Providing Internet Access for Post-Disaster Communications using Balloon Networks
Bibliographic record
Abstract
Natural disasters such as earthquake, tsunami, land sliding, wildfires, flood and hurricane have frequently happened in many places around the world. Once disaster occurred, communication networks and information systems may be damaged, depending on the level of destruction. When communication networks are seriously affected, residents in the disaster area cannot communicate their situations and needs. In recent years, social networks play a vital role in connecting people and providing support with woe for survivors and their family during natural catastrophes around the world. Therefore, it is necessary to provide Internet services to the affected area at the earliest, so that victims can use the emergency communication network to confirm their safety. From the literature study, it was observed that the balloon networks satisfy the needs of post disaster communications. In this paper, a prototype system for a balloon network is constructed using two wireless nodes in the sky for establishing emergency communications. The Internet service is provided for the network using WiFi and tested for disaster communications, e.g., accessing social network sites and VoIP calls. Finally, we evaluate the performance of the disaster communications to analyze the suitability of balloon networks during post disaster communications. From the results it is observed that a WiFi balloon network can be deployed easily to provide Internet services to the affected area to meet users' needs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".