esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Flow and Streaming in Exponential Variable Cross-Section Resonators
## 3 Physical Model and Boundary Conditions  
The physical model, shown in Figure 2, is a piston-driving exponential-shaped resonator which is closed rigidly at the small end (left end, \(x=0\)). The vibration of the piston generates the oscillatory flow field and induces standing-waves at the lowest acoustic mode in the resonator. We assume that the cross-sectional area \(s\) is a function of \(x\). That is \(s(x)\ =\ s_{0}\exp(2 mx/L)\), \(x\in[0,L]\). Here, \(m\) is the shape parameter of the resonator, \(L\) is the length, and \(s_{0}\) is the area of the resonator at the closed end. The velocity of the vibrating piston is given as \(u(t)\ =l\omega\sin(\omega t)\ =\ u_{0}\sin(\theta)\), where \(\omega\) is the angular frequency of the piston's vibration.  
An earlier study [29] describes eight different exponential resonators with \(m\) = 0, 0.6, 2.0, 2.1, 2.2, 2.3, 2.4, and 3.0; the authors revealed that the \(m\) = 2.2 exponential tube exhibits better sound-pressure amplification ability and suppression effects on shock waves, acoustic streaming, and vortices. Accordingly, in this study, the \(m\) = 2.2 exponential resonator was investigated under different acoustic vibration conditions.  
The initial state of the computation is unperturbed air, i.e., initial density \(\rho_{0}\) = 1.226 kg/m\({}^{3}\), pressure \(P_{0}\) = 101,300 Pa, kinetic viscosity coefficient \(\mu\) = 1.86 \(\times\) 10\({}^{-5}\) kg/(m.s), gas constant \(R\) = 287 J/(kg.K), ratio of specific heat \(\gamma\) = 1.4, Prandtl number \(P_{r}\) = 0.71, and acoustic speed \(c_{0}\) = 340.1 m/s. Other parameters are set as follows: the radius at the closed end is \(r_{0}\) = 0.004 m, the length of resonator is \(L\) = 0.2 m, and the angular frequency of the piston vibration is \(\omega\ =\ \Omega\ =\ c_{0}\sqrt{m^{2}+\pi^{2}}/L\); here, \(\Omega\) is the resonance angular frequency of the exponential tube and is a function of \(m\)[21].  
No-slip and adiabatic boundary conditions are applied. To compute the flux through the wall of \(b\), which relates the triangle mesh \(i\), as shown in Figure 3, a ghost triangle mesh \(g\) is constructed first. Letting \(\rho_{i}\), \(p_{i}\), and \(\textbf{U}_{i}\ =\ (u_{i},\ v_{i})\) be the average density, pressure, and velocity over mesh \(i\), respectively, the corresponding values in the ghost cell are computed as follows:  
\[\rho_{g}\ =\ \rho_{i};\quad p_{g}\ =\ p_{i};\quad\textbf{U}_{g}\ =\ -\textbf{U}_{i} \tag{11}\]  
where subscript \(g\) indicates the ghost cell. Note that the above equation should be modified at driving end, as follows:  
\[u_{g}\ =\ 2u_{0}\sin\theta-u_{i},\ \ v_{g}\ =\ -v_{i} \tag{12}\]  
Figure 2: Schematic of the exponential-shaped resonator driven by a piston.
###### Abstract  
An experimental method was developed and validated for the collection and analysis of tobacco-specific nitrosamines (TSNAs) that are present in electronic cigarette (EC) liquid or are released from aerosol samples using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. As part of this study, the relative recovery of four target TSNAs was assessed by spiking standards in a mixture of propylene glycol and vegetable glycerin. Recovery was assessed against two major variables: (1) the chemical media (solution) selected for sample dilution (acetonitrile [ACN] vs. ammonium acetate [AA]) and (2) the type of sampling filter used (Cambridge filter pad [CFP] vs. quartz wool [QW] tube). The average recovery of TSNAs in terms of variable 1 was 134 \(\pm\) 22.1% for ACN and 92.6 \(\pm\) 8.27% for AA. The average recovery in terms of variable 2 was 83.4 \(\pm\) 7.33% for QW and 58.5 \(\pm\) 12.9% for CFP. Based on these conditions, the detection limits of \(N^{\prime}\)-nitrosomornicotine (NNN), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), \(N^{\prime}\)-nitroosanatabine (NAT), and \(N^{\prime}\)-nitroosanabasine (NAB) were calculated as 4.40, 4.47, 3.71, and 3.28 ng mL\({}^{-1}\), respectively. The concentration of TSNAs in liquid and aerosol samples of six commercial EC solutions was measured as below the detection limit.  
