As manufacturing evolves towards high-quality and high-output production, the demand for intelligent, defect-free systems becomes increasingly critical. Zero Defect Manufacturing (ZDM) emerges as a key paradigm, relying on real-time data and predictive analytics to proactively detect and mitigate quality deviations. In this work, a AI-based diagnosis system is proposed as part of an integrated approach to transform quality monitoring from a reactive to a proactive strategy by enabling the early detection of potential defects. This approach considers representing algorithms for predicting product and measurement quality based on live shop floor data, and optimization algorithms for fine-tuning the regression models aiming to enhance the prediction performance. The proposed approach was applied to an automotive assembly line, and the preliminary results shown benefits in improving the results of the diagnosis process.

In the aircraft engine Maintenance, Repair, and Overhaul (MRO) process, effective task planning relies heavily on the expertise of lead engineers. However, when predictive models are used to assist decision-making, issues with incomplete, unbalanced, and inconsistent data can lead to errors in the planning task. Therefore, reliable predictions are crucial for optimising the operational efficiency. This paper proposes a methodology to improve the prediction of maintenance times for an aircraft engine MRO process by integrating K-means clustering with Cosine and Jaccard similarity methods to define a reliable prediction interval. This methodology was compared with the predictions of the Simple Linear Regression method, which resulted in the prediction interval approach significantly reducing prediction errors, increasing prediction accuracy, while optimising process management and maintenance task planning throughout the MRO process.

ESA’s BIOMASS is the seventh Earth Explorer mission. It will be devoted to the global monitoring of: 1) above ground forest biomass and biomass change maps with an accuracy90%, a 50-200 m spatial resolution, and 2-12 months temporal resolution; and 3) global forest height maps with an accuracy of 20-30%, 100 m spatial resolution, and 12 months temporal resolution.BIOMASS is based on a P-band SAR (438 MHz center frequency, 6 MHz bandwidth) orbiting in a dawn-dusk, Sun-synchronous orbit at 674 km, that will systematically acquire fully- (quad-) polarized image data in an interferometric mode over all major forested areas on the globe and a tomographic phase (7 images) to retrieve forest vertical structure information. At P-band ionospheric effects are very important and need to be corrected for. This paper describes the current status of the Ionospheric module for the Biomass End-to-end Performance Simulator (BEEPS-IOM).

ESA's BIOMASS Earth Explorer mission aims at providing global above ground forest biomass. Its payload is a UHF synthetic aperture radar. At UHF ionospheric effects, notably the group delay and phase advance, the Faraday rotation, and the intensity and phase scintillations (or rapid fluctuations) are significant and have to be properly modelled to be compensated, to understand the limitations of the proposed technique, and to select the optimum observation conditions. This paper summarizes the structure and main characteristics of the ionospheric module of the BIOMASS end-to-end performance simulator (BEEPS-IOM).

ESA's Soil Moisture and Ocean Salinity (SMOS) mission [1] has been in orbit for over 11 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions keeps being fully operational. This II-year long lifetime of SMOS, so far, has enabled the calibration and Level-1 processor team to improve the calibration procedures and the image reconstruction resulting in a new version of the Level-1 data processor, v724. To present the main performance features of this new version and the improvement in the calibration procedures constitute the main objective and content of this presentation.

Radio Frequency Interference (RFI) is a growing threat to all Earth Observation (EO) passive microwave missions. Many RFI detection algorithms are used on-board and in the ground segment data processing, but there is no single algorithm that can detect all RFI instances. The best strategy is always to combine several detection methods. This paper presents the results of the new Ground RFI Detection System (GRDS). The GRDS uses a combination of a wide variety of RFI detection algorithms to clean the Earth Observation measurements from RFI. The system is built to be able to scan for RFI for any EO mission. The initial results show the important reduction on RFI in the EO data as measured by the European Center for Mid-Range Weather Forecast (ECMWF) first guess departure statistics.

The ionosphere impacts radio-wave propagation, notably up to a few GHz. The main applications impacted by the ionosphere are GNSS positioning and timing, Earth Observations (especially low frequency SAR missions e.g. BIOMASS, and GNSS-R), and Space Weather. While most effects can be compensated by using dual-frequency receivers and circular polarization antennas, ionospheric scintillation (rapid intensity and phase fluctuations) cannot. Climatological models for the mean stable part of the electron density in the ionospheric layers (e.g. IRI or NeQuick) or for the magnetic field (e.g. WMM) have significantly improved in the past years. However, models of the inhomogeneous part, responsible for scintillation, can be improved, since they are based on relatively old data (e.g. WBMOD), or climatological inputs are limited to properly characterize all latitudes and solar conditions (e.g. GISM or WAM). This study first assesses the goodness of GISM, the model adopted by the ITU-R, by comparing GISM predictions and measured scintillation data. Then, the impact of measured intensity and phase scintillation on TDS-1 GNSS-R data is illustrated.

