esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fabrication and Measurement  
A prototype of the presented dielectric rod antenna was fabricated to verify the theoretical analysis, as shown in Figure 11. In the prototype, the helix was made of copper wire and placed into a Teflon hollow with a relative dielectric constant of 2.1. Then, the dielectric rod was fixed with aluminum reflectors. A 50 \(\Omega\) SMA connector is used as the feed port, and the inner and outer conductors connect with the helix and the bottom reflector, respectively. The printed loops were fabricated on the F4B substrate with a thickness of 2 mm and a relative dielectric constant of 2.65. Finally, these printed loops were separated and inserted into the slots on the dielectric rod in series to construct the compact structure.  
Figure 12a shows the return loss measurement of the prototype. It is seen from Figure 12b that the measured return loss is almost below \(-12\) dB in the whole band. There still exists some difference between the simulated and measured results. This is mainly due to the fabrication errors of the helix, especially the deviation of the impedance-matching segment on the first turn.  
Figure 11: Fabricated parts of Embedded Helix and dielectric rod antenna.  
Figure 10: (**a**) Radiation patterns of the whole structure for entire working band (**b**) Axial ratio for helix and final structure in entire operational band (**c**) Electric field radial profile of presented antenna.  
To verify the theoretical analysis better, the single helix, as well as the whole structure, are both measured to compare with the simulation, as shown in Figure 13. Figure 13b shows that the measured and simulated radiation patterns of the alone helix are almost the same. Similarly, in Figure 13d, it is found that the measured radiation patterns of the proposed embedded structure helix meet well with the simulated results. The small difference on the side lobe level is mainly caused by fabrication errors and environmental noise in the measurement. Additionally, the measured gain of the whole structure is about 0.4 dB lower than that of the simulated. This is mainly due to the losses caused by the solder, SMA connector, and dielectric. Further, Teflon material is used for the proposed antenna structure where Teflon dielectric constant varies from 2 to 2.04 for temperatures 10 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, respectively [23]. Furthermore, it is already discussed that the dielectric constant has little influence on return loss in the proposed structure, as mentioned in Figure 4a. It is concluded that temperature has a negligible effect on the proposed antenna's performance.  
Figure 12: (**a**) Return Loss measurement setup (**b**) Simulated and measured return loss of Embedded Helix and DR antenna.  
Figure 13: (**a**) Radiation patterns Measurement setup of Helix (**b**) simulated and measured radiation pattern comparison of Helix alone (**c**) radiation pattern measurement of the proposed structure (**d**) simulated and measured radiation pattern comparison of the proposed structure.
## 5 Performance Evaluation  
In this section, we evaluate the performance of the proposed SFPCR scheme through simulations. We compare the SFPCR scheme with three existing schemes: the FSRC scheme [33], the cluster-based scheme [21], and the mesh scheme [37] for performance evaluation. To do this, we first describe our simulation environments and scenarios and next present their performance evaluation metrics. Last, we explain the performance comparison of the FSRC and the three existing schemes by simulation results.