Keywords: tobacco specific-nitrosamines; electronic cigarette; liquid chromatography-tandem mass spectrometry +
Footnote †: journal: Applied Physics
## 5 Calculation of Losses in FESS
### Copper Loss  
Copper loss, which is also referred to as resistance loss or joule loss, is calculated using the winding resistance \(R_{s}(\Omega)\) and stator current \(i_{a}(\mathrm{A})\)[60].  
\[P_{cu}=3{i_{a}}^{2}R_{s} \tag{11}\]  
Since voltage and angular speed are linearly related [61], Equation (11) can be represented as a function of the angular speed \(\omega\) (\(\frac{\mathrm{rad}}{\mathrm{s}}\)) and voltage constant \(K_{e}\) (\(\frac{V_{\mathrm{s}}}{\mathrm{rad}}\)).  
\[P_{cu}=3\frac{\left(K_{e}\,\omega/\sqrt{2}\right)^{2}}{R_{s}} \tag{12}\]
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Trust-Aware Evidence Reasoning  
Due to the openness and low barrier to entry of social media, erroneous information may be released by artificial accounts during message propagation, which can introduce noise into a fake news detection model. To avoid the impact of low-quality posts, we must calculate the credibility of posts and take highly credible posts as evidence to detect the truthfulness of source information. From the points of the publisher features and content of posts, two indicators were considered.  
1. Authority of users who publish comments: the higher the authority of users, the more reliable their comments [40]. In other words, users tend to receive information published by users with high authority;
2. Degree of recognition of other comments in the information propagation process: comments recognized by other highly credible comments have high credibility.  
To comprehensively consider these indicators together, we constructed a random walk model based on an information dispersion network that considers these indicators as the jump probabilities of random walkers in the network. The probability that random walkers will eventually travel to each node can be considered as the credibility of posts.
# Numerical Investigation on Handling Stability of a Heavy Tractor Semi-Trailer under Crosswind
###### Abstract  
Due to the large lateral area of the trailer and variable road conditions, the handling stability of a heavy tractor semi-trailer under crosswind is very important for road safety. In this present work, numerical simulation is performed to study the crosswind effects on handling stability of a tractor semi-trailer. The aerodynamic characteristics of the tractor semi-trailer under different crosswind were computed by computational fluid dynamics (CFD). Then, mathematical models to reveal the relationship between the aerodynamic forces and crosswind were constructed to serve as inputs of the multi-body dynamics to analyze the handling stability under crosswind. The performance of crosswind stability is evaluated by the response of lateral acceleration, yaw rate and the lateral displacement. The lateral acceleration and yaw rate were decreased by a maximum of 14.6% and 16.5% compared to the truck without the deflector, which showed that the crosswind aerodynamics and stability were obviously improved.  
heavy tractor semi-trailer; crosswind stability; vehicle aerodynamic; dynamic characteristics; wind deflector +
Footnote †: journal: Applied Sciences
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 4 Experimental Results and Analysis  
### Experimental Environment and Dataset  
This study utilizes the NEU-DET [41] dataset provided by Northeastern University, which contains 1800 grayscale images covering various types of defects, including crazing (Cr), inclusion (In), patches (Pa), pitted surfaces (Ps), scratches (Sc), and rolled-in scale (Rs), with 300 samples for each type. Some examples of steel surface defects are shown in Figure 7. The dataset is divided into training, validation, and test sets in an 8:1:1 ratio.  
