Planetek Italia

An innovative architecture of a phased antenna array operating in the Ka band is reported. It operates in the 25.5 GHz to 27 GHz frequency span with a target EIRP higher than 70 dBm, even considering the maximum beam steering configuration of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm \mathbf{2 0}^{\circ}$</tex>. The whole system presented features a hybrid structure that includes a multilayer PCB hosting RF, control networks and electronics components inside the body of a phased array made with 64 hollow metallic horns as radiating elements. The proposed architecture shows flexibility in targeting different far field performance then capabilities to be employed in different space missions. Different kind of use-cases have been analyzed, spanning from inter-satellite links to EO downlink operations, together with possible evolutions in terms antenna configuration to wider the applicable scenarios.

AI-eXpress (AIX) is an AI-enabled flight acquisition and processing framework, that embarks on satellites and then brings into the market a new concept of satellite a service. AIX makes available in-orbit resources/services on-demand: services shall include EO data acquisition, processing (i.e., actionable information extraction), downlink, and distribution. Ready-made applications (e.g., fire detection and warning service) will be available on a dedicated app store, and services can be also combined to build custom applications. Valuable information can be then transferred back to Earth as notifications and alerts, or directly exploited on-board in autonomous decisions workflows. The data that is demonstrated to be not relevant to user applications can be processed and then deleted, thus freeing space in memory and using less bandwidth. Furthermore, emerging NewSpace companies may now test their innovative AI algorithms and their proof-of-concept directly in space and prove their value to the market. Traditional space institutions and research entities may test a new approach changing from “makers” to “enablers”. AIX is developed, by Planetek Italia, D-Orbit and AIKO within the ESA Incubed program and the services will be available from Q4\2024.

Ion velocity distribution functions in solar wind are often found to be non-Maxwellian. Streams of accelerated particles and temperature anisotropies are typical non-thermal features, whose origin is debated since the early years of in situ measurements. In order to disentangle the kinetic processes which may play a role in the generation of such distortions, particle double streams need to be identified and isolated. To this purpose, we have developed a numerical approach that leverages the clustering technique employed in machine learning. Here, we present the results obtained applying our technique to the ion distribution functions of a typical fast Alfv&amp;#233;nic wind stream observed by Solar Orbiter-PAS in mid-September 2022, at a heliocentric distance of about 0.58 au. We could separate up to four ion families, namely proton core and beam and alpha core and beam. This allows us to characterize and compare their features, like the relative densities and temperatures. Differently from the better-known proton beam, alpha beam represents a relevant fraction of the alpha population, around 40%. Separating such a massive beam may shed new light on alpha kinetic features like the anomalous overheating mechanism. Moreover, the study of the velocity drift of the various ion populations indicates that both the alpha core and the alpha beam are sensitive to the Alfv&amp;#233;nic fluctuations, and the surfing effect found in literature can be recovered only when considering the core and the beam as a single population. The similarities between proton and alpha beams would suggest a common generation mechanism, apparently due to local physical conditions in the plasma. &amp;#160; &amp;#160;

The sustainability of irrigated water management is hampered by a lack of precise and timely information about irrigated crops and their applied water volumes. This is particularly crucial for arid and semi-arid regions of Mediterranean areas where water withdrawals (mostly from groundwater) are unknown, preventing water authorities from effectively planning and managing water use for agriculture. Remote sensing techniques have the potential to overcome such limitations allowing to monitor the crop biophysical parameters related with crop evapotranspiration and crop water use with suitable temporal and spatial resolution. In this study the performance of some Vegetation Indexes (VIs), to identify and mapping effectively irrigated areas in Southern Italy was investigated. VIs are extracted from Sentine1-2 images and relate to irrigated and non-irrigated olive groves in Apulian region being the leader Italian olive producer. Results show that VIs’ distributions (rainfed vs. irrigated) are significantly different as confirmed by t-test and k-Smirnov test (p-value ranging between 0.0001 and 0.03). The space-time variability of irrigated crops map retrieved from remote sensed data could support water authorities in managing water resource abstractions and reduce costly monitoring practices such as insitu inspections.

The unique spectral content provided by PRISMA's hyperspectral sensor gives the possibility to study the Earth's surface and environment from space in unprecedented detail. In this respect, our work presents the results of an Italian Space Agency-funded project aiming to develop eight prototypes for providing Value Added products based on such data. Prototypes focus on vegetation, urban areas, water quality, material detection, and natural hazards, combining multiple state-of-the-art techniques based on Machine Learning, physical models, and index-based algorithms. This is particularly relevant given the increasing demand for accurate information to address sustainable policies and support decision-making processes. Through a series of case studies, we highlight the versatility and utility of PRISMA's hyperspectral data for various scientific and operational applications.

This study investigates insights concerning the future of the cooling market of the European Union (plus the United Kingdom) and its possible development for the upcoming decade (until 2030). In this manuscript, a qualitative model—Porter’s five forces analysis (PFFA)—and a quantitative tool—multi-criteria decision analysis (MCDA)—have been applied to produce a forecast and a corresponding validation technique. It has been observed that the MCDA tool came to a similar conclusion as the PFFA methodology, highlighting that, presumably, the cooling market will continue to grow moderately, mainly thanks to research and development (R&amp;D) as the central driving force. Moreover, the latter is strictly connected with R&amp;D developments, economic crises, and the welfare of the European population. Additionally, in this study, an extensive survey conducted on interviews of experts throughout each European country confirmed the slightly positive future developments forecast up to 2030 from the quantitative and qualitative methods mentioned above. The results of the study describe a steady growth of the cooling market in Europe until 2030 of about 1–2% annual increase, for a total gain of 24%.