Dependable Fiber-Wireless (FiWi) Access Networks and Their Role in a Sustainable Third Industrial Revolution Economy
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
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.
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 | 0.001 | 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 | 0.001 |
| 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