The GNSS spoofing jamming is generated by transmitting spoofing signals that are identical or similar to the authentic satellite signals but have a stronger power in order to guide a receiver to acquire and track them instead of real signals. The main aim of spoofing is to make the receiver obtain wrong timing and positioning information. In this paper, the characteristics of spoofing signals in the acquisition phase are analyzed and studied. Based on the idea of confrontation evolution of a general adverse network (GAN), this study proposes a GNSS anti-spoofing method. The performance of the proposed method is verified by simulations experiments. The experimental results show that when the pseudo-code phase difference between the spoofing signal and the authentic signal exceeds 0.5 chip, the detection probability of the GAN can reach more than 98%. The proposed method can also be applied to situations where the spoofing signal is highly synchronized with the real signal.

The vulnerability of the global navigation satellite system (GNSS) to spoofing limits its vast application in military safety and the national economy. Therefore, it is significantly important to rapidly and accurately detect GNSS spoofing. The traditional spoofing detection mainly detects spoofing signal intrusion by individual parameters such as signal power and signal quality. The single-parameter detection method can no longer manage complex and changing scenarios with the continuous development of spoofing technology. This paper proposes a GNSS multi-parameter joint detection method based on the support vector machine (SVM). The effects of spoofing on the signal acquisition, signal tracking, and position, velocity and time (PVT) solution processes are analyzed, and the composite signal quality monitoring (SQM), carrier-to-noise ratio C/N ₀ , pseudo-range Doppler consistency, PVT solving residuals, and clock difference and clock drift are extracted as detection features for training and testing of the support vector machine binary classification model. The F1-score of all nine-types parameter detection methods on TEXBAT and OAKBAT datasets improved by 22.95% and 19.6% (reach 93.97% and 97%), respectively, compared with only SQM parameters selected method. The obtained results demonstrate significant improvement in the spoofing detection performance compared with the traditional single-parameter methods.

In the last few years, the bottleneck of bandwidth in Internet of Thing (IoT) has driven expectations to figure out new ways to preprocess the information needed to be transmitted. The ways which were used before are not smart enough and they cannot align to the users’ need. Large language model (LLM)-based intelligent agent is a very hot concept in AI community, which aims to save various problems via adapting LLM to different industries. In this article, we present a collective intelligent agent system for the IoT (CASIT) that is a pioneering LLM-agent-based IoT system. We put forward a IoT framework that can be used to lots of scenarios. CASIT refers to a system based on multiple intelligent LLM agents, which realizes complex tasks through cooperation and makes full use of collective intelligence. In order to solve the problems, we designed the Memory Mechanism and Summary Mechanism that enable LLMs to efficiently process the data by comparing historical data with Local Knowledge and Chat History in the prompt. After experimental verification, we have found that our framework could accurately conclude the abnormal information, and it outperforms the single LLM system when we input 200 sets of temperature and humidity data from five different places. The system provides a new solution and method for information processing in all IoT systems. Our framework may also provide refreshing ideas for edge computing and semantic communication.

Antenna miniaturization technologies have always been the focus of antenna research. In recent years, mechanical antennas whose size does not depend on the electromagnetic wave-length have attracted researchers’ attention. Acoustically excited antennas (AEAs), as a kind of mechanical antenna, can achieve reciprocal transmission and reception. AEAs utilize internal charge and magnetization oscillations of piezoelectric materials and magnetoelectric (ME) composite materials, respectively, to radiate electromagnetic waves, which enables the size of the antenna to be related to the wavelength of the acoustic wave, reducing the size of the antenna by 4-5 orders of magnitude over conventional antennas. Additionally, it is expected to achieve breakthrough application changes in the fields of mobile low frequency antennas, portable shortwave antennas, and chip-based microwave antennas. In this paper, the theory of AEAs is summarized and different modeling and measurement studies of AEAs are described in detail. Finally, the challenges of AEAs research are elaborated and future research on AEAs is prospected.

Due to the substantial domain gaps in Remote Sensing (RS) images that are characterized by variabilities such as location, wavelength, and sensor type, Remote Sensing Domain Generalization (RSDG) has emerged as a critical and valuable research frontier, focusing on developing models that generalize effectively across diverse scenarios. However, research in this area remains underexplored: (1) Current cross-domain methods primarily focus on Domain Adaptation (DA), which adapts models to predefined domains rather than to unseen ones; (2) Few studies target the RSDG issue, especially for semantic segmentation tasks. Existing related models are developed for specific unknown domains, struggling with issues of underfitting on other unseen scenarios; (3) Existing RS foundation models tend to prioritize in-domain performance over cross-domain generalization. To this end, we introduce the first vision foundation model for RSDG semantic segmentation, CrossEarth. CrossEarth demonstrates strong cross-domain generalization through a specially designed data-level Earth-Style Injection pipeline and a model-level Multi-Task Training pipeline. In addition, for the semantic segmentation task, we have curated an RSDG benchmark comprising 32 semantic segmentation scenarios across various regions, spectral bands, platforms, and climates, providing comprehensive evaluations of the generalizability of future RSDG models. Extensive experiments on this collection demonstrate the superiority of CrossEarth over existing state-of-the-art methods.