Satellite integration into 5G: Accent on testbed implementation and demonstration results for 5G Aero platform backhauling use case
Bibliographic record
Abstract
Summary The first true multitechnology communication system is 5G, which is expected to have a large impact on society and industry. The European Commission funded H2020 5G‐PPP Phase 2 project SaT5G addressed the plug‐and‐play integration of satellite communication into 5G. One of the SaT5G use cases corresponds to the delivery of 5G connectivity services to moving platforms such as aircraft via GEO/MEO satellite backhauling. With focus on this use case, this paper elaborates on the practical implementation and measurement results obtained within the 5G Aero testbed developed as part of the SaT5G project. The 5G Aero testbed activities focus on the next generation of connectivity and content distribution services to airplanes through satellite and terrestrial integration in 5G at the user, control and management planes. Software‐defined networking (SDN) and network functions virtualisation (NFV) are key enablers to develop a powerful end‐to‐end testbed that can accelerate the adoption of multi‐access edge computing (MEC) for the next‐generation in‐flight entertainment and connectivity (IFEC) services, which use geostationary (GEO) and medium Earth orbit (MEO) satellite backhauling technologies. Hence, measurement results obtained from both over‐the‐air demonstration over the O3b MEO satellite constellation and in‐lab validation over an emulated GEO satellite link are presented, towards the next‐generation 5G‐enabled IFEC services.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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".