esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Architecture
### Scenarios  
Based on the information presented, we designed our architecture by addressing the three requirements mentioned at the beginning of this section. Our architecture relies on the scenario illustrated in Figure 1, which is described below.  
1. The user requires the license plate for his/her vehicle and configures the privacy preferences for monitoring. This information is registered in a smart contract stored in the private blockchain. At this point, the public and private keys of the user are also generated. When the user requires the license plate, the registration is done by a system connected directly to the blockchain, without going through the gateway. Moreover, only the authorized addresses can change the privacy preferences of a smart contract after it is registered.
2. When the license plate is captured by an license plate recognition (LPR) system, the captured image is sent to the gateway responsible for managing all communications.  
Figure 1: Proposed architecture.  
The gateway connects to the database that has stored the public keys corresponding to each license plate and retrieves the corresponding public key.
3. After retrieving the public key, the gateway connects to the blockchain and checks the privacy preferences of the captured license plate through the smart contract.
4. If the privacy preference of this license plate allows the image to be captured, the gateway stores its image in a storage service. On the other hand, if the user's preferences forbid the license plate capture, no image is stored.  
Figure 2 illustrates the scenario used when it is necessary to monitor an individual, as described below.  
1. To initiate the monitoring process, the government user must obtain a court order authorizing the monitoring. The court order must be sent to the gateway.
2. The order is encrypted using the license plate owner public key, and the smart contract privacy preferences are changed, allowing the license plate to be monitored for a certain time.
3. Upon completion of the monitoring time, the encrypted court order is sent to the user stating that he/she has been monitored with an order. When the monitoring time ends, the contract privacy preferences are updated to the original settings.
## 2 Literature Review
However, methods based on AHP and MILP always assumed the research environment was certain, overlooking the uncertainty in construction waste management that impacts the planning of recycling initiatives [25]. Heuristic algorithms are widely used to solve strategic and operational planning problems in various industries [34; 35; 36]. When compared to multi-objective MILP models, they can efficiently solve multi-objective problems and overcome the discontinuity of objective and constraint functions [37; 38].However, they require complex parameter settings and incur high computational costs. The swarm algorithm and GA-PSO algorithm were successfully applied to balance various objectives but failed to optimize the capacity of recycling centers in uncertain situations [39; 40; 41]. From a lifecycle perspective, [PERSON] and [PERSON] employed lifecycle assessment (LCA) to identify eco-friendly candidate locationsfor recycling centers [42]. This method is effective for optimizing environmental objectives; however, it does not align with the government's focus on recycling rates [40; 41]. Robust optimization (RO) methods were applied in solving the site selection problems under uncertain factors. By setting the maximum disturbance thresholds for the uncertainty factors, the robust methods guarantee the decision-maker against stochastic uncertainty [47]. This is proven by [PERSON] and [PERSON] [26], [PERSON] et al. [44], and [PERSON] et al. [45], where they proposed a robust optimization model for designing a construction recycling network that not only accounted for the numbers, locations, and capacities of sorting and processing facilities but also the quantities of construction waste allocated between them. Ultimately, this model emphasized the adaptation of facility capacities as a way to counteract supply uncertainty. Notably, the influence of transportation costs on the recycling rate was also factored in. Likewise, [PERSON] et al. [43] explored two uncertainties (waste generation and transportation costs) to determine optimal facility locations that can minimize the total costs. With the same optimal objective, another robust method was used to develop the facility location model, taking into consideration supply and demand uncertainties [25]. This approach aligns the model more closely with the realities of the construction industry, helping decisionmakers choose better locations. However, robust optimization methods always take into account the worst possible scenarios, leading to potentially substantial investments by decision makers to attain the objective function [25]. Stochastic optimization (SO) provides an alternative mathematical programming method to deal with uncertainty in construction waste management. This method is proven highly versatile by its wide application in many uncertain location problems, e.g.,
# Experimental Study on the Soil-Structure Responses Induced by Tunnelling in Limited Space
## 4 Results of the Model Test
### Convergence of the Tunnel  
With the excavation of a tunnel, the tunnel will inevitably experience uneven deformation. Figure 12 depicts the typical pattern of tunnel convergence [44]. In Figure 12, the diameter of the tunnel at initial state is defined as \(D\). When a tunnel is subjected to an external force, the tunnel will deform correspondingly [31; 44]. The horizontal axis of the ellipse was defined as \(D_{l}\) while the vertical axis of the ellipse was defined as \(D_{s}\). Finally, the ovality of the deformed tunnel, \(T\), can be calculated as follows:  
\[T=\frac{D_{l}-D_{s}}{D}\times 100\% \tag{2}\]  
If \(T\) is positive, this means that the tunnel will be compressed vertically and the left and right waist section of the tunnel will bulge out horizontally. If \(T\) is negative, this means that the tunnel is compressed horizontally.  
