Abstract
This paper presents an optically transparent 2-bit unit-cell for reflective intelligent surface applications in Wi-Fi 6E. The unit-cell is based on a metalmesh and can be reconfigured electronically by adjusting the voltage applied to a varactor diode. The performance of the RIS is demonstrated through simulation, which shows that the results are in good agreement with the theoretical predictions.

Abstract
In recent years, student experience (SX) has drawn the attention of researchers and librarians due to its impact on student engagement and, eventually, academic success. This study aims to explore the intellectual structure of literature focusing on the student experience with academic libraries. Through bibliometric and thematic analyses, we analyzed a sample of 160 articles published between 1995 and 2022. The findings underscore the multifaceted nature of SX research regarding academic libraries. Its intellectual structure unveils six predominant themes: (1) international students' experience; (2) servicescape and service design; (3) impact of technologies; (4) information literacy; (5) psychological and emotional aspects; and (6) engagement and motivation. Additionally, we propose a future research agenda, shedding light on prevalent theories and underexplored topics. This study serves as a valuable resource for researchers and librarians seeking insights into the nuances of SX in academic library settings. In particular, the identification of research clusters and opportunities can assist researchers in better positioning their studies and finding connections across several theoretical lenses and approaches.

Abstract
Unmanned Aerial Vehicles (UAVs) are versatile platforms for carrying communications nodes such as Wi-Fi Access Points and cellular Base Stations. Flying Networks (FNs) offer on-demand wireless connectivity where terrestrial networks are impractical or unsustainable. However, managing communications resources in FNs presents challenges, particularly in optimizing UAV placement to maximize Quality of Service (QoS) for Ground Users (GUs) while minimizing energy consumption, given the UAVs' limited battery life. Existing multi-UAV placement solutions primarily focus on maximizing coverage areas, assuming static UAV positions and uniform GU distribution, overlooking energy efficiency and heterogeneous QoS requirements. We propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which defines and optimizes UAV trajectories to reduce energy consumption while ensuring QoS based on Signal-to-Noise Ratio (SNR) in the links with GUs. Additionally, we introduce the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate energy consumption. Using both MUAVE and ns-3 simulators, we evaluate SUPPLY in typical and random networking scenarios, focusing on energy consumption and network performance. Results show that SUPPLY reduces energy consumption by up to 25% with minimal impact on throughput and delay.

Abstract
Entrepreneurs’ decision-making in sustainable open innovation practices holds significant relevance in fostering environmentally conscious and socially responsible business strategies. Sustainable decision-making not only aligns with ethical principles but also addresses pressing global challenges such as climate change and resource depletion. This study aims to characterize the decision-making role played by entrepreneurs in the context of the open innovation paradigm and to understand the factors that influence entrepreneurial performance. A quantitative methodology supported by Partial Least Squares Structural Equation Modeling was adopted, considering a sample of 407 startups incubated in science and technology parks. After that, a mixed-methods approach was employed to explore the differences between sectors of activity, in which 4 ventures were involved. The results confirm that of the 9 hypotheses formulated in the relationship between the constructs, only innovation novelty is not significant for sustainable open innovation management processes, unlike innovation openness. This study offers theoretical and practical implications for startups that intend to use open innovation networks to integrate environmental and social considerations into the core of business strategies.

Abstract
One of the fundamental tasks in Information Extraction (IE) is Event Extraction (EE), an extensively studied and challenging task [13,15], which aims to identify and classify events from the text. This involves identifying the event's central word (trigger) and its participants (arguments) [1]. These elements capture the event semantics and structure, which have applications in various fields, including biomedical texts [42], cybersecurity [24], economics [12], literature [32], and history [33]. Structured knowledge derived from EE can also benefit other downstream tasks such as Question Answering [20,30], Natural Language Understanding [21], Knowledge Base Graphs [3,37], summarization [8,10,41] and recommendation systems [9,18]. Despite the existence of several English EE systems [2,22,25,26], they face limited portability to other languages [4] and most of them are designed for closed domains, posing difficulties in generalising. Furthermore, most current EE systems restrict their scope to the sentence level, assuming that all arguments are contained within the same sentence as their corresponding trigger. However, real-world scenarios often involve event arguments spanning multiple sentences, highlighting the need for document-level EE.

Abstract
Data valuation, the process of assigning value to data based on its utility and usefulness, is a critical and largely unexplored aspect of data markets. Within the Machine Learning Data Market (MLDM), a platform that enables data exchange among multiple agents, the challenge of quantifying the value of data becomes particularly prominent. Agents within MLDM are motivated to exchange data based on its potential impact on their individual performance. Shapley Value-based methods have gained traction in addressing this challenge, prompting our study to investigate their effectiveness within the MLDM context. Specifically, we propose the Gain Data Shapley Value (GDSV) method tailored for MLDM and compare it to the original data valuation method used in MLDM. Our analysis focuses on two common learning algorithms, Decision Tree (DT) and K-nearest neighbors (KNN), within a simulated society of five agents, tested on 45 classification datasets. results show that the GDSV leads to incremental improvements in predictive performance across both DT and KNN algorithms compared to performance-based valuation or the baseline. These findings underscore the potential of Shapley Value-based methods in identifying high-value data within MLDM while indicating areas for further improvement.