esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Conventional treatment methods such as chlorination and ozonation have been proven not to be effective in eliminating and degrading contaminants such as Bisphenol A (BPA) from wastewater. Hence, the degradation of BPA using a photocatalytic reactor has received a lot of attention recently. In this study, a model-based approach using a multilayer perceptron neural network (MLPNN) coupled with back-propagation, as well as support vector machine regression coupled with cubic kernel function (CSVMR) and Gaussian process regression (EQGPR) coupled with exponential quadratic kernel function, were employed to model the relationship between the textural properties such as pore volume (V\({}_{p}\)), pore diameter (V\({}_{d}\)), crystallite size, and specific surface area (S\({}_{\text{BET}}\)) of erbium- and iron-modified TiO\({}_{2}\) photocatalysts in degrading BPA. Parametric analysis revealed that effective degradation of the Bisphenol up to 90% could be achieved using photocatalysts having textural properties of 150 m\({}^{2}\)/g, 8 nm, 7 nm, and 0.36 cm\({}^{3}\)/g for S\({}_{\text{BET}}\), crystallite size, particle diameter, and pore volume, respectively. Fifteen architectures of the MLPNN models were tested to determine the best in terms of predictability of BPA degradation. The performance of each of the MLPNN models was measured using the coefficient of determination (R\({}^{2}\)) and root mean squared errors (RMSE). The MLPNN architecture comprised of 4 input layers, 14 hidden neurons, and 3 output layers displayed the best performance with R\({}^{2}\) of 0.902 and 0.996 for training and testing. The 4-14-3 MLPNN robustly predicted the BPA degradation with an R\({}^{2}\) of 0.921 and RMSE of 4.02, which is an indication that a nonlinear relationship exists between the textural properties of the modified TiO\({}_{2}\) and the degradation of the BPA. The CSVRM did not show impressive performance as indicated by the R\({}^{2}\) of 0.397. Therefore, appropriately modifying the textural properties of the TiO\({}_{2}\) will significantly influence the BPA degradability.  
photocatalytic reactor; artificial neural network; Bisphenol A; back-propagation algorithm; multilayer perceptron; textural properties +
Footnote †: journal: Applied Sciences
## 4 Test Results  
### Porosity  
#### 4.1.1 Testing Equipment and Principle of Porosity Measurement  
Our helium porosity measuring instrument is designed using the standards of the American Petroleum Institute (API) [34] and Geology and Mineral Resources of China to measure the particle volume and helium porosity of rock (Figure 7). The helium atom has the advantages of small volume (minimal viscosity), stable ideal gas behavior and low adsorbtivity on clay mineral surfaces; this gives easy and accurate measurements without altering the nature of the specimen, which is why He measurements are standard in such cases.  
Measurement of core porosity by the gas uses [PERSON]'s law:  
\[P_{1}\cdot V_{1}=P_{2}\cdot V_{2} \tag{21}\]  
The porosity is calculated as follows:  
\[\phi=\frac{V_{P}}{V_{P}+V_{G}}\times 100\% \tag{22}\]  
Figure 7: Helium porosity measurement instrument diagram.  
where \(\phi\) is porosity, \(V_{P}\) is the pore volume, and \(V_{G}\) is the particle volume. The formulae for measuring the particle volume and pore volume are:  
\[P_{1}\cdot V_{R}=P_{2}\cdot(V_{R}+V_{M}-V_{G}) \tag{23}\]  
\[P_{1}\cdot V_{R}=P_{2}\cdot(V_{P}+V_{D}+V_{R}) \tag{24}\]  
where \(P_{1}\) is the initial pressure, \(P_{2}\) is the equilibrium pressure, \(V_{R}\) is the reference volume, \(V_{M}\) is the volume of the media cup, \(V_{D}\) is the volume of the closed system, \(V_{G}\) is the solids volume of the specimen and \(V_{P}\) is the pore volume of the specimen.  
Taking Equations (22) and (23) into Equation (21), the porosity is determined. Table 4 displays the porosity test results.
# Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies
###### Abstract  
People with visual impairment face various difficulties in their daily activities in comparison to people without visual impairment. Much research has been done to find smart solutions using mobile devices to help people with visual impairment perform tasks like shopping. One of the most challenging tasks for researchers is to create a solution that offers a good quality of life for people with visual impairment. It is also essential to develop solutions that encourage people with visual impairment to participate in social life. This study provides an overview of the various technologies that have been developed in recent years to assist people with visual impairment in shopping tasks. It gives an introduction to the latest direction in this area, which will help developers to incorporate such solutions into their research.  
smartphone; assistive technology; visually impaired; shopping; computer vision +
Footnote †: journal: Medical  
applications. Mobile devices can also be connected to cloud computing and offload tasks to be executed there, which saves power, memory, and battery [18; 19; 20]. The advantages of mobile technologies make them useful for accessing information from any place at any time and give PVI the opportunity to use smartphones in their daily activities [21; 22; 23; 24; 25]. In this way, smartphones are used with Assistive Technology (AT) to offer multiple solutions; this technology is called Mobile Assistive Technology (MAT). Researchers have conducted extensive investigations on using MAT to help PVI navigate from one place to another and shop without any support from people without disabilities [26; 27; 28; 29; 30; 31; 32; 33; 34]. In this study, we concentrate on the available solutions to help PVI in the shopping process. We divided the shopping process into two parts. The first part is how to prepare the shopping list, which provides assistance during shopping, before shopping. The second part helps them navigate inside shopping malls and identify products during shopping.  
The purpose of this literature review is twofold. The first aim is to answer the following research questions:  
Q1: What are the main categories of MAT shopping solutions for PVI?  
Q2: What are the strengths and weaknesses of the latest MAT shopping help systems for PVI?  
Q3: What capabilities do the best and most effective solution for PVI give?  
The second aim is to overview the available MAT solutions about helping PVI prepare shopping lists, navigate inside shopping malls and recognize products during shopping. It also discusses how the proposed solutions can help PVI in the shopping process and summarizes the challenges and drawbacks of the proposed solutions. This paper is structured as follows: Section 2 describes the research methodology. Section 3 discusses multiple solutions and how they can help PVI. Section 4 explains the main benefits and research challenges. Finally, Section 5 outlines the conclusions of this study.
## 2 Methods: Three-Dimensional Wear Model and Numerical Method
### Numerical Calculation Method
#### 2.2.2 Numerical Calculation Method  
Ansys Fluent 18.0 was used to perform steady numerical simulations of the computational domain of the mining pump. In the computational process, it is assumed that there is no energy or mass exchange between the particle phase and the fluid phase and that gravity is taken into account. The computation of the fluid domain adopts the SST \(k\)-\(\omega\) turbulence model, the motion of the particle phase in the computational domain adopts the DPM model based on Euler-Lagrange coordinates, the discretization of the Navier-Stokes equation adopts the second-order upwind style, and the computational convergence precision is set as \(10^{-4}\) in the numerical simulation.  
Using the \(x\)-axis direction as an example, the equation for particle motion in the computational domain can be expressed as follows:  
\[\frac{du_{p}}{dt}=F_{D}\big{(}u-u_{p}\big{)}+\frac{g_{x}(\rho_{p}-\rho)}{\rho_{ p}}+F_{V}+F_{P}+F_{x} \tag{1}\]  
where \(F_{D}(u-u_{p})\) is the drag force on a unit mass of solid-phase particles, as follows:  
\[F_{D}=\frac{18\mu}{\rho_{p}d_{p}^{2}}\frac{C_{D}R_{e}}{24} \tag{2}\]  
where, \(u\) is the fluid velocity (m/s), \(u_{p}\) is the particle velocity (m/s), \(\mu\) is the hydrodynamic viscosity (N\(\cdot\)s/m\({}^{2}\)), \(\rho\) is the fluid density (kg/m\({}^{3}\)), \(\rho_{p}\) is the particle density (kg/m\({}^{3}\)), \(d_{p}\) is the particle diameter (mm), and \(g_{x}\) is the acceleration of gravity in the \(x\)-axis direction (m/s\({}^{2}\)).
