esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Laser-Ultrasonics for Evaluating Textures and Anisotropy
## 3 Results and Discussion
### Stainless Steel-Flat Plate Specimens  
The wave paths through the plates are defined by the azimuthal angle \(\theta\) and the polar angle \(\rho\) as shown in Figure 9. Note, in particular, that the polar angles vary not only with the GD distance (from generating laser position to detection) but also with the number of reflections.  
As the number of reflections increases, the angle \(\rho\) diminishes progressively towards ND and the measured variation in velocity decreases. This is demonstrated for the as-received plate in Figure 10.  
Figure 11 also contains the calculated velocities based on EBSD textures and the [PERSON] arithmetic average procedure. As with the cylindrical specimens, the model values  
Figure 10: Velocity vs. azimuthal angle \(\theta=\pm 180^{\circ}\) for the circular scan with GD = \(\sim\)2 times the plate thickness. Echoes 1–4 correspond to polar angles of \(\rho=\) 45, 18, 11 and \(8^{\circ}\), respectively. The plot contains measurements (circles) from three full revolutions of the generation laser and the corresponding EBSD calculated velocities (solid lines) using Hill arithmetic approximation.  
Figure 9: Illustration of how the polar angles \(\rho\) towards ND and the azimuthal \(\theta\) towards RD are defined together with the measurement principle, with the green arrow representing the generation laser position and the red arrow representing the detection laser. GD is the separation between the generation and the detection lasers. For the same GD distance, different echoes will have different polar angles but the same azimuthal angle.  
lie somewhat above the measurements, but the angular variations are very similar. The two experimental approaches appear to be in good agreement with one another. Some corrections could be made, perhaps since the experimental and modelled values refer to group and phase velocities, respectively, but this discrepancy is believed to be small for the present degree of texture and birefringence. Geometrical conditions preclude measurements along wave paths that lie close to the plane of the sheet, i.e., polar angles more than about 70\({}^{\circ}\) in each case, since the vertical movements of the plate surface become smaller and disappear completely at polar angles of 90\({}^{\circ}\). Although the Galvano mirror method on the plate surfaces is not quite as precise as for the cylindrical samples, the numerical results are actually in excellent agreement and the greater simplicity and versatility of the former method weigh strongly in its favour for most industrial applications.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 3 SARDIMM: The Proposed NMP Architecture for RDA
### FPBF Module  
Figure 6 shows the architecture of the FPBF module, which is responsible for the floating-point butterfly operations. This module executes two floating-point butterfly operations in parallel. Each operator receives two 32-bit complex data inputs and a twiddle factor and performs a single butterfly operation using a floating-point adder and multiplier. In addition, the FPBF module supports the IFFT through IQ swapping and floating-point division. IQ swapping involves exchanging data in the real and imaginary parts that are applied to both the input and output of the FFT. Furthermore, the IFFT is implemented by dividing the result of each stage of the FFT by two.  
There are four types of MUXs, labeled A, B, C, and D, within the FPBF module, each supporting four different behaviors: normal FFT, normal IFFT, FFT followed by reference signal multiplication, and reference signal multiplication followed by IFFT. MUX A determines whether to perform a normal IFFT or an IFFT after the reference signal multiplication. MUX B selects between the FFT and IFFT operations. MUX C decides between the normal FFT and reference signal multiplication after the FFT. Finally, MUX D determines whether IQ swaps the FFT results. Table 1 details the operations conducted by the FPBF module based on the control signal of each MUX. Note that the control signal for MUX D is set to 1 during the final stage of the IFFT process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Control Signal**} & \multicolumn{1}{c}{**Operation of the FPBF Module**} \\ \hline
**MUX A** & **MUX B** & **MUX C** & \\ \hline
0 & 1 & 0 & Normal FFT \\
0 & 0 & 1 & FFT \& reference signal multiplication \\
1 & 1 & 0 & Reference signal multiplication \& IFFT \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operation of the FPBF module according to the MUX control signals.  
Figure 5: Hardware architecture of the proposed system: (**a**) SARDIMM system architecture; (**b**) NMP module architecture.
