Miracle-dz/UniFAR-FTUs huggingface dataset:
A finite element study of posteroanterior lumbar mobilization on elderly vertebra geometry

The applying posteroanterior (PA) load through the spinous process of the vertebral bone is one of the methods for assessment and treatment for back pain in physical therapy. Central PA mobilization is crucial to be investigated the harmful effect on osteoporotic vertebral bone. Therefore, the objective of our study was to investigate the effect of PA load on elderly L1 lumbar vertebra geometry with different bone properties. The finite element model of L1 vertebra was generated from elderly computed tomography images. A 100 N PA load was applied at posterior aspect of spinous process of L1 vertebra in two different homogeneous material properties; (1) healthy L1, (2) osteoporotic L1. The distribution of von Mises stresses and minimum principal strains in mid-transverse sections of L1 vertebra was determined. The results revealed that PA load caused higher values of stresses and strains at pedicles and laminae in transverse section of L1 vertebra. Moreover, changing the elastic modulus influenced stresses and strains distributions in transverse sections of L1 vertebra. In conclusion, applying PA force through spinous process tends to cause a crack initiation at pedicles and laminae in L1 elderly vertebra.
Light and Pressure Sensors Based on PVDF With Sprayed and Transparent Electrodes for Self-Powered Wireless Sensor Nodes

In this paper, we report on the fabrication of light and pressure sensors based on the piezo- and pyro-material polyvinylidene fluoride (PVDF). In addition, to the operation as sensors, the presented devices are characterized as energy harvesters. To form an electrical connection to the <inline-formula> <tex-math notation="LaTeX">$39\:\mu \text{m}$ </tex-math></inline-formula> thick PVDF foil, solution-based and transparent electrode (TE) materials, such as silver nanowires (AgNWs) and poly(3,4-ethylene dioxythiophene) polystyrene sulfonate (PEDOT:PSS) are utilized and compared with commercial aluminum electrodes on polymer substrate. We show that the performance with regard to sensitivity and generated output power of the TE-PVDF devices outperforms the one for the aluminum foil devices. For the piezo- and pyroelectric effect, a pressure and light sensitivity of 3.6 mV/Pa and 42 Vcm<sup>2</sup>/W, respectively, are measured. The maximum RMS power for the piezo- and pyro effect yield to 1 and <inline-formula> <tex-math notation="LaTeX">$0.42~\mu \text{W}$ </tex-math></inline-formula>, respectively, for an active PVDF area of 8 cm<sup>2</sup>. At the end of this contribution, we show that this power suffices to drive an energy autarkic wireless sensor node that is capable of measuring and transmitting an analog sensor signal using ultra-low power components. This application contributes substantially to the notion of the Internet of Things since paramount aspects, such as wireless technology, embedded electronics, and environmental sensor data together with an ultra-low power management are addressed.
Dual-Polarized and Multi-Beam Cross-Mesh Array Antenna for RF Energy Harvesting Applications

A cross-mesh array antenna is presented for RF power harvesting in this paper. To enhance the reliability of harvester, producing multiple polarizations and multiple beams is an effective method in rectenna designs. Benefited from the ability of tilting beam angle of cross-mesh array antenna, four differently tilted beams with two linear polarizations can be simply generated by four isolated feed points without any beamforming network design. To demonstrate it, a prototype operating around 6 GHz is fabricated and measured.
Annual Cropland Mapping Using Reference Landsat Time Series - A Case Study in Central Asia

Mapping the spatial and temporal dynamics of cropland is an important prerequisite for regular crop condition monitoring, management of land and water resources, or tracing and understanding the environmental impacts of agriculture. Analyzing archives of satellite earth observations is a proven means to accurately identify and map croplands. However, existing maps of the annual cropland extent either have a low spatial resolution (e.g., 250–1000 m from Advanced Very High Resolution Radiometer (AVHRR) to Moderate-resolution Imaging Spectroradiometer (MODIS); and existing high-resolution maps (such as 30 m from Landsat) are not provided frequently (for example, on a regular, annual basis) because of the lack of in situ reference data, irregular timing of the Landsat and Sentinel-2 image time series, the huge amount of data for processing, and the need to have a regionally or globally consistent methodology. Against this backdrop, we propose a reference time-series-based mapping method (RBM), and create binary cropland vs. non-cropland maps using irregular Landsat time series and RBM. As a test case, we created and evaluated annual cropland maps at 30 m in seven distinct agricultural landscapes in Xinjiang, China, and the Aral Sea Basin. The results revealed that RBM could accurately identify cropland annually, with producer’s accuracies (PA) and user’s accuracies (UA) higher than 85% between 2006 and 2016. In addition, cropland maps by RBM were significantly more accurate than the two existing products, namely GlobaLand30 and Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC).
Retrieval of the Absorption Coefficient of L-Band Radiation in Antarctica From SMOS Observations

