Failure Localization for Shared Risk Link Groups in All-Optical Mesh Networks Using Monitoring Trails
Bibliographic record
Abstract
This paper considers the problem of out-of-band failure localization in all-optical mesh networks using bidirectional monitoring trails (bm-trails), where every possible link set with up todarbitrary links is considered as a shared risk link group (SRLG). With the SRLG scenario, the bm-trail allocation problem is firstly formulated, which includes the phases of code assignment and bm-trail formation. In the first phase, each SRLG is uniquely coded by assigning each link with a nonadaptive d̅-separable combinatorial group testing code. Then, the second phase manipulates a sophisticated yet efficient bm-trail formation process through a novel greedy code-swapping mechanism, such that any SRLG failure can be unambiguously localized by collecting the alarms of the interrupted bm-trails. The algorithm prototype can be found in . Extensive simulation is conducted on hundreds of randomly generated planar topologies to verify the proposed approach in terms of the number of required bm-trails and the computational efficiency. Our approach is compared with previously reported counterparts, by which its merits are further demonstrated.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".