Radio Frequency Interference (RFI) is a serious constraint affecting the Earth Observation (EO) passive microwave missions whose effect has been increasing. There is no single algorithm that can detect all RFI instances. The best strategy always is to combine several detection methods. This paper presents a new system concept: Ground RFI Detection System (GRDS). The GRDS uses a wide variety of RFI detection algorithm to clean the Earth Observation measurements from RFI. The system architecture is modular and it is built to be able to scan for RFI for any EO mission. The initial assessments reported show the potential for this GRDS system.

The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive.

Industries are becoming more and more digitized to better implement intelligent and predictive maintenance support systems, aligned with Industry 4.0, which requires the progressive digitization of data collection and processes. Maintenance interventions, in an evolving technological context, are increasingly more complex and difficult for technicians to perform. In these environments, the use of Augmented Reality (AR) to help assist and guide in the maintenance operations, can accomplish a considerable gain in productivity. AR allows to superimpose information objects in real scenes, such as text, images, audiovisuals, and 2D/3D model animations, making available contextual information about the process, based on location and perspective. This paper describes the design and implementation of a prototype augmented reality application to support maintenance tasks inside a metal stamping production unit, that produces components for the automotive sector. It aims to train and guide personnel during the maintenance operations, and offering an extra channel to reach expert help.

Internet of Things (IoT) envisages a reality in which people and objects are interconnected in such a way that a series of services, previously unthinkable, become real. The impact of IoT technologies is already tangible in industry, particularly under the Industry 4.0 initiative, but yet far to be fully exploited in other areas, such as building automation. This paper highlights the importance of using IoT and other emergent technologies to develop building automation applications that serves as base in smart cities, particularly supporting the interoperability among home automation solutions provided by different manufacturers. For this purpose, a low cost IoT enabler solution for building automation is presented, based on the use of cyber-physical systems, as backbone to integrate different IoT technologies and building automation technologies. The proposed approach was successfully implemented in an open space laboratory.

The increase volume of vehicles circulating in large cities and the limited space for parking are factors that motivate the adoption of systems capable of dealing with such problems. In this context, smart parking systems are suitable solutions to avoid the traffic congestion, the air pollution and the long search to find a free parking spot. The inclusion of emergent ICT technologies and artificial intelligence techniques, and particularly using multi-agent systems, combined under the scope of Cyber-Physical Systems (CPS), ensure flexibility, modularity, adaptability and the decentralization of intelligence through autonomous, cooperative and proactive entities. Such smart parking systems can be easily adapted to any type of vehicle to be parked and scalable in terms of the number of parking spots and drivers/vehicles. A fundamental issue in these agent-based CPS parking systems is the interconnection between the cyber and physical counterparts, i.e. between the software agents and the physical asset controllers to access the parking spots. This paper focuses on developing an agent-based CPS for a smart parking system and particularly addressing how the software agents are interconnected with the physical asset controllers using proper Internet of Things technologies. The proposed approach was implemented in two distinct parking systems, one for bicycles and another for cars, showing an efficient, modular, adaptable and scalable operation.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission [1] has been in orbit for over 9 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions is working well. The data products are generated using version v620 of the Level-1 operational processor, a version which entered into operation in Spring 2015. During last year a comprehensive data set was processed using a new processor version v720 and the assessment of the results is expected to be completed by mid 2019. In parallel to this evaluation of v720, the following version v730 of the Level-1 processor of SMOS has been already produced. This latter version is intended for investigating the capability to reduce Radio Frequency Interferences (RFI) by applying image processing techniques. This paper describes the major features and status of the two mentioned versions of the SMOS Level-1 processor, and importantly, aims at updating the remote sensing community on those aspects of the SMOS mission.

Multi-stage manufacturing, typical in important industrial sectors, is inherently a complex process. The application of the zero-defect manufacturing (ZDM) philosophy, together with recent technological advances in Cyber-Physical Systems (CPS), presents significant challenges and opportunities for the implementation of new system architectures that contributes for the continuous improvement of the production. This paper describes the experience gained in the GO0D MAN project which aims at realizing a fully functional, replicable and therefore widely exploitable solution, employing multi-agent systems, smart on-line inspection tools, data analytics and knowledge management technologies. In particular, the paper presents the challenges tackled during the deployment of the GO0D MAN system architecture in three relevant industrial use cases, which represent more than 80% of the manufacturing sector.

