esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Performance of Concrete Dispersedly Reinforced with Sisal Fibers
## 1 Introduction
### Background
Water absorption, chloride ion permeation resistance and impact strength of samples were studied. It has been established that fiber-reinforced concrete with dispersed reinforcement in the form of sisal fibers has improved characteristics compared to conventional concrete, which determines the relevance of using sisal fiber in fiber-reinforced concrete. Studies have shown [34; 35; 36; 37] that the use of sisal fibers as a fiber reinforcement of concrete is a suitable alternative, from the point of view of economy and environmental friendliness, to the materials traditionally used as dispersed reinforcement. When compared with conventional concrete, a significant increase in strength characteristics was noted. The relevance of the use of sisal fiber for use in cement composites of finishing materials and decorative elements of buildings and structures is emphasized. The use of sisal fiber in high-strength concretes [38] did not give a significant increase in strength characteristics but changed the form of fracture from brittle to smooth. Fibrous dispersed reinforcement with sisal fibers can provide the same level of increase in strength characteristics as polypropylene fiber, but only when taking into account the equivalence of dosages of each type of fiber [39]. Sisal fibers have greater water absorption than polypropylene fibers, which can lead to the formation of microcracks and a decrease in strength over the area of contact of the cement slurry with sisal fiber. Moreover, the addition of sisal fibers to concrete reduces the fluidity of the latter due to the additional surface area of the fibers, which takes water from the concrete for wetting [40; 41; 42; 43; 44; 45; 46]. To predict the assessment of the mechanical properties of fiber-reinforced concrete with cellulose fibers, several micromechanical models are used [47; 48; 49]. They are mainly used to predict the effective mechanical properties of fiber-reinforced concrete as a composite material with a well-defined arrangement of fibers. These models can be divided into phenomenological models, which are based on the rule of mixtures: semi-empirical models that use as a basis the mixture rule modified by correcting factors (the Halpin-Tsai model [50] and the Chamis model [51]), the elastic approximation model (the Hashin-Rosen model [52], the [PERSON] model [53]) and homogenization models (Mori-Tanaka model [54], double inclusion model [55], bridge model [56; 57; 58; 59]).
# Genetic Programming to Remove Impulse Noise in Color Images
## 4 Methodology  
The proposed adaptive filter consists of two main stages for color image denoising: detection and correction. The detection stage takes a color image \(I_{N}\) perturbed by impulse noise as input. In this stage, the color image is split into R, G, and B channels to separately evaluate each pixel in a binary classification model. For each channel, the model produces a binary mask representing the set of pixels classified as noisy.  
The correction stage uses a version of a vector median filter applied only to the identified noisy pixels. The correction process of any pixel \(\vec{x}_{i}\) consists of modifying one or more of its RGB channel values according to the pixel intensity values of its neighbors. In this context, the set of neighboring pixels of \(\vec{x}_{i}\), denoted as \(\mathcal{N}(\vec{x}_{i})\), represents a \(3\times 3\) square window centered on \(\vec{x}_{i}\) in the spatial domain of the image. Modifying the pixel value of \(\vec{x}_{i}\) requires computing the sum of the absolute differences of the pixel intensities between \(\vec{x}_{i}\) and its neighborhood. These differences among pixel intensities around \(\vec{x}_{i}\) are denoted as \(d_{i}^{\mathcal{N}(\vec{x}_{i})}\) such that  
\[d_{i}^{\mathcal{N}(\vec{x}_{i})}=\sum_{\forall\vec{x}_{j}\in\mathcal{N}(\vec{ x}_{i})}||\vec{x}_{i}-\vec{x}_{j}||_{1}. \tag{6}\]  
