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Over the decades, Space Situational Awareness (SSA) has been growing in importance and public attention as thousands of civil and military assets populate the orbits around the Earth. Their survivability depends primarily on the capacity of detecting, identifying and handling potential risks or threats. The prediction of the re-entry window is still an open issue since current algorithms have limited accuracy for long period estimations, especially for the objects poorly characterized and/or moving on highly elliptical orbits. The main approaches to deal with this problem are based on numerical propagation and conventional statistical regression. The numerical propagation includes all the relevant perturbations: zonal and tesseral harmonics of the geo-potential third body attraction of the moon and the sun, solar radiation pressure, aerodynamic drag and solar activity. The ideal conditions to effectively propagate the object and estimate the re-entry window is to have a deep understanding of its physical properties and to collect several observations to determine the evolution of the orbital parameters over time. Within these conditions, the final window can be found with high accuracy, having slight variations of the epoch only during the last days and a progressive reduction of the uncertainty. Space debris and objects orbiting in highly elliptical orbits may not meet these requirements. Indeed, the characterization of the former depends on the specific genesis, but some features like size, shape, mass and the value of the exposed area of debris are usually unknown and might be estimated with regression techniques through a dedicated observation campaign. However, limited measurements related to any highly elliptical orbiting object are typically acquired. This is especially true when their orbits show significant variations, which make medium-long term predictions inaccurate and consequently the re-entry window usually converges a few orbits before the actual re-entry epoch. The recent developments in Machine Learning (ML) techniques and architectures to deal with regression problems can support the estimation of the re-entry window for these kinds of objects. This work investi- gates the advantages offered by ML architectures in providing an augmented level of awareness on the risk posed by the reentry of these objects in Earths atmosphere. More specifically, several Deep Feed-Forward architectures are studied, along with different meta-parameters and data configurations in order to assess a target baseline with a conventional ML technique. Moreover, additional analyses are carried out to verify if other artificial intelligence approaches might suit better this specific problem, along with combining the solution provided by this statistical technique with conventional propagation based on orbital dynamics. All the data used for this work were downloaded from publicly available sources and a conventional training-validation dataset split was adopted to verify the goodness of the model obtained. The results were compared with official estimations carried out by the USSTRATCOM in the frame of the publicly available space-track service, showing a tendency to reduce the error in medium-long term prediction (i.e., from 15 to 45 days before the real re-entry epoch) with a significant reduction in the computational time. As a result, the next-generation database scanning algorithms will likely take advantage of these ML approaches to provide a first guess of the re-entry window and to trigger a data acquisition process in order to refine the re-entry epoch and location predictions with all the available techniques.

Space Surveillance and Tracking (SST) is growing more and more important in the Space operations and mission analysis field, given how much the crowded environment building up near Earth can hamper them. Italy is involved in SST operations, collaborating with partners both within and outside the EU. The Italian Space Operation Centre (ISOC) has recently upgraded its systems to the ISOC 2.0 Suite, an integrated web-based platform providing multiple functions and services in the SST/SSA domain. The present work describes its sensor tasking module, called SNOS (Sensors Network Optimal Scheduler), developed thanks to a collaboration involving the Italian Air Force, the Leonardo Company and Politecnico di Milano. After the definition of the software architecture, its prototype version has been developed in Python and then translated to C++ language to grant the highest performances in the operational environment. Given the list of available sensors together with their characteristics, the time window in which the observations must be scheduled and the catalogue of objects to be observed, SNOS is able to generate a user specified number of schedules, optimized both in terms of catalogue coverage and expected quality of the measurements. The results are provided to the sensor operation authority as a JSON file conveying all the information needed for the correct execution of the task and as a Tracking Data Message (TDM), which is the Consultative Committee for Space Data Systems (CCSDS) standard for this kind of operations. SNOS can handle optical sensors as well as mono- or bi-static radar and laser sensors. The process starts from the analysis of Two Line Elements (TLE) or Orbit Ephemeris Message (OEM) representing the state of the catalogued objects to identify the visible passes, i.e. the portions of their orbits which are visible from any of the sensors of the network. These are subdivided based on the priority assigned to the related object and conflicting situations are identified, such as the ones in which multiple passes from different objects are simultaneously visible by the same sensor. The candidate solutions are then gradually built by selecting passes (or portions of them) with a heuristic optimization approach. The generated schedules are assigned a score based on the catalogue coverage and the expected quality of the measurements, which weights factors such as the elevation of the object, the distance from the observer, and the duration of the pass. The scheduler also grants the possibility to modify an already ongoing schedule to allocate tasks for unforeseen necessities highlighted by the sensor authority, while still retaining the optimality of the solution. Finally, SNOS can compute the necessary sensor pointing sequence for different kind of tasks, namely tracking tasks and various survey modes of practical interest, such as the one for the surveillance of the GEO belt and the ones designed for the identification of new debris through the inspection of areas along the orbit of an object which is expected to have suffered fragmentation. The SNOS software is currently successfully employed to run the network managed by the ISOC centre.

