Availability and cost constrained fast planning of Passive Optical Networks under various survivability policies
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a planning heuristic called Locate-ONU-with-Lowest-Availability-Requirement-First (LOWLARF) to compare the coverage capability of three previously proposed survivability policies under these constraints. The heuristic is designed to determine the length of the feeder fiber and to locate each ONU on the appropriate location in order to meet the availability requirements while not violating the budget limit. The heuristic is shown to locate the ONUs within the availability requirements and the budget limit. In the test scenarios, ONUs are assumed to contract the users requesting different availability levels while a pre-specified budget limit is set for each scenario. By our proposed planning heuristic, we compare three survivability schemes under various budget constraints and split ratios within various square regions. We show that the better the availability the less the coverage in terms of total deployed fiber length. Furthermore, we also show that better protection leads to a smaller ONU region while less protection allows to cover a larger region by the ONUs.
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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