Bibliographic record
Abstract
The range migration algorithm (RMA) is an accurate algorithm to process spotlight synthetic aperture radar (SAR) data. However, in applying the RMA to process the SAR data, especially for short-dwell data, the data length has to be extended in azimuth by zero-padding it to the length of the azimuth filter. On the other hand, the spectral analysis (SPECAN, which is a deramp followed by a fast Fourier transform) algorithm is efficient, since the data extension is unnecessary, but an assumption is made that all targets have the same FM rate and the same range cell migration. A novel algorithm is introduced here to combine the accuracy of the RMA and the efficiency of SPECAN. It involves bulk azimuth uncompressing the RMA processed data with a single linear FM filter, and then refocusing the data using SPECAN, all done without the azimuth extension in the RMA. Localized artifacts generated can be removed easily. Point target simulations, including one with a high squint angle of 60/spl deg/, were successfully performed to verify the algorithm.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".