HydRON: ESA’s end-to-end optical satellite communication system is moving towards implementation
Bibliographic record
Abstract
The ambition of the High thRoughput Optical Network (HydRON) project of European Space Agency (ESA) is to seamlessly extend terrestrial high-capacity networks into space and empower future satcom systems with capabilities commonly found in terrestrial systems only. The project originates from an accepted proposal at the occasion of the Council Meeting at Ministerial level in November 2019. It was proposed as an ESA initiated partnership project to enable the development and validation of an endto- end optical satellite communication system. This “Internet beyond Cloud(s)" has been reported in various stages of progress and has, since its initial proposal undergone, an evolution. At the core, it did not change: HydRON targets novel optical / photonic satellite communication technologies to be integrated seamlessly into terrestrial networks at terabit-per-second capacity, supporting the next generation of institutional and commercial missions, which will require advanced and secure communication capabilities that are not yet commercially unavailable. What evolved about the project is its implementation, in close coordination with the Member States of the European Space Agency as well as European and Canadian industry, who co-fund this ambitious project. This paper will present a current overview on the HydRON Project, as elements of it are entering crucial development phases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".