esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Predictive Analysis
### Neural Network Model Prediction  
Firstly, the key indicators' data is normalized by Equation (12). Then, according to Equation (13), the number of hidden layer nodes is determined to be 5-14. The number of hidden layer nodes is simulated sequentially from 5 to 14, and it is found that when the number of hidden layer nodes is set to 11, the absolute percentage error is the smallest. Therefore, this paper sets the number of nodes in the hidden layer of the GM-BP neural network to 11, and the constructed network topology is shown in Figure 2.  
GM (1,1) model and the GM-BP neural network model are used to make predictions. The input training uses the relevant data from 2010 to 2018, and the test uses the relevant data from 2019. The RCA predictive values and related errors of China's computer information service trade from 2010 to 2019 are obtained (see Table 7). As shown in Table 7, the mean absolute percentage error of the GM-BP neural network model is only 0.0553%, and the prediction results in each year are closer to the actual value than the GM (1,1) model. The results show that the model has high fitting accuracy and has good applicability for predicting the international competitiveness of industries.  
The GM-BP neural network prediction model is used to predict the international competitiveness of China's computer information service industry from 2020 to 2024 in combination with the predictive values of key indicators calculated in Table 6. The predictive results are shown in Table 8 and Figure 3. According to the chart, the international  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Actual Value**} & \multicolumn{2}{c}{**GM (1,1) Model**} & \multicolumn{2}{c}{**GM-BP Model**} \\ \cline{3-6}  & & **Predictive** & **Absolute Percentage** & **Predictive** & **Absolute Percentage** \\  & & **Value** & **Error (\%)** & **Value** & **Error (\%)** \\ \hline
2010 & 0.7170 & 0.7170 & 0 & 0.7178 & 0.1303 \\
2011 & 0.8154 & 0.8021 & 1.6297 & 0.8150 & 0.0390 \\
2012 & 0.9440 & 0.8810 & 6.6789 & 0.9437 & 0.0040 \\
2013 & 0.9440 & 0.9675 & 2.4937 & 0.9441 & 0.0178 \\
2014 & 1.0119 & 1.0626 & 5.0144 & 1.0118 & 0 \\
2015 & 1.2230 & 1.1671 & 4.5717 & 1.2231 & 0.0262 \\
2016 & 1.2879 & 1.2818 & 0.4735 & 1.2880 & 0.0093 \\
2017 & 1.4244 & 1.4078 & 13.3122 & 1.2426 & 0.0298 \\
2018 & 1.6639 & 1.5462 & 7.0759 & 1.6643 & 0.0240 \\
2019 & 1.7079 & 1.6981 & 0.5716 & 1.6833 & 0.2723 \\ Mean absolute & & & & & \\ percentage error (\%) & & 4.6468 & & 0.0553 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The predictive results of RCA value of China’s computer information service industry.  
Figure 2: Network topology.  
competitiveness of China's computer information service industry will remain stable in the future, with a slowdown in growth and increasing competitive pressure.
## 1 Introduction
The borehole optical imaging technology can effectively observe and measure the structural characteristics of the rock mass inside the borehole. At the same time, it can also obtain the geometric parameter information such as the occurrence, gap width and spacing of the structural plane. The borehole radar technology can effectively identify the possible abnormal rock mass structures around the borehole, such as karst caves and fractures. The borehole directional acoustic scanning technology can effectively find and measure the structural characteristics of the rock mass near the borehole wall, and realize the three-dimensional visualization of the surrounding rock structure of the borehole. However, these three survey technologies have their own advantages and disadvantages. In order to better understand a key survey area, these three survey technologies are simultaneously applied to common survey boreholes to realize the visualization of the wall of the survey boreholes, the identification of the abnormal structure around the boreholes and the shape carving of the rock mass structure around the boreholes [15; 16; 17]. This paper starts with the fine detection of the borehole wall, including the near-field of borehole-surrounding rock and far-field of borehole-surrounding rock, and deeply excavates the correlation characteristics between multi-source borehole data. Then, is proposed a detection technology for unfavorable geological bodies in the karst development area that integrates the optical imaging technology of borehole, directional acoustic scanning technology of borehole and borehole radar technology. The realization of the joint interpretation and fusion of the three kinds of detection data has laid an important theoretical and methodological foundation for the fine detection technology of unfavorable geological bodies in karst development areas.
