Background thermography is widely used to quantify surface temperature distributions during radiofrequency heating in biomedical, material, and industrial contexts. However, reproducible datasets capturing thermal responses of biological and inert materials under controlled RF excitation are scarce. This limitation hinders the validation of electromagnetic–thermal models, the benchmarking of image-based temperature estimation techniques, and the development of super-resolution and reconstruction algorithms. To address this gap, we generated a curated thermal image dataset that documents how different materials absorb and dissipate RF energy within an ISM band. Methods We constructed a controlled RF heating system consisting of a signal generator, a 6 W power amplifier, a circulator with matched load, and a unidirectional applicator antenna operating near 2.5 GHz. Biological tissues (pork, beef, chicken) and inert materials (foam, wood, phantom) were exposed to continuous-wave RF excitation while surface temperature was monitored using three infrared cameras with low, medium, and high resolution. Thermal images were acquired under systematically varied conditions, including RF power (0–5.5 dBm, extended to 6.5 dBm for low-loss materials), camera-to-sample distance (20–30 cm), and observation angle (0°, 30°, 60°). Each recording was captured after stabilization periods to ensure repeatability, and all images were exported as calibrated per-pixel temperature maps. Metadata documenting material type, acquisition geometry, and temperature range were embedded following a standardized naming structure. A complementary data-augmentation pipeline generated resolution-specific synthetic variations, yielding additional controlled perturbations for algorithm development. Conclusions The dataset comprises 3,600 original and 600 augmented thermal images spanning nine material categories and three spatial resolutions. Its standardized acquisition protocol and comprehensive metadata enable reproducible studies on RF–material interactions, temperature-field reconstruction, geometric correction, and super-resolution enhancement. This resource is expected to support research in computer vision, hyperthermia modelling, and the development of machine learning methods for thermal imaging under controlled RF exposure.

Background thermography is widely used to quantify surface temperature distributions during radiofrequency heating in biomedical, material, and industrial contexts. However, reproducible datasets capturing thermal responses of biological and inert materials under controlled RF excitation are scarce. This limitation hinders the validation of electromagnetic–thermal models, the benchmarking of image-based temperature estimation techniques, and the development of super-resolution and reconstruction algorithms. To address this gap, we generated a curated thermal image dataset that documents how different materials absorb and dissipate RF energy within an ISM band. Methods We constructed a controlled RF heating system consisting of a signal generator, a 6 W power amplifier, a circulator with matched load, and a unidirectional applicator antenna operating near 2.5 GHz. Biological tissues (pork, beef, chicken) and inert materials (foam, wood, phantom) were exposed to continuous-wave RF excitation while surface temperature was monitored using three infrared cameras with low, medium, and high resolution. Thermal images were acquired under systematically varied conditions, including RF power (0–5.5 dBm, extended to 6.5 dBm for low-loss materials), camera-to-sample distance (20–30 cm), and observation angle (0°, 30°, 60°). Each recording was captured after stabilization periods to ensure repeatability, and all images were exported as calibrated per-pixel temperature maps. Metadata documenting material type, acquisition geometry, and temperature range were embedded following a standardized naming structure. A complementary data-augmentation pipeline generated resolution-specific synthetic variations, yielding additional controlled perturbations for algorithm development. Conclusions The dataset comprises 3,600 original and 600 augmented thermal images spanning nine material categories and three spatial resolutions. Its standardized acquisition protocol and comprehensive metadata enable reproducible studies on RF–material interactions, temperature-field reconstruction, geometric correction, and super-resolution enhancement. This resource is expected to support research in computer vision, hyperthermia modelling, and the development of machine learning methods for thermal imaging under controlled RF exposure.

The World Health Organization (WHO) states that over 55% of road traffic accident fatalities involve vulnerable road users, including 3% who are cyclists. While autonomous vehicles are capable of detecting objects and individuals on roadways, the detection of cyclists and the prediction of their movements continue to pose significant challenges. This paper presents results from the comparison of YOLOv7, YOLOv8, and YOLO-NAS architectures for urban cyclist detection. The methodology ensures that the detectors were trained under the same conditions. Subsequently, they were evaluated using 111 cyclist images with metrics such as IoU, precision, and recall. The results highlight advantages and disadvantages within each architecture, suggesting a priority for either inference time or the quality of cyclist detection in future work. La OMS establece que más del 55% de las muertes en accidentes viales son de usuarios vulnerables, incluyendo un 3% de ciclistas. Aunque los vehículos autónomos pueden detectar objetos y personas en las carreteras, la detección de ciclistas y la predicción de sus movimientos siguen siendo desafíos significativos. Este artículo presenta resultados al comparar las arquitecturas YOLOv7, YOLOv8 y YOLO-NAS para detectar ciclistas urbanos. La metodología garantiza que los detectores se entrenaron bajo las mismas condiciones. Luego, se evaluaron con 111 imágenes de ciclistas utilizando métricas como IoU, precisión y recall. Los resultados destacan ventajas y desventajas en cada arquitectura, lo que sugiere priorizar el tiempo de inferencia o la calidad de la detección de ciclistas en futuros trabajos.

A water-rich muscle-equivalent tissue-mimicking phantom within a polymeric matrix was experimentally evaluated through a multimodal characterization methodology to determine whether it reproduces the coupled dielectric–thermal behavior of hydrated biological tissue under exposure to electromagnetic waves. The material was analyzed using thermogravimetric analysis, microwave dielectric spectroscopy from 1.5 to 4.0 GHz, VIS–NIR spectroscopy between 350 and 1200 nm, and terahertz time-domain reflection. The thermogravimetric results confirmed dominant water content, with primary mass loss below 200 °C, establishing hydration as the governing factor of its thermal response. Next, the microwave dielectric measurements show that the phantom exhibits a relative permittivity of 37.4 and an electrical conductivity of 2.4 S/m. On the other hand, the VIS–NIR spectra show smooth broadband absorption with limited spatial variability, and principal component analysis reveals macroscopic optical homogeneity without structural spectral distortion. In the THz regime, strong broadband attenuation characteristic of water-rich matrices is observed, and reflection-mode measurements enable robust assessment of temporal stability through time- and frequency-domain signatures. Finally, a microwave thermal validation demonstrates stable behavior under low-power excitation, whereas under hyperthermia-level irradiation, a significant thermal drift of −3.985 °C/h was reached under non-adiabatic conditions, identifying hydration-mediated moisture redistribution as the principal limitation during prolonged high-power exposure. Collectively, these results demonstrate cross-regime dielectric–thermal consistency while explicitly defining the hydration-driven constraints governing long-term stability, providing a validated reference material for broadband electromagnetic and thermal biomedical experimentation.