ghosh adhya 2025 mendeley optical soft failure benchmark Search Results


ghosh adhya 2025 mendeley optical soft failure benchmark  (Mendeley Ltd)
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Mendeley Ltd ghosh adhya 2025 mendeley optical soft failure benchmark
System architecture. The multi-physics stochastic simulation and <t>the</t> <t>Ghosh–Adhya</t> real-data benchmark feed a shared feature-extraction pipeline that produces 15-dimensional physics-inspired feature vectors (10 OSNR lags, velocity, acceleration, rolling mean, rolling standard deviation). The Random Forest regressor emits time-to-failure estimates; upon three consecutive sub-threshold predictions (persistence filter), the orchestration layer commits a desired-state change to a Git repository (Fig. ), triggering Kubernetes reconciliation and a Terraform-driven make-before-break migration over NETCONF/OpenROADM.
Ghosh Adhya 2025 Mendeley Optical Soft Failure Benchmark, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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System architecture. The multi-physics stochastic simulation and the Ghosh–Adhya real-data benchmark feed a shared feature-extraction pipeline that produces 15-dimensional physics-inspired feature vectors (10 OSNR lags, velocity, acceleration, rolling mean, rolling standard deviation). The Random Forest regressor emits time-to-failure estimates; upon three consecutive sub-threshold predictions (persistence filter), the orchestration layer commits a desired-state change to a Git repository (Fig. ), triggering Kubernetes reconciliation and a Terraform-driven make-before-break migration over NETCONF/OpenROADM.

Journal: Scientific Reports

Article Title: Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration

doi: 10.1038/s41598-026-52186-3

Figure Lengend Snippet: System architecture. The multi-physics stochastic simulation and the Ghosh–Adhya real-data benchmark feed a shared feature-extraction pipeline that produces 15-dimensional physics-inspired feature vectors (10 OSNR lags, velocity, acceleration, rolling mean, rolling standard deviation). The Random Forest regressor emits time-to-failure estimates; upon three consecutive sub-threshold predictions (persistence filter), the orchestration layer commits a desired-state change to a Git repository (Fig. ), triggering Kubernetes reconciliation and a Terraform-driven make-before-break migration over NETCONF/OpenROADM.

Article Snippet: The predictor is evaluated on (a) a calibrated multi-physics stochastic simulation spanning five degradation modes and (b) the Ghosh–Adhya (2025) Mendeley optical soft-failure benchmark comprising 756 real lightpaths with OSNR, BER, laser current, and received optical power across 900-sample trajectories for four failure classes.

Techniques: Extraction, Standard Deviation, Migration

Empirical characterization of the Ghosh–Adhya (2025) real-data benchmark (training split, 3,024 trajectories). Percentage of trajectories crossing the 18 dB soft-failure alarm and the 15 dB hard-failure threshold, by class. EDFA and NLI failures produce strong OSNR signatures (52% and 82% hard-threshold crossings respectively); ECL failures are OSNR-invariant due to AGC compensation (0.5% crossings, indistinguishable from no-failure baseline), establishing the scope of an OSNR-based predictor.

Journal: Scientific Reports

Article Title: Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration

doi: 10.1038/s41598-026-52186-3

Figure Lengend Snippet: Empirical characterization of the Ghosh–Adhya (2025) real-data benchmark (training split, 3,024 trajectories). Percentage of trajectories crossing the 18 dB soft-failure alarm and the 15 dB hard-failure threshold, by class. EDFA and NLI failures produce strong OSNR signatures (52% and 82% hard-threshold crossings respectively); ECL failures are OSNR-invariant due to AGC compensation (0.5% crossings, indistinguishable from no-failure baseline), establishing the scope of an OSNR-based predictor.

Article Snippet: The predictor is evaluated on (a) a calibrated multi-physics stochastic simulation spanning five degradation modes and (b) the Ghosh–Adhya (2025) Mendeley optical soft-failure benchmark comprising 756 real lightpaths with OSNR, BER, laser current, and received optical power across 900-sample trajectories for four failure classes.

Techniques: