Yuanhe Chen

Abstract As Electric Vehicle (EV) charging stations become increasingly intelligent, more Internet of Things (IoT) devices are being integrated to improve operational efficiency. Efficient and accurate detection of IoT malware is crucial to ensuring the stable and reliable operation of these charging systems. However, existing research in this field still exhibits three major limitations. First, most existing research lack unified adaptability across multiple CPU architectures.

As a kind of affordable clean energy on a wide scale, wind energy conversion and storage technologies offer a practical approach to addressing energy deficits [1]. In coastal and inland mountainous terrains, the increasing frequency of extreme weather risks the reliability and safety of wind turbines, especially ice formation on wind turbine blades, exacerbating the possibility of mechanical breakdown and being destroyed in cold and damp conditions during winter [2].