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---
title: "How to Export Fusion 360 files to Other File Types"
description: |
How to export Fusion 360 Files and detailed explanations of what each exported file type does.
date: 2018-09-12
date-modified: 2024-02-29
categories:
- 3D Printing
creative_commons: CC BY
image: banner.png
---
A major downfall of Fusion 360 is its lack of exporting options and its overall lack of interoperability with other CAD software. Its main filetype `.f3d`, seems to be closed source and isn't supported by any other programs. A naive approach of exporting to an intermediary format like `.step` or `.obj` then using something else like Autodesk Inventor or Blender to export to your final filetype from is likely to cause artifacts, loss of dimensions, and be a slow manual process. Luckily Fusion has more export options built-in, but they don't show the functionality in their desktop program.
## [Go to the Autodesk 360 Dashboard](https://myhub.autodesk360.com)
Going to [https://myhub.autodesk360.com](https://myhub.autodesk360.com) will give you a similar looking Project and file options as the data panel in the Desktop version. Navigate to your model and open it. Whether you are in the Design View or the 3D View in the top right corner, you'll have a few options to choose from.
Clicking the download button provides you with a ton more options than the desktop version offers. Unfortunately, you cannot download straight from the website as it takes time to convert, so you have to wait for an email. In my experience, this usually takes less than a minute although I would imagine more complex models take more time.
Below I have a list of every file-type that can be exported and a quick summary of what they are good for.
![List of Export Options](websiteExport.png)
## Fusion 360 Export Formats
The file extension doesn't give much information about what a format actually is so I aim to give a brief description of each export format below. Basic rule of thumb as of writing is to export as an `.stl` if your 3D printing, and `.obj` if your going to a different piece of software to work on the model.
:::{.column-body-outset}
| FORMAT | WHAT ITS GOOD FOR. |
|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Fusion 360 Archive | Provides a `.f3d` file-type which is a proprietary file-type for Fusion 360. Preserves full model functionality when opened in any Fusion 360, and is a great way to make personal backups of Fusion Models. |
| Inventor 2016 | Downloads either a single `.ipt` if the part is a single component, or multiple `.ipt` files and a `.iam` if your Fusion model contains multiple components. `.ipt` is an Inventor Part file, and `.iam` is an Inventor Assembly file. The models lose all parametric abilities, but keep dimensions and shape. |
| IGES | `.iges` is an open CAD format and should be usable in any CAD software. Does not retain any parametric elements |
| SAT | Developed by Dassault Systems but is a pretty widely accepted file-type for CAD software. Does not retain any parametric elements. |
| SMT | Only usable by Fusion 360 and Inventor 2019 |
| STEP | `.step` is an open CAD format and should be usable in any CAD software. Does not retain any parametric elements |
| DWG | Autodesk AutoCAD file format. Exports a wireframe of your model. Great for exporting to AutoCAd but otherwise useless. |
| DXF | Drawing format. Exports a wireframe of your model. Much better off making an actual drawing in fusion then exporting a `.dxf` |
| STL | `.stl` is an open standard used by most 3D modeling and CAD programs and is great for 3D printing, keep in mind that units are not stored in `.stl` formats, so it is important to remember what units your model was created in. If you aren't prompted by the soft then it likely assumed millimeters so you may need to convert yourself by scaling the model. |
| FBX | Autodesk owned file format. No real use in CAD and doesn't preserve dimensions so you end up with a tiny model. |
| SketchUp | Exports a `.skp` which opens in SketchUp perfectly, Great if you want to use your part in a SketchUp Project. |
| OBJ | `.obj` or Object is an open format and the geometry should be workable in most 3D modeling or CAD programs. |
:::
_If you have anything to add to this table please contact me._

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"Time (s)","Thrust (N)"
0.01,16.81
0.03,22.34
0.11,22.23
0.26,21.49
0.37,20
0.47,20.21
0.67,18.09
0.99,15.74
1.31,13.4
1.81,10.85
2.49,10.21
3.13,8.94
3.6,8.83
4.11,8.62
4.95,8.62
5.45,8.62
5.58,8.51
5.88,8.72
6.22,8.51
6.46,8.51
6.6,7.77
6.71,7.02
6.79,5.64
6.91,3.83
6.95,2.23
7,0.96
7.05,0
1 Time (s) Thrust (N)
2 0.01 16.81
3 0.03 22.34
4 0.11 22.23
5 0.26 21.49
6 0.37 20
7 0.47 20.21
8 0.67 18.09
9 0.99 15.74
10 1.31 13.4
11 1.81 10.85
12 2.49 10.21
13 3.13 8.94
14 3.6 8.83
15 4.11 8.62
16 4.95 8.62
17 5.45 8.62
18 5.58 8.51
19 5.88 8.72
20 6.22 8.51
21 6.46 8.51
22 6.6 7.77
23 6.71 7.02
24 6.79 5.64
25 6.91 3.83
26 6.95 2.23
27 7 0.96
28 7.05 0

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"Time (s)","Thrust (N)"
0.024,0.3704
0.066,0.8746
0.138,4.5044
0.246,6.6207
0.426,6.7351
1.218,7.3232
2.082,7.423
3.306,7.183
5.322,6.8385
6.978,6.7193
8.632,6.5512
10.144,6.4792
12.088,6.4254
15.472,6.3333
17.632,6.1305
19.108,6.0385
19.511,5.9607
19.804,4.4295
19.867,1.6687
19.937,0.3341
19.964,0
1 Time (s) Thrust (N)
2 0.024 0.3704
3 0.066 0.8746
4 0.138 4.5044
5 0.246 6.6207
6 0.426 6.7351
7 1.218 7.3232
8 2.082 7.423
9 3.306 7.183
10 5.322 6.8385
11 6.978 6.7193
12 8.632 6.5512
13 10.144 6.4792
14 12.088 6.4254
15 15.472 6.3333
16 17.632 6.1305
17 19.108 6.0385
18 19.511 5.9607
19 19.804 4.4295
20 19.867 1.6687
21 19.937 0.3341
22 19.964 0

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"Time (s)","Thrust (N)"
0,0
0.148,7.638
0.228,12.253
0.294,16.391
0.353,20.21
0.382,22.756
0.419,25.26
0.477,23.074
0.52,20.845
0.593,19.093
0.688,17.5
0.855,16.225
1.037,15.427
1.205,14.948
1.423,14.627
1.452,15.741
1.503,14.785
1.736,14.623
1.955,14.303
2.21,14.141
2.494,13.819
2.763,13.338
3.12,13.334
3.382,13.013
3.404,9.352
3.418,4.895
3.45,0
1 Time (s) Thrust (N)
2 0 0
3 0.148 7.638
4 0.228 12.253
5 0.294 16.391
6 0.353 20.21
7 0.382 22.756
8 0.419 25.26
9 0.477 23.074
10 0.52 20.845
11 0.593 19.093
12 0.688 17.5
13 0.855 16.225
14 1.037 15.427
15 1.205 14.948
16 1.423 14.627
17 1.452 15.741
18 1.503 14.785
19 1.736 14.623
20 1.955 14.303
21 2.21 14.141
22 2.494 13.819
23 2.763 13.338
24 3.12 13.334
25 3.382 13.013
26 3.404 9.352
27 3.418 4.895
28 3.45 0

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---
title: "Air Propulsion Simulation"
description: |
Simulating the performance of an air propulsion system as an alternative to solid rocket motors.
