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 surface thermal signature of buried landmines. Numerical predictions are obtained by solving an unsteady three-dimensional energy balance model for heat transport in dry soil with a buried mine using the conservative finite-volume method. The model is validated by comparing generated results against published analytical and numerical data in addition to indoor measurements performed on dry soil inside an environmental chamber. The thermal signatures are observed while cooling takes place after exposing the soil surface to a radiant heat flux for a specified period. Transient temperature profiles produced numerically agree well with thermocouple measurements recorded at shallow soil depths and with surface IR images. The difference between predicted and measured surface temperatures is less than 0.4°C and the difference in thermal signature is less than 0.3°C. Sit in. The numerical model is also used to predict perturbations of the expected thermal signatures that are compared to the real (measured) ones from the IR images. The thermographic analysis shows good promise as a method for detecting shallowly buried land mines where not only the temperature difference or contrast images generated by the thermal signatures are matched between the IR images and the simulation images with high accuracy, but also the absolute temperatures for many images generated at discrete time intervals.
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) | 0.000 | 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".