## 2 Damage Counting with Acceleration Data Only  
### Pseudo-Damage Counting  
The basic concept of damage counting in an elastic system is a stress-life approach using an S-N curve, and this counting method is valid for an elastic loading condition that is less than the yield stress of the test specimen [22; 23]. A vehicle component in a vibration test is generally exposed to fully reversible (\(R=-1\)) and high cyclic loading sources, both the combustion engine and the road, and the accumulated fatigue is within the elastic region that is suitable for the stress-life approach. If the response stress and response acceleration are given in Equations (1) and (2), respectively, the fatigue damage of a test specimen during \(T_{1}\) can be calculated as shown in Equation (3) [12; 13; 14; 15]:  
\[\sigma(\omega_{1})=\overline{\sigma}\sin(\omega_{1}t+\phi), \tag{1}\]  
\[a(\omega_{1})=\overline{a}\sin(\omega_{1}t+\phi), \tag{2}\]  
\[D(\omega_{1},T_{1})=\frac{\omega_{1}T_{1}}{\frac{\pi\cdot S_{0}^{-\
icefrac{ 1}{{h}}}}{\overline{\sigma}(\omega_{1})^{-\
icefrac{1}{{h}}}}}, \tag{3}\]  
where \(\overline{s}\) and \(\overline{a}\) are the magnitudes of the stress and acceleration, and \(\phi\) and \(\varphi\) are the phases of the stress and acceleration, respectively. In addition, \(b\) and \(S_{0}\) are the fatigue strength exponent and stress range intercepts at the location of interest, respectively. However, the vibration response is widely measured in an acceleration form, and direct information about the fatigue damage over the excitation input is impossible to obtain from the previous formula in Equation (3). Thus, quantification of the severity of the service test can be indexed using another damage concept that is derived directly from acceleration data as shown in Equation (4):  
\[D_{p}(\omega_{1},T_{1})=\frac{\omega_{1}T_{1}}{\pi\cdot\overline{a}(\omega_{ 1})^{\
icefrac{{1}}{{c}}}}, \tag{4}\]  
where \(c\) is a slope related to the fatigue damage. The new damage expression in Equation (4) does not carry any physical meaning because the calculation result does not directly relate to the creation of a crack in the responsible test specimen. Instead, the fatigue value \(D_{p}\) is the test severity index of excitation calculated from the measured acceleration response by assuming that the level of response acceleration is proportional to the fatigue damage of the service system. Therefore, the fatigue damage in Equation (4) is only valid for comparing the severity values of vibration tests, and it is defined as pseudo-damage [19]. The spectral loading can be extended to every possible frequency, and the total pseudo-damage can be derived as the summation of the pseudo-damages from single spectral loadings. The final form of the fatigue summation with duration \(T\) can be expressed as follows:  
\[D_{p}(T)=\sum_{i=1}^{N}\frac{\omega_{i}T}{\pi\cdot\overline{a}(\omega_{i})^{1/ \epsilon}}, \tag{5}\]  
where \(N\) is the discretization number of the input spectrum.