The digital transformation that is on-going worldwide, and triggered by the Industry 4.0 initiative, has brought to the surface new concepts and emergent technologies. One of these new concepts is the Digital Twin, which recently started gaining momentum, and is related to creating a virtual copy of the physical system, providing a connection between the real and virtual systems to collect and analyze and simulate data in the virtual model to improve the performance of the real system. The benefits of using the digital twin approach is attracting significant attention and interest from research and industry communities in the last few years, and its importance will increase in the upcoming years. Having this in mind, this paper surveys and discusses the digital twin concept in the context of the 4th industrial revolution, particularly focusing the concept and functionalities, the associated technologies, the industrial applications and the research challenges. The applicability of the digital concept is illustrated by the virtualisation of an UR3 collaborative robot which used the V-REP simulation environment and the Modbus communication protocol.

In recent years, the intense urbanization, and consequently the traffic congestion, has been a major concern of large cities. In this context, the development of smart parkings is a suitable solution to deal with this problem. However, the complexity and requirements imposed by such large-scale systems are an obstacle to its easy implementation. In this sense, it is fundamental to adopt emergent ICT and Artificial Intelligence technologies that are capable to address the imposed requirements. Multi-Agent Systems (MAS) is a suitable approach to face this challenge by providing modularity, flexibility, reconfigurability and fast response to condition change based on its decentralized nature. The use of such agent-based solutions to control physical assets, create novel systems entitled Cyber-Physical Systems (CPS) where the interconnection between the cyber and the physical parts is a crucial issue. This paper focuses the interface between the software agents of a smart parking system with the physical control devices of the parking spots. For this purpose, different interface practices were implemented and tested, considering different interaction schemes and technologies. These alternative interface practices were analyzed taking into consideration the response time, scalability and re-usability parameters.

In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.

Agent-based systems, an approach derived from distributed artificial intelligence, have been introduced for designing large complex systems. They are also suitable to solve challenging problems in industrial environments, being an appropriate technology for realizing cyber-physical systems. In such configuration, they need to interface with automation and control devices. However, until now there is no widely accepted practice nor pattern to interface the software agents with the automation functions. This work addresses this issue and introduces corresponding integration patterns in order to achieve full interoperability and reusability. This work, therefore, provides a methodology for mapping existing practices into a set of generic templates and also discusses the applicability of the proposed approach to different industrial application domains.

The characterization of the ionosphere and its impact on the propagation of radio-wave signals has received an increasing interest for satellite communications, Global Navigation Satellite Systems (GNSS) applications (i.e. navigation and timing), and Earth Observation (EO) missions, especially low frequency SAR missions and GNSS-Reflectometry (GNSS-R). Although the main climatological models for the mean stable part of the electron density in the ionospheric layers (e.g. IRI or NeQuick) or for the magnetic field (e.g. WMM) have been significantly improved in the past years, the inhomogeneous part, responsible for scintillation effects, can still be significantly improved, since models are based on relatively old data (e.g. WBMOD), or the climatological inputs are limited to properly characterize all latitudes and solar conditions (e.g. GISM or WAM). ESA's CLIM IONO study aims to use the experimental observations of the ionosphere collected in the past years to assess the performance of climatological ionosphere models, with the focus on scintillation models, in order to evaluate their ability to properly support future needs, to identify weak areas if any, to propose recommendations for improvements and to implement these improvements whenever possible in existing models. This work focuses on the capabilities of GNSS-R techniques to characterize ionospheric scintillation, notably at low and high latitudes.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission [1] has been in orbit for over 8 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions is working well. The data for this whole period has been and is being processed with the operational version of the current Level-l processor (version v620). Also a representative part of the same data set has been processed with a working version of a new processor (v720) which is now in preparation so that homogenous records of brightness temperatures have been made available. These rich and long data records have allowed learning important lessons from the in-flight experience, and shall eventually lead into the consolidation of the new Level-l processor version (v720) with its corresponding auxiliary calibration and configuration files. Once the improvements are confirmed the new processor version shall be recommended for the operational chain.

The increasing need for more adaptive production environments is a big motivator for the adoption of agentbased technologies in industrial systems, as they provide better mechanisms for handling dynamically and intelligently various kinds of production disturbances. Unlike with the utilization of most conventional automation languages, the use of agents enables, in an easy way, the setup of dynamic and autonomous adaptive processes to handle large and complex engineering system functions and interactions. Agent-technologies in cyberphysical systems contexts require at some point integration with automation controllers. However, most commonly available and used agent system implementations in the industry were not designed for hard real-time control use cases, and do not utilize real-time operating systems or dedicated hardware. Hence, they cannot match the hard-real-time performance of automation controllers. This work provides some insights on the performance that can be achieved with agent-based approaches that integrate with low-level automation system functions. It considers the performance of the agent-based practices in light of non-real-time dedicated hardware or operating systems. The results show that agents are well suited for the majority of soft-real-time control applications.

