esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Control Threshold for Design Based on Dynamic Shakedown Characteristics
### Shakedown Threshold of Typical Filling Materials
When designing subgrade structure, it is necessary to strictly control the filling and service quality of the surface filler to ensure that the confining pressure conditions and compactness of the filling location meet requirements. Similarly, the threshold curves for dynamic strain amplitude \(\epsilon^{*}\)d A-B and \(\epsilon^{*}\)d B-C were analyzed, as shown in Figure 14.  
The threshold curves of dynamic strain \(\epsilon_{\text{d}}\) were fitted by using the linear function form, which is expressed as  
\[\epsilon_{\text{d}}=k\sigma_{3}+\epsilon_{\text{d0}} \tag{9}\]  
where \(k\) is related to the change rate of the dynamic strain threshold with confining pressure, and \(\epsilon_{\text{d0}}\) represents the dynamic strain threshold under the unconfined condition. The values of parameters are shown in Figure 10:0 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filler Type** & **Threshold** & \(A\) & \(b\) \\ \hline Group A of bottom layer & \(CSR^{*}\)A-B & 58.87 & \(-\)0.78 \\  & \(CSR^{*}\)B-C & 129.37 & \(-\)0.80 \\ \hline Group B of bottom layer & \(CSR^{*}\)A-B & 76.63 & \(-\)0.91 \\  & \(CSR^{*}\)B-C & 140.41 & \(-\)0.86 \\ \hline Group C of bottom layer & \(CSR^{*}\)A-B & 44.09 & \(-\)0.70 \\  & \(CSR^{*}\)B-C & 124.58 & \(-\)0.78 \\ \hline Group D of bottom layer & \(CSR^{*}\)A-B & 49.24 & \(-\)0.80 \\  & \(CSR^{*}\)B-C & 107.62 & \(-\)0.82 \\ \hline Type I of surface layer & \(CSR^{*}\)A-B & 38.45 & \(-\)0.57 \\  & \(CSR^{*}\)B-C & 246.97 & \(-\)1.00 \\ \hline Type II of surface layer & \(CSR^{*}\)A-B & 59.79 & \(-\)0.84 \\  & \(CSR^{*}\)B-C & 126.84 & \(-\)0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter values of the threshold curve of \(CSR\) (\(-\)). Figure 14: Shakedown threshold of dynamic strain amplitude. The threshold curve of \(\epsilon^{*}_{\text{d A-B}}\) and \(\epsilon^{*}_{\text{d B-C}}\) tended to be linear, and the change rate was very small (\(<\)10\({}^{-5}\)-10\({}^{-6}\)), which indicated that the change in confining pressure had little effect on the shakedown threshold of dynamic strain. For the six types of high-speed railway subgrade fillers in this study, Table 4 shows that the values of \(\epsilon^{*}_{\text{d A-B}}\) were approximately in the range of 0.10-0.12% and the value of \(\epsilon^{*}_{\text{d B-C}}\) was about 0.20-0.25%. In order to ensure the long-term stability of high-speed railway subgrade, it is necessary to avoid exceeding the threshold \(\epsilon^{*}_{\text{d B-C}}\) and to control the dynamic strain of subgrade filler below the threshold \(\epsilon^{*}_{\text{d A-B}}\) as far as possible.
# Masked Style Transfer for Source-Coherent Image-to-Image Translation
## 3 Proposed System
### Training and Losses  
In order to train the proposed model, we choose to maintain the training phase of StarGANv2 without any changes. Therefore, the total loss is composed of four losses:  
* The _adversarial loss_ is used to learn the generation of realistic results: \[\mathcal{L}_{\text{adv}}=\mathbb{E}_{src}[\log D_{src}(x_{src})]+\mathbb{E }_{trg}[\log\big{(}1-D_{trg}\big{(}G\big{(}x_{src},s_{trg}\big{)}\big{)}\big{)}]\] where \(x_{src}\in\mathcal{X}\) is the input image, and \(G(\cdot)\) is the generator and takes \(x_{src}\) and \(s_{trg}\), which is the style code extracted from the reference image \(x_{ref}\).
* The _style reconstruction loss_ is introduced in order to prevent the generator \(G\) from ignoring the style \(s_{trg}\) during the generation phase: \[\mathcal{L}_{sty}=\mathbb{E}_{src,trg}\big{[}\big{\|}s_{trg}-E\big{(}x_{trg} \big{)}\big{\|}\big{]}\] where \(E\big{(}x_{trg}\big{)}\) is the style code extracted from the generated image.