# Variational Multiscale Nonparametric Regression: Algorithms and Implementation
## 1 Introduction
### Variational Denoising  
One of the most prominent regularisation methods for image analysis is total variation (TV) denoising, which was popularised by [PERSON], [PERSON] and [PERSON] [10] (for further developments towards a mathematical theory for more general variational methods, see, e.g., [17] and references therein). In the spirit of Tikhonov regularisation, the rationale behind is to enforce certain properties for the function \(f\) fitted to the model (1), which are encoded by a convex penalty term \(R\) such as the TV seminorm. Consequently, the weighted sum of a least-squares data fidelity term (corresponding to the maximum likelihood estimation in model (1)), \(R\) is minimised over all functions \(g\) (e.g., of bounded variation) and the minimiser is taken as the reconstruction or estimator for \(f\), i.e.,  
\[\hat{f}\in\arg\min_{g}\left[\frac{1}{2}\sum_{i=1}^{n}\left(Y_{i}-g\left(x_{i} \right)\right)^{2}+\alpha R\left(g\right)\right] \tag{2}\]  
with a weighting factor (sometimes called the regularisation parameter) \(\alpha>0\). As soon as \(R\) is convex, algorithmically, this leads to a convex optimisation problem, which can be solved efficiently in practice. This also applies to other convex data fidelity, e.g., when the likelihood comes from an exponential family model. To solve (2) numerically, most common are first-order methods (see, e.g., [18]), which boil down to computing the (sub-)gradients of the least-squares term and \(R\).  
One of the statistical disadvantages of methods of the form (2) is the usage of the global least-squares term, which to some extend enforces the same smoothness of \(g\) everywhere on the domain \(\Gamma_{n}\). To overcome this issue, a spatially varying choice of the parameter \(\alpha\) has been discussed in case of \(R\) being the TV seminorm (see, e.g., [19; 20]). Other options are localised least-squares fidelities [21; 22; 23] or anisotropic total variation penalties, where the weighting matrix \(A\) is also improved iteratively based on the available data (see, e.g., [24]). We discuss another powerful strategy to cope with local inhomogeneity of the signal which has its origin in statistics and has been recently shown to be statistically optimal (in a certain sense to be made precise below) for various choices of penalties \(R\).
## 3 Results
The model summary indicates that the final model achieved a \(-2\) log likelihood of 61.919. The Cox and Snell R\({}^{2}\) and Nagelkerke R\({}^{2}\) values were 0.310 and 0.438, respectively, suggesting a moderate fit of the model. Notably, the model could classify 80.6% of the cases correctly. Specifically, the variable \"Attempts on target\" significantly positively affected the likelihood of winning (B = 1.037, S.E. = 0.320, Wald = 10.507, \(p\) = 0.001), with an odds ratio of 2.821. \"Passes into attacking third\" also indicated a positive effect (B = 0.056, S.E. = 0.027, Wald = 4.361, \(p\) = 0.037), with an odds ratio of 1.058. Finally, the variables \"Attempts on target outside area\" and \"Crosses attempted\" had a significant negative effect (B = \(-1.206\), S.E. = 0.485, Wald = 6.193, \(p\) = 0.013) with an odds ratio of 0.299 and (B = \(-0.173\), S.E. = 0.062, Wald = 7.690, \(p\) = 0.006), with an odds ratio of 0.841, respectively (Table 7). These findings highlight that increasing attempts on target and passes into the attacking third significantly enhances the chances of winning. In contrast, attempts on target outside the area and crosses attempted are negatively associated with winning.
## 4 Simulation and Discussion
### Reconstruction Performance versus the Measurement Length
#### 4.4.3 Performance of FRSVsMP and SAMP versus Variable Step Sizes  
To illustrate the influence of variable step sizes on reconstruction performance, FRSVsMP were compared with SAMP in different \(M\). Figure 10a,b demonstrate success rates versus variable step sizes for synthetic and real sparse signals. The horizontal axis represents different \(M\), and the vertical axis shows the corresponding success rates (in percentages). To make the comparison clearer, FRSVsMP was compared with SAMP in different variable step sizes for synthetic and real sparse signals.  
