Ocean Wave Characterization with UAS Range-Only Measurements
Bibliographic record
Abstract
Ocean wave characterization is often performed using wave buoy measurements, and these wave characteristics are used to inform marine operations; however, wave buoy or radar measurements in a desired area are not always available. In this work, an uncrewed aerial system (UAS), integrated with two range sensors, is used to estimate wave characteristics of a local wave environment. Knowledge of sea level, the UAS pose, and the measurement from each range sensor are used to characterize the dominant wave period, significant wave height, mean wave height, top-tenth wave height, and wave direction. The discrete Fourier transform (DFT) is applied to wave height measurements from the range sensors to determine the dominant wave period and mean wave heights. The direction of wave propagation is found through a novel method of correlating range sensor measurements and analyzing the wave periods encountered by the UAS. For the simulations in this research, the sea state can be successfully characterized, and the wave propagation direction determined, from the sensor measurements in both sea states 3 and 5 conditions. Future work will focus on in-water verification of these findings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".