esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 4 Adversarial Attack Applications
### Attack in Physical World
#### 4.4.4 Face Recognition  
[PERSON] et al. [37] focused on facial biometric systems, which are widely used in surveillance and access control. They develop a systematic method to automatically carry out attacks, which are realized through printing a pair of eyeglass frames. When the eyeglass frame is worn by an adversary whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow him to evade being recognized or to impersonate another individual. Figure 13 shows impersonation using the frame. Their investigation focuses on white-box face-recognition systems, but they also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.  
Figure 12: Illustration of the transfer learning attack setup (Image Credit: [PERSON] et al. [83]).  
Figure 13: An impersonation using frames. Left: Actress [PERSON] [85]. Image classified correctly with probability 1. Middle: Perturbing frames to impersonate (actor) [PERSON]. Right: The target [86] (Image Credit: [PERSON] et al. [37]).  
[PERSON] et al. [38] studied an interesting example of an actual adversarial attack and found that infrared light completely invisible to humans can also be used to generate disturbances. As is shown in Figure 14, the adversary installed some infrared LED on the cap peak, which could illuminate the attacker's face with some points from the camera angle, but people nearby would not notice. With this technique, they successfully attacked the facial authentication system in white-box settings.  
This method differs from other attack methods in two aspects. Firstly, this method does not directly optimize perturbation. They established a model describing the infrared points generated by LED lamps with position, radius, and brightness of the points as parameters. Therefore, the optimizer optimized the model parameters to accurately search for disturbances close to the real infrared points. Secondly, they developed a real-time feedback system for adversaries to adjust the LED position to help adversaries better achieve disturbance. Therefore, adversaries do not need to print disturbance for each target; instead, they only need to adjust the device to attack different targets.
## 4 Noise Reduction Analysis of the Noise Wall
### Noise Reduction Effect of the Noise Wall  
Referring to an application case of the noise wall in a rail transit [34], the noise wall is set 10 m from the centerline of the bridge, and the wall height is set to 6m. The noise wall is established in the above bridge BEM model. To ensure the accuracy of noise prediction, the element size of the noise wall model is set to 0.2 m, and the wall surface is considered as rigid surface. The sound speed and density of air are 340 m/s and 1.225 kg/m\({}^{3}\), respectively. Taking the loaded containers condition as an example, the effect of the noise wall on the bridge radiated noise is investigated. To comprehensively evaluate the noise reduction effect of the noise wall, 12 typical field points (N1\({}^{\prime}\)-N12\({}^{\prime}\)) with heights of 2 m, 4 m, and 6 m above the ground and lateral distances of 1 m, 5 m, 10 m, and 15 m from the noise wall are selected, as shown in Figure 9.  
Figure 10 shows the comparison of SPLs at typical field points under conditions with or without the noise wall. It can be observed that the SPLs of all points are significantly reduced after setting the noise wall, and the closer the field point is to the ground and the noise wall, the better the overall noise reduction effect of the noise wall is. At field points N1\({}^{\prime}\)-N4\({}^{\prime}\), the noise reduction effect of the noise wall is significant in the frequency ranges of 5-75 Hz and above 130 Hz. In the frequency range near the main frequency (60-90 Hz), the maximum reduction values of SPLs at each point are 24.6 dB, 28.6 dB, 33.1 dB, and 20.4 dB, respectively. For field points N5\({}^{\prime}\)-N8\({}^{\prime}\), the noise wall has a great noise reduction effect in the frequency ranges below 35 Hz and above 140 Hz, and the maximum  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (Hz)** & \(\mathbf{k}\) & \(\mathbf{k_{x}}\) & \(\mathbf{k_{y}}\) \\ \hline
43.0 & 0.79 & 0.57 & 0.23 \\
70.1 & 1.30 & 0.57 & 0.47 \\
105.5 & 1.95 & 0.96 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Acoustic and modal wavenumbers of the deck at peak frequencies.  
Figure 9: Layout of typical field points.  
SPL reduction values of each point near the main frequency are 23.3 dB, 20.8 dB, 14.9 dB, and 14.6 dB, respectively. Regarding field points N9\({}^{\prime}\)-N12\({}^{\prime}\), the main action range of the noise wall is in the frequency range below 20 Hz and between 135 and 150 Hz, and the corresponding maximum reduction values of SPL in the main frequency range are 18.7 dB, 14.6 dB, 14.6 dB, and 28.8 dB, respectively.
