esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 6 Discussion and Recommendations
### Effectiveness of Different Electrode Materials  
Electrode selection significantly impacts the performance and lifespan of grounding systems. Although the general consensus is that copper electrodes are the best due to their low resistance and high conductivity, guaranteed performance, and non-degradation, their high cost makes them uneconomical for large-scale installations. Galvanized steel electrodes, conversely, exhibit lower conductivity compared to copper. With their cost  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Type of GEM** & **Standard Deviation (\(\Omega\))** \\ \hline Commercial GEM & 37 \\ \hline Copper II Sulphate & 101 \\ \hline Sodium Chloride & 172 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Fluctuation of earthing resistance.  
Figure 6: Earthing resistance of various GEM against time.  
effectiveness, adequate grounding performance is still experienced in many applications. Nevertheless, galvanized steel does corrode more easily as time progresses, particularly when buried in soils with high moisture, acid conditions, or both, hence additional reduction in longevity. Corrosion resistance and electrode lifespan are crucial factors that must be evaluated against local environmental conditions to ensure that the grounding system remains a viable prospect over time.
## 5 Main Program Design
### Real-Time Data Transmission  
Real-time data monitoring of the elevator is very important for the daily operation and maintenance of the elevator. The sampling frequency of the sensor of this system is 100 hz; in order to avoid the influence of the sensor's error on the collected data, it adopts five consecutive sampling points to seek the average value as a valid sampling point, which, together with the filtering algorithm inside the sensor, greatly reduces the error of the collected data of this system. The collected real-time speed data, front and rear offset of the elevator car, left and right offset, the number of times the elevator goes up and down, the elevator running distance, and other data are packaged and sent to the IOT platform according to the frequency of once per minute.
## 1 Introduction
The major benefit of the GA formulation is that it may produce somewhat accurate results with a simple algorithm. Selection, crossover, and mutation are all iterative processes in the GA [19; 20]. Parallel search strategies are used in a GA, and they are designed to mimic natural genetic processes. GAs have gotten a lot of interest in control systems, including searching for the optimal PID controller settings, because of their tremendous potential for optimization [21; 22; 23]. Several sorts of optimization issues have been solved using a genetic algorithm in the last 30 years. They cover a wide range of issues about not only communication network architecture, but also database query optimization and controlling the device [23]. Therefore, the GA has evolved into a dependable optimization approach for resolving issues in a variety of technological domains [24; 25]. On the other hand, the research of [26; 27; 28] enables further robust PID in systems of anon-linear nature. These works employed particle swarm optimization (PSO) and grey wolf optimizer (GWO) algorithms to optimize the PID controller in the case of an adopted second-order DC motor system. In [29], a non-dominated sorting GA was deployed to tune the PID control, considering a multi-objective problem. The work considered a tune to be a robotic manipulator. To tackle the restricted optimization problem in a servo motor system, reference [30] suggested a GA-based PID controller. When compared to PSO and evolutionary programming (EP), GA optimization was observed to yield extremely valuable results in relevance to overshoot, response time, and static error [31]. The gains of the PID speed controller were improved by GA, utilizing a weighted mixture of objective functions such as integral absolute error (IAE), integral time absolute error (ITAE), and integral square error (ISE) in comparison to the traditional DTC and a single goal function to illustrate the robustness of the suggested intelligent method. The Matlab/Simulink environment was used to assess this investigation. The following objectives outline the potential improvements discussed in this article:  
* Torque and fluxes ripples are minimized to the greatest extent possible (hysteresis comparator, variability in the parameters of the machine, fluxes, torque estimators, and inverters). * Electromagnetic torque and speed performance are both improved. * The stator and rotor currents' total harmonic distortion (THD) rate is reduced. The axes used to organize this article are as follows: The mathematical model of the DFIM in the (alpha, beta) plane is covered in Section 2. The functionality of the DTC control and its core structure is covered in Section 3. The method for attaining optimized parameters of the PID speed controller is covered in Section 4. Section 5 provides the simulation results of the GA-DTC. The analysis of the findings and a suggestion for future study are covered in Section 6.
## 5 Discussion
### Boundary Size, File Size, and Producing Time of B-Rep  
To understand the relationship between boundary size, B-Rep file size, and time needed for the creation of B-Rep, a series of additional experiments have been conducted. B-Reps with boundary size of 10, 100, 1000, and 10,000 m for unbounded half spaces were first created, then their file size and time for creation were calculated.  
Table 7 presents boundary sizes and file sizes of corresponding B-Reps for half spaces. The numbers clearly suggest that there is no relationship between them. B-Reps with a larger boundary size represent a larger space, but in terms of file size, they remain the same (1.16 KB). This relationship suggests that it is feasible to set the boundary size as large as possible, without the need to concern an over-large file size.  
According to the initial experiments, the generation of B-Rep for a half space are completed in a short time, usually less than 1/1000 second. In this situation, the measured producing time can significantly deviate from the true value. It has been observed that, in each assessment, if the B-Rep generation process has only been run once, the time for each assessment varies significantly. Therefore, to make the assessment more reliable, the B-Rep generation operation has to be repeated multiple times in each assessment, which was set at 20,000 times in this study. Six unbounded half space entities are used for the assessment, and the total processing time is presented in Table 8. For each half space entity, it is observed that the time for each boundary size is very close, in most cases, within 0.5 s. Considering the large number of repetitions (20,000), the time difference between each execution is actually small enough that it can even be neglected.
## 2 Results and Discussion
### Purification of the Antioxidant Peptides from SHCH
#### 2.2.2 Anion-Exchange Chromatography  
Ion-exchange chromatography is used to separate the charged molecules based on their affinity to the ion exchanger (anion and/or cation exchange resins), and their interaction was determined by the number and location of the charges on the molecules [5]. SHCH-I was loaded onto a Diethylaminoethyl cellulose 52 (DEAE-52) cellulose anion-exchange column and separated by stepwise elution using deionized water and 0.1, 0.5, and 1.0 M NaCl (Figure 2A). Five separated fractions (Fr.1 to Fr.5) were collected. Their HO\(\bullet\) scavenging activities were measured and are shown in Figure 2B. The HO\(\bullet\) scavenging rate of Fr.4 reached 72.03% \(\pm\) 2.64% at 10 mg protein/mL, and it exhibited significantly more efficient antioxidant activity than the other fractions (\(p<0.05\)). Peptides with basic and/or hydrophobic amino acid residues, such as His, Lys and Pro, are thought to have strong antioxidant activities [24]. Therefore, anion and cation exchange resins have been widely used to purify antioxidant peptides from protein hydrolyses [25; 26; 27]. The present data showed that Fr.4 had the strongest HO\(\bullet\) scavenging activity and was selected for further purification.
# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
## 5 DDPG-Based Resource Scheduling Algorithm
Since \(\mathbf{\mathcal{A}}_{t}=\pi(\mathbf{S}_{t};\mathbf{\Theta})\), Equation (23) is obtained to calculate the gradient of parameter \(\mathbf{\Theta}\) by the chain rule,  
\[\
abla=\frac{\partial Q(\mathbf{S}_{t},\mathbf{\mathcal{A}}_{t};\mathbf{W})}{\partial\bm {\Theta}}=\frac{\partial\pi(\mathbf{S}_{t};\mathbf{\Theta})}{\partial\mathbf{\Theta}}* \frac{\partial Q(\mathbf{S}_{t},\mathbf{\mathcal{A}}_{t};\mathbf{W})}{\partial\mathbf{ \mathcal{A}}_{t}} \tag{23}\]  
Then the actor parameters are updated with gradient ascent:  
\[\mathbf{\Theta}=\mathbf{\Theta}+\beta*\
abla \tag{24}\]  
The specific process of training the resource allocation model based on DDPG is shown in Algorithm 1. ```
1. Initialize the channel model parameters and DDPG model parameters
2. For episode = 1, 2,... E do
3. Initialize the packet model parameters
4. For \(t\) = 1, 2,... Step do
5. Given the current state \(\mathbf{S}_{t}\). 6. Get action \(\mathbf{\mathcal{A}}_{t}\) from \(\pi(\mathbf{S}_{t};\mathbf{\Theta})///determine which flows are scheduled in this TTI