* The _style diversification loss_ is used to differentiate the styles generated from two different images: \[\mathcal{L}_{div}=\mathbb{E}_{src,trg1,trg2}\big{[}\big{\|}x_{trg1}-x_{trg2} \big{\|}\big{]}\]
* The _cycle consistency loss_ maintains the domain-invariant characteristics of the generated image, like the pose and shape: \[\mathcal{L}_{cyc}=\mathbb{E}_{src,trg}\big{[}\big{\|}x_{src}-G\big{(}x_{trg},s_{src}\big{)}\big{\|}\big{]}\] where \(s_{src}\) is the estimated style code extracted from the input. The final loss is, therefore, as follows: \[\min_{G,M,E}\max_{D}\mathcal{L}_{\text{adv}}+\lambda_{sty}\mathcal{L}_{sty}- \lambda_{div}\mathcal{L}_{div}+\lambda_{cyc}\mathcal{L}_{cyc}\] It is worth noting that during training, a reference image and random noise are used alternatively for generating the style code through the mapping network.
## 4 Results  
### ANN Model Results  
Using the data from laboratory tests, an artificial neural network was created to predict the damping ratio.  
The input parameters included:  
* \(\gamma\)--the shear strain;
* G/Gmax--the normalized shear modulus;
* LL--the liquid limit;
* p\({}^{\prime}\)--the mean effective stress;
* Si--the silt content;
* PL--the plasticity limit;
* Cl--the clay content.  
The output data of the neural network were the damping ratio determined based on laboratory tests. To determine the value of the damping ratio (D) of clay soils in this study, deep feed-forward (DFF) network was used, which means that we applied more than one hidden layer in the model. The main problem with using only one hidden layer is overfitting. Therefore, by adding more hidden layers, we may achieve (not in all cases) reduced overfitting and improved generalization. The network used has the following architecture: 7-95-80-1, which means an artificial neural network with seven inputs and two hidden layers; the first with 95 neurons, the second with 80 neurons, and an output layer with 1 neuron. The relu function was used as the activation function in the first and second hidden layers. The Adam optimization algorithm was chosen to train the neural network. The input data were divided accordingly for the data used to train the network--80% and 20%--for network testing. From the training set, 20% was used to create a validation set which was used to tune the parameters of a classifier.  
The correlation between the damping ratio predicted by the ANN and their values measured in the laboratory is shown in Figure 7. The predicted values of the damping ratio of clay soils are characterized by high convergence (R\({}^{2}\) = 0.976) with their values measured in the laboratory, which justifies the use of the ANN to predict their damping ratios.  
Figure 8 illustrates the histogram of errors made by a neural network when predicting the damping ratio. The error histogram is the histogram of errors between the target values and predicted values after training a feedforward neural network. These error values indicate how the predicted values differ from the target ones. The mean absolute error (MAE) for the test set was 0.1854, while the mean square error (MSE) was 0.1704.  
Figure 7: Correlation of the damping ratios of the clay soils predicted by the artificial neural network (test cases) with the values measured in the laboratory.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Security and Privacy Requirements
#### 2.1.4 Pseudo-Anonymity  
Pseudo-anonymity ensures that users may access resources or services without having to disclose their identity, but they can still be held accountable for that use. Since the users can be accountable for the use and they are not completely anonymous, it can be viewed as conditional pseud-anonymity or reversible pseudo-anonymity. The accountability can be achieved by directly linking the user's actual identity towards some reference held by an entity such as the certificate authority, or an alias that will be used for processing purposes, such as an account number. Conditional pseudo-anonymity is essential if the identity of the violator is to be disclosed by the law enforcement authorities for the purposes of liability. There are parallels between pseudo-privacy and anonymity. Although all methods shield the identity of the user, pseudo-anonymity maintains a connection to the identity of the user for transparency because, in anonymity, the user becomes absolutely anonymous.
# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
## 2 IEEE 802.11p Physical Layer Overview and System Model
### System Model
These features are used as input to a pre-trained DCNN in order to predict the combination of QAM modulation order, channel coding rate and data payload length, that should be sent as feedback to the transmitter for using them in the forthcoming frame.
