esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Objectives  
The goal of this work was to develop a method for the quantification of target odorous VOC (using acetic acid as a model compound) with a TWA SPME approach that is more accurate and less laborious. Unlike the previous TWA SPME approaches where an SPME fiber is retracted into a metallic needle, this research proposes to use an SPME fiber that is exposed inside the GC glass liner to achieve the effect of a traditional retracted fiber _without the need to estimate and account for the inherent adsorption of VOCs onto metallic parts of SPME needle during sampling_. Since a GC glass liner has a greater cross-sectional area than a traditional retracted SPME fiber (Figure 1), the new approach should allow for greater amounts of the analyte extracted on the fiber, and increased exposure of the side surfaces of the coating to the sample, resulting in lower detection limits and greater accuracy.  
Our working hypothesis is that the glass liner enclosure might be less affected by the apparent departure from the ideal quantification model (Fick's Law, Equation (1)) that is associated with the use of metallic needle enclosures to facilitate TWA SPME. The inside of the glass liner serves as a diffusion  
Figure 1: Time-weighted average (TWA) gas sampling with solid-phase microextraction (SPME). Comparison of proposed (**A**) and conventional (**B**) TWA SPME. (**A**) Sampling with SPME fiber exposed and retracted inside of a glass liner; (**B**) a typical case of TWA SPME where the SPME fiber is retracted inside of a conventional SPME needle. Gray arrows represent the diffusion path between bulk gas (left side) and the retracted fiber tip (Z tip). Red arrows represent the “apparent” diffusion path extending beyond the tip to the SPME fiber coating side. The “apparent” diffusion path represents the extracting process enhanced by the sides of the SPME coating. \(Z^{\star}\) may continue to increase after the tip is saturated.  
path. Thus, extraction of VOCs is controlled by diffusion, and potentially can be used for sampling of VOCs in remote locations. The method utilizes GC glass liners that are readily available in many analytical laboratories. As the most abundant VOC in livestock operations, acetic acid was chosen as a model compound to prove the concept.  
The specific objectives of this research were to (1) build and verify a standard gas generation system for odorous VOC that simulates typical dynamic animal facility air in the lab; (2) test the performance of an SPME fiber retracted into a glass liner and the adherence of this air sampling concept to the Fick's Law; (3) test the new method for quantification of acetic acid on a typical Iowa swine facility and evaluate its feasibility; and (4) compare the developed method side-by-side to a standard method under field conditions.
## 3 Results
### LAI and NPP Simulation  
The original IBIS underestimated the LAI values; the RU-IBIS values, which had smaller uncertainties than the O-IBIS values, generally agreed with the observed LAI values (Figure 5). The RU-IBIS model generated LAI correlation coefficient (0.95) and R\({}^{2}\) (0.90) results that were higher than those of the O-IBIS model (0.70 and 0.50, respectively). Furthermore, the RMSE and RE values simulated by the RU-IBIS results revealed improving O-IBIS results on LAI and NPP bases (Table 5). Clearly, the RU-IBIS dynamic vegetation results are more accurate than those of the O-IBIS model.  
Figure 5c,d shows the results between the observed O-IBIS and RU-IBIS NPP at SPT and AS stations, which demonstrated that the RU-IBIS simulations maintained consistency with the observed NPP. The NPP of O-IBIS was much lower than the observed value. After modifying IBIS, the underestimation phenomenon improved significantly. The correlation coefficients were 0.52 (O-IBIS) and 0.95 (RU-IBIS), respectively. The statistical metrics for accuracy and error improved when we used the RU-IBIS results instead of the O-IBIS results.
# Research on Nonlinear Vibration Characteristics of Internal Beveloid Gear Transmission System
## 3 Numerical Solution of Internal Beveloid Gear with Parallel Axes  
When studying gear meshing, it is common to equivalently represent the meshing portion as a spring\(-\)damper connection. Essentially, solving the dynamic model of a gear transmission system involves transforming the gear dynamics model into a system of multivariate differential equations that consider time\(-\)varying meshing stiffness, meshing damping, tooth backlash, and other parameters. The Runge-Kutta method is widely applied in numerical solutions.  
