esa-sceva/satcom-chunk-collection huggingface dataset:
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 3 Materials and Methods
### Malware Detection  
In Section 3.6, among the three classifiers selected for the depth analysis, RF had the highest accuracy. In total, 49,376 malicious files and 2583 benign files corresponding to 95% of the entire dataset were randomly selected. Table 4 shows the detection accuracy results for the three classifiers.
###### Abstract  
The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.  
data cubes; deprivation; urban poverty; slums; data ecosystem; statistics +
Footnote †: journal: Sustainability
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 2 Materials and Methods
### Dynamic Models of EHPS System
#### 2.1.2 Hydraulic System  
The schematic diagram of the hydraulic system is shown in Figure 3, which mainly includes the pump, oil tank, rotating valves, screw, and nut. The rotating valves are usually modeled as a Wheatstone bridge, which contains four throttling valves and the orifice area of valves related to the torsion bar in Figure 2. The dynamic of the hydraulic system can be expressed as:  
\[\left\{\begin{array}{l}Q_{L}=C_{d}A_{1}\sqrt{2(P_{s}-P_{1})/\rho}-C_{d}A_{3} \sqrt{2P_{1}/\rho}\\ Q_{R}=C_{d}A_{2}\sqrt{2(P_{s}-P_{2})/\rho}-C_{d}A_{4}\sqrt{2P_{2}/\rho}\\ Q_{L}=-A_{P}\cdot\dot{x}_{p}+\frac{V_{s}}{K}\frac{dP_{L}}{dt}\\ Q_{R}=A_{P}\cdot\dot{x}_{p}+\frac{V_{s}}{K}\frac{dP_{L}}{dt}\end{array}\right. \tag{5}\]  
where \(Q_{L}\) is the flow through the left chamber of the cylinder; \(Q_{R}\) is the flow through the right chamber; \(C_{d}\) is the discharge coefficient; \(A_{1}\)-\(A_{4}\) are the orifice areas of valves; \(P_{s}\) is the inlet pressure; \(P_{1},~{}P_{2}\) are pressure of rotary valve; \(\rho\) is the hydraulic oil density; \(V_{1},~{}V_{2}\) are the volumes of left and right chambers of the cylinder; \(K\) is bulk modulus; \(A_{P}\) is the piston area.  
The hydraulic assistance is described in Equation (6).  
\[F_{hy}=A_{P}(P_{A}-P_{B}) \tag{6}\]  
where \(P_{A}\) is equal to \(P_{1};~{}P_{B}\) is equal to \(P_{2}\).
# Influence of the Motion of a Spring Pendulum on Energy-Harvesting Devices
## 8 Conclusions  
1. A 2 DOF nonlinear damped vibrating spring pendulum system moving in a circular path with a stationary angular velocity, in which it is connected with energy-harvesting devices, was investigated as a novel model.
2. The governing equations were derived using [PERSON]'s equation and solved asymptotically using the AMS to obtain results with high accuracy.
3. The solvability conditions were acquired in light of the resonance cases.
4. The achieved ME was solved to obtain two nonlinear algebraic equations in terms of the amplitudes and the detuning parameters.
5. The time histories of the dynamical motion, the resonance curves responses, and the solutions at the steady-state cases were graphed to reveal the excellent impact of the selected values of the model parameters on the motion.
6. The achieved asymptotic solutions were verified compared with the numerical results that reveal the high solidity between them.
7. The damping coefficients and excitation amplitudes that influence the output voltage, current, and power were checked. Moreover, the output power of the systems was reviewed according to the response of excitation frequency.
8. The nonlinear stability of the ME was examined through the stability and instability areas for the frequency response curves. In addition, the nonlinear analysis for the nonlinear amplitudes of these equations is presented.
