Monitoring cycles for fault detection in meshed all-optical networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fault detection is critical for all-optical networks (AONs). This paper introduces the concept of monitoring cycle and proposes a fault detection mechanism based on decomposing AONs into a set of cycles (a cycle cover), in which each one is defined as a monitoring cycle. Two cycle-finding algorithms are developed and compared for the proposed fault detection mechanism: heuristic depth first searching (HDFS) and shortest path Eulerian matching (SPEM). The degradation of wavelength utilization and the cardinality of cycle covers are analyzed for evaluating the proposed mechanism. The proposed mechanism is applied to four network examples: NSFNET, ARPA2, SmallNet and Bellcore. The evaluation results show that the proposed fault detection mechanism is effective and cost efficient.
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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 it