esa-sceva/satcom-chunk-collection huggingface dataset:
# Robustness of Deep Learning Models for Vision Tasks
## 4 Brain-Inspired Neural Architectures against Adversarial Attacks
### CNN-Based Visual Cortex Model  
[PERSON] et al. [164] introduced VOneNet, a hybrid CNN model depicted in Figure 30. It consists of a fixed-weight neural network called VOneBlock at the front-end, which emulates the \(V1\) area in the primate visual cortex and follows the linear-nonlinear-Poisson (LNP) framework, and a CNN-based neural network at the back end. VOneBlock features biologically inspired Gabor filters, basic and advanced cellular nonlinearities, and probability generators that mimic \(V1\) neurons. In a white-box attack experiment using the PGD attack, the author found that accuracy was closely linked to the explained variance. They also observed that the explained variance in neural activation occurred more frequently than that in nontrained models, even in models that are hardly affected by adversarial attacks, such as the adversarially trained ResNet-50. This observation implies that there is a strong positive correlation between the perturbation in adversarial attacks and the \(V1\) explained variance following the attacks.  
VOneNet in [157] is a hybrid CNN model consisting of VOneBlock and a back-end network modeled after a standard CNN, such as ResNet or Alex  
Figure 30: VOneBlock and a back-end network modeled after a standard CNN, such as ResNet or AlexNet. The (**A**) shows VOneBlock is based on the LNP framework for the \(V1\) area of the primate visual cortex and includes Gabor filters, basic and advanced cellular nonlinearities, and a generator of randomness that mimics \(V1\) neurons. The (**B**) shows the performance analysis of VOneNet.  
on the LNP framework for the \(V1\) area of the primate visual cortex and includes Gabor filters, basic and advanced cellular nonlinearities, and a generator of randomness that mimics \(V1\) neurons. VOneBlock has 256 fixed units per spatial location and two types of neurons: simple and complex cells. The standard CNN is altered by replacing the first block with VOneBlock and a trained transition layer, resulting in a VOneNet with matching spatial map dimensions but potentially more channels.  
As shown in Figure 31, all the components of VOneBlock contributed to the robustness of the model against adversarial attacks in a robustness test against PGD attacks when ResNet-50 was used as the base model. Their main contribution was that VOneNet played a distinct role against white-box attacks compared with other defense methods. Thus, the effectiveness of brain-inspired neural networks for defense against adversarial attacks was demonstrated. In addition, compared with training-based defense methods, which are known as the strongest defense methods, VOneNet had shorter computation and training times than any other optimization-based defense methods.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 2 Material Characteristic
### Optical Transparency  
Optical transparency is another parameter that has been studied. Figure 4 illustrates a comparison between the optical transparency of the ITO film and the honeycomb structure from 380 to 780 nm. Although large mesh dimensions are needed to obtain high optical transparency, this also increases sheet resistance. From this figure, the mesh structure has excellent transparency compared to the ITO throughout the entire visible spectrum. The measured transparencies remain constant. The median transparency value of the network structure is around 75.4%, while that of the ITO film is 65.1%. Note that the measured transparencies are the sum of the transparencies of two layers of the ITO and meshed structure deposited on the glass substrate.  
Global transparency results from the sum of three layers: two mesh grids and the substrate. Assuming that the contributions of the grids are identical, we can determine the contribution of the mesh as:  
\[T_{tot}=T_{mesh}\cdot T_{substr}\cdot T_{mesh} \tag{4}\] \[T_{mesh}=\sqrt{\frac{T_{tot}}{T_{substr}}} \tag{5}\]  
Inserting the previously measured transparency values in Equation (5), the transparency of the mesh can be evaluated as 95.5%. This suggests that if the OLED glass substrate and its anode can be used as the support and ground plane, respectively, the impact of a single mesh layer would be quite small. This argument goes in favor of the co-design and co-integration of the display and radiation functions.
