LoRa Network Performance Analysis on Landslide Monitor for Landslide Disaster Mitigation in the Greater Malang Area
Bibliographic record
Abstract
The Greater Malang region, consisting of Malang City, Batu City and Malang Regency, has a high potential for landslides, influenced by varying topography. This study shows the potential for landslides and mitigation efforts in this region. Data from the Central Statistics Agency (BPS) records the number of landslide incidents, and the East Java Government has formed a Regional Disaster Management Agency (BPBD) for risk management. A research team from the National Institute of Technology Malang developed Landslide Monitor (LSdM), a Wireless Sensor Network (WSN) using Long Range (LoRa) technology. The main focus is on Quality of Service (QoS) of LoRa networks. Test results demonstrate LSdM's capabilities in Line of Sight (LoS) and Non Line of Sight (NLoS) conditions, with LoRa frequency analysis highlighting differences in packet loss rates. LSdM is expected to improve landslide disaster risk management in Greater Malang.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".