Determination of propagation parameters from fully polarimetric radar data
Bibliographic record
Abstract
The polarization characteristics of the electromagnetics waves, as related to its interaction with targets, is an important aspect in remote sensing applications. This polarization information is usually contained in either the scattering matrix or the covariance matrix depending on the target under consideration. Often, propagation effects are normally not considered in obtaining them. In clear-air conditions this a reasonable assumption, but when rain, or any other type of precipitation, exists in the ray-path, depolarization of the waves may not be negligible anymore. The anisotropy that precipitation media present causes this depolarization and that could greatly affect the scattering or covariance matrix measurements. Consequently, the real target information may be hidden in those measured matrices and it could be necessary to apply different algorithms to correct for depolarization effects prior to target information extraction. Depolarization effects can be determined and accounted for if the characteristic polarizations of the medium and their respective propagation constants are known. It is found that for precipitation media that generally present reflection symmetry these polarizations are linear and orthogonal. Then it is shown that differential attenuation can be obtained from copolar power measurements and differential phase shift from copolar correlation measurements. Considering the statistics of these covariance matrix elements and temporal correlation between successive pulses from the target, statistics of propagation parameters are analyzed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".