ALASCA: the ESA Laser Guide Star Adaptive Optics Optical Feeder Link demonstrator facility
Bibliographic record
Abstract
We report on the novel ALASCA (Advanced LGS AO for Satellite Communication Assessment) facility being built for ESA by a consortium of industry and national research institutes under the ScyLight program. The aim of ALASCA is to create a facility for Optical Feeder Links (OFL) field tests, as well as to demonstrate at the ESA Optical Ground Station in Tenerife, starting in 2023, 24/7 reliable operation of optimal Optical Feeder Links based on Laser Guide Star Adaptive Optics (LGS-AO) to solve the point-ahead problem on ground-space laser communications. Space optical communication represents a technological challenge due to its specific requirements and merit parameters; the consortium's extensive experience in LGS-AO in the astronomical field allows an expert technology transfer to earth-space communication. This will enhance the review of the ALASCA's main requirements, their implementation by a proper tailoring of the modular solutions that will be adopted by the design, facing the new challenges at system level posed by the OFL applications compared to astronomical solutions. The ALASCA project will, last but not least, provide a technology assessment and a development roadmap towards the industrial exploitation of a 24/7 operational Optical Ground Station (OGS). We will provide an overview of the ALASCA project, its goals, phases and planned timeline up to the field experiments; the presentation will then focus on the project status, including also the simulations results of LGS-AO assisted OFL.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".