Monitoring and Control of a Remote Hybrid Powered Reverse Osmosis Unit for McCallum, NL
Bibliographic record
Abstract
This study presents the design and implementation of a low-cost, fully offline Supervisory Control and Data Acquisition (SCADA) system for monitoring and controlling a Reverse Osmosis (RO) water treatment unit powered by a Hybrid Energy System (HES) in the remote community of McCallum, Newfoundland and Labrador. The HES comprising PV panels, a wind turbine, batteries, and a DC diesel generator was designed and validated in prior work. To address the lack of Internet and cellular connectivity, the proposed system combines Long-Range (LoRa) communication with a local Message Queuing Telemetry Transport (MQTT) broker to facilitate real-time monitoring and bidirectional control. Two ESP32 LoRa modules form the hardware backbone, enabling wireless data transmission and control across a 400-meter range. Sensor data is visualized through FUXA, an open-source, web-based SCADA platform hosted locally. The system also provides audible alerts for fault conditions. Seven operational scenarios were tested to evaluate system performance, confirming reliable data acquisition, robust wireless communication, and effective remote actuation. Lab tests showed average end-to-end latency of 200–300 ms, zero packet loss in line-of-sight conditions, and a field-unit power demand of ~21–22 Wh/day. The modular architecture supports scaling to multiple RO units or larger communities without requiring Internet connectivity. The proposed architecture offers a scalable, energy-efficient, and Internet-independent SCADA solution for critical infrastructure in disconnected and resource-limited environments.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".