esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Improvement for PMSM Driven by DTC Based on Discrete Duty Ratio Determination Method
## 3 Analysis of the Analytical Relationships of the Synthetic Vector  
### Analysis of the Synthetic Vector  
The stator flux linkage \(\varphi_{\text{s}}\) is located in sector 1, and the impact angle is \(\delta_{\text{s}}\), as shown in Figure 3.  
Figure 2: Relationships of the variables in the control system driven by fixed sector division criterion(FS-DTC).  
The master vector \(\mathbf{V_{2}}\) and the slave vector \(\mathbf{V_{1}}\) will be selected to compensate the errors of the PMSM according to the selection rules of the master vector and the slave vector in FS-DTC [3]. It is worth mentioning that the selected master vector and slave vector in each control period are the adjacent vectors. Therefore, the direction of the equivalentsynthetic vector is between the directions of the master vector and the slave vector.  
Figure 3 shows that the principal of the accurate error compensation is that the direction of the synthetic vector is the same as that of the errors of the PMSM. Therefore, the relationship between the active angle of the synthetic vector and the error angle can be described as:  
\[\theta_{\text{sms}}=\theta_{\tau} \tag{7}\]  
where \(\theta_{\text{sms}}\) is the active angle of thesynthetic vector, which can be calculated as:  
\[\theta_{\text{sms}}=\sigma_{\text{v}}+\delta_{\text{s}} \tag{8}\]  
The error angle \(\theta_{\tau}\) can be calculated as:  
\[\theta_{\tau}=\tan^{-1}\tau \tag{9}\]  
where \(\tau\) is the state value of the error rate, which can be expressed as:  
\[\tau=\frac{r_{\text{T}}}{r_{\text{F}}} \tag{10}\]  
As the variation range of the sector angle \(\sigma_{\text{v}}\) is [0\({}^{\circ}\), 60\({}^{\circ}\)), the sector angle that can be used in the whole rotation space of the stator flux linkage can be described as  
\[\sigma_{\text{v}}=\theta_{\tau}-\delta_{\text{s}}-(N_{\text{N}}-N_{\text{s}}) \cdot 60^{\circ} \tag{11}\]  
where \(N_{\text{N}}\) is the sector number of the phase-lag active vector, \(N_{\text{s}}\) is the sector number where the stator flux linkage located in.
## 2 Vehicle Routing Problem with Time Windows
### The Parallelization Approach as a Solution  
The search to one optimum solution for the VRPTW is unattainable when the problem size grows since the execution time increases exponentially, given the NP-completeness characteristics of the problem [43]. Although metaheuristics are efficient to find reasonable solutions to complex problems, the single computer resources are not sufficient to reach results for these type of problems in a reasonable time. Parallelization is one of the most effective approaches to improve the performance of one algorithm and to overcome the single machine constraints.  
Several parallel-based approaches to solve the VRPTW have been proposed, such as one parallel version of the Branch and Bound algorithm [44], and several metaheuristic-based approaches such as SA [14], TS [45], and PSO [46]. Furthermore, various parallel GAs have been described in the existing literature. [PERSON] [47] groups these approaches in four categories: Global master-slave, island, cellular, and hierarchical parallel GA (Table 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Description** \\ \hline Global master-slave & In this approach, a single population is distributed among several nodes. Genetic operations are applied to the whole population. \\ \hline Island GA & Several subpopulations evolve separately with the occasional migration of individuals between subpopulations. \\ \hline Cellular GA & This approach consists of one spatially structured population. \\
\begin{tabular}{c} Selection and crossover are restricted to a small neighborhood. The neighborhoods are allowed to overlap, permitting some interaction among individuals. \\ \end{tabular} \\ \hline Hierarchical parallel GA & This category combines an island model with either a master-slave or cellular GA. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Categories of parallel genetic algorithms [47].
