Low-Cost IoT Communication in the Arctic Region: Using the SWARM Satellite Constellation for Remote Community Connectivity
Bibliographic record
Abstract
The Arctic region is known for its harsh and remote environment. Some of the significant system problems in that region include solving communication issues and building a high-capacity terrestrial infrastructure. This study presents an innovative solution leveraging SWARM Technologies’ low-bandwidth satellite connectivity, Sustainable Distributed Cloud Infrastructure (HIVE) cloud, and devices that are used to develop an automated system for data transfer over any distance without reliance on the Internet. Using this technology, we constructed a solution that integrates SWARM devices with Amazon Web Services (AWS), utilizing an Application Programming Interface (API) for automated notification handling, data storage, and other key functionalities. This paper presented an innovative approach utilizing AWS and the HIVE cloud for easy communication and data transfer between the SWARM device and scientists around the world. This research will help provide a cost-effective method to address the issue of collecting and transferring any type of small data without the Internet in isolated areas like the Arctic region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".