We can find that, for different \(M\), there is not much difference in reconstruction success rates between different variable step sizes. As the variable step size grows, the success rates decrease slightly since that a small variable step size approximates the actual sparsity level much more easily. It can be concluded that, for both types of sparse signals, FRSVsMP has advantage over SAMP in terms of success rates with the same step size.  
Figure 10: Reconstruction success rates vs. variable step sizes. (**a**) Synthetic signals; (**b**) Real signals.  
Figure 9: Reconstruction time vs. the measurement length. (**a**) Synthetic signals; (**b**) Real signals.
# Conjugate Gradient Hard Thresholding Pursuit Algorithm for Sparse Signal Recovery
Figure 2: Compressed Sensing model with \(s=4\). The core ideas of this paper include: (1) by combining the steps of HTP and CGIHT in each iteration, a new algorithm called Conjugate Gradient Hard Thresholding Pursuit (CGIHT) is presented; (2) by alternatively selecting a search direction from the gradient direction and the conjugate direction to improve the convergence rate of the iterative procedure; (3) furthermore, an adaptive step size selection strategy similar to that in normalized iterative hard thresholding (NIHT) is adopted in CGHTP algorithm, which eliminates the effect of step size on the convergence of HTP algorithm. The remainder of the paper is organized as follows. Section 2 reviews related work on iterative greedy algorithms for CS. Section 3 gives the key ideas and description of the proposed algorithm. The convergence analysis of the CGHTP algorithm is given in Section 4. Some simulation experiments to verify the empirical performance of the CGHTP algorithm is carried out in Section 5. Finally, the conclusion of this paper is presented in Section 6. Notations: in this paper, \(\mathcal{O}\) represents an empty set. \(\langle\mathbf{a},\mathbf{b}\rangle\) denotes the inner product of the vector \(\mathbf{a}\) and \(\mathbf{b}\). \(H_{s}(\mathbf{x})\) denotes an operator that sets all but the largest \(s\) absolute value of \(\mathbf{x}\). \(\text{supp}(\mathbf{x})\) is denoted to take the index set of nonzero entries of \(\mathbf{x}\). Let \(T,\hat{T}\subseteq\{1,2,\cdots,N\}\). Let \(\mathbf{x}_{T}\) denote a subvector consisting of elements extracted from \(\mathbf{x}\) that indexed by the elements in \(T\). \(\mathbf{A}_{T}\) denotes a submatrix that consists of the columns of \(\mathbf{A}\) with indices \(i\in T\). \(\mathbf{A}^{\text{T}}\) denotes the transpose of \(\mathbf{A}\). \(\mathbf{I}\) stands for a unit matrix whose size depends on the context. In addition, let \(\tau=M/N\) denote the sampling ratio.
# Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving
## 3 Results
### Measures of Workload for Non-Driving Tasks
#### 3.3.2 NASA TLX  
A multiple linear regression was conducted to evaluate the effects of location, gender, age group and IVIS type on the global NASA TLX score. Participants who did not report on all TLX dimensions were dropped from the analysis. The results show a significant effect of several independent variables on the TLX score (\(F_{(4,155)}=16.76\), \(p<0.001\), \(R^{2}=0.30\), \(R^{2}_{adj}=0.28\)). A significant effect was found for age, with perceived workload higher for the older participants (\(\beta=0.18\), \(t=2.13\), \(p=0.035\)), as compared to the younger. IVIS task type also had a significant effect (\(\beta=-0.52\), \(t=-7.69\), \(p<0.001\)). No significant effect was found for location (\(\beta=0.004\), \(t=0.005\), \(p=0.9\)) and gender (\(\beta=0.01\), \(t=0.12\), \(p=0.91\)).  
