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Lossy Mode Resonance and Hyperbolic Mode Resonance-Based Optical Sensors by Means of Y3Fe5O12 and SrTiO3 Films Deposition on Planar Substrates
Get PDFPublisher Copyright: © 2017 IEEE.This letter describes the fabrication of sensor devices based on lossy mode resonance (LMR) and hyperbolic mode resonance (HMR) using for the first time as generating materials of the optical resonances both, yttrium iron garnet (Y3Fe5O12) and strontium titanate (SrTiO3) with a film thickness of 739.2 and 158.7 nm for Y3Fe5O12 (YIG) and SrTiO3, respectively. First-order resonances were observed at the visible region of the electromagnetic spectrum for both materials, LMR and HMR phenomena. RF sputtering deposition was used to fabricate metal oxide thin films on coverslips in a planar waveguide configuration, the Au metallic thin films were deposited by pulsed dc sputtering in a magnetron system from Moorfield. All devices were characterized under different surrounding medium refractive index. Sensitivities achieved values of 5862 and 5865 nm/RIU (refractive index unit) for HMR versions of Y3Fe5O12 and SrTiO3r, respectively. In addition, the response of the sensors to relative humidity and different ethanol concentrations was evaluated. The best results correspond to the Y3Fe3O3-based sensor, with a sensitivity of 0.2 nm/ppm and a limit of detection (LOD) of 183 ppm for ethanol, and 64 nm/%RH for RH, with an LOD of 2.23%RH,Peer reviewe
Generalized open-circuit fault detection and diagnosis algorithms for star-connected multiphase permanent magnet synchronous machine drives
No full textPublisher Copyright: © 1972-2012 IEEE.Fault tolerance is of special interest in safety-critical transport applications, where uninterrupted electric drive operation is required. In particular, multiphase permanent magnet synchronous machine (PMSM) drives provide additional degrees of freedom that can be exploited to enhance fault tolerance. For the practical implementation of fault tolerant controllers, fast and reliable fault identification procedures must be developed. This work proposes a set of generalized open-circuit fault detection and diagnosis algorithms for star-connected m-phase PMSM drive systems with a single neutral point connection. These algorithms are then particularized for a 5-phase configuration and hybridized into a single algorithm. Experimental results in a star-connected 5-phase PMSM drive fed by a Silicon Carbide (SiC) based voltage source inverter are finally presented. The obtained results demonstrate the correctness and the high detection and diagnosis accuracy of the proposal.Peer reviewe
A True Random Number Generator for Blockchain Wallets Based on Quantum Computation
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Considering the importance of cryptography in blockchain networks, the usage of pseudo-random number generators for the secret key and nonce generation is, at least, arguable. Randomness should always be generated from non-deterministic sources, to ensure the random elements are not retrievable from third parties. In this work, a true random number generator based on quantum computation (Q-TRNG) is proposed. This Q-TRNG has allowed to create a quantum secret key generator (Q-SKG), generating keys and nonces whose entropies and serial correlation coefficients showed better values compared to the ones obtained from other widely used blockchain wallets. To the knowledge of the authors, this is the first Q-SKG that has been built for blockchain applications, enhancing the security of secret keys and transaction signatures.Peer reviewe
Extension of voxel-based lesion mapping to multidimensional neurophysiological data
Get PDFPublisher Copyright: © The Author(s) 2025.Focal brain lesions cause neurophysiological changes in local and distributed neural systems. While electroencephalography (EEG) has a long history in post-stroke neurophysiological assessment, the observed changes have rarely been linked to specific lesion locations, leaving neuroanatomical-neurophysiological relationships after stroke unclear. Current data-driven methods, such as voxel-based lesion symptom mapping (VLSM), relate lesion locations to single-feature “symptoms” but currently cannot associate anatomical injury with multidimensional data such as EEG, with its rich spatiotemporal information. To overcome this limitation, we introduce MD-VLM, an extension of VLSM to multidimensional “symptoms” that identifies relationships between lesion locations and neurophysiology. MD-VLM is data-agnostic, compatible with various lesion (e.g., lesion maps, lesion network maps) and neurophysiological (e.g., channel-level or source-localized EEG) inputs, and uses robust statistics to test for the existence of significant neuroanatomical-neurophysiological relationships. We demonstrate MD-VLM’s feasibility by applying it to EEG from chronic stroke patients performing a cued-movement task. MD-VLM revealed significant associations between frontal white-matter lesions and reduced ipsilesional parietal cue-evoked responses, consistent with damage to known fronto-parietal networks. MD-VLM is a novel data-driven extension to VLSM for multidimensional “symptoms”. Applying MD-VLM to link lesions to neurophysiological data can improve mechanistic understanding of post-stroke neurological impairments and guide future biomarker development.Peer reviewe
Local Flexibility Markets in Europe: A Review of Flexibility Products, Services, and Potential Improvements
No full textPublisher Copyright: © 2025 IEEE.With the increasing need for flexibility to ensure the safe and secure operation of electricity grids, Local Flexibility Markets (LFMs) are expected to become a key tool for mobilizing flexibility from resources connected to distribution grids. This paper reviews existing LFMs in Europe, highlighting the situation in countries such as the United Kingdom, France, the Netherlands, Norway, Italy, and Spain, which already have fully operational markets or in a precommercial stage. The analysis covers the characteristics of flexibility products, the services provided, and the market architectures. Additionally, the authors analyse the main challenges faced by LFMs and propose potential enhancements to improve various aspects of these markets.Peer reviewe
Knowledge Systematization for Security Orchestration in CPS and IoT Systems
No full textPublisher Copyright: © 2025 IEEE.Cyber-Physical Systems (CPS) and the Internet of Things (IoT) are crucial in a number of fields, including healthcare, energy, mobility, and communication. IDS, network, and application layers are among the system layers that are the primary focus of current Security Orchestration, Automation, and Response (SOAR) techniques. However, taking into account the computing continuum, there is a noticeable lack of complete SOAR techniques for multi-layered IoT/CPS systems. We aim to systematize the current SOAR approaches for IoT/CPS-based critical infrastructures. Three research topics served as the basis for our systematic review, which produced important findings: (i) IoT/CPS systems require a complete SOAR that addresses many architectural elements; (ii) AI/ML improves automation, but it is insufficient in addressing explainability and cross-layer/system/domain issues; and (iii) the incorporation of digital twin solutions into SOAR frameworks is still in its early stages. We highlight areas for further research to enhance SOAR solutions' efficacy, flexibility, and comprehensiveness in addressing evolving cybersecurity challenges.Peer reviewe
A European aerosol phenomenology – 9: Light absorption properties of carbonaceous aerosol particles across surface Europe
No full textPublisher Copyright: © 2024Carbonaceous aerosols (CA), composed of black carbon (BC) and organic matter (OM), significantly impact the climate. Light absorption properties of CA, particularly of BC and brown carbon (BrC), are crucial due to their contribution to global and regional warming. We present the absorption properties of BC (bAbs,BC) and BrC (bAbs,BrC) inferred using Aethalometer data from 44 European sites covering different environments (traffic (TR), urban (UB), suburban (SUB), regional background (RB) and mountain (M)). Absorption coefficients showed a clear relationship with station setting decreasing as follows: TR > UB > SUB > RB > M, with exceptions. The contribution of bAbs,BrC to total absorption (bAbs), i.e. %AbsBrC, was lower at traffic sites (11–20 %), exceeding 30 % at some SUB and RB sites. Low AAE values were observed at TR sites, due to the dominance of internal combustion emissions, and at some remote RB/M sites, likely due to the lack of proximity to BrC sources, insufficient secondary processes generating BrC or the effect of photobleaching during transport. Higher bAbs and AAE were observed in Central/Eastern Europe compared to Western/Northern Europe, due to higher coal and biomass burning emissions in the east. Seasonal analysis showed increased bAbs, bAbs,BC, bAbs,BrC in winter, with stronger %AbsBrC, leading to higher AAE. Diel cycles of bAbs,BC peaked during morning and evening rush hours, whereas bAbs,BrC, %AbsBrC, AAE, and AAEBrC peaked at night when emissions from household activities accumulated. Decade-long trends analyses demonstrated a decrease in bAbs, due to reduction of BC emissions, while bAbs,BrC and AAE increased, suggesting a shift in CA composition, with a relative increase in BrC over BC. This study provides a unique dataset to assess the BrC effects on climate and confirms that BrC can contribute significantly to UV–VIS radiation presenting highly variable absorption properties in Europe.Peer reviewe
