Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Native Defects in Fe-Doped β-Ga2O3

Iron impurities are believed to act as deep acceptors that can compensate for the n-type conductivity in as-grown Ga2O3, but several scientific issues, such as the site occupation of the Fe heteroatom and the complexes of Fe-doped β-Ga2O3 with native defects, are still lacking. In this paper, based on first-principle density functional theory calculations with the generalized gradient approximation approach, the controversy regarding the preferential Fe incorporation on the Ga site in the β-Ga2O3 crystal has been addressed, and our result demonstrates that Fe dopant is energetically favored on the octahedrally coordinated Ga site. The structural stabilities are confirmed by the formation energy calculations, the phonon dispersion relationships, and the strain-dependent analyses. The thermodynamic transition level Fe3+/Fe2+ is located at 0.52 eV below the conduction band minimum, which is consistent with Ingebrigtsen’s theoretical conclusion, but slightly smaller than some experimental values between 0.78 eV and 1.2 eV. In order to provide direct guidance for material synthesis and property design in Fe-doped β-Ga2O3, the defect formation energies, charge transitional levels, and optical properties of the defective complexes with different kinds of native defects are investigated. Our results show that VGa and Oi can be easily formed for the Fe-doped β-Ga2O3 crystals under O-rich conditions, where the +3 charge state FeGaGai and −2 charge state FeGaOi are energetically favorable when the Fermi level approaches the valence and conduction band edges, respectively. Optical absorption shows that the complexes of FeGaGai and FeGaVGa can significantly enhance the optical absorption in the visible-infrared region, while the energy-loss function in the β-Ga2O3 material is almost negligible after the extra introduction of various intrinsic defects.
A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.
Robust Utilization of Wind Turbine Flexibility for Grid Stabilization Robust Utilization of Wind Turbine Flexibility for Grid Stabilization

General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us at vbn@aub.aau. dk providing details, and we will remove access to the work immediately and investigate your claim. Abstract: This work considers the use of wind turbines for stabilizing an electrical grid, by employing temporary overproduction with respect to available power. We present a simple model describing a turbine, and show how the possible period of overproduction, can be maximized through a series of convex problems, where the load is distributed among several turbines in a farm. We then present an optimization scheme that guarantees a lower limit for the overproduction period and subsequently propose an adaptive implementation that is robust against parameter uncertainties.
RIS-Aided Passive Detection for LSS Targets: A GNSS Multipath-Assisted Scheme

The complex topography of urban canyons with many reflectors and scatterers makes it challenging to detect low-altitude, smaller, and slow-speed targets. In this paper, we present a novel multipath-assisted passive detection scheme based on the global navigation satellite system signals in urban canyons. We first propose an information-level target detection scheme, where a binary hypothesis test is conducted according to variation in the received signal given the presence or absence of targets in the environment. To take usage of multipath components (MPCs) in the proposed scheme, we introduce virtual anchors to model reflected signals’ propagation paths. We also introduce the reconfigurable intelligent surface to artificially improve the reflective environment and enhance the quality of received MPCs. The detection performance indicators are analyzed theoretically. Simulation results show that the proposed schemes respectively reach 90% and 94% detection probability at a signal-to-noise ratio of 5 dB. The RIS-based method outperforms the multipath-assisted method when the RIS error is less than 0.41 m.
Spatial Distribution and Molecular Diversity of Archaeal Communities in the Extreme Hypersaline Meromictic Brâncoveanu Lake (Transylvanian Basin, Romania)

ABSTRACT Dating from the Middle Miocene, the massive halite deposits lying beneath the Transylvanian Basin (Central Romania) have been valuable mineral resources quarried for millennia. Among the numerous hypersaline pit lakes that resulted from this mining, Brâncoveanu Lake is unique by its extreme salinity. Assessment of physicochemical variables, water chemistry and trophic status indicated that Brâncoveanu Lake is a permanently stratified, pH-neutral, NaCl-rich and eutrophied system. We investigated the abundance, molecular diversity and vertical distribution of archaeal community by culture-independent approaches. Additionally, the most relevant environmental parameters shaping the archaeal community composition were evaluated by statistical methods. Archaea appeared to largely outnumber Bacteria; altogether the great prevalence of Halobacteriaceae-related sequences could imply a major contribution of this group to the biogeochemical carbon turnover. The fairly distinct composition of archaeal communities reflects the lake's physicochemical stratification. Among the limnological factors, salinity and oxygen showed a significant impact on determining the composition and structure of archaeal assemblages. Furthermore, Brâncoveanu Lake might harbor novel microorganisms such as members of the recently described phylum Nanohaloarchaea. Overall, this study reported the occurrence of halophilic Archaea in a little explored hydrogeochemical system and provided a better insight into geomicrobiology of meromictic hypersaline pit lakes.
Enhancing Coccinella Beetle Biological Pest Control via a Floral Approach in Cucumber Greenhouse

