esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Definition of Numerical Simulation  
In the tank model, hexahedron mesh type was used. The element size was 10 mm. In the study, the \"multizone\" method was applied in order to reduce the number of meshes only in cases with circular and rectangular perforated baffles. The analysis models and mesh structures used in the models are presented in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline
**Baffle Structure** & **Input Excitation** & **Shape of the Baffle** \\ \hline
1. Tank without baffle & \(A=0.1\) m, \(w=5.02\) rad/s & - \\ \hline
2. Flat baffle mounted from the bottom of the tank & \(A=0.1\) m, \(w=5.02\) rad/s & \\ \hline
3. Flat baffle mounted from the ceiling of the tank & \(A=0.1\) m, \(w=5.02\) rad/s & \\ \hline
4. Circular perforated baffled tank (There are 8 holes in the baffle with a diameter of 2.5 cm) & \(A=0.1\) m, \(w=5.02\) rad/s & \\ \hline
5. Rectangular perforated baffled tank (There are 8 rectangular holes of size 20 \(\times\) 140 mm on the baffle) & \(A=0.1\) m, \(w=5.02\) rad/s & \\ \hline
6. Multi-baffled tank (3 baffles placed on the bottom) & \(A=0.1\) m, \(w=5.02\) rad/s & \\ \hline
7. Base-angled baffled tank (Two opposing baffle structures placed at 45\({}^{\circ}\) to the base surface) & \(A=0.1\) m, \(w=5.02\) rad/s & \\ \hline
8. Wall-angled baffled tank (Baffles are placed on the side walls at a height of 12 cm at 45\({}^{\circ}\). Baffle length is 9.6 cm) & \(A=0.1\) m, \(w=5.02\) rad/s & \\ \hline
9. Tank without baffle & \(A=0.02\) m, \(w=w_{n}\) & \\ \hline
10. Tank with one flat baffle & \(A=0.02\) m, \(w=w_{n}\) & \\ \hline
11. Perforated baffled tank (There are 8 holes in the baffle of diameter 2.5 cm) & \(A=0.02\) m, \(w=w_{n}\) & \\ \hline
12. Multi-baffled tank (3 baffles placed on the bottom) & \(A=0.02\) m, \(w=w_{n}\) & \\ \hline \end{tabular}
\end{table}
Table 1: Baffle structures.  
In order to give movement to the system, the sinusoidal input equation expressed by Equation (5) was written in a UDF file in the C programming language and then imported into the program. The explicit formulation was chosen in the multiphase model with the VOF method. The viscous model was laminar. In boundary conditions, the primary phase was air, and the second phase was liquid (water, kerosene). Wall motion and shear conditions were \"Stationary-wall\" and \"No-slip\", respectively. The operating pressure was 101.325 kPa. Gravity was taken into account in the y-direction. The pressure-based solver was used in transient conditions. The \"Pressure-Velocity Coupling-Fractional step algorithm\" method was chosen as the solution. \"Green-Gauss Node Based\" was determined as the spatial discretization scheme. Transient simulations were run on a multi-core Windows 64-bit PC (Intel(r) Core(tm) i7-400 HQ CPU @2.40 Ghz, 12 GB RAM).
# Study on the Multi-Mode Optimal Control of Four-Wheel Steering Vehicle
## 3 Design of Multi-Mode Optimal Decision Control System
### Principle of Multi-Mode Optimal Control Systems  
The multi-mode optimal decision control system is divided into a double monitoring system and a single layer execution system. The main function of the monitoring system is to monitor the vehicle speed. When the vehicle speed meets a certain condition, the system transmits the received signal under this condition to the next monitoring system. At this time, the second layer monitoring system monitors the front wheel angle. When the current wheel angle changes, the monitoring system will transmit the corresponding signal of the angle size to the execution system. When the execution system receives the signal of the monitoring system, the corresponding controller works. The flow chart of monitoring and control system is shown in Figure 13.  
According to the experience and access to information, the vehicle speed is defined as 0-160 km/h, which is divided into three cases, and the absolute value of the front wheel angle is defined as 0-35\({}^{\circ}\), which is divided into four cases, and then orderly combined.  
The first case: when the vehicle speed is lower than 30 km/h, the vehicle is defined as low-speed driving, without considering the size of the corner, the controller of proportional  
Figure 13: Monitoring and control system process.  
control and yaw rate feedback control is selected to ensure the most flexible steering of the vehicle under this condition.  
