Abstract

Abstract
Fiber-reinforced concrete is a crucial material for civil construction, and monitoring its health is important for preserving structures and preventing accidents and financial losses. Among non-destructive monitoring methods, Micro Computed Tomography (Micro-CT) imaging stands out as an inexpensive method that is free from noise and external interference. However, manual inspection of these images is subjective and requires significant human effort. In recent years, several studies have successfully utilized Deep Learning models for the automatic detection of cracks in concrete. However, according to the literature, a gap remains in the context of detecting cracks using Micro-CT images of fiber-reinforced concrete. Therefore, this work proposes a framework for automatic crack detection that combines the following: (a) a super-resolution-based preprocessing to generate, for each image, versions with double and quadruple the original resolution, (b) a classification step using EfficientNetB0 to classify the type of concrete matrix, (c) specific training of Detection Transformer (DETR) models for each type of matrix and resolution, and (d) and a votation committee-based post-processing among the models trained for each resolution to reduce false positives. The model was trained on a new publicly available dataset, the FIRECON dataset, which consists of 4064 images annotated by an expert, achieving metrics of 86.098% Intersection over Union, 89.37% Precision, 83.26% Recall, 84.99% F1-Score, and 44.69% Average Precision. The framework, therefore, significantly reduces analysis time and improves consistency compared to the manual methods used in previous studies. The results demonstrate the potential of Deep Learning to aid image analysis in damage assessments, providing valuable insights into the damage mechanisms of fiber-reinforced concrete and contributing to the development of durable, high-performance engineering materials. © 2025 by the authors.

Abstract
The rapid adoption of green mobility solutions-such as electric-vehicle sharing and intelligent transportation systems-has accelerated the integration of Internet of Things (IoT) technologies, introducing complex security and performance challenges. While conceptual Identity and Access Management (IAM) frameworks exist, few are empirically validated for the scale, heterogeneity, and real-time demands of modern mobility ecosystems. This work presents a data-backed, container-native reference architecture for secure and resilient Authentication, Authorization, and Accounting (AAA) in green mobility environments. The framework integrates Keycloak within a Kubernetes-orchestrated infrastructure and applies Zero Trust and defense-in-depth principles. Effectiveness is demonstrated through rigorous benchmarking across latency, throughput, memory footprint, and automated fault recovery. Compared to a monolithic baseline, the proposed architecture achieves over 300% higher throughput, 90% faster startup times, and 75% lower idle memory usage while enabling full service restoration in under one minute. This work establishes a validated deployment blueprint for IAM in IoT-driven transportation systems, offering a practical foundation for a secure and scalable mobility infrastructure.

Abstract
Grid scale Battery Energy Storage Systems (BESS) have a key role for future power systems operation and stability. However, cyclic degradation, intensified by multi-service operation, remains a major challenge, directly affecting battery lifespan and profitability. This study examines BESS participation in energy markets and in automatic frequency restoration reserve (aFRR) markets, assessing the impact of cyclic degradation costs on BESS planning and operation. The methodology involved modelling the daily dispatch of an 8.1 MW lithium-ion battery for participation in day-ahead, intraday and reserve markets, incorporating a degradation cost minimization model. The simulations were conducted using the historical data from Iberian electricity and Portuguese ancillary services market, such as energy prices, historical reserve requirements and AGC forecasts. The results show that reserve market participation is highly profitable and can be successfully complemented with day-ahead and intraday market participation. Also, incorporating cyclic degradation cost into planning extends BESS lifespan in all cases. However, this approach is beneficial only in arbitrage scenarios, while in reserve market participation, it reduces profits. The findings highlight the importance of balancing BESS degradation minimization with profitability, particularly in reserve market participation. Future research could apply this model to different battery technologies and real-world systems to validate the simulated results.

Abstract
In this paper, a robust nonlinear Super-Twisting Sliding Mode Controller (STSMC) is proposed to minimize torque ripple in Switched Reluctance Motor (SRM) drive systems, thereby reducing acoustic noise and vibration. To optimize torque ripple, the firing angles (theta(on) and theta(off)) are dynamically adjusted based on the instantaneous torque and speed error. To demonstrate its superiority, the performance of the STSMC is compared with conventional linear and Sliding Mode Control (SMC) regulators. The results confirm the robustness and effectiveness of the proposed controller. The torque ripple with PSO-optimized firing angles and STSMC is reduced by around 50% compared to conventional fixed switching angles. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
The active involvement of marginalized and vulnerable groups such as migrants and newly arrived refugees in the development of local communities has been part of many agendas across the EU and around the world. Despite the lessons gleaned from more than three decades of IUI research, there is still a shortage of systematic understanding and concrete guidance on how to design more socially inclusive and culturally sensitive interfaces targeted to these populations. In this paper, we argue that community-based citizen science approaches hold the potential to foster people-place bonds and inform the design of inclusive interactions since these initiatives are typically open to a wide audience regardless of race, ethnicity, gender, and education. From portable environmental monitoring devices to open databases providing place-related data about species observations and environmental threats, citizen scientists have a socially transformative and place-development potential that is often overlooked from an interaction design perspective. This research investigates this gap by examining digital interactions in citizen science through a systematic literature review addressing interaction possibilities for digitally enhanced place-belongingness. The results indicate three interaction themes within citizen science literature contributing to digitally enhanced sense of place-belonginess: place awareness and involvement, experience sharing, and collaboration encouragement. In addition, we found that the inclusivity goals in citizen science initiatives typically vary from urban and rural development to cultural purposes and environmental engagement and conservation. The interaction themes, along with the negative impacts of digital technologies, are discussed regarding their potential to inform technology design for place-belongingness in HCI.