To detect defects of different scales and various sizes, this paper analyzes the proportion of defect samples, the distribution of defect center coordinates within the images, and the aspect ratio distribution of the defects. As shown in Figure 8, the bar chart in Figure 8 demonstrates that the distribution of defect sample quantities is uneven, with significant disparities. To improve data quality and obtain a better-trained model, the mosaic data augmentation method was applied during training. This method randomly combines images from different categories, allowing multiple categories of objects to appear in a single training sample, thus making the dataset more balanced and mitigating the data imbalance problem to some extent. Figure 8 shows the distribution of defect center coordinates in the images, highlighting that defects are mainly concentrated in the central region of the images. Figure 8 illustrates the aspect ratio distribution of the defects, indicating that most defects have aspect ratios concentrated within a smaller range, although there are some defects with aspect ratios distributed over a larger range.  
The configuration of the experimental environment in this paper is shown in Table 2.  
The experiments in this paper used the default hyperparameter settings recommended by YOLOv8. To ensure the fairness of the experimental results, commonly used configurations from numerous related studies were referenced, and through experimental validation, the number of training epochs was set to 300. Based on the hardware capabilities, the batch size was set to 16. The specific hyperparameters are shown in Table 3.  
Figure 6: C2\(f\)-DS structure diagram.  
Figure 7: Sample diagram of various types of defects.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Average** & **Max** & **Standard Deviation** \\ \hline
1, 3, 7, 8 & 0.1 & 27.0 & 0.9 \\
2, 4, 5, 6 & 0.1 & 27.7 & 0.9 \\
9, 10, 11, 13 & 0.4 & 59.4 & 3.0 \\
12, 14, 15, 16 & 0.4 & 63.1 & 3.2 \\
17, 18, 19, 20 & 0.6 & 101.6 & 5.0 \\
21 & 1.6 & 160.4 & 9.6 \\
22, 23, 24, 25 & 4.4 & 312.3 & 20.4 \\
26, 27, 28, 29 & 5.6 & 356.8 & 24.1 \\
30, 31, 32, 33 & 10.5 & 474.4 & 36.4 \\
34, 35, 36, 37 & 13.5 & 557.4 & 45.9 \\
38, 39, 40, 41 & 15.5 & 574.5 & 48.5 \\
42 & 25.7 & 677.9 & 68.4 \\
43 & 37.4 & 829.1 & 92.2 \\
44 & 957.4 & 7266.5 & 1918.4 \\
45, 46, 47, 48 & 2051.3 & 14307.7 & 3781.4 \\
49 & 3428.9 & 23684.1 & 6000.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics for queue time forecasting (time in minutes). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Average** & **Max** & **Standard Deviation** \\ \hline
1, 3, 7, 8, 22, 23, 24, 25, & 6.2 & 7.0 & 0.6 \\
45, 46, 47, 48 & 6.2 & 8.1 & 0.6 \\
2, 4, 5, 6 & 6.2 & 8.1 & 0.6 \\
26, 27, 28, 29 & 6.4 & 9.7 & 0.7 \\
17, 18, 19, 20 & 6.6 & 12.4 & 0.9 \\
9, 10, 11, 13, 30, 31, 33, & 7.2 & 8.0 & 0.6 \\
12, 14, 15, 16 & 7.2 & 10.0 & 0.6 \\
38, 39, 40, 41 & 7.6 & 12.7 & 0.8 \\
21, 42 & 8.4 & 9.2 & 0.7 \\
34, 35, 36, 37 & 8.9 & 17.4 & 2.2 \\
43, 44 & 15.4 & 16.2 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics for lead time forecasting (time in minutes). Besides the discussion above, a final aspect needs to be explored. The machine currently has a lead time of 2 min. Nevertheless, according to the supplier, the new version of the software can carry out the task in half of it (1 min). Then, considering that the workers are not analyzing in parallel and the possible improvements in the machine, Table 7 shows the best alternatives for both scenarios. Based on Table 7, the best decision is to use 3 machines, as it requires less investment and has the same efficiency as 5 machines. Additionally, considering that trucks can wait in the queue and that the machine can be improved, the best number of workers is 3 as well. Then, it can be concluded that in the best scenario, when the machine is improved, the best arrangement is 3 workers to 3 machines. Nevertheless, if the machine is not improved, then the best arrangement is 5 workers to 3 machines.
