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Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper. [ABSTRACT FROM AUTHOR]

• Physicochemical soil properties nonlinearly explain the history of LUC in China. • An explainable machine-learning method provides both local and global explanations. • A new perspective on LUC change with reliance on soil properties as drivers. • Toward knowledge gap-filling regarding soil properties in explaining LUC history. Enhancing our comprehension of soil processes and their impact on Earth requires precise quantification of human-induced soil alterations, particularly those related to land use/cover (LUC) histories. Thoroughly validated LUC and soil maps specific to China, and an explainable machine-learning approach were applied to reveal how soil physicochemical properties, independently or in combination, explain LUC histories in the last sixty years. Validation using historical data showed that the proposed machine-learning models can adequately simulate the pattern of LUC histories. Nonlinear mappings of various soil properties in explaining LUC histories were demonstrated and critical thresholds for changes in the explanatory capacities of each soil property to specific LUC histories were identified. Specifically, physical soil properties, except for soil pH, particularly soil thickness, clay content, bulk density, and coarse fragments, play significant roles in explaining the historical trajectories of forests, croplands, and pasture/rangelands than chemical soil properties. Predicted LUC changes suggest more intensive dynamics that are characterized by expansions in forest and pasture/rangeland areas, coupled with a reduction in cropland areas. Overall, improving understanding of the bidirectional links between soil and LUC changes is a crucial and imperative step towards refining the representation of soils in Earth system models. [ABSTRACT FROM AUTHOR]

• Saturated soil hydraulic conductivity was predicted reasonably using easily accessible environmental data and limited soil observations. • Random Forest outperformed best, resulting in high-resolution hydraulic conductivity which can be used in the hydrological model at a large scale. • Silt content, Bands 7, 6, 4, 2, NDPI, and NDVI covariates explained most of the soil hydraulic conductivity variations. Saturated soil hydraulic conductivity (K sat) is a key component in hydrogeology and water management. This study aimed at evaluating popular tree-based machine learning algorithms (Random forest (RF), Quantile random forest (QRF), Cubist (Cu), and Decision tree regression (DTr)) to assess the spatial distribution of K sat in a sandy agricultural field. Soil surface reflectance derived from Landsat-8 (OLI/TIRS) (several remotely sensed data including original surface reflectances of spectral bands and 22 remote sensing indices) as well as limited ground measured soil data were used as predictor covariates. Using collected disturbed and undisturbed soil samples, physicochemical properties (K sat , porosity, organic matter, and texture contents) were determined. Based on novel supervised feature selection, covariates including Landsat-8 spectral Band2, Band4, Band6, and Band7, normalized difference pond index (NDPI), normalized difference vegetation index (NDVI), and silt content were identified as the most important factors in K sat prediction. Variable importance analysis showed Band7 and Band6 were explaining 54% of the total K sat variation. The model evaluation, using five statistics cretria, Taylor diagram and the Kruskal–Wallis (KW) test, demonstrates the outperformance of RF being followed by QRF, Cu, and DTr models. Accordingly, the RF method combined with an optimized sampling approach provides the most accurate digital soil K sat map. The uncertainty analysis showed no significant different trend in K sat predictions by applying more dense datasets confirming original-limited data (n = 28) was efficient and sufficient to derive a reliable K sat -map. The developed approach, which can be scaled up at a large scale, seems to be useful in modelling water flow and solute transport for water and nutrient management purposes toward precision agriculture. [ABSTRACT FROM AUTHOR]