A multi-beam Scan Mode Synthetic Aperture Radar processor suitable for satellite operation
Bibliographic record
Abstract
As FPGA device sizes increase, they offer greater opportunities for on-site data processing, which is potentially useful for reducing the transmission requirements for applications with large data sets. For satellite applications, unlike ASICs, designers also benefit from an FPGA's ability to be reprogrammed to update functionality over the lifetime of a satellite (15+ years) and a mission (often 5+ years), while having significantly lower power costs than GPGPUs or high performance processors. This paper presents the first custom, fully pipelined, adaptable framework for multi-beam Scan Mode Synthetic Aperture Radar (SAR), the only 24/7 remote sensing imaging system that is capable of producing high-resolution global images in any weather conditions. As high resolution SAR or even low-resolution global-coverage generates on the order of hundreds of Megabytes of raw data per second, onboard SAR processing would reduce this transmitted data by orders of magnitude. Our Scan-mode SAR processor is scalable to different bit-widths and frame-sizes. We are able to process 81×730 frames of 8-bit I-Q channels from each scan (1.4 MB) in less than 1.5 ms, approximately 102 times faster than a corresponding estimated C solution leveraging the Intel Integrated Performance Primitives, and 150 times faster than the corresponding fully vectorized software solution run in MATLAB (6-core, 3.5 Ghz CPU).
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".