In this study, the Runge-Kutta method was chosen to solve the system of multivariate differential equations due to its advantages of high accuracy, stability, flexibility, scalability, and ease of implementation. The Runge-Kutta method effectively approximates the solutions of differential equations, and by adjusting the step size and order, it allows for a balance between computational accuracy and efficiency. Furthermore, this method exhibits good applicability to nonlinear and stiff equations. By adopting the Runge-Kutta method, we can obtain more accurate numerical solutions for the system of multivariate differential equations, providing robust support for modeling and analysis in practical problems.
## 2 Materials and Methods
### Test Definitions
Here, the temperature \(T\) and the open circuit voltage \(U_{\mathrm{OCV}}\) are further introduced:  
\[\dot{Q}=I^{2}*R_{i}-I*T*\frac{dU_{\mathrm{OCV}}}{dT} \tag{1}\]  
Figure 1: Section of modulated current signal for 160 \(\mu\)Hz data point. (**a**) General behaviour, showing the amplitude and the offset; (**b**) zoomed data to show the 5 Hz carrier signal overlaid to the tested frequency. Figure 2: Section of real measured data for 160 \(\mu\)Hz data point. (**a**) Dissipated heat by irreversible heat; (**b**) measured thermal response on the centre of the cell’s surface caused by the corresponding heating in (**a**). As mentioned above, reversible heat is excluded as a quasi-stationary SOC is maintained. That leads to heat generation independent of the current direction, shown in Equation (2):  
\[\dot{Q}=I^{2}*R_{i} \tag{2}\]  
The resulting heat generation is shown for the 160 uHz frequency in Figure 2a and the corresponding temperature response in Figure 2b. The timeframe on both x-axes represents the actual test time while measuring this frequency point. The data are then preprocessed utilising the envelope function and a second-order sinus fit created by the quadratic current to acquire the maximum value for both the heat generation and the temperature response and the phase shift between both. The values lead to the thermal impedance calculated by Equation (3), using \(\Delta\varphi\) as the phase shift between the generated heat \(\dot{Q}\) and the temperature response \(\Delta T\) at a particular frequency:  
\[Z_{th}(f)=\frac{\dot{\Delta T}(f)}{\dot{\dot{Q}}(f)}=\frac{\dot{\Delta T}}{ \dot{\dot{Q}}}*e^{j\Delta\varphi} \tag{3}\]
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 4 Results
### Analysis of Temporal and Spectral Characteristics of Paddy Rice  
Figures 4 and 5 shows the temporal patterns of nine variables (visible, NIR and SWIR bands, and SAR backscattering) for each land cover class at sites A and B, respectively. Although atmospheric correction was conducted through ENVI FLAASH, there are some fluctuations (Figure 5). This is because the graphs are not in order of years, but in order of months to better show both the phenological \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline  & **S1 RF** & **S15 SVM** & **S2 RF** & **S2 SVM** & **S3 RF** & **S3 SVM** & **S4 RF** & **S4 SVM** & **SSRF** & **SSSVM** & **S6 RF** & **S6 SVM** \\ \hline S1 RF & - & - & - & - & - & - & - & - & - & - & - & - \\ S1 SVM & **2.00** & - & - & - & - & - & - & - & - & - & - & - \\ S2 RF & **2.00** & 0.00 & - & - & - & - & - & - & - & - & - & - \\ S2 SVM & 0.81 & 1.41 & 1.41 & - & - & - & - & - & - & - & - & - \\ S3 RF & **4.54** & **5.29** & **5.29** & **4.75** & - & - & - & - & - & - & - & - \\ S3 SVM & **4.02** & **5.10** & **5.10** & **4.54** & 0.45 & - & - & - & - & - & - & - \\ S4 RF & 0.81 & 1.41 & 1.41 & 0.00 & **4.91** & **4.71** & - & - & - & - & - & - \\ S4 SVM & 0.38 & **2.24** & **2.24** & 1.73 & **4.42** & **4.04** & 1.73 & - & - & - & - & - \\ S5 RF & 1.34 & 1.00 & 1.00 & 0.58 & **5.01** & **4.81** & 0.58 & 1.63 & - & - & - & - \\ S5 SVM & 0.82 & 1.41 & 1.41 & 0.00 & **4.91** & **4.71** & 0.00 & 1.34 & 1.00 & - & - & - \\ S6 RF & **2.00** & 0.00 & 0.00 & 1.41 & **5.29** & **5.10** & 1.41 & **2.24** & 1.00 & 1.41 & - & - \\ S6 SVM & **2.00** & 0.00 & 0.00 & 1.41 & **5.29** & **5.10** & 1.41 & **2.24** & 1.00 & 1.41 & 0.00 & - \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of McNemar test results (1 \(\mathrm{z}\) \(\mathrm{\SIUnitSymbolMicro}\)-values) of binary classifications for site A. The best classification for scheme 1 was used. \(\mathrm{\SIUnitSymbolMicro}\)-values are in bold if significantly different using \(\alpha\) = 0.05.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 4 Proposed Defense System
### Denoising  
The basic idea of the denoising function is to remove or reduce perturbations or noise in input audios. Here, we applied the method of noise reducing proposed in [12] as our denoising function.  
