esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Summary  
Automated feature extraction and selection do help in the development of a highly accurate classification model that could be generalizable to new, unseen time-series data. Time-series feature engineering is a substantial component of machine learning classification analytics. The irrelevant features within the training dataset make the model overfitted to a specific dataset and are not well generalizable. Thus, systematic time-series feature engineering allows automation of the overall classification process and a reduction in the difficulties faced during manual feature estimation and selection.  
In the context of stress classification, feature engineering plays a vital role in improving classification performance. The careful selection and estimation of the time-series features do help in achieving higher classification accuracy with better interpretability of the classifier's decision and achieved results. The dimensionality reduction also helps the predictive model to be computationally efficient, especially if required to run on resource-constrained devices.  
A comparison of the proposed correlation-based feature extraction algorithm with other existing methods is shown below (Table 7).  
Figure 5: Shortlisted features-based stress versus baseline classification using logistic regression, random forest, and K-nearest neighbours classifiers. (**a**) Using Pearson shortlisted features, (**b**) using Kendall shortlisted features, and (**c**) using Spearman shortlisted features.
## 2 Materials and Methods  
### Study Area and Object  
The ASG-EUPOS network consists of 106 reference stations in Poland capable of receiving GPS, GLONASS, and Galileo signals, with 100 stations compatible with BDS signals [17]. Most of these stations are able to track BDS III signals. The ASG-EUPOS can exchange border area data with systems owned by four neighbours of Poland, SKPOS, CZEPOS, SAPOS, and LITPOS, with access to 24 reference stations. Fifteen national reference stations and four foreign stations in the Polish ASG-EUPOS belong to the EPN. The ASG-EUPOS provides the user with real-time (NAWGEO, KODGIS/NAWGIS) and post-processing (POZGEO and POZGEO-D) services (Table 1).  
The data recorded by ASG-EUPOS stations are used in research such as geodynamic research [18], atmosphere and ionosphere monitoring [19], and agricultural research [20]. The ASG-EUPOS services had about 4.2 thousand subscribers in late 2019.  
The ASG-EUPOS was founded on the Trimble Pivot Platform and upgraded with the RTX Network processing module in 2018. The ASG-EUPOS has hardware and software for calculating correction data based on GPS, GLONASS, Galileo, and BDS, which is particularly valuable in network areas with dual-system receivers only. Some stations are equipped with Trimble NetR9 receivers which can track all signals from BDS III generation satellites.  
The APPS module provides relative positioning based on a network of permanent reference stations of the ASG-EUPOS. The APPS requires at least 720 measurement epochs in the user's observation file. However, it is recommended that the observation files contain at least 40 min with 1 s intervals (2400 measurement epochs). The POZGEO service uses an algorithm developed in Poland [21; 22] which is linked to a sub-service of Trimble. To determine the position of a point, the APPS of the ASG-EUPOS selects the six shortest baseline vectors between a point and the neighbouring reference stations [23].  
While there are quite a lot of publications on the real-time services of the ASG-EUPOS [24; 25; 26; 27; 28], the assessment of the POZGEO has not been a popular research topic. Analyses of measurement results with the POZGEO can be categorised as those tackling the session duration [29], the number of satellites available [30], and the impact of GNSS antenna calibration models on geometrical heights measurement [31; 32].
