Persistent rural–urban disparities in broadband connectivity remain a major policy challenge, even in digitally advanced countries. This paper examines how these inequalities manifest in northern Finland and Sweden, where sparse populations, long distances, and seasonal variations in demand create persistent gaps in service quality and reliability. Drawing on survey data (n = 148), field interviews, and spatial analysis, the study documents how variability in connectivity shapes everyday life and work in Arctic rural communities.

To assess these disparities more systematically, the paper applies the Cellular Coverage Inequality (CCI) index as a spatial Key Performance Indicator (KPI). By relating mobile coverage outcomes to degrees of rurality, the CCI highlights how network performance is unevenly distributed across space and reveals disparities that population-based or aggregate coverage statistics often obscure. While headline indicators suggest high levels of availability, experiential evidence points to recurring reliability constraints and location-specific coverage gaps affecting safety, work practices, and access to essential services.

Building on these findings, the paper develops policy reflections in six areas: shared infras- tructure and roaming frameworks, spectrum flexibility and local access models, performance- based Quality-of-Service monitoring, standardized and transparent reporting, techno-economic and environmental constraints of rural deployment, and digital-skills initiatives. Together, these reflections underscore the importance of combining spatial performance metrics with gover- nance and cost considerations when evaluating broadband policy in sparsely populated regions. The study contributes to ongoing debates on how connectivity monitoring can move beyond nominal coverage targets toward more equitable and performance-sensitive policy frameworks.

This paper presents a new quantitative means to measure how cellular areal network coverage is distributed over a country or region, in particular the extent to which coverage is concentrated to urban regions. We present a new Cellular Coverage Inequality (CCI) index useful as a quantitative measure of fairness in a urban-rural perspective. Necessary data to determine the index are, on one hand, a geographical rurality map, essentially derived from a population map and, on the other hand, a cellular coverage map of the kind typically published by cellular operators. Our CCI-index then allows different cellular networks to be compared in terms of their coverage fairness and urban-rural inequality. Alternatively, the urban-rural coverage divide can be monitored over time. We give examples of the use of the CCI index for public coverage maps of Sweden over the years 2013-2020.

This paper investigates the role of standalone Reconfigurable Intelligent Surfaces (RIS) in enhancing positioning capabilities in 6G networks. A standalone RIS differs from a conventional RIS in that the former is not controlled by the network or any other external node. Rather, it operates autonomously and its configurations and location are known to the network. We explore RIS as a tool to enable positioning through Time Difference of Arrival (TDoA) estimation with fewer base stations (BSs), leveraging its ability to act as a virtual BS. A theoretical framework is developed using the Cramér-Rao Lower Bound (CRLB) to analyze the estimation accuracy under various deployment scenarios and parameter settings. Our findings demonstrate that while RIS does not inherently improve TDoA accuracy, it facilitates sparser BS deployments by requiring only one BS and one RIS for effective estimation of TDoA. Additionally, we evaluate the impact of system parameters such as the number of subcarriers, pilot spacing, and RIS elements, identifying subcarrier spacing as the most influential factor for improving the TDoA estimate. These insights highlight the potential of RIS to complement conventional BS deployments and advance 6G TDoA estimation systems.

Nitrocarburization is a process for surface hardening of metallic components. For quality control purposes, regular inspection of the achieved hard surface layer, known as the compound layer thickness, is necessary. This is done by cutting the samples and inspecting a cross-section with microscopy. Since this is a destructive and time-consuming technique, it limits the frequency of the testing, and hence there is a need to complement this method with a rapid, non-destructive alternative. This paper demonstrates how ultrasound in combination with a supervised learning approach, can be used to estimate compound layer thickness down to 10-20 microns.

The paper discusses Structural Health Monitoring (SHM) based on ultrasonic guided waves for damage detectionin structures. Guided waves allow inspection over long distances and inaccessible features, but are also sensitiveto changes in environmental and operating conditions (EOC). The focus of this paper is on temperaturecompensation methods for guided waves. The compensation techniques include Baseline Signal Stretch (BSS),Optimal Baseline Selection (OBS), OBS+BSS, and Dynamic Time-Warping (DTW). In particular, a new, fastapproximation of DTW is evaluated and compared with conventional but computationally expensive DTW. TheFDTW algorithm utilizes a multilevel approach inspired by graph bisection principles to achieve precise warpingpath determination with linear computational complexity.The study evaluates the compensation performance of FDTW using a single baseline at a single temperature, thusaddressing the complexity and inaccessibility issues of obtaining an extensive database of baseline signals inpractical applications. The Open Guided Wave (OGW) dataset is employed for a fair comparison with othercompensation methods.Results indicate that FDTW performs well, demonstrating comparable warping performance to DTW but withsignificantly reduced computational complexity. The analysis also includes comparisons with BSS, OBS+BSS,and DTW across a range of temperatures, highlighting the effectiveness of FDTW in mitigating errors introducedby larger temperature variations.

