esa-sceva/satcom-chunk-collection huggingface dataset:
# FEM Analysis of Superplastic-Forming Process to Manufacture a Hemispherical Shell
## 4 Results and Discussion
### Experimental Results  
To make the thickness distribution of a hemispherical shell more uniform and to demonstrate the reliability of the method proposed in the second paragraph, the experimental results obtained using both a constant-thickness blank and a conical section profile blank were compared.  
At the end of the forming, the thicknesses of the shell were measured and reported in the graph of Figure 10 in the form of black (Figure 10a) and white (Figure 10b) dots. The black dots represent the results obtained using a constant-thickness blank. The white dots are associated with a conical section blank. The thickness distribution shows a different trend in the two cases:  
* An increasing trend from the pole to the edge of the shell in the case of a constant-thickness blank;
* A flatter trend from the pole to the edge in the case of a conical section blank.  
Figure 9: Thickness trend of the hemispherical shell obtained using a constant-thickness blank and a blank characterised by a conical section.  
The comparison between the results highlights the success of the conducted numerical activity, as the differences found in terms of thickness were approximately 10%.  
Figure 11 shows the comparison between the experimental results obtained from a blank with a constant thickness and a blank with a conical section profile. It underlines how the thickness trend of the hemispherical shell is indeed more uniform if a conical section profile blank is used instead of a constant-thickness blank. In particular, the thickness uniformity, represented by the \(A\%\) parameter, decreases from \(\approx\)29% when using a constant-thickness blank, to \(\approx\)20% with a conical section profile blank. This means that a conical section involves a more uniform distribution of thickness in the hemispherical shell.
# Analytical Model for Information Flow Management in Intelligent Transport Systems
###### Abstract  
The performance of this study involves the use of the zoning method based on the principle of the hierarchical relationship between probabilities. This paper proposes an analytical model allowing for the design of information and analysis platforms in intelligent transport systems. The proposed model uses a synthesis of methods for managing complex systems' structural dynamics and solves the problem of achieving the optimal balance between the information situations existing for the object and the subject under analysis. A series of principles are formulated that govern the mathematical modeling of information and analysis platforms. Specifically, these include the use of an object-oriented approach to forming the information space of possible decisions and the division into levels and subsystems based on the principles of technology homogeneity and information state heterogeneity. Using the proposed approach, an information and analysis platform is developed for sustainable transportation system management, that allows for the objective, multivariate forecasting-based record of changes in the system's variables over time for a particular process, and where decision-making simulation models can be adjusted in relation to a particular process based on an information situation existing for a particular process within a complex transport system. The study demonstrates a mathematical model that solves the optimal balance problem in organizationally and technically complex management systems and is based on vector optimization techniques for the most optimal decision-making management. The analysis involves classical mathematical functions with an unlimited number of variables including traffic volume, cargo turnover, safety status, environmental performance, and related variables associated with the movement of objects within a transport network. The study has produced a routing protocol prescribing the optimal vehicle trajectories within an organizationally and technically complex system exposed to a substantial number of external factors of uncertain nature.  
information commutation technologies; intelligent transport systems; Pareto optimal solution; vector optimization techniques; multicriterion problem; mathematical decision-making models MSC: 05-08; 15-11; 90-05; 90-08; 91-08; 91-10
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 2 Description and Diagnosis: Engineering Knowledge for Smart Data Collection
### Frequency Response Functions  
The frequency response functions (FRF) of the machine were obtained by means of impact tests. The objective was to identify the vibration modes responsible for the chatter vibrations during machining. The FRFs shown in Figure 4 were obtained by shaking the structure in both turrets close to the tip (see Figure 2) using an impact hammer (PCB086D20/C41) and measuring the response at the same point using a high sensitivity triaxial accelerometer (PCB356A17). The averaged FRFs were obtained by repeating the process four times in the machining position with the wheelset clamped.  
The dynamic properties of the main modes are summarized in Table 2:  
Considering the high vibration amplitude at around 60 Hz that was recorded during the chatter tests, the mode at 55 Hz in X direction was determined to be causing the instability. The presence of this mode at 55 Hz definitely confirmed the presence of chatter. Although the dynamic stiffness (94-117 N/\(\upmu\)m) and the damping value (5.8%) of this mode were high, the demanding operation over a heavily worn out wheel jeopardized the cutting stability. In addition, the high damping of the critical mode at 55 Hz ruled out the inclusion of the active damping technique in the chatter suppression smart system [10; 23; 24].  
