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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".