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Studying diffraction effects in the 2D position-resolved reflection phase of nanophotonic structures
No full textFundamentals and Experiments of Robust Respiration Sensing via Cell-Free Massive MIMO
No full textRespiration monitoring via radio signals enables contactless health sensing but suffers from interference caused by nearby motion. We propose a robust respiration sensing framework using Cell-free Massive MIMO (CF-mMIMO), which leverages spatial macro-diversity for interference resilience. Specifically, we analyze respiration sensing in single-antenna channels using Power Spectral Density (PSD) to reveal the impact of interference on the breathing channel’s movement spectrum. Based on this, we introduce a new metric, Sensing-Signal-to-Interference Ratio (SSIR), to evaluate local channel quality without requiring ground truth. Then, we design a Weighted Antenna Combining (WAC) method to prioritize reliable sensing links and suppress distortion. Experimental validation using a 64-antenna CF-mMIMO testbed with 100 Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers over an 18 MHz bandwidth confirms the framework’s robustness. In the presence of interference, the WAC method achieves a mean waveform correlation of 0.81 with ground truth, significantly outperforming single-antenna (0.52), averaging-based methods (0.53), and existing Wi-Fi approaches. Finally, we analyze the impact of time, frequency, and spatial resource allocation on both communication and sensing performance. Results show that increasing bandwidth and antenna count benefits both communication and sensing. With a sufficient number of antennas, respiration sensing remains accurate even with long coherence times (1 second) and narrow bandwidths (3 subcarriers), enabling its integration into communication systems with negligible overhead, making it practically “for free”. This makes CF-mMIMO a promising architecture for robust and scalable Integrated Sensing and Communication (ISAC) health monitoring.</p
Using time delayed disturbance compensation for sliding mode control
Get PDFUnder the framework of using Time Delay Control (TDC) for disturbance compensation in Sliding Mode Control (SMC), we address two practical problems. The first problem involves mitigating chattering in SMC caused by input delay, while the second problem focuses on designing TDC under conditions of limited measurements. Our research demonstrates that incorporating TDC as a phase lead compensator in the first problem can effectively accommodate larger input delays. To reduce the chattering, we propose a switching gain design, consisting of reference signals rather than measured states, resulting in an ultimately bounded solution. In the second problem, when acceleration measurements are unavailable, we provide stability conditions under which TDC can be designed with acceleration construction using delayed velocity signals. By constructing a modified sliding surface that incorporates the integral error remainder associated with the acceleration construction, our approach ensures switching gain are kept at minimal, with robust disturbance compensation at higher frequencies. We perform a simulation investigation of an autonomous underwater vehicle under input delay and disturbance to demonstrate the efficiency of the approach
Three-Phase Buck-Boost Split-Source Inverter With Improved Bus Utilization
No full textSplit source inverters (SSIs) have gained attention as potential alternatives to conventional two-stage systems in applications that require integrating a dc source into an inverter powertrain via a step-up operation such as in fuel cell-powered systems. By incorporating the voltage boost functionality directly into the inverter, SSIs eliminate the additional dc-dc stage which typically consists of semiconductor devices subjected to high current and/or voltage stresses. However, the conventional variant of the topology suffers from poor utilization of the dc-bus voltage and constraints over the dc voltage gain limiting its use in various applications. In this work, a novel variant of the converter - buck-boost split source inverter (BSI), is introduced which improves the dc-bus utilization by modifying the conventional circuitry and enables control of power injection from multiple dc sources. Furthermore, the modification provides an extra degree of freedom allowing a larger variation of the voltage gain, which can be beneficial in applications requiring different voltage levels (e.g., with different fuel cell types). In this study, the operation of the topology is analyzed, and suitable modulation methods for the converter are developed analytically and validated through PLECS simulations. Finally, a SiC-based experimental prototype is designed and tested to validate the performance of the proposed converter.</p
An open-access cross-modal forest benchmark training dataset with Sentinel-1 and Lidar data
No full textForest height estimation is critical for applications such as forest health monitoring, carbon sequestration, timber supply, biodiversity assessment, and ecosystem dynamics. The growing interest in machine learning, especially deep learning, has the potential to significantly enhance the automatic interpretation of satellite images for forest height estimation. Sentinel-1, with its regular global coverage of Synthetic Aperture Radar (SAR) data, coupled with deep learning techniques, presents a valuable opportunity to create automated tools for forest monitoring. The success of such AI-driven methods relies heavily on the availability of high-quality public benchmark datasets that include large volumes of annotated training and test data. To address the current gap in forest SAR datasets, this paper introduces a new benchmark dataset, comprising pre-processed Sentinel-1 polarimetric interferometric data annotated with forest height information derived from space-based LiDAR data. This benchmark database includes a wide range of SAR data acquired over various interferometric and temporal baselines, covering diverse forest types and regions globally. The SAR data are provided as geocoded 4×4 polarimetric-interferometric covariance matrices, which highlight key features sensitive to forest structure and height, thus facilitating the development of machine learning models for forest monitoring