Although the everlasting goal of achieving a zerodefect production system is not new, the current state of the art has not yet allowed to reach this objective. This paper describes the architectural system foundations of the European R&D GO0DMAN project where a multi-agent system is developed as the mean to provide a real-time, multi-level and multi-stage approach to reach a zero-defect production system in multi-stage environments. Particularly, the paper focuses the integration of software agents and quality control stations forming smart inspection tools, aligned with the cyber-physical systems principles. The proposed approach is validated using an electrical motor testbed, where the agent-based system allows the digitization of the running testing system in a multi-stage schema.

This paper describes the development of a multiagent system (MAS) to support the implementation of zero-defect manufacturing strategies in multi-stage production systems. The MAS infrastructure, combined with on-line inspection tools, data analytics and knowledge generation, constitutes a suitable approach to integrate process and quality control in multi-stage environments. This will allow the early detection of product defects, the adaptation to operating condition changes and the optimisation of manufacturing processes. This type of integrated management structure is aligned with a zero-defect manufacturing production model which is of paramount importance in the actual state-of-the-art manufacturing paradigms. As a proof of concept, the devised manufacturing supervision model was deployed into an experimental multi-stage system that run a set of several tests on electrical motors. The agent-based solution was implemented using the JADE framework and the exchange of information structured by proper data models and industrial based Internet-of-Things and Machine-to-Machine technologies, such as OPC-UA, REST and JSON. The obtained results demonstrate the suitability of the devised integrated management model as a vehicle to achieve dynamic and continuous system improvement in multi-stage manufacturing environments.

The digitalization advent is characterized by the increasing availability of huge amounts of data that allows to generate new knowledge to support decision-making, problem solving and process optimization. The successful implementation of this digital transformation strongly depends on the skills and competences that professionals can have in the different dimensions of the multidisciplinary vision associated to the data scientist profile. This paper describes a study, conducted under the scope of the Da.Re Erasmus + project, that surveys the existing trainings paths dedicated to the academic education of data scientists and the required hard and soft skills of the job offers for these professionals. The survey covers three countries of the project consortium, namely Portugal, Italy and UK.

Presently, many industries are facing strong challenges related to the demand of customized and high-quality products. These pressures lead to internal company's conflicts where current production systems have a rigid structure, forcing the company into a organization stall when a fast product change is required. Therefore, the need to smoothly migrate traditional systems into more feature-rich and cost-effective systems, namely Cyber-Physical Production Systems (CPPS), became a highly discussed topic. PERFoRM project focuses the conceptual transformation of existing production systems towards plug&produce ones to achieve flexible and reconfigurable manufacturing environments. In particular, the smooth migration process is considered crucial to effectively transpose existing production systems into truly CPPS. This paper describes the use of Petri nets to design the migration process under the PERFoRM perspective, taking advantage of its inherent capabilities to design, analyze, simulate and validate such complex processes.

Industrial agent technologies have been integrated in key elements coupling industrial systems and software logic, which is an important issue in the design of cyber-physical systems. Although several efforts have been tried out over the last decades to integrate software agents with physical hardware devices, and some commonalities can be observed among the existing practices, there is no uniform way overall. This work presents an empirical survey of existing practices in three application area, namely factory automation, power & energy systems and building automation. It identifies pertaining common issues and discusses how they integrate low level automation functions by utilizing industrial agents. The surveyed practices reveal high diversity, customized traditional integration focusing mostly on I/O functions, without security, and an overall approach that is mostly coupled rather than embedded.

Smart devices and Internet of Things (IoT) technologies are becoming each day more common. At the same time, besides the exponentially increasing demand to analyze the produced data, there is an evolving trend to perform the data analysis closer to the data sources, particularly at the Fog and Edge levels. In this sense, the development of testbeds that can, e.g., simulate smart devices in IoT environments, are important to explore and develop the technologies to enable the complete realization of such IoT concepts. This paper describes the digitization of an electric motor, through the incorporation of sensing and an analytical computational environment, towards the development of a testbed for IoT and Big Data technologies. The smart electric motor testbed provides real-time data streams, enabling a continuous monitoring of its operation along all the device life-cycle through advanced data analytics. Furthermore, the paper discusses how specific data analytics features fit the different IoT layers, while preliminary experiments demonstrate the testbed potentials.

