esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Computational Principle and Numerical Model
### Numerical Model
#### 3.2.2 Model Building  
Figure 6 shows the model dimensions. The overall dimensions of the model are 152 m in the x-direction, 60 m in the z-direction, and 9 m in the y-direction. Horizontal velocities are constrained on the left and right sides of the model, vertical velocities are constrained at the bottom of the model, and the top is a free boundary. For the slope of the original abandoned dregs and the cut slope, the model is built by command flow, and a total of 2500 units and 3324 nodes are generated. The same command flow is used for modeling the abandoned dreg body with the buried anti-slide pile, with hexahedral block mesh and uniform wedge mesh. The length of the anti-slide pile is 20 m, and the cross-section size is 2 m \(\times\) 1.8 m. According to engineering experience, the anti-slide pile is buried in the middle and lower position of the primary slope. The upper 5 m of the anti-slide pile is constrained by x-, y-, and z-direction velocity, and the grid is encrypted for the anti-slide pile and the surrounding soil. The total length of the bridge pile is 45.5 m, and the cross-section is 1.8 m \(\times\) 1.8 m. The pile body is not constrained.  
The ontological model of the abandoned dreg body was set as a Mohr-Coulomb plastic model. The anti-slide pile and bridge pile were modeled with reference to Won [31], where the pile was treated as a solid unit and set up as an elastic model. This can simulate the pile relatively realistically. However, since the strength discount method does not support the elastic model, the anti-slide pile is set as a Mohr-Coulomb plastic model when calculating the slope safety factor. The elastic model is still used in other cases. Considering the large difference in pile-soil properties, there is also a contact surface between the pile and soil in order to better simulate the actual situation.
# Influence of Posture Change on Train Running Safety under Crosswind
## 2 Numerical Methodology
### Parameter Definition  
Aerodynamic drag matters to energy consumption while posing a slight risk to train safety, so it was not considered in running safety in the present study. Figure 4 shows the main focus of the current study, the changing characteristics of the lateral force (\(F_{y}\)), lift force (\(F_{z}\)), rolling moment (\(M_{x}\)), pitching moment (\(M_{y}\)), and the yawing moment (\(M_{z}\)) of each car were recorded during the train's rung. Here, the directions of the moment were determined according to the right-hand principle. As shown in Figure 4, the origins of the moment coordinate system were at the center of gravity of each car, and the same motion as the train was assigned to each local coordinate system to capture the moment during its run.  
Figure 4: The parameter definition and the origin of the moment coordinate system.  
Figure 3: Computational mesh employed in the simulations: (**a**) front view of mesh density in the computational domain; (**b**) side view of mesh density in the computational domain; (**c**) mesh distribution around the bogie; (**d**) the prism layer cells attached to the train surface.  
In the present study, the aerodynamic forces and moments were given as a dimensionless form, according to Equations (1)-(5). In Addition, the dimensionless pressure distribution was also employed to show the different flow patterns on the train with and without the posture.  
\[C_{y}=\frac{F_{y}}{\frac{1}{2}\rho V^{2}S} \tag{1}\]  
\[C_{z}=\frac{F_{z}}{\frac{1}{2}\rho V^{2}S} \tag{2}\]  
\[C_{M_{x}}=\frac{M_{x}}{\frac{1}{2}\rho V^{2}SH} \tag{3}\]  
\[C_{M_{y}}=\frac{M_{y}}{\frac{1}{2}\rho V^{2}SH} \tag{4}\]  
\[C_{M_{z}}=\frac{M_{z}}{\frac{1}{2}\rho V^{2}SH} \tag{5}\]  
\[C_{p}=\frac{p-p_{0}}{\frac{1}{2}\rho V^{2}} \tag{6}\]  
where \(F_{y}\), \(F_{z}\), \(M_{x}\), \(M_{y}\), \(M_{z}\), are the side force, lift force, rolling moment, pitching moment, yawing moment of the train, respectively, and \(C_{y}\), \(C_{z}\), \(C_{M_{x}}\), \(C_{M_{y}}\), \(C_{M_{z}}\) are the corresponding dimensionless coefficients of these aerodynamic indicators. \(C_{p}\) is the pressure coefficient and \(\rho\) is the air density and takes a value of 1.225 kg/m\({}^{3}\). \(S\) is the train's cross-sectional area (10.84 m\({}^{2}\) for this train) and H is the train height, which was introduced in Section 2.1; \(V\) is the resultant velocity in the domain (85.7 m/s); \(p_{0}\) is the ambient atmospheric pressure; \(p\) is the train surface pressure; and \(H\) is the height of the train (3.89 m).
