esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of VOCs  
A three-hour integrated sequence VOC sampler was implemented at a flow rate of 50 mL/min to minimize the loss of low VOCs, which were automatically collected. A total of 123 samples were collected and analyzed during the sampling periods. VOCs (Custom (Q-QC27-1), Kemidas (M-VAVOC 503M2), Gyeonggi-do, Republic of Korea) were used as standard material for analysis. The internal standard material was chlorobenzene-d5 manufactured by Kemidas. A solid adsorption tube (C2-CAXX-5149) (Markes International, Ltd., Bridgend, UK) was utilized for sample collection and the calibration curve. VOCs were analyzed using thermal desorption (Unity2, Markes International, Ltd., Bridgend, UK) gas chromatography (GC) (7890A, Agilent, Santa Clara, CA, USA) mass spectrometry (MS) (5975C, Agilent, Santa Clara, CA, USA), and the analysis method was US EPA TO-17 (solid adsorption method). The 33 compounds used for the analysis included benzene, toluene, ethylbenzene, and xylene both for agricultural chemical analyses and orchard ambient analyses. Briefly, 1 mL of a diluted solution was collected by mixing two standard VOCs in the solid adsorption tube using nitrogen gas at a flow rate of 50 mL/min. In addition, 1 mL of chlorobenzene-d5 at the concentration of 50 mg/L was injected into the adsorption tube, with all the samples at a flow rate of 50 mL/min, using nitrogen gas to finalize the calibration curve. The detailed analytical procedure can be found elsewhere [15].
## 3 Results and Discussion
We also find that there is a decrease of the power efficiency for both of them when the applied voltage is increased from 3 to 4 V. The power efficiency is slightly increased when the applied voltage reaches 5 V, although there is a downward trend from 5 to 8 V. The decreasing trend would be linear if we ensured that the charge transport problem is normally main factor. Thus, the improved performance is attributed to the strong influence of the SPRE. Figure 6 shows the schematic diagram of the nonradiative decay process of excitons. The excitation of the surface plasmon polariton modes results in the nonradiative decay process of excitons, which propagate along the surface of the GNPs. Furthermore, the extra nonradiative decay channel at the interface between the fluorescent molecule and the GNPs results from the excitation of the surface plasmon polariton modes [30]. Therefore, the enhancement factor of the device with GNPs will be decreased when the GNPs are close to the excitons of the EML. Moreover, the extra nonradiative decay channel at the interface between the fluorescent molecule and the GNPs will be generated when the GNPs are close to the excitons of the EML. Figure 5: Luminance-voltage curves of devices with and without GNPs when the thickness of the NPB is 6 nm and the inset figure shows the power efficiency-voltage characteristics of the device under the same conditions.
## 4 Discussion
### Effect of Smartphone Use on Static Posture
#### 4.1.4 Effect of Smartphone Use on Muscle Activation and Muscle Fatigue  
Four studies have investigated the impact of smartphone use on muscle activation and muscle fatigue [14; 17; 18; 21]. Previous research focusing on cervical muscle activation revealed no statistically significant differences in activity among four muscle groups: the right splenius capitis, right upper trapezius, left splenius capitis, and left upper trapezius during maximal cervical flexion postures associated with smartphone use. However, higher average muscle fatigue values--approximately 0.2--were noted in the right splenius capitis, left splenius capitis, and left upper trapezius muscles. These findings indicate that maintaining a neutral cervical posture may be more effective in reducing muscle fatigue than adopting an excessively flexed cervical position [21]. Moreover, a study assessing the bilateral muscle activation of cervical spinal extensor muscles at four different smartphone viewing angles (\(0^{\circ},15^{\circ},30^{\circ}\), and \(45^{\circ}\)) found that maximum voluntary isometric contraction increased by approximately 2% with each \(15^{\circ}\) increment in the viewing angle [18]. Prolonged smartphone use, particularly at angles around \(50^{\circ}\), can lead to the hyperactivation of cervical extensor muscles, potentially resulting in headaches and increased cervical disk pressure. Additionally, the excessive activation of the bilateral upper trapezius muscles may exacerbate neck and shoulder pain, further intensifying forward head posture and kyphosis [14; 17].
