In mobile systems with low-altitude vehicles, integrated sensing and communication (ISAC) is considered an effective approach to increase the transmission rate due to limited spectrum resources. To further improve the ISAC performance, this paper proposes a novel method called integrated sensing and communication-movable antenna (ISAC-MA) to optimize the antenna’s position. Our goal is to support low-space vehicles by optimizing radar and communication joint beamforming and antenna position in the presence of clutter. This scheme not only guarantees the required signal-to-noise ratio (SNR) for sensing but also further improves the SNR for communication. A successive convex approximation (SCA)-based block coordinate descent (BCD) algorithm is proposed to maximize communication capacity under the condition of sensing SNR. Numerical results show that, compared with the traditional ISAC system and various benchmark schemes, the proposed ISAC-MA system can achieve higher communication capacity under the same sensing SNR constraints.

This paper investigates the security of a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) system for integrated sensing and communications (ISAC). A multi-antenna UAV transmits ISAC waveforms to simultaneously detect an untrusted target and communicate with a ground Internet-of-Things (IoT) device in the presence of an eavesdropper (Eve). To reflect practical scenarios where Eve's channel state information (CSI) is unavailable or concealed, we adopt an imperfect-CSI model. Our goal is to maximize the average secrecy rate subject to a sensing quality-of-service constraint by jointly optimizing the UAV trajectory, RIS passive beamforming, transmit beamforming, and receive beamforming. The resulting problem is highly non-convex due to the coupled design variables. We address this challenge with an efficient block coordinate descent framework, in which each block is solved via successive convex approximation built on semidefinite relaxation. Numerical results demonstrate that the proposed algorithm reliably meets sensing accuracy requirements and yields substantial secrecy-rate gains for the IoT link, even under CSI uncertainty, compared with representative baselines.

Acoustic metamaterials (AMs) composed of periodic artificial structures have extraordinary sound wave manipulation capabilities compared with traditional acoustic materials, and they have attracted widespread research attention. The sound insulation performance of thin-walled structures commonly used in engineering applications with restricted space, for example, vehicles’ body structures, and the latest studies on the sound insulation of thin-walled metamaterial structures, are comprehensively discussed in this paper. First, the definition and math law of sound insulation are introduced, alongside the primary methods of sound insulation testing of specimens. Secondly, the main sound insulation acoustic metamaterial structures are summarized and classified, including membrane-type, plate-type, and smart-material-type sound insulation metamaterials, boundaries, and temperature effects, as well as the sound insulation research on composite structures combined with metamaterial structures. Finally, the research status, challenges, and trends of sound insulation metamaterial structures are summarized. It was found that combining the advantages of metamaterial and various composite panel structures with optimization methods considering lightweight and proper wide frequency band single evaluator has the potential to improve the sound insulation performance of composite metamaterials in the full frequency range. Relative review results provide a comprehensive reference for the sound insulation metamaterial design and application.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers’ workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology’s applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community.

Along with the rapid improvement of the aviation industry, flight density also increases with the increase of flight demand, which directly leads to the increasingly prominent influence of wake vortex on flight safety and aviation control. In this paper, we propose a new joint framework—a deep learning framework—based on multisensor fusion information to address the detection and identification of wake vortices in the near‐Earth phase. By setting multiple Doppler lidar in near‐Earth flight areas at different airports, a large number of accurate wind field data are captured for wake vortex detection. Meanwhile, the airport surveillance radar is used to locate the wake vortex. In the deep learning framework, an end‐to‐end CNN‐LSTM model has been employed to identify the airplane wake vortex from the data detected by Doppler lidar and the airport surveillance radar. The variables including the wind field matrix, positioning matrix, and the variance sequence are used as inputs to the CNN channel and LSTM channel. The identification and location information of the wake vortex in the wind field image will be output by the framework. Experiments show that the joint framework based on a multisensor possesses stronger ability to capture local feature and sequence feature than the traditional CNN or LSTM model.

With the continuous growth in the volume of global air transportation, the carbon emissions of the civil aviation industry have received increasing attention. Carbon emission reduction in civil aviation is an inevitable requirement for achieving sustainable social development. This article aims to use system dynamics (SD) methods to establish a carbon emission model for the civil aviation industry that includes economic, demographic, technological, policy, and behavioral factors; analyze the key factors that affect carbon emissions; and explore effective emission reduction strategies. Researchers have found that SD-based carbon emission prediction has a high accuracy and is suitable for predicting carbon emissions in civil aviation. Through different scenario simulations, it has been found that any single emission reduction measure will struggle to effectively contribute to the expected carbon reductions in China’s civil aviation. Simultaneously adopting measures such as improving fuel efficiency, adopting clean energy, and using new-power aircraft is an effective way to reduce carbon emissions from civil aviation. In addition, policy intervention and technological innovation are equally crucial for achieving long-term emission reduction goals. The research results not only provide a scientific basis for the sustainable development of the aviation industry but also provide a reference for policymakers to formulate comprehensive emission reduction strategies.