A generic software tool to evaluate the impact of ionospheric disturbances is presented, including low and high latitude physically-based models, and low and high frequency fluctuations. This tool has been developed specifically to assess the performance of navigation receivers, but it is ready to simulate other frequencies and even receivers in dynamic conditions, which allows it to be used in other applications such as communications, GNSS-R, radar altimetry or SAR.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 7 years, with its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. This 7 year period has provided a wealth of information which has enabled us to understand and consolidate the performance of the payload in great detail. More importantly, we know now the things that work well, those that need improvement, and how the instrument could be enhanced if we were to build it again. This paper presents the lessons learnt from SMOS after 7 years in orbit.

Nowadays, many organizations intend to convert their existing production systems towards ones that are terized by adaptability, openness, flexibility and modularity. This requires a redesign of existing information processing systems especially related to control, leading possibly to cyber-physical production systems (CPPS). However, the implementation of new control technologies will have a direct impact on the normal operational status of production while engineers will also face several challenges and obstacles in adopting intelligent automation systems. New step-wise migration strategies are required to holistically support industries in their journey towards CPPS taking into account technical, economic and social aspects. This paper discusses the migration state-of-the-art strategies, analyzing them and providing a first attempt to define a migration approach for innovative production systems.

The world is moving towards to the fourth industrial revolution, usually linked with the Industrie 4.0 initiative, enables the digitization of manufacturing factories by using Cyber-Physical Systems and emergent technologies like Internet of Things and Internet of Services. The seamless reconfiguration of these complex industrial cyber-physical systems is an important challenge for the complete implementation of this revolution, being necessary to re-think the way such mechanisms can be designed and engineered. This paper presents an agentbased reconfiguration system for the dynamic and seamless reconfiguration of a physically-reconfigurable modular micro-flow production system in the area of manufacturing of aerospace engine components.

Manufacturing companies generate huge amounts of relevant data that, potentially, can help the decision makers to monitor and supervise the health status of the shop-floor. Many problems are present that prevent this monitoring, namely the identification of which data should be collected, how to execute this collection or how this data should be analysed and displayed to the user. This paper deals with the last issue, where a dynamic and responsive User Interface is developed enabling the decision maker, having an in-the-mesh intervention, to constantly monitor relevant Key-Performance Indicators (KPI). The paper describes the development of such tool, considering a web-based approach under a platform agnostic perspective. The user interface presents an dynamic view over the aggregated KPI, where data is annotated with relevant warning information.

Cyber Physical Systems (CPS) are expected to shape the evolution of production towards the fourth industrial revolution named Industry 4.0. The increasing integration of manufacturing processes and the strengthening of the autonomous capabilities of manufacturing systems make investigating the role of humans a primary research objective in view of emerging social and demographic megatrends. Understanding how the employees can be better integrated to enable increased flexibility in manufacturing systems is a prerequisite to allow technological solutions, as well as humans, to harness their full potential. Humans can supervise and adjust the settings, be a source of knowledge and competences, can diagnose situations, take decisions and several other activities influencing manufacturing performances, overall providing additional degrees of freedom to the systems. This paper, studies two different integration models: Human-in-the-Loop and Human-in-the-Mesh. They are both analysed in the context of four industrial cases of deployment of cyber physical systems in production.

The world is assisting to the fourth industrial revolution, with several domains of science and technology being strongly developed and, specially, being integrated with each other, allowing to build evolvable complex systems. Data digitization, big-data analysis, distributed control, Industrial Internet of Things, Cyber-Physical Systems and self-organization, amongst others, are playing an important role in this journey. This paper considers the best practices from previous successful European projects addressing distributed control systems to develop an innovative architecture that can be industrially deployed. For this purpose, a particular design process has to be addressed in order to consider the requirements and functionalities from various use cases. To investigate the known practices, four use cases are enlighted in this paper, which cover a wide spectrum of the European industrial force, as well as industrial standards to support a smooth migration from traditional systems to the emergent distributed systems.

With the emergence of the Industry 4.0 concept, or the fourth industrial revolution, the industry is bearing witness to the appearance of more and more complex systems, often requiring the integration of various new heterogeneous, modular and intelligent elements with pre-existing legacy devices. This challenge of interoperability is one of the main concerns taken into account when designing such systems-of-systems, commonly requiring the use of standard interfaces to ensure this seamless integration. To aid in tackling this challenge, a common format for data exchange should be adopted. Thus, a study to select the foundations for the development of such a format is hereby presented, taking into account the specific needs of four different use cases representing varied key European industry sectors.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 6 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions keeps working well. The data for almost this whole period has been reprocessed with the new fully polarimetric version (v620) of the Level-1 processor which also includes refined calibration schema for the antenna losses. This reprocessing has allowed the assessment of an improved performance benchmark, a better understanding of the observations, and the preparation of a new version (v700) of the Level-1 processor with further potential.