## Nomenclature  
\begin{tabular}{l l l}
**Symbols** & & \\ \(A\) & Channel cross-sectional area & m\({}^{2}\) \\ \(Cp\) & Constant pressure specific heat & J/(kg-K) \\ \(D_{h}\) & Hydraulic diameter & m \\ \(\dot{E}_{x}\) & Exergy expenditure rate & W \\ \(\dot{E}_{nl}\) & Exergy destruction rate & W \\ \(f\) & Friction factor & \\ g & gravity acceleration & m/s\({}^{2}\) \\ \(h\) & Convection heat transfer coefficient & W/(m\({}^{2}\cdot\)K) \\ \(k\) & Thermal conductivity & W/(m-K) \\ \(L\) & Channel length & m \\ \(Nu_{x}\) & Local Nusselt Number & \\ \(p\) & Pressure & Pa \\ \(\dot{q}\) & Heat flux & W/m\({}^{2}\) \\ \(\dot{Q}\) & Heat transfer rate & W \\ \(Re\) & Reynolds number & \\ \(T\) & Temperature & K \\ \(u\) & Fluid velocity & m/s \\ \(V\) & Volume flow rate & m\({}^{3}\)/s \\
**Greek symbols** & & \\ \(\Delta p\) & Pressure drop & Pa \\ \(\eta_{II}\) & Second law thermodynamic efficiency & \\ \(\mu\) & Viscosity & kg/(m-s) \\ \(\rho\) & Density & kg/m\({}^{3}\) \\ \(\phi\) & Volume fraction of the nanoparticles & \\ \(\Phi\) & Second phase nanoparticle volume fraction & \\ \(\psi\) & Exergy flow & J/kg \\
**Subscripts** & & \\ \(bf\) & base fluid & \\ \(f\) & fluid & \\ \(in\) & inlet & \\ m & mixture & \\ \(nf\) & nanofluid & \\ \(np\) & nanoparticles & \\ \(out\) & outlet & \\ \(s\) & solid & \\ \end{tabular}
# Model-Robust Estimation of Multiple-Group Structural Equation Models
###### Abstract
That is, an estimator in an SEM should automatically treat large deviations in \(\mathbf{\mu}-\mathbf{\mu}(\mathbf{\theta})\) and \(\mathbf{\Sigma}-\mathbf{\Sigma}(\mathbf{\theta})\) as outliers that should not (substantially) impact the estimated parameter \(\mathbf{\theta}\). We compare regularized maximum likelihood estimation with robust moment estimation approaches. Robust moment estimation has been scarcely used in SEM. We propose an alternative and more flexible loss function for robust moment estimation and demonstrate that it results in similar statistical performance compared to the computationally much more tedious maximum likelihood estimation. In this article, we discuss model-robust estimation of SEM when modeling means and covariance matrices in the more general case of multiple groups [4; 13]. Model errors in the modeled covariance matrix can emerge to incorrectly specified loadings in the matrix \(\mathbf{\Lambda}\) or unmodeled residual error correlations in the matrix \(\mathbf{\
abla}\). In the following, we only consider the case of unmodeled residual error correlations. Moreover, model errors in the modeled mean vectors are mainly due to incorrectly specified item intercepts \(\mathbf{\
u}\). In the multiple-group SEM, this case is often referred to as a violation of measurement invariance [14; 15; 16]. The investigation of model-robust SEM estimators under such a measurement noninvariance situation is also the focus of this article. For discrete items, measurement noninvariance is also referred to as differential item functioning (DIF; [17; 18]), and several approaches for model-robust estimation have been proposed (e.g., [19; 20; 21]). The remainder of the article is organized as follows: Different model-robust estimation methods are discussed in Section 2. Section 3 presents results from a simulation study with unmodeled residual error correlations. In Section 4, we focus on standard error estimation methods in a selected number of conditions of the simulation study presented in Section 3. Section 5 presents results from a simulation study with unmodeled item intercepts which indicates the presence of violations of measurement invariance (i.e., the presence of DIF). In Section 6, results from an empirical example involving data from the European social survey are offered. Finally, the article closes with a discussion in Section 7.