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 2 Methods
### Time-Domain Feature Extraction Based on Scalable Hypothesis Testing  
The current data collected during the experimental activity were time-series data. A time series is a set of observations collected in order of time [22]. In order to use a set of time series, \(D=(x_{i})_{i=1}^{N}\), as input for supervised machine learning algorithms, each time series \(x\); should be transformed into a well-defined feature space with dimensions \(M\) specific to the problem and feature vector \(\vec{x}_{i}=(x_{i,1},x_{i,2},\ldots,x_{i,M})\)[23]. In this paper, time-domain feature extraction is proposed as a method for obtaining specific features. The use of time-domain features is one of the most frequently utilized signal-processing methods since it has the lowest computational cost without neglecting useful information from motor conditions [18,19]. Several time-domain features, such as skewness and kurtosis, are robust to machine operating conditions [24]. Table 1 lists the time-domain features that were utilized in this study.  
The extracted features shown in Table 1 were filtered using hypothesis testing. Each feature \(X_{i}\) was scored according to its significance in predicting target \(Y\). As a result, each feature \(X\); was statistically investigated to determine whether \(X\); was useful for predicting target \(Y\); or irrelevant for predicting target \(Y\). Each test generated a \(p\)-value that estimated the probability that feature \(Y\) was useful for predicting the target. In this case, a \(p\)-value lower than 0.05 indicated that a certain feature \(X\) was relevant for predicting target \(Y\). All of these approaches employed sfresh, an open source Python program that  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Feature** & **Equation** \\ \hline Length & \(s_{1}=N\) \\ Maximum & \(s_{2}=max(x_{i})\) \\ Minimum & \(s_{3}=min(x_{i})\) \\ Median & \(s_{4}=\left\{\begin{array}{ll}x_{(N+1)/2},&N\text{ is odd}\\ \frac{x_{N/2}+x_{(N+1)/2}}{2},&N\text{ is even}\end{array}\right.\) \\ Sum values & \(s_{5}=\sum_{i=1}^{N}x_{i}\) \\ Mean & \(s_{6}=\frac{s_{6}}{s_{7}}\) \\ Variance & \(s_{7}=\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\bar{x})^{2}\) \\ Standard deviation & \(s_{8}=\sqrt{s_{7}}\) \\ Root mean square & \(s_{9}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_{i}^{2}}\) \\ Skewness & \(s_{10}=\frac{1}{N}\sum_{i=1}^{N}\frac{(x_{i}-s_{6})^{2}}{s_{7}}\) \\ Kurtosis & \(s_{11}=\frac{1}{N}\sum_{i=1}^{N}\frac{(x_{i}-s_{6})^{2}}{s_{7}^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the extracted time-domain features.  
Figure 4: Clutch assembly conditions of RPM equipment: (**a**) normal conditions with new grease, (**b**) warning conditions with old grease mixed with mud.  
conducts automated feature extraction based on hypothesis testing, specifically designed for time-series datasets.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 4 Deceptive Jamming Extraction
### Frequency Detection  
After phase filtering, the normal interferometric phase takes the form of continuous fringes with fewer residues, while the false phase in the interferogram appears as a constant in theory. However, in practice, due to the remaining noise and some scattered echo of true targets in the deceptive area, the false phase is not always a constant in the spatial domain, leading to unreliable detection result. Therefore, we apply principle frequency detection to identify the false regions caused by deceptive jamming.  
According to the model of the InSAR phase described in [1], for the normal interferogram, the component of fringe frequency in the radar range direction is related to the flat effect and terrain variety. Therefore, Fourier transform of interferogram in the range direction has continuous changing values in the spectrum. However, the false phase, approximately a constant in the spatial domain, will approach zero in the frequency domain.  
