ITSVA: Toward 6G-Enabled Vision Analytics over Integrated Terrestrial-Satellite Network
Bibliographic record
Abstract
The sixth-generation (6G) mobile communications system is expected to integrate the terrestrial and low Earth orbit satellite networks (LSN) to provide seamless global Internet service. This will create new opportunities for edge-assisted mobile vision analytics (MVA), which offloads frames over networks to edge servers for analysis, thereby overcoming the local computational resource constraints. With the integrated terrestrial and LSN (ITLSN), edge-assisted MVA can reach its full potential in remote and maritime areas. Nevertheless, the proximity of LEO satellites to the Earth is a double-edged sword. It offers benefits in latency and data rates but also brings challenges like frequent satellite handovers and volatile channel conditions. To demystify the in-the-wild performance of ITLSN, we carry out large-scale measurements with a major LSN service provider. The measurement results confirm the highly asymmetric and dynamic network performance of today's ITLSN, which can present non-trivial challenges for MVA frame offloading. We thus propose an ITLSN -adaptive MVA offloading framework, IT SVA, to address the inherent dynamics brought by network conditions, video content, and the offloading strategy. Extensive trace-driven simulation experiments are further conducted to verify the effectiveness of ITSVA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".