esa-sceva/satcom-chunk-collection huggingface dataset:
# Studies of Fracture Damage Caused by the Proppant Embedment Phenomenon in Shale Rock
## 1 Introduction
Another study [27] presents proppant embedment, which occurs in rock formations and can result in rapid decline of hydrocarbon production. These studies highlight the importance of the creep phenomenon (a function of confinement and temperature), of the percentage of clay content, and of the surface roughness in proppant embedment. Other parameters, such as time, temperature, and fracture fluid, can also impact the rate of proppant embedment. Also presented are numerical and analytical models representing proppant embedment [27]. The purpose of the laboratory tests presented in this paper was to determine the quantities characterizing the embedment phenomenon for dry polish shale rock (i.e., the total depth and width of the dents of proppant grains in the fracture face). The obtained values allowed to determine the percentage damage of the surface of the fracture face, the fracture width packed with proppant, and the percentage reduction in the fracture width, under given conditions at high temperature and axial compressive stress. The analyses of the obtained images of the fracture face were done on the basis of the research method of the embedment phenomenon developed at the Oil and Gas Institute-NRI.
## 1 Introduction
[29] predicted the axial load-strain curves and failure loads for two types of spiral strand cables which were in good agreement with experimental data. [PERSON] et al. [30] analyzed the stress distribution of a four-layered spiral strand bent over a curved support. Similarly, [PERSON] and [PERSON] [31] studied the bending behavior of a simple strand with different layer angles and coefficients of friction under zero tension. [PERSON] and [PERSON] [32] investigated the bending stiffness for a preloaded single-layered strand with friction. In order to reduce the computational magnitude inherent to the full 3D FE model, [PERSON] et al. [33; 34] built a concise finite element model for a simple wire strand with 1/6 pitch length for bending and another FE model for a three-layered strand for axial stretching according to the periodic and axisymmetric structure in the axial and circumferential direction, respectively. However, it is still worth noting that full 3D FE models suffer from huge computational costs and are time consuming, especially for spiral strands with long lengths or complex geometry. To overcome these drawbacks, several researchers have proposed some substitution models in which solid elements are replaced by more effective beam elements. [PERSON] and [PERSON] [35] built a mixed solid-beam element model to analyze the mechanical behavior of helically stranded cables under axial and transverse loads. Compared with the solid model, the mixed model possesses high accuracy and significantly low computational cost. For the same reason, [PERSON] et al. [36] established a mixed beam-spring element model to simulate the quasi-static bending process for structural cables on the commercial software platform ANSYS. This model incorporates user defined triaxial springs to simulate the contact extrusion and friction resistance. Similarly, [PERSON] and [PERSON] [37] presented a beam FE model to simulate pure stretching and bending for a simple strand. [PERSON] et al. [38] also developed a beam FE model into which the beam-to-beam contact algorithm and a multi-level Coulomb friction model were incorporated to deal with the interwire contact and friction forces for multi-layered strands under multiaxial loads. Recently, [PERSON] et al. [39; 40; 41] developed a curved beam to improve efficiency and accuracy with fewer numbers of beam elements. Nevertheless, previous studies seldom conduct parametric analysis to quantify the effects of friction on the bending deformation and tension distribution for spiral strands. In addition, the bending mechanism near the terminations is never examined for strands with different lengths. Therefore, this work is mainly intended to figure out the effects of friction on the bending behaviors of a single-layered strand with different lengths and subjected to multi-axial loads. This paper is organized as follows: In Section 2, a beam FE modeling strategy and data fitting methodology were presented in detail. In addition, an experiment was carried out to validate the beam FE model. In Section 3, a parametric study and comparative study were developed to validate the proposed formulas.
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## 4 Image Reconstruction  
Considering a short observation period, the actual orbit of the nanosatellite, and the platform limitations of the downlink, we could not obtain good samples of urban areas with sharp fragment boundaries, forcing us to test reconstruction quality on the forested and plain areas of the Earth. This made the reconstruction especially challenging.  
As was shown in several works, a harmonic diffractive lens (HDL) can be used to create lightweight computer vision systems for nanosatellites, small UAVs [11], and other applications [47]. However, the captured images suffer from a high level of patchwise and imagewise chromatic distortions [48]. Computational image reconstruction can significantly improve the quality of the captured images, enabling their use in remote sensing and monitoring applications [49], as proposed in [8, 10] and studied in [50]. PSNR image quality measure is typically used to evaluate the imaging quality, and computational reconstruction provides more than 10 dB peak signal-to-noise ratio (PSNR) gain in several cases [13, 48].  
Images captured with an HDL can be reconstructed using two different methods. Deconvolution with a nonlinear regularization has been studied for a single refractive lens [7, 50] and also for the HDL [7, 8]. A second approach, deep learning image reconstruction, either combined with the deconvolution [10] or used as an end-to-end pipeline, was presented in [13, 48]. Several convolutional neural network (CNN)-based architectures have been proposed, including a patchwise CNN-based chromatic deblur [49], imagewise reconstruction based on UNET-like or GAN architectures [13, 48], and a two-step reconstruction based on the combination of imagewise and patchwise CNNs [12].  
In [13, 48], it was shown that deep-learning-based reconstruction usually outperforms the deconvolution-based methods but often suffers from reconstruction artifacts [12]. In this work, we evaluate both methods applied to the images acquired in space.  
Figure 5: (**a**) The diffractive lens and 3D-printed camera casing and (**b**) CubeSat frame with the mounted camera.
## 5 Experimental Verification  
### Experiment Equipment and Method  
To further verify the effectiveness of the control strategy, the chassis system model was loaded from MATLAB into the MicroAutoBox real-time system. The embedded ECUs (Electronic Control Units) were implemented on STM32 units in order to perform tests on hard pavement. The control panel is shown in Figure 12(a). The controller mainly included single-chip modules (STM32F103 ZET6, STMicroelectronics, Geneva, Switzerland), stepper motors (YH42 BYGH47-401A, Microstep, Bratislava, Slovakia) and their drivers, bridge circuit modules, and accessory circuits. Figure 12(b) shows the detailed configuration of the FC used for test. A photoelectric encoder (GTS06-OC-RA1000B-2M, pulse: 1000; Kasei Electronics Ltd., Tokyo, Japan) was used to acquire the speeds of in-wheel motors. The off-center arm steering angles were measured using a multi-turn potentiometer (22 HP-10, 0-5 k\(\Omega\); Sakae Company, Tokyo, Japan). Acceleration sensors (WT61C232, Wit Technology Company, Dublin, Ireland) were used to detect the longitudinal and lateral FC acceleration rates. The duration of MMS was calculated by the clock integrated into the data acquisition equipment, which included a data acquisition card (USB7648B, Beijing Zhongtai Research Ltd., Beijing, China) and an industrial personal computer (610H, Advantech Technology Corporation, Beijing, China).  
The FC MMS tests were performed as follows. First, we confirmed that all parts of the FC worked correctly. This included the mechanical connections, control lines, power supply lines, data acquisition system, etc. Second, we activated the data acquisition system and started CM mode switching under distribution control. After MMS was complete, we stopped saving data and restored the FC to its original state. Similar tests were performed using the coupling control method proposed in this study. Next, we used the method described above to conduct IR, DM, and SM MMS tests. Finally, we deactivated the controller, electric wheel power supply, and overall power supply in turn, and put the FC in standby mode. The tests were conducted on hard pavement on the north campus of Northwest A&F University. Images from various MMS tests are shown in Figure 13.  
To further observe the effect of the proposed controller, we also tested the trajectory of the FC centroid. In this test, a soft marker pen fixed on the frame was used to obtain the trajectory of the FC (Figure 14(a)). The test was conducted when the FC moved with fixed posture after MMS was completed at the initial  
Figure 12: Longitudinal and lateral acceleration of four off-center arms during motion mode switching: (**a**) cross motion; (**b**) in-place rotation; (**c**) diagonal motion; (**d**) steering motion.  
position (Figure 15b). The soft marker pen has a slight contact with the ground and the driving resistance of the FC was not affected by this pen. We created a coordinate system on the ground (Figure 15b). Through measuring the trajectory coordinate, the path behavior of the FC can be obtained.
## 4 Experiments and Discussion
### Validation of Proposed Voice Trigger Approach
#### 4.2.2 Results and Discussion
In that case, the total number of parameters to be estimated for five trigger word models and a filtering model was 120,192, comprising 119,808 state parameters (\(8\times 32\times 78\times 6\)) and 384 transition matrix parameters (\(8\times 8\times 6\)). In terms of computational intensity during the decoding phase, only 9984 computations (\(8\times 32\times 39\)) were required, which represents a significant reduction of computational complexity compared to the CNN-based approach.