The manufacturing industry is facing a technology paradigm change, as also captured in the Industrie 4.0 vision as the fourth industrial revolution. Future smart industries will require to optimize not only their own manufacturing processes but also the use of products and manufacturing resources, their maintenance and their recycling. In this context the strengths and weaknesses of two key concepts, namely Cyber-Physical Systems (CPS) and Intelligent Product (IP) are discussed, and it is suggested that an integration of these two approaches to meet the introduced emergent requirements is beneficial. The integration of CPS and IP is shown via two real-world industrial cases, covering different phases of the product life-cycle, namely the production, use and maintenance phases.

In the nowadays highly unstable manufacturing market, companies are faced, on a daily basis, with important strategic decisions, such as “does the company has the necessary capacity to accept a high volume order?” or “what measures need to be implemented if the product demand increases x% a year?”. Decision-makers, i.e. company's managers, rely on their experience and insights supported by classical tools to take such decisions. Classical mathematical solvers or agent-based systems are typical architectural solutions to implement strategic planning tools to support decision-makers on this important task. Within the ARUM (Adaptive Production Management) project, a hybrid strategic planning tool was specified and developed, combining the optimization features of classical solvers with the flexibility and agility of agent systems. This paper briefly presents such architecture and focuses on the generation of the “what-if game” mechanism to support the generation of more intelligent and dynamic planning solutions.

Cyber-physical systems are an emergent paradigm to design complex, adaptive and smart systems, combining computational applications with physical hardware devices. Multi-agent systems play an important role in such systems to provide flexibility, robustness and adaptation, but their alignment will require the integration of agents with physical devices. This process is usually complex and time consuming due to the proprietary protocols provided by the hardware automation devices. This paper describes the deployment of an agent-based system in a small-scale flexible production system composed by a set of heterogeneous automation devices, such as programmable logic controllers and robots.

The continuous change in the manufacturing world is demanding more flexible, responsive and accurate planning tools, which are able to assist the decision-makers to take tactical and strategic decisions on short notice with a high level of confidence. For this purpose, these tools should dynamically explore different operative scenarios in the planning procedure and produce information about key performance indicators. This paper describes the development of an agent-based strategic planner, combining the flexibility of multi-agent systems principles with the optimization capability of a Mixed Integral Programming technique. The tool is integrated in an ecosystem of heterogeneous decision-making systems through an Enterprise Service Bus that also provides access to legacy data.

Enterprise Service Bus (ESB) is a middleware infrastructure that provides a way to integrate loosely-coupled heterogeneous software applications based on the services principles. The life-cycle management of services in such environments is a critical issue for the component's reuse, maintenance and operation. This paper introduces a service life-cycle management module that extends the traditional functionalities with advanced monitoring and data analytics to contribute for the robustness, reliability and self-organization of networks of clusters based on ESB platforms. The realization of this module was embedded in the JBoss ESB, considering a sniffer mechanism to collect the service messages crossing the bus and a Liferay portal to display relevant information related to the services' health.

The Synthetic Aperture Interferometric Radiometer (SAIR) Performance Simulator (SAIRPS) is a very versatile software tool developed by Deimos Engenharia (Portugal) and the Universitat Politècnica de Catalunya-Barcelona Tech (Spain) for the European Space Agency to simulate and compute the figures of merit of the performance of arbitrary SAIRs, using the OpenSF framework. The description and developments of this simulator were previously reported in the open literature and previous IGARSS. This paper summarizes the developments in this third year of the project, including the end-to-end validation tests, and the radiative transfer module that has been added to improve the versatility of the tool.

ESA's SMOS mission has been a cornerstone in passive Earth Observation, since it has provided for the first time ever with global and frequent observations of Soil Moisture and Ocean Salinity (where the mission acronym comes from). SMOS' single payload is MIRAS: the Microwave Imaging Radiometers by Aperture Synthesis in two dimensions. After SMOS' success, today, a number of instruments are planned or under study: the GeoStar instrument is the baseline payload for the Precipitation and All-weather Temperature and Humidity (PATH) mission from NASA (USA), the Geostationary Atmospheric Sounder (GAS) instrument is under study for post-MSG operational satellites observations (Europe), and the Geostationary Interferometric Microwave Sounder (GIMS) instrument from NSSC-CAS (China). The study of the instrument performance in terms of angular resolution and radiometric performance (radiometric sensitivity and accuracy), and the optimization of this new type of instruments is a complex task that requires dedicated ad-hoc tools. In this work the SAIRPS (Synthetic Aperture Interferometric Radiometer Performance Simulator) is presented. This complex simulator allows to analyze arbitrary receiver topologies, arbitrary array geometries, and includes noise injection calibration algorithms, and new external robust calibration algorithms, and image reconstruction algorithms that allow to evaluate the performance of almost any instrument. At present, the full simulator has been coded in Matlab, translated into C++, and integrated in OpenSF. This work summarizes the key aspects of the architecture of this simulator, completing the presentation made in IGARSS 2012, and presents a few examples of the simulation results using different instrument configurations.

