# Implementing and Testing a U-Space System: Lessons Learnt
## 1 Introduction
### Related Work  
Four phases have been defined for the implementation of U-space, each of them adding more services to allow for more complex operations. Behind this complexity, there is a more efficient and flexible use of the airspace, able to better satisfy the needs of the airspace users in different aspects. Several recent and current projects have been oriented to develop some of those envisioned services or sub-services. An example is the DACUS (Demand and Capacity Optimization for U-Space) project [2], developing a Dynamic Capacity Management service. With a duration of two years and a half, DACUS suggested a flight plan definition reflecting, on the one hand, the expected uncertainties to comply with the flight plan, which could be determined by factors like weather, communication, navigation and surveillance (CNS) accuracy or the availability of infrastructures like vertiports. And on the other hand, reflecting details of the drone and the trajectory, making it possible to determine the level of annoyance (noise, visual impact) when flying over population and the risk associated, this last value based on the data provided by a Population Density Map service. With all these inputs, DACUS determined, for a given set of flight plans, the _hotspots_, or areas where the risk of collision or the threshold of annoyance was exceeded, and measures like changes in altitude or delays were suggested in each case to avoid such hotspots. Therefore, DACUS was not providing deconfliction, but the analysis of considerations that, coordinated with the separation services, would bring us closer to a system capable of providing an accepted, safe, efficient and feasible traffic management. The topic of the Dynamic Capacity Management in U-space is also addressed in [3], where a safety condition is required in the management of the traffic density: the aircraft must remain within the range of a landing point along all of its route in case some failure or contingency forces it to land. Other services that should be in place for a successful deployment cover more technical aspects. The ICARUS project (Integrated Common Altitude Reference system for U-Space) [4] suggested four new services that were later added to edition 4 of the ConOps: Vertical Conversion service, GNSS service, Real-time Geographic Information service and Vertical Alert service. ICARUS proposed that authorities could determine a specific altitude reference in areas where there is a risk of altitude confusion. The importance of a service like this and the coordination of the altitude references was noted during the development of the work described in the present document, as will be later explained. A key piece and an input for several services is the configuration of the airspace. We should expect to find the airspace divided into different volumes associated to a diversity of constraints, like speed or altitude to mitigate the noise, layers where only drones with certain capabilities would be allowed to fly or areas dedicated to Urban Air Mobility (UAM). If the use of the airspace is to be optimized, this segmentation will probably be dynamic, and a service like the Drone Aeronautical Information Management Service will have to inform of the changes in the structure of the airspace to the airspace users and to the rest of the U-space services affected. The exploratory project USEPE [5] proposed criteria for these subdivisions together with machine learning algorithms to determine in each volume the separation between drones and from manned aviation [6]. One of the most important tasks of the system will be the contingency management. As detailed in [7], its consideration ramifies throughout the entire system and affects all stakeholders. Safety being the main principle to guide the operations, it will affect other features like flight optimization, preferences or priorities. In [8], a method is proposed to evaluate the risk of the operations and suggest measures to the path planning algorithms to mitigate the risk.