F1 Layer Modeling of Ionospheric Electron-density Distribution

As a new method to synthesise a multi-quasiparabolic (MQP) profile, the gradient of the electron density distribution of the ionosphere is used as input data. This method suits F1 layer modelling, providing a wide range of realistic shapes in the vertical ionograms by varying the gradient. Although we focus on the vertical propagation case, further simulations have shown the effect of this modelling on oblique incidence high frequency applications

Optimization and sensitivity analysis of existing deep learning models for pavement surface monitoring using low-quality images

Automated pavement distress detection systems have become increasingly sought after by road agencies to in crease the efficiency of field surveys and reduce the likelihood of insufficient road condition data. However, many modern approaches are developed without practical testing using real-world scenarios. This paper ad dresses this by practically analyzing Deep Learning models to detect pavement distresses using French Secondary road surface images, given the issues of limited available road condition data in those networks. The study specifically explores several experimental and sensitivity-testing strategies using augmentation and hyper- parameter case studies to bolster practical model instrumentation and implementation. The tests achieve adequate distress detection performance and provide an understanding of how changing aspects of the workflow influence the actual engineering application, thus taking another step towards low-cost automation of aspects of the pavement management syste

GPR monitoring of artificial debonded pavement structures throughout its life cycle during accelerated pavement testing

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