Flower-rich habitats are crucial for promoting biodiversity and ecosystem services within agricultural ecosystems, such as pollination and pest control. The present study investigates the efficacy of employing floral structures as a criterion for the selection of plant species in order to enhance the attraction of natural enemies within cucumber greenhouses, consequently augmenting floral resources. The results of our study provide evidence that flower strips have a beneficial effect on the fitness of critical natural predators, while not facilitating the proliferation of detrimental insect species. These findings exhibit potential for enhancing pest-management services in the agricultural sector. The findings of our study demonstrate that pest levels within greenhouse environments closely resemble those observed in real-world commercial cropping systems. As a result, the introduction of Coccinella septempunctata and Menochilus sexmaculatus biocontrol agents is confirmed to be a reliable and efficient method for pest management. The phenomenon of predator–prey density dependency is recognized as a crucial element in the implementation of biological control strategies. Furthermore, we investigate the impact of floral resources on the reproductive capacity of indigenous predators. The impact of Coriandrum sativum on fertility is substantial, indicating that the presence of a varied plant assortment with overlapping flowering periods can prolong the availability of floral resources. This study highlights the significance of flower-rich habitats and deliberate plant selection in augmenting biodiversity, ecosystem services, and pest management within agricultural settings. The implementation of conservation biological control technologies presents supplementary ecological advantages, thus offering practical implications for the promotion of sustainable agricultural practices.
Acute Exposure to Two Biocides Causes Morphological and Molecular Changes in the Gill Ciliary Epithelium of the Invasive Golden Mussel Limnoperna fortunei (Dunker, 1857)

Simple Summary Limnoperna fortunei, known as the golden mussel, is an invasive bivalve in South America responsible for economic damages in hydroelectric plants due to its ability to attach and grow on the walls of water pipes and tubes. Chemical compounds are often used to control golden mussel infestations in water systems. Here, we investigated the effects of two biocides—MXD-100 ™ and sodium dichloroisocyanurate (NaDCC)—through the assessment of morphological and gene-expression alterations in the gills. Both biocides were able to modulate the expression of defensive genes and morphological changes. Yet, it seems that NaDCC needs continuous exposure to control golden mussel infestation, whereas MXD-100™ can inflict severe damage to the mussels in as little as 24 h. Our results can be used to enhance control strategies for managing mussel growth in water systems, simultaneously reducing the environmental impact associated with costly and harmful chemical releases. Abstract Limnoperna fortunei, the golden mussel, is a bivalve mollusk considered an invader in South America. This species is responsible for ecological and economic damages due to its voluminous fouling capability. Chemical biocides such as MXD-100™ and sodium dichloroisocyanurate (NaDCC) are often used to control L. fortunei infestations in hydraulic systems. Thus, we proposed to investigate the effects of different periods (24, 48 and 72 h) of exposure to MXD-100™ (0.56 mg L−1) and NaDCC (1.5 mg L−1) on the gills of L. fortunei through morphological and molecular analyses. NaDCC promoted progressive morphological changes during the analyzed periods and only an upregulation of SOD and HSP70 expression during the first 24 h of exposure. MXD-100™ led to severe morphological changes from the first period of exposure, in addition to an upregulation of SOD, CAT, HSP70 and CYP expression during the first 24 h. In contrast, MXD-100™ led to a downregulation of CAT transcription between 24 and 48 h. In static conditions, NaDCC causes lethal damage after 72 h of exposure, and that exposure needs to be continuous to achieve the control of the species. Meanwhile, the MXD-100™ treatment presented several effects during the first 24 h, showing acute toxicity in a shorter period of time.
Differential Antioxidant Response to Supplemental UV-B Irradiation and Sunlight in Three Basil Varieties