To derive the range fringe frequency of the detection window, the maximum likelihood (ML) method is employed using Fourier transforms [34]. The range frequency of \((2P+1)\) pixels in the interferogram can be expressed as  
\[\hat{f}=\arg\max_{f_{x}}\left(\left|\sum_{x=a-P}^{x=a+P}\overline{S}(x)\exp(-j2 \pi(xf_{x}))\right|\right) \tag{17}\]  
where \(\overline{S}(x)\) represents the filtered complex interferogram, and \(a\) denotes the center pixel in the range fringe frequency estimation window. To speed up the optimization of Equation (17), FFT is usually employed. Then, the contours of the deceptive target can be obtained as a constant zero frequency in the range spectrum. For clarity, the range fringe frequency of partially jammed interferometric phase (Figure 2b) is presented in Figure 5. As can be seen, the real phase varies continuously in range spectrum while the false phase from 331 to 570 pixels and 611 to 660 pixels is close to zero, illustrating the validity of the zero-frequency approximation for interferometric phase of false targets.  
Therefore, a 0-1 valued mask matrix can be derived subsequently through threshold setting. Based on the mask matrix, the false area can be extracted from both interferogram and the original SAR image ultimately.
###### Abstract  
This paper addresses the practical issue of load frequency control (LFC) in multi-area power systems with degraded actuators and sensors under cyber-attacks. A time-varying approximation model is developed to capture the variability in component degradation paths across different operational scenarios, and an optimal controller is constructed to manage stochastic degradation across subaress simultaneously. To assess the reliability of the proposed scheme, both Monte Carlo simulation and particle swarm optimization techniques are utilized. The methodology distinguishes itself by four principal attributes: (i) a time-varying degradation model that broadens the application from single-area to multi-area systems; (ii) the integration of physical constraints within the degradation model, which enhances the realism and practicality compared to existing methods; (iii) the sensor suffers from fault data injection attacks; and (iv) an optimal controller that leverages particle swarm optimization to effectively balance reliability and system performance, thereby improving both stability and reliability. This method has demonstrated its effectiveness and advantages in mitigating load disturbances, achieving its objectives in just one-third of the time required by established benchmarks. The case study validates the applicability of the proposed approach and demonstrates its efficacy in mitigating load disturbance amidst stochastic degradation in actuators and sensors under FDIA cyber-attacks.  
degradation; load frequency control; Monte Carlo simulation; particle swarm optimization; cyber-attacks +
Footnote †: journal: Applied sciences
## 3 Results
### Wear Properties  
The mass loss due to wear of LHAGI, AGI, LHQTGI, QTGI, and untreated GI specimens is shown in Figure 6. The untreated GI specimens had the highest wear loss due to the low surface hardness of approximately 215 HB. After receiving quench-tempering, the wear resistance was improved. The wear resistance decreased when raising the tempering temperature due to the lower hardness of the specimens. The wear performance of GI specimens was further enhanced by applying the austempering heat treatment. It is well known that the existence of acicular ferrite and stable austenite with high carbon content provides improved wear protection on surfaces. The metastable austenite can be converted into martensite to increase the surface hardness by the high localized strain and plastic deformation [26; 27]. The wear resistance of AGI specimens also decreased when raising austempering temperature because of the lower hardness of the specimens. When the laser surface hardening treatment was utilized on AGI and QTGI specimens, the mass loss was significantly reduced because high hardness ledeburitic and martensitic structures were formed in the matrix. The LHAGI specimens had higher wear resistance than LHQTGI specimens, although the microstructure and hardness of Zone 1 and Zone 2 were quite similar, which can be attributed to two reasons. (1) The acicular ferrite and stable austenite with high carbon percentage between two adjacent laser-hardened tracks had higher wear resistance than tempered martensite under equivalent macro-hardness. (2) The combined structures of tempered acicular ferrite and tempered austenite had higher fracture toughness than tempered martensite. Similar findings were reported in a previous study on the tribological properties of ADI and tempered ADI [28].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Group 1** & \multicolumn{2}{c}{**Group 2**} & \multicolumn{2}{c}{**Group 3**} \\ \hline Austempering Temperature & & 232 °C & & 288 °C & 343 °C \\ \hline \multirow{4}{*}{Micro-Hardness Profile (HRC)} & \multirow{2}{*}{Zone 1} & AVE & SD & AVE & SD & AVE & SD \\  & & 67.5 & 0.4 & 67.8 & 0.5 & 68.2 & 0.2 \\ \cline{2-6}  & \multirow{2}{*}{Zone 2} & AVE & SD & AVE & SD & AVE & SD \\  & & 67.8 & 0.7 & 68.4 & 0.7 & 67.7 & 0.6 \\ \cline{2-6}  & \multirow{2}{*}{Zone 3} & AVE & SD & AVE & SD & AVE & SD \\  & & 38.3 & 0.4 & 33.5 & 0.4 & 24.6 & 0.5 \\ \hline \multicolumn{6}{c}{Tempering Temperature} & \multirow{2}{*}{316 °C} & \multirow{2}{*}{399 °C} & \multirow{2}{*}{482 °C} \\ \hline \multirow{4}{*}{Micro-Hardness Profile (HRC)} & \multirow{2}{*}{Zone 1} & AVE & SD & AVE & SD & AVE & SD \\  & & 68.1 & 0.4 & 68.6 & 0.6 & 68.4 & 0.2 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Zone 2} & AVE & SD & AVE & SD & AVE & SD \\  & & 66.5 & 0.6 & 66.2 & 0.3 & 66.7 & 0.8 \\ \hline \end{tabular}  
* AVE: Average Hardness, SD: Standard Deviation.  
