transision to report3

.vscode/ltex.hiddenFalsePositives.en-US.txt

@@ -2,3 +2,4 @@
 {"rule":"MORFOLOGIK_RULE_EN_US","sentence":"^\\QWikipedia gives a different definition^[Wikipedia: Characteristic Length] that defines characteristic length as the volume divided by the surface area.\\E$"}
 {"rule":"MORFOLOGIK_RULE_EN_US","sentence":"^\\QCalculation of Characteristic Length, @hillMeasurementSatelliteImpact slide 9\\E$"}
 {"rule":"MORFOLOGIK_RULE_EN_US","sentence":"^\\QCurrently, 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.\\E$"}
+{"rule":"MORFOLOGIK_RULE_EN_US","sentence":"^\\QThe summary is that using PCA determined that by far the most variance out of the current list of properties is captured by the principle moments of inertia.^Eigen Values of the inertia tensor.\\E$"}

report.qmd

@@ -266,7 +266,15 @@ CSV.write("scaled_dataset.csv", df)
 
 ---
 
-## Gathering Data
+## Machine Learning
+
+The rest of the document is an in depth look at the progress made characterizing the current fake
+satellite dataset. The summary is that using PCA determined that by far the most variance out of the
+current list of properties is captured by the principle moments of inertia.^[ Eigen Values of the
+inertia tensor. ] I'm including this because it is already typed out and may be a good reference,
+but likely isn't worth digging into once a new dataset of scans can be made.
+
+### 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
@@ -276,7 +284,7 @@ assemblies found on GrabCAD, below is an example of one of the satellites that w
 
 ![Example CubeSat Used for Analysis](Figures/assembly.jpg)
 
-## Data Preparation
+### Data Preparation
 
 The models were processed in Blender, which quickly converted the assemblies to `stl` files, giving
 108 unique parts to be processed. Since the expected final size of the dataset is expected to be in
@@ -331,7 +339,7 @@ matter for characterization. The dataset is available for download here:
 
 - [scaled_dataset.csv](https://gitlab.com/orbital-debris-research/directed-study/report-3/-/blob/main/scaled_dataset.csv)
 
-## Characterization
+### 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,
@@ -390,12 +398,12 @@ different shapes and the extreme values are slender rods or flat plates while th
 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.
 
-## Next Steps
+### 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
+glaring issue with the current dataset is the lack of any debris scans since the parts are straight
+from satellite assemblies. Getting accurate properties from the current scans we have has proved
+exceedingly difficult, 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