esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Light-Path Blockage  
In downlink visible light communication, the optical path blockage will affect the signal transmission quality. This paper uses two parameters, namely the occurrence rate and occupation rate, to model optical path obstruction. The occurrence rate denoted by \(\lambda_{u}\) is defined as the average number of blockages occurring in the optical path per unit of time. To quantify the effect of blockages, it is assumed that they follow the Poisson point process, which is generally used to model unpredictable stochastic events. The occupation rate \(\eta_{u}\) is defined as the proportion of time occupied by the optical path blockage. The range is \(0<\eta_{u}<1\)[23].  
In practice, when obstacles are near the transmitter or receiver, the LOS path is blocked, leading to a significant reduction in signal-to-noise ratio. User equipment (UE) receives minimal signals from LOS but may detect some NLOS signals. However, the total received signal power from the NLOS path is typically less than 20% [24]. In this paper, it is assumed that the received signal strength of UE is zero during the blockage period.
## 5 Experimental Results: Comparison and Analysis
### Column Drift Angle  
To investigate the lateral column displacement difference according to the connector plate shape, we calculated the upper and lower story drift angles (\(\theta_{upper}\), \(\theta_{lower}\)) for the compressive loading case (\(-\)) and tensile loading case (\(+\)), on the basis of the measurement values of the displacement  
Figure 8: Strain distribution of upper and lower beams: (**a**) K-LS; (**b**) K-LC; (**c**) K-SS; (**d**) K-SC.  
gauge installed on the test specimens and the distance between the gauges, as shown in Figure 9. The calculated drift angles are shown in Figure 10.  
Figure 10: _Cont._  
Figure 9: Calculation of drift angles on the basis of column displacement.  
For the compressive loading case, an upward reaction force was generated on the roller end, increasing the deformation of the upper story column. For the tensile loading case, a reaction force in the opposite direction was generated such that the lower story column deformation increased.  
The sum (\(\mathbf{\theta}_{upper}^{t}\)) of the drift angles (\(\mathbf{\theta}_{upper}^{+}\), \(\mathbf{\theta}_{upper}^{-}\) ) due to the \(+\) and - loadings of the upper story column were calculated separately for the K-SS and K-SC specimens, under the assumption that changes in drift angle occur up to 0.04 rad in the loading schedule. In addition, the sums (\(\mathbf{\theta}_{lower}^{t}\)) of the drift angles (\(\mathbf{\theta}_{lower}^{+}\), \(\mathbf{\theta}_{lower}^{-}\)) were calculated according to the loading direction for the lower story column. The sums of the upper story and lower story column drift angles were 0.0968 rad and 0.078 rad, respectively, for the K-SS specimen, and the sums of the upper story and lower story column drift angles were 0.0934 rad and 0.080 rad, respectively, for the K-SC specimen.  
The upper story column drift angle was 0.0034 rad greater for the straight connector plate than for the cross-shaped connector plate, while the lower story column drift angle was 0.002 rad smaller. Ultimately, the story drift angle increased by 0.0014 rad when a straight connector plate was used compared to a cross-shaped connector plate. If the story height is 3000 mm, the story drift increases by approximately 4 mm; the story drift of a 10-story structure thus reaches 40 mm, which can be an important factor for determining whether the usability criteria for lateral loads are satisfied.
## 2 Data Processing and Analysis Procedure
### Data Processing Methods
#### 2.2.2 Correlation Analysis Between Long-Wave Signals and XRA Data
In this study, the relationship between X-ray flux (XRA) and low-frequency signal strength does not always exhibit linear or instantaneous characteristics but often manifests as cumulative trends that reflect the impact of solar flares on ionospheric disturbances. The rank-based methodology of Kendall Tau is well-suited for capturing these trend consistencies, particularly in identifying the alignment of long-term and short-term trends across different lag intervals. This capability offers a supplementary perspective for understanding the trend-based variations in ionospheric dynamics and low-frequency time-code signal propagation caused by solar flares, addressing the limitations of Pearson and Spearman correlation analyses in capturing complex trends. In summary, based on the objectives of the study and the characteristics of the data, we consider [PERSON]'s rank correlation coefficient and [PERSON]'s Tau correlation coefficient to be more appropriate statistical methods. This is due to the potential nonlinearity and irregular distribution of the data, which these methods are better equipped to capture. The Pearson correlation coefficient is employed as a supplementary reference, particularly in cases where the data exhibits a linear relationship.