repository_url: https://gitlab.com/lander-team/air-prop-simulation
date: 2021-04-01
date-modified: 2024-02-29
categories:
- Julia
- Capstone
- University
creative_commons: CC BY
jupyter: julia-1.4
format:
html:
code-tools: true
code-fold: false
execute:
output: false
---
For Capstone my team was tasked with designing a system capable of moving mining equipment and materials around the surface of the Moon using a propulsive landing. The system had to be tested on Earth with something feasible for our team to build in 2 semesters. One of the first considerations my capstone advisor wanted was to test the feasibility of an air propulsion system instead of the obvious solution that of using solid rocket motors. This document is just _napkin math_ to determine if the system is even feasibly and is not meant to be a rigorous study of an air propulsion system that would easily keep a capstone team busy by itself.
```{julia}
#| code-fold: true
#| code-summary: Imports
using Plots
theme(:ggplot2); # In true R spirit
using Unitful
using DataFrames
using Measurements
using Measurements: value, uncertainty
using CSV
```
## The Simulation
I chose an off-the-shelf paintball gun tank for the pressure vessel. The primary consideration was the incredible pressure to weight ratio, and the fact that it is designed to be bumped around would be necessary for proving the safety of the system further into the project.
```{julia}
#| code-fold: false
# Tank https://www.amazon.com/Empire-Paintball-BASICS-Pressure-Compressed/dp/B07B6M48SR/
V = (85 ± 5)u"inch^3"
P0 = (4200.0 ± 300)u"psi"
Wtank = (2.3 ± 0.2)u"lb"
Pmax = (250 ± 50)u"psi" # Max Pressure that can come out the nozzle
```
The nozzle diameter was changed until the air prop system had a _burn time_ similar to a G18ST rocket motor. The propulsion system's total impulse is not dependant on the nozzle diameter, so this was just done to make it plot nicely with the rest of the rocket motors since, at this time, it is unknown what the optimal thrust profile is.
```{julia}
# Params
d_nozzle = ((1 // 18) ± 0.001)u"inch"
a_nozzle = (pi / 4) * d_nozzle^2
```
These are just universal values for what a typical day would look like during the summer in Northern Arizona. [@cengel_thermodynamics]
```{julia}
# Universal Stuff
P_amb = (1 ± 0.2)u"atm"
γ = 1.4 ± 0.05
R = 287.05u"J/(kg * K)"
T = (300 ± 20)u"K"
```
The actual simulation is quite simple. The basic idea is that using the current pressure, you can calculate $\dot{m}$, which allows calculating the Thrust, and then you can subtract the current mass of air in the tank by $\dot{m}$ and recalculate pressure using the new mass then repeat the whole process.
The bulk of the equations in the simulation came from [@cengel_thermodynamics], while the Thrust and $v_e$ equations came from [@sutton_rocket_2001, eq: 2-14].
$$ T = \dot{m} \cdot v_\text{Exit} + A_\text{Nozzle} \cdot (P - P_\text{Ambient}) $$
The initial pressure difference is `{julia} P0 - P_amb |> u"psi"`, which is massive, so the area of the nozzle significantly alters the thrust profile. The paintball tanks come with pressure regulators, in our case, 800 psi which is still a huge number compared to atmospheric pressure. While the total impulse of the system doesn't change with different nozzle areas, the peak thrust and _burn time_ vary greatly. One of the benefits of doing air propulsion and the reason it was even considered so seriously is that it should be possible to change the nozzle diameter in flight, allowing thrust to be throttled, making controlled landing easier to control.
```{julia}
df = let
t = 0.0u"s"
P = P0 |> u"Pa"
M = V * (P / (R * T)) |> u"kg"
ts = 1u"ms"
df = DataFrame(Thrust=(0 ± 0)u"N", Pressure=P0, Time=0.0u"s", Mass=M)
while M > 0.005u"kg"
# Calculate what is leaving tank
P = minimum([P, Pmax])
ve = sqrt((2 * γ / (γ - 1)) * R * T * (1 - P_amb / P)^((γ - 1) / γ)) |> u"m/s"
ρ = P / (R * T) |> u"kg/m^3"
ṁ = ρ * a_nozzle * ve |> u"kg/s"
Thrust = ṁ * ve + a_nozzle * (P - P_amb) |> u"N"
# Calculate what is still in the tank
M = M - ṁ * ts |> u"kg"
P = (M * R * T) / V |> u"Pa"
t = t + ts
df_step = DataFrame(Thrust=Thrust, Pressure=P, Time=t, Mass=M)
append!(df, df_step)
end
df
end
```
## Analysis
Below in figure 1, the result of the simulation is plotted. Notice the massive error once the tank starts running low. This is because the calculation for pressure has a lot of very uncertain variables. This is primarily due to air being a compressible fluid, making this simulation challenging to do accurately. The thrust being below 0 N might not make intuitive sense, but it's technically possible for the pressure to compress, leaving the inside of the rocket nozzle with a pressure that's actually below atmospheric pressure. The effect would likely last a fraction of a second, but the point stands that this simulation is wildly inaccurate and only meant to get an idea of what an air propulsion system is capable of.
```{julia}
#| code-folding: true
#| results: 'show'
#| fig-cap: 'Air Propulsion Simulation'
#| preview: false
#| output: true
thrust_values = df.Thrust .|> ustrip .|> value;
thrust_uncertainties = df.Thrust .|> ustrip .|> uncertainty;
air = DataFrame(Thrust=thrust_values, Uncertainty=thrust_uncertainties, Time=df.Time .|> u"s" .|> ustrip);
plot(df.Time .|> ustrip, thrust_values,
title="Thrust Over Time",
ribbon=(thrust_uncertainties, thrust_uncertainties),
fillalpha=0.2, label="Thrust",
xlabel="Time (s)",
ylabel="Thrust (N)",
)
```
In Figure 2, the air propulsion simulation is compared to commercially available rocket motors. This early in the project, we have no idea whether short burns or longer burns are ideal for a propulsive landing, so the air propulsion system was compared to a variety of different motors with unique profiles.
