Pilot3 will develop a software engine model for supporting crew decisions for civil aircraft. This software will provide a set of options to the pilot with information to help the crew select the most suitable one considering multi-criteria business objectives of the airline. Pilot3 will look integrate airlines flight policies and overall performance targets to select and rank the alternatives. The system does not only consider the flight but the whole network operations of the airline. Pilot3 is composed on four different subsystems: Indicators Estimator, Alternatives Generator, Performance Assessment Module and HMI. The first three will be developed during the project, while a HMI will be designed. Pilot3 will specify the software interface so that it can be integrated in larger systems. The Indicators Estimator will provide an estimation of the different performance indicators for a given trajectory. Pilot3 will allow the airline to select how to estimate these values: using airborne information, ground information, with analysis of data and heuristics or with machine learning predictors. The Alternatives Generator will automatically compute different alternative trajectories considering the airlines' flight policies and its performance goals. This system will also allow the pilot to add constraints and even to specify a given trajectory to be assessed. This module will perform a multi-objective optimisation. The Performance Assessment Module will compare the expected performance of each alternative trajectory produced by the Alternatives Generator and rank them according to the overall airlines' goals. Finally, the designed HMI will present the information to the pilot and allow the crew to interact with the system, accepting an alternative, rejecting them (triggering a re-computation of alternatives) or adding constraints. Pilot3 will contribute to the capture and definition of flight policies and allow airlines to define enriched policies.

Dispatcher3 will develop a software prototype for the acquisition and preparation of historical flight data in order to give support to the optimisation of future flights providing predictive capabilities and advice to dispatchers and pilots. This will be done considering airline preferences and the impact of flight missions on overall airline objectives. Dispatcher3 focuses on activities prior to departure: dispatching and pilot advice on how to operate the flight. Dispatcher3 is composed of three layers: data infrastructure, predictive capabilities and advice capabilities. The data infrastructure will be powered by DataBeacon, a multi-sided and open-source data storage and processing platform. DataBeacon provides private environments, secure data frames, a full-stack artificial intelligence environment and a scalable highly available on-demand cluster. DataBeacon has been developed and successfully been used in other initiatives by members of the consortium. The infrastructure will allow further developments, based on data science techniques, to be built on the pre-processed datasets. The predictive capabilities will be provided by the development of two modules: data acquisition and preparation, encompassing data wrangling and descriptive analytics, and a predictive model, which will perform target variable labelling and feature engineering, plus the training, testing and validation of machine learning predictive models for targeted airlines' KPIs. With the same predictions, different advice could be generated considering user policies. The advice capabilities of Dispatcher3 will be provided by a dedicated advice generator module, which will collect all the information from the predictive analytics and build a decision framework, which could be used by dispatchers and pilots. Dispatcher3 fits within the activities of CS2 Systems ITD WP1.3 "FMS and functions" and addresses some of the high-level objectives and challenges for this ITD defined by CS2.

Industry 4.0 has now extended its focus to a broader set of technologies rather than just CPS, and to the most vital processes included in the product and production systems lifecycle, rather than just to production. In all the dialects where the Industry 4.0 language is spoken, Industrial Internet of Things, Additive Manufacturing and Robotics from the technology side and Mass Customization, Product-Service Systems and Sustainable Manufacturing from the business side always represent key cornerstones and top priority challenges. FASTEN “mission” is to develop, demonstrate, validate, and disseminate an integrated and modular framework for efficiently producing custom-designed products. More specifically, FASTEN will demonstrate an open and standardized framework to produce and deliver tailored-designed products, capable to run autonomously and deliver fast and low cost additive manufactured products. This will be achieved by effectively pairing digital integrated service/products to additive manufacturing processes, on top of tools for decentralizing decision-making and data interchange. Sophisticated software technologies for self-learning, self-optimizing, and advanced control will be applied to build a full connected additive manufacturing system. ThyssenKrupp and Embraer are two of these companies that must overcome challenges of this nature, in order to cope with an increasing demand diversity, products with shorter life cycles, and the need for supplying low volumes per order, requiring flexible solutions capable to effectively manufacture and deliver personalized products.

The European aviation industry needs to leverage the surge of multi-source and multi-lingual data streams to gain augmented intelligence on its status quo and open up a wide spectrum of unprecedented services for the whole ecosystem (airlines, airports, passengers, service providers, manufacturers, local authorities, etc.). ICARUS will build a novel data value chain in the aviation-related sectors towards data-driven innovation and collaboration across currently diversified and fragmented industry players, acting as multiplier of the “combined” data value that can be accrued, shared and traded, and rejuvenating the existing, increasingly non-linear models / processes in aviation. Using methods such as big data analytics, deep learning, semantic data enrichment, and blockchain powered data sharing, ICARUS will address critical barriers for the adoption of Big Data in the aviation industry (e.g. data fragmentation, data provenance, data licensing and ownership, data veracity), and will enable aviation-related big data scenarios for EU-based companies, organizations and scientists, through a multi-sided platform that will allow exploration, curation, integration and deep analysis of original, synthesized and derivative data characterized by different velocity, variety and volume in a trusted and fair manner. ICARUS will bring together the Aerospace, Tourism, Health, Security, Transport, Retail, Weather, and Public sectors and accelerate their data-driven collaboration under the prism of a novel aviation-driven data value chain. Representative use cases of the overall domain’s value chain include: (I) Sophisticated passenger handling mechanisms and personalised services on ground facilities, (II) Enhanced routes analysis of aircrafts for improved fuel consumption optimisation and pollution awareness, (III) More accurate and realistic prediction model of epidemics, (IV) Novel Passenger experiences pre-in- and post-flight.