## 3 Results and Discussion
### Identification of Volatile Compounds in Six Citrus EOs  
The volatile compounds of six citrus EOs were detected by GC-MS and the results are shown in Table 1. A total of 101 volatile compounds were identified in HY EOs and GF EOs, including 14 monoterpenes, 29 sesquiterpenes, 14 aldehydes, 24 alcohols, 6 esters, 3 ketones and 11 others. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**CAS**} & \multirow{2}{*}{**RI 1**} & \multicolumn{3}{c}{**HYEO**} & \multicolumn{3}{c}{**GFEO**} \\ \cline{3-10}  & & & **CP** & **MADH** & **SCC** & **CP** & **MADH** & **SCC** \\ \hline \multicolumn{10}{c}{Monoterpenes} \\ \hline
1 & a-Thujene & 2867-05-2 & 930 & 3.710 \(\pm\) 0.065 & 2.460 \(\pm\) 0.182 & 0.157 \(\pm\) 0.021 & 0.040 \(\pm\) 0.006 & 0.034 \(\pm\) 0.006 & 0.004 \(\pm\) 0.001 \\ \hline
2 & a-Pinene & 80-56-8 & 937 & 14.982 \(\pm\) 0.246 & 10.456 \(\pm\) 0.634 & 0.360 \(\pm\) 0.067 & 8.581 \(\pm\) 0.132 & 7.419 \(\pm\) 0.056 & 0.137 \(\pm\) 0.046 \\ \hline
3 & Camphene & 79-92-5 & 952 & 0.117 \(\pm\) 0.012 & 0.098 \(\pm\) 0.008 & - & 0.913 \(\pm\) 0.035 & 0.042 \(\pm\) 0.001 & - \\ \hline
4 & Subinen & 3387-41-5 & 975 & 2.803 \(\pm\) 0.074 & 2.435 \(\pm\) 0.240 & 0.065 \(\pm\) 0.013 & 3.901 \(\pm\) 0.052 & 4.418 \(\pm\) 0.016 & 0.078 \(\pm\) 0.026 \\ \hline
5 & \(\beta\)-Pinene & 127-91-3 & 979 & 9.555 \(\pm\) 0.254 & 7.227 \(\pm\) 0.734 & 0.158 \(\pm\) 0.028 & 0.357 \(\pm\) 0.005 & 0.387 \(\pm\) 0.009 & 0.010 \(\pm\) 0.004 \\ \hline
6 & \(\beta\)-Myrence & 123-35-3 & 996 & 20.029 \(\pm\) 0.348 & 18.050 \(\pm\) 1.781 & 0.405 \(\pm\) 0.031 & 22.459 \(\pm\) 0.195 & 21.274 \(\pm\) 0.145 & 0.497 \(\pm\) 0.144 \\ \hline
7 & a-Phellandrene & 99-83-2 & 1004 & 0.724 \(\pm\) 0.007 & 0.686 \(\pm\) 0.101 & 0.026 \(\pm\) 0.004 & 1.329 \(\pm\) 0.120 & 0.659 \(\pm\) 0.007 & 0.023 \(\pm\) 0.014 \\ \hline
8 & 3-Carene & 13466-78-9 & 1011 & - & - & 0.001 \(\pm\) 0.000 & - & - & - \\ \hline
9 & a-Terpinene & 99-86-5 & 1017 & 2.182 \(\pm\) 0.032 & 1.712 \(\pm\) 0.153 & 0.137 \(\pm\) 0.020 & - & - & - \\ \hline
10 & \(p\)-Cymene & 99-87-6 & 1025 & 1.685 \(\pm\) 0.157 & 1.583 \(\pm\) 0.137 & 0.504 \(\pm\) 0.078 & - & - & - \\ \hline
11 & \(D\)-Limonene & 5989-27-5 & 1046 & 906.587 \(\pm\) & 871.57 \(\pm\) 83.199 & 24.767 \(\pm\) 3.052 & 953.855 \(\pm\) & 950.59 \(\pm\) 13.92 & 29.158 \(\pm\) 6.430 \\ \hline
12 & (E)-\(\beta\)-ocimene & 3779-61-1 & 1049 & 1.217 \(\pm\) 0.024 & 1.438 \(\pm\) 0.246 & 0.041 \(\pm\) 0.009 & 1.410 \(\pm\) 0.103 & 1.296 \(\pm\) 0.058 & 0.041 \(\pm\) 0.008 \\ \hline
13 & \(\gamma\)-Terpinene & 99-85-4 & 1060 & 89.911 \(\pm\) 2.797 & 70.723 \(\pm\) 6.583 & 2.317 \(\pm\) 0.302 & 0.094 \(\pm\) 0.010 & 0.116 \(\pm\) 0.011 & 0.005 \(\pm\) 0.001 \\ \hline
14 & Terpinolence & 586-62-9 & 1088 & 4.484 \(\pm\) 0.172 & 4.164 \(\pm\) 0.573 & 0.661 \(\pm\) 0.038 & 0.517 \(\pm\) 0.085 & 0.607 \(\pm\) 0.020 & 0.009 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
# Characteristics and Mechanisms of the Zigzag and Spiral Movement of Rising Bubbles in Still Water
## 3 Results and Discussion
### Vertical Movement
#### 3.1.2 Effect of Bubble Deformation  
Figure 5 shows the aspect ratio of the transition point where the bubble changes from vertical to zigzag motion, which is above 2.0. The larger the bubble diameter is, the larger the change range of the aspect ratio of the transition point is. This is caused by the fact that the larger the bubble diameter, the greater the deformation degree. The flow around bubbles is different from that of solid spheres. The liquid film on the bubble surface can slide freely, and the free slip boundary condition will be formed at the junction of the two-phase flow. When the fluid flows around the bubble, it is difficult to produce flow