esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Implementation of GPU-MOC Method
### Characteristics of a GPU  
A distinguishing feature of a GPU is that the greater number of threads that it includes, the higher efficiency that it can achieve. The reasons may be ascribed to the structure and instruction load of Compute Unified Device Architecture (CUDA). Thread switching is a zero-cost fine-grained execution on a GPU. That is, when a warp (the basic execution unit) in one block has to wait for access to off-chip memory or synchronizing instructions, a warp in another block in a ready state instead will execute the calculation immediately. In this way, computational latency is efficiently hidden and a high throughput is achieved.
## 4 Results
### Qualities of the Synthetic Data  
We checked the quality of the synthetic data generated using three indices: MSE, CC, and SMV. A detailed explanation of these indices is provided in the Methods section. The results of these indices are shown in Figure 7 and Supplementary Table S4. In all the deep generative models, all data quality indices improved with an increase in the number of epochs. Although the TVAE outperformed both GAN-based synthesizers (CTGAN and CopulaGAN) in overall quality indices, the data generated by the TVAE had no variance until 30 epochs owing to the generation of the same numbers. Accordingly, the CCs could not be calculated from 5 to 30 epochs in the TVAE. In particular, the CopulaGAN  
Figure 6: Data merge schema. Abbreviation: Resp represents the respondent. The black dot lines represent different numbers; the black straight lines represent the same numbers. The purple straight lines represent the EEG signal data. The red, blue, and green straight lines represent respondents 1, 2, and 10’s answered data, respectively.  
outperformed CTGAN in terms of CCs during early epochs and yielded better SMV performances than CTGAN during late epochs.
# Incorporating Transverse Normal Strain in the Homogenization of Corrugated Cardboards
## 4 Validation
### Homogeneous Plate  
In the case of a bi-sinusoidal load, \(q=q_{0}\text{sin}(\tau x/a)\text{sin}(\tau y/b)\) applied to a rectangular plate with four simply supported edges is examined. Parameters are set as: \(a=b=100\) mm, span-to-thickness ratio \(a/h=10\), \(q_{0}=1\) N. The material of the plate is isotropic, \(E=2\times 10^{5}\) MPa, \(\
u=0.3\).  
Simulation results from different elements, including solid element C3D8R, continuum shell element SC8R, conventional shell S8R, and the proposed element are compared in Figures 7-9. The continuum shell element is a brick element analogous to a solid element in modeling but theoretically based on shell theory [16]. A continuum shell can be discretized in a single or stacked layers. There are 10 layers of element, solid element, and continuum shell stacked in the thickness direction to ensure accuracy, which can be a benchmark for the proposed element.  
Figure 7 shows the comparison of maximum deflections. The maximum deflection of this problem occurs at the center of the plate. The result from the proposed element is almost identical to the result from S8R, which is reasonable because the proposed element is developed from FSDT. Results from these two elements are slightly smaller (about 2%) than those from C3D8R and SC8R, which might be caused by the linear simplification of transverse shear in S8R and the proposed element.  
Figure 8 shows the distribution of the transverse normal strain \(\varepsilon_{z}\) along the thickness of various elements at \(x=75\) mm, \(y=75\) mm. The \(\varepsilon_{z}\) of the proposed element is linearly distributed, which gives better characterization than the conventional shell S8R (zero) and the single-layer continuum shell SCS R (constant distribution). Compared with the stacked SC8R and the solid C3D8R, the \(\varepsilon_{z}\) distribution of the proposed element aligns closely, except near the top and bottom surface, demonstrating sufficient accuracy for the linear characterization of transverse normal strain.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Node ID** & **x-Coordinate** & **y-Coordinate** & **u** & **v** \\ \hline  
\hline \end{tabular}
\end{table}
Table 2: Nodal displacements of patch test.  
Figure 9 shows the distribution of the transverse normal stress \(\sigma_{z}\). It can be observed that \(\sigma_{z}\) exhibits a linear distribution, as the strain does. However, it does not fully match the results from the C3D8R and stacked SC8R elements as \(\sigma_{z}\) follows a cubic distribution [32]. While increasing the order of \(z\) in the displacement field could potentially resolve this discrepancy, it would also increase the model's complexity. The proposed element provides a balanced solution with its linear distribution. Notice that the stress values at the middle surface (\(z=0\)) and the top and bottom surfaces (z = \(\pm 5\)) show good agreement, verifying the correctness of the proposed element.  
Figure 8: The distribution of transverse normal strain.  
Figure 7: Comparison of maximum deflection.
