Dependable Fiber-Wireless (FiWi) Access Networks and Their Role in a Sustainable Third Industrial Revolution Economy
Bibliographic record
Abstract
According to the Organisation for Economic Co-operation and Development (OECD), broadband access networks enable the emergence of new business models, processes, inventions, as well as improved goods and services. In fact, broadband access is viewed as a so-called general purpose technology (GPT) that has the potential to fundamentally change how and where economic activity is organized. In this paper, we focus on the implications of the emerging Third Industrial Revolution (TIR) economy, which goes well beyond current austerity measures, and has recently been officially endorsed by the European Commission as the economic growth roadmap toward a competitive low carbon society by 2050. This roadmap has been receiving an increasing amount of attention by other key players, e.g., the Government of China most recently. More specifically, we describe a variety of advanced techniques to render converged bimodal fiber-wireless (FiWi) broadband access networks dependable, including optical coding based fiber fault monitoring techniques, localized optical redundancy strategies, wireless extensions, and availability-aware routing algorithms, to improve their reliability, availability, survivability, security, and safety. Next, we elaborate on how the resultant dependent FiWi access networks can be exploited to enhance the dependability of other critical infrastructures of our society, most notably the future smart power grid and its envisioned electric transportation, by means of probabilistic analysis, co-simulation, and experimental demonstration.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".