Availability and Cost-Constrained Long-Reach Passive Optical Network Planning
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Bibliographic record
Abstract
To avoid huge data loss in the last mile of Internet service, Passive Optical Networks (PONs) need to be designed with a high availability guarantee. Because next generation PON includes extending the coverage of optical broadband access networks under the name long-reach PON, availability-guaranteed planning of PONs for long-reach access is required. In this paper, we propose a Mixed Integer Linear Programming (MILP)-based approach, and a heuristic algorithm, for the planning of survivable long-reach passive optical networks. The MILP-based planning model mainly consists of cost and availability constraints, while having the objective of largest possible area coverage. The heuristic is called Locate-ONU-with-Lowest-Availability-Requirement-First (LOWLARF), and it performs a faster search for the nearly optimal locations of Optical Network Units (ONUs), Optical Line Terminal (OLT), and the optical splitter having the same objective and constraints with the MILP model. The proposed heuristic and the MILP model are compared in terms of the solution spaces provided for a small sized problem. The heuristic LOWLARF introduces the advantage of significantly degraded running time, and numerical results indicate that it can provide close results to those of the MILP-based planning. On the other hand, three survivability schemes are compared in terms of deployment cost, availability, and coverage by MILP-based planning and LOWLARF. The evaluation is done by two different availability requirement scenarios. The results show that, under both scenarios, the protection scheme offering a lower bound of 99.999% availability leads to the highest deployment cost while it covers the smallest area. The protection schemes that guarantee 99.99% availability by employing less redundancy can cover a larger area under both scenarios.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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