9. Electrical energy was generated from piezoelectric and electromagnetic devices.  
Conceptualization, M.K.A. and J.A.; Data curation, M.K.A., J.A. and T.S.A.; Formal analysis, M.K.A., R.S. and T.S.A.; Funding acquisition, J.A.; Investigation, J.A.; Methodology, M.K.A., J.A., R.S., T.S.A. and M.A.B.; Project administration, J.A.; Resources, M.K.A., J.A., T.S.A. and M.A.B.; Software, M.K.A. and R.S.; Supervision, J.A.; Validation, M.K.A., J.A. and R.S.; Visualization, J.A. and M.A.B.; Writing--original draft, M.K.A.; Writing--review & editing, M.K.A., J.A. and T.S.A. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Polish National Science Centre under the Grant OPUS 14 No. 2017/27/B/ST8/01330.  
Not applicable.  
This article was completed while the first author [PERSON] was a Doctoral Candidate in the Interdisciplinary Doctoral School at Lodz University of Technology, Poland.  
The authors declare that they have no conflict of interest.  
Figure 35: The output power versus excitation frequency with varying the damping coefficients.
# Removal of Fluoride and Arsenate from Aqueous Solutions by Aluminum-Modified Guava Seeds
## 2 Materials and Methods
### Biosorbent Characterization  
Guava seeds modified with aluminum (Al-GSs) were characterized by scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) in order to determine surface properties of this material that could be involved in fluoride and arsenate sorption from aqueous solutions. For SEM analysis, GSs and Al-GS samples were covered with copper and analyzed using a scanning electron microscope (JEOL JMS-6400, JEOL Ltd., Peabody, MA, USA) coupled with an EDS microanalysis detector (Bruker XFLASH 4010, Peabody, MA, USA). Fourier transform infrared spectroscopy analyses were performed on the GSs and Al-GS materials both before and after arsenate and fluoride adsorption tests. For this measurement, 0.3 g of dry KBr were mixed with 0.02 g of the dry sample and then compressed with a 3-ton force for 1 min. The samples were analyzed in a Bruker Tensor 27 FTIR spectrophotometer (Bruker Optics Inc., Billerica, MA, USA). The frequency range used was between 200 and 4000 cm\({}^{-1}\).
## 1 Introduction
### Radar Micro-Doppler as a Potential Solution
Ground-based radar interferometry has been used to monitor the structural oscillations of the Leaning Tower of Pisa [13]. Micro-Doppler radar is applied in laboratory settings to analyze vibrations in aerospace components such as satellite panels or aircraft wings during modal testing, which often contain lightweight, flexible structures. Radar sensors have been developed for high-frequency mechanical vibration measurements in aerospace applications, showing feasibility for vibration mode identification without contact [14]. Clearly, micro-Doppler radar represents a powerful tool for structural vibration analysis across a spectrum of applications. Its non-contact nature, capability for remote monitoring, and high sensitivity make it a valuable complement--or alternative--to conventional sensor-based structural health monitoring systems. More recently, these applications have expanded to include the detection of transmission shifts in parked cars [15]. In this study, we compare radar micro-Doppler characteristics from various vehicles with vibration measurements obtained from accelerometers attached to their bodies, focusing on transmission shifts and multi-passenger occupancy.
## 3 Method  
### Workflow  
The workflow of this study is depicted in Figure 2 which comprises the following steps: image preprocessing, spectral unmixing using MESMA, image classification via Random Forest, and accuracy assessment. First, images acquired by both HJ-CCD and HJ-IRS after the flood event were preprocessed including radiometric calibration, geometric rectification and atmospheric correction (Section 3.2). Second, MESMA was employed to decompose the multispectral images into four fraction maps consisting of water, vegetation, impervious surface, and soil (Section 3.3). Third, both the fraction maps and the five reflectance bands together with the Normalized Difference Water Index (NDWI) and topographical parameters (DEM, slope and aspect) were included for the Random Forest classifier to determine thematic land cover types after the flood (Section 3.4). Finally, accuracy assessment was done based on the confusion matrix derived from testing samples to verify the performance of the proposed approach. Meanwhile, the importance of input variables was analyzed in Section 3.5. Variable importance is similar to variable ranking, which is provided by the Random Forest classifier to identify the contribution of each variable to the classification accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sensor** & **Band** & **Wavelength/um** & **Spatial Resolution/m** & **Image Breadth/km** \\ \hline CCD & 1 & 0.43–0.52 & 30 & \\  & 2 & 0.52–0.60 & 30 & 360 \\  & 3 & 0.63–0.69 & 30 & \\  & 4 & 0.76–0.90 & 30 & \\ \hline IRS & 5 & 0.75–1.10 & 150 & \\  & 6 & 1.55–1.75 & 150 & 720 \\  & 7 & 3.50–3.90 & 150 & \\  & 8 & 10.5–12.5 & 300 & \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of HJ-1B data.
