esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Benchmark Function Testing and Analysis
### Comparison of ADGMPSO to Other Mainstream Evolutionary Algorithms
The convergence accuracy was represented by the vertical axis, while the number of iterations was represented by the horizontal axis. Figure 5 shows that the ADGMPSO algorithm achieved convergence to the desired accuracy in fewer iterations than the other algorithms for all the test functions. Although BOA, WOA, and GWO also reached the theoretical optimum on the multi-peaked functions \(f_{7}\) and \(f_{9}\), they required significantly more iterations than ADGMPSO. Figure 5: \(Cont\).
# Physical and Respiratory Rehabilitation in Spinal Muscular Atrophy: A Critical Narrative Review
## 2 Materials and Methods  
A critical narrative review was conducted. The primary aim was to investigate which rehabilitation interventions have been proposed for patients with SMA and to search for those with better evidence. The secondary aim was to give physiotherapists some technical tips about how to operate in the best way possible. The narrative review was carried out following the Scale for the Quality Assessment of Narrative Review Articles (SANRA) [41]. A comprehensive search using MEDLINE via PubMed and the PEDro databases was conducted from January 1990 to December 2023, as an arbitrary limit (e.g., last five years) may lead to potential loss of data. Papers were considered only if written in English. Case series, single-case studies, randomised controlled trials (RCTs), and prospective non-randomised studies were included. Abstracts, reports, and systematic reviews were excluded. Concerning the clinical phenotypes of SMA and the age of patients, no restrictions were set. All physiotherapeutics interventions were admitted. Studies that involved pharmacological or non-rehabilitative treatments were rejected. The outcomes included were biomarkers related to physical exercise (e.g., CK, LDH), motor function scales (e.g., Modified Hammersmith Functional Motor Scale-Extend), and physical properties tests (e.g., Time Up and Go, Six Minutes Walking Tests). The adopted search strategy involved the use of relevant \"Keywords\" and \"Boolean Operators\" (Table 1). The snowball technique was not adopted [42]. Articles were identified for potential inclusion during three stages of assessment by a single reviewer without peer evaluation: title, abstract and full text. The reviewer collected data such as the title, authors, study design, number of participants, intervention and controls, outcomes, and the main results reported by the authors.
## 5 Case Study and Algorithm Validation
### Comparisons with Dijkstra and A* Algorithms  
In this subsection, the proposed BSO method is compared with Dijkstra and A* algorithms to better evaluate its performance. In the simulation, the two parameters, including the swarm size and individual dimension of the proposed BSO method, are set as 20 and 15, respectively. Its number of iterations is 150. Table 2 below gives the cost comparisons of these three path-planning algorithms. In this table, the normalized path risk, turning angles, path length, and path cost are provided.  
From the table, it can be found that the path risk of the proposed BSO method is the least of all, which proves that the generated path is the safest when it is followed by the UAVs. It is 64.00% and 54.43% safer than Dijkstra and A* algorithms. The turning angles and path length of the proposed BSO method are still the best of the three, which are 63.00% and 55.42% better in turning angles and 64.00% and 54.43% better in path length compared with Dijkstra and A* algorithms, respectively. The overall path cost of the proposed BSO method is the lowest compared with Dijkstra and A* algorithms, which are improved by 52.00% and 44.19%, respectively. Furthermore, the A* algorithm is better than the Dijkstra  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Dijkstra Algorithm** & **A* Algorithm** & **Proposed Method** \\ \hline Path risk & 1.00 & 0.79 & 0.36 \\ Turning angles & 1.00 & 0.83 & 0.37 \\ Path length & 1.00 & 0.79 & 0.36 \\ Path cost & 1.00 & 0.86 & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cost comparisons with Dijkstra and A* algorithms.  
Figure 22: Effects of iterations on generated path.  
algorithm in all the performance indexes provided in Table 2. Dijkstra algorithm works the worst in this path-planning scenario, especially considering the ground risk, which means that the global optimum path is not found at all.  
The UAV path considering the ground risk distributions generated by the aforementioned three algorithms is shown in Figure 23. From this figure, all three paths generated by these algorithms are always trying to be away from the southeastern areas, whose ground risk is higher. It can be found that the path produced by the proposed BSO method is the closest to the straight line connecting the starting point and the destination point with the least path cost based on the data provided in Table 2. Additionally, the A* algorithm is better than the Dijkstra algorithm, with fewer turning angles and a shorter path length. The Dijkstra algorithm is the worst of all, and it is not suitable for the UAVs to follow with the highest ground risk as well as the longest path length and the most turning angles. All in all, the path provided by the proposed BSO method is the smoothest with the lowest flying cost and has proved to be the most suitable for UAVs to follow.
