Planning and optimization for a multiple space debris removal mission
Bibliographic record
Abstract
The purpose of this research is to formulate and evaluate a mission plan for de-orbiting multiple pieces of large space debris, with the use of a flexible chaser-debris connection. Within this mission plan, continuous low thrust maneuvers are used to achieve the necessary orbital transfers by considering the trade-offs between fuel mass and mission time. Two sets of five debris have been selected for demonstrating the proposed methodology - both situated in lower Earth orbit. The orbital characteristics of these debris act as inputs in the analysis, together with certain chaser specifications. For each transfer, a modified Edelbaum methodology is used to account for the changes in semi-major axis and inclination. A drift orbit is optimized as part of the rendezvous phase in order to naturally change the RAAN of the chaser to match that of the debris. The outcome is the best possible trade-off between time and fuel for the mission and the transfer characteristics required to achieve it. Finally the results are compared and contrasted with a similar scenario where the de-orbiting method using impulsive transfers is considered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".