Three basil plant varieties (Ocimum basilicum var. Genovese, Ocimum × citriodorum, and Ocimum basilicum var. purpurascens) were grown under moderate light (about 300 µmol photons m−2 s−1) in a glasshouse or growth chamber and then either transferred to an open field (average daily dose: 29.2 kJ m−2 d−1) or additionally exposed to UV-B irradiation in a growth chamber (29.16 kJ m−2 d−1), to reveal the variety-specific and light-specific acclimation responses. Total antioxidant capacity (TAC), phenolic profile, ascorbate content, and class III peroxidase (POD) activity were used to determine the antioxidant status of leaves under all four light regimes. Exposure to high solar irradiation at the open field resulted in an increase in TAC, total hydroxycinnamic acids (HCAs, especially caffeic acid), flavonoids, and epidermal UV-absorbing substances in all three varieties, as well as a two-fold increase in the leaf dry/fresh weight ratio. The supplemental UV-B irradiation induced preferential accumulation of HCAs (rosmarinic acid) over flavonoids, increased TAC and POD activity, but decreased the ascorbate content in the leaves, and inhibited the accumulation of epidermal flavonoids in all basil varieties. Furthermore, characteristic leaf curling and UV-B-induced inhibition of plant growth were observed in all basil varieties, while a pro-oxidant effect of UV-B was indicated with H2O2 accumulation in the leaves and spotty leaf browning. The extent of these morphological changes, and oxidative damage depended on the basil cultivar, implies a genotype-specific tolerance mechanism to high doses of UV-B irradiation.
Ephemeris Tracking and Error Propagation Analysis of LEO Satellites with Application to Opportunistic Navigation

—A comprehensive study is performed for low Earth orbit (LEO) space vehicles (SVs) tracking by a receiver oppor-tunistically extracting navigation observables from their down-link radio frequency signals. First, a framework to characterize the LEO SVs’ orbital motion process noise covariance is developed. Second, the tracking performance via an extended Kalman filter (EKF) is analyzed via comprehensive Monte Carlo simulations for three different sets of observables: (i) pseudorange, (ii) Doppler, and (iii) fused pseudorange and Doppler measurements. Third, experimental results are presented demonstrating the efficacy of the opportunistic tracking framework in refining the ephemeris of a LEO SV from two-line element (TLE) files. The initial position and velocity errors of over 7.1 km and 7.3 m/s, respectively, of an Orbcomm LEO SV were reduced to 698.7 m and 1.8 m/s, respectively, in just over 6 minutes of tracking with carrier phase navigation observables, extracted opportunistically. Fourth, the error propagation from the LEO SV’s state space to the measurement space and from the measurement space to the receiver’s state space is analyzed in the context of stationary receiver localization. Bounds on the magnitude of pseudorange and Doppler residuals are first derived, and the magnitude of the receiver’s estimation error is then characterized as a function of errors in the LEO SV’s state space. Fifth, experimental results are presented of a stationary receiver tracking an Orbcomm LEO SV by fusing carrier phase observables via an EKF. The tracked LEO ephemeris is then used to localize another stationary receiver, showing a reduction in the receiver’s initial horizontal error from 13,476 m to 343 m after just over 6 minutes. In contrast, it is shown that if the SGP4-propagated ephemeris was used in the EKF to localize the receiver, the error is reduced to 6,852 m, but the filter becomes inconsistent.
Assessing the potential of machine learning and integrated satellite data for canopy height mapping: a case study on the Iberian Peninsula

Understanding the structure of forests is essential to gain insights into the diversity of ecosystems, which is key to determining their resilience to abiotic and biotic hazards. Forests play a critical role in the storage of biomass and are therefore affecting the global carbon cycle and future trajectories of atmospheric CO2 concentration. Earth observations and satellite remote sensing data have become valuable resources for observing forested areas on a global scale. From this viewpoint the Global Ecosystem Dynamics Investigation (GEDI) LIDAR (Light Detection and Ranging) mission, developed by NASA (National Aeronautics and Space Administration), measures an unprecedented range of parameters that describe forest structural complexity. In this paper, we investigate the ability of machine learning tools to discern variations in canopy height and evaluate their performance with a view to determine the most accurate one for the Iberian Peninsula. A novel approach is proposed for integrating Sentinel-1, Alos-Palsar, Sentinel-2 and spectral indices data to create an input dataset for machine learning models. Random Forest (RF), XGBoost. and Multi-Layer Perceptron (MLP) are trained to leverage canopy height data obtained from GEDI metrics. Our results show that the three algorithms perform similarly in terms of accuracy and errors, presenting MLP with the highest correlation with canopy height. Additionally, we evaluated the feature importance of each model, providing insights into the variables that influence the model's decision-making. Our analysis revealed that MLP gave greater importance to Sentinel-2 bands, whereas Random Forest relied more heavily on vegetation indices. In contrast, XGBoost exhibited a balanced approach and utilized information from both features evenly. It is noteworthy that the red-edge channels and their derived indices have demonstrated a significant level of importance across all models employed in this study.
Exploratory Analysis of Immunological Responses in Wild and Laboratory Mice

