esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 The Novel Approach Results and Discussion  
By applying Theorem 1 (see Appendix C), the following outputs are shown in Tables 3 and 4 for the results generated for Network 1. The outputs' tables of the smaller network are presented in this section; however, all outputs for the bigger gas network are shown in Appendix D. The two variables of pressure and flow rate are the main outputs from the three models and used for the benchmarking of the proposed method. As can be observed from Tables 3 and 4, there was a slight deviation between the results generated using the MATLAB and SAINT model for Network 1. Applying initial approximations for the non-linear part (F(X)) of linearized Equation (16) provided the outputs of the novel method of pressure and flow rate, which can be compared with the results from the SAINT and MATLAB models and the novel approach. As an example, the pressures at Node 5 from different solution methods were 68.32, 68.28, and 67.93 barg from SAINT, MATLAB, and the novel approach, respectively. The comparison between flow rates from the numerical methods and the novel method is shown in Table 4. As a result, The explored flow rates of Pipe Number 5 were 16.38, 16.73, and 16.22 sm\({}^{3}\)/s using SAINT, MATLAB, and the novel approach, respectively.  
Furthermore, for the bigger network with 109 nodes, the average deviations of pressure and flow rate were 0.07% and 1.23%, respectively, using MATLAB, and the average deviations between the novel approach and SAINT software for pressure and flow rates were 0.77% and 3.48%. The comparisons are shown in Appendix D, Table A2. Along with accuracy, the time taken for each of the simulations was measured for both modelled networks. The proposed new method was significantly faster, taking 0.31 s, while 1 s was taken for the solution in SAINT and 2.80 for the MATLAB solution of the bigger network. This time is even less for the smaller network, with 0.058 s, shown in the Table 6. This method can be used for much bigger networks at continental scales with fast calculations.
## 3 Case Study: Experimental Verification and Comparison Results
### Experimental Setup Description
Figure 4: Experimental facility setup: (**1**) industrial robot, (**2**) spot-welding servo gun, (**3**) jig, (**4**) electrode, and (**5**) accelerometer. From the experiment, a total of 2160 data were collected. For each of the three specimens, the angle between the welding specimen and ground had two different cases, 0\({}^{\circ}\) or 45\({}^{\circ}\). Welding was performed 180 times for each normal and fault state in each angle case. In our comparison studies, the target domain data were defined as 360 data collected from GA steel with the 0\({}^{\circ}\) welding angle in order to make the target domain as similar as possible. The source domain data were defined as 1440 data collected from two different specimen materials, mild steel and GI steel. In the source domain, unlike with the target domain, all data from two materials were used regardless of the welding angle, because the source domain is not significantly affected by the data identity.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 4 Random Response Analysis
### Random Response Analysis  
By using the finite element software and mode superposition method, the random vibration response of the electric control box was analyzed based on mode analysis.  
The boundary conditions were not changed during the calculation. The applied random load spectrum is shown in Figure 10; the abscissa f represents the frequency (Hz), and the ordinate \(W_{\text{f}}\) represents the acceleration PSD (g\({}^{2}\)/Hz). The loading was performed in the X, Y, and Z directions. The calculated total acceleration root mean square (\(G_{RMSin}\)) was 8.42 g.  
Figure 8: Experimental site and equipment: (**a**) experimental equipment; (**b**) Test Lab software.  
Figure 9: Mode analysis resonance frequency comparison.  
Figure 11 shows the equivalent stress change of the electrical control box at the dangerous point in the X, Y, and Z directions; in the figure, the abscissa is the frequency, and the ordinate is the stress. The second-order mode in the X direction, the fourth-order mode in the Y direction, and the third-order mode in the Z direction exhibited the maximum stress, and the corresponding mode frequencies were \(f_{X}\) = 258.3 Hz, \(f_{Y}\) = 377.7 Hz, and \(f_{Z}\) = 309.8 Hz, respectively.  
During the actual flight, random vibrations may cause the local equivalent stress of the component to be too large and may result in the premature plastic deformation of the component and fatigue failure during service. For the electrical control box, when the vibration frequency is 250-400 Hz, the stress is large; thus, this frequency range should be avoided during its operation.
_Appl. Sci._**2015**, \(5\), 910-925; doi:10.3390/app5040910  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Double Properties of Novel Acylhydrazone Nanomaterials Based on a Conjugated System: Anion Binding Ability and Antibacterial Activity  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 3 and [PERSON] 4**  
* Department of Chemistry, Xinxiang Medical University, Xinxiang 453003, Henan, China
* Department of Immunity, Xinxiang Medical University, Xinxiang 453003, Henan, China; E-Mail: yxylwl(@)126.com
* School of Pharmacy, Xinxiang Medical University, Xinxiang 453003, Henan, China; E-Mails: [EMAIL_ADDRESS] (Y.F.); [EMAIL_ADDRESS] (X.L.)
