A low‐cost Raspberry Pi based time domain reflectometer for fault detection in electric fences
Bibliographic record
Abstract
Abstract Electric fences used to create protected areas (PAs) are prone to faults that affect their operation. The conventional method of measuring the voltage of the fence periodically to detect faults and walking along the fence to locate the faults is inefficient and time consuming. This paper presents a low‐cost Raspberry Pi time domain reflectometer (TDR) for fault detection and localisation in electric fences. The system is designed using cheap off‐the‐shelf components. It uses time domain reflectometry to detect hard (open and short circuit) faults in electric fences. Time domain reflectometry is a method of detecting and locating faults in electrical cables. The Raspberry Pi TDR is evaluated and it has successfully detected and located open circuit and short circuit faults in electric fences with a mean absolute error of 1.52 m. The Raspberry Pi TDR offers the potential to remotely monitor electric fences autonomously, hence improving their effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".