Civil Aircrafts Augmented Space–Air–Ground-Integrated Vehicular Networks: Motivation, Breakthrough, and Challenges
Bibliographic record
Abstract
In order to meet mobile users’ unprecedented communication demands and the goal of global seamless communication, space–air–ground-integrated networks (SAGINs) have attracted lots of attention in recent years. The existing works related on air segment mainly discussed unmanned aerial vehicles (UAVs), airships, and balloons near the space. However, they neglected many other valuable resources, such as civil aircrafts (CAs). Moreover, communication problems for remote areas and emergency scenarios (such as disasters and hot-spot areas) have not been solved thoroughly. Motivated by these facts, we introduce CAs to enhance the current SAGIN and present a novel architecture called “CAs augmented space–air–ground-integrated vehicular networks” (CAA-SAGIVNs). The proposed network architecture makes breakthrough in three main aspects: 1) a normal network architecture; 2) collaboration with multiple sky access platforms (SAPs); and 3) service-oriented fair allocation. Although CAA-SAGIVN can bring out many benefits, it also faces more challenges due to its high mobility and cross-layer characteristics. Therefore, we provide an exhaustive review of state-of-the-art works on modeling, mobility management, solutions of service-oriented allocation in SAGIN. On the basis of the preliminary investigation and discussion, some open issues are identified as possible future research directions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".