esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 2 Ship Hydroelasticity Theory in Regular and Irregular Waves
### Solution of Nonlinear Hydroelastic Equation  
For the solution of the hydroelastic equation of motion (i.e., Equation (2)), the fourth-order Runge-Kutta algorithm [32] is adopted in the present study. Then based on the modal superposition principle, the time-domain displacement \(w(x,t)\), vertical bending moment \(M(x,t)\), and shear force \(V(x,t)\) can be written as follows:  
\[\left\{\begin{array}{c}w(x,t)=\sum\limits_{r=1}^{m}p_{r}(t)w_{r}(x)\\ M(x,t)=\sum\limits_{r=1}^{m}p_{r}(t)M_{r}(x)\\ V(x,t)=\sum\limits_{r=1}^{m}p_{r}(t)V_{r}(x)\end{array}\right. \tag{28}\]where \(w_{r}(x)\), \(M_{r}(x)\) and \(V_{r}(x)\) are the \(r\)-th mode of vertical displacement, bending moment and shear force, respectively.
## 3 Formability Classification and Prediction Models
### Data Preprocessing  
In this study, a total of 12 beads were selected, as mentioned in Section 3.1, to predict wrinkle formation, according to the positions and forces of drawbacks. However, the presence of factors that have no significant impact on the forming results among the 12 drawbacks may degrade the accuracy of wrinkle and crack prediction and significantly increase the analysis time. Therefore, sensitivity analysis was performed to identify the degree of sensitivity to the objective function and exclude the design parameters with no significant impact. In this case, the objective function was defined as the total sum of wrinkle and crack occurrence, with each weight set at a ratio of 1:1. Sensitivity analysis was performed by applying the orthogonal array with the maximum (90%) and minimum (5%) values of design parameters using the Pareto chart [24]. The Pareto chart is a tool for representing the relative importance of several variables and is used to identify the critical variables that affect the overall results. Figure 6 shows the Pareto chart results for the total sum of wrinkles and cracks based on the positions and forces of drawbacks. Among the 12 beads, 8 beads were found to have influence on the number of wrinkles and cracks. Among the eight, no. 12 located at the curved part had the largest influence. Moreover, the positions of the beads with high influence were almost similar to those of the beads where wrinkles penetrated the product geometry when forming was performed without drawbacks, as in Figure 5b, which indicates the validity of the sensitivity analysis results. As the influence of beads 5, 7, 9, and 11 was smaller than the threshold of the standardized effect, formability prediction was performed in this study considering only the positions of the eight beads.  
As shown in Table 2, for the ranges of beads and variables used for formability prediction, 5-90% of the restraining force (7.75-251.40 N) was applied. Three levels were considered for eight design parameters, and the optimal Latin hypercube (OLP) was applied to reduce the analysis cases. OLP, a method derived from the Latin hypercube sampling technique, was designed to supplement the non-uniformity of conventional LHS. It enables efficient and even distribution of samples in the design space [25]. Therefore, it was used to efficiently create experimental cases, and a total of 256 analyses were performed. Upon the completion of the analysis, the number of wrinkles and cracks was calculated as the ratio compared to the total area by measuring the region predicted to contain wrinkles and  
Figure 6: Sensitivity analysis of wrinkle and crack.  
cracks in the entire sheet metal area based on FLD. As the objective of this study is to predict the number of wrinkles and cracks, for the uniform deviation of each amount, the data were converted into values between 0 and 1 through maximum-minimum normalization. The refined data were used for AI learning.
## 4 Experimental Results and Analysis
### Comparisons with Existing Methods  
To provide a more objective evaluation of ZWNet's performance, we have chosen to compare it with three other zero-watermarking methods. The first method, named [PERSON]'s method, is a classical zero-watermarking technique that utilizes FQGPCET and has been recognized for its robustness and discriminability [20]. The second method, [PERSON]'s method, is an end-to-end neural network-based approach centered on style transfer and removal, renowned for its robustness and is named after its creator, [PERSON] [30]. The third method, referred to as [PERSON]'s method, is a hybrid scheme that combines DWT and ResNet-101 together [26]. These three methods will be assessed alongside ZWNet in terms of their robustness, discriminability, and efficiency.