## 4 Simulation and Experiment
### Comparison of Different Methods  
The dynamic system was also modeled using the ADAMS 2020 software (Figure 9) and the results of the numerical simulation were compared. A simplified FPTLIR model  
Figure 8: The workflow of modular simulation.  
was constructed within the software with two parts. The 10 m PTL was modeled using the finite element (FE) model with 20 nodes, a damping ratio of stiffness set to 0.03, mass set to 1.0, material density set to 3595 kg/m\({}^{3}\), elastic modulus set to 79.8 GPa, and Poisson's ratio set to 0.4. The main contact parameters were configured as follows: force exponent set to 2.5, contact damping set to \(1\times 10^{4}\), penetration depth set to 0.01 m, and static friction coefficient set to 0.7. JOINT_8 and JOINT_9 were fixed constraints applied to both ends of the PTL, while JOINT_10 represented the fixed constraint between the travelling wheel and the FPTLIR, ensuring that the travelling wheel remained locked during landing to prevent slippage. The contact force represented the interaction between the travelling wheel and the PTL.  
The simulation workflow is shown in the Figure 10. The PTL was initially configured as a straight line without external forces. After stabilization under gravitational forces, the sag of the PTL at the specified landing point was obtained, and the FPTLIR was positioned accordingly. The simulation then proceeded as follows: first, the contact force was deactivated, and JOINT_11 was activated; after the PTL reached a steady state and was positioned below the travelling wheel, the contact force was activated, and JOINT_11 was deactivated, completing the coupled simulation.  
A comparison of results between the ANCF method and FEM method in ADAMS is shown in Figure 11. In the case study, the PTL length was set to 10 m, with the robot descending from the midpoint. The simulation parameters were consistent with the experimental setup, and the errors between the simulation results of the two methods and the experimental values were analyzed. Since the baseline values of the pitch and yaw angles were relatively small, our comparison focuses on the more dynamic roll angle and the displacement in the Z direction.  
As shown in Figure 11a, the Z-displacement error of the ANCF method stabilized quickly and remained below 0.02 m after 1 s, while the FEM exhibited larger fluctuations and remained below 0.02 m after 2 s. Similarly, Figure 11b indicated that the roll error of the ANCF method consistently stayed below 0.01\({}^{\circ}\) after 10 s, while the FEM demonstrated greater oscillations throughout the simulation. In terms of average errors, the Z-displacement average error of ANCF method was 0.005 m, compared to 0.014 m for FEM, representing a 64% error reduction. The average roll error of ANCF method was measured at 0.004\({}^{\circ}\), whereas FEM exhibited 0.008\({}^{\circ}\), indicating a 50% reduction in average error.  
Figure 9: Simulation diagram of the FPTLIR in the ADAMS software.
# Tin Whisker Growth Inhibition in RE-Doped Sn-Zn Soldered Joints
## 3 Results and Discussion
Meanwhile, due to the excessive addition of Ga and Nd, the thickness of IMC layer Sn-Zn-1 Ga-2 Nd soldered joints after aging treatment was obviously larger than that in soldered joint with the optimal addition. The XRD result of Sn-9 Zn-1 Ga-2 Nd soldered joint, as Figure 6 illustrated, could further verify the result, by replacing Sn-Nd phase to Ga-Nd phase, no obviously Sn-Nd phase or tin whisker growth found near the interface of soldered joint doped with exceeding content of Nd. Considering about the existence of Sn-Zn-Ga-Nd phase, there could be probably little Sn-Nd phase disperse in the interfacial microstructure of soldered joints or the amount of these Sn-Nd phase are too low to be detected after long time aging treatment. Therefore, the risk of tin whisker growth could be significantly reduced with the combined effect of Ga and Nd. The transformation in the interfacial microstructure would directly affect the mechanical property of soldered joint. Figure 7 showed the shear force of soldered joints after aging treatment, it can be seen that after eliminating the risk of tin whisker growth, the shear force of soldered joint doping excessive addition of Nd and Ga (Sn-Zn-1 Ga-2 Nd) is lower than Sn-Zn-0.06 Nd soldered joint, while  
Figure 4: Interface morphology of soldered joints with additional excess content of Nd. soldered joint with optimal addition (Sn-Zn-0.5 Ga-0.08 Nd)could maintain preferable performance after long time aging treatment. Compared with SnAgCu and SnPb soldered joints in Table 1, the shear strength of SnZnGaNNd soldered joints after long term aging treatment was significantly improved. This phenomenon can be explained as both Ga and Nd are surface activity elements, they can enhance the interface reaction during the soldering process, making the solder and Cu substrate well bonded. However, with excessive addition of Ga and Nd, the results of drastic interface action could cause the formation of a too thick IMC layer which will perform an adverse impact on the reliability of soldered joints. Figure 5: Interface morphology of soldered joints after composition adjustment. Figure 6: XRD result of Sn–9 Zn–1 Ga–2 Nd soldered joint after 1200 h.  
