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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".