# Experimental and Geant4 Simulation Study of MURA Mask for Scintimammography
## 3 Results and Discussion
### Simulation Extended Work
However, our next work will be conducted to experimentally verify those parameters for lower tumor sizes. Nevertheless, the current study can be of interest to physicians and to medical staff communities as an advancement on the topic of using MURA masks for scintillammography. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Location** & **TBR** & **FWHM** & **Contrast** & **Location** & **TBR** & **FWHM** & **Contrast** \\ \hline \multirow{6}{*}{14} & 2 & 8.037 & 3.510 & \multirow{6}{*}{20} & 2 & 7.949 & 3.830 \\ \cline{2-6}  & 3 & 8.000 & 3.707 & & 3 & 7.987 & 3.655 \\ \cline{2-6}  & 4 & 8.023 & 3.612 & & 4 & 8.030 & 3.492 \\ \cline{2-6}  & 5 & 7.925 & 3.930 & & 5 & 7.966 & 3.903 \\ \cline{2-6}  & 6 & 8.015 & 3.519 & & 6 & 7.951 & 3.582 \\ \cline{2-6}  & 7 & 7.979 & 3.843 & & 7 & 7.967 & 3.782 \\ \cline{2-6}  & 8 & 8.015 & 3.523 & & 8 & 8.017 & 3.814 \\ \cline{2-6}  & 9 & 8.033 & 3.440 & & 9 & 8.008 & 3.642 \\ \cline{2-6}  & 10 & 7.936 & 3.542 & & 10 & 8.018 & 3.451 \\ \hline \multirow{6}{*}{15} & 2 & 7.982 & 3.623 & & 2 & 8.007 & 3.502 \\ \cline{2-6}  & 3 & 8.007 & 3.745 & & 3 & 8.002 & 3.364 \\ \cline{2-6}  & 4 & 7.959 & 3.696 & & 4 & 7.982 & 3.512 \\ \cline{2-6}  & 5 & 7.975 & 3.742 & & 5 & 8.003 & 4.029 \\ \cline{2-6}  & 6 & 8.005 & 4.063 & & 6 & 8.074 & 3.803 \\ \cline{2-6}  & 7 & 7.974 & 3.718 & & 7 & 7.996 & 3.598 \\ \cline{2-6}  & 8 & 7.957 & 3.642 & & 8 & 8.021 & 3.795 \\ \cline{2-6}  & 9 & 7.935 & 3.886 & & 9 & 7.982 & 3.828 \\ \cline{2-6}  & 10 & 7.955 & 3.499 & & 10 & 8.050 & 3.636 \\ \hline \multirow{6}{*}{16} & 2 & 8.035 & 3.641 & & 2 & 7.989 & 3.661 \\ \cline{2-6}  & 3 & 7.985 & 3.487 & & 3 & 8.011 & 3.586 \\ \cline{2-6}  & 4 & 7.968 & 3.722 & & 4 & 8.050 & 3.678 \\ \cline{2-6}  & 5 & 8.069 & 3.465 & & 5 & 7.975 & 3.988 \\ \cline{2-6}  & 6 & 8.084 & 3.713 & & 6 & 7.937 & 3.867 \\ \cline{2-6}  & 7 & 7.999 & 3.587 & & 7 & 7.944 & 3.551 \\ \cline{2-6}  & 8 & 7.935 & 3.718 & & 8 & 8.019 & 4.012 \\ \cline{2-6}  & 9 & 8.054 & 3.496 & & 9 & 7.996 & 3.767 \\ \cline{2-6}  & 10 & 8.019 & 3.637 & & 10 & 7.926 & 3.874 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
**Acknowledgments:** This work was supported by The National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (12-MED2516-02). **Conflicts of Interest:** The authors declare no conflict of interest.
# A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
## 6 Bibliometric Analysis of Studies on RL in Production Scheduling
### Comparison of Applications of RL in Different Scheduling Problems

Table 5: Comparison of deterministic and uncertain scheduling methods based on RL. area(s) of production scheduling in the context of RL, deterministic scheduling and uncertain scheduling approaches has been examined in the following. Key words used for that purpose were: (a) Scheduling AND \"reinforcement learning\" AND specific deterministic scheduling problems (\"job shop\", \"flow shop\", \"open shop\", \"single machine\", and \"parallel machines\"). (b) \"Reinforcement learning\" AND specific uncertain scheduling problems (\"fuzzy scheduling\" and \"stochastic scheduling\"). Data were retrieved from ScienceDirect database using filters 'Research articles', and 'All years'; Scopus database using filter 'Article titles', 'Abstracts', 'Keywords', All years, and All document types, and Google Scholar database--All years and All document types. Obtained results are graphically presented and compared in percentage with each other in Figure 11.  
