esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Dynamic Mean Pressure Distribution  
Following the results from the preliminary simulation, in the following sections, the results presented are for the Euler-Euler model with a D24 mesh without a boundary layer and an initial turbulence intensity of 7%.  
Figure 8: Mean dynamic pressure as a function of the turbulence intensity at \(r/D_{i}\) distances of 0.00, 0.35 and 0.69 were obtained with the Euler–Euler model.  
The analysis focused on the dynamic pressure near the stagnation zone, \(r/D_{i}<0.50\). The numerical data closely matched the experimental results, with discrepancies of less than 5% for pressure transducers located along the jet centerline and at \(r/D_{i}=0.35\) (Figure 9). However, a significant deviation was observed at \(r/D_{i}=0.25\), where the recorded mean dynamic pressure did not conform to a theoretical Gaussian curve distribution, resulting in notable differences between the numerical and experimental data.  
Outside the stagnation zone, the numerical results aligned well with the experimental findings, exhibiting only minor differences.  
Figure 10 presents the dimensionless variation of the mean dynamic pressure coefficient, normalized by its axis value, as a function of the distance from the jet stagnation point divided by the pool depth. [PERSON] et al. [3] proposed an equation for this dimensionless variation, suggesting values of \(K_{2}\) equal to 30 for shallow pool depths (\(Y/D_{i}<4.0\)) and 50 for deep pool depths, based on data from five pressure transducers within \(r/Y<0.3\), Equation (8).  
\[\frac{C_{p}}{C_{p,axis}}=e^{-K_{2}(\frac{r}{r})^{2}} \tag{8}\]  
The dimensionless mean dynamic pressure coefficient, \(C_{p}\), is defined in Equation (9), where \(p_{mean}\) represents the time-averaged dynamic pressure, Y the pool depth, and \(\Phi\) a correction factor for non-uniform nozzle exit velocity distribution. Analyzing dynamic pressure during tests with a  
Figure 10: Variation of \(C_{p}/C_{p,axis}\) as a function of \(\tau/\)Y.  
Figure 9: Dynamic mean dynamic pressure as a function of the distance to the jet centerline, \(r/D_{i}\).  
jet velocity of 7.4 m/s and pool depths between 0.3 m and 0.9 m, a correction factor value of 1.083 was derived. Similar values have been noted in simulations of high-velocity water jets in analogous experimental setups [10; 11].  
\[C_{p}=\frac{p_{mean}-Y}{\Phi\,\
icefrac{{V_{2}^{2}}}{{2g}}} \tag{9}\]  
[PERSON] [12] proposed values of 25 for shallow pools (\(Y/D_{j}=4.2\)) and 250 for deep pools (\(Y/D_{j}=11.1\)), based on results from four pressure transducers at \(r/Y<0.1\). In this study, data from nineteen pressure transducers positioned up to \(r/Y=0.41\) confirmed a pool depth of 0.8 m as corresponding to a deep pool (\(Y/D_{j}=11.11\)). The numerical and experimental results better matched [PERSON]'s values for deep pools. Furthermore, [PERSON] et al. [14], analyzing data from five sensors near the jet stagnation point during the same experimental campaign, proposed a curve that closely fits the present results from numerical modeling.
# Check for special characters and punctuation
## 4 Experimental Results and Analysis
### Evaluation Methodology
#### 4.1.1 Performance Metrics  
1. Processing Time: We measured the time required for each stage of the steganographic process: \(\bullet\)Container Generation: Time to generate appropriate cover text using GPT-4-omni \(\bullet\)Secret Key Generation: Time to generate and refine synonym sets \(\bullet\)Message Encoding: Time to embed the binary message within the cover text \(\bullet\)Message Decoding: Time to extract the hidden message from stego text
2. Resource Utilization: \(\bullet\)Memory Usage: Peak memory consumption during encoding/decoding operations \(\bullet\)API Efficiency: Number of API calls to GPT-4-omni per kilobyte of message \(\bullet\)Token Consumption: Total tokens consumed for container and synonym generation
# Simulation of the Influence of Wing Angle Blades on the Performance of Counter-Rotating Axial Fan
## 2 Numerical Calculation Model and Calculation Method
### Calculation Method and Solver Settings  
Numerical simulation of the wind turbine model was carried out with ANSYS Flunet numerical calculation software. The settings were as follows:  
1. Solver setting: Choose the RNG k-\(\epsilon\) turbulence model [11] that can better reflect the rotation of the fan impeller, and ignore the influence of gravity on the fan flow field. The standard wall function was used near the wall, and the pressure and velocity coupling selected the SIMPLE algorithm, and each difference format used the second-order accuracy [12].
