Civil Aircrafts Augmented Space–Air–Ground-Integrated Vehicular Networks: Motivation, Breakthrough, and Challenges
Why this work is in the frame
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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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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 | 0.000 |
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 it