Development of a Low-Cost, Open-Source LoRA-based SCADA System for Remote Monitoring of a Hybrid Power System for an Offshore Aquaculture Site in Newfoundland
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article a low-cost and open-source Internet of Things (IoT) based Supervisory, Control and Data Acquisition (SCADA) system for remote monitoring of the hybrid power system for an offshore aquaculture site is presented. The selected site is situated 2 km away from the coastline where there is no electrical utility infrastructure and limited communication options are available. The hardware of the designed system primarily consists of six field sensors, Arduino Leonardo as Remote Terminal Unit (RTU), LoRA (Long Range) gateway, cables, AC/DC current and voltage supplies. Arduino IDE, AWS, Influx DB, and Grafana provide the software support. The field sensors are responsible for measuring the solar, battery, inverter & generator currents, along with battery voltage and temperature. All of the field sensors except the temperature sensor send the data to RTU which further delivers it to The Things Network (TTN) cloud. With the help of influx DB, AWS cloud computing services, and Grafana, the data can be stored and visualized through interactive yet informative graphs. The graphs display the historical and live data of each sensor. Further, it also gives the option to set alarms and alerts on user-defined conditions to improve control over the hybrid power system. The complete hardware is assembled and tested in Memorial University’s Power lab. The developed system was supplied with variable current/voltage supplies and the data was logged for three continuous hours. However, the data can be stored for a much longer duration as per user’s requirement. The hardware and the results presented here are a testament that the proposed design system is capable of providing a remote monitoring solution for the offshore aquaculture site.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it