## 3 Introduced Concepts and Methodologies
### Strategies for Using Only Single Genuine Reference Signature  
As mentioned in Section 2.1, handwriting signatures have high intra-class variability. This key factor presents a particularly difficult condition for signature verification with single reference signature. Some studies can overcome this disadvantage by adding additional signature samples for training or by other feather extraction methods [1; 2; 3; 4]. In this paper, we deal with the database with a constrained sample size. Therefore, we propose the following two main strategies to solve our problem of insufficient samples:  
1. We assume that there are a lot of useful local features (such as the features of strokes lines, starting, turning, and ending points) generally scattered across the whole signature image. Based on this condition, we convert a single signature image into many overlapping sub-image blocks. Since our method has the capability of extracting local features, these sub-image blocks can be used as training samples. In addition, we also apply some data preprocessing techniques which are commonly used for deep learning and image processing tasks (such as sample screening and data augmentation). In this way, the number of training samples can be expanded. Further details will be explained in Section 3.5.
2. Since we are dealing with a binary classification problem, we can define what is a genuine signature when our trained system can successfully detect the forged signatures. That is, we let the network learn more features from forged signatures.  
Fortunately, samples of forged signatures are relatively easy to obtain and basically unlimited in number (because it can be generated by artificial imitation or computer algorithms). In a later section we will use experimental results to show that our trained network is good at learning some common forgery features which exist in different signatures and authors. As more forged samples are added, the accuracy of the network will be higher.
## 4 Results
### Result and Analysis
For delignifying Chinese, although there are a lot of changes in word order and synonym replacements, they did not have a significant impact on the calculation of BLEU. The Transformer model achieved good ratings in the manual evaluation score indicator. De-colloquialization is a relatively subjective task, and each individual may have different perceptions of whether a sentence is colloquial or not, resulting in varying scores. The de-colloquialization effects of different types of colloquial sentences in the Transformer model are shown in Tables 7-11. In order to facilitate readers' comprehension, an English translation of the Chinese text has been provided. It is important to note that due to the differences between English and Chinese in terms of colloquial expressions, the translation of colloquial expressions may not be entirely accurate in order to convey the original meaning of the Chinese text. The English translation is offered solely for the purpose of assisting readers and should be used as a reference only. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Data** & **Loss** & \
\begin{tabular}{c} **BLEU** \\ **(Word)** \\ \end{tabular} &
\begin{tabular}{c} **BLEU** \\ **(Character)** \\ \end{tabular} & **Manual** & **Time/h** \\ \hline Initial BLEU & 4.9 W & / & 24.9 & 34.1 & / & / \\ Seq2 Seq & 500 W & 5.88954 & / & / & / & 70.21 \\ Seq2 Seq+attention & 100 W & 5.15372 & / & / & 49.11 \\ Seq2 Seq+attention & 500 W & 5.77583 & / & / & 209.3 \\ Transformer(base) & 500 W & 1.62311 & 26.2 & 35.3 & 6.8 & 30.3 \\ Transformer & 500 W & 1.49185 & 27.3 & 36.4 & 7.36 & 36.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of experimental results. This example highlights the model's ability to de-colloquialize in terms of grammar. In the original sentence, the conjunction \"\(\mathcal{K}\)(\" lacks a fixed collocation, and the phrase \" is unclear and lacks coherence. After applying the de-colloquialization model, the model completely restructures and summarizes the content, resulting in \", which means \"the existence theorem of two fixed points of distances\". The model removes the conjunction \"\(\mathcal{K}\)(\" and uses \" to indicate a progressive relationship.
# A 3D Numerical Model to Estimate Lightning Types for PyroCb Thundercloud
## 3 Simulation Results and Discussion
### Influence of Aerosol Concentrations on PyroCb Electrification
A parametric study has been conducted to examine the effect of increased aerosol concentration on electrical conditions under the assumption of a positive linear relationship between charge density and aerosol concentration in pyroCb. The research indicates that the frequency of both +CG and \(-\)CG lightning and their striking zones are significantly affected by the variations in charge densities resulting from different aerosol concentrations within pyroCb thunderclouds. This compact thundercloud model and its quick computational capabilities make it highly valuable for providing early warnings and protection, particularly in the context of pyroCb lightning events caused by wildfires. The proposed work has a high potential to provide a comprehensive explanation of the emergence of different cloud charge structures in pyroCb considering the charging current within the thundercloud and corona near the ground surface.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 2 Trajectory Planning Based on MINCO Trajectory Classes
### Trajectory Planning Based on MINCO Trajectories and Safe Corridors  
Based on the MINCO trajectory class, the optimal control trajectory of a mobile robot can be obtained in the unconstrained case with given boundary conditions and intermediate conditions. It is necessary to further apply the MINCO trajectory and its spatio-temporal deformation under the constraints of the underground coal mine environment to obtain the optimal motion trajectory suitable for the robot with the optimal control cost, and to propose a trajectory-planning algorithm based on the MINCO trajectory and the safety corridor.
# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 4 Experimental Results and Analysis
### Analysis of Results  
#### 4.4.1 Objective Evaluation  
To comprehensively and objectively compare the fusion performance of the AFFusion network, each fusion method was tested on two benchmark test collections. The results are shown in Tables 5 and 6, whereas Figures 5 and 6 illustrate the performance of each fusion method on each of the evaluation metrics.  
The AFFusion model achieved 10 optimal values and 2 suboptimal values for each evaluation metric in both databases, while U2 Fusion and SwinFusion methods attained  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **En** & **SD** & **MI** & **MS-SSIM** & **VIF** & **NIQMC** \\ \hline w/o-AFSF & 6.8926 & 81.0629 & 13.7852 & **0.9320** & 0.7992 & 4.5464 \\ w/o-shuffle & 6.8945 & 83.8112 & 13.7890 & 0.9248 & 0.7847 & 4.5976 \\ AFFusion & **7.1589** & **95.9510** & **14.3179** & 0.9053 & **0.8786** & **4.8369** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative results of the ablation study involving the AFF module.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Selection** & **En** & **SD** & **MI** & **MS-SSIM** & **VIF** & **NIQMC** \\ \hline SSIM & 6.7674 & 75.7708 & 13.5347 & 0.8763 & 0.8093 & 4.4303 \\ MSE & 6.8755 & 75.8543 & 13.7509 & 0.8893 & 0.7812 & 4.4854 \\ TV & 6.5573 & 59.3614 & 13.1146 & 0.8997 & 0.6567 & 3.9791 \\ SSIM + MSE & 6.9162 & 81.9746 & 13.8323 & 0.9121 & 0.8140 & 4.5599 \\ SSIM + TV & 6.9104 & 79.5288 & 13.8207 & 0.9052 & 0.7800 & 4.6015 \\ MSE + TV & 6.9942 & 83.7423 & 13.9884 & **0.9236** & 0.8143 & 4.6324 \\ SSIM + MSE + TV & **7.0708** & **90.3205** & **14.1415** & 0.9004 & **0.8784** & **4.7150** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study involving the adoption of loss functions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**\(\mathbf{\lambda_{1}}\) and \(\mathbf{\lambda_{2}}\)** & **En** & **SD** & **MI** & **MS-SSIM** & **VIF** & **NIQMC** \\ \hline (0.5, 0.5) & 6.8913 & 78.2546 & 13.7825 & 0.9299 & 0.8018 & 4.4835 \\ (0.5, 1) & 6.8717 & 75.9035 & 13.7433 & 0.9043 & 0.7970 & 4.4580 \\ (0.5, 1.5) & 6.8782 & 76.0264 & 13.7563 & 0.9196 & 0.7929 & 4.4458 \\ (1, 0.5) & 6.7920 & 71.9125 & 13.5841 & 0.8869 & 0.7875 & 4.3727 \\ (1, 1) & 7.0708 & 90.3205 & 14.1415 & 0.9004 & 0.8784 & 4.7150 \\ (1, 1.5) & 6.9237 & 80.4903 & 13.8475 & 0.9292 & 0.8150 & 4.5627 \\ (1, 0.5) & 7.0926 & 93.318 & 14.1852 & **0.9258** & 0.8492 & 4.8003 \\ (1.5, 1) & **7.1589** & **95.9510** & **14.3179** & 0.9053 & **0.8786** & **4.8369** \\ (1.5, 1.5) & 6.8789 & 76.6077 & 13.7577 & 0.9122 & 0.8157 & 4.4652 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the results of different weight settings for \(\lambda_{1}\) and \(\lambda_{2}\).  
two optimal values. These results demonstrate the superior performance of the AFSFusion network over other fusion methods, highlighting its overall effectiveness. SwinFusion, SDNet, and MUFusion, despite being recently proposed methods, exhibited inferior fusion performance compared to the proposed method.
