mirror of
https://gitlab.com/orbital-debris-research/directed-study/final-report.git
synced 2025-06-16 07:06:49 +00:00
intro
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"\n",
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"(process:28652): GLib-GIO-WARNING **: 12:50:31.472: Unexpectedly, UWP app `KDEe.V.Okular_21.1203.941.0_x64__7vt06qxq7ptv8' (AUMId `KDEe.V.Okular_7vt06qxq7ptv8!KDEe.V.Okular') supports 5 extensions but has no verbs\n"
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]
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}
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],
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"source": [
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"import IJulia\n",
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"\n",
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||||||
"# The julia kernel has built in support for Revise.jl, so this is the \n",
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||||||
"# recommended approach for long-running sessions:\n",
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||||||
"# https://github.com/JuliaLang/IJulia.jl/blob/9b10fa9b879574bbf720f5285029e07758e50a5e/src/kernel.jl#L46-L51\n",
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"\n",
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||||||
"# Users should enable revise within .julia/config/startup_ijulia.jl:\n",
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||||||
"# https://timholy.github.io/Revise.jl/stable/config/#Using-Revise-automatically-within-Jupyter/IJulia-1\n",
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"\n",
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||||||
"# clear console history\n",
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||||||
"IJulia.clear_history()\n",
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"\n",
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||||||
"# Intialize Plots w/ default fig width/height\n",
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"try\n",
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" fig_width = 7\n",
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" fig_height = 5\n",
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" fig_format = :retina\n",
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" fig_dpi = 96\n",
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" # no retina format type, use svg for high quality type/marks\n",
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" if fig_format == :retina\n",
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" fig_format = :svg\n",
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" # IJulia doesn't support PDF output so use png (if the DPI \n",
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" # remains the default of 300 then set to 96)\n",
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" elseif fig_format == :pdf\n",
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" fig_format = :png\n",
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" fig_dpi = 96\n",
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" end\n",
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" # convert inches to pixels\n",
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" fig_width = fig_width * fig_dpi\n",
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" fig_height = fig_height * fig_dpi\n",
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" using Plots\n",
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" gr(size=(fig_width, fig_height), fmt = fig_format, dpi = fig_dpi)\n",
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"catch e\n",
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" # @warn \"Plots init\" exception=(e, catch_backtrace())\n",
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"end\n",
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"\n",
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"# Set run_path if specified\n",
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"try\n",
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||||||
" run_path = \"\"\n",
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||||||
" if !isempty(run_path)\n",
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" cd(run_path)\n",
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" end\n",
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"catch e\n",
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" @warn \"Run path init:\" exception=(e, catch_backtrace())\n",
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"end\n",
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"\n",
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"# don't return kernel dependencies (b/c Revise should take care of dependencies)\n",
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"nothing\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "189db2ad",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| code-fold: true\n",
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"#| output: false\n",
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"using FileIO\n",
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"using MeshIO\n",
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"\n",
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"using stlProcess\n",
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"\n",
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"using CSV\n",
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"using DataFrames\n",
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"\n",
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"using LinearAlgebra"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5e47d862",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div class=\"data-frame\"><p>0 rows × 8 columns (omitted printing of 1 columns)</p><table class=\"data-frame\"><thead><tr><th></th><th>surface_area</th><th>characteristic_length</th><th>sbx</th><th>sby</th><th>sbz</th><th>Ix</th><th>Iy</th></tr><tr><th></th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th></tr></thead><tbody></tbody></table></div>"
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],
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"text/latex": [
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"\\begin{tabular}{r|cccccccc}\n",
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"\t& surface\\_area & characteristic\\_length & sbx & sby & sbz & Ix & Iy & \\\\\n",
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"\t\\hline\n",
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"\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & \\\\\n",
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"\t\\hline\n",
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"\\end{tabular}\n"
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],
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"text/plain": [
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"\u001b[1m0×8 DataFrame\u001b[0m"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# local path to https://gitlab.com/orbital-debris-research/fake-satellite-dataset\n",
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"dataset_path = raw\"C:\\Coding\\fake-satellite-dataset\"\n",
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"\n",
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"folders = [\"1_5U\", \"assembly1\", \"cubesat\"]\n",
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"\n",
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"df = DataFrame(;\n",
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" surface_area=Float64[],\n",
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" characteristic_length=Float64[],\n",
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" sbx=Float64[],\n",
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" sby=Float64[],\n",
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" sbz=Float64[],\n",
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" Ix=Float64[],\n",
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" Iy=Float64[],\n",
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" Iz=Float64[],\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "6c55224d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing Path: C:\\Coding\\fake-satellite-dataset\\1_5U\n",
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"Processing Path: C:\\Coding\\fake-satellite-dataset\\assembly1\n",
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"Processing Path: C:\\Coding\\fake-satellite-dataset\\cubesat\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"┌ Warning: Characteristic Length Algorithm failed to converge, this usually means stl is flat. Setting length in dir to 0.\n",
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"└ @ stlProcess C:\\Users\\albig\\.julia\\packages\\stlProcess\\8rsc7\\src\\stlProcess.jl:153\n"
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]
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}
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],
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"source": [
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"for path in dataset_path * \"\\\\\" .* folders\n",
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" println(\"Processing Path: \", path)\n",
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" Threads.@threads for file in readdir(path)\n",
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" stl = load(path * \"\\\\\" * file)\n",
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" scale = find_scale(stl)\n",
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" props = get_mass_properties(stl; scale=scale)\n",
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"\n",