# Using Artificial Intelligence for Space Challenges: A Survey
## 3 Challenges in Space Applications
### Space Exploration  
The impact of AI on defining and improving the future of space exploration is relevant. Many AI applications exist, from planetary navigation to robot exploration and deep space exploration of astronomical bodies. For example, the first image of a black hole was obtained by a Bayesian algorithm used to perform deconvolution on images created in radio astronomy [27]. Now, researchers are working on more complex AI algorithms to obtain more accurate images of black holes. These algorithms will probably help scientists identify and classify different deep-space objects [28]. An example is the discovery of two Kepler Exoplanets, namely Kepler 80g and Kepler 90i, orbiting Kepler 80 star system and Kepler 90 star system, respectively. The knowledge of the population of exoplanets is a challenge for astronomers because such retrieval of information requires an automatic and unbiased way to identify the exoplanets in these regions [29].  
The management of space debris, such as abandoned launch vehicle stages and fragmentation debris, is a demanding problem [30; 31] included in the field of space situational awareness (SSA) [32]. Indeed, researchers stress the importance of maintaining a resident space object database because of the necessary operation of SSA for a future Space Traffic Management system [33; 34]. However, the real concern with space debris is the contingency of collisions with satellites or the spacecraft, generating undesirable space accidents. Deep learning techniques can enhance the accuracy of laser-range technology traditionally used to overcome this problem. Another challenge regards the production of better maps of the planets' surface. One example is a project developed by NASA and Intel on an AI system that can help astronauts find their way on planets [35]. The program was applied to the moon's surface and then to the Mars exploration program. The collaboration of both humans and swarm robots can meet the challenges of space exploration. Indeed, multi-robot systems can be trained by humans using AI techniques [36]. Other efforts have been carried out in autonomous and intelligent space guidance problems with the support of deep and reinforcement learning [37; 38; 39; 40].  
At the same time, during the space exploration missions to Mars, monitoring and assessing the health of astronauts is crucial [41]. The applications of AI in healthcare focus on deep learning algorithms for scientific discovery or for advanced pattern recognition software for diagnosis, giving new perspectives on the future of astronauts' medical care.  
Furthermore, detecting astronauts' speech and language disorder signs to interpret their psychological behavior and to support human-robot cooperation has been explored using natural language processing [42; 43].
## 3 Results and Discussion
### Verification of Physicochemical Properties of Synthesized Peptides
#### 3.9.3 Verification of Antioxidant Activity of Synthetic Peptides
Notably, all seven peptides synthesized in our experiment contained hydrophobic amino acids to a high degree overall, which may account for their high hydroxyl radical scavenging activity [46]. Additionally, studies have found that glycine (Gly) has a significant influence on the antioxidant activity of substances. Peptide 3, which has a high glycine content [47], also exhibited high hydroxyl radical scavenging activity, aligning with these findings. Figure 13: OH\({}^{-}\) scavenging activity at various concentrations for each synthetic peptide. Determination of Superoxide Anion Scavenging Activity  
In the superoxide anion scavenging experiments, the synthetic peptides generally exhibited strong scavenging capabilities. As shown in Figure 14, among them, peptide 1 demonstrated the highest scavenging activity, reaching 42.14%, followed by peptides 6 and 7 with scavenging rates of 38.61% and 36.09%, respectively. Studies have speculated that aspartic acid present in peptide sequences may significantly contribute to their superoxide anion scavenging ability. Compared to other synthetic fragments, peptides 1 and 7 contain aspartic acid, aligning with the research direction inferred from these studies [48]. Additionally, the sequence which contains aspartic acid (D) exhibited the highest overall antioxidant activity and demonstrated strong superoxide anion scavenging ability in mouse oxidative stress response experiments [49]. Furthermore, studies have indicated that valine (Val) or leucine (Leu) at the N-terminus can positively affect the antioxidant activity of active fragments. Peptide 6, which has valine at its \(N\)-terminus and is rich in hydrophobic amino acids, was speculated to possess high superoxide anion scavenging ability due to these characteristics [50]. Figure 14: O\({}_{2}^{-}\) scavenging activity at various concentrations for each synthetic peptide. Determination of Ferrous Ion Chelating Ability  
As illustrated in Figure 15, peptides 7 and 3 exhibit strong ferrous ion chelating abilities, while peptide 2 demonstrates the weakest metal chelating ability, with a chelating rate of only 16.81% at a concentration of 1 mg/mL. At the same concentration, peptide 7 achieves a chelating rate of 44.72%, and peptide 3 reaches 40.59%. Both of these peptides contain two acidic amino acids, glutamic acid (E) and aspartic acid (D). Studies have indicated that acidic amino acid residues can chelate transition metal ions, thereby achieving antioxidant effects [51]. It was speculated that the presence of these two acidic amino acids may contribute to the strong metal chelating abilities of these two peptides [52; 53]. Similarly, peptide 7 not only contains abundant hydrophobic amino acids but also is enriched with acidic amino acids such as glutamine (Q), tyrosine (Y), tryptophan (W), and asparagine (N). This might be one of the reasons for the good antioxidant activity of peptide 7.
