Cross-Domain Heterogeneous Data Aggregation With Dynamic Group Key Agreement for Hybrid Satellite Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it