Abstract
This paper proposes the use of a Gaussian window on the array factor as an interference mitigation method, aiming to avoid the computational complexity of the MVDR algorithm at the cost of a slight performance reduction. We show that by optimizing the parameters of the Gaussian window, it is possible to effectively mitigate the interfering signal if it is received within a certain angular range from the desired signal, while being still effective beyond that range. Finally, we show that the effectiveness of this approach is maintained across the full frequency reception range of the Ka-band, and confirm its validity using 8 × 8 and 16 × 16 array sizes. © 2022 IEEE.

Abstract
<p>Flying networks, composed of Unmanned Aerial Vehicles (UAVs) acting as mobile Base Stations and Access Points, have emerged to provide on-demand wireless connectivity, especially due to their positioning capability. Still, existing solutions are focused on improving aggregate network performance using a best-effort approach. This may compromise the use of multiple services with different performance requirements. Network slicing has emerged in 5G networks to address the problem, allowing to meet different Quality of Service (QoS) levels on top of a shared physical network infrastructure. However, Mobile Network Operators typically use fixed Base Stations to satisfy the requirements of different network slices, which may not be feasible due to limited resources and the dynamism of some scenarios.</p> <p>We propose an algorithm for enabling the joint placement and allocation of communications resources in Slicing-aware Flying Access and Backhaul networks – SurFABle. SurFABle allows the computation of the amount of communications resources needed, namely the number of UAVs acting as Flying Access Points and Flying Gateways, and their placement. The performance evaluation carried out by means of ns-3 simulations and an experimental testbed shows that SurFABle makes it possible to meet heterogeneous QoS levels of multiple network slices using the minimum number of UAVs.</p>

Abstract
This article analyzes consumer intentions to purchase cannabis-based skincare cosmetics by considering the role of human values, environmental awareness, and attitudes toward cannabis-based skincare cosmetics and their industrial use. The literature enabled the definition of a set of nine hypotheses, which were tested by a quantitative study with 230 participants from Portugal. Data were collected online in 2021 using snowball sampling. Structural equation modeling and mean difference tests were used for the hypothesis testing. The results suggest that personal values regarding openness to change and conservation indirectly influence the acceptance of cannabis-derived cosmetic products by reinforcing attitudes toward cannabis-based skincare cosmetics, and that environmental awareness influences the intention to purchase cannabis-based skincare cosmetics. This article provides relevant insights for both practitioners and researchers, as it demonstrates that both attitudes toward cannabis-based skincare cosmetics and the attitude toward the use of cannabis by the cosmetic industry predict purchase intentions of cannabis-based skin care cosmetics and therefore, should be considered for the development of the strategy for communicating with consumers. The article also makes some suggestions about the profiles of consumers most willing to buy this type of product, highlighting the role of environmental awareness and human values as strong determinants that influence the purchase intention of cannabis-based skincare cosmetics.

Abstract
The deployment of smart metering technologies in the low voltage (LV) grid created conditions for the application of data-driven monitoring and control functions. However, data privacy regulation and consumers' aversion to data sharing may compromise data exchange between utility and customers. This work presents a data-driven method, based on smart meter data, to estimate linear sensitivity factors for three-phase unbalanced LV grids, which combines a privacy-preserving protocol and varying coefficients linear regression. The proposed method enables centralized and peer-to-peer learning of the sensitivity factors. Potential applications for the sensitivity factors are demonstrated by solving voltage violations or computing operating envelopes in a LV grid without resorting to its network topology or electrical parameters.

Abstract
Bidirectional electric vehicle charging stations (EVSE) offer new business models for private users and companies such as Demand Response. Even if first standards for smart charging (ISO 15118, OCPP) are established, no commercial turnkey solution for the integration of a vehicle to grid (V2G) charging station into a smart prosumer household exists yet. This study shows a possible concept for the integration of a V2G charger for a vehicle to home (V2H) use case. A smart controller for a prosumer household is developed allowing the interconnection of different types of electrical equipment like a V2G charger, a photovoltaic system for electricity generation, a heat pump for heating. Therefore, different interfaces such as Modbus TCP, Modbus RTU, OCPP, HTTP are used. An algorithm is developed to charge the vehicle at low electricity prices or at times of overproduction of the PV system respective to discharge the car at high electricity prices or times of no PV production. The modular concept allows realizing the solution as a cloud-based service which can be applied to energy communities. © 2022 IEEE.

Abstract
Our lives have been increasingly filled with technologies that use Artificial Intelligence (AI), whether at home, in public spaces, in social organizations, or in services. Like other technologies, adopting this emerging technology also requires society's attention to the challenges that may arise from it. The media brought to the public some unexpected results from using these technologies, for example, the unfairness case in the COMPAS system. It became more evident that these technologies can have unintended consequences. In particular, in the public interest domain, these unintended consequences and their origin are a challenge for public policies, governance, and responsible AI. This work aims to identify the technological and ethical risks in data-driven decision systems based on AI and conduct a diagnosis of these risks and their perception. To do that, we use a triangulation of methods. In the first stage, a search on Web of Science has been performed. We consider all the 412 papers. The second stage corresponds to a analysis of experts. The papers have been classified according to the relevance to the topic by the experts. In the third stage, we use the survey method and include risk insights from stage two in our questions. We found 24 concerns which arise from the perspective of the ethical and technological risk perspective. The perception of participants regarding the level of concern they have with the risks of a data-driven system based on AI is high than their perception of society's concern. Fairness is considered the risk whose perception is more severe. Fairness, Bias, Accountability, Interpretability, and Explainability are considered the most relevant concepts for a responsible AI. Consequently, also the most relevant for responsible governance of AI. © 2022 ACM.