Let \(D(\vec{x}_{i})\) be the set of the pixel intensity differences between every pair of pixels considering \(\vec{x}_{i}\) and its neighborhood, i.e., \(D(\vec{x}_{i})=\{d_{j}^{\mathcal{N}(\vec{x}_{i})}|\forall\vec{x}_{j}\in \mathcal{N}(\vec{x}_{i})\}\). Then, a noisy pixel \(\vec{x}_{i}\) is replaced (in one or more of its channels) with the element with the minimum values from \(D(\vec{x}_{i})\). If only one channel of pixel \(\vec{x}_{i}\) is identified as noisy (and the other two are not), only the perturbed value of this channel is replaced. On the other hand, if at least two channels are noisy in \(\vec{x}_{i}\), then the pixel is replaced entirely. Since building the set \(D(\vec{x}_{i})\) requires \(O(1)\) time given a fixed \(3\times 3\) window, the correction of the noisy pixels takes \(O(N)\) time to complete. After the correction process, a new color image \(I_{F}\) is generated. Figure 1 illustrates the two stages of the adaptive filter.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 4 Results
### Real Patient Experiments  
Several different learning rates and epochs were applied to a real human brain dataset. The best results were obtained with lr = 0.002 and 120 epochs. The training dataset contained seven objects, including 651 image pairs. The testing dataset contained two objects, which included 186 image pairs. Finally, five slices were selected from the test dataset, as shown in Figure 14. The quantitative results, including the SSIM and rRMSE, are shown in Figure 15. Compared to the SD rat whole-body dataset, the human brain dataset obtained better results in terms of reconstructed image quality. The SSIM of the test images was close to 0.94. The mean rRMSE was approximately 2.74. It is considered that the whole-body dataset of the SD rats may have finer and more complex structures than the human brain dataset. However, for a detailed structure, such as the green rectangle in Figure 14, it is difficult to obtain an accurate result. Unlike MLEM and TV, a single object can be reconstructed through a finite number of iterations; cGANs is a deep-learning-based method which achieve reconstruction by first learning the recognition processing of other objects. If there are not enough learning objects, then the leaning result is naturally not satisfied, and if the new object has some structures that the model has never seen before, it is very difficulty to reconstruct the new object accurately.  
Figure 13: Reconstruction results of different slices of SD rats. From top to bottom: sinogram images, ground truth (OSEM), reconstruction results of cGANs.
# A Wireless EEG System for Neurofeedback Training
## 3 System Architecture of the EEG Device
### Transmission Module
#### 3.2.2 Transmitter  
In the current study, a 2.4 GHz nRF24L01 transceiver module for low-power applications (1.9-3.6 V) accomplished wireless communication with a laptop (Figure 1 in [27]). This module was configured and controlled via a serial peripheral interface (SPI). Various operating modes of autonomous protocols are supported via a built-in baseband protocol engine, reducing system costs by managing all high-speed link layer operations. Data flow is provided by internal FIFOs between the MCU and the radio front-end with Gaussian frequency-shift keying (GFSK) modulation (Figures 1 and A1). The nRF24L01 supported a configurable air data rate (up to 2 Mbps), frequency channel (2.4-2.4835 GHz), and output power with a programmable range (0, \(-\)6, \(-\)12, and \(-\)18 dBm) and a wide power supply range (1.9-3.6 V; Figure 1 in [27]). Power-saving modes, combined with high air data rates in the nRF24L01 low-power design, were appropriate for the current study. The packet-based data characteristics (enhanced shock burst) of nRF24L01 enabled high-performance communication with cheap host microcontrollers and achieving very low power [27]. The nRF24L01 stayed in TX active mode during transmitting packets. In an urban environment, the power provided a transmitter-receiver distance of several tens of meters. It was possible to operate several transceiver modules simultaneously without them interfering with each other.
# Generative Image Steganography via Encoding Pose Keypoints
## 3 The Proposed Method
### Generation of Human Pose Keypoint Sequences
#### 3.1.1 Data Form  
In human pose estimation, the coordinates of human keypoints can be abstracted as a discrete point set on the image plane, which depicts the outline of human movement. Specifically, for a human image of width \(w\) and height \(h\), the pose keypoints can be represented as the sequence \(P=\{p_{i}\,|\,i=1,\,2,\,\ldots,\,n\}\) containing \(n\) two-dimensional coordinates. In this sequence, \(p_{i}\) denotes the \(i\)-th keypoint, and its coordinates (x\({}_{i}\), y\({}_{i}\)) take values within the range of (0, 0) to (\(w-1,\,h-1\)).  