As indicated in [12], the denoising method uses the spectral gating to reduce noise in an audio. Specifically, given both signal and noise audio clips, it transforms time-domain waveforms into the frequency domain, then removes the noise from the signal in the frequency domain, and finally transforms the modified signal from the frequency domain back to the time domain.  
To obtain a noise audio clip, we assume that the noise or the perturbation is white and follows a normal probability distribution, based on the observations of the adversarial audio from Figure 3. That is, we consider that the perturbations are Gaussian noise, and \(p_{i}\) in Equation (4) is assumed to have the following normal distribution with a mean of 0 and a standard deviation of \(\sigma\):  
\[p_{i}\sim N(0,\sigma^{2}). \tag{11}\]  
The transformation of a signal from the time domain to the frequency domain is based on the short-time Fourier transform (STFT), which is widely applied in digital signal processing [39]. Similarly, inverse STFT (iSTFT) is used to transform the signal from the frequency domain back to the time domain [39]. The algorithm of the denoising function is summarized in Algorithm 2.  
Figure 4: The proposed defense system.  
```
1:Input: an audio clip \(A_{1}\), noise variance \(\sigma^{2}\)
2:Output: a denoised audio clip \(A_{2}\)
3:Require: normal distribution generator \(N\), short-time Fourier transform \(STFT\), inverse short-time Fourier transform \(iSTFT\)
4:
5:noise clip \(\leftarrow[0,0,\cdots,0]\), size = length of \(A_{1}\)
6:for each signal noise[i] in noise clip do
7:noise[i] \(\leftarrow N(0,\sigma^{2})\)
8:endfor
9:\(n_{stft}\gets STFT\)(noise clip)
10:\(A_{1}\_stft\gets STFT(A_{1})\)
11:\(A_{2}\_stft\leftarrow\) Remove noise from \(A_{1}\) based on \(n_{stft}\) and \(A_{1}\_stft\)
12:\(A_{2}\gets iSTFT(A_{2}\_stft)\)
```  
**Algorithm 2** Denoising Function  
We applied the Librosa tool [40] to plot the mel spectrograms of an adversarial audio and the corresponding denoised adversarial audio with \(\sigma=0.002\) in Figure 4(a),b, respectively. The adversarial audio is the one whose waveform was shown in Figure 4(a) and is with the sampling frequency of 16 kHz. The parameters used in generating the mel spectrograms are the following: the Hann window with the length of 400 for the analysis window, 512 for the length of the fast Fourier transform (FFT), and 160 for the hop length. It can be observed from Figure 4(a),b that the background blue color of the denoised adversarial audio is much darker than that of the adversarial audio, indicating that the denoising method can effectively remove or reduce the background noise from an adversarial audio.
