Miracle-dz/UniFAR-FTUs huggingface dataset:
Delineating soil landscape facets from digital elevation models using compound topographic index in a geographic information system

Soil landscapes and their component facets (or sub-units) are fundamental information for land capability assessment and land use planning. The aim of the study was to delineate soil landscape facets from readily available digital elevation models (DEM) to assist soil constraint assessment for urban and regional planning in the coastal areas of New South Wales (NSW), Australia. The Compound Topographic Index (CTI) surfaces were computed from 25 m DEM using a D-infinity algorithm. The cumulative frequency distribution of CTI values within each soil landscape was examined to identify the values corresponding to the area specified for each unmapped facet within the soil landscape map unit. Then these threshold values and CTI surfaces were used to generate soil landscape facet maps for the entire coastal areas of NSW. Specific programs were developed for the above processes in a geographic information system so that they are automated, fast, and repeatable. The modelled facets were assessed by field validation and the overall accuracy reached 93%. The methodology developed in this study has been proven to be efficient in delineating soil landscape facets, and allowing for the identification of land constraints at levels of unprecedented detail for the coast of NSW.
Assessing the Spatiotemporal Patterns and Impacts of Droughts in the Orinoco River Basin Using Earth Observations Data and Surface Observations

Droughts impact the water cycle, ecological balance, and socio-economic development in various regions around the world. The Orinoco River Basin is a region highly susceptible to droughts. The basin supports diverse ecosystems and supplies valuable resources to local communities. We assess the spatiotemporal patterns and impacts of droughts in the basin using remote sensing data and surface observations. We use monthly precipitation (P), air temperature near the surface (T2M), enhanced vegetation index (EVI) derived from Earth observations, and average daily flow (Q) data to quantify drought characteristics and impacts. We also investigated the association between drought and global warming by correlating the drought intensity and the percentage of dry area with sea surface temperature (SST) anomalies in the Pacific (Niño 3.4 index), Atlantic (North Atlantic Index [NATL]), and South Atlantic Index [SATL]) oceans. We evaluate the modulating effect of droughts on the hydrological regime of the most relevant tributaries by calculating the trend and significance of the regional standardized precipitation index (SPI) and percentage area affected by dry conditions. El Niño events worsen the region’s drought conditions (SPI vs. Niño 3.4 index, r = −0.221), while Atlantic SST variability has less influence on the basin’s precipitation regime (SPI vs. NATL and SATL, r = 0.117 and −0.045, respectively). We also found that long-term surface warming trends aggravate drought conditions (SPI vs. T2M anomalies, r = −0.473), but vegetation greenness increases despite high surface temperatures (SPI vs. EVI anomalies, r = 0.284). We emphasize the irregular spatial-temporal patterns of droughts in the region and their profound effects on the ecological flow of rivers during prolonged hydrological droughts. This approach provides crucial insights into potential implications for water availability, agricultural productivity, and overall ecosystem health. Our study underlines the urgent need for adaptive management strategies to mitigate the adverse effects of droughts on ecosystems and human populations. The insights derived from our study have practical implications for developing strategies to address the impacts of droughts and ensure the protection of this ecologically significant region.
Red squirrel habitat mapping using remote sensing