A diffuse-interface Marangoni instability
No full textWe investigate a novel Marangoni-induced instability that arises exclusively in diffuse fluid interfaces, that is absent in classical sharp-interface models. Using a validated phase-field Navier–Stokes–Allen–Cahn framework, we linearise the governing equations to analyse the onset and development of interfacial instability driven by solute-induced surface tension gradients. A critical interfacial thickness scaling inversely with the Marangoni number, δcr ∼ Ma−1, emerges from the balance between advective and diffusive transport. Unlike sharp-interface scenarios where matched viscosity and diffusivity stabilise the interface, finite thickness induces asymmetric solute distributions and tangential velocity shifts that destabilise the system. We identify universal power-law scalings of velocity and concentration offsets with a modified Marangoni number Maδ, independent of capillary number and interfacial mobility. A critical crossover at Maδ ≈ 590 distinguishes diffusiondominated stabilisation from advection-driven destabilisation. These findings highlight the importance of diffuse-interface effects in multiphase flows, with implications for miscible fluids, soft matter, and microfluidics where interfacial thickness and coupled transport phenomena are non-negligible.</p
Mean turbulent momentum fluxes and wind deficits in nocturnal stable atmospheric boundary layers
No full textAccurately predicting the mean flow properties of wall-bounded turbulence is essential for both fundamental research and engineering applications. In atmospheric boundary layers, the mean flow within the surface layer is typically described by Monin-Obukhov similarity theory (MOST). However, beyond the surface layer, MOST no longer applies as the Coriolis effect becomes significant. To address this issue, this study introduces a novel analytical model for the mean turbulent momentum fluxes and geostrophic wind deficits in nocturnal stable atmospheric boundary layers (NSBLs), which are stably stratified near the surface and transition to neutrally stratified flow above. The model solutions are derived from the Ekman equations using the eddy viscosity approach and a new parametrisation of the flux Richardson number. The solutions show that the geostrophic wind deficits scale with (Formula presented), where (Formula presented) is the friction velocity, (Formula presented) is the boundary layer height, and (Formula presented) is the Coriolis parameter. The model’s predictions align closely with recent large-eddy simulation studies, confirming the model’s accuracy. Combined with the geostrophic drag law, the model can reliably predict the wind speed profile above the surface layer of NSBLs. This work marks a significant step in modelling atmospheric turbulence and its fundamental dynamics.</p
Anticipating uncertainty in infrastructure LCC, LCA, and S-LCA: A systematic, context-aware early identification framework
No full textUncertainty undermines the reliability of Life Cycle Costing (LCC), Life Cycle Assessment (LCA), and Social Life Cycle Assessment (S-LCA) in Infrastructure Asset Management (IAM). Many methods for uncertainty analysis exist, but practitioners often lack systematic guidance to anticipate how uncertainties will unfold in specific assessments and thereby how to manage them. We propose a pre-emptive framework that anchors uncertainty analysis in the shared modelling structure of product systems, processes, and flows, making it transferable across the three methodologies. The framework links assessment context to uncertainty through three profiling indicators—instance count, intensity level, and prospective needs—and eleven infrastructure-specific dimensions that shape them. Mapping these dimensions across IAM decision-making levels illustrates how uncertainty escalates in the assessment contexts in which individual studies are embedded. A practitioner's checklist translates the framework into an early uncertainty profiling tool, guiding analysts to target rigorous modelling and quantification where it matters most. The discussion highlights the critical interdependencies between dimensions and identifies prospective needs as the dominant driver of uncertainty. Ultimately, by making uncertainty profiles explicit up front, the framework fosters proportionate, transparent, and context-responsive uncertainty analysis practices. The paper concludes by underscoring the need for future research into methodology-specific uncertainty modelling and quantification methods—especially for S-LCA—and how to formally and explicitly link their use to different uncertainty profiles to support designing LCT studies that account for individual uncertainty needs from the start
Evaluating Conducted Emissions up to 150 kHz from DC Electric Vehicle Charging Stations
No full textElectric vehicle charging stations (EVCSs), which are known EMI sources, are expected to face upcoming standardized limits in the frequency range up to 150 kHz (also known as the supraharmonic spectrum). However, the adequacy of existing compliance tests for this range has received little attention thus far. This work examines the emissions of three high-power EVCSs under varying loads to examine whether existing compliance tests accurately assess their EMI levels. The results show significant differences between EVCSs, but existing compliance tests underestimated the EMI of all of them. Therefore, this study concludes that existing compliance test procedures are insufficient for low-frequency conducted emissions and thus need updating.</p
Continuous maximal regularity in locally convex spaces
No full textWe study maximal regularity with respect to continuous functions for strongly continuous semigroups on locally convex spaces as well as its relation to the notion of admissible operators. This extends several results for classical strongly continuous semigroups on Banach spaces. In particular, we show that Travis’ characterization of C-maximal regularity using the notion of bounded semivariation carries over to the general case. Under some topological assumptions, we further show the equivalence between maximal regularity and admissibility in this context