CYBELE generates innovation and create value in the domain of agri-food, and its verticals in the sub-domains of PA and PLF in specific, as demonstrated by the real-life industrial cases to be supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspires at demonstrating how the convergence of HPC, Big Data, Cloud Computing and the IoT can revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large scale datasets of diverse types from a variety of sources, and they are capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. CYBELE develops large scale HPC-enabled test beds and delivers a distributed big data management architecture and a data management strategy providing 1) integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources, 2) a data and service driven virtual HPC-enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimizing the features of processing large scale datasets and 3) a bouquet of domain specific and generic services on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.

The creation of the Schengen area has been one of the major achievements of the EU. However, this agreement requires countries to cooperate tightly in order to keep a high level of security at their internal borders, as well as to share the responsibility of managing external borders. Such a variety of borders (land, sea and air) and current challenges requires a consistent approach to border surveillance, based on a plethora of heterogeneous assets. These can be manned or unmanned, ranging from sensors (optical, radar, IR) to unmanned platforms (UAV, UGV, USV or UUV), and need to be combined to offer an integrated situational picture of the area under surveillance and of their location. In order to effectively control their operation and manage the large amounts of data collected by them, new approaches for command and control need to be considered, allowing efficient interaction between the operator and the different assets in the field. CAMELOT proposes to develop and demonstrate different advanced command and control service modules for multiple platform domains, based on a SOA architecture that specifies internal and external interfaces, allowing the development of a modular and scalable command and control station, customisable to the user needs. This architecture can be based on results of previous studies and work or open architectures that may prove more suitable and the interfaces can take advantage of the standardisation work that has been done already. After the definition, CAMELOT partners will prototype service modules according to their expertise, background individual technologies and practitioner needs. These will be integrated progressively in specific testing along the project. This prototype development approach will culminate in 2 final demonstrations involving end users and relevant stakeholders, to achieve a maturity of TRL6 (for most individual technologies supporting the functionalities for border surveillance) and an IRL of 7 for CAMELOT.