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 4 Results of Numerical Analysis
### Comparison of P-BF Displacement and Soil Reaction Force
#### 4.5.2 Distribution of Reactive Load  
The reinforcement section of the P-BF will certainly bear a larger part of the lateral soil reaction force while the foundation stiffness increases. Figure 12 shows the distribution of the soil reaction under five loading conditions. In addition, in Figure 12, it is observed that the depth of the subgrade reaction turning point for P-BF is also different from that of PF. The depth of the subgrade reaction of the P-BF is almost the same as the height of the bucket.  
Under these five different loading conditions, the bucket shares more than 90% of the soil reaction in the reinforcement part. Therefore, the main bearing member of the P-BF under lateral load is the bucket. Combining the above, the anti-overturning ability of the P-BF is superior to BF. It can be found that under the lateral load, the bucket in the P-BF provides lateral bearing capacity for the foundation, and the pile provides sufficient an anti-overturning moment.  
Figure 11: Lateral displacement of PF and P–BF under 5000 kN loading.  
Figure 12: _Cont._
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 4 Activation of Anti-Tumor Immunity by LTP
### The Effects of LTP on Other Immune Cells  
In addition to effecting the function of non-specific and specific immune cells, LTP can also promote activity of mixed cells extracted from immune organs and enhance anti-tumor effects. A study reported that LTP-treated prostate cancer cells increased the viability and toxicity of bone marrow cells [39]. [PERSON] et al. [40] found when LTP-treated mouse spleen cells were co-cultured with B16-F10 cells, spleen cells produced more inflammatory cytokines and enhanced the anti-tumor effects.
## 2 Model of Dynamic Imaging
### The Exposure Equation of CMOS Camera  
The synchronization between the ground imaging efficiency and the reading efficiency of the pixels on the camera focal plane is the premise of high-resolution imaging [23]. According to the object-image mapping relationship, the exposure time of the CMOS camera in the dynamic frame-by-frame imaging process is defined as the function of the ground resolution \(GSD\) and the ground imaging velocity \(V_{c}\), which can be expressed as follows:  
\[t=GSD/V_{c} \tag{5}\]  
As shown in Figure 3, an instantaneous imaging model is established by taking the single-frame image at the moment \(T_{1}\) in Figure 1 as an example. The expression of the geocentric angle \(\beta\) and the camera object distance \(L\) can be calculated from the trigonometric function:  
\[\beta=\pi-\theta-\arcsin\bigg{[}\bigg{(}\frac{R_{e}+H}{R_{e}}\bigg{)}\sin \theta\bigg{]} \tag{6}\]  
\[l=\sqrt{\left(R_{e}+H\right)^{2}+R_{e}{}^{2}-2(R_{e}+H)R_{e}\cos\delta} \tag{7}\]  
where, \(R_{e}\) is the radius of the earth and \(H\) is the height of the satellite orbit. According to the relationship among camera object distance \(L\), focal length \(f\), and pixel size \(a\), the ground resolution calculation formula can be expressed as:  
\[GSD=\frac{L\times a}{f\times\cos\Big{\{}\pi-\arcsin\bigg{[}\Big{(}\frac{R_{e }+H}{R_{e}}\Big{)}\sin\theta\bigg{]}\Big{\}}} \tag{8}\]  
Since the imaging velocity of the camera on the ground is combined velocity \(V_{c}\), which consists of camera scanning velocity \(V_{x}\) and satellite flight velocity \(V_{y}=\sqrt{GM/(R_{e}+H)}\), it can be expressed as:  
\[V_{c}=\sqrt{\left(V_{x}L\right)^{2}+GM/(R_{e}+H)} \tag{9}\]  
In the formula, \(G\) is the gravitational constant and \(M\) is the mass of the earth. It can be supposed that the number of the imaging line of the area-array CMOS camera is \(N\), while the exposure time of the area-array camera, at different attitude angles, can be expressed as:  
\[t(\theta)=\sum_{x=1}^{N}\frac{GSD_{\tau}}{V_{c}} \tag{10}\]
## 2 Materials and Methods
### Landslide Causal Factors Selection
#### 2.4.2 Factor Selection Using Information Gain Ratio  
After removing altitude, the importance of each factor in the modelling was quantitatively calculated using IGR, and the results are shown in Figure 3. According to the methodology of IGR in Section 3.1, the factor with larger average merit value made greater contributions to the accuracy of the susceptibility model. The calculation results of IGR showed that distance to rivers was the dominant causal factor in the study area, and its average merit value was 0.061.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{2}{c}{**Original Factor System**} & \multicolumn{2}{c}{**New Factor System**} \\ \cline{2-5}  & **Tolerances** & **VIF** & **Tolerances** & **VIF** \\ \hline Altitude & 0.176 & 5.687 & / & / \\ Slope & 0.535 & 1.870 & 0.536 & 1.867 \\ Aspect & 0.979 & 1.021 & 0.980 & 1.021 \\ Curvature & 0.846 & 1.183 & 0.849 & 1.178 \\ Plan curvature & 0.926 & 1.080 & 0.927 & 1.079 \\ Profile curvature & 0.876 & 1.142 & 0.876 & 1.142 \\ TRI & 0.522 & 1.916 & 0.522 & 1.914 \\ Lithology & 0.489 & 2.044 & 0.544 & 1.837 \\ Bedding structure & 0.939 & 1.065 & 0.941 & 1.063 \\ Distance to faults & 0.603 & 1.658 & 0.627 & 1.595 \\ Distance to rivers & 0.235 & 4.259 & 0.751 & 1.332 \\ Distance to guilty & 0.769 & 1.300 & 0.802 & 1.247 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multicollinearity of the causal factors. VIF: variance inflation factors.  
