Abstract
Forest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.

Abstract
A common problem in mobile networking research and development is the cost related to deploying and running real-world mobile testbeds. Due to cost and operational constraints, these testbeds usually run for short time periods but generate very unique and relevant results that are hard to reproduce. We propose the use of ns-3 as a solution to successfully reproduce real-world mobile testbed experiments. This is accomplished by feeding ns-3 with real testbed traces including node positions and radio link quality only. In order to validate our approach, the network throughput between a fixed Base Station and a Unmanned Aerial Vehicle (UAV) was measured in a real-world testbed. The experimental results were compared to the network throughput achieved using the ns-3 trace-based simulation and a plain ns-3 simulation. The obtained results show the high accuracy of the trace-based simulation, thus validating our approach.

Abstract
This work presents a multimode interference-based fiber sensor in a cavity ring-down system for sensing temperature-induced refractive index (RI) changes of water. The sensing head is based in multimodal interference (MMI) and it was placed inside the fiber loop cavity of the CRD system. A modulated laser source was used to send pulses down into the fiber loop cavity and an erbium-doped fiber amplifier (EDFA) was placed in the fiber ring to provide an observable signal with a reasonable decay time. The behavior of the sensing head to temperature was studied due to its intrinsic sensitivity to said parameter - a sensitivity of -1.6 10(-9) s/C was attained. This allowed eliminating the temperature component from RI measurement of water and a linear sensitivity of 580 mu s/RIU in the RI range of 1.324-1.331 was obtained.

Abstract

Abstract

Abstract
The PERFoRM project, an innovation action promoted within the scope of the EU Horizon 2020 program, advocates the use of an Industrie 4.0 compliant system architecture for the seamless reconfiguration of robots and machinery. The system architecture re-uses the innovative results from previous successful R&D projects on distributed control systems domain, such as SOCRADES, IMC-AESOP, GRACE and IDEAS. This paper, after describing the main pillars of the PERFoRM system architecture, focuses on mapping the system architecture into four industrial use cases aiming to validate the system architecture design before its deployment in the real environments. © Springer International Publishing AG 2017.