## 2 Problem Description and Formulation
### Entropy Generation  
It should be noted that the entropy production in a system is a key thermodynamic parameter that can improve the performance of such a system. In other words, the determination of this parameter is used to analyze the total losses and, hence, to propose an optimization procedure. In the natural convection process, the entropy generation is associated with the heat transfer and with the fluid friction. According to [PERSON] [17], the local entropy generation (\(S_{i_{0}}\)) can be expressed as:  
\[S_{i_{0}}=\frac{k}{{T_{0}}^{2}}\left(\left|\
abla T\right|\right)^{2}+\frac{2 \mu}{T_{0}}S_{i_{y}}S_{i_{y}} \tag{8}\]  
where \(k\) is the thermal conductivity of the fluid, \(\mu\) is the dynamic viscosity and \(S_{i_{y}}\) is the rate of the deformation tensor. In this expression, the first term (right-hand) is due to the heat transfer (\(S_{i,h}\)), while the second term is due to the viscous effects of the fluid (\(S_{i,d}\)). A dimensionless form of their expressions can be obtained as follows:  
\[\tilde{S}_{i_{0}}=\tilde{S}_{i,h}+\tilde{S}_{i,d} \tag{9}\]  
with:  
\[\tilde{S}_{i,h}=\left(\left|\
abla\tilde{T}\right|\right)^{2},\ \tilde{S}_{i,d}=\varphi 2 \tilde{S}_{i_{y}}\tilde{S}_{i_{y}}\ \ \text{and}\ \ \varphi=\frac{\mu T_{0}}{k}\Bigg{(}\frac{\alpha}{L \left(T_{h}-T_{c}\right)}\Bigg{)}^{2}\] (10a) Explicitly, \[\tilde{S}_{i,h}\ \text{and}\ \ \tilde{S}_{i,d}\] can be expressed as: \[\begin{split}\tilde{S}_{i,h}=&\Bigg{(}\frac{ \partial\tilde{T}}{\partial X}\Bigg{)}^{2}+\Bigg{(}\frac{\partial\tilde{T}}{ \partial Y}\Bigg{)}^{2}\ \ \text{and}\\ \tilde{S}_{i,d}=&\varphi\Bigg{[}2\Bigg{(}\Bigg{(} \frac{\partial\tilde{U}}{\partial X}\Bigg{)}^{2}+\Bigg{(}\frac{\partial\tilde {U}}{\partial Y}\Bigg{)}^{2}\Bigg{]}+\Bigg{(}\frac{\partial\tilde{U}}{ \partial X}+\frac{\partial\tilde{V}}{\partial Y}\Bigg{)}^{2}\Bigg{]},\ \text{respectively}.\end{split} \tag{10b}\]  
Here, \(T_{0}\) is \(\left(T_{h}+T_{c}\right)/\,2\) ; \(\tilde{S}_{i_{y}}\) is the dimensionless deformation rate; and \(\varphi\) (\(=\tilde{S}_{i,d}\) / \(\tilde{S}_{i,h}\) ) is the irreversibility distribution ratio [6] that informs about the relative importance of the two mechanisms of irreversibility, _i.e._, \(\tilde{S}_{i,d}\) and \(\tilde{S}_{i,h}\). A further parameter that reflects the irreversibility distribution is the average Bejan number (\(Be\)) defined as [6,7]:  
\[\text{Be}=\frac{\tilde{S}_{i,h}}{\tilde{S}_{i_{0}}} \tag{11}\]Therefore, \(\mathrm{Be}>>0.5\) indicates the dominance of the heat transfer irreversibility; \(\mathrm{Be}<<0.5\) reflects the dominance of the fluid viscous effects; and \(\mathrm{Be}=0.5\) means that the two phenomena do not compete.  
