Design, Acceptance and Capacity of Subsea Open Cables
Bibliographic record
Abstract
This article will discuss the collaboratively formed cross-industry open cables concept for characterizing optical performance of undersea cables with the intent of assessing and understanding their capacity potential. The article proposes definitions of two critical nonlinear and linear performance metrics for open cables: GSNR (Gaussian or generalized signal to noise ratio) and SNRASE(Signal to noise ratio amplified spontaneous emission), including effects such as GAWBS (guided acoustic wave Brillouin scattering) and signal droop. Measurement methodologies for these metrics are proposed, with considerations for limitations and impact of the test conditions and characteristics of the transponders used. Expanded definitions are offered to enable variable symbol rate transponders to be used for measurement, with considerations for scaling of SNR values. Considerations for using these metrics for capacity assessment and applying these techniques to concatenated multi-segment systems are introduced. Recommendations on key parameters for system specification, system characterization, and proposals for SNR-based performance budgeting tables are also discussed as foundational elements to enabling accurate estimation of the capacity potential of a subsea open cable.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".