From 8d3aebc70ca6d13c4c03f969a8530edb32bb6131 Mon Sep 17 00:00:00 2001 From: Anson Date: Sun, 24 Apr 2022 21:40:17 -0700 Subject: [PATCH] stuff --- .jupyter_cache/__version__.txt | 1 + .../base.ipynb | 248 ++++ .../base.ipynb | 237 ++++ .jupyter_cache/global.db | Bin 0 -> 28672 bytes Manifest.toml | 1012 +++++++++++++++++ Project.toml | 8 + report.html | 199 ++-- report.qmd | 72 +- 8 files changed, 1709 insertions(+), 68 deletions(-) create mode 100644 .jupyter_cache/__version__.txt create mode 100644 .jupyter_cache/executed/2f3f010a51c40fce4d85e1fa29ada8c0/base.ipynb create mode 100644 .jupyter_cache/executed/fd2f73547cc85cb9d5d855720bd105d3/base.ipynb create mode 100644 .jupyter_cache/global.db create mode 100644 Manifest.toml create mode 100644 Project.toml diff --git a/.jupyter_cache/__version__.txt b/.jupyter_cache/__version__.txt new file mode 100644 index 0000000..79a2734 --- /dev/null +++ b/.jupyter_cache/__version__.txt @@ -0,0 +1 @@ +0.5.0 \ No newline at end of file diff --git a/.jupyter_cache/executed/2f3f010a51c40fce4d85e1fa29ada8c0/base.ipynb b/.jupyter_cache/executed/2f3f010a51c40fce4d85e1fa29ada8c0/base.ipynb new file mode 100644 index 0000000..5139368 --- /dev/null +++ b/.jupyter_cache/executed/2f3f010a51c40fce4d85e1fa29ada8c0/base.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a45c1344", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "(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" + ] + } + ], + "source": [ + "import IJulia\n", + "\n", + "# The julia kernel has built in support for Revise.jl, so this is the \n", + "# recommended approach for long-running sessions:\n", + "# https://github.com/JuliaLang/IJulia.jl/blob/9b10fa9b879574bbf720f5285029e07758e50a5e/src/kernel.jl#L46-L51\n", + "\n", + "# Users should enable revise within .julia/config/startup_ijulia.jl:\n", + "# https://timholy.github.io/Revise.jl/stable/config/#Using-Revise-automatically-within-Jupyter/IJulia-1\n", + "\n", + "# clear console history\n", + "IJulia.clear_history()\n", + "\n", + "# Intialize Plots w/ default fig width/height\n", + "try\n", + " fig_width = 7\n", + " fig_height = 5\n", + " fig_format = :retina\n", + " fig_dpi = 96\n", + " # no retina format type, use svg for high quality type/marks\n", + " if fig_format == :retina\n", + " fig_format = :svg\n", + " # IJulia doesn't support PDF output so use png (if the DPI \n", + " # remains the default of 300 then set to 96)\n", + " elseif fig_format == :pdf\n", + " fig_format = :png\n", + " fig_dpi = 96\n", + " end\n", + " # convert inches to pixels\n", + " fig_width = fig_width * fig_dpi\n", + " fig_height = fig_height * fig_dpi\n", + " using Plots\n", + " 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" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "189db2ad", + "metadata": {}, + "outputs": [], + "source": [ + "#| code-fold: true\n", + "#| output: false\n", + "using FileIO\n", + "using MeshIO\n", + "\n", + "using stlProcess\n", + "\n", + "using CSV\n", + "using DataFrames\n", + "\n", + "using LinearAlgebra" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5e47d862", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

0 rows × 8 columns (omitted printing of 1 columns)

surface_areacharacteristic_lengthsbxsbysbzIxIy
Float64Float64Float64Float64Float64Float64Float64
" + ], + "text/latex": [ + "\\begin{tabular}{r|cccccccc}\n", + "\t& surface\\_area & characteristic\\_length & sbx & sby & sbz & Ix & Iy & \\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & \\\\\n", + "\t\\hline\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m0×8 DataFrame\u001b[0m" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 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", + " sbx=Float64[],\n", + " sby=Float64[],\n", + " sbz=Float64[],\n", + " Ix=Float64[],\n", + " Iy=Float64[],\n", + " Iz=Float64[],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6c55224d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Path: C:\\Coding\\fake-satellite-dataset\\1_5U\n", + "Processing Path: C:\\Coding\\fake-satellite-dataset\\assembly1\n", + "Processing Path: C:\\Coding\\fake-satellite-dataset\\cubesat\n" + ] + }, + { + "name": "stderr", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2e520bb3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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variablemeanminmedianmaxnmissingeltype
SymbolFloat64Float64Float64Float64Int64DataType
1surface_area25.20025.6086513.3338159.4060Float64
2characteristic_length79.54810.1585211.558161582.230Float64
3sbx1.402220.04173670.96707810.06630Float64
4sby3.33670.01258242.684619.683610Float64
5sbz3.911840.290061.818514.74340Float64
6Ix1.587250.03117820.2340111.13350Float64
7Iy3.743450.1785981.0159224.67350Float64
8Iz5.202070.1786861.74232.00830Float64
" + ], + "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" + ], + "text/plain": [ + "\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", + "\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" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "describe(df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.8.0-beta3", + "language": "julia", + "name": "julia-1.8" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/.jupyter_cache/executed/fd2f73547cc85cb9d5d855720bd105d3/base.ipynb b/.jupyter_cache/executed/fd2f73547cc85cb9d5d855720bd105d3/base.ipynb new file mode 100644 index 0000000..a0ebec1 --- /dev/null +++ b/.jupyter_cache/executed/fd2f73547cc85cb9d5d855720bd105d3/base.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "129f639c", + "metadata": {}, + "outputs": [], + "source": [ + "import IJulia\n", + "\n", + "# The julia kernel has built in support for Revise.jl, so this is the \n", + "# recommended approach for long-running sessions:\n", + "# https://github.com/JuliaLang/IJulia.jl/blob/9b10fa9b879574bbf720f5285029e07758e50a5e/src/kernel.jl#L46-L51\n", + "\n", + "# Users should enable revise within .julia/config/startup_ijulia.jl:\n", + "# https://timholy.github.io/Revise.jl/stable/config/#Using-Revise-automatically-within-Jupyter/IJulia-1\n", + "\n", + "# clear console history\n", + "IJulia.clear_history()\n", + "\n", + "# Intialize Plots w/ default fig width/height\n", + "try\n", + " fig_width = 7\n", + " fig_height = 5\n", + " fig_format = :retina\n", + " fig_dpi = 96\n", + " # no retina format type, use svg for high quality type/marks\n", + " if fig_format == :retina\n", + " fig_format = :svg\n", + " # IJulia doesn't support PDF output so use png (if the DPI \n", + " # remains the default of 300 then set to 96)\n", + " elseif fig_format == :pdf\n", + " fig_format = :png\n", + " fig_dpi = 96\n", + " end\n", + " # convert inches to pixels\n", + " fig_width = fig_width * fig_dpi\n", + " fig_height = fig_height * fig_dpi\n", + " using Plots\n", + " 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" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8b15e8a8", + "metadata": {}, + "outputs": [], + "source": [ + "#| code-fold: true\n", + "#| output: false\n", + "\n", + "using FileIO\n", + "using MeshIO\n", + "\n", + "using stlProcess\n", + "\n", + "using CSV\n", + "using DataFrames\n", + "\n", + "using LinearAlgebra" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f40c9594", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

0 rows × 8 columns (omitted printing of 1 columns)

surface_areacharacteristic_lengthsbxsbysbzIxIy
Float64Float64Float64Float64Float64Float64Float64
" + ], + "text/latex": [ + "\\begin{tabular}{r|cccccccc}\n", + "\t& surface\\_area & characteristic\\_length & sbx & sby & sbz & Ix & Iy & \\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & \\\\\n", + "\t\\hline\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m0×8 DataFrame\u001b[0m" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| 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", + " sbx=Float64[],\n", + " sby=Float64[],\n", + " sbz=Float64[],\n", + " Ix=Float64[],\n", + " Iy=Float64[],\n", + " Iz=Float64[],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bb8f7086", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Path: C:\\Coding\\fake-satellite-dataset\\1_5U\n", + "Processing Path: C:\\Coding\\fake-satellite-dataset\\assembly1\n", + "Processing Path: C:\\Coding\\fake-satellite-dataset\\cubesat\n" + ] + }, + { + "name": "stderr", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a51a601f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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variablemeanminmedianmaxnmissingeltype
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1surface_area25.20025.6086513.3338159.4060Float64
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4sby3.33670.01258242.684619.683610Float64
5sbz3.911840.290061.818514.74340Float64
6Ix1.587250.03117820.2340111.13350Float64
7Iy3.743450.1785981.0159224.67350Float64
8Iz5.202070.1786861.74232.00830Float64
" + ], + "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" + ], + "text/plain": [ + "\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", + "\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" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], 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a/report.html +++ b/report.html @@ -2464,51 +2464,128 @@ vertical-align: -.125em; .bi-filetype-xlsx::before { content: "\f793"; } + + + -
- -
- -
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-

Characterization of Space Debris using Machine Learning Methods

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Advanced processing of 3D meshes using Julia, and data science in Matlab.

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- - - - - -
- -
-
Author
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-

Anson Biggs

-
+
+
+
+

Characterization of Space Debris using Machine Learning Methods

+

Advanced processing of 3D meshes using Julia, and data science in Matlab.

+
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-
Published
-
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4/30/2022

-
-
- -
-
+ +
+ +
+
Author
+
+

Anson Biggs

+
+
+ +
+
Published
+
+

4/30/2022

+
+
+ +
+ + +
+ +
+ + + + +
+
+Code +
using FileIO
+using MeshIO
+
+using stlProcess
+
+using CSV
+using DataFrames
+
+using LinearAlgebra
+
+
+
+
+Code +
# local path to https://gitlab.com/orbital-debris-research/fake-satellite-dataset
+dataset_path = raw"C:\Coding\fake-satellite-dataset"
+
+folders = ["1_5U", "assembly1", "cubesat"]
+
+df = DataFrame(;
+    surface_area=Float64[],
+    characteristic_length=Float64[],
+    sbx=Float64[],
+    sby=Float64[],
+    sbz=Float64[],
+    Ix=Float64[],
+    Iy=Float64[],
+    Iz=Float64[],
+)
+
+
+
+
for path in dataset_path * "\\" .* folders
+    println("Processing Path: ", path)
+    Threads.@threads for file in readdir(path)
+        stl = load(path * "\\" * file)
+        scale = find_scale(stl)
+        props = get_mass_properties(stl; scale=scale)
+
+        eigs = eigvals(props.inertia)
+        sort_index = sortperm(eigs)
+        Ix, Iy, Iz = eigs[sort_index]
+        sbx, sby, sbz = props.sb_values[sort_index]
+
+        push!(
+            df,
+            [props.surface_area, props.characteristic_length, sbx, sby, sbz, Ix, Iy, Iz],
+        )
+    end
+end
+
+
Processing Path: C:\Coding\fake-satellite-dataset\1_5U
+Processing Path: C:\Coding\fake-satellite-dataset\assembly1
+Processing Path: C:\Coding\fake-satellite-dataset\cubesat
+
+
+
┌ Warning: Characteristic Length Algorithm failed to converge, this usually means stl is flat. Setting length in dir to 0.
+└ @ stlProcess C:\Users\albig\.julia\packages\stlProcess\8rsc7\src\stlProcess.jl:153
+
+
+
+
describe(df)
+
+

8 rows × 7 columns

variablemeanminmedianmaxnmissingeltype
SymbolFloat64Float64Float64Float64Int64DataType
1surface_area25.20025.6086513.3338159.4060Float64
2characteristic_length79.54810.1585211.558161582.230Float64
3sbx1.402220.04173670.96707810.06630Float64
4sby3.33670.01258242.684619.683610Float64
5sbz3.911840.290061.818514.74340Float64
6Ix1.587250.03117820.2340111.13350Float64
7Iy3.743450.1785981.0159224.67350Float64
8Iz5.202070.1786861.74232.00830Float64
+
+

Gathering Data

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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 1 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.