## 4 Proposed mmWave Assisted CR-IoV
### Comparative Analysis
#### 5.3.4 Analysis of BER  
BER mainly depends upon the MIMO characteristics, including channel fading, interference and noise. Here, we have presented an important analysis of the BER.  
In Figure 14, we have analyzed BER with respect to the SNR range. With the increase in SNR, the BER is reduced in both proposed and existing works. The BER is high depending on the noise range presented in the network environment. For example, in existing 2D-beamforming, BER in the presence of 2 dB SNR is 0.5, i.e., 50% of error bits are received during data transmission. For the same SNR range, the proposed work attains a BER of 0.1, which is much lower than the existing work. The main reason behind this BER reduction is the optimal beamforming achieved based on AF and BPF parameters. As the work forms beams in 3D, the generated beams are optimum and directed towards the candidate SU. Thus, the noise level can be handled through optimal beamforming. Further, we suppressed attackers by optimal procedures, which further reduced the impact of attackers in the network. In contrast, 2D information is insufficient to assure high-level transmission performance which is confirmed in the BER analysis too.
###### Abstract
In our study, which is an extended version of [18], FEM based on ANSYS Maxwell (2D) software is used to compute low-frequency magnetic fields generated by three-phase underground power cables with solid bonding according to the usual procedures applied in human exposure studies. Both trefoil and flat formations of a 12/20 kV (medium-voltage) three-phase power cable are considered for FEM model implementation. For its validation, calculated magnetic fields and induced shield currents are checked by comparison with analytical results obtained with a software tool based on the Biot-Savart law and the superposition principle, which represents an updated version of a previously developed program [19]. Finally, the application of the FEM model to multiple three-phase power cables laid together is considered. In all cases, the magnetic field reduction rate due to induced shield currents is determined as well. The computational time, generally a major drawback of using FEM [20], is, in this study, quite acceptable, in the order of 10 to 25 min (simple cable formation, depending on mesh size). Compared to the analytical approach, which is limited to simple trefoil and flat formations of cables, the FEM model can also be applied to various types of cable arrangements while taking into account different influencing factors, such as the relative phase sequences and positions of the cables. Hence, it may be used not only for assessing compliance with relevant magnetic field exposure regulations, but also as an accurate tool for optimizing cable layout and location to mitigate magnetic field problems. Usually, the modification of the total magnetic field due to induced shield currents is not considered in the commercially available software for power cable system analysis.
## 4 Results
### Performance Comparisons between DPGA and Grid-Search Approaches
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{(**a**) Steel plates faults dataset} \\ \hline
