Cross-Domain Noise Impact Evaluation for Black Box Two-Level Control CPS
Bibliographic record
Abstract
Control Cyber-Physical Systems (CPSs) constitute a major category of CPS. In control CPSs, in addition to the well-studied noises within the physical subsystem, we are interested in evaluating the impact of cross-domain noise : the noise that comes from the physical subsystem, propagates through the cyber subsystem, and goes back to the physical subsystem. Impact of cross-domain noise is hard to evaluate when the cyber subsystem is a black box, which cannot be explicitly modeled. To address this challenge, this article focuses on the two-level control CPS, a widely adopted control CPS architecture, and proposes an emulation based evaluation methodology framework. The framework uses hybrid model reachability to quantify the cross-domain noise impact, and exploits Lyapunov stability theories to reduce the evaluation benchmark size. We validated the effectiveness and efficiency of our proposed framework on a representative control CPS testbed. Particularly, 24.1% of evaluation effort is saved using the proposed benchmark shrinking technology.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".