High‐frequency radar cross‐sections of swell‐contaminated seas for a pulsed waveform
Bibliographic record
Abstract
The first‐ and second‐order monostatic cross‐sections of swell‐contaminated seas for high‐frequency ground wave pulsed radar operation are derived from the fundamental electric field equations. In this case, the ocean surface, a mixture of both swell and wind wave components, is first represented with a Fourier series. In one of the two approaches used, the total Fourier coefficient is expanded as a linear sum of the contribution from swell and wind waves without considering the possible interaction between the two regimes; whereas in a second, more general situation, the analysis includes coupling effects between the wind waves and swell. Then, the corresponding electric fields received from the swell‐contaminated sea surface are obtained. A Fourier transform of the autocorrelation of the electric fields gives the Doppler power spectral density (PSD), and a comparison between this PSD and monostatic radar range equation yields the radar cross‐sections. Simulation shows that the assumption incorporating the coupling effects leads to results that better agree with historical field observations. Overall, the model proposed here not only lays a solid foundation for the development of future swell inversion algorithms, but also reveals the existence of non‐linear energy transfer between swell and wind waves from a new perspective.
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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".