A mapping strategy was proposed to transform a two-dimensional image plane into a one-dimensional index table to reduce the complexity of sequence generation models and improve their computational efficiency. Specifically, for a two-dimensional image plane with a width of \(w\) and a height of \(h\), a size of (\(w\times h+1\)) index table was created. Each index value in this table uniquely corresponds to a coordinate point, including the placeholder coordinate (\(-1,\,-1\)). Note that the placeholder element is used to fix the sequence length and indicates that a specific keypoint in the human pose keypoint sequence cannot be detected in the image. Therefore, a mapping function g(x, y) was defined to achieve the mapping from the two-dimensional coordinate (x, y) to the one-dimensional index \(s\). This way, the original coordinate sequence \(P\) was transformed into the index sequence S = {s\({}_{i}\,|\,i=1,\,2,\,\ldots,\,n\}\). The expression of the mapping function is:  
\[\text{g}(x,y)=\left\{\begin{matrix}x+y\times h+1,&x\geq 0\wedge y\geq 0\\ 0,&\text{other}\end{matrix}\right.=s \tag{1}\]  
Specifically, \(x\) and \(y\) are the horizontal and vertical coordinates of the keypoints in the image, while s is the position of the point in the index table. To handle special cases, such as missing or invisible keypoints, placeholder elements are used as filler indexes, which do not participate in the gradient calculation and have an index value of 0.
## 4 Calibration and Analysis of Rotation Axis Position Error  
### In-Machine Measurement of the Center Point of Precision Ball Column  
#### 4.1.1 Multi-Degree of Freedom Step-by-Step Contact Scheme  
The on-machine measurement of the spherical coordinates of precision spherical column was performed in three steps: 1) Measurement of the coordinates of a set of spherical contact points of precision spherical column 1 before and after 90\({}^{\circ}\) rotation of A and C axes, 2) same measurements for precision spherical column contact point of two, and 3) solving the coordinates of the center of sphere based on the coordinates of the two sets of measuring points.  
Steps 1 and 2 involved a precise spherical measurement method, namely a multi-degree of freedom, step-by-step contact scheme. The main advantage of this scheme was that the number of contact points required for complete the detection of the center point of a sphere could be changed according to actual detection situations. To identify the center of standard sphere in this work, step-by-step detection of five points on five degrees of freedom was required. We labeled the center points of the probe as 1-1, 2, 3, 4, and 5 according to the order of detection. The position of detection points on the sphere did not need to be accurate and it was only enough to ensure that they fell within the dotted circle with one third of the diameter of the standard sphere. As shown in Figure 4a, when a = 0\({}^{\circ}\), each time the coordinates of four points on the same cross section on the precision spherical column were measured, in the counterclockwise order of +Y, +X, -Y, -X, then the coordinates of any point on the ball column were measured, which were not on the cross section determined by the other four points. In Figure 4b, the number of measurement points of the same cross section was reduced to adapt to position change, improving measurement efficiency and preventing probe collision. As seen in the Figure 4c, when a = 90\({}^{\circ}\), at least one detection had to be carried out along each direction of X, Y, and Z. Detection was carried out on the X-Y plane, and finally along the direction of the Z axis.  
The calculation method of the coordinates of spherical centers based on the coordinates of five measurement points in step 3 was as follows: Three of the five measurement points were selected for the one-time solution using Equation (27). Then, the mean values of four solutions were calculated to improve measurement accuracy.  
\[\left\{\begin{array}{l}(x_{1}-x)^{2}+(y_{1}-y)^{2}+(z_{1}-z)^{2}=(r+d/2)^{2 }\\ (x_{2}-x)^{2}+(y_{2}-y)^{2}+(z_{2}-z)^{2}=(r+d/2)^{2}\\ (x_{5}-x)^{2}+(y_{5}-y)^{2}+(z_{5}-z)^{2}=(r+d/2)^{2}\end{array}\right. \tag{28}\]  
where \(1-1(x_{1},y_{1},z_{1})\), \(1-2(x_{2},y_{2},z_{2})\), and \(1-5(x_{5},y_{5},z_{5})\) are the coordinates of measurement points, and x, y, and z are the coordinates of the center of the sphere to be determined.  
Figure 4: Detection process of multi-degree of freedom step-by-step contact scheme.