The native Eurasian red squirrel is considered endangered in the UK and is under strict legal protection. Long-term management of its habitat is a key goal of the UK conservation strategy. Current selection criteria of reserves and subsequent management mainly consider species composition and food availability. However, there exists a critical gap in understanding and quantifying the relationship between squirrel abundance, their habitat use and forest structural characteristics. This has partly resulted from the limited availability of structural data along with costefficient data collection methods. This study investigated the relationship between squirrel feeding activity and structural characteristics of Scots pine forests. Field data were collected from two study areas: Abernethy and Aberfoyle Forests. Canopy closure, diameter at breast height, height and number of trees were measured in 56 plots. Abundance of squirrel feeding signs was used as an index of habitat use. A GLM was used to model the response of cones stripped by squirrels in relation to the field collected structural variables. Results show that forest structural characteristics are significant predictors of feeding sign presence, with canopy closure, number of trees and tree height explaining 43% of the variation in stripped cones. The GLM was also implemented using LiDAR data to assess at wider scales the number of cones stripped by squirrels. The use of remote sensing -in particular Light Detection and Ranging (LiDAR) enables cost efficient assessments of forest structure at large scales and can be used to retrieve the three variables explored in this study; canopy cover, tree height and number of trees, that relate to red squirrel feeding behaviour. Correlation between field-predicted and LiDAR-predicted number of stripped cones was performed to assess LiDAR-based model performance. LiDAR data acquired at Aberfoyle and Abernethy Forests had different characteristics (in particular pulse density), which influences the accuracy of LiDAR derived metrics. Therefore correlations between field predicted and LiDAR predicted number of cones (LSC) were assessed for each study area separately. Strong correlations (rs=0.59 for Abernethy and 0.54 for Aberfoyle) suggest that LiDAR-based model performed relatively well over the study areas. The LiDAR-based model was not expected to
Sistema de Generación de Aplicaciones Interactivas para TV Digital para la evaluación de servicios masivos

Resumen: Se presenta el desarrollo del prototipo del sistema SGAi (Sistema de Generacion de Aplicaciones Interactivas), disenado para obtener la opinion de los televidentes sobre servicios masivos; de forma particular, se lo aplica para evaluar la calidad de los servicios de telecomunicaciones. SGAi esta conformado por tres componentes fundamentales: 1)Survey Composer, que permite crear y personalizar aplicaciones interactivas para encuestas y generar automaticamente el codigo NCL (Nested Context Language) que se ejecutara en el STB (Set Top Box) del televidente; 2)Una Aplicacion Web Administrable, que proporciona el sistema de gestion de encuestas, de recepcion y almacenamiento de las opiniones asi como de la visualizacion de los resultados; y, 3)El canal de retorno, encargado de la comunicacion entre el STB y el servidor de la Aplicacion Web Administrable. El sistema esta orientado, en particular Survey Composer, a facilitar la generacion de aplicaciones interactivas que empleen el canal de retorno, permitiendo que el usuario se concentre en el contenido y la parte estetica de las encuestas antes que en aprender y emplear el lenguaje NCL. SGAi ha sido desarrollado como parte de las actividades de investigacion que se realiza en el area de TV digital en la Escuela Politecnica Nacional, considerando el estandar ISDB-Tb, adoptado en el Ecuador en marzo de 2010. Abstract: The development of the prototype of the system SGAi (Generation System for InteractiveApplications) designed to obtain feedback from TV viewers about mass services, is reported; in a particularway, it is applied to evaluate the quality of telecommunication services. SGAi consists of three components: 1)Survey Composer, which lets users create and customize interactive applications for surveys and automaticallygenerate the NCL code (Nested Context Language) that will run on the STB (Set Top Box) of the viewer;2) A manageable web application that provides the survey management system, receives and storages opinionsas well as the display of results; and 3) the return channel, responsible for communications between the STBand the server of the manageable web application. The system is aimed, in particular Survey Composer, tofacilitate the generation of interactive applications that use the return channel, allowing the user to focus onthe content and aesthetics of the surveys rather than learning and using NCL. SGAi has been developed as partof research activities carried out in the digital TV area in the National Polytechnic School, considering theISDB-Tb standard, adopted in Ecuador in March 2010.
Field test of a modified numerical model for water uptake by root systems