## 3 Results and Discussion
### Limitations  
The magnetic field tester measures the EMF emitted by a phone and is limited to a fixed range of frequency; the EMF, which is out of the range of frequency, cannot be measured. Moreover, the mobile phone we used in measuring the emittance of EMF is a Samsung A40S, and the result may be different when testing other mobile phone models. Furthermore, the distance between the base stations and the phone affects the EMF emitted by the phone; this is another factor that could vary the experiment's results, which we cannot control. Finally, some of the applications of mobile phones will automatically update or run in the background during the experiment, and this could vary the experiment's results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Magnetic Field (\(\mu\)T)—Without**} & \multicolumn{2}{c}{**Magnetic Field (\(\mu\)T)—Installed**} & \\
**Cover** & \multicolumn{2}{c}{**Use of Prototype 3 (X3)**} & \multicolumn{2}{c}{**with Prototype 3 (Y3)**} & **RD3\% of** \\ \cline{2-5}
**Size** & **Mean** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **(\%)** \\ \hline
5 mm & & & N/A & N/A \\
10 mm & & & 0 & 100\% \\
15 mm & 0.66 & 0.096 & 0 & 0 & 100\% \\
20 mm & & & 0 & 0 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Percentage of phone radiation reduced (RD3) after installing Prototype 3 during an in-app phone conversation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Magnetic Field (\(\mu\)T)—Without**} & \multicolumn{2}{c}{**Magnetic Field (\(\mu\)T)—Installed**} & \\
**Cover** & \multicolumn{2}{c}{**Use of Prototype 3 (X4)**} & \multicolumn{2}{c}{**with Prototype 3 (Y4)**} & **RD4\% of** \\ \cline{2-5}
**Size** & **Mean** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **(\%)** \\ \hline
5 mm & & & 1.47 & 0.03 & 52\% \\
10 mm & & & 0.3 & 0.03 & 90\% \\
15 mm & & & 0.17 & 0.01 & 94\% \\
20 mm & & & 0.08 & 0.01 & 97\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Percentage of phone radiation reduced (RD4) after installing with Prototype 3 while Wi-Fi, data, GPS, Bluetooth, and Hotspot are on.
## 2 Theoretical Background and Concept
### The Extended Unified Theory of Acceptance and Use of Technology (UTAUT2)  
The current literature suggests that there is a controversy surrounding the applicability of technology acceptance model (TAM) and UTAUT within the customer context [21], which contributes to its the growth. The UTAUT2 was introduced by [PERSON] et al. [18] in response to the ongoing upgrades regarding the initial model, and with the aim of catering new constructs that provide a better explanation of the latest emerging technology adoption within the context of consumer. Centered on the outcomes of a Hong Kong study, the original UTAUT model was modified by [PERSON] et al. [18], and three new constructs were used to propose the UTAUT2. These constructs are habit, price and hedonic motivation. UTAUT2 clarified 74% of the variance in the behavioral intent of customers to use a technology and 52% of the variance in the use of consumer technology [22; 23].  
Due to its parsimonious structure and higher explanatory power (R2), UTAUT2 was commonly used in different studies that were related to technology acceptance and use (see Table 1). These studies involve applications, replications and extensions of the whole model or part of the model that help to expand the understanding of technology adoption. The findings are shown in Table 1 which demonstrates all the studies have explanatory power (R2) greater than 50%, suggesting that the proposed factors of UTAUT2 match the model in multiple contexts.
## 7 Passive Optical Network
### TDM-PON Application
#### 7.1.2 GPON Application  
The GPON scheme has been extensively employed by providers for delivering PON solutions. Although it is established on the earlier access networks like the BPON and the APON, GPON has a more generic and robust encapsulation structure known as G-PON Encapsulation Method (GEM). Based on this, it is capable of transporting a number of network protocols such as Ethernet, ATM, and Internet Protocol. It also provides various benefits such as high efficiency, high bandwidth, ample user interface, and extended coverage. Like EPON, it can be effectively and better employed for FTTH, FTTB, and FTTC in the OAN [144; 401].  