2. Setting of regional conditions: The calculation of the flow field adopted the moving coordinate system method, and the two-stage impeller part was set as the rotating zone, the speed was \(\pm\)2900 rad/min, and the fan part was the static zone [13].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Parameters** & **Value** \\ \hline Number of blades of first-stage impeller & 14 \\ Number of blades of second-stage impeller & 10 \\ One, two leaf height (H/mm) & 150 \\ Chord length of first and second stage blade tip (L/mm) & 111 \\ One, two wheel hub ratio & 0.6 \\ One, two stage tip clearance(mm) & 2 \\ One, two rated speed/(rad/min) & \(\pm\)2900 \\ Mounting Angle of first-stage blade (\({}^{\circ}\)) & 46 \\ Mounting Angle of two-stage blade (\({}^{\circ}\)) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of FBD No.8.0 mine cyclone machine.  
Figure 1: Three-dimensional model of wind turbine.  
3. Boundary condition setting: Define the inlet end face of the collector as the velocity inlet, and the velocity direction was the normal direction of the inlet end face. The turbulence intensity and hydraulic diameter method were selected to control the turbulence; the outlet end face of the air duct was set as a free outlet; define the interface between the two-stage impeller and the air duct area for coupling and data transmission; the two-stage blade and the wall of the hub were set with a rotating wall, and the rotation speed remained relatively static with the region of adjacent cells. The other walls were set as static walls. All walls adopted the wall condition without slippage and considering the surface roughness.
4. Convergence conditions: When the residual values of turbulent kinetic energy k, dissipation rate \(\varepsilon\), and velocities in various directions were less than \(1\times 10^{-4}\), the calculation was considered to be convergent.
## 4 Results
### Training and Evaluating the Performance of the Models
#### 4.3.5 Predicting Qualification Flag for First-Year Students  
We sought to determine the ability to predict whether students are best suited to study in mainstream or extended qualification using students' first-year data. Figure 9 shows the performance metrics for different models applied to first-year students' data using APS, mathematics and physical sciences as input parameters and qualification flag (mainstream or extended) as the target. Across the models, accuracy values range from 98.1% to 98.6%. The decision tree and random forest models achieve the highest accuracy of 98.6%, closely followed by the KNN model at 98.5%. Precision and recall scores consistently surpass 98% for all models, with the decision tree, KNN, and random forest models achieving 98.6% precision and recall. The LGBM and XGB models demonstrate slightly lower but impressive precision and recall scores of 95.8% and 98.1%, respectively. F1 scores range from 95.6% to 98.6%. The decision tree, KNN, and random forest models exhibit F1 scores of 98.6%. RMSE values range from 0.118 to 0.208, and MAE values range from 0.014 to  
Figure 8: Predicting qualifications selected using first-year students’ data for features—APS, mathematics and physical sciences.  
0.043. These results indicate that the models effectively predict the qualification flag chosen using students' first-year data with high accuracy and minimal prediction error.
## 3 Results and Discussion
### Bendability  
One of the most important criteria for edible films is flexibility. Because of the protein-polysaccharide interaction and the plasticizing impact of glycerin with papaya, gelatin, and soy protein, materials become more flexible. This phenomenon might also be attributed to the possibility of increased molecular affinity. To assess the loss of barrier quality, the edible films were exposed to bending cycles at a radius of 6.5 cm. After the bending test, there was no visible damage to the G-2 and G/PP-2 films. In order to assess the bending effect on films, all films were subjected to WVTR testing. Similar methodology was adapted as mentioned in our previous work [33]. Figure 7 shows the normalized WVTR values of the films after various bending cycles. After being bent, the edible films G-2 and G/PP-2 always returned to their original positions and did not lose internal chain adhesion. In less than 600 bending cycles, the G/PP/SP-2 films started to shatter and lose adhesion in the film matrix, which resulted in WVTR degradation and exhibited 80% degradation as compared to their initial WVTR. G-2 and G/PP-2 almost retained their initial WVTR after 2000 bending cycles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Thickness (\(\mu\)m)** & **WVTR (g.m\({}^{-2}\).Day\({}^{-1}\))** & **Weight Loss (gm)** \\ \hline G-1 & 102.85 \(\pm\) 4.87 & 82.01 \(\pm\) 0.856 & 5.75 \\ G-2 & 97.142 \(\pm\) 3.06 & 80.05 \(\pm\) 1.003 & 5.65 \\ PP-1 & 118.57 \(\pm\) 6.26 & 54.19 \(\pm\) 1.553 & 3.8 \\ PP-2 & 107.14 \(\pm\) 4.87 & 51.34 \(\pm\) 0.788 & 3.6 \\ SP-1 & 131.42 \(\pm\) 2.08 & 70.60 \(\pm\) 1.321 & 4.95 \\ SP-2 & 175.71 \(\pm\) 5.34 & 69.03 \(\pm\) 2.411 & 4.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Barrier properties of edible film.  
