esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Impact of Ventilation Methods on Droplet Nuclei Transmission in Subway Carriages
## 3 Results
### Average Contamination Range and Maximum Reach  
Figure 9 illustrates the spectrum of pollutants and the maximum dispersion of cough particles across different ventilation methods. It is notable that the calculated pollutant ranges for the two ventilation methods differ significantly. Under the SFRC method, the pollutant range is smaller and decreases with increasing wind speed.  
Both the maximum reach of cough particles under the MV method and at low wind speeds in the SFRC method exhibit relatively high values. Figure 9 shows that for the SFRC system at low wind speeds, approximately 77.1% of particles are within a 2 m range, while in the other two wind speed conditions, nearly 99.9% of particles settle within 3 m. Only a few instances reach distances beyond 3 m. In the MV system, when the air supply velocity is V = 0.8 m/s, only 47.7% of particles are within the 3 m threshold, while the majority of particles flow farther away, significantly increasing the infection risk for passengers at a distance. After increasing the wind speed, approximately 20% of particles still reach even  
Figure 8: Spatiotemporal distribution of cough droplets in the carriage with an air supply velocity of 0.2 m/s under the SFRC system.  
farther distances. At low wind speeds, both ventilation methods have high values for the maximum particle reach.  
The mass-weighted average pollution range serves as an important indicator for assessing infection risk, and Figure 9 clearly shows the differences between the two ventilation methods. Under the same air supply conditions, the mass-weighted average pollution range for the SFRC method is consistently lower than that of the MV system and remains within the social distancing threshold of 2 m. Under high wind speeds, the infection distance is significantly less than the social distancing threshold of 3 m. At a distance of 1 m from the infected individual, there is a higher risk of infection; however, the larger area of the carriage indicates the safety of this ventilation method. The SFRC system has a faster and more efficient pollutant removal capability, ensuring greater safety for passengers. The mass-weighted average range of the MV system consistently falls within the safety threshold of 2-3 m. Regardless of changes in the three wind speeds, the pollution ranges remain quite similar, and at a wind speed of 1.6 m/s, a high infection risk still exists.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 4 Workflow Stages in MLOps
### Machine Learning Preparation Phase
#### 4.3.2 ML Data Acquisition  
In traditional big data, the acquisition of data refers to the initial collection from sensors to the transport of data by interconnected networks to storage solution and a subsequent pre-processing (integration, enrichment, transformation, reduction, cleansing, etc.) [17]. Dependent on the product, not all collected data is relevant for the ML pipeline. Based on the previously declared DMP, a data engineer feeds the ML pipeline with an appropriate selection of the provided data. With support from data stewards, the optimal circumstances for obtaining problem-specific data are implemented.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 5 Experimental Result
### Impact of Re-Authentication Time
#### 5.5.2 Sensor-Level Fusion  
The sensor-level fusion provides one input sample in re-authentication time by enlarging the window size (\(T_{w}=T_{r}\)). To adjust to the longer input sequence, we introduce additional residual blocks and make the receptive field of the LFP-TCN cover the whole sequence.  
For the re-authentication time 3 and 5 s, we use 7 residual blocks with the dilation rate of \((1,2,4,8,16,32,64)\) for each block. For the re-authentication time 7 s, we use 8 residual blocks with the dilation rate of \((1,2,4,8,16,32,64,128)\) for each block.  
Table 6 tabulates the number of parameters and the averaged EERs of the sensor-level fusion at different re-authentication times. The EERs decrease for longer re-authentication  
Figure 8: Averaged EERs (%) for different training sample sizes per impostor profile.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Re-Authentication Time**} & \multirow{2}{*}{**Decision-Level Fusion**} & \multicolumn{3}{c}{**Score-Level Fusion**} \\ \cline{3-4}  & & **Mean-Score** & **Max-Score** \\ \hline
1 s & 10.95 & 10.95 & 10.95 \\
3 s & 9.97 & 8.84 & 8.09 \\
5 s & 9.15 & 8.00 & 6.98 \\
7 s & 8.65 & 7.51 & 6.24 \\
9 s & 8.21 & 7.09 & 5.73 \\
15 s & 7.21 & 6.26 & 4.81 \\
19 s & 6.77 & 5.86 & 4.20 \\
25 s & 6.12 & 5.37 & 3.74 \\
29 s & 5.86 & 5.11 & 3.55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Averaged EERs (%) of the decision and score-level fusion for different re-authentication times.  
times; however, the performances are inferior to the decision and score-level fusion. Moreover, the number of model parameters increases according to the re-authentication time, which hinders the use of memory-limited devices. This experiment shows that decision and score-level fusion are preferred for continuous authentication in terms of performance and model complexity.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 4 Error Factors of TCR RCS
### Machining Error of Length  
The limited machining accuracy will also cause the length of the TCR to not be up to the standard. Figure 10 shows the curve with the machining errors of length and RCS errors. The absolute value of the RCS error is probably proportional to the machining error of length. Again, the changes were more severe when the length decreased rather than increased. In general, the current machining accuracy of length can be guaranteed up to 1 mm, and the relative RCS errors are less than 0.1 dB for different bistatic angles. Figure 10 also shows that a length error of less than 1 mm can result from an RCS error of less than 0.1 dB at the X-band for monostatic calibration, where the RCS error is a little larger than the result of bistatic calibration. Moreover, when the length error exceeds 2 mm, we found that only  
Figure 9: The effect of interplate angle error on the RCS at different bistatic angles.  
the bistatic angle is 20\({}^{\circ}\), and the RCS error is larger than that of the monostatic calibration. Similar to the interplate orthogonality, the machining error of length does not affect the application of the TCR for bistatic radiometric calibration.
# Camouflage Backdoor Attack against Pedestrian Detection
## 2 Related Work  
### Pedestrian Detection  
Pedestrian detection is a critical research area in the field of computer vision, with a wide range of applications, including intelligent traffic monitoring, autonomous driving, crowd analysis, and video surveillance [4; 5; 6]. Over the past few decades, researchers have proposed various pedestrian detection methods, continually improving the detection accuracy and robustness. The following is a summary of some of the methods.  
Two-Stage Models: Two-stage object detection models utilize a two-step methodology, involving region proposal followed by the subsequent classification/regression of these proposals. These models consistently achieve outstanding accuracy, particularly excelling in the detection of small objects due to their two-step approach. Some representative models in this category include Cascade R-CNN [28] and Mask R-CNN [22].  
One-Stage Models: One-stage models conduct object detection in a single pass, omitting the need for explicit region proposals. These models are typically faster and well suited for real-time applications. They feature relatively simpler architectures. Representative models include You Only Look Once (YOLO) [23], SSD [24], and RetinaNet [25].  
Anchor-Based Models: Anchor-based models employ predefined anchor boxes for the prediction of object locations and classification. These models excel in multi-scale and multi-aspect-ratio object detection, offering precise object localization. Notable examples of anchor-based models include Faster R-CNN [21] and RetinaNet [25].  
Anchor-Free Models: Anchor-free models eliminate the necessity for predefined anchor boxes, instead directly predicting object locations and sizes. This approach resolves the complexities associated with anchor box design and offers improved adaptability to sparse object scenarios. Notable examples of anchor-free models include FCOS [26] and CenterNet [27].  
Previous studies have highlighted the vulnerability of pedestrian detection models to adversarial examples. In this paper, we extend this vulnerability by demonstrating their susceptibility to camouflage backdoor attacks. Our objective in this attack is to construct a tainted dataset in such a manner that any subsequent pedestrian detection model trained on it becomes contaminated with the backdoor, irrespective of the detection techniques employed.
## 3 Results Analysis and Discussion
### Example Verification
#### 3.1.2 Parameter Selection
In Model 1, the optimized quality interval objective function after determining the relevant parameters is between [0.9233 and 0.9876], so the optimized quality level of Model 1 is relatively high. Still from Table 3, it is found that the optimized period interval of Model 2 is [104; 105; 106; 107; 108; 109; 110; 111; 112; 113], the optimized cost (USS) interval is [109; 268; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123], and the optimized quality interval is [0.8003-0.9245]. The optimized results of Model 2 are consistent with the literature by [PERSON] et al. [12] on Pareto solution number, optimized period interval, and optimized cost interval. Model 2's optimized quality level, on the other hand, is greater than 0.8, which is a good level, and higher than most optimizations in the literature by [PERSON] et al. [12]. This is because [PERSON] et al. [12]'s quality model in the literature is conservative. The optimized quality does not exceed the project's average quality. Model 2's quality objective function reflects the relationship between construction period and cost, which is consistent with engineering practice. The optimized construction-period-cost-quality balance can meet the project's actual needs. Figure 7: Model 2: 3D optimal results (**a**), and 2D period–cost, period–quality, and cost–quality relationships (**b**,**c**,**d**, respectively). When the optimized results of Models 1 and 2 are compared, the optimized quality of Model 2 is lower than that of Model 1, but its optimized cost is higher than that of Model 1. When the optimized quality results of Model 1 and 2 are compared, the optimized quality differentiation of Model 1 is smaller, while that of Model 2 is larger, making it easier for decision-makers to make subjective decisions. The calculation results show that Models 1 and 2 can achieve a better balance of the three objectives of the construction period, cost, and quality. Decision-makers can select an appropriate model based on the project's actual situation to comprehensively balance and optimize the project's construction period, cost, and quality; formulate the project resource-allocation scheme and project-scheduling scheme; and effectively control the three project objectives.
## 4 Discussion  
In this paper, a systematic approach is presented for the analysis of ethane tank leakage using the FTA-FBN model. While incorporating advanced logic rules in BNs can also define conditional and data dependencies [43], logical systems have limited capabilities in handling uncertainties, particularly when dealing with large-scale problems. The BN proposed in this paper calculates the occurrence probabilities of risk events using fuzzy sets and maps the relationships among these events through fault tree diagrams. Notably, during the fuzzy processing stage, the paper innovatively employs the weakest \(t\)-norm algorithm and similarity aggregation methods, which effectively address the uncertainties and fuzziness inherent in complex system risk assessments. However, due to the lack and insufficiency of data in the oil and gas industry, the entire risk quantification process in this paper largely depends on expert opinions. Therefore, if new knowledge about risk indicators becomes available, the model needs to be continuously improved.  
In the risk assessment of the actual case of ethane storage tank leakage, the probability of occurrence of the basic event and the top event can be calculated by the FTA-FBN model, and the risk events can be ranked after the importance calculation. Through the calculation results, the key risk events of ethane storage tank leakage are identified, and the probability of occurrence is reasonable. This paper identifies the key influencing factors affecting ethane tank leakage through the minimal cut sets in FTA and conducts sensitivity analysis on these factors. This indicates that these factors are critical and need to be reduced in probability during the ethane storage process. Therefore, this paper not only identifies, quantifies, and manages the risk of ethane tank leakage but also uncovers the key influencing factors. Notably, the factor identification for ethane tank leakage risk in this paper is comprehensive, covering not only physical and chemical characteristic factors but also environmental, management, and human factors.
# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
## 4 Experiments and Analysis
### Performance of VB-Net
When we increased the number of feature nodes in BLS, we increased time consumption upon feature mapping, with simultaneous enhancement node generalization and calculation of output, while increasing only the number of enhancement nodes increased the time consumption of the last two steps. This phenomenon is explained in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature Nodes**} & \multicolumn{2}{c}{**Average Accuracy**} & \multicolumn{2}{c}{**Average Accuracy**} & \multicolumn{2}{c}{**Average Accuracy**} \\  & \multicolumn{1}{c}{**(20 \(\times\) 20 \(\times\) 20**} & \multicolumn{1}{c}{**(26 \(\times\) 26**} \(\times\) 26** & **(32 \(\times\) 32** \(\times\) 32** & **(38 \(\times\) 38** \(\times\) 38** \\  & **Voxel)** & **Voxel)** & **Voxel)** \\ \hline
100 & 81.80\% & 79.45\% & 83.99\% & 80.10\% \\
200 & 81.88\% & 80.71\% & 83.99\% & 80.42\% \\
300 & 81.88\% & 80.14\% & 83.83\% & 80.67\% \\
400 & 81.40\% & 80.59\% & 82.53\% & 80.51\% \\
500 & 81.11\% & 80.06\% & 82.33\% & 80.99\% \\
600 & 81.92\% & 79.78\% & 82.17\% & 80.18\% \\
700 & 81.19\% & 79.17\% & 82.49\% & 80.71\% \\
800 & 82.41\% & 80.02\% & 82.13\% & 80.71\% \\
900 & 81.68\% & 79.70\% & 82.41\% & 81.19\% \\
1000 & 81.44\% & 79.61\% & 81.52\% & 79.33\% \\
1500 & 81.40\% & 78.80\% & 81.03\% & 79.74\% \\
2000 & 81.72\% & 78.28\% & 80.99\% & 78.89\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy under different resolutions and feature nodes in VB-Net. Figure 4: Relationship between time cost and incremental nodes in VB-Net. To prove the proposed system had better performance in object classification tasks, we compared our classification results with some existing deep learning networks using the same dataset, as shown in Table 3. Although VoxNet [26] performed better than our method on the ModelNet10 dataset, our method ran faster than VoxNet [26]. We tested the time consumption of VB-Net and VoxNet [26] on ModelNet10, where the training epochs for VoxNet and the feature extractor were 30. The experiment showed that our method took only 39.13 s while VoxNet took 3573.78 s.
