Attitude Determination with Self-Inspection Cameras Repurposed as Earth Horizon Sensors
Bibliographic record
Abstract
Attitude determination for small satellites is a vital aspect of spacecraft operations. Earth Horizon Sensor(s) (EHS) are one of many sensors used in on-orbit attitude estimation. A conventional EHS captures infrared images of the Earth’s horizon and estimates the nadir vector in the spacecraft body frame, using the Earth’s curvature and prior knowledge of the spacecraft’s orbit. However, the design and test of new sensors increase mission cost and development time, while some spacecraft may not be able to accommodate such dedicated sensors. Therefore, it is beneficial if existing onboard optical sensors could be repurposed as effective EHS. The Space Flight Laboratory has previously designed and launched the NorSat-2 spacecraft, equipped with the Miniature Vehicle Inspection Camera (mVIC) for antenna deployment inspection. This paper proposes a generalized nadir vector estimation methodology using simulation images from an optical sensor such as the mVIC, which was not originally designed as an EHS. Nadir vector estimation accuracy with software-generated sensor images is discussed and demonstrates the viability of the mVIC to be used as an EHS.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".