esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparations of the SC Ni-Mn-Ga/Polymer Composites
#### 2.4.1 Epoxy Resin as a Matrix of the Composites  
The precursor of the thermoset epoxy resin (EPIKOTE 828; bisphenol-a epoxy resin; Japan Epoxy Resins Co., Ltd., Tokyo, Japan) in a liquid state and its corresponding hardener (TOHMIDE 280-B; modified polyaminoamide; Fuji Kasei Kogyo Co., Ltd., Osaka, Japan) were used as one of the matrix materials. The epoxy resin precursor and the hardener were mixed thoroughly using a mixer (HM-500 Hybrid Mixer, KEYENCE, Osaka, Japan) at a ratio of 1:1 (wt.%). After mixing well, a semi-liquid slurry, which was composed of the liquid epoxy resin and hardener, was obtained. The slurry was subsequently poured into an acrylic mold (5 mm \(\times\) 5 mm \(\times\) 2 mm) and was subjected to thermal curing at RT for 24 h. Before pouring the slurry, a mold release agent (BONREESU WAX; Epoch Co., Ltd., Tokyo, Japan) was applied to the acrylic mold. After curing, the density of the cured pure epoxy resin was calculated to be approximately 1.1 g cm\({}^{-3}\). The Young's modulus (\(E\)) and flow stress (\(\sigma\)) of the epoxy resin are 200-800 MPa and 15-40 MPa, respectively. It is necessary to mention that the flow stress of the epoxy resin is higher than the stress for martensite variant reorientation (\(\sigma_{\rm MVR}\)) of 1-5 MPa [40]. The epoxy resin as the matrix of the composites is abbreviated as \"epoxy\" throughout this manuscript, unless otherwise mentioned.  
The abovementioned processes are for the fabrication of pure epoxy as a control specimen. The semi-liquid slurry was used as a matrix material for the synthesis of the composites by mixing it with SC Ni-Mn-Ga alloys. The integration details of the matrix and SC Ni-Mn-Ga particles are described in Section 2.4.3.
# Measurement of High Numerical Aperture Cylindrical Surface with Iterative Stitching Algorithm
## 3 Simulation  
In order to estimate the accuracy of the proposed method, we carried out a stitching interferometry simulation for a high-NA cylindrical surface using MATLAB (MathWorks. Inc, Natick, MA, USA). First, a cylindrical surface 50 mm high and a radius of 26.4 mm was built (Figure 3a). The angular aperture equaled \(102^{\circ}\). Then, the simulated surface was divided into eight equal fragments, each of which was observed under a sub-aperture of about \(18^{\circ}\). The rotation angle of each fragment was \(12^{\circ}\), and the overlapping region was \(6^{\circ}\). Each fragment was associated with its own position error as shown in Table 1. The fourth fragment (sub-aperture) was chosen as the benchmark; therefore, its error was zero. Besides, because the errors \(\Delta x\) and \(\Delta\alpha\) were negligible, they were set zero. The errors given in the table correspond to the actual situation in the measurement.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Error** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \(\Delta z\) (μm) & 1.2 & 0.6 & 2.3 & 0 & \(-1.9\) & \(-2.1\) & \(1.1\) & \(1\) \\ \(\Delta y\) (μm) & 0.8 & 1.4 & 1.5 & 0 & 2.6 & 1.4 & \(-0.5\) & \(-0.3\) \\ \(\Delta\gamma\) (”) & \(-3.1\) & \(-3.7\) & \(-6.4\) & 0 & \(-5.6\) & \(-6.6\) & \(-2.9\) & \(-1.7\) \\ \(\Delta\beta\) (”) & \(-3.5\) & \(-2.3\) & 3.7 & 0 & \(-7.0\) & \(-3.9\) & \(-6.4\) & \(-5.6\) \\ \(\Delta x\) (μm) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \(\Delta\alpha\) (”) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Errors of the sub-apertures.  
Figure 3: (**a**) The simulation of the proposed method. An ideal cylindrical surface was built and (**b**) eight sub-apertures were segmented to cover the whole surface and each sub-aperture was attached with some position errors.  
