MODERN SPACE RADAR SYSTEMS OF EARTH MONITORING
Bibliographic record
Abstract
This article explores brief overview of modern radar systems for imaging and monitoring the Earth from space. The operating radar systems are divided into four classes: large spacecraft with global monitoring SAR, medium-sized spacecraft with detailed observation SAR, small spacecraft with detailed observation SAR, and commercial mini-spacecraft with detailed observation SAR. Listed are the main representatives of each class. Such large satellites as: European – Sentinel-1 (A, B); Japanese – ALOS-2; Canadian company MDA – Radarsat-2; Argentine – SAOCOM-1A / 1B; Chinese – Gaofen-3. Representatives of the class of mid-size spacecraft with SAR: German Aerospace Center (DLR) and the leading European space company Airbus DS – TerraSAR-X, TanDEM-X; Spanish PAZ; the Italian constellation of Cosmo-SkyMed satellites of the first and second generation; Japanese group IGS-Radar; Korean – KOMPSAT-5; Russian satellites “Kondor”. The small class includes Israeli mission satellites – TecSAR, RISAT-2 (India), Ofeq-10; Japanese – ASNARO-2, German satellites SAR-Lupe, English – NovaSAR-1. The last class of mini-spacecraft includes American - Capella and Finnish – ICEYE. The article also presents spacecraft for radar imaging, planned for launch, namely: the second generation of Italian satellites COSMO-SkyMed – CSG-2; 8 ICEYE spacecrafts (Finland); an increase in the Capella constellation, X-band radar satellites of the SuperView constellation and radar satellites Zhuhai (China); ALOS-4 JAXA (Japan); KOMPSAT-6 (Korea), radar satellites of the IRS constellation (India), American satellites XpressSAR, PredaSAR, EOS SAR, satellites of the Russian design Obzor-R1 and KondorFKA, as well as the space complex planned by ROSKOSMOS, including an orbital constellation of 6 small spacecraft for radar surveillance.
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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 | 0.002 |
| 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".