The I/R Thermal Balance Test of Radarsat-2: Approach to Verification / Correlation
Bibliographic record
Abstract
Radarsat-2 is a commercial Synthetic Aperture Radar satellite for earth observation. [1] The general stowed configuration is shown in Figure 1. In nominal operation mode, once deployed, the large SAR polarimetric Antenna (i.e. able to transmit and receive both horizontal and vertical polarisations) is inclined of about -29.8° versus the nominal direction of geodetic local surface normal (Right Looking mode). When is necessary to take images of South Pole, nominally not visible from SAR, the S/C must be rotated to the +29.8° position (Left Looking mode). During the Radarsat-2 thermal testing the S/C (PFM) was subjected to a first thermal balance/thermal cycling test in vacuum with simulation of external heat fluxes by means of I/R lamps and additional test heaters. A very complex thermal test configuration was required in order to simulate the continually varying thermal environment imposed by the S/C nominal sun-synchronous orbit and attitude. Nevertheless, a good compromise has been chosen between test set up complexity and orbital condition replication: a thermally representative test able to provide suitable verification of the S/C thermal performance. The objectives of the thermal balance testing (TBAL) of RADARSAT-2 are: Correlation of the Thermal Mathematical Model (TMM) TBAL temperature predictions to the test results; Validation of the heater/thermistor/thermostats thermal design (sizing and operation); Verification of thermal control operation and comparison with the TMM predictions. The completion of the correlation exercise will allow the validation of the Thermal Mathematical Model and the refinement of final flight predictions. The paper will mainly describe the Radarsat-2 Thermal Balance test conditions and the relevant analysis correlation that followed the test, as performed at David Florida Laboratory (Ottawa - Canada) in January 2005 (see also [2,3]). Moreover, a comparison of the correlation approach and results with the Alenia Spazio experience achieved on previous programs will be provided and discussed.
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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 | 0.008 |
| 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 | 0.001 |
| 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".