Assessment and Calibration of RCM Compact-Hybrid Modes
Bibliographic record
Abstract
The RADARSAT Constellation Mission (RCM) is equipped with three SAR satellites (RCM1, RCM2 and RCM3) flying in a constellation configuration. Each SAR is equipped with Compact polarimetry (CP) capabilities. It is now admitted that the actual SAR technology does not permit the generation of a perfectly circular polarization and this may significantly affect CP information [1], [2]. Recently, a new CP calibration model [2], which explicitly takes into account the non-circularity of transmitted polarization, in addition to polarimetric antenna distortion matrix and channel imbalances, was introduced and validated using ALOS2 CP data. In this study, the Touzi CP calibration model [2] is extended to the RCM. The high isolation of RCM antennas (cross-talk lower than -40 dB) permits a simplification of the Touzi CP model and its use as the basis of a convenient method for measurement of RCM CP axial ratio (AR) using Amazonian rainforests. The Touzi CP calibration model is also used to setup the requirements on CP calibration. These new requirements on CP calibration lead to the conclusion that the current RCM calibration meets the CP requirements for all the RCM beams with AR lower (or equal) than 0.5 dB (5m-CP2-to-14, and 30mSC beams within the incidence angle range 20° -to- 43°). A new calibration method based on the Touzi RCM CP-calibration model is developed (and validated) for the correction of the non-circularity of RCM CP beams with AR larger than 0.5dB.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".