## 2 LBV Test Methods
The early annular diverging tube (ADT) method first used a conical insert in a quartz tube to form a ring-shaped divergence. Later, the conical magnetic core was replaced by a stepped magnetic core to form the annular stepwise diverging tube (ASDT) [27]. In the ASDT, the flames exhibited minimal wrinkling or elongation, allowing the measurement of the critical LBV of a stationary flame. The development of the annular stepwise diverging tube aimed to overcome limitations in combustion spatial length and scale, reducing the influence of heat losses and flame elongation on actual flames. In the externally heated diverging channel method, a premixed fuel-air mixture enters through a rectangular intake at one end of the channel and ignites at the outlet, stabilizing at a specific position within the channel [28]. External heating of the channel walls aids flame stabilization and maintains nearly adiabatic conditions. By applying conservation of mass at the flame front, the laminar adiabatic burning velocity can be determined. In this method, the flame structure is significantly influenced by the flow field, and the flame front must remain planar without stretching or curvature to accurately calculate LBV values. A schematic diagram illustrating the principles of the six LBV testing methods is provided in Figure 1.  
In the aforementioned methods for measuring LBV, flame stretch fundamentally affects the measurement, a phenomenon that significantly cont  
Figure 1: A schematic diagram illustrating the principles of the six LBV testing methods: (**a**) spherical flame method, (**b**) stagnation flame method, (**c**) heat flux method, (**d**) Bunsen burner method, (**e**) externally heated diverging channel method, (**f**) annular stepwise diverging tube. and errors in numerous measurement data during the last century [29]. For instance, in the early stages, the spherical flame method struggled to achieve an ideal flame front, and the correction methods lacked uniformity, resulting in significant discrepancies in LBV measured by different researchers at different times. Even with the introduction of particle tracing techniques, the Bunsen burner method could only marginally mitigate its impact. By contrast, the other four methods showed much better agreement across tests. Subsequently, in the subsequent analysis of LBV for each component of aviation fuel, this issue will be summarized and discussed. Looking ahead, the primary focus in the upgrading and innovation of LBV measurement methods lies in eliminating the significant increase in laminar speed caused by flame stretch and minimizing thermal losses resulting from non-combustion.
## 4 Impact Analysis of Phase Noise in Time Domain
(**a**) Time domain noise corresponding to \(1/f\) power law coefficients; (**b**) time domain noise corresponding to \(1/f^{2}\) power law coefficients; (**c**) time domain noise corresponding to \(1/f^{3}\) power law coefficients; (**d**) time domain noise corresponding to \(1/f^{4}\) power law coefficients. Figure 9: Phase noise time-domain sequence curve of 5 MHz OCXO.