After obtaining the sub-aperture with different position errors (Figure 3b), it was necessary to stitch them using iterations if necessary (Figure 4a,b). Subtracting the obtained data (Figure 4a,b) from the simulated data (Figure 3a) enabled us to see that the peaks-and-valleys (PV) value and the root mean square (RMS) value of the stitching result without using the iterative method are larger than those obtained using the iterative method. The PV value of the residual error (Figure 3d) was 0.023 \(\upmu\)m and the RMS value was 0.000 \(\upmu\)m, which means that the sub-apertures were correctly stitched. Because the curve of the lens was not closed, some constraints in the circumference direction were lacking. So, the errors produced by the traditional method cannot be eliminated totally, especially when there is an error near the boundary. Through iterations, this situation will improve. The final value of the residual error is affected by the size of the pixel.
# Multi-Level Site Selection of Mobile Emergency Logistics Considering Safety Stocks
## 2 Model Construction  
### Problem Description  
Emergencies involve many people and cover a wide range; emergency needs are urgent, easy to spread, and must be coordinated in response to all regions. In the multilevel location of mobile emergency logistics, the security inventory strategy directly affects emergency logistics' inventory and transfer costs. Uneven resource allocation, unclear material transshipment strategies, and insufficient material stockpiles exist in the emergency material distribution process. Consider safety stock strategies for emergency site selection to minimize emergency logistics deployment, storage, and transit costs. Multi-level location selection for mobile emergency logistics is influenced by many variables. Such as demand, fixed costs of location selection, safety stock costs and transshipment costs. Mobile emergency logistics includes a logistics center, a distribution warehouse, and mobile emergency facilities. The logistics center is responsible for transporting emergency materials to the distribution warehouse. The distribution warehouse is responsible for distributing emergency materials to the mobile emergency facilities, and the distribution warehouses can transfer between each other. Mobile emergency facilities can cover emergency needs in a particular region, as shown in Figure 1.  
Compared with traditional emergency logistics, mobile emergency can effectively reduce the storage and transportation costs of emergency logistics. The security inventory strategy can ensure the rapid deployment and distribution of emergency materials. In mobile emergency logistics, emergency materials must be deployed and distributed according to actual needs to provide sufficient materials to reach the emergency area on time. To realize rapid deployment and distribution, it is necessary to set up several logistics' centers, distribution silos, and mobile emergency facilities in mobile emergency logistics and disperse emergency materials to each logistics center and distribution silos. A reasonable mathematical model must describe its structure and characteristics to solve mobile emergency logistics' multilevel location-allocation problem. According to the features of the multilevel location problem of mobile emergency logistics, its variable description is shown in Table 1, and the following hypothesis is proposed:  
**Assumption 1**. : _There is no capacity limit for the logistics center and distribution warehouse._  
**Assumption 2**. : _There are average inventory costs and safety inventory costs in the logistics center and distribution warehouse._  
**Assumption 3**. : _The emergency demand in the area covered by mobile emergency facilities is usually distributed \((\mu_{i},\sigma_{i})\)_
# An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation
## 3 Result
### Relationship Between Image Features and Fog Density  
To handle varying dynamic ranges across the extracted image features, a linear normalization process is applied \(f_{n}^{m}=(f^{m}-f_{\text{min}}^{m})/(f_{\text{max}}^{m}-f_{\text{min}}^{m})\), where \(f^{m}\) denotes \(m\)'s feature, and \(f_{\text{max}}^{m}\) and \(f_{\text{min}}^{m}\) represent the averages of the highest 0.1% and lowest 0.1% of values for feature \(m\), respectively. This normalization ensures that the adjusted features, \(f_{n}^{m}\), fall within the range \(0\leq f_{n}^{m}\leq 1\). Figure 4 demonstrates the mean normalized features derived from the patches in Figure 4a-e. Evidently, within the same scene depth, there are certain correlations between the visibility distance and normalized features, whether positive or negative.  
Table 3 presents the Pearson Correlation Coefficient (PCC) between 16 image features and fog density. Figure 5 displays the correlation matrix of features calculated using PCC, where high absolute correlation values indicate significant redundancy between two features.  
The sixteen features were fed into the Random Forest classifier to observe classification accuracy. Then, the feature with the lowest correlation was removed based on the Pearson Correlation Coefficient (PCC), and the accuracy test was repeated. This process was continued until the average accuracy reached its maximum. During the test, it was found that retaining features F1-F11 resulted in the highest classification accuracy, suggesting that these 11 features are the most important parameters for fog density estimation. Notably, sharpness (F4) was selected as a key feature despite its moderate correlation coefficient, as sharpness, which refers to the clarity of edges and details in an image, is significantly affected by light scattering in foggy weather.  
