esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling of the Steel-Concrete Composite Beam
### FE Model Verification 2  
The second FE model presented herein refers to the dynamic test conducted by [PERSON] et al. [24] to validate the effectiveness of the proposed modeling methods in modal analysis. The dynamic analysis was performed in the linear perturbation module available in ABAQUS. The test beam was simply supported with a net span of 3800 mm. The ML15 headed studs with a diameter of 13 mm and a height of 60 mm were arranged in double rows along the longitude of the SCCB. The profiled steel HM 200 \(\times\) 150 was utilized as the steel beam. Detailed dimensions and the distribution of the headed studs in the composite beam are shown in Figure 6a. Table 2 provides the detailed properties of the materials adopted for the solid-slipping beam model.  
Figure 5: FEM results of composite beam: (**a**) load vs. mid-span displacement; and (**b**) load vs. interface slip.  
Similar to the FE model 1, the normal and tangential behavior between contact surfaces was defined using the \"HARD\" and \"PENALTY\" options, respectively. The friction coefficient was set to 0.05 for the interface between the steel flange and concrete slab and 0.45 for the surface of the headed stud and the surrounding concrete by re-running the simulation and comparison. In the linear perturbation module of ABAQUS, the CDP model was disabled; therefore, the constitutive model adopted for concrete was substituted with the linear elastic model. The C3D8R element defined by the tri-linear constitutive model was adopted for the steel beam and headed studs. The three-dimensional truss element (T3D2) was designated for steel reinforcement. The embedding constraint was applied between the reinforcement truss and the concrete slab.  
A general mesh size of 50 mm was used for the overall structure, and a fine mesh size of 5 mm was adopted for head studs and the region around head studs. The vertical restraints were applied to both end supports of the SCCB; meanwhile, lateral restraints were applied to the flank side of the beam. Figure 6 provides the first two flexural modal shapes,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Yield** & **Ultimate** & **Elastic** & **Thickness** & **Density** \\  & **Strength** & **Strength** & **Modulus** & **or Diameter** & **(kg/m3)** \\  & **(MPa)** & **(MPa)** & **(GPa)** & **(mm)** & **(kg/m3)** \\ \hline Steel beam & 437 & 514 & 211 & 9/6 & 7850 \\ Headed & 350 & 467.3 & 208 & 13 & 7850 \\  stud & \(-\) & 53.67 & 35.13 & \(-\) & 2500 \\ Concrete & 270 & \(-\) & 208 & 6 & 7850 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of FE model 2.  
Figure 6: Schematic and vibration modal shapes of the composite beams: **(a)** schematic of composite beams; **(b)** 1 st flexural modal shapes; and **(c)** 2 nd flexural modal shapes.  
and Table 3 provides a comparison of the natural frequency obtained from experimental and FE analysis. The results compare well; the maximum error was about 6%.
## 2 Method for Virtual Reality Model Based on Photogrammetric Survey  
In this chapter, we shortly present how the 3D model obtained by photogrammetric surveying is prepared to be used in the Unreal Engine to create a Virtual Reality presentation. The method consists of four steps:  
1. Photogrammetric Workflow
2. Optimization and Retopology
3. Control and Analysis
4. Creating a VR environment  
Photogrammetry was achieved using the Structure from Motion algorithm which consists of surveying and data processing in Agisoft Metashape. The data acquired by the surveying process are photographs of an observed object. The first step is the processing of the obtained photographs using photogrammetry software, which results in creating depth maps. From the depth maps a high-poly triangular mesh is generated.  
The next step is Optimization and Retopology and these processes are done in ZBrush software. Through the optimization process, the polygon count decreased. By employing the retopology technique, the triangular mesh is converted to the quadrilateral mesh. The low-poly quadrilateral mesh is obtained as a result, which is then exported to Blender software for the unwrapping process. The unwrapped mesh is exported to ZBrush where the process of baking textures is performed. Also, the unwrapped mesh is exported back to AgisoftMetashape for texturing of the low-poly mesh.  
The next step is Control and Analysis of the mesh and textures. We used Substance Painter for texture correcting. From this software are exported Diffuse and Specular maps. 3 ds Max was used as a tool for texture wrap control where we checked whether the texture fits well on the low-poly mesh.  
Finally, MeshLab was used for the comparison of geometrical details of high-poly and low-poly models i.e. mesh analysis. The result of the third step is a low poly quadrilateral mesh with a high-quality texture.  