7. Calculate the numbers of allocated RB for each scheduled flow with Equation (14). 8. Execute the \(\mathbf{\mathcal{A}}_{t}\) and allocate RB numbers got in step 7 and get reward \(\mathbf{\psi}(\gamma_{t})\) with Equation (15). 9. Transit to the next state \(\mathbf{S}_{t+1}\).///After an action execution, the environment state will be updated
10. Store \((\mathbf{S}_{t},\mathbf{\mathcal{A}}_{t},\mathbf{\psi}(\gamma_{t}),\mathbf{S}_{t+1})\).//this message is stored for DDPG model training
11. Trainest num+ = 1.//calculate the size of train set
12. if train set num > min trained num do//When the size of the training set meets the training conditions, it begins to train the model. 13. \(Q_{t}=Q(\mathbf{S}_{t},\mathbf{\mathcal{A}}_{t};\mathbf{W})//\)calculate \(Q_{t}\) of action \(\mathbf{\mathcal{A}}_{t}\) in state \(\mathbf{S}_{t}\)
14. \(Q_{t+1}=Q(\mathbf{S}_{t+1},\mathbf{\mathcal{A}}_{t+1};\mathbf{W})//\)Output the \(Q_{t+1}\) with the state \(\mathbf{S}_{t+1}\) and action \(\mathbf{\mathcal{A}}_{t+1}\). 15. \(\delta_{t}=Q_{t}-(r_{t}+\psi(\gamma_{t})*Q_{t+1})//\)Get the calculation loss
16. \(\mathbf{W}=\mathbf{W}-\mathbf{\alpha}*\delta_{t}*\frac{\partial Q(\mathbf{S}_{t},\mathbf{\mathcal{ A}}_{t};\mathbf{W})}{\partial\mathbf{W}}//\)update the critic parameters using gradient descent
17. \(\
abla=\frac{\partial\pi(\mathbf{S}_{t};\mathbf{\Theta})}{\partial\mathbf{\Theta}}* \frac{\partial Q(\mathbf{S}_{t},\mathbf{\mathcal{A}}_{t};\mathbf{W})}{\partial\mathbf{\mathcal{ A}}_{t}}\)
18. \(\mathbf{\Theta}=\mathbf{\Theta}+\beta*\
abla/\)update the actor parameters with gradient ascent
19. do end
20. For end
21. For end
```  
**Algorithm 1**Resource scheduling based on DDPG
## 4 Conclusions  
The main purpose of this study was to examine the influence of the punch geometry in indentation and validate MUB as a suitable method of analysis against other analysis methods, like FEM. Different punch geometries were implemented throughout these analysis procedures.  
Based upon the numerical and analytical work carried out in this paper, the following conclusions were reached:  
* A general trend was observed. Working with deep indentations, punch geometries present low influence, always reaching a maximum force value when the punch surface has total contact with the workpiece indented, as demonstrated with FEM analysis and MUB.
* By comparing results, it could be stated that the MUB developed method shows a good correlation with FEM results. The FEM and MUB solutions agree well with each other, being results in the same range.  
Figure 16: Geometry 4 results (**A**); detail (**B**); and MUB approach (**C**).  
* On the one hand, to obtain a maximum effort value when a total contact of the punch surface is needed, the analysis of different punch geometries can be reduced to the application of the FP MUB model. On the other hand, in case a closer analysis of the deformations' initial stages is wanted, the application of the IP MUB model provides a more accurate solution.
* The evaluation of a curve punch was achieved by approximating the IP + FP geometry. The facets were increased until a curve geometry was obtained with six facets. The results obtained show similarities with the IP geometry and they also reach the same maximum force value.
* Unlike the conventional UBT method and FEM analysis, MUB presents much simpler constitutive equations and relations. In addition, its modular configurations confer versatility, being able to better adapt to different shapes with less resolution time.  
Therefore, MUB presents an easier application and resolution. It requires less calculation effort than the conventional UBT method and the analysis complexity does not increase aggregating more modules to the model. In addition, directly compared with FEM, MUB offers better computational efficiency, obtaining the same results with less fluctuation in their evolution, which makes result interpretation easier. The main disadvantage of FEM is that computational times are long--it could last minutes or hours depending on the analysis vs. seconds applying MUB--and coarse meshes are needed to solve problems in a reasonable time, which sacrifices accuracy.  