Figure 11: Distribution of the ground settlement: (**a**) tunneling in the limited space; (**b**) tunneling at “greenfield” site.  
Figure 12: Schematic of the deformed tunnel.  
Figure 13 illustrates the convergence of the tunnel during the whole experiment process. According to Figure 13, as the \(T\) remained positive during the whole process, the tunnels were all compressed vertically and bulged out horizontally under the two circumstances. The measured pattern of the tunnel deformation was consistent with the reported research findings [44]. However, when tunneling in the limited space, the ovality of the final state of the tunnel was 0.310%, which was about 23% smaller than the result induced by the construction of the tunnel at the greenfield site.
# An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes
## 1 Introduction  
The issue of electric bike helmet usage has emerged as a crucial concern for road traffic safety, along with the rapid growth of electric bikes in China. However, the detection of helmet wearing encounters numerous challenges and complexities in practical applications. Firstly, the detection algorithm must possess high precision to reduce the risks of misjudgment and missed detections owing to variations in helmet color, style, material, and other factors. Secondly, helmet wearing detection necessitates the consideration of intricate background interferences and demands swift detection capabilities to ensure both accuracy and practicality in helmet detection. Furthermore, helmet targets often constitute small components within the overall scene, presenting as diminutive objects [1, 2], which pose an additional challenge to the accuracy of the detection algorithm.  
The helmet target detection algorithms can be classified as conventional detection methods or deep learning-based detection approaches [3; 4]. The former relies on visual detection techniques that utilize manually engineered features such as Histogram of Oriented Gradient (HOG) and adaptive Gaussian mixture models [5; 6]. These methods involve separating targets, including helmets, electric bikes, and people, from the background before performing classification. This method not only affects detection accuracy, but also reduces computational speed, making it often unable to meet the real-time detection needs of traffic scenes. On the other hand, neural networks have made a big splash in deep learning and are widely used in areas such as image recognition, natural language processing, and reinforcement learning [7; 8; 9]. In terms of image recognition, these algorithms analyze and learn data features by simulating the operations of actual neural networks, offering improved detection performance compared to conventional methods.  
Deep learning-based target detection networks can be broadly classified into two-stage networks and one-stage networks. Two-stage networks, such as Fast R-CNN [10] and Faster R-CNN [11], follow a two-step process. They generate a set of candidate regions within an image and subsequently perform feature extraction and classification on these candidate regions. While these networks achieve high accuracy, they suffer from limited real-time performance, large model sizes, and are not well-suited for traffic scenes.  
In contrast, one-stage networks, including the YOLO series [12; 13; 14; 15], SSD [16], and RetinaNet [17], take a different approach. These networks eliminate the step of generating candidate regions on the image and treat target region localization as a regression problem. They directly divide the image into a grid of candidate regions and predict the target's location and category in a single step, enabling end-to-end detection. Although the accuracy of these networks is slightly lower than that of two-stage networks, they have some advantages, such as reduced model size, improved detection speed, and better suitability for real-world scenarios.