The paper explores Structural Health Monitoring (SHM) using ultrasonic guided waves, to detect structural damage. Guided waves are highly sensitive to changes in material properties and environmental and operating conditions (EOCs), with temperature being a significant factor. A key challenge in guided waves based SHM is differentiating between damage and temperature-induced changes. This paper focuses on warping-based methods for temperature compensation. Dynamic Time-Warping (DTW) encounters challenges due to its quadratic complexity. However, applying constraints to DTW accelerates the warping process by limiting the scope of the warping path to specified areas. The applied constraint should align with the characteristics of the signal. In this paper, we propose a global constraint for temperature compensation of guided waves, referred to as the Triangular global constraint (Tri-DTW). The performance of the proposed method will be compared with the Sakoe-Chiba global constraint (SC-DTW). Tri-DTW performs well, demonstrating better warping performance with four times reduced computational complexity. The analysis also includes comparisons of warping performance, warping performance with respect to temperature and damage detection performance.

Introduction: Guided waves being highly sensitive to temperature variations, temperature compensation algorithms, such as Optimal Baseline Selection, Baseline Signal Stretch and Scale Transform demonstrate effective performance under limited conditions. Dynamic Time Warping (DTW) has shown excellent compensation performance, however it comes with a substantial computational burden of O(N*N), where N represents the number of samples in each signal. Methodology: DTW works by construction of cost matrix that maps every point in one time series to all the points in the other time series, that results in complexity O(N*N).This problem can be solved by narrowing the search window using global constraints. The two most common constraints in the literature are the Sakoe-Chiba band and the Itakura Parallelogram. This paper uses Sakoe-Chiba band as a global constraint, the Sakoe-Chiba band is defined through a window size parameter which determines the largest temporal shift allowed from the diagonal. Temperature compensation performance of DTW Sakoe-Chiba is tested using the available OGW dataset #2 provided by Jochen Moll et al. The OGW dataset has been generated using 12 number of piezoelectric transducers bounded to CFRP (Carbon Fiber Reinforced Plastic) plate and varying the temperature from 20-60 degree with an increment of 0.5 degree. The signal is recorded for 1300 microseconds, that results in 13000 samples/time stamps(N). Initial results show that the Sakoe-Chiba constraint based DTW performs well and at a significantly lower cost, as indicated below. Result: The largest temporal shift (r) is estimated using Local Peak Coherence (LPC),for signal at 20 and 60 degree r is 135 samples. This results in complexity O(N*2r) which is very less than conventional DTW compensation technique O(N*N). In the final paper, the analysis will be replicated across a range of temperatures, and the performance of damage detection will be thoroughly discussed.

There are several freely available toolboxes for modeling the sound pressure field of ultrasound transducers and transducer arrays (e.g., Field II, k-Wave, and DREAM, etc.). These model the beam patterns, or how the ultrasound pulse changes depending on where we observe it, i.e., they model the spatial impulse response of the transducers. Normally, the transmitted pulse is not modeled using these toolboxes, but instead it is assumed that this pulse shape is known. Also, the models are based on assumption of an ideal behavior of the transducers, which is not necessarily the case for a real-world transducers. As a consequence, fitting these models to real measurement data, in order for them to mimic the individual transducer available in the lab, is not generally not possible with any numerical accuracy. In this paper we show, instead, how a deep learning approach can be adopted to train a model that with numerical accuracy models an transducer individual. We compare the proposed technique with real measurements and models using the Field II toolbox and show that for the actual transducer at hand, the deep learning approach outperforms the results from Field II.

Resolving more sources than sensors is always of interest to researchers. For this purpose, generation of the virtual array from the non-uniform linear array has recently gathered a lot of attention. The covariance/cumulant lags of the array output define virtual sensors and thereby virtual array. The aperture of the designed virtual array is much more than the aperture of physical array. This large aperture provides highest degrees of freedom to solve the underdetermined system. In the case of non-circular signals, pseudo-covariances/cumulants are significant, and this additional information can further be used to increase the virtual array aperture. Herein, a framework is proposed to extend the virtual array aperture by additionally using pseudo-/non-circular cumulants along with the circular cumulants of the array output for non-circular signals. The suggested framework not only increases the resolvability but also improves the DoA estimation accuracy. With fourth-order statistics, the virtual array aperture becomes almost double, and the increment is much more with further higher order statistics. Numerical simulations demonstrate the efficacy of the claims.

This paper presents a new quantitative method to measure how cellular areal network coverage is distributed over a country or region, specifically focusing on the extent to which coverage is concentrated in urban centers versus rural areas. We introduce the Cellular Coverage Inequality (CCI) index, a novel metric for assessing the equality of network distribution from a geographic perspective. The index is derived from rurality maps essentially generated from population data and operator reported cellular coverage maps. Beyond serving as a diagnostic measurement, we propose the CCI index as a regulatory tool designed to bridge the urban-rural divide. By establishing quantifiable equality benchmarks, the CCI index enables authorities to guide network evolution toward more equitable outcomes. Furthermore, the index serves as a strategic planning instrument for operators, allowing them to utilize changes in CCI to predict and quantify how specific infrastructure investments will impact regional equality. We demonstrate the utility of the CCI index by analyzing the official regulatory coverage maps of Sweden (2013–2020) and France (2019–2025).