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" eigs = eigvals(props.inertia)\n",
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" sort_index = sortperm(eigs)\n",
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" Ix, Iy, Iz = eigs[sort_index]\n",
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" sbx, sby, sbz = props.sb_values[sort_index]\n",
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"\n",
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" push!(\n",
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" df,\n",
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" [props.surface_area, props.characteristic_length, sbx, sby, sbz, Ix, Iy, Iz],\n",
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" )\n",
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" end\n",
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"end"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2e520bb3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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||||||
"<div class=\"data-frame\"><p>8 rows × 7 columns</p><table class=\"data-frame\"><thead><tr><th></th><th>variable</th><th>mean</th><th>min</th><th>median</th><th>max</th><th>nmissing</th><th>eltype</th></tr><tr><th></th><th title=\"Symbol\">Symbol</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Int64\">Int64</th><th title=\"DataType\">DataType</th></tr></thead><tbody><tr><th>1</th><td>surface_area</td><td>25.2002</td><td>5.60865</td><td>13.3338</td><td>159.406</td><td>0</td><td>Float64</td></tr><tr><th>2</th><td>characteristic_length</td><td>79.5481</td><td>0.158521</td><td>1.55816</td><td>1582.23</td><td>0</td><td>Float64</td></tr><tr><th>3</th><td>sbx</td><td>1.40222</td><td>0.0417367</td><td>0.967078</td><td>10.0663</td><td>0</td><td>Float64</td></tr><tr><th>4</th><td>sby</td><td>3.3367</td><td>0.0125824</td><td>2.68461</td><td>9.68361</td><td>0</td><td>Float64</td></tr><tr><th>5</th><td>sbz</td><td>3.91184</td><td>0.29006</td><td>1.8185</td><td>14.7434</td><td>0</td><td>Float64</td></tr><tr><th>6</th><td>Ix</td><td>1.58725</td><td>0.0311782</td><td>0.23401</td><td>11.1335</td><td>0</td><td>Float64</td></tr><tr><th>7</th><td>Iy</td><td>3.74345</td><td>0.178598</td><td>1.01592</td><td>24.6735</td><td>0</td><td>Float64</td></tr><tr><th>8</th><td>Iz</td><td>5.20207</td><td>0.178686</td><td>1.742</td><td>32.0083</td><td>0</td><td>Float64</td></tr></tbody></table></div>"
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],
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"text/latex": [
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"\\begin{tabular}{r|ccccccc}\n",
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"\t& variable & mean & min & median & max & nmissing & eltype\\\\\n",
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"\t\\hline\n",
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"\t& Symbol & Float64 & Float64 & Float64 & Float64 & Int64 & DataType\\\\\n",
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"\t\\hline\n",
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"\t1 & surface\\_area & 25.2002 & 5.60865 & 13.3338 & 159.406 & 0 & Float64 \\\\\n",
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"\t2 & characteristic\\_length & 79.5481 & 0.158521 & 1.55816 & 1582.23 & 0 & Float64 \\\\\n",
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"\t3 & sbx & 1.40222 & 0.0417367 & 0.967078 & 10.0663 & 0 & Float64 \\\\\n",
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"\t4 & sby & 3.3367 & 0.0125824 & 2.68461 & 9.68361 & 0 & Float64 \\\\\n",
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"\t5 & sbz & 3.91184 & 0.29006 & 1.8185 & 14.7434 & 0 & Float64 \\\\\n",
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"\t6 & Ix & 1.58725 & 0.0311782 & 0.23401 & 11.1335 & 0 & Float64 \\\\\n",
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"\t7 & Iy & 3.74345 & 0.178598 & 1.01592 & 24.6735 & 0 & Float64 \\\\\n",
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"\t8 & Iz & 5.20207 & 0.178686 & 1.742 & 32.0083 & 0 & Float64 \\\\\n",
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"\\end{tabular}\n"
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],
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"text/plain": [
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"\u001b[1m8×7 DataFrame\u001b[0m\n",
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"\u001b[1m Row \u001b[0m│\u001b[1m variable \u001b[0m\u001b[1m mean \u001b[0m\u001b[1m min \u001b[0m\u001b[1m median \u001b[0m\u001b[1m max \u001b[0m\u001b[1m nmis\u001b[0m ⋯\n",
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"\u001b[1m \u001b[0m│\u001b[90m Symbol \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int6\u001b[0m ⋯\n",
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"─────┼──────────────────────────────────────────────────────────────────────────\n",
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" 1 │ surface_area 25.2002 5.60865 13.3338 159.406 ⋯\n",
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" 2 │ characteristic_length 79.5481 0.158521 1.55816 1582.23\n",
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" 3 │ sbx 1.40222 0.0417367 0.967078 10.0663\n",
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" 4 │ sby 3.3367 0.0125824 2.68461 9.68361\n",
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" 5 │ sbz 3.91184 0.29006 1.8185 14.7434 ⋯\n",
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" 6 │ Ix 1.58725 0.0311782 0.23401 11.1335\n",
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" 7 │ Iy 3.74345 0.178598 1.01592 24.6735\n",
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" 8 │ Iz 5.20207 0.178686 1.742 32.0083\n",
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"\u001b[36m 2 columns omitted\u001b[0m"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"describe(df)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Julia 1.8.0-beta3",
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"language": "julia",
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"name": "julia-1.8"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.8.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "129f639c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import IJulia\n",
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"\n",
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"# The julia kernel has built in support for Revise.jl, so this is the \n",
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"# recommended approach for long-running sessions:\n",
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||||||
"# https://github.com/JuliaLang/IJulia.jl/blob/9b10fa9b879574bbf720f5285029e07758e50a5e/src/kernel.jl#L46-L51\n",
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"\n",
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"# Users should enable revise within .julia/config/startup_ijulia.jl:\n",
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"# https://timholy.github.io/Revise.jl/stable/config/#Using-Revise-automatically-within-Jupyter/IJulia-1\n",
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"\n",
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"# clear console history\n",
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"IJulia.clear_history()\n",
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"\n",
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"# Intialize Plots w/ default fig width/height\n",
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"try\n",
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" fig_width = 7\n",
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" fig_height = 5\n",
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" fig_format = :retina\n",
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" fig_dpi = 96\n",
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" # no retina format type, use svg for high quality type/marks\n",
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" if fig_format == :retina\n",
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" fig_format = :svg\n",
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" # IJulia doesn't support PDF output so use png (if the DPI \n",
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" # remains the default of 300 then set to 96)\n",
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" elseif fig_format == :pdf\n",
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" fig_format = :png\n",
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" fig_dpi = 96\n",
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" end\n",
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" # convert inches to pixels\n",
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" fig_width = fig_width * fig_dpi\n",
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" fig_height = fig_height * fig_dpi\n",
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" using Plots\n",
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" gr(size=(fig_width, fig_height), fmt = fig_format, dpi = fig_dpi)\n",
|
|
||||||
"catch e\n",
|
|
||||||
" # @warn \"Plots init\" exception=(e, catch_backtrace())\n",
|
|
||||||
"end\n",
|
|
||||||
"\n",
|
|
||||||
"# Set run_path if specified\n",
|
|
||||||
"try\n",
|
|
||||||
" run_path = \"\"\n",
|
|
||||||
" if !isempty(run_path)\n",
|
|
||||||
" cd(run_path)\n",
|
|
||||||
" end\n",
|
|
||||||
"catch e\n",
|
|
||||||
" @warn \"Run path init:\" exception=(e, catch_backtrace())\n",
|
|
||||||
"end\n",
|
|
||||||
"\n",
|
|
||||||
"# don't return kernel dependencies (b/c Revise should take care of dependencies)\n",
|
|
||||||
"nothing\n"
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"#| code-fold: true\n",
|
|
||||||
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|
|
||||||
"\n",
|
|