# A Feasibility Study of the Use of PZT Actuators for Active Control to Enrich Engine Sound
## 4 Experiment Results and Discussions
### Implementation of \(A(z)\) of the Proposed ASE Algorithm  
In order to obtain the control filter \(A(z)\) in Equation (9) of the ASE algorithm, firstly, inverse modelling was conducted to calculate the optimal FIR filter of the inverse secondary path through Equations (6)-(8), and then its modulus was taken. The main factors associated with the accuracy of the inverse FIR filter in the design process were the filter length \(I\) and the modelling delay \(\Delta\). Hence, a number of calculations were conducted to determine the optimal values of \(I\) and \(\Delta\) which minimize the mean square error (MSE) between the desired signal \(d^{\prime}(n)\) and the output signal \(\hat{y}(n)\) plotted in Figure 4a. The filter length \(I\) was varied from 1000 to 14,000 samples, and in each \(I\), the modelling delay \(\Delta\) was changed from 0 to \(I\) samples.  
Figure 9 shows the MSE surface against \(I\) and \(\Delta\), and the minimum MSE at each \(I\) (the thick line with circles) and its optimal \(\Delta\). The minimum MSE had a smaller value as \(I\) was increased, and so it may be even lower above 14,000 samples of \(I\).  
Figure 8: Impulse responses of the secondary path model.  
Figure 7: A measured magnitude response of the secondary path of the PZT-plate actuator.  
However, in this study, \(\mathbf{a}_{opt}\) in Equation (6) was designed with 4000 samples of \(I\) and its optimal \(\Delta\) of 2400 samples based on some practical considerations including adequate response speed to a rapid change of engine speed, the computational complexity, and the MSE value as shown in Figure 10.  
Then, \(\mathbf{a}_{opt}\) was transformed in the z-domain to obtain its magnitude response \(\left|\hat{S}^{-1}(z)\right|\) for \(A(z)\), as expressed in Equation (9). Figure 11 shows the comparison of the magnitude responses of \(S(z)\), \(A(z)\), and \(S(z)A(z)\) indicated by dashed, dotted, and solid lines, respectively. Here, the magnitude response \(S(z)\) is the same as that given in Figure 7. This comparison indicates that \(A(z)\) is well-designed to compensate for the magnitude of \(S(z)\) in most of the frequency range from 30 to 400 Hz within about \(\pm\)5 dB of errors, except for about 190 and 230 Hz which are the anti-resonance frequencies of \(S(z)\). At these two frequencies, the magnitude responses of \(A(z)\) were about 19 dB and 9 dB less than ideal magnitudes for perfect compensation, respectively. This is because \(A(z)\) was designed to have robust stability at those anti-resonance frequencies that can be easily changed in the secondary path by using the nominal secondary path. In addition, \(\beta\) in Equation (6) affected \(A(z)\) to prevent it from having excessive magnitudes. The designed \(A(z)\) was implemented in the ASE algorithm using a lookup table.  
Figure 10: Impulse response of the optimal FIR inverse filter \(\mathbf{a}_{opt}\).  
Figure 9: MSE surface against filter length \(I\) and modelling delay \(\Delta\), and the minimum MSE at each \(I\) (the thick line with circles).
## 4 Model Training and Experimental Results
### Experiment Based on Publicly Available Dataset
#### 4.1.2 Comparative Analysis of Identification Performance of Different Model  
This experiment utilized the NinaPro DB1 dataset, covering 17 different gestures. The InRes-ACNet model, as proposed in this study, was employed for sEMG gesture recognition. The experimental results were compared with mainstream recognition methods reported by other researchers, as illustrated in Table 2. The results indicated that traditional sEMG gesture recognition methods, such as SVM and the random forest algorithm, yielded relatively low gesture recognition rates of 69.45% and 75.36%, respectively. The adoption of deep-learning methods, specifically the convolutional neural network variant MyoCNN and VGGNet models, significantly enhanced the recognition performance, with rates of 78.25% and 81.12%, respectively. Furthermore, the accuracy of the MSCNet model  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Params** & **Best Test Acc** \\ \hline ResNet50 & 24.03 M & 82.71\% \\ ACResNet & 24.04 M & 85.98\% \\ InResNet & 24.25 M & 86.31\% \\ InRes-ACNet & 24.26 M & 87.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation experiment of the InRes-ACNet model.  
increased to 83.24% through the extraction of multi-scale features. The InRes-ACNet model proposed in this study, integrating a multi-scale module, the ResNet50 architecture, and the ACmix module, achieved a recognition rate of 86.46%, demonstrating considerable promise in gesture recognition. This outcome further substantiates the efficacy of the attention mechanism and multi-scale feature fusion strategy in enhancing the performance of sEMG gesture recognition tasks.