\end{table}
Table 7: Micro-Hardness Profiles of the LHAGI and LHQTGI Specimens with Various Heat Treatment Designs.  
Figure 6: Mass Loss in the LHAGI, AGI, LHQTGI, and QTGI Specimens.
## 3 Case Study  
### Project Specifications  
Guandi hydropower project is located in Liangshan, Sichuan Province. It is composed of a roller compacted concrete (RCC) dam, a flood discharge structure, an underground powerhouse on the right bank, and so on. The normal water level is 1330 m, with a corresponding capacity of 752.8 million m3. The maximum dam height is 168 m. The elevation of the barrage is about 1334.00 m. The dam crest length is 516.00 m, the lowest base elevation 1166.00 m, and the maximum dam bottom width 153.2 m. Guandi hydropower project is located on the west margin of the Yangtze Paraplatform, with complex cracks under the dam, strong regional neotectonic movement, relatively stable zone in the near dam reservoir area.
## 1 Introduction
Within the Armed Forces (AF), multicriteria methods are routinely employed because precise decision-making is a decisive factor for success and can reduce spending and increase defense capacity [19]. The study [20] exposes that military decisions are of great importance because they affect the sovereignty and security of countries. The MCDM methods have been applied to several recent military problems, such as [21; 22; 23; 24; 25]. The choice of personnel for missions or promotion in the military and other areas of knowledge is uncertain and subjective. Thus, the question that expresses the research problem is: How  
Figure 1: Importance of hierarchy between Data, Information, and Knowledge in MCDM. can decisions be configured to prioritize the Military of the Brazilian AF for positions and promotions transparently and reasonably? This work aims to propose a methodology for the choice of military personnel, seeking to treat the subjectivity inherent to this type of decision through the association of qualitative and quantitative data. We expect that the modeling proposed in this work will contribute to a transparent and robust decision-making process for personnel selection, with the possibility of analyzing quantitative and qualitative data intrinsic to issues related to human resources. In addition, we presented a simple and intuitive computational tool [27] that seeks to make the decision-making process more flexible, reliable, and faster, greatly facilitating complex calculations that involve MCDM. The modeling presented in this work will deliver an axiomatic structure for multiple decision-makers, allowing them to indicate their preferences and subjectivities regarding the analyzed data, aggregating their points of view and making the decision-making process more reliable and democratic. This work can contribute to academia and society, presenting an integrated, practical, and valuable model for various operational and strategic tactical applications. Given its dual nature, the developed framework will enable online applications in military and civil environments. The present study is divided into six chapters to introduce and contextualize the problem we addressed in this work. Section 2 presents the main applications of MCDM in problems related to personnel selection, mapping the state of the art of this theme, and identifying trends to guide the research of this paper. Section 3 discusses the background of the ELECTRE-MOr method. Section 4 aims to structure the problem, obtaining the criteria and subcriteria that make up the analysis. Section 5 presents the case study, while Section 6 concludes this paper, presenting the respective conclusions to the study and the proposition of future research.