The authors want to thank the University of Malaga-Andalucia Tech, International Campus of Excellence, for its financial support on this paper.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] contributed in the analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Differential Proteomic Analysis of _Listeria monocytogenes_ during High-Pressure Processing
## Materials and Methods
### Data Analysis and Protein Network Construction  
Three independent biological replicates were performed for each experiment. LC-MS/MS data were analyzed using the Maxquant software (v. 1.6.6.0; Max Planck Institute of Biochemistry, Martinsried, Germany) for label-free quantitation (LFQ). Peptides and proteins were identified using a false discovery rate of 1%. Protein sequence information on _L. monocytogenes_ serovar 1/2a was obtained from UniProtKB (downloaded on 11 December 2018). LFQ proteomic analysis of three biological replicates was performed to explore the mechanism of global proteome changes in _L. monocytogenes_ under water-washed (C), HPP-200 MPa (H2) and HPP-400 MPa (H4) treatments. Differentially expressed proteins (DEPs) were defined if a 2-fold change was observed in expression levels between control and HPP treatment groups that was statistically significant (\(p<0.05\)) [13] using Perseus software (v. 1.6.7.0; Max Planck Institute of Biochemistry, Martinsried, Germany). The functional annotation tool The Database for Annotation, Visualization, and Integrated Discovery (DAVID, v. 6.8) was employed using gene ontology (GO) and UniProtKB Keywords, STRING Protein-Protein Interaction Networks (v. 11.0) [17], the eggnog online framework of Database of Clusters of Orthologous Groups of proteins (COGs), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to determine the protein functions [18]. The dose-dependent effects of HPP in _L. monocytogenes_ proteomes were identified from three independent samples, as shown in Figure S1.
# A Multicomponent Face Verification and Identification System
## 2 Materials and Methods
### Deep Learning Model for the Face Identification and Verification  
#### 2.2.1 Dataset Curation  
The ID vs. Spot dataset [60] was used for this research. This dataset comprises a vast collection of face images captured in controlled (ID photos) and uncontrolled (Spot photos) environments. To enhance the robustness of the experiments, a meticulous curation of the dataset was undertaken to eliminate poor-quality photos against exclusion criteria such as blur (variance of Laplacian < 100), severe lighting, over/under-exposure, face coverage < 40% of frame, etc. The curated dataset ultimately utilized for the experiments encompassed a total of 1232 ID RGB photos and 3967 Spot RGB photos. Prior to inputting the images into the models, several preprocessing steps were executed, such as the following:  
1. Resizing: All images were resized to a standard dimension of 160 by 160 pixels.
2. Normalization: The pixel values of the images were normalized to achieve a mean of 0 and a standard deviation of 1.
3. Histogram equalization: Histogram equalization was applied to augment the contrast of the images.
## 1 Introduction
Due to the sandwich-like lamination and strong anisotropy of elastic properties caused by the NW-microstructure, the laser-generated wave fields are simulated within the elastodynamic model of anisotropic multilayered halfspace. The control of wave propagation in such structures presents a number of challenges. Indeed, their anisotropy complicates the physical phenomena associated with waves. Then, these waves have a multi-modal character (at a given wavelength or frequency several modes coexist) and they are dispersive (the phase and group velocities of each of the modes are frequency dependent). At present, the generally accepted means of numerical simulation are the finite element method (FEM) and similar mesh-based approaches. However, the FEM is not well suitable for the wave problems considered because it can provide the quantitative information only about the total wave field while the characteristics of individual SAW modes are of interest. Besides, the FEM can be too time-consuming due to the large relative lengthening of the simulated structures resulting in a sharp increase of the number of elements necessary for the correct SAW simulation. In view of the repeating recalculation of wave characteristics in the course of inverse problem solution (i.e., adjusting of the calculated SAW dispersion curves to the experimentally measured points), the efficiency of the simulation methods is of great importance. To avoid all the difficulties discussed above, we use an analytically based computer model. It relies on the integral and asymptotic representations in terms of the elastodynamic Green's matrix of the laminated anisotropic half-space considered. An effective computer implementation is achieved by the use of fast and stable algorithms for the Green matrix, pole and residue calculations [16; 17]. This computational model varies the materials properties (effective elastic constants, densities) to find a wave with such characteristics as were measured on experimental samples. The rest of the paper is organized as follows. In Section 2 we describe the samples and experimental technique for exciting and detection of elastic waves. The computational model and its realization are described in Section 3, followed by test numerical results illustrating the dependence of GW propagation on material parameters in agreement with the experiment data. Finally, the discussion on effective elastic parameters is provided in Section 4 and Conclusions.