In summary, it can be determined that by controlling the total addition and appropriate ratio of Ga and Nd, Sn-Zn soldered joints with optimal properties and reliability could be obtained without the risk of tin whisker growth. The results would be available for guiding the study of tin whisker growth inhibition method.
## 4 Experiments and Results
### Results  
Table 4 shows the performance of the aspect detection module for each category. In general, the model shows a very good \(F1\) value in all the categories. The model gives the best performance in the food and price categories, whereas the worst value appears in the anecdotes category.  
SentiRank was also compared with the top three systems in the ABSA task in SemEval-2014 [40], as shown in Tables 5 and 6. The best values are shown in bold. The results for SentiRank are quite close to the ones of the top system, NRC-Can-2014 [24], in both tasks.  
To get more insights into the system's performance, we analysed the confusion matrices shown in Figure 5. The figure on the top left is the confusion matrix of the overall score, when we ignore the category and consider only the polarities. The other five figures show the confusion matrices at the category level of analysis. In general the system's performance is quite good, as 82.8% instances were correctly classified. The best values  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System** & **Accuracy** \\ \hline NRC-Can-2014 [28] & **82.92** \\ \hline UNITOR [42] & 76.29 \\ \hline XRCE [43] & 78.14 \\ \hline SentiRank & 82.44 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of the polarity calculation module.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Aspect** & **P** & **R** & **F1** & **Accuracy** \\ \hline Food & 94.43 & 89.23 & 91.76 & 91.63 \\ \hline Service & 93.75 & 87.21 & 90.36 & 96 \\ \hline Price & 98.61 & 85.54 & 91.61 & 98.25 \\ \hline Ambience & 86.73 & 83.05 & 84.85 & 95.63 \\ \hline Anecdotes & 80 & 75.21 & 77.53 & 87.25 \\ \hline Average & 90.70 & 84.04 & 87.22 & 93.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of the aspect detection module.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**System** & **P** & **R** & **F1** \\ \hline NRC-Can-2014 [24] & **91.04** & **86.24** & **88.58** \\ \hline UNITOR [42] & 84.98 & 85.56 & 85.26 \\ \hline XRCE [43] & 83.23 & 81.36 & 82.28 \\ \hline SentiRank & 90.41 & 84.68 & 87.45 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of the aspect detection module.  
correspond to the positive and negative classes, whereas the worst ones appear in the conflict and neutral classes, where most samples were misclassified as positive. To understand the performance of the system for each category, we analysed the confusion matrix for each of them. The best performance appeared in the food, service and price categories. The ambience category showed the worst performance, followed by the \"Misc\" category. Such a finding can be attributed to the low number of training examples for these two categories. As the positive class had the highest number of training samples in all the categories, it showed a remarkable performance in all of them. However, on the other hand, this superiority of the positive examples caused a bias in the classifications and led to a low performance in other categories.
# A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach
## 2 Materials and Methods
### Proposed Method
#### 2.2.1 General Structure of VNSPR  
VNSPR is a hybridization of a VNS with a PR procedure. It holds benefits from both algorithms. Figure 1 depicts the elements and overall structure of the presented VNSPR. The VNS adjusted and transformed the neighborhood during the search procedure to mitigate the solution being entrapped in the local optimal solution found. Initiating with an existing solution, the VNS seeks within a subset of neighborhoods, each of which is investigated using a local search procedure. The hybrid metaheuristic includes a variable neighborhood descent (VND) section to enrich the operation of the VNS. PR is used in various strategies to incorporate search expansion and solution diversity. This method creates new solutions by tracing paths that unite superior quality solutions to elite solutions.
# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 3 Methodology  
### Multilayer Perceptron (MLP) Networks  
Multilayer perceptron (MLP) models can be considered a classic neural network approach for economic time series forecasting, widely used in the literature [28; 44; 45; 46; 47; 48]. The main reason for their extensive use is that they are considered universal approximatorsmeaning they can approximate any continuous function with a hidden layer, provided it has enough neurons. Despite their simple and easily programmable structure [3; 25], they are the subject of numerous research studies [27], showing promising results.  