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## Appendix D Multipath Interference  
By considering the geometric model used for the NLOS paths, one can assume that the NLOS paths, right after the reflection, create a secondary PPP distribution of sources with the same density \(\lambda\), where the rays have already travelled the distance \(d\). This is because the initial point process was uniformly random on the 2D space, and the reflection points were also chosen uniformly random on the perimeter of the circle with radius \(d\). Thus, for NLOS interference links (here derived for UCA for simplicity, but can be readily generalized to UcyIA), one can re-write the interference amplitude as:  
\[t=\sum_{i=1}^{\infty}\sum_{\ell=2}^{L}\frac{\beta_{i,\ell}}{((R_{i}+d)^{2}+h^{ 2})^{\frac{\delta}{2}}}G_{\mathrm{c}}(\phi_{i,\ell})x_{i,\ell}. \tag{102}\]  
By carrying out the derivations analogous to (10) to (19), and using the change of variable \(\omega\big{(}(r+d)^{2}+h^{2}\big{)}^{-b}=t\) that is slightly different with respect to the change of variables done in relation (13), one can achieve CF of the aggregated interference power for UCA for the NLOS links, as \(\Psi_{NLOS}(\omega)=\Psi_{1}(\omega).\Psi_{2}(\omega)\), where  
\[\begin{array}{rcl}\Psi_{1}(\omega)&=&\exp\Big{(}\pi\lambda a\Gamma(-\alpha) (-j\omega)^{\alpha}\,P_{G_{\mathrm{c}}}(\omega,\alpha,\sqrt{h^{2}+d^{2}})+ \pi\lambda(h^{2}+d^{2})\Big{)},\\ \Psi_{2}(\omega)&=&\exp\Big{(}-\pi\lambda da\Gamma(-\alpha/2)(-j\omega)^{\frac {\delta}{2}}P_{G_{\mathrm{c}}}(\omega,\alpha/2,\sqrt{h^{2}+d^{2}})+2\pi \lambda\,d\sqrt{h^{2}+d^{2}}\Big{)},\end{array} \tag{103}\]  
where the function \(P_{G_{\mathrm{c}}}(.)\) is defined in (20). Now, the CF of the total augmented aggregate interference is a statistically decomposable CF as a product of two CFs (for the exact mathematical definition of decomposable CF refer to [67; 68])  
\[\Psi_{I}(\omega)=\Psi_{NLOS}^{(L-1)}(\omega)\Psi_{LOS}(\omega), \tag{104}\]  
where \(\Psi_{LOS}(\omega)\) is derived as in relation (25) and \(L\) is the total number of paths. The CDF of the augmented interference can be achieved by numerical integration of this CF.
## 3 AI-Based Early Fault Detection  
After the test procedure has been described and the signal used for evaluation has been defined, this section presents the development of the two AI-based methods of predictive maintenance for early damage detection. The procedure for finding an algorithm that matches the problem is then shown step by step. The differences between the two methods are highlighted at the interfaces.  
The test series provides the opportunity to implement different types of PdM. Two of these are condition-based and statistical. Condition-based PdM therefore works with sensor data directly from the active test to derive maintenance measures. In contrast, statistical PdM uses historically collected data from series tests and thus draws on experience. In this paper, this definition is extended. Condition-based PdM works with data from the current test and can only derive measures for the same. Statistical PdM can be applied to further tests of the same type after implementation. A model can be trained at the start of the test series and applied to subsequent tests. The use of a pretrained model greatly accelerates evaluation.  
Figure 3 shows the selected methods. Only the color-coded paths are discussed in this paper. It is clearly visible which properties and differences arise.  
The red path leads to the development of an anomaly detection system for condition-based PdM using machine learning (ML). A special type of one-class support vector machine (OSVM) is to be used for this purpose. The blue path runs through statistical PdM. Using deep learning (DL), a classification task is to be solved by an artificial neural network (ANN). For this purpose, a long short-term memory (LSTM) network is selected, which can  
Figure 3: Diagram of the procedure for selecting the ML method (red) and the DL method (blue).  
be assigned to recurrent neural networks (RNN). As shown, AI is the overarching concept for ML, of which DL is a subfield. It is possible to implement these methods directly in MATLAB 2022b, which was used in this work. This simplifies application to a specifically defined problem, and, in addition, the measurement data are already available in MATLAB and do not have to be converted from other data formats before evaluation. The two selected methods are presented in detail below.