||||||
"using FileIO\n",
|
|
||||||
"using MeshIO\n",
|
|
||||||
"\n",
|
|
||||||
"using stlProcess\n",
|
|
||||||
"\n",
|
|
||||||
"using CSV\n",
|
|
||||||
"using DataFrames\n",
|
|
||||||
"\n",
|
|
||||||
"using LinearAlgebra"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
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|
|
||||||
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|
||||||
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|
|
||||||
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|
||||||
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|
|
||||||
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|
||||||
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||||||
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|
|
||||||
"<div class=\"data-frame\"><p>0 rows × 8 columns (omitted printing of 1 columns)</p><table class=\"data-frame\"><thead><tr><th></th><th>surface_area</th><th>characteristic_length</th><th>sbx</th><th>sby</th><th>sbz</th><th>Ix</th><th>Iy</th></tr><tr><th></th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th></tr></thead><tbody></tbody></table></div>"
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|
||||||
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|
|
||||||
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|
|
||||||
"\\begin{tabular}{r|cccccccc}\n",
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"\\end{tabular}\n"
|
|
||||||
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|
||||||
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|
|
||||||
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||||||
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|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"#| code-fold: true\n",
|
|
||||||
"#| output: false\n",
|
|
||||||
"\n",
|
|
||||||
"# local path to https://gitlab.com/orbital-debris-research/fake-satellite-dataset\n",
|
|
||||||
"dataset_path = raw\"C:\\Coding\\fake-satellite-dataset\"\n",
|
|
||||||
"\n",
|
|
||||||
"folders = [\"1_5U\", \"assembly1\", \"cubesat\"]\n",
|
|
||||||
"\n",
|
|
||||||
"df = DataFrame(;\n",
|
|
||||||
" surface_area=Float64[],\n",
|
|
||||||
" characteristic_length=Float64[],\n",
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
" Iz=Float64[],\n",
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
|
||||||
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|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Processing Path: C:\\Coding\\fake-satellite-dataset\\1_5U\n",
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"┌ Warning: Characteristic Length Algorithm failed to converge, this usually means stl is flat. Setting length in dir to 0.\n",
|
|
||||||
"└ @ stlProcess C:\\Users\\albig\\.julia\\packages\\stlProcess\\8rsc7\\src\\stlProcess.jl:153\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"for path in dataset_path * \"\\\\\" .* folders\n",
|
|
||||||
" println(\"Processing Path: \", path)\n",
|
|
||||||
" Threads.@threads for file in readdir(path)\n",
|
|
||||||
" stl = load(path * \"\\\\\" * file)\n",
|
|
||||||
" scale = find_scale(stl)\n",
|
|
||||||
" props = get_mass_properties(stl; scale=scale)\n",
|
|
||||||
"\n",
|
|
||||||
" eigs = eigvals(props.inertia)\n",
|
|
||||||
" sort_index = sortperm(eigs)\n",
|
|
||||||
" Ix, Iy, Iz = eigs[sort_index]\n",
|
|
||||||
" sbx, sby, sbz = props.sb_values[sort_index]\n",
|
|
||||||
"\n",
|
|
||||||
" push!(\n",
|
|
||||||
" df,\n",
|
|
||||||
" [props.surface_area, props.characteristic_length, sbx, sby, sbz, Ix, Iy, Iz],\n",
|
|
||||||
" )\n",
|
|
||||||
" end\n",
|
|
||||||
"end"
|
|
||||||
]
|
|
||||||
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|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"data": {
|
|
||||||
"text/html": [
|
|
||||||
"<div class=\"data-frame\"><p>8 rows × 7 columns</p><table class=\"data-frame\"><thead><tr><th></th><th>variable</th><th>mean</th><th>min</th><th>median</th><th>max</th><th>nmissing</th><th>eltype</th></tr><tr><th></th><th title=\"Symbol\">Symbol</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Int64\">Int64</th><th title=\"DataType\">DataType</th></tr></thead><tbody><tr><th>1</th><td>surface_area</td><td>25.2002</td><td>5.60865</td><td>13.3338</td><td>159.406</td><td>0</td><td>Float64</td></tr><tr><th>2</th><td>characteristic_length</td><td>79.5481</td><td>0.158521</td><td>1.55816</td><td>1582.23</td><td>0</td><td>Float64</td></tr><tr><th>3</th><td>sbx</td><td>1.40222</td><td>0.0417367</td><td>0.967078</td><td>10.0663</td><td>0</td><td>Float64</td></tr><tr><th>4</th><td>sby</td><td>3.3367</td><td>0.0125824</td><td>2.68461</td><td>9.68361</td><td>0</td><td>Float64</td></tr><tr><th>5</th><td>sbz</td><td>3.91184</td><td>0.29006</td><td>1.8185</td><td>14.7434</td><td>0</td><td>Float64</td></tr><tr><th>6</th><td>Ix</td><td>1.58725</td><td>0.0311782</td><td>0.23401</td><td>11.1335</td><td>0</td><td>Float64</td></tr><tr><th>7</th><td>Iy</td><td>3.74345</td><td>0.178598</td><td>1.01592</td><td>24.6735</td><td>0</td><td>Float64</td></tr><tr><th>8</th><td>Iz</td><td>5.20207</td><td>0.178686</td><td>1.742</td><td>32.0083</td><td>0</td><td>Float64</td></tr></tbody></table></div>"
|
|
||||||
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|
|
||||||
"text/latex": [
|
|
||||||
"\\begin{tabular}{r|ccccccc}\n",
|
|
||||||
"\t& variable & mean & min & median & max & nmissing & eltype\\\\\n",
|
|
||||||
"\t\\hline\n",
|
|
||||||
"\t& Symbol & Float64 & Float64 & Float64 & Float64 & Int64 & DataType\\\\\n",
|
|
||||||
"\t\\hline\n",
|
|
||||||
"\t1 & surface\\_area & 25.2002 & 5.60865 & 13.3338 & 159.406 & 0 & Float64 \\\\\n",
|
|
||||||
"\t2 & characteristic\\_length & 79.5481 & 0.158521 & 1.55816 & 1582.23 & 0 & Float64 \\\\\n",
|
|
||||||
"\t3 & sbx & 1.40222 & 0.0417367 & 0.967078 & 10.0663 & 0 & Float64 \\\\\n",
|
|
||||||
"\t4 & sby & 3.3367 & 0.0125824 & 2.68461 & 9.68361 & 0 & Float64 \\\\\n",
|
|
||||||
"\t5 & sbz & 3.91184 & 0.29006 & 1.8185 & 14.7434 & 0 & Float64 \\\\\n",
|
|
||||||
"\t6 & Ix & 1.58725 & 0.0311782 & 0.23401 & 11.1335 & 0 & Float64 \\\\\n",
|
|
||||||
"\t7 & Iy & 3.74345 & 0.178598 & 1.01592 & 24.6735 & 0 & Float64 \\\\\n",
|
|
||||||
"\t8 & Iz & 5.20207 & 0.178686 & 1.742 & 32.0083 & 0 & Float64 \\\\\n",
|
|
||||||
"\\end{tabular}\n"
|
|
||||||
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|
|
||||||
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|
|
||||||
"\u001b[1m8×7 DataFrame\u001b[0m\n",
|
|
||||||
"\u001b[1m Row \u001b[0m│\u001b[1m variable \u001b[0m\u001b[1m mean \u001b[0m\u001b[1m min \u001b[0m\u001b[1m median \u001b[0m\u001b[1m max \u001b[0m\u001b[1m nmis\u001b[0m ⋯\n",
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|
||||||
"\u001b[1m \u001b[0m│\u001b[90m Symbol \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int6\u001b[0m ⋯\n",
|
|
||||||
"─────┼──────────────────────────────────────────────────────────────────────────\n",
|
|
||||||
" 1 │ surface_area 25.2002 5.60865 13.3338 159.406 ⋯\n",
|
|
||||||
" 2 │ characteristic_length 79.5481 0.158521 1.55816 1582.23\n",
|
|
||||||
" 3 │ sbx 1.40222 0.0417367 0.967078 10.0663\n",
|
|
||||||
" 4 │ sby 3.3367 0.0125824 2.68461 9.68361\n",
|
|
||||||
" 5 │ sbz 3.91184 0.29006 1.8185 14.7434 ⋯\n",
|
|
||||||
" 6 │ Ix 1.58725 0.0311782 0.23401 11.1335\n",
|
|
||||||
" 7 │ Iy 3.74345 0.178598 1.01592 24.6735\n",
|
|
||||||
" 8 │ Iz 5.20207 0.178686 1.742 32.0083\n",
|
|
||||||
"\u001b[36m 2 columns omitted\u001b[0m"
|
|
||||||
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|
|
||||||
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|
|
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"source": [
|
|
||||||
"describe(df)"
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
||||||
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|
||||||
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|
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|
|
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|
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|
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report.qmd
86
report.qmd
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|
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cache: true
|
cache: true
|
||||||
---
|
---
|
||||||
|
|
||||||
|
## Introduction
|
||||||
|
|
||||||
|
Orbital debris is a form of pollution that is growing at an exponential pace and puts current and
|
||||||
|
future space infrastructure at risk. Satellites are critical to military, commercial, and civil
|
||||||
|
operations. Unfortunately, the space that debris occupies is increasingly becoming more crowded and
|
||||||
|
dangerous, potentially leading to a cascade event that could turn orbit around the Earth into an
|
||||||
|
unusable wasteland for decades unless proper mitigation is not introduced. Existing models employed
|
||||||
|
by NASA rely on a dataset created from 2D images and are missing many crucial features required for
|
||||||
|
correctly modeling the space debris environment. This approach aims to use high-resolution 3D
|
||||||
|
scanning to fully capture the geometry of a piece of debris and allow a more advanced analysis of
|
||||||
|
each piece. Coupled with machine learning methods, the scans will allow advances to the current
|
||||||
|
cutting edge. Physical and photograph-based measurements are time-consuming, hard to replicate, and
|
||||||
|
lack precision. 3D scanning allows much more advanced and accurate analysis of each debris sample,
|
||||||
|
focusing on properties such as moment of inertia, cross-section, and drag. Once the characteristics
|
||||||
|
of space debris are more thoroughly understood, we can begin mitigating the creation and danger of
|
||||||
|
future space debris by implementing improved satellite construction methods and more advanced debris
|
||||||
|
avoidance measures.
