esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Particle-Swarm-Optimization Algorithm for a Prediction Model of Steel Slab Temperature
## 5 Conclusions  
In this paper, the XPSO algorithm was proposed to establish a prediction model of slab temperature in a reheating furnace. A novel weight-updating strategy that combines a decreasing function and the adaptive \"stepped\" strategy was introduced into the XPSO algorithm, so it can greatly improve the search capabilities at a later stage. The validity and feasibility of the XPSO were verified by nine classical benchmark functions, simulation data sets generated by the existing mechanism model and actual data sets from Angang. The following conclusions are given.  
1. The benchmark results indicate that the XPSO algorithm has a superior performance to other PSO algorithms (PSO, IPSO, IPSO2, HPSO, CPSO).
2. The XPSO algorithm, which can accurately predict the billet temperature (99% of the prediction errors were less than 2 \({}^{\circ}\)C) while ensuring faster convergence, was more successful than all of the other optimization approaches (WOA, IA, GWO, DE, ABC).
3. The prediction model based on the XPSO algorithm can predict more accurate discharging temperatures for the operators. Consequently, the paper verifies the feasibility of the XPSO algorithm and the success of the establishment of the prediction model of slab temperature, and provides a theoretical basis for subsequent research.  
Conceptualization, M.L., P.Y. and Z.Y.; methodology, M.L.; software, M.L. and P.L.; validation, M.L. and J.Q.; formal analysis, J.Q.; investigation, P.L. and Z.Y.; resources, M.L. and P.L.; data curation, M.L.; writing--original draft preparation, M.L.; writing--review and editing, M.L. and Z.Y.; visualization, Z.Y.; supervision, P.Y. and Z.Y.; project administration, P.Y. and Z.Y.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the PhD research startup foundation of Qilu University of Technology, grant number (81110535), and the Industry-University-Research Collaborative Innovation Fund (grant number 2020-CXY46)--Development of an automated system for casting post-processing processes.  
Figure 15: The slab temperature prediction by the XPSO algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Points** & **1 (\({}^{\circ}\)C)** & **2 (\({}^{\circ}\)C)** & **3 (\({}^{\circ}\)C)** & **4 (\({}^{\circ}\)C)** & **5 (\({}^{\circ}\)C)** & **6 (\({}^{\circ}\)C)** & **7 (\({}^{\circ}\)C)** & **8 (\({}^{\circ}\)C)** & **9 (\({}^{\circ}\)C)** & **10 (\({}^{\circ}\)C)** & **Mean (\({}^{\circ}\)C)** \\ \hline XPSO & 5.43 & 2.91 & 8.93 & 7.32 & 3.47 & 5.17 & 1.17 & 5.09 & 4.09 & 6.43 & 4.99 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction errors between calculation results and measured data.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 4 Parameter Settings
### The Selection of the Threshold for Message Extraction  
According to Equation (17), \(k_{2}\) determines the threshold for message extraction, which will affect the success rate and validity of watermark information extraction. We performed a statistical analysis of the extraction results of the 29 LSFRs constructed from the eight host images with and without watermarks to select the most appropriate threshold. The experiment was set at a shooting angle of 0, 15, and 30 degrees and a shooting distance from 40 to 110 cm at intervals of 10 cm. Therefore, each LSFR was captured 24 times with different shooting conditions.  
Based on the extraction method in Section 3.3.3, a total of 696 results of watermarked LSFRs and 648 results of unwatermarked LSFRs were obtained. The average erroneous bits with a different \(k_{2}\) is shown in Figure 11(a). The extraction results of watermarked LSFRs achieve the minimum erroneous bits when \(k_{2}\) is set to 1. The distributions of erroneous bits with \(k_{2}=1\) are shown in Figure 11(b). The average of detected erroneous bits of unwatermarked LSFRs is around nineteen which is independent of \(k_{2}\).  
Therefore, \(k_{2}\) is set to 1 in our experiment.