The dimensionless total entropy generation (\(\tilde{S}_{{}_{T}}\) ) is obtained via the integration of the local entropy generation (\(\tilde{S}_{{}_{b}}\) ) throughout the entire domain, namely:  
\[\tilde{S}_{{}_{T}}=\int_{{}_{V}}\tilde{S}_{{}_{b}}dv \tag{12}\]  
For the sake of clarity, henceforth, we will omit any symbol indicating dimensionless quantities.
## 3 Results and Discussions
### Results of Combined Loading  
As the proposed model predicts the capacity with a good agreement, with the same model the combined loading analysis is carried out. The lateral response of the helical pile subjected to uplift loading is studied in the first case and later the uplift response to the laterally loaded helical pile is studied. In both cases, combined loading analyses are conducted on fully embedded piles only and the findings are reported below.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Types of Accelerometers
#### 1.3.4. Magnetic Induction  
A magnetic accelerometer is designed based on the unique physical properties of the magnetic fluid. Compared with conventional acceleration sensors, magnetic fluid accelerometers have more substantial shock resistance, sensitivity, lower energy consumption, leading to a better performance when operating with low-frequency movements [8; 9].
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 2 EMG Signals
### Feature extraction from an EMG signal
#### 2.2.4 Spatial Domain (SD)  
Spatial Domain features allow finding an improvement in the difference between postures and MES signal force levels, which provide information about the spatial distribution of the motor unit action potential (MUAP) and load between muscles [58].  
In theory, a classifier must be able to differentiate, according to the input values, to which class it belongs. An MES is, in essence, a one-dimensional pattern, so that the methods and algorithms developed for pattern recognition can be applied to its analysis. The information extracted from an MES, represented in a feature vector, is chosen to minimize the control error. The feature set should be selected as the one that separates as much as possible the desired output classes [27].
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments
### Implementation Details  
In this paper, we use the weight parameters of ResNet50 pre-trained on ImageNet as the initial weights of the network model. We follow [14; 38] as follows: (1) All experiments were performed on PyTorch. Random identity sampling is taken for each training image and resized to 256 \(\times\) 256. (2) AMANet was trained for 150 epochs. (3) For the VeRi-UAV dataset, a stochastic gradient descent (SGD) optimizer was utilized with an initial learning rate \(3\times 10^{-2}\), and the training batch size was set to 32. The model received 60 epochs of training during this phase. The training batch size for the UAV-VeID dataset is 32, and the SGD optimizer's starting learning rate is \(3\times 10^{-2}\). (4) In addition, all test batch sizes are 64. (5) For the testing phase, our main assessment metrics are Rank-n and mean average precision (mAP).
## 2 Design of the Proposed RDA with the EBGs
### Mutual Coupling Reduction with the Presence of the EBGs
Figure 3: (**a**) A simulation model of an EBG structure for the calculation of the reflection phase; (**b**) Simulated reflection phases of EBGs. EBG: Electromagnetic band gap. To examine the performance of the EBGs for the mutual coupling reduction, a 2 \(\times\) 2 array configuration is studied. A prototype was fabricated and a photograph was taken, as shown in Figure 5a. The antenna elements are spaced at a distance of \(d\) = 0.4\(\lambda\) along the \(x\)- and \(y\)-axes (\(\lambda\) is the wavelength in free space at 5.2 GHz). Figure 5b plots the measured and simulated reflection coefficients of the 2 \(\times\) 2 arrays with and without the EBGs when element #1 is excited. It can be observed that the \(-\)10 dB reflection bandwidths are centered at 5.2 GHz. The mutual coupling effects on three typical cutting planes, i.e., the horizontal, diagonal, and vertical planes, are investigated, as represented by the terms \(S_{21}\), \(S_{31}\) and \(S_{41}\), respectively. The results of a comparison of the measured and simulated results of \(S_{21}\), \(S_{31}\) and \(S_{41}\) are illustrated in Figure 6. Within the operating frequency band, maximum mutual coupling reductions of 9.47 dB, 12.78 dB and 1.38 dB were obtained by measurements on the horizontal, diagonal, and vertical planes, as shown in Figure 6a-c, respectively. Meanwhile, in the simulated results, the corresponding values were 10.78 dB, 17.1 dB and 6.62 dB. Note that due to the limited spacing between two patches, only one row of EBG cells was implemented. Therefore, the bandwidth for mutual coupling reduction was very narrow, but the designed frequency of 5.2 GHz was within this bandwidth. Reasonable agreement was also achieved between the measurement and the simulation. The discrepancy between the measurement and simulation results was mainly attributed to experimental tolerances and fabrication imperfections, especially the losses at the soldered joints between the coaxial cables and the patches. Figure 5: **(a)** The 2 \(\times\) 2 array configuration for consideration of the mutual coupling and a photograph of the fabricated prototype; **(b)** Measured and simulated reflection coefficients of the 2 \(\times\) 2 array with and without the EBGs. Figure 6: Measured and simulated results of (**a**) \(S_{21}\); (**b**) \(S_{31}\); and (**c**) \(S_{41}\).