## 3 Results and Discussion  
### Effect of MUAE Parameters on the Recovery Yield of Quercetin from DOS  
The analysis of variance (ANOVA) of response 1 (Y\({}_{1}\)) is summed up in Table 3. The model \(p\)-value of \(<\)0.0001 indicates the high significance of the regression model. The lack of fit result was insignificant with the \(p\)-value of 0.0515, which implies a significant fitting of the regression model with the data. The model terms of X\({}_{1}\)X\({}_{2}\), X\({}_{2}\)\({}^{2}\), X\({}_{3}\)\({}^{2}\) and X\({}_{5}\)\({}^{2}\) were highly significant (\(p<0.0001\)). The terms X\({}_{1}\)X\({}_{4}\), X\({}_{1}\)X\({}_{5}\), X\({}_{2}\)X\({}_{4}\), X\({}_{2}\)X\({}_{5}\), X\({}_{1}^{2}\), X\({}_{4}\)\({}^{2}\) were significant (\(p<0.05\)). The other terms were insignificant (\(p>0.10\)). The variance analysis of the model indicated the ultrasound temperature, which had three terms with significant interactions (X\({}_{1}\)X\({}_{2}\), X\({}_{2}\)X\({}_{4}\), and X\({}_{2}\)X\({}_{5}\)), and ultrasound time with three significant interaction terms (X\({}_{1}\)X\({}_{2}\), X\({}_{1}\)X\({}_{4}\), and X\({}_{1}\)X\({}_{5}\)) followed by ethanol concentration with two significant interaction terms (X\({}_{1}\)X\({}_{5}\) and X\({}_{2}\)X\({}_{5}\)) were the most highly significant terms for recovery of quercetin from DOS. The quadratic equation relating the time and temperature of ultrasound, solvent-to-solid ratio, microwave time, and ethanol concentration is shown in Equation (3):  
\[ \begin{split}\text{Y}_{1}=8.41-0.99\text{X}_{1}-0.65\text{X}_{2} +0.42\text{X}_{5}+0.39\text{X}_{1}\text{X}_{2}+0.16\text{X}_{1}\text{X}_{4}-0. 17\text{X}_{1}\text{X}_{5}-0.25\text{X}_{2}\text{X}_{4}-0.24\text{X}_{2}\text {X}_{5}\\ +0.11\text{X}_{1}{}^{2}-0.79\text{X}_{2}{}^{2}+0.32\text{X}_{3}{}^{2 }+0.14\text{X}_{4}{}^{2}+0.31\text{X}_{5}{}^{2}\end{split}  \tag{3}\]  
where Y\({}_{1}\) is the yield of quercetin, and X\({}_{1}\), X\({}_{2}\), X\({}_{3}\), X\({}_{4}\), and X\({}_{5}\) are the coded variables for ultrasound time, ultrasound temperature, solvent-to-solid ratio, microwave time, and ethanol concentration, respectively. The correlation coefficient R for the statistical relationship between actual and predicted points was measured. The R for response 1, with an absolute value of 98.55%, indicates a very strong relationship, and the adjusted R\({}^{2}\) of 97.40% for Y\({}_{1}\) indicates a strong
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 3 Protocol and Results
### Computational Requirements and Implementation of SPEKE  
The SPEKE protocol [5] is not part of the regular Spongy Castle cryptography toolset. For this reason we had to implement it ourselves with the help of the elliptic curve support from Spongy Castle. The SPEKE protocol is a modified version of the Diffie-Hellman protocol which rather than using a generator \(P\) in the key exchange uses a generator that is extracted based on a common secret \(P=h(S)\), where \(S\) represents the secret (for example a password) and \(h\) is a hash function. The steps of the key exchange protocol are the usual Diffie-Hellman protocol steps: \(A\to B:aP\), \(B\to A:bP\), with the common extracted key \(abP\), the only difference being in the generation of \(P\) from the secret \(S\). For practical implementations, there is no hash function to directly map a secret \(S\) to a group generator, but such a group generator can be found by successive attempts. When the resulting \(P\) is not a group generator, the security level of the key exchange will be lower, but a key can still be correctly extracted. For our scenario, the security is based mostly on the inability of the outsiders to hear the noise inside the car while any device inside the car can extract the same noise, a reason for which extracting a regular point on the curve, and not necessarily a generator is sufficient.  
The code is presented in Figure 9 and explained next. By using the seed resulted from the environment noise, we initialize a pseudo-random number generator prngA that will be used on [PERSON]'s side (where A is one of the participants in the key exchange). Then, prngA is used to generate the masking bits, i.e., maskA, which are going to alter the default point (which is set to xA in the implementation) by XOR-ing it with the mask, i.e., ekeshA[i] = (byte) (maskA[i] \(\wedge\) xA[i]). Note that on the side of the other participants, since the environment noise is identical and an identical seed is thus received, the same mask and the same random point on the curve will be obtained. This will ensure that both protocol participants will generate the same random point on the curve. However, the newly generated X coordinate by XOR-ing may not be part of the elliptic curve group and thus the attempt to decode the point with ecopointA = specA.getCurve().decodePoint(ekeshA)  
Figure 8: The flowchart of the proposed protocol.  
may return an exception. To circumvent this, we add a try/catch block and regenerate until a point on the curve is reached (on average this required two attempts).  
