Analysis of Physical Connectivity and Cross-Layer Service Matching in User-Service-Oriented ISTN
Bibliographic record
Abstract
Aiming at service-oriented design requirements for the Integrated Satellite Terrestrial network (ISTN), this paper proposes a cross-layer analysis framework and a distributed Cognitive Space Service Network architecture. These address challenges in traditional single-layer research, including un-quantified cross-layer deviations, incomplete link analysis, and a lack of multi-dimensional evaluation. A distributed on-orbit architecture for LEO satellites is constructed to enable cross-layer cooperation across physical-layer access, network-layer routing, and application-layer service matching. Beyond channel-fading-based analysis, a multi-link model incorporating node-induced interference is established. It derives uplink access success rate expressions, quantifies impacts of user density, link distance, and carrier bands on connectivity, and verifies interference-attenuation coupling via simulations. By integrating mutual information and entropy theory, a cross-layer deviation framework is built, using confluent hypergeometric distribution to model service matching uncertainty. This achieves quantitative modeling of “physical-network layer” cooperation gains and “network-application layer” adaptation deviations. The results provide theoretical tools for optimizing space-based intelligent networks. The architecture and methods directly support enhancing large-scale satellite network quality and constructing objective functions, providing a key technical path for service-oriented future space-ground integration systems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".