A Secure Multilayer Architecture for Software-Defined Space Information Networks
Bibliographic record
Abstract
Both space information networks (SINs) and software-defined networking (SDN) have gained considerable attention from industry and academia in recent years. Due to the unique characteristics of SDN and SINs, a hybrid version of them, e.g., software-defined SINs, can handle many complicated tasks. Some technological advances based on SDN are increasingly being deployed to satellite networks. In this context, the multilayer architecture makes it difficult to control the different physical devices with maximum network performance for vast volumes of traffic transmission. The multilayer architecture is considered to be a versatile framework to include different applications and facilities efficiently, while it enjoys the support of SDN as well. In this context, this article proposes a secure multilayer SDN architecture that separates the paradigm into terrestrial, aerial, and ground domains and facilitates security solutions. We explore the specifics of this architecture's development and implementation, hence finding out some problems and unanswered questions. In addition, our descriptive results demonstrate that the proposed architecture will significantly improve the multilayer efficiency gains of configuration upgrading and decision-making.
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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 | 0.001 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".