```{julia}
#| code-folding: true
#| results: 'show'
#| fig-cap: "Rocket Motor Data: [@thrustcurve]"
#| preview: false
#| output: true
f10 = CSV.read("AeroTech_F10.csv", DataFrame);
f15 = CSV.read("Estes_F15.csv", DataFrame);
g8 = CSV.read("AeroTech_G8ST.csv", DataFrame);
plot(air.Time, air.Thrust, label="Air Propulsion", legend=:topleft);
for (d, l) in [(f10, "F10"), (f15, "F15"), (g8, "G8ST")]
plot!(d[!, "Time (s)"], d[!, "Thrust (N)"], label=l)
end
title!("Propulsion Comparison");
xlabel!("Time (s)");
ylabel!("Thrust (N)")
```
In the end, the air propulsion system's performance has a very impressive total impulse and, with more time and resources, could be a serious option for a propulsive landing on Earth. One of the largest abstractions from the Moon mission that the mission here on Earth will have to deal with is the lack of Throttling engines since any propulsion system outside of model rocket motors is well beyond the scope of this Capstone.
## Future Work
After determining that solid model rocket motors are the best option for the current mission scope, the next step is determining what motor to use. There are many great options, and deciding what thrust profile is ideal may have to wait until a Simulink simulation of the landing can be built so that the metrics of each motor can be constrained more. Instead of throttling motors, the current working idea is that thrust vector control may be a way to squeeze a little more control out of a solid rocket motor. Thrust Vector Control will undoubtedly be challenging to control, so another essential piece that needs exploring is whether an LQR controller is feasible or if a PID controller is accurate enough to control our system.
Codebase: https://gitlab.com/lander-team/air-prop-simulation

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---
title: "ISS Eclipse Determination"
description: |
Determining how much sunlight a body orbiting a planet is receiving.
date: 2021-05-01
date-modified: 2024-02-292
categories:
- Julia
- Astrodynamics
creative_commons: CC BY
image: preview.png
format:
html:
code-tools: true
code-fold: false
kernel:
id: julia-1.4
project: ../../
---
Determining the eclipses a satellite will encounter is a major driving factor when designing a mission in space. Thermal and power budgets have to be made with the fact that a satellite will periodically be in the complete darkness of space where it will receive no solar radiation to power the solar panels and keep the spacecraft from freezing.
## What is an Eclipse
![Geometry of an Eclipse](geometry.svg)
The above image is a simple representation of what an eclipse is. First, you'll notice the Umbra is complete darkness, then the Penumbra, which is a shadow of varying darkness, and then the rest of the orbit is in full sunlight. For this example, I will be using the ISS, which has a very low orbit, so the Penumbra isn't much of a problem. However, you can tell by looking at the diagram that higher altitude orbits would spend more time in the Penumbra.
![Body Radius's and Position Vectors](vectors_radiuss.svg)
Here is a more detailed view of the eclipse that will make it easier to explain what is going on. There are 2 Position vectors, and 2 radius that need to be known for simple eclipse determination. More advanced cases where the atmosphere of the body your orbiting can significantly affect the Umbra and Penumbra, and other bodies could also potentially block the Sun. However, we will keep it simple for this example since they have minimal effect on the ISSs orbit. <code style="color:#0b7285">Rsun</code> and <code style="color:#c92a2a">Rbody</code> are the radius of the Sun and Body (In this case Earth), respectively. <code style="color:#5f3dc4">r_sun_body</code> is a vector from the center of the Sun to the center of the target body. For this example I will only be using one vector, but for more rigorous eclipse determination it is important to calculate the ephemeris at least once a day since it does significantly change over the course of a year. The reason that I am ignoring it at the moment is because there is currently no good way to calculate [Ephemerides](https://ssd.jpl.nasa.gov/?ephemerides) in Julia but the package is being worked on so I may revisit this and do a more rigorous analysis in the future. <code style="color:#5c940d">r_body_sc</code> is a position vector from the center of the body being orbited, to the center of our spacecraft.
## The Code
```{julia imports, code_folding=TRUE}
#| code-fold: true
#| code-summary: Imports
using Unitful
using LinearAlgebra
using SatelliteToolbox
using Plots
using Colors
theme(:ggplot2)
```
To get the orbit for the ISS, I used a [Two-Line Element](https://en.wikipedia.org/wiki/Two-line_element_set) which is a data format for explaining orbits. The US Joint Space Operations Center makes these widely available, but https://live.ariss.org/tle/ makes the TLE for the ISS way more accessible [@ariss]. The Julia Package [SatelliteToolbox.jl](https://github.com/JuliaSpace/SatelliteToolbox.jl) makes it super easy to turn a TLE into an orbit that can be propagated. Simply putting the TLE in a string and using the `tle` string macro like below, we now have access to the information to start making our ISS orbit.
```{julia ISS, results='show'}
ISS = tle"""
ISS (ZARYA)
1 25544U 98067A 21103.84943184 .00000176 00000-0 11381-4 0 9990
2 25544 51.6434 300.9481 0002858 223.8443 263.8789 15.48881793278621
"""
```
Now that we have the TLE, we can pass that into SatelliteToolbox's orbit propagator. Before propagating the orbit, we need to have a range of time steps to pass into the propagator. The TLE gives the mean motion, n, which is the revolutions per day, so using that, we can calculate the amount of time required for one orbit, which is all that we're worried about for this analysis. The propagator returns a tuple containing the Orbital elements, a position vector with units meters, and a velocity vector with units meters per second. For this analysis were only worried about the position vector.
```{julia mean-motion, result='show'}
ISS[1].n
```
```{julia orbit-propagation}
orbit = init_orbit_propagator(Val{:twobody}, ISS[1]);
time = 0:0.1:((24/ISS[1].n).*60*60); # ISS[1].n gives the mean motion, or orbits per day.
o, r, v = propagate!(orbit, time);
```
We just need to use the radii and vectors discussed earlier to determine if the ISS is in the penumbra or umbra. This is a lot of trigonometry and vector math that I won't bore anyone with. However, using the diagrams above and following the code in the sunlight function, you should follow what's happening. For a rigorous discussion, check out [@vallado].
```{julia sunlight-function}
function sunlight(Rbody, r_sun_body, r_body_sc)
Rsun = 695_700u"km"
hu = Rbody * norm(r_sun_body) / (Rsun - Rbody)
θe = acos((r_sun_body ⋅ r_body_sc) / (norm(r_sun_body) * norm(r_body_sc)))
θu = atan(Rbody / hu)
du = hu * sin(θu) / sin(θe + θu)
θp = π - atan(norm(r_sun_body) / (Rsun + Rbody))
dp = Rbody * sin(θp) / cos(θe - θp)
S = 1
if (θe < π / 2) && (norm(r_body_sc) < du)
S = 0
end
if (θe < π / 2) && ((du < norm(r_body_sc)) && (norm(r_body_sc) < dp))
S = (norm(r_body_sc .|> u"km") - du) / (dp - du) |> ustrip
end
return S
end
```
Then we can pass all the values we've gathered into the function we just made.
```{julia}
S = r .|> R -> sunlight(6371u"km", [0.5370, 1.2606, 0.5466] .* 1e8u"km", R .* u"m");
```
## Plotting the Results
The `sunlight` function returns values from 0 to 1, 0 being complete darkness, 1 being complete sunlight, and anything between being the fraction of light being received. Again since the ISS has a very low orbit, the amount of time spent in the penumbra is almost insignificant.
