Resident space object (RSO) attitude and optical property estimation from space-based light curves
Bibliographic record
Abstract
With the increase in the number of objects orbiting Earth, Space Situational Awareness (SSA) has becoming an important area of research in the space sector. Currently most sensors that contribute to SSA are large dedicated optical or radar stations, such as space fence (Pechkis, et al., 2015). With the increase in low resolution sensors in LEO there is a growing potential to utilize these to augment current SSA efforts. Star trackers are readily available and used in space for attitude determination, with recent work performed to demonstrate the benefit of using spaced-based optical measurements for Resident Space Object (RSO) detection. In this paper, we describe the interpretation of space-based measurement for light curve of an RSOs to estimate the RSOs shape, attitude and optical properties. In this model, two Bidirectional Reflectance Distribution Functions (BDRF’s) are compared, namely a defined facet model and an anthropic Phong model. From the initial results an RSOs shape, attitude, optical properties can be estimated with basic a-priory information on the shape of the RSO with both models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".