Data obtained from careful water balance studies on water uptake by the roots of red cabbage are compared with results obtained from a modified numerical model of Nimah and Hanks. In the modified model the air dry moisture content at the soil surface may vary with time depending on meteorological conditions. The maximum possible rate of evapotranspiration is calculated by considering both meteorological conditions and crop properties. Data are quoted to suggest that the coefficient of the root sink may sometimes vary exponentially with depth. A period of 7 weeks was simulated, and the calculated weekly moisture profiles did not agree completely with those measured in the field. On the other hand, the calculated cumulative rates of evaporation and transpiration were in excellent agreement with the field data. When the original model was used without the suggested modifications, the agreement of these rates with the field data was not as good, an indication that some of these modifications actually improve the predictive capabilities of the model.
A Two-Stage Hierarchical One-Class Classification Structure for HFSWR Ship-Target Detection

A high-frequency surface wave radar (HFSWR) is an effective tool for monitoring an exclusive economic zone (EEZ). However, the presence of diverse clutters and noises that contaminate the echo signals of the radar hinders its maritime surveillance. To address this issue, this article presents a two-stage hierarchical one-class classification network (HOCN) designed specifically for ship-target detection in range–Doppler (RD) images. In Stage 1, the plausible region of interest (PROI) is extracted. This stage uses a dynamic threshold optimization strategy and Laplacian kernel to identify the potential regions of interest. In Stage 2, the proposed one-class deconvolutional-and-convolutional network (OC-DCNet) is used for fine detection of ship targets. This stage comprises two submodules: the deconvolutional submodule, which expands the input into a 2-D matrix, and the convolutional submodule, which classifies the input pattern as either a ship target or a nonship target. The experimental results on a newly collected dataset called HFRD demonstrate the effectiveness of the proposed HFSWR ship-target detection algorithm.
DACOD360: Deadline-Aware Content Delivery for 360-Degree Video Streaming Over MEC Networks

The proliferation of 360-degree video applications has brought significant challenges to existing networks. To meet the requirements of high transmission rate, low interaction latency, and high reliability, Mobile Edge Computing (MEC) has emerged as a promising technology that enables caching and processing at network edges. In this article, we present DACOD360, a deadline-aware content delivery system for the 360-degree video streaming over MEC networks. To address the challenges such as unpredictable viewports, uneven cached tiles, concurrent requests, and dynamic bandwidth, we formulate the deadline-aware delivery problem as a long-term integer program model to maximize the Quality of Experience (QoE) under the constraints of network bandwidth, cache capacity, and deadline. This optimization problem is a complex sequential decision that considers both deadline-constrained service quality at the temporal scale and multi-user resource allocation at the spatial scale. To solve it, we decompose the original problem into two sub-problems and solve them iteratively using Deep Reinforcement Learning (DRL) and Cooperative Bargaining Game (CBG). Comprehensive experiments are conducted in a wide variety of environments, and the results demonstrate that our proposed scheme outperforms the state-of-the-art schemes in terms of long-term QoE, traffic reduction, and other metrics.
Cyclicity in the middle Eocene central Arctic Ocean sediment record: Orbital forcing and environmental response

[1] Continuous X-ray fluorescence scanning of middle Eocene (� 46 Ma) core M0002A-55X (� 236–241 m composite depth), recovered during Integrated Ocean Drilling Program Expedition 302, revealed a strong cyclical signal in some major and trace geochemical elements. We performed a multiproxy study of the same core, which included organic geochemical, sedimentological, and biological parameters, and integrated our results with available geochemical and physical properties data. The target was to look for cyclicity in the several proxies, investigate their frequency, and understand the environmental response to the potential forcing. Results indicate that a higher terrigenous component corresponds to lower organic carbon concentration, smaller contributions by angiosperm pollen and spores, organic-walled dinoflagellate cysts, and chrysophyte cysts (lower productivity, shorter growing season for flowering plants, and lower stratification) but higher contributions by bisaccate pollen and diatoms (drier conditions on land, more marine conditions) and higher terrigenous sand (ice-rafted debris (IRD)). Our investigation shows that physical proxy parameters hold cyclicity with periods of about 50 and 100 cm and that these frequency components are compatible with a Milankovitchtype orbital forcing, representing precession and obliquity, respectively. The longer 100 cm cyclicity is also present in the biological (pollen, dinoflagellate cysts, and siliceous microfossils) and in the sedimentological (IRD) proxies. The environmental signal derived from the integrated multiproxy analysis suggests that in an enclosed Arctic Ocean at time of ice (sea ice and glacial ice) initiation the biological proxies responded more strongly to growing season length/darkness, whereas the terrigenous components, directly driven by sea ice and/ or glacial ice formation and extent, responded more directly to seasonal insolation.
Assessing forest mortality patterns using climate and FIA data at multiple scales