The last step in the workflow is creating a VR environment. This step is done by the Unreal Engine where we used assets and these are the mesh and texture from the third step. In the last step, a scene was created with appropriate lights and interaction. The block diagram presenting the method is shown in Figure 1. A more detailed technical explanation is given in Section 3.2.
# Evaluating the Effect of Intensity Standardisation
## 2 Materials and Methods
### Considered Intensity Standardisation Techniques
#### 2.3.4 WhiteStripe  
The WhiteStripe method proposed by [PERSON] et al. [17] estimates statistics of intensities of the white matter to normalise all other regions of interest. The process is as follows. First, the method uses a penalised spline smoother to estimate the latest non-background mode of the histogram of intensities, \(\mu_{WS}\). In T1-w scans, this mode would coincide with the white matter mode. Second, the method estimates the standard deviation, \(\sigma_{WS}\) from a window of 10% of the intensity values around the detected mode. Third, all intensities are normalised in a z-score fashion using these two statistics (Equation (1)). This standardisation method has been shown to be robust to the presence of white matter lesions in ageing populations [17].
## 2 Dual-User Operation of Remote Robot System with Force Feedback
### Dual-User Operation  
The system configuration of dual-user operation is shown in Figure 3. As depicted in Figure 3, the system comprises two master terminals (referred to as _master terminals 1 and 2_, herein) and one slave terminal. Both users manipulate their respective haptic interface devices to collaboratively control the robot arm. The video captured by the web camera is multicast to both master terminals, allowing the two users to view the same video screen.  
As in [23], the reaction force exerted on the two haptic interface devices, denoted as \(\mathbf{F}_{t}^{(\text{m}_{1})}\) and \(\mathbf{F}_{t}^{(\text{m}_{2})}\), at master terminals 1 and 2 (the haptic interface devices are also called _haptic interface devices_ 1 and 2, respectively) are calculated as follows:  
\[\mathbf{F}_{t}^{(\text{m}_{1})}=\mathbf{F}_{t}^{(\text{m}_{2})}=K_{\text{scale}}^{( \text{F})}\mathbf{F}_{t-1}^{(\text{s})} \tag{3}\]  
where \(\mathbf{F}_{t}^{(\text{s})}\) represents the force transmitted from the slave terminal at time \(t\), and \(K_{\text{scale}}^{(\text{F})}\) represents the mapping scale for force between the industrial robot and the haptic interface device. The position vector of the robot is determined by robot position control, which we  
Figure 3: System configuration of dual-user operation.  
Figure 2: Block diagram of stabilization control with filter.
# Conditional Generative Adversarial Networks for Domain Transfer: A Survey
## 4 The Applications of CGAN
### Image Style Transfer  
The task of image style transfer is to transform one type of image into another style of image, including black and white to color, line coloring, texture transformation (for example, oil painting generated from photos), and so on. Similar to SR, it falls into the category of low-level semantic image generation but locally requiring high-level semantic support.  
[PERSON] et al. [35] proposed Pix2 Pix as a universal model for solving different image-to-image tasks in the supervised setting, i.e., the training data would include the source image and its corresponding destination image. [PERSON] et al. [25] extended the framework to generate high-resolution images.  
As a basis, CycleGAN [15] has made significant progress in unsupervised settings by introducing cyclic consistency loss to ensure that the destination image correctly retains domain invariant features, such as pose, of the source image. Although CycleGAN learns deterministic one-to-one mapping, it fails to capture the multi-modal attributes of the image distribution. MUNIT [38] recognizes this limitation and extends the framework to learn multi-modal mapping, which can generate different and real translation output. However, it trains different encoder-decoder models for each domain, so it is not easy to scale up to multiple domains. StarGAN [27, 39] solves this problem and proposes a unified model for multi-domain translation. FUNIT [62] tries to generalize images from unseen domains using some reference images from the target domain, but it requires fine-grained class labels during training and cannot model unspecified changes within the class.  
Due to the in-depth research on image style transfer, CGAN has been applied to other fields. For example, in the case of Geographic Information Systems (GIS), CGAN is used to transfer satellite images to maps [18, 87]. In the case of Computer Graphics, reference [88] uses CGAN to generate the SyntheticFur dataset by transferring the style of photos and [89] uses it to denoise blurred images.
