esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Method
### Optimization of Die Design
#### 2.4.1 Optimization Objectives  
In this study, effective strain (\(\overline{\epsilon}\)), strain inhomogeneity (CV\(\varepsilon\)p), and maximum pressing load (ML) were considered as optimization objectives. These parameters play a crucial role in enhancing the mechanical properties of the material and improving the overall effectiveness of the process. High effective strain values correspond to improved mechanical properties. Therefore, numerous studies, both in the past and present, have been conducted to increase effective strain [8; 47; 48; 49]. Strain inhomogeneity, on the other hand, plays a critical role in assessing the effectiveness of SPD processes. Consequently, numerous studies have been conducted by researchers in recent years to evaluate strain inhomogeneity [50; 51; 52]. Strain inhomogeneity is measured by dividing the standard deviation of the effective strain values by the average effective strain value and is called the Coefficient of Inhomogeneity (CV\(\varepsilon\)p). The formula for calculating CV\(\varepsilon\)p is given as follows [53]:  
\[\mathrm{CV}\varepsilon\mathrm{p}=\frac{\mathrm{Stde}\mathrm{v}\varepsilon \mathrm{p}}{\mathrm{Av}\varepsilon\mathrm{p}}\]  
This coefficient provides a normalized measurement to assess the level of strain inhomogeneity by relating the variability of strain values to their mean, which is crucial in optimization studies focused on improving the uniformity of material deformation. After the FEM analysis, to find the CV\(\varepsilon\)p, 72 points were chosen across the cross-sectional plane of the workpiece as seen in Figure 4. Thanks to the DEFORM 3D (V11.0) software, the effective strain values at these points were extracted and evaluated to determine how evenly the effective strain was distributed throughout the material.  
Figure 4: Selected point across the cross-sectional plane.  
Pressing load represents the amount of force applied to the workpiece during the deformation process. Although achieving high effective strain and homogeneity are key objectives, it is equally important to minimize the pressing load. High pressing loads can result in several undesirable consequences, such as increased energy consumption, higher tool wear, and potential damage to the die or workpiece. To address these challenges and improve process efficiency, numerous studies in recent years have focused on minimizing the pressing load [22, 26]. After the FEAs, the stroke-force graphs of the process were extracted and recorded.
## 2 System and Channel Models
### Statistical Models
#### 2.2.3 Pointing Errors  
In line-of-sight (LOS) wireless coherent THz communication links, the loss of pointing error due to misalignment is another important factor to determine the performance and reliability of the link as illustrated in Figure 2.  
As shown in Figure 2, consider a receiver (RX) with a circular detection beam that propagates a distance \(z\) from the transmitter to a circular detector with an aperture radius \(a\). The distance \(r\) represents the instantaneous radial displacement between the center of the detector and the beam's centroid. The fraction of power collected by the receiver, due to pointing errors, can be approximated by [25]  
\[h_{p}(r;z)\approx A_{0}\exp(\frac{-2r^{2}}{w_{z,eq}^{2}}) \tag{8}\]  
where \(A_{0}\) is the fraction of the collected power at \(r=0\), which is defined as \(A_{0}=[\text{erf}(v)]^{2}\), and the equivalent beam width \(w_{z,\text{eq}}\) is defined as \(w_{z,\text{eq}}^{2}=w_{z}^{2}\sqrt{\pi}\frac{\text{erf}(v)}{2v\exp(-v^{2})}\). In this case, \(v=\sqrt{\pi}d/(\sqrt{2}w_{z})\) represents the ratio of the aperture radius to the beam width. The parameter \(d\) signifies the receiver aperture's radius, while \(w_{z}\) denotes the beam waist at distance \(z\). For a Gaussian beam, the beam waist \(w_{z}\) is defined as \(w_{z}=w_{0}\sqrt{1+\left(\frac{zA}{\pi w_{0}^{2}}\right)^{2}}\), where \(w_{0}\) is the beam waist at \(z=0\). Consequently, the PDF of \(h_{p}\) can be derived as follows:  
\[f_{h_{p}}(h_{p})=\frac{\gamma^{2}}{A_{0}^{\gamma^{2}}}h_{p}^{\gamma^{2}-1}, \quad 0\leq h_{p}\leq A_{0} \tag{9}\]  
where \(\gamma=w_{z,eq}/2\sigma_{5}\) represents the ratio of the equivalent beam width to the jitter standard deviation, quantifying the severity of the pointing error effect.  
