diff --git a/Logbook.md b/Logbook.md
index ac22e73b8..04c2b82f5 100644
--- a/Logbook.md
+++ b/Logbook.md
@@ -6,6 +6,28 @@
 
 Updated the Leios Slide Deck with reusable slides about this sim
 
+### Micro-mainnet
+
+To accommodate the speed limitations of the Haskell simulation, an even smaller "micro-mainnet" has been created.
+
+- Methodology: [topology-v3.ipynb](data/simulation/pseudo-mainnet/topology-v3.ipynb)
+- Network: [topology-v3.yaml](data/simulation/pseudo-mainnet/topology-v3.yaml)
+- Metrics: [topology-v3.md](data/simulation/pseudo-mainnet/topology-v3.md)
+- Experiment comparing micro- vs mini-mainnet: [analysis.ipynb](analysis/sims/micro-mainnet/analysis.ipynb)
+
+| Metric                       | Value      |
+| ---------------------------: | ---------: |
+| Total nodes                  | 100        |
+| Block producers              | 22         |
+| Relay nodes                  | 78         |
+| Total connections            | 2123       |
+| Network diameter             | 4 hops     |
+| Average connections per node | 21.23      |
+| Average latency              | 97.5 ms    |
+| Maximum latency              | 529.1 ms   |
+| Bidirectional connections    | 389        |
+| Asymmetry ratio              | 63.35%     |
+
 ## 2025-09-11
 
 ### Compendium of data for Delta QSD modeling
diff --git a/analysis/sims/micro-mainnet/analysis.ipynb b/analysis/sims/micro-mainnet/analysis.ipynb
new file mode 100644
index 000000000..6080819ec
--- /dev/null
+++ b/analysis/sims/micro-mainnet/analysis.ipynb
@@ -0,0 +1,3256 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "11c10d93-fe1b-4019-8b44-3bdf1cc851e2",
+   "metadata": {},
+   "source": [
+    "# Leios simulation analysis for network topology"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b605859-ed4f-4e03-9f40-8891c3122a01",
+   "metadata": {},
+   "source": [
+    "## Set up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45599565-dbe9-415e-b356-0db87b421c63",
+   "metadata": {},
+   "source": [
+    "### Load packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "26fe5452-fe6b-4383-b383-1de25d28bd0b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "suppressMessages({\n",
+    "    require(RColorBrewer, quietly=TRUE)\n",
+    "    require(data.table, quietly=TRUE)\n",
+    "    require(ggplot2, quietly=TRUE)\n",
+    "    require(magrittr, quietly=TRUE)\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4313649-8871-435c-a405-42e99d7f7145",
+   "metadata": {},
+   "source": [
+    "## Experiment: Varying throughput and network topology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "73cc37a3-f119-4252-8d14-47c4fd504e6a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "simVersion <- readLines(\"sim-cli.hash\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "065dffd1-932a-4fa3-afd4-046a81e0866b",
+   "metadata": {},
+   "source": [
+    "### Analysis of lifecycles and efficiencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a101227-6be8-46e8-b20c-b73d82475bd5",
+   "metadata": {},
+   "source": [
+    "#### Read the lifecycle results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b492fce1-9267-4379-ad0d-9b775e0a424a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded Rdata file: sampleSize = 1 \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "        Network         Bandwidth               CPU        \n",
+       " topology-v2:278926   10 Mb/s:557902   4 vCPU/node:557902  \n",
+       " topology-v3:278976                                        \n",
+       "                                                           \n",
+       "                                                           \n",
+       "                                                           \n",
+       "                                                           \n",
+       "                                                           \n",
+       "        Diffusion duration         Voting duration     Max EB size    \n",
+       " L_diff = 7 slots:557902   L_vote = 4 slots:557902   12 MB/EB:557902  \n",
+       "                                                                      \n",
+       "                                                                      \n",
+       "                                                                      \n",
+       "                                                                      \n",
+       "                                                                      \n",
+       "                                                                      \n",
+       "      Tx size              Throughput      Tx start [s]  Tx stop [s] \n",
+       " 1500 B/Tx:557902   0.100 TxMB/s:120250   Min.   :60    Min.   :960  \n",
+       "                    0.150 TxMB/s:180240   1st Qu.:60    1st Qu.:960  \n",
+       "                    0.200 TxMB/s:257412   Median :60    Median :960  \n",
+       "                                          Mean   :60    Mean   :960  \n",
+       "                                          3rd Qu.:60    3rd Qu.:960  \n",
+       "                                          Max.   :60    Max.   :960  \n",
+       "                                                                     \n",
+       "  Sim stop [s]  Message          Item           Size [B]        References   \n",
+       " Min.   :1500   EB:   308   0      :     6   Min.   :  1024   Min.   :0.000  \n",
+       " 1st Qu.:1500   RB:   446   1      :     6   1st Qu.:  1500   1st Qu.:2.000  \n",
+       " Median :1500   TX:557148   10     :     6   Median :  1500   Median :3.000  \n",
+       " Mean   :1500               100    :     6   Mean   :  1623   Mean   :3.075  \n",
+       " 3rd Qu.:1500               1000   :     6   3rd Qu.:  1500   3rd Qu.:4.000  \n",
+       " Max.   :1500               10000  :     6   Max.   :256240   Max.   :8.000  \n",
+       "                            (Other):557866                                   \n",
+       "  Created [s]      To IB [s]        To EB [s]         To RB [s]     \n",
+       " Min.   :  20.07   Mode:logical   Min.   :  81.12   Min.   : 115.2  \n",
+       " 1st Qu.: 285.07   NA's:557902    1st Qu.: 331.23   1st Qu.: 345.3  \n",
+       " Median : 510.13                  Median : 589.34   Median : 622.3  \n",
+       " Mean   : 510.23                  Mean   : 576.18   Mean   : 617.9  \n",
+       " 3rd Qu.: 735.21                  3rd Qu.: 812.34   3rd Qu.: 840.3  \n",
+       " Max.   :1496.07                  Max.   :1171.20   Max.   :1188.2  \n",
+       "                                  NA's   :1954      NA's   :10091   \n",
+       "   In RB [s]     \n",
+       " Min.   :  81.1  \n",
+       " 1st Qu.: 288.1  \n",
+       " Median : 516.3  \n",
+       " Mean   : 511.4  \n",
+       " 3rd Qu.: 718.2  \n",
+       " Max.   :1091.3  \n",
+       " NA's   :548422  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if (file.exists(\"results/lifecycle.Rdata\")) {\n",
+    "    load(file=\"results/lifecycle.Rdata\")\n",
+    "    cat(paste(\"Loaded Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "} else {\n",
+    "    lifecycle <- fread(\"results/lifecycle.csv.gz\", stringsAsFactors=TRUE)\n",
+    "    sampleSize <- 1\n",
+    "    save(lifecycle, sampleSize, file=\"results/lifecycle.R\")\n",
+    "    cat(paste(\"Saved Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "}\n",
+    "setnames(lifecycle, old=\"Kind\", new=\"Message\")\n",
+    "lifecycle %>% summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f5305e22-8b33-47c4-baaa-97f5c388e71b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'sim-cli 1.3.0-90106d8e2, 10 Mb/s, 4 vCPU/node, L_diff = 7 slots, L_vote = 4 slots, 12 MB/EB, 1500 B/Tx'"
+      ],
+      "text/latex": [
+       "'sim-cli 1.3.0-90106d8e2, 10 Mb/s, 4 vCPU/node, L\\_diff = 7 slots, L\\_vote = 4 slots, 12 MB/EB, 1500 B/Tx'"
+      ],
+      "text/markdown": [
+       "'sim-cli 1.3.0-90106d8e2, 10 Mb/s, 4 vCPU/node, L_diff = 7 slots, L_vote = 4 slots, 12 MB/EB, 1500 B/Tx'"
+      ],
+      "text/plain": [
+       "[1] \"sim-cli 1.3.0-90106d8e2, 10 Mb/s, 4 vCPU/node, L_diff = 7 slots, L_vote = 4 slots, 12 MB/EB, 1500 B/Tx\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "label <- lifecycle[, unique(paste(simVersion, `Bandwidth`, `CPU`, `Diffusion duration`, `Voting duration`, `Max EB size`, `Tx size`, sep=\", \"))]\n",
+    "label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a24ff377-42d3-433f-8bf8-2232d01d4ea6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fixed <- c(\"Bandwidth\", \"CPU\", \"Diffusion duration\", \"Voting duration\", \"Max EB size\", \"Tx size\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0690d98a-084b-49bc-aa2d-e7426cf29c3d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "variedX <- c(\"Network\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "16fff188-e2fd-44c6-afd0-d5c36cb9308d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "variedY <- c(\"Throughput\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1a99fa00-7bf6-4f26-adf5-878263220fed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "varied <- c(variedX, variedY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0661dbfd-b117-42bf-9a4e-13d5d914e5d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "lifecycle[, `:=`(\n",
+    "    `VariedX`=`Network`,\n",
+    "    `VariedY`=`Throughput`\n",
+    ")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0a2ca1db-3514-4b1f-a641-07e576b7d9d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "facet_varied_x <- function(scales=\"fixed\", wide=FALSE) {\n",
+    "    if (wide)\n",
+    "        facet_grid(`VariedX` ~ ., scales=scales)\n",
+    "    else\n",
+    "        facet_grid(. ~ `VariedX`, scales=scales)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "59bc00ba-5522-4d62-b071-7c822833d7ef",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "facet_varied <- function(scales=\"fixed\", wide=FALSE) {\n",
+    "    if (wide)\n",
+    "        facet_grid(`VariedX` ~ `VariedY`, scales=scales)\n",
+    "    else\n",
+    "        facet_grid(`VariedY` ~ `VariedX`, scales=scales)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59e0fd0a-aaf4-4d57-8867-d2ffe73f49b2",
+   "metadata": {},
+   "source": [
+    "#### Range of transaction injection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fc066f9a-5a0c-474b-a07a-8814511179ef",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "txFirst <- lifecycle[, median(`Tx start [s]`)]\n",
+    "txLast <- lifecycle[, median(`Tx stop [s]`)]\n",
+    "txWindow <- txLast - txFirst\n",
+    "simFinish <- lifecycle[, median(`Sim stop [s]`)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9d3746a3-d2ac-44e0-8549-d13f6741ea9a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Pzyy6KwHYsQonfv3rVq1cpXT75HL1mvtjTq1KnTvHnzefPmubq6CiGmT5/esGHD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\nIrN0AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(lifecycle, aes(x=`Created [s]`)) +\n",
+    "    geom_histogram(binwidth=5, fill=\"lightgray\") +\n",
+    "    geom_vline(xintercept=txFirst, color=\"blue\") +\n",
+    "    geom_vline(xintercept=txLast, color=\"red\") +\n",
+    "    ylab(\"Number of Transactions\") +\n",
+    "    ggtitle(\"Time range of Transactions\", label)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76469aa1-6fc7-4fee-9596-5edd49339927",
+   "metadata": {},
+   "source": [
+    "#### Actual TPS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "84519b57-0a07-48c6-91c3-bd54f25a26ec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 6 x 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Bandwidth</th><th scope=col>CPU</th><th scope=col>Diffusion duration</th><th scope=col>Voting duration</th><th scope=col>Max EB size</th><th scope=col>Tx size</th><th scope=col>Network</th><th scope=col>Throughput</th><th scope=col>Demand [tx/s]</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>10 Mb/s</td><td>4 vCPU/node</td><td>L_diff = 7 slots</td><td>L_vote = 4 slots</td><td>12 MB/EB</td><td>1500 B/Tx</td><td>topology-v2</td><td>0.100 TxMB/s</td><td> 66.66778</td></tr>\n",
+       "\t<tr><td>10 Mb/s</td><td>4 vCPU/node</td><td>L_diff = 7 slots</td><td>L_vote = 4 slots</td><td>12 MB/EB</td><td>1500 B/Tx</td><td>topology-v2</td><td>0.150 TxMB/s</td><td>100.00111</td></tr>\n",
+       "\t<tr><td>10 Mb/s</td><td>4 vCPU/node</td><td>L_diff = 7 slots</td><td>L_vote = 4 slots</td><td>12 MB/EB</td><td>1500 B/Tx</td><td>topology-v2</td><td>0.200 TxMB/s</td><td>142.85778</td></tr>\n",
+       "\t<tr><td>10 Mb/s</td><td>4 vCPU/node</td><td>L_diff = 7 slots</td><td>L_vote = 4 slots</td><td>12 MB/EB</td><td>1500 B/Tx</td><td>topology-v3</td><td>0.100 TxMB/s</td><td> 66.66778</td></tr>\n",
+       "\t<tr><td>10 Mb/s</td><td>4 vCPU/node</td><td>L_diff = 7 slots</td><td>L_vote = 4 slots</td><td>12 MB/EB</td><td>1500 B/Tx</td><td>topology-v3</td><td>0.150 TxMB/s</td><td>100.00111</td></tr>\n",
+       "\t<tr><td>10 Mb/s</td><td>4 vCPU/node</td><td>L_diff = 7 slots</td><td>L_vote = 4 slots</td><td>12 MB/EB</td><td>1500 B/Tx</td><td>topology-v3</td><td>0.200 TxMB/s</td><td>142.85778</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 6 x 9\n",
+       "\\begin{tabular}{lllllllll}\n",
+       " Bandwidth & CPU & Diffusion duration & Voting duration & Max EB size & Tx size & Network & Throughput & Demand {[}tx/s{]}\\\\\n",
+       " <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t 10 Mb/s & 4 vCPU/node & L\\_diff = 7 slots & L\\_vote = 4 slots & 12 MB/EB & 1500 B/Tx & topology-v2 & 0.100 TxMB/s &  66.66778\\\\\n",
+       "\t 10 Mb/s & 4 vCPU/node & L\\_diff = 7 slots & L\\_vote = 4 slots & 12 MB/EB & 1500 B/Tx & topology-v2 & 0.150 TxMB/s & 100.00111\\\\\n",
+       "\t 10 Mb/s & 4 vCPU/node & L\\_diff = 7 slots & L\\_vote = 4 slots & 12 MB/EB & 1500 B/Tx & topology-v2 & 0.200 TxMB/s & 142.85778\\\\\n",
+       "\t 10 Mb/s & 4 vCPU/node & L\\_diff = 7 slots & L\\_vote = 4 slots & 12 MB/EB & 1500 B/Tx & topology-v3 & 0.100 TxMB/s &  66.66778\\\\\n",
+       "\t 10 Mb/s & 4 vCPU/node & L\\_diff = 7 slots & L\\_vote = 4 slots & 12 MB/EB & 1500 B/Tx & topology-v3 & 0.150 TxMB/s & 100.00111\\\\\n",
+       "\t 10 Mb/s & 4 vCPU/node & L\\_diff = 7 slots & L\\_vote = 4 slots & 12 MB/EB & 1500 B/Tx & topology-v3 & 0.200 TxMB/s & 142.85778\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 6 x 9\n",
+       "\n",
+       "| Bandwidth &lt;fct&gt; | CPU &lt;fct&gt; | Diffusion duration &lt;fct&gt; | Voting duration &lt;fct&gt; | Max EB size &lt;fct&gt; | Tx size &lt;fct&gt; | Network &lt;fct&gt; | Throughput &lt;fct&gt; | Demand [tx/s] &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 10 Mb/s | 4 vCPU/node | L_diff = 7 slots | L_vote = 4 slots | 12 MB/EB | 1500 B/Tx | topology-v2 | 0.100 TxMB/s |  66.66778 |\n",
+       "| 10 Mb/s | 4 vCPU/node | L_diff = 7 slots | L_vote = 4 slots | 12 MB/EB | 1500 B/Tx | topology-v2 | 0.150 TxMB/s | 100.00111 |\n",
+       "| 10 Mb/s | 4 vCPU/node | L_diff = 7 slots | L_vote = 4 slots | 12 MB/EB | 1500 B/Tx | topology-v2 | 0.200 TxMB/s | 142.85778 |\n",
+       "| 10 Mb/s | 4 vCPU/node | L_diff = 7 slots | L_vote = 4 slots | 12 MB/EB | 1500 B/Tx | topology-v3 | 0.100 TxMB/s |  66.66778 |\n",
+       "| 10 Mb/s | 4 vCPU/node | L_diff = 7 slots | L_vote = 4 slots | 12 MB/EB | 1500 B/Tx | topology-v3 | 0.150 TxMB/s | 100.00111 |\n",
+       "| 10 Mb/s | 4 vCPU/node | L_diff = 7 slots | L_vote = 4 slots | 12 MB/EB | 1500 B/Tx | topology-v3 | 0.200 TxMB/s | 142.85778 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Bandwidth CPU         Diffusion duration Voting duration  Max EB size\n",
+       "1 10 Mb/s   4 vCPU/node L_diff = 7 slots   L_vote = 4 slots 12 MB/EB   \n",
+       "2 10 Mb/s   4 vCPU/node L_diff = 7 slots   L_vote = 4 slots 12 MB/EB   \n",
+       "3 10 Mb/s   4 vCPU/node L_diff = 7 slots   L_vote = 4 slots 12 MB/EB   \n",
+       "4 10 Mb/s   4 vCPU/node L_diff = 7 slots   L_vote = 4 slots 12 MB/EB   \n",
+       "5 10 Mb/s   4 vCPU/node L_diff = 7 slots   L_vote = 4 slots 12 MB/EB   \n",
+       "6 10 Mb/s   4 vCPU/node L_diff = 7 slots   L_vote = 4 slots 12 MB/EB   \n",
+       "  Tx size   Network     Throughput   Demand [tx/s]\n",
+       "1 1500 B/Tx topology-v2 0.100 TxMB/s  66.66778    \n",
+       "2 1500 B/Tx topology-v2 0.150 TxMB/s 100.00111    \n",
+       "3 1500 B/Tx topology-v2 0.200 TxMB/s 142.85778    \n",
+       "4 1500 B/Tx topology-v3 0.100 TxMB/s  66.66778    \n",
+       "5 1500 B/Tx topology-v3 0.150 TxMB/s 100.00111    \n",
+       "6 1500 B/Tx topology-v3 0.200 TxMB/s 142.85778    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dtmp <- lifecycle[\n",
+    "    `Message` == \"TX\" & `Created [s]` >= txFirst & `Created [s]` <= txLast, \n",
+    "    .(\n",
+    "        `Demand [tx/s]`=.N/txWindow\n",
+    "    ),\n",
+    "    c(fixed, varied)\n",
+    "]\n",
+    "setorderv(dtmp, varied)\n",
+    "dtmp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7747da73-31c5-4ff0-b842-ab4e762fd7f6",
+   "metadata": {},
+   "source": [
+    "#### Size of persisted data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5b72b808-5ce0-47e3-be8f-8e7972d53b89",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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WQy1dDK/fz8amjNuA33igUAAFAJih0AAIBKUOwAAABUgmIHAACgEhQ7AAAAlaDY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\nxQ4AAEAQFDsAAABB/D/nhCD8t6cHyQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "    lifecycle[\n",
+    "        ,\n",
+    "        .(`Size [kB/s]`=sum(`Size [B]`)/1e3/600),\n",
+    "        c(\"VariedX\", \"VariedY\", \"Message\")\n",
+    "    ],\n",
+    "    aes(x=\"\", y=`Size [kB/s]`, fill=`Message`)\n",
+    ") + geom_bar(stat=\"identity\") +\n",
+    "    facet_varied(scales=\"fixed\") +\n",
+    "    xlab(variedX)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "048eba3e-f592-4d0b-9329-ca98aed18951",
+   "metadata": {},
+   "source": [
+    "#### Spatial efficiency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4b9cf49e-e58a-4df6-aa3a-a58ba3a3f0b9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 6 x 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Network</th><th scope=col>Throughput</th><th scope=col>Space efficiency [%]</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>topology-v2</td><td>0.100 TxMB/s</td><td>95.80366</td></tr>\n",
+       "\t<tr><td>topology-v2</td><td>0.150 TxMB/s</td><td>96.32882</td></tr>\n",
+       "\t<tr><td>topology-v2</td><td>0.200 TxMB/s</td><td>95.70599</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.100 TxMB/s</td><td>92.76436</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.150 TxMB/s</td><td>94.88802</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.200 TxMB/s</td><td>94.89913</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 6 x 3\n",
+       "\\begin{tabular}{lll}\n",
+       " Network & Throughput & Space efficiency {[}\\%{]}\\\\\n",
+       " <fct> & <fct> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t topology-v2 & 0.100 TxMB/s & 95.80366\\\\\n",
+       "\t topology-v2 & 0.150 TxMB/s & 96.32882\\\\\n",
+       "\t topology-v2 & 0.200 TxMB/s & 95.70599\\\\\n",
+       "\t topology-v3 & 0.100 TxMB/s & 92.76436\\\\\n",
+       "\t topology-v3 & 0.150 TxMB/s & 94.88802\\\\\n",
+       "\t topology-v3 & 0.200 TxMB/s & 94.89913\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 6 x 3\n",
+       "\n",
+       "| Network &lt;fct&gt; | Throughput &lt;fct&gt; | Space efficiency [%] &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| topology-v2 | 0.100 TxMB/s | 95.80366 |\n",
+       "| topology-v2 | 0.150 TxMB/s | 96.32882 |\n",
+       "| topology-v2 | 0.200 TxMB/s | 95.70599 |\n",
+       "| topology-v3 | 0.100 TxMB/s | 92.76436 |\n",
+       "| topology-v3 | 0.150 TxMB/s | 94.88802 |\n",
+       "| topology-v3 | 0.200 TxMB/s | 94.89913 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Network     Throughput   Space efficiency [%]\n",
+       "1 topology-v2 0.100 TxMB/s 95.80366            \n",
+       "2 topology-v2 0.150 TxMB/s 96.32882            \n",
+       "3 topology-v2 0.200 TxMB/s 95.70599            \n",
+       "4 topology-v3 0.100 TxMB/s 92.76436            \n",
+       "5 topology-v3 0.150 TxMB/s 94.88802            \n",
+       "6 topology-v3 0.200 TxMB/s 94.89913            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dtmp <- lifecycle[\n",
+    "        , \n",
+    "        .(`Tx size [B]`=sum(ifelse(is.na(`To RB [s]`) & is.na(`In RB [s]`), 0, 1.0 * `Size [B]`))),\n",
+    "        varied\n",
+    "    ][\n",
+    "        lifecycle[\n",
+    "            , \n",
+    "            .(`Non-tx size [B]`=sum(as.numeric((`Message` == \"TX\" | `Message` == \"EB\" | `Message` == \"RB\") * `Size [B]`))), \n",
+    "            varied\n",
+    "        ],\n",
+    "        on=varied\n",
+    "    ][, .(`Space efficiency [%]`=100*`Tx size [B]`/`Non-tx size [B]`), varied]\n",
+    "setorderv(dtmp, varied)\n",
+    "dtmp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9250da59-54a6-4550-a91b-274ad6fcef3e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wqpIr9T/LyMeOc7K9oOaerOpu0vkqp9cFB9AMljSBJA7lwhje/1zGbLSszudlc7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\n/wC+Wx576hh4tAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "    lifecycle[\n",
+    "        `Message` == \"TX\", \n",
+    "        .(`Tx size [B]`=sum(ifelse(is.na(`To RB [s]`) & is.na(`In RB [s]`), 0, 1.0 * `Size [B]`))),\n",
+    "        .(`VariedX`, `VariedY`)\n",
+    "    ][\n",
+    "        lifecycle[\n",
+    "            `Message` != \"VT\", \n",
+    "            .(`Non-tx size [B]`=sum(as.numeric((`Message` == \"TX\" | `Message` == \"EB\" | `Message` == \"RB\") * `Size [B]`))), \n",
+    "            .(`VariedX`, `VariedY`)\n",
+    "        ],\n",
+    "        on=c(\"VariedX\", \"VariedY\")\n",
+    "    ][, .(`Space efficiency [%]`=100*`Tx size [B]`/`Non-tx size [B]`), .(`VariedX`, `VariedY`)],\n",
+    "    aes(x=\"\", y=`Space efficiency [%]`)\n",
+    ") +\n",
+    "    geom_bar(stat=\"identity\") +\n",
+    "    facet_varied(scales=\"fixed\") +\n",
+    "    ylim(0, 100) +\n",
+    "    xlab(variedX)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b38a7ba-fc08-4129-90f8-049ff423cfba",
+   "metadata": {},
+   "source": [
+    "#### Time to reach the EB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9159b85a-7e54-4d6e-a380-a587fce7ca27",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 6 x 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Network</th><th scope=col>Throughput</th><th scope=col>Time to reach EB [s]</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>topology-v2</td><td>0.100 TxMB/s</td><td> 17.95809</td></tr>\n",
+       "\t<tr><td>topology-v2</td><td>0.150 TxMB/s</td><td> 21.10878</td></tr>\n",
+       "\t<tr><td>topology-v2</td><td>0.200 TxMB/s</td><td>135.57301</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.100 TxMB/s</td><td> 15.66623</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.150 TxMB/s</td><td> 18.10662</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.200 TxMB/s</td><td>107.58791</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 6 x 3\n",
+       "\\begin{tabular}{lll}\n",
+       " Network & Throughput & Time to reach EB {[}s{]}\\\\\n",
+       " <fct> & <fct> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t topology-v2 & 0.100 TxMB/s &  17.95809\\\\\n",
+       "\t topology-v2 & 0.150 TxMB/s &  21.10878\\\\\n",
+       "\t topology-v2 & 0.200 TxMB/s & 135.57301\\\\\n",
+       "\t topology-v3 & 0.100 TxMB/s &  15.66623\\\\\n",
+       "\t topology-v3 & 0.150 TxMB/s &  18.10662\\\\\n",
+       "\t topology-v3 & 0.200 TxMB/s & 107.58791\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 6 x 3\n",
+       "\n",
+       "| Network &lt;fct&gt; | Throughput &lt;fct&gt; | Time to reach EB [s] &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| topology-v2 | 0.100 TxMB/s |  17.95809 |\n",
+       "| topology-v2 | 0.150 TxMB/s |  21.10878 |\n",
+       "| topology-v2 | 0.200 TxMB/s | 135.57301 |\n",
+       "| topology-v3 | 0.100 TxMB/s |  15.66623 |\n",
+       "| topology-v3 | 0.150 TxMB/s |  18.10662 |\n",
+       "| topology-v3 | 0.200 TxMB/s | 107.58791 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Network     Throughput   Time to reach EB [s]\n",
+       "1 topology-v2 0.100 TxMB/s  17.95809           \n",
+       "2 topology-v2 0.150 TxMB/s  21.10878           \n",
+       "3 topology-v2 0.200 TxMB/s 135.57301           \n",
+       "4 topology-v3 0.100 TxMB/s  15.66623           \n",
+       "5 topology-v3 0.150 TxMB/s  18.10662           \n",
+       "6 topology-v3 0.200 TxMB/s 107.58791           "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dtmp <- lifecycle[\n",
+    "        `Message` == \"TX\" & \n",
+    "        !is.na(`To EB [s]`) &\n",
+    "        `Created [s]` >= txFirst & `Created [s]` <= txLast, \n",
+    "        .(`Time to reach EB [s]`=mean(`To EB [s]`-`Created [s]`)), \n",
+    "        varied\n",
+    "    ]\n",
+    "setorderv(dtmp, varied)\n",
+    "dtmp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d451bc57-36d0-41ad-9cbd-8ebb3324267c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OHUqVOzsrKEECdOnFiyZMlVV12ldVznpKWl9ejRY+7cuUaj8eabb67Kqnr06DFj\nxoy+ffv279//wIED8+bNu/rqqwNXsKWmpgoh5syZ84c//OGaa66ZNWtWt27d2rRpM3LkSKPR\n+H//939ffPHFa6+9duHZTIfD8de//vXOO+/s3Llz//79S0pKFi9eXL9+/QcffDBkGGHW3KJF\ni/r167/00kter7dp06afffbZqlWr6tevv3HjxmXLlt19992RB5menv7kk08+9thjHTp0GDBg\nQF5e3tKlSzt37rx169aq7EMKOwAA9GvUqFHjx48fMmRInTp1/H7/8ePHmzdvPmrUKK3jOk+/\nfv3WrVvXs2fPC0+DVkouceTpyZMnFxUV/fvf/966dWvHjh1XrVp18ODBzz//XH5W7kb4xRdf\nzM/Pv+aaa9q2bfvFF1888cQTr776akFBweWXX7569eo+ffqE3Mrw4cNr1679zDPPzJo1Kykp\nqUePHs8880zgZtVywqzZYrGsWbMmOzv7b3/7W1paWteuXT/99NNjx4498cQT27Ztu/vuuxUF\nmZ2dXbdu3ZdeeumFF15o3rz5jBkzOnbsOHnyZEWXIZYj+f1+1Y1VOHPmjNvtrujZzMxMn893\n+vTpWrvPdjz96aEdgWeb9i9/n4vMaDQmJyefOXNGUSQmkyktLc3lclV0M0tFbDabwWBQem+2\nw+FwOBz5+fmB48ARSk1NLS4u9ng8ilo5nU6z2Xzy5ElFrYQQGRkZp06dUtREkqTMzEy3263i\nLbDb7YH+ISNksVjkfRLmMo5K08zr9ebl5dX65uxfRaPO3B54tlPh2YkWQXE17f+rujQzm81O\np1NFmslX8gaOz0dITrPwLz8kdWmWlpZmMpnimWZlZWWBHuojZDabbTab0jSzWq0pKSlFRUUh\nL+6R5eXlhdljWVlZHo8nLy9P0XZNJpP8RaGolZxmxcXFSr+X1KVZUlKS3W5XkWZOp1PuGEJR\nq7S0NKPRqGJIdRVpZjAYMjIy1KWZ1WotLCysfNEggTQ7duxYuRN55fj9/t27d//4448Gg6FR\no0ZXXHFF5CdnW7RooSgqVHccsQMAQHf2798f/DA5OTnQW8d3330nqNhQAQo7AAB0p6LLv4QQ\nZrPZ4XC8++678YwH1QWFHQAAuhMYZWvnzp1z5sz54x//eMUVVxiNxn379r366qsPPfSQtuFB\ntyjsADHf3ikw3alwR5glASA+Ard2Ll68ePTo0V26dJEfduzYsWHDhtOnT6/6aDWpLDUAACAA\nSURBVPFISPRjBwCAfv3yyy/lbjVNT08/evSoVvFA5yjsAADQrxYtWrz++uuBsQ18Pt/y5cub\nNmWUTYTGqVgAAPRr9OjRf/rTn4YOHfq73/3OaDTu37+/sLDwxRdf1Dou6BSFHQAA+tWkSZM3\n3nhj3bp1hw8fliSpf//+vXr1CtPDImo4vRd2+1POTXPcGfFE7gHQCYfD0axZM5PJJElSo0aN\nwnTPDui9sAP0IGXWNCGET4gUIQrGPal1OABqkNOnT48fP/7gwYO1a9cWQhw/fvySSy6ZOXOm\n0+nUOjToEYUdAAD6NW/ePLPZ/MYbb9SqVUsIcfz48alTp86bN2/SpElahaR0jL4IpaSkVL4Q\nKkNhBwCAfu3Zs+epp56SqzohRO3atR988MHp06drG5VlRjTLyrLJT0dxbTUc3Z0AAKBrkiRp\nHQKqDQo74Dw7ks/+AwA9aNu27cKFC0+ePCk/PHHixJIlS6666ipto4Ju6fVUrPmG36YY3wna\ny01/KTBtEdw8ASB+Ro0aNX78+CFDhtSpU8fv9x8/frx58+ajRo3SOi7olF4LOwAAIER6evrf\n//733bt3//jjjwaDoVGjRldccQUnZ1ERCjsAAPTL6/UKIdq0adOmTRt5js/nC17AaDRqEBb0\nisIOAAD96tmzZ/gFNm3aFJ9IUC1Q2AEAoF+LFi3SOgRUJxR2QGjBN8Z21y4MADVcixYt/H7/\n3r175bFiucYO4VHYAZU7mn5umnFjAcQTQ4oFKysru/jii3NycjIzM7WORafoxw4AAP0KDCn2\n+m/kmVrHFW9ut/vrr78eOXJkbm6u1rHoWryP2FmtVpvNVtGzkiQZDIaKRouTB2IXQohps8q1\nMhqNSseYMxgMQgiLxSJPRE6+/0jpXUjy8na73Wq1KmpoMpkcDoff71exORXj7kmSpG60PnVv\nQVXeuDDL2Gy28GmmYrsBihpWMc1MJmWfUHl5h8NR7qa5SBqqSDP5RcUzzUwmk4qECfOtEqaV\nECL8p9Vms4U/HaYizSRJUvcahRBWq1Xd91Lc0sxoNCYlJan4NlOXMCpayW+ortJM6HVIsfj7\n29/+Nnfu3LKyMq0D0bt4F3Yej0e+czski8Xi9/tLSkrCr6TcAvInqtJW5RiNRrPZ7PF4lDa0\nWCySJJWWlipqZbVaTSZTWVmZx+NR1NBgMJSVlYXZaSEZjUYV+0QIYTablbaSJMlqtfp8PhVv\ngdVqVdrKbDbLb1yYXyO32x1mj6mLNkBRQ5PJFM80s9lsRqNRRZrJrZSmmfwW6DzNTCaTxWKJ\nUZqFqWxUfyhUfHJVp5lcUsQzzUpLS1X81WE0GuOWZhaLxev1qnsL1KWZ2+2OJDBFa05I48aN\nGzdu3K5du9q3b691LLoW78LO6/WGT2K/319plpdbwGg0RtLqwg0JIXw+n9KG8jev0lZms1lE\n8PIv5Pf7PR6P0i9Q+dUp3ZZMaSv5G0fdWxDhl9qFmwv/2xB+P/v9/rPRelop2rRMxV5VkWZy\nPaEuzTwej9KGPp+vWqSZij0phDCZTEpbyYdSwle6Xq83/B5T96FQ9xqFqq8XdWkmHy9XkWby\nt5nSvx/OfWCVU/e+q9uc0WhUt7lKK115SLGpU6dmZWUJhhRDZbh5AgAA/WJIMShCYQcAgH4x\npBgUobADAECPTp06JYTIyMjweDx5eXmnTp0ymUzp6ek+n49hxFARvRZ2ElcPAABqrp07d06e\nPHnixInNmzd/7LHHCgsLmzVrJknSv//974yMjBdeeEG+5A4oR6+FXSi56S/JExbxpLaRAAAQ\nUy+//PLAgQO7du06fvz4Sy65ZOLEiXIvTsXFxTNmzJgzZ87TTz+tdYzQIzooBgBAdw4fPnz7\n7bcbjcZ9+/YNHz480Denw+EYPnz4l19+qW14WmnXrp3f72fYiTCq0xE7QA8CHWUXjOPIMYBY\nSU5OLi4uzsjIaNy48enTp4Ofys3NrVOnjlaBQeco7AAhzDecm3bt0C4OADirQ4cOs2fPHj16\n9OjRo5999tnCwsLLLrvM7/d/9dVXixcvzs7O1jpA6BSFHQAAujNq1KhFixY9/PDDcj/YM2bM\nCDwlSdLTTz+9Zs0a7aKDflHYAeeZb+8kT4zi0B0A7SQlJWVnZ48ZMyY/P//MmTNKh2JDjcXN\nEwAA6IvP59u3b5/X6zUYDGlpaY0aNWrym8aNGxcXF69du1brGKFTej9ityP53HSL9LMTTTUJ\nBQCAuDh27Ngf//jH1atXJyUlyXN8Pt9XX321ZcuWzZs35+XltW7dWtsIoVt6L+wAAKhp6tSp\nU7t27cmTJw8aNMhisWzZsuXjjz8uLCy86qqr7rnnni5duqSlpWkdI3SKwg4AAH0xGo2LFi1a\nsmTJ9OnTXS6X0WgcMGDAiBEjAgfwNFc2me6RdYrCDgAA3XE6nY8//vgjjzzyySefbNy4ceXK\nlVu3bu3evfv111/fpEkTraODflHYAQCgUzabrXv37t27dz9z5sxHH320YcOG1157rUmTJt27\ndx8+fLiGgRXMT43i2lJG5UdxbTUchR0QWqDfEyFEp5Sgrk+anh2zuCljFgOIF6fTedttt912\n223Hjh378MMPN27cqG1hB92iuxMAAKoBr9e7efPmunXrDh8+fNmyZVqHA52isAMAoBooKSmZ\nOnWq1lFA7yjsAAAAEgTX2AFCSFcFPdigWRgAAFQNR+wAAKgG7Hb7q6++qnUU0DsKOwAAqgGD\nwdCgQQOXy/Xhhx9OmTJF63CgU5yKBQBA70pKSj799NNNmzbt2LFDkqSOHTtqHRF0qloWdimz\npgWmC8bRlxjUKmx4diJtn6ZxAECFtmzZ8tFHH23fvt1sNnfp0mXKlCnt27e3Wq1axxVvx48f\nHzdu3MaNG10u19VXX/3Xv/71iiuu0DooPaqWhR0AADXEX/7yF6fTmZ2d3b17d6PRqHU4mhk2\nbNjJkydff/31pKSk559/vnv37l999VXdunW1jkt3qlNhtz/l7EQbTcMAZBw5BhAHkyZN+uCD\nD5577rk1a9Zcd911v//97zMyMrQOKt5++umnDz/8cOvWrV27dhVCvP7663Xq1Hn//fcfeOAB\nrUPTHb0XducN61S4I8ySQJSZbwhM7kg+l3stCrQIBkBN1bNnz549e548eXLDhg3vvvvu3Llz\nL7/88u7du996661ahxY/Xq936tSp7du3lx+63e6SkhKfz6dtVPqk98IOiK6UWdPK5AkhRJfn\ntA0GACKUlZV1xx133HHHHTk5OevXr3/llVdqVGHXsGHDv/zlL/J0cXHxXXfdlZKSMmjQIG2j\n0qdqWdjlpr8UmLYwEDsAIHGtXLmyefPmbdq0kSRJCNGyZcu0tLSaWdP4/f7XXntt8uTJF110\n0UcffVQDT0lHoloWdkAMBZ2BBQDNzZ8/X5Kkli1bzpw50+l0CiHWrVu3bNmydu3aTZo0KT09\nXesA4+TXX38dNGjQ4cOHZ86cOWTIEIOBjnhDY78AAKBrEydOvOiiiwLnIocOHTp37ty8vLy/\n//3v2gYWN36//w9/+ENmZuY333wzdOhQqrow2DUAAOhaRkbGxIkTT5w4sX79eiGE2Wy+/PLL\nH3nkkZ07d2odWpz897//3bVrV79+/T755JMPf3P06FGt49IjvZ6K9bTSOgIAAPTCarXec889\nS5Ys6datm81mE0LYbLaysjKt44qTvXv3+v3+YcOGBc+cN2/eqFGjtApJtzhiBwBANdC9e/e0\ntLSZM2eWlJR4vd4333yzVauachAkOzvbfwGqupD0esQOAAAEMRgMEydOHDt27O233242myVJ\nmjNnjtZBQXeqZWF3NOgeoKbahQEAQKz96U9/atCggTzdqFGjf/7zn5s2bZIkqWvXrvT3gQtV\ny8IOUO2tNi8FPapSB8V7m55bVVP6UwQQG3379g1+mJKSUqO6JoZSFHZABaSrQs4OjFnM2GIA\nAL3h5gkAAIAEQWEHAACQIDgVC5yvgjOwAADoX3Uq7HYkn53g2iYAADSUMipf6xAQGqdiAQAA\nEkR1OmIH6AFHjgFg34rUKK6t1VCO/0UNR+wAAAAShO6P2JlvODft2qFdHKjR5ts7BaZHkYdA\n9VHrm63yxK+/u0bbSID40H1hB8SBJ2ggbdM+7eIAAKBKdFTYpcyaViZPCCFuHKVtMMB51R4A\nANUB19gBAAAkCAo7AACABEFhBwAAkCB0dI0doAtcWgcAqLbiXdglJSWZTKE3WqpqhVlZWeUm\nFLHb7Xa7XUVDh8OholVqqpoeHS0Wi4pWQu0+UdfKbDara2i1WlW0Cr//w6RZjIR/7arTLCkp\nSUUrp9OpolW1SDOLxRLPNAu//1NSUoxGY5gFTCZTPHeOw+FQ970UzzRLT09X0Uoo3CfS5ner\nsgbVaWaz2VS0Urf/a6Bvv/02Ozt7x44dJpPpuuuumz17doMGDbQOSo/iXdj5fD6PxxPyqdz0\nl849KHzu7ERaJSOyezweSZIMBoPX61UUiSRJRqPR5/P5fD5FDQ0GgxBCRSs5SL/fr6ihHKSK\nVpIkVbSrwzCZTOpa+f1+FW9BVd44+Y0IKUyaxUhFmyPNQkqMNAu/nzWJljQLI8I1qH7jJElS\nuicDb5yiVjVTaWlpnz59LrvsshUrVpSVlU2dOrV///6fffaZ1nHpUbwLO5fL5Xa7o7jCvLw8\no9GYnJx85swZRQ1NJlNaWlppaWlRUZGihjabzWAwFBcXK2ol/zFdVFRUVlamqGFqampxcbHS\nLzWn02k2m/Py8hS1EkJkZGQobSVJUmZmpsfjUfEW2O32ggJlI3NZLJbU1NSSkpIwByciTLOR\nJ88eRVuaHH7B8wQ6K75TnOupuKKdZjabnU6nijSTj/C5XC5FreQ0KywsVPopU5dmaWlpJpMp\nnmnmdrvz85UNPWQ2m202m9I0s1qtKSkpLpcrzNGU8HssKyvL6/UqfZkmk8nhcKh4jU6ns6Sk\nROn3kro0S0pKstvtKtLM6XQWFhYqrZnS0tKMRqOKNCsnkjUYDIaMjAx1aWa1WgsLCxW1CqSZ\nolY10549ew4dOrRz5075oK/f7+/bt29hYWFyspKv75qhOl1jF/IHFQAAJLb27dsXFhYmJSV5\nvd4TJ0588MEHHTp0oKoLSUeF3VGVl14ACuwI/h6I/O/k4KHt3BuiFw6AOAmMLVYOQ41VC0aj\nUT58ft11123dujU9PX3btm1aB6VTOirsIvdq3XPTUzWLAjUdeQjoSkWlGxLJe++9V1hYuHjx\n4m7duh06dCglJUXriHSHfuwAADVXrW+2yv+0DgThfPXVV+vWrRNCZGRkNGzYcPr06cXFxR99\n9JHWcemRjo7Y7afsBgAAF9i7d292dvZPP/1kNpuFEGfOnCkpKVHdSVNi44gdapb59k6Bf1rH\nAgCIyE033eTz+e67776dO3du27Zt8ODBzZo1+/3vf691XHpEYQcAAHQtMzNzzZo1P/zwQ48e\nPQYMGJCWlrZhwwZ1PXInPB2digUAAAipY8eOmzdv1jqKaqDaF3Yps6YJIXxCpAhRMO5JrcMB\nAADQDKdiAQAAEkS1P2IHxFugs2IXI6AAAPSl2hd2uekvBaYtglOxiJ/g+2qnahcGUJPV+nru\nuQfSVSGW8LQ6O2HaF4+AAK1V+8IOiAfvsHPTxte1iwNABALFHFDzcI0dAABAgqhWR+y4tgnx\nVNjw3HTyj9rFAQC602povtYhILRqVdiFcjT93HRT7cIAAADQXLUs7IIvWr9TcPQOAFCBwKH3\nNG6eQI2g+8Iu+BpY4xfaxQEAiaPs5VpCiDIhnI8Wah1L9IS/ZyKvV+j5aR/I/9f6ZuuFT/76\nu2uqGlWCeu/91Ciu7bZbOLEbNbov7IDoClypCeB8crUnhLDc96u2kcRVoOD7rcIDqjXuigUA\nAEgQOjpityM56IFfszBQ0wXfDAskkMABOQAJTEeFXeXO9Sq+QcswUMORh4B+hBxtAqjBqlVh\nB+gKl+uh+jvzUnLlCwGoPrjGDgAAIEFUzyN25x0poR87VFnQdXUjT9rliaVZrshXkDJrmjxR\nMO7JKMYFAIAi1bOwA2IvUOEJIZZytgoA9OHjjz++7rrrTpw4kZmZqXUseqTTwu7cURN+UAEA\n56v19dyzU9w8UcOcOXNmxIgRPp9P60D0S0eFXfBAYSM1jAM1T/DBOQCAbj388MMXXXTR4cOH\ntQ5Ev3RU2FUd1zkBAOiNMlEtX758586dS5Ysue6667SORb+qVWEXGAeQQWOhGiduAKAa+v77\n78eMGbN27VqDgQ49wmHvAAp5Wp39BwCIC6/XO2LEiLFjx3bo0EHrWPSuWh2xC+CgCwAgAuo6\nMILevPjiiydPnuzbt29OTs4PP/wghPjuu+/cbnedOnW0Dk13qmdhF+TVuuemZx/SLg4ASBSB\n65VFAl2yXNE9UucKvrxeoZ4vilVAUOK7777Lyclp3bp1YE7nzp3vvvvupUuXahiVPlX7wg7Q\nzHlHjv+rWRgAZDG4Z6LWN1sD07/+7pqorx8RWrhw4cKFC+XpXbt2tW/f/uTJk/RjFxKFHRCB\nkD8YafviHgeAKIv8XG2tvbnyxK9tqCegX3oq7Ko8pHpu+kvyhEUkyLkDVBd7m57NvabkHgDE\nUrt27fx+v9ZR6JeeCjsAACpwbrQJABWjsAOAhFX2ci2tQ4gxOh4CzqeysPN6vWvXrvX5fNdd\nd11qamp0YzpP8LVNyT+eneCTDAAAcIFIOyguKiq6//77W7ZsKT/s27fvLbfcctttt7Vt2/bH\nH38M3xYAAABxEGlh95e//OXll1+uX7++EGL79u2rV6++7777/vOf/+Tl5c2YMSOWEaqRMmta\n4J/WsQAAAMRJpKdiV61a1adPn9WrVwshVq9ebbVan3/+eafT2bdv3w8//DCWEQIAcD7GHwIq\nEGlh98svv9x7773y9LZt2zp27Oh0OoUQLVu2XLFiRayiU+ho+tmJzNOaxoFqYsixsxNv1g27\nHFDDBLqOEvQeBVQ3kRZ29erV27NnjxAiNzf3k08+mThxojz/m2++qVUrSndd8RcYAKDKKho9\nDFF02y35WoeA0CIt7AYMGDB79uwxY8Z8/PHHXq930KBBxcXFixYtWrly5a233hrTEMObb+8U\nmL5T7NAwEiSYwPE8IcTSrFBLhB5ZEgAAzURa2E2aNOnbb7+dO3euEGLatGmXXXZZTk5OdnZ2\nkyZNpk2L1w0Kga5PkrkPF9WAfO9OqRBi6nNaxwIAqBEiLexSUlLefffd/Px8SZJSUlKEEHXq\n1Nm4cWOnTp2SkpJiGSEQV8EH6iJ3ddOzR45/jWYsALQWfGCeYwpBJmyKZhe2z17Pid2oUdZB\ncXBfxE6ns0ePHtGOp7zgSyUqHaE5gCt/USF6twZqkljcI1Vrb25g+tc2mVFbLxANkRZ2+fn5\nY8eO3bhxY3FxcbmnMjIycnJyoh2YGq/+9rmdyF2xiKrI/8Cg60ToVqDfgGD1K/u2DAxKZrlP\n78ejw98zUdHB+EDBF7L5eZ93LgdCdRBpYffYY48tW7bsxhtvrFevniRJwU8ZjcYYBAbEj7rT\nr4BuBf7A2NtU20CqAbo9QoKJtLB7//33FyxY8OCDD8Y0GgAAAKgWaWEnSVLv3r1jGgoAABXS\nT1+ngXOyQghRpFkYQCiRFnbdunXbtWtXo0aNYhpNeIELIJaKoA+V84bA5PzfJibSoR0qct43\nMoBqjk80cL5IC7unnnpq8ODBqampPXv2rNL2TKY4X5Nns9lCzpfDMJlMFS1QEbPZLEmS0lYm\nk0kIYbFYDAaDooYGg8FqtcrNFbUSFb/2MFS8tMAWlTY0Go1Go1Hdngy/Q8xmcyRpdsUvhfLE\nl3WSFcUQapOBPzDyLnxS3cuUJMnv9yttJYSwWCxKP2UGg8FisShNM/ly2/ikmbwtFXsypmkW\nfgEVL9NgMKj7KAkhjE+NT1HUTKFAVPLm1KWZ1Wr1+XxKW6n+XooDObDYpRmgVKT5NGHCBJvN\ndsMNN2RkZDRs2LBcIn7++ecRrkdpWVN1FX31yJFIkqTiuynMaisi/ywZDAYVDVXstMCvoNKG\nqlup25Oq93+5O3iqHkxM+ac8bv5t2hdZZ8Xq0kxupS5hjEZj+L0aspWIV5oFYotnmoXfk5Xu\n5zh/KGItEFVV0kx+gVXZut7IgckVedw+sDXTzJkzJ0yYEHhoMpncbreG8ehWpIVdSUlJRkZG\n1S+zKysrq/CdCOpgrIq3KdVrd26csV+LQl8AYTKZrFar2+0uqmCBithsNoPBcGG3L+E5HA6z\n2VxSUlJWVqaoodFodLlcHo9HUSuTyWQwGJS+NCGE1WpV2kqSJLvd7vV6lTY0mUx2u11pK4vF\nYrFY3G632WyuaJlwaSYi7U87cDxPCPFm3Sof0vtNhK/XbrcLIVyuSPtulDkcDpPJVFJSovT7\nTl2amc3meKaZzWZTkWZms9lmsyltZbVaLRZLWVlZmDQrLS0Ns8fsdrvP51PxoXA4HCpeo9Vq\nVdREhUBUSUlJ6tLMZDK5XC6v16uolXySREWaxYccmPwWqE6z2ISWaHJycvr06fPoo4/KD9X9\nhVATRFrYrV27NqZxADoRXM8FBPeHMuTY2Ws9e11eSdUV6Cg78/Sj0QkOAGqqnJycwYMH9+rF\nIN2VUHZq3+/3Hz58+ODBgx6P55JLLmncuDHHkAEAsWPe+lun38F3xTKETM2Tk5OzcePGWbNm\nFRcXd+nS5YUXXmjRooXWQemRgrJsw4YNbdq0adKkSc+ePXv37t2sWbPLL798w4YNsQsOAADg\n5MmTp06dMhgMK1asWLlyZVFRUffu3fPzGWE2hEiP2O3cubNPnz4XXXTRtGnTWrdubTAYvvnm\nm4ULF/bp02fHjh1XXRXX7oXOG98pedi5JwIXw7gpN6GN4HGKAWiCsWQST1pa2tGjR+vWrSuf\nJ7zqqqsuvvji1atXDx06VOvQdCfSwm7KlCkXX3zxrl27MjPPDnh82223PfTQQ+3atZs8efKa\nNWtiFiEQcyGvqwMA6ITJZKpXr17gYVpaWuPGjY8cOaJhSLoV6anY3bt3Dxs2LFDVyTIyMoYP\nH7579+7oxFLY8Ny/6EmZNU3+F8V1AkBiOJp+7h+gW6tXr77iiityc3Plh4WFhUeOHLn00ku1\njUqfIj1iF6ajVKV9qEYi6AjKuW4mFHR9cq63WBGyw1gAQEX+G/THdfcfz06sW18rMHP40Cp9\n7dfafUtg+te274dY4Ou5IZpxw0QNdu211+bm5g4bNuyxxx6z2+1PP/10kyZN/vCHP2gdlx5F\nesSubdu2r7/+eqBYlp0+ffr1119v27ZtDAIDAEBjI0/aA/+0jqVGS0lJ+eCDD3w+34ABAwYN\nGlSrVq0NGzaE6WayJov0iN306dO7du3apk2bhx9+uHXr1kKI//3vfwsXLjx27Ni//vWvWEYI\nxMpFxYfkiROOplVdl36GJweE2NtUzU08/w11FUzImctXnO0btveNv0a+fstH3VREFbkq3jMR\nsnnIM0XnF3k67Tk58bRu3Xr9+vVaR1ENRFrYdejQYfXq1dnZ2ZMnTw7MvOyyyxYvXtyhQ4fY\nxAbo2nl3Z6eFWzL4VlmLeDJ2IQGIHQ7aoVpQ0EHxjTfe+OWXX/7www8HDhzw+/3Nmzdv0qQJ\nHRQDAADohLKRJwwGQ9OmTZs2rfJ5K1VCDyAbfAtt2m+nw/xfxCkm1Awh+0MJOYBsyFsL65+O\nekQAAIRQSWEnSVKdOnWOHTsW/nzr559/HtWooilwFoxTYNCDQM87BeNISESTuuvq9CP0nbCV\n4fQoUE4lhV2dOnVq1aolhMjKyopLPOUFHyn5sk6IAyShcSU7KhCrn4E8xqUGAGivksLu2LGz\npz/Xrl0b+2AAANVJcOd2iu6QjZvA0QEFhwaU+PN7Z496LBhWEov1A0pFeuvDiBEjvv322wvn\nf/zxx4888khUQwLi7aLiQ4F/kbcacuzcPwAA9KCSI3aBHomXL18+cOBA+bRsgM/nW7t27dKl\nS+fNmxerAEMJ/h1dqs0pYgBAFJwbheK8EYOgd89en691CAitksIu+NK62267LeQy3bt3j2ZE\nVREYcMa0T9M4ACCGgge/Tvy7cLzDQsyM6pDiQCKppLB7/vnn5YnHH3/84YcfbtasWbkFUlNT\nBw4cGJPQLlD5pRKBj3paiMIu+KtQViqEmPpclKIDAOhCyP6JEF2pO26M4tryOzGkRNRUUtg9\n9thj8sTq1asffPDBNm3axD6kKKvXrpM8UbJR20CgOwbLV/KEr+zywMxojjMGAEB8RdpB8aZN\nm/Lz81955ZVGjRr16NFDCPHmm29+//33Dz74YEZGRlRCCe6H4qLiL+WJSn9czxvWKcsVlUgA\noIbYn6J1BACiKtLC7ocffujRo8ehQ4eee+45ubA7cuTIxIkTFyxYsHXr1kaNGsUySOWCOxVz\ncLIVagTfIRv50burW04JTH+aMz3KMaHmqVmX0wGoskgLuwkTJpw8efKVV14ZPny4PGfcuHE3\n3nhjr169Jk6c+Prrr8csQqC6ChR5P+04V+ExFApUu/BC4cDMUiFEqL8+gg/ItSiIdEM7gi5j\n7lQYbmalzt30GqzSG2BD3TBx7J16wUvI/01sW8magv5Cu6KSRYGEEGlh99FHH91///0jR44M\nntmmTZv7779/2bJl0Y8LSCDBA8gybizi4NXfBtQOrsAqPeu6I9RtaeFnVlThha7nYuCZ3ZHW\nmD0PfalqC+fKwcAw5SE3mrdbCGGTp01T6awYmom0sCstLU1NTb1wvs1mKyoqimpIlQu+4ynk\nQOznOfenYd6FTxqmPhH4ruM0BwDEUDXvqS7yIhLQUKSFXbt27VatWjVu3Di7/dzNCqWlpatW\nrbryyitjE1tEgjsrHnLsbGy9Lg9xF0XgFFiwzNOPxiYu1CwffPVb7l2jhGQOEwAAIABJREFU\nbSCAZuJ2lA5AGJEWdlOnTr3uuus6d+48evToyy67zGQy5eTkvPjii3v27Fm/nu5nUL0F+j0R\n53d9Amgi5LV0ABCJSAu7rl27rlq1Kjs7+9577w3MrFu37muvvdazZ8/YxAboWvAlATEaXxzQ\nuZBX4KkkXRW9dSExLVu2bN68efv37+/YseP8+fNbtmypdUR6FGlhJ4S49dZbb7rppt27dx84\ncKCsrKx58+bt2rULPjMbRYEjKBcVn5sZeZcT53Vu54xeWKj+gs/dA4mEHumQ2JYtW/boo4++\n+OKLjRs3fuaZZ2655ZZ9+/YZjUat49IdBYWdEMJsNnfs2LFjx46BOcuWLdu2bduSJUuiHRig\njZDDUQD6F82DZ5oIDPYNXMDv9z/77LPPPvvsPffcI4S45JJLsrOzjxw50rhxY61D0x0Fhd1b\nb721cePG4uJzx9B8Pt/GjRtbtYrOp5FDKQAA4ELffvvt/v37+/fv7/P5Tp482aBBg7feekvr\noHQq0sJuyZIlDzzwQGpqqsfjKS4ubtCgQWlp6YkTJ+rXrz9z5syYhqhCcI24tDnXbUBjwcNR\nBHdWDACIxNGjR00m0/Lly6dPn15QUHDxxRfPnTu3f//+WselR5EWdvPnz7/iiis+++yzgoKC\nZs2aLVu2rHv37uvXr7/zzjvr1q1beftoCDk6e+QXsNfrNOXCmcG/sowEUBMEJ0x4wbfKRn6t\n5wdbK7uf0URhh6ra2/Rc501tDoXrs0ndcBG6cv6AE6ihTp486fF4tm/f/tVXX6Wnp8+fP3/o\n0KF79uyJ1jnDRBJpYXfw4ME//vGPVqvVarW2bdt2586d3bt3v/HGG/v16xetIcUi/8WtdA3q\nblEMdDFAT8WIg7KXawWmLff9qmEkAKBztWrVEkIsWLCgTp06QogJEyYsWrTogw8+oLC7UKSF\nncFgSE8/Oy5S8+bNc3Jy5OmOHTtOnTo1FpFFTV6vsxPOLzSNAwAQD0HjwyJBXHrppQaD4dSp\nU3Jh5/F4XC5XWlqa1nHpUaSFXcuWLd95550HHnggIyOjVatWCxcu9Pv9kiQdOnQoLy/EUF3R\nUvWeYxkSANEV8pIAoCI6PxVQ7e+l1SXPVFtgmnFjo6J+/foDBgwYMWLEX//6V6fTOWfOHJPJ\ndOutt2odlx5FWtiNGTNm2LBhjRs3Pnz4cJ8+fcaPHz9y5MimTZsuWLAguPeT+Aj+a4wfVyhy\n3p/ylqittoqXAQAAwlu2bFl2dvY999xTWFh4zTXXfPTRRxkZGVoHpUeRFnZDhw612WzLly/3\n+XyXXnrpCy+8MG7cuNLS0gYNGsyePTumIQJAjVXpfRLBC0TLfHunwPQo144IZ4ZmviHEzMoG\nmQh5w0Qdz3sXzvzFdFslAVRNz0NfBqY5jqAtu92+cOFCraOoBhT0Y9evX79+/frJ048++ug9\n99zz/ffft2jRwmKJ3nEPAEAFYlHDIXJchoFqQdnIEwFer3fTpk0+n69hw4bRKuxidI4svOA+\nUErWxvBiQQA1XOBiu2AhL7x7q825Aq5FQdQCqPRyupCH