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 5 ML Algorithms Application  
### Algorithms' Training  
The initial phase of ML model development involves collecting training and validation data, demonstrated through the Gabel El-Zeit Wind Farm case study (580 MW), highlighting its regional applicability. Anemometers provide 10 min measurements, enabling very short-term wind speed forecasting (VSTWSF). Predictions span various time scales: 10 min, 30 min, 6 h, 24 h, and 36 h. Seven predictors--atmospheric pressure, humidity, wind vane, temperature, hour, height, and month--serve as inputs for 12 ML models. Models include Decision Trees (CART method), SVM (RBF kernel), and deep neural networks (ANNs, LSTMs) with three hidden layers (100 neurons each), optimizing VSTWSF (7-100-200-100-1) based on heuristic guidelines.  
The Gaussian-error linear unit (GELU) activation function is utilized across all layers in ANNs because it promotes sparse representations, enhancing efficiency in variable-size representation and linear separability [65]. Additionally, multiple linear and fourth-order polynomial regression models with (\(k_{p}=4\)) have been applied. The backpropagation (BP) algorithm has been used to train the weights in the ANNs. To further improve performance, ensemble learning algorithms such as Random Forest (RF), bagging, boosting, Stochastic Gradient Descent (SGD), Extra Trees, and XGBoost have been employed as well. These ensemble models can integrate base learners such as ANN, Decision Tree (DT), and Support Vector Machine (SVM), with DT being the base learner for all ensemble models in this study. The very short-term wind power prediction (VSTWPF) process utilizes the same dataset with seven predictors as VSTWSF, incorporating its optimal model as an eighth predictor. This integration enhances accuracy, with DNNs using 309 neurons (8-100-200-100-1).
## 2 Materials and Methods
### Sample Collection and Extraction  
#### 2.2.1 Pm2.5  
Medium-volume samplers (Tish, Cleves, OH) were located in the selected locations; PM2.5 and vapor samples were collected continuously for 48 h as one time collections. The LB and SB samples were collected in early summer (June). The CM samples were collected continuously over a 5 d period in late spring (May). Teflon-coated glass fiber filters (Pall Corp, East Hills, NY) were used for PM2.5 collection and XAD-4 resin beds (Acros, Thermo Fisher Scientific) for the vapor-phase. Sampling details and matrix cleaning procedures have been previously published (16). Estimates of the volume equivalent for each vapor sample analyzed were based on the air volume collected divided by the fraction of the total extract used in each analysis. Aqueous suspensions of PM2.5 samples were prepared by sonicating filter punches in cell culture water for 20 min and the corresponding volume of air (m\({}^{3}\)/cm\({}^{2}\)) calculated to normalize the results. As the mass of the PM2.5 on the filter was not measured, the volume equivalent of air was used to describe the final concentration of the particles in the aqueous suspension, which was 2.2-6.0 m\({}^{3}\)/mL.
## 2 Related Work  
### Battery Balancing Type  
There are two general types of balancing methods: Passive and active methods, as depicted in Figure 1. The main advantage of the passive method is that it has the simplest circuit configuration of all battery balancing algorithms. However, its disadvantage is that the energy is dissipated through resistance as heat. By contrast, active balancing methods balance overcharge/over-discharge batteries using capacitors, inductors, and power converters [9].  
Active methods can be categorized into charge and discharge methods and the charge method can be further divided into a central type of balancing method and a cell control balancing method. When the cell voltage becomes unbalanced as the battery discharges, the central balancing method causes the primary side switch to turn off while the secondary battery starts charging the battery cell. The advantages of this approach are that it is inexpensive and has a simple control structure. However, the disadvantage is that the cells cannot be independently controlled.  