|
||||||
|
|
||||||
|
### Current Progress
|
||||||
|
|
||||||
|
This project aims to fix very difficult issues, and although great progress has been made there is
|
||||||
|
still plenty of work to be done. Currently, algorithms have been made that are capable of getting
|
||||||
|
many key features from solid ^[A mesh with a surface that is fully closed and has no holes in its
|
||||||
|
geometry.] models that are in the `stl` format. The algorithm for processing the 3D meshes is
|
||||||
|
implemented in the Julia programming language. Syntactically the language is very similar to Python
|
||||||
|
and Matlab. Julia was chosen because it is nearly as performant as compiled languages like C, while
|
||||||
|
still having tooling geared towards engineers and scientists. The code produces a struct with all
|
||||||
|
the calculated properties as follows:
|
||||||
|
|
||||||
|
```julia
|
||||||
|
struct Properties
|
||||||
|
# Volume of the mesh
|
||||||
|
volume::Float64
|
||||||
|
# Center of gravity, meshes are not always center at [0,0,0]
|
||||||
|
center_of_gravity::Vector{Float64}
|
||||||
|
# Moment of inertia tensor
|
||||||
|
inertia::Matrix{Float64}
|
||||||
|
# Surface area of mesh
|
||||||
|
surface_area::Float64
|
||||||
|
# Average orthogonal dimension of the mesh
|
||||||
|
characteristic_length::Float64
|
||||||
|
# Projected length of farthest two points in [x,y,z] directions
|
||||||
|
solidbody_values::Vector{Float64}
|
||||||
|
end
|
||||||
|
```
|
||||||
|
|
||||||
```{julia}
|
```{julia}
|
||||||
#| code-fold: true
|
#| echo: false
|
||||||
#| output: false
|
#| output: false
|
||||||
|
|
||||||
using FileIO
|
using FileIO
|
||||||
@ -29,8 +75,11 @@ using stlProcess
|
|||||||
|
|
||||||
using CSV
|
using CSV
|
||||||
using DataFrames
|
using DataFrames
|
||||||
|
using Plots
|
||||||
|
theme(:ggplot2)
|
||||||
|
|
||||||
using LinearAlgebra
|
using LinearAlgebra
|
||||||
|
using Statistics
|
||||||
```
|
```
|
||||||
|
|
||||||
```{julia}
|
```{julia}
|
||||||
@ -40,7 +89,8 @@ using LinearAlgebra
|
|||||||
# local path to https://gitlab.com/orbital-debris-research/fake-satellite-dataset
|
# local path to https://gitlab.com/orbital-debris-research/fake-satellite-dataset
|
||||||
dataset_path = raw"C:\Coding\fake-satellite-dataset"
|
dataset_path = raw"C:\Coding\fake-satellite-dataset"
|
||||||
|
|
||||||
folders = ["1_5U", "assembly1", "cubesat"]
|
# folders = ["1_5U", "assembly1", "cubesat"]
|
||||||
|
folders = ["cubesat"]
|
||||||
|
|
||||||
df = DataFrame(;
|
df = DataFrame(;
|
||||||
surface_area=Float64[],
|
surface_area=Float64[],
|
||||||
@ -55,8 +105,9 @@ df = DataFrame(;
|
|||||||
```
|
```
|
||||||
|
|
||||||
```{julia}
|
```{julia}
|
||||||
|
#| output: false
|
||||||
|
|
||||||
for path in dataset_path * "\\" .* folders
|
for path in dataset_path * "\\" .* folders
|
||||||
println("Processing Path: ", path)
|
|
||||||
Threads.@threads for file in readdir(path)
|
Threads.@threads for file in readdir(path)
|
||||||
stl = load(path * "\\" * file)
|
stl = load(path * "\\" * file)
|
||||||
scale = find_scale(stl)
|
scale = find_scale(stl)
|
||||||
@ -64,21 +115,44 @@ for path in dataset_path * "\\" .* folders
|
|||||||
|
|
||||||
eigs = eigvals(props.inertia)
|
eigs = eigvals(props.inertia)
|
||||||
sort_index = sortperm(eigs)
|
sort_index = sortperm(eigs)
|
||||||
Ix, Iy, Iz = eigs[sort_index]
|
I1, I2, I3 = eigs[sort_index]
|
||||||
sbx, sby, sbz = props.sb_values[sort_index]
|
sbx, sby, sbz = props.sb_values[sort_index]
|
||||||
|
|
||||||
push!(
|
push!(
|
||||||
df,
|
df,
|
||||||
[props.surface_area, props.characteristic_length, sbx, sby, sbz, Ix, Iy, Iz],
|
[props.surface_area, props.characteristic_length, sbx, sby, sbz, I3, I2, I1],
|
||||||
)
|
)
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
```
|
```
|
||||||
|
|
||||||
|
:::{.column-body-outset}
|
||||||
|
|
||||||
```{julia}
|
```{julia}
|
||||||
|
#| echo: false
|
||||||
|
|
||||||
describe(df)
|
describe(df)
|
||||||
```
|
```
|
||||||
|
|
||||||
|
:::
|
||||||
|
|
||||||
|
```{julia}
|
||||||
|
S = cov(Matrix(df))
|
||||||
|
|
||||||
|
eig_vals = eigvals(S);
|
||||||
|
|
||||||
|
# sorting eigenvalues from largest to smallest
|
||||||
|
sort_index = sortperm(eig_vals; rev=true)
|
||||||
|
|
||||||
|
lambda = eig_vals[sort_index]
|
||||||
|
names_sorted = names(df)[sort_index]
|
||||||
|
|
||||||
|
lambda_ratio = cumsum(lambda) ./ sum(lambda)
|
||||||
|
|
||||||
|
plot(lambda_ratio, marker=:x)
|
||||||
|
xticks!(sort_index,names(df), xrotation = 15)
|
||||||
|
```
|
||||||
|
|
||||||
## Gathering Data
|
## Gathering Data
|
||||||
|
|
||||||
To get started on the project before any scans of the actual debris are made available, I opted to
|
To get started on the project before any scans of the actual debris are made available, I opted to
|
||||||
@ -165,7 +239,7 @@ eig_vals = eig(S);
|
|||||||
lambda_ratio = cumsum(lambda) ./ sum(lambda)
|
lambda_ratio = cumsum(lambda) ./ sum(lambda)
|
||||||
```
|
```
|
||||||
|
|
||||||
Then plotting `lambda_ratio`, which is the `cumsum`/`sum` produces the following plot:
|
Then plotting `lambda_ratio`, which is the `cumsum ./ sum` produces the following plot:
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
460
report.tex
460
report.tex
@ -1,460 +0,0 @@
|
|||||||
% Options for packages loaded elsewhere
|
|
||||||
\PassOptionsToPackage{unicode}{hyperref}
|
|
||||||
\PassOptionsToPackage{hyphens}{url}
|
|
||||||
\PassOptionsToPackage{dvipsnames,svgnames,x11names}{xcolor}
|
|
||||||
%
|
|
||||||
\documentclass[
|
|
||||||
letterpaper,
|
|
||||||
DIV=11,
|
|
||||||
