Bibliographic record
Abstract
This article explores the relevance of radar sensing methods. The principles of its operation and the advantages of use are considered: the independence of obtaining images from weather conditions and the time of day, the possibility of wide viewing at long ranges with high resolution and the flexibility of controlling and changing radar parameters, which allows you to vary the position and size of the viewing area, resolution and forms of information. In addition, the main satellite radar systems were considered: satellites of the European Space Agency ERS-1,2 and ENVISAT; Canadian satellites of MDA Radarsat-1,2; satellites launched by the German Aerospace Center (DLR) and the leading European space company Airbus DS-TerraSAR-X, TanDEM-X; Spanish satellite PAZ; Japanese satellites ALOS and ALOS-2; Italian constellation of Cosmo-SkyMed satellites; Indian satellite RISAT-1; English satellite NovaSAR-1; Finnish ICEYE satellites; Korean satellite KOMPSAT-5, Chinese satellite Huan Jing 1C, European satellites Sentinel-1 (A, B) and Russian satellites Condor. Also in this article are considered radar spacecraft planned to launch, namely: the second generation of Italian satellites COSMO-SkyMed – CSG – 2; 2 spacecraft ICEYE (Finland); 4 X-band radar satellites SuperView (China); 2 radar satellites Zhuhai (China); ALOS-4 JAXA (Japan); KOMPSAT-6 (Korea), 3 radar spacecraft of the IRS constellation (India), SAOCOM (Argentina), Russian-made satellites Obzor-P1 and Kondor-FKA, in addition, ROSKOSMOS plans to create a space complex that includes an orbital constellation of 6 small-sized spacecraft for radar observation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".