* Department of Chemistry, Nankai University, Tianjin 300071, China; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-373-302-9128, Fax: +86-373-302-9959.  
Academic Editor: [PERSON]  
_Received: 15 August 2015 / Accepted: 12 October 2015 / Published: 21 October 2015_  
A series of new compounds (**1-12**) containing 1,5-diaza-fluorenone, 1,10-phenanthroline-5,6-dione, ferrocene-1,1'-dione, anthracene-9-carbaldehyde have been synthesized and optimized. The nanomaterials were also developed successfully. The binding properties were evaluated for biologically important anions (F\({}^{-}\), Cl\({}^{-}\), Br\({}^{-}\), I\({}^{-}\), AcO\({}^{-}\), and H\({}_{2}\)PO\({}_{4}\)-) by theoretical investigation, UV-vis, and fluorescence experiments, and compound **6** displayed the strongest binding ability for AcO\({}^{-}\) ion among the synthesized compounds. Theoretical investigation analysis revealed that the intramolecular hydrogen bond existed in the structure of compound **6** and the roles of molecular frontier orbitals in molecular interplay. In addition, compound **6** showed wide antibacterial activity for colon bacillus, typhoid bacillus, and _Pseudomonas aeruginosa_, and inferior activity for hay bacillus and _Staphylococcus aureus_. This series of acylhydrazone nanomaterials showed double properties, anion binding ability, and antibacterial activity.  
binding ability; antibacterial activity; acylhydrazone derivative; nanomaterials; synthesis
## 5 Machine Learning Techniques
### Logistic Regression  
Logistic regression is one of the statistical models that is popular for complex problems. It has a function known as logistic function that is able to model a binary dependent variable [66]. In a mathematical point of view, a binary logistic regression depends on two existing values, i.e., 0 or 1. The label value 1 represents a collection of one or more independent variables. The independent variable exists between 0 and 1, depending on logistic function. In the machine learning prediction problems, usually, sigmoid function is used for evaluation [67]. The mathematical equation is as follows:  
\[logistic(\eta)=\frac{1}{1+exp(-\eta)}. \tag{5}\]  
Based on the probability, the logistic regression between 0 and 1 related to classification. The output is as follows:  
\[P\Big{(}O^{(i)}=1\Big{)}=\frac{1}{1+exp\Big{(}-\Big{(}\beta_{0}+\beta_{1}x_{1 }^{(i)}+\cdots,\beta_{n}x_{n}^{(i)}\Big{)}\Big{)}}, \tag{6}\]  
\[log\Big{(}\frac{P(y=1)}{1-P(y=1)}\Big{)}=log\Big{(}\frac{P(y=1)}{P(y=0)}\Big{)} =\beta_{0}+\beta_{1}x_{1}^{(i)}+\cdots,\beta_{n}x_{n}^{(i)}. \tag{7}\]  
The above equation is called a log odd function.  
If one element, i.e., the \(x_{j}\) attribute, is replaced, then it will effect on the prediction, such as:  
\[\left(\frac{P(y=1)}{1-P(y=1)}\right)=odds=exp\Big{(}\beta_{0}+\beta_{1}x_{1 }^{(i)}+\cdots,\beta_{n}x_{n}^{(i)}\Big{)}. \tag{8}\]  
Now, let us check if one of the feature value is raised to 1.  
\[\frac{odds_{x_{j+1}}}{odds}=\frac{exp\Big{(}\beta_{0}+\beta_{1}x_{1}+\cdots \beta_{j}(x_{j}+1)\cdots,\beta_{n}x_{n}^{(i)}\Big{)}}{exp\Big{(}\beta_{0}+ \beta_{1}x_{1}+\cdots\beta_{j}x_{j}\cdots,\beta_{n}x_{n}^{(i)}\Big{)}}, \tag{9}\]  
\[\frac{exp(a)}{exp(b)}=exp(a-b), \tag{10}\]  
\[\frac{odds_{x_{j+1}}}{odds}=exp(\beta_{j}(x_{j+1})-\beta_{j}x_{j})=exp(\beta _{j}). \tag{11}\]  
Conclusively, we can say that one replacement in the feature can affect the ratio by an \(\exp\) factor\((\beta_{j})\).
