Bibliographic record
Abstract
Reliable real-time and uninterrupted data tele-communication between subsystems in a Cyber Physical System (CPS) is at the core of secure operation of the CPS. Time-delay and Denial-of-Service (DoS) cyberattacks are among CPS cyber threats that could impair normal functioning in the CPS, through intentional latency in the data tele-communication systems by infliction of data congestion, routing issues, parasitic electro-magnetic interference, etc. Load Frequency Control (LFC) in smart grids are among critical CPS controllers where certain time-delays in the transmission of remote system parameters could cause instability and pervasive havocs within seconds, if not detected and contained promptly. This paper considers a general interconnected CPS of heterogenous subsystems where information is randomly delayed from one subsystem to another. The paper circumvents time-delays caused at the tele-communication layer by augmenting a Multi-Agent System (MAS) of cooperative filters that restore random information delays with negligible and controllable time-delays. Consequently, real-time information would be accessible by the subsystem controllers at all times, regardless of the delays inflicted in the tele-communication layer. The LFC problem has been considered as an example of a challenging CPS that is highly vulnerable to time-delay cyberattacks, and the proposed cooperative filters were used to validate the theoretical results through numerical simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".