New manufacturing control architectures rely on the decentralization of the system entities to handle in a more effective way the current constraints imposed to the highly dynamic manufacturing world. Despite of their benefits, these architectures miss the performance levels of those achieved by traditional centralized systems running without failures. This paper proposes a holonic multi-agent system architecture, based on ADACOR foundations, which considers the self-organization concept to create a two vector solution embedded in micro and macro levels. The paper describes the self-organization mechanism embed at the micro level, named behavioural self-organization, being validated using the AIP-PRIMECA flexible manufacturing system.

This paper discusses the methodology for the development of a strategic planning tool, and particularly the architectural solution to be adopted, within an integrated ICT solution for the improvement of planning and scheduling systems for the manufacturing of small production lots of complex products. After the analysis of different possible architectural solutions, the paper proposes a hybrid solution that combines existing solvers with the multi-agent system principles. The proposed approach introduces several benefits, namely in terms of flexibility, robustness, and achievement of exploratory alternative solutions, playing interactive what-if simulation supporting decision-makers to take strategic decisions.

ESA's SMOS mission has demonstrated that Synthetic Aperture Interferometric Radiometers can be a useful tool for Earth observation. Today, the NASA's GeoStar instrument (USA), ESA's GAS instrument (Europe), or CSSAR's GIMS instrument (China) are just three examples of planned geostationary millimeter-wave sounders using synthetic aperture interferometric radiometry for continuous atmospheric monitoring with a much improved spatial resolution. The study of the performance in terms of angular resolution and radiometric performance, and the optimization of this new type of instruments is a complex task that requires dedicated ad-hoc simulators. The SAIPRS (Synthetic Aperture Interferometric Radiometer Performance Simulator) is an ESA project aiming at the development of a generic simulator in terms of receiver architecture and array configuration, including moving antennas (and eventually changing polarization reference frames). This work summarizes the key aspects of the architecture of this simulator.

The Soil Moisture and Ocean Salinity (SMOS) mission was launched by ESA on November 2009 and it comprises a single payload Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) that measures the radiation emitted by the Earth in the protected frequency of 1.4 GHz (the L-band) and uses it to retrieve geophysical parameters. During the preparation of the mission it was foreseen that there might be some Radio Frequency Interference (RFI) affecting the data produced by SMOS and several algorithms to detect and flag the data were designed, but it has been shown at the early stages of the In-Orbit Commissioning Phase that these algorithms were un-effective. One of the first independent and self contained methods to detect, mitigate and flag data affected by RFIs was developed by DEIMOS Engenharia as part of the Level 1 Prototype Processor (L1PP) and it's fully functional since November 2010. The Detection Algorithm in L1PP uses the multi-angular SMOS data to detect the probable position of RFIs. The Mitigation Algorithm is based on a technique to remove the influence of point-like sources (like the Sun [1]) from SMOS data. The Flagging Algorithm has been developed with ESA and the Level 2 Teams so that geolocated pixels are flagged depending on the intensity of the RFI affecting them.

The Soil Moisture and Ocean Salinity (SMOS) mission by ESA has been delivering Level 1 data to the Level 2 users since 2010. The Level 1 processing is divided in three levels: Level 1a, comprising the conversion of the raw data from MIRAS into calibrated visibilities; Level 1b, where the calibrated visibilities are corrected from the effects of unwanted sources (for example, the Sun) and are transformed into Brightness Temperatures; finally, in the Level 1c the Brightness Temperatures are geolocated over the Earth in a discrete grid and several auxiliary parameters are computed and annotated in the L1c final product. The algorithms responsible for the Calibration (L1a), Image Reconstruction (L1b) and Geolocation (L1c) have been validated during the In-Orbit Commissioning Phase that took place in the first half of 2010. However, as the knowledge on the performance of MIRAS increases, it has become necessary to set in motion a review on the current algorithms. This paper focuses on the review of the Image Reconstruction algorithms being conducted by DEIMOS Engenharia and IRAP and it is divided in the following sections: in Section 1 the theory for the proposed alternatives with respect to the baseline algorithm is demonstrated, along with several examples to ilustrate them. All the results have been obtained using the baseline algorithm or the alternatives that have been implemented in the Level 1 Prototype Processor (L1PP); in Section 2 the impact on the Level 1 Operational Processor (L1OP) is assessed, using as reference the L1PP results; and finally, in Section 3 the main conclusions found throughout this paper are summarized.

