Remote Monitoring, Control and Data Visualization for a Solar Water Pumping System
Bibliographic record
Abstract
Access to clean water is a significant challenge in many regions, including Sukkur, Pakistan. The effective management of water resources is a critical challenge, particularly in areas with limited access to surface water sources. This paper presents a remote monitoring of water pumping system designed to address water distribution challenges in Sukkur, Pakistan. The system utilizes a combination of hardware components, including Arduino Uno, Raspberry Pi 2, ultrasonic sensor, and GSM modules, to enable remote monitoring, control, and data visualization. The system architecture incorporates Node-RED, a powerful flow-based programming tool, to facilitate data communication, storage, and visualization. To enable remote monitoring and control, users can send SMS commands to the Arduino Uno, connected to the GSM module, to query the system's status and control the pump's operation. Additionally, a mobile application developed using the MIT App Inventor platform allows users to interact with the system, visualize real-time and historical data, and receive updates on water levels and pump status. The Raspberry Pi 2 serves as a server and cloud storage for the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".