Reliability Calculus: A Theoretical Framework to Analyze Communication Reliability
Bibliographic record
Abstract
Communication reliability is one of the most important concerns and fundamental issues in network systems, such as cyber-physical systems, where network components, sensors, actuators, controllers are interconnected with each other. These systems are prevalent in many safety-critical areas, including aerospace, automotive, civil infrastructure, energy, healthcare, manufacturing, and transportation, etc. In such systems, a single link failure, or communication delay could lead to catastrophic consequences. Hence, there is an urgent demand on efficient methodologies to model and analyze the delay distribution of control messages or feedback signals, especially when networks grow more complex and more heterogenous. In this paper, a calculus based on frequency domain analysis is developed to address this goal, so we can model and analyze the reliability of communication in large-scale compositional networked systems. Several network structures (e.g. serial, parallel, circular and backup) are defined as building blocks to model a wide variety of connections in networked systems. The advantages of the proposed theoretical framework over the traditional time domain approaches include the capability to capture higher order moments of system characteristics, calability to analyze the reliability of complex systems, efficiency in calculation and practicability in simulation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".