# Optimization of Multilayer Absorbers Using the Bald Eagle Optimization Algorithm
## 3 Results
### Normal Incidence
#### 3.1.4 Design-4  
Five-layer microwave absorbers were optimized for a 1-20 GHz FR for normal incidence (Table 5). The materials and thicknesses of layers were optimized to minimize the RC in this FR. An MMA for a maximum RC of \(-18.0721\) dB with a total thickness of 4.69 mm is designed. The comparison of RC versus frequency with similar designs is illustrated in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**LAYER**} & \multicolumn{4}{c}{**2-18 GHz**} \\ \cline{2-7}  & \multicolumn{2}{c}{**BESOA**} & \multicolumn{2}{c}{**DS-ABC**} & \multicolumn{2}{c}{**PSO**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Design-3**} & \multicolumn{2}{c}{**[21]**} & \multicolumn{2}{c}{**[18]**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Material**} & \multicolumn{1}{c}{**d**} & \multicolumn{1}{c}{**Material**} & \multicolumn{1}{c}{**d**} & \multicolumn{1}{c}{**Material**} & \multicolumn{1}{c}{**d**} \\ \hline
1 & 16 & 0.23961 & 14 & 0.2112 & 16 & 0.24332 \\ \hline
2 & 6 & 1.19691 & 6 & 1.4993 & 8 & 0.33948 \\ \hline
3 & 16 & 0.15684 & 14 & 0.4176 & 7 & 0.81477 \\ \hline
4 & 5 & 0.44143 & 2 & 0.4211 & 6 & 0.76302 \\ \hline
5 & 11 & 0.99422 & 15 & 0.4775 & 15 & 1.05037 \\ \hline
**Max. RC [dB]** & \(-17.00\) & \(-17.0\) & \(-17.0\) & & \(-16.102\) \\ \hline
**Total thick. [mm]** & \(3.02\) & \(3.02\) & \(3.2109\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for 5-layer absorber optimized for 2-18 GHz FR for normal incidence (Design-3).  
Figure 6: Comparison of RC of Design-2 versus frequency.  
Figure 8: RC of 5-layer absorber optimized for 1–20 GHz FR for normal incidence (Design-4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**1–20 GHz 0\({}^{\circ}\) TE/TM**} \\ \cline{2-5}
**LAYER** & \multicolumn{2}{c}{**BESOA**} & \multicolumn{2}{c}{**DS-ABC**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Design-4**} & \multicolumn{2}{c}{**[**21**]**} \\ \cline{2-5}  & **Material** & **d** & **Material** & **d** \\ \hline
1 & 16 & 0.20551 & 16 & 0,2046 \\ \hline
2 & 6 & 1.90707 & 6 & 1,5898 \\ \hline
3 & 16 & 0.61099 & 14 & 0,3953 \\ \hline
4 & 10 & 0.94470 & 5 & 1,4635 \\ \hline
5 & 9 & 1.03169 & 10 & 1,0846 \\ \hline
**Max. RC [dB]** & \multicolumn{2}{c}{\(-\)18.0721} & \multicolumn{2}{c}{\(-\)18.06} \\ \hline
**Total thick. [mm]** & \multicolumn{2}{c}{4.69} & \multicolumn{2}{c}{4.73} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Design parameters of 5-layer MA for normal incidence for 1–20 GHz FR.  
Figure 7: RC of 5-layer absorber optimized for 2–18 GHz FR for normal incidence (Design-3).
## 6 Analysis of the eLPRT Protocol
### Choice of the Superframe Period  
In the 2.4 GHz band, the minimum superframe period defined by the IEEE 802.15.4 standard (according to Figure 2, with \(SO=0\)) is 15.46 ms, with the closest allowed superframe periods around 100 ms being 61.44 ms (\(SO=2\)) and 122.88 ms (\(SO=3\)). The sampling rate (\(f_{s}\)) required by the application scenario (30 Hz) corresponds to a sampling period of 33.33 ms. However, neither of these two superframe periods is multiple of the sampling period, so the number of samples per packet and, consequently, the packet length, vary, as shown in Table 3. Another example where the sampling rate is 10 Hz is also presented. This analysis assumes that the first sample is generated at \(t=0\), which also corresponds to scheduled time for the first transmission.  
The eLPRT protocol uses 8 bits to encode the superframe period, allowing 256 options in comparison with the 15 options provided by the GTS scheme. It also provides the superframe period of 100 ms, which means that all packets carry the same number of samples in this case, except for the first one. To allow a fair comparison of the other features, the following results assume that both the eLPRT protocol and the GTS scheme are using a superframe period of 100 ms.
