Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Tap Hybrid Beamforming in Wideband mmWave Systems: A Non-Iterative Approach

Hybrid beamforming design on millimeter wave channels with frequency selectivity is a challenging problem. Most of the existing approaches adopt multi-carrier modulation, which is a bit more complicated as a baseband beamformer is used for each subcarrier. To reduce the complexity, we propose a single carrier hybrid beamforming scheme. Our design aims to mitigate both the inter-symbol and multi-user interferences using a multi-tap analog beamformer followed by a single tap baseband beamformer. Specifically, we propose a non-iterative algorithm based on orthogonalization process. Simulation results show that the proposed scheme achieves a promising achievable sum rate performance in low signal-to-noise ratio regions.
Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis

Plant phenology depends largely on temperature, but temperature alone cannot explain the Northern Hemisphere shifts in the start of the growing season (SOS). The spatio–temporal distribution of SOS sensitivity to climate variability has also changed in recent years. We applied the partial least squares regression (PLSR) method to construct a standardized SOS sensitivity evaluation index and analyzed the combined effects of air temperature (Tem), water balance (Wbi), radiation (Srad), and previous year’s phenology on SOS. The spatial and temporal distributions of SOS sensitivity to Northern Hemisphere climate change from 1982 to 2014 were analyzed using time windows of 33 and 15 years; the dominant biological and environmental drivers were also assessed. The results showed that the combined sensitivity of SOS to climate change (SCom) is most influenced by preseason temperature sensitivity. However, because of the asymmetric response of SOS to daytime/night temperature (Tmax/Tmin) and non-negligible moderating of Wbi and Srad on SOS, SCom was more effective in expressing the effect of climate change on SOS than any single climatic factor. Vegetation cover (or type) was the dominant factor influencing the spatial pattern of SOS sensitivity, followed by spring temperature (Tmin > Tmax), and the weakest was water balance. Forests had the highest SCom absolute values. A significant decrease in the sensitivity of some vegetation (22.2%) led to a decreasing trend in sensitivity in the Northern Hemisphere. Although temperature remains the main climatic factor driving temporal changes in SCom, the temperature effects were asymmetric between spring and winter (Tems/Temw). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. Vegetation adaptation has a greater influence on the temporal variability of SOS sensitivity relative to each climatic factor (Tems, Temw, Wbi, Srad). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. This study provides a basis for vegetation phenology sensitivity assessment and prediction.
Extended Nonlinear Two-Time Scale Estimation Approach for Antilock Braking Systems

This article is motivated by an interesting recent result on antilock braking system (ABS) design using two-time scale (TTS)-based estimation. In this article, the basic method of TTS is extended to estimate not only the disturbances but also their derivatives, improving the accuracy of estimation by an order of magnitude. Further, the problem of ABS is extended to an eight-DOF vehicle model to include the effects of load transfer, steering dynamics, and actuator dynamics. Since analysis showed that the use of basic TTS to ABS design results in a poor transient in the estimation error dynamics, this article proposes a new formulation of the TTS method specifically for ABS. Two new ABS-specific sliding surfaces are proposed to compensate the actuator dynamics where the second sliding surface is proposed as an improvement over the first one. The stability, and convergence of estimation, and tracking errors are analyzed. The proposed methods are validated on MATLAB/Simulink, and CarSim platforms for a D-class sedan car. The results are also validated on an experimental ABS platform in the laboratory.
Benchmarking Classical and AI-based Caching Strategies in Internet of Vehicles

Edge caching has emerged as a promising approach to deal with the redundant traffic, to improve the Quality of Service (QoS) and to optimize the energy use in Internet of Vehicles (IoV). However, the intrinsic storage limitations of edge servers pose a critical challenge for IoV edge caching scheme. To solve these issues, several caching policy management techniques have been proposed in literature. In this paper, we perform a systematic comparison among the recent Artificial Intelligence (AI) based caching approaches and the classical caching techniques for IoV. Our objective is to provide a roadmap for choosing the best caching strategy for a given constrained environment. Through a practical scenario, the simulation results show that AI-based edge caching methods achieve high performance in terms of total content access cost and edge hit rate while maintaining a relatively low average delay. On the other hand, hash routing strategies tend to maximize the edge hit rate to the detriment of delivery latency.
Blind Receiver Design for Downlink Cognitive-Radio NOMA Networks

Non-orthogonal multiple access (NOMA) and cognitive radio (CR) are two promising technologies that provide efficient spectrum utilization, which is required for future wireless networks to support heterogeneous user requirements. In this work, we study the throughput performance of a downlink CR-NOMA network comprising a primary user (PU) and a secondary user (SU). Considering a blind cognitive SU which receives its packets opportunistically in an uncoordinated manner, the base station adapts the transmission mode (TM) to satisfy the users’ bit error rate quality of service requirements. The network falls under the hybrid underlay-interweave paradigm, and it is considered uncoordinated since SU does not receive frequent signalling about current TM. This keeps the overhead signalling at minimum levels and maintains the spectral-efficiency. The obtained numerical results show that the proposed network can provide substantial throughput improvement over conventional NOMA networks, even when considering non-ideal cognitive receiver in block fading or frequency selective fading channels.
Using Landsat Imagery to Assess Burn Severity of National Forest Inventory Plots

