esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Case Information
Principal component analysis (PCA) by the maximum orthogonal rotation was used to extract seven factors whose eigenvalues were greater than 1. The explained cumulative variance explained rate was 68.564%, and the common factor explained most of the variance of the observed variables. 2) Confirmatory factor analysis  
Confirmatory factor analysis (CFA) was used to test whether the number of factors and the factor load of the observed variables was consistent with the theoretical expectations. In the 218 samples of the CFA confirmatory factor analysis, the job distribution of the respondents was 40 in mechanical inspection, 36 in electrical inspections, 66 in control room operation, 30 in technical management, 21 in process design, and 6 in equipment maintenance. The city distribution was Hebei, Liaoning, Sichuan, Beijing, Guangdong, Guangxi, Hubei, Jiangsu, Shandong, Zhejiang and etc. The age distribution was 60 people were aged 28 to 40 years, 51 people were aged 41 to 50 years, 90 people were aged 51 and over, and 17 people did not answer. The distribution of educational background was 69 were college students and below, 80 were undergraduates, 41 were job graduates, 20 were doctoral students, and 8 respondents did not answer. To verify the validity of the preliminary index factor structure, the remaining 218 questionnaires were used for confirmatory factor analysis. The results of confirmatory factor analysis (Table 5) and path coefficients (Table 6) were obtained by using structural equation AMOS 22.0 software. 3) Competency measurement model for major equipment safety operators  
By using the same exploratory factor analysis and confirmatory factor analysis, the operator's competency model is obtained as shown in Figure 4.  
4) Computation of the comprehensive evaluation value of personnel competence and post complexity  
A total of 49 operators were selected. According to the established operator's competency measurement model, the comprehensive evaluation value of competency was calculated by using the structural identification model given in Section 3.2 (1). \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Fitting Index** & \(\chi^{2}\) & \(\mathbf{df}\) & \(\mathbf{p}\) & \(\mathbf{\chi^{2}/df}\) & **NNFI** & **CFI** & **RMSEA** \\ \hline Output results & 2426.580 & 1020 & 0.067 & 2.379 & 0.916 & 0.907 & 0.071 \\ \hline \end{tabular}
\end{table} 
Table 5: Confirmatory factor analysis model fitting index output. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Measurement** & \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**COEFFICIENT**} & \multirow{2}{*}{**C.R. **} & \multirow{2}{*}{**P**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{2}{c}{**Measurement**} & \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Coefficient**} & \multirow{2}{*}{**C.R.
## 2 Materials and Methods
### Qualitative and Quantitative Analysis  
Chromotographic peak deconvolution and analysis were performed using the AMDIS algorithm integrated into TraceFinder 5.1 software. Compound identification was achieved through spectral matching against the NIST reference database, with additional validation via retention index (RI) comparisons. The RI values were determined through linear interpolation of n-alkane standards (C-T\({}_{40}\)) using the formula: RI = 100n + 100 \(\times\) (t\({}_{\rm a}\) - t\({}_{\rm n}\))/(t\({}_{\rm n+1}\) - t\({}_{\rm n}\)), where n represents the carbon count of the alkane homolog, t\({}_{\rm a}\) corresponds to analyte retention time, while t\({}_{\rm n}\) and t\({}_{\rm n+1}\) denote retention times of consecutive n-alkanes. Quantitative analysis was conducted using an internal standard semi-quantitative method with the equation: C\({}_{\rm i}\) = (C\({}_{\rm is}\)\(\times\) A\({}_{\rm i}\))/A\({}_{\rm is}\), where C\({}_{\rm i}\) indicates internal standard concentration, and A\({}_{\rm i}\)/A\({}_{\rm is}\) represents the analyte-to-standard peak area ratio [33].  
The relative odor activity value (ROAV) was used to identify key aroma compounds contributing to the overall flavor profile of the samples. ROAV is calculated as the ratio of the compound's concentration to its sensory detection threshold, reflecting its potential contribution to the aroma. Compounds with ROAV \(\geq\)1 were considered key volatile compounds. The calculation formula is as follows: ROAV\({}_{\rm i}\) = C\({}_{\rm i}\)/T\({}_{\rm i}\). Among them, ROAV\({}_{\rm i}\) is the relative odor activity value of compound i, and C\({}_{\rm i}\) is the relative content of the compound; T\({}_{\rm i}\) is the threshold of the compound [31].