# A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data
## Appendix A DBL-FS Variational Inference
### Mean Field Approximation of \(\tau\)  
Following the same steps as in the two previous approaches, we use the mean field approximation over variable \(\tau\) to obtain the logarithm of the approximate posterior:  
\[\ln\left(q(\tau)\right)=E[\ln\left(p(\mathbf{y}|\mathbf{X},\mathbf{a},\tau)\right)] +E[\ln\left(p(\tau)\right)]+const. \tag{109}\]  
Therefore, the first term on this equation is:  
\[\ln\left(p(\mathbf{y}|\mathbf{X},\mathbf{a},\tau)\right)=\frac{N}{2}\ln(\tau)-\frac {\tau}{2}\Bigg{(}\sum_{n=1}^{N}y_{n}^{2}-2\text{Tr}\Big{\{}\mathbf{y}^{\text{T}} \mathbf{Ka}\Big{\}}+\text{Tr}\Big{\{}\mathbf{K}^{\text{T}}\mathbf{Kaa}^{\text {T}}\Big{\}}\Bigg{)}+const, \tag{110}\]  
and, applying the expectation we obtain:  
\[\mathbb{E}_{a}[\ln\left(p(\mathbf{y}|\mathbf{X},\mathbf{a},\tau)\right)] =\frac{N}{2}\ln(\tau)\] \[\quad-\frac{\tau}{2}\Bigg{(}\sum_{n=1}^{N}y_{n}^{2}-2\text{Tr} \Big{\{}\mathbf{y}^{\text{T}}\mathbf{K}\langle\mathbf{a}\rangle\Big{\}}+\text{Tr }\Big{\{}\mathbf{K}^{\text{T}}\mathbf{K}\langle\mathbf{a}\mathbf{a}^{\text{T }}\rangle\Big{\}}\Bigg{)}+const. \tag{111}\]  
The second term is defined as:  
\[\mathbb{E}[\ln\left(p(\tau)\right)]=\ln\left(p(\tau)\right)=-b_{0}^{\tau}\tau+ \left(a_{0}^{\tau}-1\right)\ln\left(\tau\right)+const. \tag{112}\]  
Now, if we sum Equations (111) and (112), we obtain:  
\[\ln\left(q(\alpha)\right)=\left(\frac{N}{2}+a_{0}^{\tau}-1\right) \ln\left(\tau\right)\\ -\frac{\tau}{2}\Bigg{(}\sum_{n=1}^{N}y_{n}^{2}-2\text{Tr}\Big{\{} \mathbf{y}^{\text{T}}\mathbf{K}\langle\mathbf{a}\rangle\Big{\}}+\text{Tr}\Big{\{} \mathbf{K}^{\text{T}}\mathbf{K}\langle\mathbf{a}\mathbf{a}^{\text{T}}\rangle \Big{\}}+2b_{0}^{\tau}\Bigg{)}+const. \tag{113}\]  
Therefore, following the same procedure as with the previous variables, we identify terms from the distribution and obtain:  
\[q(\tau)=\Gamma(\tau|a_{\tau},b_{\tau}), \tag{114}\]  
where the first parameter is:  
\[a_{\tau}=\frac{N}{2}+a_{0}^{\tau}, \tag{115}\]  
and the second one is:  
\[b_{\tau}=\frac{1}{2}\big{(}\sum_{n=1}^{N}y_{n}^{2}-2\text{Tr}\Big{\{}\mathbf{y}^{ \text{T}}\mathbf{K}\langle\mathbf{a}\rangle\Big{\}}+\text{Tr}\Big{\{}\mathbf{ K}^{\text{T}}\mathbf{K}\langle\mathbf{a}\mathbf{a}^{\text{T}}\rangle\Big{\}} \big{)}+b_{0}^{\tau}. \tag{116}\]
## 2 Methods
### Numerical Method
\begin{array}{ll}R_{rij}=h_{ij}-h_{c}+\left(z_{TPij}-z_{TPi,k_{j}}\right)- \left(u_{ij}-u_{i,j_{c}}\right)&(a)\\ J_{ijIJ}=\frac{\partial R_{rij}}{\partial h_{ij}}=\delta_{ijIJ}-\left(K^{ij}_{ IJ}-K^{i,c}_{IJ}\right)\frac{\partial P_{Il}}{\partial h_{lj}}&(b)\\ R^{\prime}_{rij}=h_{ij}-l\sin\theta-z_{r}+z_{TPij}-u_{ij}&(c)\\ J^{\prime}_{ijIJ}=\frac{\partial R^{\prime}_{rij}}{\partial h_{lj}}=\delta_{ ijIJ}-K^{ij}_{IJ}\frac{\partial P_{Il}}{\partial h_{lj}}&(d)\\ \left(\delta_{ijIJ}=\left\{\begin{array}{ll}1,&if\,\,i=I\,and\,\,j=J\\ 0,&else\end{array}\right.\right)\end{array}  \tag{20}\]  
where \(i\), \(I=0\):\(M\), and \(j\), \(J=0\):\(N\). \(M\) and \(N\) are the total numbers of mesh points in \(x\) and \(y\) directions. \(i_{\text{c}}\) and \(j_{\text{c}}\) stand for the grid nodes at \(x=0\) and \(y=0\), respectively. \(K^{ij}_{IJ}=K^{ij}_{4 IJ}+K^{ij}_{6 IJ}\), in which \(K_{4}\) and \(K_{6}\) denote the influence coefficients for the elastic deformation due to pressure on the surface of the slider and that of the disk, respectively. (4) Determination of the elastic deformations \(u_{4}\) and \(u_{6}\):  
