On indirect method of RCS calculation of conducting targets in random media
Bibliographic record
Abstract
One of the random medium effects is the enhancement in radar cross-section (RCS) of targets, and it can be explained by the coherent addition of doubly scattered waves. In several earlier studies (Tateiba, M. and Tomita, E., 1992; El-Ocla, H. and Tateiba, 2001, 2002, 2003), we have proved that the spatial coherence length (SCL) of waves around the target in a random medium, together with the target configuration, affects the RCS and backscattering enhancement, apart from the polarization of the incident waves. This conclusion is important in radar detection and remote sensing applications. These results need a lot of computation time if we use our method directly. We propose an indirect estimate for the RCS by using a beam wave incident on a conducting target in free space. This method presents an approximate solution to the scattering problem in a random medium. This indirect estimate reduces the processing time that is so important in radar detection, especially in real time applications. H-wave scattering is quite different from E-wave scattering, especially in the resonance region, because of waves creeping along objects. Therefore, we need to analyze the scattering for both polarizations. The time factor is assumed and suppressed. This method is applicable for low and high frequency ranges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".