This paper presents a novel Spiral Slot Planar Inverted-F Antenna (SSPIFA) specifically designed for telemedicine and healthcare applications, featuring compact size, biocompatible safety, and high integration suitability. By replacing the conventional top metal patch of a Planar Inverted-F Antenna (PIFA) with a slot spiral radiator whose geometry is precisely matched to the ground plane, the proposed antenna achieves a significant size reduction, making it ideal for encapsulation in miniaturized medical devices—a critical requirement for implantation scenarios. Tailored for the ISM 915 MHz band, the antenna is fabricated with a four-turn slot spiral etched on a 30 mm-diameter dielectric substrate, achieving an overall height of 22 mm and an electrically small profile of approximately 0.09λ × 0.06λ (λ: free-space wavelength at the center frequency). Simulation and measurement results demonstrate a −16 dB impedance matching (S11 parameter) at the target frequency, accompanied by a narrow fractional bandwidth of 1% and stable right-hand circular polarization (RHCP). When implanted in a layered biological tissue model (skin, fat, muscle), the antenna exhibits a near-omni directional radiation pattern in the azimuthal plane, with a peak gain of 2.94 dBi and consistent performance across the target band. These characteristics highlight the SSPIFA’s potential for reliable wireless communication in implantable medical systems, balancing miniaturization, radiation efficiency, and biocompatible design.

Accurate pavement-distress detection in unmanned aerial vehicle (UAV) imagery remains challenging because defects exhibit weak texture, irregular geometry, scale variation, and visual similarity to markings, shadows, water, and repaired pavement. This study presents YOLO-MCG, an engineering adaptation of YOLOv10 that integrates an adapted Context-Guided (CG) feature-fusion block and Multi-Scale Dilated Attention (MSDA) at the stride-8 P3 node, with fixed summation fusion. A direct audit of UAV-PDD2023 identified 11,158 boxes in 2440 images, including 44.8% small targets. Under the primary parent–frame–disjoint split, YOLO-MCG achieved precision 0.781, recall 0.503, mAP@0.5 0.562, and mAP@0.5:0.95 0.308. Across three independent parent–frame–disjoint splits, the corresponding mean metrics were $0.783\pm 0.010$, $0.505\pm 0.011$, $0.564\pm 0.004$, and $0.309\pm 0.006$, respectively. The mAP@0.5 improvement over YOLOv10n was statistically significant ($p<0.01$). Small-object AP@0.5 was $0.433\pm 0.015$, compared with $0.643\pm 0.008$ for large objects, while the mean AP of the rare repair and pothole classes was $0.535\pm 0.016$. Direct-transfer mAP@0.5 on the India, Japan, and Czech subsets of RDD2022 was $0.418\pm 0.015$, $0.392\pm 0.018$, and $0.374\pm 0.020$, respectively. A same-site, multi-condition pilot covering three altitudes and two pavement types obtained recall ranging from 0.43 to 0.52. Given the current limits of split-level resampling, multi-site validation, and survey-grade geolocation, the evidence supports human-in-the-loop screening rather than autonomous or survey-grade inspection.