Numerical rough surface scattering simulations using the FVTD method
Bibliographic record
Abstract
A method for numerically calculating the scattering from randomly rough surfaces has been developed and implemented in a finite-volume time-domain (FVTD) computational engine. A formulation that computes the total-field for planar multi-layered media was implemented and used as the source in a scattered-field implementation of the FVTD method. Computational geometries with rough surfaces exhibiting Gaussian statistics were created. Monte Carlo simulations for the scattering from the rough surfaces were performed using a scattered-field formulation of the FVTD method, which represents the rough surface as contrast sources. These contrast sources generate the scattered-fields and are more easily absorbed by the absorbing boundary conditions (ABCs), improving efficiency and accuracy. A far-field transformation is made and the normalized radar cross-section is computed. The validity of the technique is demonstrated by showing favorable comparisons with the small perturbation model (SPM). We demonstrate the applicability with a practical example for sea ice remote sensing.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".