Forest Inventory and Analysis (FIA) and PRISM climate data from 1991-2000 were obtained for 10 states in the southeastern United States. Mortality was calculated for each plot, and annual values for precipitation and maximum and minimum temperature were extracted from the PRISM data. Data were then stratified by upland/bottomland for red oak species, and classification and regression tree (CART) analysis was used to determine the influence of climate variables on mortality at ecoregion province and section levels. The results presented here will provide a basis for future research on the causal factors related to red oak mortality.
CNN‐SCNet: A CNN net‐based deep learning framework for infant cry detection in household setting

Infants are vulnerable to several health problems and cannot express their needs clearly. Whenever they are in a state of urgency and require immediate attention, they cry, which is a form of communication for them. Therefore, the parents of the infants always need to be alert and keep continuous supervision of their infants. However, parents cannot monitor their infants all the time. An infant monitoring system could be a possible solution to monitor the infants, determine when the infants are crying, and notify the parents immediately. Although many such systems are available, most cannot detect infant cries. Some systems have infant cry detection mechanisms, but those mechanisms are not very accurate in detecting infant cries because the mechanisms either include obsolete approaches or machine learning (ML) models that cannot identify infant cries from noisy household settings. To address this limitation, in this research, different conventional and hybrid ML models were developed and analyzed in detail to find out the best model for detecting infant cries in a household setting. A stacked classifier is proposed using different state‐of‐the‐art technologies, outperforming all other developed models. The proposed CNN‐SCNet's (CNN‐Stacked Classifier Network) precision, recall, and f1‐score were found to be 98.72%, 98.05%, and 98.39%, respectively. Infant monitoring systems can use this classifier to detect infant cries in noisy household settings.
3-D Joint Inversion of DC Resistivity and Time-Domain Induced Polarization With Structural Constraints in Undulating Topography

Addressing the significant impact of undulating terrain on 3-D direct current (dc) inversion, the unreliability of traditional linearized polarizability inversion for highly polarizable anomalies, and the nonuniqueness of separate resistivity and time-domain-induced polarization (TDIP) inversion, this article conducts a joint inversion study of 3-D dc resistivity and TDIP with structural constraints in undulating topography. A regularly arranged deformed hexahedron mesh simulates undulating surface terrain, transformed into regular hexahedron elements for 3-D undulating terrain dc resistivity modeling. Based on the exact inversion of polarization calculated from the inversion results of apparent resistivity and equivalent apparent resistivity data, a joint inversion of resistivity and polarization constrained by cross-gradient is implemented. Synthetic data examples show that the application of arbitrary hexahedron elements significantly reduces the influence of terrain on inversion, and the implementation of joint inversion markedly improves the recovery of high-polarization anomalies while enhancing both the model resolution and the inversion accuracy. The proposed algorithm is applied to the joint inversion of resistivity and polarizability in the lead–zinc mining area of Xiagalaiaoyi River in Huzhong area, the Great Khingan Mountains, and Northwestern Heilongjiang Province, achieving good results.
Discovery of 7-Azanorbornane-Based Dual Agonists for the Delta and Kappa Opioid Receptors through an In Situ Screening Protocol