## 5 Experimental Results
Figure 13: VDC regulation by the backscattered data. Figure 15: (**a**) Configuration data transmission and the power boosting (**b**) Configuration data transmission and acknowledgement (**c**) the power boosting for stimulation. Prior to current stimulation, power boosting is activated such that the transmission power is increased to deliver adequate power for the stimulation. As shown in Figure 15c, before the stimulation time, the Power Require bit P\({}_{\text{R}}\) in the backscattered data is set to \"0\" to command the external control unit to increase the transmission power immediately. The design parameters and measured data are summarized in Table 1. The primary coil has good quality factor (98), but that of the secondary coil is not as high (41). The inductance of the detection coil is around 300 nH, and the coupling factor with the primary coil is around 0.3 that achieves good sensitivity for backscattered data detection and does not have excessive interference on the primary coil. The 3-V LDO and the 1.2-V LDO have good power supply rejection ratio (PSRR) at both \(<\)1 kHz and 80 MHz such that the two ripple components are rejected. The total quiescent current of the secondary chip is around 40 uA. The maximum power transfer efficiency from the PA input to the rectifier output is around 8% when the distance of the two coils is 1 cm and rectifier loading current is 300 uA.
# 2-D Cross-Plot Model Analysis Using Integrated Geophysical Methods for Landslides Assessment
## 4 Discussion
### 2-D Cross-Plot Model of Resistivity-Velocity
The model reveals, at a depth of \(<\)5 m, the layer dominated with values from Q1 region, which classified as low velocity and resistivity values, indicating poorly compacted soil or the existence of higher porosity material, such as sand. The Q4 region looks dominant for this profile, which indicates the weathered zone; the region of Q2 at a depth of \(<\)5 m (solid rectangle box) shows low velocity but high resistivity, and may indicate the presence of a boulder at shallow subsurface, or represent as moderate weathered material for this profile. The Q3  
Figure 12: 2-D cross-plot model of integrated 2-D resistivity and seismic refraction: **(a)** HR1 and HS1; (b) HR2 and HS2; (c) HR4 and HS3. region, with high resistivity and velocity, may represent the hard material at a depth of >15 m. The suspected failure plane was marked with a solid black line. From the 2-D cross-plot model, the information of the highly weathered zone was retrieved and used to estimate the volume of mass sliding. The estimated volume of the mass sliding was calculated using Equation (2), where the calculation of the volume of total area is subtracted from the volume under the failure plane. The Kriging interpolation methods were used in generating the model. From the previous figure of the study area (Figure 1), the boundary of the landslide area was digitized and plotted, as shown in Figure 13a. In order to identify the volume, m\({}^{3}\) of mass sliding the Universal Transverse Mercator (UTM) coordinate in meter, m unit was used with the elevation map (m unit) of the study area. It is very important to ensure that the unit used for all parameters is the same to avoid wrong interpretations. From the elevation map, the elevation map of the landslide area was digitized and plotted, as shown in Figure 13b. The elevation map under the failure plane was plotted (Figure 13c) after being digitized from geophysical results. This map represents the surface under the mass sliding. From this data, the volume was computed using the volume function in Surfer tools. The volume in the landslide area was extracted as the volume of the surface (Figure 14a) and the volume under the failure plane (Figure 14b). The volume difference between the two figures of 15a and b represent the volume of mass sliding. Table 2 shows a summary of the calculation. Figure 13: The elevation map of Hulu Yam study areas: (**a**) surface elevation map of the study area; (**b**) extracted surface elevation of landslide area; (**c**) extracted lower surface of failure plane at landslide area. The parameters above were generated by the Surfer volume function as three methods of volume calculation were presented. The final volume is represented by positive volume, which is 167,904.81 m\({}^{3}\), and indicates the volume of mass sliding. The negative and net volume is neglected since the input data of upper and lower surfaces (Figure 14c) did not overlap each other.
