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Earth exploration satellite service (EESS) plays a crucial role in environmental monitoring and weather forecasting by utilizing passive sensing technologies. However, the rapid expansion of terrestrial and satellite communication networks has introduced significant interference challenges, particularly in frequency bands that overlap with or are adjacent to EESS sensors. In this work, we develop a system model that explicitly characterizes EESS interference by considering reflected signal effects and spatial interference accumulation. Based on this model, we propose an EESS-aware resource allocation (EARA) framework that jointly optimizes power allocation and user association, while ensuring that interference to EESS sensors remains within acceptable limits. A non-convex joint optimization problem is formulated and efficiently solved leveraging the Lagrangian dual transform and Dinkelbach’s method. Simulation results demonstrate that the proposed EARA scheme achieves up to 26.3% higher sum rate compared to genetic algorithm and binary whale optimization algorithm, while strictly satisfying the ITU-defined interference threshold. This work establishes a foundation for future research on the coexistence of communication networks and passive Earth observation systems, offering practical strategies for interference mitigation and spectrum sharing in next-generation networks. 

This review highlights recent advances in additive manufacturing of rare-earth-free (REF) magnetic materials, emphasizing innovations in material synthesis, ink formulation, printing techniques, and device applications. 

Abstract Magnetic particle imaging (MPI) is a tracer-based tomographic imaging technique utilized in applications such as lung perfusion imaging, cancer diagnosis, stem cell tracking, etc. The goal of translating MPI to clinical use has prompted studies on further improving the spatial-temporal resolutions of MPI through various methods, including image reconstruction algorithm, scanning trajectory design, magnetic field profile design, and tracer design. Iron oxide magnetic nanoparticles (MNPs) are favored for MPI and magnetic resonance imaging (MRI) over other materials due to their high biocompatibility, low cost, and ease of preparation and surface modification. For core–shell MNPs, the tracers’ magnetic core size and non-magnetic coating layer characteristics can significantly affect MPI signals through dynamic magnetization relaxations. Most works to date have assumed an ensemble of MNP tracers with identical sizes, ignoring that artificially synthesized MNPs typically follow a log-normal size distribution, which can deviate theoretical results from experimental data. In this work, we first characterize the size distributions of four commercially available iron oxide MNP products and then model the collective magnetic responses of these MNPs for MPI applications. For an ensemble of MNP tracers with size standard deviations ofσ, we applied a stochastic Langevin model to study the effect of size distribution on MPI imaging performance. Under an alternating magnetic field (AMF), i.e., the excitation field in MPI, we collected the time domain dynamic magnetizations (M-t curves), magnetization-field hysteresis loops (M-H curves), point-spread functions (PSFs), and higher harmonics from these MNP tracers. The intrinsic MPI spatial resolution, which is related to the full width at half maximum (FWHM) of the PSF profile, along with the higher harmonics, serve as metrics to provide insights into how the size distribution of MNP tracers affects MPI performance.