## 2 Lempel-Ziv Data Compression
### LZ1 Compression  
Given an alphabet \(A\) and a string \(S\) in \(A^{*}\), the LZ1 factorization of \(S\) is \(S=f_{1}f_{2}\cdots f_{i}\cdots f_{k}\) where \(f_{i}\) is the shortest substring which does not occur previously in the prefix \(f_{1}f_{2}\cdots f_{i}\) for \(1\leq i\leq k\). With such factorization, the encoding of each factor leaves one character uncompressed. To avoid this, a different factorization was introduced (LZSS factorization) where \(f_{i}\) is the longest match with a substring occurring in the prefix \(f_{1}f_{2}\cdots f_{i}\) if \(f_{i}\
eq\lambda\), otherwise \(f_{i}\) is the alphabet character next to \(f_{1}f_{2}\cdots f_{i-1}\)[15]. \(f_{i}\) is encoded by the pointer \(q_{i}=(d_{i},\ell_{i})\), where \(d_{i}\) is the displacement back to the copy of the factor and \(\ell_{i}\) is the length of the factor (LZSS compression). If \(d_{i}=0\), \(l_{i}\) is the alphabet character. In other words the dictionary is defined by a window sliding its right end over the input string, that is, it comprises all the substrings of the prefix read so far in the computation. It follows that the dictionary is both _prefix_ and _suffix_ since all the prefixes and suffixes of a dictionary element are dictionary elements. The position of the longest match in the prefix with the current position can be computed in real time by means of a suffix tree data structure [16; 17].
# Effect of Camera Choice on Image-Classification Inference
## 3 Materials and Methods  
### Camera Selection  
The cameras employed in this study are listed in Table 1 with their important properties. We focused exclusively on webcams firstly because they are small and, therefore, easy to position accurately. Secondly, they can be controlled from a common software application (in this study, the Microsoft Windows Camera application), so there is a consistent means of capturing and managing images from the different cameras. There is significant cross-support for certain resolutions among these webcams, which facilitates a valid comparison between them. In addition, we selected webcams from different vendors to increase the diversity of features and implementations. Note that some properties, such as the image sensor size and the aperture f-value, are not included in Table 1 because not all webcam vendors publish these values in their data sheets.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Property** & **Angetube** & **EMEET** & **Logitch** & **Logitch** & **Microsoft** & **Razer Kiyo X** \\  & **962A** & **SmartCam C960** & **C270** & **C920e** & **LifeCam Cinema** & **Razer Kiyo X** \\ \hline Field of View a  & 78\({}^{\circ}\) & 90\({}^{\circ}\) & 55\({}^{\circ}\) & 78\({}^{\circ}\) & 73\({}^{\circ}\) & 82\({}^{\circ}\) \\ \hline Focus Method & Autofocus & Fixed Focus & Fixed Focus & Autofocus & Autofocus & Autofocus \\ \hline Light Correction & Yes & Yes & No & Yes & No & No \\ \hline Number of supported resolutions b  & 13 & 7 & 16 & 13 & 9 & 4 \\ \hline  & \(1920\times 1080\) & \(1920\times 1080\) & & \(1920\times 1080\) & & \(1920\times 1080\) \\  & \(1600\times 896\) & & & \(1600\times 896\) & & \\  & \(1280\times 720\) & \(1280\times 720\) & \(1280\times 720\) & \(1280\times 720\) & \(1280\times 720\) & \(1280\times 720\) \\  & \(1024\times 576\) & \(1024\times 576\) & \(1024\times 576\) & \(1024\times 576\) & & \\  & \(800\times 448\) & & \(800\times 448\) & \(800\times 448\) & \(800\times 448\) & \\  & \(640\times 360\) & \(640\times 360\) & \(640\times 360\) & \(640\times 360\) & \(640\times 360\) & \(640\times 360\) \\  & \(1280\times 960\) & \(1280\times 960\) & & \(1280\times 960\) & & \\ Supported resolutions b  & 1024 \(\times\) 768 & & & & \\ (16:9 resolutions in green, & & & & & \\  & \(800\times 600\) & \(800\times 600\) & \(800\times 600\) & \(800\times 600\) & \\  & \(43\) resolutions in blue, & & \(640\times 480\) & \(640\times 480\) & \(640\times 480\) & \(640\times 480\) & \\ other aspect ratios in black) & \(320\times 240\) & & \(320\times 240\) & \(320\times 240\) & \(320\times 240\) & \\  & \(352\times 288\) & & \(352\times 288\) & \(352\times 288\) & \(352\times 288\) & \\  & \(960\times 544\) & & \(960\times 544\) & & \(960\times 544\) & \\  & \(1184\times 656\) & & & & & \\  & \(848\times 480\) & & & \(864\times 480\) & \(864\times 480\) & \\  & & \(752\times 416\) & & & & \\  & & \(544\times 288\) & & & & \\  & & \(432\times 240\) & \(432\times 240\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cameras and Their Properties (All cameras sourced in Australia).