# Development of an Artificial Intelligence-Based System for Predicting Weld Bead Geometry
## 2 Materials and Methods  
### Systematic Procedure for Developing the Predictive System  
For developing the predictive system, the systematic procedure is shown in Figure 1. The system enables the prediction of the weld bead shape (including bead width, reinforcement, and penetration) of SMAW, MIG, and TIG welding processes. From analyzing the mechanism of the SMAW, MIG, and TIG welding processes as well as the welding joint types, the input parameters that are the welding process parameters and the allowed values of these parameters were determined. To develop mathematical models showing the relationship between the input parameters and the weld bead shape, experiments were carried out. These experiments are designed by using the factorial design technique. Then, the MiniTab tool was used to establish the relationship between the input parameters and the response. From the established mathematical models, the main factors affecting the weld bead shape were determined. These data were used for training the artificial intelligence (AI)-based models. For developing the AI-based model, the network architecture for SMAW, MIG, and TIG welding process with the input layer including the welding process parameters and the output layer that is the weld bead shape was used. The deep learning technique and Python platform were used for programming modules for the predictive system. These modules include interface, SMAW, MIG, and TIG modules.  
Figure 1: Systematic procedure for developing the predictive system.
## 2 Materials and Methods
### Data Preparation
#### 2.2.4 Land Cover Datasets  
The GlobeLand30 is a global land cover product produced by the National Geomatics Center of China [16]. The FROM-GLC30 is the first global 30 m spatial resolution land cover product, produced by Tsinghua University in 2013 based on Landsat images [17]. The FROM-GLC10 data were the latest results for the 2017 land cover produced by Tsinghua University, which used Sentinel-2 images and previous training samples based on Landsat data [18]. The Qilian Mountains 30 m land cover classification product data set (1985-2019) V2.0 (LCD-QLM (V2.0)) was also used in this study as auxiliary reference data [28]. The data were downloaded from the National Qinghai-Tibetan Plateau Science Data Center ( [[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)), accessed on 24 September 2021). The basic information about the land cover products is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Products** & **Data Source** & **Time** & **Spatial Resolution** & \begin{tabular}{c} **Classification** \\ **Algorithms** \\ \end{tabular} \\ \hline FROM-GLC30 & Landsat TM/ETM+ & 2010, 2015, 2017 & 30 m & \begin{tabular}{c} SVM, supervised \\ classification \\ \end{tabular} \\ \hline GlobeLand30 & Landsat TM/ETM+, & 2000, 2010 & 30 m & \begin{tabular}{c} Pixel-object- \\ knowledge-based \\ (POK-based) method \\ \end{tabular} \\ \hline FROM-GLC10 & Sentinel-2 & 2017 & 10 m & \
\begin{tabular}{c} RF, supervised \\ classification \\ \end{tabular} \\ \hline Land Cover Dataset at & Landsat 8 & 1985, 1990, 1995, 2000, & & \\ Qilian Mountain Area & TM/ETM+/OLI & 2005, 2010, 2015, 2016, & 30 m &
\begin{tabular}{c} Supervised \\ classification \\ \end{tabular} \\ from 1985 to 2019 (V2.0) & 2017, 2018, 2019 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of existing land cover products.