Figure 2: Detector and beam footprint with misalignment on the detector plane.
## 2 Background of the Proposed Method
### Speeded-Up Robust Features (SURF)
#### 2.1.2 Descriptor Generation  
After the features are detected as mentioned above, SURF generates a descriptor, which describes the characteristics of the features, such as the shape and color, using the sum of the Haar wavelet responses [19]. The Haar wavelets enable SURF to increase the robustness and decrease the computational time. To generate the descriptor, the first step is constructing a square region around the points of interest and assigning a reproducible orientation based on the orientation-selection method introduced in [17]. The region is then split equally into \(4\times 4\) sub-regions to retain some of the spatial information as shown in Figure 2. In each sub-region, we compute the Haar wavelet responses at regularly \(5\times 5\) spaced sample points. [PERSON] et al. suggested that the level of performance is best at the each of the above sizes [17]. The wavelet responses are calculated in the \(x\)- and \(y\)-axis directions (\(d_{x}\) and \(d_{y}\)) and summed over each sub-region. The wavelet responses are then weighted with a Gaussian centered on the point of interest to increase the robustness toward geometric deformations and localization errors. Moreover, to obtain information regarding the polarity of the intensity changes, the absolute values of the responses \(\left|d_{x}\right|\) and \(\left|d_{y}\right|\) are also summed. Thus, each sub-region has a four-dimensional descriptor vector as shown in Equation (4).  
\[v=\big{|}\sum d_{x},\sum d_{y},\sum\left|d_{x}\right|,\sum\left|d_{y}\right| \big{|}. \tag{4}\]  
As a result, the overall descriptor has a 64-dimension vector because 4 vectors are created for each \(4\times 4\) sub-region. Figure 2 shows a descriptor vector obtained by summing the wavelet response in a sub-region.  
Finally, the descriptor vectors for each sub-region are normalized to reduce the impact on the environment, such as an external light or illumination. Eventually, we can obtain the robust features and descriptors of the PCB image using SURF. The descriptor for a single feature is generated, as shown in Figure 3, where \(m\) is the number of extracted features.
# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 4 Augmentation of the Concrete Cracks Dataset  
The impact of light on propagation events varies based on seabed topography and streamer depth. By inputting these variables into an underwater wave propagation model, we can anticipate the locations where optical effects will affect the underwater visuals. Wave models are increasingly employed to study and forecast surf conditions in different coastal areas. These models can be developed using wave action or motion systems, which are also known as phase averaging or phase resolving models. The latest generation of phase average models is commonly used in various applications, including commercial tools for flow simulation and wave modeling in advanced professional engineering [59]. In this paper, we have used the model presented in [59] to generate the 3D wave geometry for image augmentation based on the JONSWAP spectrum, which has been proven to be more suitable for a fetch-limited sea with increasing waves [60].  
In this study, it is important to have realistic images of underwater concrete structures, so the technique for rendering illumination effects is presented in [59]. First, it is assumed that the surface of the water is level, and the sun's refraction vector is detected. Then, the texture of the surface is developed, representing underwater depth. Finally, the perspective of the scene chosen is that of the human eye. This method was developed with the Blender program [59].  