# A Study on the Applicability of Waste Glass Wool and Waste Mineral Wool as Fiber Reinforcement
## 3 Methodology
### Freezing and Thawing Resistance  
Concrete is a porous and brittle material, and defects may occur due to reduced strength or crumbling when constructed in a site where freezing and thawing environments are repeated, such as in a cold area [36]. In particular, when water absorbed into the concrete structure is frozen by the surrounding environment, volume expansion occurs, and there is a larger space for moisture to move into the concrete. After then, as the freezing and thawing process repeatedly occurs, the deterioration of concrete accelerates, significantly reducing overall durability such as penetration resistance of chlorine ions and harmful substances. Therefore, evaluating the freezing-thawing resistance of concrete is one of the parameters to evaluate the durability of concrete during the service life. This experiment was performed to evaluate whether the addition of waste fibers to concrete had an effect on the freezing-thawing resistance. The experiment was conducted according to ASTM C 666 \"Standard Test Method for Resistance of Concrete to Rapid Freezing and Thawing\" [37]. After the specimens in the form of a prism of 100 \(\times\) 100 \(\times\) 400 mm at the age of 14 days was placed in the equipment, the specimens were subjected to freezing-thawing in one cycle of 4 h in the temperature range of \(-\)18 \({}^{\circ}\)C to 4 \({}^{\circ}\)C as shown in Figure 6. The evaluation was conducted through visual rating and weight retention ratio every 30 cycles, and the reduction ratio of the relative dynamic modulus of elasticity was reviewed. The dynamic  
Figure 5: Principle of flexural strength test.  
elasticity modulus of elasticity was measured using the equipment as shown in Figure 7. The test was conducted until the relative dynamic modulus of elasticity ratio was reduced to 60% or less or until the freezing-thawing action reached 300 cycles as specified in ASTM C666.
## 3 Materials and Methods
### Selection of Features  
Selecting appropriate features is critical for the sEMG-based gait phase classification. The features can be divided into three categories: Time-domain (TD), frequency domain (FD), and time-frequency domain (TFD).  
The TD feature can be obtained directly from the raw sEMG, which results in a small computation overhead and guarantees the real-time performance of the classifier [39]. In this study, we choose five TD features, including [PERSON]'s set (mean absolute value (MAV), zero crossings (ZC), waveform length (WL), and slope sign changes (SSC)), and RMS. Hudgin's set is the most widely used feature set in the sEMG- based pattern recognition with fair performance [40]. RMS is chosen because it can characterize the change of the sEMG amplitude caused by muscle fatigue [15].  
The FD feature is extracted from the Fourier transform of the signal. In the FD features, we choose MDF and MPF because of their abilities to reflect the fatigue-caused frequency changes [18; 37; 38]. Although the TFD features also perform well in fatigue evaluation [19; 20], their practical application is still limited by the computational complexity. In this paper, the TFD features are not included.  
In addition, a newly proposed feature spinal module action (SMA) is included in this study, which carries deeper physiological information in a \"population coding\" way to express movement.  
In a word, eight features are considered in this paper, and they are MAV, ZC, WL, SSC, RMS, MDF, MPF, and SMA.  
Figure 4: A sample segment of the raw sEMG signal. The color of the curve denotes the label (gait phases) of the signal.  
Figure 3: The positions of the sEMG sensors.
## 3 Results and Analysis
### SCB Test and Analysis
#### 3.1.2 Digital Image Measurement System  
The digital image measurement system consisted of two fixed light sources, image acquisition equipment (two CCD cameras), calibration equipment, image processing equipment, a loading system, etc. The basic principle of digital image correlation (DIC) technology is shown in Figure 7. Speckle images before and after the surface deformation of the measured object were recorded by image acquisition equipment. The reference subregion with displacement point (x, y) as the center and side length of (2 M + 1) \(\times\) (2 M + 1) was selected in the speckle images before deformation. By searching the speckle image after deformation, the target subregion with the greatest correlation with the reference subregion was found. The center point of the target subregion was defined as (x', y'). The horizontal and vertical displacement components u and v of the subregion before and after deformation were obtained by the changes of the center points of the reference subregion and the target subregion after deformation.