Additionally, to attend to the rising traffic requirements, XG-PON1 has been introduced. The XG-PON1 can offer relatively higher data transmission than the legacy GPON scheme. Furthermore, another economic advantage is that it is backward compatible with the existing GPON. This enables the operators to keep their GPON investments while still supporting XG-PON1. Likewise, apart from the ODN, XG-PON1 also inherits the framing and management from GPON. In the same way, the XG-PON1 transmission convergence layer enhances the GPON TC layer main processing mechanisms by improving the dynamic bandwidth allocation (DBA) algorithm, framing structure, as well as activation systems. This implementation helps the operators in reusing the current network elements [143; 402].  
Figure 23: Potential Ethernet solutions: (**a**) PtP Ethernet, (**b**) curb-switched Ethernet, and (**c**) EPON.
## 2 Materials and Methods
### Computational Modeling of Buckling
#### 2.1.3 Computational Model Discretization  
SHELL finite elements were used to reduce the computational complexity of 3D analyses to 2D. By minimizing shear effects, which are negligible in thin plate behavior, these elements accurately determine the ultimate buckling stress for thin and moderately thick plate structures.  
For complex nonlinear analyses, the SHELL281 finite element, recommended by ANSYS [21] and employed by [PERSON] et al. [11], was utilized in this computational model, due to its greater accuracy in nonlinear problems and in representing the effects of bending and plasticity. This eight-node element, with six degrees of freedom per node (translations and rotations in the \(x\), \(y\), and \(z\) axes), allows for the creation of quadrilateral or triangular elements. SHELL281 offers versatility through comprehensive configuration options, including adjustable integration point quantities for complex geometries, thickness offset, and advanced curved shell formulation. In this model, nine integration points were used along with the shell thickness. This choice, aligned with the ANSYS recommendation to use five or more points in simulations involving plasticity, ensures adequate representation of the material's nonlinear behavior. Additionally, result storage was configured for the top, bottom, and middle surfaces (KEYOPT (8) = 2), and the other element configurations were considered as standard value from ANSYS [21].  
For the case study, the element size was standardized according to the mesh convergence test conducted prior to the numerical simulations, and the element size for verification and validation was determined based on the results of the mesh convergence test for each specific case.
# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 5 Overview of the AI Market and Current State of the Telecommunications Industry
### Current State of the Telecommunications Industry
#### 5.1.2 The Rise of Over-the-Top (OTT) Services  
Suppose one has a data plan with their mobile operator to which they purchased a smartphone with which they can make GSM calls and short message service (SMS). One could be able to make Skype calls or any other voice-over-IP (VoIP) services for cheaper and free voice calls and chats over the network. Here, Skype is referred to as the OTT service. OTT services, namely voice and video services over the wireless network, have been revolutionized by smartphones since they have multimedia and advanced communication functions [86]. The network carrier utilized for an OTT service has (i) no control, (ii) no rights, (iii) no responsibilities, as well as (iv) no claim on the OTT served by its network infrastructure. What it does is only carry the internet protocol (IP) packets from source to destination. The network carrier can be aware of the packets and their contents but cannot do anything about it, which makes VoIP an alternative to expensive phone calls. In VoIP, the caller does not pay for the dedicated phone line but utilizes the existing internet without dedication. However, there have been restrictions imposed by network carriers on their networks on VoIP services. For instance, when Apple released the iPhone, AT&T imposed some restrictions over its user on its network. However, these restrictions were lifted due to pressure from the FCC, and since then, carrier networks realized that they should stop fighting and reap the benefits of offering good connectivity for users of OTT services.
# Rapid Measurement and Identification Method for the Geometric Errors of CNC Machine Tools
## 4 Stepwise Identification of Geometric Errors
### Squareness Error Identification  
The squareness error between the X and Y-axis is taken as an example. The two-dimensional position coordinates of the initial sampling point \(A_{0}\) are set to be \((0,0)\). As shown in Figure 3, the X-axis drives the micro-angle sensor to scan each paraboloid surface, and the two-dimensional displacement \(A_{1}(x_{1},y_{1})\), \(A_{2}(x_{2},y_{2})\),..., \(A_{i}(x_{i},y_{i})\) of each sampling point, relative to the initial one, can be obtained, as discussed in Section 3.1.2. As shown in Figure 9, the least-squares method is used to fit the two-dimensional displacement of each sampling point \(A_{0}\), \(A_{1}\), \(A_{2}\),..., \(A_{i}\), and the linear equation of the actual motion path OX' can be obtained. Then, the Y-axis drives the micro-angle sensor to scan each paraboloid surface, the two-dimensional displacement \(A_{1}(x_{1},y_{1})\), \(A_{2}(x_{2},y_{2})\),..., \(A_{i}(x_{i},y_{i})\) of each sampling point, relative to the initial one, can be obtained, and the linear equation of the actual motion path OY' is obtained.  