numbers=noendperiod,
|
|
||||||
oneside]{scrartcl}
|
|
||||||
\usepackage{amsmath,amssymb}
|
|
||||||
\usepackage{lmodern}
|
|
||||||
\usepackage{iftex}
|
|
||||||
\ifPDFTeX
|
|
||||||
\usepackage[T1]{fontenc}
|
|
||||||
\usepackage[utf8]{inputenc}
|
|
||||||
\usepackage{textcomp} % provide euro and other symbols
|
|
||||||
\else % if luatex or xetex
|
|
||||||
\usepackage{unicode-math}
|
|
||||||
\defaultfontfeatures{Scale=MatchLowercase}
|
|
||||||
\defaultfontfeatures[\rmfamily]{Ligatures=TeX,Scale=1}
|
|
||||||
\fi
|
|
||||||
% Use upquote if available, for straight quotes in verbatim environments
|
|
||||||
\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
|
|
||||||
\IfFileExists{microtype.sty}{% use microtype if available
|
|
||||||
\usepackage[]{microtype}
|
|
||||||
\UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts
|
|
||||||
}{}
|
|
||||||
\makeatletter
|
|
||||||
\@ifundefined{KOMAClassName}{% if non-KOMA class
|
|
||||||
\IfFileExists{parskip.sty}{%
|
|
||||||
\usepackage{parskip}
|
|
||||||
}{% else
|
|
||||||
\setlength{\parindent}{0pt}
|
|
||||||
\setlength{\parskip}{6pt plus 2pt minus 1pt}}
|
|
||||||
}{% if KOMA class
|
|
||||||
\KOMAoptions{parskip=half}}
|
|
||||||
\makeatother
|
|
||||||
\usepackage{xcolor}
|
|
||||||
\IfFileExists{xurl.sty}{\usepackage{xurl}}{} % add URL line breaks if available
|
|
||||||
\IfFileExists{bookmark.sty}{\usepackage{bookmark}}{\usepackage{hyperref}}
|
|
||||||
\hypersetup{
|
|
||||||
pdftitle={Characterization of Space Debris using Machine Learning Methods},
|
|
||||||
pdfauthor={Anson Biggs},
|
|
||||||
colorlinks=true,
|
|
||||||
linkcolor={blue},
|
|
||||||
filecolor={Maroon},
|
|
||||||
citecolor={Blue},
|
|
||||||
urlcolor={Blue},
|
|
||||||
pdfcreator={LaTeX via pandoc}}
|
|
||||||
\urlstyle{same} % disable monospaced font for URLs
|
|
||||||
\usepackage[left=1in,marginparwidth=2.0666666666667in,textwidth=4.1333333333333in,marginparsep=0.3in]{geometry}
|
|
||||||
\setlength{\emergencystretch}{3em} % prevent overfull lines
|
|
||||||
\setcounter{secnumdepth}{-\maxdimen} % remove section numbering
|
|
||||||
% Make \paragraph and \subparagraph free-standing
|
|
||||||
\ifx\paragraph\undefined\else
|
|
||||||
\let\oldparagraph\paragraph
|
|
||||||
\renewcommand{\paragraph}[1]{\oldparagraph{#1}\mbox{}}
|
|
||||||
\fi
|
|
||||||
\ifx\subparagraph\undefined\else
|
|
||||||
\let\oldsubparagraph\subparagraph
|
|
||||||
\renewcommand{\subparagraph}[1]{\oldsubparagraph{#1}\mbox{}}
|
|
||||||
\fi
|
|
||||||
|
|
||||||
\usepackage{color}
|
|
||||||
\usepackage{fancyvrb}
|
|
||||||
\newcommand{\VerbBar}{|}
|
|
||||||
\newcommand{\VERB}{\Verb[commandchars=\\\{\}]}
|
|
||||||
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
|
|
||||||
% Add ',fontsize=\small' for more characters per line
|
|
||||||
\usepackage{framed}
|
|
||||||
\definecolor{shadecolor}{RGB}{241,243,245}
|
|
||||||
\newenvironment{Shaded}{\begin{snugshade}}{\end{snugshade}}
|
|
||||||
\newcommand{\AlertTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
|
|
||||||
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
|
|
||||||
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.40,0.45,0.13}{#1}}
|
|
||||||
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
|
|
||||||
\newcommand{\BuiltInTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
|
|
||||||
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
|
|
||||||
\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
|
|
||||||
\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{\textit{#1}}}
|
|
||||||
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{#1}}
|
|
||||||
\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
|
|
||||||
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
|
|
||||||
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
|
|
||||||
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{\textit{#1}}}
|
|
||||||
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
|
|
||||||
\newcommand{\ExtensionTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
|
|
||||||
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
|
|
||||||
\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.28,0.35,0.67}{#1}}
|
|
||||||
\newcommand{\ImportTok}[1]{\textcolor[rgb]{0.00,0.46,0.62}{#1}}
|
|
||||||
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
|
|
||||||
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
|
|
||||||
\newcommand{\NormalTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
|
|
||||||
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
|
|
||||||
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
|
|
||||||
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
|
|
||||||
\newcommand{\RegionMarkerTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
|
|
||||||
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
|
|
||||||
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
|
|
||||||
\newcommand{\StringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
|
|
||||||
\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.07,0.07,0.07}{#1}}
|
|
||||||
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
|
|
||||||
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{\textit{#1}}}
|
|
||||||
|
|
||||||
\providecommand{\tightlist}{%
|
|
||||||
\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}\usepackage{longtable,booktabs,array}
|
|
||||||
\usepackage{calc} % for calculating minipage widths
|
|
||||||
% Correct order of tables after \paragraph or \subparagraph
|
|
||||||
\usepackage{etoolbox}
|
|
||||||
\makeatletter
|
|
||||||
\patchcmd\longtable{\par}{\if@noskipsec\mbox{}\fi\par}{}{}