# Computer Vision System: Measuring Displacement and Bending Angle of Ionic Polymer-Metal Composites
## 4 Results and Discussion
#### Limitations and Recommendations  
The proposed vision system has several limitations--one of them involving the detection of the IPMC tip. The system detects the IPMC tip by finding the contour point with a maximum y-coordinate. Assuming a rigid, straight IPMC sample initially positioned verti  
Figure 19: IPMC bending angle values obtained from manual calculation and vision system.  
Figure 18: IPMC displacement values obtained from manual calculation and vision system.  
cally, this method will not work once the sample is in a horizontal position, i.e., when the bending angle equals 90\({}^{\circ}\). However, in reality, some samples will have a slight curvature to them. In these instances, the detection method will cease to work at bending angles less than 90\({}^{\circ}\). Furthermore, the proposed system only approximates the linear displacement of IPMCs, which can be quite accurate for small movements, up to about half the length of the IPMC [12]. However, as mentioned earlier, the bending is more accurately described by the arc length between points of the IPMC tip.  
This proposed vision system requires specific conditions for the measurements to be accurate, such as having a good contrast between the sample and the background, having no shadows present and camera configurations with specific settings. These factors need to be adjusted depending on the sample, since colour uniformity between samples might differ. Several pre-processing (crop video, extract frames, feed images to programme) steps are also required to obtain displacement values. Results are not real-time. The system can be more streamlined by automating these processes, cutting down the need for human intervention. An example would be the setup presented in [12].  
Regarding lens distortion, another avenue to minimize the effects is through software correction. OpenCV has an inbuilt function for camera calibration. Through analysing sets of checkerboard images, the intrinsic and extrinsic properties of the camera can be calculated and used to calibrate images accordingly [38].
# Fully Symmetric Convolutional Network for Effective Image Denoising
## 4 Experimental Results
### Comparative Analysis of Algorithm
#### 4.2.3 Running Time  
In addition to visual quality, another important aspect in the image restoration process is the testing speed with respect to image resolution. Figure 12 lists the running times of different algorithms for denoising gray images of sizes 128 \(\times\) 128 and 256 \(\times\) 256 with noise level 0.09. Unlike CSF and DnCNN algorithms, the running time for the proposed model remained consistent irrespective of the image resolution. It is highly efficient such that it can denoise an image of size 256 \(\times\) 256 in just 0.2 s. Therefore, taking denoising performance and flexibility into consideration, the proposed model is competitive for practical applications as it achieves very appealing computational efficiency. The evaluation was performed in MATLAB(R2015b) environment on a computer with a dual-core Intel(R) Xeon(R) CPU E5-2650 LV3 @1.80 GHZ, 32 GB of RAM for BM3D, WNNM, PCLR, and MLP and an NVidia Titan X GPU was used for CSF, DNCNN, and FSCN algorithms. The NVidia cuDNN-v5.1 deep learning library was used to accelerate the computation of the FSCN model. However, different execution environments did not influence the finding that the deep-learning-based approach has the performance advantage for the restoration stage of image denoising.  
Figure 11: Visual results of image denoising with noise level 0.09.
# Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS
###### Abstract  
A current-assisted single-photon avalanche diode (CASPAD) is presented with a large and deep absorption volume combined with a small p-n junction in its middle to perform avalanche trigger detection. The absorption volume has a drift field that serves as a guiding mechanism to the photo-generated minority carriers by directing them toward the avalanche breakdown region of the p-n junction. This drift field is created by a majority current distribution in the thick (highly-resistive) epi-layer that is present because of an applied voltage bias between the p-anode of the avalanching region and the perimeter of the detector. A first CASPAD device fabricated in 350-nm CMOS shows functional operation for NIR (785-nm) photons; absorbed in a volume of \(40\times 40\times 14\)\(\upmu\)m\({}^{3}\). The CASPAD is characterized for its photon-detection probability (PDP), timing jitter, dark-count rate (DCR), and after pulsing.  
single photon detector; avalanche breakdown; current-assistance +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Data Sources and Processing  
Remote sensing image data were acquired from the Google Earth Engine (GEE) platform database, specifically from the Tier 1 Surface Reflectance (SR) products of Landsat5 (TM) and Landsat8 (OLI). These datasets have been processed for geometric, radiometric, and atmospheric corrections, yielding a spatial resolution of 30 m. Through the GEE platform, images for the summer months (June to September) of the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020 were selected. A cloud mask algorithm was employed to eliminate pixels affected by clouds, and the median of the cloud-free pixels was used to generate the composite images with the least cloud cover for the target years. Additionally, a water index was employed to mask the water body information [29].  
