Attitude determination and control for the Self Contained Orbit Termination Tool (SCOTT)
Bibliographic record
Abstract
This thesis presents the development of a guidance and control system for a Self-Contained Orbit Determination Tool (SCOTT). SCOTT is designed to provide CubeSats with the capability to autonomously deorbit to meet the 25-year deorbiting guideline set by the United Nations Committee On the Peaceful Use of Outer Space (UNCOPUOS), as a means to mitigating the growing space debris problem. The SCOTT module is equipped with Sun sensors, magnetometer, gyroscope, and thrusters. This use of low-cost, simple sensors provides an autonomous, self-contained solution that does not require communication with spacecraft operators or other satellites. SCOTT's guidance system measures the Sun and magnetic field vectors over an orbit and uses the angle between the two measured vectors to determine the retrograde direction through a particle swarm optimization algorithm and a power spectral density analysis. SCOTT's control strategy consists of pointing the spacecraft's deorbit thrusters in the retrograde direction by controlling to prescribed Sun sensor and magnetometer measurements. The prescribed measurements represent the sensor readings that would be collected if the spacecraft was pointed in the correct direction for a retrograde burn. Using simulation, I developed an integrated guidance and control operational procedure that is able to self-assess its own optimization solution and autonomously carry out a deorbit maneuver. The results show that SCOTT is successfully able to determine its position, and perform a deorbit burn that maneuvers the spacecraft into a lower orbit that accelerates deorbit and ensures conformance to the deorbiting guideline.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".