Prediction of Multi-Scale Communication Demand in LEO Satellite Networks Based on Multi-Source Data Fusion
Bibliographic record
Abstract
With the rapid development of Low Earth Orbit (LEO) satellite communication networks, spatial modeling of communication demand has become essential for onboard resource scheduling and interference management. However, existing models largely rely on simplified distributional assumptions and single-source data, making it difficult to capture the spatial heterogeneity of communication demand and the complexity of its multifactorial drivers. This paper proposes a predictive framework for the spatial distribution of communication demand by fusing multi-source datasets. The core idea lies in combining the spatial feature extraction capability of Convolutional Neural Network (CNN) with the analytical power of Geographic Information Systems (GIS). The approach first constructs a multi-source data model incorporating spatial constraints. Based on this model, key influencing factors are identified and quantified through statistical-spatial analysis. Finally, a CNN-based model is employed to perform multi-scale spatial prediction of communication demand. Experiments show that the proposed method outperforms Random Forest and BP neural networks in terms of fitting accuracy and other performance metrics, and exhibits strong generalization capability across different spatial scales.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".