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Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks. However, completely training a large general-purpose model from the scratch is challenging for time series analysis, due to the large volumes and varieties of time series data, as well as the non-stationarity that leads to concept drift impeding continuous model adaptation and re-training. Recent advances have shown that pre-trained LLMs can be exploited to capture complex dependencies in time series data and facilitate various applications. In this survey, we provide a systematic overview of existing methods that leverage LLMs for time series analysis. Specifically, we first state the challenges and motivations of applying language models in the context of time series as well as brief preliminaries of LLMs. Next, we summarize the general pipeline for LLM-based time series analysis, categorize existing methods into different groups (\textit{i.e.}, direct query, tokenization, prompt design, fine-tune, and model integration), and highlight the key ideas within each group. We also discuss the applications of LLMs for both general and spatial-temporal time series data, tailored to specific domains. Finally, we thoroughly discuss future research opportunities to empower time series analysis with LLMs. 

Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources. 

There is an increasing need to fly unmanned aerial vehicles (UAVs) to enable a wide variety of beneficial applications such as emergency/disaster response, observation and study of weather phenomena including severe storms. However, UAVs are subject to cybersecurity threats stemming from increasing reliance on computer and communication technologies. There is a need to foster a robust workforce with integrated UAV and cybersecurity competencies. In addition to technique challenges, current UAV cybersecurity education also faces two significant non-technical challenges: first, there are federal or state rules and regulations on UAV flights; second, the number of designated UAV test sites is limited. A three years NSF SaTC funded project in 2020 will specifically address these challenges. We propose to develop a laboratory platform for UAV cybersecurity education. To be specific, our platform integrates software simulation with hardware-in-the-loop (HIL) simulation to simulate different UAV scenarios, on the top of which cybersecurity components are developed for hands-on practicing. We use a firmware for UAV system development, Pixhawk with related open-source software packages, as the basic simulation framework. On the top of the simulation environment, a series of hands-on exercise modules will be developed to cover UAV cybersecurity issues. Motivated by different types of cybersecurity threats to UAVs, we will adopt the scenario based design and set up several categories of exercise modules including common threats in UAV and additional modules for newly identified threats with corresponding actors, goals, actions, and events. In such a manner offense and defense tasks can be further developed. The proposed platform has the potential to be adopted by universities with limited resources to UAV cybersecurity. It will help educate future workforce with integrated UAV and cybersecurity competencies, towards secure and trustworthy cyberspace around UAVs.