Three important conclusions were obtained from the tests carried out in Section 2 to inform the design of a hybrid edge-cloud-based smart system for train wheel repair. First, the cutting tests and the FRF measurement showed that regenerative chatter related to a structural mode around 55 Hz was the responsible for the marks that are endangering the productivity of the portal lathe. Therefore, the smart system focused on the detection, control, and suppression of chatter. Secondly, the industrial accelerometer located close to the tool tip in the left turret was selected as the main sensor to determine the status of the process, and the critical vibration levels in this position for the sensors were defined. Finally, the self-excited mode had considerable damping and the ratio between the chatter frequency and the cutting frequency \(k\) was very high (\(k>200\)). These two characteristics were considered to select the most proper techniques to suppress chatter.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Left Turret**} & \multicolumn{3}{c}{**Right Turret**} \\ \hline  & **Frequency** & **Modal Stiffness** & **Damping** & **Frequency** & **Modal Stiffness** & **Damping** \\  & **(Hz)** & **(N/\(\upmu\)m)** & **(\%)** & **(Hz)** & **(N/\(\upmu\)m)** & **(\%)** \\ \hline X & 55 & 94.5 & 5.8 & 55 & 116.8 & 5.8 \\ Y & 154 & 31.3 & 5 & 151 & 33.7 & 5.5 \\ Z & 149 & 62.2 & 5.4 & 39.3 & 33.2 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic properties of the main modes.  
Figure 4: FRF on the (**a**) left turret and (**b**) right turret.
# Study on Airflow Field Distribution and Dust Distribution Characteristics at Blast Piles
## 4 Conclusions  
This paper, utilizing a combination of testing trials and numerical simulation methods, presents an in-depth study of the flow field around blast piles and examines how natural airflow and shoveling operations influence dust migration characteristics. The primary conclusions are as follows:  
(1) Particle size testing: Dust particle size testing at the open-pit coal mine revealed that medium and large particle sizes constitute about 82% of the total and the particles have a relatively high sphericity which indicates a near-spherical shape.  
(2) Effect of wind direction on flow field: The influence of different wind directions on the flow field at blast piles is mainly reflected in the changes in vortices. When wind enters from the dump side, vortices form around the leeward slope of the blast pile platform and the bottom of the pit, creating a complex airflow with multiple vortex fields. As wind speed increases, the vortices at the bottom of the pit shift from the upper right to the lower left. Conversely, when wind enters from the stope side, the airflow traces smoothly throughout the whole mine pit with vortex fields only existing at the bottom of the pit and remaining confined in this area.  
(3) The priority of airflow over shovel's impact on dust migration: For dust at a single blast pile, higher inlet wind speeds mean lower dust concentration and facilitate to expulsion of dust, particularly benefiting the movement of larger particles. Compared to the dust at stationary blast piles, the dust from shoveling operations which obtain its initial velocity, exhibits greater concentration and a wider range of impact. However, the initial velocity from shoveling is insufficient to alter the dust's trajectory, as the movement is still predominantly governed by airflow with the initial velocity merely playing a supportive role.  
The above research can be a good guide to the treatment of blast pile dust in open-pit mines, such as the use of dust suppression agent to consolidate dust particles in advance or spray dust removal during shoveling operation.  
Conceptualization, J.Z.; methodology, R.L.; software, H.W.; formal analysis, Y.H.; resources, J.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (5204088) and Chongqing Science and Technology Bureau (CSTB2022 NSQCQ-MSX1595).  
Not applicable.  
Figure 17: Distribution of shoveled dust particles at 600 s under different wind speeds.
## 3 Results  
### Optimization for Operation Condition of Reactors  
This section outlines the method to determine the operating temperature of reactors that yields the highest conversion to CH\({}_{4}\) and consequently generates the highest CH\({}_{4}\) composition. The conversion and yield of CH\({}_{4}\) depend upon the operating temperature and the catalysts utilized. Moreover, catalyst deactivation, carbon, and coke formation are related to the high temperature operation of the reactors. The preferred operating temperature must not only ensure catalyst productivity but also hinder catalyst thermal deactivation. Ni-based ZrO\({}_{2}\) catalysts can perform higher CH\({}_{4}\) selectivity within the temperature range of 200-300 \({}^{\circ}\)C [12].  