Figure 6: Stress–strain diagram of edible film with different concentration of gelatin (red line), papaya (black line), and soy protein (blue line).
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 3 Technical Challenges and Requirements  
Compared to terrestrial networks, the use of UAVs in RANs offers additional degrees of freedom at the designer's disposal. However, their integration in future network infrastructures poses important technical challenges and requirements. Although some of these issues are application-dependent, we describe herein the main common problems. Moreover, even though such issues are presented separately, they might be closely intertwined in practice.
## 3 Methodology and Models
### Model Training  
The main model was trained using the prepared sequences of meteorological data and corresponding energy outputs.  
* Loss Function: The mean squared error (MSE) loss function was utilized to quantify the error between the predicted and actual energy production values. This choice was driven by the need to penalize large errors more heavily, which is crucial in the context of energy production forecasting.
* Optimizer: The Adam optimizer was chosen for its efficiency in handling sparse gradients and its adaptability to the complex, non-convex optimization landscape typical of deep learning models.
* Training: The model was trained for 2000 epochs, with the optimizer iteratively adjusting the model weights to minimize the MSE loss. The training process was carefully monitored to prevent overfitting by utilizing dropout method (dropout = 0.3), and a batch size equal to 256.
## 3 Mathematical Modeling and Design Methodology
### System Design Methodology
#### 3.4.4 Particle Swarm Optimization (PSO) Algorithm  
The PSO algorithm is one of the popular nature-inspired metaheuristic optimization algorithms. It was developed by [PERSON] and [PERSON] in 1995 [47]. Moreover, it has been revitalized several times in order to solve practical issues related to engineering and power system optimization [48; 49; 50]. Recently, PSO has emerged as one of the promising algorithms in solving various optimization problems in the field of science and engineering as well [51; 52; 53; 54; 55].  
As such, PSO is inspired by the social and cooperative behavior displayed by various species to look for their needs in the search space. The algorithm is guided by personal experience (\(P_{best}\)), overall experience (\(G_{best}\)), and the present movement of the particles to decide their next positions in the search space. Further, the experiences are accelerated by two factors \(c_{1}\) and \(c_{2}\), and two random numbers generated between [0,1]. Whereas, the present movement is multiplied by an inertia factor, \(w\), varying between [\(w_{min}:w_{max}\)].  
Figure 9: System operation flow chart for an on-grid system.  