As shown in Figure 9, \(\varphi_{1}\) is caused by the misalignment of the L-shape line with the X-axis, which can be corrected by software. \(\varphi_{2}\) is caused by the misalignment of the L-shape line with the Y-axis and the squareness error of oxy. Obviously, oxy can be calculated as  
\[\alpha_{xy}=\varphi_{2}-\varphi_{1} \tag{29}\]  
Figure 8: (a) Measurement in the XOZ plane; (b) Measurement in the YOZ plane.  
Figure 9: The identification principle of the squareness error.  
In the actual situation, both the sign and the size relationship of \(\varphi_{1}\) and \(\varphi_{2}\) are uncertain, while through the linear equation of OX' and OY', the angle \(\xi\) of the actual motion between the X and Y-axis can be calculated, and \(\alpha_{xy}\) can be expressed as Equation (30), without discussing the situations.  
\[\alpha_{xy}=\frac{\pi}{2}-\xi \tag{30}\]  
Similarly, as shown in Figure 8, the squareness error of \(\alpha_{xz}\) and \(\alpha_{yz}\) can be derived by placing the L-shape standard piece in the XOZ plane and YOZ plane, respectively.
## 2 Research Status on Anhydrous Hydrazine Fuel  
Understanding anhydrous hydrazine fuel combustion kinetics is crucial in various engineering and scientific applications. Combustion kinetic studies of hydrazine, often based on incomplete kinetic mechanisms [20; 21; 22; 23], started with rate constants [24; 25; 26; 27; 28; 29; 30; 31] estimation for the reactions of N\({}_{2}\)H\({}_{4}\) with atoms or radicals [25; 32; 33; 34; 35; 30; 31]. In 1953, [PERSON] et al. [24] constructed a model for hydrazine decomposition in an excitation tube. They later adjusted [36] this model, and the simulation results were consistent with the overall decomposition rate of N\({}_{2}\)H\({}_{4}\) measured experimentally [25]. In 1998, [PERSON] et al. [36] adjusted the reaction rate constant for the reaction stated in Equation (1) based on the decomposition of N\({}_{2}\)H\({}_{4}\) and flame experimental data.  
\[\mathrm{N}_{2}\mathrm{H}_{4}+\mathrm{NH}_{2}=\mathrm{N}_{2}\mathrm{H}_{3}+ \mathrm{NH}_{3} \tag{1}\]  
They updated a primary mechanism for the decomposition of N\({}_{2}\)H\({}_{4}\) based on a complete set of N/H reactions [36], mainly by correcting the thermodynamic data of N\({}_{2}\)H\({}_{\mathrm{x}}\) substances and the reaction rate constants [37] of several reactions closely related to the decomposition of N\({}_{2}\)H\({}_{4}\). The updated hydrazine decomposition model at high temperatures [38] contained 51 reactions and 11 substances. [PERSON] et al. [39] also developed a detailed N\({}_{2}\)H\({}_{4}\) decomposition chemical kinetic mechanism, consisting of 13 chemicals and 33 essential reactions, to simulate the explosion of pure N\({}_{2}\)H\({}_{4}\) vapor. [PERSON] and [PERSON] et al. [40] studied the gas-phase reactions of N\({}_{2}\)H\({}_{4}\) with O\({}_{2}\), NO, and NO\({}_{2}\). They showed that the high activity reactions of N\({}_{2}\)H\({}_{4}\) and NO\({}_{2}\) were caused by the continuous abstraction of H atoms on N\({}_{2}\)H\({}_{4}\) by NO\({}_{2}\).