## 2 Acoustic Emission Method and Wavelet Transform
By entering a = 1/2j and b = k/2j (k, j \(\in\) C) into Formula (5) the wavelet family is created:  
\[\psi_{\mathrm{j,k}}(\mathrm{t})=2^{(\mathrm{j}/2)}\cdot\psi\left(2^{\mathrm{ j}}\cdot\mathrm{t-k}\right), \tag{6}\]  
where j and k are scale and translation parameters, respectively. The multiresolution analysis of the discrete signal f(t) can be written according to the Mallat pyramid algorithm [11]:  
\[\mathrm{f_{j}}=\mathrm{S_{j}}+\mathrm{D_{j}}+\ldots+\mathrm{D_{n}}+\ldots+ \mathrm{D_{1}},\;\mathrm{n}=\mathrm{J-j}. \tag{7}\]  
Each component in the signal representation (7) is associated with a specific frequency range and provides information at different levels of decomposition j = 1,..., J. The level of multiresolution analysis (MRA) is determined by the discrete parameter J, S\({}_{\mathrm{j}}\) is the approximation of the signal, D\({}_{\mathrm{n}}\) and S\({}_{\mathrm{n}}\) are, respectively, details and rough parts at different decomposition levels, and D\({}_{1}\) corresponds to the most detailed representation of the signal. To satisfy the dyadic requirements of DWT, the function f\({}_{\mathrm{j}}\) must be approximated by N = 2 J discrete values. The multiresolution analysis, while using the Mallat pyramid algorithm, is presented schematically in Figure 1.  
In the considerations regarding damage detection in steel structures, the Daubechies wavelet was applied. This is a family of orthogonal wavelets with compact support, asymmetrical with sharp edges. It requires a relatively small number of wavelet coefficients, and its order is between 2 and 20, always even numbers. The selection of this wavelet results from the authors' previous experience in detecting damages in various types of structures [4; 5; 7]. The basic Daubechies wavelet (mother) and scaling wavelet (father) are shown in Figure 2.
## 3 Results and Discussions
### MMT Parameters for Tendon Tissues in Different States
We can see from Figure 5 and Table 2 that, among the four states, the tendon tissues in the TDO state have the smallest mean \(t_{1}\) values, while the ones in the MDO states have the largest mean \(t_{1}\) values. The reason for this is that stretching the tendon tissue along the direction of the fibers leads to them being arranged more  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\alpha_{1}\)} & \multicolumn{3}{c}{**Origin**} & \multicolumn{3}{c}{**TDO**} & \multicolumn{3}{c}{**MDO**} & \multicolumn{3}{c}{**ODO**} \\ \cline{2-13}  & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) \\ \hline \(I_{1}\) & \(0.04\) & \(0.00\) & \(0.03\) & \(0.03\) & \(0.03\) & \(-0.07\thus strengthening the anisotropy. Meanwhile, stretching the tendon tissue perpendicular to the direction of the fibers results in them spreading apart and weakening the anisotropy. Moreover, it can be noticed from Table 2 that the second-order moment \(I_{2}\) values of all the samples are very close to 0, which results from the very small \(I_{1}\) values. The third and fourth-order moments \(I_{3}\) and \(I_{4}\) of the parameter \(t_{1}\) are largest in TDO, and the difference in the remaining three states is not obvious because the values of anisotropy in the TDO state are generally the smallest. In summary, the analysis in this section confirms that for the MMT parameter \(\alpha_{1}\), its central moments can be used to distinguish among different stretching states of tendon tissues. For the MMT parameter \(t_{1}\), its first-order moment can be used to distinguish between TDO and MDO states, and its third and fourth-order moments can be used to distinguish between TDO and other states.
# Practical Guide to Ultrasound Beam Forming: Beam Pattern and Image Reconstruction Analysis
## 7 Other Beam-Forming Strategies  
Several emerging beam forming strategies, besides those dealt with in this paper, have been reported and discussed in the literature, including approaches based on machine learning [52]. Particularly interesting concepts are those explored with null subtraction imaging (NSI), and coherence beam forming. With NSI, particular sets of apodization functions are used to achieve a lateral  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Beam Forming Strategy** & **Spatial Resolution** & **Data Acquisition Rate** & **Array Aperture Size** & **Penetration Depths** \\ \hline MLA–MLT & - & + & 0 & 0 \\ Plane and Diverging Wave & - & ++ & - & - \\ Synthetic Aperture & + & - & - & - \\ \hline \end{tabular}
\end{table}
Table 1: This table summarizes the peculiarities of each modality. A plus or minus sign means that the performance with respect to that specific imaging feature (one for each column) is improved or reduced, respectively. The 0 sign means that there is no significant variation. The evaluation is performed with respect to standard beam-forming performance.  
