In situ calibration of offsetting magnetometer feedback transients on the Cassiope spacecraft
Bibliographic record
Abstract
Abstract. We present an in situ calibration process to derive the transient behavior of the offsetting fluxgate magnetometer (MGF) instruments on the Cassiope spacecraft. The dynamic behavior of the MGF changed on orbit following a software update. Characterizing the new instrument dynamics during normal spacecraft operations and then removing the transients was confounded by significant magnetic interference from the reaction wheels used to orient the spacecraft. Special operations were performed where data were taken in a safehold mode, with the reaction wheels stopped, following a single-event upset of the spacecraft bus flight computer after transiting the South Atlantic Anomaly. The slow single-axis rotation of the safehold mode was used to characterize the fluxgate's new feedback dynamics. This characterization process was then adapted for routine operation intervals with slow reaction wheel rates to allow the transient behavior to be characterized over long intervals of data spanning a wide range of temperatures. Subtracting these characterized transients from the flight data improves the instrument's noise floor and allows the instrument to accurately track rapidly changing local fields without loss of measurement fidelity. More generally, this characterization process should apply to other situations where the dynamics of an offsetting instrument must be calibrated in situ.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".