###### Abstract  
Tunnels generally operate underground or underwater in a complex environment. As a result, the health monitoring system is inevitably affected by various environmental factors, which introduces noise to the system. However, the noise contained in the monitoring sequence may disrupt structural damage identification and health state assessment as the real structural response may be overwhelmed by the noise. To properly eliminate the noise in an objective way, this study proposed an improved wavelet threshold denoising method. Firstly, it adopts a quantitative factor, namely the Sparse Index, to assist the selection of the best wavelet basis in numerous wavelet packages. Then, the decomposition layer and threshold are optimized by a comprehensive evaluation based on a variation coefficient method. At last, the application of the concrete strain health monitoring data of the Hong Kong-Zhuhai-Macao Bridge immersed tunnel verified the effectiveness of the proposed method. It is found that the combination of sym12 and five decomposition layers can obtain the best denoising results within the selected wavelet families and decomposition levels. Moreover, the proposed method achieves good denoising results under different fluctuation levels. Thus, the proposed method is reliable, can solve the problem of optimal parameter selection such as decomposition level and wavelet basis in wavelet denoising, and can be applied in the structural health monitoring of critical infrastructures.  
wavelet threshold denoising; health monitoring system; variation coefficient method; Hong Kong-Zhuhai-Macao Bridge; immersed tunnel +
Footnote †: journal: Applied sciences
## 6 Simulation Results
### Scenario Setup  
We considered a network with 100 fixed GNs uniformly distributed in a rectangular area of size 5000 m \(\times\) 5000 m. We set three different scenarios by varying the value of \(\mu\). This parameter is used to discretize the area inside the convex hull formed by the GNs. Different from our previous work [12] where UAVs were only allowed to fly at a fixed altitude, here, a UAV may fly at a given altitude \(h\) uniformly selected from the set \(\mathcal{H}\)= {40, 80, 120} m. We assume that the transmit power among the nodes is fixed at 23 dBm. Previously, in Section 4, it was stated that potential UAV placement points will be identified within a convex hull formed by the GNs. The convex hull is found by the Graham scan algorithm [28] based on the GN deployment positions \(q_{(x,y,0)}^{c}\).  
Our scenarios consider a suburban environment and use log-distance path loss model for the signal attenuation. Table 2 shows all possible data rates and their corresponding minimum sensitivities at the receiver, i.e., \(P_{r}(d)\). These values were used to compute the maximum achievable distance \(\mathcal{D}_{i}\) with \(\alpha\) in Equation (1) set to 2.2 [23] and \(d_{0}\) fixed at 1 m. We compute \(P_{r}(d_{0})\) using the free space propagation model, \(P_{r}(d_{0})=P_{t}G_{t}G_{r}(c/4\pi d_{0}f)^{2}\)[29], where \(P_{t}\) is the transmit power, \(G_{t}\) and \(G_{r}\) are the transmitter and receiver antenna gains, respectively, and \(c\) and \(f\) are the speed of light and carrier frequency, respectively. We set \(G_{t}=G_{r}=1\) (0 dBm). Moreover, each data rate in Table 2 is considered to be using a different transmission mode.  
For the set of UAV candidate position \(Q\), we chose \(\mathcal{D}_{i}\) with the lowest minimum sensitivity and adjusted it by using the parameter \(\mu\) to ensure that two UAVs positioned side by side can communicate with each other. As already stated, we assumed that there was a wireless communication technology between UAVs that was capable of efficiently relaying all the traffic from the GNs, never causing a bottleneck. The parameters that were common in different scenario are detailed in Table 3 as follows.  
We have adjusted NSGA-II parameters such as the probability of crossover (\(p_{c}\)) and mutation (\(p_{m}\)) and the population size so that the algorithm did not prematurely converge or perform an excessive number of computations due to either low values of the probability of crossover or a high population size. The NSGA-II parameters are summarized in Table 4.