3yKfeZ6QB+q8wypppVzIw3ghf0Gi\nOJZM6I1W4KToVflCQPREWtgVFRWNGTNmy5Yt8v2wffv2Xb16tRCiadOmmzZtatiwYQxj/E3I\nsZ5CXm8XsnO7SjsYy02X5Ak6tAOgE8EjwIYv8kKeKkUUBd/MB+hWpIXdX/7yl5dffrl79+5C\niO3bt69evfq+++679dZb77777hkzZixevDiWQQJRw1cz4iBl1rRSeULjQOIr5FE6APEVaWG3\natWqPn36yEfpVq9ebbVan3/+eafT2bdv3w8//DAqoVT9Fzf80BTcrghAb4LPz+qzPxQA1Uuk\nhd0vv/xy7733ytPbtm3r2LGj0+kUQrRs2XLFihWxiq4CVe/cDqi6kJ2g9qysY9RfWoSYySgU\nEEKsW1+r8oUAIKxIC7t69ert2bNHCJGbm/vJJ59MnDhRnv/NN9/IA30ACSyKf0t87fHLE61N\nUpViAhILY8ICURFpYTdgwIDZs2ePGTPm448/9nq9gwYNKi4uXrRo0cqVK+n6GQCAGiW/03qt\nQ0BokRZ2kyZN+vbbb+fOnSuEmDZt2mWXXZaTk5Odnd2kSZNp0yq52xQAgJopa/IH8sTJGfR7\ngniItLBLSUl599138/PzJUlKSUkRQtSpU2fjxo2dOnVKSkqKZYSVCHmOLIoXPwEAgHJSd82K\n4try242L4tpqOGUdFKempgamnU5njx49oh2PlgIXP10lTmobCQBApqg3YACRFnb5+fljx47d\nuHFjcXFxuacyMjLkXosBAHoQ6KyYnooD6E4BNUSkhd1jjz22bNmyG2+8sV69epJ03t18RqMx\nBoEBAOIr0MOwe4OmcQBQL9LC7v3331+wYMGDDz4Y02h0gk7FANQQ81OnVL4QgOrDEOFykiT1\n7t07pqEAAACgKiI9YtetW7ddu3Y1atSoituzWq02m62KK4mRnoe+lCeCb4+VbwEOZjQaJUlS\negJaXt5ut1utVkUNTSZTUlKSz+dTsbkLg69U4K5npYxGo9KGBoNBXSshhMViCbOMzWarXmkW\nTN4h8jtoMim7vUle3uFwKE0YdWkmvxfxTDOTyRTPNAv/abXZbOWuS4m1/aFeRIuCcE0CF9sJ\nRdfbBY/6GjgtW+lMhHVhEkaSZoBSkt/vj2S5b7/9dvDgwbNnz+7Zs2dVtufxeJT+VgFKkWaI\nA6/XyxXGiIoDBw4o/bMqci1aRL8rr4KCglh0d6Lu7z2UE+mP34QJE2w22w033JCRkdGwYcNy\nv5qff/55hOspLi52u90VPZuRkeHz+fLy8iJcm8xoNCYlJeXn5ytqZTKZnE5nSUlJUVGRooY2\nm81gMFx4d3B4drvd4XAUFBSUlZUpapiSkuJyuTwej6JWqampZrM5NzdXUSshRHp6+unTpxU1\nkSQpIyPD7XareAtsNlthYaGiVhaLJSUlpbi42OFwVLRMpWnm9XrPnDmjaLvq0sxsNqemprpc\nLhUJI4RwuVyKWjkcDrvdnp+fH+blh6QuzZxOp8lk0nmamc1mq9UaizQrLCwMs8cyMzM9Ho/S\nNDOZTHa7vaAg7FG4C1SXNEtNTS0qKvJ6vYpaOZ1Oo9F46tQpRa2EqjQzGAzp6ellZWUq3gIV\naWa1WpOTk5X+BgHhRVrYlZSUZGRkVP0yO7/fX+kxwggPIpZbXnUrFQ1VtApuG59WQvk+qUor\nFQ2r8sZVuoyu0izOm0vgNFP3ga3K5qoYjM7f9yrunMRLs6p8YFXvf0Ri1apVAwYMKDfz7rvv\nXrp0qSbx6Fmkhd3atWtjGgcAAEBI11xzzbp16wIPy8rK7r77boaqD6mq1yEtW7Zs27ZtS5Ys\niUo0AAAA5dSuXbtXr3OD7c6YMWP48OG33367hiHploLC7q233io38oTP59u4cWOrVq1iEBgA\nAEB5OTk5K1as2L17t9aB6FSkhd2SJUseeOCB1NRUj8dTXFzcoEGD0tLSEydO1K9ff+bMmTEN\nEQAAQAjh9/vvv//+p556im5iKhJpB8Xz58+/4oorTpw4cfjw4dTU1GXLlh0/fvyDDz5wu911\n69aNaYgAAABCiNdeey0/P3/gwIFaB6JfkRZ2Bw8e7N27t9VqzcrKatu27c6dO4UQN954Y79+\n/SZOnBjLCAEAAIQQYs6cOQ888IDWUehapIWd3LuPPN28efOcnBx5umPHjtu2bYtJaAAAAL/5\n5JNP/ve//w0bNkzrQHQt0sKuZcuW77zzjtxFZKtWrTZv3ix3wHPo0CGl/QkDAAAo9fbbb199\n9dVOp1PrQHQt0sJuzJgxn332WePGjU+fPt2nT5/Dhw+PHDly2rRpCxYs6NixY0xDBAAAWLNm\nzbXXXqt1FHoX6V2xQ4cOtdlsy5cv9/l8l1566QsvvDBu3LjS0tIGDRrMnj07piECAAD873//\n0zqEaiDSI3ZCiH79+r399tuZmZlCiEcffTQ3N/err746cODA5ZdfHrPwAAAAEKmICrvPPvus\nSZMmCxcuDJ6ZlJTUunVri8USm8AAAACgTESFXYMGDX7++efNmzfHOhoAAACoFtE1dnXr1l22\nbNl99923dOnSu+66y2BQcAK3HIPBYDQaK3rW5/P5fL4wC1S0Tr/fr6KVz+cTQihtKEmSilZC\nCJ/PJ0mS0oZ+vz/8TquolYo9KYRQ0UqSJJ/Pp+4tEKr2v/zGhV9z+DRTF20800wIoSJb5J2j\nLmGqRZoJ5XtS3Rsnb07+sIdZs67STF3CqNicEKIqaaZ0WzXh28xsNlf6tQZESJJ7LanUwIED\nv/vuu71796alpdWrV89utwc/+/nnn8cmPAAAoC8FBQWpu2ZFcYX57cYJIVJSUqK4zhor0rti\nCwsL69aty+hhAAAAuhVpYbd27dqYxgEAAIAqivRU7IgRIyZNmnTppZeWm//xxx//61//mjdv\nXgxiAwAAulNQUBCL1XIqNioqKexyc3PliaysrPfee69r167Bz/p8vjlz5rz44otFRUUxjBEA\nAOgGhZ2eVVLYhb8pTNa9e/cPP/wweiEBAAD9KigoSP0imr/7+Vf1EBR2UVLJNXbPP/+8PPH4\n448//PDDzZo1K7dAamrqwIEDYxIaAOD/2bvz+Cjq84Hjs/eRbLJJQAjhCpcXh1yCRxUBDxQU\noSIoHlgKIgWVmkotWgQrVGqLIAWBAkWo/lSiVEUEFFSKoKjcEAQBRREIhNzJnr8/Bpcl7E52\nJrs7s5vP+8VLZ2fnO99nZp/dffLdOQBAjkiPsbvhhhtmzpzZqVOnWAcEAAC0jBE7LYv0rNj1\n69fHNA4AAADUUaSFXbQUFxe73e5wz2ZlZfl8vqKiIlnrNBgMqampxcXFsloZjUan01lZWSn3\nzA+r1arX6ysqKmS1stvtdru9pKTE5XLJapiWllZRUeHxeGS1Sk9PN5lMhYWFsloJgpCZmXn6\n9GlZTXQ6XVZWltvtVvAS2Gw2uQfhms1mcZ/Y7fZwy9SaZl6v98yZM7L6VZZmJpMpPT1dQZqJ\n1wCvrKyU1UpMM+nND0lZmjmdTqPRGM80c7lcJSUlshqaTCar1So3zSwWi8PhKC8vT0lJCbfM\nmTNnJPZYgwYNPB6P3DQzGo3iB4WsVmKaVVRUyP1cUpZmKSkpNptNQZqlp6eXlZV5vV5ZrZxO\np8FgCJzJFzkFaabX6zMzM5WlmcViKSsrk9UqkGbHjh2L3Z0n2rVrF6M1Q5uU3xwMAAAAmkJh\nBwAAkCQo7AAAAJKEVGE3aNCgwDkT/fr127lzZ1xCAgAAgBJSJ0989NFHOp0uJyfHYrGsXr36\nwQcfTEtLC7lkixYtYhMeAACAcPz48SeeeGLNmjVer7dPnz5/+9vfmjVrpnZQWiRV2D3wwAOz\nZ8/Oz88XHw4dOjTckhFeDA8AAECBIUOGlJSUvPLKK0aj8bnnnhswYMC2bdvUDkqLpAq7WbNm\nDRo06LvvvvP7/SNHjszLy7v44ovjFhkAAIAgCFVVVZ999tlrr702cOBAQRB0Ol3//v2PHz/e\nqFEjtUPTnFquY9erV69evXoJgiD+FHvZZZfFIygAAIBfWK3Wa6+9dvHixVdccYXRaFywYEHH\njh2p6kKK9ALFb775piAIfr//yJEjBw8e9Hg8bdu2bdmypV7PebUAACC2VqxYcemll15yySWC\nIKSlpe3evVvtiDRKRlm2du3aTp065ebm9u3b95ZbbmndunWHDh3Wrl0bu+AAAADKy8v79Olz\nyy237NixY/fu3UOHDu3bt6/c+1TVE5GO2G3duvW222676KKLpkyZ0r59e71ev3v37rlz5952\n222bN2/u0qVLTKMEAAD11gcffHD48OGvv/7aaDQKgjBv3rymTZv+97//feCBB9QOTXN0EZ7Q\n2q9fv71793711VdZWVmBmadPn+7ateull166atWqCPtzu93iqxI6Gp1OUHSOrU4X6YZEpTtl\nFAcZz01LlO78fr/YaUgej8dgMEg0FxJh5yiQKJuWKN2RZtHtK1Fed2XdKSMGefDgwcS6V2xp\naWna1x9FcYUlXfoIguBwOMIt8J///GfUqFGnT582m82CIHi93pycnClTpowaNSqKYSSHSEfs\nvvnmm9/85jfBVZ0gCJmZmcOHD1+4cKGsLiXSV/yUlJvf4vtQQSvxHaWsoeLu5H5e6PV6Za0U\nBCkIgsFgUNZK2Z4U6vDCSXzjSgejSrRyG4pHrypoRZrVQJpJIM1Ctor/94KsVvXTLbfckp6e\nPnTo0CeffFKn082aNcvr9d5+++1qx6VFkRZ2EpknKykrKircbne4Z7Oysnw+n9xfzQ0GQ2pq\nanFxsaxWRqPR6XRWVVWVl5fLami1WvV6fUVFhaxWdrvdbreXlZW5XC5ZDdPS0ioqKjwej6xW\n6enpJpNJwfEHmZmZclvpdLqsrCyPx6PgJbDZbKWlpbJamc3mtLS0qqoqu90ebpla08zr9Z45\nc0ZWv8rSzGQypaenK0gzm80mCEJlZaWsVmKalZaWSmx+SMrSzOl0Go3GeKaZ2+0uKSmR1dBk\nMlmtVrlpZrFYHA5HZWVlSkpKuGXKy8sl9liDBg0UpJnRaLTb7Qq2MT09vbKyUu7nkrI0S0lJ\nEd+5ctMsPT29rKzM6/XKauV0Og0GQ3zSTK/XZ2ZmKkszi8VSVlYmq1UgzWS1qp8yMzPXr18/\nceLEAQMGeL3eq666av369Y0bN1Y7Li2KtLDr3Lnz8uXLJ0yYEDxoV1RUtHz58s6dO8cmNgAA\nAEEQhHbt2gXumAAJkRZ2U6dOveaaazp16jRmzJj27dsLgrBnz565c+ceO3bs//7v/2IZIQAA\nACISaWHXvXv39957b8KECZMmTQrMvOyyy+bPn9+9e/fYxAYAAAAZIi3sBEG46aabduzYcfjw\n4QMHDvj9/jZt2uTm5nKBYgAAAI2QUdgJgqDX61u1atWqVasYRQMAAADF5BV2QDJxzJgiTpTm\nPaNuJAAARAU/pAIAACQJRuwAAIA84r0ioEGM2AEAACSJiAq7L774Ijc3d+7cubGOBgAAAIpF\n9FNss2bNfvrpp08++WTMmDGxDggAAGhc2hc/RXFtJVc2ieLa6rmIRuyys7OXLFny7rvvLl68\nWME9lQEAABAHkZ48kZ+f37Zt24ceemjChAk5OTniraMDvvzyyxjEBgAAABkiLezKysqys7Oz\ns7NjGg0AAAAUi7Sw++CDD2IaBwAAAOpI3nXsysrKtmzZcvLkyV69ejmdTpPJZDAYYhQZAAAA\nZJFxHbuFCxc2adKkb9++w4YNKygo2LJlS7NmzZYvXx674AAAABC5SAu7999/f9SoUV27dl2x\nYoU4p127dpdffvnw4cNXrVoVs/AAAAAQqUgLu7/+9a/t27dfu3btoEGDxDnZ2dkffvhhly5d\npk+fHrPwAAAAhO+///7uu+9u2LBhs2bNHnrooZKSErUj0qhIC7tt27b9+te/NhrPOyZPr9ff\ndtttO3fujEFgAAAAgiAI5eXlvXv3rqioePfdd1999dV9+/YFhplQQ6QnT2RkZFRWVl443+Px\nOByOqIYEAABwzocffvjjjz/u2LHDbrcLgvDGG280a9Zs586dHTp0UDs0zYl0xK5Hjx6vvvpq\nUVFR8MwTJ04sWbKke/fuMQgMAABAEAShuLjYbDYHbo6QkZGh1+t37dqlblTaJOMYu5KSkiuu\nuOL5558XBGH16tVPPfXU5ZdfXlpayjF2AAAgdnr37u3xeJ566qkzZ8789NNPDz/8sM/nO378\nuNpxaVGkhV1ubu5nn32Wm5v7pz/9SRCE6dOnT5s2rVOnTp9++mnbtm1jGSEAAKjXWrRo8eab\nby5btiwjI6NVq1YtW7bMyMho0KCB2nFpkYwLFHfq1GnDhg1FRUUFBQVms7lNmzZpaWmxiwwA\nAEB06623/vDDD8eOHcvKyvJ4PH/5y1+aNm2qdlBaJOMCxYIgHDlyZOXKle+9997777+/atWq\nGofcAQAARN2JEyeGDRu2b9++7Oxss9n8zjvvNGjQ4Oqrr1Y7Li2SMWL35JNPzpw50+VyBeY4\nnc6pU6f+7ne/i0FgAAAAgiAIF1100b59+0aOHDl16tRTp06NHz/+ySefNJvNaselRZGO2P3z\nn/984YUXunbtunr16hMnThw/fnzVqlWXXHLJuHHj8vPzYxoiAACo595++22Hw3HHHXdMmTJl\n0qRJeXl5akekUZGO2C1atOjyyy//6KOPAicb9+vXr1evXt27d585cybXCQQAALHTsmXLDz74\nQO0oEkCkI3b79+8fOHBgoKoT2Wy2wYMH79ixIwaBAQAAQJ5IC7vLLrustLT0wvmFhYUXX3xx\nVEMCAACAEpEWduPHj1+yZMmWLVuCZ37yySeLFy9+6KGHYhAYAAAA5JE6xu7ZZ58NftisWbOr\nrrqqb9++7du39/v927dvX79+fY8ePdq0aRPjIAEAAFA7qcJu8uTJF85cu3bt2rVrAw+3bNky\nffr0Pn36RD0yAAAAyCJV2Hk8nkhWodPpohQMAAAAlJMq7AwGQ9ziAAAAQB1Feh27o0ePPv74\n41u2bKmsrKzxVEZGxv79+6MdGAAA0KiSK5uoHQJCi7SwGzVq1OrVq3v06NGpU6cav70ysAcA\nAKAFkRZ2GzdufP3114cMGRLTaAAAAKBYpIVdw4YNu3XrFtNQALU4ZkwJTJfmPaNiJACQENI+\njrR+iERJ74hO1kQkIr1A8e23375s2bKYhgIAAIC6iLTifuGFF6655prdu3f36dMnJSWlxrP3\n3ntvtAMDYu5UxmxxIqtonLqRAAAQFZEWdu+///727du//PLLN95448JnKewAAABUF2lhN3Xq\n1G7duj366KMdO3aMwxWJA8c8ccATAABAhCIt7A4ePPj5559feumlMY0GAAAAikV68kT37t1L\nSkpiGgoAAADqItLCbvr06U899dSRI0diGg0AAAAUi/Sn2Oeee+7HH39s3bp1q1atLjwr9ptv\nvol2YAAAAOdxuVxNmjQpKCjIysoS53g8nieffHLFihVut3vAgAEvvfSSxWJRN0h1RVrYeTye\ntm3btm3bNqbRAAAAXMjtdhcUFEybNu3UqVPB83//+9+vWLFi3rx5JpNpzJgxv/3tb5cuXapW\nkFoQaWH37rvvxjQOAACAcGbOnDlr1iyXyxU8s7S0dNGiRYsWLerfv78gCHPmzLnjjjv+9re/\nXXTRRSqFqb5Ij7EDAABQS15e3g8//LBq1argmbt27SorK7vxxhvFh3369PF4PPX88LBIR+w6\ndOgQ7qmePXsuWLAgSvEAAABE5NixY2az2el0ig/NZnNGRsaxY8fUjUpdkRZ2LVu2DH5YXV19\n4MCBQ4cO9ezZs3v37tGPCwAAQJLf77/wpgkej0eVYDSiTsfYrVq16p577mnTpo2M/oxGvV7q\n91+dThd8Pksk57bo9Xq9Xi/3LBiDwSD+V25Do9FYI8jIuzOZTHLv26HX681ms9hcVishsr1X\ng4JNC/SoYJ8o2//CL/tTYhmJNNPpdGK0rlDPhotHr9crft0Vb6ayVmazWfpddiG9Xm8ymeSm\nmZjM8UkzsS9laaaglbgnxf9KLCO9xxRsZl0+zYxGY3zSLPBpJjfNdDqd2Wz2+XxyWyn7XIpn\nmonJILeVyWQSakszSMvOzq6uri4tLXU4HIIgeDyeM2fO5OTkqB2XmuqUT7feeuvYsWNnzJjR\nu3fvCJuIH1vhnhXfVMFZHknGi297ue8NMQy9Xi+3YeAzVEF3cr84BUHQ6XQGg0FuOXjhnoyc\nslbKXgIFrcR9KP2NIp1monD9hpsvloNxSzOxYdy6E18IBd+4QrzSTOxLWZopfptLZ1Gtb+e4\nvSlUSTMFn0t6vV6ss2W1SuI0C7xwslohWPv27e12+/r162+//XZBEDZu3GgwGK644gq141JT\nXf9QaNOmzdy5cyNf3uVyud3ucM9arVafz1deXu74ZU55eXmt6xQHfiJZMpj4163b7Zbb0Gq1\n6vX6iooKWa3sdrvJZKqqqqpxRk+tDAZDZWWl3IFlcchK7qYJgmCxWOS20ul0NpvN6/UqeAls\nNpvcVmaz2Ww2u91u8Y/dkCJMs5DPhpuvLM1MJpOyNLPZbIIgVFZWymplt9uNRmNVVZXE5oek\nLM3EMZu4pZnValWQZiaTyWq1ym1lsVjMZrPL5ZJIs+rqaok9ZrPZJNIsHKPRaLfblaWZy+WS\n+7mkLM1SUlKUpZnRaKysrPR6vbJaiT90xCfN9Hq94jRT0F0gzWS1QrC0tLSHHnooLy+vadOm\ner3+scceGzZsWHZ2ttpxqalOfyh4vd4VK1akpqZGKxoAAIDI/eMf/+jXr9/AgQNvu+22q666\nav78+WpHpLJIR+wGDBhQY47P59u7d++hQ4cmTJgQ7agAAABq6tq1q9/vD55jNBpnzpw5c+ZM\ntULSmkgLu6NHj144s3Hjxvfee+/TTz8d1ZAAAACgRKSFXT2/3B8AAID2cTIOAABAkpAasZO4\n20QNO3fujEYwgDpOZcwOTJuFZ1SMBACAupAq7Go93XXv3r3FxcVRjQcAAAAKSRV2n3/+ebin\njh8/npeXt3nz5szMzGnTpsUgMAAAAMgj+xg7n8/3z3/+85JLLlm2bNlDDz1UUFAwatSoWEQG\nAAAAWeTdeWLr1q1jxozZunVrx44d586de/XVV8coLEAtjhlTxInSPA62A4DQSnrLu1EN4ibS\nEbszZ86MHTu2R48eBQUFf//737/66iuqOgAAAE2JaMTu1VdffeKJJ06cOHH33Xf//e9/b9Kk\nSazDCpylyCmKAABozaNvOGpfKGIvDSmN4trquVpG7Hbv3n399dfff//9Tqdz7dq1r7/+ehyq\nOgAAACggVdg9+eSTnTt3/vLLL6dOnbpz586+ffvGLSwAAADIJVVVM5RWAAAgAElEQVTYvfDC\nC263u7Ky8umnn7ZYLLrw4hYuAAAAwpE6xm7kyJFxiwMAAAB1JFXYLViwIG5xAAAAoI7kXccO\nSCZHM85ONC1SNQ4AAKJE9p0nAAAAoE0UdgAAAEmCn2KB0AL3FhO4vRgAaIPL5WrSpElBQUFW\nVlYk8+shRuwA4WjGuX8AAA1yu927du0aMWLEqVOnIplfbzFiBwAAtG7mzJmzZs1yuVwRzq+3\nGLEDAABal5eX98MPP6xatSrC+fUWhR0AAECSoLADAABIEhR2AAAASYLCDgAAIElQ2AEAACQJ\nCjsAAIAkwXXsAABAYujatavf7498fj1EYQec51TGbHEiq2icupEAACCXRgu7wJ2dWqkaBuqJ\n/Y5z002L1IsDAIC64Rg7AACAJEFhBwAAkCQo7AAAAJIEhR0AAECS0OjJE0AcBJ8zAQBAEqCw\nAwAA8rw0pFTtEBAaP8UCAAAkCUbsAACAPJ+/FM1jWa56lPG/qGHEDgAAIEkwYgeEFri3mCAI\nZuEZFSMBACBCjNgBAAAkCQo7AACAJKHRn2IDFxhrpWoYAAAACYQROwAAgCRBYQcAAJAkKOwA\nAACSBIUdAABIDC6Xq0GDBqdOnQrMOX78+P3339+kSZOMjIxbbrllx44dKoanBRR2qL82p579\nF+xoxtl/AADtcLvdu3btGjFiRHBVJwjCvffeu2PHjuXLl3/44YdpaWm9e/c+duyYWkFqgUbP\nigUAAAiYOXPmrFmzXC5X8Mwff/zxo48+2rhx4zXXXCMIwvLlyxs3bvzuu++OGjVKpTDVx4gd\nAADQury8vB9++GHVqlXBM71e7+TJk7t16yY+dLvdVVVVPp9PjQC1It4jdhaLxWq1hntWp9Pp\n9XqH49ythR0zppx7esqMcK0MBkNwq0jo9XpBEMxmszgROYPBIPYot5UgCDabzWKxyGpoNBpT\nUlLkpqnYndx9IgiCTqdT0ErsUcFLUJcXTmIZq9UqnWY1+g3+NbZdqFtROxyO+KeZIAhGo7x3\nqLi83W6XmzDK0kzcqHimmdFojGeaSb9brVarTqeTWCDObwqLxaLscyluaWYwGFJSUvx+v9xW\nyhJGQSvxBVWWZjW+vCJsJdSWZpDWvHnzP//5z+J0RUXFAw884HA4hgwZom5U6op3YWc0GqU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8smDBgqlTp1ZWVhoMhl//+tf33XdfYAAP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\nYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        lifecycle[\n",
+    "            `Message` == \"TX\" & \n",
+    "            !is.na(`To EB [s]`) &\n",
+    "            `Created [s]` >= txFirst & `Created [s]` < txLast, \n",
+    "            .(`Time to reach EB [s]`=(`To EB [s]`-`Created [s]`)), \n",
+    "            .(`VariedX`, `VariedY`, `Minute created`=factor(floor(`Created [s]`/60)))\n",
+    "        ],\n",
+    "    aes(x=`Time to reach EB [s]`, fill=`Minute created`)\n",
+    ") + geom_histogram(bins=50, position=\"stack\") +\n",
+    "    facet_varied(wide=TRUE) +\n",
+    "    xlab(\"Time for transaction to reach EB [s]\") +\n",
+    "    ylab(\"Number of transactions\") +\n",
+    "    theme(axis.text.y = element_blank(), axis.ticks.y = element_blank())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc5aba94-7a59-44c3-98bf-5d5928ecb20a",
+   "metadata": {},
+   "source": [
+    "#### Time to reach the ledger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "06dc0e72-6337-4eca-9cbd-3b7576397c99",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 6 x 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Network</th><th scope=col>Throughput</th><th scope=col>Time to reach ledger [s]</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>topology-v2</td><td>0.100 TxMB/s</td><td> 54.85769</td></tr>\n",
+       "\t<tr><td>topology-v2</td><td>0.150 TxMB/s</td><td> 58.95047</td></tr>\n",
+       "\t<tr><td>topology-v2</td><td>0.200 TxMB/s</td><td>177.09873</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.100 TxMB/s</td><td> 57.04782</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.150 TxMB/s</td><td> 58.07758</td></tr>\n",
+       "\t<tr><td>topology-v3</td><td>0.200 TxMB/s</td><td>149.63690</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 6 x 3\n",
+       "\\begin{tabular}{lll}\n",
+       " Network & Throughput & Time to reach ledger {[}s{]}\\\\\n",
+       " <fct> & <fct> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t topology-v2 & 0.100 TxMB/s &  54.85769\\\\\n",
+       "\t topology-v2 & 0.150 TxMB/s &  58.95047\\\\\n",
+       "\t topology-v2 & 0.200 TxMB/s & 177.09873\\\\\n",
+       "\t topology-v3 & 0.100 TxMB/s &  57.04782\\\\\n",
+       "\t topology-v3 & 0.150 TxMB/s &  58.07758\\\\\n",
+       "\t topology-v3 & 0.200 TxMB/s & 149.63690\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 6 x 3\n",
+       "\n",
+       "| Network &lt;fct&gt; | Throughput &lt;fct&gt; | Time to reach ledger [s] &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| topology-v2 | 0.100 TxMB/s |  54.85769 |\n",
+       "| topology-v2 | 0.150 TxMB/s |  58.95047 |\n",
+       "| topology-v2 | 0.200 TxMB/s | 177.09873 |\n",
+       "| topology-v3 | 0.100 TxMB/s |  57.04782 |\n",
+       "| topology-v3 | 0.150 TxMB/s |  58.07758 |\n",
+       "| topology-v3 | 0.200 TxMB/s | 149.63690 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Network     Throughput   Time to reach ledger [s]\n",
+       "1 topology-v2 0.100 TxMB/s  54.85769               \n",
+       "2 topology-v2 0.150 TxMB/s  58.95047               \n",
+       "3 topology-v2 0.200 TxMB/s 177.09873               \n",
+       "4 topology-v3 0.100 TxMB/s  57.04782               \n",
+       "5 topology-v3 0.150 TxMB/s  58.07758               \n",
+       "6 topology-v3 0.200 TxMB/s 149.63690               "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dtmp <- lifecycle[\n",
+    "        `Message` == \"TX\" & \n",
+    "        !is.na(`To RB [s]`) &\n",
+    "        `Created [s]` >= txFirst & `Created [s]` <= txLast, \n",
+    "        .(`Time to reach ledger [s]`=mean(`To RB [s]`-`Created [s]`)), \n",
+    "        varied\n",
+    "    ]\n",
+    "setorderv(dtmp, varied)\n",
+    "dtmp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "da2fc48b-51bb-4cef-bf80-dc199206b405",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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lRZ7k7srH9SrwmDwWA2m9WrXwhhNBqt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\nAACASZDYAQAAmASJHQAAgEn8f1o96H6w0FbIAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        lifecycle[\n",
+    "            `Message` == \"TX\" & \n",
+    "            !is.na(`To RB [s]`) &\n",
+    "            `Created [s]` >= txFirst & `Created [s]` < txLast, \n",
+    "            .(`Time to reach ledger [s]`=`To RB [s]`-`Created [s]`), \n",
+    "            .(`VariedX`, `VariedY`, `Minute created`=factor(floor(`Created [s]`/60)))\n",
+    "        ],\n",
+    "    aes(x=`Time to reach ledger [s]`, fill=`Minute created`)\n",
+    ") + geom_histogram(bins=50) +\n",
+    "    facet_varied(wide=TRUE) +\n",
+    "    xlab(\"Time for transaction to reach ledger [s]\") +\n",
+    "    ylab(\"Number of transactions\") +\n",
+    "    theme(axis.text.y = element_blank(), axis.ticks.y = element_blank())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "cdf2ab4e-3e2c-45ae-90f1-3f1f336dc897",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ggsave(\"plots/reach-rb-tx.svg\", units=\"in\", dpi=150, width=16, height=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26e3bfa5-85eb-4bc1-9f97-a0379a1a5ac8",
+   "metadata": {},
+   "source": [
+    "#### Number of references\n",
+    "\n",
+    "A *reference* is one of the following:\n",
+    "\n",
+    "- A transaction is included in an IB.\n",
+    "- An IB is referenced by an EB.\n",
+    "- An EB is referenced by another EB.\n",
+    "\n",
+    "In an efficient Leios, the number of references for each data item would be one.\n",
+    "\n",
+    "- *Zero references* indicates that the item was not used.\n",
+    "- *Two or more references* indicates that duplication has occurred."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e3963046-2ff4-4f56-af87-cb0d6fb18ea0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BGQpIDkbhyQxBGQpIDkbhyQxBGQpIDk\nbhyQxBGQpIDkbhyQxBGQpIDkbhyQxBGQpIDkbhyQxBGQpIDkbhyQxBGQpIDkbhyQxBGQpIDk\nbhyQxBGQpPZrSHtsmT16aaNv9qT36puk8Rl165tB4zPqtRf3Vjj5QJ9+Ff3GZzSgb3qNz2hQ\n33Qbn1HSXbYnL7g3IoPk89aT9Rl5vRlmfUZd9mLvBEAatGUH9bJG3wxmE/omY3xGHpu0zxWl\nk/biQIWTp1P6VaSMz8hjk/S5omRa3ySMzyjjLlc4eSIySNGlH8rzzbDgMg/t1HhoJ48m6qFd\ndOmHqsuHdu43BpIUkCYq/VBAcgFJHgFJCkg2IMkjIEkByQYkeQQkKSDZgCSPgCQFJBuQ5BGQ\npIBkA5I8ApIUkGxAkkdAkgKSDUjyCEhSQLIBSR4BSQpINiDJIyBJAckGJHkEJCkg2YAkj4Ak\nBSQbkOQRkKSAZAOSPAKSFJBsQJJHQJICkg1I8ghIUlMFUmT3KZCEgKSMgGQDkhCQlBGQbEAS\nApIyApINSEJAUkZAsgFJCEjKCEg2IAkBSRkByQYkISApIyDZgCQEJGUEJBuQhICkjIBkA5IQ\nkJQRkGyTDlJkJweSFJCqDEhCQFJGQLIBSQhIyghINiAJAUkZAckGJCEgKSMg2YAkBCRlBCQb\nkISApIwkSDOBVBKQ5BGQgiJIX5tzZljDmWcCqSggySMgBUWQjlp+bVjDtdeOH6Ra34PlTRSk\nWp+zPCBJjQHSue1Dv8wbz4d2tb4HywOSEJCUUeX3kXbed9NXvrdzXD/YUOt7sDwgCQFJGVWE\n9MzpjSed1HjGFiCVBCR5BKSgCNJ5s56Kx5884wIglQQkeQSkoAjSjPvDp/fNGDWkPTdfcO4X\nXzImffuFi1cngQSkoXuj1ueskH6oMUF6YIyQrrx4R3xl627TtmRL+7KbgQSkoXuj1ueskH6o\nsUA6f9bGePxnHxz1Q7tdzR25f41a1/af/ZQxW+d1AQlIwVSE9MzpjSef1Dhz82gh/fE7uYdz\ngwt/2Nnca0yqpT182d5cA7tsdQipa5fagPEYDXbbi3vc3y22/lqfs7xB/VBdZsBjlHCX7cl7\n3L1R63NWSD/UrnTWY5R61V4s+vD3vTeO9cPfgyvP7356fnipdX349JSmpqaVBa+v9T1Yntex\nqitd6YW1Pmd543ByV6qeTz4eR0/mxTziGj2k7ONLL/mN2bggvNy6Lnz6qU984hP3Jm3ZWt+D\n5aWSahnjM3KbQXfv2tK1Pmd5Gf1QKeMzKtjYkw+6l9X6nBXSD5XMGn2TzLqLg3kxDSMdevSo\nIXVdsWxD1pjO5v7c38otW+3L3WPKOnxox/tIQryPpIzK30fq6Oi4+123bt56z6z7Rwspe8kN\nifDXvoWbjdkxr9KbE5DqKCBJjeGDDe+9M3z60w+OFtK2lg3bcgXmluUvvHjxKvcK9xsDqY4C\nktQYIL3zwfDpjqNGC+nB5qEeNem2pYvX8AlZIA3fG7U+Z4X0Q40F0tyFO+LxzsvmjuWjdhVz\nvzGQ6iggSY0B0tojZ5zTetJR64BUEpDkEZCC4m+jeO76i5bf8FwcSCUBSR4BKeD7kYAkBSRl\nxPcj2YAkBCRlxPcj2YAkBCRlND7fjwSk4YA0xSGN9fuRgDQckKY2pLF+PxKQ8gFpakMa6/cj\nASkfkKY2pCi+HwlIAZCmOqQOF5AKA5I8AlJQBKnBBaTCgCSPgBQUQar0TbJACoCkjYAU8CVC\nQJICkjLiS4RsQBICkjLiS4RsQBICkjLiS4RsQBICkjLiS4RsQBICkjLiS4RsQBICkjLiS4Rs\nQBICkjLiS4RsQBICkjKqAGnTpvjz31zx+ds6gFQakOQRkAIH6dvTbn3ixCPmzD38lI1AKglI\n8ghIgYM087KOlnO2x+M/P2shkEoCkjwCUuAgTd8UP3zoS+wePAJIJQFJHgEpcJCOezw+6+7w\nwm3vB1JJQJJHQAocpAvnrn3k3Ws2PPH1GXcAqSQgySMgBQ7ScxdNa2wMvxXp4MOAVBKQ5BGQ\ngsLPI3Vu/vHaoYBUEpDkEZACC6nzofznj3Y+8GkglQQkeQSkwEL6SUP4P8/v/N4nj22cA6SS\ngCSPgBRYSB3vmnvXd5Yfc+j8VUVfagekMCDJIyAF7n2kLR8/rKHxkuKf6QKk4YAkj4AUFH6w\nYfs3z2o8ecVaIJUFJHkEpKD4q7/jW26a1fCeq4BUEpDkEZCCEki5Nlx1GpBKApI8AlJQCqmj\njfeRygOSPAJSUArpuQYglQckeQSkAEhAkgKSMgKSDUhCQFJG+4LUuR5I5QFJHgEpKP+o3bY1\nH4kcUtpman0PlpdJq2WNz8htkhVOnq31OcvL6ofKGJ9RwcaePOFeVutzVkg/VO4P3WfkLiYK\n2WxvWzRt+rn8i1QS/yLJI/5FCor+Rbr1o9OPXHrHDh7alQYkeQSkoPgHjR39z8X/Ly4gDQck\neQSkoAjS6pbGuTdtAlJZQJJHQApKPtiw8ep3HzLnK0AqCUjyCEhB+Uftvv/Jo4FUEpDkEZCC\nIkjX3Rf+f7+ffBJIJQFJHgEpKP5gw8Hhj728oqH5GSAVBSR5BKSgGNLqc+fE48//+/uWAKko\nIMkjIAXFkO7d0fSNePHP7ANSACRtBKSgBFJ89Qnb4/GHjwBSUUCSR0AKSiF1nr5oe8eiDwOp\nKCDJIyAFpZDiPz5m+pFH/QhIRQFJHgEpKIJ041O5J1u/8tXCL24AUgAkbQSkoPwTsiUBKQCS\nNgJSACQgSQFJGQHJBiQhICkjINmAJAQkZQQkG5CEgKSMgGQDkhCQlBGQbEASApIyApINSEJA\nUkZAsgFJCEjKCEg2IAkBSRkByQYkISApIyDZgCQEJGUEJBuQhICkjIBkA5IQkJQRkGxAEgKS\nMgKSDUhCQFJGQLIBSQhIyghINiAJAUkZAckGJCEgKSMg2YAkBCRlBCQbkISApIyAZAOSEJCU\nEZBsQBICkjICkg1IQkBSRkCyAUkISMoISDYgCQFJGQHJBiQhICkjINmAJAQkZQQkG5CEgKSM\nxgtSqrU79zR9+4WLVyeBBKShe6PW56yQfqiaQkq//NXmEFLbki3ty24GEpCG7o1an7NC+qFq\nCun+peeFkPrPfsqYrfO6gASkAEijemj3yxBSZ3Nv7kFeS3v4gvktLS23pG2m1vdgeZm0Wtb4\njNzGPaoteHWtz1leVj9UxviMCjb25An36lqfs0L6oXJ/6D4jdzERPaSn54cXW9eHT2fNnDnz\nn7K2OoSU1TPGY1RQyt4fBS+s9TnL8zmK19ELNvbkyXo+eWR/6AWbZPSQNi4YgrTOvtT9U1iH\nfzvx0E6Ih3bKaJwf2vXnHtS0bAUSkAIgjR5S38LNxuyYV+nNCUh1FJCk6gCSuWX5Cy9evMq9\n1P3GQKqjgCRVD5DSbUsXr+ETskAavjdqfc4K6YfiS4SqDUhCQFJGQLIBSQhIyghINiAJAUkZ\nAckGJCEgKSMg2YAkBCRlBCQbkISApIyAZAOSEJCUEZBsQBICkjICkg1IQkBSRkCyAUkISMoI\nSDYgCQFJGQHJBiQhICkjINmAJAQkZQQkG5CEgKSMgGQDkhCQlBGQbEASApIyApINSEJAUkZA\nsgFJCEjKCEg2IAkBSRkByQYkISApIyDZgCQEJGUEJBuQhICkjIBkA5IQkJQRkGxAEgKSMgKS\nDUhCQFJGQLIBSQhIyghINiAJAUkZAckGJCEgKSMg2YAkBCRlBCQbkISApIyAZAOSEJCUEZBs\nQBICkjICkg1IQkBSRkCyAUkISMoISDYgCQFJGQHJBiQhICkjINmAJAQkZQQkG5CEgKSMgGQD\nkhCQlBGQbEASApIyApINSEJAUkYTCyltM7W+B8vLpNWyxmfkNskKJ8/W+pzlZfVDZYzPqGBj\nT55wL6v1OSukHyr3h+4zchcTEwBpl60O/0Xq2qU2YHxG3fbingon76/1Ocsb0A/VZXxGCXfZ\nnrzH3Ru1PmeF9EPtSmd9Rq/aizy00/8B56GdPOKhXcD7SECSApIyApINSEJAUkZAsgFJCEjK\nCEg2IAkBSRkByQYkISApIyDZgCQEJGUEJBuQhICkjIBkA5IQkJQRkGxAEgKSMgKSDUhCQFJG\nkUD67b+t21P2QiABCUiekHac/+5PPmvuek0s9hffBdLICEgBkKqC1P7a2OsPfP2Drzn0a3ee\nceBmIOVHQAqAVBWkv421mb4Fsdf/2pjU8WcBKT8CUgCkqiC99dTck87YsvDyZ/8XkPIjIAVA\nqgpS7P/lniRjV4aXr/b5YJ77jYFURwFJaiIgXemeAsmOgBQACUhSQJJHQApGB+nvXsg1/PRi\nII2MgBQAqTpIRQEpPwJSAKSqIH2mKCDlR0AKgFQVpKpzvzGQ6iggSU0wpFeeAVJ+BKQASFVB\nOuSm8OmS+8OnfNTOjoAUAKkqSHz4u+IISAGQgCQFJHkEpABIUxhSZE0+SJEFJBuQxhyQgASk\nCAISkIAUQUAaJaT53801/HQhkIAEpFFC4mvtgFQYkEYH6VtFAQlIQOJr7YA09oAEJCBFEJCA\nBKQIAhKQIgxIQAJSBAEJSECKICABqTpI839izOwdQCoJSECqDtLrF8Rfjn335XxAygckIFUH\n6VPaVzakb79w8eokkIAEJPF9pCduvy32D7flqwCpbcmW9mU3AwlIQBIh5Vr4iwqA8vWf/ZQx\nW+d1AQlIQFIgGZN9af3aFzOVIHU29xqTamkPL39uxYoVDw7asrU++DiWtKccsHeFO3m61jdv\nHMu4Y1Y4eaLWN288S7hzjhLSj48N30H6qx9XgPT0/PBp6/rw6SlNTU0rK2nbn0vX+gbUQala\n34CJLjk6SM8eePA1Dzx03SEHtpdf5cYF4dPWdeHTvbkGdtkyu/RSxme0W98kjM+oS98MGI/R\nYLe96H6yrnt1f69+Fb2mx2PUr2+6TZ/HaFDfdJkBj1HCXbYndyfZk9Sv4lXjM0rrm11Zn5HP\nm2E66zFKvWovjvKh3exDh+6zV98xpxxSZ3N/7m/llq32BQXvIwV6KaNvgtSr+iZhfEYT9bV2\nPfpV9BS8Z7HvUZ++2Wt8RhP1fxFK6lexy/iM0vomyPqMvN4MJ+b/tPqWK4Z/vfKt5ZD6Fm42\nZse8Sm9OQJICkjzaHyH95Qikt5RDMrcsf+HFi1e556s8AZDEEZCkJhukM4cf2u2eNrsCpHTb\n0sVrKn9C1ufGAUkcAUlqskHacuDBX37ooesb/3RLBUilVXkCIIkjIElNNkhm3TFDH/7+kYcj\nIAVAkpvCkEzmxXVrX6j4CVkgAQlI3pCqqMoTAEkcAUkKSO7GAUkcAUkKSO7GAUkcAUkKSO7G\nAUkcAUkKSO7GAUkcAUlqkkHa/I41QCoZASkAUrWQfveac4BUMgJSAKSqH9p953V3+H0OKWy3\nLbVbL5nxGe3RN4MZn9FefTOQ8Rn12It7K5y8v1e/it5Mj8eoT990Z3xGA/pmb8ZnNOgu25O7\n43Yl9KvYnfEZ+bz1pH1GXm+GaZ+Rewvr+5XYr/cBaeHxsTcec1KYNyciKvt+JFuNbg7R5GwM\nH7UjopFKIfWsv/f3A/zfCYiqqwTSrQfFYhs2vM3nB/YRka0Y0qMHnH5/bMPvPhj7QY1uDtHk\nrBjS+45NmdgGkznxfTW6OUSTs2JIB33JhJDMVW+s0c0hmpwVQ3r75cOQPtdYo5tDNDkrhnT2\nwbtDSH942wKP/7TKr83gS4TEEV8iJDXZvkToVwe9/brY5Z9785//B5DyIyAFQKoaktn2gfB/\nfvI3z3k4AlIAJLkpDMmY3Zva95a+DEhhQAKSP6SX7/z8NffuNj5VeQIgiSMgSU06SJe9Jnxo\n98ZvAGlkBKQASFVDWh07be0f//DDU2P3Ayk/AlIApKohNR3TH/7Sf4zPVzZUeQIgiSMgSU02\nSAd9fvjXL7wBSPkRkAIgVQ3plIuHf/27vwZSfgSkAEhVQ/r2658Jf9nwZ7cAKT8CUgCkqiBd\nHXbMAbMu+czM2CnrgZQfASkAUlWQYoV9EEj5EZACIFUFKV2Yz/+Uq8oTAEkcAUlqMkGquipP\nACRxBCSpyQbpNwsb3zzU4UDKj4AUAKlqSHMOOPWi5WF/7wEpZSu4uM+yXqPIriitbzLGZ+Q2\niQonz2Si+n08rihtfEaRXVHBH4Y9eaLiq/eZ8foDjeqKxuPNMDE6SAfd5wFopF22zKKo2r1L\nLWF8Rl36ZsD4jLrtxT0VTt7fo19Fr/EZ9embbuMzGtA3XcZnlHCX7cndSfYk9at41fiM0vpm\nV9ZnlPHYpLM+o1ftxVH+izT9xSogFfybGhkkHtoJ8dBOro4e2n3mS0AqGQEpAFLVkJInf+Rf\nvjUUkPIjIAVAqhrSg68Z+YwskPIjIAVAqhrSie++r3PnUEDKj4AUAKlqSG/o8AAEJPdqIElN\nXUgffAZIJSMgBUCqGtLWM14GUvEISAGQqoY078j/cfgJQwEpPwJSAKSqIX3IBqT8CEgBkKqG\nVFUFJwCSEJDkEZDcCYAkBCR5tD9CmjHSMiDlR0AKgDS695FmTYud+i9Ayo+AFABptA/tfvCG\nx4GUHwEpANKo30e6YjaQ8iMgBUAaNaQ7/gJI+RGQAiCNFlL6b31+hmzBCYAkBCR5tD9CGv5k\n7NxpsUuBlB8BKagMKbI/8/0R0vCXB51w2pUJo1dwgsjuVCAJAUmujiBVVcEJIrtTgSQEJDkg\nuYAkBCS5OoE0oygg5UdACoBUFaRTXW/g/9lgR0AKgDS6h3b/dX7sTXyJ0MgISAGQRgMps/qN\nB3wsKH0pkIAEpGogPXtS7LiNPoyAFAaksbc/Qtrzd39y0M0p41fBCSK7U4EkBCS5+oF091/G\nzvmtJyMghQFp7O13kJ5/f+yIx7wZASkMSGNvf4N02YF/dq3PVwYBCUgBkPYNqeiHMfN5pJER\nkAIgVQVpWVFAyo+AFACpuveRqq3gBJHdqUASApIckFxAEgKSHJBcQBICkhyQXEASApIckFxA\nEgKSHJBcQBICkhyQXEASApIckFxAEgKSHJBcQBICkly9Q/rN1YvOW5m7L9O3X7h4dRJIQAoD\nUrWQkh+/pn3zpy81pm3JlvZlNwMJSGFAqhZSvLnHmGeaB/rPfsqYrfO6gASkAEjVQ8oMmMzu\nNZeazuZeY1It7eHLPiD/gdwAABYPSURBVLdixYoHB23ZyO7UxKBaxviMkvom7XNFaXdFA/Yu\nKXh1Sr+KlPEZpfVN0ueKkh5XlDA+v1vGXa5w8kRkf+ZZ/bYMZr1GPhujbwazBW8YEb6PZMyK\n5kW/Nk/PDy+2rg+fntLU1LSyYBDZnepza2pVutY3oA4q+N8QTIk/c5OMFFL3H+/5aP/GBeHF\n1nXh09++8sore3bbontoV3Cl+yppujxGe/XNoPEZ9diL7kGte3V/n34VfabXY9Svb3qMz2hQ\n3+w1PqOku2xPXnBvRPZnntZvy+5sxmPks0lnfUbuzbA3Okgvhw/msgs3dzb35/5WbtlqX1Hw\n4DSyO5X3kYR4H0muzt9HeuK83GOa3pb2voWbjdkxr9KbE5CkgCQ3VSB1t676ZccXLho0tyx/\n4cWLV7lXFJwgsjsVSEJAkqtzSCZ++TkX3PiH3MO6tqWL1/AJWSANBaSqIe2rghNEdqcCSQhI\nckByAUkISHJAcgFJCEhyQHIBSQhIckByAUkISHJAcgFJCEhyQHIBSQhIckByAUkISHJAcgFJ\nCEhyQHIBSQhIckByAUkISHJAcgFJCEhyQHIBSQhIckByTTpIkZ0cSFJAqjIgCQFJDkguIAkB\nSQ5ILiAJAUkOSC4gCQFJDkguIAkBSQ5ILiAJAUkOSC4gCQFJDkguIAkBSQ5ILiAJAUkOSC4g\nCQFJDkguIAkBSQ5ILiAJAUkOSC4gCQFJDkguIAkBSQ5ILiAJAUkOSC4gCQFJDkguIAkBSQ5I\nLiAJAUkOSC4gCQFJDkguIAkBSQ5ILiAJAUkOSC4gCQFJDkguIAkBSQ5ILiAJAUkOSC4gCQFJ\nDkguIAkBSQ5ILiAJAUluskIasGUju1MHB9TSxmeU8Lkin1HSXuyvcPJUZCdP6bclYXxGaZ8r\n8hll3OUKJx+M7ORZ/bYMZL1GPhujbwayBW9hEwCpyxbdv0jdXWpJs9dj1KNvEsZn1Gcvdlc4\neXRvTgP6bekzPqOEvukxPqOku2xP3mtf1B3ZyTP6benK+ox8Numsz8i9hfXx0E6Nh3byiId2\nAe8jAUkKSMoISHYEJCEgKSMg2RGQhICkjIBkR0ASApIyApIdAUkISMoISHYEJCEgKSMg2RGQ\nhICkjIBkR0ASApIyApIdAUkISMoISHYEJCEgKSMg2RGQhICkjIBkR0ASApIyApIdAUkISMoI\nSHYEJCEgKSMg2RGQhICkjIBkR0ASApIyApIdAUkISMoISHYEJKH6ghRd+qGA5AKSPAKSFJBs\nQJJHQJICkg1I8ghIUkCyAUkeAUkKSDYgySMgSQHJBiR5BCQpINmAJI+AJAUkG5DkEZCkgGQD\nkjwCkhSQbECSR0CSApINSPIISFJAsgFJHgFJCkg2IMkjIEkByQYkeQQkKSDZgCSPgCQ1dkgz\ngVQSkOQRkIIiSF+bc2ZYw5lnAqkoIMkjIAVFkI5afm1Yw7XXAqkoIMkjIAVFkM5tH/pl3qR4\naBdZkw5SZAFJaizvI+2876avfG/n5PhgQ2QBSQhIyqgipGdObzzppMYztgCpJCDJIyAFRZDO\nm/VUPP7kGRcAqSQgySMgBUWQZtwfPr1vBpBKApI8AlJQDOmBMULac/MF537xJWPSt1+4eHUS\nSEAaujdqfc4K6YcaC6TzZ22Mx3/2wdE/tLvy4h3xla27TduSLe3LbgYSkIbujVqfs0L6ocb4\nwYaTT2qcuXm0kHY1d+T+NWpd23/2U8ZsndcFJCAFUxFSfOe9N47lw99//E7u4dzgwh92Nvca\nk2ppD1/2r3fdddemXlu21vdgef29aknjMUoN2ot99i4puI5an7O8lH6ofpP0GKXdZXtyd4/1\n1/qcFdIP1Zsx+qY30+eOmRfziGssH2wYXHl+99Pzw0ut68OnpzQ1Na0seH2t78HyvI5VXelK\nL6z1Ocsbh5O7UvV88vE4ejIvpmGkQ48ePaTs40sv+Y3ZuCC83LoufLo510tdtjr8F6mnSy1h\nfEZ99mK3vUPcqwdrfc7yEvqheozPKOUu25P3ulfX+pwV0g/Vlc76jPbai315MR0dHXe/69bN\nW++Zdf+oIXVdsWxD1pjO5v7c38otW+3L3WNK3keqo3gfSWoM7yO9987w6U8/OFpI2UtuSIS/\n9i3cbMyOeZXenIBURwFJagyQ3vlg+HTHUaOFtK1lw7Zcgbll+QsvXrzKvcL9xkCqo4AkNQZI\ncxfuiMc7L5s7WkgPNg/1qEm3LV28hk/IAmn43qj1OSukH2oskNYeOeOc1pOOWjeWj9pVzP3G\nQKqjgCQ1ls8jPXf9RctveC4OpJKAJI+AFPD9SECSApIy4vuRbEASApIy4vuRbEASApIy4vuR\nbEASApIyGp/vRwLScECa2pDG/P1IQBoOSFMb0li/HwlI+YA0tSGN9fuRgJQPSFMbUocLSIUB\nSR4BKSiC1OACUmFAkkdACoogVfomWSAFQNJGQAr4EiEgSQFJGfElQjYgCQFJGfElQjYgCQFJ\nGfElQjYgCQFJGfElQjYgCQFJGfElQjYgCQFJGfElQjYgCQFJGfElQjYgCQFJGVWAtGlT/Plv\nrvj8bR1AKg1I8ghIgYP07Wm3PnHiEXPmHn7KRiCVBCR5BKTAQZp5WUfLOdvj8Z+ftRBIJQFJ\nHgEpcJCmb4ofPvQldg8eAaSSgCSPgBQ4SMc9Hp91d3jhtvcDqSQgySMgBQ7ShXPXPvLuNRue\n+PqMO4BUEpDkEZACB+m5i6Y1NobfinTwYUAqCUjyCEhB4eeROjf/eO1QQCoJSPIISIGF1PlQ\n/vNHOx/4NJBKApI8AlJgIf2kIfyf53d+75PHNs4BUklAkkdACiykjnfNves7y485dP6qoi+1\nA1IYkOQRkAL3PtKWjx/W0HhJ8c90AdJwQJJHQAoKP9iw/ZtnNZ68Yi2QygKSPAJSUPzV3/Et\nN81qeM9VQCoJSPIISEEJpFwbrjoNSCUBSR4BKSiF1NHG+0jlAUkeASkohfRcA5DKA5I8AlIA\nJCBJAUkZAckGJCEgKaN9QepcD6TygCSPgBSUf9Ru25qPRA6p15at9T1YXn+vWtL4jAbsxb4K\nJ0/W+pzlJfVD9RufUdpdtid391h/rc9ZIf1QvRmjb3ozfe6YhWy2ty2aNv1cIJUEJHkEpN4i\nSLd+dPqRS+/YwUO70nhoJ494aBcU/6Cxo/+5+P/FBaThgCSPgBQUQVrd0jj3pk1AKgtI8ghI\nQckHGzZe/e5D5nwFSCUBSR4BKSj/qN33P3k0kEoCkjwCUlAM6dnVa+PxjvufB1JxQJJHQAqK\nID189NH3xOPbG056DEhFAUkeASkogjRnydAH7drPmg+kooAkj4AUFEE6/Pvx+NYzO+L3HAGk\nooAkj4AUlP0M2ScbNsXv/CsgFQUkeQSkoAhS64fbd6444hN3nr4ISEUBSR4BKSiC9NOTD5l+\n9LqZDTOfBFJRQJJHQAqKP/y9/e47n4vHtxW8BEhhQJJHQArKPyFbEpACIGkjIAVAApIUkJQR\nkGxAEgKSMgKSDUhCQFJGQLIBSQhIyghINiAJAUkZAckGJCEgKSMg2YAkBCRlBCQbkISApIyA\nZAOSEJCUEZBsQBICkjICkg1IQkBSRkCyAUkISMoISDYgCQFJGQHJBiQhICkjINmAJAQkZQQk\nG5CEgKSMgGQDkhCQlBGQbEASApIyApINSEJAUkZAsgFJCEjKCEg2IAkBSRkByQYkISApIyDZ\ngCQEJGUEJBuQhICkjIBkA5IQkJQRkGxAEgKSMgKSDUhCQFJGQLIBSQhIymi8IKVau3NP07df\nuHh1EkhAGro3an3OCumHqimk9MtfbQ4htS3Z0r7sZiABaejeqPU5K6QfqqaQ7l96Xgip/+yn\njNk6rwtIQAqANKqHdr8MIXU29+Ye5LW0hy94/LHHHuvstmVrfQ+W19utljB9+ijZby/2uDcn\n22Ctz1leUj9Un/EY9abcZXtyd4/11vqcFdIP1Z3JeozSBXdV9JCenh9ebF0fPj2lqalpZcGg\n1vdgeT7HqrJ0pRfW+pzljcPJXal6Pvl4HD0ZPaSNC8KLrevCp/961113beq11eG/SP29aknj\nMUoN2ot99v4ouI5an7O8lH6ofpP0GKXdZXtyd4/11/qcFdIP1Zsx+qY30+eOOR4P7fpzfyu3\nbLUvdY8peR+pjuJ9JKk6eB+pb+FmY3bMq/TmBKQ6CkhSdQDJ3LL8hRcvXuVe6n5jINVRQJKq\nB0jptqWL1/AJWSAN3xu1PmeF9EPxJULVBiQhICkjINmAJAQkZQQkG5CEgKSMgGQDkhCQlBGQ\nbEASApIyApINSEJAUkZAsgFJCEjKCEg2IAkBSRkByQYkISApIyDZgCQEJGUEJBuQhICkjIBk\nA5IQkJQRkGxAEgKSMgKSDUhCQFJGQLIBSQhIyghINiAJAUkZAckGJCEgKSMg2YAkBCRlBCQb\nkISApIyAZAOSEJCUEZBsQBICkjICkg1IQkBSRkCyAUkISMoISDYgCQFJGQHJBiQhICkjINmA\nJAQkZQQkG5CEgKSMgGQDkhCQlBGQbEASApIyApINSEJAUkZAsgFJCEjKCEg2IAkBSRkByQYk\nISApIyDZgCQEJGUEJBuQhICkjIBkA5IQkJQRkGxAEgKSMgKSDUhCQFJGQLIBSQhIyghINiAJ\nAUkZAckGJCEgKaOJhdRlq0NIPV1qCeMz6rMXuyucfLDW5ywvoR+qx/iMku6yPXmvuzdqfc4K\n6YfqSmd9Rnvtxb4JgDRgy9b6HiwvMaCWNj6jpL3YX+HkqVqfs7y0fqiE8Rll3OUKJ6/Dv0IW\n6YcayBp9M5AddJd5aKfGQzt5xEO7gPeRgCQFJGUEJBuQhICkjIBkA5IQkJQRkGxAEgKSMgKS\nDUhCQFJGQLIBSQhIyghINiAJAUkZAckGJCEgKSMg2YAkBCRlBCQbkISApIyAZAOSEJCUEZBs\nQBICkjICkg1IQkBSRkCyAUkISMoISDYgCQFJGQHJBiQhICkjINmAJAQkZQQkG5CEgKSMgGQD\nkhCQlBGQbEASApIyApINSEJAUkZAsgFJCEjKCEg2IAkBSRkByQYkISApIyDZgCQEJGUEJBuQ\nhICkjIBkA5IQkJQRkGxAEtpfIUUWkGxAEgKSEpBsQBICkhKQbEASApISkGxAEgKSEpBsQBIC\nkhKQbEASApISkGxAEgKSEpBsQBICkhKQog9IQAJSBAEJSECKICABCUgRBCQgASmCgASkiCGl\nb79w8eokkIAEpDFBaluypX3ZzUACEpDGAqn/7KeM2TqvC0hAAtIYIHU29xqTamkPL//2lVde\n2bPbtj9D6rGndH+FuJP31/rmlTe4W22v8Rkl3WV78oJ7o9bnHM/c23Zv9JCenh8+bV0fPj2l\nqalpZcHran3wccwdMl3pXqn1zSvP+w90NKXq+eTR5U6ZjB7SxgXh09Z14dPPrVix4sFBW3ZQ\nL2t8Rgl9kzE+o6S+SftcUdpd0YC9KwpendKvImV8Rml9k/S5oqTHFSWMz++WcZcrnDyRKf9P\nyjIeo4TXW4/XyGdT7ZvheDy068/9rdyy1b6g4H0k/RF3kDL6Jki9qm8Sxmc0UV9r16NfRU/B\nexb7HvXpm73GZzRR7yMl9avYZXxGaX0TZH1GXm+Gtf+fn/Qt3GzMjnmV3pyAJAUkeTTVIJlb\nlr/w4sWr3PNVngBI4ghIUvsVpHTb0sVrKn9C1ufGAUkcAUlqv4JUWpUnAJI4ApIUkNyNA5I4\nApIUkNyNA5I4ApIUkNyNA5I4ApIUkNyNA5I4ApIUkNyNA5I4ApIUkNyNA5I4ApIUkNyNA5I4\nApIUkNyNA5I4ApIUkNyNA5I4ApIUkNyNA5I4ApLUfg2puu64LqIr+rfreqK5onXX/S6aK1Jq\nv257NFcUv25jNFf0++vWRnNFSqnr7onomv5xTURX1HZjRFdU2oRBuuDkiK7os02BPvLpxqZf\nRHNFSv/W9Eg0V/TTptujuaKOpvF6cyou0fR/I7qmM+ZHdEXnvieiKyoNSOMekMYekGxAGnNA\nGntAsgFpzAEp+iYMEtH+HJCIIghIRBEEJKIIAhJRBE0QpNIfCTOGUq3dUVzNnpsvOPeLL0Vx\nTXIRnnwKH73+Tz5BkEp/JMyoS7/81eZI7tMrL94RX9m6Wx+OschOPoWPPhlOPjGQyn4kzKi7\nf+l5kdynu5o7cn9AreP+NWfRnXwKH30ynHxiIBX+SJix9stI7tM/fif3mGNw4Q8juCqxKE8+\nhY9e/yefGEiFPxJmrEVzn4YNrjw/qqvaZ1GefAofvf5PPjGQCn8kzFiL6j7NPr70kt9Eck1S\nUZ58Ch+9/k8+UQ/tSn4kzBiK6D7tumLZhmwUVyQX5cmn8NHr/+QTA6nsR8KMoWju0+wlNyQi\nuBq1KE8+hY9e/yefoA9/l/5ImDEUzX26rWXDtlwRfSG5UIQnn8JHr/+TT9QnZEt+JMwYiuY+\nfbB5qEcjuCq5CE8+hY9e/yfnS4SIIghIRBEEJKIIAhJRBAGJKIKARBRBQCKKICARRRCQiCII\nSEQRBKQa92hsqLec8ZPil3/qDQtqc4NoVAGpxj0am3PllVde/pHXHPBk4YufiC34yb7+E6rD\ngFTjHo3dMvTrY7G5hS++PRavyc2hUQakGjcCybxpWuGLb4u9XDL8/eYJukU0moBU4xyko3NP\nfvWRQ1///h8YszD3btNs96yZvfDe176j8Pl5O89561s/vjd3ceP/eVPDopcL/lvT/bl3/tn0\nz/bW6kRTMyDVuBFIT8Q+a8y21x98+dUzDrjN/OIfYt/d4Z41s49/3UdWFz5/ynH//tKaAz5m\nzMN/euzVlx50WHfBK+f96VnXzI0tq/HBplhAqnGPxj509dVXX3ne/5zdb8zpb3/VmOTpB/UM\nP7Rzz86O3WGKn38s9/zst5vkYcfn/rs7cq+2r9x7wKdzr5t5RI0PNsUCUo3Lf/g7duBqY3bH\nvhy+6P7Y+iFIBc/OfmOm6NWz3xReuvDNZnMs/OFjyRvXu1d2H9C0q0aHmcIBqcblH9r959/m\n/o3ZlEcVu3cIUsGzs4/JbQqfPyH8j5a92Xw7tmn4egpeec2fvOYDV2yq2YmmZkCqcSPvI/1H\n7HLTHrt8w1C/H4JU8Ozsk3KT0udDSHfGnh2+noJXmue/+N7XxprTtTrSlAxINW4EUiq2zOyN\nXRFe/N2GgSFIBc8OwSl9PoT0s9i3w0s33ete2bWzz5g9y2IR/eRa8gpINW4EUia2yJi/efMf\nc5dmvTU9/MEG9+wwnNLnc5D63vbXCWO2xa53r1wfC38CxPdjD9fuUFMwINU4+3mk151szHN/\n/rYrrjoxdk/+E7Lu2WE4pc/nIJl7Djjxuqvecsir7pW90163+KYL//e0vbU71BQMSDXOQjo1\ndn/uPaX5h7zhveH/d234Kxvss8NwSp+/6PDckx+f/saDW4vG8XMOfu07lv3nxJ9lKgckoggC\nElEEAYkogoBEFEFAIoogIBFFEJCIIghIRBEEJKIIAhJRBAGJKIKARBRBQCKKICARRdB/A5OB\nqrc2j+LUAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "    lifecycle[\n",
+    "        `Message` == \"EB\",\n",
+    "        .(`References`),\n",
+    "        .(`VariedX`, `VariedY`)\n",
+    "    ], \n",
+    "    aes(x=`References`)\n",
+    ") +\n",
+    "    geom_histogram(binwidth=1) +\n",
+    "    facet_varied(wide=TRUE) +\n",
+    "    ylab(\"Number of EB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3ef7c12c-b295-4260-aa59-7959fb2ce5f3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LTzrvDSzsVLO7FdSb60UwYEkgtIIiAB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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "    lifecycle[\n",
+    "        `Message` == \"TX\",\n",
+    "        .(`References`),\n",
+    "        .(`VariedX`, `VariedY`)\n",
+    "    ], \n",
+    "    aes(x=`References`)\n",
+    ") +\n",
+    "    geom_histogram(binwidth=1) +\n",
+    "    facet_varied(wide=TRUE) +\n",
+    "    ylab(\"Number of TX\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d44b6a56-2399-4b22-9060-b74c093fc86e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ggsave(\"plots/references-tx.svg\", units=\"in\", dpi=150, width=16, height=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75593061-5257-4960-b50f-6f07dce58d8c",
+   "metadata": {},
+   "source": [
+    "#### Temporal efficiency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a80c61d8-0c51-4c63-be1e-696923483fd9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "toElapsed <- function(created, toRb, inRb) {\n",
+    "    if (!is.na(toRb))\n",
+    "        toRb - created\n",
+    "    else if (!is.na(inRb))\n",
+    "        inRb - created\n",
+    "    else\n",
+    "        as.numeric(NA)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "183a915d-c959-4117-bba0-340e9f06058e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "outcomes <- lifecycle[\n",
+    "    `Message` == \"TX\" & `Created [s]` < txLast,\n",
+    "    .(\n",
+    "        `Submitted [minute]`=factor(floor(`Created [s]`/60)), \n",
+    "        `Time to reach ledger [s]`=mapply(toElapsed, `Created [s]`, `To RB [s]`, `In RB [s]`)\n",
+    "    ),\n",
+    "    .(`VariedX`, `VariedY`)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3c5e829b-185d-42ed-a936-82b5c893aec2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "totals <- \n",
+    "    outcomes[\n",
+    "        , \n",
+    "        .(\n",
+    "            `Total`=.N, \n",
+    "            `Lost`=sum(is.na(`Time to reach ledger [s]`)),\n",
+    "            `1st min`=sum(`Time to reach ledger [s]`<=60,na.rm=TRUE),\n",
+    "            `2nd min`=sum(`Time to reach ledger [s]`>60&`Time to reach ledger [s]`<=120,na.rm=TRUE),\n",
+    "            `3rd min`=sum(`Time to reach ledger [s]`>120&`Time to reach ledger [s]`<=180,na.rm=TRUE)\n",
+    "        ), \n",
+    "        .(`VariedX`, `VariedY`, `Submitted [minute]`)\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb43e073-4d4e-455a-b861-9fea5b848b25",
+   "metadata": {},
+   "source": [
+    "#### Transactions reaching the ledger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f65eae2e-99b4-4fd3-9a56-7c78041bf66e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OwAAAAkgmAHAAAgEQQ7AAAAibCuBxTX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\nXwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        melt(\n",
+    "            totals[\n",
+    "                , \n",
+    "                .(\n",
+    "                    `Ledger in 1st min`=`1st min`/`Total`, \n",
+    "                    `Ledger in 2nd min`=`2nd min`/`Total`, \n",
+    "                    `Ledger in 3rd min`=`3rd min`/`Total`, \n",
+    "                    `Ledger later`=(`Total`-`Lost`-`1st min`-`2nd min`-`3rd min`)/`Total`,\n",
+    "                    `Lost`=`Lost`/`Total`\n",
+    "                ), \n",
+    "                .(`VariedX`, `VariedY`, `Submitted [minute]`)\n",
+    "            ],\n",
+    "            id.vars=c(\"VariedX\", \"VariedY\", \"Submitted [minute]\"),\n",
+    "            variable.name=\"Outcome\",\n",
+    "            value.name=\"Fraction\"\n",
+    "        )[, .(\n",
+    "            `VariedX`, `VariedY`,\n",
+    "            `Submitted [minute]`, \n",
+    "            `Outcome`=factor(`Outcome`,levels=c(\"Ledger in 1st min\", \"Ledger in 2nd min\", \"Ledger in 3rd min\", \"Ledger later\",\"Lost\")), \n",
+    "            `Fraction of transactions [%]`=100*`Fraction`\n",
+    "        )],\n",
+    "    aes(x=`Submitted [minute]`, y=`Fraction of transactions [%]`, fill=`Outcome`)\n",
+    ") +\n",
+    "    geom_bar(stat=\"identity\") +\n",
+    "    facet_varied(wide=TRUE) +\n",
+    "    scale_fill_manual(values=c(\n",
+    "        \"Ledger in 1st min\"=brewer.pal(n=5, name=\"Set1\")[[2]], \n",
+    "        \"Ledger in 2nd min\"=brewer.pal(n=5, name=\"Set1\")[[3]], \n",
+    "        \"Ledger in 3rd min\"=brewer.pal(n=5, name=\"Set1\")[[4]], \n",
+    "        \"Ledger later\"=brewer.pal(n=5, name=\"Set1\")[[5]],\n",
+    "        \"Lost\"=brewer.pal(n=5, name=\"Set1\")[[1]]\n",
+    "    )) +\n",
+    "    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e5f627be-7986-406a-8058-ef7fa742680f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ggsave(\"plots/temporal-efficiency-bar.svg\", units=\"in\", dpi=150, width=16, height=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "773a3a51-42ab-484a-b2ff-189e02ce9f28",
+   "metadata": {},
+   "source": [
+    "#### Release memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "47d9923b-7280-4634-9ba8-f32589bde5b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rm(lifecycle, outcomes, totals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "00edc3c4-170b-4ec1-91cc-a43d9b170531",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A matrix: 2 x 6 of type dbl</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>used</th><th scope=col>(Mb)</th><th scope=col>gc trigger</th><th scope=col>(Mb)</th><th scope=col>max used</th><th scope=col>(Mb)</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>Ncells</th><td> 990083</td><td>52.9</td><td> 2122184</td><td>113.4</td><td> 2122184</td><td>113.4</td></tr>\n",
+       "\t<tr><th scope=row>Vcells</th><td>1890521</td><td>14.5</td><td>20910268</td><td>159.6</td><td>26137834</td><td>199.5</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A matrix: 2 x 6 of type dbl\n",
+       "\\begin{tabular}{r|llllll}\n",
+       "  & used & (Mb) & gc trigger & (Mb) & max used & (Mb)\\\\\n",
+       "\\hline\n",
+       "\tNcells &  990083 & 52.9 &  2122184 & 113.4 &  2122184 & 113.4\\\\\n",
+       "\tVcells & 1890521 & 14.5 & 20910268 & 159.6 & 26137834 & 199.5\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A matrix: 2 x 6 of type dbl\n",
+       "\n",
+       "| <!--/--> | used | (Mb) | gc trigger | (Mb) | max used | (Mb) |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| Ncells |  990083 | 52.9 |  2122184 | 113.4 |  2122184 | 113.4 |\n",
+       "| Vcells | 1890521 | 14.5 | 20910268 | 159.6 | 26137834 | 199.5 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       used    (Mb) gc trigger (Mb)  max used (Mb) \n",
+       "Ncells  990083 52.9  2122184   113.4  2122184 113.4\n",
+       "Vcells 1890521 14.5 20910268   159.6 26137834 199.5"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gc()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "407ce1ea-b984-4fc5-8440-6f02905c6cdf",