Figure 3: Composition of the new dataset.  
_Appl. Sci._**2024**, 14, 9657  
Figure 4: Foggy images of different fog density: (**a**) very thick fog, (**b**) thick fog, (**c**) dense fog, (**d**) moderate fog, (**e**) light fog, and (**f**) the average normalized features for foggy images of different fog density shown in (**a–e**), respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fog-Relevant Features** & **Serial Number** & **Correlation Coefficient** \\ \hline Coefficients of MSCN Variance & F1 & 0.493 \\ Dark channel & F2 & 0.562 \\ Colorfulness & F3 & 0.581 \\ Sharpness & F4 & 0.457 \\ Coefficient of sharpness variance & F5 & 0.477 \\ Entropy & F6 & 0.481 \\ Combination of saturation and value in HSV space & F7 & 0.440 \\ Chroma & F8 & 0.632 \\ Variance of chroma & F9 & 0.534 \\ Weber contrast of luminance & F10 & 0.555 \\ Local contrast & F11 & 0.512 \\ Contrast energy (gray) & F12 & 0.354 \\ Contrast energy (yb) & F13 & 0.313 \\ Contrast energy (rg) & F14 & 0.367 \\ Gradient magnitude & F15 & 0.295 \\ Color variance & F16 & 0.308 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation coefficient between visibility range and eleven features by PCC.
# Numerical Study on the Route of Flame-Induced Thermoacoustic Instability in a Rijke Burner
## 1 Introduction
On the research of the interaction of turbulence and combustion, numerical simulation has also shown its extraordinary ability [26]. These CFD simulations help in understanding the detailed flow parameters, such as pollution control, efficiency enhancement, and mass transport processes, for further analysis and can thus serve as an aid to engineers and designers for building combustion systems with a decreased possibility of thermoacoustic instabilities. The present study concentrates on the numerical simulation of a 2-D Rijke burner to understand the dynamics of self-excited thermoacoustic instability. A finite volume-based approach is used to simulate reacting flows under both laminar and turbulent flow conditions. The onset of growth of the pressure, velocity, and reaction heat are captured. The oscillation frequency and mode are calculated and compared with the theoretical prediction, and they show good agreement. Finally, the unsteady flow fields at different times show that flame-induced vortices occur inside the combustor, and the temperature distributions show that the velocity perturbation in the tube changes the distance between the flame and honeycomb in turn, forming a forward feedback loop in the tube. The results of this work show that there are vortices in the Rijke-type burner when thermoacoustic instability occurs, and it should be considered a turbulent reacting flow field.
###### Abstract  
Fifth-generation (5G) networks provide network slicing capabilities, enabling the deployment of multiple logically isolated network slices on a single infrastructure platform to meet specific requirements of users. This paper focuses on modeling and analyzing resource capacity planning and reallocation for network slicing, specifically between two providers transmitting elastic traffic, such during as web browsing. A controller determines the need for resource reallocation and plans new resource capacity accordingly. A Markov decision process is employed in a controllable queuing system to find the optimal resource capacity for each provider. The reward function incorporates three network slicing principles: maximum matching for equal resource partitioning, maximum share of signals resulting in resource reallocation, and maximum resource utilization. To efficiently compute the optimal resource capacity planning policy, we developed an iterative algorithm that begins with maximum resource utilization as the starting point. Through numerical demonstrations, we show the optimal policy and metrics of resource reallocation for two services: web browsing and bulk data transfer. The results highlight fast convergence within three iterations and the effectiveness of the balanced three-principle approach in resource capacity planning for 5G network slicing.  
5G; network slicing; capacity planning; resource reallocation; controller; elastic traffic; Markov decision process (MDP); queuing system; signal; continuous-time Markov chain (CTMC) +
Footnote †: journal: Future Internet
# Smart Grid for Industry Using Multi-Agent Reinforcement Learning
## 2 Fundamentals
### Approaches for Production Control  
The main task of production control is to ensure the manufacturing of the predefined jobs by the production resources, although stochastic events like machine break-downs can occur [11]. Therefore, production control needs to make decisions and react to unforeseen events within a few minutes or seconds. Although the jobs are mostly assigned to resources in advance, production control determines the time and order sequence of the various jobs to be manufactured in every resource. In general, there exist two production control strategies for accomplishing this task, and these are briefly outlined in the following [26; 27]:  
* **Reactive control strategy:** The jobs are reactively scheduled based on simply dispatching rules like first-in-first-out (FIFO) or earliest-deadline-first (EDF). After finishing a job, the next job is selected, so no fixed production schedule is determined. In doing so, decisions can be made very quickly, whereas the solution quality is limited.
