Improved Accuracy of a Single-Slit Digital Sun Sensor Design for CubeSat Application Using Sub-Pixel Interpolation
Bibliographic record
Abstract
In recent years, we have seen significant improvements in the digital sun sensor (DSS) design enabled by advanced micro-systems fabrication and optical sensing technologies. In this paper, we propose a simple single-slit DSS concept with improved accuracy using sub-pixel interpolation. In considering the DSS design, we focused on several characteristics of the sun sensor, including field-of-view, sensor accuracy, complexity, and computational requirements. First, the optimal mask design was determined based on the simple geometry of the slit size, mask height and pixel width. Then, a two-step pixel read-out algorithm was implemented for sub-pixel level interpolation to determine the illumination ratio using 1-, 2-, 4- and 8-bit readouts. Lastly, the improved DSS was integrated with typical CubeSat, commercial-grade attitude sensors suite and a simple TRIAD method to determine the attitude of a CubeSat in LEO. Compared to standard 1-bit read-out mode (0.32 deg RMSE), 8-bit DSS achieves better than 0.01 deg RMSE. In a CubeSat scenario, improvements in attitude knowledge and control accuracy are marginal when using TRIAD, due to the significantly lower accuracy in other CubeSat-scale sensors (magnetometer, for example).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".