Evolutionary Strategy for Practical Design of Passive Optical Networks
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
Passive optical networks (PONs) are an important and interesting technology for broadband access as a result of the growing demand for bandwidth over the past 10 years. An arduous and complex step in the design of such networks involves determining the placement of equipment, optical fiber cables and several other parameters relevant to the proper functioning of the network. In this paper, we propose an evolutionary strategy to optimize the infrastructure design of PONs by using genetic algorithm technique. This meta-heuristic is capable of elaborating fast, automatic and efficient solutions for the design and planning of PONs. Our proposal has been developed using real maps, aiming to minimize deployment costs and time spent to carry out PON projects, achieving pre-defined quality criteria. We considered, in our simulations, two scenarios (non-dense and dense), four possible topologies and two regions of interest. The non-dense consists of a scenario in which subscribers are distributed in a dispersed manner in the region of interest. The dense has a considerably higher number of subscribers distributed in a very close way to each other. Based on the obtained results, the potential of our proposal is quite clear, as well as its relevance from a technical, economic, and commercial point of view.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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