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
###### Abstract
Hence, this paper aims to answer the following questions: What are the key applications and modes of TerraSAR-X and how does the scientific user community generally utilize the mission? Therefore, we briefly summarize the TerraSAR-X mission, its technical capabilities and the product provision mechanism for scientific purposes. Based on the analysis of 1636 data utilization proposals, we determine the thematic scope as well as user motivations and expectations for using high resolution X-band SAR data. We examine all data takes acquired on requests by scientific users to assess the share of the different modes and polarizations and to identify the preferred settings and geographic areas of interest. We analyze the published results based on TerraSAR-X data in peer-reviewed scientific literature. Finally, we compare and discuss the revealed findings.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 3 IoT Architectures for EW Systems
### Reference Architecture  
IoT systems' functions and peculiarities can be described starting from their architectural configuration. As for the most basic IoT solutions [24], a three-layered architecture can be used to describe a generic EW system based on the IoT. As shown in Figure 1 the common IoT architecture basically consist of a Perception layer, a Communication layer, and an Application layer [23].  
Figure 1: Reference IoT architecture.  
While specific architectures may introduce or specify new layers and intermediate layers, such as Edge/Fog Layers, Middleware and Interface Layers, a generic and simple architecture can be considered to be one that senses data from the environment, processes it, and/or forwards it to a central server which will then use the current and previously stored data to generate alarms through different methods, such as signal processing, statistical methods, computer vision, or Artificial Intelligence (AI), specifically through the adoption of Machine Learning (ML), and Deep Learning algorithms. Below is a brief description of each layer with its main characteristics.
# A Recursive Optimization Approach for Buffer Allocation in Large Production Lines
## 3 Problem Formulation  
### Production Line Model  
A serial production line consists of \(K\) machines \((M_{1},\ldots,M_{K})\) separated by \(K-1\) buffers \((B_{1},\ldots,B_{K-1})\), as depicted in Figure 1. We consider the classic continuous flow model of production lines [17]:  
1. The material is modelled as a continuous fluid.
2. Each machine is unreliable and can be in either the up or down state. The processing rate of machine \(M_{i}\) is \(s_{i}\), i.e., if \(M_{i}\) operates in isolation, when it is up, a quantity of material \(s_{i}dt\) is processed in an infinitesimal time \(dt\). If all machines have the same processing rate, the line is said to be homogeneous. In the case of homogeneous lines, time is scaled and the processing rate is 1.
3. Buffer \(B_{i}\) has a capacity of \(b_{i}\geq 0\), \(i=1,\ldots,K-1\), meaning that it can accommodate \(b_{i}\) units of material.
4. Machine \(M_{i}\), \(i=2,\ldots,K\), is starved at time \(t\) if one of its upstream machines is down and all buffers between this machine and \(M_{i}\) are empty. The first machine \(M_{1}\) is never starved.
5. Machine \(M_{i}\), \(i=1,\ldots,K-1\), is blocked at time \(t\) if one of its downstream machines is down and all buffers between this machine and \(M_{i}\) are full. The last machine \(M_{K}\) is never blocked.
6. The machines can fail only when they are working, i.e., they are up and neither starved nor blocked.
7. The time to failure of machine \(M_{i}\), \(i=1,\ldots,K\), is exponentially distributed, with rate \(\lambda_{i}\). The mean time to failure (MTTF) is \(1/\lambda_{i}\).
8. The time to repair of machine \(M_{i}\), \(i=1,\ldots,K\), is exponentially distributed, with rate \(\mu_{i}\). The mean time to repair (MTTR) is \(1/\mu_{i}\).