The discharge-type balancing technique controls the balance of the discharge cell with either a 1:N structure or a 1:1 structure. The 1:\(N\) structure is a serial connection to the battery, and an individual cell equalizer (ICE) is included in each cell. The configuration of an ICE consists of a power semiconductor switch, a transformer, and a diode. When the cell becomes unbalanced due to overcharging, the ICE of the corresponding cell is operated to discharge the battery. At this point, the energy is discharged into an adjacent cell, the structure of which is bundled into a battery string such that high-voltage stress is applied to the diode. The 1:1 scheme is a method of balancing cells by controlling the discharge of adjacent cells. The control method is as follows. The overall balance among the cells is maintained by discharging cells with higher voltages in the upper layers into cells with lower voltages in the lower layers. In contrast to the 1:\(N\) scheme, the 1:1 scheme has low-voltage stress but a long balancing time because it operates by controlling the discharges of two adjacent cells.
# Propagation Characteristics of a Twisted Cosine-Gaussian Correlated Radially Polarized Beam
## 3 Numerical Results
With the increase of the beam order \(n\), the blueshift appears and the maximum blueshift occurs in the focal plane. In addition, one also finds that the maximum redshift occurs off-axis in the focal plane and grows rapidly as the off-axis distance \(y\) increases. In order to learn more about the effect of the twist phase on spectral shift, Figure 9b-d plots the relative spectral shift of a focused polychromatic twisted CGC radially polarized beam versus the propagation distance for different values of the twist phase. One finds that the maximum value of redshift and blueshift are significantly reduced as the twist phase increases. The direct reason is that the divergence of the beam increases with the growth of the twist phase. As a result, the spectral density distribution becomes more uniform, leading to a gradual decrease in the difference in spectral shift. This is similar to the correlation-induced spectral change [2; 5]. Moreover, different from a polychromatic radially polarized SM beam [47], one finds from Figure 9f that there is a bimodal blueshift observed near the focal plane for a polychromatic CGC radially polarized beam with \(n=1\). It is important to note that the effect of the twist phase on the spectral change is similar to the coherence, which is achieved by modulating the spectral density distribution upon propagation.
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 4 Experiment
### Dataset and Parameter Settings  
The CVRP dataset for testing comprises 20 problems from CVPLIB (accessed on 20 January 2024). [[http://vrp.atd-lab.inf.puc-rio.br/index.php/en/](http://vrp.atd-lab.inf.puc-rio.br/index.php/en/)]([http://vrp.atd-lab.inf.puc-rio.br/index.php/en/](http://vrp.atd-lab.inf.puc-rio.br/index.php/en/)), which are widely recognized standard problems for VRP. Each problem's size ranges from 22 to 200 cities. The dataset includes city coordinates, demands, the number of vehicles, and the capacity of each vehicle. Each dataset has uniform and clustered node distribution patterns, as depicted in Figure 6.  
In Figure 6, the red points (node 0) represent depots, and the blue points represent the locations of disaster victims. The problem codes have the following meanings: A, B, E, and M, which denote the problem set. N denotes the number of cities. K denotes the maximum number of vehicles. An additional factor introduced into the problem to test the efficiency of the developed model is the level of flood risk on each road segment.  
The adjustment of parameters for evaluating the performance of VRP-RIDFU consists of the GA and environmental parameters, as shown in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**GA Parameters** & & **Environment Parameters** & \\ \hline Population size: & 20–30 & Flooded probabilities: & 0.0–1.0 \\ Generation: & 100–2000 & Uncertainty situation: & 1–10 times \\ Crossover rate: & 0.7–1.0 & Vehicle speed: & 3–88 km/h [32] \\ Mutation rate: & 0.1–0.3 & & \\ Selection rate: & 0.5 & & \\ Number of runs: & 20 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting.  
Figure 6: The example of CVRP instances’ characteristics.