For the layered structure, the Green function for the elastic deformation in the spatial domain is difficult to be derived; however, its frequency response function in the frequency domain can be obtained in light of the Papkovich-Neuber potentials, continuity condition at the layer/substrate interface and boundary conditions at the contact surface. Details can be referred to in the study of [PERSON] [30]. Discrete forms of the elastic deformations \(u_{4}\) and \(u_{6}\) can be expressed as  
\[u_{4}\Big{(}x_{i},y_{j}\Big{)} =\sum_{I=0}^{M}\sum_{J=0}^{N}K_{4 IJ}^{ij}P_{IJ} \tag{21}\] \[u_{6}\Big{(}x_{i},y_{j}\Big{)} =\sum_{I=0}^{M}\sum_{J=0}^{N}K_{6 IJ}^{ij}P_{IJ} \tag{22}\]  
The influence coefficients \(K_{4}\) and \(K_{6}\) can be derived from the frequency response functions \(\overline{\widetilde{g}_{4}}\) and \(\overline{\widetilde{g}_{6}}\) based on the convolution theorem and the fast Fourier Transform technique [30]. Then, the elastic deformations can be solved according to Equations (21) and (22) through the DC-FFT (Discrete convolution and fast Fourier transform) algorithm [30].
## 2 Applications of RIM
### Environmental Engineering: Discrete Particle-Flow Interaction
[PERSON] and [PERSON] (2018) developed a novel approach for visualizing and quantifying the dynamics of liquid-granular mixtures in open channel flows. The method used a combination of transverse and longitudinal laser scans with PTV to capture the in-plane velocities of both liquid tracers and solid grains in a three-dimensional volume. The study utilized PMMA spheres as the solid phase and para-cymene as the liquid phase to achieve RI matching and enhance optical access. The integrals of the mapped discharge intensities over the channel cross-section were found to be consistent with bulk outlet measurements of liquid and solid discharges, validating the accuracy of the imaging approach. The findings are of great importance for the comprehension of the intricate interactions that occur in granular flows. In conclusion, the section highlights the RIM method's crucial role in visualizing soil contaminant transport, with applications ranging from grout permeation to NAPL distribution. The use of transparent media and matched fluids provides detailed insights and highlights the need for non-toxic, environmentally friendly materials. Future advances in imaging and RIM integration will significantly improve environmental remediation strategies.
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 3 Experiments
### Results and Discussion
#### 3.4.4 Prediction Performance of Typical Stations  
In addition to network scale prediction, we also explored the effect of individual station scale prediction through typical stations. Inspired by Ref. [36], we selected transfer stations, terminal stations, and regular stations as representatives to comprehensively understand the passenger flow characteristics of different types of metro stations. The selected stations include Xianghu Station, Fengqi Road Station, and Qingchun Square Station. Xianghu Station is a terminal station on Metro Line 1. Fengqi Road Station is a transfer station on Metro Line 2. Qingchun Square Station is a regular station on Metro Line 2. Time series comparison charts were generated by integrating the ground truth with predicted results derived from our proposed model, as shown in Figure 18.  
