An open source SCADA for a solar water pumping system designed for Pakistani Conditions
Bibliographic record
Abstract
This paper is about an open source SCADA system Emoncms which is used to monitor different parameters of a solar water pumping system for Pakistani Conditions. A prototype built in lab for the overall system is also discussed in this paper. The parameters monitored are environmental parameters which include temperature, humidity and solar irradiance; hydro parameters include water level in the water tank, water flow rate into the tank and electrical parameters include Photovoltaic panel voltage, battery voltage and load current. These parameters are collected by Arduino Mega 2560 which acts as a hub for the sensors and then a string of data which contains information about different parameters which were measured using sensors is sent through a serial communication at a sampling rate of 30 seconds to Raspberry Pi which has open source SCADA Emoncms server installed. The Emoncms identify the different parameters sent to it, perform logging and display data in three different dashboards. The first dashboard has live parameters displayed over it, the second dashboard gives graphical view of real time changes in the parameters and the third dashboard displays the historically logged data of different parameters monitored.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".