## 2 Current Technologies for Detecting Falls
### Artificial Intelligence  
Artificial intelligence gives the system the ability to detect falls based on the dataset and data trends. Sensors produce data connected with various fall parameters during the data collection procedure. As a result, machine learning methods are utilized to categorize or identify fall actions based on the requirements of an application [12]. For fall detection as well as other activity detection, deep learning techniques are becoming the preferred method, particularly for visual detectors and sensor fusion [13]. Another area of fall detection examination is deep reinforcement learning, which is based on psychological and neuroscientific theories about how humans adapt to changing environments and improve their behaviors. Deep reinforcement knowledge incorporates both deep and reinforcement learning to expand detection alternatives that respond to changing environments, while preserving accuracy and robustness.  
Figure 2 shows the five processes that make up the overall system for fall detection methodology. Depending on the application's needs, data collection from sensing devices is the first step. In the second step, the noisy and undesired signals are removed from the collected fall data. Feature extraction from fall datasets, which takes the preprocessed data and extracts the desired features, is the third step. In the fourth step, machine learning techniques are applied to classify abnormal falls and ADL. It separates the data into two categories: training and testing. Finally, different evaluation matrices, such as accuracy and confusion matrices, are used in the performance assessment step to analyze the system's overall outcome.
## 5 Conclusions  
Based on analyzing the influences of different airfoil morphing methods (simultaneous variable camber and chord (VCaCh), variable camber (VCa) alone, and variable chord (VCh) alone) on aerodynamic characteristics during four typical flight stages of a conceptual aircraft, the following conclusions can be drawn:  
1. Both VCaCh and VCa morphing methods could improve the lift during the take-off stage. The benefits of VCaCh morphing method are more significant when the trailing edge deflects at the same angle. Therefore, if the take-off weight is large, the VCaCh morphing method is required.
2. During the first high-speed cruise stage of flying to the destination, the shock wave is quite strong at a larger \(L/W_{0}\). The VCaCh morphing method can significantly weaken the shock wave, reduce the shock-wave drag, and thus increase the lift-to-drag ratio.
3. During the low-speed flight stage above the destination, the pressure drag of VCaCh and VCa airfoils can be significantly reduced, which highly improves the lift-drag ratio.
4. The required lift force is small during the second high-speed cruise stage of flying back to the airport, and the shock wave is weak at a small \(L/W_{0}\). At this time, using the VCa morphing method of trailing-edge downward deflection could improve the lift-drag ratio, but the benefits are pretty small. This can be explained in that a variable camber improves the lift-drag ratio mainly through reducing the shock-wave drag, but the original shock-wave drag is actually small, so the influence of the variable camber on total drag is small.
5. During all stages of the flight, the adaptation of a VCaCh or VCa morphing method in a real-time manner can not only improve the high lift characteristics at take-off stage, but also increase the lift-to-drag ratio at transonic cruise and low-speed task stages during which the required lift is continuously decreasing due to the consumption of fuel, which implies that aircraft with proper morphing airfoil can achieve all-stage high aerodynamic performance.  
Conceptualization, Q.C., P.B.; methodology, writing--review, and supervision, Q.C.; investigation, Y.C., J.Z., Q.C., H.L.; computation, visualization, Y.C., J.Z.; formal analysis, Y.C., J.Z., Q.C.; writing--original draft, Y.C., J.Z., Q.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 91741102), the Shenzhen Fundamental Research Program (Grant No. JCYJ20190807160413162), and the Fundamental Research Funds for the Central Universities (Grant No. 19 lgzd15).  
Figure 12: Pressure coefficient distributions of morphing airfoil at \(Ma=0.734\), \(L/W_{0}=0.7\).