## 2 System Model
Moreover, \(u=2.07123\frac{\sin\varphi}{\sin\varphi_{\mathrm{3 dB}}}\), where \(\varphi\) is the angle between the location of the satellite user and the beam center with respect to the satellite, and \(\varphi_{\mathrm{3 dB}}\) is the 3-dB angle. \(L_{S}\) is the free space loss of the secondary link. Besides, \(h_{S}\) is the fading channel power gain of the secondary link. Herein, we employ the widely-adopted Shadowed-Rician fading model with closed formula, which can be used for mobile/fixed terminals operating in various propagation environment. According to [34], the probability density function (PDF) of \(h_{S}\) is shown as  
\[f_{h_{S}}\left(x\right)=\alpha\exp\left(-\beta x\right){{}_{1}}F_{1}\left({{m_ {S}},1,\delta x}\right), \tag{4}\]  
where \({{}_{1}}F_{1}\left(\cdot,\cdot,\cdot\right)\) denotes the confluent hypergeometric function [35] and \(\alpha\), \(\beta\) and \(\delta\) can be calculated as  
\[ \begin{array}{l}\alpha=\frac{1}{2b_{S}}{\left(2b_{S}m_{S}+\Omega_{S}\right) }\\ \beta=\frac{1}{2b_{S}}\alpha\frac{\Omega_{S}}{2b_{S}(2b_{S}m_{S}+\Omega_{S})}, \end{array}  \tag{5}\]  
where \(2b_{S}\) is the average power of the scatter component, \(\Omega_{S}\) is the average power of the line-of-sight (LOS) component and \(m_{S}\) is the Nakagami fading parameter. Similarly, the interference power \(P_{i}\) at the base station (BS) in primary terrestrial networks can be calculated as  
\[P_{i}=P_{t}G_{t}\left(\theta^{\prime}\right)G_{BS}L_{p}h_{I}, \tag{6}\]  
where \(G_{t}\left(\theta^{\prime}\right)\) is the equivalent transmit antenna gain for terrestrial interference link with off-axis angle \(\theta^{\prime}=\arccos\left(\cos\left(\theta\right)\cos\left(\psi\right)\right)\) and \(\psi\) denotes the angle between the over horizon projected main lobe of the satellite user and the BS [36]. In addition, \(G_{BS}\) is the receive antenna gain at the BS and, \(L_{p}\) and \(h_{I}\) are free space loss and the fading channel power gain of the terrestrial interference link, respectively. As for \(h_{I}\), Nakagami fading distribution is considered and \(h_{I}\) follows the PDF given by [16]  
\[f_{h_{I}}\left(x\right)=\frac{\varepsilon^{m_{I}}x^{m_{I}-1}}{\Gamma\left(m_{I} \right)}\exp\left(-\varepsilon x\right), \tag{7}\]  
where \(\Gamma\left(\cdot\right)\) is the Gamma function [35], \(m_{I}\) is the Nakagami fading parameter, \(\Omega_{I}\) is the average power and \(\varepsilon\ =\ m_{I}/\Omega_{I}\). For brevity, we denote \(G_{S}=G_{t}\left(\theta\right)G_{r}\left(\varphi\right)L_{S}\) and \(G_{I}=G_{t}\left(\theta^{\prime}\right)G_{BS}L_{p}\) in the rest of the paper. To facilitate the analysis of the average EE limits in cognitive satellite terrestrial networks, it is assumed that the satellite user has perfect channel state information (CSI) about \(h_{S}\) and \(h_{I}\) at all fading states. Note that \(h_{S}\) can be obtained by estimating it at the satellite and sending it back to the satellite user through a feedback link. Furthermore, \(h_{I}\) can be obtained through cooperation with the BS, or from a third party such as the spectrum manager [37].