As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots.
An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.
Deep Learning-based Sub-6GHz/mmWave Partitioning for Short-Packet Communications

A seamless use of Sub-6GHz and mmWave frequency bands will be capital in order to counter the severe spectrum scarcity and to meet the stringent sets of requirements imposed by Beyond 5G applications. In particular, future IoT applications are expected to generate a large amount of short packets. Unlike existing works, we propose a unifying framework devoted to short-packet communications, that jointly optimizes the user partitioning over both bands and the resource scheduling over each band. The proposed partitioning leverages Deep Learning, so as to best tackle the uncertainties inherent to the wireless environment, in particular the varying user Line-of-Sight (LoS) situations, as well as their individual QoS requirements. Numerical results validate the proposed approach which outperforms conventional user partitioning schemes in terms of sum-rate, while better satisfying delay-reliability requirements.
Near-Ultraviolet to Near-Infrared Band Thresholds Cloud Detection Algorithm for TANSAT-CAPI

Cloud and aerosol polarization imaging detector (CAPI) is one of the important payloads on the China Carbon Dioxide Observation Satellite (TANSAT), which can realize multispectral polarization detection and accurate on-orbit calibration. The main function of the instrument is to identify the interference of clouds and aerosols in the atmospheric detection path and to improve the retrieval accuracy of greenhouse gases. Therefore, it is of great significance to accurately identify the clouds in remote sensing images. However, in order to meet the requirement of lightweight design, CAPI is only equipped with channels in the near-ultraviolet to near-infrared bands. It is difficult to achieve effective cloud recognition using traditional visible light to thermal infrared band spectral threshold cloud detection algorithms. In order to solve the above problem, this paper innovatively proposes a cloud detection method based on different threshold tests from near ultraviolet to near infrared (NNDT). This algorithm first introduces the 0.38 μm band and the ratio of 0.38 μm band to 1.64 μm band, to realize the separation of cloud pixels and clear sky pixels, which can take advantage of the obvious difference in radiation characteristics between clouds and ground objects in the near-ultraviolet band and the advantages of the band ratio in identifying clouds on the snow surface. The experimental results show that the cloud recognition hit rate (PODcloud) reaches 0.94 (ocean), 0.98 (vegetation), 0.99 (desert), and 0.86 (polar), which therefore achieve the application standard of CAPI data cloud detection The research shows that the NNDT algorithm replaces the demand for thermal infrared bands for cloud detection, gets rid of the dependence on the minimum surface reflectance database that is embodied in traditional cloud recognition algorithms, and lays the foundation for aerosol and CO2 parameter inversion.
Synthesis and evaluation of potential atmospheric eye irritants

Photochemical smog is manifested by several chemical, physical, and physiological symptoms, one of which is eye irritation. A compound of the peroxyacetyl nitrate-type (PAN), peroxybenzoyl nitrate (PBzN), has been synthesized and shown to be about 200 times more powerful an eye irritant than formaldehyde. In order to further characterize atmospheric eye irritation, several irritants of the PBzN-type have been investigated.
Evaluating a Low-Cost Strain Gauge Breathing Sensor for Sleep Apnea Detection at Home

We report results and experience from a clinical evaluation of a low-cost strain gauge breathing sensor for sleep apnea detection. We collected from 34 individuals 57 nights of unattended sleep monitoring data at home with the certified Type 3 monitor Nox-T3 and the Flow strain gauge sensor from SweetZpot. The quality of the Flow data does not match the quality of the Nox-T3 data due to the much simpler build of the Flow sensor and issues during data acquisition. The Flow data can still be used to accurately discriminate between normal and disrupted breathing. With appropriate data pre-processing, we achieve a mean breath detection sensitivity of 97.2% and positive prediction value of 94.2%.
Detecting Winter Cover Crops and Crop Residues in the Midwest US Using Machine Learning Classification of Thermal and Optical Imagery

Cover crops are an increasingly popular practice to improve agroecosystem resilience to climate change, pests, and other stressors. Despite their importance for climate mitigation and soil health, there remains an urgent need for methods that link winter cover crops with regional-scale climate mitigation and adaptation potential. Remote sensing is ideally suited to provide these linkages, yet, cover cropping has not been analyzed extensively in remote sensing research. Methods used for remote sensing of crops from satellites traditionally leverage the difference between visible and near-infrared reflectance to isolate the signal of photosynthetically active vegetation. However, using traditional greenness indices like the Normalized Difference Vegetation Index (NDVI) for remotely sensing winter vegetation, such as winter cover crops, is challenging because vegetation reflectance signals are often confounded with reflectance of bare soil and crop residues. Here, we present new and established methods of detecting winter cover crops using remote sensing observations. We find that remote sensing methods that incorporate thermal data in addition to traditional reflectance metrics are best able to distinguish between winter farm management practices. We conclude by addressing the potential of existing and upcoming hyperspectral and thermal missions to further assess agroecosystem function in the context of global change.
Multithreading Based Parallel Processing for Image Geometric Coregistration in SAR Interferometry