Land use data were derived from the 1985-2020 China Land Cover product of Wuhan University [30], which, after reclassification, were used to characterize the degree of human disturbance [31]. DEM data were obtained from the Geospatial Data Cloud [32], and slope information was then extracted using ArcGIS spatial analysis functions. Precipitation data were sourced from the National Earth System Science Data Center [33]. The ecological function area vectors originated from the Resource and Environmental Science Data Center, Chinese Academy of Sciences [34]. The soil type data were sourced from the Harmonized World Soil Database [35].  
Utilizing ArcGIS 10.2 software, the study area was divided into a regular grid. Balancing sampling point density and model computation efficiency, the grid size was set to 1 km by 1 km. Samples were taken from the center of each grid cell for the purpose of calculations and analyses using the geodetector and multiscale geographically weighted regression (MGWR).  
Figure 2: Location of study area.
# Molecular Identification of Keratinase DogkerA from Deinococcus gobiensis for Feather Degradation
###### Abstract  
Keratin is a tough fibrous structural protein that is difficult to digest with pepsin and trypsin because of the presence of a large number of disulfide bonds. Keratin is widely found in agricultural waste. In recent years, especially, the development of the poultry industry has resulted in a large accumulation of feather keratin resources, which seriously pollute the environment. Keratinase can specifically attack disulfide bridges in keratin, converting them from complex to simplified forms. The keratinase thermal stability has drawn attention to various biotechnological industries. It is significant to identify keratinases and improve their thermostability from microorganism in extreme environments. In this study, the keratinases DogKerA was identified in _Deinococcus gobiensis_ I-0 from the Gobi desert. The amino acid sequence analysis revealed that DogKerA was 58.68% identical to the keratinase MtaKerA from _M. thermophila_ WR-220 and 40.94% identical to the classical BliKerA sequence from _B. licheniformis_ PWD-1. In vitro enzyme activity analysis showed that DogKerA exhibited an optimum temperature of 60 \({}^{\circ}\)C, an optimum pH of 7 and a specific enzyme activity of 51147 U/mg. DogKerA can degrade intact feathers at 60 \({}^{\circ}\)C and has good potential for industrial applications. The molecular modification of DogKerA was also carried out using site-directed mutagenesis, in which the mutant A350S enzyme activity was increased by nearly 30%, and the results provide a theoretical basis for the development and optimization of keratinase applications.  
Deinococcus gobiensis; keratinase; thermostability; feather degradation; site-directed mutation +
Footnote †: journal: Medical Informatics
## 3 Morphology Similarity and Coplanarity Measurement
### Coplanarity Measurement  
The coal seam floor is composed of \(k\) surfaces, which are formed by orderly connecting the \(k+1\) floor series, as shown in Figure 3. Clustering based on coplanarity automatically classifies the \(k\) surfaces into a number of clusters. Each cluster center is used to represent a floor zone of the coal seam that is covered by the cluster. In each zone, the gradient in the face direction of each cluster center is take as the extension direction of the cluster centers obtained with clustering based on morphology similarity.  
Each surface sample, composed of two neighboring floor series, F\({}_{i}\) and F\({}_{i+1}\), is defined as  
Figure 4: Schematic of morphology similarity measurement according to PLR of floor series.  