## 3 Numerical Simulation Results
### Numerical Simulation of Failure Modes  
From the stress contour in Figure 6, it can be seen that as the blasting stress wave propagates in the surrounding rock, the stress of the tunnel and surrounding rock was redistributed. When the stress applied to the element of the numerical model exceeded the stress threshold, it became invalid, as shown in Equation (3).  
When the source of blasting disturbance was located from the left side of the tunnel, the left side of the straight wall was most severely damaged, followed by the right side, and the damage at the arch shoulder and arch foot of the straight wall was most obvious, as shown in Figure 6(a). When the blasting disturbance source was located above the roof of the tunnel, there was a small range of damage at the roof and arch foot of the tunnel, and the integrity maintenance was more complete than the other two locations, as shown in Figure 6(b). When the blasting disturbance source was located below the floor of the tunnel, the damage of the floor was the most severe, with a smaller range of damage to the arch crown but greater than the range of incident stress waves above the roof. As a whole, when the blasting disturbance is located above the roof, the integrity of the tunnel remains relatively intact, and the damage area of the surrounding rock is much smaller compared to the other two positions, as shown in Figure 6(c). However, when the blasting loads act from a horizontal position, the damage degree is significantly more obvious than in the vertical direction, and more attention should be paid to the impact of excavation blasting on the same horizontal tunnel during tunnel supporting.  
Figure 6: Dynamic response process of the tunnel under blast stress wave.
# The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review
## 2 Axial Compressive Stress
### Bourdon Effect Due to Internal Pressure  
As the fluid travels across the pipe component, pipe bends are subjected to internal pressure. The difference in the surface area of the pipe bend at the intrados and extrados results in unbalanced thrust forces [37; 38]. These forces contribute to large stress concentrations on the wall of the pipe bend, resulting in the deformation of the cross-section of the pipe bend [28; 39]. In the industry, the CSA-Z662 standard is used to assess the influence of internal pressure on a pipe bend. The Tresca stress distribution is one of the criteria used for assessment. According to the Tresca criterion, plastic deformation occurs when the maximum shear stress at a point of interest reaches the maximum stress in a uniaxial tension material at yield. [PERSON] et al. [29] showed that the maximum Tresca stress is located at the intrados of the pipe bend, while the minimum is at the extrados, as illustrated by Figure 1. This study showed that the Bourdon effect results in an in-plane bending moment for pipe bends with fixed ends.  
In the presence of a bending moment acting on a pipe bend, the pipe component is subjected to axial forces above the neutral axis, as illustrated in Figure 2. As a result, a resultant force which displaces the cross-section away from the neutral axis is created. This is known as the ovalization of the pipe bend's cross-section when subjected to internal pressure.  
Ovalization results in the internal surface of the pipe wall being subjected to tension while the external surface is subjected to axial compressive stress. The surface area of the pipe wall at the extrados is acted upon by a thrust force, displacing it away from the neutral axis, causing a reduced ovalization effect at this region. As a result, the maximum impact of ovalization is observed at the intrados of the pipe bend.  
The effects of axial compressive stress resulting from the ovalization of a pipe bend are more significant than the tensile stresses acting on the pipe [38]. Under this condition, the pipe bends are more susceptible to local buckling and failure.  
Figure 1: Tresca stresses on a 90\({}^{\circ}\) pipe elbow. (Reprinted/adapted with permission from [29]).