Figure 3: The average merit of each causal factor.  
Support vector machine has many advantages, such as a stable result and fast operation speed; thus, it was used to test the prediction accuracy of different factor combinations, and the accuracy was calculated using receiver operating characteristic [35]. As shown in Table 5, when eliminating TWI, curvature, plan curvature, and profile curvature, the accuracy of susceptibility modelling was the highest of 0.922. However, when the aspect was excluded, the accuracy of susceptibility modelling was significantly reduced to 0.908. The elimination of inconsequential factors can improve the accuracy of susceptibility modelling. Finally, nine important causal factors were selected for susceptibility modelling.
## 3 NFV Network Architecture with AI-Based Resource Allocation
Next we denote with \(c^{a}_{h,j}\) (\(\bar{a}\in\bar{\varOmega}_{NP};h\in[1..N^{\bar{u}}_{VNFI}];j\in[1..\infty)\)) the processing capacity measured for the \(h\)-th VNFI in the \(j\)-th Monitoring Interval (MI) that is in \(t\in[(j-1)T_{m},jT_{m})\);
* Step-2: The measured processing capacities \(c^{a}_{h,j}\) are retrieved on the Vi-Vnfm ETSI interface [12] from the AA that trains a neural network in order to determine the processing capacity to be allocated to the VNFIs; we assume the allocation procedure is performed on a periodic basis with duration \(T_{a}\); we denote with \(S\) the ratio of \(T_{a}\) to \(T_{m}\) and with \(\bar{c}^{a}_{h,nS}\) (\(\bar{u}\in\bar{\varOmega}_{NP};h\in[1..N^{\bar{u}}_{VNFI}];n\in[1..\infty)\)) the processing capacity to be allocated to the \(h\)-th VNFI in \(t\in[nT_{a},(n+1)T_{a}]\); \(\bar{c}_{h,nS}\) is evaluated by a neural network based on knowledge of the required processing capacities required and measured in \(L\) previous MIs that is the values \(\{c^{a}_{h,j},h\in[1..N^{\bar{u}}_{VNFI}];j\in[nS..nS-L+1]\}\). * Step-3: The NFVO receives on the Or-Vi interface [12] from all of the VIMs the processing capacities \(\bar{c}^{a}_{h,nS}\) (\(\bar{u}\in\bar{\varOmega}_{NP};h\in[1..N^{\bar{u}}_{VNFI}];n\in[1..\infty)\)) to be allocated and decides if new VNFI placement, VNFI migration and updating of processing capacity allocated to the VNFI have to be performed. * Step-4: The placement and reconfiguration operations are conducted through the VIM and using the Or-Vi and Nf-Vi interfaces. Figure 1: ETSI Compliant NFV Network Architecture with AI-based Resource Allocation. The determination of the processing capacities \(\hat{c}^{a}_{R,nS}\) (\(\bar{u}\in\bar{U}_{NP};h\in[1..N^{a}_{VNFI}]\), \(n\in[1..\infty)\)) to be allocated to the VNFIs are determined by a convolutional and LSTM neural network and trained by minimizing the network cost. Such a cost is characterized by two components: The first one is the cloud resource allocation cost; the second one occurs when the resources are under-allocated and QoS degradation is introduced for a fraction of the offered traffic. Finally many reconfiguration and placement algorithms have been proposed in literature [30]. The goal of this paper is not to propose a new one. We adopt a simple reconfiguration algorithm, inherited from [27] in which migrations are not involved and allowing us to verify the effectiveness of the proposed allocation procedures.