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ghosh adhya 2025 mendeley optical soft failure benchmark  (Mendeley Ltd)

 
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    Structured Review

    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
    https://www.bioz.com/product/ghosh+adhya+2025+mendeley+optical+soft+failure+benchmark/pmc13201549-43-18-20?v=Mendeley+Ltd
    Average 86 stars, based on 1 article reviews
    ghosh adhya 2025 mendeley optical soft failure benchmark - by Bioz Stars, 2026-08
    86/100 stars

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    1) Product Images from "Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration"

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

    Journal: Scientific Reports

    doi: 10.1038/s41598-026-52186-3

    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.
    Figure Legend 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.

    Techniques Used: 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.
    Figure Legend 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.

    Techniques Used:



    Similar Products

    ghosh adhya 2025 mendeley optical soft failure benchmark  (Mendeley Ltd)
    86
    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
    https://www.bioz.com/product/ghosh+adhya+2025+mendeley+optical+soft+failure+benchmark/pmc13201549-43-18-20?v=Mendeley+Ltd
    Average 86 stars, based on 1 article reviews
    ghosh adhya 2025 mendeley optical soft failure benchmark - by Bioz Stars, 2026-08
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    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: