esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Influence of Dust Removal Fan Structure Parameters on Dust Removal Performance
## 4 Numerical Simulation Results and Analysis
### Influence of the Number of Driving Impeller Blades on the Performance of Dust Removal Fan  
The number of blades of the driving impeller of the original dust removal fan is 13. Now, keeping the shape of the blades unchanged, six dust removal fan impeller models with different numbers of driving impeller blades are established. The number of blades for each model is shown in Table 5.  
In the numerical simulation of six dust removal fan models with different numbers of driving impeller blades, the inlet speed was set to 16.405 m/s, the speed of the driving impeller was set to 2900 r/min, and the dust particle size was set to 10 \(\upmu\)m when the discrete phase was added. The numerical simulation results are summarized in Table 6.  
The relationship between the total pressure efficiency, static pressure efficiency, and dust removal efficiency in the table and the number of blades is depicted in a line chart, as shown in Figure 14.  
According to the numerical simulation results, the total pressure efficiency, static pressure efficiency, and dust removal efficiency of the dust removal fan all decrease with an increase in the number of driving impeller blades. This occurs because when the number of dust removal fan blades is too large, the excessive number of blades reduces the flow channel area, obstructing the flow and diminishing the total pressure efficiency and static pressure efficiency of the dust removal fan. Consequently, the decrease in total pressure and static pressure reduces the likelihood of dust particles being captured by the water film on the wall of the dust removal fan, resulting in reduced dust removal efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of** **Blades**} & \multirow{2}{*}{**Total** **Pressure/Pa**} & \multirow{2}{*}{**Static** **Pressure/Pa**} & \multirow{2}{*}{**Impeller Shaft Power/kW**} & \multirow{2}{*}{**Total Pressure Efficiency/\%**} & \multirow{2}{*}{**Static Pressure Efficiency/\%**} & \multirow{2}{*}{**Dust Removal Efficiency/\%**} \\  & & & & & & \\ \hline
10 & 1525.90 & 1204.85 & 19.136 & 52.60 & 41.54 & 83.73 \\ \hline
11 & 1540.24 & 1217.53 & 19.660 & 52.02 & 40.86 & 83.24 \\
12 & 1543.52 & 1223.60 & 20.053 & 50.78 & 40.25 & 82.84 \\
13 & 1546.77
# Effects of the Configuration of Trailing Edge on the Flutter of an Elongated Bluff Body
## 3 Results and Discussions
### Phase Lag between Torsional and Vertical Vibration  
In order to investigate the coupling characteristics of the vertical and torsional degrees of freedom when the bridge deck vibrates, the amplitude ratio of torsional vibration to vertical vibration, and the phase difference \(\varphi_{d}\) between the torsional degrees of freedom \(\varphi_{t}\), and the vertical degrees of freedom \(\varphi_{\text{p}}\) when bending-torsional coupling flutter occurs are analyzed.  
The amplitude ratio of torsional vibration to vertical vibration is calculated as follows:  
\[\gamma=\frac{\text{RMS}(\theta\times B/2)}{\text{RMS}(y)} \tag{4}\]  
Figure 19 shows the amplitude ratio of torsional vibration to vertical vibration at various apex angles of the trailing edge. It can be found that the torsional vibration dominates the bending-torsional coupling system in case 30c-45, where the amplitude ratio of torsional vibration to vertical vibration is 5.96, and then amplitude of vertical vibration gradually increase as the trailing edge becomes blunter. At case 30c-180, the amplitude ratio of torsional vibration to vertical vibration is 0.65.  
The phase difference between torsional vibration and vertical vibration is calculated as follows:  
\[\varphi_{d}(f)=\left\{\begin{array}{ccc}\arctan\frac{\text{imag}\big{\{}S_{ \theta y}(f)\big{\}}}{\text{real}\big{\{}S_{\theta y}(f)\big{\}}}&\text{if}& \text{real}(S_{\theta y})\geq 0\\ \arctan\frac{\text{imag}\big{\{}S_{\theta y}(f)\big{\}}}{\text{real}\big{\{}S_{ \theta y}(f)\big{\}}}+\pi&\text{if}&\text{real}(S_{\theta y})<0\end{array} \right\} \tag{5}\]  
where \(\varphi_{d}\) is phase difference, \(S_{\theta y}\) is the cross-power spectrum, and \(f\) is the coupling vibration frequency.  
Figure 20 shows the phase difference between torsional vibration and vertical vibration at various apex angles of the trailing edge. For the 30c-30, 30c-45, 30c-60, 30c-90, and 30c-180 cases, the vertical vibration lags behind the torsional vibration by \(-3.8\), \(33.5\), \(-7.8\)  
Figure 19: The amplitude ratio of torsional vibration to vertical vibration at various apex angles of the trailing edge.  
Figure 18: Phase trajectory of case 30c-180 in different time periods when \(U_{t}\) is increased from 148.75 to 153.13. (**a**) Torsional degree of freedom; and (**b**) vertical degree of freedom.  
\(-12.6\), and \(-17.2\) degrees, respectively. Except in the case 30c-45, the phase gradually decreases (gradually moves away from 0 degrees) as the trailing edge becomes blunter.
# An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
## 3 System Model
### Description
This multi-hop capability extends the reach of critical messages like accident/congestion notifications, ensuring safety information is disseminated as far as needed despite link interruptions. In summary, Direct LoS VLC links will only be available between a subset of vehicles based on obstruction/spacing. NLoS is supported through reflections off parked vehicles. Multi-hop VLC/RF relaying extends the reach of safety messages for vehicles without direct connections. In mixed LoS/NLoS topologies, vehicles utilize the available VLC, RF, or multi-hop options based on their location and surrounding links. Very congested intersections may still rely more on RF broadcasts, acknowledging VLC limitations with total vehicle obstruction. VLC provides high bandwidth for real-time beaconing when LoS is available, powering dynamic topology maps. RF enables basic emergency alerts even when blocked, and partitions coverage into managed zones. Together they maximize dissemination and connectivity everywhere from open roads to dense intersections. These techniques are implicitly part of achieving comprehensive connectivity even in NLoS conditions or blockages, alongside direct LoS communication when available. Together this ensures critical safety messages disseminate as widely as needed. This paper limits its scope to the rapid notification of accident and congestion information although there are several other potential message types that could be disseminated using our approach, such as road condition updates reporting slippery roads, potholes or debris, planned lane closure or roadwork alerts to guide traffic rerouting, weather alerts informing about upcoming heavy rain, emergency vehicle notifications to clear a path, just to name a few. In our work, we have considered two types of messages, i.e., accident information and congestion/traffic jam data. The accident information originates from the vehicle directly involved in the incident (i.e., source s) and must be back-propagated to the preceding vehicles that have not yet reached the location. The source located in the intersection (as illustrated in Figure 2--top) broadcasts the accident message simultaneously over its assigned VLC frequencies/beams and the RF channels. In an intersection (as illustrated in Figure 2--bottom), a hybrid VLC-RF system is considered and the region is divided into \(n\) regions of interest (ROI) zones. Only the vehicles located in these ROI zones that precede the source need to receive the emergency message. The proposed approach utilizes simultaneous VLC and RF broadcasts from the source vehicle, while spatially restricting the message region via zoning. This is intended to disseminate accident alerts efficiently to impacted vehicles in the surrounding area. By leveraging both technologies and restricting the broadcast region, the goal is to disseminate alerts to relevant vehicles without overloading those not directly affected. Figure 2: Regions of interests for a direct street and an intersection.
## 3 Polynomial Chaos Expansion (PCE) for the Gear System with Random Error
### Precision Analysis of Polynomial Chaotic Expansion Model  
In order to effectively compare the accuracy and computational efficiency of PCE models of different orders, this paper dosed a random sampling based on the original system model and the PCE model and solved the dynamic uncertain response of the system by using the Monte Carlo simulation (MCS) and the PCE method, as shown in Figure 15. Due to the limitation of calculation time, the sample size was set at 1000. In the figure, the red curve is the dynamic meshing force of the system without considering the random disturbance of error. The black curves are the system response results of 1000 samples, based on the MCS method. The blue curves in Figure 15a,b are, respectively, the system response results of 1000 samples based on the two-order PCE model, with \(N=30\) and \(N=60\). The blue curves in Figure 15c,d are, respectively, the system response results of 1000 samples based on the three-order PCE model, with \(N=70\) and \(N=140\). It can be seen that when the number of samples needed to determine the PCE coefficient was set as \(N=4M\), the calculation accuracy was significantly improved, compared with \(N=2M\) for the two-order or three-order PCE model.  
In order to make a more intuitive comparison, this paper further calculated the mean value and standard deviation of the peak-to-peak value of the dynamic meshing force (shown in Table 3) and the relative error (shown in Figure 16) for the two methods. It is worth noting that the computing time shown in the table is the time when four-core parallel computing was adopted, and the calculation time of the PCE model was mainly consumed by solving the PCE coefficient. It can be seen intuitively from Table 3 and Figure 16 that when \(N=4M\), the mean values obtained by the two-order and three-order PCE models were basically consistent with the calculated results of the MCS method, and the relative errors were both less than 1%. However, for the standard deviation, only the relative error between the three-order PCE model with \(N=4M\) and the MCS method was less than 2%. By comparing the accuracy and calculation time, this paper used the three-order PCE model (\(N=4M\)) to analyze the dynamic uncertainty of the gear system under the influence of random error.  
Figure 16: Relative errors between the MCS method and PCE models of different orders: (**a**) the mean value and (**b**) the standard deviation.  
Figure 15: Dynamic response curve of the gear system under random disturbance with multiple errors, based on the MCS method and PCE models of (**a**) two-order with \(N=30\), (**b**) two-order with \(N=60\), (**c**) three-order with \(N=70\) and (**d**) three-order with \(N=140\).
## 2 Proposed Methodology for Industrial Electric Load Identification
### Introduction to the Joint Time-Frequency Entropy Feature
#### 2.2.1 Time-Frequency Domain Features  
The time domain feature (TD) used in this paper consists of 16 time-domain statistics. These include unitary statistics and unitless statistics. The unitary statistics usually have an intuitive physical meaning and include mean value, maximum value, minimum value, root mean square, peak-to-peak value, standard deviation, mean amplitude, root mean square amplitude, skewness, and kurtosis. Unitless statistics are usually written as the product or ratio of two statistical units whose final units cancel each other, and include skewness metrics, kurtosis metrics, margin metrics, impulse metrics, peak metrics, and waveform metrics. The frequency domain features (FD) used in this paper consist of 13 frequency-domain statistics. These 13 features include the mean value, standard deviation, center of gravity, root mean square, mean value of spectral cliff, standard deviation of spectral cliff, skewness of spectral cliff, two main band position change indicators and four correlation statistics showing the concentration or dispersion of the frequency spectrum.  
The frequencies required for the calculation are obtained by the fast Fourier transform, a fast algorithm that performs the discrete Fourier transform on a computer. In signal processing, the computation of the discrete Fourier transform has an important place. The discrete Fourier transform can achieve correlation, filtering, and spectral estimation of signals. The following defines a discrete-time signal \(x(n)\) to continuous Fourier transform.  
\[X\Big{(}e^{j\omega}\Big{)}=\sum\limits_{n=0}^{N-1}x(n)e^{j\omega n} \tag{1}\]  
Because it is a continuous function, it cannot be calculated on a computer. Therefore, we can discretely approximate the spectrum of \(x(n)\) and then perform the spectrum analysis on the computer. The definition of a discrete signal \(x(n),n=0,1,2,\cdots,N-1\) to discrete Fourier transform, is provided as follows:  
\[X(k)=\sum\limits_{k=0}^{N-1}X(k)w_{N}^{-nk} \tag{2}\]  
where \(w_{N}=\mathrm{e}^{(-j2\sigma/N)},n=0,1,2,\cdots,N-1\). The inverse transformation of the defined transformation is as follows:  
\[x(n)=\frac{\sum\limits_{k=0}^{N-1}X(k)w_{N}^{-nk}}{N} \tag{3}\]The discrete Fourier variation in matrix form is \(X=A\cdot x\), where the transformation matrix \(A\) is:  
\[A=\begin{pmatrix}1&1&\cdots&1\\ 1&w_{N}^{1}&\cdots&w_{N}^{N-1}\\ \vdots&\vdots&\ddots&\vdots\\ 1&w_{N}^{N-1}&\cdots&w_{N}^{(N-1)^{2}}\end{pmatrix} \tag{4}\]
## 2 Review of Robust Statistics  
Robust estimation procedures have been developed and studied in statistical data analysis to replace the LSE for unknown estimation. Robust estimation algorithms perform statistical analysis of test data to detect and exclude the outlier data measurements for more accurate estimation. In normal Gaussian distribution conditions, LSE has several advantages such as simplicity of calculation. In addition, the properties of the stochastic and functional models do not change from start to end.  
However, multiple classes of robust estimators have been introduced for data sets having multiple outlying observations and with the nonlinear behavior. A robust estimation algorithm is defined on three features: efficiency, stability and breakdown point. Efficiency is the performance of an estimator for an assumed model. Stability refers to the size of impairment in the performance for small deviations from the model assumptions. A robust estimation process must be stable in the sense that small deviations from the model assumptions should not impair the performance. The measure of the maximal fraction of outlier observations in any data for a robust estimator, without spoiling the final estimate, is defined as the breakdown point. A robust procedure should have an optimal or near optimal efficiency at the assumed model. It should be stable in case of small deviations from the assumed model and avoid catastrophe in case of large deviations from the model. The Least Median Square Estimator (LMSE), proposed by [PERSON] [22] and further improved by [PERSON] [23], is the first estimator that achieved the maximum asymptotic break down point of 1/2. Conventional LSE is based on minimizing the sum of the squared residuals. In the LMSE, the sum is replaced by the median of the squared residuals. However, the relative efficiency of this estimator is 0 with respect to LSE because of its low rate of convergence [24]. In [25], [PERSON] and [PERSON] proposed the first high breakdown robust regression estimator--named S-estimator--that can achieve both high breakdown point and convergence rate. However, [PERSON] proved that the regression S-estimator has maximum asymptotic efficiency 0.329 at the normal distribution [26]. Under the condition of normality, [PERSON] introduced the concept of weighted LSE (WLSE) by designing a procedure to minimize the weight of outlying observations to reduce the effect of the outliers [27]. Later on, this concept became very popular in navigation research where weights are assigned based on the geometry (elevation and azimuth) and quality of signals [28; 29]. [PERSON] proposed a robust estimator that can achieve nearly optimal efficiency as well as maximum breakdown point and called as MM-estimator [21]. [PERSON] and [PERSON] introduced a robust and efficient reweighted least squares estimator (REWLSE), which combines a high breakdown point with the full asymptotic efficiency for normal errors [30].
## 2 Principle of PSO-LSSVM
### Construction of PSO Optimized LSSVM Model  
1. The majority of the samples obtained from the data are used as learning samples for the PSO-optimized LS-SVM model, and several examples are used as test samples. To avoid the influence of the inconsistent data dimension and to improve the training speed, the training samples are normalized to the interval [0,1].
2. The initial settings of the PSO-SVM, including setting the adaptation threshold e, the particle dimension n, the population size m, the iteration number P, the learning factor  
Figure 1: Particle swarm optimization schematic.  
c1, c2, and the inertia factor \(\varepsilon\), randomly gives the initial solution space position \(\mathrm{X_{i0}}\) of the particle and the particle's initial velocity \(\mathrm{V_{i0}}\).
3. The test sample is predicted by the support vector machine model corresponding to each particle vector, and the prediction error of the predictive test sample is used as the individual fitness value to reflect the promotion and prediction abilities of the support vector model. \[\mathrm{E=}\frac{1}{2}\sum_{j=1}^{\mathrm{N}}\left(y_{\mathrm{j}}^{\mathrm{d}} -y_{\mathrm{j}}\right)^{2}\] (8) where N is the number of particle samples participating in the LS-SVN training, and \(y_{\mathrm{j}}^{\mathrm{d}}\) and \(y_{\mathrm{j}}\) are the LS-SVM training output values and expected output values of the Jth particle, respectively.
4. The fitness value calculated for each particle is compared with the fitness value of the current individual optimal solution. If \(\mathrm{Si(x)<pbesti}\), the current particular optimal solution is replaced by the particle; that is, \(\mathrm{pbesti=Si(x)}\), and \(\mathrm{xpbesti=xi}\).
