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Spacecraft missions venturing beyond Earth rely upon ranging, specific payloads or supports systems, claiming the usage of facilities such as Deep Space Network or ESTRACK. This requires a significant amount of resources both on Earth and onboard, especially in monetary terms. Furthermore, satellite link is not always guaranteed, and results are not available in real-time. Therefore, to cruise independently of Earth-based operators and to achieve the requirements raised by the next planetary exploratory missions, this manuscript proposes a novel visual-based terrain relative navigation (TRN) system. TRN is promising because can be applied to a wide range of space missions, e.g. planetary exploration (rocky planets), the study of moons of gaseous planets, approach phase of comets, asteroid, and other celestial bodies. In essence, a spacecraft can retrieve its absolute position by matching a pattern of observed craters with a database. The measurements thus obtained can be integrated into a navigation filter to estimate the spacecraft state (position and velocity). The ability to detect match surface features to a map is crucial for TRN. However, craters largely vary their appearances also depending on image qualities, lighting geometry, and noises. For these reasons, realizing a crater detector able to generalize to different scenarios is complex. It is worth considering that this task must be performed in a robust way to keep high the navigation accuracy. In past, this has led to least square approaches, creating situations where corrupted navigation states render otherwise good images ineffectual, leading to unnecessary filter reinitializations, trajectory aborts (e.g. during lunar descent), or other undesirable events. Contrarily, the solution proposed is reliable, combining the strengths of a region-based convolutional neural network (Mask R-CNN) with the robustness of projective invariants theory. An extended Kalman filter completes the TRN system, further increasing the stability of the system. Despite the usage of medium resolution (118 m/px) data, results showed that the navigation accuracy lies below 400 meters in the best-case scenario for a satellite orbiting around the Moon at about 50 km altitude. This is expected to guarantee real-time autonomous onboard operations with no need for ground support.

The ARGO project brings forward an innovative concept in the field of Maritime Situational Awareness (MSA). Currently, the maritime traffic monitoring is mainly supported by the Automatic Identification System (AIS), a system in which medium and large ships (>300 tons) are equipped by a transponder transmitting vessels identification data and information about their route, enabling ship-to-ship and ship-to-shore data exchange within about 60 nautical miles from the coast. The AIS system is conceived on a collaborative approach: only the ships whose AIS transponder is turned on can be detected and monitored. Hence, non-collaborative vessels do not leave a trace in the AIS system. The ARGO project aims to improve the security of ships providing an effective understanding of maritime traffic, including also non-collaborative vessels. Currently, several space-based monitoring systems have been proposed and tested. All of them carry out the ship detection based on echoes in optical or radar images. This have strongly improved the MSA in the last decade, but such systems are useless to fight against illegal activities performed by go-fast boats. In fact, this kind of vessels, thanks to their design, materials and high speed, cannot be imaged in radar images and the illegal activities are typically performed during the night-time when optical sensors cannot provide data. However, thanks to their high velocity and narrow beam, these boats are likely to produce long visible wakes. Such wakes are very persistent, lasting for distances of several kilometers, and they can be observed in Synthetic Aperture Radar (SAR) imagery. Hence, ARGO project will enable the space-based surveillance of seas under all weather and day-and-night conditions, overcoming the idea of the ship detection based on echoes in optical or radar images and exploiting the wake presence as an indirect signature of the ship presence. The unique capabilities of the proposed system make it suited for the detection of very fast ships, typically used for illegal affairs, strongly contributing to border security and passengersgoods safety.

Plant nursery industry makes extensive use of non-biodegradable plastic pots (polypropylene, PP); in Italy, it is estimated an annual consumption of about 440 million PP pots which, at the end of their use, represent an expensive waste to be disposed of; therefore, the replacement of PP pot with similar products based on biodegradable materials, may reduce significantly the environmental impact of this sector. ECO-POT project aims at a cost-effectively production and successive field validation of biodegradable pots realized in different sizes; the mechanical properties of pots, i.e. resistance to lateral deformation and crushing, and rate of biodegradation should match needs of both operators and consumers. In particular, the biodegradation will be tested on plantable pot which are left intact on the root ball and planted into the field. In this case pots are designed to allow root to grow through the container walls and to decompose rapidly after being planted. In addition, the rate of biodegradation will be also tested on pots which are designed to be removed before final planting, thus speculating on the compostability of the used containers (test ISO UNI 14855-1) Different commercial biodegradable products, alone or mixed, will be tested. ECOPOT project involves different plant nursery farms located in Pistoia; up to 10,000 ornamental plants, placed in pots of different sizes, in different environments and belonging to 10 different species / varieties, will be tested. The most important expected results are: reduction in the consumption of non-renewable resources; economic benefits for nurseries gained in the international markets thanks to the adoption of recognizable low environmental impact trademarks (ECOLAB).