The second case: when the speed is in the range of 30 km/h-90 km/h, the speed is considered medium and high speed, and when the wheel angle is between 0-15\({}^{\circ}\), the self-tuning double yaw rate feedback controller is selected; when the wheel angle is between 15\({}^{\circ}\) and 40\({}^{\circ}\), the proportional feedback controller of the yaw rate is selected to ensure that the vehicle has good handling stability based on flexible steering.  
The third case: when the speed is in the range of 90-200 km/h, the speed is high, and when the current wheel angle is in the range of 0-15\({}^{\circ}\), the proportional feedforward controller is selected; when the front wheel angle is in the range of 15-35\({}^{\circ}\), the cooperative controller is selected to ensure the optimal handling stability of the vehicle.
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
## 5 Providing Information Based on Prospect Theory
### Example of Calculation of the Value of Information  
#### 5.4.1 Calculation of the Original Value of Information  
We assume that the value factor \(I_{1}=5\), value factor \(I_{2}=4\), and value factor \(I_{3}=3\) are set and the reference points range from 1 to 5. By using the Formula (3), the original information value is \(\Delta I_{i}=I_{i}\) because \(I_{0}=0\). From the Formula (2), \(V(\Delta I_{1})=\Delta{I_{1}}^{a}=5^{0.88}\), \(V(\Delta I_{2})=\Delta{I_{2}}^{a}=4^{0.88}\), \(V(\Delta I_{3})=\Delta{I_{3}}^{a}=3^{0.88}\). Then, the probability weighting function \(W_{i}\) is calculated by using Formulas (4) and (5), and the decision matrix is shown in Table 7. The result is shown in Table 8.  
As a result, \(W_{1}=0.4167\), \(W_{2}=0.333\), and \(W_{3}=0.2500\). Then, from the Formula (1), \(U=\sum_{i=1}^{n}W_{i}\cdot V(\Delta I_{i})\), the original information value is calculated as \(U=5^{0.88}*0.4167+4^{0.88}*0.3333+3^{0.88}*0.25=3.5038\).
# Engineering Biomimetic Gelatin Based Nanostructures as Synthetic Substrates for Cell Culture
## 4 Discussion
The PCL/gelatin/P188 scaffold with 20% P188 also exhibited highest cell proliferation on day two of the cell viability study when compared to the scaffold with 10% and 5% P188. This indicated the initial increase in cell proliferation, with respect to increasing in the concentration of the P188 in the scaffold. However, by day four the percentage fluorescence of the scaffold (PCL/gelatin/P188) with 20% P188 only increased by 17%, whereas those with 5% and 10% P188 showed 29% and 33% increase, respectively. Although the scaffold with 20% P188 had the highest roughness of all, this decrease in cell proliferation could be because of the toxic effect of high P188 concentrations or the beads that are formed due to the low concentration of PCL in the scaffold. As previous studies have observed, beaded scaffolds reduce the surface area to volume ratio, reducing the cell adhesion and growth irrespective of the nanoscale diameter of the fibers [54]. Hence, further studies have to be performed to understand its significance in chemical and physical characteristics of the fiber and its impact on cell proliferation. The significance of using gelatin in this experiment is attributed to its innate ability to increase biocompatibility of PCL nanofibers [37]. Gelatin possesses many integrin binding sites for efficient cell adhesion and growth [37]. Moreover, gelatin imparts hydrophilicity to the fibers which are known to increase cell adhesion [25; 32]. This ability of gelatin to improve biocompatibility can be clearly observed in this experiment. The cell metabolic activity associated with PCL/gelatin increased to a value higher than that associated with PCL by the fourth day of study. Additionally, the higher roughness of PCL/gelatin is likely to be beneficial when compared to PCL nanofibers. Interestingly, the roughness of PCL/gelatin was higher than PCL/gelatin/P188 nanofibers with 5% and 10% P188 concentration, yet the cell proliferation on scaffolds with P188 was significantly higher than PCL/gelatin. This clearly indicates the major role played by P188 to promote cell growth. The least biocompatible PCL nanofibers, on the other hand, exhibited improved cell proliferation when compared to that of control. This is attributed to the nanofibrous architecture of the scaffold which is known to provide the physical cues required for efficient cell adhesion and growth [12; 19; 20]. In short, the cell proliferation study clearly indicates that the nanofibrous nature of the scaffold, its roughness, and its composition play a very important role in cell adhesion and proliferation
# Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem
## 3 Unified Candidate Order-Based Genetic Algorithm
### Existing GAs for the FJSP
An example of a crossover operation of the existing GA for the FJSP is illustrated in Figure 2. Consider two parent solutions, \(P_{1}\) and \(P_{2}\), in panel (a) in Figure 2. Typically, existing GAs for the FJSP apply different kinds of crossover operators to the OS and MS parts of parent solutions. In panel (b) in Figure 2, POX and classical one-point crossover are applied to the OS and MS part, respectively. POX is a well-known crossover operator for sequencing problems. In panel (b), the OS parts of the solutions are represented in the form of a sequence of operations, in order to apply POX. Job set 1 indicates that an operation \(o_{ij}\) has an identical position \(OS\big{(}o_{ij}\big{)}\) in both \(P_{x}\) and \(C_{x}\) (\(x=1\), 2) if it belongs to \(J_{3}\), where \(C_{x}\) denotes an offspring solution. In contrast, job set 2 means \(C_{1}\) (\(C_{2}\)) inherits precedence relationships among operations of \(J_{1}\) and \(J_{2}\) from \(P_{2}\) (\(P_{1}\)). The one-point crossover operator divides a chromosome into two subparts, i.e., the earlier part and later part, and generates \(C_{1}\) (\(C_{2}\)) by combining the earlier part of \(P_{1}\) (\(P_{2}\)) and the later part of \(P_{2}\) (\(P_{1}\)). Panel (c) in Figure 2 shows a multichromosome representation of \(C_{1}\) and \(C_{2}\), obtained by applying POX and one-point crossover. Similarly, most existing GAs  
Figure 2: An example of crossover for an existing GA for solving the FJSP. for solving the FJSP use different crossover operators to recombine the multichromosome, which consists of the OS and MS parts. In Figure 2, we can see that the OS part and MS part evolve separately. That is, the crossover procedure of the OS part is not correlated with that of the MS part. In this context, the traditional approach based on two different crossover operators is called the separate evolution strategy in this paper. This strategy has a critical limitation, i.e., a combination of the OS gene and MS gene for an identical operation can be easily broken. For instance, \(o_{31}\) has two associated genes: \(OS(o_{31})\) and \(MS(o_{31})\). For \(P_{1}\) in panel (a) in Figure 2, \(OS(o_{31})=1\) and \(MS(o_{31})=M_{2}\). If these two genes indicate the optimal assignment order and machine for \(o_{31}\), the combination of \(OS(o_{31})=1\) and \(MS(o_{31})=M_{2}\) should be preserved during the crossover procedure. However, this combination is not found in offspring solutions \(C_{1}\) and \(C_{2}\) in panel (c) in Figure 2. In more detail, \(OS(o_{31})\) of \(P_{1}\) is inherited to \(C_{1}\), whereas \(MS(o_{31})\) of \(P_{1}\) is inherited to \(C_{2}\). The u-COGA proposed in this paper is designed to overcome the limitation of the traditional separate evolution strategy for the FJSP.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 3 Proposed Method
### Training Process
#### 3.3.1 Weakly-Supervised Transfer Learning  
Transfer learning is the concept of applying pre-trained models to fine-tune them on other target data. Transfer learning performs a projection of all the new inputs through a pre-trained model. For instance, we have a pre-trained model function \(f(\cdot)\) and attempt to learn a new function \(g(\cdot)\). To this end, we simplify \(g(\cdot)\) by \(g(f(\cdot))\); in this way \(g(\cdot)\) perceives all the data through \(f(\cdot)\). In this work, we first trained EDC-Net on the ABC dataset [35], and then considered this model as a pre-trained model. Afterwards, in order to improve the performance of the model with various types of shapes, we fine-tuned the model on ShapeNet [34] (the details about the datasets are explained in Section 4.1). Since there is no available edge detection ground-truth for ShapeNet, we considered [12] as a baseline edge detection technique (the implementation is publicly available from [51]). The edge detection of the aforementioned technique is not perfectly precise; therefore, this stage is considered as weakly-supervised learning since we trained the model with these imprecise labels.
