Backscattering enhancement analysis for targets in continuous random media based on wave polarization
Bibliographic record
Abstract
Owing to the double passage effect, the phenomenon of backscattering enhancement arises in which the radar cross-section (RCS) in a random medium is twice that in free space. In a previous study, it was proved that the enhancement in radar cross-section (ERCS) deviates from two and has large and anomalous fluctuations, sometimes as a result of the wave polarization and other parameters, especially for targets in strong random media. Linear, including horizontal and vertical, polarizations were considered. In this paper, a numerical analysis is presented to show that the fluctuations can be reduced and make ERCS dependent almost only on the double passage effect under certain conditions. Therefore, we will have a better detection technique of targets of large sizes in continuous random media. In doing that, the linear and circular polarizations of incident waves are considered. We assume the case where a directly incident wave is produced by a line source in the far field distributed uniformly along the axis parallel to the conducting cylinder (target) axis.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".