# Phase Compensation Technique for Effective Heat Focusing in Microwave Hyperthermia Systems
## 3 Electromagnetic Focusing for Microwave Hyperthermia
### Phase Compensation Technique for Multiple Focusing  
As mentioned in the previous section, unwanted E-field focusing occurred due to the interactions between the sub-arrays. In circular antenna arrays, the interferences between the antennas on opposite sides are most severe. An inaccurate synthesis of the E-field inside the breast phantom can degrade the success of the hyperthermia treatment. Therefore, in this section, the phase compensation technique is applied to enhance the SAR focusing in the target points. To minimize the number of unwanted interferences, each input phase was calculated to satisfy the in-phase condition in the target tumors, and the phase compensation technique was applied to allow destructive interference to occur between the sub-arrays, as shown in  
Figure 7: Configuration of the six-element antenna sub-array focusing system for two breast tumors. Figure 8a. In the calculated input phase of sub-array #2, the modified phase \(\varphi_{\rm m}\) was added to antennas #7-#12 to compensate for phase \(\varphi_{n2}\) in Equation (10):  
\[\varphi_{n2}^{*} = \varphi_{n2}+\varphi_{\rm m} \tag{10}\]  
where \(\varphi_{n2}^{*}\) is the modified phase applied to sub-array #2. The input phases depending on the different values of the modified phases are reported in Table 3, and the SAR distributions with phase compensation depending on the modified phase values are presented in Figure 9. The added modified phases \(\varphi_{\rm m}\) are \(0^{\circ}\), \(60^{\circ}\), \(120^{\circ}\), and \(180^{\circ}\), respectively. In Figure 9, the modified phase is insufficient for achieving destructive interference in the unwanted spot, and inaccurate E-field synthesis is still observed. However, when the modified phase \(\varphi_{\rm m}\) is \(180^{\circ}\), sub-arrays #1 and #2 are out-of-phase, so destructive interference occurs outside the cancer spots, and maximum SAR occurs at (15,10) mm and (\(-15,-10\)) mm, as shown in Figure 9d. In this condition, the E-fields are well concentrated on the desired positions, leading to successful thermal therapeutic effects. To confirm the out-of-phase condition between sub-array #1 and #2, the modified phase \(\varphi_{\rm m}=180^{\circ}\) is applied in sub-array #2 for two other cases of tumor locations: case 1--two tumors are at (\(-10\), \(20\)) mm and (\(15,-10\)) mm; case 2--two tumors are at (20,0) mm and (\(-15\),\(-15\)) mm. The input phases for case 1 and 2 are shown in Table 4. In Figure 10a,b, the SAR distributions in accordance with the input phases in Table 4 are presented. As a result, the maximum SARs are presented at (\(-10\), \(20\)) mm and (\(15,-10\)) mm in Figure 10a, and (20,0) mm and (\(-15,-15\)) mm in Figure 10b, respectively. By applying the modified phase, \(\varphi_{\rm m}=180^{\circ}\) in sub-array #2, the out-of-phase condition is achieved between sub-array #1 and #2 and the EM energy is well-focused in the target areas.
# Arbitrary Motion Style Transfer via Contrastive Learning
## 2 Related Work
### Motion Style Transfer  
In earlier research, various data-driven methods have been proposed to learn representations of motion styles. [PERSON] et al. [26] introduced a linear time-invariant system to capture style differences between different motions, enabling style transfer for motions with similar content. [PERSON] et al. [27] employed a Gaussian process model based on kinematics and dynamics to manipulate input motions and achieve desired styles. [PERSON] et al. [28] combined K-nearest neighbor search and a regression model to facilitate real-time style transfer between motion segments. [PERSON] and [PERSON] [1] focused on using spectral intensity features to express style variations between different motions and used them for style transfer and control.  
Recent studies have explored deep learning methods for motion style transfer. [PERSON] et al. [2; 29] proposed an approach that manipulates the Gram matrix of feature variables in the latent space to achieve style transfer. [PERSON] et al. [30] introduced a conditional variational autoencoder that directly learns the distribution of style motions with a few style examples. [PERSON] et al. [4] employed AdaIN to combine style features and content features of motions, enabling a more concise style transfer. Moreover, they utilize an asymmetry encoder architecture in their framework. [PERSON] et al. [5] incorporated spatio-temporal graph convolutional networks (ST-GCN) to learn the representation of motions, enhancing the network's ability to extract in a local spatio domain. [PERSON] et al. [31] adopted a novel recurrent module to enable online motion style transfer. [PERSON] et al. [32] presented a style modeling system based on the local motion phases. [PERSON] et al. [33] conducted a hierarchical structure to achieve real-time controllable motion transition. [PERSON] et al. [6] proposed a system that enables independent style transfer for different body parts of characters and a loss function to implement arbitrary motion style transfer. [PERSON] et al. [34] proposed a multi-condition diffusion model that separates and reintegrates trajectories, content, and style from motion, enhancing style transfer performance while preserving the core content of motion.  