The test of the deep learning method was performed using concrete crack images with and without underwater optical effects. The method used for the generation of underwater optical effects was presented in [59]. The collection contains 40,000 photos of surfaces with and without concrete fractures (20,000 with cracks--\"Positive\" and 20,000 without cracks--\"Negative\") from [PERSON]'s released dataset, which was used for the test [61]. Optical water illusion effects were generated using the technique depicted in [59], and various modifications (random rotation, zoom, height, width) and different blending techniques were employed for dataset augmentation. For the image classification, 40,000 distinct optical underwater effects were generated and applied to the whole dataset of images with and without cracks; see Figure 1. The technique of image augmentation is presented in detail in [59].  
Figure 1: Example of the augmented dataset. Concrete images without cracks and with underwater optical effects are depicted in panel (**a**). Concrete images with cracks and with underwater optical effects are depicted in panel (**b**).  
Machine learning methods used for image classification are presented in the next section.
## 4 Finite-Time Fault-Tolerant Control Using Synchronous Terminal Sliding Mode Control
** The Lyapunov function can be selected as:=  
\[V=\frac{1}{2}S^{T}S \tag{28}\]  
The time derivative of V in Equation (28) is:  
\[ \begin{array}{ll}\dot{V}&=S^{T}\dot{S}\\ &=S^{T}\Big{(}\dot{e}+\Lambda\pi|\varepsilon|^{\Lambda-I}\cdot\dot{\varepsilon }\Big{)}\\ &=S^{T}\Big{(}\ddot{q}_{d}-\ddot{q}+\Lambda\pi|\varepsilon|^{\Lambda-I}\cdot \dot{\varepsilon}\Big{)}\\ &=S^{T}\Big{(}\begin{array}{c}\ddot{q}_{d}-\mathbf{M}^{-1}(q)\Big{(}\tau- \mathbf{H}(q,\dot{q})\Big{)}\\ +\mathbf{M}^{-1}(q)\zeta(q,\dot{q},\tau,t)+\Lambda\pi|\varepsilon|^{\Lambda-I} \cdot\dot{\varepsilon}\end{array}\Big{)}\end{array}  \tag{29}\]  
Substituting Equation (27) into Equation (29) we have:  
\[\dot{V}=-S^{T}K_{1}sign(S)\leq-\sigma_{1}V^{\frac{1}{2}}\leq 0 \tag{30}\]where \(\sigma_{1}=\lambda_{\text{min}}(K_{1})\). When the sliding mode achieves, the system become as Equation (20) and shown as above. Therefore, Theorem 2 is proven. In Figure 2, the block diagram of the proposed controller AFTC-S-TSMC2 was shown. The block T is the synchronization matrix in Equation (13). The block of Fault Estimation uses the same extended state observer in Equation (8) to estimate the lumped uncertainties, disturbances and faults. Compared with the first proposed controller, the second proposed controller has less computation, and the ability to converge in a finite-time. **Remark 2**. : _A singularity may occur at \(|\varepsilon|^{\Lambda-I}\)and \(|E|^{\Lambda-I}\) in Equation (17) and Equation (27) as \(S\) approaches zero. By using saturation function \(sat(x^{\Lambda-I},u_{s}),x=|\varepsilon|\) and \(|E|\) where \(u_{s}>0\), the singularity can be avoided and the system retains finite-time stability [23]._  
**Remark 3**. : The time convergence can be shown as:  
\[t=t_{r}+t_{\varepsilon_{i}} \tag{31}\]  
_where \(t_{r}\)is as shown in Equation (23), and \(t_{\varepsilon_{i}}\) is as shown in Equation (25)._
## 3 Results and Discussions
### Self-Nucleation Isothermal Crystallization Kinetics
\(b_{0}\) is the molecular thickness, \(\triangle h_{f}\) is the enthalpy of fusion, and \(k\) is the Boltzmann constant, \(k\) = 1.38 \(\times\) 10\({}^{-23}\) J/K. It has been reported that, for iPP resins, crystallization occurring at 139-154 \({}^{\circ}\)C was carried out in Regime II. In this study, since the self-nucleation isothermal crystallization temperature is 141-145 \({}^{\circ}\)C, \(n_{c}\) is taken to be 2 [62; 63]. \(\sigma\) can also be estimated as:  
