Cross-Domain Heterogeneous Data Aggregation With Dynamic Group Key Agreement for Hybrid Satellite Networks
Bibliographic record
Abstract
Hybrid satellite networks, composed of Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) systems, are capable of ensuring seamless and flexible data exchange across entities. However, the inherent heterogeneity presents critical challenges for cross-domain data aggregation. Specifically, the following issues remain unsolved for current cross-domain data aggregation designs, including insufficient adaptability to the dynamic hierarchical network topologies, inflexible leader election for intra-domain data aggregation, and unsound privacy preservation for inter-domain data transmission. To overcome these limitations, a cross-domain heterogeneous data aggregation scheme for hybrid satellite networks is developed, providing dynamic group key agreement. First, an efficient re-authentication mechanism is constructed to ensure de-synchronization resistance. Meanwhile, a flexible and adaptive leader election strategy is proposed to enhance stable and seamless data exchange among dynamic LEO networks. Additionally, a secure dynamic cross-domain data transmission method is designed to resist eavesdropping and replay attacks. The security proofs and discussions regarding vital security properties are presented, while the performance analysis follows. Compared with the state-of-the-art, advantages in terms of security and performance properties can be proved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".