Inspired by previous works, our framework incorporates contrastive learning into arbitrary motion style transfer. Unlike previous end-to-end methods, our approach focuses on constructing more comprehensive feature representations by considering the various differences between different motions. This leads to improved stylization and accurate preservation of content.
# NURBS-Enhanced Meshfree Method with an Integration Subtraction Technique for Complex Topology
## 4 Numerical Examples
### Bubble with Arbitrary Holes  
As the problem of a single circular hole dug in the predesigned domain of a rectangle has been verified above, we now propose another example of an arbitrary domain: a bubble with arbitrary holes. In FEM, this bubble is discretized with an edge length of 0.03 with payer quad4 meshes, as illustrated in Figure 15. The material properties and constraints are as follows: [PERSON]'s modulus \(E=10^{3}\), [PERSON]'s ratio \(v=0.3\), the left and right side of the bubble are fixed in X and Y directions (shown with purple labels \"12\" in Figure 15), and a single force of 1.0 is applied to the upper side. As for the presented method, the predesigned domain and holes are all discretized in a point-style homotopic mesh, with \(n\) numbers in the circumferential direction and \(m\) numbers in the radial direction, as marked in Figure 16. Field nodes are deployed at a uniform interval of 0.04 within the predesigned domain with the same nodes as FEM along the boundary, as shown in Figure 16c.  
Figure 16: Discretization of RSUB in a point-style homotopic mesh: (**a**) predesigned domain, (**b**) arbitrary holes, (**c**) deployment of field nodes, (**d**) actual equivalent problem.  
Figure 15: Discretization and constraints in FEM.  
In the presented RSUB method, the displacement and stress of the example are solved in the stiffness matrix constructed on the field nodes, as in Figure 16c. Then, the displacement and stress in the y-direction are compared for FEM, RPIM and RSUB, as shown in Figure 17. In addition, the detailed results of the displacement of nodes on the upper side of the boundary are listed in Figure 18 at the cusp where the force is loaded.  
Comparing the solutions of displacement and stress in Figures 17 and 18, it is obvious that the RSUB results are consistent with those of RPIM and FEM. However, little oscillation is found near the boundary of the right-side irregular hole in the stress results of RSUB. The reason would be that the difference between the displacement of field nodes inner and outer the hole is too great. While in the stress recovery process, both of inner and outer field nodes are needed. In addition, stress is only accurately solved at the Gaussian interpolation point, so oscillation may appear in the recovery result on the boundary of hole.  
Figure 17: Displacement in y-direction of different methods: (**a**) FEM; (**c**) RPIM; (**e**) RSUB. Stress in y-direction of different methods: (**b**) FEM; (**d**) RPIM; (**f**) RSUB.
## 5 Security Analysis, Performance Evaluation, and Discussion
### Key Strength Analysis of Proposed Key/Subkey Generation Technique
#### 5.1.3 Bitwise Uncorrelation Tests (BUCT)  
* A bitwise uncorrelation test finds out if all subkeys are bitwise uncorrelated with each other.
* A new sequence is generated by using Equation (15). Sequence generation is conducted by XORing all possible combinations of bits of subkeys X\({}_{\rm i}\) and X\({}_{\rm j}\).  
Binary sequence = (\(X_{1}\oplus X_{2}\)) \(\mid\mid\) (\(X_{1}\oplus X_{3}\)) \(\mid\mid\)....... \(\mid\mid\) (\(X_{2}\oplus X_{3}\))...... \(\mid\mid\) (\(X_{\rm i}\oplus X_{\rm j}\)) \(\mid\mid\)....... \(\mid\mid\) (\(X_{\rm r-1}\oplus X_{\rm r}\))  
(15)  
where (\(Xi\oplus Xj\)) = (Xi [1] \(\oplus\) Xj) \(\mid\) (Xi[2] \(\oplus\) Xj) \(\mid\)....... \(\mid\mid\) (\(Xi[L]\oplus Xj\))  
Here, \(i\
eq j\), \(i=1,2,\ldots,r,j=1,2,\ldots,r\).  
* Frequency test: this test is the same as the one explained in Section 5.1.1. However, this test is carried out on the sequence generated by Equation (15).