The performance of methanation at temperatures ranging from 240 to 300 \({}^{\circ}\)C was analyzed in this study. The temperature of the gases generated from the catalytic reaction were measured. Figure 4a indicates the quantity of CO\({}_{2}\) converted to CH\({}_{4}\) from both reactors. The CO\({}_{2}\) conversion to CH\({}_{4}\) was calculated as shown in Equation (1):  
\[\text{CO}_{2}\text{ conversion}=\frac{V_{\text{CH}_{4}}\text{output}\times 100 \%}{V_{\text{CH}_{4}}\text{output}+V_{\text{CO}_{2}}\text{output}}, \tag{1}\]  
where \(V_{\text{CH}4}\) output and \(V_{\text{CO}_{2}}\) output are the volumes of CH\({}_{4}\) and CO\({}_{2}\) analyzed by QMS. The maximum conversion of CO\({}_{2}\) to CH\({}_{4}\) was 97.6%, which was achieved at a temperature of 260 \({}^{\circ}\)C. CO formation and catalyst deactivation will increasingly occur at temperatures higher than 320 \({}^{\circ}\)C [23; 24]. Therefore, a temperature of 260 \({}^{\circ}\)C was utilized in our system. The reaction towards CH\({}_{4}\) can be controlled by varying the stoichiometric ratio of the reaction. Increased CO\({}_{2}\) conversion and CH\({}_{4}\) yields occur  
Figure 3: View of the two adiabatic fixed-bed reactors used for the methanation system.  
at the stoichiometric ratio of 1:4 depending upon the types of the catalyst used [13]. Additionally, carbon deposition and carbon monoxide formation also depend upon the stoichiometric ratio of the reaction. The formation of carbon was observed when a molar ratio of 1:4 for CO2/H2 was not maintained [15]. This carbon generation may affect the catalyst performance and deactivate the catalyst. Consequently, it was necessary to determine the stoichiometric ratio that yielded the highest CO2 conversion without negatively impacting the system. The CO2 conversion to CH4 was analyzed by changing the flow rate of CO2 via MFC. As shown in Figure 4b, 1:4 was the best stoichiometric ratio for our system, which yielded a CO2 conversion efficiency of 97.6%. The reactor temperatures were 260 \({}^{\circ}\)C for each reactor. When the stoichiometric ratio was changed to 0.9:4, reactor performance, as measured by CH4 concentration, was 91%; when it changed to 1.1:4, 89% of CO2 conversion was yielded, i.e., an efficiency lower than that of 1:4.
## 4 Discussion  
Our findings highlight the substantial potential of spectral indices derived from Sentinel-2 imagery for assessing forest characteristics. The analysis of remote sensing ecological variables in the Arid Chaco region, encompassing both plains and foothills landscapes, effectively captures differences in forest conservation status associated with distinct management regimes. These differences, identified through field data (e.g., \(SAI\)) and corroborated by existing literature, further support the effectiveness of integrating remote sensing and field-based approaches for ecosystem assessment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Landscape**} & \multirow{2}{*}{**RS ind**} & \multirow{2}{*}{**R\({}^{2}\) adj**} & \multirow{2}{*}{**Sigma**} & \multirow{2}{*}{**Statistic**} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{**logLik**} & \multirow{2}{*}{**AIC**} & \multirow{2}{*}{**AIC Null**} & \multirow{2}{*}{**Nobs**} & \multicolumn{2}{c}{\(p\)-Value**} \\  & & & & & & & & & & & **Shapiro-** \\ \hline \multirow{3}{*}{**Plains**} & BI\({}_{2}\) & 0.71 & 349.35 & 36.99 & \(1.80\times 10^{-8}\) & \(-\)216.6 & 441.341 & 483 & 30 & 0.97 \\  & MCARI\({}_{\text{Sent}}\) & 0.15 & 17.75 & 6.25 & 0.018 & \(-\)127.8 & 261.679 & 265 & 30 & 0.16 \\  & NDWI\({}_{\text{Gao}}\) & 0.71 & 0.047 & 73.58 & \(2.50\times 10^{-9}\) & 50.04 & \(-\)94.083 & \(-\)57 & 30 & 0.38 \\ \hline \multirow{3}{*}{**Foothills**} & BI\({}_{2}\) & 0.54 & 87.4 & 22.25 & 0.0002 & \(-\)110.8 & 227.68 & 241 & 19 & 0.43 \\  & MCARI\({}_{\text{Sent}}\) & 0.39 & 6.66 & 6.87 & 0.01 & \(-\)61.35 & 130.7 & 138 & 19 & 0.36 \\ \cline{1-1}  & NDWI\({}_{\text{Gao}}\) & 0.52 & 0.03 & 20.15 & 0.00032 & 38.52 & \(-\)71.04 & \(-\)58 & 19 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Linear regressions between the Structural Alteration Index (\(SAI\)—predictor) and the spectral indices (BI\({}_{2}\), MCARI\({}_{\text{Sent}}\), and NDWI\({}_{\text{Gao}}\)—response variables) for plain and foothill landscapes. The columns include landscape type; spectral index used as the response variable; adjusted Adj R\({}^{2}\) (model fit); sigma (residual error); F-statistic and its _p_-value (model significance); log likelihood and Akaike Information Criterion (AIC) for model evaluation; AIC of the null model (baseline comparison); number of observations (nobs); and _p_-value from the Shapiro-Wilk test assessing residual normality.
## 4 Conclusions  
This study presents a CP imaging method for enhancing the vision in rainy-conditions by combining the multi-scale analysis in the frequency domain. This method can enhance the contrast of rainy-day images and recover image color. It is based upon the interaction between light and water spray including absorbance and scattering. The key step is using the CP memory effect in the low-frequency parts to improve the quality of images in rainy conditions and compensate for the specific structure information by the high-frequency parts. By this method, much clearer images with better image colors can be reconstructed. This processing method provides a new way to remove the influences of water spray on images and enhance visibility on rainy days. For future work, we will extend this method to video monitoring such as a driving safety assistance device.  
Conceptualization, F.L. and X.L.; methodology, F.L. and X.L.; validation, F.L. and P.H.; formal analysis, F.L. and X.S.; resources, F.L. and X.S.; data curation, X.L. and P.H.; writing--original draft preparation, F.L.; writing--review and editing, P.H. and X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China, under grant 62075175, 62005203.  
Not applicable.  
The authors would like to acknowledge [PERSON] from Xidian University for helping to acquire experimental data in this paper. Thanks to [PERSON] from Xidian University, [PERSON] from Stanford University and [PERSON] from Georgia Tech. for checking the English grammar of this paper.  
The authors declare no conflict of interest.  
Figure 8: The image of Scene Three. (**a**) The original rainy-day image. (**b**) The reconstructed image. (**c,d**) The intensity statistic of each pixel of the original rainy-day image and reconstructed image.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Data Extraction and Categorization
#### 2.4.5 Methods Employed for Data Processing  
Generally, the approaches used in data processing across the studies follow similar strategies. The process typically starts with sensor alignment and data fusion. Subsequently, filtering techniques and smoothing methods are applied, along with dimensionality reduction and other signal processing techniques, facilitating the selection and extraction of pertinent features. This culminates in the final step of pattern classification. Despite this shared framework, the specific methods employed do vary among the studies.  
In the domain of aligning sensors and data fusion, several approaches have been adopted. Some studies utilized the Kalman filter [62; 66] and MARG (Magnetic, Angular Rate, and Gravity) systems [59; 66], while others employed [PERSON]'s algorithm [59; 60; 66]. Additionally, the least squares method was used from six stationary positions [50; 52], and there was also the rotation of sensors' reference systems [54].  
For filtering and smoothing methods, a range of techniques have been employed, including Type-II Chebyshev low-pass filter [67], Butterworth high-pass or low-pass filter [48; 49; 52; 56; 60; 61; 62; 69], Infinite Impulse Response Butterworth high-pass or low-pass filter [51; 53; 57; 59], Finite Impulse Response filter [50; 51; 54], Savitzky-Golay smoothing filter [62], and moving average [48; 59; 60].  