+   "metadata": {},
+   "source": [
+    "### Resource usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e83759ec-0ab5-4b7a-8bd0-9fb965a5c0c5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded Rdata file: sampleSize = 1 \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "        Network       Bandwidth             CPU              Diffusion duration\n",
+       " topology-v2:2250   10 Mb/s:2550   4 vCPU/node:2550   L_diff = 7 slots:2550    \n",
+       " topology-v3: 300                                                              \n",
+       "                                                                               \n",
+       "                                                                               \n",
+       "                                                                               \n",
+       "                                                                               \n",
+       "                                                                               \n",
+       "         Voting duration   Max EB size        Tx size            Throughput \n",
+       " L_vote = 4 slots:2550   12 MB/EB:2550   1500 B/Tx:2550   0.100 TxMB/s:850  \n",
+       "                                                          0.150 TxMB/s:850  \n",
+       "                                                          0.200 TxMB/s:850  \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "  Tx start [s]  Tx stop [s]   Sim stop [s]       Node        Egress [B]       \n",
+       " Min.   :60    Min.   :960   Min.   :1500   node-0 :   6   Min.   :5.408e+03  \n",
+       " 1st Qu.:60    1st Qu.:960   1st Qu.:1500   node-1 :   6   1st Qu.:9.945e+06  \n",
+       " Median :60    Median :960   Median :1500   node-10:   6   Median :9.215e+07  \n",
+       " Mean   :60    Mean   :960   Mean   :1500   node-11:   6   Mean   :1.426e+08  \n",
+       " 3rd Qu.:60    3rd Qu.:960   3rd Qu.:1500   node-12:   6   3rd Qu.:1.952e+08  \n",
+       " Max.   :60    Max.   :960   Max.   :1500   node-13:   6   Max.   :1.253e+09  \n",
+       "                                            (Other):2514                      \n",
+       "    Disk [B]       Total CPU [s]    Maximum CPU [s/s]\n",
+       " Min.   :      0   Min.   : 50.06   Min.   :0.4248   \n",
+       " 1st Qu.:      0   1st Qu.: 69.19   1st Qu.:0.6399   \n",
+       " Median :      0   Median : 90.76   Median :0.6861   \n",
+       " Mean   :  27296   Mean   : 92.48   Mean   :0.7099   \n",
+       " 3rd Qu.:      0   3rd Qu.:124.36   3rd Qu.:0.8155   \n",
+       " Max.   :1313360   Max.   :128.93   Max.   :1.0922   \n",
+       "                                                     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if (file.exists(\"results/resources.Rdata\")) {\n",
+    "    load(file=\"results/resources.Rdata\")\n",
+    "    cat(paste(\"Loaded Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "} else {\n",
+    "    resources <- fread(\"results/resources.csv.gz\", stringsAsFactors=TRUE)\n",
+    "    sampleSize <- 1\n",
+    "    save(resources, file=\"results/resources.Rdata\")\n",
+    "    cat(paste(\"Saved Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "}\n",
+    "resources %>% summary\n",
+    "resources[, `:=`(\n",
+    "    `VariedX`=`Network`,\n",
+    "    `VariedY`=`Throughput`\n",
+    ")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "bd364b0a-20ca-4e21-8182-bd3dad46ec41",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VikSfCUq3cKxI7BBImEh65IeSv6dPCc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\n67/hdgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        resources[, .(`VariedX`, `VariedY`, `Node`, `Nodal egress [Mb/s]`=8*`Egress [B]`/simFinish/1e6)], \n",
+    "    aes(x=\"\", y=`Nodal egress [Mb/s]`)\n",
+    ") +\n",
+    "    geom_violin() +\n",
+    "    facet_varied(wide=FALSE) +\n",
+    "    xlab(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "af07b739-6a24-4794-8b81-be607edc13d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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L5GD2KapcdI29ZE21DXO1H/M7+JfEVZ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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        resources[, .(`VariedX`, `VariedY`, `Node`, `1-Second peak CPU [vCPU⋅s/s]`=`Maximum CPU [s/s]`)], \n",
+    "    aes(x=\"\", y=`1-Second peak CPU [vCPU⋅s/s]`)\n",
+    ") +\n",
+    "    geom_boxplot() +\n",
+    "    ylim(0, NA) +\n",
+    "    facet_varied(wide=FALSE) +\n",
+    "    xlab(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4e6bf086-3d52-40a1-a440-bfd7981caf7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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L5GD2KapcdI29ZE21DXO1H/M7+JfEVZ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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        resources[, .(`VariedX`, `VariedY`, `Node`, `1-Second peak CPU [vCPU⋅s/s]`=`Maximum CPU [s/s]`)], \n",
+    "    aes(x=\"\", y=`1-Second peak CPU [vCPU⋅s/s]`)\n",
+    ") +\n",
+    "    geom_boxplot() +\n",
+    "    ylim(0, NA) +\n",
+    "    facet_varied(wide=FALSE) +\n",
+    "    xlab(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "de6c017b-0969-4fc5-83db-829005493d0b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HZIz9W0n7xmbAohdUBIlNQOafDoXZrm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\n/j/omvGx6xg6ZAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        resources[, .(`VariedX`, `VariedY`, `Node`, `Mean CPU [vCPU⋅s/s]`=`Total CPU [s]`/simFinish)], \n",
+    "    aes(x=\"\", y=`Mean CPU [vCPU⋅s/s]`)\n",
+    ") +\n",
+    "    geom_boxplot() +\n",
+    "    ylim(0, NA) +\n",
+    "    facet_varied(wide=FALSE) +\n",
+    "    xlab(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47949581-0655-49a5-9f3b-6ef154e7d2db",
+   "metadata": {},
+   "source": [
+    "#### Release memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "bca64570-d004-4ac6-bb29-91a865a4e957",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rm(resources)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "c45f56ad-a3ce-41f8-84e3-4dc18f45637b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A matrix: 2 x 6 of type dbl</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>used</th><th scope=col>(Mb)</th><th scope=col>gc trigger</th><th scope=col>(Mb)</th><th scope=col>max used</th><th scope=col>(Mb)</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>Ncells</th><td>1019390</td><td>54.5</td><td> 2122184</td><td>113.4</td><td> 2122184</td><td>113.4</td></tr>\n",
+       "\t<tr><th scope=row>Vcells</th><td>1941589</td><td>14.9</td><td>16728215</td><td>127.7</td><td>26137834</td><td>199.5</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A matrix: 2 x 6 of type dbl\n",
+       "\\begin{tabular}{r|llllll}\n",
+       "  & used & (Mb) & gc trigger & (Mb) & max used & (Mb)\\\\\n",
+       "\\hline\n",
+       "\tNcells & 1019390 & 54.5 &  2122184 & 113.4 &  2122184 & 113.4\\\\\n",
+       "\tVcells & 1941589 & 14.9 & 16728215 & 127.7 & 26137834 & 199.5\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A matrix: 2 x 6 of type dbl\n",
+       "\n",
+       "| <!--/--> | used | (Mb) | gc trigger | (Mb) | max used | (Mb) |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| Ncells | 1019390 | 54.5 |  2122184 | 113.4 |  2122184 | 113.4 |\n",
+       "| Vcells | 1941589 | 14.9 | 16728215 | 127.7 | 26137834 | 199.5 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       used    (Mb) gc trigger (Mb)  max used (Mb) \n",
+       "Ncells 1019390 54.5  2122184   113.4  2122184 113.4\n",
+       "Vcells 1941589 14.9 16728215   127.7 26137834 199.5"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gc()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "150a80d3-3029-4a84-a215-002012d0d512",
+   "metadata": {},
+   "source": [
+    "### Receipt of messages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4138490-c36f-4d16-adce-4554ca20e676",
+   "metadata": {},
+   "source": [
+    "#### Read results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "fb6267b0-7a8f-4ad7-bb10-357bb9dd3e03",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded Rdata file: sampleSize = 0.33 \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "        Network           Bandwidth                 CPU          \n",
+       " topology-v2:75124898   10 Mb/s:84330347   4 vCPU/node:84330347  \n",
+       " topology-v3: 9205449                                            \n",
+       "                                                                 \n",
+       "                                                                 \n",
+       "                                                                 \n",
+       "                                                                 \n",
+       "                                                                 \n",
+       "        Diffusion duration           Voting duration       Max EB size      \n",
+       " L_diff = 7 slots:84330347   L_vote = 4 slots:84330347   12 MB/EB:84330347  \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "      Tx size                Throughput        Tx start [s]  Tx stop [s] \n",
+       " 1500 B/Tx:84330347   0.100 TxMB/s:18805939   Min.   :60    Min.   :960  \n",
+       "                      0.150 TxMB/s:27185909   1st Qu.:60    1st Qu.:960  \n",
+       "                      0.200 TxMB/s:38338499   Median :60    Median :960  \n",
+       "                                              Mean   :60    Mean   :960  \n",
+       "                                              3rd Qu.:60    3rd Qu.:960  \n",
+       "                                              Max.   :60    Max.   :960  \n",
+       "                                                                         \n",
+       "  Sim stop [s]  Message                 Item             Producer       \n",
+       " Min.   :1500   EB:   40138   623-node-565:    2621   node-26:  289657  \n",
+       " 1st Qu.:1500   RB:   69809   692-node-565:    2556   node-52:  279624  \n",
+       " Median :1500   TX:77961295   144-node-14 :    2359   node-40:  260235  \n",
+       " Mean   :1500   VT: 6259105   288-node-132:    2329   node-28:  256150  \n",
+       " 3rd Qu.:1500                 81-node-582 :    2328   node-45:  253619  \n",
+       " Max.   :1500                 718-node-580:    2312   node-36:  252833  \n",
+       "                              (Other)     :84315842   (Other):82738229  \n",
+       " Generated [s]        Size [B]        Recipient         Received [s]    \n",
+       " Min.   :  20.07   Min.   :   164   node-14:  193879   Min.   :  20.08  \n",
+       " 1st Qu.: 289.95   1st Qu.:  1500   node-4 :  193451   1st Qu.: 290.22  \n",
+       " Median : 514.65   Median :  1500   node-11:  193435   Median : 514.93  \n",
+       " Mean   : 515.48   Mean   :  1510   node-65:  193370   Mean   : 515.74  \n",
+       " 3rd Qu.: 738.25   3rd Qu.:  1500   node-18:  193341   3rd Qu.: 738.50  \n",
+       " Max.   :1496.07   Max.   :256240   node-92:  193270   Max.   :1498.40  \n",
+       "                                    (Other):83169601                    \n",
+       "  Elapsed [s]    \n",
+       " Min.   :0.0004  \n",
+       " 1st Qu.:0.0907  \n",
+       " Median :0.1313  \n",
+       " Mean   :0.2617  \n",
+       " 3rd Qu.:0.3038  \n",
+       " Max.   :3.6663  \n",
+       "                 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if (file.exists(\"results/receipts.Rdata\")) {\n",
+    "    load(\"results/receipts.Rdata\")\n",
+    "    cat(paste(\"Loaded Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "} else {\n",
+    "    receipts <- fread(\"results/receipts.csv.gz\", stringsAsFactors=TRUE)\n",
+    "    sampleSize <- 1\n",
+    "    save(receipts, file=\"results/receipts.Rdata\")\n",
+    "    cat(paste(\"Saved Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "}\n",
+    "setnames(receipts, old=\"Kind\", new=\"Message\")\n",
+    "receipts %>% summary\n",
+    "receipts[, `:=`(\n",
+    "    `VariedX`=`Network`,\n",
+    "    `VariedY`=`Throughput`\n",
+    ")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "f26dc9a0-828f-45ec-84f9-6443dc8f5aa8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>EB</li><li>RB</li><li>TX</li><li>VT</li></ol>\n",
+       "\n",
+       "<details>\n",
+       "\t<summary style=display:list-item;cursor:pointer>\n",
+       "\t\t<strong>Levels</strong>:\n",
+       "\t</summary>\n",
+       "\t<style>\n",
+       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
+       "\t.list-inline>li {display: inline-block}\n",
+       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "\t</style>\n",
+       "\t<ol class=list-inline><li>'EB'</li><li>'RB'</li><li>'TX'</li><li>'VT'</li></ol>\n",
+       "</details>"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item EB\n",
+       "\\item RB\n",
+       "\\item TX\n",
+       "\\item VT\n",
+       "\\end{enumerate*}\n",
+       "\n",
+       "\\emph{Levels}: \\begin{enumerate*}\n",
+       "\\item 'EB'\n",
+       "\\item 'RB'\n",
+       "\\item 'TX'\n",
+       "\\item 'VT'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. EB\n",
+       "2. RB\n",
+       "3. TX\n",
+       "4. VT\n",
+       "\n",
+       "\n",
+       "\n",
+       "**Levels**: 1. 'EB'\n",
+       "2. 'RB'\n",
+       "3. 'TX'\n",
+       "4. 'VT'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] EB RB TX VT\n",
+       "Levels: EB RB TX VT"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kinds <- receipts[, unique(`Message`) %>% sort]\n",
+    "kinds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53dc780b-3a2b-4910-abba-8d84e2d01532",
+   "metadata": {},
+   "source": [
+    "#### Arrival histograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5c1b28c-9146-4940-89bb-27b6355b2c1f",
+   "metadata": {},
+   "source": [
+    "##### Compare the distributions of elapsed times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b4684b8f-6db0-4dad-895c-457e19c51c68",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "arrivalDelayHistogram <- function(rs, kind, title=\"\", scales=\"fixed\", outfiles=c(), width=16, height=8, dpi=150) {\n",
+    "    g <- ggplot(\n",
+    "            rs[, .(`VariedX`, `VariedY`, `Elapsed [s]`, `Minute created`=factor(floor(`Generated [s]`/60)))],\n",
+    "        aes(x=`Elapsed [s]`, fill=`Minute created`)\n",
+    "    ) +\n",
+    "        geom_histogram(binwidth=0.1) +\n",
+    "        facet_varied(wide=TRUE, scales=scales) +\n",
+    "        xlab(\"Time from generation to receipt at node [s]\") +\n",
+    "        ylab(paste(\"Number of\", kind, \" received\")) +\n",
+    "        theme(axis.text.y = element_blank(), axis.ticks.y = element_blank())\n",
+    "    for (outfile in outfiles)\n",
+    "        ggsave(outfile, units=\"in\", width=width, height=height, dpi=dpi)\n",
+    "    g\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7457b10a-93d7-407f-9352-1f753cf54494",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w65BaPR6HA4atBalSwWi9FoLC0tVeUAIR8K1YpNPkFgs9lUfLFlZWV2u12V1hIS\nElT8RchnsT1s4DnN5G/bKsbje5r5sk1FmqkRmjAajVFRUWGeZqp8f5BjC3GaeSiz5DSr2dGs\nSiaTqbp3XjHCWPRwWaryKXJ/fHimmSRJ0dHR5eXlajUYExOjYprFx8ermGa+nG6eP39+9+7d\nGzZsWHnl/v37r7/+elVigM7o5+IJAAD0p02bNiNHjoyPj6+88ttvvx0wYIBWIWni9OnT48eP\n37hxY0lJSceOHV944YVrrrlG66DCEYUdAADha8qUKU6nc+/evb///rvBYGjSpMk111wzc+ZM\nreMKtbvvvvvs2bPvvvtuXFzciy++mJGRsX///rS0NK3jCjsUdgAAhK/z588/8cQTv/76a/36\n9YUQp0+fbtmy5YwZM5KSkrQOLXT++OOPL774Yvv27V27dhVCvPvuuw0aNPjkk09GjRqldWhh\nhwmKAQAIX/PmzTObze+99967/yWv1DqukLLb7c8++2y7du3kRfmyJBUn5NITCjsAAMLX3r17\n77vvvnr16smL9evXHz169HfffadtVCF2ySWXPPPMM/JV6sXFxcOGDUtISIi0UYY+orADguho\nrMs/AKgBtaYEr+2cTuc777xz+eWX//bbb5s3b05NTdU6onCk2zF2Fdf/G4WQJ/bmJmMAgFrn\n2muvnT9//rPPPlu3bl0hxJkzZxYuXHjddddpHVeo/fXXXwMGDPj9999nzJgxcOBAtW5Moj+6\nLewAANCBBx988Iknnhg4cGCDBg2cTufp06dbtGjx4IMPah1XSDmdzltuuaVJkyZr165V965I\n+qPbwi4vcZNijSTosQMQ6eSzGXYh7EIkCCE4mxH2UlJS3njjjT179hw7dkySJHm6k0g7Ofvl\nl19+++2348aN27FjR8XKyy67rFGjRhpGFZ70U9i5V3IKisnZOZYB0Ae3o197fzbmS2+YOnTo\nUOXF+Pj4K6+8Uv75l19+EUK0atVKg7A0sm/fPqfTeffdd1deOW/evEjrufSFfgo7AAB0Y/To\n0dU9ZDabY2NjP/zww1DGo62cnJycnByto6gd9FPY5SYcqLxYp6C1YgPF91S+pAKIQIpDpRCi\nniZxwJuNGzfKP+zevXv27NkPPPDANddcYzQaf/rpp3feeee+++7TNjyELf0UdgAA6IbRaJR/\nePPNN8eOHdulSxd5sUOHDpdccsnUqVNfe+017aJD+NJPYXfS5f7Iok6BRnEAAIz8IxsAACAA\nSURBVKCeP//8Mzk5ufKalJSUEydOaBUPwhzTwAAAEL5atWr17rvvlpaWyosOh2PZsmWXXnqp\ntlEhbOmnx84rxcgShpUA0Cfj1VpHADWNHTv24YcfHjRo0FVXXWU0Gg8dOlRYWDhnzhyt40KY\niqDCDgCAWqdZs2bvvffeunXrfv/9d4PBkJ2d3aNHj7i4OK3jQpjSbWHnfuUXAAC1UWxsbPPm\nzU0mk8FgaNKkSWwsd55GtXRb2AEAoAPnz59/4oknfv311/r16wshTp8+3bJlyxkzZiQlJWkY\nVczYHd43ghYo7ACgdvPrBIViAgHBgOOwN2/ePLPZ/N5779WrV08Icfr06WeffXbevHmTJk3S\nOjSEIwo7AADC1969e6dMmSJXdUKI+vXrjx49eurUqdpGdWJxooqtNRqRr2JrEU63hZ37t9KL\nC7WIAwCAwBgMBq1DQK3BPHYAAISva6+9dv78+WfPnpUXz5w5s3Dhwuuuu07bqBC2dNtjBwCA\nDjz44INPPPHEwIEDGzRo4HQ6T58+3aJFiwcffFDruBCmKOyAINof47J4m0ZhAKi9UlJS3njj\njT179hw7dkySpCZNmlxzzTWcnEV1KOwAAAhfdrtdCJGenp6eni6vcTgclTcwGo0ahIVwFUGF\nneJyCq7wB6BTrbQOAGrKysryvMGmTZtCEwlqhQgq7AAAqHUWLFigdQioTSjsAAAIX61atXI6\nnfv27ZPvFcsYO3hGYQeoKWHWcy7LXTSKA4BehOctxULv559/zsnJ+fe//20ymbp37/7SSy81\nbtxY66DCEfPYAUDtdjLe5R90puKWYu/+l7xS67hCqrS09NZbbzUajcuXL1+0aNHhw4ezs7O1\nDipM0WMHqOlIw1e1DgGAroTnLcVCbO/evUeOHNm9e3dKSooQwul09u3bt7CwMD6erzJKFHaA\nmugvAaA6RtS1a9eusLAwLi7ObrefOXNm/fr17du3p6qrEqdiAQAIX9xSTAhhNBrj4uKEEN27\nd7/44otXrFjx9ttvax1UmKLHDgCA8MUtxSr76KOPCgsL33zzzRtuuOHIkSMJCQlaRxR2KOwA\nAAhf3FJMCLF///4//vijZ8+eqampqampU6dOnT179ubNm3v37q11aGGHU7EAAISjc+fOnTt3\nTghhs9kuXLhw7ty5Cxcu5OfnK24pFgn27ds3dOjQ8vJyeTEvL89qtUZFRWkbVXiisAMAIOzs\n3r170KBBBw4cOHny5NChQ2fPnv3999/v2bNn5syZI0aMqBhyFyFuvvlmh8Nx77337t69+6uv\nvrrzzjubN2/+t7/9Teu4wpFuT8UejVWuaVqsRRwAAPhv0aJF/fv379q16xNPPNGyZcuJEyda\nLBYhRHFx8bRp02bPnj19+nStYwydOnXqrF27dvz48ZmZmbGxsTfccMOGDRtiY90+6aHjwg4A\ngNrr999/f/75541G408//fTyyy/LVZ0QIjY2dvDgwRMmTNA2vNDr0KHDli1btI6iFuBULAAA\nYSc+Pr64uFgI0bRp0/Pnz1d+KDc3t0GDBhrFhXBHYQcAQNhp3779Sy+99Ntvv40dO/aNN974\n4osvTp06dfLkyfXr17/yyivDhw/XOkCEKU7FAkAEcR9/nK5FGPDqwQcfXLBgwf3332+z2YQQ\n06ZNq3jIYDBMnz597dq12kWH8BVBhZ3icMaxDAAQtuLi4nJych555JH8/Py8vLwInOIENRNB\nhR0QenMsdSovTtYqDgC1isPhOHjwYKtWrYxGY3JycnJycsVDTqfzxx9/3LJlywMPPKBhhAhb\nFHYAULu5n11FbXfq1KkHHnhgzZo18g1ShRAOh2P//v1bt27dsmXLhQsXWrdurW2ECFsUdgAA\nhJcGDRrUr19/8uTJAwYMiIqK2rp167Zt2woLC6+77rp77rmnS5culfvwgMpCXdiZTCZJqvZS\nXPnmd0ajMTo6OtiR1GAXcuTR0dFOpzPwAAwGg8FgUOuVmkwm+X+1GpQkKSoqSsVRHZIkqfhi\nPWSR1w3kh9SNx+Fw+NKaL9tUpJkKkQkhSZK6r1SonWbR0dFhm2aeb8dpMpmMRqOHSISqRzOj\n0Viz24N6DSDwCCVJUv1oZjabVWlNCGEwGKKiotQ6bgu106zK9UajccGCBQsXLpw6dWpJSYnR\naOzXr9+QIUMqOvA012hEvtYhoGqhLuyMRqOHQ6H8N2MwGAL/k94fo1xzdYnLYg12IYdX3d9h\nDVpT5ZXK5HfVw3vrL4PBYDKZVDkUVjSo4ov1/AnnS5pJkqRWPPKnmi9b+rJHuSm1YiPNasxr\nmnk+FGiYZgpeAwg8QoPBoO4rFWqnmdlsVjHNQvNrTUpKeuyxxx566KEdO3Zs3Ljx/fff3759\ne0ZGxo033tisWTNV9g5dCnVhV1paWnETX3fy1yCbzVZYWBjsSGqwi6SkJEmSioqKVDlASJKU\nmJio1iu1WCxms7m0tNRqtarSYFJSUnFxsd1uV6U1i8Vit9tVfLGSJHn4WPWcZnInioppFhsb\n63A4fHnnfdmjnGZqxWY0GuPj48M5zYqKilTpsTMYDCFOM6vVKs9DUSU5zcrLy9WKJy4uzmaz\nlZaW+vtErwEEHqG6aRYTE2M2m61Waw1ebJWSk5PVTTMVjx4xMW6dEK4sFktGRkZGRkZeXt7m\nzZs3bNiwdOnSZs2aZWRkDB48WJUYaubrlYkqttahP/1/qmGMHQBEEPezGbdpEQb8lZSUdNtt\nt912222nTp364osvNm7cqG1hh7Cln8KO68IAADpmt9u3b9/erVu3wYMHU9WhOvop7AAgMrl3\nwkGXrFbrs88+u2nTJq0DQVjjXrEAAAA6QWEHAACgE/o5FcvJCACAjsXExLzzzjtaR4Fwp5/C\nDghH5j5aRwBAJyRJaty4cUlJyY4dOzZv3jx16lStI0I4orAD1PRJHa0jAKBHVqt1165dmzZt\n+ve//20wGDp06KB1RAhTFHYAAISvrVu3bt68eefOnWazuUuXLk899VS7du1CcOPNsLVt27bu\n3bufOXOmTh2+SVeBwg4AgPD1zDPPJCUl5eTkZGRkqHintVoqLy9vyJAhKt5gWn+4KhYAgPA1\nadKkli1bzpw587HHHvvoo4/OnTundURauv/++y+66CKtowhrEdRjp7hslrvoAADCX1ZWVlZW\n1tmzZzds2PDhhx/OnTv36quvzsjI6NMn4q7NWrZs2e7duxcuXNi9e3etYwlf9NgBABDu6tat\ne9dddy1evPj1119v3rz5W2+9pXVEofbbb7898sgj7777bnx8vNaxhLUI6rEDAMyxKMebT9Yk\nDvjs/fffb9GiRXp6usFgEEJcdtllycnJAwYM0DqukLLb7UOGDBk3blz79u2//fZbrcMJa/TY\nAQAQvl577bWcnJwHHnggLy9PXrNu3bqBAwc+9thj58+f1za2kJkzZ87Zs2f79u178ODBo0eP\nCiF++eWXP//8U+u4whE9dkDNJcx6Tv7BKIRZ/qmLdtEA0KmJEydu27btmWeeeeWVV4QQgwYN\nuu666+bMmfPGG288+eSTWkcXCr/88svBgwdbt25dsaZz587Dhw9fvHixhlGFJ3rsAEBf7E1d\n/qH2S01NnThx4pkzZz7//HMhhNlsvvrqqx966KHdu3drHVqIzJ8/3/lf8qs+e/YsVV2V6LED\nQqeih09WMP5prSIBULtER0ffc889CxcuvOGGGywWixDCYrGUlZVpHRfCDj12QOjkJW6q/E/r\ncADUJhkZGcnJyTNmzLBarXa7fcWKFVdccYXWQWmgbdu2TqeT205Uhx47ANAzRT+xyNAoDgRM\nkqSJEyeOGzfu9ttvN5vNBoNh9uzZWgeFsENhB4RObsKByov1tIoDkcwccbPa1nYPP/xw48aN\n5Z+bNGny9ttvb9q0yWAwdO3aNTU1VdvYEIYo7ABAz9xO+rfXJg7UVN++fSsvJiQkROA9J+A7\nCjsA0BfrxVpHAEAzEVTYKeZbZ7J1AACgM7ot7NxvmwMAuqQ83Nk1igNAGNBtYQcAEG6X7DDG\nDtA3CjsAAOCfDv3ztQ4BVaOwAwB9yU/ROgIAmqGwAwA9OxmvdQTQo399mqhiawNupf9PNdxS\nDAAAQCfosQPUxOXYAAANUdgBQC3HXcIA/Jd+CjvvPSUc+wAAgK7pp7ADQs/tLpwAAGipFhd2\nCbOec1nO0CgOAACA8FCLCzsvOPGK4HOb018I4WlIgGLiiXoqhwMAiHRMdwIAAKATFHYAACDc\nzZgxw1CJ2WzWOqIwpd9TsYAmGAMAAEFw8ODBW2+9dcyYMfKiwWDQNp6wFbmFneLai4LxT2sV\nCQCEjvFqrSMAauLgwYN33nlnjx49tA4k3HEqFqi5k/HKfwCAYDh48ODGjRsbNWqUmpraq1ev\nQ4cOaR1RmIrcHjvFDGSSoMcOAIBwdPbs2XPnzkmStHz5cpvNNnXq1IyMjB9//DExMVHr0MJO\n5BZ2AKBLz5x3XY7WJgxARcnJySdOnEhLS5MkSQhx3XXXXXzxxWvWrBk0aJDWoYUdCjsAqOUY\nNge9M5lMDRs2rFhMTk5u2rTp8ePHNQwpbEXuGLvchAOV/2kdDgAAqNqaNWuuueaa3NxcebGw\nsPD48eOXX365tlGFJx312DHNBAC4ORqrdQRAwLp165abm3v33Xc/+uijMTEx06dPb9as2S23\n3KJ1XOFIR4UdEIY4R4aw00rrAAC/JSQkrF+/Picnp1+/fnFxcVlZWUuWLGGO4iqFurCTJ4z2\n8KjXbYLE9z2qFVvFi1WltYo2VWwwbFvz2pSP+9J8fssqA1A3MYLRmooNCvUSQ/XYAkwz1Y9m\nFRPue93yb3+5LB5N8d5yAHH9r4VIOJoF49eqSjuRoHXr1p9//rnWUdQCoS7sYmNjTSYvO42K\niqpTx9Od1GVnXOcrEaJ9AHEJX/YoS01NDWRHNd6vL+Li4uLi4tRqLTk5Wa2mhBAmk0ndF+uB\nL2kWHR0dHa3x5YIe3hB13yt1W4uPj4+PV23KvpQUb3WHP8xmc8jSLD4+3mg0et5G9TRT8Z2v\noNY7RpoB4SDUhZ3dbnc6ndU9ajAYTCaTw+Gw2+2hjEoIUV5e7nUbk8lkMBh82dJHJpPJZrOp\n0pQkSUaj0W63OxwOVRpUMTYhhNlsdjqdKr5YIYSHz9SwTTOFKnOJNKuxEKeZzWbz8D6onmZG\no9HpdKr1zlemSrKRZjUjpxmgolAXdlar1cNBRJKk1NTU8vLygoICr03tT1NcyhpQj11eXp7X\nbZKSksxmc35+voeiwXeSJCUmJvqyX19YLJb4+PiSkhKr1apKg0lJSYWFhWp9JtWtW9dms6n4\nYiVJio2tdky45zQzGo0pKSk+pllQVfmGyGmm1ntlNBrj4+PDOc0KCgpU+fw2GAx16tQJZZqV\nlJR4+HSX06ysrKywsFCVeOLi4mw2W2lpqSqtVRb4O6ZumsXExMTFxRUXF6v1YpOTk9VNs/Ly\n8vz8/MBbE0LExMSo0g5Qge8KAAAAOsFVsUDoKCaeSNcoDESU/XQJAZGEHjsAAACdiNweu5Ou\nl1vV0ygMAABqnQG3qjPKEKqjxw4AAEAnKOwAAAB0InJPxQIAgJp56stEFVubmsGJXdXQYwcA\nAKAT9NgBQcUN1wEAoUNhB9Tc0WrvSgCEEt8fAPwHp2IBAAB0IpJ67IxXax0BIp3iHgC3aRQG\nAECv6LEDAADQiUjqsXPFXTsROO7CibBgbxqsjQHUNjoq7DjTCgBu5ljqaB0CoI4lS5bMmzfv\n0KFDHTp0eO211y677DKtIwpHnIoFVGW82uUfAEANS5YsGTNmzAMPPPDhhx8KIXr37m2327UO\nKhzpqMcOAADokdPp/Oc///nPf/7znnvuEUK0bNkyJyfn+PHjTZs21Tq0sENhBwAAwtrPP/98\n6NCh7Oxsh8Nx9uzZxo0br1y5UuugwhSnYgEAQFg7ceKEyWRatmxZcnJy/fr1GzZsuGrVKq2D\nClMR1WPH5OzQmGIY+2St4gCAWuXs2bM2m23nzp379+9PSUl57bXXBg0atHfv3iuuuELr0MKO\np8IuLy/PpyZMpri4OJXiCR2migUAoFaoV6+eEOL1119v0KCBEOLJJ59csGDB+vXrKezceSrs\nkpOTfWkiKytrw4YNKsUDAADg4vLLL5ck6dy5c3JhZ7PZSkpKfKxSIo2nwu7FF1+s+NnpdL7+\n+uu///57z54909PTjUbjgQMHPvnkk86dO0+bNi34cQIAasTcR+sIgEA1atSoX79+Q4YMeeGF\nF5KSkmbPnm0ymfr0Iber4Kmwe/TRRyt+fu21186cOfPVV1916tSpYuWePXu6dev29ddfd+zY\nMYgx1pCXEXWMdkJw+DGUM2HWc4o1BeOfVjUYANCJJUuW5OTk3HPPPYWFhddff/3mzZtTU1O1\nDioc+XrxxFtvvTV06NDKVZ0Q4tprrx0xYoQ8Z2AQYgN0x7XvJC9xk+JxSVDYwX/Wiysvtcq1\nahUIEDwxMTHz58/XOopawNfpTn755ZcqS+Pk5OTDhw+rGhIAAABqwtceu6uuuuqDDz6YOHFi\nbGxsxcri4uJVq1a1bt06OLF5cTTW+zaeMO4EAeMunACAsOJrj92YMWN+/PHHbt26ffjhh0eP\nHj169OhHH33UvXv3H374gfOwAFBrWC9W/gOgI7722A0aNOjUqVNTpky5/fbbK1YmJSW9/PLL\nAwcODE5sXigmomP+YdQ6uQkHFGvqaRIHAEAv/LjzxKOPPjps2LDNmzcfPnzYZDI1b968e/fu\nKSkpwQsOAFBx9XSUEFFCCK6eBlA9/24pZrFYUlJSmjZt2r179+TkZLPZHKSwAAAA4C9fx9gJ\nIRYtWnTxxRdnZWXdddddBw8e3LVrV+PGjd99993gBQfo28l45T8AAALha4/dp59+OmrUqG7d\nuo0ZMyY7O1sI0apVq6uuumrw4MEpKSm33HJLMIMEAKgk3338zFkNwkAtNzUjX+sQUDVfC7uZ\nM2e2bt16w4YNJtN/npKWlrZ+/fr27dvPmDGDwg4ANKOs1ZigGIhcvp6K3bt3b79+/Sqquv88\nWZJuvfXW/fv3ByEwQBfsTV3+AQAQTL722KWkpJSUlLivt9lsCQkJqobkK+aGhfb8nebaeLXn\nxxNmPecQolQI+Y+Kix8BhKfEHc1UbC2/y28qthbhfC3sOnbsuHTp0scff7zy/CZnzpxZsmRJ\n586dgxMbEHYqJp74jx4qt6+4eyy3joXgnsIA/OHHGLv09PQ2bdqMHj1aCLFu3br169cvXLjQ\narXOmDEjmBECEUQxZTHzFSMEHAturLwojVbWkQBqEV8Lu2bNmm3btu3hhx+eNGmSEEIu5jIz\nM2fNmtWyZcsgBggAkU3dO5Q8c95L+3ydAGo1PyYoTk9P37x58/nz5w8ePBgVFdWiRYvExMTg\nRQYAAAC/+FrY9ezZc9iwYX379k1JSenUqVNQYwL0Q3GH9TiXpaOxys0vLgxuOIA7xczY9NgB\ntZqvhd327dvXr1+fmJjYv3//oUOH/u1vfzMYDEGNLOi8XZ8IAPrzt7/cVjXSIAwAQeLrPHZn\nzpxZuXJlz549V6xY0a1bt0svvfSZZ545fPhwUIMDAACA73ztsYuNje3Xr1+/fv1KSkrWrl27\ncuXKl1566bnnnuvatevQoUNHjRoV1Cir5u8UYoD2WmkdAGof95sIK86Wul8P4YKzE6j9Vq1a\n1a9fP8XK4cOHL168WJN4wpkfF0/IYmJisrOzs7Oz8/PzJ0yYsGDBgq+++kqbws5finn/jYe0\nCQMAAPjj+uuvX7duXcViWVnZ8OHD+/Shf6cKfhd2xcXFn3/++erVq9esWXP+/Pnk5OS+ffsG\nIzK/Keu2o5pEAbhQ3MTT9eKJ/THKzbsENxrohHKi7KZzNQoECJH69ev36PG/GeGnTZs2ePDg\n22+/XcOQwpavhd358+fXrFnzwQcfrF+/vri4ODEx8bbbbhswYMBNN90UFRUV1BCByMH1iQjc\nRUV/ah0CEEQHDx5cvnz5nj17tA4kTPla2F100UU2my0+Pr5v374DBgzo2bNndHR0UCMDAISA\nYtqddI3CAHzhdDpHjhw5ZcoUipDq+FrYZWdnDxgw4Oabb46JcTt75I/Y2FhJ8nIpblRUVOU7\n0taQ4sxsFVyGsfuyRzny5OTkGgfl3qAKr1QIIYQ8+0xsbGyAv6AKkiSpOwG1yWRS8cV6nm0n\neGlm8/cJgVHlHQvzNEtKSlKlKZm6aeZ5g7i4OK9pFh0dbTabVYknZGr2BqqeZnFxcbGxbjM9\n1ojqaWY2m0OWZlBYunRpfn5+//79tQ4kfPla2K1YsUKV/RUXF5eXl1f3qCRJqampZWVlBQUF\nquzOd+fPe76uTAghkpKSzGbzhQsXnE5n4HuUK6cLFy4E3pQQwmKxxMfHFxcXW61WVRpMSkoq\nLCy02+2qtFa3bl2bzZaXl6dKaxaLRZIkDwd9z2lmNBpTUlJqlmYJXrfw/o3CD76kpWdGozE+\nPl7Fd171NCsoKHA4HIE3ZTAY6tSpE8o0KyoqstmqLfXlNCstLS0sDHTWafeJrK/3rwH/rsWu\nQdapm2YxMTFxcXFFRUWlpaWqNJicnJyfn69impWXl+fn5wfemhBCre9IkWP27Nm143pN7Xgp\n7AwGQ4MGDU6dOtW+fXsPm33zzTeqRqWBv5a7DGeqN8h9Ek/AjftEEupUwsD/uF9kA0SmHTt2\n/Pjjj3fffbfWgYQ1L4VdgwYN6tWrJ4SoW7duSOIJJuXNnY5qEwbwX3MsdRRreotcTSJBJFMU\njrdpFAbg1erVqzt27KjuiXX98VLYnTp1Sv7hs88+C34wAAAAVVu7dm12drbWUYQ7/+axKyws\n3LVr119//dW9e/fk5GSz2Ww0GoMUmfo8zigGhIK3EXhcn4gaqOL2r4Ae/fjjj1qHUAv4Udgt\nWrQoJydHHm++efNmIcRdd901a9ascDnbrTjTajmpURwAoCb3U/Yzg9n+ZFUbBxBivhZ2n376\n6ahRo7p16zZmzBi5I7RVq1ZXXXXV4MGDU1JSbrnllmAGWQ1ugIiQ237Zq64rXva8veImnlMu\nUjkeRATuiw3AZ74WdjNnzmzduvWGDRtMpv88JS0tbf369e3bt58xY4Y2hZ1nig48AAAAvfO1\nsNu7d+9jjz1WUdXJJEm69dZbX3311eqeFWT+Tc4EBEFgSVhFT8zigBoE3Kk6tyKAMOdrYZeS\nklJSUuK+3mazJSR4n7QVABAa0c6jriu45zAQQbzcD6dCx44dly5dqpiR/MyZM0uWLPE8dzEA\nAABCw48xdunp6W3atBk9erQQYt26devXr1+4cKHVap0xY0YwIwSAyOZ2odj2y3Jclk/M9fR0\n1wHHrXLVuR0cgPDka2HXrFmzbdu2Pfzww5MmTRJCyMVcZmbmrFmzWrZsGcQAPWDgCMJQYFft\ncA8AaICrbuG//C6/aR0CqubHPHbp6embN28+f/78wYMHo6KiWrRokZiYGLzIgo66EEAkUMzN\nLrz02CXMeq7yYsH4p9UOCEAQ+VHY5efnv//++02aNMnMzBRCrFix4rfffhs9enRqamrQwvPI\nc9eI8ljmxcl4l0X3wcYJs55zCFEqhLwhBzsEA1PFoiohnQHgSEOXiQ7qCY51qELiNw+r2Fp+\n+zkqthbhfC3sjh49mpmZeeTIkZkzZ8qF3fHjxydOnPj6669v3769SZMmwQzSN35Wcv7iYAcg\nTChuPddU1ca9fssFEM58LeyefPLJs2fPvvXWW4MHD5bXjB8//qabburRo8fEiRPffffdoEVY\nrWd+d6nkpgRW13GPTvjiE+W9nbxQ3sTzMm9P8Dja6a/lLh+y9QZxi1CEmmPBjYo10uhNmkQC\noEq+FnabN28eOXLkiBEjKq9MT08fOXLkkiVL1I/LB8qPzOB22AGARjQdEKz4OnEyTbkBX4OB\nsOJrYVdaWlrlpRIWi6WoqEjVkGpIcVNOdwF26QEqUAwMtfj3bM6RQaa4erppMPelyDoAYc7X\nwq5t27arVq0aP358TMz/jiilpaWrVq1q06ZNcGILKaaZgAbc5icDqsCdrwH4zNfC7tlnn+3e\nvXvnzp3Hjh175ZVXmkymgwcPzpkzZ+/evZ9//nlQQwwNr1cj0lkC4ZYn2mLIXcRS5GFvreIA\nEH58Ley6du26atWqnJycf/zjHxUr09LSli5dmpWVFZzYVKY4VzvlIteHmaITqvB8dbbi0Tj3\naSwO+b4rvmxECvek8mdgyV9ZZysvJmws9Ly98pLbYj/2BUBzfsxj16dPn5tvvnnPnj2HDx8u\nKytr0aJF27ZtK5+ZjSjM4RmhvH0B8DrWE/CXe1JNuciPL6LzVtatvPikcCvsXIcEKAfwUdgB\ntYofhZ0QorS0tKCgQJKknj17Jicnm83mIIVVJUUtJeI83h7Rjd8TT3iUl+hyhb/EtHYA9IgO\nPISJ06dPP/bYY59//rndbs/MzHzxxRcbN26sdVDhyI/CbtGiRTk5OQUFBUKIzZs3CyHuuuuu\nWbNm3X333UEKLpztTztQeZEL/lET7tNYGF1OxSq/zAT2bQS1lPJLqVA9E1yGBCgG8D0scisv\nhriwU0ybx5x5kWzAgAH5+fkLFiwwmUzTpk3r3bv33r17tQ4qHPla2H366aejRo3q1q3bmDFj\nsrOzhRCtWrW66qqrBg8enJKScssttwQzyJqo4lAIqE79CcZcPmKPNMxRu31EHL8Phq7jDeaI\nj10ereNS54kgf63lKzRkVqt127Zt7733Xt++fYUQBoOhV69ep0+frl+/vtahhR1fC7uZM2e2\nbt16w4YNJtN/npKWlrZ+/fr27dvPmDEjDAu7EOP6xEihmKDErnxc3W8UTCEWmQIcdhJU7heG\nB/WOxllNXWbF58AasSwWy/XXX7948eI2bdqYTKaFCxdec801VHVV8rWwFQ9ZagAAIABJREFU\n27t372OPPVZR1ckkSbr11ltfffXV6p4V1gKbKlaB6xMjRkhvxw5UzfULRqtcq+9PjXYe9W9f\n2s4YYMzWcu8IJ6tWrbriiisuv/xyIURiYuIPP/ygdURhytfCLiUlpaSkxH29zWZLSEhQNSSN\nMFUsaiC0M8cyjD1CTOji8m259z73HjvtvmCE+FCp6e3UED6KiooyMzN79uw5YcIEo9E4Z86c\nrKysnTt3pqRwUyklXwu7jh07Ll269PHHH6/8Jp45c2bJkiWdO3cOTmz+8fsUmPcZxTzhIxah\nd2+Ky1mwjcXK0U7QhznJL1defNSfDjkVKMcb7Hd9mE5raOCzzz47evTod999J585fOONNxo1\navTxxx8PGzZM69DCjh9j7NLT09u0aTN69GghxLp169avX79w4UKr1TpjxoxgRvg/2y9zPed7\nQstxJ0z1BA24nhQ7Gru48iLjylFDil4xo+ujRi+V3L6PXcaepPdhIBzUV1ZW5nA4HA6HvOhw\nOOx2e2lpqbZRhSdfC7tmzZpt27bt4YcfnjRpkhBCLuYyMzNnzZrVsmXLIAYYNhRddG639KHv\nJDIoPgI932ci4PY/8XgDM25wrFcbt4xzXeGlx+6ioj89POrXCLzAMXk7gqFnz55JSUkDBw6c\nMGGCwWCYO3eu3W7v04dbRlXBj3ns0tPTN2/efP78+YMHD0ZFRbVo0SIxMTF4kXmlOFodqhPY\n5Q+uFJMnCSH2t3Zdpu8EgXMfomc56Wl713NkcxJcLhgcvVx50Q5XZ9dS1x876mWLoi6uy3nB\nCsV/ilMr6UzeDjWkpqZu2rTpiSee6N27t91u79y586ZNmxo0aKB1XOHIp8Lu66+/vvPOOx9/\n/PH7778/JSWlU6dOwQ5Lc7kJBxRrPN/9/d6LXD5iuzD7iV4oJ57oNqXy0kY/7uxaFfcOP9fC\nzi3rXE+KuZ4jOxnv8u1CcHV2rRUdvcFl2ep2ViS/u+uyp8LOc38eUFu0atVq9erVWkdRC/hU\n2DVu3PjkyZNbtmy5//77gx2QB1kNXT7kju0L7e49X/Pv2pWi7uwnf7n1xChQOIZOYOdeFTf9\nnOLe2JmrXBb9uSUooA7lkLujmkQBoGZ8KuzS0tKWLFly7733Ll68eNiwYZIkBTusqgV1OiXX\nY5n3iWFDOLuSezCKoVdBnSAUlbnfjh0IvUC7ikNI2efthkF4gLp8HWO3evXqli1b3nPPPTk5\nOQ0bNoyJcRm2/c033wQhNjeROtWc4roN4TYbAoVdyETyreq4vUqE8taB53nupyMNXYbcuX9N\nZRAeoC5fC7vCwsK0tLS0tLSgRuNNhM6fpJi9TAgh7DdpEUgk8mGqWD8o60Kv53X9+TLjfgmt\nuhfx7LjIZZGLcEPH8otiRatcVQc6KwcAeJzQ323GYMUI40VnlGM9PeMqWkBdvhZ2n332WVDj\n8ElQpyD3egsB5aSdTV0fPVp5SfEV1t/PV+U1uZ0ZaKUZRedob1Ubdz+xW8WoO58pLpIVanfl\nKj6/KexQpXvT1lRe3Phrr8qL7ucfLnbt0qun6MBTFJ3ibIDhAbrnx3Qnmtu4XfEXrurkTK6D\n4t2PPn71FyoObf5+BO5Pc70m1+g2BbTbvecRNC6/96BOsuN1715OigU27tN9LJRb38mjgbQP\n3fKYeIqOZPfpBZQ3UHE942/9/rfKiwVZXoc/A5GuNhV2nqn7iVvF2U8NcbdEVCmwqxcVNwy4\nuKFyA2XfCcJGcGcw8evMrDfKSs7tGrishh9XXlx03qXOu9Rb+/V+2FV58a+rOvoXH6A7+ins\n/KW4rCxL0R+n6hW47tMdS6M3VV5UdJZ8oph8FNrZuOVW1xVe+okD/IKhnA8lsEGtft3ryX1U\nex3lkIAVAUWDoIl2Hg3k6V4OhgGyTPJr83tdh3IO9voEvvdqJL/9HK1DQNVqU2EX4hvjKHk+\nfHjsO1GeWnUbdaeYq93zZMgIpRBnnfLqimB+aHm+X5kQQghl3qI28rfsU367uKia7XzlrU5U\n3JrWvj/A/QERrjYVdiHlPmokmMPalB+xQZ2xD2Ej2JOnKPLqYtfRS3Mu83KO7OoSPy5vrPf9\nacWav66p7/vT4ZcAu+g8U6ZlE9cLyzzf9U54GyHg/l1FUdgZI3T2g1on8btFKraWf929KrYW\n4Sjs/kN5MkLVeV2ymiovV1QcOZXXMyrKSi6ViFiBzUMxJ9nlIp7euS7XJyorObcvM/emfCwQ\nAbyMH1DcbcX95iueu/S89jr70y2tGF0ghBBN6VcGXFDYVUPdU2BuH5nKw1PzlwXCg2K8Y4l4\nwcPGXk/UqnwVrdXPvhNXWco089Y1Qs9xqNT7/vHKi07ROZR7d7sUw+XG6hsPeUnarEDP1foh\nq/ka5Sq+9wKuKOx8pvhMVXxtVXSleLtcsYrDk4ene+PXGHl4ppiR+NEvPRV2oabIOj8LOy+V\nXBXnyBRzN7osuZ97RY053x3qsuw2I3EoKeq8Q3Waqti426xVSm6Xbhx1WXLLUuvyuMqLBdfU\nKCxAR8K3sHPvcm8lSkK3e+WsmKHluYgUyjoyy/VkBGVdIOaUOCsvPqrudImB8XtUe4Adz9zg\nJGT8reQU21tbBrJzzyP2WuU28PCoEF4GDCgqueuPKfd1Js6l/WM7vezNM8X3DQZ6IgKFb2Hn\nfsmeX7Ojej1H5vmkmL/32M4SHsdC+fn5unG3l/sPeD73QQdeII7tDKNKzkseprhmXaLbjSw8\ndukpPnGrmORC0aCfHYRMMOaB4o80U7ypVSReNS78t+cNNh7qXnlRcTB0r+Q8t19qaOprZD74\nb53332rP7eQJaQn9MTidTu9bubHb7Z999pnD4ejevXtiYqLvT7RarQaDodpoDIaoqCiHw1Fe\nXm756rDi0WMfNa9BqD7yd/CT57FT21x7G6dc5mWGT8VHrNeBWcrPYI9j6jceba18usfhxtau\nLYQQZrPZZrPVLD3cRUdHy79WVVozGo0Gg8FoNFa3QWlpqYenu6TZZuW0vMfWnlAlSF+4/2Y9\nfyEJcIhegAP+/Mo671w/Yq2dsgJqTQgR8jTz5Whmt9ttNpv7o4rjm3PVjkBCdYvMYweee+9g\nYB1+ilIswCt21S3sFCyDigJ5unxsVD3NhBBHjx51OByqNOiuVSv1LzQuKCgIxlWxCQkJKrYZ\nsXwt7IqKih555JGtW7cePHhQCNG7d+81a9YIIS699NJNmzZdcsklPu7PZrOZTOHbTQh9IM0Q\nAna73UPZB/ju8OHDFHaCwk4lvn74PfPMM4sWLcrIyBBC7Ny5c82aNffee2+fPn2GDx8+bdq0\nN9/09TxCcXGxhy86kiSlpKSUlZUVFBT42KBnsbGxDofDalXn5FpiYqLZbD537pwq/ViSJCUk\nJOTl5QXelBAiOjo6Pj6+qKhIxRdbVFRkt6tzyVmdOnVsNptaL9ZisUiSFBMTU90GvqRZaWlp\nYWGhKvGom2ZJSUkmkyk3N9f7pj4wGo1xcXH5+fmqtGaxWOLi4goLCz33ifouMTGxsLBQlY80\ng8GQmppaXl6u4ov1nGaFhYVV9sbJjEZjcnKyimkWFxdns9nUeucjKs2SkpIKCgrCM808JBhQ\nM74WdqtWrbr11lvlXro1a9ZER0e/+OKLSUlJffv2/eKLL3zfn9Pp9FAVyQ953sYvzv9SpbXK\nbarSTsX/alH3xYZta16b8nFf4Zlm6iZGMFpTsUGhdpoJtX+tNd6gthzNVGyHo1nNmlKlnUhw\n7Nix8ePHf/nllxaL5e9///srr7zi10iwyCH5uN2ff/7ZqVMn+eevvvqqQ4cOSUlJQojLLrvs\n5El/51wAAADwVVFRUUZGRnFx8SeffLJ06dKff/75jjvu0DqoMOVrj13Dhg337t0rhMjNzd2x\nY8fEiRPl9T/88EO9em5TgQMAAKhk/fr1f/zxx/fffx8bGyuE+Ne//tW4ceP9+/dfffXVXp8b\naXztsevXr99HH330yCOP3HTTTXa7fcCAAcXFxbNnz37//fe7du0a1BABAEAky8vLi4qKqhiS\nmJKSIknSgQPcUK4KvhZ2kyZNuvXWW+fOnbtnz54pU6ZceeWVx48fz8nJqV+//nPPPef9+QAA\nADWSkZFhs9kmTpx44cKFkydP3nfffQ6H4/Rp7n9TBV8Lu4SEhA8//PDChQt5eXmTJ08WQjRo\n0GDjxo379+9v2TKgOZAAAAA8aNKkycqVK5ctW5aSknLppZc2bdo0JSWlbt26WscVjvyb60u+\nAuXkyZM7duxITEzs0KFDXFyc12cBAAAE4pZbbjl+/PipU6fk+bOmT5/eqFEjrYMKR1567Pbv\n3z906NCuXbuOGTNm9+7dQoi33367WbNm/fv379Gjx6WXXvr//t//C0mcAAAgQp05c+auu+76\n+eef09LSoqKiPvzww7p163bp0kXruMKRpx677777rkuXLqWlpYmJid98880777zz9ttvjxo1\nKi0tLScnJzExcenSpUOGDGnWrFmHDh1CFjEAAIgoF1100c8//3zvvfdOnTo1Nzd37NixEyZM\niIqK0jqucOSpx+7pp58uLS1988038/LyLly4kJWVdfvtt1sslm3bto0dO3b48OHr16+/8sor\nX3jhhZCFCwAAItAHH3yQkJBw2223Pffcc5MnTx4/frzWEYUpTz123377badOnUaOHCmEiI2N\nnT59+urVqwcMGNC4ceP/PNlk+vvf/z5//vxQRAoAACJV06ZNP/vsM62jqAU89dj9+eefleeo\na968uRCiQYMGlbeR708apOAAAADgOy8XT1S+P7HZbA5yMAAAAKg5X+exAwAAQJjzMo/duXPn\nfv31Vw9rzp0759f+JEkyGo0eHnU4HEIID9v4y2AwqNWa0+l0OBxGo9HpdAbemvxi1YrNYDA4\nHA51X6wkqVb3OxwOp9Op4ov1vIE+0kyV1iRJUvedVz3NjEaj11+oL+TYSDMfhXOaCSGCcTRT\nMc2E2r9Ws9ksNwsEzuChRvH9z0CVQgcAAIS/goKCxO8Wqdhg/nX3CiESEhJUbDNieeqxe+SR\nR0IWBwAAAALkqbCbPXt2yOIAAABAgDydivXFH3/8ceLEiY4dO6oVEAAACGcFBQXBaJZTsarw\nNDq+cePGs2bNqrxmxIgRq1evrrxm0aJFnTp1CkpoAAAA8IenU7EnTpzIz8+vvGbJkiWNGjW6\n4447ghwVAAAIX4nfbVWxtfzr/j97dx7fRJn/AfyZmaRN0/QuRylHOcpZqNAWFHVBqCtXAUER\nAZdDBBFBRboioICoxbuAgggLrILnUs9FDllQ+CEoyFGuIkgLCAItvdI2zfn7Y9xsMkknSTPJ\nM0k+7xcvXp3J5JnvTL5Jvnlm5pm/SNhaiMM4dgAAAABBAoUdAAAAQJBAYQcAAAAQJFzceUJy\nlZWVBoOhoUdZlo2Pj6+vr5fqihu1Wm02m3U6nSStxcTEKJXKsrIyqe48ER0dXVFR4X1ThBCV\nSqXRaLRarYQbq9VqTSaTJK0lJiYaDIbKykpJWlOpVCzLqtXqhhYQTzOO4+Li4mSeZqWlpZK0\nxnGcRqORcM9LnmbV1dWSjLnPMExCQoI/06yiosJoNDb0KJ9mOp1Oq9VKEk9kZKTRaKyvr5ek\ntdjYWIVCIc80i4iIiIyMrK6ulnBjq6qqJEwzvV4vOAG90fgbsl+9etV3d57o2LGjj1oGeUKP\nHQAAAECQcNFjd/LkyU8++URkzokTJ3wSFwAAAAB4yEVh9/nnn3/++eficwAAAABADsQKu02b\nNvktDgAAAADwklhhN378eL/FAQAAACBOr9e3aNGiqKgoISGBn2M0Gp955pktW7YYDIacnJzl\ny5eHh4fTDZIuXDwBAAAAcmcwGE6cODF58uSysjLb+U8//fQnn3zy9ttvr1+/fseOHY888git\nCGXC38OdAAAAAHgqPz9/xYoVer3edmZ1dfX69evXr18/bNgwQsg777wzYsSI119/vWnTppTC\npA89dgAAACB3ubm5ly5d2rp1q+3MEydOaLXau+++m58cOHCg0Wg8cuQIjQDlAoUdAAAABKSr\nV6+GhYXFxsbyk2FhYXFxcVevXqUbFV0o7AAAACAgWSwWhmEEM0VuCRMKxAq7UaNG7d69m/97\n8ODBhYWFfgkJAAAAwLWkpCTb+0MajcaKiork5GS6UdEldvHErl27GIZJTk4ODw/ftm3bpEmT\noqOjnS7Zpk0b34QHAAAA4FxaWppard69e/fw4cMJIfv27eM47pZbbqEdF01ihd3EiRNXrlxZ\nUFDAT44dO7ahJS0Wi8RxAQAAAIiKjo6eMmVKbm5uy5YtWZZ98sknH3zwwaSkJNpx0SRW2K1Y\nsWLUqFG//fabxWKZOnVqbm5up06d/BYZAAAAgLi33npr7ty5I0eONJlMw4cPz8/Ppx0RZS7G\nsevfv3///v0JIfyh2K5du/ojKAAAAAAHGRkZgoOECoUiPz8f9ZyVuwMUf/bZZ4QQi8VSUlJy\n/vx5o9GYmpqakpLCsriuFgAAAEAWPLjzxM6dO59++mnba2O7du2an59vHRiQrqjXXrCdrM59\nnlYkAAAAAFS4W9gdOnRo6NChTZs2feGFF9LS0liWPXny5OrVq4cOHXrgwIFevXr5NEoAAAAA\ncIlx84LWwYMHnz59+vDhwwkJCdaZN2/ezMjI6NKli+AWHyIMBoNCIVZN8iMNNuIyW/28J2wn\nw5Yt97QFlxodm0iDodMakXrXiTxqNBo5jvNnPBJCmnnTGpH6ZRXJND+nmcz3lcwTQ867jhBy\n/vx5s9ksYYO2OnbsKHmb1dXV0b/8IGGDVb3+QgiJioqSsM2Q5W6P3ZEjRx5++GHbqo4QEh8f\nP2HChHXr1rm/vtraWoPB0NCjLMvGx8fbDjboPkE6lJWVEULUarXZbNbpdJ625lRMTIxSqbx5\n86Ykb2mWZaOjoysqKrxvihCiUqk0Gk1NTY2EG6vVak0mkyStJSYmGgyGyspKSVpTqVQsy6rV\n6oYWqKmpEUkzjuPi4uIal2ZO+SLN+AT2HsdxGo1Gwj0veZpVV1dL8pXGMExCQoI/00yr1YoM\ncM+nmU6n02q1ksQTGRlpNBrr6+slaS02NlahUMgzzSIiIiIjI7VarYQbW1VVJWGa6fX6qqoq\n71sjhEREREjSDoCVu5c+iJQy8uz2AAAAAAg17vbY9ezZc/PmzXPmzLHttCsvL9+8eXPPnj19\nExsAAADIEX/wFGTI3cJu6dKlt99+e3p6+owZM9LS0gghp06dWr169dWrVz/55BNfRggAAAAA\nbnG3sMvKyvrmm2/mzJmzcOFC68yuXbu+9957WVlZvokNAAAAADzgwTh2f/3rX48fP15cXHzu\n3DmLxdKhQ4e2bdtigGIAAIBQE/3zRQlbq8pqLWFrIc6Dwo4QwrJsu3bt2rVr56NoAAAAAKDR\n0N8GAAAAECRQ2AEAAAAECRR2AAAAAEEChR0AAABAkHCrsPvpp5/atm27evVqX0fjjcro3bb/\naIcDAAAA4G9uFXatWrW6cuXK999/7+toAAAAAKDR3CrskpKSNm7c+PXXX2/YsEGS+ygDAAAA\neEqv1ycmJpaVlbk5PwS5O45dQUFBamrqlClT5syZk5ycHBERYfvozz//7IPYAAAAAAghxGAw\nFBUV5eXlCaq3huaHLHcLO61Wm5SUlJSU5NNoAAAAABzl5+evWLFCr9e7OT9kuVvYffvttz6N\nAwAAAKAhubm5ubm5hw8fzszMdGd+yPLslmJarfbgwYM3btzo379/bGysUqnkOM5HkQEAAACA\nRzwYx27dunUtWrTIzs5+8MEHi4qKDh482KpVq82bN/suOAAAAABwn7uF3b///e9p06ZlZGRs\n2bKFn9OxY8du3bpNmDBh69atPgsPAAAAANzl7qHYV155JS0tbefOnQrFn09JSkravn17VlbW\nsmXLhgwZ4rMIAQAAAMAt7vbYHT169L777rNWdX8+mWWHDh1aWFjog8AAAAAAwDPuFnZxcXF1\ndXWO841GY1RUlKQhAQAAAEBjuFvY9enT54MPPigvL7edef369Y0bN2ZlZfkgMAAAAAA7GRkZ\nFoslISHBzfkhyN3C7pVXXqmqqrrllltefvllQsi2bdvmz5/frVu36urqZcuW+TJCAAAAAHCL\nu4Vd27Zt9+7d27Zt2wULFhBCli1blpeXl56e/sMPP6SmpvoyQgAAAABwiwcDFKenp+/Zs6e8\nvLyoqCgsLKxDhw7R0dG+iwwAAAAAPOLZnSdKSkp279597ty58PDw1NTUe+65Jy4uzkeRAQAA\nAIBHPCjsnnnmmfz8fNv77MbGxi5duvTxxx/3QWAAAAAA4Bl3z7FbtWrVq6++mpGRsW3btuvX\nr1+7dm3r1q2dO3eeNWtWQUGBT0MEAAAAAHe422O3fv36bt267dq1KyIigp8zePDg/v37Z2Vl\n5efnjxo1ymcRAgAAAIBb3C3szp49O3v2bGtVx4uIiBg9evTy5ct9EBgAAADIVFVWa9ohgHPu\nHort2rVrdXW14/zS0tJOnTpJGhIAAAAANIa7hd3s2bM3btx48OBB25nff//9hg0bpkyZ4oPA\nAAAAAMAzYodilyxZYjvZqlWr2267LTs7Oy0tzWKxHDt2bPfu3X369OnQoYOPgwQAAAAZid5r\nlLC1qjs9G3wNRIjtysWLFzvO3Llz586dO62TBw8eXLZs2cCBAyWPDAAAAAA8IlbYGY1u1eMM\nw0gUDAAAAAA0nlhhx3Gc3+IAAAAAAC+5e1T78uXLTz311MGDB+vq6gQPxcXFnT17VurAAAAA\nAMAz7hZ206ZN27ZtW58+fdLT0wXHXtGxByEr6rUX+D84QpSEEEKqc5+nGA8AAIQ4dwu7ffv2\nffzxx2PGjPFpNAAAAADQaO6OY9ekSZPMzEyfhgIAAAAA3nC3sBs+fPimTZt8GoqXyqJO2P6j\nHQ4AAABITK/XJyYmlpWVWedcu3btb3/7W4sWLeLi4gYNGnT8+HGK4cmBu4diX3311dtvv/3k\nyZMDBw6MjIwUPDp+/HipA3PNenoT70ay/0MAAAAAfzAYDEVFRXl5ebZVHSFk/PjxpaWlmzdv\njoyMfP311wcMGFBYWJiUlEQrTurcLez+/e9/Hzt27Oeff/70008dH6VS2AEAAECIyM/PX7Fi\nhV6vt535+++/79q1a9++fbfffjshZPPmzc2bN//666+nTZtGKUz63C3sli5dmpmZ+cQTT/To\n0cObEYnFL6FlWZb/X6lUNnoVPL4FjuMYhvG+NR6/4Uql0mKxeN8ay7ISxsbvWI7jJNxYpVLJ\nvyKSkORl5XEcJx6Y39LMkfdtWtNMinAkfgv4KM3MZrMkTVkb9L414l6aiXwYSp5mLMtKu+eJ\nXNOM33VyTjNpP80kaScU5Obm5ubmHj582PaMf5PJtHjxYuscg8Gg0+kkea0Dl7uF3fnz53/8\n8ccuXbp4uT6VSqVQuFipUqmMiYlx2VS96KO2LURERLgVnHuio6MlbM2dLXVfRESEhBur0Wik\naooQwnGctBsrQsI0E+eYhFJto7T7Ss5pFhUVJVVThBCFQuG3NFOr1S6/lcPCwsLCwvwTTyPI\nOc3UarWErQVumoG41q1bL1q0iP+7trZ24sSJUVFRIT6Ch7uFXVZWVlVVlffrMxgMBoOhoUcZ\nhlGpVCaTSdDX6pR4VxI/kLJCobBYLCaTyeNAnQkPD2dZ1nGI5sZhGCYsLKy+XrxAdRfHcWFh\nYQaDwc0bwbkUHh6u1+sl6ZskhERERJjNZgk3Vrx7QMI0E+eYhN6nh5zTTKFQKJVKvV4v4Xsq\ncNNMPH+kTLPFzwjmmBe/4mWbSLPGkfBl5bn8CQrusFgsH3zwwcKFC5s2bbpnz574+HjaEdHk\nbkotW7bs73//+/r169u0aePN+vR6vcg3LsuyKpXKaDTW1NS4bEr89xffglqtNpvNOp3O40Cd\nUSgULMvW1tZKdShWoVC4s6XuUKlU/AerhBtbV1cn1QdrRESEyWSScGPFD4WIpxnHce6nmTjH\nJPS+TT7NpNpXHMdxHCfhnue/cSVMs9raWqmOkfk5zerr60V+R/FpZjAY5Jlm/IkW8kyziIgI\npVJZX18vVaWoVColTDO+sJNwYyVpJ5TduHFjzJgxJSUly5YtGzt2rIRnEAUodwu7F1988fff\nf2/fvn27du0cr4o9cuSI1IEBAAAAiLFYLEOGDGnTps3WrVtRJfPcLeyMRmNqampqaqpPowEA\nAABw03/+85/Dhw8/9dRT+/fvt87s1KlTy5YtKUZFl7uF3ddff+3TOAAAAAA8cuzYMYvFIhhz\n7e233545cyatkKjDaZsAAAAQGDIyMmxPc58zZ86cOXMoxiND7hZ23bt3b+ihW2+9de3atRLF\nAwAAAACN5G5hl5KSYjtZX19/7ty5Cxcu3HrrrVlZWdLHBQAAAAAe8uocu61bt44bN65Dhw6S\nhgQAAAAAjeHVcC9DhgyZOXPma6+9JlU0AAAAANBo3o7j16FDh4MHD0oSCgAAAAB4w6vCzmQy\nbdmyRdo7igIAAABA47h7jl1OTo5gjtlsPn369IULF3ClMQAAAIAcuFvYXb582XFm8+bNx48f\n/9xzz0kaEgAAAMha1Z0YB1em3H1hcDdYAAAAAJlDxQ0AAACeif53lIStVQ2tlrC1ECdW2Inc\nbUKgsLBQimC8csX+Eo4mlMIAAJBWZfRuwRyWPE8lEgCQP7HCzuXlrqdPn66srJQ0HgAAAABo\nJLHC7scff2zooWvXruXm5h44cCA+Pj4vL88HgQEAAACAZzwex85sNq9atapz586bNm2aMmVK\nUVHRtGnTfBEZAAAAAHjEs4snDh06NGPGjEOHDvXo0WP16tV9+/b1UVgAAAAA4Cl3e+wqKipm\nzpzZp0+foqKiN9988/Dhw6jqAAAAAGTFrR67Dz74YO7cudevX3/WBLCsAAAgAElEQVTggQfe\nfPPNFi1a+DosAAAAAPCUi8Lu5MmTjz322A8//NCxY8fNmzdnZ2f7Jyx3OA4BAAAAABDKxA7F\nPvPMMz179vz555+XLl1aWFgoq6oOAAAAQo1er09MTCwrK7POOXPmzJAhQ+Lj45s2bTpmzJhL\nly5RDE8OxAq7V1991WAw1NXVPffcc+Hh4UzD/BYuAAAAhCCDwXDixInJkyfbVnX19fVDhw7l\nOO7DDz9ct27duXPnRo8eTTFIORA7FDt16lS/xQEAAADQkPz8/BUrVuj1etuZR48e/e233w4d\nOhQXF0cIsVgsI0eO1Gq1Lu+wEMTECru1a9f6LQ4AAACAhuTm5ubm5h4+fDgzM9M6MzMzU6vV\nRkZGmkym69evb9++PSsrK5SrOuLpOHYAAAAAMsFxXGRkJCGkf//++/bti4uL+7//+z/aQVHm\n8Z0nAAAAAGTlyy+/LCkpeeyxx/7yl79UV1fTDocmFHYAjVcZvVvwj3ZEAAAhpLCwcNu2bYSQ\n+Pj41q1bL126tLa