## 3 Research Methodology
### Data Collection
#### 3.1.2 Article Selection  
According to the objective of this review, it is necessary to collect as much of the literature of ANN applications in CM as possible. To realize this target, keywords were selected following related previous review studies in CM [31, 38]. As a result, the key search words \"neural network\" and \"construction management/engineering management\" were used. The keyword search in web of science was set as the \"topic\" in order to retrieve all the publications containing the selected keywords in their title, abstract, or selected keywords section. The search period was set to include the last 20 years, from January 2000 to December 2020. The \"document type\" was limited to \"article\", because journal articles usually provide more comprehensive and higher-quality information than other types of publications [39] and most reviews of CM study only focus on journal articles [40]. As a result, four hundred potential documents were initially retrieved from the selected journals. Adopting a two-round strategy, the same process as in Reference [31], the contents of the articles were further examined through manual screening. The first  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & **Journal** & **Cite Score 2019** & **No. of Paper** \\ \hline
1 & Journal of Construction Engineering and Management (JCEM) & 5.8 & 38 \\
2 & Automation in Construction (AC) & 9.5 & 34 \\
3 & Journal of Civil Engineering and Management (JCiEM) & 4.7 & 13 \\
4 & Engineering, Construction and Architectural Management (ECAM) & 2.5 & 9 \\
5 & Journal of Management in Engineering (JME) & 6.7 & 8 \\
6 & International Journal of Project Management (IJPM) & 13.0 & 6 \\
7 & Journal of Computing in Civil Engineering (JCCE) & 7.6 & 4 \\ Total & & & 112 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of the selected papers among different journals.  
Figure 2: Outline of the three-stage process of research methodology.  
round of selection checked the articles' title, abstract and keywords information, followed by a second round of selection conducted by reading and analyzing the whole article to double-check articles. The following criteria were used [41]: ANN was studied as the main technology or played an important role rather than a comparison. Finally, 112 articles were selected and used in this study (Table 2).
## 3 Methods
### Application Requirements  
The provision of real-time traffic congestion estimation and tourism spots is one of the main functions of the application. The acquisition of locations with participant consent was also an essential requirement of the application to collect data on the travel trajectory (probe data). Additionally, a questionnaire was required to record descriptive information when the participants made a stopover. An experimental application satisfying these requirements was developed before the experimental period.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 4 Remote Sensing Applications
### Land Use and Land Cover Assessment and Density Assessment
Additionally, the implementation of real-time monitoring systems, facilitated by remote sensing technologies, can provide stakeholders with actionable insights for adaptive land management practices. By encouraging collaboration between researchers, conservationists, and policymakers, we can ensure the sustainable management of Agrane stands and other species, enhancing their resilience in the face of environmental challenges.
# Improved Anticancer Activity of the Malloapelta B-Nanoliposomal Complex against Lung Carcinoma
## 3 Results
### Inhibition of Colony Formation malB-Nanoliposome Complex  
One of the most important characteristics of cancer is self-renewal ability, which allows tumor cells to proliferate and grow from a single cancer stem cell (CSC). To evaluate the effects of malB and malB-nanoliposomes on cellular self-renewal, a colony-forming assay was performed on the A549 cell line, cells of which were able to form colonies in vitro. The outcome after 8 days of treatment showed that both malB-liposomes and free malB decreased the numbers of colonies derived from A549 cells (Figure 3). The number of colonies decreased markedly at 5 \(\upmu\)M and 2.5 \(\upmu\)M malB in comparison to the controls. However, a lower concentration of malB had a minimal effect on the number of colonies. In addition, malB-nanoliposome C also inhibited colony formation by \(>\)50% at concentrations of 5 \(\upmu\)M and 2.5 \(\upmu\)M. The IC\({}_{50}\) values of malB and malB-nanoliposomes were calculated as 2.28 \(\upmu\)M and 1.75 \(\upmu\)M, respectively. These inhibitory activities of the compound in both forms were significant compared to the control (\(p<0.001\)). The results suggested that malB encapsulated in nanoliposomes had a stronger inhibitory effect on the formation of A549 lung adenocarcinomas than the free form. In addition, the effects of free malB and malB-nanoliposomes were different as the concentration increased. In detail, at the same dose of 0.75 \(\upmu\)M, malB-nanoliposomes inhibited the formation of A549 colonies by up to 40.66%, whereas the free malB showed only 17.21% inhibition of A549 colony formation. The same was also true at a dose of 1.25 \(\upmu\)M.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{4}{c}{**IC\({}_{50}\) Values (\(\upmu\)M)**} \\ \cline{2-5}  & **SK-LU-1** & **LLC** & **CL141** & **A549** \\ \hline MalB compound & 8.67 \(\pm\) 0.89 & 9.59 \(\pm\) 0.52 & 9.81 \(\pm\) 0.81 & 10.35 \(\pm\) 0.32 \\ \hline MalB-nanoliposomes & 13.12 \(\pm\) 1.47 & 12.35 \(\pm\) 1.45 & 12.24 \(\pm\) 1.38 & 11.86 \(\pm\) 0.99 \\ \hline Blank nanoliposomes & NA & NA & NA & NA \\ \hline \multicolumn{5}{c}{Note: NA means not available.} \\ \end{tabular}
\end{table}
Table 3: Cytotoxic activities of malB-nanoliposomes and untrapped compound malB on different lung cancer cell lines.  
