Abstract
Speckle-based fiber optic sensors are well-known to offer high sensitivity but are strongly limited on the in terrogation side by low camera frame rates and dynamic range. To address this limitation, we present a novel interrogation framework that explores event-based vision to achieve high throughput, high bandwidth, and low-latency speckle analysis of a multimode optical fiber sensor. In addition, leveraging an optimized decomposition of the raw event streams through multi-point calibration and machine learning optimization, our approach also proves capable of isolating simultaneous deformations applied at distinct points. The experimental results vali date the methodology by separating the signals of four piezoelectric actuators over a 400 Hz-20 kHz range with minimal crosstalk applied over varying distances from 3 cm to 75 cm. Overall, these results establish event-driven speckle interrogation as a new versatile platform for real-time, multi-point acoustic sensing and pave the way for its application in complex and unstructured environments in future works.

Abstract
The dataset reports an up-to-date overview of the selective biowaste collection with a focus on food waste and organic kitchen waste across 308 municipalities in Portugal, to assess the compliance with the EU Waste Framework Directive that made biowaste collection mandatory from 1st January 2024. Data were collected through a structured survey sent to the totality of the municipalities, complemented by systematic research in secondary official sources such as municipal web-sites, reports and statistical data. The questionnaire covered aspects such as coverage, collection models (nearby bring points, door-to-door, co-collection), sector-specific deployment (household collection, non-domestic collection), operational characteristics, and performance indicators (capture rates, cost per tonne). The dataset was structured and validated through cross-checking the multiple sources assessed, prioritising direct municipal questionnaire responses. It includes disaggregated data at a municipality level, including detailed information on the characteristics and efficiency of the initiatives, when available. The database allows the cross-comparison across Portuguese regions and potentially with other international systems, in terms of biowaste collection strategies with focus on food waste and organic kitchen waste. Municipalities in Portugal have been carrying out pilot experiences within their territories, but there is no systematic assessment of what has been carried out nor the results obtained. Given the limited available data, this dataset provides a valuable resource for policy design and further research on biowaste management initiatives to further assess their efficiency and adaptability to different municipal realities at a national and even European level. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Abstract
Speckle-based fiber optic sensors are well-known to offer high sensitivity but are strongly limited on the interrogation side by low camera frame rates and dynamic range. To address this limitation, we present a novel interrogation framework that explores event-based vision to achieve high throughput, high bandwidth, and low-latency speckle analysis of a multimode optical fiber sensor. In addition, leveraging an optimized decomposition of the raw event streams through multi-point calibration and machine learning optimization, our approach also proves capable of isolating simultaneous deformations applied at distinct points. The experimental results validate the methodology by separating the signals of four piezoelectric actuators over a 400Hz - 20kHz range with minimal crosstalk applied over varying distances from 3cm to 75cm. Overall, these results establish event-driven speckle interrogation as a new versatile platform for real-time, multi-point acoustic sensing and pave the way for its application in complex and unstructured environments in future works.

Abstract
This study examines the scientific contributions of the Higher Institute of Accounting and Administration at the University of Aveiro (ISCA-UA) from 2019 to 2022, focusing on how these align with Education 5.0 and Society 5.0 goals. Using a case study approach, data were collected from institutional records, analyzing publications by type and thematic focus, emphasizing areas that promote societal well-being, multiliteracy, and educational innovation. The methodology involves a mixed-methods approach: quantitative analysis assesses publication trends, distribution by faculty rank, and output frequency, while qualitative analysis identifies themes relevant to societal and educational advancements. This approach provides insights into how ISCA-UA's research aligns with Education 5.0 objectives, fostering both technical and socio-emotional skills needed for a super-smart society. Findings highlight an increase in publications addressing digital transformation, sustainability, and governance, reflecting the institution's adaptability and responsiveness to societal shifts, particularly noticeable during the COVID-19 pandemic. This emphasis supports Education 5.0s aims of preparing students with versatile skills for modern challenges. The study contributes to the academic literature by showing how higher education institutions can align research outputs with global educational frameworks, promoting interdisciplinary skills and social responsibility. Future research could explore the impact of these themes on curriculum design and student development, further supporting the evolution toward Education 5.0.

Abstract
Recent research on online censorship has provided valuable insights into common censorship strategies and censors' tolerance for collateral damage. A consistent finding across these studies is that censors tend to favour cost-effective techniques such as proxy enumeration, active probing, and deep packet inspection (DPI), rather than more complex and non-deterministic methods such as deep learning-based traffic analysis. For example, a recent study on the Snowflake censorship evasion system reinforced this finding by demonstrating that authoritarian regimes primarily relied on DPI to target the system. However, as censorship techniques continue to evolve, two critical questions arise: (1) What future attack vectors are likely to emerge based on current research and observed censor capabilities? (2) How can these emerging threats, along with previously utilised censorship methods, be effectively mitigated? In this paper, we present Obscura, a censorship evasion system designed to resist cost-effective, historically grounded censorship techniques while also defending against a class of plausible future attacks within a cost-effective threat model targeting WebRTC-based censorship evasion systems. Obscura is built upon four core features: (1) encapsulation of traffic within WebRTC media streams, (2) the use of a reliability layer, (3) support for both browser-based and Pion-based clients and proxy instances, and (4) the use of ephemeral proxies. Each feature is intended to mitigate either a known attack observed in the wild or a theoretically plausible attack consistent with the capabilities of a cost-effective censor. We provide a security analysis to justify our design choices and a performance evaluation to demonstrate that Obscura maintains reasonable throughput for typical online activities.

Abstract