The DOSSA (Decentralization of Space Situational Awareness) project, led by SpaceAble in collaboration with Unistellar and the Laboratoire d’Astrophysique de Marseille, aims to enhance space surveillance through a collaborative effort involving amateur astronomers, researchers, and industrial networks, leveraging advanced technology and data collection to improve space situational awareness in an era of escalating satellite numbers. This project aims to create a comprehensive sky map of objects transiting around Earth. It benefits from a large dataset collected by the Unistellar telescope network, a global network comprised primarily of robotically controllable evScopes 1 and 2 telescopes, each featuring a 11.4 centimeter aperture diameter. We develop dedicated deep learning algorithms to account for the relatively compact diameters of the telescopes and to extend detection thresholds. Firstly, we use traditional convolutional neural networks (CNNs) based classifiers to detect images including a satellite streak, and secondly, we use UNet to identify pixels affected by such streaks. Both neural networks are trained on realistic simulations generated using our optical Fourier simulation software, by combining observed sky backgrounds and synthetic satellite streaks spanning a wide range of orbital parameters. With this strategy, we reach excellent performance on the segmentation of images in test set, with a recall of 79.6% pixels belonging to the satellites masks, at a false positive rate of 0.001%. For 90% of streak pixels recovered by the neural networks, we obtain a precision of 96.3% on the predicted satellite masks. This exceeds the performance of non-ML algorithms and paves the way to measuring accurate satellite positions over broader magnitudes ranges and down to lower S/N, in order to increase the precision on their orbital parameters.

Orbit Determination of Resident Space Objects Using the P-Band Mono-Beam Receiver of the Sardinia Radio Telescope The population of space debris in near-Earth space is continuously growing and it represents a serious problem for active satellites and spacecraft. A performant ground-based and space-based network of sensors is necessary for space surveillance and consequently to prevent new collisions and monitoring atmospheric reentry of these objects. This paper illustrates the possible role of the Italian ground-based novel bi-static radar sensor, named BIRALET, for space monitoring and resident space objects tracking. The main characteristics of the receiver system, the Sardinia Radio Telescope with its P-band mono-beam receiver, are described in detail. Then, a preliminary analysis of the performance of the sensor is presented, and the results of numerical simulations are shown, providing a general overview on both observation capabilities and orbit determination accuracy achievable with the Sardinia Radio Telescope. Published in Applied Sciences

Enhancing the Capability of a Ground-Based Optical Telescope for Thai National Space objects Observation ^† Nowadays, the space operations environment have to face with space safety problems because of the growing of space debris in resident of space objects (RSOs) that can cause a catastrophic collision. In order to prevent debris-related risks in operational orbit, ground-based passive optical telescope network were used as a primary equipment for space debris observation due to the lowest maintenance costs. Furthermore, in technical, a precise tracking (position and velocity) of space objects can be beneficial towards not only orbit determination but also estimation spacecraft collision probability especially, in Low-Earth Orbit regime. National Astronomical Research Institute of Thailand (NARIT) has long experience operate in an observatory to perform both passive & active optical instruments for astrophysics and space sciences missions.

network of space surveillance sensors capable of searching, tracking , and characterizing satellites in all Earth orbits . This network includes a variety