Large Scale Satellite-Based Wireless Sensor Networks for Arctic Monitoring
Bibliographic record
Abstract
Abstract Nowadays wireless sensor networks (WSNs) have been widely used as a field information gathering technology in remote monitoring and control areas. However, deploying WSNs in the Arctic areas is still facing some special challenges. The extremely low temperature (below -40°C degrees) and frequent snow/ice covering may affect the stable operation of regular electronic circuitry. And inaccessibility makes the Arctic WSNs be isolated from human's maintenance most of the time. In this paper, we propose a Large-Scale Satellite-based Wireless Sensor Network (LSSWSN) architecture for the Arctic areas. Based on ZigBee-Pro protocol, our proposed LSSWSN holds the capacity of 64,000 nodes in total, which are divided into 100 sub-networks with 640 nodes for each sub-network. This proposed design can make sure some critical network faults to be isolated into small sub-network domain. Moreover, FPGA-based hardware implementation of AES has been integrated to improve communication security. Special considerations have been also taken into account for the enclosure design of the sensor nodes, routers, and coordinator within LSSWSN.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".