###### Abstract
In the present work, we explore two important aspects of the analytical approach to the one diode/two resistor (1-D/2-R) equivalent circuit model for solar panels:  
* Selection of a proper ideality factor for extraction of the model parameters based on analytical methods, as this forms the cornerstone for extracting the other four parameters [57], and;
* Development of a simplified approach to the Lambert W-function, which is required in this analytical methodology in order to:
* Obtain the value of the first parameter of the 1-D/2-R equivalent circuit model, i.e., the value of the resistance of the series-connected resistor, and;
* Solve the implicit equation of this model to derive the output current in relation to the output voltage. Our simplified approach to the Lambert W-function is also proven to be a relatively powerful tool for extracting the parameters of some of the most accurate explicit equations for photovoltaic modeling that can be found in the literature: the models of [PERSON] and [PERSON] [58; 59] and Das [60]. The aim of this paper is to define very simple methodologies to measure the performance of photovoltaic devices (solar cells/panels), thus that these can be implemented as part of more complex simulations. One example of this type of simulation is the coupled thermo-electrical modeling of space systems with ESATAN(r) [61]. It should also be emphasized that these methodologies may be useful tools for professionals within small and medium-sized enterprises in the solar energy sector to allow them to estimate the performance of the systems they design. Additionally, the increasing use of photovoltaic generation in small grids [62; 63] could increase the worth of the easy approximations to solar panels performance included in this paper. This paper is organized as follows. A simple model of solar panels is described in Section 2, and simplified equations for solving the 1-D/2-R equivalent circuit model and the Lambert W-function are given in Section 3. A case study is also described and solved in Section 3, using the procedures described in the preceding sub-sections. Finally, conclusions are presented in Section 4.
## 4 Experimental Design  
The experimental setup for tool fault detection under operating conditions is illustrated in Figure 2. The GSVM714A NC milling machine (Deusi Numerical Control Technologies Co., Ltd., Nanjing, China) is used for the experimental test. The tools used in this test are carbide cutting tools (#RPMW1003#) and a face milling cutter with four teeth (#EMR 5R160-40-8T#). Owing to their effectiveness and convenience [35; 36], vibration signals were collected indirectly in operation states  
Figure 1: Damage diagnosis procedure based on hybrid Stationary subspace analysis, SSA: stationary subspace analysis, LS-SVM: least squares support vector machine.  
using a laser vibrometer from Polytec Inc. (shown in Figure 3). These vibration signals were sent to a data acquisition instrument (MI-7016 Avant, Econ Technologies Co., Ltd., Hangzhou, China) and stored on a personal computer.  
Tools in this experiment are considered to be in one of three states: (1) normal; (2) mild wear; or (3) severe wear (as shown in Figure 4). Aluminum alloy samples #7075-T351# (150 mm \(\times\) 100 mm \(\times\) 500 mm) were fixed on the machining center. Cutting speed was set to 1000 rpm and cutting depth to 1 mm.  
Figure 4: Samples of different tool states, (**a**) mild wear tool; (**b**) severe wear tool.  
Figure 3: Measuring vibration signal by a laser vibrometer.  
Figure 2: Experiment design.  
It was assumed that all investigated tools have the same damage mechanism. Vibration signals in three tool fault classes are measured and total 180 samples were taken, out of which 60 samples from each condition of the tool for a time interval of 1s at sampling frequency of 48 kHz. 60 samples of each tool condition divided into two parts: 40 samples as training set and 20 samples as testing set. The training set is used to train the LS-SVM model in MATLAB 2012R.
## 3 Results and Discussion
### Method Validation  
#### 3.2.1 Specificity  
Method specificity was evaluated by comparing retention time and UV spectra of standard mixtures and HST samples. Comparisons showed retention times and UV absorbances of HST samples and the four reference standards were well matched (Figure 4). The wavelengths used for analysis were as follows; berberine (\(\lambda_{\rm max}\) = 228.3, 263.8, and 346.2 nm), palmitate (\(\lambda_{\rm max}\) = 226.0, 273.3, and 343.8 nm), geniposide (\(\lambda_{\rm max}\) = 239.0 nm), and paeoniflorin (\(\lambda_{\rm max}\) = 231.9 and 274.5 nm).