\[\sigma=\alpha\Delta h_{f}\sqrt{a_{0}b_{0}} \tag{7}\]  
where \(\alpha\) was empirically obtained to be 0.1 and \(a_{0}b_{0}\) represents the cross-sectional area of the polymer chain [60]. \(\triangle h_{f}\), \(a_{0}\), and \(b_{0}\) of iPP are supposed to be 1.96 \(\times\) 10\({}^{8}\) J/m\({}^{3}\), 5.49 \(\times\) 10\({}^{-10}\) m, and 6.26 \(\times\) 10\({}^{-10}\) m, respectively, based on the literature [60]. Therefore, a value of \(\sigma\) = 11.5 erg cm\({}^{-2}\) is obtained from Equation (7). From the plot of \(InG+\frac{U^{*}}{R(T_{c}-T_{\infty})}\) of against \(\frac{1}{T_{c}\cdot\Delta T\cdot f}\) (shown in Figure 9), the value of \(K_{g}\) can be directly calculated from the slope. Therefore, the value of \(\sigma_{e}\) was simply estimated by Equations (5) and (6). The results of \(K_{g}\) and \(\sigma_{e}\) for samples are listed in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & \(K_{g}\times\)**10\({}^{-4}\) (K\({}^{2}\))** & \(\sigma_{e}\) (erg cm\({}^{-2}\))** \\ \hline PP & 23.4 & 96.6 \\ GPP & 20.2 & 83.1 \\ GHPP & 17.9 & 76.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The kinetic parameter for crystal growth (\(K_{g}\)) and interfacial free energies of crystal growth (\(\sigma_{e}\)) under self-nucleation isothermal crystallization process. The results in Table 3 suggest that, from PP, GPP to GHPP, both the \(K_{g}\) and \(\sigma_{e}\) decrease gradually, indicating that the surface free energy for crystal growth decreases and the ability for crystal growth increases after the addition of GO and GO-H202. The addition of GO might facilitate the molecular chain regularly arranging into lamellae to a larger degree and therefore increases the growth rate of the crystal. Moreover, the addition of GO-H202 further increases the crystal growth rate of PP benefit from the grafting of H202 on GO, which might make the GO's dispersion more uniform and act as a foreign nucleating agent for iPP. In summary, the crystallization kinetics analysis of this manuscript demonstrates that the presence of GO not only enhances the nucleation rate and overall crystallization rate, but also encourages the crystal growth of the PP matrix, possibly due to the external nucleating agent effect of GO; moreover, the grafting of hyperbranched polyester onto GO provides a better compatibility (i.e., better dispersion) with iPP matrix and possibly new nucleation sites for iPP, resulting in the further enhancement of both nucleation rate, crystal growth rate, and overall crystallization rate.
## 3 Message Flow for Location Information
For example,  
<dtmf len=\"2\" value=\"*1\"/> \(<\)grid x1=\"123\",y1=\"456\",w1=\"24\",h1=\"3\"/> means that when the user presses the screen area of width 24 and height 3 at the coordinate (123, 456), the UE will generates two DTMF digits *1. The Media Server sends the touch screen information to the UE through SIP INFO message. **Step 13** The UE replies the 200 OK to the MMAS Server. At this point, the user can appropriately touch the screen. **Step 14** When the user touches the screen (e.g., he/she wants to see the menu of this restaurant), the UE will sends the corresponding DTMF digits to the Media Server through the RTP connection [10]. The Media Server then sends the new screen to the user based on the DTMF signals. In the above example, the UE is assumed to be a mobile phone. This service can also be accessed from STB/TV.