Exploring the effectiveness of negative and positive inserts in machining Inconel 718 alloy: a comparative study
No full textPublisher Copyright: © The Author(s) 2024.Inconel 718 alloy is characterised by high strength and corrosion resistance and remains stable at high temperatures, so it is widely used in the energy and aerospace industries. However, machining this material is difficult due to its high strength, hardness, and high specific force coefficient exceeding 3000 MPa. Turning of the Inconel 718 alloy can be carried out with negative and positive inserts. Therefore, the impacts of the insert geometry on the turning process of Inconel 718, cutting force components, and surface roughness were studied. Three positive and three negative insert geometries were tested. It was shown that the key influence on the active components of the cutting force is the effective rake angle. The surface roughness, on the other hand, depends mainly on the cutting-edge radius. It has been shown that the negative insert geometry with γ = 6° and rn=22 μm provides a 30% lower cutting force than the positive inserts and the same surface roughness. The developed models of the cutting force components proved that when cutting with positive inserts, a higher specific cutting force occurs for the Inconel 718 alloy than for the negative insert. It was shown that technological parameters had a very similar effect on the cutting force components and surface roughness parameters regardless of the blade geometry. It was proven that the use of positive inserts makes sense only for depths of cut no greater than the size of the corner radius.Peer reviewe
Variational Autoencoder-Based Alert System for Onshore Wind Turbine: Application to a Real Case Study
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Structural Health Monitoring (SHM) of wind turbines is still far from a practical and effective implementation. One of the main limitations is the access to long-term experimental data from operative systems, as well as the access to a computational counterpart (e.g., Finite Element (FE) model) for testing purposes. This work proposes an unsupervised learning approach based on Deep Neural Networks (DNNs) for structural damage detection in a wind turbine operating within an onshore wind farm. The target system belongs to a wind farm in Portugal, where a long-term acceleration dataset was recorded and postprocessed to estimate the modal properties (eigenfrequencies and eigenmodes). Since the available monitoring data corresponds to what we assume is the healthy state, an unsupervised approach is required to learn from the data. We propose a Variational Autoencoder (VAE) approach to compress the measured features (particularly the eigenfrequencies) into a latent space variable and subsequently expand them into the original data space with minimal loss of information. This approach can be seen as a single-class classifier, where we learn to represent and reconstruct data from a known class, and any measurement that comes from a different generation process (e.g., damaged system) will be raised as an outlier. Given the stochastic character of the architecture, we explore the damage detection capability during testing by comparing statistic indicators. In order to generate damage scenarios, we employ a simple FE model, from which some damage simulations are resolved. We prevent the modeling error from being transferred to the experimental data by obtaining the relative change between the healthy and the damaged synthetic scenarios. These ratios are free from modeling error and can be applied, assuming the similarity of the domains to the experimental data. The results demonstrate that the VAE successfully detects the presence of damage. For slight damage cases, we find some scenarios where the histogram from the reconstruction error in (i) the healthy and (ii) the damaged scenario are almost overlapped, indicating some limitations. We explore the Receiver Operating Curves (ROC), which represent one of the most extensively employed techniques to measure the capability of single-class classifiers.Peer reviewe