2ds+ePbTjogmFHQAAAASkY8eO/e1vfzMYDPxkZWWlTqcLCwujGxVdKOwA\nAAAgIA0ePNhsNk+dOvXQoUP/93//98ADD7Rv3/7OO++kHRdNKOwAAAAgICUkJGzdurW4uHjg\nwIH33XdfbGzszp071Wo17bhowlWxAAAAEBgyMjIsFovtnN69e3///fe04pEh9NgBAAAABIlA\n6rGLeu0F28nKaLtHi+17XtN9Hw8AAACArKDHDgAAACBIoLADAAAACBIo7AAAAACCBAo7AAAA\ngCCBwg4AAAAgSATSVbEAAAAgB1VDq2mHAM6hxw4AAAAgSKDHDgAAADzzxqYoCVt7egL6/ySD\nHjsAAACAIOHvHrvw8PDw8PCGHmUYhhCiUCg0Go2nLRdG2E1O+O9tKjhClIQQQixLXvW0TQGO\n4wghkZGRXrbDYxiGZdlGbKlTfGzh4eEKhTSvKcdxarVacEs+LxuUcGP5VGmI79JMoMphjvdt\n8i+lVPuKYRhp9zyROs0iIyMDNM1UKpXIo/xzlUqlPNOMZVlJ2uH5Is1UKpVSqZSkQZZlpU0z\nST49eC7TDMBT/i7sjEajyWRq6FGWZcPDw81mc319veOjYh+ibnDapkcUCgXLsnq9XpIPCJZl\nFQqF91HxwsLClEql0WjU6/WSNKhQKPR6vdlslqQ1lUplsVgk3Fh+7zW0gDdp5iWp0kyq2FiW\n5ThOtmmmVCqlSjOGYVQqlYQva1hYGMMwImlmMBhEIufTzGQyyTPN+JpJzmlmMBgMBoMkDSqV\nyvr6ekk+tyVPM5GfoACN4+/CzmQyibxX+R+RZrPZ6TJeFnbef0bwnwsGg0Gqws5isUj1ycX/\nxhXfvR6xWCzi5ZGnGnpZG4HjOPGXQHw/8PtKwnhsSZhmUoTz576SbZrxr4JUhR0hRNqN5T+R\nGmIymYxGo8jTCdKsUfhiWvJPMwnTTMKXVarObwArnGMHAAAAECTwWwEAIKhE/fcMY1517vO0\nIgEA/0NhBwAQYMRLt9+SV9pONiEo7ABCCA7FAgAAAAQJFHYAAAAQGPR6fWJiYllZmeNDe/fu\n5TjO6UMhBYUdAAAAyJ3BYDhx4sTkyZOdlm6VlZUPPfSQVEN0BTScYwcAIGtlUScEc2Kq7qIS\nCQBF+fn5K1asaGgQzRkzZjRt2rSkpMTPUckQeuwAAABA7nJzcy9durR161bHhzZt2nTo0KHX\nXnvN/1HJEHrsAAAAIFBduHDhySef/Pbbb8VHFA8d2AsAAAAQkEwm00MPPfTUU09lZWXRjkUu\nArjHzvG8EwAAAAgdy5cvLy0tHTlyZFFRUXFxMSHk119/NRgMzZs3px0aNQFc2AEAgKMrGrvJ\nJpTCAPCDX3/9taioKC0tzTrntttumzRp0oYNGyhGRVfwFHbLVQm2k/OjdwsWYDH8OgAAQBBZ\nvXr16tWr+b8PHz6cmZlZWlqakJAg/qzghnPsAAAAAIJE8PTYAfif44meOOwFAOA7GRkZFovF\n04dCCnrsAAAAAIIECjsAAACAIIHCDgAAACBIoLADAAAACBIo7AAAAACCBAo7AAAAgCCB4U4A\nAAJb1Gsv2E13ohQHAMgACjsAAADwzNMTqmmHAM4FUmFXaX+XsP1NxRbGyLEAAAAQagKpsAMA\nAAA5+OmtKAlb6/0U+v8kg4snAAAAAIIECjsAAACAIIHCDgAAACBIoLADAAAACBIo7AAAAACC\nBAo7AABZu6IR/gN49NFHaYcAMoXhTgAAAORr27Zt27ZtM5vNtjOLiopmz55NCFmxYgWluECm\nUNgBAAQYwWjtMVV32U4Wq+0WTvdDQOBLq1ev7t+/f3Jysu3MwsLCO+64g1ZIIGco7AAAAOTr\nlltueeSRRzQau2Pwhw8fHjNmDK2QKNLr9S1atCgqKkpISODnLFu27Nlnn7UuoFAoDAYDpehk\nAYUdAACAfC1ZssRisRw9erSkpIRhmDZt2vTo0eOVV16hHZe/GQyGoqKivLy8srIy2/lFRUVD\nhw6dNWsWP8kwDI3oZASFHQAAgHyVl5fPmzfv/PnzzZo1I4Rcu3YtNTV12bJlMTExtEPzq/z8\n/BUrVuj1esH8oqKiBx544J577qESlQzhqlgAKUW99oLtP9rhAEDAe/vtt5VK5UcffbT5v/iZ\ntOPyt9zc3EuXLm3dulUwv6io6LvvvmvZsmV8fPywYcPOnj1LJTz5QI8dgAcEtdqN5IYWBACQ\nxtGjR5csWdKkSRN+slmzZtOnT1+6dCndqGSitLT05s2bLMt++OGHRqNx6dKlAwYMOHXqVHR0\nNO3QqEFhBwAAIGs4b6whsbGxly9fTkpKYlmWENKrV68WLVp8880348aNox0aNTgUCwAAIF89\ne/ZcvXp1aWkpP3n9+vW1a9f26tWLblQyoVAokpOT+aqOEBIbG5uSknLp0iW6UdGFwg4AAEC+\nZs6caTAYxo4dO2HChPHjx48bN85kMs2cOZN2XLLwzTff9OjRw3qdrFarvXTpUufOnelGRRcO\nxQIABDbBeMUQZOLi4t59990jR45cvHiRZVl+uBMcnOX169evrKxs/PjxTz/9dERExEsvvdS2\nbdshQ4bQjosmFHYAAACyI7i6U6PRdO3alf/7119/JYR07NiRQlgyExUVtX379jlz5tx3332R\nkZHZ2dkbN25UKpW046IJhR0AgKwJbhFGCOlOIwzws+nTpzf0kFKpVKvVX3zxhT/jkYmMjAyL\nxWI7Jy0tbceOHbTikSF/F3Zqtdp6kmNDwsLC4uLiHOeXOc7yhNM2PcJHHhsb62U7PIZhGIbx\nPipra4QQtVodEREhSYMsy8bExAjeP95QKBQSbqz4YQhv0kyc0cPlG7EKPnJp95Wc0yw2NjZA\n0ywyMtJlmoWHh1PvPHC6Q+SfZpGRkWq1Q0nbKJKnmVKplGpjRVLou+++4/84dOjQW2+99dhj\nj/Xo0YPjuNOnT7///vuPPvqoJAFA8KHQY+fyo5BhGJfL+GK9LvEfNxLGJvmWuvwq8qgpIuk1\n9j56WRu9LkniuaIRzmlnP9mIVSDNvGww+NLMS04DQJp52aAfXlaO4/g/3nvvvdmzZ/ft25ef\n7N27d+vWrZcuXfrOO+/4OgYIRP4u7Gpra0XuzsuybHx8fH19fXV1teSrFtxdrhFiYmKUSuXN\nmzcl+eXHsmx0dHRFRYX3TRFCVCqVRqOpqanR6XSSNBgTE6PVak0mkyStJSYmGgyGyspKSVpT\nqVQsy4r8mq+pqRFJM47j4uLiGpdmUR4u34is49PM+3TlcRyn0Wgk3POSp1l1dbXZbPa+KYZh\nEhIS/JlmWq3WaGywD5dPM51Op9VqJYmn0ZzmUmxsrEKhkGeaRUREREZGarXa+vp6SRqMjY2t\nqqqSMM30en1VVZX3rRFC3On8/uOPPwRHiuLi4i5fvixJABB8gna4kysa4T8AAICA07Fjx82b\nN1vLXLPZvGnTpnbt2ok/C0IWLp4AAACQr9mzZz/xxBPjxo3r1q0bx3Fnz57VarXLly+nHRfI\nVAgVdoK7fFbnPk8rEgAAADe1bdv2o48+2rZtW0lJCcMwo0ePvueeeyIjI2nHBTIVRIWdcrj9\n9AY6YQAAAEhKrVa3b99eoVAwDNOmTRuprhf2Ru+npD8VHiQRRIUdgAwI7gHAEnQMA4BXysvL\n582bd/78+WbNmhFCrl27lpqaumzZspiYGNqhgRwF7cUTAAAAQeDtt99WKpUfffTR5v/iZ9KO\nC2QKPXYAAADydfTo0SVLljRp0oSfbNas2fTp05cuXUo3qoqFUo68HftigwNUgacCuLAr9HDo\n+9+SV9pONsExMvAc7rYOMlQWdYJ2COBbEo6uDEEPh2IBAIJKYYTdPwh0PXv2XL16dWlpKT95\n/fr1tWvX9urVi25UIFsB3GMHAACOlqsSbCcX0ooDJDJz5sx58+aNHTu2efPmFovl2rVrHTp0\nmDlzJu24QKZQ2AEAAMhXXFzcu+++e+TIkYsXL7Is26ZNmx49euDgLDQEhR0AAIB88ffsTk9P\nT09P5+cI7nvLcRyFsECuUNgBAADIV3Z2tvgCu3fjoi74HxR2AAAA8rVmzRraIUAgwVWxAAAA\n8tWxY8fU1NTa2trTp0+fOXOmrq4uNTW1ow3aAfqVXq9PTEwsKyuznblx48bMzMzo6Ojs7Oyi\noiJasclEIPXYFSbZjdW0PGoyrUgAAAD8A7cU4xkMhqKiory8PMeqbtasWcuXL09JSXn55Zdz\ncnJOnz4dyucdBlJhV0z/rscAAAB+Zb2lGH/ziWvXri1evPjtt99esGAB7dD8Kj8/f8WKFXq9\n3namxWLJy8vLy8ubMmUKISQ1NXXOnDmXLl1KSUmhE6UM4FAsAECAKYs6YfuPdjjgW0ePHn30\n0UcFtxT75Zdf6Eblf7m5uZcuXdq6davtzDNnzpw9e3b06NFms/n69eutWrX67LPPQrmqIyjs\nAAAAZA6j1jXk8uXLCoVi06ZNsbGxzZo1S05O3rJlC+2gKENhBwAAIF+4pZiI0tJSo9H4448/\nFhYWVlZWPv744+PGjTt9+jTtuGgKpHPsvHRFYzfZhFIYAADSEny4QZDBLcVE8EeoV61a1bx5\nc0LIs88+u2bNmu3bt3fp0oV2aNSEUGEHAAAQcHBLMRGdO3dmWfbmzZt8YWc0Guvq6mJjY2nH\nRRMKOwCA4KIcTjsCkMbNmzcJIfHx8UajsaKi4ubNmwqFIi4uzmw2h/JwHrZatmx53333PfTQ\nQ6+++mpMTMxbb72lUCiGDw/pt0DQFnaOY6Ok1NKIA4KaY5q1sL9EEUf8AaBxDh06tHDhwvnz\n53fo0OHpp5/WarXt27dnGObTTz+Nj49/8803ExMTaccoCxs3bpwzZ86UKVO0Wu0dd9yxZ8+e\n+Ph42kHRFLSFHQAAQOBat27d/ffff/vtt8+bNy81NXX+/PkqlYoQUltb++KLL7711lsvvfQS\n7RgpyMjIsFgstnMiIiJWr15NKx4ZwlWxAAAAslNSUnLvvfdyHHf69OkJEybwVR0hRK1WT5gw\n4fjx43TDA9lCYQcAACA7Go2mtraWEJKSklJeXm77UFlZGX+tAIAjFHYAAACyk5WV9cYbb1y4\ncGH27Nnvvvvurl27rl69euXKle3bt+fn50+aNIl2gCBTOMcOAABAdmbOnLlmzZoZM2YYjUZC\nyIsvvmh9iGGYl156SXBzLQAeCjsAD+C+nOB/hRHCOX1phAF+FhkZOWfOnCeffLKqqqqystJs\nNtOOCAIDDsUCAADIi9lsPn36tMlkYlk2Nja2TZs2bf8rJSWltrb222+/pR0jyBR67AAAAOTl\n6tWrjz322DfffBMZGcnPMZvNhYWFP/zww/fff19RUZGWlkY3QpCtoC3sHA9eYIBiAAAICM2b\nN2/WrNnChQvHjBkTFhb2ww8/7N27V6vV9urVa8qUKX379qV+16zYFw10A4CGBG1hBwAQIhzv\ngAKBjuO4NWvWrF27dunSpXV1dRzH8TfOsnbgATQEhR0AAIDsxMTEzJ079/HHH9+/f/933333\nr3/9a9++fQMGDLjrrrvatm1LOzqQLxR2AAAAMqVSqQYMGDBgwIDKyso9e/bs3Lnzgw8+aNu2\n7YABAyZMmEAxsLCnyiRsTf9WgoSthTgUdgAAAHIXExMzYsSIESNGXL16ddeuXd999x3dwg5k\nK4gKO6477QgAAAB8xWQy7du3r1+/fhMmTEBVBw3BOHYAAAABQKfTLV68mHYUIHdB1GMHIANX\nNHaTTSiFAcFNkGYAAFZBW9gtVwnPxMwhUp7pCQDgH/g0AwD3BW1hBwAQJJTDHWZtoBAG0BYR\nEfH+++/TjgLkDufYAQAABACWZVu1alVXV7dr167nnnuOdjggU+ixA/ChqNdesJ2szn2eViQA\nENB0Ot3Bgwd379594MABhmF69+5NOyI69Hp9ixYtioqKEhISCCFbtmy57777BMtMmjRpw4bQ\n7dX2d2HHcZzIoyzL8v8rlUpfR9KIVTAMwz/RYrF4HwDLsgzDSLWl/I7lOE6qBhmGUSgU/Csi\nCQlfVo7jxAPzW5p5ektid9ZoTbPGh2VD5mnGvwpms9n7pvj9Ju3G8m02bgF/fpqJcxqAnNOM\n33WSf5pJ8rnN7zdpP81cLvPDDz/s2bPnxx9/VCqVffv2fe655zIzM8PDwyUJIIAYDIaioqK8\nvLyysv+dY3rHHXds27bNOqnX6ydNmjR8uOPZCyHE34WdQqEQeT/w7xmO41Qqla8jacQq+I8b\nqd5ODMMwDCPVlvKfDkqlUqpSjGXZ8PBwST4KedJurPg3rlKplEmaCbizRv4VlCo2hmFYlpVt\nmjEMI22aSb6xIpRKpUgeUkwzAacBBESauVP0uIOPLUDTjBCyaNGimJiYOXPmDBgwQKp9Eojy\n8/NXrFih1+ttZzZr1uyee+6xTr744osTJky49957/R6djPi7sKuvrzcYDA09yrJsfHy8wWCo\nrq72dSSNWEVMTAzLslqtVqoeu+joaKm2VKVSaTQanU6n0+kkaTAmJqampsZkMknSWnh4uMlk\nknBjWZZVqxu887lOpxNJM47jwsLCJEkzTy9XdGeNfJpJta84jtNoNHJOM61WK1WPnS/STKFo\n8ENSp9MZjcaGHrWmmVarlSSeRnO6Q2JjYxUKhTzTLCIiQqFQ6HS6+vp6SRqMjY2VNs2MRqOE\nG+tymQULFmzfvv2VV17ZunVr//7977zzzvj4eEnWHlhyc3Nzc3MPHz6cmZnpdIGioqIPP/zw\nyJEjfg5MbnCOHYAH9jelHQEAhJjs7Ozs7OzS0tKdO3d+8cUXK1as6N69+4ABA0L8gKOAxWJ5\n5JFHlixZEoIHqQVwVSwAAIDcJSYmPvjggxs2bFi1alX79u3Xr19POyJ5+eCDD6qqqu6//37a\ngdCHHjsAAABZq6qq+umnn9q3b9+2bdtOnTp16NDhrrvuMhgM1C/NkY+33npr2rRptKOQhRAq\n7Irtz8hKpxQGAIC0HK/OhmBy5syZefPmEULmz5/ftm1bQojBYJg1a1aLFi3y8vJat25NO0D6\n9u/ff+rUqfHjx9MORBZwKBYAILhw3e3+QYB79913+/Tps2XLFuvYdSqV6uuvv27Tps2qVavo\nxiYTBQUFffr0iYmJoR2ILKCwAwCQN0GhhlotxJw7d27UqFH8QCfV1dWzZ882mUwajWbkyJEn\nT56kHZ0sbN26tV+/frSjkIsQOhQrOFoxglIYENwER/zvoBQGAASN8PBw6/hNtbW1hYWFlZWV\n8fHxRqNRZDieYJWRkeE44tipU6eoBCNPgZQTU+OEY4YByFxl9G7bSZbglmIA4JkePXq8//77\nzz//fGRk5L///W+NRvP+++/37t37n//8Z3o6ThcHIRyKBQAAkK/p06dfuXJlxIgRQ4YM+eqr\nr1auXHnmzJkFCxYwDDNjxgza0YHsBFKPHVFiMEaQGSc5Gbp3ngYAX2jevPm6deuOHTtmMpnS\n09MjIyPffffduro6d+5aASEooAo7jzh+4+rwjQsAoaAj7QBAYiqVqk+fPrZzUNVBQ3AoFgAA\nACBIBG+PHQBAkBD2wAkuvl4eNdl/sQCAvAVUYScYvclUSCkOAAAAADkKqMJOgBttP32WThgA\nAAAhRv8WBiCTKZxjBwAAABAkAqvHDpd6QYApizphO9mEVhwAAJKKevKwhK1V52dI2FqIC6zC\nDgAg9JhSaEcAAAEjoAq7mr52k5H7KcUBAAAAIEc4xw4AAAAgSARUj51HBGOjAAAEK3zcAcB/\nBVRhVxVnNxlJKQwAAAAAWQqowg5A9grt79/Yt4HFAAAAfAGFHQBAcMFVtAAhDIUdAECAEXQM\nAwBYhVBht1xld/+ThbTiAADwjuDTDCB06PX6Fi1aFBUVJST8+S64du3a3Llzd+zYYTKZBg4c\n+Prrr7dq1YpukHQFU2EnuC8Fbh0L9F3R2E3izhPgG7grDwQ/g8FQVFSUl5dXVlZmO3/MmDFV\nVVVr1qxRKBQvvvhiTk7O0aNHaQUpB8FU2AHQJ+hKySFlDS0JAADuy8/PX7FihV6vt52p0+n2\n7t370UcfjRw5khDCMMywYcOuXbvWrFkzSmHSh8IOQEzUay/YTfekFAcAQGjLzc3Nzc09fPhw\nZmamdaZKpbrjjjs2bNhwyy23KBSKtWvX9ujRI5SrOoLCDgAAAALXli1bunTp0rlzZ0JIdHT0\nyZMnaUdEWSDfUuzqULt/AAAAEEpqamoGDhw4aNCg48ePnzx5cuzYsdnZ2eXl5bTjogk9dgAA\nABCQvv322+Li4l9++UWhUBBC3n333ZYtW3711VcTJ06kHRo1KOwAvOB4j07DVzTigBCjHG43\niRGJIVTp9Xqz2Ww2m/lJs9lsMpnq6+vpRkVXIB+KBQAAgBA2aNCgmJiYsWPHHjx48Keffpo4\ncaLJZBo+fLjrZwYvFHYAklIOt/sHAAA+Ex8fv3v3bkJITk7O4MGDKyoqdu/e3bx5c9px0YRD\nsQAAABAYMjIyLBaL7ZyOHTsWFBTQikeG0GMHAAAAECRCqccOx8UAAAAgqIVSYQfgtcII2hEA\nuKRrYT9tohMGANCAwg4AAOSCv4lfPSFhhIQRUp37PO2IAAIMzrEDAAAACBLosQMACC5VcfbT\npXTCAAAaUNgBAMib8Jw5QlQ0wgCAQIDCDsADy1UJtEMAAKCvOj+DdgjgnL8LO6VSyd+p1ymG\nYQghHMdFRPj84sNGrIJlWUKISiXNj2WGYViWlWpL+b2qVCr5feg9lmVVKpX1BnySNCjVxrrc\nTB+mmXeD5vAnhlsZn89zXIZPM6n2Fcuy8k8zwXCjXjbotzQLCwtTKpUikRBCFAqFFPHovXmy\n0wDknGa2JGlTwjST/EtKJIUAGsffhV1YWJjINy5PoVC4XMZ7kZGRfn6iH1oLDw8PDw+XqjVp\nP6Y5jpN2Y0VImGbS3kq6Mnq37WTThndI6KSZWq2Wqini3zQLDw/nOE58GYk+zbwq7ER2iAzT\nTPCOkyrCwE0z2Yqa97GErVUvGythayHO34WdTqczmRocVIll2aioKIPBUFtb6+zxGAkjqays\n9PQpkZGRCoWiqqpKkl9+LMuq1WqtVut9U4SQsLCwiIiIuro6vd6r7wCryMhI8RfLIzExMSaT\nScKNZVlWpLbwLs3sSHsuU1nUCdvJcGdJyKdZI/LTKY7jVCpVTU2NJK35Is3q6uok6RhmGCY6\nOtpoNEq4seJpVltbKxI5n2Z6vb6urk6SeBrNaS5pNBqO42SYZoJ3nCQRajQa8RfLfZKnmYS/\nkQB4/i7sTCaTwWBo6FH+6IDZbBZZRiqNWAVfzxkMBqkKO4vFItWW8j0H4rvXI3xsUhV2RNKX\nleM48ZdAfD/w+8rNeHx6krrTAKxpJskqzGZzeHi4zNNMqm9ca4Pet0bcSzOj0SjydNLYtBcc\nsif9lnjagq3ASjPBO06SNiVPMwk/zfxweApCTRCllCnFbpI7SycMCHFcd9upYvvjPy2k6bIE\nAABwLogKOwAKOjrMEftFcUVjN9lE4mAgZNj/fsA9wwDAKpQKO8FHIUAjcKNpRwAAANCgIC7s\nPOtKAQAAAAh0QVzYAUhAMEAJIfd49PRC+xFjUlxfhgsAANB4KOwApOXYVQwA7hL8lGLJ87Qi\nAQhQQVTYCW6nGFlMJwwAAJ+z//1g/+m3qNyvoQD4k16vb9GiRVFRUULCnzd4vHjxYm5u7n/+\n8x+VSnX33Xfn5+dHR0fTDZKuICrsAEBSgtHUqnPRdwLSE6RZZUh/I4MYg8FQVFSUl5dXVlZm\nnVlTUzNgwIAuXbp8/fXXOp1u/vz5o0aN+u677yjGSR0KOwAAeauKE84J9dtZQSjKz89fsWKF\n4LY327dv//33348fP87fNe7TTz9t1apVYWFh9+6hOw4GSzsAAACAP5VFnbD9RzsckJHc3NxL\nly5t3brVdmZlZSV/q0N+Mi4ujmXZEydCOnNQ2AF4wpRi968RCwAAgEQGDBhgNBrnz59fUVFx\n5cqVRx991Gw2X7t2jXZcNKGwAwAAgIDUpk2bzz77bNOmTXFxce3atUtJSYmLi0tMTKQdF004\nxw7AC6765JarEmwnc0hZQ0sCAEAjDBky5NKlS1evXk1ISDAajS+99FLLli1pB0UTCruAhMsV\nAQAArl+//sQTTyxatKhz586EkH/961+JiYl9+/alHRdNKOwAAEAurmjsJptQCgMCRdOmTc+c\nOTN16tSlS5eWlZXNnj37mWeeCQsLox0XTSjsAAAAIFB9/vnnM2bMGDFiREpKysKFC5988kna\nEVGGwg7Al5TD7ac30AkDACAoZGRkWCwW2zkpKSnffvstrXhkCIVdABCcUQcAIcXxFmFLmqZQ\niAMAAgGGOwEAAAAIEqHbY4cLSwE8wr9l6glREqLEWwYAQP+BXlMAACAASURBVJaCqLAT3E4R\n91IEAAg0xWq7yTvwCxzAQ0FU2AEAQKCpjN5NOwSAoBK6hd1vySttJ5sQ/BAEgMAkOF4BACEs\ndAs7DIMJIE68K4XFbyEAAPkJ3cIuoAm+cfEVCwAA/lS9bCztEMA5FHYA4JayqBO2k+jk9h2c\ndgYyFxUVRTsEaBAKOwAAkCmcDA3gqUAq7ByHX7e1BGcPgxzoWthNqiiFASHszhu0IwAAegKp\nsPOMKUU4h6MQBUDQwPVGsiX+o1fmBIf4AcBLwVvYBTWc7RQwuO62U4LBV9P9GorH8I0L/lcY\nYTeZgp8TAB5CYQcAEFQ6lulsJ2V++0RBT/ByVYLtZA4p82s0AIEPhV1AwkEx8ANBmgEAgPyh\nsAPwqY60A4BQh2EvAUIKCjsAgGBWmGR3rqTMz+wEAC8FT2EnuC5sSVOHJeyvkw2s09gBAAAA\nXGJpBwAAAAAA0gieHjsAgKDkZMDhNi2cLAcAEMyFnc7hg091hUYcAP8jGKNrBKUwIKQE9Gkn\nAR08ABXBW9gFEdwRnCJpB+kVjNG1UMKmAQAAUNgB+Jbg1nbK4XTCcINgGFtCSHFPu8mUWv8F\nE2oEO78ymlYgsjM1zu630ASHLJXbeMsA1KGwAxDjYpBexyP+AH7n5CS8hpnX3EUIuWkzh50u\np2MCgh8/hq8oxQEQqFDYAYBbcLaTjFTFNfqpjmcXBNCta/Z1WimYk47xlgHsBW9h5/jBZ3/x\nBE5jB58QJB4u2QHaBJ91fSmFAQD+EbyFnaOAPWom7fn74BFBNxWAL/jzAinHswvo9tgJ32Jc\nd7tJHIoF8JC/Czu1Ws2yLkZFDgsLi4tr/IGGxnFnjXzksbGxUq2UZVl31uvy/Jm4uDiGYQgh\narU6IiLC1eJuYVk2OlrKU7gVCoVULyvDMPz2NsSHaSbpzwOnAfCRS/gWcDPNjK4WEJzGPkmK\nCFmWjYmJ8b4dK2nTTHyByMhIl2kWHh6uVCpdrqvMg7g8JrgWO8dhbZLsMTfTTHJufnRLm2ZK\npdJvaQbgKX8XdrW1tQaDoaFHWZaNj4/X6/XV1dXOHk/0XWDl5eUul4mJiVEqlRUVFRaLxfs1\n8pVTRUVFI54r+I1bXl6uUqk0Gk1tba1Op/M+NkJITEyMVqs1mUyStJaYmGg0GisrKyVpTaVS\nsSyrVjfYmSaeZhzHxcXFNZxm/uM06/g0cych3cFxnEajcWfPRznMKRT9jSBJhDExMdXV1Waz\n2fumGIZJSEjwZ5rV1NQYjQ3Ww3ya1dfXa7VaSeLxHe9fSvfTzJF4mhHVAvvpOYLH3Qk+Nja2\nqqpKwjQzGAxVVVXet0YIkeqnOIBVKB2KtRdAI4o5HjpxOgRAPSFKQpS4/l/OBIeZ5MTxaKDg\nPQK0dCxz/LWmohCHbyDNAKQVuoVdAMFpXgFMcOg2klIYbihMcjyVE9+4QcF+AJFi9QbB47jA\nGSCYoLADAEII+RpVnB9hRGK3daQdAECAQWEXDH5LthvbqQkGdgJJYKhYfxF0l3a/mkYrEtkR\n3LsFAFwJ2sJukcMJtUsEv4llfHMn1+yDv6KxO7YSQMONBjyH4RIFiSfMOhlbHjVZOEvGZwQC\nAIBTQVvYBRMXV41BIJHzcaWnHebsoBBFaPBy4DrHH64hIjtZeMaAJ3dTAwgJIVzYBU5vhJOu\nFAD/C5y3TMARXCAl8Y6W+QsX0AdPAOQnhAu7gCbzT2oIRI5jL8v4Gt5A59/byci5n9iV693s\nJikMgQwQYFDYQUgTXJxIMAog0CAYmfIarTgAIPChsAMAQoiTC0HQY+c/wsORP/tuVYIikhAy\nwncrAwC/k29h59iVQlJW0AhEBnDgVa5C9hx2kBVnt6awIRgxBOe0AQQ1+RZ2AH7geHEiKzoK\n4KISD8/xEXSDBXYfWCCfqiUzXdPsr4gS/njzYY+d7Ai3XTTNUJUCuILCTo6EvZVD7nFY5Ky/\nYgk55jV32U23pxSH7AmyFOcmeoYbbTcpHIZ3vf8iCSw4fAHgCgo7AGiMfZ3s7neSjvudeERQ\nyQmu/fRO05o/7KYF1zurJFyVD+BWEwDeQWEXoHBQTBrNbvtYMOfaj2OpRCJLSDNfWVRkV8kt\nsT9iLxgMRXPdDxEBQJAI5cLO7ktLVseVhCd+maYLl+CK/RVLyPHvAGPyhr4Tn7lTcMMEfw7P\nJjgK7F9Oroob0tZu0nE8RQDwRCAVdg+fsbvy6x+dpTyisKmn3XGlEbI6ruRk5Nhi2ynBsPXp\nvo0mqFz8UngO3Vn73Tc16U3bySC+DNbJ7ZWb0ogjNAiuY11ULvZpJjy0SgghKRIHJB+Ow+4A\ngCfkW9g5uZei6BX9rgXV9Yl2BANTYVQqbwiqZIkPR9b0tZ82Sdm4d4R9SISQThTCCBGttAds\nJx8+c6vtpFbaS3aC96MPABzJt7ALZTgaKCMhczjSxVhoROIT/MGWoM477Wp5J1V4sPjO/or/\nvU3sJpck+TMWgICEwk6O9rs8BBYy1Yb/OY7LH7pEz3bCCQC+0zXN7qed7rhwAddVuFw5ORTj\neA4xAHhBvoWdY68VLg1rEAbtBM8JTmOvI6969HRBBTwBd90NFPL+WSioWfc2kfnoLACyI9/C\nzrHX6q/nvWpQcG54YHfpB9bAVIFF0ipZmHXRErYNEIIwBA+AC/It7MADGI1dNgQnPwnOEAow\nnlyfKBivmGDIYtkQ/rqgerEzTiAG8LUALuyC6hvUXmEE7QhCmaBKltF1qxITnu0UqGdtQTAT\nXiYi7+PIAHIg38JuatPJgjkXpV2BjD8glsd+Yzd91aHjJDp4h1Ojz5fHeoR9YKU+XBeA/FzR\neLa88DIRJ5dm400EYEe+hV1IE4525hlZ3UUj8Pix4j/2lbCfOX148I5jAZQIO73aOF7sHLz9\n0gChR8aF3fX1DrMkPVYUVDeuEXQyXaATBXjIYTDkgBo0RHiVyQY6YQQl4adTJZ0wfMAx54Pr\noxiAPhkXdh7y9Sl3Ua+9YCaknhD+SALlbjDBET1VCp0wgpLwiuMrvluV48mU/rxryNft7U5j\n/6unwwnhkh2QhPD8BJzsCeCV4CnsvET3rlzmNXfZTff8wbPn4yevhIRFsw8LOwA/EJ6m5uRi\nZ/+dpjY1+qZwVpXfVg4QElDYyRJugy1X0nYML1cJ73Kx0Kv2JCYcJgNZGRpwki5AQENh91+q\nBRRXLjgoJrDI4RJY8a9YwYhiGE7MI34d9MthMGTrEf8oQoiPv1MFF55LfNU5BCzBODistB8g\n3v1qdfwwBAABFHbuEnzYxbxm96i0X8D48KJIeAlhJzph+MXTtAMA5xaVCKof4cUTTWv+sJ28\nHtncm9UJuuh+S7b7nSnt6coXfxSeQtf6Ng/unCN8ewKAAxR2siDoO3FZ2OFGVb4TuLdXl5zw\nSxSHYoOC3H43yi0egEAXwIWd4Av4bILdzz7XP+zaCNvzaO2/Jdsd7mzi7dEKu76TIL6pBtjh\nRgtmVEbPs52U+CiYvYtfthdfQPAWW1Ru9xZb0vSvtpPZycLzBdG3EigEhyMEYwj7+uPHo044\n/O4CcCmACzuJuRqWVnCLw66d7L7GvPwO+26f43DqEAIcRqIuTLJLs+72l0uz0+1vAgZACHE4\nMivOsZAqi7XLOsFQcy7HVrzxoV3t12ScZx+HqNUApCXfws7f/fPCEUOEQ7Hv9+IkesEpLMTh\nnDzxj7a9TTw4BwXAR1zcwIDzYyggqTX2hy+613n2dD/38AkIbt+Ce7cAyLewkxvhWLIO1zOK\nEN5t3cNDbA+fEZZ9/+iMUi84CTpLWkR5cBq7cDREqXv40LMSHBxfR8GwO+vqyvwYjrcE5wCg\nrANAYeeu5bHf2E2btthOYeSn0OTy9CAX1x9IOmCh4DAuIaSFd8fIXBAE77Ap6EppNH9f+2n6\n1X463nbC5evo6aFbiXnyGxsgFIRuYScYUGBJGxeHfhedGmq3fFe7R/d1GmY7KRg67uU04Teu\nl+PQOhwUs5vKbv+m7SS+Tr1yXUanP3r6+4HuMTIIGPY1+tQkuw8QonvJbsrhxBLS0ydBuQu3\ntgOwFzyFnfhFsr72tf0VgYLfrMtj7T8oZXaDAZAPwcA368gG28krGrGhpx1vr55SK2VsnsIx\nsoAlNkSA44klgtNUBPdjdDhD4GfB070ck2/Rqafsprv57/ZoAPIUPIUdXYLSLcf+4AVp7nB7\nRIeLMzzi6mwnu89lwZEUgoNigUNQ5wkI+04cOk6yU+ye7ttX3fHeLaZCn64Q3OSycnK420qK\n3bTqn7ZThUl2RyeIwyl6r9in5TN97Y5XPP0fF9F6yvEUZIAQF7qFnfjRTNcEw6NwZ22nhH0n\nfr7rtquhW8B93511vYz/2B912tRzju3k1DjhSHKC8RFd9p1IzJNjZC6vHA8pA0v22E7WMyk+\nXZ2L652J3WS2489U5RXbqcrosbaTy6Psfl34+W4nfF6ZCIkkhDhLKpwJCsEndAs7T7n47IsU\ne67j0C1NDne2nbxILjc6MCerK7I/LSxcwrZDnbRH/J3cBTjJvhi6+pRwCRtTm7rqErMfxEdw\nS+K/XnfxbHGCkjc7yWGJqx4cIxPc4JjgHsc+42zQO7s+vO8O2f0QzXY5drvKrrBrdscyD9cu\nxtMDtYJfCKoBH9pO3nBIKpwwAMEHhZ1zTU4eFMz5jthdPOHQCTfJdmKq/ZeckzH5HI9beUP0\n4krHX9g3vDsQHNDqlmoIIVrC12ca8YWJ1GN8CDLB2cWPdt+i4qM5LkkS3LjCoXdReFK83ZXd\nF8Xadk24Z0RrUJdw4wqKHIotu+Lp4o92jzmOtbREMMan/REDhxucCAu7cEuxyNo9JajkXH/S\nSvtRDCAD/i7sGIbhuAZHMmVZ1uUybnL8Pvayc0X89kqkXHCN7STbSZff314S3Ffb5Sh3gh+1\ntfOWOC7DMAz/crgUb/+5f/M2u8lfPrcbOqHXvY6nG3qMZVmGYcQX8D6F/MBJ1Xh1qLMF/yRM\nJOHCwue6OHfKxwRpyd3j5BXh04xhmItbhZ3WXG/PxsnlU0KSTw+el2nGPyRJKjqUPr49OCve\nqfbwGWHhdecNu0MEgqxupT1gO+kyck+79AQcE8kW18Mhqex/kHA9xH5LqZctIoTUE0IIiXL2\nyRl/fKPt5M0ek0Ra44nnGEAjMBaLxZ/rMxqNCoXzavLSE749B1a8sMvOdDHcycXtdpd+CYon\nwTeu4OCF4BvOz5xseEu7j5KOx+w+7Jqq/m47Gf7KCvH2BS9cq+V2q2O22u3Y7/6wq/MIIQOn\nSJ+Bfkszx33r0bFax8Ku9W12ywsOdwqWF09Cd1YnIcefE4Kz2s+m272D7iiaZTt5Xfeq4OmC\nXSF4E4k/KkhCHzGZTO4WbQ/vkXbV3hR2jmWieOOC5R1XLTg8KqjMXK5OfO0ueXoVrYAgkSxj\nG1qQEEKYj+2n4xKFS5TbnW/gflqeO3fObDaLrdsLHTtK2Y8A8ufvwk6n04n8QGEYJiwszGw2\nGwwGSVanUCgsFovJJM2RR6VSybJsfX29JK0xDKNUKvV6vSStcRynUCiMRqOEG2s0GqVKj/Dw\ncAlfVo7jxDtmxF+jUEszhUIh4Z5Hmlm582lmMpmMRqMk8UibZmFhYQzDyDnNDAaDVLVOWFiY\nwWCQbZoRQoqLi1HYgVT8fSi2vr5e5P3Asmx8fLzBYKiurpZkdWq12mw263TSdFTExMSwLKvV\naiX5gGBZNjo6WqotValUGo1Gp9NJuLE1NTVSfYuEh4ebTCYJN5ZlWbXaYdy2/9LpdCJpxnEc\n/0Ev5zSTKjaO4zQajZzTTKvVSvKVxjCM/9NMpGizpplWq5UknsjISKPRKFUpFhsbq1Ao5Jlm\nERERCoVCp9NJuLHSppnRaJRwYyVpB8DKrZOoAAAAAED+UNgBAAAABAkUdgAAAABBAoUdAAAA\nQJBAYQcAAAAQJFDYAQAAAAQJFHYAAAAAQQKFHQAAAECQQGEHAAAAECRQ2AEAAAAECRR2AAAA\nAEEChR0AAABAkEBhBwAAABAkUNgBAAAABAnGYrHQjuF/qqurV65c2blz51GjRtGOxYnNmzeX\nlJTk5uYqlUrasQgdP378m2++GTRoUK9evWjHImQ2m5ctW9ayZcu//e1vtGMhhJCKiopVq1Z1\n7dp15MiRtGNx4v333798+fK8efNYVna/u3755Zdt27YNGzasR48etGMRMhqNr776aps2bcaP\nH087FkIIKSsrW7NmTffu3XNycmjH4sSGDRuuXr06f/582oE4cejQoR07dowYMaJbt260YxHS\n6XRvvvlmu3btxo4dSzsWAOfk9c1RV1dXUFBw4MAB2oE4t2/fvoKCApPJRDsQJ0pKSgoKCi5c\nuEA7EOcKCgq+//572lH8qba2tqCg4Oeff6YdiHM//PBDQUGBrH5xWRUXFxcUFBQXF9MOxAmT\nyVRQULB3717agfypurq6oKDgl19+oR2Ic3v27Pniiy9oR+Hc+fPnCwoKLl68SDsQJwwGQ0FB\nwf79+2kHAtAgeRV2AAAAANBoKOwAAAAAggQKOwAAAIAgIa+LJwAAAACg0dBjBwAAABAkUNgB\nAAAABAkUdgAAAABBAoUdAAAAQJBQ0A7gf0wm0z//+c/9+/cbjcbevXs/8sgjcrvBg9FonDhx\n4rvvvhsVFUU7FjsVFRUbNmw4evSoXq/v1KnTpEmTUlJSaAf1p8uXL//jH/8oKiriOC4tLe3h\nhx9OTEykGA/SrHHknGMEaeY5pFkjyC3NAJySUY/d+vXr9+7dO3369NmzZx85cuTtt9+mHdH/\nmEymkpKS5cuXV1dX047FiTfeeKO4uHju3LlLliyJiIhYsGBBeXk57aAIIcRgMLzwwgssy86d\nO3fWrFlXr17Ny8ujGxLSrHFkm2MEaeYhpFnjyDDNAJyzyENtbe3999+/b98+fvLQoUMjR46s\nqKigG5XVli1bJk+ePGHChJycnKqqKtrh2CktLc3JyTl16hQ/aTQax40bt23bNrpR8YqKinJy\ncqqrq/nJAwcO5OTk1NXV0YoHadY4cs4xC9LMQ0izxpFbmgE0RC49diUlJTqd7pZbbuEn09PT\nzWbz+fPn6UZlNWrUqPXr1y9atIh2IE6YzeYHH3ywQ4cO/KTRaNTr9WazmW5UvA4dOnz66aca\njcZsNpeXl//yyy+pqakqlYpWPEizxpFzjhGkmYeQZo0jtzQDaIhczrErLy9XKBSRkZH8pEKh\n0Gg08umEl7MmTZo8+OCD/N/19fX5+fkRERF33HEH3ah4LMvyH3zz588/deqURqN55ZVXKMaD\nNGscOecYQZoFC6QZgCTkUthZLBaGYQQzTSYTlWACkcVi2b1796ZNm2JjY19++WVZnRBNCFmw\nYIFOp9u+ffuzzz67du3aiIgIKmEgzbwh8xwjSLOggDQD8JJcCrv4+HiDwVBXV8e/SUwmk1ar\nTUhIoB1XYKisrHz11VevX78+ceLEv/zlL45fKrSUlJSUlZX16tUrKioqKipq/PjxX375ZWFh\nYe/evanEgzRrNNnmGEGaBRGkGYD35HKOXevWrcPDwwsLC/nJU6dOsSzbrl07ulEFBIvFsmTJ\nkqioqHfeeadfv36y+ii8cOHCW2+9Ze2rqK2t1ev1CgW1nxNIs8aRc44RpFmwQJoBSEIuSalW\nq7Ozszds2JCQkMAwzLp16/r16xcXF0c7rgBw/Pjx8+fPjxgx4vTp09aZycnJchhgKSMjY+3a\ntStXrhw2bJjBYPj444+TkpK6detGKx6kWePIOccI0ixYIM0AJMFYLBbaMfzJZDKtX7/+xx9/\nNJvNffr0mTp1qtyG9Dx37tycOXM2b94sq9M+vvjii/Xr1wtmTp8+fejQoVTiETh79uyGDRsu\nXLgQHh7erVu3SZMmNW3alGI8SLNGkHmOEaSZ55BmjSC3NANwSkaFHQAAAAB4Qy7n2AEAAACA\nl1DYAQAAAAQJFHYAAAAAQQKFHQAAAECQQGEHAAAAECRQ2AEAAAAECRR2AAAAAEEChR0AAABA\nkEBhBwAAABAkUNiFusmTJzMNS01N9Wcws2fPjo2NHT16tD9XCiAHb7zxBsMwlZWVnj6xe/fu\n/Lt11qxZIovNmDGDX6x79+5ehAkAcqegHQBQlpOT07JlS/7vy5cvb9y4sV+/fnfeeSc/Jz4+\nnhCSlJT0xx9/+Pruc3v27Fm5cuWoUaMef/xxn64ocL3xxhtz584tLS1NSEggPntdBGuRJ/e3\n3ZvNCYhdQQjJysp65pln2rdvL7LMtGnTsrOz8/Ly6uvr/RYYAPgfCrtQN2rUqFGjRvF/Hzx4\ncOPGjXffffeCBQtsl2nSpIkfIvntt98IIXl5eR07dvTD6oKAf14XeQrlbXeUnJzssp+7Z8+e\nPXv23LhxY3FxsV+CAgA6cCgWXDt+/PjVq1d9vRa+9yU8PFww/48//vjpp598vXa6GreN/nld\nGsfXr5qctx0AgCIUduDa4MGDs7Ky+L9zcnLuvffew4cP//Wvf42Li8vMzPzyyy8NBsOcOXNS\nU1NjYmKGDRv2+++/W5974cKFBx54ICUlJSYmpl+/flu3bnW6ivvvv3/q1KmEkJSUlMGDB/Mr\nvf/++z/++OOUlJQHHniAX+zQoUNDhgxp3rx5UlLSkCFDDh8+bG3B08AEtm3b1r9//9jY2D59\n+rz33nuvv/56VFSUO1sxePDge++9t6ioaOzYsUlJSUlJSdOmTauqqnLzuY7b+OGHH/bu3Ts2\nNjY6Orpnz57r1q3j5991111z584lhCQmJj700EOC10V857gM0spxLeItCzjdIvEc2L9//z33\n3JOQkJCcnDxu3LiSkhI3d5112xmG+cc//vHpp5/269cvJibm1ltv3bhxo8jmCLi/wx03Vnyv\niu+3jz766Pbbb4+JicnMzFy1apWgcTffOI6qq6vnz5+fmpqqVqvbt2+fm5tbU1Pj5nMBIBhY\nAP7rwIEDhJAXX3xRMH/QoEGZmZn838OGDevUqdOAAQN+/PHHU6dO9e3bNywsLCsra/HixefO\nnfv4448Zhrn//vv5hY8ePRodHZ2cnDxv3rzFixenpaUxDLNu3TrHVZ88eTI3N5cQ8vHHHx8/\nfpxfaXp6ulqtHjNmzDvvvGOxWHbs2KFUKlu3bj1v3rxnn322TZs2SqVyx44djQhM4OOPP2ZZ\nNj09fcmSJY8++mh4eHhycrJGo3FnKwYNGtSnT58ePXr861//unDhwqpVqxiGmTJlipvPFWzj\nli1bCCFZWVkvv/xybm4uf577Z599xjc1Y8YMQsiXX355+vRpwesivnPEg7TluBbxlh1TRbBF\n4nvgyy+/VCgU3bt3X7x48Zw5c6Kiotq3b19VVeXOrrNuOyFk4MCB7du3//TTT/fu3fvoo48S\nQl5++WWnmyPg0Q533FiRvSq+315//XVCSJcuXebPn//oo4+q1eq2bdsSQioqKlxuu0BaWtrI\nkSOtkyNHjlQoFKNHj37hhReGDBlCCJk6dar10WHDhqWlpTltBwCCAwo7+B83CzuO44qLi/nJ\nTz75hBAyZswY68K33nprq1at+L/79+/funXrsrIyflKv1/fv3z8qKqq6utpx7XxPibXlQYMG\nEULWr1/PT5pMprS0tOTk5Bs3bvBzSktLk5OTe/ToYTabPQ3MVn19fevWrbOysurq6vg5X331\nFSHEWtiJbwUf586dO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+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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oV9CSFvvfXWzJkz8/Pza9WqRRkqeiRPUgmNHQAAgFJ5PB5CSNu2bdu2bcve4vV6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AAJ/yfvlyh0DFsSpR7hBCRWxid/Xq1bvf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\nMmvUm15mAAy05Rq70tJSfX19Y2NjalFfX9/ExERL/mQ1i7p06TJt2jTqcU1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Dycmpd+/ep06dUuXJQqGwuLhY6ZJEANAluMYO3nYSiaR7\n9+7Lli2j1zx58uT06dO42BwAAN44uMYO3nbGxsZz5szZt29ffX39qFGjSktLY2Ji9PX158+f\nz3ZoACp59OhRUlKSnZ2d4i+7leTk5BQXFz9+/Lg9AwOA9oczdgAkNjZ2/fr1qampwcHBmzdv\ndnJyysrK6tKlC9txAagkIyPDz88vLi6O4Tl79+718/PLzMxst6gAgBW4xg4AAABAR+CMHQAA\nAICOQGMHAAAAoCPQ2AEAAADoCDR2AAAAADoCjR0AAACAjkBjBwAAAKAj0NgBAAAA6Ag0dgAA\nAAA6Ao0dAAAAgI74H1y42fp4QZzkAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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kiJDT6KJHySJJlMpkAgoC9WkiQdtyCSZVmWZZ/Pp6O3NplMfr9fX6DudWs2m3UH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\nuU2bNunp6b169Vq+fLlqFzfddNOIESOEEG3atLn66quVTm+66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+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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tTflDTLUQaIP80MBoMqacZxXOrTTNp/UC9z6lmWMkSVNEtylqMv53h25JGtWfTB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\nANr/SJPZY+ruAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for (k in kinds)\n",
+    "    arrivalDelayHistogram(\n",
+    "        receipts[`Message` == k],\n",
+    "        paste0(k, \"s\"),\n",
+    "        paste(\"Arrival delay for\", k),\n",
+    "        scales=\"free_y\",\n",
+    "        outfiles=paste0(\"plots/elapsed-\", k, \".svg\")\n",
+    "    ) %>% print"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c20c8c5-bc7a-48e5-81cc-b552867469ff",
+   "metadata": {},
+   "source": [
+    "#### Bandwidth usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c62b1ae5-aa58-40b0-91ff-640a586160c4",
+   "metadata": {},
+   "source": [
+    "##### Total network bandwidth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "6a7ee9fa-d782-431d-a161-2f0767cc8e71",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "totalBandwidthPlot <- function(rs, title=\"\", scales=\"fixed\", outfiles=c(), width=16, height=8, dpi=150) {\n",
+    "    g <- ggplot(\n",
+    "            rs[,\n",
+    "                .(`Size [Gb]`=8*sum(`Size [B]`, rm.na=TRUE)/1e9/sampleSize),\n",
+    "                by=.(`VariedX`, `VariedY`, `Slot`=floor(`Received [s]`), `Message`)\n",
+    "            ],\n",
+    "        aes(x=`Slot`, y=`Size [Gb]`, fill=`Message`)\n",
+    "    ) +\n",
+    "        geom_area() +\n",
+    "        facet_varied(wide=TRUE, scales=scales) +\n",
+    "        xlab(\"Slot [s]\") +\n",
+    "        ylab(\"Total network ingress [Gb/s]\")\n",
+    "    for (outfile in outfiles)\n",
+    "        ggsave(outfile, units=\"in\", width=width, height=height, dpi=dpi)\n",
+    "    g\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "41cbb3b6-73cb-4b84-8685-b9eaa61992dc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ty4Bg0aaG8bNmx433337du3L7ZZoTIKO6CuS3nuaao6hC/mrSXmCdRZfs9wR3yi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1qL3GYCEAaK0FnYrloOKtRdUTAAC0EC2osItPFKpD9BMAAB52SURBVFgAkHCk6X+N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\nAACBEHYAAIF4AH/Ef25J/5vIAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "totalBandwidthPlot(\n",
+    "    receipts,\n",
+    "    \"Total bandwidth\",\n",
+    "    scales=\"free_y\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9970168-bf24-4aba-bf7b-dae5bca2feb3",
+   "metadata": {},
+   "source": [
+    "##### Bandwidth usage per node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "f56bfd5b-9008-4ef9-ac86-de5be8e1dcdc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "nodeCountTable <- receipts[, .(`Node count`=length(unique(`Recipient`))), .(`Network`)]\n",
+    "nodeCount <- function(network)\n",
+    "    nodeCountTable[`Network` == network, `Node count`]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "217e3844-0f98-47f2-a259-1594157c209b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "meanNodalIngressPlot <- function(rs, title=\"\", scales=\"fixed\", outfiles=c(), width=16, height=8, dpi=150) {\n",
+    "    g <- ggplot(\n",
+    "            rs[,\n",
+    "                .(`Size [Mb/node]`=8*sum(`Size [B]`)/1e6/nodeCount(`VariedX`)/sampleSize),\n",
+    "                by=.(`VariedX`, `VariedY`, `Slot`=floor(`Received [s]`), `Message`)\n",
+    "            ],\n",
+    "        aes(x=`Slot`, y=`Size [Mb/node]`, fill=`Message`)\n",
+    "    ) +\n",
+    "        geom_area() +\n",
+    "        facet_varied(wide=TRUE, scales=scales) +\n",
+    "        xlab(\"Slot [s]\") +\n",
+    "        ylab(\"Mean network ingress among nodes [Mb/s]\")\n",
+    "    for (outfile in outfiles)\n",
+    "        ggsave(outfile, units=\"in\", width=width, height=height, dpi=dpi)\n",
+    "    g\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "523cd963-0b7e-4557-ad1f-f323d4fb5f31",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Q53FKjsyEOETGZmpt1u16jzRo0aadQz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wYEFeXl63bt3Gjh0rhNi4ceOmTZuWLVtW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\nrkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "meanNodalIngressPlot(\n",
+    "    receipts, \n",
+    "    \"Mean nodal ingress\",\n",
+    "    scales=\"free_y\",\n",
+    "    outfiles=paste0(\"plots/ingress-average-area.svg\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "2184aef9-4218-42d5-a768-9f5e6aa05654",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "peakNodalIngressPlot <- function(rs, title=\"\", scales=\"fixed\", outfiles=c(), width=16, height=8, dpi=150) {\n",
+    "    g <- ggplot(\n",
+    "            rs[,\n",
+    "                .(`Size [Mb]`=8*sum(`Size [B]`, rm.na=TRUE)/1e6/sampleSize),\n",
+    "                by=.(`VariedX`, `VariedY`, `Slot`=floor(`Received [s]`), `Message`, `Recipient`)\n",
+    "            ][,\n",
+    "                .(`Size [Mb]`=max(`Size [Mb]`)),\n",
+    "                by=.(`VariedX`, `VariedY`, `Slot`, `Message`)\n",
+    "            ],\n",
+    "        aes(x=`Slot`, y=`Size [Mb]`, color=`Message`)\n",
+    "    ) +\n",
+    "        geom_point(size=0.5) +\n",
+    "        facet_varied(scales=scales) +\n",
+    "        xlab(\"Slot [s]\") +\n",
+    "        ylab(\"Maximum network ingress among nodes [Mb/s]\")\n",
+    "    for (outfile in outfiles)\n",
+    "        ggsave(outfile, units=\"in\", width=width, height=height, dpi=dpi)\n",
+    "    g\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "3d3b6b61-11bb-4bd5-bb9e-2d9e4fb0949c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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m2Bv1SXZc7XDk5ZTSlDuzx9SefHta+WNHv/rklXNuFlhsTwrs8xZ/NbTSnZnevZ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\nMc1ov19Fns3jY+PQTa3kzFWJEsiKMlReKvUdgBkWAKTefV2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sUiGqsAtF14e1ugAAIABJREFUtAk75HLply5BHjdmWPeNt8rWOLK8RPfZ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\nAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "peakNodalIngressPlot(\n",
+    "    receipts, \n",
+    "    \"Peak nodal ingress\",\n",
+    "    scale=\"free_y\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ce920bc-a21a-44b8-b74f-29370edcd28b",
+   "metadata": {},
+   "source": [
+    "#### Release memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "a0cbb25a-88cf-43b5-a68b-e6ef1262dce1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rm(receipts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "793b6e31-b429-46c3-8081-04f518190b4b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A matrix: 2 x 6 of type dbl</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>used</th><th scope=col>(Mb)</th><th scope=col>gc trigger</th><th scope=col>(Mb)</th><th scope=col>max used</th><th scope=col>(Mb)</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>Ncells</th><td>   1171363</td><td>  62.6</td><td>   2815774</td><td>  150.4</td><td>   2815774</td><td>  150.4</td></tr>\n",
+       "\t<tr><th scope=row>Vcells</th><td>1056415075</td><td>8059.9</td><td>3439615229</td><td>26242.2</td><td>4299200151</td><td>32800.3</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A matrix: 2 x 6 of type dbl\n",
+       "\\begin{tabular}{r|llllll}\n",
+       "  & used & (Mb) & gc trigger & (Mb) & max used & (Mb)\\\\\n",
+       "\\hline\n",
+       "\tNcells &    1171363 &   62.6 &    2815774 &   150.4 &    2815774 &   150.4\\\\\n",
+       "\tVcells & 1056415075 & 8059.9 & 3439615229 & 26242.2 & 4299200151 & 32800.3\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A matrix: 2 x 6 of type dbl\n",
+       "\n",
+       "| <!--/--> | used | (Mb) | gc trigger | (Mb) | max used | (Mb) |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| Ncells |    1171363 |   62.6 |    2815774 |   150.4 |    2815774 |   150.4 |\n",
+       "| Vcells | 1056415075 | 8059.9 | 3439615229 | 26242.2 | 4299200151 | 32800.3 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       used       (Mb)   gc trigger (Mb)    max used   (Mb)   \n",
+       "Ncells    1171363   62.6    2815774   150.4    2815774   150.4\n",
+       "Vcells 1056415075 8059.9 3439615229 26242.2 4299200151 32800.3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gc()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11398578-b1c9-4e63-9d1a-fa3175ba090b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### CPU usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e2e0ea5-c590-410e-bc5f-02a612e81e33",
+   "metadata": {},
+   "source": [
+    "#### Read results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "dbb4f0b4-cc41-4c05-9113-d9b052d531fd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded Rdata file: sampleSize = 0.33 \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "        Network           Bandwidth                 CPU          \n",
+       " topology-v2:75219797   10 Mb/s:84439242   4 vCPU/node:84439242  \n",
+       " topology-v3: 9219445                                            \n",
+       "                                                                 \n",
+       "                                                                 \n",
+       "                                                                 \n",
+       "                                                                 \n",
+       "                                                                 \n",
+       "        Diffusion duration           Voting duration       Max EB size      \n",
+       " L_diff = 7 slots:84439242   L_vote = 4 slots:84439242   12 MB/EB:84439242  \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "      Tx size                Throughput        Tx start [s]  Tx stop [s] \n",
+       " 1500 B/Tx:84439242   0.100 TxMB/s:18840563   Min.   :60    Min.   :960  \n",
+       "                      0.150 TxMB/s:27220216   1st Qu.:60    1st Qu.:960  \n",
+       "                      0.200 TxMB/s:38378463   Median :60    Median :960  \n",
+       "                                              Mean   :60    Mean   :960  \n",
+       "                                              3rd Qu.:60    3rd Qu.:960  \n",
+       "                                              Max.   :60    Max.   :960  \n",
+       "                                                                         \n",
+       "  Sim stop [s]       Slot             Node               Task         \n",
+       " Min.   :1500   Min.   :  20.0   node-15:  194011   ValTX  :77963104  \n",
+       " 1st Qu.:1500   1st Qu.: 290.0   node-17:  193837   ValVote: 6258005  \n",
+       " Median :1500   Median : 515.0   node-21:  193811   ValRB  :   69656  \n",
+       " Mean   :1500   Mean   : 515.5   node-12:  193692   ValRH  :   58581  \n",
+       " 3rd Qu.:1500   3rd Qu.: 738.0   node-11:  193665   ValEH  :   40299  \n",
+       " Max.   :1500   Max.   :1500.0   node-78:  193657   ValEB  :   40184  \n",
+       "                                 (Other):83276569   (Other):    9413  \n",
+       "  Duration [s]      \n",
+       " Min.   :0.0002800  \n",
+       " 1st Qu.:0.0006201  \n",
+       " Median :0.0006201  \n",
+       " Mean   :0.0009211  \n",
+       " 3rd Qu.:0.0006201  \n",
+       " Max.   :0.3393539  \n",
+       "                    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if (file.exists(\"results/cpus.Rdata\")) {\n",
+    "    load(file=\"results/cpus.Rdata\")\n",
+    "    cat(paste(\"Loaded Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "} else {\n",
+    "    cpus <- fread(\"results/cpus.csv.gz\", stringsAsFactors=TRUE)\n",
+    "    sampleSize <- 1\n",
+    "    save(cpus, file=\"results/cpus.Rdata\")\n",
+    "    cat(paste(\"Saved Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "}\n",
+    "cpus %>% summary\n",
+    "cpus[, `:=`(\n",
+    "    `VariedX`=`Network`,\n",
+    "    `VariedY`=`Throughput`\n",
+    ")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "bd842a2d-028c-43e1-bcf9-973afe6f2eef",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cpusNode <-\n",
+    "        cpus[\n",
+    "            ,\n",
+    "            .(`Duration [s]`=sum(`Duration [s]`)),\n",
+    "            by=.(`VariedX`, `VariedY`, `Node`, `Slot`)\n",
+    "        ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dfc94b5-c8ed-4b2b-87ea-6ebbf65ce3a3",
+   "metadata": {},
+   "source": [
+    "#### Peak CPU usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8d56f5e-b724-4afb-b14a-7b82a5743f85",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "##### Histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "933ae82a-44e6-4a17-a160-f441455aaa11",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "peakCpuHistogram <- function(cs, title=\"\", wide=TRUE, scales=\"fixed\", outfiles=c(), width=16, height=8, dpi=150) {\n",
+    "    g <- ggplot(\n",
+    "            cs[,\n",
+    "                .(`Duration [%]`=100*max(`Duration [s]`)),\n",
+    "                by=.(`VariedX`, `VariedY`, `Slot`)\n",
+    "            ], \n",
+    "        aes(x=`Duration [%]`)\n",
+    "    ) +\n",
+    "        geom_histogram(binwidth=10) +\n",
+    "        facet_varied(wide, scales=scales) +\n",
+    "        xlab(\"Peak CPU load [%]\") +\n",
+    "        ylab(\"Number of slots\")\n",
+    "    for (outfile in outfiles)\n",
+    "        ggsave(outfile, units=\"in\", width=width, height=height, dpi=dpi)\n",
+    "    g\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "92b34cec-a998-40a7-9700-3d7497e98039",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KTP7b8jt1Yfnggk0zFAcgr9wgU9YeQhvf9ya3vlB4BkOgZITqFfuKAnjDyk9+Uh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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "peakCpuHistogram(\n",
+    "    cpusNode, \n",
+    "    \"Peak CPU load among all nodes\",\n",
+    "    scales=\"fixed\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "536fc45f-54b5-4692-9c51-4ab385d99ab6",
+   "metadata": {},
+   "source": [
+    "##### Time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "0519518a-9e15-4f1e-bbd1-064eef6b3044",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "peakCpuTimeseries <- function(cs, title=\"\", scales=\"fixed\", outfiles=c(), width=16, height=8, dpi=150) {\n",
+    "    g <- ggplot(\n",
+    "            cs[,\n",
+    "                .(`Duration [%]`=100*mean(`Duration [s]`)/sampleSize),\n",
+    "                by=.(`VariedX`, `VariedY`, `Slot`)\n",
+    "            ], \n",
+    "        aes(x=`Slot`, y=`Duration [%]`)) +\n",
+    "        geom_line() +\n",
+    "        facet_varied(wide=TRUE, scale=scales) +\n",
+    "        xlab(\"Slot\") +\n",
+    "        ylab(\"Mean CPU load [%]\")\n",
+    "    for (outfile in outfiles)\n",
+    "        ggsave(outfile, units=\"in\", width=width, height=height, dpi=dpi)\n",
+    "    g\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "143992f2-580b-4fab-89a7-d78771aea1ef",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0h4r7oaFBI7eCB37e4jpMqarh1C+hohPUBIfDjfZ0EiI/FrjdPAYAGpMIP0cu3z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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "peakCpuTimeseries(\n",
+    "    cpusNode,\n",
+    "    \"Mean CPU load among all nodes\",\n",
+    "    outfiles=\"plots/cpu-mean-timeseries.svg\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9d35a8a-28f7-4e60-9f73-6ab7e4b186e4",
+   "metadata": {},
+   "source": [
+    "#### Mean CPU usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7056cbb6-558c-441b-83ae-8f244188eb0b",
+   "metadata": {},
+   "source": [
+    "##### Histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "bab18a7f-63b5-4d3e-98a8-34d1c5126d26",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "meanCpuHistogram <- function(cs, nodeSeconds, title=\"\", scales=\"fixed\", wide=FALSE, outfiles=c(), width=16, height=8, dpi=150) {\n",
+    "    g <- ggplot(\n",
+    "            cs[,\n",
+    "                .(`Duration [%]`=100*sum(`Duration [s]`)/nodeSeconds/nodeCount(`VariedX`)),\n",
+    "                by=.(`VariedX`, `VariedY`, `Task`)\n",
+    "            ], \n",
+    "        aes(x=\"\", fill=`Task`, y=`Duration [%]`)) +\n",
+    "        geom_bar(stat = \"identity\") +\n",
+    "        facet_varied(wide=wide, scales=scales) +\n",
+    "        xlab(\"\") +\n",
+    "        ylab(\"Mean CPU load [%]\")\n",
+    "    for (outfile in outfiles)\n",
+    "        ggsave(outfile, units=\"in\", width=width, height=height, dpi=dpi)\n",
+    "    g\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "39e888f7-5105-4776-962b-2559477f00c2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hUrVkhdAqyLM3YAAAAyQbADAMCh/fTT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y8vK+/PJLpVMAeCQUOwAAAElQ7AAAACRB\nsQMAAJAEN08AAABIgiN2AAAAkqDYAQAASIJiBwAAIAmKHQAAgCQodgAAAJKg2AEAAEiCYgcA\nACAJih0AAIAkKHYAAACSoNgBAABIgmIHAAAgCYodAACAJCh2AAAAkqDYAQAASIJiBwAAIAmK\nHQAAgCQodgAAAJKg2AEAAEiCYgcAACAJih0AAIAkKHYAAACSoNgBAABIgmIHAAAgCYodAACA\nJCh2AAAAkqDYAQAASIJiBwAAIAmKHQAAgCQodgAAAJKg2AEAAEiCYgcAACAJih0AAIAkKHYA\nAACSoNgBAABIgmIHAAAgCYodAACAJCh2AAAAkqDYAQAASIJiBwAAIAmKHQAAgCQodgAAAJKg\n2AEAAEiCYgcAACAJih0AAIAkKHYAAACSoNgBAABIgmIHAAAgCYodAACAJCh2AAAAkqDYAQAA\nSIJiBwAAIAmKHQAAgCT+P72pIbOF4hqUAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "meanCpuHistogram(\n",
+    "    cpus[`Slot` >= txFirst & `Slot` < txLast],\n",
+    "    txWindow * sampleSize, \n",
+    "    \"Mean CPU load among all nodes\", \n",
+    "    scales=\"fixed\",\n",
