Pre-Coded Inter-Satellite Routing Algorithm with Load Balancing for Mega-Constellation Networks
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Bibliographic record
Abstract
In order to achieve global multiple seamless coverage, space-based internet usually adopts low Earth orbit (LEO) mega-constellation networks structure, which has the characteristics of high network topology dynamics, limited on-board computing and storage capacity, and uneven distribution of ground traffic. Such features may cause problems such as high transmission delay, network congestion and link interruption. Establishing a stable, efficient and balanced satellite communication link can effectively alleviate the performance of the transmission delay, load balancing, and network throughput. Taking advantage of the regularity of network topology, a pre-coded inter-satellite routing algorithm with load balancing is proposed, which includes 3 parts: (a) the routing sequence coding method and the concept of gateway satellite Service Region (GSSR) are proposed; (b) the initial routing sequence of GSSR is generated based on the maximum network flow method under the ideal situation of uniform satellite traffic distribution; (c) aiming at the uneven distribution of traffic, the Sinkhorn algorithm is used to improve the load balancing performance of inter-satellite links. Simulation results show that, for the Starlink Group-4 constellation, the proposed method can maintain a low transmission delay and improve the load balancing together with the network throughout performance with minimal hops and low time complexity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it