12,070 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Rapid detection of soil carbonates by means of NIR spectroscopy, deep learning methods and phase quantification by powder Xray diffraction

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    Soil NIR spectral absorbance/reflectance libraries are utilized towards improving agricultural production and analysis of soil properties which are key prerequisite for agroecological balance and environmental sustainability. Carbonates in particular, represent a soil property which is mostly affected even by mild, let alone extreme, changes of environmental conditions during climate change. In this study we propose a rapid and efficient way to predict carbonates content in soil by means of FT NIR reflectance spectroscopy and by use of deep learning methods. We exploited multiple machine learning methods, such as: 1) a MLP Regressor and 2) a CNN and compare their performance with other traditional ML algorithms such as PLSR, Cubist and SVM on the combined dataset of two NIR spectral libraries: KSSL (USDA), a dataset of soil samples reflectance spectra collected nationwide, and LUCAS TopSoil (European Soil Library) which contains soil sample absorbance spectra from all over the European Union, and use them to predict carbonate content on never before seen soil samples. Soil samples in KSSL and in TopSoil spectral libraries were acquired in the spectral region of visNIR, however in this study, only the NIR spectral region was utilized. Quantification of carbonates by means of Xray Diffraction is in good agreement with the volumetric method and the MLP prediction. Our work contributes to rapid carbonates content prediction in soil samples in cases where: 1) no volumetric method is available and 2) only NIR spectra absorbance data are available. Up till now and to the best of our knowledge, there exists no other study, that presents a prediction model trained on such an extensive dataset with such promising results on unseen data, undoubtedly supporting the notion that deep learning models present excellent prediction tools for soil carbonates content.Comment: 39 pages, 5 figure

    Human wellbeing responses to species’ traits

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    People rely on well-functioning ecosystems to provide critical services that underpin human health and wellbeing. Consequently, biodiversity loss has profound negative implications for humanity. Human-biodiversity interactions can deliver individual-level wellbeing gains, equating to substantial healthcare cost-savings when scaled-up across populations. However, critical questions remain about which species and/or traits (e.g. colours, sounds, smells) elicit wellbeing responses. The traits that influence wellbeing can be considered ‘effect’ traits. Using techniques from community ecology, we analyse a database of species’ effect traits articulated by people, to identify those that generate different types of wellbeing (physical, emotional, cognitive, social, spiritual and ‘global’ wellbeing, the latter being akin to ‘whole-person health’). Effect traits have a predominately positive impact on wellbeing, influenced by the identity and taxonomic kingdom of each species. Different sets of effect traits deliver different types of wellbeing. However, traits cannot be considered independently of species because multiple traits can be supported by a single species. Indeed, we find numerous effect traits from across the ecological community can elicit multiple types of wellbeing, illustrating the complexity of biodiversity experiences. Our empirical approach can help implement interdisciplinary thinking for biodiversity conservation and nature-based public health interventions designed to support human wellbeing

    Improved accuracy and spatial resolution for bio-logging-derived chlorophyll a fluorescence measurements in the Southern Ocean

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    The ocean’s meso- and submeso-scales (1-100 km, days to weeks) host features like filaments and eddies that have a key structuring effect on phytoplankton distribution, but that due to their ephemeral nature, are challenging to observe. This problem is exacerbated in regions with heavy cloud coverage and/or difficult access like the Southern Ocean, where observations of phytoplankton distribution by satellite are sparse, manned campaigns costly, and automated devices limited by power consumption. Here, we address this issue by considering high-resolution in-situ data from 18 bio-logging devices deployed on southern elephant seals (Mirounga leonina) in the Kerguelen Islands between 2018 and 2020. These devices have submesoscale-resolving capabilities of light profiles due to the high spatio-temporal frequency of the animals’ dives (on average 1.1 +-0.6 km between consecutive dives, up to 60 dives per day), but observations of fluorescence are much coarser due to power constraints. Furthermore, the chlorophyll a concentrations derived from the (uncalibrated) bio-logging devices’ fluorescence sensors lack a common benchmark to properly qualify the data and allow comparisons of observations. By proposing a method based on functional data analysis, we show that a reliable predictor of chlorophyll a concentration can be constructed from light profiles (14 686 in our study). The combined use of light profiles and matchups with satellite ocean-color data enable effective (1) homogenization then calibration of the bio-logging devices’ fluorescence data and (2) filling of the spatial gaps in coarse-grained fluorescence sampling. The developed method improves the spatial resolution of the chlorophyll a field description from ~30 km to ~12 km. These results open the way to empirical study of the coupling between physical forcing and biological response at submesoscale in the Southern Ocean, especially useful in the context of upcoming high-resolution ocean-circulation satellite missions

    A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling

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    The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently

    Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape

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    The Earth\u27s surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale-dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower-based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso-ÎČ, meso-Îł) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid-latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface-atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller-scale turbulent and larger-scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single-point tower measurements through traditional time-domain half-hourly Reynolds decomposition. We report scale-resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface-atmospheric interactions over unstructured heterogeneities and can help inform multi-scale model-data integration of weather and climate models at a sub-grid scale

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

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    This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
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