Figure 3: _Cont._
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 3 The Proposed Attention-Based Full-Scale Fusion Network
### Implementation Details  
The proposed AFSF network is implemented using the PyTorch 2.0 [53] framework on two NVIDIA GTX 3090 GPUs. We train the proposed AFSF network 1000 epochs from scratch on the new roof dataset. The adjustment strategy of the learning rate is the same as that of [44]. We set the batch size to 128. In each batch, 64 images contain buildings (roofs), while the remaining 64 images do not contain any building (roof). At the training phase, each image is first randomly cropped to \(128\times 128\). To increase the diversity of the training data, we adopt the following data augmentation operations: (1) geometric deformation enhancement operations, i.e., RandomAffine, RandomErasing, RandomFisheye, RandomHorizontalFlip, RandomVerticalFlip, RandomRotation, and RandomThinPlateSpline; and (2) non-geometric deformation enhancement operations, i.e., RandomGaussianBlur, RandomElasticTransform, RandomPlasmaBrightness, RandomPlasmaContrast, RandomGamma, RandomGrayscale, RandomGaussianNoise, RandomHue, RandomMotionBlur, RandomPosterize, RandomSaturation, RandomSharpness, RandomSolarize, RandomMedianBlur, RandomSnow, RandomRain, RandomBoxBlur, RandomBrightness, RandomContrast, RandomInvert, RandomPerspective, RandomPlanckianJitter, and RandomPlasmaShadow, implemented in kornia [54]. To add more detail, in each training iteration, we randomly select one to three geometric deformation enhancement operations and one to five non-geometric deformation enhancement operations, while for the testing phase, we do not use any data augmentation method.
# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 2 Related Work
### Sub-Pixel Convolution  
Sub-pixel convolution [21] rearranges the feature map to extend the feature map like deconvolution [22; 23]. Super resolution is a kind of reasoning problem that restores information lost in HR images by using the redundancy of LR images. In order to utilize this redundancy, a large number of convolutions are used or features are extracted using convolutions with various filters. This causes the problem that the network becomes complicated or a large number of parameters are used. To solve this problem, sub-pixel convolution rearranges several feature maps to create extended feature maps. This structure increases the redundancy of LR patches rather than one deconvolution. Recently, a super resolution method using sub-pixel convolution has been applied, and a method of reducing the amount of calculation and parameters by using an LR image as an input image is being studied.  
Figure 6 is the process of sup-pixel convolution; a is the input feature map, b is the feature map resulting from the convolution, and c is the feature map rearranged from the convolution feature map. For rearraying in sub-pixel convolution, the number of channels equal to the square of the expansion coefficient is required.  
\[f^{pos}(x)=PS(W*x+b), \tag{5}\]  
Equation (5) is an equation for the sub-pixel convolution process. _PS_() is the rearrangement of the LR feature map, \(W\) is the weight, \(x\) is the input feature map, and b is the  
Figure 4: Channel Attention Module.  
