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Abstract With a high signal-to-noise ratio and a great depth of exploration, the wireless electromagnetic method (WEM) has wide applications in the exploration of deep mineral resources and oil and gas reservoirs. Extremely low-frequency electromagnetic (ELF) waves emitted from a horizontal antenna are used to achieve synchronous acquisition for different receivers of multi-coverage information in a global region. However, previous research based on a planar model ignored the curvature of the Earth. This work focuses on the electromagnetic fields (EM fields) in the model of a spherical ‘Earth ionosphere’ to extend the coverage of WEM. By transferring the EM fields from a vertical electric dipole (VED) as well as a vertical magnetic dipole (VMD) in the multi-layered medium of the Earth, we obtain the formulae for the EM fields emitted by a horizontal electric dipole (HED) by using a reciprocity theorem. The correctness of the proposed method is verified by comparing it with the approximate analytical formula and previous work. Based on the above results, we have studied the propagation and frequency characteristics of electromagnetic fields in a spherical waveguide consisting of the ionosphere and earth. The results show that the electromagnetic fields under the spherical model produce interference effects that are different from those of the planar model. The electromagnetic response of the layered Earth was then discussed, and its potential as an electromagnetic technique for exploring the deep Earth was demonstrated.

Abstract The wireless electromagnetic method (WEM) is an emerging new method for deep resource exploration. This method, based on magnetotellurics and the controlled-source electromagnetic method, features advantages such as high signal strength and signal-to-noise ratio, large penetration depth, and easy survey geometry setups, which gives WEM potential for deployment in mineral exploration of large depths. As for now, there is little research into the controlling factors of the ionosphere in WEM exploration, and whether the ionosphere is related to the modeling result remains unclear. In this work, we first developed a modified quadrature with extrapolation (QWE) method to settle the oscillation in the calculation of a WEM response. Then we compared its performance and accuracy with other methods and find that our method has better numerical stability even with highly oscillated integral kernels, which is ideal for WEM emulation. With the emulation tools, we can further investigate how the ionosphere’s height and resistivity affect the ground-received signal. We compared signals obtained with different ionospheric properties under various earth resistivity models. The conclusion is that these ionospheric properties only impact the received signal’s strength and have no influence on the Cagniard resistivity or phase of impedance.

Fully coupled lithosphere, atmosphere, and ionosphere theory has demonstrated that extremely low-frequency electromagnetic (ELF-EM) fields present a broad application prospect in deep resource exploration, but previous studies have ignored the contribution of the Earth’s curvature. This study extends the theory of ELF-EM over a stratified Earth to the case where the Earth’s curvature must be taken into account, and presents an analytical solution of the ELF-EM field excited by a grounded horizontal antenna in a spherical Earth–ionosphere model, whose theoretical approach and solution method are notably different from the flat Earth–ionosphere model. Additionally, the Earth is treated as a concentric-layered sphere rather than an ideal homogeneous sphere. We aim to investigate the effects of the Earth’s curvature on the surface field, so as to broaden the coverage of the ELF wave in resource exploration. The solution is mathematically accurate and physically reasonable, since it reflects the sphericity and radially stratified structure of the Earth. We first verify the correctness and reliability of the proposed method by comparing the results with FDTD in a full-space spherical model. Additionally, we then compared the spherical results with the conventional controlled-source electromagnetic method and flat Earth–ionosphere results. The results show that when the distance between the transmitter and the receiver is comparable to the Earth radius, the spherical model better reflects the resonance of the wave in the cavity, suggesting that the effect of the Earth’s curvature is not negligible. Then, the numerical simulations conducted to investigate the properties of the EM fields and their sensitivities to the conductivity at depth in the Earth are discussed. Finally, the EM responses of some simple electrical conductivity structures models are modeled to illustrate their prospects in future resource exploration.

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

Networks of sensors placed on the skin can provide continuous measurement of human physiological signals for applications in clinical diagnostics, athletics and human-machine interfaces. Wireless and battery-free sensors are particularly desirable for reliable long-term monitoring, but current approaches for achieving this mode of operation rely on near-field technologies that require close proximity (at most a few centimetres) between each sensor and a wireless readout device. Here, we report near-field-enabled clothing capable of establishing wireless power and data connectivity between multiple distant points around the body to create a network of battery-free sensors interconnected by proximity to functional textile patterns. Using computer-controlled embroidery of conductive threads, we integrate clothing with near-field-responsive patterns that are completely fabric-based and free of fragile silicon components. We demonstrate the utility of the networked system for real-time, multi-node measurement of spinal posture as well as continuous sensing of temperature and gait during exercise. Though wireless near-field communication (NFC) technologies that connect wearable sensors for health monitoring have been reported, the short range of NFC readers limits sensor functionality. Here, the authors report a wireless and battery-free body sensor network with near-field-enabled clothing.