One can see from Figure 11a that RL-based scheduling algorithms are mostly used to solve job shop scheduling problems [106], while flow shop production environment is the second most important type of scheduling problems, where RL is applied. Obviously, RL-based algorithms found application in other types of scheduling problems. As the most noticeable of them, it is possible to mention energy efficiency scheduling, multi-objective scheduling, and distributive scheduling. The last-mentioned scheduling problem is exploited especially in intelligent manufacturing systems [107]. Analyzing the results from Figure 11b, it is observed that RL is predominantly used in stochastic scheduling methods comparing with its application in fuzzy scheduling methods. Moreover, the effectiveness of stochastic scheduling using RL has been also demonstrated through benchmarking studies [29; 108; 109]. Although the application of RL in fuzzy scheduling is promising, it remains less common due to additional complexities involved in integrating fuzzy logic  
Figure 11: Comparison of (**a**) deterministic scheduling methods and (**b**) uncertain scheduling methods from their occurrences in literature. with RL methods [110; 111]. However, the results of this quantitative approach for comparison of application of RL in different scheduling problems provide some useful managerial insights into scheduling practice. Nevertheless, further exploration and research on qualitative aspects of RL-based scheduling, particularly in the context of smart manufacturing, is required.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 1 Introduction
The CEEMDAN method is utilized to decompose unstable residual component into multiple stable components, identifying uncertain characteristics of the load series. Subsequently, the model designs an advanced residual BiLSTM (ARBiLSTM) block to learn the temporal features of the different components and generate the forecasting results for the following day. The primary contributions of this paper are outlined as follows:  
1. The paper proposes the forecasting model based on Prophet-CEEMDAN-ARBiLSTM. The model can decompose the original electricity load series into different components without considering the external factors affecting the change in the load data. Then, these components are used as inputs of the ARBiLSTM block to generate the final output. 2. The paper proposes an advanced residual BiLSTM (ARBiLSTM) block achieved by restructuring the BiLSTM layer into a dense connected structure. The ARBiLSTM block effectively enhances the forecasting ability of the method and relieves the gradient vanishing. 3. The effectiveness of the proposed method is verified using real data, and the experiments show that the Prophet-CEEMDAN-ARBiLSTM yields higher accuracy than existing models. The remainder of this paper is organized as follows. Section 2 presents the Prophet-CEEMDAN-ARBiLSTM model. Section 3 details the training process of the proposed model and provides the comparative results with existing models. Section 4 concludes the research of this paper.
## 3 Comparison of Theoretical Simulation and Performance Tests  
### Test Platform Construction  
In order to verify the reasonableness and accuracy of the model and simulation parameter settings, the submersible well pump performance experimental test platform was built, and the test principle is shown in Figure 5. It mainly includes the CYB type intelligent pressure transmitter, the LDTH type DN400 electromagnetic flow meter (accuracy \(\pm\) 0.3%), the ZDLM type electric control valve, and the 381 LS type electronic electric actuator.  
Before the start of the test, put the submersible well pump into the water. Running the submersible well pump makes the pipeline discharge air, so close the valve. The experiment starts with zero flow, and then the valve opening is gradually increased. In the process of increasing the flow from small to large, the flow rate is stable during the corresponding data collection. In standard working conditions, more data points are collected, while in small and large flow conditions, a relatively small number of data points are collected.  
Pump performance test schematic diagram. (1) Computer; (2) data acquisition instrument; (3) distribution cabinet; (4) DN400 electromagnetic flow meter; (5) ZDLM type electric control valve; (6) CYB type intelligent pressure transmitter; (7) pump body; (8) electric motor.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 3 Establishment of Rigid Body Dynamics Model for Mechanisms with Clearances  
### Structural Characteristics of an Eight-Bar Mechanism  
The schematic structure of planar eight-bar mechanism is illustrated in Figure 6. The red dotted line indicates the objects at the same vertical position in the Y-axis direction. Mechanism including a crank 1 (\(L_{1}\)), triplex member 3 (\(L_{BO3},L_{CO3},L_{DO3}\)), connecting rods 2 (\(L_{2}\)), 4 (\(L_{4}\)), 5 (\(L_{5}\)), and 6 (\(L_{6}\)), a slider 7, and a frame 8. Crank 1 is the active member, and the slider undergoes reciprocal linear motion within a guideway.  
Figure 6: Simplified structural diagram of a planar eight-bar mechanism.  
This study investigates an eight-bar mechanism that has excellent kinematic characteristics, including low-speed stability of the slider in the lower dead center position, pronounced fast slewing characteristics, and strong load carrying capacity. The study focuses on the eight-bar mechanism, which has excellent kinematic characteristics. At the same time, output motion pattern of the eight-bar press can be easily adjusted by controlling and regulating the servomotor without changing the topology of the mechanism. This adjustment improves the stroke, speed, and acceleration volatility of the press slide during the working stroke. As a result, the vibration and shock loads generated during press operation are reduced, thereby improving the operating efficiency of the press.  
The design of the eight-bar mechanism is adapted to the needs of diversified products and material processing technologies and has a wide range of potential applications in different industrial fields, for example, machining, automotive manufacturing, and aerospace. High-performance output realized through the intelligent control method not only enhances the adaptability and flexibility of the equipment, but also provides scientific basis and technical support for improving the precision and efficiency of the industrial production process. Therefore, it is of great value for both academic research and engineering applications.  