Figure 5: Spatial Attention Module.  
bias. \(PS(T)_{x,y,z}\) is rearranged for expansion during the sub-pixel convolution process and is expressed in the following formula.  
\[PS(T)_{x,y,z}=T_{\lfloor x/r\rfloor\lfloor y/r\rfloor,C\cdot r\cdot mod(y,r)+C \cdot mod(x,r)+c\epsilon} \tag{6}\]  
In Equation (6), \(T\) is the tensor of the feature map, \(x\), \(y\), \(c\) are the \(x\), \(y\), and \(z\) axes of the input \(T\), and \(C\) is the channel of the extended feature map. To expand a feature map of size \(x*y*c\) to \(n\cdot x*n\cdot y*c\), the number of channels of the output feature map needs to be \(c\cdot n^{2}\), where \(n\) is the expansion factor. To this end, the number of channels of the feature map must be increased through convolution, and the size of the feature map is expanded through rearrangement. Through this process, in this paper, it was used to expand feature maps to secure redundancy, and high-quality, HR images were created by resolving checkerboard artifact.
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 3 Voice Spoof Detection and Algorithm Recognition System
### Incremental Spoof Algorithms Recognition
#### 3.2.2 Incremental Learning Module  
Voice spoof algorithms are constantly evolving and constantly updating the dataset over time. Therefore, the model cannot initially include all voice spoof algorithm data. The incremental learning module enables the model to continuously learn new voice spoof algorithms through prototypes and example sets, avoiding the need for the model to retrain all the data and saving a significant amount of training time.  
As shown in Figure 5, the model learns the prototype at the \((t-1)\)th iteration stage and saves a small number of examples for each classification algorithm. At the \(t\)th iteration learning, the model adds \(n\) classes of prototypes to the \((t-1)\)th iteration prototypes, each containing identical \(K\) prototypes, based on the number \(n\) of voice spoof algorithm types used in the newly added dataset. The model is then trained on the new voice spoofing dataset and the example set.  
Figure 4: Schematic for prototype training. Different colored circles indicate different classes of prototypes. The size of the circle indicates the size of the value.  
The amount of data stored in each class in the example set is much smaller than in the new voice spoofing training set. Therefore, the model suffers from a data imbalance during training. For this reason, we added different weights to each category when calculating the loss of each category based on the number of examples saved in the old category and the number of voices generated by the spoof algorithm in each of the new datasets. Thus, the impact on the recognition results due to data imbalance is reduced. The representation is as follows:  
\[loss(x,y)=weight[y](-x[y]+\log(\sum_{j}\exp(x[j]))) \tag{11}\]  
where \(y\in\{1,2,\dots,C\}\) denotes the classification labels, and weight denotes the loss weights for each category.  
Thus, the total loss function of the incremental learning module is denoted as  
\[loss_{total}=w_{1}*loss_{1}+w_{2}*loss_{2}+loss_{3} \tag{12}\]  
where \(w_{1}\) and \(w_{2}\) are the weight parameters set to 0.1 and 0.01, respectively, in our experiments. \(loss_{3}\) is the cross-entropy loss function for classification used to train the classification ability of the neural network, denoted as  
\[loss_{3}=-\frac{1}{S}\sum_{i=1}^{S}[y_{i}*\log(softmax(f_{i}))] \tag{13}\]  
where \(S\) is the batch size, and \(f_{i}\) is the feature of the \(i\)th sample in the batch.
## 2 Materials and Methods
### Reliability Analysis and Model Fit Test  
Reliability analysis was used to confirm the internal consistency of the measurement variables. The measurement variables for each latent variable were analyzed for internal consistency using Cronbach's \(\alpha\) value. Additionally, a factor analysis through Varimax factor rotation was performed to assess the construct validity.  
Figure 1: Conceptual structural equation model (SEM) of this study. Rectangle, measurement variable; ellipse, latent variable; Di, disturbance or residual; ei, measurement error.  
In this study, the model was verified by a model fit test and composite reliability analysis. The model fit test was performed using the goodness of fit indices, such as \(\chi^{2}\) and p values, NFI, CFI, GFI, TLI, and RMSEA values. Composite reliability of the model was analyzed by average variance extracted (AVE), composite reliability (CR), and correlation coefficients between variables. SPSS version 18.0 and AMOS 18 were used as statistical analysis tools.