Figure 4: This figure illustrates the different driving schemes for MLA, multi-line transmission (MLT), plane wave, diverging wave, and synthetic aperture beam forming. The transmit beams are represented in orange, and different shades of orange are used to highlight multiple beams for MLT, and separate the beam profiles for plane and diverging wave, respectively. For MLA, the receive beams are also shown in shades of blue. The duration of the transmit phase is also emphasized for MLT.  
(and potentially elevation) spatial resolution that goes beyond the diffraction limit. This technique requires the application of signal-processing techniques only in the receive phase. In essence, the idea is to combine the images formed using zero mean and non-zero mean apodization functions. A zero-mean apodization generates a beam with a \"hole\" along the beam axis (see Huygens' principle). Consequently, this beam can be subtracted from that generated by a non-zero mean apodization function, thus obtaining an extremely sharp beam. However, the gain in spatial resolution is costly in terms of contrast [53; 54; 55].  
When using coherence beam forming, each imaging pixel is obtained from the integration of the normalized covariance matrix that is calculated between the signals received by all of the elements forming the array [56]. This follows after appropriate time-delay compensations are applied. This technique is clearly more computationally expensive compared to standard beam forming, and generally results in a smaller dynamic range. However, it is particularly suitable for applications in low SNR imaging conditions. Contrast is generally improved, and noise is significantly reduced [56; 57; 58].
# Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives
## 3 Few- to Multi-Layer Graphene-Based Stacked Laminates
### Challenges  
Currently, commercial polymeric membranes for CO\({}_{2}\)/CH\({}_{4}\) separation comes in two configurations: (1) flat-sheet in spiral-wound membrane modules, and (2) hollow fiber in hollow fiber membrane modules. Spiral wound modules based on cellulose acetate flat-sheet membranes from UOP Separex(tm) have been around for more than 25 years, while other companies, such as Cameron and Ube Industries in Japan, produce cellulose triacetate and polyimide hollow fiber membranes, respectively, for CO\({}_{2}\)/CH\({}_{4}\) separation [45]. Hollow fibers membranes are widely used for industrial gas separation membranes [15], especially for high-pressure separation, such as natural gas sweetening, owing to their high packing density, large membrane surface area per module and relatively lower module production costs [46]. On the other hand, spiral-wound flat-sheet membranes are more commonly used in low-pressure or vacuum-driven gas separation [46]. However, to date, GO-based stacked laminates are only often demonstrated as flat-sheet membranes with reason plausibly attributed to the ease of membrane fabrications and performance evaluations. Reports on GO-based stacked laminates are, in comparison, more limited. Hence, there exists a gap between current academic pursue and industrial needs, making technology transfer and commercialization less straightforward. Owing to the large membrane surface area particularly for hollow fiber membranes, a larger quantity of graphene-based materials is also needed to produce GO- or graphene-based stacked laminates at a mass production level. This means that the cost of producing GO-based stacked laminates as gas separation membranes will become less competitive. While we foresee that the production cost is much lower than nanoporous single-layer CVD graphene membranes, it is likely to stay higher as compared to the mixed-matrix membrane design and definitely more costly than current polymeric membranes [11]. The question, therefore, lies in whether the higher CO\({}_{2}\)/CH\({}_{4}\) separation performances are worth the additional capital investment on these graphene-based laminates.
## 4 Discussion
### Portable Analyzer Performance  
The MHZ19B sensor has a response time of less than 60 s, so the analyzer was programmed with a response time of one minute to perform a better analysis. On the other hand, open-path analyzers such as LI--7500A and LI--7550 have selectable response times of 0.1, 0.05, and 0.0025 s [35]. In addition, a regression analysis was performed between NEE fluxes calculated from data collected with the portable analyzer and NEE fluxes calculated using EC by [PERSON] et al. [11], [PERSON] et al. [12], and [PERSON] et al. [13]. The determination coefficients have values of 0.661, 0.7873, and 0.5943, respectively (Figure 12). These values can be improved if an additional calibration method is taken into consideration and by improving the Tc and RHc conditions. Furthermore, the sensitivity of the sensors is also an important factor to consider since the MHZ19B has a sensitivity of \(\pm\)50 ppm compared with LI--7500A, with \(\pm\)0.11 ppm [35].