* Poker test: this test finds out how many times the p-bit block appears in the sequence derived from Equation (18). The sequence is divided into N non-overlapping blocks, each of length P. bi is the ith bit of a P-bit sequence. Equation (16) is used to find a distribution of P-bit blocks.  
\[Z=\frac{2^{p}}{B}\sum_{i=1}^{2^{p}}{(bi)^{2}-B} \tag{16}\]  
Results Discussion:  
The threshold level is set at 10%, which means that 10 out of 100 sequences generated by Equation (15) can be rejected i.e., minimum of 90% of the sequence should pass the test. The results tabulated in Table 4 suggest that the proposed techniques successfully pass the BUCT test, as the randomness factor is 98% and 94.5% of sequences pass the poker test. Figure 6 shows the plot that helps us to understand the behavior of the proposed method and AES-128 for the BUCT test. The proposed SARNC technique has a higher pass percentage compared to AES-128.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Frequency Test in (\%)** & **Poker Test in (\%)** \\ \hline Proposed & 98 & 94.5 \\ SARNC technique & & \\ \hline Original & & \\ AES-128 & 97 & 93.6 \\ \end{tabular}
\end{table}
Table 4: Bitwise uncorrelation test (BUCT).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Completeness** & **Avalanche** & **Strict Avalanche** \\  & **d\({}_{\rm c}\)** & **d\({}_{\rm a}\)** & **d\({}_{\rm sa}\)** \\ \hline Proposed & 1 & 1 & 0.904879 \\ SARNC technique & & & \\ \hline Original & & & \\ AES-128 & 0.7 & 0.7 & 0.605883 \\ \hline \end{tabular}
\end{table}
Table 3: Bit independence test (BIT).
## 3 Control Strategies
### Short-Circuit Proof Algorithm for Grid-Disconnected Mode  
As detailed in Section 2, the inverter operates as the voltage master when the ATENEA microgrid is grid-disconnected operated. The inverter must provide sinusoidal currents even under an over-load or short-circuit occurrence reducing the voltage accordingly. In this way, the aim of the microgrid inverter and this paper is to generate a totally controlled short-circuit power regulating sinusoidal currents. Right after, this information can be used to (re)configure certain protection devices' distributed thresholds along the AC microgrid [25]. In this sense, the time response of the breaker that feeds the fault should be affected minimally.  
Figure 7 shows the proposed block diagram of the short-circuit proof mechanism. The conventional voltage and current stationary frame controllers can be seen in black, i.e., Proportional Resonant with Harmonic Compensator (PRHC) controllers, where the subscript \(u\) and \(i\) refer to voltage and current, respectively. \(u_{C}^{*}\) is the objective AC capacitor voltage, \(U_{C}\) the current AC capacitor voltage, and \(u_{I}\) the inverters' output voltage. \(i_{L1}\) and \(i_{L2}\) are the inverters' output inductance and grid coupling inductance. The superscript \({}^{*}\) designs the reference, and the subscript \(rms\) is the root mean square (rms) computed value.  
The blue parts are added with respect to a conventional voltage-current nested loop for the short-circuit proof enhancement. The algorithm is based on the per phase rms value of the current reference on the AC side, \(i_{L1}^{*}\), and two factors \(k_{1}\) and \(k_{2}\). The first factor \(k_{1}\) allows for dynamically regulating the voltage set-point to attenuate it under short-circuit or high over-load situations, as follows:  
\[k_{1}=\left\{\begin{array}{ll}1,&\text{if}\qquad i_{\text{rms}}\leq I,\\ (K+1)-\frac{i_{\text{rms}}K}{I},&\text{if}\quad I<i_{\text{rms}}<(K+1)\;I,\\ 0,&\text{if}\qquad i_{\text{rms}}\geq(K+1)\;I,\end{array}\right. \tag{2}\]  
Figure 6: Scheme of the power over-load supervisor algorithm.  
where \(I\) is the maximum desired rated output current (at the \(L_{1}\) inductance). The parameter \(K\) in \(k_{1}\) calculation allows for adapting the system response speed to face the over-load or short-circuit occurrence.  
The second factor \(k_{2}\) limits the current to the rated value when the fault situation appears as  
\[k_{2}=\left\{\begin{array}{ll}1,&\text{if}\quad i_{\text{rms}}<I,\\ \frac{I}{i_{\text{L1 rms}}},&\text{if}\quad i_{\text{rms}}\geq I.\end{array}\right. \tag{3}\]  
If only the previous algorithm is applied, the behaviour under the fault clearance is undesired. This is due to the fast response of the \(k_{1}\) factor. To avoid this kind of undesired dynamics, when the current \(k_{1 in}(k)\) value is higher than the previous computed one \(k_{1}(k-1)\), i.e., this criterion is used as a fault recovery indicator, the applied \(k_{1}(k)\) gain for the inner current control reference is filtered according to Figure 7b.