# A Deep Neural Network Model for Speaker Identification
## 3 Results  
### Training  
In order to speed up the training procedure, our proposed network schemes are implemented using TensorFlow deep learning library written in Python, which can be executed on a graphics processing unit (GPU). Due to parallel processing, GPU is generally more efficient than a central processing unit (CPU) on neural network processing. Our experiments are conducted on a dedicated Nvidia TITAN Xp GPU. In 2-D CNN and RNN-LSTM network models training stage, we choose the gradient descent algorithm with a linear decreasing learning rate schedule from 0.0001 to 0.00001. The model is trained for 80 epochs with a minibatch size of 32. Training pairs are re-shuffled in each training epoch. The validation set is used for hyper-parameter tuning and early stopping.  
In the training phase for the deep GRU network model, the batch size is determined as 32, and the optimization is deployed by the Nadam optimizer [55; 56], which incorporates Nesterov momentum into Adam. The initial learning rate is set at 0.0001. The loss function is selected as the categorical cross-entropy as mentioned in (16), where \(y_{i}\) is the truth target vector, and \(a_{i}\) is the output vector of the model for \(i\)-th class. The learning rate and convolutional kernel size of the network are conducted with various settings by trial-and-error approach, and the optimal setting for each network that yields the best performance results is chosen. The weights in the models were initialized randomly at the start of the training process, and progressively updated throughout the process. At the same time, to address the difficult problem of overfitting in deep learning model training, the dropout layer is also applied in the 2-D CNN layer and the GRU layer. In this study, a dropout of 0.3 at the 2-D CNN layer of the networks, and a dropout of 0.2 at the stacked GRU layers were deployed to avoid an overfitting problem typically encountered in deep neural networks. Moreover, layer normalization in GRU layers and batch normalization in convolutional layers and dense layers were deployed to avoid an overfitting problem typically encountered in deep neural networks. An example of the deep GRU network model training progress is given in Figure 8.  
In order to verify the feature extraction ability of the CNNs proposed in our experiments, we specially designed a 2-D CNN network model for speaker identification experiments. In the following picture, the model training results are shown in the following picture. It can be seen from Figure 9 that the 2-D CNN network model used in the  
Figure 8: Loss and accuracy of the deep GRU network model with the Aishell-1 dataset. The left block presents loss vs. training epochs and the right block represents the accuracy vs. training epochs.  
experiment has converged after approximately 66 rounds of training and the results of the validation set are also relatively ideal. One training progress of the 2-D CNN network model is given in Figure 9.
## 6 Travel Planning  
The node insertion algorithm presented above generates the set of viewpoints, but it does not tell us how to move from one viewpoint to another in order to cover the surface. In general the problem of determining a minimum length motion path for a set of points such that each point is reached at least once is part of the Traveling Salesman Problem (TSP) which is NP hard. We should note here that in the type of application planned here (Thermographic NDT), the inspection time is much greater than the travel time, and thus there is no need to emphasize optimality in the path planning process.  
The points generated from a single start point (using the indirect method as the stoppage criteria) are set out corresponding to a 2D grid. We propose a simple algorithm which would give a satisfactory solution to the problem. Note that this is only applicable to the case where these points are mapped roughly to a grid on the surface.  
Figure 33 shows us the 2D space in which nodes have been mapped, each node representing a viewpoint. We proceed to develop a boustrophedon-like coverage path, starting from the right-lowermost node and moving up until all the nodes in that column have been covered. Then we move to the previous column's uppermost node and change the direction of motion. This path planning is quite intuitive and so we do not describe the algorithm in more detail. Refs. [1; 15] describe the boustrophedon cell decomposition methods which is what we follow but with a small difference. The 'cutout regions' where there are no points in the 2D space do not correspond to 'obstacles' in the conventional coverage path planning sense, since we can just pass over them (there is no physical obstacle). Hence we do not need to break down the region into cells and so on. We can see how the 'cutout region' is handled in Figure 33, which shows the travel planning in  
process. In this figure, black dots denote the viewpoints (represented by their (i,j) values) already covered by the travel plan and red dots denote the viewpoints yet to be covered. Although this travel path is not necessarily optimal, it is good enough for practical purposes.  
