Browsing by Author "Garzaniti, Nicola"
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Item Open Access FSSCat: the Federated Satellite Systems 3 Cat Mission: demonstrating the capabilities of CubeSats to monitor essential climate variables of the water cycle(IEEE, 2022-11-30) Camps, Adriano; Munoz-Martin, Joan Francesc; Ruiz-de-Azua, Joan Adrià; Garzaniti, NicolaThe Federated Satellite Systems/ 3 Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S 3 ) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3 Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a softwaredefined radio (SDR), and 2) the HyperScout-2 onboard 3 Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3 Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3 Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results.Item Open Access Spacecraft conjunction assessment optimisation using deep learning algorithms applied to conjunction data messages (CDMs)(International Astronautical Federation (IAF), 2023-10-06) Rosales Ruiz, Jose Javier; Garzaniti, NicolaThe lack of global regulations on space debris management during the early days of the space era until the last few decades of the 20th century resulted in a consistent increase in space debris. Spacecraft collisions in orbit and the industry's growing interest in launching constellations of satellites are now exacerbating the problem. To address those concerns, multiple space organisations worldwide have implemented Situational Space Awareness programmes with integrated Conjunction Assessment systems that allow the detection of spacecraft conjunctions with an estimated collision risk probability. While this approach has proved effective in the last two decades, the foreseen increment of artificial space objects in orbit in the coming years will put any existing system under severe stress if the technology does not evolve to match the new demands. The objective of this research is two-fold: it evaluates different architectures used in the field of Deep Learning to increase the accuracy of on-orbit Conjunction Events forecasting. It provides a multi-purpose modular, Machine Learning based Python library to support Conjunction Assessment activities. The results of this study show that simpler cell architectures used in the Recurrent Neural Networks outperform the corresponding Vanilla versions in terms of accuracy for the problem at hand. It also demonstrates that the attention mechanism provides the best performance with up to 40% more accuracy.Item Open Access Spacecraft conjunction assessment optimization using deep learning algorithms applied to conjunction data messages (CDMs)(International Astronautical Federation (IAF), 2023-10-06) Rosales Ruiz, Jose Javier; Garzaniti, NicolaThe lack of global regulations on space debris management during the early days of the space era until the last few decades of the 20th century resulted in a consistent increase in space debris. Spacecraft collisions in orbit and the industry's growing interest in launching constellations of satellites are now exacerbating the problem. To address those concerns, multiple space organisations worldwide have implemented Situational Space Awareness programmes with integrated Conjunction Assessment systems that allow the detection of spacecraft conjunctions with an estimated collision risk probability. While this approach has proved effective in the last two decades, the foreseen increment of artificial space objects in orbit in the coming years will put any existing system under severe stress if the technology does not evolve to match the new demands. The objective of this research is two-fold: it evaluates different architectures used in the field of Deep Learning to increase the accuracy of on-orbit Conjunction Events forecasting. It provides a multi-purpose modular, Machine Learning based Python library to support Conjunction Assessment activities. The results of this study show that simpler cell architectures used in the Recurrent Neural Networks outperform the corresponding Vanilla versions in terms of accuracy for the problem at hand. It also demonstrates that the attention mechanism provides the best performance with up to 40% more accuracy.Item Open Access System design study of a constellation of small spacecraft to deliver seamless 5G connectivity to unmodified cell phones through an end-to-end non-terrestrial network(International Astronautical Federation (IAF), 2023-10-06) Vargas Avila, Gerardo; Anastasopoulos, Marios; Liao, Zuliang; Morea, Albert; Capell, Elliot; Koenig, Anton; López Buqué, Iñigo; Ravishankar, Sreekrishna; Ruiz, Diego Larrauri; Cordova, Asis; Haynez, Guillaume; Kommareddy, Sai Tejaswi; Garzaniti, NicolaThe increasing demand for high-speed mobile data services has led to the development of 5G and 6G technology, which promises to revolutionize the way people access and use the internet. However, the full exploitation of 5G network potential is often limited by the challenges related to the deployment of the physical infrastructure required to support these networks. In order to address these limitations, a new approach is needed to bring 5G services to areas that are currently underserved. This paper presents the results of a system design study that explores the use of a constellation of small spacecraft to deliver seam-less 5G connectivity to unmodified cell phones, through an endto-end non-terrestrial network. Within the study, several use cases have been considered including offering enhanced service to cities and connecting areas not served by traditional mobile services such as remote regions, ships and offshore platforms, regions hit by natural disasters and contested battlefields. A trade space exploration approach was undertaken to identify the optimal solution for meeting stakeholders’ requirements associated with the different use cases. The analysis explores the effects of key architectural decisions on overall system performance and lifecycle cost, benchmarking them against foreseen customers’ needs and market demand. A variety of alternatives were evaluated including the number of satellites, types of orbits, number of orbital planes, satellite size, weight and power, antenna technologies, inter-satellite links technologies and routing schemes among others. As a result, it was proposed the use of a constellation of about 3000 satellites in a sun-synchronous Low Earth Orbit (LEO) orbit, with a satellite lifetime of 5 years. Each satellite is equipped with a phased array antenna in 5G non-terrestrial band frequency n256 for direct connectivity to unmodified user cell phones and free space optical telecommunication terminals for on-orbit backhauling. Commercial Off-The-Shelf (COTS) components for spacecraft subsystems and sensors were considered when available. With more than 95% of Earth coverage and high system scalability, the mission represents a promising solution for providing global 5G connectivity paving the way for a more connected world.