separation at its tail. Through direct vertical simulation of flat bubbles, [PERSON] [5] found that there is a critical value for the instability of the bubble wake, that is, an aspect ratio \(\lambda\) of 2.21. Only when the aspect ratio \(\lambda\) is greater than 2.21 can the vorticity brought about by the nonshear flow make the wake flow unstable, and the larger the aspect ratio \(\lambda\), the wider the range that can cause wake instability. The critical value of the aspect ratio in this experiment is about 2.0, which is close to the simulation result.
## 3 Results and Discussion
### Drinking Water Quality Index  
Table 4 shows the DWQI obtained in the four sampling sites of the Cuernavaca aquifer. DWQI range from 11.2 to 78.2 were obtained from 2012 to 2019, where 70% of the samples showed an excellent groundwater quality, mainly in the P1, P3, and P4 sampling sites, as shown in Figure 10. P1 showed the best water quality, possibly because this sampling site is at the highest elevation and close to a protected green area. In contrast, P2 showed a high variation of groundwater quality because it is in a highly-populated area. Similar DWQI results are reported by [PERSON] et al. (2014), who mentioned that the DWQI ranged from 1.86 to 82.25 for water samples from different sampling sites of an aquifer in India.  
Figure 9: Hierarchical cluster analysis for groundwater quality parameters monitored in the Cuernavaca aquifer from 2012 to 2019.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 5 Conclusions  
We propose an end-to-end splicing location network, which includes the image stream and the illumination stream. The image stream extracts global features such as strong contrast differences and unnatural tampered boundaries. The illumination stream extracts inconsistent color illumination features in the IIC and GE illumination maps. In addition, the MFPN is used to fuse the multi-scale features of dual-stream network and C-RPN is proposed to generate candidate tampered regions with greater retention of location information. Extensive experiments, evaluated on the NIST16 and CASIA datasets, show that our proposed algorithm is superior to some state-of-the-art algorithms, which achieves accurate tampered location at the pixel level, and has good robustness to post-processing operations, such as noise, blur and JPEG recompression. However, the illumination maps are also effective in detecting splicing forgery. In the future, we will try to explore common features for image forensics.  
Conceptualization: Y.Z., X.S., S.L., X.Z. and G.Y.; methodology: Y.Z. and X.S.; software: Y.Z., X.S. and S.L; writing--original draft preparation: Y.Z. and X.S.; writing--review and editing: [PERSON] and [PERSON]; supervision: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Major Program of the National Natural Science Foundation of China (Grant No. 91746207), the National Natural Science Foundation of China (Grant No. 6210071784 and Grant No. 61806071), the Natural Science Foundation of Hebei Province, China (Grant No. F2021202030, Grant No. F2019202381 and Grant No. F2019202464), the Sci-tech Research Projects of Higher Education of Hebei Province, China (Grant No. QN2019207 and Grant No. QN2020185), Key Research and Development Program of Xinjiang Province (Grant No. 2020B03001).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request due to restrictions. The data presented in this study are available on request from the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Examples of visual results on the CASIA and nist16 datasets. From left to right: the first three columns are original image, tampered images and Ground-Truth, the last two columns are the results of Ours (GE) and Ours (IIC).