###### Abstract  
Numerous variables can introduce errors into the measurement chain of a digital image correlation (DIC) system. These can be grouped into two categories: measurement quality and the correlation principle. Although previous studies have attempted to investigate each error source in isolation, there are still no comprehensive, standardized procedures for calibrating DIC systems for full-field strain measurement. The aim of this study, therefore, was to develop an applied experimental method that would enable a DIC practitioner to perform a traceable full-field measurement calibration to evaluate the accuracy of a particular system setup in a real-world environment related to their specific application. A sequence of Speckle Pattern Boards (SPB) that included artificial deformations of the speckle pattern were created, allowing for the calibration of in-plane deformations. Multiple deformation stages (from 10% to 50%) were created and measured using the GOM ARAMIS system; the results were analysed and statistical techniques were used to determine the accuracy. The measured strain was found to be slightly over-estimated (nominally by 0.02%), with a typical measurement error range of 0.34% strain at a 95% confidence interval. Location within the measurement volume did not have a significant effect on error distributions. It was concluded that the methods developed could be used to calibrate a DIC system in-situ for full-field measurements of large deformations. The approach could also be used to benchmark different DIC systems against each other or allow operators to better understand the influence of particular measurement variables on the measurement accuracy.  
DIC; traceable calibration; accuracy; error +
Footnote †: journal: Applied Sciences
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Steady State Phase (SSP)
#### 3.3.2 Sharable Slot  
To achieve distributed data collection from all tree nodes to sink in a session, the following factors are considered in this study:  
1. The lower the level (i.e., the closer to the sink) of a tree node, the more data the tree node needs to send, and the longer it takes to send the data;
2. CH has a data fusion function. The amount of data that a tree node sends to its parent is less than the sum of the amount that the node receives from its children and the amount that it collects from member nodes. It leads to increasing the amount of data level-by-level from the leaf nodes to sink.  
The SS-MAC protocol divides different data transmission periods, called sharable slots, for tree nodes at different levels. A tree node can calculate its sharable slot length and start time as long as it knows its level. We introduce the exponential wait time function from [8], \(Wtime(l)=W_{s}\times a^{l-1}\), and construct a Session_ST beacon delay function, \(Sdelay(l)=(L-l)\times T_{B}\), to determine the start time and length of each level's sharable slot, where \(l\) is the level of the node.  
As shown in Figure 6, the time that a node at level \(l\) waits to send or receive data packets after receiving the beacon is:  
\[WT(l)=\begin{cases}0,&l=L\\ Sdelay(l),&l=L-1\\ Sdelay(l)+Wtime(l+1),&\text{else}\end{cases}\]  
Tree nodes at level \(l\) (\(l\
eq L\)) wake up at \(WT(l)\), then keep actively receiving data from tree nodes at level \(l+1\). This period is defined as a receiving sharable slot, \(SS^{RX}(l)\), which is equal to the difference between \(Wtime(l)\) and \(Wtime(l+1)\); after receiving data from level \(l+1\), the tree nodes at level \(l\) perform fusion processing, then send fused data to the tree nodes at level \(l-1\) in the transmitting sharable slot, \(SS^{TX}(l)\). Note that the tree nodes at level \(L\) directly enter \(SS^{TX}(l)\) after receiving the Session_ST beacon. The size of the sharable slot at level \(l\), \(SS(l)\) is equal to the sum of \(SS^{RX}(l)\) and \(SS^{TX}(l)\). We have:  
\[SS^{RX}(l) =Wtime(l)-Wtime(l+1)\] \[=\begin{cases}W_{s}(1-\alpha)a^{l-1},&l\
eq L\\ 0,&l=L\end{cases}\] \[SS^{TX}(l) =SS^{RX}(l-1)\] \[=\begin{cases}W_{s}(1-\alpha)a^{l-2},&l\
eq 1\\ 0,&l=1\end{cases} \tag{1}\] \[SS(l) =SS^{TX}(l)+SS^{RX}(l)\] \[=\begin{cases}W_{s}(\alpha^{l-1}-\alpha^{l}),&l=1\\ W_{s}(\alpha^{l-2}-\alpha^{l-1}),&l=L\\ W_{s}(\alpha^{l-1}-\alpha^{l}),&\text{else}\end{cases} \tag{2}\]  
Figure 6: Sharable slots in SSP.  
The tree nodes at the highest two levels enter sharable slots first, while other tree nodes go to sleep until their sharable slots arrive.