## 3 Results
### Synthesis of Results
#### 3.4.2 Effect of AI-Assisted Physiotherapy on Functional Impairment  
Functional impairment was assessed in seven of the eight included studies. Among these, five studies reported significant improvements in functional impairment following AI-assisted physiotherapy [29; 30; 31; 32; 34], while two studies found no significant differences compared to usual physiotherapy [21; 33]. Of the six studies included in the meta-analysis, five measured functional impairment and activity limitations both before and after the intervention [29; 30; 31; 33; 34]. Three studies used the Roland Morris Disability Questionnaire (RMDQ) [29; 30; 31], one used the Oswestry Disability Index (ODI) [29], and the remaining  
Figure 3: (**a,b**) Pain Intensity Outcomes: Forest Plot, Funnel Plot, and Sensitivity Analysis Comparing AI-Assisted Physiotherapy to Usual Therapeutic Interventions [29; 30; 31; 33; 34; 35].  
two employed the Modified Oswestry Disability Index (MODI) [34] and the High Fear Avoidance Beliefs Questionnaire (HFAB) [33] as outcome measures.  
Pooled effect sizes were calculated using a random-effects model. The results showed that AI-assisted physiotherapy demonstrated a potential for greater efficacy in reducing functional impairment compared to usual treatment; however, this difference did not reach statistical significance (SMD = \(-\)0.2508, 95% CI: \(-\)0.5574 to 0.0559, \(p\) = 0.1089) (See Figure 4a). A heterogeneity test revealed a fairly high degree of variability between studies (I\({}^{2}\) = 65.86%, \(\tau^{2}\) = 0.0707, \(p\) = 0.0433).  
Publication bias was evaluated using the Egger regression test, which showed no significant evidence of bias (intercept = \(-\)0.0387, 95% CI: \(-\)0.83689 to 0.7594, t = \(-\)0.1545, \(p\) = 0.887). This finding was further supported by a visual inspection of the funnel plot (See Figure 4b). Although minor asymmetries were observed in the funnel plot, they were deemed insignificant, confirming the absence of substantial publication bias.  
To explore the sources of heterogeneity, a sensitivity analysis was performed using a random-effects model (Figure 4c). The sensitivity analysis involved the sequential exclusion of individual studies to assess their impact on the overall effect size. The results suggested that AI-assisted therapies and usual physiotherapy were largely comparable in managing functional impairment, with no single study disproportionately influencing the overall findings.  
Figure 4: (**a–c**) Functional Impairment Outcomes: Forest Plot, Funnel Plot, and Sensitivity Analysis Comparing AI-Assisted physiotherapy to Usual Therapeutic Interventions [29; 30; 31; 33; 34].
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 3 Experimental Verification
### Evaluation of Water Quality in Aquaculture Areas  
According to different cruising paths, the unmanned boat carries out water quality data during cruising to obtain multiple sampling points. Seventy water quality sampling points were established in the aquaculture area to collect pH values, water temperature, and TDS values. Using the ordinary kriging method to interpolate, one can obtain the distribution of water quality in the aquaculture area. Based on the pH value, temperature, and TDS affiliation functions established in the previous section, a fuzzy comprehensive evaluation can be conducted using the kriging interpolation results. Figure 13 shows the distribution of the kriging interpolation results and the values of the membership function for pH, water temperature, and TDS values.  
The Root Mean Squared Error (RMSE) and the Root Mean Squared Percentage Error (RMSPE) of the predicted results for pH, temperature, and TDS values obtained through the kriging interpolation, compared to the actual sampling point data, are shown in Table 3.  
Based on the results of the pH, water temperature, and TDS membership functions, the distribution of water quality information evaluation within the entire fishing area can be clearly obtained (Figure 14).  
Figure 13: Ordinary kriging interpolation and distribution of membership functions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Water Quality Index** & **pH** & **Temperature** & **TDS** \\ \hline RMSE & 0.2 & 0.4 \({}^{\circ}\)C & 20 ppm \\ RMSPE & 2.67\% & 1.6\% & 2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Water quality index with their corresponding RMSE and RMSPE.