As illustrated in Figure 6, for a planar eight-bar mechanism, the formula for calculating its degree of freedom is:  
\[F=3n-2P_{L}-P_{H} \tag{34}\]  
In the equation, \(F\) represents the degree of freedom of mechanism, \(n\) denotes the total number of moving links in mechanism, \(P_{L}\) is the number of lower pairs, and \(P_{H}\) is the number of higher pairs.  
In this particular mechanism, \(n=7,P_{L}=10,P_{H}=0\); therefore, the degree of freedom \(F\) is 1. The mechanism is driven by a single motor. Consequently, this mechanism possesses a definite motion.
## 1 Introduction
In particular, mechanical properties of cement composites incorporating 0.6% MWNT and 10% silica future showed increase of 36.07% and 18.11% in terms of compressive and flexural strengths, respectively [29]. [PERSON] et al., 2019 studied mechanical properties and autogenous shrinkage of MWNT-incorporated cement composites [30]. The authors demonstrated that 0.01% MWNT-incorporated cement composites brought 12.4% increase in compressive strength and 8.5% reduction in autogenous shrinkage [30]. [PERSON] and [PERSON], 2014 compared the mechanical properties of NS, silica fume (SF), or fly ash (FA)-incorporated cement mortars and determined that the greatest enhancements in the compressive and flexural strengths of 84% and 32%, respectively, were achieved by incorporating 10% NS [13]. These improvements were higher than those of the control mortar after 28 days [13]. [PERSON] et al., 2020 investigated the early age setting, hydration, mechanical characteristics, and other properties of NS-incorporated cementitious composites [4]. In particular, the compressive strength was enhanced by 21% after 28 days compared to control mortar, and the mechanical characteristics were analyzed by thermogravimetric analysis (TGA), mineralogical characterization, and microstructure observations [4]. [PERSON] et al., 2014 achieved enhancements of the flexural and compressive strengths as high as 82.2% and 50.5% compared to plain cement paste, respectively, by incorporating 0.75% MWNTs and 0.5% NS simultaneously [10]. [PERSON] et al., 2018 studied mechanical properties and durability of MWNT/NS-incorporated cement composites demonstrating enhanced corrosion resistance and increase of 12-76% in the compressive strength [31]. To date, various experimental studies have been conducted in which NS and/or CNTs were incorporated into the cementitious matrix. However, studies on the mechanical performances of cement composites fabricated with the simultaneous incorporation of NS and CNTs are scarce, and analyses on the enhancements of the mechanical characteristics by means of thermal analysis or porosity examinations have been inadequate. In the present study, the mechanical characteristics of the cement composites fabricated with various contents of NS and CNTs were examined, and the measured mechanical characteristics were analyzed. MWNT/NS-incorporated cement composites were prepared by varying the MWNT content from 0% to 1.0% and the NS content from 0% to 5%. To understand the microstructure and CNT distribution, scanning electron microscopy (SEM) observations and electrical conductivity tests were performed. Moreover, TGA and porosity examinations were carried out to identify the factors affecting the enhancements of the mechanical characteristics.
## 5 Conclusions  
In this paper, an IFA-LADRC scheme was designed for offshore wind power SOFC systems, and the IFA based on inertia weights was used to search the optimal controller parameters. To verify the feasibility and correctness of the above methods, a PID control scheme was established for comparison with the LADRC control scheme. In the given value tracking experiments, the overshoots of the four control schemes IFA-LADRC, FA-LADRC, IFA-PID, and FA-PID are 1.95%, 1.78%, 14.13%, and 15.41%, respectively. It can be seen that the overshoots of the system using LADRC are smaller, where the overshoot of the system using the FA-LADRC method is 13.6% less than that using the FA-PID method, and the overshoot of the system using IFA-LADRC method is 12.2% less than that of the system using IFA-PID method. By increasing the voltage perturbation, it can be seen that when T = 100 s, the voltage increases by 7.8%, and at this time, the overshoot of the system using LADRC is reduced by about 5.7% compared to that of the system using PID controller. That is, the LADRC using the improved firefly algorithm is more effective than the PID controller control. In addition, the robustness of the proposed method was verified by Monte Carlo experiments. An offshore wind power hydrogen production system is very complex, and the control problem of the fuel cell can be improved by using an optimization algorithm to improve the controller but also by using a deep learning method to build an SOFC neural network model to improve the control accuracy.  