Within the framework of multi-temporal Synthetic Aperture Radar (SAR) interferometric processing, image coregistration is a fundamental operation that might be extremely time-consuming. This paper explores the possibility of addressing fast and accurate SAR image geometric coregistration, with sub-pixel accuracy and in the presence of a complex 3-D object scene, by exploiting the parallelism offered by shared-memory architectures. An efficient and scalable processor is proposed by designing a parallel algorithm incorporating thread-level parallelism for solving the inherent computationally intensive problem. The adopted functional scheme is first mathematically framed and then investigated in detail in terms of its computational structures. Subsequently, a parallel version of the algorithm is designed, according to a fork-join model, by suitably taking into account the granularity of the decomposition, load-balancing, and different scheduling strategies. The developed parallel algorithm implements parallelism at the thread-level by using OpenMP (Open Multi-Processing) and it is specifically targeted at shared-memory multiprocessors. The parallel performance of the implemented multithreading-based SAR image coregistration prototype processor is experimentally investigated and quantitatively assessed by processing high-resolution X-band COSMO-SkyMed SAR data and using two different multicore architectures. The effectiveness of the developed multithreaded prototype solution in fully benefitting from the computing power offered by multicore processors has successfully been demonstrated via a suitable experimental performance analysis conducted in terms of parallel speedup and efficiency. The demonstrated scalable performance and portability of the developed parallel processor confirm its potential for operational use in the interferometric SAR data processing at large scales.
Adaptive scale weighted fuzzy C-Means clustering for the segmentation of purple soil color image

The segmentation and extraction of the purple soil region from purple soil color image can effectively avoid the influence of background on recognition of soil types. A scale weighted fuzzy c-means clustering algorithm(SWFCM) is proposed for effective segmentation of purple soil color image. The main work is to establish the maximum difference optimization model with the mean of Gaussian distance between each pixel and each peak of the image histogram, and optimize the clustering number and the initial clustering centers. Then, the compactness of each class is defined to weight the Euclidean distance between the pixel and the clustering center and improve the optimization model of FCM for raising its clustering performance. Aiming at the problem of removing scattered small soil blocks in the background and filling holes in the purple soil region, the algorithm of extracting the boundary of the purple soil region and the algorithm of filling the purple soil region are proposed. Finally, the normal and robust experiments are carried out on the normal sample set and robust sample set. And the performances of relative algorithms are compared, which involves the previously released FCM algorithms and some methods for the segmentation of purple soil color image and our proposed algorithm. Experimental results show that performance of SWFCM is better and it can provide a high reference for adaptive segmentation of purple soil color images. Especially for robust experiment images, its average segmentation accuracy is improved by 6 . 64% ∼ 8 . 25 % compared with other purple soil segmentation algorithms.
A method for efficient clustering of spatial data in network space

Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time.
From Remote Sensing to Species Distribution Modelling: An Integrated Workflow to Monitor Spreading Species in Key Grassland Habitats

Remote sensing (RS) has been widely adopted as a tool to investigate several biotic and abiotic factors, directly and indirectly, related to biodiversity conservation. European grasslands are one of the most biodiverse habitats in Europe. Most of these habitats are subject to priority conservation measure, and several human-induced processes threaten them. The broad expansions of few dominant species are usually reported as drivers of biodiversity loss. In this context, using Sentinel-2 (S2) images, we investigate the distribution of one of the most spreading species in the Central Apennine: Brachypodium genuense. We performed a binary Random Forest (RF) classification of B. genuense using RS images and field-sampled presence/absence data. Then, we integrate the occurrences obtained from RS classification into species distribution models to identify the topographic drivers of B. genuense distribution in the study area. Lastly, the impact of B. genuense distribution in the Natura 2000 (N2k) habitats (Annex I of the European Habitat Directive) was assessed by overlay analysis. The RF classification process detected cover of B. genuense with an overall accuracy of 94.79%. The topographic species distribution model shows that the most relevant topographic variables that influence the distribution of B. genuense are slope, elevation, solar radiation, and topographic wet index (TWI) in order of importance. The overlay analysis shows that 74.04% of the B. genuense identified in the study area falls on the semi-natural dry grasslands. The study highlights the RS classification and the topographic species distribution model’s importance as an integrated workflow for mapping a broad-expansion species such as B. genuense. The coupled techniques presented in this work should apply to other plant communities with remotely recognizable characteristics for more effective management of N2k habitats.