Numerical and Experimental Investigation of Thermal Signatures of Buried Landmines in Dry Soil
Bibliographic record
Abstract
This paper reports a numerical and experimental investigation conducted to study the thermal signature of buried landmines on soil surface. A finite-volume-based numerical model was developed to solve the unsteady three-dimensional heat transport equation in dry homogeneous soil with a buried mine. Numerical predictions of soil thermal response were validated by comparison with published analytical and numerical values in addition to data obtained experimentally. Experiments were performed inside an environmental chamber and soil temperatures were measured during cooling, using two measurement techniques, after exposing the soil surface to a radiant heat flux for a specified period. In the first technique, the temporal variation of the surface and internal soil temperatures were recorded using thermocouples. In the second technique, the soil surface temperature was measured using an infrared camera that revealed the thermal signature of the mine. The transient temperature profiles generated numerically agreed with measurements, and the difference between predicted and measured values was less than 0.3°C at both the soil surface and in depth. The accurate matching of numerical and IR images at the surfaces was found to strongly depend on the use of a smaller soil thermal conductivity at the surface than at greater depths. The numerical model was used to predict the dependence of the peak thermal contrast on time, depth, and heating period. The thermographic analysis, when combined with numerical predictions, holds promise as a method for detecting shallowly buried land mines.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".