## 4 Passenger Flow Prediction Results and Discussion  
### Experimental Design  
In the TCN-LSTM model with an attention mechanism developed in this study, the TCN module has three residual blocks. The first residual block has 32 filters, a convolution kernel size of 3, and an expansion coefficient of 1. The second residual block has 32 filters, 3 convolution kernels, and 4 expansion coefficients. There are three layers in the LSTM module, and each layer sets the parameters of the hidden layer to 128. The number of neurons in the Dense layer is set to 1, and the output is one-dimensional data. The model training number is set to 300 times to follow the new weight; the time step is set to 32, and other parameters remain unchanged.  
Based on the data proposed by the Chongqing Rail Transit Group, the first 80% of the data were used as the training set for model training, and the remaining 20% of the data were used as the testing set to verify the accuracy and effectiveness of the model prediction. The platform for the experimental model is Python 3.10, and third-party libraries such as Scikit learn, Keras, and TensorFlow were built in this environment to construct a short-term passenger flow prediction model.
## 3 Proposed Cross-Working Condition RUL Prediction Method
### JTMN-Based RUL Prediction
#### 3.2.1 Joint Domain Adaptation Loss
Aiming at the essential characteristics of the RUL regression prediction problem, an adaptive method of the unsupervised transferable domain called representation subspace distance (RSD) is introduced to build joint domain adaptation loss for realizing accurate RUL prediction [29]. RSD belongs to a similarity measurement criterion to measure the two domains. For source domain \(\mathbf{F}_{s}\) and target domain \(\mathbf{F}_{t}\), the orthogonal bases of intrinsic feature subspaces are firstly calculated using the SVD algorithm:  
\[\mathbf{F}_{s}=\mathbf{U}_{s}\mathbf{SV}_{s}{}^{T} \tag{17}\]  
\[\mathbf{F}_{t}=\mathbf{U}_{t}\mathbf{SV}_{t}{}^{T} \tag{18}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) are the left and right singular  
\[Sim_{\mathbf{s},\mathbf{t}}=tr(\mathbf{L}_{\mathbf{U}_{\mathbf{t}}\leftrightarrow \mathbf{U}_{\mathbf{t}}}+\mathbf{L}_{\mathbf{V}_{\mathbf{s}}\leftrightarrow \mathbf{V}_{\mathbf{t}}}) \tag{21}\]  
The joint domain adaptation loss, called VDR-RSD, combines the representation ability of the distribution discrepancy of VDR and the representation ability of feature manifold distribution of RSD to effectively improve the model's ability to learn features in different domains, further enhancing cross-working condition RUL prediction performance.
###### Abstract  
Due to pronounced work-hardening induced by the complex interplay of deformation mechanisms such as dislocation slip, twinning and/or martensitic phase transformation, high-manganese steels represent a class of materials well-suited for mechanical surface treatment. In the present study, the fatigue behavior of a high-manganese steel showing twinning-induced plasticity (TWIP) effect at room temperature (RT) was investigated after deep rolling at 550 \({}^{\circ}\)C. Results are compared to a former study discussing the behavior after RT deep rolling. Evolution of the near-surface microstructure was analyzed by scanning electron microscopy (SEM), microhardness measurements and residual stress depth profiles obtained by X-ray diffraction (XRD). Both uniaxial tensile tests and uniaxial tension-compression fatigue tests have been conducted in order to rationalize the macroscopic material behavior. Following deep rolling at 550 \({}^{\circ}\)C, SEM measurements employing electron backscatter diffraction (EBSD) revealed a heavily deformed surface layer as well as localized deformation twinning. Specimens showed inferior hardness and residual stress depth profiles when compared to RT deep rolled counterparts. Tensile tests indicated no difference between the conditions considered. Fatigue properties however were improved. Such behavior is rationalized by a more stable residual stress state induced by dynamic strain aging.  
high-manganese steel; deep rolling; TWIP; near-surface properties; residual stresses; residual stress stability; fatigue behavior
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation
#### 2.2.8 Multiple Linear Regression (MLR)  
MLR is a widely used statistical modeling technique for establishing the relationship between a dependent variable and multiple independent variables. When the relationship between the dependent variable and two or more predictors is assumed to be linear, the method is referred to as multiple linear regression. The general form of an MLR model can be expressed as follows:  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\ldots+\beta_{n}x_{n}+\varepsilon \tag{1}\]  
where \(y\) is the dependent variable, \(x_{1}\), \(x_{2}\), \(\ldots x_{n}\) are the independent variables, \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{n}\) are the slope coefficients, \(\beta_{0}\) is a constant term, and \(\varepsilon\) is the error term.