This paper presents the progress made in the calibration and image reconstruction of the brightness temperature data provided by the SMOS mission of the European Space Agency. This progress has been made thanks to the learning through the accumulation of results since the launch of the satellite. Current performance of the payload on-board SMOS, the MIRAS interferometer, our main findings on its behavior and open lines of investigation are included in this contribution.

The presence of radio frequency interferences (RFIs) in the data collected by the Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency is larger than what was anticipated, and it has a strong impact on the retrieval of these geophysical parameters. The first efforts to deal with this problem were proposed by DEIMOS Engenharia in the Level 1 Prototype Processor released in November 2010, and they are also a part of its latest versions. DEIMOS focused on three key aspects to detect the RFIs, to mitigate these sources, and to flag the data in SMOS products affected by RFIs. The detection algorithm is based on a statistic approach that computes the most probable position of the RFI source based on multiangular observations made by SMOS. The mitigation algorithm has been designed taking as reference the existing technique to remove the sun effect. The mechanism to flag the data has been developed in cooperation with the European Space Agency and consists in flagging geolocated pixels according to the strength of the RFI. In this paper, three algorithms will be presented, as well as their results. For completion, a high-level description of the data processing for SMOS is also a part of this text.

SMOS imagery has been analyzed to study: 1) radio-frequency interference (RFI) detection and mitigation algorithms, and 2) the statistical properties of RFI. Results show that with a high probability of detection (~0.75), the probability of false alarm is very high as well (~0.68), and most snap-shots seem to be contaminated, even though the estimated RFI value is so weak, and the impact in the SMOS imagery is not even noticeable. Results of the detection and mitigation algorithm are presented, with the statistical analysis of more than 13000 L1b snap-shots.

The Level 1 Processing of SMOS transforms the data acquired by MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) into geolocated TOA Brightness Temperatures, providing observation angles and additional parameters for the Level 2 Processor. Prior to SMOS launch in November 2009 the Level 1 Prototype Processor (L1PP) lead the way for specifying product types and contents, as well as to define, implement and validate all processing algorithms. During the six months of Commissioning, L1PP continued to be the testing environment for all new algorithms and proposed modifications to the Level 1 products. Particular emphasis should be given to LIPP's capability to produce the first image from SMOS. Within less than three hours after the data acquisition at ESAC, L1PP generated images. L1PP has also been tuned to identify unforeseen hardware problems that have been spotted only with the satellite in-orbit. This paper is divided in 3 sections: I) a high level description of the LI processing strategy and functional blocks of the processor (called processing units); II) important results obtained during the In-Orbit Commissioning Phase (IOCP), namely for calibration optimization, image reconstruction improvement, geolocation assessment and the impact on scientific results, in particular, to insure optimal input to Level 2 Soil Moisture and Ocean Salinity retrieval; and III) conclusions from the Commissioning Phase.

The performance requirements of the SMOS payload are demanding in terms of spatial resolution, accuracy, stability and precision, all critical to fulfill its scientific objectives. For this reason a commissioning plan for MIRAS was carefully devised to verify, calibrate and characterize all instrument parameters which could have an impact on its performance. This presentation describes the most important results from the instrument commissioning phase.

The Corbella's revision of the fundamental equation of the interferometric aperture synthesis radiometry is verified using airborne and spaceborne data. This experimental verification uses the measurements of the airborne instrument HUT-2D from the Aalto University (Helsinki), and the unique spaceborne microwave imaging radiometer by aperture synthesis (MIRAS) from the ESA's Soil Moisture and Ocean Salinity (SMOS) mission. The data acquired with those sensors support the Corbella's revision of the visibility function. The revised function predicts that visibilities depend on the contrast between the target's brightness temperature and the backward noise of the receivers emitted through the antennas.

L1 processing of SMOS data will transform MIRAS instrument raw outputs into geolocated Brightness Temperatures, providing observation angles and additional parameters for the L2 Processor. The objectives of the L1 processor prototype (L1PP) activities were the definition of the product types and contents, the definition of the data processing models and the implementation and validation of the baseline processing algorithms. The L1 prototype is intended to be used in studies on the feasibility of the algorithms as well as on the advantages/disadvantages of other relevant proposed algorithms before they are taken to the SMOS operational chain. This paper will begin by presenting a very high level description of the L1 processing strategy and functional blocks of the processor (called processing units). A brief definition of the three L1 products types (L1a, L1b and L1c) will be presented, together with data-driven approach selected for the processor. The main focus will be on three aspects of the processing: the practical approach taken for the calibration of MIRAS data, implementing the in-orbit calibration Plan; the reconstruction algorithms chosen and their implementation, converting Calibrated Visibilities into Brightness Temperature Fourier components; and the geolocation algorithm, computing brightness temperatures over an Earth fixed grid along with observation angles, pixel size and accuracy.