The use of mice as a model organism in biomedical research is widespread due to their similarities with humans in anatomy and physiology. However, the genetic isolation of inbred strains from natural populations raises concerns about the reliability of these models. Studies have shown differences in immune responses between wild and laboratory mice, and the use of exploratory tools for large datasets can help to analyze and interpret these differences. The aim statistical analysis of dataset can provide insights into the differences between variables and individuals and guide further investigations. The results showed that LDA provided clear separation between the different groups, and successfully differentiated between stimulation types and mouse strains, with distinct clustering of data points. The KNN algorithm performed best for smaller values of K. However, the selected gender characteristic did not possess strong discriminatory power in separation and further investigation into alternative features or methodologies may be necessary. In conclusion, the aim of providing comparative immunological analysis of wild and laboratory mice types is achieved. This study underscores the importance of careful statistical analysis, acknowledges the limitations of imputation methods, and highlights the potential of LDA and KNN algorithms in analyzing immune response data. As well, highlighting the need for improved models that are able to capture the complexities of immune responses and their relevance to human immunology.
Person Identification Method Based on PointNet++ and Adversarial Network for mmWave Radar

As a 3-D point cloud has the ability to present the contour of an object clearly, it provides more spatial information for person identification (PI) task. Aiming at the improvements on the quality of point cloud and distribution of features, an innovative treatment method for point cloud and a novel network structure are investigated in this article. First, spatiotemporal feature of point cloud is enhanced by implementing dual-stage density-based spatial clustering of applications with noise (DST-DBSCAN) method, which can filter most invalid points and decrease the sparsity of point cloud. After that, the optimized point cloud is input into neural network, which contains three parts for feature extraction, classification and feature optimization. Specifically, PointNet++ is adopted to extract features and realize PI recognition. In addition, an adversarial network is designed for optimizing feature distribution of point clouds by encouraging the feature extractor of PointNet++ to generate features of the same person as similar as possible. Experimental results demonstrate that the proposed method can improve the accuracy by 3.77% than original PointNet++ network with raw data.
Approaching simulation-based controller design: heat exchanger case study

Supported by an identification experiment using random-phase multisines, uncertain parameters in a grey-box model for a multiple-input multiple-output laboratory-scale heat exchanger are fitted to experimental data. By defining desired trajectories for the controlled system concerning setpoint changes, simulations and a cost function taking control signal activity into account, we determine both a linear and a nonlinear PI-controller. The resulting control systems are evaluated through practical experiments and analysis with encouraging results. The approach to modelling and controller design raises questions about what is needed from an educational point of view, e.g., what skills are needed for simulation-based control design and analysis?
Auditory and reward structures reflect the pleasure of musical expectancies during naturalistic listening

Enjoying music consistently engages key structures of the neural auditory and reward systems such as the right superior temporal gyrus (R STG) and ventral striatum (VS). Expectations seem to play a central role in this effect, as preferences reliably vary according to listeners’ uncertainty about the musical future and surprise about the musical past. Accordingly, VS activity reflects the pleasure of musical surprise, and exhibits stronger correlations with R STG activity as pleasure grows. Yet the reward value of musical surprise – and thus the reason for these surprises engaging the reward system – remains an open question. Recent models of predictive neural processing and learning suggest that forming, testing, and updating hypotheses about one’s environment may be intrinsically rewarding, and that the constantly evolving structure of musical patterns could provide ample opportunity for this procedure. Consistent with these accounts, our group previously found that listeners tend to prefer melodic excerpts taken from real music when it either validates their uncertain melodic predictions (i.e., is high in uncertainty and low in surprise) or when it challenges their highly confident ones (i.e., is low in uncertainty and high in surprise). An independent research group (Cheung et al., 2019) replicated these results with musical chord sequences, and identified their fMRI correlates in the STG, amygdala, and hippocampus but not the VS, raising new questions about the neural mechanisms of musical pleasure that the present study seeks to address. Here, we assessed concurrent liking ratings and hemodynamic fMRI signals as 24 participants listened to 50 naturalistic, real-world musical excerpts that varied across wide spectra of computationally modeled uncertainty and surprise. As in previous studies, liking ratings exhibited an interaction between uncertainty and surprise, with the strongest preferences for high uncertainty/low surprise and low uncertainty/high surprise. FMRI results also replicated previous findings, with music liking effects in the R STG and VS. Furthermore, we identify interactions between uncertainty and surprise on the one hand, and liking and surprise on the other, in VS activity. Altogether, these results provide important support for the hypothesized role of the VS in deriving pleasure from learning about musical structure.