|
|
||||||
\makeatother
|
|
||||||
% Allow footnotes in longtable head/foot
|
|
||||||
\IfFileExists{footnotehyper.sty}{\usepackage{footnotehyper}}{\usepackage{footnote}}
|
|
||||||
\makesavenoteenv{longtable}
|
|
||||||
\usepackage{graphicx}
|
|
||||||
\makeatletter
|
|
||||||
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth\else\Gin@nat@width\fi}
|
|
||||||
\def\maxheight{\ifdim\Gin@nat@height>\textheight\textheight\else\Gin@nat@height\fi}
|
|
||||||
\makeatother
|
|
||||||
% Scale images if necessary, so that they will not overflow the page
|
|
||||||
% margins by default, and it is still possible to overwrite the defaults
|
|
||||||
% using explicit options in \includegraphics[width, height, ...]{}
|
|
||||||
\setkeys{Gin}{width=\maxwidth,height=\maxheight,keepaspectratio}
|
|
||||||
% Set default figure placement to htbp
|
|
||||||
\makeatletter
|
|
||||||
\def\fps@figure{htbp}
|
|
||||||
\makeatother
|
|
||||||
\newlength{\cslhangindent}
|
|
||||||
\setlength{\cslhangindent}{1.5em}
|
|
||||||
\newlength{\csllabelwidth}
|
|
||||||
\setlength{\csllabelwidth}{3em}
|
|
||||||
\newlength{\cslentryspacingunit} % times entry-spacing
|
|
||||||
\setlength{\cslentryspacingunit}{\parskip}
|
|
||||||
\newenvironment{CSLReferences}[2] % #1 hanging-ident, #2 entry spacing
|
|
||||||
{% don't indent paragraphs
|
|
||||||
\setlength{\parindent}{0pt}
|
|
||||||
% turn on hanging indent if param 1 is 1
|
|
||||||
\ifodd #1
|
|
||||||
\let\oldpar\par
|
|
||||||
\def\par{\hangindent=\cslhangindent\oldpar}
|
|
||||||
\fi
|
|
||||||
% set entry spacing
|
|
||||||
\setlength{\parskip}{#2\cslentryspacingunit}
|
|
||||||
}%
|
|
||||||
{}
|
|
||||||
\usepackage{calc}
|
|
||||||
\newcommand{\CSLBlock}[1]{#1\hfill\break}
|
|
||||||
\newcommand{\CSLLeftMargin}[1]{\parbox[t]{\csllabelwidth}{#1}}
|
|
||||||
\newcommand{\CSLRightInline}[1]{\parbox[t]{\linewidth - \csllabelwidth}{#1}\break}
|
|
||||||
\newcommand{\CSLIndent}[1]{\hspace{\cslhangindent}#1}
|
|
||||||
|
|
||||||
\KOMAoption{captions}{tableheading}
|
|
||||||
\makeatletter
|
|
||||||
\makeatother
|
|
||||||
\makeatletter
|
|
||||||
\@ifpackageloaded{caption}{}{\usepackage{caption}}
|
|
||||||
\AtBeginDocument{%
|
|
||||||
\ifdefined\contentsname
|
|
||||||
\renewcommand*\contentsname{Table of contents}
|
|
||||||
\else
|
|
||||||
\newcommand\contentsname{Table of contents}
|
|
||||||
\fi
|
|
||||||
\ifdefined\listfigurename
|
|
||||||
\renewcommand*\listfigurename{List of Figures}
|
|
||||||
\else
|
|
||||||
\newcommand\listfigurename{List of Figures}
|
|
||||||
\fi
|
|
||||||
\ifdefined\listtablename
|
|
||||||
\renewcommand*\listtablename{List of Tables}
|
|
||||||
\else
|
|
||||||
\newcommand\listtablename{List of Tables}
|
|
||||||
\fi
|
|
||||||
\ifdefined\figurename
|
|
||||||
\renewcommand*\figurename{Figure}
|
|
||||||
\else
|
|
||||||
\newcommand\figurename{Figure}
|
|
||||||
\fi
|
|
||||||
\ifdefined\tablename
|
|
||||||
\renewcommand*\tablename{Table}
|
|
||||||
\else
|
|
||||||
\newcommand\tablename{Table}
|
|
||||||
\fi
|
|
||||||
}
|
|
||||||
\@ifpackageloaded{float}{}{\usepackage{float}}
|
|
||||||
\floatstyle{ruled}
|
|
||||||
\@ifundefined{c@chapter}{\newfloat{codelisting}{h}{lop}}{\newfloat{codelisting}{h}{lop}[chapter]}
|
|
||||||
\floatname{codelisting}{Listing}
|
|
||||||
\newcommand*\listoflistings{\listof{codelisting}{List of Listings}}
|
|
||||||
\makeatother
|
|
||||||
\makeatletter
|
|
||||||
\@ifpackageloaded{caption}{}{\usepackage{caption}}
|
|
||||||
\@ifpackageloaded{subcaption}{}{\usepackage{subcaption}}
|
|
||||||
\makeatother
|
|
||||||
\makeatletter
|
|
||||||
\@ifpackageloaded{tcolorbox}{}{\usepackage[many]{tcolorbox}}
|
|
||||||
\makeatother
|
|
||||||
\makeatletter
|
|
||||||
\@ifundefined{shadecolor}{\definecolor{shadecolor}{rgb}{.97, .97, .97}}
|
|
||||||
\makeatother
|
|
||||||
\makeatletter
|
|
||||||
\@ifpackageloaded{sidenotes}{}{\usepackage{sidenotes}}
|
|
||||||
\@ifpackageloaded{marginnote}{}{\usepackage{marginnote}}
|
|
||||||
\makeatother
|
|
||||||
\makeatletter
|
|
||||||
\makeatother
|
|
||||||
\ifLuaTeX
|
|
||||||
\usepackage{selnolig} % disable illegal ligatures
|
|
||||||
\fi
|
|
||||||
|
|
||||||
\title{Characterization of Space Debris using Machine Learning Methods}
|
|
||||||
\usepackage{etoolbox}
|
|
||||||
\makeatletter
|
|
||||||
\providecommand{\subtitle}[1]{% add subtitle to \maketitle
|
|
||||||
\apptocmd{\@title}{\par {\large #1 \par}}{}{}
|
|
||||||
}
|
|
||||||
\makeatother
|
|
||||||
\subtitle{Advanced processing of 3D meshes using Julia, and data science
|
|
||||||
in Matlab.}
|
|
||||||
\author{Anson Biggs}
|
|
||||||
\date{4/30/2022}
|
|
||||||
|
|
||||||
\begin{document}
|
|
||||||
\maketitle
|
|
||||||
|
|
||||||
\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[interior hidden, borderline west={3pt}{0pt}{shadecolor}, boxrule=0pt, enhanced, breakable, sharp corners, frame hidden]}{\end{tcolorbox}}\fi
|
|
||||||
|
|
||||||
\hypertarget{gathering-data}{%
|
|
||||||
\subsection{Gathering Data}\label{gathering-data}}
|
|
||||||
|
|
||||||
To get started on the project before any scans of the actual debris are
|
|
||||||
made available, I opted to find 3D models online and process them as if
|
|
||||||
they were data collected by my team. GrabCAD is an excellent source of
|
|
||||||
high-quality 3D models, and all the models have, at worst, a
|
|
||||||
non-commercial license making them suitable for this study. The current
|
|
||||||
dataset uses three separate satellite assemblies found on GrabCAD, below
|
|
||||||
is an example of one of the satellites that was used.