```{julia}
#| code-fold: true
#| fig-cap: ISS Sunlight
# Get fancy with the line color.
light_range = range(colorant"black", stop=colorant"orange", length=101);
light_colors = [light_range[unique(round(Int, 1 + s * 100))][1] for s in S];
plot(
LinRange(0, 24, length(S)),
S .* 100,
linewidth=5,
legend=false,
color=light_colors,
);
xlabel!("Time (hr)");
ylabel!("Sunlight (%)");
title!("ISS Sunlight Over a Day")
```
Looking at the plot, the vertical transition from 0% to 100% makes it pretty clear that the time in the penumbra is limited. Still, almost counterintuitively, it also looks like the ISS gets more sunlight than it does darkness. So, using the raw sunlight data, we can calculate precisely how much time is spent in each region.
Time in Sun:
```{julia sun-time, results='show'}
sun = length(S[S.==1]) / length(S) * 100
```
Time in Darkness:
```{julia dark-time, results='show'}
umbra = length(S[S.==0]) / length(S) * 100
```
Time in Penumbra:
```{julia penum-time, results='show'}
penumbra = 100 - umbra - sun
```
This means that even with the ISS's low orbit, it still gets sunlight ~62% of the time and spends almost no time in the penumbra. This would vary a few percent depending on the time of year, but in a circular orbit like the ISS, the amount of sunlight would remain pretty constant. There are other orbits like a polar orbit, lunar orbit, or highly elliptic earth orbits that can have their time in the sunlight vary widely by the time of year.

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---
title: "Continuous Integration and Systems Engineering"
description: |
CI is helpful for more than running tests and pushing code to production. My team is currently using it to build continuous reports of our code to keep everyone updated with its status.
date: 2021-10-04
date-modified: 2024-02-29
categories:
- Systems Engineering
- Capstone Assignment
- University
---
## The Problem
Working on a complex project that spans multiple domains can be challenging for a small team. In addition, the small team size encourages every team member to keep entirely up to date on the inner workings of every system, but that can add a ton of overhead and make progress slow to a halt.
<aside>Teammates can also slow meetings to a halt or make low quality project contributions if tasked to contribute on a subsystem they aren't knowledgeable of.</aside>
My [Capstone](https://projects.ansonbiggs.com/#category:Capstone) team makes this even harder since all, but one of us are Astronautical Engineers. Astronautical is just a fancy way of saying, "Systems, but every problem is a satellite" so a project like ours with a massive Embedded software aspect can be challenging since the most crucial part of the project is essentially an enigma to a majority of the team.
So what we needed was a way for me to write code and to keep the rest of the team up to date without ever having to touch the code and ideally without me having to generate any kind of report manually.
## The Solution
With Continuous Integration and a little Python, my GitLab repository automatically builds the latest code, runs the code, produces plots with the results, and makes an executable available if someone is inclined to run the code themselves. The automation means that the project readme is always up to date, and if anyone is wondering how progress is proceeding, they can check the readme and view the current performance in an accessible format.
The data availability is also excellent for when my team has to produce a progress report because there is never waiting on the person who can run the code and then build some plots. Unfortunately, the same can't be said for the other sub-teams who usually have difficulty compiling their progress into a report.
## The Result
All of the code being used for the Capstone is public, and the specific repository that I've been talking about can be accessed here: https://gitlab.com/lander-team/lander-cpp As you can see, the readme has charts that are always up to date. The `.gitlab-ci.yml` file contains all the build info, and you'll likely be surprised how simple of a setup it is.

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{
"cSpell.words": [
"cryptonomist"
]
}

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CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
PlotlyJS = "f0f68f2c-4968-5e81-91da-67840de0976a"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
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@misc{Jain_2021,
title = {Ripple guilty of fraud, but not securities law violations: {Messari}s {Selkis}},
url = {https://ambcrypto.com/ripple-guilty-of-fraud-but-not-securities-law-violations-messaris-selkis/},
author = {Jain, Anjali},
month = nov,
year = {2021},
note = {Abstract Note: Ripple Labs has been fighting the United States Securities and Exchange Commission (SEC) in an ugly court battle for almost a year now. And yet, still, there is no respite in sight. When the blockchain firm was first accused by the watchdog of violating securities laws, many in the crypto-community came out to support Ripple. […]}
}
@article{Shiba,
title = {Shiba inu ecosystem},
url = {https://woofpaper.org/SHIBAINU_Ecosystem_WOOF_Paper.pdf},
note = {Publisher: Shiba Inu}
}
@article{LeMahieu,
title = {Nano: {A} feeless distributed cryptocurrency network},
url = {https://content.nano.org/whitepaper/Nano_Whitepaper_en.pdf},
author = {LeMahieu, Colin},
note = {Abstract Note: Recently, high demand and limited scalability have increased the average transaction times and fees in popular cryptocurrencies, yielding an unsatisfactory experience. Here we introduce Nano, a cryptocurrency with a novel block-lattice architecture where each account has its own blockchain, delivering near instantaneous transaction speed and unlimited scalability. Each user has their own blockchain, allowing them to update it asynchronously to the rest of the network, resulting in fast transactions with minimal overhead. Transactions keep track of account balances rather than transaction amounts, allowing aggressive database pruning without compromising security. To date, the Nano network has processed 4.2 million transactions with an unpruned ledger size of only 1.7GB. Nanos feeless, split-second transactions make it the premier cryptocurrency for consumer transactions.
Publisher: nano.org}
}
@misc{Srinivasan_2017,
title = {Quantifying decentralization},
url = {https://news.earn.com/quantifying-decentralization-e39db233c28e},
publisher = {Earn.com},
author = {Srinivasan, Balaji S.},
month = oct,
year = {2017},
note = {Abstract Note: We must be able to measure blockchain decentralization before we can improve it.}
}
@misc{NLReps,
title = {{NanoLooker} representatives},
url = {https://nanolooker.com/representatives},
abstract = {Explore the nano crypto currency blockchain.},
urldate = {2021-11-28}
}
@misc{nano,
title = {Nano is a digital currency for everyone, without fees.},
url = {https://nano.org/},
abstract = {Nano is a new currency that happens to be digital. Just like the cash in your pocket, choosing to transact with nano ensures that 100\% of the value is transferred directly to the recipient. Created to facilitate both local and international payments, choosing to use nano makes moving money across borders effortless and feeless.},
urldate = {2021-11-28}
}
@misc{ripple,
title = {Ripple {\textbar} global instant payments},
url = {https://ripple.com/},
abstract = {Efficiently Move Money to all Corners of the world},
urldate = {2021-11-28}
}
@article{kraken_deposit,
title = {Kraken},
url = {https://support.kraken.com/hc/en-us/articles/203325283-Cryptocurrency-deposit-processing-times},
urldate = {2021-11-28},
month = oct,
year = {2021},
note = {Abstract Note: For security reasons, we require cryptocurrency deposits to receive a number of confirmations on its blockchain before the funds can be credited to your account.