In the case where we have multiple start points, we can link the endpoint of the first travel path to the start point of the second travel path thus generated, and so on. Figure 34 illustrates this process.  
For the case of path planning using the direct method for stoppage criteria, or to ensure optimality in general, we can implement the solution using the traveling salesman approach. Since solutions to the traveling salesman problem are well known in the literature, they are not explored here.  
Since each node corresponds to a viewpoint, and each viewpoint can be represented by the position and orientation of the sensor, we can thus derive the motion of the machine or robot for moving to these viewpoints in sequence. We could thus generate CNC G-code or robot code for this purpose to be used in the actual application.
## 3 Results
### Simulation Results by the Fluspect Model with/without the FQE Inversion Code
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & \(\mathbf{C_{ab}}\) & \(\mathbf{C_{av}}\) & \(\mathbf{C_{dm}}\) & \(\mathbf{C_{s}}\) & \(\mathbf{C_{ca}}\) & \(\mathbf{N}\) & **FQE** & **FQE** \\
**Setup** & **(ug cm\({}^{-2}\))** & **(mg m\({}^{-2}\))** & **(mg cm\({}^{-2}\))** & & **(ug cm\({}^{-2}\))** & & **I** & **II** \\ \hline HT leaf A & 46.43 & 0.02 & 0.005 & 0.32 & 6.35 & 1.43 & 0.002 & 0.01 \\  & (46.40) & (-) & (-) & (0.33) & (5.99) & (-) & (0.004) & (0.005) \\  & 55.65 & 0.02 & 0.004 & 0.19 & 7.47 & 1.82 & 0.002 & 0.01 \\ HT leaf B & (55.63) & (-) & (-) & (?) & (?) & (?) & (0.003) & (0.004) \\  & 75.19 & 0.02 & 0.003 & 0.05 & 6.67 & 1.86 & 0.002 & 0.01 \\  & (-) & (-) & (0.004) & (-) & (6.47) & (-) & (0.004) & (0.005) \\  & 53.69 & 0.02 & 0.011 & 0.32 & 9.07 & 1.79 & 0.002 & 0.01 \\ HT leaf D & (-) & (-) & (0.012) & (-) & (?) & (?) & (?) & (0.004) & (0.005) \\ \hline HM leaf A & 50.46 & 0.02 & 0.02 & 0.08 & 8.63 & 1.47 & 0.002 & 0.01 \\  & (50.45) & (-) & (-) & (?) & (8.34) & (-) & (0.003) & (0.004) \\  & 51.44 & 0.02 & 0.003 & 0.03 & 8.01 & 1.64 & 0.002 & 0.01 \\  & (51.43) & (-) & (-) & (?) & (?) & (?) & (0.003) & (0.005) \\ HM leaf C & 38.33 & 0.01 & 0.02 & 0.12 & 8.34 & 1.56 & 0.002 & 0.01 \\  & (38.32) & (-) & (-) & (0.13) & (8.16) & (-) & (0.003) & (0.005) \\  & 61.44 & 0.02 & 0.02 & 0.26 & 7.68 & 1.87 & 0.002 & 0.01 \\  & (61.45) & (-) & (-) & (?) & (?) & (?) & (?) & (0.004) & (0.005) \\ \hline LT leaf A & 20.96 & 0.01 & 0.01 & 0 & 6.02 & 1.42 & 0.002 & 0.01 \\  & (-) & (-) & (?) & (?) & (?) & (?) & (?) & (0.005) \\ LT leaf B & 22.26 & 0.01 & 0.01 & 0.014 & 5.25 & 1.32 & 0.002 & 0.01 \\  & (22.29) & (-) & (0.02) & (0.012) & (5.42) &