+    "    outfiles=paste0(\"plots/cpu-mean-histogram.svg\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d65f9b6-3552-4fff-a479-5a8d0718d0a5",
+   "metadata": {},
+   "source": [
+    "##### Time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "aece6abc-dfe0-4009-8345-51544a89fc8b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "meanCpuTimeseries <- function(cs, nodeSeconds, title=\"\", scales=\"fixed\", outfiles=c(), width=16, height=8, dpi=150) {\n",
+    "    g <- ggplot(\n",
+    "            cs[,\n",
+    "                .(`Duration [%]`=100*sum(`Duration [s]`)/nodeSeconds/nodeCount(`VariedX`)/sampleSize),\n",
+    "                by=.(`VariedX`, `VariedY`, `Slot`, `Task`)\n",
+    "            ], \n",
+    "        aes(x=`Slot`, y=`Duration [%]`, color=`Task`)\n",
+    "    ) +\n",
+    "        geom_point(size=0.25, alpha=0.75) +\n",
+    "        facet_varied(wide=TRUE, scales=scales) +\n",
+    "        scale_y_sqrt() +\n",
+    "        xlab(\"Slot\") +\n",
+    "        ylab(\"Mean CPU load [%]\")\n",
+    "    for (outfile in outfiles)\n",
+    "        ggsave(outfile, units=\"in\", width=width, height=height, dpi=dpi)\n",
+    "    g\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "e2a5b736-fcb2-49b6-9b17-29be5f20f5ac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bj79agVZIQTfbwrPa/zPKzdUt+NcZGm5/uy9SHbv5uTdd1Q8aAeU5O8F797+5Nh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\n06q+7JLCHrliaPDMvn0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1vhpNESUifd09L+bDR2nCL/orK8tB+hhV2m+UokWiGK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\nZrZTmhWg+IBlR6V1TRwjAigb5IOuud1uRiD7lp1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Tz6MoI7yy0S4ncJbRiA6OLqQ2IAyUZCSRs192bjdzh4gAACAASURBVBaoo7EIIJzR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\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yrg2M+6nQCgaLEjvQ9ZOP/B7gfq8r84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VgZBSSqSebMS9hFwZitC3m+q+m8mWhXxd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\n7wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "meanCpuTimeseries(\n",
+    "    cpus, \n",
+    "    sampleSize, \n",
+    "    \"Mean CPU load among all nodes\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "160674d3-500d-4ba9-a0cc-8bb05213d62e",
+   "metadata": {},
+   "source": [
+    "#### Release memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "df3178d7-68ce-431b-a69b-3f033e75a790",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rm(cpus, cpusNode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "b01b72e4-cdf9-4357-a263-71d994d5b62f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A matrix: 2 x 6 of type dbl</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>used</th><th scope=col>(Mb)</th><th scope=col>gc trigger</th><th scope=col>(Mb)</th><th scope=col>max used</th><th scope=col>(Mb)</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>Ncells</th><td>  1038295</td><td>  55.5</td><td>   2815774</td><td>  150.4</td><td>   2815774</td><td>  150.4</td></tr>\n",
+       "\t<tr><th scope=row>Vcells</th><td>761983681</td><td>5813.5</td><td>2751692184</td><td>20993.8</td><td>4299200151</td><td>32800.3</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A matrix: 2 x 6 of type dbl\n",
+       "\\begin{tabular}{r|llllll}\n",
+       "  & used & (Mb) & gc trigger & (Mb) & max used & (Mb)\\\\\n",
+       "\\hline\n",
+       "\tNcells &   1038295 &   55.5 &    2815774 &   150.4 &    2815774 &   150.4\\\\\n",
+       "\tVcells & 761983681 & 5813.5 & 2751692184 & 20993.8 & 4299200151 & 32800.3\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A matrix: 2 x 6 of type dbl\n",
+       "\n",
+       "| <!--/--> | used | (Mb) | gc trigger | (Mb) | max used | (Mb) |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| Ncells |   1038295 |   55.5 |    2815774 |   150.4 |    2815774 |   150.4 |\n",
+       "| Vcells | 761983681 | 5813.5 | 2751692184 | 20993.8 | 4299200151 | 32800.3 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       used      (Mb)   gc trigger (Mb)    max used   (Mb)   \n",
+       "Ncells   1038295   55.5    2815774   150.4    2815774   150.4\n",
+       "Vcells 761983681 5813.5 2751692184 20993.8 4299200151 32800.3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gc()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bcfeba8-b66e-42da-a0c5-ea6c1fbd575c",
+   "metadata": {},
+   "source": [
+    "### Block contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a35433d7-2006-486e-8032-c9e582d4fa5d",
+   "metadata": {},
+   "source": [
+    "### Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "63a4eac4-d691-4cb4-9077-95ca488109ae",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded Rdata file: sampleSize = 1 \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "        Network      Bandwidth            CPU             Diffusion duration\n",
+       " topology-v2:352   10 Mb/s:754   4 vCPU/node:754   L_diff = 7 slots:754     \n",
+       " topology-v3:402                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "                                                                            \n",
+       "         Voting duration   Max EB size       Tx size           Throughput \n",
+       " L_vote = 4 slots:754    12 MB/EB:754   1500 B/Tx:754   0.100 TxMB/s:248  \n",
+       "                                                        0.150 TxMB/s:238  \n",
+       "                                                        0.200 TxMB/s:268  \n",
+       "                                                                          \n",
+       "                                                                          \n",
+       "                                                                          \n",
+       "                                                                          \n",
+       "  Tx start [s]  Tx stop [s]   Sim stop [s]  Message           Item    \n",
+       " Min.   :60    Min.   :960   Min.   :1500   EB:308   115-node-11:  6  \n",
+       " 1st Qu.:60    1st Qu.:960   1st Qu.:1500   RB:446   117-node-74:  6  \n",
+       " Median :60    Median :960   Median :1500            123-node-12:  6  \n",
+       " Mean   :60    Mean   :960   Mean   :1500            140-node-17:  6  \n",
+       " 3rd Qu.:60    3rd Qu.:960   3rd Qu.:1500            144-node-14:  6  \n",
+       " Max.   :60    Max.   :960   Max.   :1500            144-node-24:  6  \n",
+       "                                                     (Other)    :718  \n",
+       " Generated [s]      Transactions          Endorses  \n",
+       " Min.   :  20.07   Min.   :   0   401-node-207:  6  \n",
+       " 1st Qu.: 331.23   1st Qu.:   0   428-node-100:  5  \n",
+       " Median : 623.34   Median :  60   123-node-12 :  3  \n",
+       " Mean   : 679.56   Mean   :2275   144-node-14 :  3  \n",
+       " 3rd Qu.: 977.08   3rd Qu.:4602   157-node-25 :  3  \n",
+       " Max.   :1496.07   Max.   :8000   (Other)     :131  \n",
+       "                                  NA's        :603  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if (file.exists(\"results/sizes.Rdata\")) {\n",
+    "    load(file=\"results/sizes.Rdata\")\n",
+    "    cat(paste(\"Loaded Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "} else {\n",
+    "    sizes <- fread(\"results/sizes.csv.gz\", stringsAsFactors=TRUE)\n",
+    "    sampleSize <- 1\n",
+    "    save(sizes, file=\"results/sizes.Rdata\")\n",
+    "    cat(paste(\"Saved Rdata file: sampleSize =\", sampleSize, \"\\n\"))\n",
+    "}\n",
+    "setnames(sizes, old=\"Kind\", new=\"Message\")\n",
+    "sizes %>% summary\n",
+    "sizes[, `:=`(\n",
+    "    `VariedX`=`Network`,\n",
+    "    `VariedY`=`Throughput`\n",
+    ")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "9634539d-fe2c-4c41-95e1-d2c2788af38c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1500"
+      ],
+      "text/latex": [
+       "1500"
+      ],
+      "text/markdown": [
+       "1500"
+      ],
+      "text/plain": [
+       "[1] 1500"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "txSize <- sizes[, as.numeric(sub(\" .*$\", \"\", unique(`Tx size`)))]\n",
+    "txSize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30397903-91e4-42f8-aaaa-029e695b0868",
+   "metadata": {},
+   "source": [
+    "### EB contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca305cb5-29d2-4d93-b86a-81f042f62918",
+   "metadata": {},
+   "source": [
+    "#### Transactions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "d6c487d8-22f2-4e3c-9f58-f132101a8f96",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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67iF7KENApC4ilCWUtIMoRESMUQkgIh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\nOvD/AZ11vbDFheJxAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(sizes[`Message` == \"EB\" & `Transactions` > 0], aes(x=`Transactions`)) +\n",
+    "    geom_histogram(binwidth=1000) +\n",
+    "    facet_varied(wide=TRUE) +\n",
+    "    ylab(\"Number of EBs\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e662832-3e83-4b5f-b022-037396f352a3",
+   "metadata": {},
+   "source": [
+    "#### Sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "1d0fa517-18fc-4b05-82f2-751efa94800f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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frgB5S66a+B5DkGJCD5hXTUOWrvgVcrdUEjkDzHgAQkv5Auf/3F73ndLyPL/+Y8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\ngg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        sizes[`Message` == \"EB\" & `Transactions` > 0, .(`EB size [MB]`=`Transactions`*txSize/1e6), .(`VariedX`, `VariedY`)], \n",
+    "    aes(x=`EB size [MB]`)\n",
+    ") +\n",
+    "    geom_histogram(binwidth=1) +\n",
+    "    facet_varied(wide=TRUE) +\n",
+    "    ylab(\"Number of EBs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "5fac54a0-d037-4ffb-8a92-3d45d466ee96",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ggsave(\"plots/contents-ebs-size.svg\", units=\"in\", dpi=150, width=16, height=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac31b43d-5a38-44a4-91b1-c75b778ff66c",
+   "metadata": {},
+   "source": [
+    "### RB contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ea11c8a-fb6a-49f4-8782-aa1e36b7c6b3",
+   "metadata": {},
+   "source": [
+    "#### Transactions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "72e71e9b-35e2-46ac-8a71-939fbdaa732c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0nyJHUwSZ8kL1G3xTSej6j5Cu4/e0ox/+mIpKMZ9pvUxa+usg1JbF/5C8+PfnFG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\n9bVD3IUTAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(sizes[`Message` == \"RB\" & `Transactions` > 0], aes(x=`Transactions`)) +\n",
+    "    geom_histogram(binwidth=10) +\n",
+    "    facet_varied() +\n",
+    "    ylab(\"Number of RBs\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96b443d6-1b6d-4328-9f25-a90c714a3d32",
+   "metadata": {},
+   "source": [
+    "#### Sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "0aab9e66-18c6-416e-a0b8-9b2ba3254807",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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raY5uMRtV7B/WdPKdY/HZFUNLmvJS9+c6FjSGLHmmd9P322Q/rRXXjpnU17xhif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\nuOD/A3f33/r2gn1FAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        sizes[`Message` == \"RB\" & `Transactions` > 0, .(`RB size [kB]`=`Transactions`*txSize/1e3), .(`VariedX`, `VariedY`)],\n",
+    "    aes(x=`RB size [kB]`)\n",
+    ") +\n",
+    "    geom_histogram(binwidth=10) +\n",
+    "    facet_varied() +\n",
+    "    ylab(\"Number of RBs\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28b97c74-4047-4dc7-a2e8-3cc277a7a0a8",
+   "metadata": {},
+   "source": [
+    "### Disposition of blocks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c129b14-1072-4bbe-afe4-e2c556eb7df1",
+   "metadata": {},
+   "source": [
+    "#### Data processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "1e665b0d-c4c2-4c75-b0e6-4a5f3c8fcabf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ebSizes <- \n",
+    "        sizes[`Message` == \"RB\" & !is.na(`Endorses`), .(`Certified`=TRUE), .(`VariedX`, `VariedY`, `Item`=`Endorses`)][\n",
+    "        sizes[`Message` == \"EB\", .(`EB Transactions`=`Transactions`), .(`VariedX`, `VariedY`, `Item`)], \n",
+    "            on=c(\"VariedX\", \"VariedY\", \"Item\")\n",
+    "        ][\n",
+    "            ,\n",
+    "            .(\n",
+    "                `EB txs later not certified`=ifelse(is.na(`Certified`), as.numeric(`EB Transactions`), 0),\n",
+    "                `EB txs later certified`=ifelse(is.na(`Certified`), 0, as.numeric(`EB Transactions`))\n",
+    "            ),\n",
+    "            .(`VariedX`, `VariedY`, `Item`)\n",
+    "        ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "a9d73765-4b37-439b-af87-c9e2e8c2e237",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rbSizes <- \n",
+    "        ebSizes[\n",
+    "            ebSizes[, .(`EB txs certified now`=`EB txs later certified`), .(`VariedX`, `VariedY`, `Endorses`=`Item`)][\n",
+    "                sizes[`Message` == \"RB\", .(`Generated [s]`, `RB Transactions`=as.numeric(`Transactions`)), .(`VariedX`, `VariedY`, `Item`, `Endorses`)],\n",
+    "                on=c(\"VariedX\", \"VariedY\", \"Endorses\")\n",
+    "            ],\n",
+    "            on=c(\"VariedX\", \"VariedY\", \"Item\"),\n",
+    "            nomatch=0\n",
+    "        ][\n",
+    "            ,\n",
+    "            .(\n",
+    "                `Generated [s]`,\n",
+    "                `RB`=`RB Transactions`,\n",
+    "                `EB later not certified`=`EB txs later not certified`,\n",
+    "                `EB later certified`=`EB txs later certified`,\n",
+    "                `EB now certified`=ifelse(is.na(`EB txs certified now`), 0, `EB txs certified now`)\n",
+    "            ),\n",
+    "            , .(`VariedX`, `VariedY`, `Item`)\n",
+    "        ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "05b7d0b4-ac84-45f8-94a2-9e596caf6356",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "blocks <- melt(\n",
+    "    rbSizes, \n",
+    "    id.vars=c(\"VariedX\", \"VariedY\", \"Item\", \"Generated [s]\"),\n",
+    "    measure.vars=c(\"RB\", \"EB later not certified\", \"EB later certified\", \"EB now certified\"),\n",
+    "    variable.name=\"Block\",\n",
+    "    value.name=\"Transactions\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce3e57c4-5ee2-4608-aea5-f154bc1ee97d",
+   "metadata": {},
+   "source": [
+    "#### Transactions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "436afbc7-4373-4f7f-8805-a85313e2a3a8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PQHkbgvWJsJreXgaKur15Uf/91CcI8i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cqFNputc+fODTSrAwAA0J6wErvzzz//+PHjmzdvruto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\nAAAASUVORK5CYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        blocks[, .(`Transactions`=sum(`Transactions`)), .(`VariedX`, `VariedY`, `Block`, `Minute generated`=(floor(`Generated [s]`/60)))],\n",
+    "    aes(x=`Minute generated`, y=`Transactions`, fill=`Block`)\n",
+    ") +\n",
+    "    geom_bar(stat=\"identity\") +\n",
+    "    facet_varied(wide=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "1fda2095-9968-427e-898e-328895f8f53e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AcAQG3jx4+/fPnyypUrhRA9e/ZMTU21WCx79+79/fffJ0yYcNdddyldIFA3gh0A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\n/wP742UV/14G5gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        blocks[, .(`Transactions`=sum(`Transactions`)), .(`VariedX`, `VariedY`, `Block`)],\n",
+    "    aes(x=\"\", y=`Transactions`, fill=`Block`)\n",
+    ") +\n",
+    "    geom_bar(stat=\"identity\") +\n",
+    "    facet_varied()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9aadae91-726b-4158-8155-96bf40964bd9",
+   "metadata": {},
+   "source": [
+    "#### Sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "22ac0aaf-9ca4-4c54-a1ba-0670b06c8b73",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KAQFHYAAAAKQWEHAACgEBR2AAAACkFh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\nLgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        blocks[, .(`Total size of transactions [MB]`=sum(`Transactions`)*txSize/1e6), .(`VariedX`, `VariedY`, `Block`, `Minute of block generation`=(floor(`Generated [s]`/60)))], \n",
+    "    aes(x=`Minute of block generation`, y=`Total size of transactions [MB]`, fill=`Block`)\n",
+    ") +\n",
+    "    geom_bar(stat=\"identity\") +\n",
+    "    facet_varied(wide=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "7aba29a7-197a-46f4-83f7-bfd3a7bce0fc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ggsave(\"plots/disposition-size-timeseries.svg\", units=\"in\", dpi=150, width=16, height=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "18976ec9-8ea9-4a60-a141-b28f240d5b50",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Vu3fuCBBx544AGj0bh///6VK1devXp1+fLlStcF1IBgBwBADQ4dOqTRaCIjI81m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glZWVVVVVctdylxJF0c3Nra6tZWVlvFFQ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\nAAhC2AEABPEXonCgJ2P/CS4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "        blocks[, .(`Total size of transactions [MB]`=sum(`Transactions`)*txSize/1e6), .(`VariedX`, `VariedY`, `Block`)], \n",
+    "    aes(x=\"\", y=`Total size of transactions [MB]`, fill=`Block`)\n",
+    ") +\n",
+    "    geom_bar(stat=\"identity\") +\n",
+    "    facet_varied()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e382fcee-18e0-4fea-8f1e-2e678f5d6949",
+   "metadata": {},
+   "source": [
+    "#### Release memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "12c5df92-53dc-4b5a-8756-9fac5439a323",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rm(sizes, ebSizes, rbSizes, blocks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "9918bf71-983d-4d02-84cb-54c183149c53",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A matrix: 2 x 6 of type dbl</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>used</th><th scope=col>(Mb)</th><th scope=col>gc trigger</th><th scope=col>(Mb)</th><th scope=col>max used</th><th scope=col>(Mb)</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>Ncells</th><td>1038321</td><td>55.5</td><td>   2815774</td><td>  150.4</td><td>   2815774</td><td>  150.4</td></tr>\n",
+       "\t<tr><th scope=row>Vcells</th><td>1979701</td><td>15.2</td><td>2201353748</td><td>16795.0</td><td>4299200151</td><td>32800.3</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A matrix: 2 x 6 of type dbl\n",
+       "\\begin{tabular}{r|llllll}\n",
+       "  & used & (Mb) & gc trigger & (Mb) & max used & (Mb)\\\\\n",
+       "\\hline\n",
+       "\tNcells & 1038321 & 55.5 &    2815774 &   150.4 &    2815774 &   150.4\\\\\n",
+       "\tVcells & 1979701 & 15.2 & 2201353748 & 16795.0 & 4299200151 & 32800.3\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A matrix: 2 x 6 of type dbl\n",
+       "\n",
+       "| <!--/--> | used | (Mb) | gc trigger | (Mb) | max used | (Mb) |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| Ncells | 1038321 | 55.5 |    2815774 |   150.4 |    2815774 |   150.4 |\n",
+       "| Vcells | 1979701 | 15.2 | 2201353748 | 16795.0 | 4299200151 | 32800.3 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       used    (Mb) gc trigger (Mb)    max used   (Mb)   \n",
+       "Ncells 1038321 55.5    2815774   150.4    2815774   150.4\n",
+       "Vcells 1979701 15.2 2201353748 16795.0 4299200151 32800.3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gc()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "r-minimal kernel",
+   "language": "r",
+   "name": "r-minimal"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "4.2.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/analysis/sims/micro-mainnet/combine-results.sh b/analysis/sims/micro-mainnet/combine-results.sh
new file mode 100755
index 000000000..76f09c832
--- /dev/null
+++ b/analysis/sims/micro-mainnet/combine-results.sh
@@ -0,0 +1,41 @@
+#!/usr/bin/env nix-shell
+#!nix-shell -i bash -p gnused gzip pigz "rWrapper.override { packages = with rPackages; [ data_table R_utils bit64 ggplot2 magrittr stringr ]; }"
+
+set -e
+
+mkdir -p results/$d
+for f in lifecycle resources receipts cpus sizes
+do
+  DIR=$(find experiments -type f -name $f.csv.gz \( -not -empty \) -printf %h\\n -quit)
+  HL=$(sed -n -e '1p' "$DIR/case.csv")
+  HR=$(zcat "$DIR/$f.csv.gz" | sed -n -e '1p')
+  if [[ "$f" == "lifecycle" || "$f" == "resources" || "$f" == "sizes" ]]
+  then
+    FRACT=1.00
+  else
+    FRACT=0.33
+  fi
+  (
+    echo "$HL,$HR"
+    for g in $(find experiments -type f -name $f.csv.gz \( -not -empty \) -printf %h\\n)
+    do
+      if [ ! -e "$g/stderr" ]
+      then
+        echo "Skipping $g because it has no stderr." >> /dev/stderr
+      elif [ -s "$g/stderr" ]
+      then
+        echo "Skipping $g because its stderr is not empty." >> /dev/stderr
+      else
+        BL=$(sed -n -e '2p' "$g/case.csv")
+        zcat "$g/$f.csv.gz" | gawk 'FNR > 1 && rand() <= '"$FRACT"' { print "'"$BL"'" "," $0}'
+      fi
+    done
+  ) | pigz -p 3 -9c > results/$f.csv.gz
+R --vanilla << EOI > /dev/null
+require(data.table)
+sampleSize <- $FRACT
+print(sampleSize)
+$f <- fread("results/$f.csv.gz", stringsAsFactors=TRUE)
+save($f, sampleSize, file="results/$f.Rdata", compression_level=9)
+EOI
+done
diff --git a/analysis/sims/micro-mainnet/experiments.list b/analysis/sims/micro-mainnet/experiments.list
new file mode 100644
index 000000000..9d96e2b63
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments.list
@@ -0,0 +1,10 @@
+experiments/topology-v2,0.100/run.sh
+experiments/topology-v3,0.100/run.sh
+experiments/topology-v2,0.150/run.sh
+experiments/topology-v3,0.150/run.sh
+experiments/topology-v2,0.200/run.sh
+experiments/topology-v3,0.200/run.sh
+experiments/topology-v2,0.250/run.sh
+experiments/topology-v2,0.300/run.sh
+experiments/topology-v3,0.250/run.sh
+experiments/topology-v3,0.300/run.sh
diff --git a/analysis/sims/micro-mainnet/experiments/config.yaml b/analysis/sims/micro-mainnet/experiments/config.yaml
new file mode 100644
index 000000000..4cd4c76ed
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/config.yaml
@@ -0,0 +1,110 @@
+
+# Simulation parameters.
+timestamp-resolution-ms: 0.1         
+simulate-transactions: true                    
+cleanup-policies:                        
+- cleanup-expired-vote           
+
+# Protocol parameters for Linear Leios.
+leios-variant: linear-with-tx-references
+linear-eb-propagation-criteria: eb-received
+linear-vote-stage-length-slots: 4
+linear-diffuse-stage-length-slots: 7
+praos-fallback-enabled: true      
+leios-stage-active-voting-slots: 1
+leios-mempool-sampling-strategy: ordered-by-id
+relay-strategy: request-from-first
+treat-blocks-as-full: false                       
+eb-diffusion-strategy: peer-order
+eb-referenced-txs-max-size-bytes: 12000000
+eb-size-bytes-constant: 240
+eb-size-bytes-per-ib: 32
+
+# A conservative upper bound based on empiricial data of empty Praos blocks on
+# Cardano mainnet.
+leios-header-diffusion-time-ms: 1000.0
+
+# Scenario-specific parameters for transaction generation.
+tx-start-time: 60                                               
+tx-stop-time: 960
+tx-generation-distribution:        
+  distribution: constant               
+  value: 10.0                             
+tx-size-bytes-distribution:        
+  distribution: constant      
+  value: 1500              
+tx-conflict-fraction: 0                   
+tx-overcollateralization-factor-distribution:
+  distribution: constant                   
+  value: 0                             
+
+# Based on a linear model fit from `db-analyser` measurements of mainnet blocks
+# for the `Apply` operation, either with or without a term for Plutus execution
+# steps.
+#
+# `Apply CPU [ms]` / `Tx count` ~ (0.6201 ms/tx)
+#
+# `Apply CPU [ms]` ~ 0 + (0.2624 ms/tx) * `Tx count`
+#                      + (0.9487 ms/Gstep) * `Tx exec [Gstep]`
+#
+tx-validation-cpu-time-ms: 0.6201
+
+# Inherited from Cardano mainnet protocol parameters for Praos.
+tx-max-size-bytes: 16384
+rb-generation-probability: 0.05
+rb-head-size-bytes: 1024
+rb-body-max-size-bytes: 90112
+
+# Worst case based on benchmark cluster "Forging: Slot start to announced".
+rb-generation-cpu-time-ms: 71.02
+
+# Based on `apply` statistics for empty blocks using `db-analyser` on blocks
+# from the Cardano mainnet.
+rb-head-validation-cpu-time-ms: 0.4438
+eb-header-validation-cpu-time-ms: 0.4438
+
+# Based on a linear model fit from `db-analyser` measurements of mainnet blocks
+# for the `Reapply` operation, either with or without a term for Plutus
+# execution steps.
+#
+# `Reapply CPU [ms]` ~ (0.3539 ms) + (0.02151 ms/kB) * `Block size [kB]`
+#
+# `Reapply CPU [ms]` ~ (0.3478 ms) + (0.01943 ms/kB) * `Block size [kB]`
+#                                  + (0.02127 ms/Gstep) * `Block exec [Gstep]`
+#
+rb-body-legacy-praos-payload-validation-cpu-time-ms-constant: 0.3539
+rb-body-legacy-praos-payload-validation-cpu-time-ms-per-byte: 0.00002151
+eb-body-validation-cpu-time-ms-constant: 0.3539
+eb-body-validation-cpu-time-ms-per-byte: 0.00002151
+
+# Based on analysis of wFA+LS sortition for realistic stake distribution similar
+# to that of Cardano mainnet, and on the security requirement.
+vote-generation-probability: 600
+vote-threshold: 450
+
+# Based on CDDL for votes, worst case for non-persistent voters.
+vote-bundle-size-bytes-constant: 0
+vote-bundle-size-bytes-per-eb: 164
+
+# Worst case based on benchmarks from cryptography prototype.
+vote-generation-cpu-time-ms-constant: 0.280
+vote-generation-cpu-time-ms-per-tx: 0
+vote-validation-cpu-time-ms: 2.9
+
+# Vote diffusion settings.
+vote-diffusion-strategy: peer-order
+vote-diffusion-max-bodies-to-request: 1
+vote-diffusion-max-headers-to-request: 100
+vote-diffusion-max-window-size: 100
+
+# Worst case based on CDDL for certificates and on empirical study of wFA+LS
+# sortition.
+cert-size-bytes-constant: 8000
+cert-size-bytes-per-node: 0
+
+# Worst case based on benchmarks from cryptography prototype.
+cert-generation-cpu-time-ms-constant: 92.5
+cert-generation-cpu-time-ms-per-node: 0
+cert-validation-cpu-time-ms-constant: 157.2
+cert-validation-cpu-time-ms-per-node: 0
+
diff --git a/analysis/sims/micro-mainnet/experiments/run.sh b/analysis/sims/micro-mainnet/experiments/run.sh
new file mode 100755
index 000000000..85e6afb08
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/run.sh
@@ -0,0 +1,111 @@
+#!/usr/bin/env nix-shell
+#!nix-shell -i bash -p ansifilter gnugrep gnused gzip pigz bc
+
+set -eo pipefail
+
+cd "$(dirname "${BASH_SOURCE[0]}")"
+
+TX_START=60
+TX_STOP=960
+SIM_STOP=1500
+BW=10
+CPU_COUNT=4
+SIM=Rust
+VARIANT=linear-with-tx-references
+BLOCK_SIZE=12
+TX_SIZE=1500
+LABEL=$(basename "$PWD")
+PROPAGATION=eb-received
+STAGE_LENGTH_DIFF=7
+STAGE_LENGTH_VOTE=4
+NETWORK=$(echo -n "$LABEL" | sed -e 's/^\(.*\),\(.*\)/\1/')
+THROUGHPUT=$(echo -n "$LABEL" | sed -e 's/^\(.*\),\(.*\)/\2/')
+TX_SPACING_HONEST=$(echo "scale=3; $TX_SIZE / $THROUGHPUT / 1000" | bc)
+
+ulimit -S -m 48000000 -v 48000000
+
+if [[ -e sim.log ]]
+then
+  rm sim.log
+fi
+
+mkfifo sim.log
+
+sed -e 's/"bandwidth-bytes-per-second":125000000/"bandwidth-bytes-per-second":'"$((125000 * BW))"'/g' \
+    -e 's/"cpu-core-count":6,/"cpu-core-count":'"$CPU_COUNT"',/g' \
+    "../../../../../data/simulation/pseudo-mainnet/$NETWORK.yaml" \
+  > network.yaml
+
+yaml2json ../config.yaml \
+| jq '. + 
+{
+  "leios-variant": "'"$VARIANT"'"
+, "linear-eb-propagation-criteria": "'"$PROPAGATION"'"
+, "linear-diffuse-stage-length-slots": '"$STAGE_LENGTH_DIFF"'
+, "linear-vote-stage-length-slots": '"$STAGE_LENGTH_VOTE"'
+, "leios-stage-length-slots": '"$STAGE_LENGTH_VOTE"'
+, "eb-referenced-txs-max-size-bytes": ('"$BLOCK_SIZE"' * 1000000)
+, "eb-body-avg-size-bytes": ('"$BLOCK_SIZE"' * 1000000)
+, "tx-size-bytes-distribution": {distribution: "constant", value: '"$TX_SIZE"'}
+, "tx-generation-distribution": {distribution: "constant", value: '"$TX_SPACING_HONEST"'}
+, "tx-start-time": '"$TX_START"'
+, "tx-stop-time": '"$TX_STOP"'
+}
+' > config.yaml
+
+function cleanup() {
+  rm sim.log
+  rm network.yaml
+}
+trap cleanup EXIT
+
+grep -E -v '(Slot|CpuTask|Lottery)' sim.log | pigz -p 3 -9c > sim.log.gz &
+
+case "$SIM" in
+  Rust)
+    ../../sim-cli --parameters config.yaml network.yaml --slots "$SIM_STOP" --conformance-events sim.log > stdout 2> stderr
+    ;;
+  Haskell)
+    ../../ols sim leios --leios-config-file config.yaml --topology-file network.yaml --shared-log-format JSON --conformance-events --output-file sim.log --output-seconds "$SIM_STOP" > stdout 2> stderr
+    ;;
+  *)
+    false
+esac
+
+wait
+
+cat << EOI > case.csv
+Network,Bandwidth,CPU,Diffusion duration,Voting duration,Max EB size,Tx size,Throughput,Tx start [s],Tx stop [s],Sim stop [s]
+$NETWORK,$BW Mb/s,$CPU_COUNT vCPU/node,L_diff = $STAGE_LENGTH_DIFF slots,L_vote = $STAGE_LENGTH_VOTE slots,$BLOCK_SIZE MB/EB,$TX_SIZE B/Tx,$THROUGHPUT TxMB/s,$TX_START,$TX_STOP,$SIM_STOP
+EOI
+
+zcat sim.log.gz \
+| ../../leios-trace-processor \
+  +RTS -N5 -RTS \
+  --trace-file /dev/stdin \
+  --lifecycle-file lifecycle.csv \
+  --cpu-file cpus.csv \
+  --resource-file resources.csv \
+  --receipt-file receipts.csv
+
+(
+  echo 'Kind,Item,Generated [s],Transactions,Endorses'
+  zcat sim.log.gz \
+  | grep -E '(EB|RB)Generated' \
+  | jq -r '
+    .message.type[0:2]
+    + "," + .message.id
+    + "," + (.time_s | tostring)
+    + "," + (.message.transactions | length | tostring)
+    + "," + (if .message.endorsement then .message.endorsement.eb.id else "NA" end)
+  '
+) > sizes.csv
+  
+pigz -p 3 -9f {cpus,lifecycle,receipts,resources,sizes}.csv
+
+cat case.csv
+
+if [[ "$SIM" == "Rust" ]]
+then
+  ansifilter stdout | grep -E '^ INFO (praos|leios|network): ' > summary.txt
+fi
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/case.csv b/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/case.csv
new file mode 100644
index 000000000..935445db3
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/case.csv
@@ -0,0 +1,2 @@
+Network,Bandwidth,CPU,Diffusion duration,Voting duration,Max EB size,Tx size,Throughput,Tx start [s],Tx stop [s],Sim stop [s]
+topology-v2,10 Mb/s,4 vCPU/node,L_diff = 7 slots,L_vote = 4 slots,12 MB/EB,1500 B/Tx,0.100 TxMB/s,60,960,1500
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/config.yaml b/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/config.yaml
new file mode 100644
index 000000000..f4ebff47a
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/config.yaml
@@ -0,0 +1,67 @@
+{
+  "timestamp-resolution-ms": 0.1,
+  "simulate-transactions": true,
+  "cleanup-policies": [
+    "cleanup-expired-vote"
+  ],
+  "leios-variant": "linear-with-tx-references",
+  "linear-eb-propagation-criteria": "eb-received",
+  "linear-vote-stage-length-slots": 4,
+  "linear-diffuse-stage-length-slots": 7,
+  "praos-fallback-enabled": true,
+  "leios-stage-active-voting-slots": 1,
+  "leios-mempool-sampling-strategy": "ordered-by-id",
+  "relay-strategy": "request-from-first",
+  "treat-blocks-as-full": false,
+  "eb-diffusion-strategy": "peer-order",
+  "eb-referenced-txs-max-size-bytes": 12000000,
+  "eb-size-bytes-constant": 240,
+  "eb-size-bytes-per-ib": 32,
+  "leios-header-diffusion-time-ms": 1000.0,
+  "tx-start-time": 60,
+  "tx-stop-time": 960,
+  "tx-generation-distribution": {
+    "distribution": "constant",
+    "value": 15.000
+  },
+  "tx-size-bytes-distribution": {
+    "distribution": "constant",
+    "value": 1500
+  },
+  "tx-conflict-fraction": 0,
+  "tx-overcollateralization-factor-distribution": {
+    "distribution": "constant",
+    "value": 0
+  },
+  "tx-validation-cpu-time-ms": 0.6201,
+  "tx-max-size-bytes": 16384,
+  "rb-generation-probability": 0.05,
+  "rb-head-size-bytes": 1024,
+  "rb-body-max-size-bytes": 90112,
+  "rb-generation-cpu-time-ms": 71.02,
+  "rb-head-validation-cpu-time-ms": 0.4438,
+  "eb-header-validation-cpu-time-ms": 0.4438,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-constant": 0.3539,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-per-byte": 0.00002151,
+  "eb-body-validation-cpu-time-ms-constant": 0.3539,
+  "eb-body-validation-cpu-time-ms-per-byte": 0.00002151,
+  "vote-generation-probability": 600,
+  "vote-threshold": 450,
+  "vote-bundle-size-bytes-constant": 0,
+  "vote-bundle-size-bytes-per-eb": 164,
+  "vote-generation-cpu-time-ms-constant": 0.28,
+  "vote-generation-cpu-time-ms-per-tx": 0,
+  "vote-validation-cpu-time-ms": 2.9,
+  "vote-diffusion-strategy": "peer-order",
+  "vote-diffusion-max-bodies-to-request": 1,
+  "vote-diffusion-max-headers-to-request": 100,
+  "vote-diffusion-max-window-size": 100,
+  "cert-size-bytes-constant": 8000,
+  "cert-size-bytes-per-node": 0,
+  "cert-generation-cpu-time-ms-constant": 92.5,
+  "cert-generation-cpu-time-ms-per-node": 0,
+  "cert-validation-cpu-time-ms-constant": 157.2,
+  "cert-validation-cpu-time-ms-per-node": 0,
+  "leios-stage-length-slots": 4,
+  "eb-body-avg-size-bytes": 12000000
+}
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/run.sh b/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/run.sh
new file mode 120000
index 000000000..74f7c8c42
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/run.sh
@@ -0,0 +1 @@
+../run.sh
\ No newline at end of file
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/summary.txt b/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/summary.txt
new file mode 100644
index 000000000..1a189932a
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.100/summary.txt
@@ -0,0 +1,93 @@
+ INFO praos: sim_cli::events: 60001 transactions(s) were generated in total.
+ INFO praos: sim_cli::events: 64 naive praos block(s) were published.
+ INFO praos: sim_cli::events: 1436 slot(s) had no naive praos blocks.
+ INFO praos: sim_cli::events: 60001 transaction(s) (90.00 MB) finalized in a naive praos block.
+ INFO praos: sim_cli::events: 0 transaction(s) (0 B) did not reach a naive praos block.
+ INFO praos: sim_cli::events: Pool 3 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 38 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 41 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 42 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 43 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 45 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 46 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 50 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 53 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 54 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 58 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 59 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 62 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 67 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 68 failed to publish 1 naive praos block(s) due to slot battles.
+ INFO praos: sim_cli::events: Pool 70 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 74 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 93 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 108 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 109 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 121 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 132 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 136 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 138 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 351 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 361 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 362 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 367 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 371 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 418 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 429 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 431 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 439 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 442 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 446 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 477 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 478 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 481 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 510 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 518 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 524 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 526 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 527 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 528 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 532 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 535 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 536 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 537 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 539 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 540 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 560 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 562 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 565 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 568 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 739 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 741 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 742 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 745 published 2 naive praos block(s)
+ INFO leios: sim_cli::events: 0 IB(s) were generated, on average 0.000 IB(s) per slot.
+ INFO leios: sim_cli::events: 0 out of 60001 transaction(s) were included in at least one IB.
+ INFO leios: sim_cli::events: The average age of the pending transactions is NaNs (stddev NaN).
+ INFO leios: sim_cli::events: Each transaction was included in an average of NaN IB(s) (stddev NaN).
+ INFO leios: sim_cli::events: Each IB contained an average of NaN transaction(s) (stddev NaN) and an average of 0 B (stddev 0 B). 0 IB(s) were empty.
+ INFO leios: sim_cli::events: Each node received an average of 0.000 IB(s) (stddev 0.000).
+ INFO leios: sim_cli::events: 45 EB(s) were generated; on average there were 0.030 EB(s) per slot.
+ INFO leios: sim_cli::events: Each EB contained an average of 3569.356 transaction(s) (stddev 1592.821). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each EB contained an average of 0.000 IB(s) (stddev 0.000). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each IB was included in an average of NaN EB(s) (stddev NaN).
+ INFO leios: sim_cli::events: 0 out of 0 IBs were included in at least one EB.
+ INFO leios: sim_cli::events: 0 out of 0 IBs expired before they reached an EB.
+ INFO leios: sim_cli::events: 21 out of 45 EBs expired before an EB from their stage reached an RB.
+ INFO leios: sim_cli::events: 59941 out of 60001 transaction(s) were included in at least one EB.
+ INFO leios: sim_cli::events: 23569 total votes were generated.
+ INFO leios: sim_cli::events: Each stake pool produced an average of 109.116 vote(s) (stddev 30.701).
+ INFO leios: sim_cli::events: Each EB received an average of 535.659 vote(s) (stddev 145.277).
+ INFO leios: sim_cli::events: There were 7876 bundle(s) of votes. Each bundle contained 2.993 vote(s) (stddev 1.625).