## 4 Findings Results
### Descriptive Findings
#### 4.1.4 The Different Methodological Approaches and Article Proportion  
Annotations regarding the titles and abstracts of the 119 reviewed publications will guide the reader toward identifying the methodological focus (see Figure 6). Among these articles, modeling as a system of symbols used to obtain reproducible results or data falls under various categories: 44 papers focus on modeling, 43 on case studies, 25 on surveys, and 7 on conceptual discussions. For instance, \"modeling\" accounts for around 37 percent, while \"case study\" encompasses about 36 percent of the entire literature on demand forecasting. This is significant because it suggests that these approaches are crucial in identifying the most reliable demand estimation techniques based on numerous performance metrics, with a substantial body of literature supporting them.  
Figure 5: Literature statistics of research directions.  
The prominence of modeling and case studies indicates that researchers are particularly interested in assessing the realism of models by testing their theories against real-world scenarios, thereby enhancing their reliability. As mentioned in the previous section, as of October 2023, the data utilized in this study from various articles constitutes 21%. Surveys are among these documents, providing a general overview of many modern trends, challenges, and improvements related to AI-driven demand prediction, thus offering informative perspectives on how users understand and apply these technologies.  
Although limited in number, conceptual discussions play an important role in identifying new research ideas, frameworks, and theoretical foundations for future studies. This methodological diversity reaffirms that researchers adopt a holistic approach to tackling the issue of demand forecasting in supply chain management, combining theoretical exposition, empirical assessment, and comprehensive examination to advance this field.
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 3 Proposed Quasi-Uniform Quantizer for SCMA System
### The Standard Uniform Quantizer for SCMA  
By defining the quantization step \(\Delta\), for a real number \(a\), the quantizer can be described as  
\[Q(a,\Delta)=sign(a)\Delta\left\lfloor\frac{|a|}{\Delta}+0.5\right\rfloor, \tag{18}\]  
where \(sign(a)\) determines the sign of a real number \(a\) and \(\lfloor a\rfloor\) outputs an integer not larger than \(a\). It is obvious that the output of \(Q(a,\Delta)\) in (18) obeys the form of \(l\Delta\), where \(l\) is an integer. For a \(q\)-bit standard uniform quantizer, the quantization results can be expressed as  
\[Q(a,\Delta)=\begin{cases}L\Delta,&a\geq(L-0.5)\Delta\\ \par l\Delta,&(l-0.5)\Delta\leq a<(l+0.5)\Delta,0<l<L\\ \par 0,&-\Delta/2<a<\Delta/2\\ \par l\Delta,&(l-0.5)\Delta<a\leq(l+0.5)\Delta,-L<l<0\\ \par-L\Delta,&a\leq-L\Delta+\Delta/2\end{cases}, \tag{19}\]  
where \(L=2^{q-1}-1\) and its output alphabet contains \(2L+1=2^{q}-1\) number of elements, given by \[\{-L\Delta,(-L+1)\Delta,\cdots,-\Delta,0,\Delta,\cdots,(L-1)\Delta,L\Delta\}. \tag{20}\]  
Note that the \(q\) binary bits are composed of \((q-1)\) bits to apply binary representation and one sign bit with a value of \(0\) when \(l\) is positive, otherwise with a value of \(1\).
# Design of an Energy Harvester Based on a Rubber Bearing Floating Slab Track
## 5 Parameter Optimization Design  
### Parameter Analysis  
To investigate the optimal size of the spherical energy harvester, the parameter analysis of its geometry was carried out to obtain the optimal energy harvesting effect. The parametric geometry is shown in Figure 13.  
Here, H represents the thickness of the piezoelectric layer, R represents the radius of the piezoelectric layer, r\({}_{1}\) represents the vertical axis radius of the inner cavity of the spherical shell, and r\({}_{2}\) represents the horizontal axis radius of the inner cavity of the spherical shell. The outer radius of the spherical shell is aligned with the radius of the piezoelectric layer. The axis ratio I is defined as I = r\({}_{1}\)/ r\({}_{2}\), which is used to characterize the ellipticity of the hollow shell; the thickness of the spherical shell is T = R-r\({}_{2}\). The parameters of the initial spherical energy harvester are shown in Table 4.  
