60Co in Cast Steel Matrix: a European Interlaboratory Comparison for the Characterisation of New Activity Standards for Calibration of Gamma-ray Spectrometers in Metallurgy

International audience; Two series of activity standards of Co-60 in cast steel matrix, developed for the calibration of gamma-ray spectrometry systems in the metallurgical sector, were characterised using a European interlaboratory comparison among twelve National Metrology Institutes and one international organisation. The first standard, consisting of 14 disc shaped samples, was cast from steel contaminated during production ("originally"), and the second, consisting of 15 similar discs, from artificially-contaminated ("spiked") steel. The reference activity concentrations of Co-60 in the cast steel standards were (1.077 +/- 0.019) Bq g(-1) on 1 January 2013 12h00 UT and (1.483 +/- 0.022) Bq g(-1) on 1 June 2013 12h00 UT, respectively

The Role of Education in Sustainable Dietary Patterns in Slovenia

The most sustainable dietary patterns involve the consumption of plant-based (vegan) foods, excluding or reducing animal products, including meat, fish, and dairy, yet there is a lack of research on determinants of sustainable dietary patterns in central European countries. The present article aimed to examine the prevalence of sustainable dietary practices and attitudes among the Slovenian public and to investigate the role of education in fostering sustainable dietary patterns. We analyzed a representative national sample of Slovenians, with data gathered in 2019 (ISSP/Slovenian Public Opinion; N = 1079; 51.2% females). The results indicate that most Slovenians do not have sustainable dietary practices or attitudes with regard to health, the environment, animals, and dietary minorities. One in four Slovenians consume meat at least once per day and one in two consume meat three to six times per week. In addition, 78.2% of Slovenians consume milk at least three times per week; more than half consume milk daily or more often. Fish consumption is the least frequent among the three food groups. At least two-thirds of Slovenians also hold attitudes that are low in sustainability. Results also show that, after controlling for confounding variables, higher educational level emerged as an independent predictor of lower meat consumption, but not of lower fish or milk consumption. Furthermore, those in the lowest educational group are significantly less likely to hold sustainable attitudes than those in the higher educational group. Finally, current student status only decreases meat consumption. Since our results show an educational gradient in meat consumption and attitudes, public health and environmental campaigns should focus on the less-educated groups, encouraging them to reduce meat intake and fostering more sustainable attitudes

The Role of Education in Sustainable Dietary Patterns in Slovenia

The most sustainable dietary patterns involve the consumption of plant-based (vegan) foods, excluding or reducing animal products, including meat, fish, and dairy, yet there is a lack of research on determinants of sustainable dietary patterns in central European countries. The present article aimed to examine the prevalence of sustainable dietary practices and attitudes among the Slovenian public and to investigate the role of education in fostering sustainable dietary patterns. We analyzed a representative national sample of Slovenians, with data gathered in 2019 (ISSP/Slovenian Public Opinion; N = 1079; 51.2% females). The results indicate that most Slovenians do not have sustainable dietary practices or attitudes with regard to health, the environment, animals, and dietary minorities. One in four Slovenians consume meat at least once per day and one in two consume meat three to six times per week. In addition, 78.2% of Slovenians consume milk at least three times per week; more than half consume milk daily or more often. Fish consumption is the least frequent among the three food groups. At least two-thirds of Slovenians also hold attitudes that are low in sustainability. Results also show that, after controlling for confounding variables, higher educational level emerged as an independent predictor of lower meat consumption, but not of lower fish or milk consumption. Furthermore, those in the lowest educational group are significantly less likely to hold sustainable attitudes than those in the higher educational group. Finally, current student status only decreases meat consumption. Since our results show an educational gradient in meat consumption and attitudes, public health and environmental campaigns should focus on the less-educated groups, encouraging them to reduce meat intake and fostering more sustainable attitudes