+ INFO leios: sim_cli::events: 25 L1 block(s) had a Leios endorsement.
+ INFO leios: sim_cli::events: 58741 tx(s) (88.11 MB) were referenced by a Leios endorsement.
+ INFO leios: sim_cli::events: 1260 tx(s) (1.89 MB) were included directly in a Praos block.
+ INFO leios: sim_cli::events: Spatial efficiency: 88.11 MB/5.35 MB (1646.737%) of Leios bytes were unique transactions.
+ INFO leios: sim_cli::events: 35836 tx(s) (37.891%) referenced by a Leios endorsement were redundant.
+ INFO leios: sim_cli::events: Each transaction took an average of NaNs (stddev NaN) to be included in an IB.
+ INFO leios: sim_cli::events: Each transaction took an average of 17.958s (stddev 14.561) to be included in an EB.
+ INFO leios: sim_cli::events: Each transaction took an average of 54.463s (stddev 20.201) to be included in a block.
+ INFO network: sim_cli::events: 44940749 TX message(s) were sent. 44940749 of them were received (100.000%).
+ INFO network: sim_cli::events: 0 IB message(s) were sent. 0 of them were received (NaN%).
+ INFO network: sim_cli::events: 33249 EB message(s) were sent. 33249 of them were received (100.000%).
+ INFO network: sim_cli::events: 5899124 Vote message(s) were sent. 5899124 of them were received (100.000%).
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/case.csv b/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/case.csv
new file mode 100644
index 000000000..294aa86e2
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/case.csv
@@ -0,0 +1,2 @@
+Network,Bandwidth,CPU,Diffusion duration,Voting duration,Max EB size,Tx size,Throughput,Tx start [s],Tx stop [s],Sim stop [s]
+topology-v2,10 Mb/s,4 vCPU/node,L_diff = 7 slots,L_vote = 4 slots,12 MB/EB,1500 B/Tx,0.150 TxMB/s,60,960,1500
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/config.yaml b/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/config.yaml
new file mode 100644
index 000000000..71fb7808d
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/config.yaml
@@ -0,0 +1,67 @@
+{
+  "timestamp-resolution-ms": 0.1,
+  "simulate-transactions": true,
+  "cleanup-policies": [
+    "cleanup-expired-vote"
+  ],
+  "leios-variant": "linear-with-tx-references",
+  "linear-eb-propagation-criteria": "eb-received",
+  "linear-vote-stage-length-slots": 4,
+  "linear-diffuse-stage-length-slots": 7,
+  "praos-fallback-enabled": true,
+  "leios-stage-active-voting-slots": 1,
+  "leios-mempool-sampling-strategy": "ordered-by-id",
+  "relay-strategy": "request-from-first",
+  "treat-blocks-as-full": false,
+  "eb-diffusion-strategy": "peer-order",
+  "eb-referenced-txs-max-size-bytes": 12000000,
+  "eb-size-bytes-constant": 240,
+  "eb-size-bytes-per-ib": 32,
+  "leios-header-diffusion-time-ms": 1000.0,
+  "tx-start-time": 60,
+  "tx-stop-time": 960,
+  "tx-generation-distribution": {
+    "distribution": "constant",
+    "value": 10.000
+  },
+  "tx-size-bytes-distribution": {
+    "distribution": "constant",
+    "value": 1500
+  },
+  "tx-conflict-fraction": 0,
+  "tx-overcollateralization-factor-distribution": {
+    "distribution": "constant",
+    "value": 0
+  },
+  "tx-validation-cpu-time-ms": 0.6201,
+  "tx-max-size-bytes": 16384,
+  "rb-generation-probability": 0.05,
+  "rb-head-size-bytes": 1024,
+  "rb-body-max-size-bytes": 90112,
+  "rb-generation-cpu-time-ms": 71.02,
+  "rb-head-validation-cpu-time-ms": 0.4438,
+  "eb-header-validation-cpu-time-ms": 0.4438,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-constant": 0.3539,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-per-byte": 0.00002151,
+  "eb-body-validation-cpu-time-ms-constant": 0.3539,
+  "eb-body-validation-cpu-time-ms-per-byte": 0.00002151,
+  "vote-generation-probability": 600,
+  "vote-threshold": 450,
+  "vote-bundle-size-bytes-constant": 0,
+  "vote-bundle-size-bytes-per-eb": 164,
+  "vote-generation-cpu-time-ms-constant": 0.28,
+  "vote-generation-cpu-time-ms-per-tx": 0,
+  "vote-validation-cpu-time-ms": 2.9,
+  "vote-diffusion-strategy": "peer-order",
+  "vote-diffusion-max-bodies-to-request": 1,
+  "vote-diffusion-max-headers-to-request": 100,
+  "vote-diffusion-max-window-size": 100,
+  "cert-size-bytes-constant": 8000,
+  "cert-size-bytes-per-node": 0,
+  "cert-generation-cpu-time-ms-constant": 92.5,
+  "cert-generation-cpu-time-ms-per-node": 0,
+  "cert-validation-cpu-time-ms-constant": 157.2,
+  "cert-validation-cpu-time-ms-per-node": 0,
+  "leios-stage-length-slots": 4,
+  "eb-body-avg-size-bytes": 12000000
+}
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/run.sh b/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/run.sh
new file mode 120000
index 000000000..74f7c8c42
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/run.sh
@@ -0,0 +1 @@
+../run.sh
\ No newline at end of file
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/summary.txt b/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/summary.txt
new file mode 100644
index 000000000..6972ddfea
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.150/summary.txt
@@ -0,0 +1,93 @@
+ INFO praos: sim_cli::events: 90001 transactions(s) were generated in total.
+ INFO praos: sim_cli::events: 64 naive praos block(s) were published.
+ INFO praos: sim_cli::events: 1436 slot(s) had no naive praos blocks.
+ INFO praos: sim_cli::events: 90001 transaction(s) (135.00 MB) finalized in a naive praos block.
+ INFO praos: sim_cli::events: 0 transaction(s) (0 B) did not reach a naive praos block.
+ INFO praos: sim_cli::events: Pool 3 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 38 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 41 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 42 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 43 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 45 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 46 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 50 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 53 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 54 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 58 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 59 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 62 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 67 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 68 failed to publish 1 naive praos block(s) due to slot battles.
+ INFO praos: sim_cli::events: Pool 70 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 74 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 93 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 108 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 109 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 121 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 132 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 136 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 138 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 351 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 361 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 362 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 367 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 371 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 418 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 429 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 431 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 439 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 442 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 446 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 477 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 478 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 481 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 510 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 518 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 524 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 526 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 527 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 528 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 532 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 535 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 536 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 537 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 539 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 540 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 560 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 562 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 565 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 568 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 739 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 741 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 742 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 745 published 2 naive praos block(s)
+ INFO leios: sim_cli::events: 0 IB(s) were generated, on average 0.000 IB(s) per slot.
+ INFO leios: sim_cli::events: 0 out of 90001 transaction(s) were included in at least one IB.
+ INFO leios: sim_cli::events: The average age of the pending transactions is NaNs (stddev NaN).
+ INFO leios: sim_cli::events: Each transaction was included in an average of NaN IB(s) (stddev NaN).
+ INFO leios: sim_cli::events: Each IB contained an average of NaN transaction(s) (stddev NaN) and an average of 0 B (stddev 0 B). 0 IB(s) were empty.
+ INFO leios: sim_cli::events: Each node received an average of 0.000 IB(s) (stddev 0.000).
+ INFO leios: sim_cli::events: 45 EB(s) were generated; on average there were 0.030 EB(s) per slot.
+ INFO leios: sim_cli::events: Each EB contained an average of 5647.067 transaction(s) (stddev 1966.076). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each EB contained an average of 0.000 IB(s) (stddev 0.000). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each IB was included in an average of NaN EB(s) (stddev NaN).
+ INFO leios: sim_cli::events: 0 out of 0 IBs were included in at least one EB.
+ INFO leios: sim_cli::events: 0 out of 0 IBs expired before they reached an EB.
+ INFO leios: sim_cli::events: 21 out of 45 EBs expired before an EB from their stage reached an RB.
+ INFO leios: sim_cli::events: 89941 out of 90001 transaction(s) were included in at least one EB.
+ INFO leios: sim_cli::events: 23440 total votes were generated.
+ INFO leios: sim_cli::events: Each stake pool produced an average of 108.519 vote(s) (stddev 30.832).
+ INFO leios: sim_cli::events: Each EB received an average of 532.727 vote(s) (stddev 144.860).
+ INFO leios: sim_cli::events: There were 7830 bundle(s) of votes. Each bundle contained 2.994 vote(s) (stddev 1.626).
+ INFO leios: sim_cli::events: 26 L1 block(s) had a Leios endorsement.
+ INFO leios: sim_cli::events: 88801 tx(s) (133.20 MB) were referenced by a Leios endorsement.
+ INFO leios: sim_cli::events: 1200 tx(s) (1.80 MB) were included directly in a Praos block.
+ INFO leios: sim_cli::events: Spatial efficiency: 133.20 MB/8.35 MB (1595.118%) of Leios bytes were unique transactions.
+ INFO leios: sim_cli::events: 57442 tx(s) (39.278%) referenced by a Leios endorsement were redundant.
+ INFO leios: sim_cli::events: Each transaction took an average of NaNs (stddev NaN) to be included in an IB.
+ INFO leios: sim_cli::events: Each transaction took an average of 21.109s (stddev 15.889) to be included in an EB.
+ INFO leios: sim_cli::events: Each transaction took an average of 58.708s (stddev 19.553) to be included in a block.
+ INFO network: sim_cli::events: 67410749 TX message(s) were sent. 67410749 of them were received (100.000%).
+ INFO network: sim_cli::events: 0 IB message(s) were sent. 0 of them were received (NaN%).
+ INFO network: sim_cli::events: 33249 EB message(s) were sent. 33249 of them were received (100.000%).
+ INFO network: sim_cli::events: 5864670 Vote message(s) were sent. 5864670 of them were received (100.000%).
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/case.csv b/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/case.csv
new file mode 100644
index 000000000..33ba4628a
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/case.csv
@@ -0,0 +1,2 @@
+Network,Bandwidth,CPU,Diffusion duration,Voting duration,Max EB size,Tx size,Throughput,Tx start [s],Tx stop [s],Sim stop [s]
+topology-v2,10 Mb/s,4 vCPU/node,L_diff = 7 slots,L_vote = 4 slots,12 MB/EB,1500 B/Tx,0.200 TxMB/s,60,960,1500
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/config.yaml b/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/config.yaml
new file mode 100644
index 000000000..a72fb84a2
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/config.yaml
@@ -0,0 +1,67 @@
+{
+  "timestamp-resolution-ms": 0.1,
+  "simulate-transactions": true,
+  "cleanup-policies": [
+    "cleanup-expired-vote"
+  ],
+  "leios-variant": "linear-with-tx-references",
+  "linear-eb-propagation-criteria": "eb-received",
+  "linear-vote-stage-length-slots": 4,
+  "linear-diffuse-stage-length-slots": 7,
+  "praos-fallback-enabled": true,
+  "leios-stage-active-voting-slots": 1,
+  "leios-mempool-sampling-strategy": "ordered-by-id",
+  "relay-strategy": "request-from-first",
+  "treat-blocks-as-full": false,
+  "eb-diffusion-strategy": "peer-order",
+  "eb-referenced-txs-max-size-bytes": 12000000,
+  "eb-size-bytes-constant": 240,
+  "eb-size-bytes-per-ib": 32,
+  "leios-header-diffusion-time-ms": 1000.0,
+  "tx-start-time": 60,
+  "tx-stop-time": 960,
+  "tx-generation-distribution": {
+    "distribution": "constant",
+    "value": 7.500
+  },
+  "tx-size-bytes-distribution": {
+    "distribution": "constant",
+    "value": 1500
+  },
+  "tx-conflict-fraction": 0,
+  "tx-overcollateralization-factor-distribution": {
+    "distribution": "constant",
+    "value": 0
+  },
+  "tx-validation-cpu-time-ms": 0.6201,
+  "tx-max-size-bytes": 16384,
+  "rb-generation-probability": 0.05,
+  "rb-head-size-bytes": 1024,
+  "rb-body-max-size-bytes": 90112,
+  "rb-generation-cpu-time-ms": 71.02,
+  "rb-head-validation-cpu-time-ms": 0.4438,
+  "eb-header-validation-cpu-time-ms": 0.4438,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-constant": 0.3539,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-per-byte": 0.00002151,
+  "eb-body-validation-cpu-time-ms-constant": 0.3539,
+  "eb-body-validation-cpu-time-ms-per-byte": 0.00002151,
+  "vote-generation-probability": 600,
+  "vote-threshold": 450,
+  "vote-bundle-size-bytes-constant": 0,
+  "vote-bundle-size-bytes-per-eb": 164,
+  "vote-generation-cpu-time-ms-constant": 0.28,
+  "vote-generation-cpu-time-ms-per-tx": 0,
+  "vote-validation-cpu-time-ms": 2.9,
+  "vote-diffusion-strategy": "peer-order",
+  "vote-diffusion-max-bodies-to-request": 1,
+  "vote-diffusion-max-headers-to-request": 100,
+  "vote-diffusion-max-window-size": 100,
+  "cert-size-bytes-constant": 8000,
+  "cert-size-bytes-per-node": 0,
+  "cert-generation-cpu-time-ms-constant": 92.5,
+  "cert-generation-cpu-time-ms-per-node": 0,
+  "cert-validation-cpu-time-ms-constant": 157.2,
+  "cert-validation-cpu-time-ms-per-node": 0,
+  "leios-stage-length-slots": 4,
+  "eb-body-avg-size-bytes": 12000000
+}
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/run.sh b/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/run.sh
new file mode 120000
index 000000000..74f7c8c42
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/run.sh
@@ -0,0 +1 @@
+../run.sh
\ No newline at end of file
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/summary.txt b/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/summary.txt
new file mode 100644
index 000000000..8dd68681a
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v2,0.200/summary.txt
@@ -0,0 +1,101 @@
+ INFO praos: sim_cli::events: 128572 transactions(s) were generated in total.
+ INFO praos: sim_cli::events: 77 naive praos block(s) were published.
+ INFO praos: sim_cli::events: 1423 slot(s) had no naive praos blocks.
+ INFO praos: sim_cli::events: 128572 transaction(s) (192.86 MB) finalized in a naive praos block.
+ INFO praos: sim_cli::events: 0 transaction(s) (0 B) did not reach a naive praos block.
+ INFO praos: sim_cli::events: Pool 3 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 4 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 35 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 38 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 41 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 42 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 45 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 46 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 48 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 50 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 53 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 54 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 58 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 59 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 62 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 67 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 68 failed to publish 1 naive praos block(s) due to slot battles.
+ INFO praos: sim_cli::events: Pool 70 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 93 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 98 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 99 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 108 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 109 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 121 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 125 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 131 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 132 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 136 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 138 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 139 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 351 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 362 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 369 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 371 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 377 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 429 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 431 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 442 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 446 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 477 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 478 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 481 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 482 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 510 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 518 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 521 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 522 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 527 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 528 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 529 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 531 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 535 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 536 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 537 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 539 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 540 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 548 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 552 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 560 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 562 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 564 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 568 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 739 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 741 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 742 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 745 published 1 naive praos block(s)
+ INFO leios: sim_cli::events: 0 IB(s) were generated, on average 0.000 IB(s) per slot.
+ INFO leios: sim_cli::events: 0 out of 128572 transaction(s) were included in at least one IB.
+ INFO leios: sim_cli::events: The average age of the pending transactions is NaNs (stddev NaN).
+ INFO leios: sim_cli::events: Each transaction was included in an average of NaN IB(s) (stddev NaN).
+ INFO leios: sim_cli::events: Each IB contained an average of NaN transaction(s) (stddev NaN) and an average of 0 B (stddev 0 B). 0 IB(s) were empty.
+ INFO leios: sim_cli::events: Each node received an average of 0.000 IB(s) (stddev 0.000).
+ INFO leios: sim_cli::events: 54 EB(s) were generated; on average there were 0.036 EB(s) per slot.
+ INFO leios: sim_cli::events: Each EB contained an average of 7662.296 transaction(s) (stddev 1198.144). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each EB contained an average of 0.000 IB(s) (stddev 0.000). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each IB was included in an average of NaN EB(s) (stddev NaN).
+ INFO leios: sim_cli::events: 0 out of 0 IBs were included in at least one EB.
+ INFO leios: sim_cli::events: 0 out of 0 IBs expired before they reached an EB.
+ INFO leios: sim_cli::events: 27 out of 54 EBs expired before an EB from their stage reached an RB.
+ INFO leios: sim_cli::events: 128212 out of 128572 transaction(s) were included in at least one EB.
+ INFO leios: sim_cli::events: 27690 total votes were generated.
+ INFO leios: sim_cli::events: Each stake pool produced an average of 128.194 vote(s) (stddev 36.853).
+ INFO leios: sim_cli::events: Each EB received an average of 522.453 vote(s) (stddev 151.975).
+ INFO leios: sim_cli::events: There were 9249 bundle(s) of votes. Each bundle contained 2.994 vote(s) (stddev 1.619).
+ INFO leios: sim_cli::events: 29 L1 block(s) had a Leios endorsement.
+ INFO leios: sim_cli::events: 127072 tx(s) (190.61 MB) were referenced by a Leios endorsement.
+ INFO leios: sim_cli::events: 1500 tx(s) (2.25 MB) were included directly in a Praos block.
+ INFO leios: sim_cli::events: Spatial efficiency: 190.61 MB/13.49 MB (1413.439%) of Leios bytes were unique transactions.
+ INFO leios: sim_cli::events: 80000 tx(s) (38.634%) referenced by a Leios endorsement were redundant.
+ INFO leios: sim_cli::events: Each transaction took an average of NaNs (stddev NaN) to be included in an IB.
+ INFO leios: sim_cli::events: Each transaction took an average of 135.573s (stddev 60.208) to be included in an EB.
+ INFO leios: sim_cli::events: Each transaction took an average of 176.880s (stddev 54.127) to be included in a block.
+ INFO network: sim_cli::events: 96300428 TX message(s) were sent. 96300428 of them were received (100.000%).
+ INFO network: sim_cli::events: 0 IB message(s) were sent. 0 of them were received (NaN%).
+ INFO network: sim_cli::events: 39988 EB message(s) were sent. 39988 of them were received (100.000%).
+ INFO network: sim_cli::events: 6927501 Vote message(s) were sent. 6927501 of them were received (100.000%).
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/case.csv b/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/case.csv
new file mode 100644
index 000000000..8c4988b57
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/case.csv
@@ -0,0 +1,2 @@
+Network,Bandwidth,CPU,Diffusion duration,Voting duration,Max EB size,Tx size,Throughput,Tx start [s],Tx stop [s],Sim stop [s]
+topology-v3,10 Mb/s,4 vCPU/node,L_diff = 7 slots,L_vote = 4 slots,12 MB/EB,1500 B/Tx,0.100 TxMB/s,60,960,1500
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/config.yaml b/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/config.yaml
new file mode 100644
index 000000000..f4ebff47a
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/config.yaml
@@ -0,0 +1,67 @@
+{
+  "timestamp-resolution-ms": 0.1,
+  "simulate-transactions": true,
+  "cleanup-policies": [
+    "cleanup-expired-vote"
+  ],
+  "leios-variant": "linear-with-tx-references",
+  "linear-eb-propagation-criteria": "eb-received",
+  "linear-vote-stage-length-slots": 4,
+  "linear-diffuse-stage-length-slots": 7,
+  "praos-fallback-enabled": true,
+  "leios-stage-active-voting-slots": 1,
+  "leios-mempool-sampling-strategy": "ordered-by-id",
+  "relay-strategy": "request-from-first",
+  "treat-blocks-as-full": false,
+  "eb-diffusion-strategy": "peer-order",
+  "eb-referenced-txs-max-size-bytes": 12000000,
+  "eb-size-bytes-constant": 240,
+  "eb-size-bytes-per-ib": 32,
+  "leios-header-diffusion-time-ms": 1000.0,
+  "tx-start-time": 60,
+  "tx-stop-time": 960,
+  "tx-generation-distribution": {
+    "distribution": "constant",
+    "value": 15.000
+  },
+  "tx-size-bytes-distribution": {
+    "distribution": "constant",
+    "value": 1500
+  },
+  "tx-conflict-fraction": 0,
+  "tx-overcollateralization-factor-distribution": {
+    "distribution": "constant",
+    "value": 0
+  },
+  "tx-validation-cpu-time-ms": 0.6201,
+  "tx-max-size-bytes": 16384,
+  "rb-generation-probability": 0.05,
+  "rb-head-size-bytes": 1024,
+  "rb-body-max-size-bytes": 90112,
+  "rb-generation-cpu-time-ms": 71.02,
+  "rb-head-validation-cpu-time-ms": 0.4438,
+  "eb-header-validation-cpu-time-ms": 0.4438,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-constant": 0.3539,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-per-byte": 0.00002151,
+  "eb-body-validation-cpu-time-ms-constant": 0.3539,
+  "eb-body-validation-cpu-time-ms-per-byte": 0.00002151,
+  "vote-generation-probability": 600,
+  "vote-threshold": 450,
+  "vote-bundle-size-bytes-constant": 0,
+  "vote-bundle-size-bytes-per-eb": 164,
+  "vote-generation-cpu-time-ms-constant": 0.28,
+  "vote-generation-cpu-time-ms-per-tx": 0,
+  "vote-validation-cpu-time-ms": 2.9,
+  "vote-diffusion-strategy": "peer-order",
+  "vote-diffusion-max-bodies-to-request": 1,
+  "vote-diffusion-max-headers-to-request": 100,
+  "vote-diffusion-max-window-size": 100,
+  "cert-size-bytes-constant": 8000,
+  "cert-size-bytes-per-node": 0,
+  "cert-generation-cpu-time-ms-constant": 92.5,
+  "cert-generation-cpu-time-ms-per-node": 0,
+  "cert-validation-cpu-time-ms-constant": 157.2,
+  "cert-validation-cpu-time-ms-per-node": 0,
+  "leios-stage-length-slots": 4,
+  "eb-body-avg-size-bytes": 12000000
+}
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/run.sh b/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/run.sh
new file mode 120000
index 000000000..74f7c8c42
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/run.sh
@@ -0,0 +1 @@
+../run.sh
\ No newline at end of file
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/summary.txt b/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/summary.txt
new file mode 100644
index 000000000..73b2cd826
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.100/summary.txt
@@ -0,0 +1,58 @@
+ INFO praos: sim_cli::events: 60001 transactions(s) were generated in total.
+ INFO praos: sim_cli::events: 80 naive praos block(s) were published.
+ INFO praos: sim_cli::events: 1420 slot(s) had no naive praos blocks.
+ INFO praos: sim_cli::events: 60001 transaction(s) (90.00 MB) finalized in a naive praos block.
+ INFO praos: sim_cli::events: 0 transaction(s) (0 B) did not reach a naive praos block.
+ INFO praos: sim_cli::events: Pool 3 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 4 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 5 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 6 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 7 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 8 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 9 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 10 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 11 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 13 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 14 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 15 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 16 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 16 failed to publish 1 naive praos block(s) due to slot battles.
+ INFO praos: sim_cli::events: Pool 17 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 18 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 34 published 10 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 62 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 72 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 73 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 74 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 75 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 76 published 2 naive praos block(s)
+ INFO leios: sim_cli::events: 0 IB(s) were generated, on average 0.000 IB(s) per slot.
+ INFO leios: sim_cli::events: 0 out of 60001 transaction(s) were included in at least one IB.
+ INFO leios: sim_cli::events: The average age of the pending transactions is NaNs (stddev NaN).
+ INFO leios: sim_cli::events: Each transaction was included in an average of NaN IB(s) (stddev NaN).
+ INFO leios: sim_cli::events: Each IB contained an average of NaN transaction(s) (stddev NaN) and an average of 0 B (stddev 0 B). 0 IB(s) were empty.
+ INFO leios: sim_cli::events: Each node received an average of 0.000 IB(s) (stddev 0.000).
+ INFO leios: sim_cli::events: 57 EB(s) were generated; on average there were 0.038 EB(s) per slot.
+ INFO leios: sim_cli::events: Each EB contained an average of 3564.789 transaction(s) (stddev 1821.075). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each EB contained an average of 0.000 IB(s) (stddev 0.000). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each IB was included in an average of NaN EB(s) (stddev NaN).
+ INFO leios: sim_cli::events: 0 out of 0 IBs were included in at least one EB.
+ INFO leios: sim_cli::events: 0 out of 0 IBs expired before they reached an EB.
+ INFO leios: sim_cli::events: 35 out of 57 EBs expired before an EB from their stage reached an RB.
+ INFO leios: sim_cli::events: 59941 out of 60001 transaction(s) were included in at least one EB.
+ INFO leios: sim_cli::events: 26765 total votes were generated.
+ INFO leios: sim_cli::events: Each stake pool produced an average of 1216.591 vote(s) (stddev 355.286).
+ INFO leios: sim_cli::events: Each EB received an average of 469.561 vote(s) (stddev 180.175).
+ INFO leios: sim_cli::events: There were 978 bundle(s) of votes. Each bundle contained 27.367 vote(s) (stddev 5.260).
+ INFO leios: sim_cli::events: 23 L1 block(s) had a Leios endorsement.
+ INFO leios: sim_cli::events: 58021 tx(s) (87.03 MB) were referenced by a Leios endorsement.
+ INFO leios: sim_cli::events: 1980 tx(s) (2.97 MB) were included directly in a Praos block.
+ INFO leios: sim_cli::events: Spatial efficiency: 87.03 MB/6.70 MB (1299.006%) of Leios bytes were unique transactions.
+ INFO leios: sim_cli::events: 18306 tx(s) (23.984%) referenced by a Leios endorsement were redundant.
+ INFO leios: sim_cli::events: Each transaction took an average of NaNs (stddev NaN) to be included in an IB.
+ INFO leios: sim_cli::events: Each transaction took an average of 15.666s (stddev 12.678) to be included in an EB.
+ INFO leios: sim_cli::events: Each transaction took an average of 56.618s (stddev 22.831) to be included in a block.
+ INFO network: sim_cli::events: 5940099 TX message(s) were sent. 5940099 of them were received (100.000%).
+ INFO network: sim_cli::events: 0 IB message(s) were sent. 0 of them were received (NaN%).
+ INFO network: sim_cli::events: 5643 EB message(s) were sent. 5643 of them were received (100.000%).
+ INFO network: sim_cli::events: 96822 Vote message(s) were sent. 96822 of them were received (100.000%).
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/case.csv b/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/case.csv
new file mode 100644
index 000000000..16e2489c6
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/case.csv
@@ -0,0 +1,2 @@
+Network,Bandwidth,CPU,Diffusion duration,Voting duration,Max EB size,Tx size,Throughput,Tx start [s],Tx stop [s],Sim stop [s]
+topology-v3,10 Mb/s,4 vCPU/node,L_diff = 7 slots,L_vote = 4 slots,12 MB/EB,1500 B/Tx,0.150 TxMB/s,60,960,1500
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/config.yaml b/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/config.yaml
new file mode 100644
index 000000000..71fb7808d
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/config.yaml
@@ -0,0 +1,67 @@
+{
+  "timestamp-resolution-ms": 0.1,
+  "simulate-transactions": true,
+  "cleanup-policies": [
+    "cleanup-expired-vote"
+  ],
+  "leios-variant": "linear-with-tx-references",
+  "linear-eb-propagation-criteria": "eb-received",
+  "linear-vote-stage-length-slots": 4,
+  "linear-diffuse-stage-length-slots": 7,
+  "praos-fallback-enabled": true,
+  "leios-stage-active-voting-slots": 1,
+  "leios-mempool-sampling-strategy": "ordered-by-id",
+  "relay-strategy": "request-from-first",
+  "treat-blocks-as-full": false,
+  "eb-diffusion-strategy": "peer-order",
+  "eb-referenced-txs-max-size-bytes": 12000000,
+  "eb-size-bytes-constant": 240,
+  "eb-size-bytes-per-ib": 32,
+  "leios-header-diffusion-time-ms": 1000.0,
+  "tx-start-time": 60,
+  "tx-stop-time": 960,
+  "tx-generation-distribution": {
+    "distribution": "constant",
+    "value": 10.000
+  },
+  "tx-size-bytes-distribution": {
+    "distribution": "constant",
+    "value": 1500
+  },
+  "tx-conflict-fraction": 0,
+  "tx-overcollateralization-factor-distribution": {
+    "distribution": "constant",
+    "value": 0
+  },
+  "tx-validation-cpu-time-ms": 0.6201,
+  "tx-max-size-bytes": 16384,
+  "rb-generation-probability": 0.05,
+  "rb-head-size-bytes": 1024,
+  "rb-body-max-size-bytes": 90112,
+  "rb-generation-cpu-time-ms": 71.02,
+  "rb-head-validation-cpu-time-ms": 0.4438,
+  "eb-header-validation-cpu-time-ms": 0.4438,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-constant": 0.3539,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-per-byte": 0.00002151,
+  "eb-body-validation-cpu-time-ms-constant": 0.3539,
+  "eb-body-validation-cpu-time-ms-per-byte": 0.00002151,
+  "vote-generation-probability": 600,
+  "vote-threshold": 450,
+  "vote-bundle-size-bytes-constant": 0,
+  "vote-bundle-size-bytes-per-eb": 164,
+  "vote-generation-cpu-time-ms-constant": 0.28,
+  "vote-generation-cpu-time-ms-per-tx": 0,
+  "vote-validation-cpu-time-ms": 2.9,
+  "vote-diffusion-strategy": "peer-order",
+  "vote-diffusion-max-bodies-to-request": 1,
+  "vote-diffusion-max-headers-to-request": 100,
+  "vote-diffusion-max-window-size": 100,
+  "cert-size-bytes-constant": 8000,
+  "cert-size-bytes-per-node": 0,
+  "cert-generation-cpu-time-ms-constant": 92.5,
+  "cert-generation-cpu-time-ms-per-node": 0,
+  "cert-validation-cpu-time-ms-constant": 157.2,
+  "cert-validation-cpu-time-ms-per-node": 0,
+  "leios-stage-length-slots": 4,
+  "eb-body-avg-size-bytes": 12000000
+}
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/run.sh b/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/run.sh
new file mode 120000
index 000000000..74f7c8c42
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/run.sh
@@ -0,0 +1 @@
+../run.sh
\ No newline at end of file
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/summary.txt b/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/summary.txt
new file mode 100644
index 000000000..7324d949a
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.150/summary.txt
@@ -0,0 +1,57 @@
+ INFO praos: sim_cli::events: 90001 transactions(s) were generated in total.
+ INFO praos: sim_cli::events: 78 naive praos block(s) were published.
+ INFO praos: sim_cli::events: 1422 slot(s) had no naive praos blocks.
+ INFO praos: sim_cli::events: 90001 transaction(s) (135.00 MB) finalized in a naive praos block.
+ INFO praos: sim_cli::events: 0 transaction(s) (0 B) did not reach a naive praos block.
+ INFO praos: sim_cli::events: Pool 3 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 4 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 5 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 6 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 7 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 8 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 9 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 10 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 11 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 13 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 14 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 15 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 16 published 6 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 17 published 7 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 18 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 34 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 62 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 72 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 74 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 74 failed to publish 1 naive praos block(s) due to slot battles.
+ INFO praos: sim_cli::events: Pool 75 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 76 published 5 naive praos block(s)
+ INFO leios: sim_cli::events: 0 IB(s) were generated, on average 0.000 IB(s) per slot.
+ INFO leios: sim_cli::events: 0 out of 90001 transaction(s) were included in at least one IB.
+ INFO leios: sim_cli::events: The average age of the pending transactions is NaNs (stddev NaN).
+ INFO leios: sim_cli::events: Each transaction was included in an average of NaN IB(s) (stddev NaN).
+ INFO leios: sim_cli::events: Each IB contained an average of NaN transaction(s) (stddev NaN) and an average of 0 B (stddev 0 B). 0 IB(s) were empty.
+ INFO leios: sim_cli::events: Each node received an average of 0.000 IB(s) (stddev 0.000).
+ INFO leios: sim_cli::events: 49 EB(s) were generated; on average there were 0.033 EB(s) per slot.
+ INFO leios: sim_cli::events: Each EB contained an average of 5272.286 transaction(s) (stddev 2404.808). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each EB contained an average of 0.000 IB(s) (stddev 0.000). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each IB was included in an average of NaN EB(s) (stddev NaN).
+ INFO leios: sim_cli::events: 0 out of 0 IBs were included in at least one EB.
+ INFO leios: sim_cli::events: 0 out of 0 IBs expired before they reached an EB.
+ INFO leios: sim_cli::events: 28 out of 49 EBs expired before an EB from their stage reached an RB.
+ INFO leios: sim_cli::events: 89821 out of 90001 transaction(s) were included in at least one EB.
+ INFO leios: sim_cli::events: 21897 total votes were generated.
+ INFO leios: sim_cli::events: Each stake pool produced an average of 995.318 vote(s) (stddev 260.064).
+ INFO leios: sim_cli::events: Each EB received an average of 446.878 vote(s) (stddev 201.203).
+ INFO leios: sim_cli::events: There were 799 bundle(s) of votes. Each bundle contained 27.406 vote(s) (stddev 5.058).
+ INFO leios: sim_cli::events: 21 L1 block(s) had a Leios endorsement.
+ INFO leios: sim_cli::events: 88381 tx(s) (132.57 MB) were referenced by a Leios endorsement.
+ INFO leios: sim_cli::events: 1620 tx(s) (2.43 MB) were included directly in a Praos block.
+ INFO leios: sim_cli::events: Spatial efficiency: 132.57 MB/8.45 MB (1569.506%) of Leios bytes were unique transactions.
+ INFO leios: sim_cli::events: 18405 tx(s) (17.235%) referenced by a Leios endorsement were redundant.
+ INFO leios: sim_cli::events: Each transaction took an average of NaNs (stddev NaN) to be included in an IB.
+ INFO leios: sim_cli::events: Each transaction took an average of 18.107s (stddev 12.415) to be included in an EB.
+ INFO leios: sim_cli::events: Each transaction took an average of 57.875s (stddev 21.489) to be included in a block.
+ INFO network: sim_cli::events: 8910099 TX message(s) were sent. 8910099 of them were received (100.000%).
+ INFO network: sim_cli::events: 0 IB message(s) were sent. 0 of them were received (NaN%).
+ INFO network: sim_cli::events: 4851 EB message(s) were sent. 4851 of them were received (100.000%).
+ INFO network: sim_cli::events: 79101 Vote message(s) were sent. 79101 of them were received (100.000%).
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/case.csv b/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/case.csv
new file mode 100644
index 000000000..6badfac19
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/case.csv
@@ -0,0 +1,2 @@
+Network,Bandwidth,CPU,Diffusion duration,Voting duration,Max EB size,Tx size,Throughput,Tx start [s],Tx stop [s],Sim stop [s]
+topology-v3,10 Mb/s,4 vCPU/node,L_diff = 7 slots,L_vote = 4 slots,12 MB/EB,1500 B/Tx,0.200 TxMB/s,60,960,1500
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/config.yaml b/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/config.yaml
new file mode 100644
index 000000000..a72fb84a2
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/config.yaml
@@ -0,0 +1,67 @@
+{
+  "timestamp-resolution-ms": 0.1,
+  "simulate-transactions": true,
+  "cleanup-policies": [
+    "cleanup-expired-vote"
+  ],
+  "leios-variant": "linear-with-tx-references",
+  "linear-eb-propagation-criteria": "eb-received",
+  "linear-vote-stage-length-slots": 4,
+  "linear-diffuse-stage-length-slots": 7,
+  "praos-fallback-enabled": true,
+  "leios-stage-active-voting-slots": 1,
+  "leios-mempool-sampling-strategy": "ordered-by-id",
+  "relay-strategy": "request-from-first",
+  "treat-blocks-as-full": false,
+  "eb-diffusion-strategy": "peer-order",
+  "eb-referenced-txs-max-size-bytes": 12000000,
+  "eb-size-bytes-constant": 240,
+  "eb-size-bytes-per-ib": 32,
+  "leios-header-diffusion-time-ms": 1000.0,
+  "tx-start-time": 60,
+  "tx-stop-time": 960,
+  "tx-generation-distribution": {
+    "distribution": "constant",
+    "value": 7.500
+  },
+  "tx-size-bytes-distribution": {
+    "distribution": "constant",
+    "value": 1500
+  },
+  "tx-conflict-fraction": 0,
+  "tx-overcollateralization-factor-distribution": {
+    "distribution": "constant",
+    "value": 0
+  },
+  "tx-validation-cpu-time-ms": 0.6201,
+  "tx-max-size-bytes": 16384,
+  "rb-generation-probability": 0.05,
+  "rb-head-size-bytes": 1024,
+  "rb-body-max-size-bytes": 90112,
+  "rb-generation-cpu-time-ms": 71.02,
+  "rb-head-validation-cpu-time-ms": 0.4438,
+  "eb-header-validation-cpu-time-ms": 0.4438,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-constant": 0.3539,
+  "rb-body-legacy-praos-payload-validation-cpu-time-ms-per-byte": 0.00002151,
+  "eb-body-validation-cpu-time-ms-constant": 0.3539,
+  "eb-body-validation-cpu-time-ms-per-byte": 0.00002151,
+  "vote-generation-probability": 600,
+  "vote-threshold": 450,
+  "vote-bundle-size-bytes-constant": 0,
+  "vote-bundle-size-bytes-per-eb": 164,
+  "vote-generation-cpu-time-ms-constant": 0.28,
+  "vote-generation-cpu-time-ms-per-tx": 0,
+  "vote-validation-cpu-time-ms": 2.9,
+  "vote-diffusion-strategy": "peer-order",
+  "vote-diffusion-max-bodies-to-request": 1,
+  "vote-diffusion-max-headers-to-request": 100,
+  "vote-diffusion-max-window-size": 100,
+  "cert-size-bytes-constant": 8000,
+  "cert-size-bytes-per-node": 0,
+  "cert-generation-cpu-time-ms-constant": 92.5,
+  "cert-generation-cpu-time-ms-per-node": 0,
+  "cert-validation-cpu-time-ms-constant": 157.2,
+  "cert-validation-cpu-time-ms-per-node": 0,
+  "leios-stage-length-slots": 4,
+  "eb-body-avg-size-bytes": 12000000
+}
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/run.sh b/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/run.sh
new file mode 120000
index 000000000..74f7c8c42
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/run.sh
@@ -0,0 +1 @@
+../run.sh
\ No newline at end of file
diff --git a/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/summary.txt b/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/summary.txt
new file mode 100644
index 000000000..ea087ee89
--- /dev/null
+++ b/analysis/sims/micro-mainnet/experiments/topology-v3,0.200/summary.txt
@@ -0,0 +1,59 @@
+ INFO praos: sim_cli::events: 128572 transactions(s) were generated in total.
+ INFO praos: sim_cli::events: 75 naive praos block(s) were published.
+ INFO praos: sim_cli::events: 1425 slot(s) had no naive praos blocks.
+ INFO praos: sim_cli::events: 128572 transaction(s) (192.86 MB) finalized in a naive praos block.
+ INFO praos: sim_cli::events: 0 transaction(s) (0 B) did not reach a naive praos block.
+ INFO praos: sim_cli::events: Pool 3 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 4 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 5 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 6 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 6 failed to publish 1 naive praos block(s) due to slot battles.
+ INFO praos: sim_cli::events: Pool 7 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 8 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 9 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 10 published 5 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 11 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 13 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 14 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 15 published 1 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 16 published 4 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 16 failed to publish 1 naive praos block(s) due to slot battles.
+ INFO praos: sim_cli::events: Pool 17 published 6 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 18 published 6 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 34 published 6 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 62 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 72 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 72 failed to publish 1 naive praos block(s) due to slot battles.
+ INFO praos: sim_cli::events: Pool 74 published 3 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 75 published 2 naive praos block(s)
+ INFO praos: sim_cli::events: Pool 76 published 5 naive praos block(s)
+ INFO leios: sim_cli::events: 0 IB(s) were generated, on average 0.000 IB(s) per slot.
+ INFO leios: sim_cli::events: 0 out of 128572 transaction(s) were included in at least one IB.
+ INFO leios: sim_cli::events: The average age of the pending transactions is NaNs (stddev NaN).
+ INFO leios: sim_cli::events: Each transaction was included in an average of NaN IB(s) (stddev NaN).
+ INFO leios: sim_cli::events: Each IB contained an average of NaN transaction(s) (stddev NaN) and an average of 0 B (stddev 0 B). 0 IB(s) were empty.
+ INFO leios: sim_cli::events: Each node received an average of 0.000 IB(s) (stddev 0.000).
+ INFO leios: sim_cli::events: 58 EB(s) were generated; on average there were 0.039 EB(s) per slot.
+ INFO leios: sim_cli::events: Each EB contained an average of 7166.966 transaction(s) (stddev 1646.360). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each EB contained an average of 0.000 IB(s) (stddev 0.000). 0 EB(s) were empty.
+ INFO leios: sim_cli::events: Each IB was included in an average of NaN EB(s) (stddev NaN).
+ INFO leios: sim_cli::events: 0 out of 0 IBs were included in at least one EB.
+ INFO leios: sim_cli::events: 0 out of 0 IBs expired before they reached an EB.
+ INFO leios: sim_cli::events: 33 out of 58 EBs expired before an EB from their stage reached an RB.
+ INFO leios: sim_cli::events: 128092 out of 128572 transaction(s) were included in at least one EB.
+ INFO leios: sim_cli::events: 25755 total votes were generated.
+ INFO leios: sim_cli::events: Each stake pool produced an average of 1170.682 vote(s) (stddev 315.532).
+ INFO leios: sim_cli::events: Each EB received an average of 444.052 vote(s) (stddev 203.764).
+ INFO leios: sim_cli::events: There were 940 bundle(s) of votes. Each bundle contained 27.399 vote(s) (stddev 5.234).
+ INFO leios: sim_cli::events: 27 L1 block(s) had a Leios endorsement.
+ INFO leios: sim_cli::events: 126652 tx(s) (189.98 MB) were referenced by a Leios endorsement.
+ INFO leios: sim_cli::events: 1920 tx(s) (2.88 MB) were included directly in a Praos block.
+ INFO leios: sim_cli::events: Spatial efficiency: 189.98 MB/13.53 MB (1403.937%) of Leios bytes were unique transactions.
+ INFO leios: sim_cli::events: 51081 tx(s) (28.740%) referenced by a Leios endorsement were redundant.
+ INFO leios: sim_cli::events: Each transaction took an average of NaNs (stddev NaN) to be included in an IB.
+ INFO leios: sim_cli::events: Each transaction took an average of 107.588s (stddev 56.928) to be included in an EB.
+ INFO leios: sim_cli::events: Each transaction took an average of 149.203s (stddev 54.499) to be included in a block.
+ INFO network: sim_cli::events: 12728628 TX message(s) were sent. 12728628 of them were received (100.000%).
+ INFO network: sim_cli::events: 0 IB message(s) were sent. 0 of them were received (NaN%).
+ INFO network: sim_cli::events: 5645 EB message(s) were sent. 5645 of them were received (100.000%).
+ INFO network: sim_cli::events: 93060 Vote message(s) were sent. 93060 of them were received (100.000%).
diff --git a/analysis/sims/micro-mainnet/sim-cli.hash b/analysis/sims/micro-mainnet/sim-cli.hash
new file mode 100644
index 000000000..0bc8d8834
--- /dev/null
+++ b/analysis/sims/micro-mainnet/sim-cli.hash
@@ -0,0 +1 @@
+sim-cli 1.3.0-90106d8e2
diff --git a/data/simulation/pseudo-mainnet/ReadMe.md b/data/simulation/pseudo-mainnet/ReadMe.md
index a02bb35d7..0c508e11c 100644
--- a/data/simulation/pseudo-mainnet/ReadMe.md
+++ b/data/simulation/pseudo-mainnet/ReadMe.md
@@ -13,7 +13,7 @@ The aim of the pseudo-mainnet topology is to have a Leios network that is genera
 - Geographic distribution (countries and autonomous systems) consistent with measurements by the [Cardano Foundation](https://cardanofoundation.org/)
 
 
-## Version 1
+## Version 1: "pseudo-mainnet"
 
 - Network: [topology-v1.yaml](topology-v1.yaml)
 - Results of [topology checker](../../../topology-checker): [topology-v1.md](topology-v1.md)
@@ -22,3 +22,19 @@ The aim of the pseudo-mainnet topology is to have a Leios network that is genera
 > [!WARNING]
 > 
 > This is the first cut at a realistic mainnet-scale topology for Leios, but it likely contain imperfections, but it likely contain imperfections because several compromises were made during its construction, so as to smooth out inconsistencies in source data. It does pass the topology checks, however, and approximately matches the marginal distributions of key network metrics.