To obtain the optimal size of the spherical harvester, the dimensions of the piezoelectric layer and hollow shell were parametrically analyzed, and the parameters mainly included the piezoelectric layer thickness H, the piezoelectric layer radius R, the axis ratio I, and the spherical shell thickness T. The material properties of each working condition were consistent during the analysis, and PZT-5H was used for the piezoelectric layer.
## 4 Case Study and Verification
### Experimental Parameter Configuration
Furthermore, considering to further illustrate the effectiveness of the proposed data preprocessing method in this paper, time-domain, frequency-domain, CWT time-frequency domain data, and the proposed preprocessing data are used to train the constructed neural network, respectively. The results are shown in Figure 12(a), the diagnostic accuracies of the proposed method are above 98% under different noise interferences, and they have lower error. Therefore, compared with other input data, the proposed data preprocessing method shows better effectiveness and robustness under different strong background noise environments. Figure 11: Training process of loss function and accuracy curve. Figure 10: Influence of hyperparameters on model accuracy. To evaluate the anti-noise interference ability of the proposed neural network, we added Gaussian white noise with varying decibels to the collected fault signal. We compared the performance of the CNN [29], LSTM [16], and proposed methods in identifying fault features, as shown in Figure 13b. The results demonstrate that the proposed method outperforms the CNN and LSTM methods under different levels of noise interference during the 10 training sessions. This highlights the superior anti-noise interference capabilities and robustness of the proposed method. Due to the characteristics of the input data, framework, and parameters of the neural network, the LSTM has better identification performance than the CNN for the noise-added input signal.
## 3 Investigation of Flow Characteristics Based on Numerical Analysis Results
### Aerodynamic Interaction of Rotating Blades and Tower under Wind Shear Condition  
The aerodynamic performance (shaft power) history of a WTG on each rotating blade with the various azimuth angles is shown in Figure 7.  
Two different aerodynamic fluctuations with 3P frequency are distinctively observed. The first phenomenon is the wind shear effect determined by Equation (1) in Section 2.2. In the case of 0\({}^{\circ}\) azimuth angle, the aerodynamic performance of the rotor 1 is approximately 18.7\(\sim\)31.9% higher than that of the rotor 2 and 3, while the performance rapidly decreased at the 45\({}^{\circ}\) azimuth angle. The second phenomenon is the blade-tower interaction, which leads to high pressure drop as blades pass close to tower as shown in Figure 7, which showed additional 22.1% drop of shaft power when the blade 2 passed tower very closely (50\({}^{\circ}\) azimuth angle). Figure 8 shows the visualization of the pressure distribution at two different azimuth angles, 33\({}^{\circ}\) and 50\({}^{\circ}\).  
The flow field around the tip area of the rotor blade 2 has a strong pressure difference between the rotor suction surface and the rotor pressure surface, which rapidly dropped the aerodynamic performance by the blade-tower interaction. Figure 9 provides more intuition on the blade-tower interaction based on the axial induction factor distribution.  
Based on the one-dimensional momentum theory and Betz limit [31], axial induction factor (_a_) and related aerodynamic performance coefficient (\(C_{p}\)) are expressed in Equations (3) and (4).  
\[a=(V_{a-inlet}-V_{a-outlet})/V_{a-inlet} \tag{3}\]  
\[C_{p}=4a(1-a)^{2} \tag{4}\]  
Figure 8: Pressure distribution due to blade-tower interaction.  
Figure 7: Simultaneous pressure distribution around tower.  
where \(V_{a-inlet}\) is an inlet free-stream velocity and \(V_{a-outlet}\) is a rotor outlet free-stream velocity.  
When the axial induction factor becomes \(1/3\), the aerodynamic performance coefficient has maximum value with 0.593. As shown in Figure 9, the axial induction factor of the rotor blade passage between the rotor 2 and the rotor 3 around the tower is remarkably high, compared with other rotor blade passages. Based on Equation (3), the above phenomenon influences the aerodynamic characteristics of the wind turbine rotor blade.