Disaster resilience of optical networks: State of the art, challenges, and opportunities
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
For several decades, optical networks, due to their high capacity and long-distance transmission range, have been used as the major communication technology to serve network traffic, especially in the core and metro segments of communication networks. Unfortunately, our society has often experienced how the correct functioning of these critical infrastructures can be substantially hindered by massive failures triggered by natural disasters, weather-related disruptions and malicious human activities. In this position paper, we discuss the impact on optical networks of all major classes of disaster events mentioned above, and we overview recent relevant techniques that have been proposed to increase the disaster resilience of optical networks against the various classes of disaster events. We start by presenting some proactive methods to be applied before the occurrence of a disaster. Then we move our focus also on other preparedness methods that can be executed in the (typically short) time frame between the occurrence of an early alert of an incoming disaster and the time a disaster actually hits the network. Finally, we discuss reactive procedures that allow performing post-disaster recovery operations effectively. The analysis of disaster resilience mechanisms provided in this paper covers both wired and optical wireless communication infrastructures and also contains explicit remarks covering the role of emerging technologies (e.g., fixed-mobile convergence in the 5G era and beyond) in disaster resilience.
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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