ALMASat-1, ALMASat-EO and beyond: evolution of structural concepts and technologies towards multifunctional structures for microsatellites

Multifunctional Structures (MFS) represent one of the most promising disruptive technologies in the space industry. The possibility to merge spacecraft primary and secondary structures as well as attitude control, power management and onboard computing functions is expected to allow for mass, volume and integration effort savings. Additionally, this will bring the modular construction of spacecraft to a whole new level, by making the development and integration of spacecraft modules, or building blocks, leaner, reducing lead times from commissioning to launch from the current 3-6 years down to the order of 10 months, as foreseen by the latest Operationally Responsive Space (ORS) initiatives. Several basic functionalities have been integrated and tested in specimens of various natures over the last two decades. However, a more integrated, system-level approach was yet to be developed. The activity reported in this thesis was focused on the system-level approach to multifunctional structures for spacecraft, namely in the context of nano- and micro-satellites. This thesis documents the work undertaken in the context of the MFS program promoted by the European Space Agency under the Technology Readiness Program (TRP): a feasibility study, including specimens manufacturing and testing. The work sequence covered a state of the art review, with particular attention to traditional modular architectures implemented in ALMASat-1 and ALMASat-EO satellites, and requirements definition, followed by the development of a modular multi-purpose nano-spacecraft concept, and finally by the design, integration and testing of integrated MFS specimens. The approach for the integration of several critical functionalities into nano-spacecraft modules was validated and the overall performance of the system was verified through relevant functional and environmental testing at University of Bologna and University of Southampton laboratories.

Flower cover and bee diversity estimate by drone RGB images

Worldwide, biodiversity is decreasing due to climate change, habitat fragmentation and agricultural intensification. Bees are essential crops pollinator, but their abundance and diversity are decreasing as well. For their conservation, it is necessary to assess the status of bee population. Field data collection methods are expensive and time consuming thus, recently, new methods based on remote sensing are used. In this study we tested the possibility of using flower cover diversity estimated by UAV images (FCD-UAV) to assess bee diversity and abundance in 10 agricultural meadows in the Netherlands. In order to do so, field data of flower and bee diversity and abundance were collected during a campaign in May 2021. Furthermore, RGB images of the areas have been collected using Unmanned Aerial Vehicle (UAV) and post-processed into orthomosaics. Lastly, Random Forest machine learning algorithm was applied to estimate FCD of the species detected in each field. Resulting FCD was expressed with Shannon and Simpson diversity indices, which were successively correlated to bee Shannon and Simpson diversity indices, abundance and species richness. The results showed a positive relationship between FCD-UAV and in-situ collected data about bee diversity, evaluated with Shannon index, abundance and species richness. The strongest relationship was found between FCD (Shannon Index) and bee abundance with R2=0.52. Following, good correlations were found with bee species richness (R2=0.39) and bee diversity (R2=0.37). R2 values of the relationship between FCD (Simpson Index) and bee abundance, species richness and diversity were slightly inferior (0.45, 0.37 and 0.35, respectively). Our results suggest that the proposed method based on the coupling of UAV imagery and machine learning for the assessment of flower species diversity could be developed into valuable tools for large-scale, standardized and cost-effective monitoring of flower cover and of the habitat quality for bees.

Monitoring of macro- and microlitter in the Venice lagoon and coastal area

Marine litter and plastics are a significant and growing marine contaminant that has become a global problem. Macrolitter is subject to fragmentation and degradation due to physical, chemical and biological processes, leading to the formation of micro-litter, the so-called microplastics. The purpose of this research is to assess marine litter pollution by using remote sensing tools to identify areas of macrolitter accumulation and to evaluate the concentrations of microplastics in different environmental matrices: water, sediment and biota (i.e. mussels and fish) and to contribute to the European project MAELSTROM (Smart technology for MArinE Litter SusTainable RemOval and Management). The aim is to monitor the presence of macro- and microlitter at two sites of the Venice coastal area: an abandoned mussel farm at sea and a lagoon site near the artificial Island of Sacca Fisola; The results showed that both study areas are characterised by high amounts of marine litter, but the type of observed litter is different. In fact, in the mussel farm area, most of the litter is linked to aquaculture activities (ropes, nets, mooring blocks and floating buoys). In the Venice lagoon site, the litter comes more from urban activities and from the city of Venice (car tyres, crates, wrecks, etc.). Microplastics is present in both sites and in all the analysed matrices. Generally, higher microplastics concentrations were found at Sacca Fisola (i.e., in surface waters, mussels and fish). Moreover, some differences were also observed in shapes and colours comparing the two sites. At Sacca Fisola, white irregular fragments predominate in water samples, blue filaments in sediment and mussels, and transparent irregular fragments in fish. At the Mussel Farm, blue filaments predominate in water, sediment and mussels, while flat black fragments predominate in fish. These differences are related to the different types of macrolitter that characterised the two areas.