Fine lateral and longitudinal sensor (FLLS) on-board ESA’S PROBA-3 mission
Bibliographic record
Abstract
PROBA-3 is a European Space Agency (ESA) mission to study the Sun’s corona, and is the world’s first precision formation flying mission. The mission will comprise a pair of satellites separated by 150 m, whose relative displacement must be monitored to within 300 µm in order to produce an accurate coronagraph. This measurement is provided by FLLS – the Fine Lateral and Longitudinal Sensor – being designed and built by Neptec UK and Neptec Design Group Canada. FLLS uses a retro-reflected laser beam to monitor the position of the occulter-disc satellite with respect to the coronagraph satellite. Phase measurements of the reflected beam are used to determine the longitudinal displacement between the two satellites – up to 250 m – while the motion of the returning beam on a CMOS sensor measures the lateral displacement. This system is being designed in collaboration with Surrey Space Centre, and presents exciting challenges in performance testing and ground-based calibration over its full operating range. The completed FLLS system will be suitable for any type of mission requiring accurate displacement measurements. This could be between a constellation of satellites observing the Earth, or within a science mission monitoring instrumentation positions. FLLS could allow large-scale structures to be deployed and maintained in space, monitoring structural distortion before, during, and after deployment, and providing in-flight corrections to data collection. Examples of such future applications include in-orbit observatories, positioning of telecommunication satellite antennas, and deployable mechanisms on lunar or Martian missions. The paper and anticipated presentation will provide a comprehensive overview of FLLS including technical designs, calibration and performance test plans, as well as the envisaged scope for future applications.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".