Abstract
Innovation is a key driver to address the challenges of sustainability. To achieve this goal, the various entities participating in the innovation process must work together to ensure that research methodologies and results are aligned with the needs and expectations of society. Having a conceptual and practical approach that allows exploring and measuring how projects are fulfilling this expectation is key in this process. In this sense, this study aims to explore the relevance of this phenomenon for the development of innovation practices and to characterize and compare several responsible innovation assessment tools (RIATs) that can be used by research teams. A qualitative methodology is used to identify, map, and compare the characteristics of each RIAT. The main strengths and limitations of each approach are also explored. The findings reveal a total of 18 RIATs and 16 dimensions. The challenge is to get a harmonious balance between these various dimensions and to have this analysis performed in the early stages of each project. This study makes practical contributions by identifying areas that each project must assess and ensure for its innovation activities to be socially desirable and ethically acceptable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Management of network resources in advanced IoT applications is a challenging topic due to their distributed nature from the Edge to the Cloud, and the heavy demand of real-time data from many sources to take action in the deployment. FANETs (Flying Ad-hoc Networks) are a clear example of heterogeneous multi-modal use cases, which require strict quality in the network communications, as well as the coordination of the computing capabilities, in order to operate correctly the final service. In this paper, we present a Virtual Network Embedding (VNE) framework designed for the allocation of dataflow applications, composed of nano-services that produce or consume data, in a wireless infrastructure, such as an airborne network. To address the problem, an anypath-based heuristic algorithm that considers the quality demand of the communication between nano-services is proposed, coined as Quality-Revenue Paired Anypath Dataflow VNE (QRPAD-VNE). We also provide a simulation environment for the evaluation of its performance according to the virtual network (VN) request load in the system. Finally, we show the suitability of a multi-parameter framework in conjunction with anypath routing in order to have better performance results that guarantee minimum quality in the wireless communications.

Abstract
This work presents a methodology for characterizing ultrasonic and LASER sensors aimed at detecting obstacles within the context of electrical inspections by multirotor Unmanned Aerial Vehicles (UAVs). A set of four ultrasonic and LASER sensor models is evaluated against eight target components, typically found in high-voltage towers. The results show that ultrasonic sensor arrays displaced 25. apart reduce the chances of problems related to crosstalk and angular uncertainty. Within the LASER sensor suite, solar exposure directly affects the detection behavior among lower power sensors. Based on the results obtained, a set of sensors capable of detecting multiple obstacles belonging to a high-voltage tower was identified. In this reasoning, it becomes possible to model sensor architectures for multirotor UAVs to detect multiple obstacles and advance in the state of the art in obstacle avoidance systems by UAVs in inspections of high-voltage towers.

Abstract
METIS SCAO uses a wavefront control concept that deploys a 2-stage spatial reconstruction where the wavefront is first reconstructed on an intermediate space we call the virtual DM, and then projected onto the actual control space. This document addresses the projection of the wavefront estimation on the virtual deformable mirror (VDM) onto the control modes developed for METIS (Mid-infrared ELT Imager and Spectrograph). We present a new approach to project onto the control modes using an intermediate regularized projection on the M4 mirror and then convert to modes. This method enables us to utilise all modes for the projection and control in a stable manner, achieving high Strehl ratios for a wide range of conditions without the need for complex parameter tuning.

Abstract

Abstract
The use of contrast-enhanced computed tomography (CTCA) for detection of coronary artery disease (CAD) exposes patients to the risks of iodine contrast-agents and excessive radiation, increases scanning time and healthcare costs. Deep learning generative models have the potential to artificially create a pseudo-enhanced image from non-contrast computed tomography (CT) scans. In this work, two specific models of generative adversarial networks (GANs) - the Pix2Pix-GAN and the Cycle-GAN - were tested with paired non-contrasted CT and CTCA scans from a private and public dataset. Furthermore, an exploratory analysis of the trade-off of using 2D and 3D inputs and architectures was performed. Using only the Structural Similarity Index Measure (SSIM) and the Peak Signal-to-Noise Ratio (PSNR), it could be concluded that the Pix2Pix-GAN using 2D data reached better results with 0.492 SSIM and 16.375 dB PSNR. However, visual analysis of the output shows significant blur in the generated images, which is not the case for the Cycle-GAN models. This behavior can be captured by the evaluation of the Fr ' echet Inception Distance (FID), that represents a fundamental performance metric that is usually not considered by related works in the literature.