## 3 Presentation and Discussion of the Results
### Surface Hardness Test  
Figure 17 illustrates the graph with the correlation obtained between the rebound number (R) and the mean concrete compressive strength (f\({}_{\text{cm}}\)) for the studied SCC as well as the corresponding equation for the straight line (continuous line) used to fit the results with a correlation coefficient of  
Figure 16: Comparison between correlations for SCC and NVC using ultrasonic pulse velocity test.  
Figure 15: Ultrasonic pulse velocity versus average concrete compressive strength.  
about 0.96. In Figure 18 the obtained correlation for SCC from Figure 17 is compared to that obtained by [PERSON] and [PERSON] [4; 9], as previously referred in Section 3.1. From Figure 18 some differences are visualized between SCC and NVC, which are probably due to the different mortar/coarse aggregate ratios and different maximum size of the coarse aggregate. Also, the wall effect and the external vibration used to compact NVC certainly contributed to densify the near surface area of concrete. In fact, from Figure 18 it seems that, for concretes with equal compressive strength, NVC presents higher surface hardness when compared to SCC.  
The results of Table 5 show that \(\mathsf{S_{d}}\) varied from 0.63 to 2.24, the average being 1.21. Again, there is no significant difference when testing NVC of compressive strength up to 82 MPa [4; 9], which correlation is shown in Figure 18, since the \(\mathsf{S_{d}}\) varied from 0.66 to 1.93, the average being 1.11. Similar analysis was done for the \(\mathsf{C_{v}}\), showing that for SCC the \(\mathsf{C_{v}}\) varied from 1.3 to 4.9%, the average being 2.7% (Table 5), while for NVC [4; 9] the \(\mathsf{C_{v}}\) varies between 1.3 to 5.0% with the average being 3.0%. These results are according to those reported by [PERSON] [24], which indicates as typical a \(\mathsf{C_{v}}\) of 4% when testing different locations of the same element. The values of \(\mathsf{S_{d}}\) and \(\mathsf{C_{v}}\) are quite similar for NVC and SCC and no abnormal circumstances were detected. Previous analysis on the repeatability of surface hardness test, undertaken by [PERSON] and [PERSON] [10], bringing together NVC and SCC results here reported, have revealed that \(\mathsf{C_{v}}\) tends to be slightly lower as concrete compressive strengths increases, while \(\mathsf{S_{d}}\) remains almost constant, which indicates that \(\mathsf{S_{d}}\) is the statistical parameter which better represents the repeatability.  
Figure 17: Rebound number versus average concrete compressive strength.  
Figure 18: Comparison between correlations for SCC and NVC using surface hardness test.
###### Abstract  
The physics-informed neural network (PINN) is an effective alternative method for solving differential equations that do not require grid partitioning, making it easy to implement. In this study, using automatic differentiation techniques, the PINN method is employed to solve differential equations by embedding prior physical information, such as boundary and initial conditions, into the loss function. The differential equation solution is obtained by minimizing the loss function. The PINN method is trained using the Adam algorithm, taking the differential equations of motion in structural dynamics as an example. The time sample set generated by the Sobol sequence is used as the input, while the displacement is considered the output. The initial conditions are incorporated into the loss function as penalty terms using automatic differentiation techniques. The effectiveness of the proposed method is validated through the numerical analysis of a two-degree-of-freedom system, a four-story frame structure, and a cantilever beam. The study also explores the impact of the input samples, the activation functions, the weight coefficients of the loss function, and the width and depth of the neural network on the PINN predictions. The results demonstrate that the PINN method effectively solves the differential equations of motion of damped systems. It is a general approach for solving differential equations of motion.  
physics-informed neural networks; differential equations of motion; loss function; multiple degrees of freedom; activation function +
Footnote †: journal: Applied sciences