Reliable fiber-wireless access networks: Less an end than a means to an end
Bibliographic record
Abstract
In coming years, broadband access networks are expected to undergo a couple of paradigm shifts. First, copper will play a less important role and eventually give way to bimodal fiber-wireless (FiWi) access networks. Second, broadband access networks will become less an end in itself than a means to an end by exploiting them not only for telecommunications per se but also other relevant economic sectors of the future low carbon society. Despite recent research activities demonstrating that both fiber and wireless media might be also used to transfer small amounts of energy over limited distances, it is anticipated that fiber and wireless technologies represent the two remaining complementary building blocks of future converged communications networks, while copper remains the energy (but not necessarily data) transmission medium of choice in future smart power grids. In this paper, we explore ways of using dependent FiWi access networks to enable or enhance the dependability of other critical infrastructures of today's society, most notably the future smart power grid. After discussing the respective pros and cons of a variety of available access networking technologies, we elaborate on the rationale behind the design choices of our Uber-FiWi network and showcase its suitability as a holistic end-to-end smart grid communications infrastructure for next-generation power distribution networks via illustrative experimental and co-simulation studies on emulated power blackouts during a security breach and coordinated plug-in electric vehicle (PEV) charging without a deviated voltage profile and deteriorated power quality.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".