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 2 Cause Analysis of Abnormal Red Ribbon
### Influence of Unbalance Current Intrusion on Cloke Transformer
#### 2.3.2 DC Magnetic Bias Analysis of Transformer  
As the transformer is an electromagnetic device, the iron core has saturation characteristics [20]. When there is an interference current flowing into the transformer, the magnetic flux will increase on the basis of the original normal operation, and the working point will also move, making the iron core saturated; the waveform will also be distorted; and finally, the secondary side voltage will also be interfered with and reduced [21]. Through the analysis of unbalanced impulse current, it can be seen that there is a DC component in the unbalanced impulse current, which will lead to the generation of magnetic flux with the DC component in the transformer. It can be intuitively seen from Figure 5 that due to the existence of the DC component in the unbalanced impulse current, the magnetic flux curve of the transformer moves upward, which eventually causes  
Figure 4: Equivalent circuit of unbalanced impulse current invading choke transformer.  
the excitation current of the transformer to be distorted, causing interference to the track circuit equipment.
# Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
###### Abstract  
Vehicle-to-everything services are in the implementation phase, and automakers agree that V2X would improve the safety-critical applications already deployed. 3 GPP Release 12 introduces LTE-V for V2V and V2I services. The LTE-V is extended to C-V2X to support V2N. Because of the challenge of high mobility in the V2X system, cutting-edge technologies, such as SDN and small cell in 5G networks, pave the way to the next generation of vehicular networks. SDN is a network technology concept that divides the data and control planes. The OpenFlow protocol is used for communication between the control layer and the network layer in SDN. Different from wireless traditional cellular base stations, small cells are lower-power cell sites that are deployed every few blocks. Small cells can transmit data using mid- and high-band spectrums. Small cell-linked road side unit (RSU) is considered a key enabling technology because it has the capability to create a logical cluster platform residing at the edge of the network, which provides high computation performance. Accordingly, we consider a novel distributed software-defined small cell-linked road side unit vehicular network architecture (diSRsVN). Based on diSRsVN, logical software-defined on-board wireless vehicle, and topology discovery over diSRsVN are presented. The proposed architecture is evaluated under an omnet++ network simulator. The simulation results show the effectiveness of the proposed architecture, which improves the packet delivery ratio and minimizes end-to-end delay.  
5G small cell networks; software-defined network; V2X; topology discovery; 5G small cell; vehicular network Article
# Study on Fire Smoke Distribution Characteristics in Mountain Spiral Highway Tunnels
## 2 Materials and Methods
### Verification of Numerical Simulation Reliability  
We attempt to compare the simulation results with the experimental results in order to ascertain the reliability of the simulation results. [PERSON] et al. [1] conducted six full-scale tunnel fire experiments in a tunnel at a high altitude of 4100 m. The smoke spreading characteristics, maximum temperature, and longitudinal temperature distribution were studied. In this study, at altitude of 4100 m, the ambient pressure was 62.63 kPa, the ambient temperature was 9-11 \({}^{\circ}\)C, and the ambient air density was 0.84 kg/m\({}^{3}\). The longitudinal ventilation velocity of Test 6 varied from 0 to 1 m/s. The fire area was 2.0 m\({}^{2}\), and the fire scale was 1.55 MW. Test 3 in this paper had a longitudinal ventilation velocity varying from 0 to 1 m/s, a fire area of 1.0 m\({}^{2}\), and a fire scale of 0.94 MW. A special test was conducted to compare with the experiment results based on the above environmental conditions. The comparison results shown in Figure 3 showed that the FDS predictions were in good agreement with the experimental results.  
Slight discrepancies were observed near the fire source, with slightly higher temperatures in the experimental results compared to the FDS results. However, these discrepancies become negligible away from the fire source due to complex turbulent and heat transfer phenomena that occur in close proximity to fire source. Additionally, a constant longitudinal wind velocity was set in FDS model, which continuously varied during [PERSON]'s experiment. Overall, the results of comparison proved the reliability of FDS simulation.