|
|
||||||
|
|
||||||
\begin{figure}
|
|
||||||
|
|
||||||
{\centering \includegraphics{Figures/assembly.jpg}
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
\caption{Example CubeSat Used for Analysis}
|
|
||||||
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
\hypertarget{data-preparation}{%
|
|
||||||
\subsection{Data Preparation}\label{data-preparation}}
|
|
||||||
|
|
||||||
The models were processed in Blender, which quickly converted the
|
|
||||||
assemblies to \texttt{stl} files, giving 108 unique parts to be
|
|
||||||
processed. Since the expected final size of the dataset is expected to
|
|
||||||
be in the magnitude of the thousands, an algorithm capable of getting
|
|
||||||
the required properties of each part is the only feasible solution. From
|
|
||||||
the analysis performed in
|
|
||||||
\href{https://gitlab.com/orbital-debris-research/directed-study/report-1/-/blob/main/README.md}{Report
|
|
||||||
1}, we know that the essential debris property is the moments of inertia
|
|
||||||
which helped narrow down potential algorithms. Unfortunately, this is
|
|
||||||
one of the more complicated things to calculate from a mesh, but thanks
|
|
||||||
to a paper from (Eberly
|
|
||||||
2002)\marginpar{\begin{footnotesize}\leavevmode\vadjust pre{\protect\hypertarget{ref-eberlyPolyhedralMassProperties2002}{}}%
|
|
||||||
Eberly, David. 2002. {``Polyhedral {Mass Properties} ({Revisited}).''}
|
|
||||||
\url{https://www.geometrictools.com/Documentation/PolyhedralMassProperties.pdf}.\vspace{2mm}\par\end{footnotesize}}
|
|
||||||
titled
|
|
||||||
\href{https://www.geometrictools.com/Documentation/PolyhedralMassProperties.pdf}{Polyhedral
|
|
||||||
Mass Properties}, his algorithm was implemented in the Julia programming
|
|
||||||
language. The current implementation of the algorithm calculates a
|
|
||||||
moment of inertia tensor, volume, center of gravity, characteristic
|
|
||||||
length, and surface body dimensions in a few milliseconds per part. The
|
|
||||||
library can be found
|
|
||||||
\href{https://gitlab.com/MisterBiggs/stl-process}{here.} The
|
|
||||||
characteristic length is a value that is heavily used by the NASA
|
|
||||||
DebriSat project (Murray et al.
|
|
||||||
2019)\marginpar{\begin{footnotesize}\leavevmode\vadjust pre{\protect\hypertarget{ref-DebriSat2019}{}}%
|
|
||||||
Murray, James, Heather Cowardin, J-C Liou, Marlon Sorge, Norman
|
|
||||||
Fitz-Coy, and Tom Huynh. 2019. {``Analysis of the DebriSat Fragments and
|
|
||||||
Comparison to the NASA Standard Satellite Breakup Model.''} In
|
|
||||||
\emph{International Orbital Debris Conference (IOC)}. JSC-E-DAA-TN73918.
|
|
||||||
\url{https://ntrs.nasa.gov/citations/20190034081}.\vspace{2mm}\par\end{footnotesize}}
|
|
||||||
that is doing very similar work to this project. The characteristic
|
|
||||||
length takes the maximum orthogonal dimension of a body, sums the
|
|
||||||
dimensions then divides by 3 to produce a single scalar value that can
|
|
||||||
be used to get an idea of thesize of a 3D object.
|
|
||||||
|
|
||||||
\begin{figure}
|
|
||||||
|
|
||||||
{\centering \includegraphics{Figures/current_process.pdf}
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
\caption{Current mesh processing pipeline}
|
|
||||||
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
The algorithm's speed is critical not only for the eventual large number
|
|
||||||
of debris pieces that have to be processed, but many of the data science
|
|
||||||
algorithms we plan on performing on the compiled data need the data to
|
|
||||||
be normalized. For the current dataset and properties, it makes the most
|
|
||||||
sense to normalize the dataset based on volume. Volume was chosen for
|
|
||||||
multiple reasons, namely because it was easy to implement an efficient
|
|
||||||
algorithm to calculate volume, and currently, volume produces the least
|
|
||||||
amount of variation out of the current set of properties calculated.
|
|
||||||
Unfortunately, scaling a model to a specific volume is an iterative
|
|
||||||
process, but can be done very efficiently using derivative-free
|
|
||||||
numerical root-finding algorithms. The current implementation can scale
|
|
||||||
and process all the properties using only 30\% more time than getting
|
|
||||||
the properties without first scaling.
