Bibliographic record
Abstract
Wide-swath SAR imagery obtained by the RADARSAT ScanSAR mode can suffer from radiometric artifacts. These artifacts arise from improper application of Range Dependent Gain Corrections (RDGCs), mainly due to insufficient knowledge of the satellite's roll angle. Specifically, roll angle estimation errors as small as 0.1 degrees can cause noticeable gain errors of 1 dB or more. Beam-stitching techniques exist which can reduce, but not eliminate, these errors in the beam overlap region Current roll angle estimation algorithms do not consistently provide adequate results. These algorithms are susceptible to RDGC uncertainties in terms of pattern shape and gain offsets. This paper proposes a new data acquisition method, in which signal data is obtained during the beam switchover by transmitting pulses through one beam and receiving them with another beam. This "2-beam data" is then used in a modified algorithm to provide a more accurate and robust roll estimate. The logistics of acquiring 2-beam data are also explored. The effects of various roll angle estimation errors on different beam combinations are simulated. The algorithm results from a current and two proposed algorithms are compared. Algorithms using this 2-beam data can tolerate an overall lower mean scene /spl sigma//sup o/ and more RDGC uncertainty than standard data.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".