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 1 Introduction
### Background  
With the introduction of the latest 3 GPP R17, 5G system architecture was expanded to support ATSSS, as shown in Figure 1. According to the ATSSS rules, provided by the traffic scheduling strategy model, the UE supports traffic steering, switching, and splitting across 3 GPP and non-3 GPP access. Furthermore, several theoretical studies using NWA have been implemented. The data transmitted from the UE to the data network have parallel links to the 3 GPP (NR) and non-3 GPP (WLAN) networks. Therefore, following a reasonable traffic scheduling strategy, the UE can choose between passing all its data in a single stream through the NR network or dividing it into two streams, one that passes through the NR network and another that passes through the WLAN. Given that NR networks and WLANs exhibit significantly different network coverage, signal-to-noise-ratio (SNR) reliability, and user distance, a reasonable allocation of the power and network resources is necessary to optimize the network capacity [13].  
The service type of the UE determines the characteristics of its uplink data. Additionally, the development of new mobile applications gives rise to different services, including traditional voice services and multimedia services, such as data and video streaming. Thus, user service requirements are gradually demanding more broadband, diversification, and individualization. Moreover, different Quality of Service (QoS), i.e., data transmission rates and time delays, are required for different technologies and terminals [14; 15; 16]. Access to a technology that meets all user-service requirements is difficult, and the contradiction between complex service requirements and inefficient resource scheduling has become increasingly prominent. Conversely, services in heterogeneous converged networks face burstiness, which results in an asymmetric network service distribution. Presently, users mostly connect to a single network to transmit all business data according to their personal  
Figure 1: Architecture for ATSSS support.  
preferences and habits. This can easily lead to problems, such as access congestion and low QoS, in a single network, while large amounts of alternative network resources remain idle.
## 7 Practical Implications  
Figure 19 shows practical Wi-Fi-based VANET deployment scenarios in a shopping small parking lot in Auckland City. Wireless access points (APs) serve as central transmitters and receivers for Wi-Fi networks. Each AP has its radio range, typically 20 m to 100 m, depending on the technology and the environment in which it operates. Multiple APs ensure the coverage and capacity of the system. Careful channel assignment can avoid co-channel interference and therefore improve the system's performance. Minimizing this inter-access-point interference is crucial for co-channel users, and achieved through careful RF channel assignment and deployment. Migrating to the 5 GHz spectrum in 802.11n networks can reduce channel reuse problems and boost the system performance. Wi-Fi extenders can be used to extend coverage by rebroadcasting signals from the base AP to users at a distance or behind barriers. Smart antenna technology with multiple elements enhances performance by increasing gain, coverage, and data rates. Metal shielding and toroidal helical antennas improve signal propagation. These steps and techniques optimize inter-floor signal propagation in the buildings in which a Wi-Fi network operates.  
Figure 19: A practical VANET deployment scenario in a shopping mall parking lot in Auckland.  
One can observe that two access points (APs) are deployed on each floor for better coverage and performance, given the floor plan of the WestCity shopping mall. If more than two APs (per floor) are deployed, the cost versus coverage (i.e., deployment costs) will be higher and co-channel interference will potentially be increased.  
We provide the following four best practice guidelines for practitioners:  
* Identifying the VANET deployment requirements (a feasibility study) such as the facility required and any issues/restrictions for system deployment.
* Determining practical deployment scenarios such as shopping mall parking lots, single-floor, multi-floor, or campus environments.
* Identifying technical requirements such as the minimum signal strength, signal-to-noise ratio, delay/jitter tolerance, and the maximum transmitting power.
* Conducting preliminary field trial measurements for Wi-Fi-based VANETs using real hardware and software.
# Study on Compaction Characteristics and Compaction Process of an Unsaturated Silt Based on PFC3D
## 3 Discrete Element Simulation of an Unsaturated Silt Static Triaxial Test  
### Modeling  
The discrete element simulation process can be roughly divided into three stages: boundary constraint establishment, particle generation, and preloading. Firstly, according to the conventional indoor static triaxial test, the model rigid boundary constraint wall is generated according to the ratio of 1:1, and the wall size is a cylinder of \(\varphi\)39.1 mm * \(\times\) 80 mm. Then the contact between the particles is set to Hill contact, and the particles are generated inside the wall according to the particle gradation.  