The processing units that constitute the network (neurons) are structured into multiple layers, hence the term \"multilayer\". The first layer represents the input data fed into the neural network, while the final layer is responsible for producing an output that corresponds to the network's response to the processed data. Between these two, there may be one or more intermediate layers. Each neuron in each layer processes the information it receives from all the neurons in the preceding layer:  
\[x_{j}^{l}=f\left(\sum_{i}w_{ji}^{l}x_{i}^{l-1}+b_{j}\right). \tag{1}\]  
In this equation, \(x_{j}^{l}\) represents the output of neuron \(j\) in layer \(l\). This output is derived from the outputs of neurons in the previous layer, \(x_{i}^{l-1}\), which are multiplied by the weights \(w_{ji}^{l}\), representing the connections between this neuron and those in the preceding layer. For this reason, these layers are often referred to as dense layers [49]. The output \(x_{j}^{l}\) is calculated by applying an activation function \(f\) (0) to the weighted sum of the inputs plus a bias term \(b_{j}\). In hidden layers, common activation functions include the hyperbolic tangent or the sigmoid function, while a linear function is generally used in the output layer.  
The power of neural models, such as MLPs, lies in their ability to learn from data, enabling them to perform artificial intelligence tasks. Therefore, it is essential to train them before use. In this process, the available data are split into two groups: one for training and another for validation. The validation set is used to assess the performance of the trained network, which is a standard procedure in neural models.  
During training, the data are organized into pairs of inputs and desired outputs. These data are fed into the network, which generates an output. This output is then compared to the expected one to compute the error. The error is propagated backward through the layers to adjust the weights of the neurons to minimize it. The algorithm that performs this adjustment is known as \"backpropagation\" [50].
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 3 Surface Preparation and Its Impact on Debonding
### Behaviour of IRP System in 152.4 Mm (6 inch) Discontinuous Host Pipe  
This section investigates how the adhesive layer affects the behavior of an IRP system within a host pipe featuring a 152.4 mm (6 inch) wide circumferential gap. This study commences with the preparation of the ablated surface within the host pipe, exhibiting an adhesive strength of 40 MPa (5.8 ksi), to determine the maximum load capacity for the broader discontinuity system. Figure 27 depicts the stress response of the 152.4 mm (6 inch) discontinuous IRP system when subjected to 80% of the full load (71.2 kN or 16 kip) utilized for the 12.7 mm (0.5 inch) discontinuous IRP system, which is equivalent to 56.9 kN (12.8 kip). It is observed that when the applied force exceeds 56.9 kN (12.8 kip), the 152.4 mm (6 inch) discontinuity width IRP system experiences local deformation failure on the crown of the discontinuity section. To maintain simplicity, the load of 56.9 kN (12.8 kip) is defined as the maximum applied load for the 152.4 mm (6 inch) discontinuous IRP system while considering various levels of adhesive strengths resulting from different surface preparation methods before bonding the host pipe and the IRP system.
## 2 Materials and Methods
### The Model Used for the Statistical Analysis of the Results  
The following models were used in the work to estimate the tolerance field of the basic pitch:  
_Model_\(\mathbf{0}\) is implicitly the nominal dimension and its deviations (the original tolerance zone). _Model_\(\mathbf{I}\) assumes a normal, symmetric distribution. The ISO estimator will be here \(\pm 3\sigma\), also called estimator \(6\sigma\). _Average_ is used as \(N\!S\):  
\[p_{b_{design}}=\overline{X}=\frac{1}{N}\cdot\sum_{i=1}^{N}X_{i} \tag{5}\]  