* **Predictive-reactive control strategy:** A deterministic schedule is calculated for a specified production period so as to optimize the given objectives. However, every stochastic event during the production period requires a schedule update, which makes this approach computationally expensive.  
Moreover, multi-agent systems (MAS) display a further control strategy and are mainly characterized by their decentralized system architecture, so they cannot be clearly assigned to either one of the aforementioned categories [26]. In most cases of MAS, physical production resources like machines are represented by an individual agent, which pursues given goals and thereby either cooperates or competes with other agents [28]. Although classic MAS approaches are very fault tolerant and robust, they often provide a low solution quality because agents can only provide local optimization and can hardly pursue long-term goals [29]. However, these downsides can be surpassed using multi-agent reinforcement learning (MARL). A basic understanding of MARL is provided in the following.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 2 Definition of Algorithm
### Failure Maintenance Decision  
There are different kinds, phenomena and processing methods of communication faults in the information system based on the inverse symmetry algorithm. The maintenance decisions of different communication faults in information system are summarized as follows.
## 3 Results and Discussion
### Development of Map for Soil Amplification Factor
#### 3.2.2 Plotting Map of Soil Amplification Factor  
Before the seismic microzonation map, a map for the soil amplification factor (AF) was plotted to ease the related parties in obtaining an overview of the AF prediction at the Malay Basin. Figure 11 portrayed a complete map for the soil AF and the scale and showing map of the soil AF for the 100-year return period. The black square on the map represents the study area. The soil AF contour maps of this study area, according to the five return periods, can be seen in Figure 12. These maps were plotted by using ArcGIS software by implementing the IDW method. This maps consist of soil types D and E. Values of the soil AFs predicted in the map can be used to predict the amount of soil amplification in the  
Figure 10: Soil amplification factor for soil types D and E under far-field seismic events.  
study area to determine the reliability of the existing structure or to design a new structure in the study area.  
Referring to Figure 12, results show that the soil amplification factor decreased as the return period increased. Soil type E obtained the soil amplification factor, which was higher than soil type D. The same pattern can be found in [PERSON] et al. (2017) [37]; the lowest soil class used in the study consisted of clay soil, with a higher plasticity index producing the highest soil amplification factor of 4.5. The higher amplification is suggested to occur when the seismic and soil frequency coincide. [PERSON] and [PERSON] (2016) [41] obtained soil amplification factors between 1.4 and 2.8 for a site in Northern Thailand. It was suggested that this happened due to site geological conditions where the thickness of the sediment for this area was relatively thin and there was bedrock underlying this part of Lamphun city. [PERSON] et al. (2015) [23] agreed with this scenario, as soil conditions and local sites can influence the soil amplification (this was experienced in the Adapazari region before the Kocaeli earthquake in 1999). Furthermore, [PERSON] et al. (2018) [42] found soil amplification factors for Banda Acheh, which is underlain by soft soil up to 4.3. Thus, the soil amplification factor should be used to estimate the amount of vibration received by the ground surface when a seismic event occurs.
## Appendix A  
In Section 3.1, the dynamic model of BSR for trajectory tracking and the linearized error model of BSR was given. The following describe the concrete values of matrixes \(\mathbf{A}(t)\), \(\mathbf{B}(t)\) in Equation (15).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Traditional MPC** & **Backstepping** & **Proposed MPC** \\ \hline \(x[m^{2}]\) & 0.046 & 0.043 & **0.037** \\ \(y[m^{2}]\) & 0.038 & 0.027 & **0.026** \\ \(\theta[rad^{2}]\) & 0.336 & 0.329 & **0.317** \\ \hline \hline \end{tabular}
\end{table}
Table 4: MSEs of Tracking square trajectory based on different control methods.  