Dimensionality reduction and other signal processing techniques include Power Spectral Density [66], Principal Component Analysis [48; 65; 66; 69], and covariance matrix analysis [48]. Additionally, Symbolic Aggregate Approximation [52], Synchrosqueezing Transform [63], Hilbert Transformation, and Fourier Transformation [67] have been employed, along with methods such as Fixed-size Nonoverlapping Sliding Window and Fixed-size Overlapping Sliding Window [52]. Independent Component Analysis has been utilized as well [58], and phase shift determination has been applied using techniques like peak and troughs detection algorithms [51; 57; 59; 60; 61; 62; 63; 65; 66; 67] and Lissajous figures or the Konno-Mead loop method [50; 52; 53].  
Several studies have outlined their methodologies for pattern classification. These encompass a diverse range of techniques, including Modified Long Short-Term Memory Recurrent Neural Network [63], K-means clustering [48], Correlation-based Feature Selection, Decision Tree, Decision Tree Bagging, Linear Discriminant Analysis, k-Nearest Neighbors, Support Vector Machines, Multilayer Artificial Neural Networks [52], One-dimensional Convolutional Neural Network [55; 56; 64], Markov chain Monte Carlo [60], and Wavelet Cross-Spectrum analysis [62]. Furthermore, certain researchers have developed proprietary models [57], while others have employed alternative graphical methods, such as the Bland-Altman plot [61], to assess the level of agreement or disagreement between sensors and reference data.
## 3 Result and Discussion
### EK Remediation Experiments
#### 3.2.3 PH and Residual Concentration in the Post-Experiment Sample  
Figure 6 shows the concentration and pH of the residual copper and phenanthrene measured in the post-experiment sample. As described in Figure 6a, the concentration of copper was detected to be slightly high at the point of x/L = 0.7, which was relatively lower than the initial concentration of the contaminant.  
At this point, the concentration of the residual copper was detected as the lowest with the acetic acid improver, implying that the degree of purification in the contaminated ground was greater than that of other cases. In addition, the pH at x/L = 0 - 0.7 was determined to be 2-4, lower than pH 5 where the precipitation of copper occurs, indicating that no precipitation occurred based on the results of a previous study [27]. However, as described in Figure 6b, the characteristics of the removal of phenanthrene were not clearly defined due to the low recovery of the pretreated contaminant sample. Therefore, further study of the pretreatment of phenanthrene might need to be conducted.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 4 Discussion
### Parameter Settings  
To ensure the effectiveness of the algorithm comparison, we discuss the parameter settings of the four comparison methods of LSD, EDLines, Linelet, and AG3 line. Finally, we use the source code and default parameters provided by the original author of the algorithm. We list some important parameters of the four methods. LSD sets the minimum gradient magnitude to \(5.2\); EDLines uses a \(5\times 5\) Gaussian kernel filter with \(\sigma=1\) to filter the image; Linelet sets the linelet length difference threshold \(\tau_{len}\) to \(3\); the jump distance of anchor points in AG3 lines is set to \(10\), and the gradient orientation threshold is \(\pi/8\). Except for EDLines, the implementations of the other three methods are available on the websites provided by the relevant authors. Since the original EDLines code is no longer available on the website provided by the author, we used the optimized code of the original authors to improve the performance of EDLines and enhance the comparability of EDLines. Their implementations are available on the authors' websites [46; 47; 48; 49]. Our algorithm determines the internal parameters during the experiment. The parameters of ST_LSD are as follows:  
1. Suppressing image noise: in Section 2.2, the image is filtered by using an adaptive bilateral filter with a \(3\times 3\) filter window.
2. Gradient orientation tolerance: in Section 2.3, following the LSD [21] method, two pixels' gradient orientations are aligned when the difference between their angles is smaller than \(\pi/8\).
3. Angle difference threshold: in Section 2.4, following the LSM [29] method, the coefficients in the angle difference threshold are set as \(\alpha=-2\), \(\beta=-1.5\), and \(\gamma=22.5\lx@math@degree\).
4. Endpoint distance threshold: in Section 2.4, \(\varepsilon=0.09\), and the algorithm has the best effect in the experiment.