# Parameter Analysis on Seismic Response of Long Lined Tunnel by 2.5D Substructure Method
## 3 The 2.5D Finite Element Substructure Method  
### Review 2.5D Finite Element Method  
In the substructure method, the finite domain only includes long lined tunnel structures. Through the 2.5D finite element method, the plane element with three degrees of  
Figure 1: 2.5D soil–structure interaction model with oblique incidence of seismic wave.  
freedom at each element node is obtained. The seismic response of the tunnel mentioned in the previous section can be expressed as follows:  
\[\widetilde{R}(x,y,z,t)=\mathbf{N}\overline{\mathbf{R}}(y,z)\exp(-ik_{x}x)\exp(-i \omega t) \tag{1}\]  
where \(\mathbf{N}\) is the shape function matrix in 2D finite element analysis; \(\mathbf{R}\) is the dynamic response amplitude of the structure in the \(y\)-\(o\)-\(z\) plane; the superscript represents the frequency-wavenumber domain; \(i\) is an imaginary unit; \(k_{x}\) is the wave number in the \(x\) direction and \(\omega\) is the circular frequency.  
Assuming that the urban underground soil layer is a linear elastic solid medium, its motion equation under the global coordinate system \((x,y,z)\) can be expressed as  
\[\mathbf{L}^{\mathrm{T}}\mathbf{\sigma}=\rho\ddot{\mathbf{u}} \tag{2}\]  
where \(\mathbf{\sigma}\) is stress; \(\rho\) is the density; \(\mathbf{\bar{u}}\) is the nodes acceleration; \(\mathbf{L}\) is a 3D differential operator matrix and the superscript T of the matrix represents transposition.  
\[\mathbf{L}=\begin{bmatrix}\frac{\partial}{\partial x}&0&0&\frac{\partial}{ \partial y}&0&\frac{\partial}{\partial z}\\ 0&\frac{\partial}{\partial y}&0&\frac{\partial}{\partial x}&\frac{\partial}{ \partial z}&0\\ 0&0&\frac{\partial}{\partial z}&0&\frac{\partial}{\partial y}&\frac{\partial}{ \partial x}\end{bmatrix}^{\mathrm{T}} \tag{3}\]  
By replacing the partial derivative of \(x\) in the 3D differential operator with--\(ik_{x}\), the equilibrium equation in the form of 2.5D finite element method is obtained.  
\[\left(\mathbf{K}-\omega^{2}\mathbf{M}\right)\mathbf{\bar{u}}=\mathbf{\overline {F}} \tag{4}\]  
where \(\mathbf{K}\) and \(\mathbf{M}\) are the stiffness and mass matrices derived according to the standard finite element process. \(\mathbf{F}\) is the interaction between the structure and the soil.  
So far, the 2.5D equilibrium equation of the finite domain of the tunnel structure has been obtained.  
\[\begin{bmatrix}\mathbf{S}_{m}&\mathbf{S}_{nm}\\ \mathbf{S}_{mn}&\mathbf{S}_{mm}\end{bmatrix}\begin{Bmatrix}\overline{\mathbf{ u}}_{n}\\ \overline{\mathbf{u}}_{m}\end{Bmatrix}\mathbf{=}\begin{Bmatrix}\mathbf{0}\\ -\mathbf{\overline{F}}_{m}\end{Bmatrix} \tag{5}\]  
where the subscript \(n\) refers to the number of internal nodes of the finite domain except the soil and tunnel interface. The subscript \(m\) refers to the number of nodes on the soil and tunnel interface.  
The dynamic stiffness matrix is as follow  
\[\mathbf{S}=\mathbf{K}-\omega^{2}\mathbf{M} \tag{6}\]
# A Fast Method for Calculation of Marine Gravity Anomaly
## 2 Method  
The free-air gravity anomaly is important in studying specific marine areas. On the ground, the free-air gravity anomaly \(\Delta g\) can be obtained by \(\Delta g=g_{obs}-g_{fa}-g_{0}\), where \(g_{obs}\) is the observed gravity, \(g_{fa}\) is the free-air correction, and \(g_{0}\) is the reference field. Generally, the marine gravity is measured on shipboard. Because the geoid corresponds to sea level, the shipboard gravity minus \(g_{0}\) is the free-air gravity anomaly. The gravity anomaly calculated from the inverse Stokes formula is also the marine free-air gravity anomaly.