The group velocity of lightning electromagnetic signals plays an important role in lightning location systems using the time difference of arrival (TDOA) method. Accurate estimation of group velocity is difficult due to the space- and time-varying properties of the Earth-ionosphere waveguide. Besides, the analytical solution of the group velocity is difficult to obtain from the classic mode theory, especially when higher-order modes, anisotropic geomagnetic background, diffuse ionosphere profile, and propagation path segmentation are all taken into consideration. To overcome these challenges, a novel numerical method is proposed in this paper to estimate the group velocity of the lightning signal during ionospheric quiet periods. The well-known Long Wavelength Propagation Capability (LWPC) code is used to model the propagation of VLF/LF radio waves. Since LWPC uses a simplified ionospheric model which is unable to describe the subtle variations of ionospheric parameters over time and space, the IRI-2016 model is incorporated into the numerical modeling process to provide more accurate ionosphere parameters. Experimental results of a VLF/LF lightning location network are demonstrated and analyzed to show the effectiveness of our method. The proposed method is also applicable when there is a sudden ionospheric disturbance as long as the parameters of the ionosphere are obtained in real time by remote sensing methods.

With the advancement of deep Earth exploration and urban underground space detection, addressing the challenges posed by environmental complexity and increasing data acquisition accuracy are increasingly critical. Conventional geophysical exploration techniques and their associated networking approaches cannot fulfill contemporary exploration requirements. Herein, the fundamental principles and inherent connections between seismic exploration and electromagnetic exploration are analyzed by comparing the circuit design for seismic and electromagnetic data-acquisition systems. Consequently, a technical solution for a seismo-electromagnetic joint acquisition system with high accuracies in both seismic and electromagnetic explorations is developed. A real-time host monitoring system is developed for both personal computers and mobile terminals. Moreover, a distributed hybrid networking scheme for this distributed seismo-electromagnetic high-accuracy acquisition system that incorporates wired, wireless, and self-storage intelligent arbitrary clipping is innovatively implemented, achieving a synchronization accuracy of <100 ns among various acquisition systems. The developed system is used for networking deployment in Zhangjiakou (Hebei, China), verification of key indicators in a coal mine with a depth of 848 m in Huainan (Anhui, China), and practical onsite explorations in Changzhi (Shanxi, China). The experimental and application results verify the reliability, stability, and practicality of the system. The proposed joint exploration technique fully leverages the advantages of distributed data-acquisition systems and can potentially shed light on the development of other joint research methodologies. The findings can increase the efficiency and accuracy of joint geophysical explorations, providing a solid foundation with regard to both machinery and technical aspects.

The atmospheric noise widely present in very-low-frequency and low-frequency (VLF/LF) communication systems is usually considered as a type of impulsive noise, which can degrade the performance of signal processing methods based on Gaussian noise. To solve the problems of difficult model training and complex noise parameter estimation under impulsive noise in other demodulation schemes of minimum shift keying (MSK) signals, we use one-dimensional convolutional neural networks (1D-CNNs) to replace the integrator and decision module in coherent demodulation instead of the entire demodulation process. Our scheme preserves the operations that are beneficial for neural network training in the conventional coherent demodulation process, and the use of neural networks avoids the estimation of noise parameters. To further improve the symbol error rate (SER) performance, we remove the symbol conversion before output generation and add the symbol conversion before MSK modulation. The simulation results show that our scheme with fewer parameters and less calculation has better SER performance than other neural network demodulators. Our scheme’s SER performance exceeds the performance of the demodulation algorithm based on a myriad branch metric, whose performance is very close to the maximum likelihood (ML) performance. And, our scheme does not require complex noise parameter estimation.

Conventional PID control exhibits limited robustness when applied to folding-wing unmanned aerial vehicles (UAVs) operating under realistic flight conditions, where strong nonlinearities, model uncertainties, and external disturbances are present. In particular, the coupled lateral-directional dynamics make reliable attitude regulation challenging. This paper presents a structured neural compensation approach that augments a baseline PID controller with a bounded auxiliary compensator learned via Proximal Policy Optimization (PPO). Instead of directly generating control commands, the policy operates in a low-dimensional residual space, preserving the classical control structure while improving robustness to disturbances and uncertainties. The proposed framework is evaluated via software-in-the-loop simulations on a high-fidelity folding-wing UAV model. Comparative results show improved tracking performance and disturbance rejection over conventional PID control and unstructured reinforcement learning-based controllers.

This paper presents an optimized long-wave (10–300 kHz) wideband direction-finding system for scientific research. The antenna unit of the system comprises one vertical electric field sensor and two horizontal magnetic field sensors oriented in the north–south and east–west directions, respectively. The overall design prioritizes compactness, engineering feasibility, and ease of deployment, enabling the effective reception of long-wave radio signals within the 10–300 kHz range. The magnetic field sensitivity reaches $8fT/\sqrt{\mathrm{Hz}}@10\mathrm{kHz}$, while the electric field sensitivity achieves $3.2\mathsf{\mu }\mathrm{V}/\mathrm{m}/\sqrt{\mathrm{Hz}}@10\mathrm{kHz}$. The overall sensitivity of the receiver is $1\mathsf{\mu }\mathrm{V}$ (300 Hz bandwidth, 10 dB signal-to-noise ratio). The synchronization accuracy of the system is within 10 ns. Theoretically, with a baseline length of 5 km and a signal incidence angle ranging from 9.9° to 170.1°, the direction finding error is less than 2°. Additionally, direction-finding methods for MSK and ASK modulated signals are analyzed. To evaluate the system’s actual performance, initial measurements were conducted in Qingdao, Shandong.