Sentinel-1 Mission operations concept
Bibliographic record
Abstract
As part of the European Copernicus programme (formerly Global Monitoring for Environment and Security — GMES), the Sentinel-1 mission, based on a constellation of two SAR satellites, will ensure continuity of C-band SAR observations, building on ESA's and Canada's heritage on satellite SAR systems (ERS, ENVISAT and RADARSAT). 2014 marks the launch of the first Sentinel-1 satellite, which took place from Kourou on 3rdApril on a Soyuz launcher. The Sentinel-1 mission operations concept follows the overall Copernicus Space Component operations concept. The mission will be operated based on stable and predefined observation scenarios and associated systematic production schemes. During the Full Operations Capacity of the mission, the observation plans will make optimum use of the maximum SAR duty cycle within the technical constraints of the overall system, with the main objective of satisfying the observation requirements from the Copernicus services and for use by ESA and EU Member States. In addition, a secondary objective is to some extent ensure the continuity of ERS/ENVISAT SAR data exploitation, including in particular the requirements from the scientific communities. For programmatic and technical reasons, a so-called Ramp-Up operations phase has been defined, during which the capacity of the overall system, including the ground segment, will progressively increase, together with the gradual release of the operationally qualified products. The Ramp-Up phase will lead to the Full Operational Capacity of the system with the two-satellite constellation in orbit. The process of collecting the Sentinel-1 observation needs and deriving the baseline observation scenarios is described in the paper at high level. An overview of the main types of services and applications to be supported by Sentinel-1 and the category and sources of observation requirements is provided. The resulting observation plans for the first months of operations are described at high level.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".