On the Development of a Second-Order Bistatic Radar Cross Section of the Ocean Surface: A High-Frequency Result for a Finite Scattering Patch
Bibliographic record
Abstract
The development of a model for the second-order bistatic high-frequency (HF) radar cross section on an ocean surface patch remote from the transmitter and receiver is addressed. A new approach is taken that allows a direct comparison with existing monostatic cross sections for finite regions of the ocean surface. The derivation starts with a general expression for the bistatically received second-order electric field in which the scattering surface is assumed to be of small height and slope. The source field is taken to be that of a vertically polarized dipole, and it is assumed that the ocean surface can be described, as is usually done, by a Fourier series in which the coefficients are zero-mean Gaussian random variables. Subsequently, a bistatic cross section of the surface, normalized to patch area, is derived. The result is verified by the following two means: 1) the complete form of the bistatic HF radar cross section in backscattering case is shown to contain an earlier monostatic result that has, itself, been used extensively in radio oceanography applications; and 2) the bistatic electromagnetic coupling coefficient is shown to reduce exactly to the monostatic result when backscattering geometry is imposed. The model is also depicted and discussed based on simulated data
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".