5. The fitness value pbesti of the current individual optimal solution of each particle is compared with the current population optimal value fitness value gbesti. If \(\mathrm{pbesti<gbesti}\), the particle is used to replace the original population optimal solution; that is, gbesti = pbesti, and \(\mathrm{xgbest=xi}\).
6. After the entire group of particles is calculated, it is determined whether the termination condition is satisfied; if it is not satisfied, a new particle group is generated, and the process returns to step (3). If the termination requirement is met, the calculation ends, and the calculation result is given as an output (Figure 2).  
Figure 2: Flow chart of a calculation based on PSOLSSVM.
# Protection against Shiga Toxins
## 1 Introduction
### Gb3 and Its Interaction with Stx
Stx1 binding to Gb3 has also been shown to depend on cholesterol levels in the membrane [56; 57]. Stx2 has been shown to be more potent in mice [3] and is more often associated with disease in humans [58], although the binding affinity of Stx2 to Gb3 is lower than that of Stx1, when measured using Gb3 adsorbed on a microtiter plate [59], and Stx2 is less toxic to Vero cells than Stx1 [60]. The different pathology observed for Stx1 and Stx2 might be caused by differences in receptor binding and thus differential targeting to susceptible tissues [61; 62; 63], as well as differences in intracellular transport of the toxins [64; 65; 37; 66]. In the human body, the expression of Gb3 is restricted to only certain tissues. Normally, the highest Gb3 content is found in the microvascular glomeruli and proximal tubule cells of the kidney, consistent with the renal pathology of HUS [63; 67; 68; 69]. Gb3 is also found in microvascular endothelial cells, and during infection with STEC, main Stx-target sites are the vascular endothelium of the colon [70; 71] and the central nervous system [16; 72]. Moreover, Gb3 is expressed in platelets [73; 74], and in the carbohydrate defined P histo-blood group system, Gb3 constitutes the rare P\({}^{k}\) antigen present on erythrocytes [75]. In the immune system, Gb3 represents a lymphocyte differentiation antigen, termed CD77, which is expressed in a subset of germinal center B lymphocytes [76]. It should also be noted that Gb3 expression is frequently increased in cancer cells [77]. However, the physiological role of Gb3 is still unclear, and it is not known why Gb3 expression is restricted to certain tissues. In vivo studies of Gb3 synthase knock-out mice, which displayed a total loss of Gb3 and other globo-series glycosphingolipids, showed no changes in birth-rates and no apparent abnormalities over a year of nurturing, with the exception of total loss of sensitivity to Stx1 and Stx2 as compared to wild-type mice [78].
## 3 Results and Discussion
### BMP--Comparison of WS, RM and DDGS as AD Feedstock
As a consequence, the increased  
Figure 1: Cumulative methane production by untreated and steam-exploded wheat straw samples. WS + N: wheat straw with the addition of NH4 Cl; SE + N: steam-exploded wheat straw with the addition of NH4 Cl; WS: wheat straw; SE: steam-exploded wheat straw. Figure 2: Cumulative methane production examining the anaerobic digestion of wheat straw (WS), steam-exploded wheat straw (SE), dried distillers’ grains with solubles (DDGS) and rapeseed meal (RM) at (**A**) I/S ratios of 3.5 (_n/v_) and (**B**) 1.75 (_n/v_). availability of nitrogen inside an AD system is expected to increase the biomethane yields of the system, but only in cases where ammonium levels in the system do not exceed the AD inhibition levels [21]. With respect to methane production, the digestion of DDGS, RM, WS and SE at an I/S ratio of 1.75 (_w/w_) resulted in higher yields (up to 14%) compared to the I/S ratio of 3.5 (_w/w_). AD microorganisms usually utilise the available carbon and nitrogen sources to produce biogas and energy for maintenance. At the same time, part of the available carbon and nitrogen is channelled towards microbial proliferation. It is possible that the increased availability of the feedstock in the cases of the I/S ratio 1.75 (_w/w_) also resulted in an increase in the microbial population, and as a result, the produced gas yields were also increased.