Publisher: Kraken}
}
@misc{coingecko,
title = {{CoinGecko}},
url = {https://www.coingecko.com/},
abstract = {CoinGecko provides a fundamental analysis of the crypto market. In addition to tracking price, volume and market capitalisation, CoinGecko tracks community growth, open-source code development, major events and on-chain metrics.},
urldate = {2021-11-28}
}
@misc{cryptonomist_xrp,
title = {Who {Owns} the {Most} {XRP}},
url = {https://en.cryptonomist.ch/2020/01/25/ripple-who-owns-the-most-xrp/},
urldate = {2021-11-28},
month = jan,
year = {2020},
note = {Abstract Note: This is how Ripple ownership is distributed}
}
@misc{BTC_ECI,
title = {Bitcoin {Energy} {Consumption}},
url = {https://digiconomist.net/bitcoin-energy-consumption/},
urldate = {2021-11-28},
note = {Abstract Note: The Bitcoin Energy Consumption Index provides the latest estimate of the total energy consumption of the Bitcoin network.}
}
@misc{CanadianVelociraptor_2018,
title = {The entire {Nano} network is so efficient that, operating at 7000tps, it can be powered by a single wind turbine.},
url = {https://www.reddit.com/r/nanocurrency/comments/7ucw1a/the_entire_nano_network_is_so_efficient_that/},
urldate = {2021-11-28},
author = {{CanadianVelociraptor}},
month = feb,
year = {2018}
}
@misc{CoinbaseHowSend,
title = {Coinbase - {How} to send crypto},
url = {https://www.coinbase.com/learn/tips-and-tutorials/how-to-send-crypto},
abstract = {Learn how to send crypto to another wallet — to a friend, family member, or store.},
language = {en},
urldate = {2021-12-09},
file = {Snapshot:C\:\\Users\\anson\\Zotero\\storage\\UM9W87NV\\how-to-send-crypto.html:text/html}
}
@misc{harperNanoNetworkFlooded2021,
title = {Nanos {Network} {Flooded} {With} {Spam}, {Nodes} {Out} of {Sync}},
url = {https://www.coindesk.com/tech/2021/03/11/nanos-network-flooded-with-spam-nodes-out-of-sync/},
abstract = {Developers encouraged node operators to throttle their bandwidth in response. They had to make it worse before it could get better.},
language = {en},
urldate = {2021-12-09},
author = {Harper, Colin},
month = mar,
year = {2021},
note = {Section: Tech},
file = {Snapshot:C\:\\Users\\anson\\Zotero\\storage\\JFG5PHT8\\nanos-network-flooded-with-spam-nodes-out-of-sync.html:text/html}
}

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---
title: "Notes on Nano"
description: |
Nano (Ӿ) is a fast, feeless and severely underrated currency.
date: 2021-12-08
date-modified: 2024-02-29
categories:
- Cryptocurrency
- Notes
bibliography: citations.bib
creative_commons: CC BY
preview: banner.jpg
---
## The Current State of Crypto
In 2021 news about cryptocurrency has been dominated by _meme coins_. It is without a doubt that the crypto bull runs of 2021 have been thanks to the hype around coins with zero utility besides the ability to rapidly shoot up in value due to carefully constructed tokenomics^[@Shiba]. Unfortunately, the hype surrounding coins with actual utility has been disappointingly low in 2021.
The current narrative of the most influential coins is that Bitcoin will change financial infrastructure and keep governments from taking our freedom. Ethereum will be the backbone for everything from the internet, to art, to the mortgage on your house. The rest of the top 10 (and most of the top 100) coins by market cap are either smart-contract platforms with little community gunning for Ethereum's spot or meme coins promising valuations that challenge the wealth of the economy of the world.
## Return of the Utility
The coin I want to talk about is Nano (XNO). As of writing, it is ranked #175 by market capitalization. Nano does one thing and executes it perfectly: being a currency. The current market capitalization is far lower than other coins with similar amounts of development and community. Ethereum has a vibrant ecosystem where usage causes more usage. [The figure here illustrates this nicely.](https://twitter.com/domothy/status/1462988092848156677/photo/1) The only way to increase demand for Nano and increase the price is to get it used for everyday transactions which would make the limited supply more scarce. Currencies have so much more to offer the world than just stores of value that can shoot up in price, and I think Nano is the perfect example of that. A coin of pure utility.
<!-- ![Data from [@kraken_deposit] and [@coingecko]](caps.svg) -->
:::{.column-body-outset}
<embed
type="text/html"
src="caps.html"
width="100%"
style="height: 60vh"
/>
:::
<figcaption>Data from [@kraken_deposit] and [@coingecko]</figcaption></br>
Transactions must happen in seconds for a currency to be usable. Above are all the coins listed on Kraken, along with their confirmation time. Confirmation time is the amount of time Kraken recommends waiting before a transaction can be trusted not to be reversed on the blockchain. Kraken's times are conservative, but I believe them to be a trusted authority on the topic. It's important to note that most of the coins on this list are not designed with buying coffee in mind, but I think the comparison is still relevant since it is commonly believed that the only thing holding Bitcoin and its derivatives back from being used everywhere is hitting a critical mass of adoption among retail. Bitcoin is making progress, but Bitcoin today is far from being used as a currency.
![Data from [@coingecko]](fast.svg)
Above are all of the coins capable of settling in less than 1 second, which is about on par with a credit card. I also believe that confirming in less than a second is a benchmark that must be hit to be useful for everyday transactions. Solana stands out the most since it is the only smart contract platform on the list and has the highest market cap. Solana has a ton of potential, but I don't believe that a coin with a smart-contract platform can compete long-term with a coin specifically designed to be a currency. The DeFi and Parachain coins are more governance tokens than actual cryptocurrencies, so they aren't designed with the scale required for use as a currency. That leaves Ripple and Nano, which have wildly different market caps. Ripple is quite controversial since it is centralized to the point that I would argue it's closer to Visa than Bitcoin^[@Jain_2021]. I believe a fraudulent organization runs Ripple, so I don't want to justify it by talking about it too much here. However, it would be biased not to compare the performance and tokenomics since they are the only two serious projects with the goal to become a currency.
:::{.column-body-outset}
| | Nano^[@nano] | Ripple^[@ripple] |
| ----------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------------------------------------- |
| Ticker | XNO | XRP |
| Symbol | Ӿ | N/A |
| Market Cap^[@coingecko] | .650B | 46B |
| Transaction Speed | <1 second | 3+ seconds |
| Transaction Fee | 0 | 0.00001 XRP^[Subject to change at any moment.] |
| Minimum Nakamoto Coefficient^[@Srinivasan_2017] | 10^[@NLReps] | 1 |
| Supply | All coins minted, no mechanism to remove coins from network. | All coins minted, transactions reduce overall supply. |
| Foundation Ownership | Ӿ7 million Developer Fund | Ripple foundation and the founder own well over half of all the coins.^[cryptonomist_xrp] |
| Initial Distribution | Faucets | Initial Coin Offering |
:::
## What is Nano
**Nano is fast** because it uses a block-lattice architecture. This means that there is no concept of block times which are the limiting factor for most cryptocurrency's transaction time. Every wallet has a unique blockchain, and each "block" only contains a single transaction. Since the block is only needed when a transaction is made, wallets can pre-mine a block and hold onto the proof of work. Since each wallet manages its blockchain and proof of work, nodes only need to worry about the balance of each wallet. This enables transactions to be instantly propagated to the network, and the nodes that govern the network only keep track of the balance of each wallet.