\[\mathbf{A}(t)=\left[\begin{array}{cccc}0&0&-u_{d}\sin(\theta_{d})&\cos(\theta_{d} )&0\\ 0&0&u_{d}\cos(\theta_{d})&\sin(\theta_{d})&0\\ 0&0&0&0&1\\ \textbf{O}_{2\times 3}&&\textbf{A}_{22}\end{array}\right]_{5\times 5} \tag{10}\]  
\[\textbf{A}_{22}=\mathbf{M^{-1}}\textbf{D} \tag{11}\]  
\[\mathbf{B}(t)=\left[\begin{array}{c}\textbf{O}_{3\times 2}\\ \textbf{M^{-1}}\end{array}\right]_{5\times 2} \tag{12}\]  
In the Equation (23), the matrix \(\Theta\) in the definition of \(\mathbf{H}\) is expressed as the following:  
\[\Theta=\left[\begin{array}{cccc}\mathbf{CB}&\textbf{0}&\textbf{0}&\textbf{0 }\\ \textbf{CAB}&\mathbf{CB}&\textbf{0}&\textbf{0}\\ \cdots&\cdots&\ddots&\cdots\\ \textbf{CA}^{N_{c}-1}\textbf{B}&\textbf{CA}^{N_{c}-2}\textbf{B}&\cdots& \textbf{CB}\\ \textbf{CA}^{N_{c}}\textbf{B}&\textbf{CA}^{N_{c}-1}\textbf{B}&\cdots& \textbf{CAB}\\ \vdots&\vdots&\ddots&\vdots\\ \textbf{CA}^{N_{p}-1}\textbf{B}&\textbf{CA}^{N_{p}-2}\textbf{B}&\cdots& \textbf{CA}^{N_{p}-N_{c}-1}\textbf{B}\end{array}\right] \tag{13}\]  
The matrix \(\Psi\) in the definition of \(\mathbf{G}\) is written as follows:  
\[\Psi=\left[\begin{array}{c}\textbf{CA}\\ \textbf{CA}^{2}\\ \cdots\\ \textbf{CA}^{N_{c}}\\ \cdots\\ \textbf{CA}^{N_{p}}\end{array}\right] \tag{14}\]
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
Thus, this is the first time that a multi-service end-to-end performance is assessed in the LBT-based 3 GPP standards--WiFi coexistence literature leading to key conclusions on the performance of each priority and recommendations for future QoE-based use of LBT channel access configurations. From these results, a definition is derived and the framework about traffic prioritization for 3 GPP standards in unlicensed band is implemented. The structure of this work can be summarized as follows. In Section 2, the access characteristics to the unlicensed channel in WiFi and 3 GPP LBT are described. Section 3 presents the evaluation framework designed for user end-to-end multi-service performance evaluation. Thereafter, a description of the relevant indicators being evaluated is presented in Section 4, followed by Section 5 presenting an exhaustive description of the scenario and parameters of the simulation leading to the performance assessment analysis. Finally, Section 6 summarizes the conclusions of this work
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 3 Probe Test and Validation
### Isolated Voltage Probe as an Oscilloscope Probe
#### 3.1.3 Power Arbitrary Function Generator  
The last set of tests is designed to verify the capability of the proposed probe to determine the purity of a voltage power signal according to the European standard EN 50160. For this, a 200 V amplitude-50 Hz frequency (European mains frequency) signal is combined with several harmonics whose amplitude is set to the maximum allowable limit according to the EN 50160 standard indications (Table 2).  
For this test, an AC/DC power source GW Instek APS-1102 was used as an arbitrary function generator connected to a Keysight X 3014T Digital Storage Oscilloscope through a Tektronix P5205 probe and the probe under test, using the commercial probe as a reference.  
Figure 16 shows some of the signals used in this test, acquired using a Keysight X-3014T Digital Storage Oscilloscope. These signals consist of the combination of the main voltage signal and harmonics 3, 5 and 7 (Figure 16a), harmonics 10 and 25 (Figure 16c), and harmonics 2, 4, and 22 (Figure 16e). In these figures, Channel 1 (yellow) presents the signals measured using a Tektronix P5205, while Channel 2 (green) shows the signals acquired using the proposed differential probe. As in the previous examples, the proposed probe presents a gain error that can be adjusted by electronics calibration.  
The frequency spectra obtained by applying the fast Fourier transform (FFT) oscilloscope function to the signals acquired using the proposed probe are presented in Figure 16b,d,f. These figures show how the probe detects all those harmonics, thus validating its application for the evaluation of the purity of a power signal.