## 1 Introduction
In particular, we consider the following two-dimensional Heston volatility model interpreting the behavior of the asset value \(S\) and its volatility \(y\) at time \(t\geq 0\),  
\[\frac{dS(t)}{S(t)} = \mu dt+\sqrt{y(t)}dW_{1}(t), \tag{1}\] \[dy(t) = \kappa(\eta-y(t))dt+\sigma\sqrt{y(t)}dW_{2}(t),\] (2) \[\mathrm{cov}(dW_{1}(t),dW_{2}(t)) = \rho dt, \tag{3}\]  
where \(\mu\) is the expected return of the asset, \(\kappa\) is the rate of reversion to the mean level of the volatility, \(\eta\) is the mean level of the volatility, \(\sigma>0\) is the volatility parameter, and \(\mathrm{cov}(u,v)\) is the covariance between \(u\) and \(v\)[6; 15]. The two Wiener processes \(W_{1}(t)\) and \(W_{2}(t)\) describe the random noise in asset and volatility, respectively; they are assumed to be correlated with a constant correlation coefficient \(\rho\in[-1,1]\). Let \(v(S,y,t),\ t\geq 0\), denote the value of a European put option that is a function of asset price, volatility, and time. An application of [PERSON]'s Lemma and the non-arbitrage principle with a riskless portfolio construction leads to [4; 6; 9; 16; 17],  
\[v_{t}+\frac{1}{2}yS^{2}v_{SS}+\rho\sigma ySv_{Sy}+\frac{\sigma^{2}y}{2}v_{yy} +rSv_{S}+\kappa(\eta-y)v_{y}=rv,\ S,y>0. \tag{4}\]  
Let  
\[v(S,y,T)=\max\left\{K-S,0\right\},\ \ S,y\geq 0,\]  
be the terminal condition to use, where \(T\) is the payoff time and \(K\) is the strike price. We adopt the following mixed boundary conditions for \(S,y>0\) and \(T>t\geq 0\)[8]:  
\[v(0,y,t) = Ke^{-r(T-t)}, \tag{5}\] \[\lim_{S\to\infty}v(S,y,t) = 0,\] (6) \[v_{y}(S,0,t) = 0,\] (7) \[\lim_{y\to\infty}v_{y}(S,y,t) = 0. \tag{8}\]Set \(\tau=T-t\). Equation (4) can be rewritten as  
\[v_{\tau}=\frac{yS^{2}}{2}v_{SS}+\rho\sigma ySv_{Sy}+\frac{\sigma^{2}y}{2}v_{yy}+ rSv_{S}+\kappa(\eta-y)v_{y}-rv,\ \ \ T>\tau>0.\]  
Let \(x=\ln\frac{S}{K}\) and \(u=\frac{v}{K}e^{\tau\tau}\). For \(-\infty<x<\infty,\ y>0,\ T>\tau>0\), we observe that  
\[dy(t) = \kappa(\eta-y(t))dt+\sigma\sqrt{y(t)}dW_{2}(t),\]0  
together with constraints [4; 8; 14],  
\[dy(t) = \kappa(\eta-y(t))dt+\sigma\sqrt{y(t)}dW_{2}(t),\]1 \[dy(t) = \kappa(\eta-y(t))dt+\sigma\sqrt{y(t)}dW_{2}(t),\]2 (11) \[dy(t) = \kappa(\eta-y(t))dt+\sigma\sqrt{y(t)}dW_{2}(t),\]3 (12) \[dy(t) = \kappa(\eta-y(t))dt+\sigma\sqrt{y(t)}dW_{2}(t),\]4 (13) \[dy(t) = \kappa(\eta-y(t))dt+\sigma\sqrt{y(t)}dW_{2}(t),\]5  
We may extend the temporal domain for (9)-(14) by allowing \(T=\infty\). Further, for the sake of computation, we consider a truncated spatial domain \(\Omega=\{(x,y):-X<x<X;\ 0<y<Y\}\), for sufficiently large \(X\) and \(Y\), in the rest of our investigation. In the next section, a nonuniform spatial mesh will be introduced. Based on it, a semi-discretized system will be derived for solving (9)-(14). Dynamically balanced up-downwind difference approximations will be presented. A general linear stability analysis and computational experiments will be carried out in Section 2. Computationally evaluated rates of convergence of the scheme will also be provided. Finally, conclusions and future research intentions will be given in Section 3.
