Development of a Traceable Dielectric-Property Dataset for Femoral-Fracture Monitoring in the 1–4-GHz Band With Cross-Database Validation and Machine-Learning Proof of Concept

Reliable dielectric-property models are a prerequisite for credible forward electromagnetic simulation of microwave-based femoral-fracture monitoring systems. This paper presents a traceable dielectric-property dataset for the 1 to 4 GHz band, with explicit surrogate justification and cross-database validation. Reference targets are consolidated from canonical wideband sources and the IT’IS Tissue Properties Database (v5.0). Compact single-term Cole–Cole parameters are fitted for fracture-relevant tissues and healing-stage constituents. Fracture scenarios (fracture type × healing stage) are constructed using mixing rules with uncertainty bounds derived from physiological variability and volume-fraction uncertainty. We explicitly separateproperty fitting from forward simulation:the fitted properties are intended as inputs to a forward FDTD solver (with CPML) for design studies; no inversion is performed. Across representative tissues, bandwise agreement between fitted models and reference targets yields NRMSE below 10% with Pearson correlation ρ ≥ 0.97. Cross-database comparison against four independent sources (Gabriel, IT’IS, IFAC-CNR, Sasaki/NICT) demonstrates inter-database consistency within 2.4% maximum deviation at 2 GHz for representative tissues; the additional Cole–Cole fitting approximation introduces deviations of up to 6.9%, which remain within the assigned physiological variability bounds. Surrogate selections for healing-stage constituents are justified physiologically based on water content similarity and assigned conservative uncertainty bounds. As a supplementary downstream demonstration, a machine-learning proof of concept reports (i) 56.1% accuracy on a challenging fine-grained 7-class synthetic task and (ii) 57.1% accuracy on an independent external bio-phantom dataset, confirming that the moderate classification accuracy reflects genuine physical overlap between adjacent healing stages. The dataset and protocols are archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.18027914).