In medicinal chemistry, the copper-catalyzed click reaction is used to prepare ligand candidates. This reaction is so clean that the bioactivities of the products can be determined without purification. Despite the advantages of this in situ screening protocol, the applicability of this method for transmembrane proteins has not been validated due to the incompatibility with copper catalysts. To address this point, we performed ligand screening for the µ, δ, and κ opioid receptors using this protocol. As we had previously reported the 7-azanorbornane skeleton as a privileged scaffold for the G protein-coupled receptors, we performed the click reactions between various 7-substituted 2-ethynyl-7-azanorbornanes and azides. Screening assays were performed without purification using the CellKeyTM system, and the putative hit compounds were re-synthesized and re-evaluated. Although the “hit” compounds for the µ and the δ receptors were totally inactive after purifications, three of the four “hits” for the κ receptor were true agonists for this receptor and also showed activities for the δ receptor. Although false positive/negative results exist as in other screening projects for soluble proteins, this in situ method is effective in identifying novel ligands for transmembrane proteins.
Estimating basin scale evapotranspiration (ET) by water balance and remote sensing methods

Evapotranspiration (ET) is an important hydrological process that can be studied and estimated at multiple spatial scales ranging from a leaf to a river basin. We present a review of methods in estimating basin scale ET and its applications in understanding basin water balance dynamics. The review focuses on two aspects of ET: (i) how the basin scale water balance approach is used to estimate ET; and (ii) how ‘direct’ measurement and modelling approaches are used to estimate basin scale ET. Obviously, the basin water balance‐based ET requires the availability of good precipitation and discharge data to calculate ET as a residual on longer time scales (annual) where net storage changes are assumed to be negligible. ET estimated from such a basin water balance principle is generally used for validating the performance of ET models. On the other hand, many of the direct estimation methods involve the use of remotely sensed data to estimate spatially explicit ET and use basin‐wide averaging to estimate basin scale ET. The direct methods can be grouped into soil moisture balance modelling, satellite‐based vegetation index methods, and methods based on satellite land surface temperature measurements that convert potential ET into actual ET using a proportionality relationship. The review also includes the use of complementary ET estimation principles for large area applications. The review identifies the need to compare and evaluate the different ET approaches using standard data sets in basins covering different hydro‐climatic regions of the world. Copyright © 2011 John Wiley & Sons, Ltd.
Common-conversion-point stacking of receiver functions for estimating the geometry of dipping interfaces

SUMMARY We develop a new method to estimate geometries of dipping seismic velocity discontinuities from common conversion point stacking of receiver functions. The multistage fast-marching method is applied to the ray tracing with refraction at dipping interfaces. Although geometry of interfaces dipping at 30°–70° cannot be correctly estimated with a 1-D velocity model, it can be correctly estimated when refraction at dipping interfaces is taken into account. We apply this method to synthetic and observed data and confirm that geometries of dipping interfaces are successfully estimated. This method will contribute to revealing the structures beneath subduction zones where dipping interfaces exist.
Lattice dynamics and thermodynamic properties of (Mg,Fe2+)SiO3 postperovskite

[1] Phonon dispersion relations and vibrational density of states of (Mg0.9375Fe0.0625)SiO3 postperovskite have been determined by direct first-principles calculations of the dynamical matrix up to 150 GPa. Incorporation of iron in the postperovskite phase, irrespective of the two investigated configurations and the spin state, was found to decrease the acoustic phonon frequencies but to have a minor effect on the optic modes at high frequencies. The phonon dispersion curves exhibit negative phonon frequencies below 10 GPa when iron is incorporated in the high or low spin state and indicate unstable dynamic structures. Then, the calculated phonon frequencies of dynamically stable structures are used to determine vibrational contributions to the Helmholtz free energy within the quasi-harmonic approximation. The high temperature equation of state and several thermodynamic properties are then derived for (Mg0.9375Fe0.0625)SiO3 postperovskite and compared with those of pure MgSiO3 postperovskite. The results show that a low concentration of iron, irrespective of high spin or low spin, in MgSiO3 postperovskite has a minor effect on the thermodynamic properties at pressure-temperature conditions of the lowermost part of the Earth's mantle.