In the last decades, the growing in-orbit population of resident objects has become one of the main concerns for space agencies and institutions worldwide, and several initiatives have been promoted to tackle this issue. In the resulting Space Surveillance and Tracking (SST) services, ground-based sensors (optical or radar) are typically used to track orbiting objects. Within this framework, the Italian Space Operation Centre (ISOC) has recently upgraded its systems to the ISOC 2.0 Suite, an integrated platform providing multiple functions and services in the SST domain. It is a web-based platform giving users the ability to connect and use the system both locally and remotely. The software has been designed and implemented in partnership with industry and academia. The present work describes the orbit determination module developed for the ISOC 2.0 Suite thanks to a collaboration involving the Italian Air Force, Leonardo Company and Politecnico di Milano. After the definition of the software architecture, its prototypal version has been developed and then translated to C++ language to be used in the operational environment, guaranteeing the highest performances in terms of computational times. The process starts correlating the observation data reported in Tracking Data Message (TDM) files to objects contained in the catalogue, which can be provided in one of the Consultative Committee for Space Data Systems (CCSDS) formats. The correlation is evaluated according to a statistical correlation index quantifying the probability that a candidate object is related to the input TDM. If the index is above a pre-defined threshold, the object is discarded, otherwise it is added to a list of possible correlated objects. If the process correlates the measurements to a catalogued object, an orbit determination refinement is run, by using either a sequential or a batch filter and exploiting the available ephemerides as first guess. Instead, if the correlation process does not link observation data to any catalogued object, an initial orbit determination (IOD) is performed with the available measurements. If both angular coordinates and slant range are available, a radar IOD is run, which first computes the orbital position for each observation epoch and then matches them to derive the orbital state. If instead only angular coordinates are provided, an optical IOD process is run, which first tries to retrieve the range associated to the observation by means of orbital geometry and dynamics. Then, the orbital state is computed either similarly to the radar IOD process, or by exploiting the range rate derived from the computed range. Finally, the compliance between the determined orbital state and the measurements is tested against the same statistical index as in the correlation process, both for ROD and IOD. The performance of the resulting software is assessed and provided on both synthetic and real data.

The Earth-Moon system is constantly bombarded by meteoroids of different size and impact speed. Observation of the impacts on the Moon can enable thorough characterization of the Lunar meteoroid flux, which is similar to that of the Earth. While Earth-based Lunar observations are restricted by weather, geometric and illumination conditions, a Lunar-based observation campaign can improve the detection rate and, when observing the Lunar far side, complement in both space and time the observations taken from Earth. The Lunar Meteoroid Impact Observer (LUMIO), one of the two winning concepts of the ESA SysNova Lunar CubeSats for Exploration challenge, is a mission designed to observe, quantify, and characterize the micro-meteoroid impacts on the Lunar far side. It is based on a 12U CubeSat that carries the LUMIO-Cam, a custom-designed optical instrument capable of detecting light flashes in the visible spectrum. The spacecraft is placed on a halo orbit about the Earth–Moon L2 point, where permanent full-disk observation of the Lunar far side can be performed with excellent quality, given the absence of Earth background noise. After passing Phase 0 and an independent feasibility study in the ESA Concurrent Design Facility, the mission has successfully completed its Phase A in March 2021. Although the Phase 0 design of the LUMIO spacecraft was assessed as feasible by the ESA CDF study, a number of critical issues were identified, which have been tackled by the Phase A design. The paper presents the outcome of this Phase A design effort for the LUMIO spacecraft. Particularly relevant changes or updates in the spacecraft design include: a consolidated design of the LUMIO-Cam, with longer baffle for straylight protection; a set of ADCS sensors and actuators with increased redundancy; a combination of Direct-to-Earth communication and inter-satellite link with a mothership in Lunar orbit; use of Earth ranging to complement and validate the current innovative autonomous navigation strategy based on optical observations of the Moon by means of the LUMIO-Cam; re-assessment of the COTS components selection for the power and propulsion systems.

Questo documento è il terzo deliverable dell'accordo di collaborazione tra ASI e INAF in ambito "Detriti Spaziali- Supporto alle attività IADC e SST 2019-2021".