# Ultrasonic Surface Rolling Process: Properties, Characterization, and Applications
## 2 Mechanism of USRP  
USRP is a novel surface SPD technique that is an integration of UIP and DR techniques. In USRP, the combined effect of ultrasonic vibrating motion and static force on the specimen applied through a rolling ball tip induces SPD on the specimen surface. Figure 2 demonstrates the working of USRP. A standard USRP mechanism consists of an ultrasonic wave generator, an air compressor, and a working tip usually fixed on a lathe carriage to perform the deep rolling. The high-frequency ultrasonic vibration and static force are transmitted to the roller, rotating along the substrate surface at high velocity [46]. The static force used in the USRP process is lower than the conventional DR process; hence USRP can maintain the geometrical accuracy [28]. This repeated high-frequency impact integrated with rolling produces large elastic and plastic deformation on the substrate. Once the USRP is completed, the elastic deformation recovers while the plastic deformation plays a predominant role in reducing the surface roughness. Therefore, the integration of UIP and DR can induce a significant amount of grain refinement, and eventually, it will produce a uniform microstructure and GNS layer on the substrate.  
In addition, the ultrasonic vibration during the USRP process allows easy entry of the lubricant between the rolling tip and specimen surface. The purpose of providing lubrication between the working tip and processing surface is to reduce friction. The  
Figure 2: The fundamental working process of USRP.  
lubricant also acts as a coolant and improves the aesthetics of the surface. Simultaneously, the high-frequency impact improves the plastic deformation ability, which prevents the material from cracking and promotes the reconstruction of the material structure [47]. In this manner, USRP can provide superior surface integrity and surface mechanical properties.
# Two-Dimensional Profile Simulation Model for Five-Axis CNC Machining with Bull-Nose Cutter
## 1 Introduction
In this regard, numerous parameters, including tool orientation, tremor, runout, cutting force, and plastic deformation, were considered in the calculation. Further, this method is also based on the Z-Map. [PERSON] et al. [27] analyzed the motion of the cutting edge of a ball-end cutter and constructed a 3D surface topography model accordingly. Based on the N-buffer method and combined with inverse kinematic transformation of five-axis machining, [PERSON] et al. [28] proposed a 3D surface topography model for machining. [PERSON] et al. [29] combined geometric and mechanical models and established a surface roughness prediction model considering the influence of elastic and plastic deformation. Based on the Z-Map, [PERSON] et al. [30] established a model to predict the surface topography of four-axis milling by analyzing the cutting-edge motion of a bull-nose cutter. However, this method does not consider tool deformation, tool wear, or vibration in machining. [PERSON] et al. [31] used the N-Buffer method to develop a 3D topography prediction model for free-form surfaces machined by a bull-nose cutter, and investigated the effect of inclination angle on surface topography using the prediction model and milling experiments. Recently, [PERSON] et al. [32] proposed a symbolic-numerical approach for approximating the curve. This approach is an efficient method for approximating the curve using graphs of critical points. Bull-nose cutters are increasingly used in CNC machining due to their curvature adaptiveness. However, most current studies focus on modeling surface topography for machining with ball-end and flat-end cutters, while only a few focus on using bull-nose cutters. Simultaneously, the prediction model of surface topography after machining by a five-axis CNC with a bull-nose cutter can be used to verify the machining parameters and provide a theoretical basis to optimize them. Additionally, the profile method is a widely adopted method to evaluate surface topography. Therefore, a 2D profile simulation model is proposed in this study to predict the surface topography of five-axis CNC machining with a bull-nose cutter. This model and the corresponding simulation algorithm are presented in Sections 2 and 3, respectively. Simulation and machining experiments for the bull-nose cutter machining plane and free-form surface are discussed in Section 4. Finally, the main achievements and conclusions are summarized in Section 5.
## 4 The Proposed Key Distribution Framework
### A Spatiotemporal Diversification-Based Multipath-Multi-Key Distribution
#### 4.1.3 Testing Phase  
The core concept of this phase is that, in the event of a successful interception of a relay node, the key can be rerouted to alternative paths during the same time step. Unlike conventional multipath distribution methods, where the process stops after a key compromise, the proposed model activates an inspection/testing phase to identify and evaluate the compromised nodes. This phase introduces a mechanism to assess all nodes involved in the key distribution process, assigning rewards or penalties based on their behavior. Compromised nodes are identified and penalized, and if a node consistently fails to improve its behavior, it is excluded from the network. This proactive approach not only addresses vulnerabilities as they occur but also adapts to potential threats dynamically, maintaining robust security over time.