Data-Oriented Perspective on Hybrid Satellite-Terrestrial Uplink Communication
Bibliographic record
Abstract
Satellite networks have become central to advancing modern communication standards due to their potential for extensive global coverage. Despite this, high latency in satellite systems poses critical challenges for delay-sensitive applications, underscoring the need for reliable metrics that reflect ultra-reliable, low-latency communication. To address these challenges, data-oriented approaches, widely adopted in terrestrial networks, offer a fresh perspective by assessing transmission performance through delay outage rates. This work introduces the data-oriented approach to hybrid satellite-terrestrial networks (HSTNs), focusing on an uplink non-orthogonal multiple access (NOMA) scheme where grant-based and grant-free users coexist. The proposed analytical framework reveals the relationship between the amount of information data, power consumption, and the satellite distance, providing valuable pathway for optimizing the performance of HSTNs in the context of delay-sensitive applications.
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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 | 0.001 | 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 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".