## 4 Aerodynamic Design Method Investigation
### Trimmed Configuration Assessment
#### 4.5.3 Wind Tunnel Experiment of Trimmed Configuration  
To verify the trimmed configurations, the trimmed configuration is subjected to a wind tunnel experiment. The experiment is carried out in a direct blowdown transonic wind tunnel with a test section of 0.6 m \(\times\) 0.6 m and a test length of 2.5 m. The accuracies of wind tunnel test in transonic condition are \(\Delta\)Ma \(\leq\) 0.003, \(\Delta\alpha\ \leq\ 0.02\). The experimental conditions are Re = 7.1 \(\times\) 10\({}^{6}\) and Ma = 0.85. Numerical simulations are also carried out at these conditions. Besides, the wind tunnel test model is made of all steel, the scale of the model is 1:95, and the test model is installed on the wind tunnel by means of rear sting support. Geometric modification is arranged at the rear of the test model because of the rear sting support. Six-component strain balance is used in the force measuring system. The comparison of the results of the wind tunnel experiment and the numerical simulation are depicted in Figure 36.  
As shown in Figure 36, the lift and drag at small and medium angles of attack are in good agreement with the wind tunnel test results. However, at high angles of attack, the lift and drag of the wind tunnel and the numerical simulation results differ. This can be attributed to the model differences. The difference between the calculated and experimental values of the pitching moment could be due to the rear sting support of the wind tunnel experiment. In comparison with the experimental results, the CFD results obtained from the numerical simulation are sufficiently accurate.
## 4 Results and Discussion
### Actual Urban Pipeline Sensor Layout Optimization
Looking ahead, future research will focus on further reducing system costs and improving the algorithm's applicability and robustness, particularly in real-time and large-scale urban gas networks. This data-driven optimization method is expected to find broader applications in commercial and industrial environments, offering new perspectives and approaches for research and practice in related fields. Conceptualization: Z.S. ; data curation: Z.S., X.Z. and Z.H. ; methodology: Z.S. and F.Y. ; resources: B.C. ; visualization: Q.D. and Z.Z. ; validation: X.S. ; funding acquisition resources: B.C. and X.Y. ; writing--original draft: Z.S. ; writing--review and editing: X.S. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key Research and Development Program of China, grant number 2022 YFC3004801; the National Natural Science Foundation of China, grant number 52325107, 52171287; High tech Ship Research Project of Ministry of Industry and Information Technology, grant number 2023 GXB01-05-004-03, GXBZH2022-293; the Science Foundation for Distinguished Young Scholars of Shandong Province, grant number ZR2022 JQ25; and the Fundamental Research Funds for the Central Universities, grant number 24 CX10006A. No new data were created or analyzed in this study. Authors [PERSON] and [PERSON] was employed by the company Bokang Intelligent Information Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats
## 4 Numerical Simulations  
In this section, the effectiveness of the proposed guidance and navigation scheme is assessed by means of a numerical simulation campaign of the 3-DOF equations of motion. The first simulation consists of a nominal case in which only the guidance law is tested. The second simulation tests both the guidance and the navigation systems on a set of 100 Monte Carlo runs. The main parameters of the simulation are reported in Table 1, where \(\Delta t_{F}\) is the duration of the firing impulse of the spacecraft that generates the \(\Delta v\) of Equation (8). The nominal initial condition \(x_{0}\) in both simulations consists in the two satellites being on the same circular, equatorial orbit at a distance of 10 km along the tangent direction.
## 4 Results
### Ablation Results  
#### 4.3.1 Comparisons in Image Pair Matching Strategies  
To validate the superiority of the proposed image pair matching strategy using the similarity of graph framelet transform coefficients (GraphSim), we compare it with four other commonly used similarity measures, including the SSIM used in SSECNN, the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Porcine Hearts**} & \multicolumn{2}{c}{**Early Systole**} & \multicolumn{2}{c}{**End Systole**} & \multicolumn{2}{c}{**End Diastole**} \\ \cline{2-9}  & **SNR** & **CNR** & **SNR** & **CNR** & **SNR** & **CNR** & **SNR** & **CNR** \\ \hline Noisy & 0.59 & 76.20 & 16.81 & 137.48 & 17.38 & 133.63 & 16.78 & 158.94 \\ LPCA & 4.71 & 179.65 & 43.74 & 301.06 & 46.10 & 314.66 & 42.50 & 361.03 \\ AONLM & 3.62 & 168.07 & 16.77 & 137.48 & 17.38 & 16.31 & 16.81 & 158.94 \\ BM3D & 1.39 & 79.79 & **75.70** & **469.04** & **80.56** & **464.33** & **77.05** & **603.10** \\ DIP & 0.72 & 31.83 & 27.11 & 195.25 & 30.90 & 187.01 & 32.46 & 236.86 \\ NB2 NB & 2.31 & 59.28 & 40.58 & 200.50 & 42.49 & 215.58 & 37.59 & 241.08 \\ P2S & 1.79 & 120.46 & 30.12 & 184.71 & 32.21 & 196.76 & 29.82 & 220.70 \\ SSECNN & 4.34 & 134.85 & 48.90 & 293.19 & 52.24 & 278.21 & 47.12 & 334.24 \\ Node2 Node & **6.50** & **203.58** & 71.29 & **477.57** & 75.60 & **465.23** & 67.52 & 540.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SNRs and CNRs obtained with different methods for real datasets. The optimal and suboptimal results are highlighted in bold and with underline, respectively.  
