State of the Art of Telecommunication Systems in Isolated and Constrained Areas
Bibliographic record
Abstract
Smart objects are deployed globally, contributing to improved communications and the growth of industrial systems’ performances. Unfortunately, isolated territories are generally excluded from this progress. Remote areas in Canada are no exception. Thus, about two hundred thousand people are living in isolated regions in Canadian territory. The development of these communities is slowed down not only by an outdated energy supply, but they are also dependent on telecommunication systems not fully deployed in those regions, thus contributing to the amplification of those populations’ isolation. Furthermore, in many remote regions of the planet, radio communication remains difficult due to geographical constraints, environmental attenuation, and the lack of telecommunication infrastructure. These regions are often referred to as “white areas,” meaning zones with little or no communication coverage. As a part of this article, a state of the art of telecommunication solutions available in an isolated environment is applied with a critical analysis based on several criteria. It shows the ability to use an original approach based on a captive balloon. Despite the proposed solution’s feasibility, several challenges need to be addressed before formally adopting it. These challenges include: (i) controlling the height of the balloon; (ii) stabilization of the balloon; and (iii) powering the system. The list of references given at the end of the paper should offer aids for the industry and for researchers working in this field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".