Microwave emissions at the L-band (1–2 GHz) in Antarctica are characterized by a significant contribution of ice layers at great depth, from hundreds to a thousand meters. Brightness temperatures, thus, could provide the internal temperature of the ice sheet. However, there are two difficulties to overcome in developing an accurate retrieval algorithm. First, it is difficult to know precisely from which depths waves are emanating because the ice-absorption coefficient is uncertain at the L-band, despite several formulations proposed in the literature over the past few decades. Second, emissivity potentially varies in Antarctica due to remnant scattering in firn (or ice), even at the Brewster angle, and despite the low frequency, limiting the accuracy of the estimate of the physical temperature. Here, we present a retrieval method able to disentangle the absorption and emissivity effects from brightness temperature over the whole continent. We exploit the fact that scattering and absorption are controlled by different physical parameters and phenomena that can be considered as statistically independent. This independence provides a constraint to the retrieval method, that is then well-conditioned and solvable. Our results show that (1) the retrieved absorption agrees with the permittivity model proposed by Mätzler et al. (2006), and (2) emissivity shows significant variations, up to 6% over the continent, which are correlated with wind speed and accumulation patterns. A possible cause of this latter point is density heterogeneity and sastrugi buried in the firn. These two results are an important step forward for the accurate retrieval of internal temperature using low-frequency microwave radiometers.
Automated Performance Management for the Big Data Stack

More than 10,000 enterprises worldwide today use the big data stack that is composed of multiple distributed systems. At Unravel, we have worked with a representative sample of these enterprises that covers most industry verticals. This sample also covers the spectrum of choices for deploying the big data stack across on-premises datacenters, private cloud deployments, public cloud deployments
A wideband millimeter-wave bidirectional circularly polarized antenna array using sequential rotation feeding

A single-layer wideband antenna array with bidirectional dual-orthogonally circularly polarized (CP) property at millimeter-wave band is proposed. The CP waves are generated by a perturbation using a shorting via in the SIW cavity. The operating bandwidths are improved by adopting the sequential rotation feeding technique. To validate the design, a prototype operating at 42 GHz was fabricated and measured. The measured 3-dB axial ratio (AR) bandwidths in +z and -z directions can cover a frequency band of 39.5-44 GHz, and the measured peak gains can reach 10.6 dBic at 43 GHz.
Identification and Separation of Multiple Scattering from Complex Cavities

A joint sliding window statistic parameters and adaptive decomposition algorithm is presented to identify and separate multiple scattering from complex cavity. The statistic parameters of the sliding window along the line of sight of radar are used to identify the multiple scattering centers. Adaptive decomposition based on modulated sinc functions is applied to separate the cavity multiple scattering fields. An example using measured data is given to illustrate the feasibility and correctness.
Analysis of Multichannel Signal Transmission using a flexible spectral allocation method in Elastic Optical Networks

A flexible spectral allocation method is proposed and evaluated considering the impact of the number of channels on the required spectral bandwidth. The performance of elastic optical networks (EONs) is examined using a multilevel modulation format and coherent transmission. Network design parameters such as spectral bandwidth and channel symbol error rate (SER) are adopted as metrics for analysis. Transmission of quadrature phase shift-keying (QPSK) signals modulated at 56 and 100 Gbps is simulated and results for B-B, 50 and 200km are discussed.
Image Steganography Based on Edge Detection Algorithm

This article proposes a new steganography technique to embed secret message in grayscale images. It is an adaptive edge scheme that dissimulates the secret data in the edge pixels. The selection of the edge pixels is based on oversegmentation using Modified Simple Linear Iterative Clustering (M-SLIC). The M-SLIC algorithm makes it possible to segment an image into K regions called superpixel. This segmentation is very useful for the detection of objects and the contours between these objects. The number of superpixel K depends on the amount of data to be embedded and the regularity of the cover image. The experiment results have shown that the proposed technique improve the performance of the stego image in terms of capacity, imperceptibility and robustness compared to recent steganographic techniques.
Further Observations on the Shape Synthesis of Closely-Spaced Antennas Using Sub-Structure Characteristic Modes

Earlier work devised a shape synthesis approach for closely-spaced electrically-small antennas operating at different frequencies. The method uses the substructure characteristic mode concept. Definition of the objective function of this shape optimization process in terms of such modes allows the feedpoint to only be specified after shaping is complete. Certain problem issues identified in this work are resolved in the present paper, and an illustrative example of such shape optimized antennas is provided.
DSM-to-LoD2: Spaceborne Stereo Digital Surface Model Refinement

