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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".