**DR Algo.** & **PR Para.** & **RFE Algo.** & **RFE Para.** & **PCA Flag** & **PCA Para.** & **LM Para.** \\ \hline SSMOTE & \(r_{2 tt}=0.934\) & FAG & \(n_{FAG}=2\) & TRUE & \(e=98.964\) & \(sv_{\text{WO}}=adam\wedge n_{\text{HN}}=20\wedge act_{\text{HL}}=tanh\) \\  & \(r_{2 tt}=0.933\) & FAG & \(n_{FAG}=3\) & TRUE & \(e=98.964\) & \(sv_{\text{WO}}=adam\wedge n_{\text{HN}}=19\wedge act_{\text{HL}}=tanh\) \\  & \(r_{2 uu}=1\) & PCA & \(n_{FCG}=1\) & TRUE & \(e=93.742\) & kNN: \(n_{\text{HN}}=2\wedge wf_{\text{HN}}=distance\) \\  & \(r_{2 tt}=0.918\) & PCA & \(n_{\text{PC}}=1\) & TRUE & \(e=93.742\) & kNN: \(n_{\text{HN}}=3\wedge wf_{\text{HN}}=distance\) \\  & \(r_{2 tt}=0.834\) & PCA & \(n_{\text{PC}}=1\) & TRUE & \(e=93.742\) & kNN: \(n_{\text{HN}}=3\wedge wf_{\text{HN}}=distance\) \\  & \(r_{2 tt}=0.928\) & PCA, LSA, FAG & \(n_{\text{PC}}=2\) & TRUE & \(e=98.157\) & SVM: \(C=7\) \\  & \(r_{2 tt}=0.934\) & \(n_{FAG}=3\) & TRUE & \(e=98.634\) & SVM: \(C=8\) \\  & \(r_{2 tt}=0.931\) & PCA, LSA, FAG & \(n_{FCG}=1\) & TRUE & \(e=98.216\) & RF: \(n_{\text{true}}=9\) \\  & \(r_{2 tt}=0.993\) & PCA, LSA, FAG & \(n_{FCG}=1\) & TRUE & \(e=98.859\) & RF: \(n_{\text{true}}=10\) \\  & \(r_{2 tt}=0.874\) & FAG & \(n_{FAG}=3\) & TRUE & \(e=98.216\) & RF: \(n_{\text{true}}=10\) \\ \hline  & & & \multicolumn{4}{c}{(**b**) SECOM dataset} \\ \hline
**DR Algo.** & **DR Para.** & **RFE Algo.** & **RFE Para.** & **PCA Flag** & **PCA Para.** & **LM Para.** \\ \hline BSMOTE1 & \(r_{2 tt}=0.910\) & LSA, FAG & \(n_{FCG}=53\) & TRUE & \(e=97.867\) & \(sv_{\text{WO}}=adam\wedge n_{\text{HN}}=20\wedge act_{\text{HL}}=relu\) \\  & \(r_{2 tt}=0.907\) & LSA, FAG & \(n_{FCG}=54\) & TRUE & \(e=96.521\) & MLP: \\  & \(r_{2 tt}=0.910\) & None & None & TRUE & \(e=98.027\) & \(sv_{\text{WO}}=adam\wedge n_{\text{HN}}=17\wedge act_{\text{HL}}=relu\) \\  & \(r_{2 tt}=0.763\) & None & None & TRUE & \(e=97.678\) & kNN: \(n_{\text{HN}}=2\wedge wf_{\text{HN}}=uniform\) \\  & \(r_{2 tt}=0.968\) & PCA, LSA, FAG & \(n_{FAG}=20\) & TRUE & \(e=98.322\) & SVM: \(C=6\) \\  & \(r_{2 tt}=0.803\) & FAG & \(n_{FAG}=19\) & TRUE & \(e=90.882\) & RF: \(n_{\text{true}}=9\) \\  & SMOTE & \(r_{2 tt}=0.934\) & LSA & \(n_{FCG}=9\) & TRUE & \(e=91.980\) & RF: \(n_{\text{true}}=9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best solutions of our DPGA approach in fault-types classification datasets. (**a**,**b**) Results of the steel plates faults and SECOM datasets, respectively.
# Dynamic Performance of a New-Type PSC I-girder for Railway Bridge Application
## 3 Performance Evaluation on Full-Scale PSC I-girder Specim
### Evaluation of Ultimate Load Bearing Capacity  
#### 3.3.1 Test Method  
Four-point bending test was carried out on the specimen to evaluate its structural performance. Figure 17 illustrates the four-point bending test setup. The specimen was placed on two supporting pins positioned at a distance of 500 mm from the ends. Two loading actuators were placed at an equal 3 m apart from center of specimen, which let this distance be subjected to pure bending moment. Two actuators with capacity of 3500 kN were used to apply loading and, the deflection, the strains in concrete and steel as well as the crack width at the segmental interfaces were measured. The test was planned to apply loading from the service load to the ultimate load and up to failure but failure could not be attained due to the limited capacity of the actuators and the maximum load reached 6097 kN.  