The initial population (swarm) of size \(N\) and dimension \(D\) is denoted by \(X=\left[X_{1},X_{2},\ldots,X_{N}\right]^{\mathsf{T}}\). Each individual (particle) is given by \(X_{i}=\left[X_{i,1},X_{i,2}\,\ \ldots,X_{i,D}\right]\). Additionally, the initial velocity of the population is denoted by \(V=\left[V_{1},V_{2},\ldots,V_{N}\right]^{\mathsf{T}}\). The index \(i\) varies from \(1\) to \(N\) whereas the index \(j\) varies from \(1\) to \(D\). The velocity and position of the particles are computed using Equations (21) and (22), respectively [56]:  
\[V_{i,j}^{k+1}=w\ V_{i,j}^{k}+c_{1}r_{1}\big{(}\textit{Pbest}_{i,j}^{k}-X_{i,j} ^{k}\big{)}+c_{2}r_{2}\big{(}\textit{Gbest}_{j}^{k}-X_{i,j}^{k}\big{)} \tag{21}\]  
\[X_{i,j}^{k+1}=X_{i,j}^{k}+V_{i,j}^{k+1} \tag{22}\]
## 4 Empirical Evaluation of Proposed Algorithms
### Experiments with Reference Datasets
#### 4.1.4 Results for Scalability
We concentrated on the running time of the initialization and the corresponding SSE. We performed scalability experiments in two parts: (1) Tests with TIN: #processing elements was varied from 8 to 64 and \(K=100\) was fixed; (2) Tests with SVH: The number of clusters was varied as \(K\in\{100,200,400,800\}\) and #processing elements was fixed to 32. Otherwise, we used the same parameter settings as in the previous experiments. Median running time and SSE curves out of 10 runs are shown in Figure 1. Results for the experiment 1 are shown in Figure 1. In terms of [PERSON]'s law [51], K-means\(\parallel\) and SK-means\(\parallel\) perform equally well: running time is nearly halved when #processing elements is doubled from 8 to 16 and from 16 to 32. From this perspective, performance of SRPK-means\(\parallel\) is slightly worse than K-means\(\parallel\) and SK-means\(\parallel\). The results for the experiment 2 are shown in Figure 1b,c. Clearly, for very high-dimensional data, SRPK-means\(\parallel\) runs much faster compared to K-means\(\parallel\) and SK-means\(\parallel\). As analyzed in Section 3.2, the speedup for SRPK-means\(\parallel\) is increased when \(K\) is increased. A similar observation was made between K-means++ and RPK-means++ in [28]. Furthermore, when \(K=800\), the speedup for SRPK-means\(\parallel\) with respect to K-means\(\parallel\) is 7-8 and with respect to SK-means\(\parallel\) 9-10. Moreover, according to Figure 1c, SRPK-means\(\parallel\) (when \(p=40\)) and SK-means\(\parallel\) sustain their accuracy when \(K\) is increased in a frame of K-means\(\parallel\).
## 4 Experiments
### Results on COVID-SemiSeg Dataset  
In this section, we introduce quantitative and qualitative results to validate the performance of our model on COVID-SemiSeg dataset.  
The quantitative results allow numerical benchmarking of segmentation accuracy compared to ground truth annotations. Metrics such as the Dice coefficient, Hausdorff distance, and boundary F1-score provide precise measures of overlap, contour adherence, and delineation quality. Together, they offer a comprehensive and reliable assessment.  
We also showcase qualitative segmentation visualizations on real CT scans. These predicted maps give intuitive examples of segmentation behavior on complex shapes and textures. The visual results demonstrate generalization to variable infection morphology and localization across lung anatomy.  
Through both quantitative evaluations and qualitative demonstrations, we rigorously validate effectiveness on the COVID-SemiSeg challenge. The multi-pronged evidence builds confidence by aligning numerical scores with visual segmentations. This confirms the robustness of our semi-supervised approach to inconsistent novel cases.
## 4 Performance Analysis of Multi-Constellation Kinematic PPP
### Results and Analysis
#### 4.2.3 Convergence Time  
To evaluate the convergence performance of multi-constellation kinematic PPP, we started from the data sets in five-minute intervals (i.e., 12 times an hour) until convergence was achieved. In this study, the position filter was considered to be converged when the positioning errors reached \(\pm\)0.1 m and remained within that range. Table 5 presents the mean convergence time of RTK. Figures 5 and 6 show the kinematic PPP positioning errors of the four different processing cases. In these figures, we only present the convergence performance of PPP based on six replicates because of the space limitations. The convergence time was defined as the period from the first epoch to the converged epoch (indicated as a red line in these figures). Based on these figures and the table, we found that the GPS-only PPP required substantially more time to converge than the other processing cases. In contrast, the convergence performance of the GPS/GLONASS/BDS system was the best for all three coordinate components. Relative to that of GPS-only PPP, the convergence times of GPS/GLONASS and GPS/BDS PPP improved, particularly in the east direction. This result was very similar to that of [PERSON] et al. (2017).
# Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence
One characteristic consists of the kinematics of wheeled mobile robots. There are different types of kinematics: internal, external, direct, and inverse. The kinematic describes the relationship between mobile and the robot internal and external variables as a function of inputs and the environment. During the last few years, several types of research are applying artificial intelligence to solve problems in different fields, such as engineering, finance, marketing, health, gaming, telecommunications, transportation, and many others. Obstacle avoidance for mobile robots has not been an exception. Authors in [11] developed a hybrid AI algorithm combining Q-learning and Autowisard that will permit an autonomous mobile robot to self-learn. Using five layers Neuro-fuzzy architecture to learn the environment and the navigation map of a place, the mobile robot was routed based wholly on the sensor input (3D vision) by [12]. In this paper, we extend and analyze two systems designed for Obstacle-Avoidance implemented to perform in real-world scenarios [13,14]. The first system [13] presents the case of a mobile robot that applies fuzzy logic to realize soft turns basing its decision on depth image information obtained by a Microsoft Kinect device; the second system [14]introduces the case of a mobile robot based on a multi-layer perceptron, navigating in an uncontrolled environment with the help of ultrasonic sensors to decide how to move. The main contribution of this work lies in the discussion of aspects learned through the experience of designing and implementing these mobile robots in real-world conditions, which allow comparing both approaches in terms of different considerations, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. Although several works have approached this kind of comparison, such as the ones presented in [15; 16], there is valuable information that a detailed discussion on environmental inputs and general operational conditions, along with the analysis of both options, can offer to help in the decision of selecting one approach or the other. Hence, this kind of analysis and discussion offers perspectives to choose: (i) between reactive Obstacle-Avoidance systems based on ultrasonic or Kinect sensors, and (ii) between models that infer optimal decisions applying fuzzy logic or artificial neural networks; with elements and methods that are key when designing wheeled mobile robots. In the next sections, we present a brief explanation of two widely used AI techniques applied to mobile robots and some critical considerations and methods for obstacle avoidance and navigation applications.
## 6 Results and Analysis
### Analysis of the Results  
The logistics company before the implementation of Algorithm 1 routed the auto-carriers empirically, i.e., the personnel in charge of this process did it without the assistance of some planning or optimization software, meaning the construction of inefficient routes [17]. Similarly, the loading of the vehicles in the auto-carrier was the responsibility of the operators, a process prone to damage during the unloading of the vehicles upon arrival at the dealership. Due to the absence of a LIFO policy that considers the route of the auto-carrier in the process of vehicle allocation.  
The planning and routing of the auto-carriers of the logistics company were favorably impacted by the implementation of Algorithm 1, obtaining as output the generated routes (e.g., \(d_{0}->d_{44}->d_{35}->d_{0}\)) for the auto-carriers, the unloading vehicles in each dealership and auto-carrier schedules, as shown in Table 10. These results were possible to obtain thanks to the heuristic routing algorithm that considers the time (\(CT\) variable and time matrix). In addition, the proposed Algorithm 2 allowed to automate the process of allocation of vehicles in the auto-carrier, making it easier for operators to load the vehicles, examples of the output of this Algorithm 2 are shown in Figure 9. The allocation constraints can be adjusted to the requirements of different vehicles to be transported, but retaining the allocation logic.  
The efficiency of the proposed algorithm was demonstrated by being able to generate planning (routes, schedules, vehicle accommodations) in a reasonable time for more than 2000 routes, using auto-carriers of 3 vehicles of capacity. With the same performance, the results were obtained in a real case of the logistics company, using a heterogeneous fleet were generated 660 routes as shown in Table 9. As a consequence of the size of the demand, the routes constructed by Algorithm 1 contain from one dealership to four dealerships in their planning.
## 4 Results
### Conventional MES vs. the System with the DT-MES  
To analyze the results of the proposed architecture, the generation of errors in the process was carried out where error states occurred in different scenarios in the production cycle of the MES.  
1. Twenty orders are made from the conventional MES with different characteristics, and during execution, three errors are generated: (1) turning the plate, (2) changing the transport carriage, and (3) removing a fuse from the assembled plate.
2. Subsequently, the same number of orders, characteristics, and the same errors are generated as in the conventional system; however, the system works with the proposed DT-MES architecture.  
During testing, the time it takes to complete the requested order after the generation of failures is measured. In the first case, the operator waits until one of the sensors at the next station where the product arrives generates an error and this is solved by intervening. In the second case, the digital system warns of the error, and the DT provides the opportunity to visualize the digital environment and replicate the behavior of the physical environment in real time, as shown in Figure 7.  
After taking the times in which each of the processes is carried out for each of the cases, the results shown in Figure 8 are obtained, where it is observed that these times are decreased by a total average of 40.2 s, which represents a percentage improvement of 14.2%. These results show that the proposed architecture provides the possibility of streamlining production and reducing downtime generated by internal or external disturbances that may occur during production.  
Figure 7: (a) Digital twin–MES production line; (b) MES production line.