## 2 Materials and Methods
### Process Description  
The process was modeled using as reference two extraction plants of crude palm oil located in Bolivar and Cesar departments in North Colombia and complementary data reported in the literature [22]. The processing of palm fruit into crude palm oil involves five basic operations: sterilization, threshing, fruit digestion, oil extraction, and oil purification [23], as shown in Figure 1. The African palm bunch (30 t/h) is sterilized by the action of saturated steam to avoid the effect of the enzyme lipase on the free fatty acids and to hydrolyze the palm rachis to soften the pulp tissues [15]. From the sterilization stage, the sterilized bunch, condensed water, and steam leave the system. Then, the sterilized bunches are sent to the threshing stage where the fruits are separated from the rachis by a rotating drum of 5 kW power. The rachis is discarded while the fruits are reheated in the digestion stage to facilitate the expulsion of the oil. The digestion equipment power is 10.5 kW. The digested fruits are pressed by a horizontal perforated basket cylindrical shape, where a liquor containing a large amount of oil is extracted. Subsequently, water is added to the liquor to facilitate oil separation and purification. In the clarification stage, up to 90% of the oil is separated and sent to a drying process. The heavy sludge from the clarification goes to the centrifugation step where 10% of the oil is recovered; the water and heavy sludge from this stage leave the process, while the recovered oil is recirculated to the clarification stage. In the drying stage, the percentage of moisture and impurities still contained in the oil is reduced to the maximum; due to the high temperature at which the oil leaves, this drying is performed under vacuum conditions, the pressure of the stream is reduced and causes the remaining water to evaporate. The vacuum dryer power is 15 kW. Finally, the dry palm oil is sent to its respective storage.  
Figure 1: Process diagram for crude palm oil production.
# Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT
###### Abstract  
High-pixel-number synchrotron radiation computed tomography (CT) has the advantages of high sensitivity, high resolution, and a large field of view. It has been widely used in biomedicine, cultural heritage research, non-destructive testing, and other fields. The Nyquist sampling theorem states that when the detector's pixels per row are increased, it requires more CT projections, resulting in a lengthened CT scan time and increased radiation damage. Sparse-view CT can significantly reduce radiation damage and improve the projection data acquisition speed. However, there is insufficient sparse projection data, and the slices reconstructed show aliasing artifacts. Currently, aliasing artifact correction processes more medical CT images, and the number of pixels of such images is small (mainly \(512\times 512\) pixels). This paper presents an aliasing artifact correction algorithm based on deep learning for synchrotron radiation CT with a high pixel number (\(1728\times 1728\) pixels). This method crops high-pixel-number CT images with aliasing artifacts into patches with overlapping features. During the network training process, a convolutional neural network is utilized to enhance the details of the patches, after which the patches are reintegrated into a new CT slice. Subsequently, the network parameters are updated to optimize the new CT slice that closely approximates the full-view slice. To align with practical application requirements, the neural network is trained using only three samples to optimize network parameters and applied successfully to untrained samples for aliasing artifact correction. Comparative analysis with typical deep learning aliasing artifact correction algorithms demonstrates the superior ability of our method to correct aliasing artifacts while preserving image details more effectively. Furthermore, the effect of aliasing artifact correction at varying levels of projection sparsity is investigated, revealing a positive correlation between image quality after deep learning processing and the number of projections. However, the trade-off between rapid experimentation and artifact correction remains a critical consideration.  
synchron radiation CT; spare-view; artifact correction; deep learning Article Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT  
19 February 2024  
19 April 2024  
12 April 2024  
17 April 2024
## 5 The Case Study Results and Analysis
### Case Analysis  
Using the multi-attribute decision-making method for product form design proposed in this paper, which integrates expert knowledge and user preference, the weights of the expert group and the user group were determined to be 0.52 and 0.48 respectively, through the Deviation Minimization method. The final weighting resulted in the order of proximity of the alternatives to the idealized scheme in this case as follows: \(\mathbf{P}_{6}{\rightarrow}\mathbf{P}_{7}{\rightarrow}\mathbf{P}_{8}{ \rightarrow}\mathbf{P}_{2}{\rightarrow}\mathbf{P}_{4}{ \rightarrow}\mathbf{P}_{5}{\rightarrow}\mathbf{P}_{3}{ \rightarrow}\mathbf{P}_{1}\).  