Figure 16: Dynamic characteristics of PSC I-girder specimen: (**a**) acceleration response under forced vibration by exciter; (**b**) FFT analysis; and (**c**) free vibration for analysis of damping ratio.
## 2 Materials and Methods
### Absolute Robustness  
Robustness (RB) in general terms can be defined as \"... the capacity of a system to maintain a function in the face of perturbation\" ([18] p. 169). In the present work, RB is the measure of the degree of variability for DStr and DSize as a function of GAI binarization. If RB = 1, then DStr and DSize remain unchanged. This suggests that the highest degree of robustness, RB = 0, indicates the lowest robustness level for DStr and DSize. Let us now consider the procedures of RB quantification in these scales.  
The set of global thresholds denoted by \(\text{tr}_{1}\),..., \(\text{tr}_{k}\),..., \(\text{tr}_{p}\) allow us to convert a GAI into a set of binary images, \(\text{BIm}_{1}\),..., \(\text{BIm}_{k}\),..., \(\text{BIm}_{p}\). The set of angles \(\varphi_{1}\),..., \(\varphi_{r}\),..., \(\varphi_{q}\) is used to calculate the morphological characteristics of a binary image as the function of its rotation. DStr(\(\text{tr}_{k}\),\(\varphi_{r}\)), is the result of calculating DSt for \(\text{tr}_{k}\) and \(\varphi_{r}\). The parameter DStr(\(\text{tr}_{k}\), \(\varphi_{r}\)) is averaged over \(\text{tr}_{k}\), k = 1,p for the angle of rotation \(\varphi_{r}\), and is denoted by \(\text{AvDStr}(\varphi_{r})\).  
\[\text{AvDStr}(\varphi_{r})=\frac{1}{P}\sum_{k=1}^{P}\text{DStr}(\text{tr}_{k},\varphi_{r})\.\]  
The absolute deviation of DStr from the arithmetical mean is denoted by (\(\Delta\text{DStr}(\text{tr}_{k}\),\(\varphi_{r}\)) for \(\text{tr}_{k}\) and \(\varphi_{r}\) and is equal to:  
\[\Delta\text{DStr}(\text{tr}_{k}\),\(\varphi_{r})=\left\lvert\text{DStr}(\text{tr}_{k}\),\(\varphi_{r})-\text{AvDStr}(\varphi_{r})\right\rvert.\]  
The average deviation of \(\Delta\text{DStr}\) from the arithmetic mean for \(\varphi_{r}\) is equal to:  
\[\text{Av}[\Delta\text{DStr}(\varphi_{r})]=\frac{1}{P}\sum_{k=1}^{P}\Delta \text{DStr}(\text{tr}_{k},\varphi_{r}).\]  
Finally, the robustness of DStr with respect to \(\text{BIm}_{1}\), \(\text{BIm}_{2}\), and \(\text{BIm}_{3}\) on an absolute scale is equal to:  
\[\text{absRB}(\text{DStr})=1-\frac{1}{q}\ \sum_{r=1}^{q}\text{Av}[\Delta\text{DStr}( \varphi_{r})] \tag{1}\]  
The algorithm for calculating absRB(DSize) is identical to that for calculating absRB(DStr).
## 5 Discussion  
### Relative Uncertainty from TCH  
Figure 5 illustrates the pixel-by-pixel relative uncertainty and statistical histograms of the TSEB-SM, PML-V2, and SEBAL ET products in the Heihe River Basin. Overall, the TSEB-SM and PML-V2 products have substantially smaller relative uncertainty than SEBAL ET. From the statistical histograms, it appears that most of the relative uncertainty values for TSEB-SM ET are distributed between 50 and 70%, those for PML-V2 ET are mainly in the range of 70-90%, and those for SEBAL ET are in the range of 60-70% in the upstream portion of the region, with most of the rest exceeding 200%. The high relative uncertainty values of TSEB-SM ET are mostly distributed in the downstream arid Gobi area and a few grassland areas in the upstream and midstream. Most of the high relative uncertainty values of PML-V2 ET are distributed in the downstream Gobi Desert region, with some being distributed in the midstream irrigation regions and upstream alpine meadow region. The high relative uncertainty values for SEBAL ET are also predominantly distributed in the downstream and midstream regions. This is similar to the results of the previous (Section 4.1) comparison with the ground station observations, where all three products were underestimated in the downstream, resulting in high relative uncertainties.  
