Abstract
The assessment of deposited material in tunnel reinforcement operations can be performed using a 3D model generated from multiple scans. For this purpose, an accurate alignment of the scanned models is required. Aligning existing structure model with data scanned after surface deformations can be challenging, particularly if reference markers are not available or were displaced. For scenarios where the surrounding structure is largely changed, certain procedures can be adapted when processing the scanned data to achieve consistent alignment between scanned and reference structure models. This work proposes a methodology to cope with these situations, analysing the impact of different approaches. Experiments were performed in a realistic scenario related with shotcrete of railway tunnels wall surfaces, with the results showing the applicability of the developed work. The proposed procedure relies in highlighting the importance of specific points that describe the same feature in the reference and aligning PC. The proposed methodology achieved an RMS difference of 0.0173 m, which lead to a drastic improvement in the point cloud alignment compared to the use of standard ICP algorithm without data preprocessing, which achieved 0.0518 m in the studied use-case.

Abstract
While a significant number of databases are deployed in cloud environments, pushing part or all data storage and querying planes closer to their sources (i.e., to the edge) can provide advantages in latency, connectivity, privacy, energy, and scalability. This article dissects the advantages provided by databases in edge and fog environments by surveying application domains and discussing the key drivers for pushing database systems to the edge. At the same time, it also identifies the main challenges faced by developers in this new environment and analyzes the mechanisms employed to deal with them. By providing an overview of the current state of edge and fog databases, this survey provides valuable insights into future research directions.

Abstract
Nanoparticle-based plasmonic optical fiber sensors can exhibit high sensing performance, in terms of refractive index sensitivities (RISs). However, a comprehensive understanding of the factors governing the RIS in this type of sensor remains limited, with existing reports often overlooking the presence of surface plasmon resonance (SPR) phenomena in nanoparticle (NP) assemblies and attributing high RIS to plasmonic coupling or waveguiding effects. Herein, using plasmonic optical fiber sensors based on spherical Au nanoparticles, we investigate the basis of their enhanced RIS, both experimentally and theoretically. The bulk behavior of assembled Au NPs on the optical fiber was investigated using an effective medium approximation (EMA), specifically the gradient effective medium approximation (GEMA). Our findings demonstrate that the Au-coated optical fibers can support the localized surface plasmon resonance (LSPR) as well as SPR in particular scenarios. Interestingly, we found that the nanoparticle sizes and surface coverage dictate which effect takes precedence in determining the RIS of the fiber. Experimental data, in line with numerical simulations, revealed that increasing the Au NP diameter from 20 to 90 nm (15% surface coverage) led to an RIS increase from 135 to 6998 nm/RIU due to a transition from LSPR to SPR behavior. Likewise, increasing the surface coverage of the fiber from 9% to 15% with 90 nm Au nanoparticles resulted in an increase in RIS from 1297 (LSPR) to 6998 nm/RIU (SPR). Hence, we ascribe the exceptional performance of these plasmonic optical fibers primary to SPR effects, as evidenced by the nonlinear RIS behavior. The outstanding RIS of these plasmonic optical fibers was further demonstrated in the detection of thrombin protein, achieving very low limits of detection. These findings support broader applications of high-performance NP-based plasmonic optical fiber sensors in areas such as biomedical diagnostics, environmental monitoring, and chemical analysis. (c) 2024 Chinese Laser Press

Abstract
Due to the exponential increase in energy consumption and CO2 emissions, new sustainable energy sources have emerged, and hydrogen (H2) is one of them. Despite all the advantages, H2 has high flammability, so constant monitoring is essential. Two optical techniques were numerically studied and compared with the goal of H2 sensing: surface plasmon polaritons (SPP) and Tamm plasmon polaritons (TPP). The H2-sensitive material used was palladium (Pd) in both techniques. The SPP structure was found to have more sensitivity to H2 than TPP, 23 and 5nm/4vol% H2, respectively. However, the latter has lower FWHM, with the minimum of the band showing reflectivity near 0%. In addition, TPP also uses more cost-effective materials and can be interrogated at normal incidence with depolarized light. The potential of using each of these optical techniques for H2 sensing was demonstrated. © The Authors.

Abstract
This article proposes a methodology for high loss detection in LV network, based on a very small set of commonly available data/metadata from networks connected to an MV/LV substation. The approach is based on a combination of predictors from several distinct categories, including network data, metadata, and measured smart meter data. Several independent groups of unranked real networks were simulated, and it was possible to find the top ten networks with the highest level of losses with a very satisfactory success rate (76% to 98%), depending on selected groupings folds. Due to the impracticability of analyzing all LV networks, the identification of the highest loss ones is essential for the definition of loss reduction planning since, with this list filtering, it is possible to determine with a good degree of certainty which networks require maintenance or upgrade.

Abstract
The advent of Industry 4.0 has created a need for more flexible and adaptable manufacturing systems. This paper proposes the integration of AAS (Asset Administration Shells), SBM (Skill-based manufacturing) and OPC UA (Open Platform Communications Unified Architecture), to enable more flexible manufacturing systems. The integration of these concepts provides a solution for achieving faster and easier dynamic reconfiguration in manufacturing systems, which is essential for fulfilling the demand of customization and flexibility in modern production systems. An Asset Administration Shell provides a standardized structure for describing assets and their administration, while Skill-based manufacturing enables the deployment of task-oriented machines that can self-configure, self-diagnose, and self-optimize their performance. The use of OPC UA as a communication protocol ensures that these systems can communicate with one another in a secure and reliable way. This paper presents a conceptual framework for the integration of these three open technologies. This framework contributes to having a single interface and source of information for every asset, which can lead to increased efficiency by reducing changeover times, thus reducing the overall cost in flexible manufacturing system scenarios. Future work will focus on the implementation and validation of this framework in a real-world manufacturing setting.