## 3 Experiments and Discussion
The best-fit values for the LC substrate were shown to be \(\varepsilon_{\perp}\) = 2.63 at 0 V and \(\varepsilon_{//}\) = 3.67 at 10 V. There was an approximate match between the predicted shift in the resonant frequency and the normalized experimental values of the phase test at bias voltages of 1.1 V, 1.2 V, 1.5 V, 2 V, 3 V, and 10 V. By increasing the bias voltage the resonant frequency was reduced and the matching relative permittivity was increased. As shown in Figure 10, the prototype achieved a phase shift of about 300\({}^{\circ}\) over the frequency range 121.5-126 GHz (3.6% tunable rage). The maximum phase shift of 306\({}^{\circ}\) occurred at 124.5 GHz. Tunable phase shift as a function of the applied voltage is shown in Figure 11. Compared with the simulation results, the maximum phase shift error was less than 12\({}^{\circ}\) in the working frequency range. This phase shift error might be caused by the following reasons. First, the device was not an infinite periodic structure as it was in the simulation, so there might be some effects caused by the boundary conditions. Second, there were finite fabrication tolerances. Third, the numerical analysis was carried out assuming the ideal LC layers, where the relative permittivity varied continuously between \(\varepsilon_{\perp}\) and \(\varepsilon_{//}\). The assumption of isotropy and homogeneity for each state of the relative permittivity of the LC in the cell might produce a small interfering resonance within the reflected field. Figure 8: (**a**) Measured reflection amplitude of the liquid crystal based phase shifter; (**b**) simulated reflection amplitude of the liquid crystal based phase shifter. Figure 9: Simulated (dashed line) and measured (solid line) reflection phase of the liquid crystal based phase shifter at different relative permittivity and bias.
## 4 Experiments
### Performance Along Different Forecasting Horizons
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**RMSE**} \\ \cline{2-6}  & **3 Steps** & **6 Steps** & **12 Steps** & **24 Steps** & **48 Steps** \\ \hline
**LSTM**. & 10.0 & 13.09 & 21.24 & 66.46 & 313.35 \\ \hline
**ETS** & 41.23 & 55.08 & 45.01 & 48.59 & 47.47 \\ \hline
**AR** & 8.39 & 16.75 & 32.14 & 36.95 & 39.75 \\ \hline
**CNN** & 12.1 & 14.93 & 17.12 & 20.5 & 23.46 \\ \hline
**MLP** & 11.83 & 14.45 & 17.89 & 21.53 & 25.01 \\ \hline
**Prophet** & 31.5 & 31.48 & 37.0 & 78.84 & 57.95 \\ \hline
**ARIMA** & 11.38 & 12.25 & 12.57 & 13.87 & 17.94 \\ \hline
**CNN-LSTM** & 12.24 & 14.72 & 13.93 & 14.93 & 20.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: This table shows how varies the RMSE error from a three step forecasting case to six and up to 48 steps. For this type of error, the ARIMA method shows a slightly better performance than deep learning methods such as CNN-LSTM, in particular in the long run. Figure 17: The graphics shows how is each type of error (RMSE, MAPE and MAXAE) distributed for the CNN-LSTM hybrid model in five different multi-step cases : 3, 6, 12, 24 and 48 steps.