A digital surface model (DSM) provides the geometry and structure of an urban environment with buildings being the most prominent objects in it. Built-up areas change with time due to the rapid expansion of cities. New buildings are being built, existing ones are expanded, and old buildings are torn down. As a result, 3D surface models can increase the understanding and explanation of complex urban scenarios. They are very useful in numerous fields of remote sensing applications, in tasks related to 3D reconstruction and city modeling, planning, visualization, disaster management, navigation, and decision-making, among others. DSMs are typically derived from various acquisition techniques, like photogrammetry, laser scanning, or synthetic aperture radar (SAR). The generation of DSMs from very high resolution optical stereo satellite imagery leads to high resolution DSMs which often suffer from mismatches, missing values, or blunders, resulting in coarse building shape representation. To overcome these problems, we propose a method for 3D surface model generation with refined building shapes to level of detail (LoD) 2 from stereo half-meter resolution satellite DSMs using deep learning techniques. Mainly, we train a conditional generative adversarial network (cGAN) with an objective function based on least square residuals to generate an accurate LoD2-like DSM with enhanced 3D object shapes directly from the noisy stereo DSM input. In addition, to achieve close to LoD2 shapes of buildings, we introduce a new approach to generate an artificial DSM with accurate and realistic building geometries from city geography markup language (CityGML) data, on which we later perform a training of the proposed cGAN architecture. The experimental results demonstrate the strong potential to create large-scale remote sensing elevation models where the buildings exhibit better-quality shapes and roof forms than just using the matching process. Moreover, the developed model is successfully applied to a different city that is unseen during the training to show its generalization capacity.
Numerical Modeling and Analysis of Physical Properties in Biomaterials with Fractal Structure

The synthesized two-dimensional mathematical models of non-isothermal humidity transfer in media with fractal structure taking into account the effects of memory and spatial correlation. Built explicit and implicit difference schemes for equations related heat-mass transfer in two-dimensional domain with boundary conditions of the third kind. These algorithmic aspects for the realization of the obtained difference equations using method predictor-corrector and analyzed the conditions of stability of difference schemes.
Array-Fed Fabry-Perot Cavity Antenna for Two-Dimensional Beam Steering

We propose a simple phased-array design based on a Fabry-Perot cavity antenna for the generation of a highly-directive pencil beam steerable along two directions in 3-D space. Each array element generates an element pattern in the far-field obtained through the excitation of a dominant cylindrical TM leaky wave inside the cavity. Hence, the resulting conical pattern is combined with the array factor of a $N \times N$ square or circular arrangement of ideal vertical electric dipoles. By proper phasing such sources at the operating frequency, a pencil beam with continuous scanning both in azimuth and elevation is achieved. The proposed leaky-wave phased array is of interest for future wireless power transfer systems as well as for advanced radar and localization systems.
Characterisation of Functional-Trait Dynamics at High Spatial Resolution in a Mediterranean Forest from Sentinel-2 and Ground-Truth Data

The characterisation of functional-trait dynamics of vegetation from remotely sensed data complements the structural characterisation of ecosystems. In this study we characterised for the first time the spatial heterogeneity of the intra-annual dynamics of the fraction of absorbed photosynthetically active radiation (FAPAR) as a functional trait of the vegetation in Prades Mediterranean forest in Catalonia, Spain. FAPAR was derived from the Multispectral Instrument (MSI) on the Sentinel-2 satellite and validated by comparison with the ground measurements acquired in June 2017 at the annual peak of vegetation activity. The validation results showed that most of points were distributed along the 1:1 line, with no bias nor scattering: R2 = 0.93, p < 0.05; with a root mean square error of 0.03 FAPAR (4.3%). We classified the study area into nine vegetation groups with different dynamics of FAPAR using a methodology that is objective and repeatable over time. This functional classification based on the annual magnitude (FAPAR-M) and the seasonality (FAPAR-CV) from the data on one year (2016–2017) complements structural classifications. The internal heterogeneity of the FAPAR dynamics in each land-cover type is attributed to the environmental and to the specific species composition variability. A spatial autoregressive (SAR) model for the main type of land cover, evergreen holm oak forest (Quercus ilex), indicated that topographic aspect, slope, height, and the topographic aspect x slope interaction accounted for most of the spatial heterogeneity of the functional trait FAPAR-M, thus improving our understanding of the explanatory factors of the annual absorption of photosynthetically active radiation by the vegetation canopy for this ecosystem.
Statistical and Hydrological Evaluations of Multi-Satellite Precipitation Products over Fujiang River Basin in Humid Southeast China