# Investigation of Cavitation Noise in Cavitating Flows around an NACA0015 Hydrofoil
## 3 Computational Domain and Boundary Conditions  
The NACA0015 hydrofoil, with chord length \(C=0.07\) m, was selected and its angle of attack was \(\alpha=6^{\circ}\). Figure 1 shows the shape of the computational domain and the boundary conditions. The inlet boundary was \(3\)\(C\) ahead of the hydrofoil, where the inflow velocity was fixed, \(V_{\mathrm{x}}=7.2\) m/s (\(Re=5.62\times 10^{5}\)). The outlet boundary was \(5.5\)\(C\) behind the hydrofoil, and a pressure outlet was set to control the cavitation number by setting different pressures. The span wise was \(0.1\)\(C\). The water-tunnel wall and the hydrofoil surface were set as the no-slip wall boundary condition. Twenty-one monitor points were set around the hydrofoil to monitor pressure pulsation. Their locations are shown in Figure 2. Monitor points were arranged at intervals of \(0.1\)\(C\) along the upper surface of the hydrofoil. Their names were U0, U1, U2, U3, U4, U5, U6, U7, U8, U9, U10, B1, B2, B3, B4, B5, B6, B7, B8, B9, and B10, in order.  
Structural mesh was adopted in the simulation. The mesh near the leading-edge and the tail was refined deliberately to precisely capture the complex characteristics of the fierce flow change. To control the overall nodes of the mesh, the mesh density of the other parts of the flow field was reduced appropriately. After the grid independence validation, the total mesh nodes were set as 946,082. The mesh distribution of the computational domain is shown in Figure 3.  
In the present study, seven different cavitation numbers with different cavitation types were selected, and are summarized in Table 1.
# Combining Higher-Order Statistics and Array Techniques to Pick Low-Energy P-Seismic Arrivals
## 5 Results  
In this section, we configure the HOSA method according to the settings reported in Table 3 and apply it to the traces registered by DETECT stations derived from the catalog described in Section 2. We run HOSA on a Workstation DELL Precision 7820 Tower with 2 Intel Xeon Silver 4214R 2.4 GHz and 12 \(\times\) 8 GB DDR4 2933 RDIMM ECC. Processing the entire dataset of about 30,700 traces takes less than 5 min. The method assigns a HOSA onset to 8113 of the total traces. We will refer to these onsets with the symbol \(o^{H}\). We recall that the number of traces associated with a catalog onset (indicated with \(o^{C}\)) is 8685. The two sets of traces (with an onset by HOSA and with an onset in the catalog) do not completely overlap. Specifically, the number of traces with onsets identified by both the catalog and HOSA is equal to \(N_{common}=4797\). Traces with onsets solely detected by the catalog (and not by HOSA) are \(N_{catalog}=3888\), whereas there exist \(N_{HOSA}=3316\) traces with onsets exclusively identified by the HOSA method. In the following, we analyze the quality of the HOSA onsets.
# Effect of Compression Tights on Skin Temperature in Women with Lipedema
## 3 Results
### Maximum Skin Temperature  
Table 2 shows the data of maximum skin temperature measured in the different tests, moments, and types of compression tights. No differences were observed in the different ROIs (\(p\) > 0.05) in the interaction between tests, moment, and type of compression tights.  
The moment (pre-exercise or post-exercise) had a primary effect on maximum skin temperature. A lower maximum skin temperature was measured after exercise than before exercise in the anterior thigh (IC95% (\(-\)1.25, \(-\)0.69 \({}^{\circ}\)C) \(p\) < 0.001), knee (IC95% (\(-\)0.74, \(-\)0.25 \({}^{\circ}\)C) \(p\) < 0.001) and posterior thigh (IC95% (\(-\)1.16, \(-\)0.52 \({}^{\circ}\)C) \(p\) < 0.001).