|
|
||||||
|
|
||||||
\begin{Shaded}
|
|
||||||
\begin{Highlighting}[]
|
|
||||||
\NormalTok{ Row │ variable mean min median max}
|
|
||||||
\NormalTok{─────┼───────────────────────────────────────────────────────────────────}
|
|
||||||
\NormalTok{ 1 │ surface\_area 25.2002 5.60865 13.3338 159.406}
|
|
||||||
\NormalTok{ 2 │ characteristic\_length 79.5481 0.158521 1.55816 1582.23}
|
|
||||||
\NormalTok{ 3 │ sbx 1.40222 0.0417367 0.967078 10.0663}
|
|
||||||
\NormalTok{ 4 │ sby 3.3367 0.0125824 2.68461 9.68361}
|
|
||||||
\NormalTok{ 5 │ sbz 3.91184 0.29006 1.8185 14.7434}
|
|
||||||
\NormalTok{ 6 │ Ix 1.58725 0.0311782 0.23401 11.1335}
|
|
||||||
\NormalTok{ 7 │ Iy 3.74345 0.178598 1.01592 24.6735}
|
|
||||||
\NormalTok{ 8 │ Iz 5.20207 0.178686 1.742 32.0083}
|
|
||||||
\end{Highlighting}
|
|
||||||
\end{Shaded}
|
|
||||||
|
|
||||||
Above is a summary of the current 108 part with scaling. Since all the
|
|
||||||
volumes are the same it is left out of the dataset, the center of
|
|
||||||
gravity is also left out of the dataset since it currently is just an
|
|
||||||
artifact of the \texttt{stl} file format. There are many ways to
|
|
||||||
determine the `center' of a 3D mesh, but since only one is being
|
|
||||||
implemented at the moment comparisons to other properties doesn't make
|
|
||||||
sense. The other notable part of the data is the model is rotated so
|
|
||||||
that the magnitudes of \texttt{Iz}, \texttt{Iy}, and \texttt{Ix} are in
|
|
||||||
descending order. This makes sure that the rotation of a model doesn't
|
|
||||||
matter for characterization. The dataset is available for download here:
|
|
||||||
|
|
||||||
\begin{itemize}
|
|
||||||
\tightlist
|
|
||||||
\item
|
|
||||||
\href{https://gitlab.com/orbital-debris-research/directed-study/report-3/-/blob/main/scaled_dataset.csv}{scaled\_dataset.csv}
|
|
||||||
\end{itemize}
|
|
||||||
|
|
||||||
\hypertarget{characterization}{%
|
|
||||||
\subsection{Characterization}\label{characterization}}
|
|
||||||
|
|
||||||
The first step toward characterization is to perform a principal
|
|
||||||
component analysis to determine what properties of the data capture the
|
|
||||||
most variation. \texttt{PCA} also requires that the data is scaled, so
|
|
||||||
as discussed above the dataset that is scaled by \texttt{volume} will be
|
|
||||||
used. \texttt{PCA} is implemented manually instead of the Matlab
|
|
||||||
built-in function as shown below:
|
|
||||||
|
|
||||||
\begin{Shaded}
|
|
||||||
\begin{Highlighting}[]
|
|
||||||
\CommentTok{\% covaraince matrix of data points}
|
|
||||||
\VariableTok{S}\OperatorTok{=}\VariableTok{cov}\NormalTok{(}\VariableTok{scaled\_data}\NormalTok{)}\OperatorTok{;}
|
|
||||||
|
|
||||||
\CommentTok{\% eigenvalues of S}
|
|
||||||
\VariableTok{eig\_vals} \OperatorTok{=} \VariableTok{eig}\NormalTok{(}\VariableTok{S}\NormalTok{)}\OperatorTok{;}
|
|
||||||
|
|
||||||
\CommentTok{\% sorting eigenvalues from largest to smallest}
|
|
||||||
\NormalTok{[}\VariableTok{lambda}\OperatorTok{,} \VariableTok{sort\_index}\NormalTok{] }\OperatorTok{=} \VariableTok{sort}\NormalTok{(}\VariableTok{eig\_vals}\OperatorTok{,}\SpecialStringTok{\textquotesingle{}descend\textquotesingle{}}\NormalTok{)}\OperatorTok{;}
|
|
||||||
|
|
||||||
|
|
||||||
\VariableTok{lambda\_ratio} \OperatorTok{=} \VariableTok{cumsum}\NormalTok{(}\VariableTok{lambda}\NormalTok{) }\OperatorTok{./} \VariableTok{sum}\NormalTok{(}\VariableTok{lambda}\NormalTok{)}
|
|
||||||
\end{Highlighting}
|
|
||||||
\end{Shaded}
|
|
||||||
|
|
||||||
Then plotting \texttt{lambda\_ratio}, which is the
|
|
||||||
\texttt{cumsum}/\texttt{sum} produces the following plot:
|
|
||||||
|
|
||||||
\begin{figure}
|
|
||||||
|
|
||||||
{\centering \includegraphics{Figures/pca.png}
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
\caption{PCA Plot}
|
|
||||||
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
The current dataset can be described incredibly well just by looking at
|
|
||||||
\texttt{Iz}, which again the models are rotated so that \texttt{Iz} is
|
|
||||||
the largest moment of inertia. Then including \texttt{Iy} and
|
|
||||||
\texttt{Iz} means that a 3D plot of the principle moments of inertia
|
|
||||||
almost capture all the variation in the data.
|
|
||||||
|
|
||||||
The next step for characterization is to get only the inertia's from the
|
|
||||||
dataset. Since the current dataset is so small, the scaled dataset will
|
|
||||||
be used for rest of the characterization process. Once more parts are
|
|
||||||
added to the database it will make sense to start looking at the raw
|
|
||||||
dataset. Now we can proceed to cluster the data using the k-means method
|
|
||||||
of clustering. To properly use k-means a value of k, which is the number
|
|
||||||
of clusters, needs to be determined. This can be done by creating an
|
|
||||||
elbow plot using the following code:
|
|
||||||
|
|
||||||
\begin{Shaded}
|
|
||||||
\begin{Highlighting}[]
|
|
||||||
\KeywordTok{for} \VariableTok{ii}\OperatorTok{=}\FloatTok{1}\OperatorTok{:}\FloatTok{20}
|
|
||||||
\NormalTok{ [}\VariableTok{idx}\OperatorTok{,\textasciitilde{},}\VariableTok{sumd}\NormalTok{] }\OperatorTok{=} \VariableTok{kmeans}\NormalTok{(}\VariableTok{inertia}\OperatorTok{,}\VariableTok{ii}\NormalTok{)}\OperatorTok{;}
|
|
||||||
\VariableTok{J}\NormalTok{(}\VariableTok{ii}\NormalTok{)}\OperatorTok{=}\VariableTok{norm}\NormalTok{(}\VariableTok{sumd}\NormalTok{)}\OperatorTok{;}
|
|
||||||
\KeywordTok{end}
|
|
||||||
\end{Highlighting}
|
|
||||||
\end{Shaded}
|
|
||||||
|
|
||||||
Which produces the following plot:
|
|
||||||
|
|
||||||
\begin{figure}
|
|
||||||
|
|
||||||
{\centering \includegraphics{Figures/kmeans.png}
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
\caption{Elbow method to determine the required number of clusters.}
|
|
||||||
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
As can be seen in the above elbow plot, at 6 clusters there is an
|
|
||||||
``elbow'' which is where there is a large drop in the sum distance to
|
|
||||||
the centroid of each cluster which means that it is the optimal number
|
|
||||||
of clusters. The inertia's can then be plotted using 6 k-means clusters
|
|
||||||
produces the following plot:
|
|
||||||
|
|
||||||
\begin{figure}
|
|
||||||
|
|
||||||
{\centering \includegraphics{Figures/inertia3d.png}
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
\caption{Moments of Inertia plotted with 6 clusters.}
|
|
||||||
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
From this plot it is immediately clear that there are clusters of
|
|
||||||
outliers. These are due to the different shapes and the extreme values
|
|
||||||
are slender rods or flat plates while the clusters closer to the center
|
|
||||||
more closely resemble a sphere. As the dataset grows it should become
|
|
||||||
more apparent what kind of clusters actually make up a satellite, and
|
|
||||||
eventually space debris in general.
|
|
||||||
|
|
||||||
\hypertarget{next-steps}{%
|
|
||||||
\subsection{Next Steps}\label{next-steps}}
|
|
||||||
|
|
||||||
The current dataset needs to be grown in both the amount of data and the
|
|
||||||
variety of data. The most glaring issue with the current dataset is the
|
|
||||||
lack of any debris since the parts are straight from satellite
|
|
||||||
assemblies. Getting accurate properties from the current scans we have
|
|
||||||
is an entire research project in itself, so hopefully, getting pieces
|
|
||||||
that are easier to scan can help bring the project back on track. The
|
|
||||||
other and harder-to-fix issue is finding/deriving more data properties.
|
|
||||||
Properties such as cross-sectional or aerodynamic drag would be very
|
|
||||||
insightful but are likely to be difficult to implement in code and
|
|
||||||
significantly more resource intensive than the current properties the
|
|
||||||
code can derive.
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
\end{document}
|
|
||||||
Loading…
x
Reference in New Issue
Block a user