+
+
+## Version 2: "mini-mainnet"
+
+- Network: [topology-v2.yaml](topology-v2.yaml)
+- Results of [topology checker](../../../topology-checker): [topology-v2.md](topology-v2.md)
+- Jupyter notebook used for creating the network: [topology-v2.ipynb](topology-v2.ipynb)
+
+
+## Version 3: "micro-mainnet"
+
+- Network: [topology-v3.yaml](topology-v3.yaml)
+- Results of [topology checker](../../../topology-checker): [topology-v3.md](topology-v3.md)
+- Jupyter notebook used for creating the network: [topology-v3.ipynb](topology-v3.ipynb)
+
+
diff --git a/data/simulation/pseudo-mainnet/topology-v3.ipynb b/data/simulation/pseudo-mainnet/topology-v3.ipynb
new file mode 100644
index 000000000..d01c3e94d
--- /dev/null
+++ b/data/simulation/pseudo-mainnet/topology-v3.ipynb
@@ -0,0 +1,3475 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c7d1d4b8-32d6-4899-8fdf-f8f45d306411",
+   "metadata": {},
+   "source": [
+    "# Synthesis of a micro-mainnet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f3cfe5b-618d-4780-ad4b-8a2247a2975f",
+   "metadata": {},
+   "source": [
+    "## Set up packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "62630a48-074a-449e-9d25-e84a285c4f88",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "suppressMessages({\n",
+    "    require(data.table, quietly=TRUE)\n",
+    "    require(ggplot2, quietly=TRUE)\n",
+    "    require(igraph, quietly=TRUE)\n",
+    "    require(jsonlite, quietly=TRUE)\n",
+    "    require(magrittr, quietly=TRUE)\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c105ce0b-8a77-4c8e-afb6-16f893127850",
+   "metadata": {},
+   "source": [
+    "## Read data files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3faccca-6dc2-4007-b430-e8997ab88490",
+   "metadata": {},
+   "source": [
+    "### Probes of mainnet nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "106fc8ed-b781-4ebc-b90c-8ff265433748",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                                                                id      \n",
+       " 0049eca4703be755cef74ed20e1815b50b5fa0ceb07c6b822c78355dd12fdaa6:   1  \n",
+       " 0078d0f5d825d8946e0809e85fe0f21589294cae912104b3ca9f0e4bb93552c3:   1  \n",
+       " 008c441677a37d339c96a2bf2c9aa377e2411a00eb9d4588e3362043f0d7b744:   1  \n",
+       " 012200f2a61b1d302998036a06091f074f95125848e9caff9f0d23e392538936:   1  \n",
+       " 012c09d9b08f7c26d9076a0aea43ead69de74de251de49c6ba67e40d0613053b:   1  \n",
+       " 01567bed7019b7452a243cb66b3e8cc433c2eafe4d7569e49227d61bcfd6d33f:   1  \n",
+       " (Other)                                                         :2195  \n",
+       "               n2n_version   peer_sharing      asn                   country   \n",
+       " NodeToNodeVersionV10:   1   f: 745       Min.   :   209   Germany       :528  \n",
+       " NodeToNodeVersionV11:   1   t:1456       1st Qu.: 14061   United States :502  \n",
+       " NodeToNodeVersionV13: 869                Median : 20860   Canada        :154  \n",
+       " NodeToNodeVersionV14:1330                Mean   : 61266   Japan         :123  \n",
+       "                                          3rd Qu.: 51167   United Kingdom:108  \n",
+       "                                          Max.   :401116   Finland       : 90  \n",
+       "                                          NA's   :64       (Other)       :696  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ptNodes <- fread(\"pt_nodes.csv\", stringsAsFactors=TRUE)\n",
+    "ptNodes %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "533787ae-0954-473a-9d81-9f1aa50b6f6a",
+   "metadata": {},
+   "source": [
+    "### Probes of mainnet edges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "58e6a7e7-95e4-401b-80a7-5893139cf393",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                                                              source     \n",
+       " 99f4b029b0864e19334824015c8d05b34c34353ab7fe36e9da91689ceb512b66:   67  \n",
+       " 7e0520b4ebf8159bd1e219d49fc847efc9fe54332cc36aa5ac2350020c2a4939:   64  \n",
+       " 624d8747537834fe5aa3da126f5647904f2425fb002c72be6fe811fbd671412b:   61  \n",
+       " 73a4c479d08b254dab522708a611981c48e8323ae39ab6ad838c30ba3c8c8811:   59  \n",
+       " 1b80ab54864c57bf4f1e9646d30edf48e4d98e8d70ecaacac89adb3a48be5726:   57  \n",
+       " 4a5c7640e2e4f40870dfe3df1fe942e90f718394bd68de31e5ec9730c5ece414:   57  \n",
+       " (Other)                                                         :49569  \n",
+       "                                                              target     \n",
+       " 99f4b029b0864e19334824015c8d05b34c34353ab7fe36e9da91689ceb512b66:  194  \n",
+       " 2d8b0cb5e44c3e746e7e29fb025dcb258aaf4df658f41c11bdc6824a9059fb84:  185  \n",
+       " 9b88f53637710f0b577b5cc060e67ceb1fddbee24c18f600b4497829d2e2323f:  181  \n",
+       " d651218f17c10c040eb944416864971abd396ff8142bda7e2d97e26a7fc3a987:  177  \n",
+       " d0cca0575254a0c01423d9532b4223e4dc467405ca40bd530e4d1befb3c5c222:  168  \n",
+       " 450bb4474418bd71728346d338824776690e9a3a1971e0df5885e938ee2d0c1c:  162  \n",
+       " (Other)                                                         :48867  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ptEdges <- fread(\"pt_edges.csv\", stringsAsFactors=TRUE)\n",
+    "ptEdges %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d4aed06-2a77-4a96-828b-8532a4cea7aa",
+   "metadata": {},
+   "source": [
+    "### Snapshot of mainnet stakepools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "65d2bd28-97dc-429b-984e-1bcbcafa7cf0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    epoch_no        country    no_of_pools         stake           \n",
+       " Min.   :524   Australia: 1   Min.   :  1.00   Min.   :    120272  \n",
+       " 1st Qu.:524   Austria  : 1   1st Qu.:  1.00   1st Qu.:  32641441  \n",
+       " Median :524   Belgium  : 1   Median :  5.00   Median :  94785845  \n",
+       " Mean   :524   Bolivia  : 1   Mean   : 37.09   Mean   : 616847011  \n",
+       " 3rd Qu.:524   Brazil   : 1   3rd Qu.: 23.50   3rd Qu.: 517550850  \n",
+       " Max.   :524   Canada   : 1   Max.   :537.00   Max.   :7077609940  \n",
+       "               (Other)  :29                                        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "poolCountries <- fread(\"pool_country.csv\", stringsAsFactors=TRUE)\n",
+    "poolCountries %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "487f5b4c-541d-4406-b97b-4cef4c0115eb",
+   "metadata": {},
+   "source": [
+    "### Boundary boxes for countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "644ea425-7144-4d27-b647-b5c238322a98",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "        country    ISO3166 country code    longmin              latmin       \n",
+       " AT         :  1      :248              Min.   :-179.9850   Min.   :-78.000  \n",
+       " AV         :  1   ABW:  1              1st Qu.: -59.9168   1st Qu.: -7.080  \n",
+       " Afghanistan:  1   AE :  1              Median :  10.0000   Median : 10.000  \n",
+       " Albania    :  1   AT :  1              Mean   :  -0.1206   Mean   :  9.275  \n",
+       " Algeria    :  1   ATG:  1              3rd Qu.:  39.6500   3rd Qu.: 32.858  \n",
+       " Andorra    :  1   AV :  1              Max.   : 176.1000   Max.   : 62.000  \n",
+       " (Other)    :248   BD :  1                                                   \n",
+       "    longmax              latmax           Wrapped   \n",
+       " Min.   :-174.4170   Min.   :-54.386          :242  \n",
+       " 1st Qu.:   0.4457   1st Qu.:  9.544   WRAPPED: 12  \n",
+       " Median :  32.5915   Median : 21.000                \n",
+       " Mean   :  35.9674   Mean   : 23.508                \n",
+       " 3rd Qu.:  75.0000   3rd Qu.: 42.918                \n",
+       " Max.   : 180.0000   Max.   : 89.000                \n",
+       "                                                    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "countryBoundingBoxes <- fread(\"country-boundingboxes.csv.gz\", stringsAsFactors=TRUE)\n",
+    "countryBoundingBoxes %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcc58c31-9d01-43f5-95b6-fb8534f51886",
+   "metadata": {},
+   "source": [
+    "### ASN to ASN round-trip pings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f276086d-9ae6-4d05-a18e-e02b0fe35e4d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      asn1             asn2           rtt_cnt            rtt_avg         \n",
+       " Min.   :     0   Min.   :     0   Min.   :       1   Min.   :     0.03  \n",
+       " 1st Qu.:  4493   1st Qu.: 34347   1st Qu.:       6   1st Qu.:    30.22  \n",
+       " Median : 12276   Median : 57073   Median :    2169   Median :    95.40  \n",
+       " Mean   : 26245   Mean   :115727   Mean   :    6231   Mean   :   114.11  \n",
+       " 3rd Qu.: 32167   3rd Qu.:203296   3rd Qu.:    4326   3rd Qu.:   173.23  \n",
+       " Max.   :401332   Max.   :401612   Max.   :44991955   Max.   :301077.44  \n",
+       "                                                                         \n",
+       "    rtt_std             rtt_min             rtt_max       \n",
+       " Min.   :     0.00   Min.   :     0.00   Min.   :      0  \n",
+       " 1st Qu.:     0.82   1st Qu.:    22.19   1st Qu.:     56  \n",
+       " Median :     3.64   Median :    75.87   Median :    154  \n",
+       " Mean   :    17.62   Mean   :   101.80   Mean   :    424  \n",
+       " 3rd Qu.:    13.39   3rd Qu.:   161.65   3rd Qu.:    287  \n",
+       " Max.   :173798.40   Max.   :301077.44   Max.   :3592381  \n",
+       " NA's   :58552                                            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "asn_rtt_stat <- fread(\"asn_rtt_stat.csv.gz\")\n",
+    "asn_rtt_stat %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4343e7b0-501b-4739-a079-1195d8b429a1",
+   "metadata": {},
+   "source": [
+    "### Intra ASN round-trip pings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "60f7b435-5508-4a75-8ea1-45301ff2dfd1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    rtt_cnt             rtt_avg         rtt_std         rtt_min \n",
+       " Min.   :145057189   Min.   :80.41   Min.   :103.5   Min.   :0  \n",
+       " 1st Qu.:145057189   1st Qu.:80.41   1st Qu.:103.5   1st Qu.:0  \n",
+       " Median :145057189   Median :80.41   Median :103.5   Median :0  \n",
+       " Mean   :145057189   Mean   :80.41   Mean   :103.5   Mean   :0  \n",
+       " 3rd Qu.:145057189   3rd Qu.:80.41   3rd Qu.:103.5   3rd Qu.:0  \n",
+       " Max.   :145057189   Max.   :80.41   Max.   :103.5   Max.   :0  \n",
+       "    rtt_max      \n",
+       " Min.   :249626  \n",
+       " 1st Qu.:249626  \n",
+       " Median :249626  \n",
+       " Mean   :249626  \n",
+       " 3rd Qu.:249626  \n",
+       " Max.   :249626  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "intra_rtt_stat <- fread(\"intra_rtt_stat.csv.gz\")\n",
+    "intra_rtt_stat %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "676b297c-c5ee-4450-a5a5-b3e212df5074",
+   "metadata": {},
+   "source": [
+    "### Mainnet stake"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "13361019-3c46-4784-87ac-54488164aada",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "     Epoch         Pool           Stake [Lovelace]         Stake [Fraction]   \n",
+       " Min.   :500   Length:2884        Min.   :         11118   Min.   :0.000e+00  \n",
+       " 1st Qu.:500   Class :character   1st Qu.:    1442483670   1st Qu.:6.300e-08  \n",
+       " Median :500   Mode  :character   Median :  127668335529   Median :5.596e-06  \n",
+       " Mean   :500                      Mean   : 7923945372784   Mean   :3.467e-04  \n",
+       " 3rd Qu.:500                      3rd Qu.: 4675668176601   3rd Qu.:2.048e-04  \n",
+       " Max.   :500                      Max.   :78570314564092   Max.   :3.438e-03  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mainnetStake <- fread(\"mainnet-stake.csv.gz\")[order(`Epoch`, `Stake [Lovelace]`)][`Stake [Lovelace]` > 0]\n",
+    "mainnetStake %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "096e2327-1c5b-46b4-b692-0f2b43a4e101",
+   "metadata": {},
+   "source": [
+    "## Create ASN-level totals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9afe2d8c-05fc-4bd0-b585-dcc11f8284d9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Compare node countries to pool countries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "746600ab-be30-4579-9928-fac33db8c32f",
+   "metadata": {},
+   "source": [
+    "All of the pool countries appear in the list of node countries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "994e1b0e-e429-4685-a8c1-aa8efda5da05",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<details>\n",
+       "\t<summary style=display:list-item;cursor:pointer>\n",
+       "\t\t<strong>Levels</strong>:\n",
+       "\t</summary>\n",
+       "\t<style>\n",
+       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
+       "\t.list-inline>li {display: inline-block}\n",
+       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "\t</style>\n",
+       "\t<ol class=list-inline><li>'Australia'</li><li>'Austria'</li><li>'Belgium'</li><li>'Bolivia'</li><li>'Brazil'</li><li>'Canada'</li><li>'China'</li><li>'Finland'</li><li>'France'</li><li>'Germany'</li><li>'Hungary'</li><li>'India'</li><li>'Indonesia'</li><li>'Ireland'</li><li>'Japan'</li><li>'Latvia'</li><li>'Lithuania'</li><li>'Netherlands'</li><li>'New Zealand'</li><li>'Norway'</li><li>'Poland'</li><li>'Romania'</li><li>'Russian Federation'</li><li>'Seychelles'</li><li>'Singapore'</li><li>'South Africa'</li><li>'South Korea'</li><li>'Spain'</li><li>'Sweden'</li><li>'Switzerland'</li><li>'Taiwan'</li><li>'Thailand'</li><li>'United Arab Emirates'</li><li>'United Kingdom'</li><li>'United States'</li></ol>\n",
+       "</details>"
+      ],
+      "text/latex": [
+       "\n",
+       "\\emph{Levels}: \\begin{enumerate*}\n",
+       "\\item 'Australia'\n",
+       "\\item 'Austria'\n",
+       "\\item 'Belgium'\n",
+       "\\item 'Bolivia'\n",
+       "\\item 'Brazil'\n",
+       "\\item 'Canada'\n",
+       "\\item 'China'\n",
+       "\\item 'Finland'\n",
+       "\\item 'France'\n",
+       "\\item 'Germany'\n",
+       "\\item 'Hungary'\n",
+       "\\item 'India'\n",
+       "\\item 'Indonesia'\n",
+       "\\item 'Ireland'\n",
+       "\\item 'Japan'\n",
+       "\\item 'Latvia'\n",
+       "\\item 'Lithuania'\n",
+       "\\item 'Netherlands'\n",
+       "\\item 'New Zealand'\n",
+       "\\item 'Norway'\n",
+       "\\item 'Poland'\n",
+       "\\item 'Romania'\n",
+       "\\item 'Russian Federation'\n",
+       "\\item 'Seychelles'\n",
+       "\\item 'Singapore'\n",
+       "\\item 'South Africa'\n",
+       "\\item 'South Korea'\n",
+       "\\item 'Spain'\n",
+       "\\item 'Sweden'\n",
+       "\\item 'Switzerland'\n",
+       "\\item 'Taiwan'\n",
+       "\\item 'Thailand'\n",
+       "\\item 'United Arab Emirates'\n",
+       "\\item 'United Kingdom'\n",
+       "\\item 'United States'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "**Levels**: 1. 'Australia'\n",
+       "2. 'Austria'\n",
+       "3. 'Belgium'\n",
+       "4. 'Bolivia'\n",
+       "5. 'Brazil'\n",
+       "6. 'Canada'\n",
+       "7. 'China'\n",
+       "8. 'Finland'\n",
+       "9. 'France'\n",
+       "10. 'Germany'\n",
+       "11. 'Hungary'\n",
+       "12. 'India'\n",
+       "13. 'Indonesia'\n",
+       "14. 'Ireland'\n",
+       "15. 'Japan'\n",
+       "16. 'Latvia'\n",
+       "17. 'Lithuania'\n",
+       "18. 'Netherlands'\n",
+       "19. 'New Zealand'\n",
+       "20. 'Norway'\n",
+       "21. 'Poland'\n",
+       "22. 'Romania'\n",
+       "23. 'Russian Federation'\n",
+       "24. 'Seychelles'\n",
+       "25. 'Singapore'\n",
+       "26. 'South Africa'\n",
+       "27. 'South Korea'\n",
+       "28. 'Spain'\n",
+       "29. 'Sweden'\n",
+       "30. 'Switzerland'\n",
+       "31. 'Taiwan'\n",
+       "32. 'Thailand'\n",
+       "33. 'United Arab Emirates'\n",
+       "34. 'United Kingdom'\n",
+       "35. 'United States'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "factor(0)\n",
+       "35 Levels: Australia Austria Belgium Bolivia Brazil Canada China ... United States"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "poolCountries[!(`country` %in% ptNodes$`country`), unique(`country`)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "866b6263-ce21-453c-92e1-a2d8ad6c99d7",
+   "metadata": {},
+   "source": [
+    "Twenty-one of the node countries do not appear in the list of pool countries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "be12a026-9317-44e7-a942-f2a5c3d7b45d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li></li><li>Estonia</li><li>Argentina</li><li>Viet Nam</li><li>Czechia</li><li>Italy</li><li>Portugal</li><li>Slovenia</li><li>Greece</li><li>Denmark</li><li>Chile</li><li>Cayman Islands</li><li>Israel</li><li>Malaysia</li><li>Luxembourg</li><li>New Caledonia</li><li>Iran</li><li>Malta</li><li>Bulgaria</li><li>Mexico</li><li>Peru</li></ol>\n",
+       "\n",
+       "<details>\n",
+       "\t<summary style=display:list-item;cursor:pointer>\n",
+       "\t\t<strong>Levels</strong>:\n",
+       "\t</summary>\n",
+       "\t<style>\n",
+       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
+       "\t.list-inline>li {display: inline-block}\n",
+       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "\t</style>\n",
+       "\t<ol class=list-inline><li>''</li><li>'Argentina'</li><li>'Australia'</li><li>'Austria'</li><li>'Belgium'</li><li>'Bolivia'</li><li>'Brazil'</li><li>'Bulgaria'</li><li>'Canada'</li><li>'Cayman Islands'</li><li>'Chile'</li><li>'China'</li><li>'Czechia'</li><li>'Denmark'</li><li>'Estonia'</li><li>'Finland'</li><li>'France'</li><li>'Germany'</li><li>'Greece'</li><li>'Hungary'</li><li>'India'</li><li>'Indonesia'</li><li>'Iran'</li><li>'Ireland'</li><li>'Israel'</li><li>'Italy'</li><li>'Japan'</li><li>'Latvia'</li><li>'Lithuania'</li><li>'Luxembourg'</li><li>'Malaysia'</li><li>'Malta'</li><li>'Mexico'</li><li>'Netherlands'</li><li>'New Caledonia'</li><li>'New Zealand'</li><li>'Norway'</li><li>'Peru'</li><li>'Poland'</li><li>'Portugal'</li><li>'Romania'</li><li>'Russian Federation'</li><li>'Seychelles'</li><li>'Singapore'</li><li>'Slovenia'</li><li>'South Africa'</li><li>'South Korea'</li><li>'Spain'</li><li>'Sweden'</li><li>'Switzerland'</li><li>'Taiwan'</li><li>'Thailand'</li><li>'United Arab Emirates'</li><li>'United Kingdom'</li><li>'United States'</li><li>'Viet Nam'</li></ol>\n",
+       "</details>"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item \n",
+       "\\item Estonia\n",
+       "\\item Argentina\n",
+       "\\item Viet Nam\n",
+       "\\item Czechia\n",
+       "\\item Italy\n",
+       "\\item Portugal\n",
+       "\\item Slovenia\n",
+       "\\item Greece\n",
+       "\\item Denmark\n",
+       "\\item Chile\n",
+       "\\item Cayman Islands\n",
+       "\\item Israel\n",
+       "\\item Malaysia\n",
+       "\\item Luxembourg\n",
+       "\\item New Caledonia\n",
+       "\\item Iran\n",
+       "\\item Malta\n",
+       "\\item Bulgaria\n",
+       "\\item Mexico\n",
+       "\\item Peru\n",
+       "\\end{enumerate*}\n",
+       "\n",
+       "\\emph{Levels}: \\begin{enumerate*}\n",
+       "\\item ''\n",
+       "\\item 'Argentina'\n",
+       "\\item 'Australia'\n",
+       "\\item 'Austria'\n",
+       "\\item 'Belgium'\n",
+       "\\item 'Bolivia'\n",
+       "\\item 'Brazil'\n",
+       "\\item 'Bulgaria'\n",
+       "\\item 'Canada'\n",
+       "\\item 'Cayman Islands'\n",
+       "\\item 'Chile'\n",
+       "\\item 'China'\n",
+       "\\item 'Czechia'\n",
+       "\\item 'Denmark'\n",
+       "\\item 'Estonia'\n",
+       "\\item 'Finland'\n",
+       "\\item 'France'\n",
+       "\\item 'Germany'\n",
+       "\\item 'Greece'\n",
+       "\\item 'Hungary'\n",
+       "\\item 'India'\n",
+       "\\item 'Indonesia'\n",
+       "\\item 'Iran'\n",
+       "\\item 'Ireland'\n",
+       "\\item 'Israel'\n",
+       "\\item 'Italy'\n",
+       "\\item 'Japan'\n",
+       "\\item 'Latvia'\n",
+       "\\item 'Lithuania'\n",
+       "\\item 'Luxembourg'\n",
+       "\\item 'Malaysia'\n",
+       "\\item 'Malta'\n",
+       "\\item 'Mexico'\n",
+       "\\item 'Netherlands'\n",
+       "\\item 'New Caledonia'\n",
+       "\\item 'New Zealand'\n",
+       "\\item 'Norway'\n",
+       "\\item 'Peru'\n",
+       "\\item 'Poland'\n",
+       "\\item 'Portugal'\n",
+       "\\item 'Romania'\n",
+       "\\item 'Russian Federation'\n",
+       "\\item 'Seychelles'\n",
+       "\\item 'Singapore'\n",
+       "\\item 'Slovenia'\n",
+       "\\item 'South Africa'\n",
+       "\\item 'South Korea'\n",
+       "\\item 'Spain'\n",
+       "\\item 'Sweden'\n",
+       "\\item 'Switzerland'\n",
+       "\\item 'Taiwan'\n",
+       "\\item 'Thailand'\n",
+       "\\item 'United Arab Emirates'\n",
+       "\\item 'United Kingdom'\n",
+       "\\item 'United States'\n",
+       "\\item 'Viet Nam'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. \n",
+       "2. Estonia\n",
+       "3. Argentina\n",
+       "4. Viet Nam\n",
+       "5. Czechia\n",
+       "6. Italy\n",
+       "7. Portugal\n",
+       "8. Slovenia\n",
+       "9. Greece\n",
+       "10. Denmark\n",
+       "11. Chile\n",
+       "12. Cayman Islands\n",
+       "13. Israel\n",
+       "14. Malaysia\n",
+       "15. Luxembourg\n",
+       "16. New Caledonia\n",
+       "17. Iran\n",
+       "18. Malta\n",
+       "19. Bulgaria\n",
+       "20. Mexico\n",
+       "21. Peru\n",
+       "\n",
+       "\n",
+       "\n",
+       "**Levels**: 1. ''\n",
+       "2. 'Argentina'\n",
+       "3. 'Australia'\n",
+       "4. 'Austria'\n",
+       "5. 'Belgium'\n",
+       "6. 'Bolivia'\n",
+       "7. 'Brazil'\n",
+       "8. 'Bulgaria'\n",
+       "9. 'Canada'\n",
+       "10. 'Cayman Islands'\n",
+       "11. 'Chile'\n",
+       "12. 'China'\n",
+       "13. 'Czechia'\n",
+       "14. 'Denmark'\n",
+       "15. 'Estonia'\n",
+       "16. 'Finland'\n",
+       "17. 'France'\n",
+       "18. 'Germany'\n",
+       "19. 'Greece'\n",
+       "20. 'Hungary'\n",
+       "21. 'India'\n",
+       "22. 'Indonesia'\n",
+       "23. 'Iran'\n",
+       "24. 'Ireland'\n",
+       "25. 'Israel'\n",
+       "26. 'Italy'\n",
+       "27. 'Japan'\n",
+       "28. 'Latvia'\n",
+       "29. 'Lithuania'\n",
+       "30. 'Luxembourg'\n",
+       "31. 'Malaysia'\n",
+       "32. 'Malta'\n",
+       "33. 'Mexico'\n",
+       "34. 'Netherlands'\n",
+       "35. 'New Caledonia'\n",
+       "36. 'New Zealand'\n",
+       "37. 'Norway'\n",
+       "38. 'Peru'\n",
+       "39. 'Poland'\n",
+       "40. 'Portugal'\n",
+       "41. 'Romania'\n",
+       "42. 'Russian Federation'\n",
+       "43. 'Seychelles'\n",
+       "44. 'Singapore'\n",
+       "45. 'Slovenia'\n",
+       "46. 'South Africa'\n",
+       "47. 'South Korea'\n",
+       "48. 'Spain'\n",
+       "49. 'Sweden'\n",
+       "50. 'Switzerland'\n",
+       "51. 'Taiwan'\n",
+       "52. 'Thailand'\n",
+       "53. 'United Arab Emirates'\n",
+       "54. 'United Kingdom'\n",
+       "55. 'United States'\n",
+       "56. 'Viet Nam'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       " [1]                Estonia        Argentina      Viet Nam       Czechia       \n",
+       " [6] Italy          Portugal       Slovenia       Greece         Denmark       \n",
+       "[11] Chile          Cayman Islands Israel         Malaysia       Luxembourg    \n",
+       "[16] New Caledonia  Iran           Malta          Bulgaria       Mexico        \n",
+       "[21] Peru          \n",
+       "56 Levels:  Argentina Australia Austria Belgium Bolivia Brazil ... Viet Nam"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ptNodes[!(`country` %in% poolCountries$`country`), unique(`country`)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcee352d-5297-40d4-9423-629a12be822d",
+   "metadata": {},
+   "source": [
+    "### Use hot-deck imputation to assign missing nodes to countries and ASNs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "461053bf-81fe-4733-a900-4c8aab39b061",
+   "metadata": {},
+   "source": [
+    "If the country is missing, then the ASN is also missing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6b50a019-6832-425f-b9b5-f5cc1eb0942d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 x 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Has Country</th><th scope=col>Has ASN</th><th scope=col>N</th></tr>\n",
+       "\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>FALSE</td><td>FALSE</td><td>  64</td></tr>\n",
+       "\t<tr><td> TRUE</td><td> TRUE</td><td>2137</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 x 3\n",
+       "\\begin{tabular}{lll}\n",
+       " Has Country & Has ASN & N\\\\\n",
+       " <lgl> & <lgl> & <int>\\\\\n",
+       "\\hline\n",
+       "\t FALSE & FALSE &   64\\\\\n",
+       "\t  TRUE &  TRUE & 2137\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 x 3\n",
+       "\n",
+       "| Has Country &lt;lgl&gt; | Has ASN &lt;lgl&gt; | N &lt;int&gt; |\n",
+       "|---|---|---|\n",
+       "| FALSE | FALSE |   64 |\n",
+       "|  TRUE |  TRUE | 2137 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Has Country Has ASN N   \n",
+       "1 FALSE       FALSE     64\n",
+       "2  TRUE        TRUE   2137"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ptNodes[, .N, .(`Has Country`=`country`!=\"\", `Has ASN`=!is.na(`asn`))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7e270cd8-5093-46e5-a417-02eb75cb883f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>2137</li><li>2</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 2137\n",
+       "\\item 2\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 2137\n",
+       "2. 2\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 2137    2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nonMissing <- ptNodes[`country` != \"\", .(`country`, `asn`)]\n",
+    "nonMissing %>% dim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1e5163a1-b85a-4f29-938b-77208b6cd7fa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2137"
+      ],
+      "text/latex": [
+       "2137"
+      ],
+      "text/markdown": [
+       "2137"
+      ],
+      "text/plain": [
+       "[1] 2137"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(nonMissing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a5447ea7-a7a1-4c41-89fa-96d5e87c01d8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "assignments <- sample(1:nrow(nonMissing), ptNodes[`country`==\"\", .N])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a8020c95-28f4-418d-a863-bb41f437e913",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ptNodes[`country` ==\"\", `:=`(`country`=nonMissing[assignments, `country`], `asn`=nonMissing[assignments, `asn`])]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fa01f60-ecde-4c3f-8297-bf5eeb12465a",
+   "metadata": {},
+   "source": [
+    "### Distribute the stake among the ASNs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "48a846c5-e010-4523-9cd6-cf3b232fc0d4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 6 x 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>country</th><th scope=col>asn</th><th scope=col>no_of_nodes</th><th scope=col>fraction</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>United States</td><td> 16276</td><td>26</td><td>0.04961832</td></tr>\n",
+       "\t<tr><td>United States</td><td> 14618</td><td>31</td><td>0.05916031</td></tr>\n",
+       "\t<tr><td>United States</td><td>396982</td><td>33</td><td>0.06297710</td></tr>\n",
+       "\t<tr><td>United States</td><td>  5650</td><td> 8</td><td>0.01526718</td></tr>\n",
+       "\t<tr><td>United States</td><td> 16509</td><td>29</td><td>0.05534351</td></tr>\n",
+       "\t<tr><td>United States</td><td> 16591</td><td> 9</td><td>0.01717557</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 6 x 4\n",
+       "\\begin{tabular}{llll}\n",
+       " country & asn & no\\_of\\_nodes & fraction\\\\\n",
+       " <fct> & <int> & <int> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t United States &  16276 & 26 & 0.04961832\\\\\n",
+       "\t United States &  14618 & 31 & 0.05916031\\\\\n",
+       "\t United States & 396982 & 33 & 0.06297710\\\\\n",
+       "\t United States &   5650 &  8 & 0.01526718\\\\\n",
+       "\t United States &  16509 & 29 & 0.05534351\\\\\n",
+       "\t United States &  16591 &  9 & 0.01717557\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 6 x 4\n",
+       "\n",
+       "| country &lt;fct&gt; | asn &lt;int&gt; | no_of_nodes &lt;int&gt; | fraction &lt;dbl&gt; |\n",
+       "|---|---|---|---|\n",
+       "| United States |  16276 | 26 | 0.04961832 |\n",
+       "| United States |  14618 | 31 | 0.05916031 |\n",
+       "| United States | 396982 | 33 | 0.06297710 |\n",
+       "| United States |   5650 |  8 | 0.01526718 |\n",
+       "| United States |  16509 | 29 | 0.05534351 |\n",
+       "| United States |  16591 |  9 | 0.01717557 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  country       asn    no_of_nodes fraction  \n",
+       "1 United States  16276 26          0.04961832\n",
+       "2 United States  14618 31          0.05916031\n",
+       "3 United States 396982 33          0.06297710\n",
+       "4 United States   5650  8          0.01526718\n",
+       "5 United States  16509 29          0.05534351\n",
+       "6 United States  16591  9          0.01717557"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodeFractions <- ptNodes[, .(`no_of_nodes`=.N), .(`country`, `asn`)][, .(`asn`, `no_of_nodes`, `fraction`=`no_of_nodes`/sum(`no_of_nodes`)), .(`country`)]\n",
+    "nodeFractions %>% head"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223f59ea-5818-4f12-b648-b85b112bb529",
+   "metadata": {},
+   "source": [
+    "Randomly distribute the pools to ASNs according to a multinomial distribution weighted by the number of nodes in the ASN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "de271d8d-b0e9-44d2-ab60-89f0d55df8fd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rpools <- function(no_of_pools, fractions) {\n",
+    "    if (sum(no_of_pools) == 0)\n",
+    "        0\n",
+    "    else\n",
+    "        as.numeric(rmultinom(1, no_of_pools, fractions))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3aaec209-a66b-49bd-9833-e56cee86899d",
+   "metadata": {},
+   "source": [
+    "Distribute the country's stake in proportion to the distribution of pools within the ASN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b08b5692-a84d-413b-80e3-a44851502793",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 6 x 5</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>country</th><th scope=col>asn</th><th scope=col>no_of_nodes</th><th scope=col>no_of_pools</th><th scope=col>stake</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>United States</td><td> 16276</td><td>26</td><td>10</td><td>148295395</td></tr>\n",
+       "\t<tr><td>United States</td><td> 14618</td><td>31</td><td>17</td><td>252102171</td></tr>\n",
+       "\t<tr><td>United States</td><td>396982</td><td>33</td><td>14</td><td>207613552</td></tr>\n",
+       "\t<tr><td>United States</td><td>  5650</td><td> 8</td><td> 4</td><td> 59318158</td></tr>\n",
+       "\t<tr><td>United States</td><td> 16509</td><td>29</td><td>19</td><td>281761250</td></tr>\n",
+       "\t<tr><td>United States</td><td> 16591</td><td> 9</td><td> 4</td><td> 59318158</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 6 x 5\n",
+       "\\begin{tabular}{lllll}\n",
+       " country & asn & no\\_of\\_nodes & no\\_of\\_pools & stake\\\\\n",
+       " <fct> & <int> & <int> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t United States &  16276 & 26 & 10 & 148295395\\\\\n",
+       "\t United States &  14618 & 31 & 17 & 252102171\\\\\n",
+       "\t United States & 396982 & 33 & 14 & 207613552\\\\\n",
+       "\t United States &   5650 &  8 &  4 &  59318158\\\\\n",
+       "\t United States &  16509 & 29 & 19 & 281761250\\\\\n",
+       "\t United States &  16591 &  9 &  4 &  59318158\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 6 x 5\n",
+       "\n",
+       "| country &lt;fct&gt; | asn &lt;int&gt; | no_of_nodes &lt;int&gt; | no_of_pools &lt;dbl&gt; | stake &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| United States |  16276 | 26 | 10 | 148295395 |\n",
+       "| United States |  14618 | 31 | 17 | 252102171 |\n",
+       "| United States | 396982 | 33 | 14 | 207613552 |\n",
+       "| United States |   5650 |  8 |  4 |  59318158 |\n",
+       "| United States |  16509 | 29 | 19 | 281761250 |\n",
+       "| United States |  16591 |  9 |  4 |  59318158 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  country       asn    no_of_nodes no_of_pools stake    \n",
+       "1 United States  16276 26          10          148295395\n",
+       "2 United States  14618 31          17          252102171\n",
+       "3 United States 396982 33          14          207613552\n",
+       "4 United States   5650  8           4           59318158\n",
+       "5 United States  16509 29          19          281761250\n",
+       "6 United States  16591  9           4           59318158"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "asnPools <- poolCountries[\n",
+    "    nodeFractions,\n",
+    "    on=\"country\"\n",
+    "][\n",
+    "    `no_of_pools`>0,\n",
+    "    .(`asn`, `no_of_nodes`, `no_of_pools`=rpools(`no_of_pools`, `fraction`), `stake`), \n",
+    "    .(`country`)\n",
+    "][,\n",
+    "  .(`asn`, `no_of_nodes`, `no_of_pools`, `stake`=`stake`*`no_of_pools`/sum(`no_of_pools`)),\n",
+    "  .(`country`)\n",
+    "]\n",
+    "asnPools %>% head"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e31f29cd-734b-4079-85db-a5311cebec22",
+   "metadata": {},
+   "source": [
+    "Check that the resulting disaggregation matches the original marginal distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4ac2954d-216b-4a44-a785-cbc0c189603f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "TRUE"
+      ],
+      "text/latex": [
+       "TRUE"
+      ],
+      "text/markdown": [
+       "TRUE"
+      ],
+      "text/plain": [
+       "[1] TRUE"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    asnPools[, .(`no_of_pools`=sum(`no_of_pools`), `stake`=round(sum(`stake`))), .(`country`=as.character(`country`))][order(`country`)] == \n",
+    "    poolCountries[, .(`no_of_pools`, `stake`), .(`country`=as.character(`country`))][order(`country`)]\n",
+    ") %>% all"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e309e84e-262a-4b21-bbcc-4fe222f3768f",
+   "metadata": {},
+   "source": [
+    "#### Adjust the number of pools to aproximately match the mainnet registration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85541a37-5cf8-4598-8343-5572760b737e",
+   "metadata": {},
+   "source": [
+    "Count to total number of pools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0144e2b5-cf15-4349-8fe5-8a93a7cdb1cb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1298"
+      ],
+      "text/latex": [
+       "1298"
+      ],
+      "text/markdown": [
+       "1298"
+      ],
+      "text/plain": [
+       "[1] 1298"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "asnPools[, sum(no_of_pools)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c4393d-52e9-4da6-ad81-d14f4d83c253",
+   "metadata": {},
+   "source": [
+    "We want only 3% for this smaller network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6b72efbd-161e-4547-a59e-3beadb92c4e1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "asnPools <- asnPools[, .(`country`, `asn`, `no_nodes`=round(`no_of_nodes`/30), `no_of_pools`=round(`no_of_pools`/30), `stake`)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e8c6cfca-37a3-45e1-891a-6da553539d74",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "26"
+      ],
+      "text/latex": [
+       "26"
+      ],
+      "text/markdown": [
+       "26"
+      ],
+      "text/plain": [
+       "[1] 26"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "poolCount <- asnPools[, sum(no_of_pools)]\n",
+    "poolCount"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "980c7f20-b4b6-4866-bd82-d2ddeb2ff072",
+   "metadata": {},
+   "source": [
+    "## Create nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bb5a1f4-90dd-4806-9c4e-551e7a60feff",
+   "metadata": {},
+   "source": [
+    "We want 100 nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3c86606b-f958-4a9f-9bce-d56d1f3379e9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "nodeCount <- 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d470bcd4-d3b5-4f10-b9ad-33fa2fd1a72c",
+   "metadata": {},
+   "source": [
+    "However, the data set should not contain any block producers, so we need to reserve `poolCount` indices for those block producers. We'll also reserve `poolCount` for the second relay."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "722cab79-9916-48c1-a3c6-a3bcd13be58c",
+   "metadata": {},
+   "source": [
+    "### Sample to decrease the number of nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d2abea3f-001b-4a45-ab7f-23370385b80f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "74"
+      ],
+      "text/latex": [
+       "74"
+      ],
+      "text/markdown": [
+       "74"
+      ],
+      "text/plain": [
+       "[1] 74"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "expandedNodes <- ptNodes[sample(nodeCount - poolCount), .(`country`, `asn`, `id`)]\n",
+    "expandedNodes %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69fd5ac0-c573-4780-a3d3-870138af04e2",
+   "metadata": {},
+   "source": [
+    "### Assign stakepools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e0772fd2-89f9-4326-bb98-21853d504203",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rpools <- function(no_of_pools, no_of_nodes) {\n",
+    "    if (no_of_pools[1] == 0)\n",
+    "        0\n",
+    "    else if (no_of_pools[1] > no_of_nodes)\n",
+    "        rep(1, no_of_nodes)\n",
+    "    else {\n",
+    "        selection <- rep(0, no_of_nodes)\n",
+    "        pools <- sample(1:no_of_nodes, no_of_pools[1])\n",
+    "        selection[pools] <- 1\n",
+    "        selection\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c7d6523e-4d51-4b60-8a39-5d5675526bec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "22"
+      ],
+      "text/latex": [
+       "22"
+      ],
+      "text/markdown": [
+       "22"
+      ],
+      "text/plain": [
+       "[1] 22"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodeStakes <- asnPools[\n",
+    "    expandedNodes, on=c(\"country\", \"asn\")\n",
+    "][,\n",
+    "    .(`id`, `country`, `asn`, `no_of_pools`=fcoalesce(`no_of_pools`,0), `stake`=fcoalesce(`stake`,0))\n",
+    "][, \n",
+    "  .(`id`, `stake`=rpools(`no_of_pools`, .N) * `stake`/ `no_of_pools`), \n",
+    "  .(`country`, `asn`)\n",
+    "]\n",
+    "nodeStakes[`stake` > 0, .N]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4afa3b68-86f0-4239-81e7-ee1b928798d9",
+   "metadata": {},
+   "source": [
+    "Some of the pools were lost in this process, but they were tiny pools."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d7f927c-4f52-4dff-a581-f9cd47abbdb8",
+   "metadata": {},
+   "source": [
+    "### Assign block producers, relays, and other nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d0ad47c7-db4e-4275-966d-e972019399e3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "100"
+      ],
+      "text/latex": [
+       "100"
+      ],
+      "text/markdown": [
+       "100"
+      ],
+      "text/plain": [
+       "[1] 100"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodeStakes <- rbind(\n",
+    "    nodeStakes[`stake` > 0, .(`country`, `asn`, `id`, `stake`, `kind`=factor(\"BPROD\"), `kindex`=.I)],\n",
+    "    nodeStakes[`stake` > 0, .(`country`, `asn`, `id`, `stake`=0, `kind`=\"RELAY1\", `kindex`=.I)],\n",
+    "    nodeStakes[`stake` > 0, .(`country`, `asn`, `id`, `stake`=0, `kind`=\"RELAY2\", `kindex`=.I)],\n",
+    "    nodeStakes[`stake` == 0 | is.na(`stake`), .(`country`, `asn`, `id`, `stake`=0, `kind`=\"OTHER\", `kindex`=NA)]\n",
+    ")[1:nodeCount]\n",
+    "nodeStakes %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "36d7c854-0514-4c2a-a9dc-eddfbdc783bd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 4 x 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>kind</th><th scope=col>N</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>BPROD </td><td>22</td></tr>\n",
+       "\t<tr><td>RELAY1</td><td>22</td></tr>\n",
+       "\t<tr><td>RELAY2</td><td>22</td></tr>\n",
+       "\t<tr><td>OTHER </td><td>34</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 4 x 2\n",
+       "\\begin{tabular}{ll}\n",
+       " kind & N\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t BPROD  & 22\\\\\n",
+       "\t RELAY1 & 22\\\\\n",
+       "\t RELAY2 & 22\\\\\n",
+       "\t OTHER  & 34\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 4 x 2\n",
+       "\n",
+       "| kind &lt;fct&gt; | N &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| BPROD  | 22 |\n",
+       "| RELAY1 | 22 |\n",
+       "| RELAY2 | 22 |\n",
+       "| OTHER  | 34 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  kind   N \n",
+       "1 BPROD  22\n",
+       "2 RELAY1 22\n",
+       "3 RELAY2 22\n",
+       "4 OTHER  34"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodeStakes[, .N, .(`kind`)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b58dce25-eea7-4ab4-ab2e-d6a8bff90ae3",
+   "metadata": {},
+   "source": [
+    "### Add geographic locations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29a7f4b3-7c74-4c75-a020-ad2460a36180",
+   "metadata": {},
+   "source": [
+    "First make sure that the names align."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "fc49c9f6-c253-4b1c-9005-c36893822ab5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 0 x 6</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>country</th><th scope=col>asn</th><th scope=col>id</th><th scope=col>stake</th><th scope=col>kind</th><th scope=col>kindex</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 0 x 6\n",
+       "\\begin{tabular}{llllll}\n",
+       " country & asn & id & stake & kind & kindex\\\\\n",
+       " <fct> & <int> & <fct> & <dbl> & <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 0 x 6\n",
+       "\n",
+       "| country &lt;fct&gt; | asn &lt;int&gt; | id &lt;fct&gt; | stake &lt;dbl&gt; | kind &lt;fct&gt; | kindex &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     country asn id stake kind kindex"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodeStakes[!(`country` %in% countryBoundingBoxes$`country`)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "50901349-f49e-4577-82fb-6a7e8a9fec34",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rlong <- function(longmin, longmax, wrapped) {\n",
+    "    if (wrapped)\n",
+    "        (runif(1, longmax, 360 + longmin) + 180) %% 360 - 180\n",
+    "    else\n",
+    "        runif(1, longmin, longmax)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "184e1b00-2df5-40f0-9039-3865d6ab0b09",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rlat <- function(latmin, latmax) {\n",
+    "    runif(1, latmin, latmax)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "bc40f4d1-75eb-48f8-a7d1-9256a95b25b5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "100"
+      ],
+      "text/latex": [
+       "100"
+      ],
+      "text/markdown": [
+       "100"
+      ],
+      "text/plain": [
+       "[1] 100"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodes <- nodeStakes[\n",
+    "    countryBoundingBoxes[, .(`country`, `longmin`, `longmax`, `latmin`, `latmax`, `wrapped`=`Wrapped`==\"WRAPPED\")], \n",
+    "    on=\"country\",\n",
+    "    nomatch=0\n",
+    "][,\n",
+    "  .(`index`=.I-1, `country`, `asn`, `id`, `kind`, `kindex`, `stake`, `long`=mapply(rlong, `longmin`, `longmax`, `wrapped`), `lat`=mapply(rlat, `latmin`, `latmax`))\n",
+    "][,\n",
+    "  .(`index`=as.integer(`index`), `subindex`=1:.N, `kindex`, `kind`, `stake`, `long`, `lat`),\n",
+    "  .(`country`, `asn`, `id`)\n",
+    "][\n",
+    "    order(-`stake`)\n",
+    "]\n",
+    "nodes %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a2481de9-7852-4319-acc9-fd11d4422ea4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 6 x 10</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>country</th><th scope=col>asn</th><th scope=col>id</th><th scope=col>index</th><th scope=col>subindex</th><th scope=col>kindex</th><th scope=col>kind</th><th scope=col>stake</th><th scope=col>long</th><th scope=col>lat</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Japan  </td><td> 16509</td><td>65b17e5f8ad4c27a470c17675fb79b38b562665f391f3b7441d427409537d849</td><td>65</td><td>1</td><td>22</td><td>BPROD</td><td>506301108</td><td>126.89372</td><td>40.79217</td></tr>\n",
+       "\t<tr><td>Finland</td><td> 24940</td><td>d0974c20be0dd022d27cb07ef809d3e644b28159bfc111b3620f7ac244f6e2bd</td><td> 4</td><td>1</td><td>18</td><td>BPROD</td><td>454700774</td><td> 34.61739</td><td>57.16145</td></tr>\n",
+       "\t<tr><td>Germany</td><td>197540</td><td>3d74284f50bbd9f0cf1e9962116635f55b86b40828dd4309b702274bb6bc7f86</td><td>21</td><td>1</td><td>11</td><td>BPROD</td><td>434936924</td><td> 10.06439</td><td>46.25508</td></tr>\n",
+       "\t<tr><td>Germany</td><td>197540</td><td>ba80534841443d36ab2aa78db04503c9475f348e11ccb90050073fc4b06f6cf3</td><td>22</td><td>1</td><td>12</td><td>BPROD</td><td>434936924</td><td> 11.36001</td><td>52.74827</td></tr>\n",
+       "\t<tr><td>Germany</td><td>197540</td><td>900ca8f289f52688db79315711824f25c5862c50bf8aa17f4419a8f3b5e75c7c</td><td>23</td><td>1</td><td>13</td><td>BPROD</td><td>434936924</td><td> 11.71072</td><td>46.08612</td></tr>\n",
+       "\t<tr><td>Germany</td><td> 24940</td><td>5a29be9fe25a3727b3f1cdc1296ea2ab7b61780cf76527d950593519bb805beb</td><td>11</td><td>1</td><td> 1</td><td>BPROD</td><td>408577110</td><td> 13.63801</td><td>46.69807</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 6 x 10\n",
+       "\\begin{tabular}{llllllllll}\n",
+       " country & asn & id & index & subindex & kindex & kind & stake & long & lat\\\\\n",
+       " <fct> & <int> & <fct> & <int> & <int> & <int> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t Japan   &  16509 & 65b17e5f8ad4c27a470c17675fb79b38b562665f391f3b7441d427409537d849 & 65 & 1 & 22 & BPROD & 506301108 & 126.89372 & 40.79217\\\\\n",
+       "\t Finland &  24940 & d0974c20be0dd022d27cb07ef809d3e644b28159bfc111b3620f7ac244f6e2bd &  4 & 1 & 18 & BPROD & 454700774 &  34.61739 & 57.16145\\\\\n",
+       "\t Germany & 197540 & 3d74284f50bbd9f0cf1e9962116635f55b86b40828dd4309b702274bb6bc7f86 & 21 & 1 & 11 & BPROD & 434936924 &  10.06439 & 46.25508\\\\\n",
+       "\t Germany & 197540 & ba80534841443d36ab2aa78db04503c9475f348e11ccb90050073fc4b06f6cf3 & 22 & 1 & 12 & BPROD & 434936924 &  11.36001 & 52.74827\\\\\n",
+       "\t Germany & 197540 & 900ca8f289f52688db79315711824f25c5862c50bf8aa17f4419a8f3b5e75c7c & 23 & 1 & 13 & BPROD & 434936924 &  11.71072 & 46.08612\\\\\n",
+       "\t Germany &  24940 & 5a29be9fe25a3727b3f1cdc1296ea2ab7b61780cf76527d950593519bb805beb & 11 & 1 &  1 & BPROD & 408577110 &  13.63801 & 46.69807\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 6 x 10\n",
+       "\n",
+       "| country &lt;fct&gt; | asn &lt;int&gt; | id &lt;fct&gt; | index &lt;int&gt; | subindex &lt;int&gt; | kindex &lt;int&gt; | kind &lt;fct&gt; | stake &lt;dbl&gt; | long &lt;dbl&gt; | lat &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| Japan   |  16509 | 65b17e5f8ad4c27a470c17675fb79b38b562665f391f3b7441d427409537d849 | 65 | 1 | 22 | BPROD | 506301108 | 126.89372 | 40.79217 |\n",
+       "| Finland |  24940 | d0974c20be0dd022d27cb07ef809d3e644b28159bfc111b3620f7ac244f6e2bd |  4 | 1 | 18 | BPROD | 454700774 |  34.61739 | 57.16145 |\n",
+       "| Germany | 197540 | 3d74284f50bbd9f0cf1e9962116635f55b86b40828dd4309b702274bb6bc7f86 | 21 | 1 | 11 | BPROD | 434936924 |  10.06439 | 46.25508 |\n",
+       "| Germany | 197540 | ba80534841443d36ab2aa78db04503c9475f348e11ccb90050073fc4b06f6cf3 | 22 | 1 | 12 | BPROD | 434936924 |  11.36001 | 52.74827 |\n",
+       "| Germany | 197540 | 900ca8f289f52688db79315711824f25c5862c50bf8aa17f4419a8f3b5e75c7c | 23 | 1 | 13 | BPROD | 434936924 |  11.71072 | 46.08612 |\n",
+       "| Germany |  24940 | 5a29be9fe25a3727b3f1cdc1296ea2ab7b61780cf76527d950593519bb805beb | 11 | 1 |  1 | BPROD | 408577110 |  13.63801 | 46.69807 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  country asn   \n",
+       "1 Japan    16509\n",
+       "2 Finland  24940\n",
+       "3 Germany 197540\n",
+       "4 Germany 197540\n",
+       "5 Germany 197540\n",
+       "6 Germany  24940\n",
+       "  id                                                               index\n",
+       "1 65b17e5f8ad4c27a470c17675fb79b38b562665f391f3b7441d427409537d849 65   \n",
+       "2 d0974c20be0dd022d27cb07ef809d3e644b28159bfc111b3620f7ac244f6e2bd  4   \n",
+       "3 3d74284f50bbd9f0cf1e9962116635f55b86b40828dd4309b702274bb6bc7f86 21   \n",
+       "4 ba80534841443d36ab2aa78db04503c9475f348e11ccb90050073fc4b06f6cf3 22   \n",
+       "5 900ca8f289f52688db79315711824f25c5862c50bf8aa17f4419a8f3b5e75c7c 23   \n",
+       "6 5a29be9fe25a3727b3f1cdc1296ea2ab7b61780cf76527d950593519bb805beb 11   \n",
+       "  subindex kindex kind  stake     long      lat     \n",
+       "1 1        22     BPROD 506301108 126.89372 40.79217\n",
+       "2 1        18     BPROD 454700774  34.61739 57.16145\n",
+       "3 1        11     BPROD 434936924  10.06439 46.25508\n",
+       "4 1        12     BPROD 434936924  11.36001 52.74827\n",
+       "5 1        13     BPROD 434936924  11.71072 46.08612\n",
+       "6 1         1     BPROD 408577110  13.63801 46.69807"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodes %>% head"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d736c132-8685-467f-80d5-2559fdf7fc9f",
+   "metadata": {},
+   "source": [
+    "Most of the stake is preserved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ec1da05d-10e0-4c93-8177-c60d3107cb70",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.385410907735196"
+      ],
+      "text/latex": [
+       "0.385410907735196"
+      ],
+      "text/markdown": [
+       "0.385410907735196"
+      ],
+      "text/plain": [
+       "[1] 0.3854109"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodes[, sum(`stake`)] / poolCountries[, sum(as.numeric(`stake`))]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72c1af98-2039-4344-af13-98c3c1ed97ff",
+   "metadata": {},
+   "source": [
+    "## Adjust the stake distribution to match mainnet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cb8df28a-3a14-4faf-8e66-869d5811c45c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oIyHFJhxjWe5hGSMkKCMhzSy+JeT/MI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\noOKHdN54B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+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(nodes[`stake` > 0][order(`stake`)][, .(.I, `stake`)], aes(x=`I`, y=`stake`)) + geom_line()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c2ce1ba9-af0a-47f0-9c71-4c3b8de44e6c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s8aWJzqAOmSi/O3159ppp2+jpDsREjo3EJaXvsci1MdIJ3wsDFNhxtzdxYh2YmQ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\nREiMWej/AVZcmkHCi/hQAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(mainnetStake[`Epoch` == 500][order(`Stake [Lovelace]`)][, .(.I, `stake`=`Stake [Lovelace]`/1e6)], aes(x=`I`, y=`stake`)) + geom_line()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "84410ccc-3c53-4555-9391-3b0e0a9c7d2e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "100"
+      ],
+      "text/latex": [
+       "100"
+      ],
+      "text/markdown": [
+       "100"
+      ],
+      "text/plain": [
+       "[1] 100"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nodes <- rbind(\n",
+    "    nodes[`stake` == 0],\n",
+    "    nodes[order(-`stake`)][`stake` > 0, .(`country`, `asn`, `id`, `index`, `subindex`, `kindex`, `kind`, `long`, `lat`, `srank`=.I)][\n",
+    "        mainnetStake[`Epoch` == 500][order(-`Stake [Lovelace]`)][, .(`srank`=.I, `stake`=`Stake [Lovelace]`/1e6)],\n",
+    "        on=\"srank\",\n",
+    "        nomatch=0\n",
+    "    ][, .(`country`, `asn`, `id`, `index`, `subindex`, `kindex`, `kind`, `stake`, `long`, `lat`)]\n",
+    ")\n",
+    "nodes %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06c8b1db-5b01-4cb5-b99e-270e1c441666",
+   "metadata": {},
+   "source": [
+    "## Create edges"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96f4da0d-484c-49ca-a650-131ea2cf21b3",
+   "metadata": {},
+   "source": [
+    "### Examine summary statistics for edge connectivity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "126dd6ed-bd69-4991-a2ca-e3d88bb361ad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
+       "   1.00   31.00   36.00   34.46   40.00   67.00 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ptEdges[, .(`degree`=.N), .(`source`)][, `degree`] %>% summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a54deb46-dc08-4d3f-85b3-73856321704d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GTXb0jZLT95+MO3bj2R3bnjwYeuv13A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\nYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "    ptEdges[, .(`degree`=.N), .(`source`)],\n",
+    "    aes(x=`degree`)\n",
+    ") +\n",
+    "    geom_histogram(binwidth=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d54b63ea-099e-48e1-ba44-e785798ad350",
+   "metadata": {},
+   "source": [
+    "### Connect the empirically observed edges, but not for the second relay because it needs more randomization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4ed72760-6749-43c0-ac61-6f51d2a605d3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "10"
+      ],
+      "text/latex": [
+       "10"
+      ],
+      "text/markdown": [
+       "10"
+      ],
+      "text/plain": [
+       "[1] 10"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "edges <- nodes[`kind` %in% c(\"RELAY1\", \"OTHER\"), .(`tindex`=`index`, `tasn`=`asn`, `subindex`, `target`=`id`)][\n",
+    "    nodes[`kind` %in% c(\"RELAY1\", \"OTHER\"), .(`sindex`=`index`, `sasn`=`asn`, `subindex`, `source`=`id`)][\n",
+    "        ptEdges, on=\"source\", allow.cartesian=TRUE\n",
+    "    ], \n",
+    "    on=c(\"target\", \"subindex\"),\n",
+    "    nomatch=0\n",
+    "][,\n",
+    "  .(`source_index`=`sindex`, `source_asn`=`sasn`, `target_index`=`tindex`, `target_asn`=`tasn`)\n",
+    "]\n",
+    "edges %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "965cbaaf-c21f-477c-80bb-4a92edc7d37c",
+   "metadata": {},
+   "source": [
+    "### Connect the second relay randomly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "3d5d4ae0-f7e7-4ce9-b682-d656fdec2d7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "192"
+      ],
+      "text/latex": [
+       "192"
+      ],
+      "text/markdown": [
+       "192"
+      ],
+      "text/plain": [
+       "[1] 192"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "edges <- rbind(\n",
+    "    edges,\n",
+    "    nodes[`kind` == \"RELAY2\", .(`tindex`=`index`, `asn`)][\n",
+    "        nodes[`kind` != \"BPROD\", .(`sindex`=`index`, `asn`)], \n",
+    "        on=\"asn\", \n",
+    "        allow.cartesian=TRUE\n",
+    "    ][\n",
+    "        `tindex` != `sindex`,\n",
+    "        .(`tindex`, `asn`, `copy`=1:.N),\n",
+    "        .(`sindex`)\n",
+    "    ][\n",
+    "        `copy` <= 10,\n",
+    "        .(`source_index`=`sindex`, `source_asn`=`asn`, `target_index`=`tindex`, `target_asn`=`asn`)\n",
+    "    ]\n",
+    ")\n",
+    "edges %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f3f4934-7114-45ce-8a03-4dcbf4e55e7b",
+   "metadata": {},
+   "source": [
+    "### Ensure that each node connects to at least one other node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "dfdba9e6-6059-4d7f-b254-826d8913cf3d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "14"
+      ],
+      "text/latex": [
+       "14"
+      ],
+      "text/markdown": [
+       "14"
+      ],
+      "text/plain": [
+       "[1] 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "disconnected <- nodes[`kind` != \"BPROD\" & !(`index` %in% edges[, unique(`source_index`)]), .(`source_index`=`index`, `source_asn`=`asn`)]\n",
+    "disconnected %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "fb67a5b2-8cca-4f88-b04e-92b6bcb02f5f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "redges <- function(missings) {\n",
+    "    nodes[\n",
+    "        missings[, .(`source_index`, `source_asn`, `target_index`=sample(nodes[`kind` != \"BPROD\", `index`], .N, replace=TRUE))], \n",
+    "        on=c(\"index\" = \"target_index\")\n",
+    "    ][,\n",
+    "        .(`source_index`, `source_asn`, `target_index`=`index`, `target_asn`=`asn`)\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "5c97908b-5f0e-423b-ab65-90d86035e9c6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "edges <- rbind(edges, redges(disconnected))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "156cefcd-2d32-43ec-8d89-6e342aaebc0a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "poorlyConnected <- function()\n",
+    "    edges[, .(`degree`=.N), .(`source_index`, `source_asn`)][`degree` < 20][, .(`source_index`, `source_asn`)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "050fa531-41a8-4a80-a15e-30bf17a768eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0"
+      ],
+      "text/latex": [
+       "0"
+      ],
+      "text/markdown": [
+       "0"
+      ],
+      "text/plain": [
+       "[1] 0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for (i in 1:20) {\n",
+    "    deficient <- poorlyConnected()\n",
+    "    edges <- rbind(edges, redges(deficient))\n",
+    "}\n",
+    "poorlyConnected() %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "5ce755a8-12b4-451d-b94a-eed525f3e1d6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAACylBMVEUAAAABAQECAgIDAwME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\ngg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "    edges[, .(`degree`=.N), .(`source_index`)],\n",
+    "    aes(x=`degree`)\n",
+    ") +\n",
+    "    geom_histogram(binwidth=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2739489-6f02-4806-9861-66e2e1605fb3",
+   "metadata": {},
+   "source": [
+    "We could smear the spike out, but the recommended node configuration suggests 20 active peers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2401b3cc-e207-4fc1-be80-f3224c792927",
+   "metadata": {},
+   "source": [
+    "### Repeat in the reverse direction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "013ae30e-c4ce-4794-a049-ab927f468f3c",
+   "metadata": {},
+   "source": [
+    "#### Ensure that each node connects to at least one other node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "261a2780-e682-4b4a-baf2-b06422c06488",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0"
+      ],
+      "text/latex": [
+       "0"
+      ],
+      "text/markdown": [
+       "0"
+      ],
+      "text/plain": [
+       "[1] 0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "disconnected <- nodes[`kind` != \"BPROD\" & !(`index` %in% edges[, unique(`target_index`)]), .(`target_index`=`index`, `target_asn`=`asn`)]\n",
+    "disconnected %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e524ec0b-02c0-4d99-ab88-8348c67c1872",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "redgesReverse <- function(missings) {\n",
+    "    nodes[\n",
+    "        missings[, .(`target_index`, `target_asn`, `source_index`=sample(nodes[`kind` != \"BPROD\", `index`], .N, replace=TRUE))], \n",
+    "        on=c(\"index\" = \"source_index\")\n",
+    "    ][,\n",
+    "        .(`source_index`=`index`, `source_asn`=`asn`, `target_index`, `target_asn`)\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a3fbccfe-5d59-4668-94dc-d38630391a64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "edges <- rbind(edges, redgesReverse(disconnected))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ebd78e5a-4811-4fc7-8796-926d0bae90ec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "poorlyConnectedReverse <- function()\n",
+    "    edges[, .(`degree`=.N), .(`target_index`, `target_asn`)][`degree` < 20][, .(`target_index`, `target_asn`)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "ab219ee8-8a12-451c-b9f4-3bc9311e547d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0"
+      ],
+      "text/latex": [
+       "0"
+      ],
+      "text/markdown": [
+       "0"
+      ],
+      "text/plain": [
+       "[1] 0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for (i in 1:20) {\n",
+    "    deficient <- poorlyConnectedReverse()\n",
+    "    edges <- rbind(edges, redgesReverse(deficient))\n",
+    "}\n",
+    "poorlyConnectedReverse() %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23e4e45b-78c7-494c-8fae-e10bad872136",
+   "metadata": {},
+   "source": [
+    "### Give every node ten more random peers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "226c430e-7dee-4a11-86a4-dd8555979d60",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "for (i in 1:10) {\n",
+    "    nonBprods <- nodes[`kind` != \"BPROD\"]\n",
+    "    edges <- rbind(\n",
+    "        edges, \n",
+    "        redges(\n",
+    "            nonBprods[\n",
+    "                sample(nrow(nonBprods), nrow(nonBprods), replace=TRUE),\n",
+    "                .(`source_index`=`index`, `source_asn`=`asn`)\n",
+    "            ]\n",
+    "        )\n",
+    "    )\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "2911304b-2df1-46a0-8564-42b8afa88ad2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CS7EEIiJA+EZBdCSITkgZDsQgiJkDwQ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\nmgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(\n",
+    "    edges[, .(`degree`=.N), .(`source_index`)],\n",
+    "    aes(x=`degree`)\n",
+    ") +\n",
+    "    geom_histogram(binwidth=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "9474f6c7-9b8f-454f-808c-df5b5d1d085e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  source_index       degree     \n",
+       " Min.   : 0.00   Min.   :22.00  \n",
+       " 1st Qu.:35.25   1st Qu.:30.00  \n",
+       " Median :54.50   Median :32.50  \n",
+       " Mean   :54.24   Mean   :32.32  \n",
+       " 3rd Qu.:79.75   3rd Qu.:34.75  \n",
+       " Max.   :99.00   Max.   :40.00  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "edges[, .(`degree`=.N), .(`source_index`)] %>% summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "903dd1d4-eef3-4b04-bf6e-56a2f837f4cc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2521"
+      ],
+      "text/latex": [
+       "2521"
+      ],
+      "text/markdown": [
+       "2521"
+      ],
+      "text/plain": [
+       "[1] 2521"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "edges %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f19539d-59bd-4900-a200-f5ece8340975",
+   "metadata": {},
+   "source": [
+    "### Check that the graph is connected"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "0a846a78-d05b-475d-95ed-fac18410792f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g <- graph_from_data_frame(edges[, .(`source_index`, `target_index`)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "32f2bcfe-a238-4e6d-83a7-798eeb76e6ae",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "TRUE"
+      ],
+      "text/latex": [
+       "TRUE"
+      ],
+      "text/markdown": [
+       "TRUE"
+      ],
+      "text/plain": [
+       "[1] TRUE"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "is.connected(g, mode=\"strong\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "195b6a3c-8ab3-4060-9911-d26898a02ba9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g <- graph_from_data_frame(edges[, .(`target_index`, `source_index`)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "5f53f737-1207-4a24-87b0-0c2b78c02da4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "TRUE"
+      ],
+      "text/latex": [
+       "TRUE"
+      ],
+      "text/markdown": [
+       "TRUE"
+      ],
+      "text/plain": [
+       "[1] TRUE"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "is.connected(g, mode=\"strong\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2c21247-7ef7-475a-a1d6-09fb320f43e3",
+   "metadata": {},
+   "source": [
+    "## Assign latencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72cd408e-4339-4413-93b9-ff2397933e53",
+   "metadata": {},
+   "source": [
+    "### Assign as many latencies as possible at the ASN to ASN level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "c62e7f58-4c8b-44ad-95e8-65852bdba3e8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rrtt <- function(rtt_min, rtt_max, rtt_avg, rtt_std) {\n",
+    "    if (is.na(rtt_std))\n",
+    "        rtt_avg / 2\n",
+    "    else\n",
+    "        max(rtt_min, min(rtt_max, rnorm(1, rtt_avg, rtt_std))) / 2\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "27fff747-277e-4332-b27f-4a5fba352bfd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  source_index    target_index      latency         \n",
+       " Min.   : 0.00   Min.   : 0.00   Min.   :  0.01956  \n",
+       " 1st Qu.:31.00   1st Qu.:34.00   1st Qu.:  2.27972  \n",
+       " Median :46.00   Median :45.00   Median :  7.10913  \n",
+       " Mean   :47.14   Mean   :46.94   Mean   : 21.39632  \n",
+       " 3rd Qu.:61.00   3rd Qu.:59.00   3rd Qu.: 22.70819  \n",
+       " Max.   :99.00   Max.   :99.00   Max.   :191.18955  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies <- rbind(\n",
+    "    asn_rtt_stat[edges, on=c(\"asn1\" = \"source_asn\", \"asn2\" = \"target_asn\"), nomatch=0],\n",
+    "    asn_rtt_stat[edges, on=c(\"asn2\" = \"source_asn\", \"asn1\" = \"target_asn\"), nomatch=0]\n",
+    ")[,\n",
+    "    .(\n",
+    "        `latency`=mapply(rrtt, `rtt_min`, min(`rtt_max`, 1000), min(`rtt_avg`, 1000), `rtt_std`)\n",
+    "    ),\n",
+    "    .(\n",
+    "        `source_index`,\n",
+    "        `target_index`\n",
+    "    )\n",
+    "]\n",
+    "latencies %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e307efdf-f3ac-476a-9962-140a7e243c21",
+   "metadata": {},
+   "source": [
+    "### Assign intra ASN latencies for edges that don't yet have latencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "c7d1448f-d7cf-49b3-9e36-fb54a52400cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  source_index    target_index      latency       \n",
+       " Min.   : 0.00   Min.   : 0.00   Min.   :  0.000  \n",
+       " 1st Qu.:32.00   1st Qu.:34.00   1st Qu.:  2.286  \n",
+       " Median :47.00   Median :46.00   Median :  7.364  \n",
+       " Mean   :48.11   Mean   :47.87   Mean   : 22.513  \n",
+       " 3rd Qu.:63.00   3rd Qu.:60.00   3rd Qu.: 26.021  \n",
+       " Max.   :99.00   Max.   :99.00   Max.   :191.190  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies <- rbind(\n",
+    "    latencies,\n",
+    "    latencies[edges, on=.NATURAL][\n",
+    "        is.na(`latency`) & `source_asn` == `target_asn`,\n",
+    "        .(`latency`=rrtt(intra_rtt_stat$rtt_min, intra_rtt_stat$rtt_max, intra_rtt_stat$rtt_avg, intra_rtt_stat$rtt_std)),\n",
+    "        .(`source_index`, `target_index`)\n",
+    "    ]\n",
+    ")\n",
+    "latencies %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82bf3885-1dcd-44d6-9a9d-dff088bedff9",
+   "metadata": {},
+   "source": [
+    "### Assign remaining latencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "020f0020-705b-49ca-8d90-0747c0cb3166",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 1 x 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>rtt_avg</th><th scope=col>rtt_std</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>114.1144</td><td>17.62252</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 1 x 2\n",
+       "\\begin{tabular}{ll}\n",
+       " rtt\\_avg & rtt\\_std\\\\\n",
+       " <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t 114.1144 & 17.62252\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 1 x 2\n",
+       "\n",
+       "| rtt_avg &lt;dbl&gt; | rtt_std &lt;dbl&gt; |\n",
+       "|---|---|\n",
+       "| 114.1144 | 17.62252 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  rtt_avg  rtt_std \n",
+       "1 114.1144 17.62252"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "generic_rtt <- asn_rtt_stat[, .(`rtt_avg`=mean(`rtt_avg`), `rtt_std`=mean(`rtt_std`, na.rm=TRUE))]\n",
+    "generic_rtt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "74a2ceba-c3f2-48ff-8136-e92c4e2ab8db",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  source_index    target_index      latency       \n",
+       " Min.   : 0.00   Min.   : 0.00   Min.   :  0.000  \n",
+       " 1st Qu.:35.00   1st Qu.:36.00   1st Qu.:  6.368  \n",
+       " Median :54.00   Median :53.00   Median : 43.415  \n",
+       " Mean   :53.95   Mean   :54.08   Mean   : 37.417  \n",
+       " 3rd Qu.:79.00   3rd Qu.:80.00   3rd Qu.: 59.841  \n",
+       " Max.   :99.00   Max.   :99.00   Max.   :191.190  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies <- rbind(\n",
+    "    latencies,\n",
+    "    latencies[edges, on=.NATURAL][is.na(`latency`)][, .(`latency`=rrtt(50, 1000, generic_rtt$`rtt_avg`, generic_rtt$`rtt_std`)), .(`source_index`, `target_index`)]\n",
+    ")[, .(`latency`=mean(`latency`)), .(`source_index`, `target_index`)]\n",
+    "latencies %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6d94383-d04e-48bd-88b4-35999bb83f84",
+   "metadata": {},
+   "source": [
+    "### Ensure that latencies between different ASNs are at least 5 milliseconds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "d975e67c-5c1f-4789-b0b3-9f4c29e35887",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "latencies <- latencies[edges, on=.NATURAL, nomatch=0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "b11eee94-60c1-4331-b6ed-3d17a4165c26",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "latencies[`source_asn` == `target_asn` & `latency` < 5, `latency`:=5+`latency`]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "801b8017-a468-4f20-a218-637c3cdaad98",
+   "metadata": {},
+   "source": [
+    "### Ensure that no latency is less than 0.05 ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "2fc2bb74-c5de-45eb-abfe-daddf1f684ea",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "latencies[`latency` < 0.1, `latency`:=0.05+`latency`]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7f4353b-ad94-497e-893b-32619c424aad",
+   "metadata": {},
+   "source": [
+    "### Ensure that there are no duplicate edges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "af38e103-4006-41e2-803c-49b66d48e9ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2521"
+      ],
+      "text/latex": [
+       "2521"
+      ],
+      "text/markdown": [
+       "2521"
+      ],
+      "text/plain": [
+       "[1] 2521"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "c4a9ba3e-58ce-4670-a3e9-56f8aed03867",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "latencies <- latencies[\n",
+    "    `source_index` != `target_index`, \n",
+    "    .(`latency`=mean(`latency`)), \n",
+    "    .(`source`=`source_index`, `target`=`target_index`)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "c58c70b0-4730-44a2-b951-3e3e49eb8d94",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2035"
+      ],
+      "text/latex": [
+       "2035"
+      ],
+      "text/markdown": [
+       "2035"
+      ],
+      "text/plain": [
+       "[1] 2035"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67e0d200-7553-4323-8b2b-436928763ec5",
+   "metadata": {},
+   "source": [
+    "### View the distribution of latencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "78958f45-66bd-48f9-aec0-6950a17984b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5DCgKSbCSQgSQNSLCDZbAYkVUCyD0hh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\nTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(latencies, aes(x=`latency`)) + geom_histogram(bins=50) + scale_x_log10()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84e2f72e-5934-496c-81bc-ee8150942eb6",
+   "metadata": {},
+   "source": [
+    "### Add the edges between block producers and their relays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "84964dfb-bff0-4afe-b29f-1b4d1ae4ead3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2123"
+      ],
+      "text/latex": [
+       "2123"
+      ],
+      "text/markdown": [
+       "2123"
+      ],
+      "text/plain": [
+       "[1] 2123"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies <- rbind(\n",
+    "    latencies,\n",
+    "    nodes[`kind` == \"BPROD\", .(`source`=`index`, `kindex`)][\n",
+    "        nodes[`kind` == \"RELAY1\", .(`target`=`index`, `kindex`)],\n",
+    "        on=\"kindex\"\n",
+    "    ][, .(`source`, `target`, `latency`=runif(.N, 0.05, 0.25)/8)],\n",
+    "    nodes[`kind` == \"BPROD\", .(`target`=`index`, `kindex`)][\n",
+    "        nodes[`kind` == \"RELAY1\", .(`source`=`index`, `kindex`)],\n",
+    "        on=\"kindex\"\n",
+    "    ][, .(`source`, `target`, `latency`=runif(.N, 0.05, 0.25)/8)],\n",
+    "    nodes[`kind` == \"BPROD\", .(`source`=`index`, `kindex`)][\n",
+    "        nodes[`kind` == \"RELAY2\", .(`target`=`index`, `kindex`)],\n",
+    "        on=\"kindex\"\n",
+    "    ][, .(`source`, `target`, `latency`=runif(.N, 0.05, 0.25)/8)],\n",
+    "    nodes[`kind` == \"BPROD\", .(`target`=`index`, `kindex`)][\n",
+    "        nodes[`kind` == \"RELAY2\", .(`source`=`index`, `kindex`)],\n",
+    "        on=\"kindex\"\n",
+    "    ][, .(`source`, `target`, `latency`=runif(.N, 0.05, 0.25)/8)]\n",
+    ")\n",
+    "latencies %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c3db623-b355-4c1e-90a1-022bacf70488",
+   "metadata": {},
+   "source": [
+    "### Symmetrize the latencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "ef1326a1-04cf-4c61-a51b-4b73ff037d9e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "3468"
+      ],
+      "text/latex": [
+       "3468"
+      ],
+      "text/markdown": [
+       "3468"
+      ],
+      "text/plain": [
+       "[1] 3468"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bidirectional <- merge(latencies, latencies, by.x=c(\"source\", \"target\"), by.y=c(\"target\", \"source\"), all=TRUE)\n",
+    "bidirectional %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "8007a8d3-b910-4a33-9ac9-3522255c1906",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "bidirectional[!is.na(`latency.x`) & !is.na(`latency.y`) & `latency.x` < `latency.y`, `latency.x`:=`latency.y`]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "f503c311-b34a-47fb-af44-b33c455d7ce9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2123"
+      ],
+      "text/latex": [
+       "2123"
+      ],
+      "text/markdown": [
+       "2123"
+      ],
+      "text/plain": [
+       "[1] 2123"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies <- bidirectional[!is.na(`latency.x`), .(`source`, `target`, `latency`=`latency.x`)]\n",
+    "latencies %>% nrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "379bac2c-30fa-45d8-b015-eef43c1bde0c",
+   "metadata": {},
+   "source": [
+    "### Patch up locations that violate the triangle inequality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "a8c0a6d6-183c-4576-9077-328c55a42a0a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IGRAPH 7e3743b DNW- 100 2123 -- \n",
+      "+ attr: name (v/c), weight (e/n)\n"
+     ]
+    }
+   ],
+   "source": [
+    "g <- graph_from_data_frame(latencies[, .(`source`=as.character(`source`), `target`=as.character(`target`), `weight`=`latency`)])\n",
+    "g %>% summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "1cd8ef7b-f51e-41c5-821d-be680e321d76",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "TRUE"
+      ],
+      "text/latex": [
+       "TRUE"
+      ],
+      "text/markdown": [
+       "TRUE"
+      ],
+      "text/plain": [
+       "[1] TRUE"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for (pass in 1:10) {\n",
+    "    changed <- 0\n",
+    "    for (src in latencies[, sort(unique(`source`))]) {\n",
+    "        targets <- latencies[`source` == src, .(`target`=as.character(`target`), `latency`)]\n",
+    "        shortestLatencies <- distances(g, as.character(src), targets$`target`, mode=\"all\") %>% as.numeric\n",
+    "        replacements <- targets$`latency` > shortestLatencies\n",
+    "        if (any(replacements)) {\n",
+    "            latencies[`source` == src, `latency`:=ifelse(replacements, 0.99 * shortestLatencies, targets$`latency`)]\n",
+    "            changed <- changed + 1\n",
+    "        }\n",
+    "    }\n",
+    "    if (changed == 0)\n",
+    "        break\n",
+    "}\n",
+    "changed == 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3894995-3bb6-4c01-a5a2-6c4defd75f38",
+   "metadata": {},
+   "source": [
+    "### Rescale the latencies because the triangle correction reduced them by too much"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "a1c993e8-377a-4ff9-8c0f-7c7c445a73cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hy3lWcJYb7Co6VCKv0NWokvoT7NrCyh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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(latencies, aes(x=`latency`)) + geom_histogram(bins=50) + scale_x_log10()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "0389e93b-2a66-41f5-bcc6-4d70ef6dc4ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AQkIGlAMgISkDQgGQEJSBqQjIAEJA1I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\nKIWARJRC/w/JWgwImiwy7AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplot(latencies, aes(x=`latency`)) + geom_histogram(bins=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "3ccb5401-b379-43c8-a76f-508d0dfd4075",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "     source          target        latency        \n",
+       " Min.   : 0.00   Min.   : 0.0   Min.   : 0.00804  \n",
+       " 1st Qu.:34.00   1st Qu.:35.0   1st Qu.: 2.33421  \n",
+       " Median :54.00   Median :53.0   Median : 4.54747  \n",
+       " Mean   :53.37   Mean   :53.5   Mean   :12.19080  \n",
+       " 3rd Qu.:79.00   3rd Qu.:79.0   3rd Qu.:11.28334  \n",
+       " Max.   :99.00   Max.   :99.0   Max.   :66.13572  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies %>% summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "a2cc83ef-d110-439e-b669-fce3798e0ec0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "latencies[, `latency`:=8*`latency`]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "8ffbf190-a7a5-4906-8fe6-6ae3ed2f8767",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "     source          target        latency        \n",
+       " Min.   : 0.00   Min.   : 0.0   Min.   :  0.0643  \n",
+       " 1st Qu.:34.00   1st Qu.:35.0   1st Qu.: 18.6737  \n",
+       " Median :54.00   Median :53.0   Median : 36.3798  \n",
+       " Mean   :53.37   Mean   :53.5   Mean   : 97.5264  \n",
+       " 3rd Qu.:79.00   3rd Qu.:79.0   3rd Qu.: 90.2667  \n",
+       " Max.   :99.00   Max.   :99.0   Max.   :529.0858  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latencies %>% summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93e04604-8d54-4412-a175-5b215e6b477f",
+   "metadata": {},
+   "source": [
+    "## Export the results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ff0e8ee-4884-4fd3-9573-d87674d6caf2",
+   "metadata": {},
+   "source": [
+    "### CSV files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "dcd26a64-3cac-4dec-95dc-782f3e3cd5cc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fwrite(nodes[, .(`index`, `kind`, `stake`, `long`, `lat`)], \"nodes-v3.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "ae985899-e3e4-4c7b-98e6-e61d6ec26a6a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fwrite(latencies, \"edges-v3.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0963cd15-de37-43f5-a169-1cf29c11ae04",
+   "metadata": {},
+   "source": [
+    "### Leios topology file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e177b495-d757-4101-bc2d-65308a87d219",
+   "metadata": {},
+   "source": [
+    "Assume to 1 Gps bandwidth for all connections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "b975dd28-95bc-4bf1-8781-8200270d830c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1.25e+08"
+      ],
+      "text/latex": [
+       "1.25e+08"
+      ],
+      "text/markdown": [
+       "1.25e+08"
+      ],
+      "text/plain": [
+       "[1] 1.25e+08"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bandwidth <- 1 * 1e9 / 8\n",
+    "bandwidth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25beabae-f9df-4ed7-a89c-4fa2e590bc24",
+   "metadata": {},
+   "source": [
+    "Assume 6 cpu cores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "41099208-4913-4c93-91ac-30bc7893b4b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cpus <- 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8be3379-de9a-45e2-baac-4285eb8fc941",
+   "metadata": {},
+   "source": [
+    "Create the JSON for the topology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "7c9a11d4-796d-45a4-8133-1ed17fdc7af3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "makeTopology <- function(nodes, latencies, bandwidth, cpus) {\n",
+    "    nodeData <- list()\n",
+    "    for (i in 1:nrow(nodes)) {\n",
+    "        src <- nodes[i, `index`]\n",
+    "        edgeData <- list()\n",
+    "        edgeRows <- latencies[`source` == src, .(`target`=paste0(\"node-\",`target`), `latency`)]\n",
+    "        for (j in 1:nrow(edgeRows))\n",
+    "            edgeData[[edgeRows[j, `target`]]] <- as.list(edgeRows[j, list(`bandwidth-bytes-per-second`=unbox(bandwidth), `latency-ms`=unbox(`latency`))])\n",
+    "        nodeData[[paste0(\"node-\", src)]] <-\n",
+    "            list(\n",
+    "                `location` = c(nodes[i, `long`], nodes[i, `lat`]),\n",
+    "                `cpu-core-count` = unbox(cpus),\n",
+    "                `stake` = unbox(nodes[i, round(`stake`)]),\n",
+    "                `producers` = edgeData\n",
+    "            )\n",
+    "    }\n",
+    "    list(`nodes` = nodeData)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "f3b53c8e-f6a3-4561-b21b-88d01b004cf7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "topology <- makeTopology(nodes, latencies, bandwidth, cpus)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "0097aad0-524e-446e-a041-206679de6b52",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{\"location\":[-56.1778,-54.3602],\"cpu-core-count\":6,\"stake\":0,\"producers\":{\"node-1\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":355.723},\"node-3\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":327.4158},\"node-9\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":307.3528},\"node-30\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":304.5782},\"node-33\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":330.6291},\"node-35\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":329.8456},\"node-44\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":307.6525},\"node-46\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":325.6989},\"node-50\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":329.4704},\"node-55\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":339.2679},\"node-56\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":307.3528},\"node-59\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":309.6877},\"node-66\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":291.2674},\"node-69\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":321.0809},\"node-71\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":325.4763},\"node-73\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":329.4704},\"node-79\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":384.4852},\"node-80\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":457.6372},\"node-81\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":321.2192},\"node-91\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":339.9682},\"node-93\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":328.6463},\"node-96\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":386.0898},\"node-99\":{\"bandwidth-bytes-per-second\":125000000,\"latency-ms\":324.3329}}} "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "topology$`nodes`$`node-0` %>% toJSON"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "297ff754-53fe-4c4c-b44b-93984d4cc855",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "write_json(topology, \"topology-v3.yaml\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "r-minimal kernel",
+   "language": "r",
+   "name": "r-minimal"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "4.2.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/simulation/pseudo-mainnet/topology-v3.md b/data/simulation/pseudo-mainnet/topology-v3.md
new file mode 100644
index 000000000..de2d16c5d
--- /dev/null
+++ b/data/simulation/pseudo-mainnet/topology-v3.md
@@ -0,0 +1,48 @@
+# Topology Analysis Report
+
+Analysis of: topology-v3.yaml
+
+## Network Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total nodes | 100 |
+| Block producers | 22 |
+| Relay nodes | 78 |
+| Total connections | 2123 |
+| Network diameter | 4 hops |
+| Average connections per node | 21.23 |
+| Clustering coefficient | 0.841 |
+| Average latency | 97.5ms ms |
+| Maximum latency | 529.1ms ms |
+| Stake-weighted latency | 0.0ms ms |
+| Bidirectional connections | 389 |
+| Asymmetry ratio | 63.35% |
+
+## Stake Distribution
+
+| Metric | Value |
+|--------|-------|
+| Total stake | 1621928450 |
+| Gini coefficient | 0.782 |
+
+### Top 5 Stake Holders
+
+| Node | Stake | % of Total |
+|------|--------|------------|
+| node-21 | 75578906 | 4.66% |
+| node-22 | 74130184 | 4.57% |
+| node-23 | 74078634 | 4.57% |
+| node-4 | 76601989 | 4.72% |
+| node-65 | 78570315 | 4.84% |
+
+### Geographic Stake Distribution
+
+| Region | Nodes | Total Stake | % of Network |
+|---------|--------|-------------|-------------|
+
+## Geographic Validation
+
+✅ No geographic violations found
+
+
diff --git a/data/simulation/pseudo-mainnet/topology-v3.yaml b/data/simulation/pseudo-mainnet/topology-v3.yaml
new file mode 100644
index 000000000..645def98a
--- /dev/null
+++ b/data/simulation/pseudo-mainnet/topology-v3.yaml
@@ -0,0 +1 @@
+{"nodes":{"node-0":{"location":[-56.1778,-54.3602],"cpu-core-count":6,"stake":0,"producers":{"node-1":{"bandwidth-bytes-per-second":125000000,"latency-ms":355.723},"node-3":{"bandwidth-bytes-per-second":125000000,"latency-ms":327.4158},"node-9":{"bandwidth-bytes-per-second":125000000,"latency-ms":307.3528},"node-30":{"bandwidth-bytes-per-second":125000000,"latency-ms":304.5782},"node-33":{"bandwidth-bytes-per-second":125000000,"latency-ms":330.6291},"node-35":{"bandwidth-bytes-per-second":125000000,"latency-ms":329.8456},"node-44":{"bandwidth-bytes-per-second":125000000,"latency-ms":307.6525},"node-46":{"bandwidth-bytes-per-second":125000000,"latency-ms":325.6989},"node-50":{"bandwidth-bytes-per-second":125000000,"latency-ms":329.4704},"node-55":{"bandwidth-bytes-per-second":125000000,"latency-ms":339.2679},"node-56":{"bandwidth-bytes-per-second":125000000,"latency-ms":307.3528},"node-59":{"bandwidth-bytes-per-second":125000000,"latency-ms":309.6877},"node-66":{"bandwidth-bytes-per-second":125000000,"latency-ms":291.2674},"node-69":{"bandwidth-bytes-per-second":125000000,"latency-ms":321.0809},"node-71":{"bandwidth-bytes-per-second":125000000,"latency-ms":325.4763},"node-73":{"bandwidth-bytes-per-second":125000000,"latency-ms":329.4704},"node-79":{"bandwidth-bytes-per-second":125000000,"latency-ms":384.4852},"node-80":{"bandwidth-bytes-per-second":125000000,"latency-ms":457.6372},"node-81":{"bandwidth-bytes-per-second":125000000,"latency-ms":321.2192},"node-91":{"bandwidth-bytes-per-second":125000000,"latency-ms":339.9682},"node-93":{"bandwidth-bytes-per-second":125000000,"latency-ms":328.6463},"node-96":{"bandwidth-bytes-per-second":125000000,"latency-ms":386.0898},"node-99":{"bandwidth-bytes-per-second":125000000,"latency-ms":324.3329}}},"node-1":{"location":[1.958,47.9821],"cpu-core-count":6,"stake":0,"producers":{"node-2":{"bandwidth-bytes-per-second":125000000,"latency-ms":382.0704},"node-9":{"bandwidth-bytes-per-second":125000000,"latency-ms":55.4557},"node-28":{"bandwidth-bytes-per-second":125000000,"latency-ms":52.0751},"node-29":{"bandwidth-bytes-per-second":125000000,"latency-ms":55.4557},"node-30":{"bandwidth-bytes-per-second":125000000,"latency-ms":55.4557},"node-32":{"bandwidth-bytes-per-second":125000000,"latency-ms":74.5036},"node-35":{"bandwidth-bytes-per-second":125000000,"latency-ms":77.9485},"node-40":{"bandwidth-bytes-per-second":125000000,"latency-ms":85.0288},"node-41":{"bandwidth-bytes-per-second":125000000,"latency-ms":55.8389},"node-42":{"bandwidth-bytes-per-second":125000000,"latency-ms":52.4588},"node-44":{"bandwidth-bytes-per-second":125000000,"latency-ms":55.7554},"node-49":{"bandwidth-bytes-per-second":125000000,"latency-ms":77.9565},"node-58":{"bandwidth-bytes-per-second":125000000,"latency-ms":50.8937},"node-61":{"bandwidth-bytes-per-second":125000000,"latency-ms":53.7654},"node-64":{"bandwidth-bytes-per-second":125000000,"latency-ms":73.5793},"node-68":{"bandwidth-bytes-per-second":125000000,"latency-ms":399.2192},"node-71":{"bandwidth-bytes-per-second":125000000,"latency-ms":73.5793},"node-72":{"bandwidth-bytes-per-second":125000000,"latency-ms":107.0204},"node-73":{"bandwidth-bytes-per-second":125000000,"latency-ms":80.9539},"node-82":{"bandwidth-bytes-per-second":125000000,"latency-ms":349.2272},"node-85":{"bandwidth-bytes-per-second":125000000,"latency-ms":232.4767},"node-87":{"bandwidth-bytes-per-second":125000000,"latency-ms":352.533},"node-93":{"bandwidth-bytes-per-second":125000000,"latency-ms":76.2036},"node-97":{"bandwidth-bytes-per-second":125000000,"latency-ms":133.8964},"node-99":{"bandwidth-bytes-per-second":125000000,"latency-ms":72.5799}}},"node-2":{"location":[-91.4924,70.2075],"cpu-core-count":6,"stake":0,"producers":{"node-5":{"bandwidth-bytes-per-second":125000000,"latency-ms":333.3759},"node-6":{"bandwidth-bytes-per-second":125000000,"latency-ms":333.759},"node-9":{"bandwidth-bytes-per-second":125000000,"latency-ms":336.7565},"node-27":{"bandwidth-bytes-per-second":125000000,"latency-ms":333.3759},"node-29":{"bandwidth-bytes-per-second":125000000,"latency-ms":336.7565},"node-32":{"bandwidth-bytes-per-second":125000000,"latency-ms":308.1886},"node-35":{"bandwidth-bytes-per-second":125000000,"latency-ms":350.3826},"node-39":{"bandwidth-bytes-per-second":125000000,"latency-ms":329.4379},"node-40":{"bandwidth-bytes-per-second":125000000,"latency-ms":348.5679},"node-41":{"bandwidth-bytes-per-second":125000000,"latency-ms":333.759},"node-44":{"bandwidth-bytes-per-second":125000000,"latency-ms":337.0562},"node-55":{"bandwidth-bytes-per-second":125000000,"latency-ms":348.9061},"node-56":{"bandwidth-bytes-per-second":125000000,"latency-ms":337.1927},"node-59":{"bandwidth-bytes-per-second":125000000,"latency-ms":331.9965},"node-60":{"bandwidth-bytes-per-second":125000000,"latency-ms":338.4468},"node-61":{"bandwidth-bytes-per-second":125000000,"latency-ms":335.0662},"node-64":{"bandwidth-bytes-per-second":125000000,"latency-ms":338.2039},"node-66":{"bandwidth-byte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diff --git a/docs/technical-report-2.md b/docs/technical-report-2.md
index 58d74b25a..623447472 100644
--- a/docs/technical-report-2.md
+++ b/docs/technical-report-2.md
@@ -652,49 +652,50 @@ The security-related impacts of adversarial behavior upon Leios fall into three
 
 The table below constitutes a historical record of the simulation experiments used to study the behavior of Leios variants and to test the correctness of the simulators. Only the [cip/](../analysis/sims/cip/) and [regression/](../analysis/sims/regression/) experiments are re-run each time the Rust or Haskell simulator is revised. The configurations and results reside in the folders [analysis/sims/](../analysis/sims/) in the Leios repository. Archives of the full results (simulation logs etc. and post-processed data files) reside in an AWS S3 bucket. Each folder contains sufficient information to re-run the experiment at the relevant commit or hash; with minor modification of the configuration file `config.yaml`, the experiment can be re-run with the latest version of the Haskell and/or Rust simulator. See [analysis/sims/ReadMe.md](analysis/sims/ReadMe.md) for general guidance regarding re-running the experiments.
