Magnetic Signature Attenuation of an Unmanned Aircraft System for Aeromagnetic Survey
Bibliographic record
Abstract
A novel magnetic signature attenuation technique based on reconfiguring the location and orientation of the onboard magnetic sources of an unmanned aircraft system (UAS) is presented in this paper. The UAS, GeoSurv II, is intended for high-resolution aeromagnetic survey which requires the magnetic signature of the aircraft to be very low. Genetic algorithm (GA) is used to find an optimum configuration given multiple objective functions motivated by the application. The magnetic field contribution from a single servomotor onboard GeoSurv II is modeled as a single permanent magnet dipole, which is then used to build the cost function for the GA routine. The optimization/simulation outcome suggests very little alteration in the current configuration of the GeoSurv II servomotors resulting in a substantial improvement of the overall magnetic signature of the UAS. The simulation results are validated by practical experimentation. The experimental results, in addition to the simulation results, further confirm that the GA optimized configuration substantially outperforms the current configuration in terms of magnetic signature of GeoSurv II.
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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) | 0.000 | 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 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".