## 3 Performance-Based Analysis of the Target Axial-Flow Fan Unit
### Numerical Analysis of the Target Axial-Flow Fan System  
#### 3.2.1 Numerical Methodology  
To simulate the flow performance of the target axial-flow fan system, three-dimensional incompressible unsteady Reynolds-averaged Navier-Stokes (URANS) equations are used as the governing equations:  
\[\frac{\partial}{\partial x_{j}}(u_{j})=0 \tag{1}\]  
\[\frac{\partial}{\partial t}(u_{i})+\frac{\partial}{\partial x_{j}}(u_{j}u_{i })=-\frac{1}{\rho_{0}}\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}\Bigg{(}v\Bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}\Bigg{)}-\overrightarrow{u_{j}u_{i}^{\prime }}\Bigg{)} \tag{2}\]  
These equations are numerically solved using the finite volume method based on unstructured meshes. The Re-Normalization Group (RNG) k-\(\varepsilon\) model is employed as a turbulence model that has been shown to be well adapted to flows with complex swirling and secondary separation, such as vortex flows and flows driven by axial-flow fans [15]. The standard wall function is used as a wall treatment method, and the first-order upwind scheme is used to discretize the turbulent kinetic energy and dissipation rate equations. The second-order upwind discretization scheme is used to discretize the momentum term, and the SIMPLE scheme is used to solve the pressure-velocity coupling equations. The first-order implicit method is selected for the transient discretization. To simulate the unsteady flows due to the rotating fan blades, the sliding mesh technique is used. All these numerical methods are implemented using the commercial CFD solver of ANSYS Fluent, version 19.1. The Ffowcs Williams and Hawkings (FW-H) equation is used as an acoustic solver in the following form:  
\[4\pi c^{2}H\rho^{\prime}\Big{(}\overset{\rightarrow}{x},t\Big{)}=\frac{ \partial}{\partial t}\int_{S}\frac{\rho_{0}\overset{\rightarrow}{v}\cdot \overset{\rightarrow}{n}}{r|1-M_{r}|}dS\Big{(}\overset{\rightarrow}{\eta} \Big{)}-\frac{\partial}{\partial x_{i}}\int_{S}\frac{p_{ij}n_{j}}{r|1-M_{r}|} dS\Big{(}\overset{\rightarrow}{\eta}\Big{)}+\frac{\partial^{2}}{ \partial x_{i}\partial x_{j}}\int_{V}\frac{T_{ij}}{r|1-M_{r}|}d^{3}\overset{ \rightarrow}{\eta} \tag{3}\]  
The surface and volume integral terms in Equation (3) can be used to predict the density fluctuation. The surface integrals, which are the first and second terms on the right-hand side of Equation (3), correspond to the monopole and dipole acoustic sources, respectively. The volume integral is the third term on the right-hand side of Equation (3), and it represents the contributions from quadrupole sources that are typically negligible for low Mach number flows. For this study, as depicted in Figure 3, the fan blade and housing shroud surfaces are selected as the source integral surfaces for the FW-H equation. These impermeable integral surfaces evaluate the monopole and dipole terms, while the quadrupole terms are disregarded.
###### Abstract  
The automation of the detection infrastructure in GPR imagery is a key issue, particularly in the context of the non-invasive acquisition of radargrams with a multi-antenna ground-penetrating radar. Due to the fact that the dataset acquired with a multi-antenna GPR is very large, in the context of automating the process of detecting hyperbolas, the authors have proposed an adaptive approach to the selection of GPR images. The aim of this project was to develop a method for the selection of GPR images by means of applying the appropriate quality indicators. The authors propose a new, adaptive approach to the selection of radargrams that were recorded during the route of a GPR in a single profile, where several radargrams were recorded. Depending on the obtained initial values of the standard indicators for the assessment of the quality and quality maps of the radargrams, those images from selected channels that will ensure the highest possible quality and efficiency of hyperbola detection were selected. The stage of image quality assessment is essential in the context of improving the effectiveness of the automated detection of underground infrastructure. The quality assessment was performed based on the entropy indicator, PIQE, and Laplacian variance. The selected quality indicators allowed the authors to assess the degree of blurring, noise, and the number of details representing the underground structures that are present in GPR images. An additional product of the quality assessment were the generated maps that present the distribution of entropy in the analyzed images. The image selection was verified based on the results of the parameters that assess the effectiveness of the detection of hyperbolas that represent underground networks. The proposed innovative adaptive approach to the selection of images acquired by GPR enabled a significant improvement in the efficiency of the detection of hyperbolas representing underground utility networks, by 15-40%, shortening data processing and infrastructure detection times.  