Accurate characterization and simulation of electromagnetic propagation can be obtained by ray-tracing methods, which are based on a high frequency approximation to the Maxwell equations and describe the propagating field as a set of propagating rays, reflecting, diffracting and scattering over environment elements. However, this approach has been usually too computationally costly to be used in large and dynamic scenarios, but this situation is changing thanks the increasing availability of efficient ray-tracing libraries for graphical processing units. In this paper we present Opal, an electromagnetic propagation simulation tool implemented with ray-tracing on graphical processing units, which is part of the Veneris framework. Opal can be used as a stand-alone ray-tracing simulator, but its main strength lies in its integration with the game engine, which allows to generate customized 3D environments quickly and intuitively. We describe its most relevant features and provide implementation details, highlighting the different simulation types it supports and its extension possibilites. We provide application examples and validate the simulation on demanding scenarios, such as tunnels, where we compare the results with theoretical solutions and further discuss the tradeoffs between the simulation types and its performance.

Against the backdrop of the rapid development of wireless communication technology, the complex signal interference issues in the electromagnetic spectrum environment have become a key factor affecting the quality and reliability of signal transmission. Existing solutions, such as traditional interference suppression techniques that rely on static spectrum allocation and fixed interference patterns, are no longer able to adapt to the rapidly changing electromagnetic environment and face computational complexity challenges when processing large amounts of real-time data. This study proposes an intelligent anti-interference algorithm that combines deep neural networks and game theory, and constructs a model based on near-end strategy optimization. By extracting and processing signal features through deep neural networks, and dynamically adjusting communication strategies with near-end optimization, the model effectively addresses the recognition and prediction of signal transmission feature parameters in target communication systems, generates interference signals with the same feature parameters, and achieves effective interference suppression. Experiments show that the proposed model achieves an accuracy rate of 95.23% in identifying interference signals and an anti-interference accuracy rate of 85.47%, significantly outperforming random forest and deep Q-network models. The study not only clarifies the limitations of existing solutions but also precisely defines the goals of the new model, which are to reduce error rates and improve adaptability in dynamic environments. The results further explain the significance of the used metrics and test conditions, providing new means and strategies for the development of anti-interference communication technology, especially in dealing with new complex electromagnetic spectrum interference.

In this paper, we propose a reconfigurable intelligent surface (RIS) that can dynamically switch the transmission and reflection phase of incident electromagnetic waves in real time to realize the dual-beam or quad-beam and convert the polarization of the transmitted beam. Such surfaces can redirect a wireless signal at will to establish robust connectivity when the designated line-of-sight channel is disturbed, thereby enhancing the performance of wireless communication systems by creating an intelligent radio environment. When integrated with a sensing element, they are integral to performing joint detection and communication functions in future wireless sensor networks. In this work, we first analyze the scattering performance of a reconfigurable unit element and then design a RIS. The dynamic field scattering manipulation capability of the RIS is validated by full-wave electromagnetic simulations to realize six different functions. The scattering characteristics of the proposed unit element, which incorporates two p-i-n diodes have been substantiated through practical implementation. This involved the construction of a simple prototype and the subsequent examination of its scattering properties via the free-space measurement method. The obtained transmission and reflection coefficients from the measurements are in agreement with the anticipated outcomes from simulations.

Optogenetics is a powerful technique that allows target-specific spatiotemporal manipulation of neuronal activity for dissection of neural circuits and therapeutic interventions. Recent advances in wireless optogenetics technologies have enabled investigation of brain circuits in more natural conditions by releasing animals from tethered optical fibers. However, current wireless implants, which are largely based on battery-powered or battery-free designs, still limit the full potential of in vivo optogenetics in freely moving animals by requiring intermittent battery replacement or a special, bulky wireless power transfer system for continuous device operation, respectively. To address these limitations, here we present a wirelessly rechargeable, fully implantable, soft optoelectronic system that can be remotely and selectively controlled using a smartphone. Combining advantageous features of both battery-powered and battery-free designs, this device system enables seamless full implantation into animals, reliable ubiquitous operation, and intervention-free wireless charging, all of which are desired for chronic in vivo optogenetics. Successful demonstration of the unique capabilities of this device in freely behaving rats forecasts its broad and practical utilities in various neuroscience research and clinical applications. Although wireless optogenetic technologies enable brain circuit investigation in freely moving animals, existing devices have limited their full potential, requiring special power setups. Here, the authors report fully implantable optogenetic systems that allow intervention-free wireless charging and controls for operation in any environment.