## 3 Theory and Modeling
### Principal and Modeling of Electret Transducer Component
The change in charge allows for current output between the two plates, where the short-circuit current \(I_{\rm sc}\) and open-circuit voltage \(U_{\rm oc}\) of alternating positive-negative unipolar electret structure conform to the following Equations (6) and (7) in this model:  
\[\begin{array}{l}I_{sc}=\frac{dQ_{1}}{dt}\\ \\ =\frac{\omega_{\rm r}\tau^{2}\delta}{\frac{\omega_{\rm r}\tau}{\alpha t}-2}- \frac{(-5\omega_{\rm r})^{2}\delta}{\frac{\omega_{\rm r}\tau}{\alpha t}-10} \ \ \ [  
The simulation results indicate that when the distance between the electrode and the electric is sufficiently small, the induced charge on the electrode surface is dominated by the nearest electret film. Although the interleaved polarities of the electret structure lead to certain degree of reduction in the absolute charge amount on the copper electrode plates due to the partial cancelation of the opposing electric fields, this configuration can significantly enhance the variation in the induced charge on the electrode and the charge difference between paired electrode. As a result, the open-circuit voltage and short-circuit  
Figure 8: Simulation optimization results of the electret transducer component while rotating: (**a**) induced charge on the plates, (**b**) open-circuit voltage between the plates, and (**c**) short-circuit current in the component. Figure 7: Simulation modeling of the alternating polarities electret transducer, the upper layer is the electret and the lower layer is copper: (**a**) model and (**b**) charge distribution. current of the component are improved. With a rotational speed set at 60 rad/s, the short-circuit current output of the component during the rotation of the electret layer is derived from Equation (6). The simulation results align closely with theoretical expectations, and electret transducer component exhibits characteristics of high open-circuit voltage, low-output current, and high dynamic equivalent resistance. And all the simulation results converge successfully, achieving the tolerance requirement of \(1\times 10^{-6}\)after nine iterations. Moreover, from Equations (6) and (7), it can be inferred that with fixed size parameters and structure, both the amplitude of the current and the frequency of voltage, as well as the current variations, are brief and positively correlated with rotational speed, unaffected by complex factors. Thereby, it presents potential for inversion analysis to derive rotational speed and wind speed information from the output signals of the paired plates.
# The Effect of Countermeasure Readability on Security Intentions
## 4 Methodology
### Formative Measurement Model Evaluation  
As this work sought to supplement PMT, the data resulting from the questionnaire had to be analyzed using a suitable method. The partial least squares structural equation modeling (PLS-SEM) method is the perfect fit for this research, as it allows for the estimation of causal systems that include latent variables that can be endogenous, exogenous, or both [61]. Moreover, [PERSON] and [PERSON] [10], [PERSON] [11], and [PERSON] [28] use this method. All calculations concerning the PLS algorithms were done in R using the SEMinR package [62]. The link to the code and data used for the analysis can be found in Appendix D.  
This research's conceptual model translated into a formative measurement model in PLS-SEM. Each construct was considered a latent variable since they could not be directly measured but merely estimated by an operationalization of the concept. This operationalization resulted in manifest variables (a.k.a. indicators in SEM) that measured, or 'formed,' the latent constructs.  
Contrary to reflective models, in formative models evaluating the internal consistency of constructs is inappropriate. [PERSON] [63] stresses, when \"formative measurement is involved, reliability becomes an irrelevant criterion for assessing measurement quality\". Criteria, such as [PERSON]'s \(\alpha\) and composite reliability, were therefore not applicable to this research.  
In a critical assessment of the usage of PLS-SEM [PERSON] et al. [64] find inter alia that researchers do not employ and sometimes even misuse criteria for model evaluation. Considering this, this study will be attentive to their proposed guidelines, elaborated upon by [PERSON] [65].  
Within PLS-SEM, there are certain recommended methods for model assessment. For formative outer models, there are three criteria to assess. First, multicollinearity, measured by the variance inflation factor (VIF). Second, the indicators' relative contribution to their constructs, noted by their weights. Third, construct validity, estimated by the significance of weights and convergent validity.  
Since formative measurement model construction should be primarily founded on theory rather than statistics [66, 67], the latter two of these criteria do not have a fixed value that must be achieved. [PERSON] et al. [64] propose important statistics concerning these criteria should at least be reported.
