Powering Environmental Monitoring Systems in Arctic Regions: A Simulation Study
Bibliographic record
Abstract
This paper describes a long-term simulation of an environmental monitoring system. This innovative approach combines harvesting-aware power management with primary batteries used as a back-up. It significantly extends the operational life of the device, while avoiding loss of data due to insufficient solar energy during winter in the harsh Arctic environment. The simulation considers the device to be located in the Arctic environment. Its main operation modes involve measurement from sensor interface, data storage and transmission. To perform an effective data-for-energy exchange, the device is controlled by a fuzzy energy management strategy. The new structure of the fuzzy rule-based system independently controls two separate variables related to data collection and the operation of a data buffer. The simulator uses meteorological data from Inuvik, Northwest Territories, Canada, to estimate the amount of energy available for solar harvesting. This site, located above the polar circle, receives very limited amounts of solar radiation during winter. Operation of the device is evaluated over a two-year period. The simulation results are described both numerically and using time-series plots of energy- and data-related variables. The performance is adequate for unsupervised operation of the system with annual maintenance visits to replace batteries. DOI: http://dx.doi.org/10.5755/j01.eee.20.7.8020
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".