Performance Analysis of HSTC Network with Non-Static Terrestrial Nodes in a Fading Environment
Bibliographic record
Abstract
In this paper, Hybrid satellite-terrestrial systems (HSTC) are preferred in dynamic type environments with high mobility of nodes due to the performance enhancement features in multiple relay-based selective decode-and-forward (DF) approach. Because of the satellite link, aerial satellite to destination and satellite to relay links, are not the same due to time-selective shadowed Rician fading. Time-selective Rician fading depends on parameters like the angle of elevation of the satellite, the terrestrial relay node, and the node destination links, considered to be distinctively time-selective Nakagami faded. Here, per-frame average symbol error rate (SER) and outage probability are derived in a closed-form expression with consideration of M-ary PSK modulated symbols transmission. After evaluation, it was detected that the system performance is significantly degraded due to the time-varying nature of the links (dynamic environment). After various simulations and calculations, it was established that the error rate of the HSTC is significantly low by increasing the elevation angle of the satellite on the relay point. The performance enhancement can be observed by enhancing the satellite angle at the destination node of user equipment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".