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
###### Abstract  
In light of the increasing scarcity of frequency spectrum resources for satellite communication systems based on the transparent transponder, fast and efficient satellite resource allocation algorithms have become key to improving the overall resource occupancy. In this paper, we propose a reinforcement learning-based Multi-Branch Deep Q-Network (MBDQN), which introduces TL-Branch and RP-Branch to extract features of satellite resource pool state and task state simultaneously, and Value-Branch to calculate the action-value function. On the one hand, MBDQN improves the average resource occupancy performance (AOP) through the selection of multiple actions, including task selection and resource priority actions. On the other hand, the trained MBDQN is more suitable for online deployment and significantly reduces the runtime overhead due to the fact that MBDQN does not need iteration in the test phase. Experiments on both non-zero waste and zero waste datasets demonstrate that our proposed method achieves superior performance compared to the greedy or heuristic methods on the generated task datasets.  
deep reinforcement learning (DRL); resource allocation; transparent transponder; satellite communications; Deep Q-Network (DQN) Article
# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 2 Method
### Indicator Weight Determination
#### 2.3.1 Determining Weight Using the AHP  
(1) Constructing the judgment matrix  
Experts conduct pairwise comparisons of the n indicators within the same level, assessing their relative importance. A proportional scale of 1 to 9 is used for this evaluation, with the specific scoring criteria shown in Table 1 [35].  
Construct the judgment matrix A (orthogonal matrix); \(\mathrm{a_{ij}}\) represents the comparison results of the i-th factor relative to the j-th factor [35]:  
\[\mathrm{A}=\left(\mathrm{a_{ij}}\right)_{\mathrm{n:n}}=\begin{pmatrix}\mathrm{a _{11}}&\mathrm{a_{12}}&\dots&\mathrm{a_{1n}}\\ \mathrm{a_{21}}&\mathrm{a_{21}}&\dots&\mathrm{a_{2n}}\\ \vdots&\vdots&\vdots&\vdots\\ \mathrm{a_{n1}}&\mathrm{a_{n2}}&\dots&\mathrm{a_{m}}\end{pmatrix} \tag{2}\]  
(2) Calculating weights  
Perform geometric averaging (root method) on each row vector of matrix A, then normalize it to obtain the weights of each evaluation indicator and the characteristic vector W:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale** & **Meaning** \\ \hline
1 & Two elements are equally important. \\ \hline
3 & The former is slightly more important than the latter. \\ \hline
5 & The former is significantly more important than the latter. \\ \hline
7 & The former is strongly more important than the latter. \\ \hline
9 & The former is extremely more important than the latter. \\ \hline
2, 4, 6, 8 & Intermediate values between the above adjacent judgments. \\ \hline Reciprocal & The importance scale of the latter compared to the former. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of judgment matrix scale.  
\[\mathrm{w_{i}}=\frac{\overline{\mathrm{w_{i}}}}{\sum_{i=1}^{n}\overline{\mathrm{w_{ i}}}},\ \mathrm{w}=\begin{cases}\mathrm{w_{1}}\\ \mathrm{w_{2}}\\ \vdots\\ \mathrm{w_{n}}\end{cases} \tag{3}\]
3. Consistency check Calculate the maximum eigenvalue \(\lambda_{\mathrm{max}}\): \[\lambda_{\mathrm{max}}=\frac{1}{n}{\sum_{i=1}^{n}\frac{\left(\mathrm{AW} \right)_{i}}{\mathrm{w_{i}}}}.\] (4)  
Calculate consistency index (CI), random consistency index (RI), and consistency ratio (CR):  
\[\mathrm{CI}=\left(\lambda_{\mathrm{max}}-\mathrm{n}\right)/\left(\mathrm{n}-1\right) \tag{5}\]  
The RI is related to the order of the judgment matrix. Generally, the higher the order of the matrix, the greater the likelihood of random consistency deviation. The specific corresponding relationship is shown in Table 2.  
To check the consistency of the judgment matrix, it is necessary to compare the CI with the RI. The formula for the CR is as follows:  
\[\mathrm{CR}=\mathrm{CI}/\mathrm{RI} \tag{6}\]  
Typically, if CR \(<\) 0.1, the judgment matrix is considered to have passed the consistency test; otherwise, the matrix does not exhibit satisfactory consistency.