As can be seen in Table 5, although the maximum group utility values \(\mathbf{S_{i}}\) of \(\mathbf{P_{1}}\) are all greater than the rest of the schemes in the expert-knowledge-based decision-making process, its minimum individual regret value \(\mathbf{R_{i}}\) is also the largest, indicating that this scheme has the largest gap between it and the best scheme as well as the worst performance under each criterion. \(\mathbf{P}_{6}\) has the smallest individual regret value \(\mathbf{R}_{i}\), indicating that this scheme has the smallest gap with the best scheme and performs relatively well under each criterion, with the least compromise across them. The ratio value \(\mathbf{Q}_{i}\) measures the degree of balance between \(\mathbf{R}_{i}\) and \(\mathbf{Q}_{i}\). \(\mathbf{P}_{6}\) has the smallest benefit ratio, indicating a superior balance between the attributes. Therefore, \(\mathbf{P}_{6}\) is the best scheme, scoring 1 after the normalization process. From Table 9, it is evident that \(\mathbf{P}_{6}\) has the maximum performance scores under each attribute in user-preference-based decision making. Therefore, the scores of each attribute for \(\mathbf{P}_{6}\) are defined as the positive ideal solution in the TOPSIS calculation, resulting in a final scheme score of 1. Therefore, an analysis of the calculations reveals that the combined scores for \(\mathbf{P}_{6}\) are greater than those of the other scheme, regardless of the decision-making weights of the groups.  
However, in the actual decision-making process, to mitigate the impact of differing weights between experts and user groups on scheme ordering, this study employed three methods of fusion: the entropy weight method, variance method, and chi-square method. These methods were used to verify the scores from both sides. The calculated weights for the expert group and user group were, respectively, 0.6394 and 0.3606 for the entropy weight method, 0.5057 and 0.4943 for the variance method, and 0.4451 and 0.5549 for the chi-square method. The final scheme sorting obtained using these methods demonstrated consistency in scheme ordering. This further proves the robustness and reliability of the method in selecting the optimal product form design solution. However, in practical applications, it is still necessary to select appropriate weight coefficients according to the actual situation.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 6 Smooth Pulses
### Smooth Exponential Pulse  
By following the same procedure, one can replace the singular exponential with the following smooth exponential pulse:  
\[A(z=0,t)=\frac{1}{2}A_{0}\exp(at)erfc\left(\frac{t}{\Delta}\right) \tag{76}\]  
which propagates in dispersive medium according to  
\[A(z>0,t)=\frac{A_{0}}{2}\exp\left(-i\frac{\beta_{2}}{2}a^{2}z+at\right)erfc \left(\frac{t-i\beta_{2}az}{\sqrt{\Delta^{2}-2i\beta_{2}z}}\right). \tag{77}\]  
This function is illustrated in Figure 21 for two different distances.  
Figure 20: Same as Figure 12, but for Equation (75) with the transition width \(\Delta=0.4\theta\).  
Sample Application: A pulse with a 10% to 90% rise time of \(\Delta t_{10-90}=1\) ps and a decay time of \(\sim\)3.7 ps with a carrier wavelength of \(\lambda=2.0\)\(\upmu\)m can be simulated by the pulse presented by Equation (76) with \(\Delta=0.67\) ps, \(a=0.3\) ps\({}^{-1}\). Since \(a\Delta\cong 0.2\), Figure 21 is an illustration of this pulse. If the pulse penetrates a BK7 glass, then for this wavelength, \(\beta_{2}\cong 98\) ps\({}^{2}\)/Km, and therefore \(\zeta=0.1\) and \(\zeta=0.01\) in the figure correspond to \(z=\zeta\theta^{2}/\beta_{2}\cong 11\) m and \(z\cong 1.1\) m, respectively.
## 4 Simulation and Analysis  
By using a collaborative simulation platform involving Matlab/Simulink, PreScan, and CarSim, three different control systems depicted in Table 1 are validated through simulation tests. The validation process consists of constructing a collision avoidance scenario in PreScan, configuring key parameters of the test vehicle in CarSim (as listed in Table 2), and implementing the distinct control systems in Matlab/Simulink. For the constructed scenario, we set the lane width to 3.75 m and the inner radii of the lanes to 60, 90, and 120 m. The road friction is set to 0.9, and the initial distance between the host vehicle and the obstacle vehicle is 100 m. In the scenario, the obstacle vehicle remains stationary on the lane, while the host vehicle, equipped with the control systems, approaches the obstacle vehicle on the same lane at speeds of 50 km/h and 60 km/h, respectively, on the three different curved paths.  
Figure 7: Lane deviation scenario.  