The purpose of the paper is to evaluate the quality and hydrological utility of four popular satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product (3B42V7), near real-time product (3B42RT), and the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD) and bias-corrected product (CMORPH CRT) over Fujiang River basin, China. First, we provided a statistical assessment of the four precipitation products at multiple spatiotemporal scales. The results show that: (1) all the products except 3B42RT capture the spatial pattern of annual precipitation fairly well; (2) in general, CMORPH BLD benefits from the application of the probability density function-optimal interpolation (PDF-OI) gauge adjustment algorithm and performs best among all the products with Pearson correlation coefficients (CC) of 0.84 and 0.94, equitable threat score (ETS) of 0.56 and 0.63 in grid and basin scales, respectively, followed by 3B42V7 and CMORPH CRT; whereas 3B42RT performs worst across all the metrics; (3) according to the occurrence frequencies of rainfall, satellite estimates mainly fall into the bin of 0–1 mm/day and tend to underestimate light precipitation. In addition, the performance of all the products in warm season is much better than in cold season in both grid and basin scales. Subsequently, a physically based distributed model is established to further evaluate the hydrological utility of different precipitation products. The results reveal that: (1) the errors in precipitation products mainly propagate into hydrological simulations, resulting in the best hydrological performance in CMORPH BLD in both daily and monthly scales after recalibrating the model, while 3B42RT shows limited skills in reproducing the daily observed hydrograph; (2) after recalibrating the model with the respective satellite data, significant improvements are observed for all the products; (3) CMORPH BLD no longer shows its superiority during near-real-time monitoring of floods. There is still a great challenge for the application of current satellite-based estimates into local flood monitoring. This study could be used as guidance for choosing alternative satellite precipitation products for hydrological applications in a local community, particularly in basins in which rainfall gauges are scarce.
Physical Layer Security based on Time Reversal Technique for Urban Radio Channels

This work presents experimental characterization of feasibility of physical layer security based on time reversal technique for radio channels. The investigation is for outdoor urban microcellular radio channels for both scenarios of fixed and mobile receivers. The investigation is at frequency range of 2.4 GHz and bandwidth of measurement system is 100 MHz. A metric based on difference between strongest and 2nd strongest peak on power delay profile is proposed and presented as performance indicator.
A Region Merging Segmentation with Local Scale Parameters: Applications to Spectral and Elevation Data

Region merging is the most effective method for the segmentation of remote sensing data. The quality and the size of the resulted image objects is controlled by a global heterogeneity threshold, termed as the scale parameter. However, the multidimensional nature of the visible features in a scene defies the use of an even optimum single global scale parameter. In this study, a novel region merging segmentation method is proposed, where a local scale parameter is defined for each image object by its internal and external heterogeneity measures (i.e., local variance and Moran’s I). This method allows image objects with low internal and external heterogeneity to be further merged with higher scale parameter values, since they are more likely to be a part of an adjacent object, than objects with high internal and external heterogeneity. The proposed method was applied in spectral and elevation data and its results were evaluated visually and with supervised and unsupervised evaluation methods. The comparison with multi-resolution segmentation (MRS) showed that the proposed region merging method can produce improved segmentation results in terms of maximizing intra-object homogeneity and inter-object heterogeneity as well as in the delimitation of specific target objects, present in spectral and elevation data. The unsupervised evaluation results of the (1) Côte d’Azur, (2) Manchester, and (3) Szada images from the SZTAKI-INRIA building detection dataset showed that the proposed method (overall goodness, OGf (1): 0.7375, (2): 0.7923, (3): 0.7967) performs better than MRS (OGf (1): 0.7224, (2): 0.7648, (3): 0.7823). The higher values of OGf indicate their ability to produce segmentation results with reduced over-segmentation effects and without the need of presegmented input data, in contrast to the objective heterogeneity and relative homogeneity (OHRH) hybrid segmentation method (OGf (1): 0.5864, (2): 0.5151, (3): 0.6983).
Automated Open Cotton Boll Detection for Yield Estimation Using Unmanned Aircraft Vehicle (UAV) Data

Unmanned aerial vehicle (UAV) images have great potential for various agricultural applications. In particular, UAV systems facilitate timely and precise data collection in agriculture fields at high spatial and temporal resolutions. In this study, we propose an automatic open cotton boll detection algorithm using ultra-fine spatial resolution UAV images. Seed points for a region growing algorithm were generated hierarchically with a random base for computation efficiency. Cotton boll candidates were determined based on the spatial features of each region growing segment. Spectral threshold values that automatically separate cotton bolls from other non-target objects were derived based on input images for adaptive application. Finally, a binary cotton boll classification was performed using the derived threshold values and other morphological filters to reduce noise from the results. The open cotton boll classification results were validated using reference data and the results showed an accuracy higher than 88% in various evaluation measures. Moreover, the UAV-extracted cotton boll area and actual crop yield had a strong positive correlation (0.8). The proposed method leverages UAV characteristics such as high spatial resolution and accessibility by applying automatic and unsupervised procedures using images from a single date. Additionally, this study verified the extraction of target regions of interest from UAV images for direct yield estimation. Cotton yield estimation models had R2 values between 0.63 and 0.65 and RMSE values between 0.47 kg and 0.66 kg per plot grid.