Middleware for Smart Heterogeneous Critical Infrastructure Networks Intercommunication
Bibliographic record
Abstract
Abstract Critical Infrastructures (CIs) are physical assets and organizations responsible for the production and distribution of society’s vital goods and services. The increasing interconnection of CIs has resulted in interdependencies which might lead to propagation of failure from one infrastructure to another. Most of current critical infrastructures are equipped with data collection and communication capabilities that can be used to inform and warn other CIs about such events and alarms. In this paper, a publish/subscribe-based communication system among dissimilar (heterogeneous) CIs is presented. The proposed system improves the manageability of CIs by providing an exchange medium for status information and alerts. It achieves this via a uniform architecture, within and across infrastructure boundaries, that maintains data restrictions that reflect real life organizational, administrative, and policy boundaries. Finally, the proposed system is modeled using the OMNET++ simulation framework, and a network performance study investigating scalability is presented. Simulation results showed that system scalability depends on service time per packet, subscription density, and number of clients per router.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".