 
-| Experiment                                        | Folder                                      | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | Findings                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
-| ------------------------------------------------- | ------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| CIP figures                                       | [cip/](../analysis/sims/cip/)               | Proposes a set of protocol parameters and throughputs for inclusions in the *Evidence* section of the draft CIP.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | • Modest resources (4 vCPU/node, 10 Mb/s bandwidth) are adequate up to at least 0.3 TxMB/s.<br>• It is likely that Plutus-heavy workloads could also be supported.<br>• Stage length of 7 slots allows for diffusion while having a low probability of discarding an EB.<br>• Maximum of 12 MB of transactions in an EB allows for occasional fully utilized EBs to “catch up” on throughput when sortition is unlucky.<br>• At lower TPS, most of the EBs are small.<br>• Maximum block size could be reduced at the expense of longer waits when sortition is unlucky.                                                                                                                                                                                                                                                                                                                                                                                              |
-| Regression in Rust simulator                      | [regression/](../analysis/sims/regression/) | Compares the behavior of all of the tagged versions of the Rust simulator, `sim-cli`, against each other when the same network topology and configuration file are used.                                                                                                                                                                                                                                                                                                                                                                                                                                                         | Differences in simulation output as `sim-cli` is revised.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| Parameter sweep                                   | [params/](../analysis/sims/params/)         | Vary delta header and the durations of votes and diffusion.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | There is a wide range of viable parameters for Linear Leios, but the best-performing ones depend upon assumptions for network topology and latencies.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| Network degradation                               | [degraded/](../analysis/sims/degraded/)     | Studied Leios's behavior when network topology is thinned. The number of connections to/from each node was randomly thinned by up to 87% of its original mainnet-like topology. Beyond that level (i.e., more than 88% of connections lost), the network topology splits into disconnected regions where some nodes can no longer communicate with each other. That degradation resulted in the diameter of the network increasing from 5 hops to 8 hops and the number of connections per node dropping form 23.5 to 6.0. Both honest cases and cases where adversaries delay the release or transactions and EBs were studied. | The experiment was inconclusive, in that the protocol operated properly when 87% of connections where lost, but it was impractical to further thin the network topology without breaking it into non-communicating components.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-| Low bandwidth                                     | [bandwidth/](../analysis/sims/bandwidth/)   | The inter-node bandwidth was reduced to values as low as 1 Mb/s.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | Leios operates successfully at a throughput of 0.250 TxkB/s even at a 2 Mb/s bandwidth but breaks down at 1 Mb/s.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-| Late EB and TX attacks                            | [2025w33b/](/../analysis/sims/2025w33b/)    | An experiment involving late EB and/or late transaction diffusion was run, where tx and the EB propagation scheme were also varied.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | • The attack successfully thwarts Leios throughput under some conditions.<br>• Efficiency starts dropping when EBs and transactions are delayed 6.5 seconds.<br>• Efficiency doesn’t continue dropping much after delays of 7 seconds.<br>• $L_\text{diff} = 0\text{s}$ performs better than $L_\text{diff} = 7 \text{s}$.<br>• None of the cases, using `txs-received`, loses transactions or bogs down.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| Late diffusion                                    | [2025w33/](../analysis/sims/2025w33/)       | An experiment involving late EB and/or late transaction diffusion was run, where  and the EB propagation scheme were also varied.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | • The attack successfully thwarts Leios throughput under some conditions. • Follow-up analysis determined that transactions were lost in some cases due to a misformulation of the memory-pool rules in the simulator.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Haskell vs Rust                                   | [2025w32b/](..analysis/sims/2025w32b/)      | Compares Haskell vs Rust simulation output at newer commits.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | Results indicate that a previous discrepancy in vote diffusion has been corrected.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| Draft figures for CIP                             | [2025w32/](../analysis/sims/2025w32/)       | Proposes a set of protocol parameters and throughputs for inclusions in the *Evidence* section of the draft CIP.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | • Modest resources (4 vCPU/node, 10 Mb/s bandwidth) are adequate up to at least 0.3 TxMB/s.<br>• It wasn't studied here, but it is likely that Plutus-heavy workloads could also be supported.<br>• Stage length of 7 slots allows for diffusion while having a low probability of discarding an EB.<br>• Maximum of 12 MB of transactions in an EB allows for occasional fully utilized EBs to “catch up” on throughput when sortition is unlucky.<br>• At lower TPS, most of these blocks are small.<br>• Maximum block size could be reduced at the expense of longer waits when sortition is unlucky.<br>• This experiment raised questions about whether the mempool rules are adequate.                                                                                                                                                                                                                                                                         |
-| Plutus validation time                            | [2025w31c/](../analysis/sims/2025w31c/)     | Varies the CPU time used by Plutus phase 2 validation in Linear Leios at 100 TPS for a 6-vCPU node.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | • Simulation with 6-vCPU nodes supported doubling the Plutus per-transaction budget on Linear Leios.<br>• Simulations at a sixfold increase in the Plutus per-transaction budget failed: nodes bogged down validating transactions and cannot put them in new EBs.<br>• EB could likely handle 5000 Gstep of Plutus computation in Linear Leios, which is 250x the Praos per-block budget.<br>• This could support a handful of Plutus transactions with a 20x greater Plutus budget.<br> • Alternatively every Plutus transaction could have its budget increased by 50%.<br> • However, intentionally late diffusion of Plutus-heavy transactions could interfere with EB adoption.<br>• These results are uncertain due to the variability in measured Plutus CPU costs: there is quite a bit of variability in the CPU time actually used by a Plutus script given its execution steps.                                                                           |
-| Haskell vs Rust                                   | [2025w31b/](../analysis/sims/2025w31b/)     | Compares the early draft of the Haskell simulator for Linear Leios to the more mature Rust simulator.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | Discrepancies related to CPU usage, network usage, and vote diffusion must be investigated.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| Experiment varying stage length in "No IBs" Leios | [2025w31/](../analysis/sims/2025w31/)       | Varied the stage-length protocol parameter of "No IBs" Leios from 5 to 12 slots/stage.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | • Results indicate that the settlement time is not strongly affected by this parameter within this rage of values.<br>• Note that larger stage lengths result in less frequent voting.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Effect of network topology                        | [2025w30b/](../analysis/sims/2025w30b/)     | Compared Linear Leios simulation results on the 750-node mini-mainnet vs the 10,000-node pseudo-mainnet networks in the Rust simulator.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | • Results indicate that the smaller network is slightly more stressful to the protocol than the larger network, but in general there are no substantial differences between simulation results for the two networks.<br>• The same findings and conclusions would result from using either network.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Linear Leios at 1000 TPS                          | [2025w30/](../analysis/sims/2025w30/)       | Tested whether Linear Leios with transaction references and Stracciatella can support throughputs of 1000 tx/s at 300 B/tx.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | Linear Leios with transactions embedded in the EBs cannot support such throughput.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| 100 TPS for Stracciatella and Linear Leios        | [2025w29b/](../analysis/sims/2025w29b/)     | Tested the viability of Stracciatella and Linear Leios at 100 TPS if appropriate stage lengths and maximum EB sizes are chosen.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | • 5 slot/stage is too short for Linear Leios at 100 tx/s.<br>• Including transactions in EBs (instead of references) results in congestion and delays at 100 tx/s.<br>• 10 MB/EB is sufficient for 100 tx/s but 5 MB/EB is not.<br>• EB-sortition unluckiness in Stracciatella can lengthen the transaction lifecycle, but this could be remedied by increasing the EB production rate.<br>• CPU and network usage peak high when transactions are included in EBs.<br>• Caveat: this conclusion may change when better transaction validation times are used in the simulator configuration.                                                                                                                                                                                                                                                                                                                                                                         |
-| Linear Leios                                      | [2025w29/](../analysis/sims/2025w29/)       | A comprehensive set of simulations for the Linear Leios variant was completed, aiming to map out the protocol's performance under varies loads and comparing several versions of the variant to Stracciatella.                                                                                                                                                                                                                                                                                                                                                                                                                   | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Stracciatella                                     | [2025w28/](../analysis/sims/2025w28/)       | The Stracciatella variant of Leios (i.e., no IBs, tx refrences in EBs, and a two-stage pipeline) is analyzed.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | • 1000+ TPS is attainable.<br>• Congestion does appear at this throughput.<br>• 5 slot/stage performs less well but appears to scale better than 8 slot/stage.<br>• Spatial efficiency is better than 95%.<br>• Time to ledger is better than two minutes (subject to RB randomness, of course).<br>• Only the tiniest fraction of transactions don't reach the ledger, likely due to EBs expiring before the reach an RB.<br>• Network usage is slightly heavy.<br>• CPU usage seems suspiciously light.                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| Small transactions                                | [2025w28/](../analysis/sims/2025w28/)       | A small-transaction, high-throughput experiment.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | • 1000 tx/s with 300 B/tx is feasible in Leios variants<br>• Time vs space tradeoff<br>• `full-with-ib-references` uses space more efficiently than `full-without-ibs`<br>• `full-without-ibs` has shorter transaction lifecycle than `full-with-ib-references`<br>• `full-without-ib` loses some transactions, likely due to the aggressive pruning of the memory pool<br>• 2 CPU cores are sufficient<br>• Network usage is modest                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-| Timestep effects in simulators                    | [2025w27/](../analysis/sims/2025w27/)       | We compared the 1000 TPS Full Leios simulation results (Rust) at two time resolutions, 0.100 ms and 0.025 ms.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | • No significant differences in results were found.<br>• This means that we can safely run simulations at the coarser time step; such enable greater parallelism in the simulator and shorter wallclock times.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-| 1000 TPS experiment                               | [2025w27/](../analysis/sims/2025w27/)       | A 1000 TPS experiment of basic transactions (300-byte non-Plutus transactions) was completed to demonstrate the viability of Leios at high throughput.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Praos simulations                                 | [2025w26/](../analysis/sims/2025w26/)       | Rust simulations on mini-mainnet for 50 and 100 TPS on Praos. These can serve as a reference when evaluating Leios performance.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | • Praos _simulations_ perform well at 50 TPS.<br>• They exhibit congestion-induced forking at 100 TPS.<br>• All transaction reach the ledger.<br>• Bursty usage of network and CPU does not tax infrastructure resources.<br>• Should we request cardano-node benchmark studies of large Praos blocks?                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Nine candidate variants of Leios                  | [2025w26/](../analysis/sims/2025w26/)       | Three basic variants and three sharding strategies.<br>- Basic<br>    - Full<br>    - Full without IBs<br>    - Full with transaction references<br>- Sharding<br>    - Unsharded<br>    - Sharded<br>    - Overcollateralized 1x (i.e., each tx has a probability of being included in two shards instead of one)                                                                                                                                                                                                                                                                                                               | The network and CPU metrics for all of the variants were acceptable, but they had different spatial and temporal efficiencies.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-| Conflicting transactions                          | [2025w26/](../analysis/sims/2025w26/)       | Exploring the effect of conflicting transactions at 100 TPS for the simplest Leios variant.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | • Spatial efficiency can be as low as 55%, which is due to the occasional production of an IB before the previous one is received.<br>•  Transaction typically reach the ledger within 75 seconds.<br>•  All non-conflicted transactions reach the ledger.<br>•  NIC bandwidth of 20 Mb/s is sufficient: see figure below.<br>•  Four vCPU cores are sufficient.<br>•  Even the largest IBs (up to 2 MB) diffuse globally within 5 seconds: see figure below.<br>•  IB traffic does not interfere with transaction, vote, EB, or RB traffic.<br>•  The occasional large IBs benefit performance by diffusing before the mean time between IBs, which permits pruning of the memory pool before the next IB is typically produced.                                                                                                                                                                                                                                     |
-| Bandwidth                                         | [2025w25/](../analysis/sims/2025w25/)       | Exploring the effect of bandwidth limits at 100 TPS and 300 TPS.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | • The protocol parameters are sufficient for Leios high performance at 100 tx/s (or 300 tx/s): mini-mainnet topology, 1 IB/s (or 2 IB/s), 10 slot/stage, 328 kB/IB maximum, 1.5 EB/stage, and multiple shards.<br>•  Spatial efficiency is 80%.<br>•  All transactions reach the ledger, typically within two minutes.<br>•  A 30 Mbps network interface card (NIC) is sufficient for the Leios node.<br>•  A 4-core vCPU is also sufficient.<br>•  The results are insensitive to inter-nodal link bandwidths greater than 50 Mb/s, and even at 10 Mb/s the effect of link bandwidth is small.                                                                                                                                                                                                                                                                                                                                                                       |
-| Mini-mainnet                                      | [2025w24/](../analysis/sims/2025w24/)       | The 750-node pseudo-mainnet network was used in Haskell and Rust experiments to study the limits of transaction and IB throughput for realistic scenarios up to 300 TPS and 32 IB/s.                                                                                                                                                                                                                                                                                                                                                                                                                                             | • The 750 node mini-mainnet is a suitable replacement for the 10,000-node pseudo mainnet, in that either topology would result in similar performance measurements and resource recommendations.<br>• The Haskell and Rust simulations substantially agree for mini-mainnet simulations.<br>• Block propagation less than 1 second, which is consistent with empirical observations from pooltool.io. Note that this has implications for our discussion of the IB-concurrency period.<br>• With 1 Gb/s links/NICs, the protocol can support 25 MB/s throughput before it starts degrading.<br>• Mean time from mempool to ledger is about 150 seconds for transactions.<br>• Disk-space efficiency is about 80%.<br>• About 20% of network traffic is wasted.<br>• Even at 300 ts/x, a 6-core VM is sufficient for peak demand, but average demand is less than 2 cores.                                                                                             |
-| Pseudo-mainnet                                    | [2025w23/](../analysis/sims/2025w23/)       | The 10,000-node pseudo-mainnet network was used in Rust experiments to study the limits of transaction and IB throughput for realistic scenarios up to 300 TPS and 32 IB/s.                                                                                                                                                                                                                                                                                                                                                                                                                                                      | • Transactions took an average of 100 seconds to travel from the memory pool to the ledger.<br>• Disk and network usage was approximately 80% efficient.<br>• Even at high TPS, six CPU cores were sufficient to handle peak load.<br>• Block propagation time averaged under one second.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| Limited CPU                                       | [2025w22/](../analysis/sims/2025w22/)       | Lifecycle analysis with nodes limited to eight vCPU cores.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Excess capacity                                   | [2025w20/](../analysis/sims/2025w20/)       | The previous transaction lifecycle simulations raised the question of whether the duplication of transactions in IBs was starving other transactions from ever being included in an IB, and hence never making it to the ledger. Those earlier simulations had at total IB capacity that was only modestly larger than the size of all of the transactions submitted during the course of the simulation. To evaluate this hypothesis, we re-ran the experiment with IBs being produced at three times a higher rate, which leaves plenty of space in IBs for transaction duplication.                                           | The loss of transactions persists, indicating that the hypothesis was incorrect and that some other factor is preventing transactions from making it to the ledger.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Lifecycle and resource analyses                   | [2025w19/](../analysis/sims/2025w19/)       | We executed the first high-tps simuations of Leios using the Rust simulator, with TPS ranging as high as 1000 tx/s. Two efficiency measures are used to quantify the inclusion of transactions in the ledger and the amount of data that must be persisted as the ledger.                                                                                                                                                                                                                                                                                                                                                        | Recently proposed revisions to Full Short Leios will increase both efficiencies, as will tuning the protocol parameters involved. In the simulations it took approximately 100 seconds for a transaction to reach the ledger, measured from the time the transaction was submitted.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Full Leios                                        | [2025w17/](../analysis/sims/2025w17/)       | Simulation of 270 scenarios of Full Leios for varied IB production rate, IB size, and EB production rate, stage length, and CPU constraints.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | • All outstanding discrepancies between Rust and Haskell simulation results have either been resolved or explained.<br>• Inclusion of a transaction in an IB typically occurs in 2.4 seconds.<br>• Referencing of a transaction via an EB typically occurs in 27.6 seconds.<br>• Referencing of a transaction via an RB typically occurs in 67.2 seconds.<br>• The current version of Full Leios exhibits some undesirable side effects of never referencing some transactions in RBs and duplicating inclusion or referencing of transactions.                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| Full Leios                                        | [2025w16/](../analysis/sims/2025w16/)       | Simulation of 648 scenarios of Full and Short Leios for varied IB production rate, IB size, and EB production rate, stage length, and CPU constraints.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | • Agreement between the Rust and Haskell simulations is generally quite close.<br>• The Haskell simulation experiences network congestion at 16 IB/s, but the Rust simulation does not.<br>• The Rust simulation uses more CPU at high IB rates than the Haskell simulation does.<br>• The Rust simulation sometimes does not produce enough votes to certify an EB.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-| Arrival times                                     | [2025w15/](../analysis/sims/2025w15/)       | Study of diffusion of IB, EB, votes, and transactions.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Edinburgh                                         | [pre-edi/](../analysis/sims/pre-edi/)       | Simulations for discussion at the Leios workshop in Edinburgh.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Short Leios                                       | [2025w13/](../analysis/sims/2025w13/)       | Simulation of 198 scenarios of Short Leios for varied IB production rate, IB size, and network topology, CPU limits, and protocol flags.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | • The simulations resolved most of the prior outstanding issues that arose in comparing simulators, but they unearthed new ones.<br>• Studied how limiting available CPU affects the propagation of messages like IBs, where they might potentially be lost if insufficient CPU is available. _CPU can impact diffusion of IBs under some stressful scenarios._<br>• Compared freshest-first versus oldest-first vote propagation. _Freshest first (arguably) may improve reliability of IB delivery._<br>• Compared an extended voting period versus a limited one in the Haskell simulation. _Extended voting makes little difference except for rare improvements in reliable delivery of votes._<br>• The qualitative discrepancies between the Haskell and Rust simulators' results are under investigation to determine whether they are _bona fide_ differences due to the resolution of the simulators instead of infidelities in the simulations themselves. |
-| Realism                                           | [2025w12xl/](../analysis/sims/2025w12xl/)   | Study of more realistic network settings.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Short Leios with varied IB production and size    | [2025w12/](../analysis/sims/2025w12/)       | Simulation of 18 scenarios of Short Leios for varied IB production rate, IB size, and network topology.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | • In the simulations the Leios protocol scales well to mainnet-size networks.<br>• The protocol tends to experience congestion once the input-block rate reaches 30 IB/s.<br>• Even at the highest data rates studied, it appears that six vCPUs are sufficient to handle cryptographic operations.<br>• Allowing IBs that are larger than current Praos RBs may have advantages in TCP efficiency, in network usage, and in adapting to fluctuating transaction loads.<br>• A few minor unexplained differences remain when comparing the Haskell and Rust results, and these are under active investigation.<br>• Overall, the two simulators are in essential agreement for the protocol parameters and network configurations studied.                                                                                                                                                                                                                            |
-| Mainnet-scale simulation                          | [2025w11xl/](../analysis/sims/2025w11xl/)   | Mainnet-scale network simulation, using the Rust simulator.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | It is interesting to see that the 3000-node mainnet-scale network transports IBs faster than the artificial 100-node network that was previously analyzed: likely this is because the larger network has long-range "shortcut" edges that speed transport.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Haskell vs Rust                                   | [2025w11/](../analysis/sims/2025w11/)       | Compares 90 scenarios of the Rust and Haskell simulations                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | Github issues were created for any significant differences between the pairs of simulation results.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Haskell                                           | [2025w10/](../analysis/sims/2025w10/)       | • Compare simulations with and without accounting for CPU usage.<br>• Vary key protocol parameters<br>• IB production rate<br>• IB size<br>• Length of Leios stages- Delay between the generation of an IB, EB, vote, or RB and its receipt at nodes.<br>• Peak and mean CPU usage over time<br>• Breakdown of CPU usage by type of task<br>• Sizes of IBs, EBs, and RBs<br>• Duplicate IB references in EBs<br>• Reference to EBs from RBs<br>• Number of IBs referenced by EB<br>• Timing of references to IBs and EBs from EBs and RBs, respectively                                                                          | • The protocol supports data rates up to 30 IB/s with input blocks up to 163 kB/IB.<br>• Verification of IBs dominates CPU usage, except at low throughputs.<br>• Short Leios includes the same IB in multiple EBs.<br>• Short Leios sometimes discards IBs because not EB was produced.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
-| Haskell and Rust                                  | [2025w09/](../analysis/sims/2025w09/)       | Compares the Haskell and Rust simulations.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | •  The ELT ("extract/load/transform") workflow for processing simulation data was refactored so that complete logs from both simulations are organized for simpler querying.<br>• The Rust simulator was temporarily modified to generate IBs of fixed size, which is what the Haskell simulator does, so that output from the two can be compared.<br>• Discrepancies between congestion metrics for two simulators were partially resolved.<br>• Analyses of the elapsed time from IB generation to receipt at the various nodes and also of time-in-flight over node-to-node links were developed.<br>• It appears that both network bandwidth and CPU bottlenecks play a role in congestion at very high transaction throughput, but this needs more detailed investigation.                                                                                                                                                                                      |
-| Varied IB rate                                    | [2025w08/](../analysis/sims/2025w08/)       | Compares the Haskell and Rust simulators for input-block production varying from 1 IB/s to 100 IB/s.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | The Haskell simulation results indicate that network congestion occurs at high IB production rates, causing both the average propagation time and the tail of extremely slow propagation to lengthen.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| Experiment                                        | Folder                                            | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | Findings                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| ------------------------------------------------- | ------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| CIP figures                                       | [cip/](../analysis/sims/cip/)                     | Proposes a set of protocol parameters and throughputs for inclusions in the *Evidence* section of the draft CIP.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | • Modest resources (4 vCPU/node, 10 Mb/s bandwidth) are adequate up to at least 0.3 TxMB/s.<br>• It is likely that Plutus-heavy workloads could also be supported.<br>• Stage length of 7 slots allows for diffusion while having a low probability of discarding an EB.<br>• Maximum of 12 MB of transactions in an EB allows for occasional fully utilized EBs to “catch up” on throughput when sortition is unlucky.<br>• At lower TPS, most of the EBs are small.<br>• Maximum block size could be reduced at the expense of longer waits when sortition is unlucky.                                                                                                                                                                                                                                                                                                                                                                                              |
+| Regression in Rust simulator                      | [regression/](../analysis/sims/regression/)       | Compares the behavior of all of the tagged versions of the Rust simulator, `sim-cli`, against each other when the same network topology and configuration file are used.                                                                                                                                                                                                                                                                                                                                                                                                                                                         | Differences in simulation output as `sim-cli` is revised.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| Parameter sweep                                   | [params/](../analysis/sims/params/)               | Vary delta header and the durations of votes and diffusion.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | There is a wide range of viable parameters for Linear Leios, but the best-performing ones depend upon assumptions for network topology and latencies.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| Micro- vs mini-mainnet                            | [micro-mainnet/](../analysis/sims/micro-mainnet/) | Comparison of simulation results for micro- vs mini-mainnet.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | Most results metrics for the two topologies are consistent.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+| Network degradation                               | [degraded/](../analysis/sims/degraded/)           | Studied Leios's behavior when network topology is thinned. The number of connections to/from each node was randomly thinned by up to 87% of its original mainnet-like topology. Beyond that level (i.e., more than 88% of connections lost), the network topology splits into disconnected regions where some nodes can no longer communicate with each other. That degradation resulted in the diameter of the network increasing from 5 hops to 8 hops and the number of connections per node dropping form 23.5 to 6.0. Both honest cases and cases where adversaries delay the release or transactions and EBs were studied. | The experiment was inconclusive, in that the protocol operated properly when 87% of connections where lost, but it was impractical to further thin the network topology without breaking it into non-communicating components.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Low bandwidth                                     | [bandwidth/](../analysis/sims/bandwidth/)         | The inter-node bandwidth was reduced to values as low as 1 Mb/s.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | Leios operates successfully at a throughput of 0.250 TxkB/s even at a 2 Mb/s bandwidth but breaks down at 1 Mb/s.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| Late EB and TX attacks                            | [2025w33b/](/../analysis/sims/2025w33b/)          | An experiment involving late EB and/or late transaction diffusion was run, where tx and the EB propagation scheme were also varied.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | • The attack successfully thwarts Leios throughput under some conditions.<br>• Efficiency starts dropping when EBs and transactions are delayed 6.5 seconds.<br>• Efficiency doesn’t continue dropping much after delays of 7 seconds.<br>• $L_\text{diff} = 0\text{s}$ performs better than $L_\text{diff} = 7 \text{s}$.<br>• None of the cases, using `txs-received`, loses transactions or bogs down.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| Late diffusion                                    | [2025w33/](../analysis/sims/2025w33/)             | An experiment involving late EB and/or late transaction diffusion was run, where  and the EB propagation scheme were also varied.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | • The attack successfully thwarts Leios throughput under some conditions. • Follow-up analysis determined that transactions were lost in some cases due to a misformulation of the memory-pool rules in the simulator.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Haskell vs Rust                                   | [2025w32b/](..analysis/sims/2025w32b/)            | Compares Haskell vs Rust simulation output at newer commits.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | Results indicate that a previous discrepancy in vote diffusion has been corrected.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| Draft figures for CIP                             | [2025w32/](../analysis/sims/2025w32/)             | Proposes a set of protocol parameters and throughputs for inclusions in the *Evidence* section of the draft CIP.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | • Modest resources (4 vCPU/node, 10 Mb/s bandwidth) are adequate up to at least 0.3 TxMB/s.<br>• It wasn't studied here, but it is likely that Plutus-heavy workloads could also be supported.<br>• Stage length of 7 slots allows for diffusion while having a low probability of discarding an EB.<br>• Maximum of 12 MB of transactions in an EB allows for occasional fully utilized EBs to “catch up” on throughput when sortition is unlucky.<br>• At lower TPS, most of these blocks are small.<br>• Maximum block size could be reduced at the expense of longer waits when sortition is unlucky.<br>• This experiment raised questions about whether the mempool rules are adequate.                                                                                                                                                                                                                                                                         |
+| Plutus validation time                            | [2025w31c/](../analysis/sims/2025w31c/)           | Varies the CPU time used by Plutus phase 2 validation in Linear Leios at 100 TPS for a 6-vCPU node.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | • Simulation with 6-vCPU nodes supported doubling the Plutus per-transaction budget on Linear Leios.<br>• Simulations at a sixfold increase in the Plutus per-transaction budget failed: nodes bogged down validating transactions and cannot put them in new EBs.<br>• EB could likely handle 5000 Gstep of Plutus computation in Linear Leios, which is 250x the Praos per-block budget.<br>• This could support a handful of Plutus transactions with a 20x greater Plutus budget.<br> • Alternatively every Plutus transaction could have its budget increased by 50%.<br> • However, intentionally late diffusion of Plutus-heavy transactions could interfere with EB adoption.<br>• These results are uncertain due to the variability in measured Plutus CPU costs: there is quite a bit of variability in the CPU time actually used by a Plutus script given its execution steps.                                                                           |
+| Haskell vs Rust                                   | [2025w31b/](../analysis/sims/2025w31b/)           | Compares the early draft of the Haskell simulator for Linear Leios to the more mature Rust simulator.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | Discrepancies related to CPU usage, network usage, and vote diffusion must be investigated.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+| Experiment varying stage length in "No IBs" Leios | [2025w31/](../analysis/sims/2025w31/)             | Varied the stage-length protocol parameter of "No IBs" Leios from 5 to 12 slots/stage.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | • Results indicate that the settlement time is not strongly affected by this parameter within this rage of values.<br>• Note that larger stage lengths result in less frequent voting.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Effect of network topology                        | [2025w30b/](../analysis/sims/2025w30b/)           | Compared Linear Leios simulation results on the 750-node mini-mainnet vs the 10,000-node pseudo-mainnet networks in the Rust simulator.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | • Results indicate that the smaller network is slightly more stressful to the protocol than the larger network, but in general there are no substantial differences between simulation results for the two networks.<br>• The same findings and conclusions would result from using either network.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Linear Leios at 1000 TPS                          | [2025w30/](../analysis/sims/2025w30/)             | Tested whether Linear Leios with transaction references and Stracciatella can support throughputs of 1000 tx/s at 300 B/tx.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | Linear Leios with transactions embedded in the EBs cannot support such throughput.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| 100 TPS for Stracciatella and Linear Leios        | [2025w29b/](../analysis/sims/2025w29b/)           | Tested the viability of Stracciatella and Linear Leios at 100 TPS if appropriate stage lengths and maximum EB sizes are chosen.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | • 5 slot/stage is too short for Linear Leios at 100 tx/s.<br>• Including transactions in EBs (instead of references) results in congestion and delays at 100 tx/s.<br>• 10 MB/EB is sufficient for 100 tx/s but 5 MB/EB is not.<br>• EB-sortition unluckiness in Stracciatella can lengthen the transaction lifecycle, but this could be remedied by increasing the EB production rate.<br>• CPU and network usage peak high when transactions are included in EBs.<br>• Caveat: this conclusion may change when better transaction validation times are used in the simulator configuration.                                                                                                                                                                                                                                                                                                                                                                         |
+| Linear Leios                                      | [2025w29/](../analysis/sims/2025w29/)             | A comprehensive set of simulations for the Linear Leios variant was completed, aiming to map out the protocol's performance under varies loads and comparing several versions of the variant to Stracciatella.                                                                                                                                                                                                                                                                                                                                                                                                                   | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Stracciatella                                     | [2025w28/](../analysis/sims/2025w28/)             | The Stracciatella variant of Leios (i.e., no IBs, tx refrences in EBs, and a two-stage pipeline) is analyzed.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | • 1000+ TPS is attainable.<br>• Congestion does appear at this throughput.<br>• 5 slot/stage performs less well but appears to scale better than 8 slot/stage.<br>• Spatial efficiency is better than 95%.<br>• Time to ledger is better than two minutes (subject to RB randomness, of course).<br>• Only the tiniest fraction of transactions don't reach the ledger, likely due to EBs expiring before the reach an RB.<br>• Network usage is slightly heavy.<br>• CPU usage seems suspiciously light.                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| Small transactions                                | [2025w28/](../analysis/sims/2025w28/)             | A small-transaction, high-throughput experiment.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | • 1000 tx/s with 300 B/tx is feasible in Leios variants<br>• Time vs space tradeoff<br>• `full-with-ib-references` uses space more efficiently than `full-without-ibs`<br>• `full-without-ibs` has shorter transaction lifecycle than `full-with-ib-references`<br>• `full-without-ib` loses some transactions, likely due to the aggressive pruning of the memory pool<br>• 2 CPU cores are sufficient<br>• Network usage is modest                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| Timestep effects in simulators                    | [2025w27/](../analysis/sims/2025w27/)             | We compared the 1000 TPS Full Leios simulation results (Rust) at two time resolutions, 0.100 ms and 0.025 ms.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | • No significant differences in results were found.<br>• This means that we can safely run simulations at the coarser time step; such enable greater parallelism in the simulator and shorter wallclock times.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| 1000 TPS experiment                               | [2025w27/](../analysis/sims/2025w27/)             | A 1000 TPS experiment of basic transactions (300-byte non-Plutus transactions) was completed to demonstrate the viability of Leios at high throughput.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Praos simulations                                 | [2025w26/](../analysis/sims/2025w26/)             | Rust simulations on mini-mainnet for 50 and 100 TPS on Praos. These can serve as a reference when evaluating Leios performance.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | • Praos _simulations_ perform well at 50 TPS.<br>• They exhibit congestion-induced forking at 100 TPS.<br>• All transaction reach the ledger.<br>• Bursty usage of network and CPU does not tax infrastructure resources.<br>• Should we request cardano-node benchmark studies of large Praos blocks?                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Nine candidate variants of Leios                  | [2025w26/](../analysis/sims/2025w26/)             | Three basic variants and three sharding strategies.<br>- Basic<br>    - Full<br>    - Full without IBs<br>    - Full with transaction references<br>- Sharding<br>    - Unsharded<br>    - Sharded<br>    - Overcollateralized 1x (i.e., each tx has a probability of being included in two shards instead of one)                                                                                                                                                                                                                                                                                                               | The network and CPU metrics for all of the variants were acceptable, but they had different spatial and temporal efficiencies.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Conflicting transactions                          | [2025w26/](../analysis/sims/2025w26/)             | Exploring the effect of conflicting transactions at 100 TPS for the simplest Leios variant.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | • Spatial efficiency can be as low as 55%, which is due to the occasional production of an IB before the previous one is received.<br>•  Transaction typically reach the ledger within 75 seconds.<br>•  All non-conflicted transactions reach the ledger.<br>•  NIC bandwidth of 20 Mb/s is sufficient: see figure below.<br>•  Four vCPU cores are sufficient.<br>•  Even the largest IBs (up to 2 MB) diffuse globally within 5 seconds: see figure below.<br>•  IB traffic does not interfere with transaction, vote, EB, or RB traffic.<br>•  The occasional large IBs benefit performance by diffusing before the mean time between IBs, which permits pruning of the memory pool before the next IB is typically produced.                                                                                                                                                                                                                                     |
+| Bandwidth                                         | [2025w25/](../analysis/sims/2025w25/)             | Exploring the effect of bandwidth limits at 100 TPS and 300 TPS.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | • The protocol parameters are sufficient for Leios high performance at 100 tx/s (or 300 tx/s): mini-mainnet topology, 1 IB/s (or 2 IB/s), 10 slot/stage, 328 kB/IB maximum, 1.5 EB/stage, and multiple shards.<br>•  Spatial efficiency is 80%.<br>•  All transactions reach the ledger, typically within two minutes.<br>•  A 30 Mbps network interface card (NIC) is sufficient for the Leios node.<br>•  A 4-core vCPU is also sufficient.<br>•  The results are insensitive to inter-nodal link bandwidths greater than 50 Mb/s, and even at 10 Mb/s the effect of link bandwidth is small.                                                                                                                                                                                                                                                                                                                                                                       |
+| Mini-mainnet                                      | [2025w24/](../analysis/sims/2025w24/)             | The 750-node pseudo-mainnet network was used in Haskell and Rust experiments to study the limits of transaction and IB throughput for realistic scenarios up to 300 TPS and 32 IB/s.                                                                                                                                                                                                                                                                                                                                                                                                                                             | • The 750 node mini-mainnet is a suitable replacement for the 10,000-node pseudo mainnet, in that either topology would result in similar performance measurements and resource recommendations.<br>• The Haskell and Rust simulations substantially agree for mini-mainnet simulations.<br>• Block propagation less than 1 second, which is consistent with empirical observations from pooltool.io. Note that this has implications for our discussion of the IB-concurrency period.<br>• With 1 Gb/s links/NICs, the protocol can support 25 MB/s throughput before it starts degrading.<br>• Mean time from mempool to ledger is about 150 seconds for transactions.<br>• Disk-space efficiency is about 80%.<br>• About 20% of network traffic is wasted.<br>• Even at 300 ts/x, a 6-core VM is sufficient for peak demand, but average demand is less than 2 cores.                                                                                             |
+| Pseudo-mainnet                                    | [2025w23/](../analysis/sims/2025w23/)             | The 10,000-node pseudo-mainnet network was used in Rust experiments to study the limits of transaction and IB throughput for realistic scenarios up to 300 TPS and 32 IB/s.                                                                                                                                                                                                                                                                                                                                                                                                                                                      | • Transactions took an average of 100 seconds to travel from the memory pool to the ledger.<br>• Disk and network usage was approximately 80% efficient.<br>• Even at high TPS, six CPU cores were sufficient to handle peak load.<br>• Block propagation time averaged under one second.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| Limited CPU                                       | [2025w22/](../analysis/sims/2025w22/)             | Lifecycle analysis with nodes limited to eight vCPU cores.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Excess capacity                                   | [2025w20/](../analysis/sims/2025w20/)             | The previous transaction lifecycle simulations raised the question of whether the duplication of transactions in IBs was starving other transactions from ever being included in an IB, and hence never making it to the ledger. Those earlier simulations had at total IB capacity that was only modestly larger than the size of all of the transactions submitted during the course of the simulation. To evaluate this hypothesis, we re-ran the experiment with IBs being produced at three times a higher rate, which leaves plenty of space in IBs for transaction duplication.                                           | The loss of transactions persists, indicating that the hypothesis was incorrect and that some other factor is preventing transactions from making it to the ledger.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Lifecycle and resource analyses                   | [2025w19/](../analysis/sims/2025w19/)             | We executed the first high-tps simuations of Leios using the Rust simulator, with TPS ranging as high as 1000 tx/s. Two efficiency measures are used to quantify the inclusion of transactions in the ledger and the amount of data that must be persisted as the ledger.                                                                                                                                                                                                                                                                                                                                                        | Recently proposed revisions to Full Short Leios will increase both efficiencies, as will tuning the protocol parameters involved. In the simulations it took approximately 100 seconds for a transaction to reach the ledger, measured from the time the transaction was submitted.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Full Leios                                        | [2025w17/](../analysis/sims/2025w17/)             | Simulation of 270 scenarios of Full Leios for varied IB production rate, IB size, and EB production rate, stage length, and CPU constraints.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | • All outstanding discrepancies between Rust and Haskell simulation results have either been resolved or explained.<br>• Inclusion of a transaction in an IB typically occurs in 2.4 seconds.<br>• Referencing of a transaction via an EB typically occurs in 27.6 seconds.<br>• Referencing of a transaction via an RB typically occurs in 67.2 seconds.<br>• The current version of Full Leios exhibits some undesirable side effects of never referencing some transactions in RBs and duplicating inclusion or referencing of transactions.                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| Full Leios                                        | [2025w16/](../analysis/sims/2025w16/)             | Simulation of 648 scenarios of Full and Short Leios for varied IB production rate, IB size, and EB production rate, stage length, and CPU constraints.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | • Agreement between the Rust and Haskell simulations is generally quite close.<br>• The Haskell simulation experiences network congestion at 16 IB/s, but the Rust simulation does not.<br>• The Rust simulation uses more CPU at high IB rates than the Haskell simulation does.<br>• The Rust simulation sometimes does not produce enough votes to certify an EB.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| Arrival times                                     | [2025w15/](../analysis/sims/2025w15/)             | Study of diffusion of IB, EB, votes, and transactions.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Edinburgh                                         | [pre-edi/](../analysis/sims/pre-edi/)             | Simulations for discussion at the Leios workshop in Edinburgh.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Short Leios                                       | [2025w13/](../analysis/sims/2025w13/)             | Simulation of 198 scenarios of Short Leios for varied IB production rate, IB size, and network topology, CPU limits, and protocol flags.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | • The simulations resolved most of the prior outstanding issues that arose in comparing simulators, but they unearthed new ones.<br>• Studied how limiting available CPU affects the propagation of messages like IBs, where they might potentially be lost if insufficient CPU is available. _CPU can impact diffusion of IBs under some stressful scenarios._<br>• Compared freshest-first versus oldest-first vote propagation. _Freshest first (arguably) may improve reliability of IB delivery._<br>• Compared an extended voting period versus a limited one in the Haskell simulation. _Extended voting makes little difference except for rare improvements in reliable delivery of votes._<br>• The qualitative discrepancies between the Haskell and Rust simulators' results are under investigation to determine whether they are _bona fide_ differences due to the resolution of the simulators instead of infidelities in the simulations themselves. |
+| Realism                                           | [2025w12xl/](../analysis/sims/2025w12xl/)         | Study of more realistic network settings.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | n/a                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Short Leios with varied IB production and size    | [2025w12/](../analysis/sims/2025w12/)             | Simulation of 18 scenarios of Short Leios for varied IB production rate, IB size, and network topology.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | • In the simulations the Leios protocol scales well to mainnet-size networks.<br>• The protocol tends to experience congestion once the input-block rate reaches 30 IB/s.<br>• Even at the highest data rates studied, it appears that six vCPUs are sufficient to handle cryptographic operations.<br>• Allowing IBs that are larger than current Praos RBs may have advantages in TCP efficiency, in network usage, and in adapting to fluctuating transaction loads.<br>• A few minor unexplained differences remain when comparing the Haskell and Rust results, and these are under active investigation.<br>• Overall, the two simulators are in essential agreement for the protocol parameters and network configurations studied.                                                                                                                                                                                                                            |
+| Mainnet-scale simulation                          | [2025w11xl/](../analysis/sims/2025w11xl/)         | Mainnet-scale network simulation, using the Rust simulator.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | It is interesting to see that the 3000-node mainnet-scale network transports IBs faster than the artificial 100-node network that was previously analyzed: likely this is because the larger network has long-range "shortcut" edges that speed transport.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| Haskell vs Rust                                   | [2025w11/](../analysis/sims/2025w11/)             | Compares 90 scenarios of the Rust and Haskell simulations                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | Github issues were created for any significant differences between the pairs of simulation results.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Haskell                                           | [2025w10/](../analysis/sims/2025w10/)             | • Compare simulations with and without accounting for CPU usage.<br>• Vary key protocol parameters<br>• IB production rate<br>• IB size<br>• Length of Leios stages- Delay between the generation of an IB, EB, vote, or RB and its receipt at nodes.<br>• Peak and mean CPU usage over time<br>• Breakdown of CPU usage by type of task<br>• Sizes of IBs, EBs, and RBs<br>• Duplicate IB references in EBs<br>• Reference to EBs from RBs<br>• Number of IBs referenced by EB<br>• Timing of references to IBs and EBs from EBs and RBs, respectively                                                                          | • The protocol supports data rates up to 30 IB/s with input blocks up to 163 kB/IB.<br>• Verification of IBs dominates CPU usage, except at low throughputs.<br>• Short Leios includes the same IB in multiple EBs.<br>• Short Leios sometimes discards IBs because not EB was produced.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| Haskell and Rust                                  | [2025w09/](../analysis/sims/2025w09/)             | Compares the Haskell and Rust simulations.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | •  The ELT ("extract/load/transform") workflow for processing simulation data was refactored so that complete logs from both simulations are organized for simpler querying.<br>• The Rust simulator was temporarily modified to generate IBs of fixed size, which is what the Haskell simulator does, so that output from the two can be compared.<br>• Discrepancies between congestion metrics for two simulators were partially resolved.<br>• Analyses of the elapsed time from IB generation to receipt at the various nodes and also of time-in-flight over node-to-node links were developed.<br>• It appears that both network bandwidth and CPU bottlenecks play a role in congestion at very high transaction throughput, but this needs more detailed investigation.                                                                                                                                                                                      |
+| Varied IB rate                                    | [2025w08/](../analysis/sims/2025w08/)             | Compares the Haskell and Rust simulators for input-block production varying from 1 IB/s to 100 IB/s.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | The Haskell simulation results indicate that network congestion occurs at high IB production rates, causing both the average propagation time and the tail of extremely slow propagation to lengthen.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
 
 ## Test networks
 
@@ -721,9 +722,8 @@ The aim of [the pseudo-mainnet topology](../data/simulation/pseudo-mainnet/) i
 |Network diameter|6 hops|
 |Average connections per node|29.88|
 |Clustering coefficient|0.122|
-|Average latency|77.0ms ms|
-|Maximum latency|636.8ms ms|
-|Stake-weighted latency|0.0ms ms|
+|Average latency|77.0ms|
+|Maximum latency|636.8ms|
 |Bidirectional connections|10800|
 |Asymmetry ratio|92.77%|
 
@@ -742,7 +742,7 @@ Because the 10,000 [pseudo-mainnet](../data/simulation/pseudo-mainnet/topology-
 - Metrics: [topology-v2.md](../data/simulation/pseudo-mainnet/topology-v2.md)
 
 | Metric                       | Value      |
-| ---------------------------- | ---------- |
+| ---------------------------- | ---------: |
 | Total nodes                  | 750        |
 | Block producers              | 216        |
 | Relay nodes                  | 534        |
@@ -750,12 +750,33 @@ Because the 10,000 [pseudo-mainnet](../data/simulation/pseudo-mainnet/topology-
 | Network diameter             | 5 hops     |
 | Average connections per node | 25.75      |
 | Clustering coefficient       | 0.332      |
-| Average latency              | 64.8ms ms  |
-| Maximum latency              | 578.3ms ms |
-| Stake-weighted latency       | 0.0ms ms   |
+| Average latency              | 64.8ms  |
+| Maximum latency              | 578.3ms |
 | Bidirectional connections    | 1463       |
 | Asymmetry ratio              | 84.85%     |
 
+### Micro-mainnet
+
+To accommodate the speed limitations of the Haskell simulation, an even smaller "micro-mainnet" has been created.
+
+- Methodology: [topology-v3.ipynb](../data/simulation/pseudo-mainnet/topology-v3.ipynb)
+- Network: [topology-v3.yaml](../data/simulation/pseudo-mainnet/topology-v3.yaml)
+- Metrics: [topology-v3.md](../data/simulation/pseudo-mainnet/topology-v3.md)
+
+| Metric                       | Value      |
+| ---------------------------- | ---------: |
+| Total nodes                  | 100        |
+| Block producers              | 22         |
+| Relay nodes                  | 78         |
+| Total connections            | 2123       |
+| Network diameter             | 4 hops     |
+| Average connections per node | 21.23      |
+| Clustering coefficient       | 0.841      |
+| Average latency              | 97.5ms  |
+| Maximum latency              | 529.1ms |
+| Bidirectional connections    | 389        |
+| Asymmetry ratio              | 63.35%     |
+
 ## Empirical data
 
 ### Inter-datacenter bandwidth measurements