In Table 3 we show the computational time for the pairing operation on our smartphones using SPEKE. All smartphones from our experiments can handle the SPEKE protocol in no time, i.e., a dozen milliseconds or so. This proves that handling the cryptography part of the protocol is not an issue for modern smartphones.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 3 Methodology
### Proposed Model Architecture
#### 3.4.2 BiLSTM Architecture  
LSTM tackles the problem of limited memory concerning previous data in traditional neural networks by having neurons called memory cells to keep track of the most relevant information. The LSTM networks can capture long-term dependencies in the given data [38]. We preferred BiLSTM instead of LSTM because it can understand context-dependent sequences by scanning the data both forwards and backwards simultaneously [30]. In this work, the architecture of the BiLSTM-based model contains a single BiLSTM layer followed by a one-dimensional global max pooling layer.
## 2 Theory and Analysis
In the co-moving frame (\(x^{\prime}=x-\beta_{x}t\)) with the longitudinal electric field \(E_{x}\), the ion motion is governed by:  
\[H(\xi,p_{i}^{\prime})=\gamma_{\beta}\Bigg{[}(1+p_{i}^{\prime 2})^{1/2}-p_{i}^{ \prime}\beta_{x}-\frac{m_{e}n_{c}a_{0}^{2}}{2m_{i}n_{e}}\xi^{2}\Bigg{]} \tag{2}\]  
where \(\xi=x^{\prime}\omega_{p}\) and \(p_{i}^{\prime}=p_{i}/m_{i}c\) are the normalized coordinate and momentum of the ion, the Lorentz factor \(\gamma_{\beta}=1/(1-\beta_{x}^{2})^{1/2}\) corresponds to the normalized velocity \(\beta_{x}=v_{x}/c\) of the excited longitudinal electric field, and \(v_{x}\simeq v_{laser}\). The propagating velocity \(v_{laser}\) of the laser front can be estimated as \(v_{laser}\sim\exp(-4n_{e}/n_{cr})(1-n_{e}/n_{cr})^{1/2}c\) and \(n_{cr}\simeq(1+0.48a_{0}^{2})^{1/2}n_{c}\) is the relativistic critical density for CP ultra-intense laser pulses [44; 45]. The equation \(H(\xi,p_{i}^{\prime})\leq 1\) defines the region of the ions accelerating in the \(\xi-p_{i}^{\prime}\) phase space. In overdense plasma with \(n_{e}=2n_{c}\), the ion acceleration condition \(H(\xi,p_{i}^{\prime})<1\), as shown in Figure 1a, is satisfied for ions with \(p_{i}/m_{i}c\geq 0.7\). It means that the accelerated proton with \(v_{p}\simeq 0.73c\) is already able to enter the accelerating region for \(a_{0}
## 2 Materials and Methods
### Data Processing  
Raw MALDI-MS(/MS) and RPLC-ESI-MS(/MS) files were processed using the software Data Explorer 4.11 and Xcalibur 4.1, respectively. mMass 5.5.1 was used for the peak list extraction from txt files. Bioinformatic research for peptides was carried out with Protein Prospector MS Fit and MS Tag in SwissProt database among all taxonomies, tolerance set on 0.2 Da for parent ion and 0.3 Da for ion fragments in the case of MS/MS datasets, up to four acceptable missed cleavages and the following variable peptide modifications: oxidation of methionine, proline and tryptophan, deamidation of asparagine and glutamine and phosphorylation of serine, threonine and tyrosine. Carbamidomethylation of cysteine was set as a constant modification only for conventionally digested samples. Bioinformatic research for lipids was made with the software Online Lipid Calculator and LIPID MAPS\({}^{\otimes}\) Lipidomics Gateway setting a 0.2 Da tolerance. ATR-FTIR spectra were processed with the software used for acquisition; resulting data were exported in txt format to plot the spectrum in OriginPro 2016.