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Record W6887997394 · doi:10.18130/wc03-rv89

Senior AE Capstone: Starting the Design Process for a Space Debris Tracking Cubesat; The Tragedy of the Not-So-Commons: Megaconstellations Viewed Through the Framework of Environmental Justice

2025· article· en· W6887997394 on OpenAlexaboutno aff

Bibliographic record

VenueLibra · 2025
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsnot available
Fundersnot available
KeywordsSpace debrisCubeSatResource (disambiguation)SatellitePopulationProcess (computing)Space explorationNatural resource

Abstract

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The exponential increase of space pollution since the 1958 Vanguard 1 launch poses serious risks to current and future satellite operations. Though the U.S. Space Surveillance Network (SSN) tracks objects over 10 cm in diameter, the vast majority—roughly 70%—of orbital debris remains untracked, endangering critical satellite infrastructure. If any collide with satellites in orbit around the Earth, they could cause catastrophic failures of current satellite missions and produce more space debris. To mitigate this gap, my technical senior design capstone has begun designing a space-debris tracking CubeSat that tracks space debris smaller than 10 cm in diameter. This fills a key gap as the United States Space Surveillance Network (SSN) already tracks all objects in Earth’s orbit over 10 cm (Matney, 2016). Our work has been primarily focused on developing radar detection technology. In parallel, I used the Environmental Justice (EJ) framework to analyze similarities between current space pollution trends and past ground-side pollution trends. EJ is rooted in the idea that everyone has the right to the same environmental protection and benefits, and thus provides a basis for evaluating the finite resource that Earth’s orbital space is. The ever-increasing orbital debris population poses a growing threat to satellite infrastructure, yet over 70% of debris—particularly objects smaller than 10 cm—remains untracked. To address this, our project developed a novel debris-tracking sensor optimized for CubeSat deployment using continuous-wave (CW) radar technology. Originally centered on CubeSat design, the project shifted focus to prototyping a radar-based sensor capable of detecting and characterizing small debris. Ideally operating at 30 GHz, the sensor leverages Doppler shifts and signal amplitude to determine debris velocity, distance, and size. However, the Universal Software Radio Peripheral (USRP) used for prototyping could only reach ~5.85 GHz. Testing was conducted using GNU Radio software and 3D-printed, metal-coated spheres spun to simulate debris for sensor validation. However, due to some technical limitations, we switched to a SparkFun A111 radar sensor, though that had some limitations as well. Supporting these physical tests, three software simulations were developed: a Debris Distribution Model (DDM), Orbit Determination Model (ODM), and Orbital Debris Detection Model (ODDM). These evaluated orbital performance, sensor placement, and debris detection capability, while helping identify an optimal orbit for CubeSat deployment. Results indicate the feasibility of radar-based detection at reduced frequencies and validate predictive modeling for mission planning. This research contributes to safer space operations by improving debris-tracking systems, lowering collision risk, and informing future radar sensor integration into CubeSat platforms. While technical advances offer practical tools for collision avoidance, more comprehensive responses to orbital debris require examining the sociotechnical systems fueling current trends. Satellite megaconstellations—like Starlink and OneWeb—have exponentially increased satellite launches and debris production. Though they promise global connectivity, their unregulated growth escalates collision risks and monopolizes orbital space, raising sustainability concerns. My research applies the Environmental Justice (EJ) framework—initially developed to assess unequal exposure to terrestrial pollution—to megaconstellations and space debris. It explores how EJ principles can inform equitable environmental policy for space. Drawing from historical analogs like CFC pollution and the Montreal Protocol, the analysis reveals the urgent need for binding global governance. Current voluntary and fragmented national regulations fall short. Without coordinated action grounded in EJ’s equity and precautionary values, rapid satellite expansion risks permanent damage to orbital ecosystems and exclusion of future actors from equitable access to space. For the work done on the technical thesis, we were not able to achieve everything we set out to do. While the DDM and ODM were successful, the ODDM required some input from the physical simulation for detection parameters. While it works, the accuracy of its findings cannot be validated without successful testing of the prototype sensor. GNU Radio was too difficult to learn in a short time period, as we were not able to procure the desired hardware. While disappointing, our efforts were not in vain, as we proved that our design choices for the ideal sensor made sense. Furthermore, our documentation of working with GNU Radio and the SparkFun A111 should help follow researchers. I recommend that future capstone teams finish the radar system before worrying about the other CubeSat subsystems. My STS thesis was successful in evaluating the megaconstellation and orbital debris situation. Clear parallels were made between CFC production in the 1900s and the growth of orbital debris and megaconstellation populations. Future researchers might choose to focus on how lessons learned from other global commons, such as international waters or Antarctica, could be applied to megaconstellations. I also think that a qualitative analysis of the socioeconomic impact of space debris and megaconstellations could prove useful in understanding the current and future megaconstellation situations, as well as identifying potential actors who stand to gain or bear the risks of the current rate of expansion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0300.018

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.274
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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