Furthermore, prior to activating the AEB system, the vehicles are controlled using the driver model described in Section 3.1 to satisfy the lateral control requirements for normal movement on curved roads. In System 0, it is assumed that the driver does not respond promptly after the AEB system's warning is activated, resulting in no braking or steering actions. In terms of longitudinal control, the AEB system initiates emergency braking action. At the same time, it is assumed that the steering wheel remains unchanged during the braking process. Meanwhile, in both System 0 and System 0, the initial conditions remain the same, but after the AEB system is triggered, the LKA system takes over lateral control of the vehicle based on specific triggering conditions.
## 4 Results on ML/DL-Based Adaptive Modulation  
This section analyzes ML/DL techniques applied to adaptive modulation in UWA communication systems. The analysis includes an overview (Section 4.1), system characteristics (Section 4.2), and performance comparison (Section 4.3) for SC-UWA (Section 4.4) as well as MC-UWA systems (Section 4.5).
# Experimental Study on Settlement Behavior of Ballasted Tracks with Polymer Compound-Coated Gravel
## 1 Introduction
As such, a pressing need exists for studies on developing a track bed ballast with good durability that can be used semi-permanently while maintaining the shape of the ballast in the face of impacts, wear, and friction. In previous research, differences in rough stones, particle size distribution, material strength, number of repetitions, and setting behavior were analyzed according to load conditions, etc. Herein, the static spring stiffness and long-term setting behavior of the ballast coated with a high-molecular compound are experimentally compared with those of the normal ballast. In addition, the proposed coating is different from binding with epoxy or urethane or inserting separately processed aggregate to stabilize the ballast, as discussed in previous studies [15; 16]. In this study, a railway ballast was developed with an impact-resistant, highly elastic polymer compound coating in order to quantify and increase the ballasted track's level of elasticity and mitigate issues in the field, such as mud-pumping and track bed drainage difficulties. In addition, to check the performance of the proposed ballast, ballast box tests and finite element analyses were performed to confirm that the ballast is a suitable substitute for the existing ballast. It was experimentally proven that the long-term settlement behavior of the ballast can be improved by coating it with a polymer compound, and this imparts elasticity and maintains the ballast shape even after cyclic loading.
## 5 Validation of Proposed Method
### Evaluation Method  
In this study, some crowded environments similar to the simulation in [12], where it is challenging for the PMV to reach the destination efficiently due to avoidance behavior, were focused on. Therefore, numerical simulations were conducted in a corridor, considered sufficiently wide for the vehicle to execute avoidance behaviors, where numerous pedestrians approach the vehicle from the front.  
In the simulation, as shown in Figure 13, the width and length of the pathway were 6.0 m and 40 m, respectively. The PMV was controlled by either the proposed method or the existing method. The PMV started from \((x,\ y)=(0,\ 1)\) heading for \((x,\ y)=(40,-1)\), which is assumed to be a waypoint on the global path. A trial of autonomous navigation was considered complete if the PMV reached the goal line \((x=32.5\ \text{m})\) within 60 s after starting. Pedestrians were assigned random initial positions, velocities, and destinations and moved according to an SFM model [15]. Regarding the path-planning method, a parameter study was conducted in the environment for the weight constants \(\beta_{goal}\) and \(\beta_{inv}\). Then, the combination of the constants was set \(\left(2^{-5},2^{-5}\right)\), a combination optimal from the  
Figure 12: Scale reduction of PS for tolerable pedestrians.  
perspective of passenger comfort, which was emphasized in [12]. A constant value of 0.995 was adopted for \(\gamma_{R}\) as the value that satisfies Equation (7) in Section 4.2.  
To investigate the differences in the behavior between the existing and proposed methods due to differences in pedestrian density, five conditions with different numbers of pedestrians (10, 20, 30, 40, and 50) were set. Additionally, one simulation trial was conducted for the condition with no pedestrians. Thus, this simulation's pedestrian density ranged from approximately 0.0 to 0.2/m\({}^{2}\). One thousand simulation trials were conducted for each pedestrian density condition with different initial conditions. The same pedestrian initial conditions were applied to both existing and proposed methods.