Figure 4: Spatial distribution of annual cumulative ET modeled by TSEB-SM.  
It is worth noting that TSEB-SM ET has lower relative uncertainty in the midstream irrigation region compared to the upstream alpine meadow region. This is primarily due to the input of soil moisture into the TSEB-SM model, which monitors the large amount of water depletion in the yield of the early crop-growing season and, thus, simulates surface evapotranspiration with greater accuracy. Also, the relative uncertainties of all three products are lower in the upstream of the Heihe River Basin, which is mainly due to the fact that the upstream is primarily alpine meadows with abundant rainfall, and for both TSEB-SM ET and PML-V2 ET based on the energy balance and PML-V2 ET based on the P-M equations of the physical model, a better simulation of surface evapotranspiration can be carried out. Generally, TSEB-SM ET outperformed PML-V2 ET and SEBAL ET in arid and semi-arid regions at the watershed scale.
## 3 Proposed Method--Depression Ensemble Stacking Generalization Model (DESGM)
### Stacked Ensemble Model  
_DESGM_  
The idea of meta-level-based learning is used in stacking ensemble learning. As shown in Figure 1, various ML methods are used to generate the set of base learners. Each primary classifier's hyperparameter can be optimized to increase accuracy, and diversity can be attained through various techniques, such as various training samples, sets of features, and factors.  
### Stacked Ensemble Model  
_DESGM_  
The DESGM model employs various supervised learning techniques to classify patients who are at risk. The approach utilizes both training and testing data to construct a model capable of predicting the appropriate output label for new observations.  
Perceptron:  
The perceptron is the most basic type of neural network and is used to categorize patterns that are claimed to be linearly separable. A single neuron-based perceptron can only conduct pattern categorization with two classes. The perceptron algorithm follows the steps below.  
Input: X represents a set of m training samples, each denoted as \((x_{1},x_{2},\ldots,x_{m})^{T}\).  
1. Initialize w = 0.
2. For each training sample \(X_{i}\), the output value \(\hat{y}^{(i)}\) is computed using the following activation function:  
\[\hat{y}^{(i)}\Big{(}:=\Phi\Big{(}w^{T}x^{(i)}\Big{)}\Big{)} \tag{2}\]
3. Update the weights.
4. The weight vector w is then updated based on the computed output and the actual class label \(\hat{y}^{(i)}\) of the ith training sample using the update rule:  
\[w:=w+\Delta w,\Delta w=\eta\Big{(}y^{(i)}-\hat{y}^{(i)}\Big{)}x^{(i)} \tag{3}\]  
where \(\eta\) is the learning rate, \(0<\eta<1\). The activation function \(\Phi(z)\) is defined in the following equation:  
\[\Phi(z)=\{1\ \text{ if }z\geq 0,-1\text{ otherwise, }z=w^{T}=w_{0}+w_{1}x_{1}+w_{2}x_{2}+\ldots+w_{m}x_{m} \tag{4}\]where \(\rm{w_{0}}\) functions as a bias term, and the binary classifier uses the values of 1 for the depression class and \(-1\) for the non-depression type. The model's ability to generalize to larger sets of data is enhanced by including the bias term, which narrows in on particular traits.
# Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
## 3 Results
### Test of the Model Using the 2015 Nepal Mw 7.8 Earthquake
Considerably different findings were obtained from the analysis of maps. Figure 5 shows the DTEC maps computed using GIM values (Figure 5a) and VTEC estimated values (Figure 5b). Regarding DOY 111, [PERSON] et al. [66] report a negative anomaly in the DTEC map at 08 UT that is visible in Figure 5a. Despite a slightly different shape (and size) of the anomaly, the same is visible also in Figure 5b. In Figure 6, we show the maps of the VTEC rate SNR at 03 UT (Figure 6a) and 08 UT (Figure 6b). In maps at 03 UT, anomalies over the earthquake zone appear for DOYs between 110 and 115 (the main shock was at 06:11 UT of DOY 115). Other anomalies also appear in DOYs 106 and 107, during which, however, there are high values of Kp that indicate an intense solar activity in such a time span. The same ionospheric anomalies are also visible in maps at 08 UT, in which that of DOY 111 is clearer here than in maps at 03 UT. Figure 4: VTEC rate SNR time series, 24 h VTEC rate SNR, and Kp index for an overall comparison (all the values were computed at the epicenter of the Nepal earthquake): (**a**) VTEC rate SNR (red dot line) along with its 4 h moving average (black line). Labels refer to midnight. (**b**) 24 h VTEC rate SNR per DOY. Labels refer to midday. (**c**) The geomagnetic activity index Kp. Labels refer to midnight.
## 4 Discussion
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Hypothesis** & **chi2** & **df** & **Prob \textgreater{} chi2** \\ \hline \hline \(\ln\) GDP does not Granger cause DfG & 1.971 & 1 & 0.160 \\ \(\ln\) GDP does not Granger cause ECI & 0.365 & 1 & 0.546 \\ \(\ln\) GDP does not Granger cause ln GDP & 63.338 & 1 & 0.000 ***1 \\ \(\ln\) G does not Granger cause ECI & 19.434 & 1 & 0.000 *** \\ ECI does not Granger cause ln GDP & 227.91 & 1 & 0.000 *** \\ ECI does not Granger cause DfG & 69.104 & 1 & 0.000 *** \\ \hline \hline \end{tabular}  
* **Note: *** \(p<0.01\).  
\end{table} 
Table 3: Granger causality Wald tests for Panel VAR. the amount of export by counting the number of products with RCA \(\geq 1\). Our observation that after the 2008 global financial crisis, the correlation between DfG and GDP is stronger than that between ECI and GDP, can at least be partially attributed to the enhancement of the global participation in production chains, or simply, greater participation in global value chains (GVCs). Therefore, simply focusing on RCA \(\geq 1\), which has been used in designing ECI and its variants, is no longer sufficient. In other words, information contained in products with RCA \(<1\) can no longer be simply discarded. Therefore, in future, it would be extremely interesting to develop a new economic complexity index by using DfG alone, or by combining DfG and ECI (or its variants). Conceptualization, J.G. and B.L. ; methodology, J.G. and B.L. ; software, B.L. ; validation, J.G. and B.L. ; formal analysis, J.G. and B.L. ; investigation, J.G. and B.L. ; resources, B.L. ; data curation, B.L. ; writing--original draft preparation, J.G. and B.L. ; writing--review and editing, J.G. and B.L. ; visualization, B.L. ; supervision, J.G. ; project administration, J.G. ; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Key Research and Development Program of China under Grant No. 2019 AAA0103402, by the Fundamental Research Funds for the Central Universities, and by Innovation Project of Guangxi Graduate Education under Grant No. YCBZ2017017. It is also supported by the National Natural Science Foundation of China under Grant No. 41671532. One of the authors ([PERSON]) also benefited tremendously from participating the long program on culture analytics organized by the Institute for Pure and Applied 746 Mathematics (IPAM) at UCLA, which was supported by the National Science Foundation. Not applicable. Data available in a publicly accessible repository. The authors thank [PERSON] and [PERSON] for help plotting some of the figures presented in the paper. The authors declare no conflict of interest. References