## 6 Conclusions  
First, this paper models the high-capacity hybrid energy storage system in DC microgrids and describes principles of active disturbance rejection control (ADRC) and improved MPC. Second, droop control is adopted to obtain power distribution between n battery converters, and a DC bus voltage compensator based on ADRC is presented to compensate for DC bus voltage deviations. A compensatory approach to eliminating DC bus voltage deviations introduced by droop control is presented, effectively increasing accuracy and maintaining constant DC bus voltage. Then, the design procedures of ADRC in outer voltage control loops and improved MPC in inner current control loops of n battery converters are proposed, and the ADRC combined with MPC for supercapacitor converter is also presented. Compared with traditional observers, the voltage expansion state observer of the proposed ADRC control is independent of the system model and parameters and consequently has strong disturbance immunity and significantly reduces voltage overshoots during power fluctuations. The MPC-based inner current control loop accelerates current response speed and significantly decreases switching losses.  
Finally, simulations and experiments are conducted, and comparisons among the proposed ADRC combined with improved MPC, dual PI control, and dual ADRC control are accomplished. The results indicate that the derived control strategies have strong disturbance immunity and significantly reduce voltage overshoots during power fluctuations,  
Figure 26: The DC bus voltage, the load power decrease, the battery currents, and the supercapacitor current of HESS when ADRC combined with improved MPC used with voltage compensation. The non-English word “\(\overline{Y}\)\(\overline{Y}\)\(\overline{Y}\)” in the figure means “average”.  
accelerate current response speed, and significantly decrease switching losses. ADRC, combined with improved MPC, could greatly improve the service life of power electronic converters and is extremely applicable for large-capacity hybrid energy storage systems.  
However, there are some limitations to the proposed control strategy. First, when ADRC is used as the voltage outer loop of HESS, the ADRC parameters may be difficult to choose and there are couplings between parameters. Second, the inner loop adopts the improved MPC to reduce the switching loss, more states of power devices are considered, and complicated calculations are introduced.  
Study design, literature search, and writing the manuscript, X.L.; graph production, data analysis, and data processing, J.C.; supervision and data collection, Y.S.; resources and writing\(-\)review and editing, X.S.; translation and literature search, Y.J. All authors have read and agreed to the published version of the manuscript.  
The project is supported by the National Natural Science Foundation of China (52237008) and supported by the Beijing Natural Science Foundation (L243003).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# On-Machine Precision Form Truing and In-Situ Measurement of Resin-Bonded Spherical Diamond Wheel
## 2 System and Methods
### Precision Form Truing of Spherical Diamond Wheel  
Figure 3 shows the diamond roller truing process of the resin-bonded spherical diamond wheel. In the _yoz_-plane of the machine tool coordinate system, the rotary axes of the tool spindle and the truing spindle are perpendicular to each other. During the truing process, the hemispherical diamond wheel and the diamond roller rotate at speed \(\omega_{t}\) and \(\omega_{d}\), respectively. The diamond roller cuts in along the \(x\)-axis direction from the top of the spherical diamond wheel, then feeds along the generatrix of the spherical surface through the \(x\)-, \(y\)-, and \(z\)-axes interpolation motion to achieve the truing process. Finally, the diamond roller cuts out parallel to the tool spindle axis. The truing depth should be set in both \(x\) and \(z\) directions, and the detailed on-machine form truing parameters are shown in Table 2. The diamond roller is a disc-shaped coarse-grained electroplated grinding wheel (type 1A1). As the diamond roller has good wear resistance, the truing process can be approximated as a deterministic material removal process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Details** & **Values** \\ \hline Type & Keyence LK-G5000 \\ Sampling frequency/kHz & 10 \\ Resolution/\(\upmu\)m & 0.1 \\ Measuring range/mm & 6 \\ Working distance/mm & 20 \\ Grinding wheel RPM/rpm & 0 and 600 \\ Scanning speed/mm/min & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Working parameters of the laser scan micrometer.  
Figure 2: Principle of in-situ measurement system.  