Figure 7: Denoised results for in vivo human cardiac DTI acquired at end diastole phase with different methods. The 2 nd and 7 th rows demonstrate the zoomed-in red rectangle regions for DW images and fiber orientations, respectively. P2S achieves the worst performance.  
Euclidean distance used in NLM and BM3D, and histogram and Cosine similarities. To make a fair comparison, the baseline network structure, the training process, and the optimizing parameters of Node2 Node are kept unchanged except its image pair matching strategies. As demonstrated in Figure 8, the proposed image pair matching method results in the smallest random residual maps with a minimum RMSE value of 8.85, which is improved by 67.62%, 80.21%, 68.27%, and 67.58% compared against the SSIM, Euclidean distance, and cosine and histogram similarity measures, respectively. In addition, compared with the other image pair matching methods, the proposed GraphSim can remove the noise without losing the structure information.
# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
## 2 Relate Work
### Convolutional Neural Networks for Sequence-to-Sequence Data
Further, a TCN was proposed to extend the input large memory processing using a dilated causal convolution architecture and residual connections to preserve more accurate prediction results. A TCN block was regularly selected using the combinational 1D fully convolutional architecture followed by a nonlinear activation function (rectified linear unit, ReLU) to determine the weights of an input sequence that improve classification accuracy in model prediction, then adding a dropout function to reduce overfitting. In other words, a dropout function in a dense layer decreases the number of parameters to be learned and helps reduce overfitting. From a design perspective, the TCN is capable of handling a series of uncertain length and outputs it with the same length. Experimental results [17; 18; 19; 20] have shown that TCNs exhibit better performance with sequences of uncertain length, where the TCN architecture performs well in very long sequences of inputs. Sequence data prediction is the most frequently encountered issue in the TCN model, which is illustrated as follows [11]. Consider a given training dataset \(D(x_{t},\ y_{t})\) in a sequence modelling task, where \(x_{t}\) denotes input sequence (\(x_{t}\in R^{n},t=1,\ldots,\ T\)) and \(\widetilde{y}_{0},\widetilde{y}_{1},\ldots,\widetilde{y}_{T}\) is a prediction sequence with the corresponding sequential input (\(x_{0},\ x_{1},\ldots,\ x_{T}\)). The mapping function \(f\) is defined for a temporal convolutional network as follows:  
\[\widetilde{y}_{0},\widetilde{y}_{1},\ldots,\ \widetilde{y}_{T}=f(x_{0},\ x_{1}, \ldots,\ x_{T}), \tag{8}\]where \(\tilde{y}_{t}\) depends only on previous observations \(x_{0},\ x_{1},\ldots,\ x_{t}\) at time \(t\). The network is trained by supervised learning to find the function \(f\) that minimizes the error between the actual outputs (\(y_{0},\ \ldots,\ y_{T}\)) and the predictions (\(\tilde{y}_{0},\tilde{y}_{1},\ldots,\ \tilde{y}_{T}\)). Under the causal constraint, the TCN develops dilated causal convolutions to expand the receptive field exponentially, taking more historical information into consideration. The dilated convolution operation \(F\) on element \(s\) of the 1-D sequence \(x_{t}\in R^{n}\) for a filter \(f\): (0, 1, \(\ldots,\ k-1\)) \(\rightarrow\)\(R\) is formulated as Equation (9) [13]:  
\[F(s)=x_{t}*_{d}(f)(s)=\sum_{i=0}^{k-1}f(i)\cdot x_{s-d,i}, \tag{9}\]  
where \(k\) is the stack size, \(d\) is the dilation factor, and symbol \({}^{*}\) is the convolution operator. The dilated causal convolution used in this paper is illustrated in Figures 1 and 2 with dilation factors \(d=1\), 2, 4, 8 and stack size \(k=2\). Definitely, the larger the stack sizes \(k\) or dilation factor \(d\) are set, the broader the receptive field of the network is.