Xianghu Station emerges as a vital link connecting urban centers and suburban regions, facilitating the transit of passengers between these spheres. Its passenger inflow displays a characteristic of traversing through suburban areas, while the passenger outflow exhibits notable morning and evening peaks. Fengqi Road Station is surrounded by many commercial and residential areas. So, it exhibits a pronounced concentrated peak pattern, especially during the morning and evening commuting peak hours. In contrast, Qingchun Square Station presents the characteristic attribute of a large passenger flow. As a significant transportation hub located within a commercial district, this station experiences a pronounced influx of passengers during weekday mornings, which is closely associated with the surrounding urban dynamics. These three stations encompass diverse geographic contexts, showcasing a high degree of representativeness and applicability. In general, the proposed model can achieve accurate predictions at both the network level and station level.
## 4 Determination of Individuality Indexes Weights and Calculation of Comprehensive Score
### The Calculation of Weights for Individuality Indexes  
Since the individuality indexes upon various DSER are different, and the relative importance of each index is difficult to divide artificially, therefore, the entropy weight method is used to evaluate the individuality index.  
Entropy, one of the parameters that characterize the state of matter in thermodynamics, is a measure of the degree of chaos in the system. Entropy method is to use the degree of variation of information entropy to calculate the weight of each index, to evaluate the importance of each index [26].  
Assuming that there are \(m\) regions, \(n\) evaluation indexes, the weights of indexes are determined as follows:  
(1) Calculate \(P_{ij}\), the weight of the index values of each region under various indexes  
\[P_{ij}=\frac{x_{ij}}{\sum_{i=1}^{m}x_{ij}} \tag{8}\]  
where \(i\) represents the \(i\)th region, \(j\) represents the \(j\)th index, and \(x_{ij}\) represents the index value of the \(i\)th region under the \(j\)th index.  
(2) Calculate \(e_{j}\), the entropy of each index  
\[e_{j}=-\frac{1}{\ln(m)}\sum_{i=1}^{m}p_{ij}\ln p_{ij} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(n\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(RI\) & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average random consistency.  
Calculate \(g_{j}\), the difference coefficient of each index  
\[g_{j}=1-e_{j} \tag{10}\]  
Calculate \(W_{j}\), the weight of each index  
\[W_{Bj}=\frac{g_{j}}{\sum_{j=1}^{n}g_{j}} \tag{11}\]
## 1 Introduction
Additionally, 100 PD data images were collected from each of the three test samples. PD data were recorded by gradually reducing the voltage from the PDIV to the PDEV to test the CNN. Ten PD data images with a duration of one minute each were captured to evaluate the performance of the CNN model. Additionally, one PD data image was continuously captured for one hour to represent a saturated condition. The duration of PD data collection and presence of saturated PD patterns affecting the accuracy of defect recognition in epoxy resins were assessed using these different approaches for training and testing the proposed CNN model. A comparison of the CNN performances under these different scenarios provided insights into the optimal data collection duration and conditions for accurate defect recognition. The remainder of this paper is organized as follows. The PD test process, fabrication of test samples, experimental setup, and data collection are presented in Section 2. The analysis of the PRPD patterns under different conditions from the PDIV to the PDEV are presented in Section 3. The CNN algorithm modeling is presented and the training and testing results and their implications are discussed in detail in Section 4. Finally, significant remarks and suggestions for future research are provided in Section 5.
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 7 Discussion and Evaluation
### Performance Analysis  
To analyze the general performance of the SSBI prototype implementation, we implemented different test scenarios and stress testing with recorded customer groups. The complete flow of the payment card issuance and applet installation was performed using the desktop personalization tool at an early step, followed by card initialization, setting the customer PIN, and key generation. During the Create_DID process, the card is presented to perform the signing operation, with the average time to respond being 3.2 s, assuming that the customer presents the physical card to the mobile app to eliminate the human action time of fetching the card. Moreover, to be able to perform real-world stress testing on the system throughput and latency, we isolated the card signing time during the stress testing and prepared a set of pre-signed transactions (10, 25, 50, and up to 300) to push the network.  
