Real‐time compensation of magnetic data acquired by a single‐rotor unmanned aircraft system
Bibliographic record
Abstract
ABSTRACT Two methods for low‐altitude calibration of a single‐rotor unmanned aircraft system using a real‐time compensator are tested: (1) a stationary calibration where the unmanned aircraft system executes manoeuvres while hovering in order to minimize ambient field changes due to the local geology; and (2) an adapted box calibration flown in four orthogonal directions. Both methods use two compensator‐specific limits derived from established methods for manned airborne calibration: the lowest frequency used by the compensator for the calibration algorithm and the maximum variation of the ambient magnetic intensity experienced by the unmanned aircraft system during calibration. Prior to flying, the unmanned aircraft system was magnetically characterized using the heading error and fourth difference. Magnetic interference was mitigated by extending the magnetometer‐unmanned aircraft system separation distance to 1.7 m, shielding, and demagnetization. The stationary calibration yielded an improvement ratio of 8.595 and a standard deviation of the compensated total magnetic intensity of 0.075 nT (estimated Figure‐of‐Merit of 3.8 nT). The box calibration also yielded an improvement ratio of 3.989 and a standard deviation of the compensated total magnetic intensity of 0.083 nT (estimated Figure‐of‐Merit of 4.2 nT). The stationary and box calibration solutions were robust with low cross‐correlation indexes (1.090 and 1.048, respectively) when applied to a non‐native data set.
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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 | 0.001 | 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) | 0.001 | 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".