## 2 Selection of Pulse-like Ground Motions and the Engineering Background  
### Selection of Pulse-like Ground Motions  
The presence of long-period velocity pulses is a significant characteristic of pulse-like ground motions. The effect of long-period velocity pulses would cause velocity and displacement shocks to any engineering structures in the vicinity, resulting in significant damage that is markedly different from that caused by non-pulse ground motions. Therefore, the selection of pulse-like ground motions is a prerequisite for accurately analyzingthe dynamic response of engineering structures. As mentioned above, fault fractures are the main reason for pulse-like ground motions, which is why pulse-like ground motions are usually selected from near-field ground motions with rupture distances of less than 20 km. However, during the propagation of ground motions, when they are passing through complex crustal structures, such as geological basin margins, the velocity pulse effect may also be caused by constructive interference from ground motions.  
[PERSON] [27] proposed the wavelet analysis approach to quantitatively identify whether a ground motion contains a velocity pulse. Since the wavelet analysis approach is fully quantitative, reproducible, and inexpensive in terms of computational expense, this approach has been widely used in pulse-like ground motion identification. Using the wavelet analysis approach, [PERSON] identified 91 pulse-like ground motions, and detailed information on these pulse-like ground motions is reported in the literature [27]. Hence, based on these 91 pulse-like ground motions, for this study, we have selected 9 additional pulse-like ground motions from the PEER NGA-West2 database using the wavelet analysis approach. This has led to the number of selected pulse-like ground motions reaching 100, and detailed information regarding the 9 additional pulse-like ground motions is given in Table 1. The relationship between the peak acceleration, pulse period, pulse amplitude, and fault distance of the selected 100 pulse-like ground motions is shown in Figure 1, and the response spectra are shown in Figure 2.  
It can be seen from Figure 1 that the value range of the peak ground acceleration (PGA) of the selected 100 pulse-like ground motions in this study is 0.09-1.44 g, while the value range of the pulse period is from 0.40 s to 12.94 s, the value range of the pulse amplitude is from 30.02 cm/s to 191.17 cm/s, and the maximum fault distance reaches 102.39 km. In addition, there are a total of 19 pulse-like ground motions with a fault distance greater than 20.0 km among the selected 100 pulse-like ground motions. Although the PGA of these ground motions is relatively low, the range of pulse period and pulse amplitude values is 0.79-9.11 s and 30.02-69.96 cm/s, respectively. This indicates that the pulse eigenvalues of far-field pulse-like ground motions are not smaller than those of near-field pulse-like ground motions. Hence, when selecting pulse-like ground motions, it is not advisable to limit the selection to near-field ground motions with fault distances of less than 20 km.
# Study on Penetration Sensing Method Based on Pool Oscillation and Arc Voltage during Pulsed GMAW
## 1 Introduction
Compared with P-GTAW, the dynamic behavior of weld pool in P-GMAW is more complicated, which increases the difficulty of sensing weld penetration status based on weld pool oscillation and voltage signal. [PERSON] and [PERSON] found that the depth of the weld penetration can be determined with adequate accuracy from the change of the weld pool surface depth during the peak current period [8]. Based on this study, [PERSON] and [PERSON] established the empirical equation of relation between the penetration status and the fluctuation amplitude in voltage signal at peak current period for stationary welding with different wire feeding speeds [13]. The voltage signal of GTAW can reflect the change of weld pool height in real time because the spatial position of the tungsten electrode is constant and the weld pool is relatively flat [11]. In contrast, in P-GMAW, the droplet growth and oscillation at the tip of the welding wire at peak current period have a significant influence on the arc length, which results in the voltage signal not accurately reflecting the change in the weld pool height [14]. However, there is not a lot of literature that expounds on the internal relationships among the oscillation characteristics of the weld pool, the voltage signal and the penetration status of the traveling welding joint in P-GMAW. Therefore, the researches about the relation among the weld pool oscillation, the penetration status and the voltage signals were of considerable significance to realize P-GMAW weld penetration sensing based on voltage signal with low cost and high reliability. To study the internal relations between the oscillation characteristics of the weld pool and the penetration status of the weld joint in P-GMAW, a high-speed camera and image analysis system was used to obtain the direct information about the weld pool dynamic behavior in this paper. Generally, the welding parameters cannot be changed during the welding process, and the welders adjust the welding speed to control weld penetration. Based on this, Weld joints with different penetration status were obtained with varying speeds of welding under the same welding parameters in this paper. The relationship between voltage signal and weld pool oscillation was analyzed under different penetration status, and a new method is proposed to sense the penetration status for traveling P-GMAW which was based on the voltage signal during pulse base value phase.
# Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
## 3 Results and Discussion  
### Comparison of EDGAR v4.3.2 and UNFCCC Reports  
We compared the annual total methane emissions from national reports of the UNFCCC and EDGAR for the top emitting countries (Figure 1). The global anthropogenic methane emissions stayed stable from 1990 to 2000 and increased again at the start of the 21 st century [60]. The main sources of methane, the production and transmission of coal, oil and natural gas, livestock, waste, and wastewater, along which coal mining, livestock, and natural gas production and distribution have contributed the most to the increase in the last ten years [19]. Countries with the largest absolute increases over these ten years were China, India, Brazil, and Indonesia, whereas the largest decreases occurred in the European Union, Russian Federation, Nigeria, and Ukraine [59].  
We divided countries into three groups according to the ratio of the UNFCCC-reported emissions to those by EDGAR. In the first group, G1, shown in Figure 1, Russia reported 1.7 to 2-fold of the EDGAR estimates. Some part of this increase might be due to accidental methane release, which is not included in EDGAR and is not a negligible methane source in Russia [61]. The differences reported by most countries in the second group, G2, were within 20%. The United States of America (USA) reported within 10% more than EDGAR estimate. China reported 66.3 Tg in the year 2012, which is 16% lower than the EDGAR estimate. France and the United Kingdom reported lower but within 10% of the EDGAR estimates and Germany reported 17% less than the EDGAR estimate. In the third group, G3, Japan and India reported 60%-70% of the EDGAR estimate, while Bangladesh and Iran updated their last reports in 2005 and 2000, estimating less than 50% of the EDGAR estimates.
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 3 Results
### Buildings Damage and Sentinel-1 A-DInSAR  
Most of the buildings in the La Miera landslide area showed damage below the level of category 3 because they presented no visible landslide ground damage. However, two buildings presented damage within category 3 (moderate), and only one building presented category 4 (serious) damage (Figures 4 and 5A,B). These buildings are in the west of the landslide near the access road to Los Campos, La Miera, and El Valle neighborhoods (Figure 4, B-1). They were built in 1941. The damage consisted of vertical open cracks with a width of various centimeters (Figure 5A,B). Furthermore, in the building with category 4, the damage had already been repaired, but cracks of several meters in length and several centimeters in width had developed again (Figure 5B). In this same sector, some tension cracks several meters long and >10 mm in width were observed in the access road and in walls (Figures 4 and 5C). Here, the descending VLOS detected displacement velocities of \(-7.1\) to \(-7.9\) mm/year and a horizontal velocity of \(-7.2\) mm/year (westward direction).  
Figure 4: La Miera landslide with the new geomorphological features of activity, building damage inventory [56] and the updated landslide limits. Digital terrain model with a \(5\times 5\) m pixel resolution. B-1, B-2, and B-3 squares are the buildings with observed damage. Line A-A’ indicates the position of the W-E cross-section of the landslide.  
In the village of El Valle, the damage to most of the buildings did not reach category 3 (Figure 4). Only four buildings presented category 3 (moderate) levels of damage (Figure 4, B-2, and B-3). They were built in the first half of the 20 th century. They presented some open cracks with widths between 1 and 5 cm in facades and in walls (Figure 5D,E). The maximum VLOS detected in these buildings was \(-\)11.6 mm/year, while the maximum horizontal velocity detected was \(-\)7.2 mm/year (westward direction) (Figure 4).  
At the foot of the landslide, numerous tension cracks were observed in the path. These were several meters in length and had maximum widths of several centimeters (Figures 4 and 5F).