According to the diamond roller truing principle shown in Figure 3, the form-truing error is not introduced from tool setting in the \(y\)- and \(z\)-axes directions as the 1A1 type diamond roller is used. However, the alignment error in the \(x\) direction has a great influence on the form-truing accuracy of the spherical diamond wheel. The tool setting in the \(x\) direction involves finding the \(x\)-axis coordinates of the machine tool that make the diamond roller rotation axis and the diamond wheel rotation axis coplanar. The difference between the practical tool position and the ideal tool position \(\Delta x\) is the alignment error in the \(x\) direction, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Details** & **Values** \\ \hline Truing Spindle RPM \(\omega_{d}\)/rpm & 7200 \\ Tool spindle RPM \(\omega_{l}\)/rpm & 900 \\ Depth of truing/imm/pass & 5–20 \\ Feed rate/mm/min & 10 \\ Diameter of grinding wheel/mm & \(\approx\)50 \\ Grain size and concentration & D46 C100 \\ Coolant, Challenge 300-HT & 3 vol\% in water \\ \hline \hline \end{tabular}
\end{table}
Table 2: Form truing parameters.  
Figure 4: Influence of the alignment error on the profile of the spherical diamond wheel: (**a**) diamond roller not to center, (**b**) diamond roller past center.  
Figure 3: Precision form truing of the spherical diamond wheel.
# Dilated Filters for Edge-Detection Algorithms
## 3 Methodology
### First-Order Derivative Orthogonal Gradient Operators  
First-order derivative orthogonal gradient operators are the most basic operators and have been extensively researched over the decades. We consider in our analysis the following edge-detected operators and their extensions: pixel difference operator [32], separated pixel difference operator [32], [PERSON] operator and the extension to a \(5\times 5\) or \(7\times 7\) kernel [33; 34; 35; 36; 37], [PERSON] operator and the extension to a \(5\times 5\) or \(7\times 7\) kernel [34; 36], [PERSON] operator [38] and the \(5\times 5\) kernel expansion [33], [PERSON] and Malin operator [39], [PERSON] operator, Scharr operator and the extensions to \(5\times 5\) kernel [36; 40], [PERSON] operator [41] and Orhei operator [42].  
All the kernel masks for the operators are presented in Figures 1, 2 and 3 from Appendix A. All those operators are orthogonal discrete filters so we represent only one of the kernels. To obtain the other kernels we just need to rotate it by a fraction of \(\frac{\pi}{2}\).  
The gradient is a measure of change in a function, and an image can be considered to be an array of samples of some continuous function of image intensity, typically the two-dimensional equivalent of the first derivative. The magnitude is calculated using Equation (4), where \(f(x,y)\) is the image and \(G_{x}\), \(G_{y}\) are the gradient elements on \(x\) and \(y\) directions. The direction of the gradient is calculated using Equation (5) [23].  
\[G[f(x,y)]=\sqrt{G_{x}^{2}+G_{y}^{2}}\approx|G_{x}|+|G_{y}| \tag{4}\]  
\[\theta=\tan^{-1}\left[\frac{G_{y}}{G_{x}}\right] \tag{5}\]  
The result of this algorithm is an edge map formed by edges that are not topically 1 pixel width, this aspect can deform the result of our evaluation. So for a better evaluation, we choose to thin the resulting edges beforehand using the Guo-Hall algorithm [31]. We add two steps to this algorithm that are commonly used, see [43]: smoothing and thresholding. Smoothing of images is a common practice for enhancing the results as thresholding will eliminate \"weak\" edges that are found. We present the flowchart for these edge detections in Algorithm 2.  
```
1:for edge operator do
2: Convert image to gray-scale image
3: Apply Gaussian filter smoothing
4: Apply convolution with kernel \(\triangleright\) kernel rotated with \(\pi/2\), found in Figure A1 (for Gradient magnitude) \(\triangleright\) kernel rotated by a fraction of \(\pi\), as in Figure A2 (for Compass magnitude) \(\triangleright\) kernel found in Figure A7 (for Frei-Chen)
5: Calculate gradient magnitude using \(\triangleright\) Equation (4) (for Gradient magnitude) \(\triangleright\) Equation (6) (for Compass magnitude) \(\triangleright\) Equation (7) (for Frei-Chen)
6: Apply global threshold algorithm
7: Apply Guo-Hall thinning algorithm [31] to remove the excess edge points
8:endfor
```  
**Algorithm 2** First-Order Derivative Operators steps.