**Nano is free** to transact because it does not rely on paying miners for proof of work, and it is designed to be efficient so running a node is cheap and does not require support from the network. Bitcoin requires 1900 kWh of power for a single transaction^[@BTC_ECI], whereas Nano is only 0.111 Wh per transaction^[CanadianVelociraptor_2018] which is a difference of about 17 million percent.^[Numbers as of writing. Mining difficulty is variable, and hardware efficiency gets better every day.] There are many arguments that no one will run the nodes required to keep the Nano network healthy without proper incentives. Nano can work around this by keeping the requirements to run a Node very low so that the indirect benefits of running a node far outweigh the costs of the hardware. Companies will likely run nodes since running a node will be cheaper than paying transaction fees for other payment methods. The Nano whitepaper goes into great detail about the types of nodes.
## Why isn't Nano Mainstream
Most cryptocurrencies have features that directly benefit other users or network maintainers from being used. Transaction fees fed back into the coin's ecosystem are a great way to create feedback loops that generate more use for a coin. The incentive structure for Nano is much less direct but arguably much more potent. Established players in the cryptocurrency and payment processor world benefit significantly from having transactions go through them. To single out Coinbase, they have a feature where you can transfer crypto between any other member on the platform instantly and free^[@CoinbaseHowSend]. This proprietary feature could create a walled garden that Nano threatens to dismantle.
It would be unfair to suggest that Nano's problems are from big established players fighting innovation. Nano's most vital features also make it susceptible to spam attacks. Since there is no fee and the network supports dividing 1 Nano into 30 decimal places, attackers can spam the network by sending trace amounts of Nano to random addresses. The ability to send these dust transactions creates a ton of stress on the network and has even ground Nano to a halt in the past^[@harperNanoNetworkFlooded2021]. However, the network did perform better than most had hoped, and the spam attack worked as an excellent verification of Nano's capabilities. Since then, the development team has focused on solutions to ensure that the issue doesn't occur again. It is worth noting that although Nano is feeless, it is not free. So, while sending Nano does not cost any Nano, it does cost computation, so attacks on the network are still costly.
---
I'll close this off by saying that I highly recommend any cryptocurrency enthusiast to please read the [Nano Whitepaper](https://content.nano.org/whitepaper/Nano_Whitepaper_en.pdf) since it is one of the best cryptocurrency whitepapers, and in my opinion, second only to the original [Bitcoin Whitepaper](https://bitcoin.org/en/bitcoin-paper). I hope that this article was insightful, and even though I am a holder of Nano, I hope that the information is unbiased. If you think Nano is worth checking out, the Nano Foundation has an excellent getting started page: https://nano.org/try-nano

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using PlotlyJS
using CSV
using HTTP
using DataFrames
using StringViews
using JSON
using Dates
kraken = """
cryptocurrency confirmationMinutes
0x (ZRX) 5
1inch (1INCH) 5
Aave (AAVE) 5
Aavegotchi (GHST) 5
Algorand (ALGO) 0.75
Ankr (ANKR) 5
Aragon (ANT) 5
Augur (REP) 5
Augur v2 (REPV2) 5
Axie Infinity (AXS) 5
Badger DAO (BADGER) 5
Balancer (BAL) 5
Bancor (BNT) 5
Band Protocol (BAND) 5
Basic Attention Token (BAT) 5
Bifrost (BNC) 4
Bitcoin (BTC) 40
Cardano (ADA) 10
Cartesi (CTSI) 5
Chainlink (LINK) 5
Chiliz (CHZ) 5
Compound (COMP) 5
Cosmos (ATOM) 0
Covalent (CQT) 5
Curve (CRV) 5
Dash (DASH) 5
Dai (DAI) ERC-20 5
Decentraland (MANA) 5
Dogecoin (DOGE) 40
dYdX (DYDX) 5
Energy Web Token (EWT) 1.75
Enjin Coin (ENJ) 5
Enzyme Finance (MLN) 5
EOS (EOS) 0
Ethereum (ETH) 5
Flow (FLOW) 1
Gnosis (GNO) 5
ICON (ICX) 0
Injective Protocol (INJ) 5
Karura (KAR) 4
Kava (KAVA) 0
Keep Network (KEEP) 5
Kusama (KSM) 2
Kyber Network (KNC) 5
Lisk (LSK) 51
Litecoin (LTC) 30
Livepeer (LPT) 5
Loopring (LRC) 5
Maker (MKR) 5
Mina (MINA) 60
Mirror Protocol (MIR) 5
Monero (XMR) 30
Moonriver (MOVR) 4
Nano (XNO) 0
Ocean (OCEAN) 5
OmiseGO (OMG) 5
Orchid (OXT) 5
Origin Protocol (OGN) 5
Oxygen (OXY) 0
PAX Gold (PAXG) 5
Perpetual Protocol (PERP) 5
Phala (PHA) 5
Polkadot (DOT) 2
Polygon (MATIC) 5
Qtum (QTUM) 60
Rarible (RARI) 5
Raydium (RAY) 0
REN Protocol (REN) 5
Ripple (XRP) 0
Serum (SRM) 0
Shiden (SDN) 4
Siacoin (SC) 60
Solana (SOL) 0
Stellar Lumens (XLM) 0
Storj (STORJ) 5
Sushi (SUSHI) 5
Synthetix (SNX) 5
tBTC (TBTC) 5
Tether USD (USDT) ERC-20 5
Tezos (XTZ) 15
The Graph (GRT) 5
The Sandbox (SAND) 5
Tron (TRX) 1
Uniswap (UNI) 5
USD Coin (USDC) 5
Waves (WAVES) 10
Wrapped Bitcoin (WBTC) 5
Yearn Finance (YFI) 5
Zcash (ZEC) 60
"""
df = CSV.File(IOBuffer(kraken)) |> DataFrame
df.symbol = [split(split(sym, "(")[end], ")")[1] |> lowercase for sym in df.cryptocurrency]
coins = HTTP.get("https://api.coingecko.com/api/v3/coins/list").body |>
StringView |> JSON.parse .|> DataFrame |> x -> vcat(x...)