Moreover, there are many studies on electrothermal hydrogen energy management for offshore wind power systems, which is also a topic that can be further investigated. However, this paper is discussed from the simulation point of view, and no actual experiments have been performed. Since the simulation results are good, they can be applied within the actual system in the next step.  
Figure 14: Performance indicators of four control methods under Monte Carlo simulation.  
**Author Contributions:** Conceptualization, Z.P. and J.L. ([PERSON]); methodology, Z.P., J.L. ([PERSON]), Z.Q. and [PERSON]. ([PERSON]); validation, Z.P. and X.N.; formal analysis, L.H. and Z.Q.; investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); data curation, X.N. and Z.Q.; writing--original draft preparation, Z.P. and J.L. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, X.N. and Z.Q.; supervision, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Technology Project of Shanghai Investigation, Design & Research Institute, grant number 2021 FD(8)-011.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data of this work is available by contacting the corresponding author.  
The authors declare no conflict of interest.
# MD-TransUNet: An Image Segmentation Network for Car Front Face Design
## 2 Materials and Methods
### DCGCN Module
To reduce computational complexity, the function \(g(\mathbf{\Lambda})\) can be closely estimated using a truncated series expansion of Chebyshev polynomials:  
\[g(\Lambda)\approx\sum_{k=0}^{K}\Theta_{k}T_{k}\Big{(}\widetilde{\Lambda} \Big{)}, \tag{12}\]  
Figure 3: Structure of the DCGCN module. In the equation, \(\widetilde{\Lambda}=\left(\frac{2}{\lambda max}\right)\Lambda-\mathbf{I_{N}}\), where \(\lambda max\) is the largest eigenvalue of \(L\), and \(\theta k\) are the Chebyshev coefficients. The recursive definition of the Chebyshev polynomials is in Equation (13). To compute the filter response, the signal is expanded in terms of these Chebyshev polynomials. This method effectively approximates the spectral graph convolution by using a polynomial of the Laplacian, making it computationally efficient. The approximation allows the convolution operation to be performed in the graph domain without explicitly computing the eigen-decomposition of the Laplacian matrix, thus significantly reducing the computational burden. \[T_{k}(x)=2 xT_{k-1}(x)-T_{k-2}(x)T_{k-2}(x), \tag{13}\]  
In the equation, \(T_{0}(x)=1\) and \(T_{1}(x)=x\). Hence, the signal \(x\) convolved with the filter \(g\) can be represented in the following manner:  
\[g(\Lambda)*x\approx\sum_{k=0}^{K}\theta_{k}T_{k}\Big{(}\widetilde{\Lambda} \Big{)}x, \tag{14}\]  
In the equation, \(\widetilde{\mathbf{L}}=\left(\frac{2}{\lambda max}\right)\mathbf{L}-\mathbf{I_ {N}}\). In summary, this defines the convolution operation in graph convolution networks. Similarly, an efficient two-layer GCN is employed to conduct convolutional operations on the graph data \(G\), avoiding redundant convolution layers that could cause node smoothing. According to the spectral convolution approximation process detailed in graph convolution networks described in references [30; 31; 32], the rule for forward propagation in a graph convolutional network can be articulated in the following manner:  
\[\mathbf{H^{(I)}}=\delta\Big{(}\widetilde{A}XW\Big{)}, \tag{15}\]  
In the equation, \(\mathbf{H^{(I)}}\) represents the output produced by the \(l\)-th layer in the neural network architecture, \(W\) represents the learnable parameters tuned during the training process, \(\delta(\cdot)\) represents the nonlinear activation function exemplified by the commonly used ReLU, and \(X\) represents the node feature matrix of the nodes within the entire graph structure \(G\). \(\widetilde{A}\) replaces the original adjacency matrix \(A\) to effectively mitigate the issue of gradient vanishing or explosion. The formula for the calculation is presented below:  
\[\widetilde{\mathbf{A}}=\mathbf{D}^{-\frac{1}{2}}(\mathbf{A}+\mathbf{I_{N}}) \mathbf{D}^{-\frac{1}{2}}, \tag{16}\]  
In the equation, the matrix \(\mathbf{I_{N}}\) denotes the identity matrix, while \(\mathbf{D}=\text{diag}(d_{1},d_{2},\ldots,d_{n})\) signifies the matrix of node degrees in \(\mathbf{A}+\mathbf{I_{N}}\).