The testing environment was as follows:  
* Number of organizations: two banks and one agency;
* Access to Ethereum network: Infura;
* Consensus mechanism: Proof of Authority (PoA);
* Nodes: four nodes (one validator and three peer nodes);
* Customer device: Samsung Galaxy x4 and iPhone 6;
* The system ran on: Intel(R) Core(TM) i7-10850H CPU @ 2.70 GHz;
* OS: Windows 10;
* State database: Firestore;
* Mocha tool for unit testing;
* Caliper benchmarking tool;
* Solidity to write smart contracts.  
To perform the transaction on the Ethereum network, the computation cost is expressed in the term of gas expenses. The gas amount is the parameter of computation complexity. There is a significant cost of the operations that deploy smart contracts such as VC and DID creation. The function of SSBI was validated for general comparison and the evaluation results were gathered for the system throughput and latency, followed by assessment of the complexity and consistency of the Blockchain interaction operations. The test was conducted based on a peer network of four nodes: one node was configured as a validity node to perform PoA consensus and sign the transaction; the remaining controller nodes sent the transactions to the validation node.
## 2 Theory and Modeling  
### Determining Photon-Free Path Length in Inhomogeneous Water  
Detailed MC simulation for light propagating in sea water requires random determination of photon path length, photon scattering angle, and reflection or transmission at sea surface or bottom boundaries [16]. Most existing MC simulation methods assume that sea water is optically homogeneous, while true sea water is sometimes optically vertical inhomogeneous. It should be noted that \"inhomogeneous\" water in this study mainly means that vertical optical properties of the water are inhomogeneous. In order to simulate optically inhomogeneous sea water, inhomogeneous layers are divided into voxels with different optical properties. We allow \(\alpha(\mathrm{z})\) and \(b(\mathrm{z})\) vary with depth z in this study, where \(\alpha(z)\) and \(b(z)\) are sea water absorption and scattering coefficient at the depth of z, respectively. Therefore, a series of photon path lengths at different depths are generated as follows:  
\[s=\frac{-\ln(\Re)}{c(z)} \tag{2}\]  
where \(\Re\) is a series of random numbers, which follow uniform distribution over the interval \([0,1]\), and \(c(z)\) is sea water attenuation coefficient, which is the sum of \(\alpha(z)\) and \(b(z)\). In the case of homogeneous water, the average distance that a photon travels is \(\frac{1}{c}\).
# Residual Stress in Laser Welding of TC4 Titanium Alloy Based on Ultrasonic laser Technology
## 5 Conclusions  
An ultrasonic laser technique was used to measure the residual stress in laser welding of TC4 titanium alloy. A pre-stress loading method was proposed and the acoustoelastic coefficients of TC4 titanium alloys were first obtained. On the basis of known acoustoelastic coefficients, the longitudinal and transverse welding residual stresses were measured with an ultrasonic laser technique. The results showed that the longitudinal residual stress was obviously larger than the transverse stress. The longitudinal residual stress was high tensile stress near the welding seam, and the maximum value of longitudinal residual stress was about 420 MPa, which was half of the yield strength. Then, the effect of the process parameters on the features of the residual stress was discussed. The results showed that heat input is the dominant parameter determining laser welding residual stress. With increasing heat input, the welding zone widened and the peak value of the residual stress increased gradually. Higher welding speeds should be chosen as much as possible when the welding power is constant.  
Figure 11: Comparison of the longitudinal residual stress before and after heat treatment. The maximum of longitudinal residual stress was reduced from 423 MPa to 92 MPa, a decrease of 78%.  
Heat treatment can reduce the maximum of the longitudinal residual stress by about 80% and lead to the redistribution of stress.  
The work presented here was performed in collaboration among all authors. [PERSON] designed, analyzed, and wrote the paper. [PERSON] guided the full text. [PERSON] and [PERSON] provided and analyzed the experimental data. All authors contributed to and approved the manuscript.  
The study is supported by the National Natural Science Foundation of China Project (Grant No. 51771051), Doctoral Research Foundation of Liaoning Province Project (Grant No. 20170520439) and the Fundamental Research Funds for the Central Universities (Grant No. N170504021).  
Authors would like to acknowledge the help of senior engineer [PERSON] of AVIC 601 Institute for providing the welding specimens and related process parameters. Furthermore, the authors would like to thank engineer [PERSON] of Shenyang Aircraft Industrial Corporation for helping us to complete the post-veld heat treatment experiment.  
The authors declare no conflict of interest.