The field of wireless communication and network security is witnessing significant advancements with a focus on innovative solutions to long-standing challenges. Researchers are exploring new approaches to improve the performance and efficiency of wireless systems, including the use of deep learning techniques, hybrid receiver architectures, and intelligent metasurfaces. Additionally, there is a growing emphasis on developing practical tools and frameworks for network traffic generation, radio propagation modeling, and outdoor positioning. Noteworthy papers in this area include WiFiSim, which achieves realistic simulation of WiFi probe requests, and ConCap, which provides a practical tool for network traffic generation. The paper on Hybrid Neural/Traditional OFDM Receiver with Learnable Decider also presents a promising approach to overcoming the limitations of deep learning-based receivers. Furthermore, the work on Integrating Stacked Intelligent Metasurfaces and Power Control for Dynamic Edge Inference via Over-The-Air Neural Networks showcases a novel framework for edge inference that leverages the wireless channel itself for computation. Overall, these advancements have the potential to significantly impact the field of wireless communication and network security, enabling more efficient, reliable, and secure systems.