The original particle size distribution curve obtained through particle analysis experiments is shown in Figure 2a. From the figure, it can be seen that the content of fine sand (0.002-0.075 mm) in the roadbed is relatively large, accounting for about 60%, while the proportion of the silt and clay (<0.002 mm) is relatively small, accounting for about 7%. In the process of constructing a discrete element model, a small particle size can lead to an increase in the number of generated particles, making calculation difficult. In order to simplify the calculation, particles smaller than 0.002 mm were filtered out during the generative model. In the PFC3D software, particles are generated by dividing the particle size intervals. In order to facilitate the construction of the model, each interval is grouped based on the slope of the straight line. Intervals with similar slopes are divided into one group, and the eight sections in the original grading curve are simplified into four sections (2-0.25 mm, 0.25-0.1 mm, 0.1-0.05 mm, 0.05-0.002 mm). The simplified grading distribution is shown in Figure 2b, and a static triaxial test discrete element model is constructed based on this particle grading curve.  
In the process of indoor testing, the test soil sample is wrapped by a rubber membrane. Under the combined action of an axial load and circumferential confining pressure, due to the positive Poisson effect, the sample will produce an irregular lateral expansion. For the conventional discrete element model, the boundary constraint is a rigid wall, which is quite different from the indoor test and will have a certain impact on the simulation results. Therefore, the boundary conditions need to be adjusted to meet the flexible membrane boundary conditions. When establishing the flexible servo boundary, it is first necessary to delete the rigid boundary constraint. Then, at the rigid boundary position, the particles of the same particle size are used as the basic unit to generate the particle wall boundary. Then, the circumferential confining pressure is equivalent to the concentrated load, which is uniformly applied to each boundary particle. The contact between the boundary particles is set to linear contact, and finally, the flexible particle servo boundary is generated, as shown in Figure 3b.  
Figure 2: Particle grading curve. (**a**) Original gradation. (**b**) Simplified gradation.
## 3 Modeling of the System and Optimization
### Optimization of the System  
Equation (25) represents the exergetic efficiency of the system. Optimum operating and design condition is obtained by maximizing Equation (25) for exergy efficiency subject to the constraints, i.e., Equations (4)-(9). For this, parameters such as \(T_{a}\), \(T_{s}\), \(G_{T}\), \(\tau\alpha\), \(\eta_{o}\), \(S\), \(C_{p}\), and \(\rho\) have been considered as constants. \(A_{a}\) is considered as independent parameter and others as the dependent parameters. MATLAB optimization toolbox is used for numerical optimization. Active set algorithm is used for this non-linear optimization problem. The values of some parameters used for optimization are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Absorber plate material & : & Copper \\ Dimensions of the absorber plate: & : & 1 m \(\times\) 0.6 m \\ Plate thickness & : & 0.2 mm \\ Serpentine pipe material & : & Copper \\ Serpentine pipe inner diameter & : & 10 mm \\ Serpentine pipe thickness & : & 1.5 mm \\ Glazing material & : & Tempered glass \\ Glazing dimensions & : & 1.05 m \(\times\) 0.81 m \\ Glazing thickness & : & 5 mm \\ Insulation & : & Glass wool and cork sheet \\ Collector tilt angle & : & & 27\({}^{\circ}\) \\ Thermal conductivity of absorber & : & 401 W/(m K) \\ Thermal conductivity of insulation & : & 0.04 W/(m K) \\ transmittance-absorptance product & : & 0.855 \\ Apparent sun temperature & : & 4350 K \\ Ambient temperature & : & 303 K \\ Irradiance & : & 900 W/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some parameters specification.  
After optimization, following result has been obtained:  
\[A_{a}=0.61449\;\mathrm{m}^{2},\;\dot{m}=0.001999\;\mathrm{kg/s},\;T_{ out}=343.21\;\mathrm{K},\;T_{p}=345.4\;\mathrm{K},\;U_{l}=8.465\;\mathrm{W/m^{2}K},\] \[Q_{u}=252.50\;\mathrm{W},\;\Delta P=45\;\mathrm{Pa},\;F_{R}=0.60, \;F^{\prime}=0.83\]  
At optimum condition, the exergetic efficiency becomes: \(\epsilon=3.72\%\).