ground-penetrating radar (GPR); automatic utilities detection; image quality; entropy; Laplacian variance; PIQE; underground infrastructure +
Footnote †: journal: Applied Sciences  
1
## 2 Experimental Section
### General Procedure for the Synthesis of 5-pyrazol-4,5-dihydropyrazoles Derivatives 3
#### 2.2.10 Compound **3j**  
5-Chloro-4-(1-(4-chlorophenylophenyl)-4,5-dihydro-3-(4-methoxyphenyl)-1_H_-pyrazol-5-yl)-3-methyl-1-phenyl-1_H_-pyrazol. Yellow solid, 80%. mp 128-130 \({}^{\circ}\)C. \({}^{1}\)H NMR (400 MHz CDCl\({}_{3}\) RT) 8 (ppm): 2.13 (CH\({}_{3}\)), 3.19 (m, 1H, CH\({}_{2}\)), 3.78 (m, 1H, CH\({}_{2}\)), 3.85 (s, 3H, OCH\({}_{3}\)), 5.27 (q, 1H, CH), 6.93 (d, 2H, Hm 3-aryl, \(J=8.78\) Hz), 7.00 (d, 2H, Ho \(N\)-aryl \(J=9.03\) Hz), 7.15 (d, 2H, Hm \(N\)-aryl, \(J=9.04\) Hz), 7.39 (t, 1H, Hp, N-Ph, \(J=7.78\) Hz), 7.47 (t, 2H, Hm, N-Ph, \(J=8.03\) Hz), 7.52 (d, 2H, Ho, N-Ph, \(J=7.53\) Hz), 7.68 (d, 2H, Ho 3-aryl, \(J=8.79\) Hz). \({}^{13}\)C NMR 8 (ppm): 13.3 (CH\({}_{3}\)), 41.6 (CH\({}_{2}\)), 55.4 (OCH\({}_{3}\)), 55.7 (CH), 114.8 (Cm, 3-aryl), 128.0 (Co, 3-aryl), 125.7 (Ci, 3-aryl), 161.3 (Cp, 3-aryl), 148.6 (C3 pyrazoline), 118.1 (C4 pyrazole), 124.0 (Co, N1-aryl), 124.6 (Ci, N1-aryl), 114.9 (Cm, N1-aryl), 144.2 (C5 pyrazole), 148.3 (C3 pyrazole), 137.8 (Ci, N-Ph), 125.6 (Co, N-Ph), 128.9 (Cp, N-Ph), 129.6 (Cm, N-Ph). MS (70 eV) \(m\)/z (%) = 480/478 (M\({}^{+2}\)/M\({}^{+}\), 11/71), 477/475 (30/100), 315 (46), 127 (23), 125 (64), 90 (28), 77 (56), 51 (30). FT-IR (KBr, v en cm\({}^{-1}\)), 1597 (C=N, st), 1498 (C=C, st).
###### Abstract  
Most applications of land cover maps that have been derived from satellite data over the Arctic require higher thematic detail than available in current global maps. A range of application studies has been reviewed, including up-scaling of carbon fluxes and pools, permafrost feature mapping and transition monitoring. Early land cover mapping studies were driven by the demand to characterize wildlife habitats. Later, in the 1990s, up-scaling of in situ measurements became central to the discipline of land cover mapping on local to regional scales at several sites across the Arctic. This includes the Kuparuk basin in Alaska, the Usa basin and the Lena Delta in Russia. All of these multi-purpose land cover maps have been derived from Landsat data. High resolution maps (from optical satellite data) serve frequently as input for the characterization of periglacial features and also flux tower footprints in recent studies. The most used map to address circumpolar issues is the CAVM (Circum Arctic Vegetation Map) based on AVHRR (1 km) and has been manually derived. It provides the required thematic detail for many applications, but is confined to areas north of the treeline, and it is limited in spatial detail. A higher spatial resolution circumpolar land cover map with sufficient thematic content would be beneficial for a range of applications. Such a land cover classification should be compatible with existing global maps and applicable for multiple purposes. The thematic content of existing global maps has been assessed by comparison to the CAVM and regional maps. None of the maps provides the required thematic detail. Spatial resolution has been compared to used classes for local to regional applications. The required thematic detail increases with spatial resolution since coarser datasets are usually applied over larger areas covering more relevant landscape units. This is especially of concern when the entire Arctic is addressed. A spatial resolution around 30 m has been shown to be suitable for a range of applications. This implies that the current Landsat-8, as well as Sentinel-2 missions would be adequate as input data. Recent studies have exemplified the value of Synthetic Aperture Radar (SAR) inundra regions. SAR missions may be therefore of added value for large-scale high latitude land cover mapping.  
classification; tundra; land cover; optical; radar 2016 8979