Bibliographic record
Abstract
The accuracy of a star tracker attitude solution is directly dependent on the number stars visible to the sensor and its ability to extract direction vectors from point spread functions of stars. Improper focus of a star tracker can negatively aect both of these parameters. We propose a procedure for focusing the optics of a nanosatellite star tracker. The procedure utilizes the Siemens Sinusoidal Star pattern to measure the modulation transfer function of the sensor. We then replicate the measured data through simulation by convolving variously sized point spread functions with an image of the star pattern. The shape of the defocused systems point spread function is then used to estimate the position of image detector at various points across the sensor eld of view. Initial results show the procedure is capable of measuring the position of the image detector with an average error of approximately 0.055mm. Precise calibration of the sensor focus can lead to improved sensor performance, specically in terms of star detection and centroid accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".