Determination of Broadband Complex EM Parameters of Powdered Materials: 2. Ilmenite‐Bearing Lunar Analogue Materials
Bibliographic record
Abstract
Abstract We present systematic measurements of the frequency‐dependent complex dielectric permittivity of lunar regolith analogue samples with increasing amounts of the mineral ilmenite along with Bayesian model fits using a one‐pole Cole‐Cole model. We use these results to calculate a lower bound for the attenuation of radar signals in dB/m based on ilmenite content. We compare our measurement results with previous efforts to use Earth‐based radar maps to calculate the effect of ilmenite on radar attenuation and find that they are in agreement. We also revisit the ilmenite‐dependent loss tangent relationships of Carrier III et al. (1991) and demonstrate the significant frequency‐dependent effect of ilmenite content on signal attenuation as well as the effect of minor variations in loss tangent for depth‐to‐feature determinations. The results presented here are the first systematic laboratory measurements investigating the effect of ilmenite on radar attenuation and show future promise for the application of dielectric spectroscopy, or the identification of materials based on their electromagnetic properties in the radar and microwave range.
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 | 0.001 | 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".