coins = coins[coins.id.!="xeno-token", :]
coins = unique(coins[end:-1:1, :], :symbol);
df = innerjoin(df, coins, on = :symbol)
mktcap_url = "https://api.coingecko.com/api/v3/simple/price?vs_currencies=usd&include_market_cap=true&include_24hr_vol=true&ids=" * join(df.id, ",")
caps = HTTP.get(mktcap_url).body |>
StringView |> JSON.parse
df.mktcap = [caps[id]["usd_market_cap"] for id in df.id]
df.volume = [caps[id]["usd_24h_vol"] for id in df.id]
df.logvol = log.(df.volume)
df = df[df.mktcap.!=0, :]
p = plot(df,
x = :confirmationMinutes,
y = :mktcap, mode = "markers",
text = :cryptocurrency,
marker_color = :logvol,
marker = attr(colorscale = "Viridis", line = attr(width = 0.4, color = "DarkSlateGrey")),
Layout(title = "Market Cap vs. Confirmation Time, Data From: $(today())",),
labels = Dict(
:confirmationMinutes => "Confirmation Time (Minutes)",
:mktcap => "Market Capitalization (USD)"),
)
open("./caps.html", "w") do io
PlotlyBase.to_html(io, p.plot)
end

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using CSV
using DataFrames
using HTTP
using StringViews
using JSON
using Dates
using Plots
using StatsPlots
using Colors
theme(:ggplot2)
kraken = """
cryptocurrency confirmationMinutes
0x (ZRX) 5
1inch (1INCH) 5
Aave (AAVE) 5
Aavegotchi (GHST) 5
Algorand (ALGO) 0.75
Ankr (ANKR) 5
Aragon (ANT) 5
Augur (REP) 5
Augur v2 (REPV2) 5
Axie Infinity (AXS) 5
Badger DAO (BADGER) 5
Balancer (BAL) 5
Bancor (BNT) 5
Band Protocol (BAND) 5
Basic Attention Token (BAT) 5
Bifrost (BNC) 4
Bitcoin (BTC) 40
Cardano (ADA) 10
Cartesi (CTSI) 5
Chainlink (LINK) 5
Chiliz (CHZ) 5
Compound (COMP) 5
Cosmos (ATOM) 0
Covalent (CQT) 5
Curve (CRV) 5
Dash (DASH) 5
Dai (DAI) ERC-20 5
Decentraland (MANA) 5
Dogecoin (DOGE) 40
dYdX (DYDX) 5
Energy Web Token (EWT) 1.75
Enjin Coin (ENJ) 5
Enzyme Finance (MLN) 5
EOS (EOS) 0
Ethereum (ETH) 5
Flow (FLOW) 1
Gnosis (GNO) 5
ICON (ICX) 0
Injective Protocol (INJ) 5
Karura (KAR) 4
Kava (KAVA) 0
Keep Network (KEEP) 5
Kusama (KSM) 2
Kyber Network (KNC) 5
Lisk (LSK) 51
Litecoin (LTC) 30
Livepeer (LPT) 5
Loopring (LRC) 5
Maker (MKR) 5
Mina (MINA) 60
Mirror Protocol (MIR) 5
Monero (XMR) 30
Moonriver (MOVR) 4
Nano (XNO) 0
Ocean (OCEAN) 5
OmiseGO (OMG) 5
Orchid (OXT) 5
Origin Protocol (OGN) 5
Oxygen (OXY) 0
PAX Gold (PAXG) 5
Perpetual Protocol (PERP) 5
Phala (PHA) 5
Polkadot (DOT) 2
Polygon (MATIC) 5
Qtum (QTUM) 60
Rarible (RARI) 5
Raydium (RAY) 0
REN Protocol (REN) 5
Ripple (XRP) 0
Serum (SRM) 0
Shiden (SDN) 4
Siacoin (SC) 60
Solana (SOL) 0
Stellar Lumens (XLM) 0
Storj (STORJ) 5
Sushi (SUSHI) 5
Synthetix (SNX) 5
tBTC (TBTC) 5
Tether USD (USDT) ERC-20 5
Tezos (XTZ) 15
The Graph (GRT) 5
The Sandbox (SAND) 5
Tron (TRX) 1
Uniswap (UNI) 5
USD Coin (USDC) 5
Waves (WAVES) 10
Wrapped Bitcoin (WBTC) 5
Yearn Finance (YFI) 5
Zcash (ZEC) 60
"""
df = CSV.File(IOBuffer(kraken)) |> DataFrame
df.symbol = [split(split(sym, "(")[end], ")")[1] |> lowercase for sym in df.cryptocurrency]
coins = HTTP.get("https://api.coingecko.com/api/v3/coins/list").body |>
StringView |> JSON.parse .|> DataFrame |> x -> vcat(x...)
coins = coins[coins.id.!="xeno-token", :]
coins = unique(coins[end:-1:1, :], :symbol);
df = innerjoin(df, coins, on = :symbol)
mktcap_url = "https://api.coingecko.com/api/v3/simple/price?vs_currencies=usd&include_market_cap=true&ids=" * join(df.id, ",")
caps = HTTP.get(mktcap_url).body |>
StringView |> JSON.parse
df.mktcap = [caps[id]["usd_market_cap"] ./ 1e9 for id in df.id]
df = df[df.mktcap.!=0, :]
function imp_func(name)
important = ["Bitcoin" "Ethereum" "Nano" "Dogecoin" "XRP" "Solana"]
if name in important
println(name)
name |> typeof |> println
return name
else
return ""
end
end
df.important = imp_func.(df.name)
sort!(df, :confirmationMinutes)
let
@df df[df.important.!="", :] scatter(:confirmationMinutes, :mktcap, group = :cryptocurrency, markersize = 6, alpha = 0.9)
@df df[df.important.=="", :] scatter!(:confirmationMinutes, :mktcap, alpha = 0.5, label = "Other")
title!("Market Cap vs. Confirmation Time")
xlabel!("Confirmation Time (Minutes)")
ylabel!("Market Cap (Billions USD)")
# annotate!(12, 435, "Ethereum")
# annotate!(45, 1010, "Bitcoin")
annotate!(2, 1090, text("Prices as of: $(today())", :left, 8))
plot!(legend = :topleft)
savefig("caps.svg")
end
fast = df[df.confirmationMinutes.==0, :]
cats = [
"cosmos" "Parachain"
"icon" "Parachain"
"kava" "DeFi"
"nano" "Currency"
"oxygen" "DeFi"
"raydium" "DeFi"
"serum" "DeFi"
"solana" "Smart Contract"
"stellar" "Parachain"
"ripple" "Currency"
] |> x -> DataFrame(x, [:id, :category])
fast = innerjoin(cats, fast, on = :id)
let
@df fast bar(
:id,
:mktcap,
group = :category,
xrotation = 15,
legend = :topleft,
# yaxis = :log10
# fillcolor = [jlc.blue jlc.green jlc.purple jlc.red],
)
title!("Market Caps of Fast Coins")
ylabel!("Market Cap (Billions USD)")
savefig("fast.svg")
end

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---
title: "Machine Learning Directed Study: Report 1"
description: |
Orbital Debris Characterization using K-means clustering.
repository_url: https://gitlab.com/orbital-debris-research/directed-study/report-1
date: 2022-02-14
date-modified: 2024-02-29
categories:
- Matlab
- Orbital Debris
- University
image: clusters.svg
draft: false
---
## Gathering Data
To get started on the project before any scans of the actual debris are made available, I opted to find similar 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 of the models have at worst a non-commercial license making them suitable for this study. To start, I downloaded a high-quality model of a 6U CubeSat, which coincidentally enough was designed to detect orbital debris. This model consists of 48 individual parts, most of which are unique.
