## 1 Introduction  
Space missions have always been at the forefront of scientific and technological advancement. These missions, whether they involve satellite deployments, deep space explorations, or future manned missions to the Moon and Mars, require extremely high levels of precision and reliability. One of the critical factors ensuring the success of these missions is maintaining electromagnetic cleanliness in instrument locations for accurate and noise-free data acquisition. Electric cleanliness refers to the state of minimizing or eliminating unwanted electric fields and currents within a spacecraft or satellite. These unwanted electric phenomena can arise from various sources, including internal electric components, the spacecraft's interaction with the space environment, or even other onboard systems. Maintaining electric cleanliness is crucial because electromagnetic interference (EMI) can disrupt the operation of sensitive instruments and systems, leading to malfunctions or failures that could jeopardize the entire mission [1]. Moreover, the extremely low-frequency (quasi-static) electric field emitted by planetary and various objects, is measured by specialized instrumentation that presents innate sensitivity to electric fields. In order to provide accurate field measurements and capture the electric signature of these celestial bodies, it is necessary to operate in zero-field conditions. Thus, it is required to measure, model, and minimize the EMI generated from the equipment inside the spacecraft or satellite to the sensor's position at these frequencies [2; 3]. Many space programs, such as Bepi Colombo, Juice, and Solar Orbiter, have studied and developed methods to adhere to the strict electric cleanliness requirements. In these methods, the electric field measurements are carried out either at the system level, providing the electric signature of the entire spacecraft, or at the unit level, with each spacecraft unit measured and modeled separately [4; 5; 6; 7]. When the measurements are carried out at the unit level, the quasi-static electric near-field signatures from all individual units are utilized to evaluate the total electric field. Field probes are placed in various angles/orientations near the unit under test in order to capture its spatial electric signature in the frequency range of interest [2]. A common method to characterize the unit from the collected data is multiple dipole modeling (MDM). MDM relies on the fact that the measured signature can be reproduced from a set of virtual dipoles assigned to the measured unit and by solving in this way an inverse electromagnetic problem. In the low frequencies, usually, one dipole is sufficient to accurately express the unit behavior. The parameters that are required to describe the dipole source, for the electric field in this case, are the electric moment vectors and the electric dipole position vectors inside the unit spatial boundaries [6]. The MDM method can be implemented by either deterministic or heuristic techniques, which predict the electric signature with adequate accuracy [3; 8]. These techniques, for simple dipole sources, have a satisfactory convergence speed; however, they are post-process methodologies due to the time that the algorithm needs to explore the solution space and converge to the best candidate solution. As the number of sources comprising the unit increases, so does the number of unknowns, and thus the solution speed gradually decreases [6; 9]. In recent years, the advent of advanced computational techniques, particularly neural networks, has opened new avenues for addressing the challenges of electric cleanliness in space missions [10; 11; 12]. Neural networks, a subset of artificial intelligence (AI) and machine learning (ML), are computational models inspired by the human brain's structure and function. They excel in pattern recognition, learning from data, and making predictions, making them highly suitable for complex, data-intensive tasks.