INGR Roadmap Satellite Chapter
Bibliographic record
Abstract
The fifth generation (5G) wireless communication systems development has brought about a paradigm shift using advanced technologies; including softwarization, virtualization, massive MIMO, and ultradensification, in addition to introducing new frequency bands. However, as societal needs for any form of information grow, it is necessary to satisfy the UN's Sustainable Development Goals (SDGs). Migrations to 6G and beyond systems are envisioned to provide augmented capacity, so massive IoT, with better performance relying on optimization made possible by artificial intelligence, it is absolutely necessary. Non-Terrestrial Networks (NTNs), including satellite systems, High-Altitude Platforms (HAPs), and Unmanned Aerial Vehicles (UAVs), provide the best solutions to connect the unconnected, unserved, and underserved in remote and rural areas. Over the past few decades, Geo Synchronous Orbits (GSO) satellite systems have been deployed to support broadband services, backhauling, Disaster Recovery and Continuity of Operations (DR-COOP), and emergency services. Recently, novel non-GSO satellite systems are attracting significant interest. Within the next few years, several thousands of Low Earth Orbit (LEO) satellites and mega-LEO constellations will provide global internet services, offering user throughput comparable to terrestrial mobile or fixed access networks. This report represents the 2023 Edition of the INGR Satellite Working Group Report, following the previous three editions [1]–[3]. This edition of the INGR Satellite Working Group Report addresses NTN and 6G more in detail, adding further contributions on optical wireless communications, artificial intelligence techniques, seamless handover, security, and recent standardization efforts given the prospected unification of terrestrial and NTN components of 6G.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.436 | 0.387 |
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".