\[\mathbf{M}_{i} =\mathrm{Fs}_{i}\cup\mathrm{Fs}_{i+1}=\left\{(x_{i},y_{i1},z_{i1}), \cdots(x_{i},y_{in},z_{in}),(x_{i+1},y_{(i+1)1},z_{(i+1)1}),\cdots,\ (x_{i+1},y_{(i+1)n},z_{(i+1)n})\right\} \tag{2}\]  
Figure 5 shows the schematic of the coplanarity measure between surfaces \(\mathbf{M}_{i}\) and \(\mathbf{M}_{j}\). A(\(x_{ip}\), \(z_{iv}\)) and B(\(x_{(i+1)v}\), \(z_{(i+1)v}\)), with a same coordinate in the face-advancing direction \(y_{v}\), are the points belonged to \(\mathrm{Fs}_{i}\) and \(\mathrm{Fs}_{i+1}\), respectively. Line AB is the intersecting line of surface \(\mathbf{M}_{i}\) and the X-Z plane at \(y_{v}\). The slope of \(\mathbf{M}_{i}\) at \(y_{v}\) is defined as \(\theta_{v}^{<,i,i+1>}\). Analogously, the slope of \(\mathbf{M}_{j}\) at \(y_{v}\) is defined as \(\theta_{v}^{<,j,i+1>}\). Points C\({}^{\prime}\) and D\({}^{\prime}\) are determined with the slope \(\theta_{v}^{<,i,j+1>}\) and the \(X\)-axis coordinates of points C and D. The collinearity between lines AB and CD, \(col_{v}^{,ij}\), is calculated as follows,  
\[\left\{\begin{array}{l}col_{v}^{,ij}=|\ \varepsilon_{jv}\ |+|\ \varepsilon_{(i+1)v}\ |+| \varepsilon_{jv}-\varepsilon_{jv}|\\ \varepsilon_{jv}=\frac{z_{(i+1)v}-z_{in}}{x_{(i+1)v}-x_{in}}(x_{jv}-x_{iv})\\ \varepsilon_{(i+1)v}=\frac{z_{(i+1)v}-z_{in}}{x_{(i+1)v}-x_{in}}(x_{(j+1)v}-x_ {iv})\end{array}\right. \tag{3}\]  
The coplanarity measurement between surfaces \(\mathbf{M}_{i}\) and \(\mathbf{M}_{j}\), \(Col(\mathbf{M}_{i},\mathbf{M}_{j})\), is defined as the collinearity summation of all sub-samples in the two surfaces,  
\[Cop(\mathbf{M}_{i},\mathbf{M}_{j})=\sum_{v=1}^{n}col_{v}^{,ij} \tag{4}\]  
where \(n\) is the length of the floor series \(\mathrm{Fs}_{i}\).
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 4 Challenges Faced in the Development of AM Metal Mirror
### Porosity
#### 4.1.2 Hot Isostatic Pressing (HIP)  
HIP is an effective method used for densifying metal parts which began in the 1950s. It is mainly used to prepare powder superalloy and improve casting properties as well as metallic structures. The principle is to place a part in a closed chamber and heat. At the same time, inert gas is used as the working medium to exert the same pressure on the part from all directions, which densities it under high temperature and pressure (Figure 33) [86]. Hence, this process is termed isostatic pressure. The high pressure and temperature can eliminate the most pores and cracking in the parts. Because the part is extruded from all directions, HIP has relatively little effect on the part's dimensions. Moreover, it positively impacts residual stress relief, anisotropy reduction, and fatigue behavior improvement [87; 88].  
Figure 33: Schematic diagram of the HIP [23].  
HIP is necessary post-processing for AM mirror blanks. Figure 34 presents a comparison of Songnian Tan's product before and after HIP. The porosity of the mirror blanks reduced from 0.092% to 0.005% [37]. The result of the study showed that in the following HIP treatment, the internal pores were reduced, and the tensile strength was reduced due to the microstructure changes. It further illustrated the feasibility of HIP in post-processing.
# A Multi-Segment Expanded Anchor for Landslide Emergency Management
## 3 Field Tests
### Test Method  
As shown in Figure 3, the test procedure mainly included segmental underreaming, installing the towing wheel, preparing MSEAs, installing MSEAs, conducting initial tensile tests (failure tests for partial MSEAs), grouting, and curing, and conducting secondary tensile tests.  
1. Preparing MSEAs. Figure 4 illustrates the assembly process of a MSEA during the field tests. The bearing blades are depicted in Figure 4a. Each load-bearing body is composed of four bearing blades, each of which has a guide rail at the end. An assembled load-bearing body is shown in Figure 4b. First, the anchor with a P-type anchorage passed through the sliding anchorage, and the guide rails at the ends of the bearing blades were inserted into the sliding groove reserved at the guide head. Then, the sliding anchorage was wrapped and surrounded by the blades. The surfaces of the blades were serrated to increase the frictional drag between the bearing blades and the perforated wall. Figure 4c shows a photo of a MSEA with a length of 23 m, that used two load-bearing bodies to share the pressure. To facilitate the installation of the MSEAs, a traction rope was used to pass through the load-bearing bodies.
2. Initial tensile tests. To determine the initial bearing capacity of the MSEAs without grouting, six MSEAs each with a length of 23 m were tested in the field without grouting.
3. Failure tests without grouting. The failure tests without grouting were conducted for MSEAs Nos. 4-6 to determine their ultimate bearing capacity without grouting. During loading, the two load-bearing bodies of each MSEA were cyclically stressed under the control of oil pressure in order to achieve uniform stress between them. Load-bearing bodies of each MSEA were tensioned successively under an oil pressure of 10 MPa and were then tensioned in sequence under an oil pressure of 12.5 MPa. In this way, a staged cyclic tension was performed.  
Figure 3: Flow chart of the field tests.