User-Centric Proximity Estimation Using Smartphone Radio Fingerprinting

The integration of infectious disease modeling with the data collection process is crucial to reach its maximum potential, and remains a significant research challenge. Ensuring a solid empirical foundation for models used to fill gaps in data and knowledge is of paramount importance. Personal wireless devices, such as smartphones, smartwatches and wireless bracelets, can serve as a means of bridging the gap between empirical data and the mathematical modeling of human contacts and networking. In this paper, we develop, implement, and evaluate concepts and architectures for advanced user-centric proximity estimation based on smartphone radio environment monitoring. We investigate innovative methods for the estimation of proximity, based on a person-radio-environment trace recorded by the smartphone, and define the proximity parameter. For this purpose, we developed a smartphone application and back-end services. The results show that, with the proposed procedure, we can estimate the proximity of two devices in terms of near, medium, and far distance with reasonable accuracy in real-world case scenarios

Direction of arrival estimation for BLE

The use of smart antenna arrays in wireless communication systems is becoming increasingly popular, as they improve the performance of the radio link and allow the integration of communication and sensing functions. In this paper, we present the design and performance evaluation of a uniform circular antenna array (UCAA) with 12 monopole antennas and RF switches to select the active antenna for the signal direction of arrival (DoA) estimation. The antenna configuration was optimized through simulations for a different number of antennas and antenna array radius. The frequency tuning of the antennas considered the coupling of the active antenna with adjacent antennas. The performance of the antenna array was evaluated by (1) simulations that examined the proximity of the antenna to the edge of the antenna array and the influence of adjacent antennas on the performance of the active antenna, and (2) measurements in outdoor and indoor environments. The results of the simulations show that antenna coupling can cause nulls in the radiation pattern, which affects the DoA estimation by reducing the signal-to-noise ratio. In a semi-controlled environment, we achieved a DoA estimation error of less than 1 degree by using the Bluetooth Low Energy (BLE) Angle of Arrival feature

Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibilities in the design and optimization of wireless networks and the development of new radio services. In this paper, we introduce, formalize, and evaluate a framework for machine learning (ML) based wireless sensing of indoor surface materials in the form of EM properties. We apply the radio-environment (RE) signatures of the wireless link, which inherently contains environmental information due to the interaction of the radio waves with the environment. We specify the content of the RE signature suitable for surface-material classification as a set of multipath components given by the received power, delay, phase shift, and angle of arrival. The proposed framework applies an ML approach to construct a classification model using RE signatures labeled with the environmental information. The ML method exploits the data obtained from measurements or simulations. The performance of the framework in different scenarios is evaluated based on standard ML performance metrics, such as classification accuracy and F-score. The results of the elementary case prove that the proposed approach can be applied for the classification of the surface material for a plain environment, and can be further extended for the classification of wall materials in more complex indoor environments

Geometric Simplifications of Natural Caves in Ray-Tracing-Based Propagation Modelling

Natural caves show some similarities to human-made tunnels, which have previously been the subject of radio-frequency propagation modelling using deterministic ray-tracing techniques. Since natural caves are non-uniform because of their inherent concavity and irregular limestone formations, detailed 3D models contain a large number of small facets, which can have a detrimental impact on the ray-tracing computational complexity as well as on the modelling accuracy. Here, we analyse the performance of ray tracing in repeatedly simplified 3D descriptions of two caves in the UK, i.e., Kingsdale Master Cave (KMC) Roof Tunnel and Skirwith Cave. The trade-off between the size of the reflection surface and the modelling accuracy is examined. Further, by reducing the number of facets, simulation time can be reduced significantly. Two simplification methods from computer graphics were applied: Vertex Clustering and Quadric Edge Collapse. We compare the ray-tracing results to the experimental measurements and to the channel modelling based on the modal theory. We show Edge Collapse to be better suited for the task than Vertex Clustering, with larger simplifications being possible before the passage becomes entirely blocked. The use of model simplification is predominantly justified by the computational time gains, with the acceptable simplified geometries roughly halving the execution time given the laser scanning resolution of 10 cm