Satellite angular motion classification for active on-orbit debris removal using robots
Bibliographic record
Abstract
Purpose This paper aims to present a novel method for identification and classification of rotational motion for uncontrolled satellites. These processes are shown in context of close proximity orbital operations. In particular, it includes a manipulator arm mounted on chaser satellite and used to capture target satellites. In such situations, a precise extrapolation of the target’s docking port position is needed to determine the manipulator arm motion. The outcome of this analysis might be used in future debris removal or servicing space missions. Design/methodology/approach Nonlinear, and in some special cases, chaotic nature of satellite rotational motion was considered. Four parameters were defined: range of motion toward docking port, dominant frequencies, fractal dimension of the motion and its time dependencies. Findings The qualitative analysis was performed for presented cases of spacecraft rotational motion and for each case the respective parameters were calculated. The analysis shows that it is possible to detect the type of rotational motion. Originality/value A novel procedure allowing to estimate the type of satellite rotational motion based on fractal approach was proposed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".