![CubeSat Used for Analysis](assembly.jpg)
## Data Preparation
To begin analysis of the satellite, fundamental properties from each piece needed to be derived. The most accessible CAD software for me is Fusion 360, but almost any CAD software can give properties for a model. The only way to export the properties from fusion is to my computer's clipboard. This creates a long process that requires clicking on each part of the CubeSats assembly, then creating a text file and pasting my clipboard to the file. This task was easily automated using an AutoHotkey script to automatically push my computer's clipboard to a file. Below is an example of each file's contents, but only a portion of the file for brevity. The text file is generated in a way that makes it difficult to parse, so a separate piece of code was used to collect all of the data from all of the part files and then turn it into a `.csv` file for easy import into Matlab.
```txt
...
Physical
Mass 108.079 g
Volume 13768.029 mm^3
Density 0.008 g / mm^3
Area 19215.837 mm^2
World X,Y,Z 149.00 mm, 103.80 mm, 41.30 mm
Center of Mass 101.414 mm, 102.908 mm, -0.712 mm
...
```
The entire file of the compiled parts properties from Fusion 360 can be seen [here.](https://gitlab.com/orbital-debris-research/directed-study/report-1/-/blob/main/compiled.csv) This method gave 22 columns of data, but most of the columns are unsuitable for the characterization of 3D geometry. The only properties considered must be scalars independent of a model's position orientation in space. Part of the data provided was a moment of inertia tensor. The tensor was processed down to $I_x$, $I_y$, and $I_z$, which was then used to calculate an $\bar{I}$. Then bounding box length, width, and height were used to compute the total volume that the object takes up. In the end, the only properties used in the analysis of the parts were: mass, volume, density, area, bounding box volume, $\bar{I}$, and material. Some parts also had to be removed because the final dataset is 44 rows and 7 columns. Below is a _Splom_ plot which is a great way to visualize data of high dimensions. As you can see, most of the properties correlate with one another.
:::{.column-body-outset}
<embed
type="text/html"
src="prepped.html"
width="100%"
style="height: 40vh"
/>
:::
<figcaption>[View Plot as an image](prepped.svg)</figcaption></br>
Now that the data is processed and clean, characterization in Matlab can begin. The original idea was to perform _PCA_, but the method had difficulties producing meaningful results. This is likely because the current dataset is tiny for machine learning and the variation in the data is high. The application of _PCA_ will be revisited once the dataset grows. The first step for characterization is importing our data into Matlab.
```m
data = readmatrix('prepped.csv');
```
Next _k-means_ will be used to cluster the data. Since it is hard to represent data in higher dimensions than two, only two columns of data will be provided for the clustering. For now, I think it makes most intuitive sense to treat volume and mass as the most critical columns since the volume vs. mass plot shows 3 reasonably distinct groups.
```m
[idx,C] = kmeans(data(:,1:2),3);
```
We can look at the distribution of parts in each cluster to ensure that each cluster has at least a few data points. Since _k-means_ is an iterative method that relies on a user guess and randomness, it's essential to ensure that the clusters make some sense.
```m
histcounts(idx) =
22 13 9
```
Then plotting Volume vs. Mass using our clusters produces the following plot. These make intuitive sense, but it is clear that the dataset needs much more data for <strong style="color:#00ff00;">Cluster 3</strong>.
![Volume and Mass clusters](clusters.svg)
Below is another _Splom_, but with the clusters found above. Since the _k-means_ only used Mass and Volume to develop its clusters, some of the properties do not cluster well against each other. This is also a powerful cursory glance at what properties are correlated.
:::{.column-body-outset}
<embed
type="text/html"
src="prepped_clustered.html"
width="100%"
style="height: 40vh"
/>
:::
<figcaption>[View Plot as an image](prepped_clustered.svg)</figcaption></br>
## 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 only two different material types. Modern satellites, and therefore their debris, is composed of dozens of unique materials. The other and harder to fix issue is finding/deriving more data properties. Properties such as cross-sectional are or aerodynamic drag would be very insightful, but there is no good way to collect that data. Thankfully, the 3D scanner methods to obtain more properties can be developed and applied over the entire dataset.
Once the dataset is grown, more advanced analysis can begin. PCA is the current goal and can hopefully be applied by the next report.
Check out the repo for this report for all the code and raw data. https://gitlab.com/orbital-debris-research/directed-study/report-1

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---
title: "Machine Learning Directed Study: Report 2"
description: |
Advanced processing of 3D meshes using Julia, and data science in Matlab.
repository_url: https://gitlab.com/orbital-debris-research/directed-study/report-2
date: 2022-04-03
date-modified: 2024-02-29
categories:
- Matlab
- Orbital Debris
- Julia
- University
image: Figures/inertia3d.png
draft: false
---
## 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.
![Example CubeSat Used for Analysis, @interfluo6UCubeSatModel](Figures/assembly.jpg)
## 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
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
[Report 1](https://gitlab.com/orbital-debris-research/directed-study/report-1/-/blob/main/README.md),
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 [@eberlyPolyhedralMassProperties2002] titled
[Polyhedral Mass Properties](https://www.geometrictools.com/Documentation/PolyhedralMassProperties.pdf),
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
[here.](https://gitlab.com/MisterBiggs/stl-process) The characteristic length is a value that is
heavily used by the NASA DebriSat project [@DebriSat2019] 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.
![Current mesh processing pipeline](Figures/current_process.svg)
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.
```txt
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](https://gitlab.com/orbital-debris-research/directed-study/report-3/-/blob/main/scaled_dataset.csv)
## 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:
```matlab
% 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:
![PCA Plot](Figures/pca.png)
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:
```matlab
for ii=1:20
[idx,~,sumd] = kmeans(inertia,ii);
J(ii)=norm(sumd);
end
```
Which produces the following plot:
![Elbow method to determine the required number of clusters.](Figures/kmeans.png)
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:
![Moments of Inertia plotted with 6 clusters.](Figures/inertia3d.png)
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.
## 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.

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# options specified here will apply to all posts in this folder
# freeze computational output
# (see https://quarto.org/docs/projects/code-execution.html#freeze)
freeze: true
# Enable banner style title blocks
title-block-banner: true
bibliography: ../citations.bib
author:
- name: Anson Biggs
url: https://ansonbiggs.com
license: "CC BY"
copyright:
holder: "Anson Biggs"
year: 2024