+

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.

@@ -2530,16 +2607,16 @@ Murray, James, Heather Cowardin, J-C Liou, Marlon Sorge, Norman Fitz-Coy, and To

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.

-
 Row │ variable               mean      min        median     max
-─────┼───────────────────────────────────────────────────────────────────
-   1 │ surface_area           25.2002   5.60865     13.3338     159.406
-   2 │ characteristic_length  79.5481   0.158521    1.55816     1582.23
-   3 │ sbx                     1.40222  0.0417367   0.967078    10.0663
-   4 │ sby                     3.3367   0.0125824   2.68461     9.68361
-   5 │ sbz                     3.91184  0.29006     1.8185      14.7434
-   6 │ Ix                      1.58725  0.0311782   0.23401     11.1335
-   7 │ Iy                      3.74345  0.178598    1.01592     24.6735
-   8 │ Iz                      5.20207  0.178686    1.742       32.0083
+
 Row │ variable               mean      min        median     max
+─────┼───────────────────────────────────────────────────────────────────
+   1 │ surface_area           25.2002   5.60865     13.3338     159.406
+   2 │ characteristic_length  79.5481   0.158521    1.55816     1582.23
+   3 │ sbx                     1.40222  0.0417367   0.967078    10.0663
+   4 │ sby                     3.3367   0.0125824   2.68461     9.68361
+   5 │ sbz                     3.91184  0.29006     1.8185      14.7434
+   6 │ Ix                      1.58725  0.0311782   0.23401     11.1335
+   7 │ Iy                      3.74345  0.178598    1.01592     24.6735
+   8 │ Iz                      5.20207  0.178686    1.742       32.0083

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 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 Iz, Iy, and 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:

  • scaled_dataset.csv
    • @@ -2548,17 +2625,17 @@ Murray, James, Heather Cowardin, J-C Liou, Marlon Sorge, Norman Fitz-Coy, and To

    Characterization

    The first step toward characterization is to perform a principal component analysis to determine what properties of the data capture the most variation. PCA also requires that the data is scaled, so as discussed above the dataset that is scaled by volume will be used. PCA is implemented manually instead of the Matlab built-in function as shown below:

    -
    % covaraince matrix of data points
-S=cov(scaled_data);
-
-% eigenvalues of S
-eig_vals = eig(S);
-
-% sorting eigenvalues from largest to smallest
-[lambda, sort_index] = sort(eig_vals,'descend');
-
-
-lambda_ratio = cumsum(lambda) ./ sum(lambda)
    +
    % covaraince matrix of data points
+S=cov(scaled_data);
+
+% eigenvalues of S
+eig_vals = eig(S);
+
+% sorting eigenvalues from largest to smallest
+[lambda, sort_index] = sort(eig_vals,'descend');
+
+
+lambda_ratio = cumsum(lambda) ./ sum(lambda)

    Then plotting lambda_ratio, which is the cumsum/sum produces the following plot:

    @@ -2568,10 +2645,10 @@ Murray, James, Heather Cowardin, J-C Liou, Marlon Sorge, Norman Fitz-Coy, and To

    The current dataset can be described incredibly well just by looking at Iz, which again the models are rotated so that Iz is the largest moment of inertia. Then including Iy and 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:

    -
    for ii=1:20
-    [idx,~,sumd] = kmeans(inertia,ii);
-    J(ii)=norm(sumd);
-end
    +
    for ii=1:20
+    [idx,~,sumd] = kmeans(inertia,ii);
+    J(ii)=norm(sumd);
+end

    Which produces the following plot:

    @@ -2594,13 +2671,7 @@ Murray, James, Heather Cowardin, J-C Liou, Marlon Sorge, Norman Fitz-Coy, and To
    - -

    Footnotes

    - -
      -

    1. This is a test↩︎

      2. -
    -
+