## 2 Materials and Methods
### Evaluation of Contribution to BrC Light Absorption Coefficient  
BC, BrC, and inorganic dust components primarily cause light absorption in atmospheric aerosol particles. In this study, the aethalometer's measured optical absorption coefficient represents the combined contributions of these three components, necessitating their separation. In order to evaluate BrC's contribution, this study utilized the BC absorption Angstrom exponent (AAE) value. Previous studies have reported that BC AAE values emitted from fossil fuel combustion are -1.0, while those emitted from BB are -2.0, and desert dust exhibits values between 1 and 2 [1]. Thus, the light absorption by BC particles does not vary significantly with wavelength and exhibits an AAE value of approximately 1.0. On the other hand, BrC, which absorbs significant light in the ultraviolet wavelength range, is associated with emission sources exhibiting AAE values greater than 1.0 [25; 26]. This study used the AAE to separate BrC, which strongly absorbs light near the ultraviolet wavelength, from the total light absorption coefficient. The total light absorption coefficient (\(\mathrm{b_{abs}}\), \(\lambda\)) at each wavelength was defined as the sum of the light absorption coefficients of BC (BC\(\lambda\)) and BrC (BrC\(\lambda\)). The light absorption coefficients at 880 and 950 nm, where BrC absorption is assumed to be negligible, were attributed solely to BC absorption, with an assumed BC AAE of 1.0. Following this assumption, the light absorption coefficients due to \(\mathrm{b_{BC,\lambda}}\) were computed. This methodology enabled the determination of the optical absorption coefficient of BC at 370 nm (BC\({}_{370}\)). The total light absorption coefficient of aerosol particles at each wavelength \(\mathrm{b_{abs}}\) can be represented according to Equation (1) as the sum of the light absorption coefficients of BC and BrC, denoted as \(\mathrm{b_{BC,\lambda}}\) and \(\mathrm{b_{BrC,\lambda}}\), respectively, as follows:  
\[\mathrm{b_{abs,\lambda}}=\mathrm{b_{BC,\lambda}}+\mathrm{b_{BrC,\lambda}} \tag{1}\]  
In this study, the absorption coefficient of light attributed to BrC\({}_{370}\) at a wavelength of 370 nm was employed, calculated by subtracting the light absorption coefficient due to BC\({}_{370}\) from the total light absorption coefficient at 370 nm.
## 4 Discussion
### Limitations  
While this study has investigated WUE in the YRB, it is not without limitations. The data for GPP and ET used in this research were primarily derived from remote sensing observations or model simulations, and the accuracy of these data can be influenced by various factors [29, 31, 58]. Additionally, our analysis focused mainly on climatic factors affecting WUE, without comprehensively considering the impacts of hydrological conditions and land use changes [14, 15, 69]. Therefore, future research should delve deeper into the mechanisms by which climate change, land use change, and drought stress interact with WUE. It should also integrate multiple data sources and analytical tools to enhance the acquisition of high-precision data, optimize model parameterization, and improve validation methods, thereby advancing the understanding and prediction of ecosystem WUE.
## 4 Experiments
### Results and Discussion
#### 4.1.1 Discussion on Complexity Trade-Offs in Training Phase Efficiency  
The \(O(m\times n\times K)\) training complexity is generally more efficient compared to models like Random Forest, where tree construction can be computationally expensive for large feature spaces.  
* Classification phase complexity: The \(O(n\times K^{2})\) complexity per test sample may be higher than that of models like k-NN (\(O(n\times K)\)), especially for datasets with a large number of classes \(K\). However, this trade-off is often justified by the higher classification accuracy and robustness provided by the resemblance-based decision mechanism.
* Space complexity consideration: For percentile calculations (Algorithm 1, lines 10-12), selection-based algorithms require \(O(m)\) space. However, alternative approaches can reduce this to \(O(1)\) additional space. If \(m>n\times K\), the space complexity of percentile computation could dominate.