_Model_\(\mathbf{II}\) assumes a symmetrical normal distribution. The ISO estimator here will be _PCR_. _Average_ is used as \(N\!S\). The estimated tolerance zone is calculated according to the following formula:  
\[T_{design}=6\cdot\sigma\cdot C_{p} \tag{6}\]  
where the \(C_{p}\) coefficient is equal to \(1.33\). _Model III_ is the Disper parameter calculated according to the following formula:  
\[Disper=2\cdot\max[(Max-Average),(Average-Min)] \tag{7}\]  
The half Disper is the upper and lower deviations of the base pitch. _Average_ is used as _NS_. _Model IV_ and _Model V_ are the _Range_ parameters calculated according to the formula:  
\[R=Max-Min \tag{8}\]  
Half-_Range_ is the upper and lower deviation of the base pitch. As _NS_, _Median_:  
\[p_{b_{design}}=Med=\frac{X_{i_{max}}+X_{i_{min}}}{2} \tag{9}\]  
and Average are used for the models. From _Model VI_ to _Model XI_, it is the adoption of a symmetric _S-F_ distribution. The ISO estimator will be \(\pm\frac{1}{T_{N,\,\alpha}\sigma_{r}}\). The confidence level was assumed appropriately for the models: \(\alpha=99\%\), \(\alpha=95\%\), \(\alpha=90\%\), \(\alpha=80\%\), \(\alpha=60\%\) and \(\alpha=40\%\). _Average_ is used as _NS_. The standard deviation was calculated in various ways. When a symmetric Gaussian distribution was adopted, the standard deviation for the sample was calculated according to the following formula:  
\[\sigma=\sqrt{\frac{1}{N}\cdot\sum_{i=1}^{N}\left(X_{i}-\overline{X}\right)^{2}} \tag{10}\]  
When a symmetric _S-F_ distribution was adopted, the standard deviation of the sample was calculated according to the formulas:  
\[\sigma_{t-S}=\sigma_{r}\cdot t_{N,\alpha} \tag{11}\]  
\[S=\sqrt{\frac{1}{N-1}\cdot\sum_{i=1}^{N}\left(X_{i}-\overline{X}\right)^{2}} \tag{12}\]  
\[\sigma_{r}=\frac{S}{\sqrt{N}} \tag{13}\]  
The value of Student's t-coefficient depends on the adopted confidence level. The data necessary to present the results were calculated according to the following formulas:  
\[T_{true}=USL-LSL \tag{14}\]  
\[T_{design}=6\cdot\sigma\cdot C_{p} \tag{6}\]0  
The measurement uncertainty of the disk micrometer should be added to each estimated zone; however, in order to determine the value of the base pitch, measurements were made twice: first across \(n+1\) teeth and then across \(n\) teeth. Each of these measurements is burdened with the _MPE_ of the micrometer.
## 2 Materials and Methods  
### Synthesis  
The BG powders were synthesized using spray drying method based on three most common compositions of 58S (60 mol% SiO\({}_{2}\), 36 mol% CaO, and 4 mol% P\({}_{2}\)O\({}_{5}\)), 68S (70 mol% SiO\({}_{2}\), 26 mol% CaO, and 4 mol% P\({}_{2}\)O\({}_{5}\)), and 76S (80 mol% SiO\({}_{2}\), 16 mol% CaO, and 4 mol% P\({}_{2}\)O\({}_{5}\)). By using tetraethyl orthosilicate (TEOS, Si(OC\({}_{2}\)H\({}_{5}\))\({}_{4}\), 99.9 wt%, Showa, Tokyo, Japan), calcium nitrate tetrahydrate (CN, Ca(NO\({}_{3}\))\({}_{2}\)-4H\({}_{2}\)O, 98.5 wt%, Showa, Tokyo, Japan), and triethyl phosphate (TEP, (C\({}_{2}\)H\({}_{5}\))\({}_{3}\)PO\({}_{4}\), 99.0 wt%, Alfa Aesar, Heysham, UK) as the source of Si, Ca and P, precursor solutions of various BG powders were prepared by dissolving TEOS, CN and TEP in 10.0 g of 0.5 M HCl and 360.0 g of ethanol based on their given Si, Ca and P ratios. All precursor solutions were stirred at room temperature for 4 h to achieve homogeneity. For the spray drying process, the final precursor solutions were diluted with 1 L deionized water with concentration of 1.8 M and dispersed into fine droplets using a high-speed rotating disc at 20,000 rpm, with flow rate around 50 mL/min, into the spray dryer machine (SD DD-03, IDTA machinery Co., New Taipei, Taiwan), with the hot chamber set at 200 \({}^{\circ}\)C to form BG powders. The resulting dried BG powders were collected in a drying tube and calcined at 600 \({}^{\circ}\)C for 1 h to form the final products. Finally, the BG powders were washed by 1 M nitric acid to eliminate any impurity and un-reacted species. Note that following the procedure as described above, a precursor solution of 1 L can yield BG powder around 100 g, corresponding to a yield of up to 90%.