Post hoc Tukey's HSD showed significant differences in NASA TLX scores between several IVIS types, comparable to the results of objective workload. Results in Table 4 show differences in subjective and objective evaluation of IVIS workload. The NASA TLX index was normalized for the comparison with the task completion rate.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multicolumn{5}{c}{**Task Completion**} \\ \cline{2-6}  & **Est.** & \(SE\) & \(z\)**Value** & \(p\)**Value** & **95\% CI** \\ \hline (Intercept) & **0.573** & 0.047 & 12.170 & 0.000 & [0.481, 0.666] \\ Location (US) & 0.099 & 0.053 & 1.880 & 0.060 & [\(-0.004\), 0.203] \\ Task (base: A/C) & & & & & \\ contact & **0.403** & 0.057 & 7.126 & 0.000 & [0.292, 0.514] \\ dial & **0.169** & 0.057 & 2.979 & 0.003 & [0.058, 0.280] \\ playlist & **0.391** & 0.057 & 6.897 & 0.000 & [0.280, 0.502] \\ radio & \(-0.077\) & 0.057 & \(-1.345\) & 0.179 & [\(-0.189\), 0.035] \\ Age (older: 46–57) & \(-0.117\) & 0.058 & \(-2.018\) & 0.044 & [\(-0.231\), \(-0.003\)] \\ Gender (female) & \(-0.020\) & 0.045 & \(-0.459\) & 0.647 & [\(-0.108\), 0.067] \\ \hline \hline \end{tabular}  
* Significant estimates are in bold.  
\end{table}
Table 3: Estimated coefficients for task completion (objective workload measure) 1.  
As shown in Figure 2, the global TLX scores for each IVIS type correspond with the objective measurement of the IVIS workload. Note the inverse relationship: the IVIS task types that were perceived as difficult on the TLX index (with higher mean score indicating higher workload) had a lower mean completion rate, whereas those perceived as easy on TLX (lower mean score indicating lower workload) had a higher mean completion rate.
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 1 Introduction
[22] proposed to revise the FPP approaches by combining one code pattern and the geometric constraints in fringe projection to reduce the projection number of grey patterns with a low frame rate projector. To implement 3D face measurements with an FPP first approach is to project patterns with infrared or near-infrared projection, which can minimize interference to the human eye and avoid discomfort. Then, the projection should be as fast as possible and as few as possible, which can make the imaging process less sensitive to the motion of moving surfaces. Finally, the cost of the projection hardware should be minimized to reduce the cost of the final products and improve its market application. For example, the successful application of three-dimensional measurement technologies from Microsoft Kinect, Intel RealSense, and Apple iPhone X propels the application development and simultaneously drives the need for better 3D imaging technologies [23], especially for high-precision 3D data measurements for 3D face recognition. In this research, a galvanometer scanner (labelled as MEMS) device of micro-vibration mirror for sinusoidal laser scanning is used. This device is made by xiaoU technology Co., Ltd. of Qingdao China, and the model of the device is UFGKH0A1 IR01. It has one scanning axis with a 32 kHz frequency and an 830 nm laser. In this system, the laser beam is used in combination with a cylindrical mirror to form the scanning line. In principle, the projection speed of the sinusoidal fringes can be up to 32 kHz. However, due to the optical setup, the light intensity is too low to perform any measurement at full speed. Therefore, the fastest scanning speed of the MEMS is 500 fps. This MEMS, with an infrared laser scanning cost less than 100 USD, does not interfere with the human eye and meetsthe needs of 3D face recognition for measurement accuracy and speed. With an IDS 3250 camera (manufactured by IDS Imaging Development Systems GmbH in Obersulm, Germany), a system that can achieve 3D face acquisition at 8 Hz rate with good quality when using a three-step phase-shifting algorithm is developed successfully. The FPP methods of the multi-frequency (hierarchical) method [24] and multi-wavelength (heterodyne) method [25; 26] are investigated and compared in detail both analytically and experimentally. At the same time, the measurement accuracy based on the MEMS applied projector and the traditional DLP projector is compared. This setup demonstrates the fast 3D shape measurements of a real human face. The paper is organized as follows. The basic principles of fringe projection and the phase-shifting approach of the multi-frequency method and multi-wavelength method are explained in Section 2. Section 3 presents the experimental verifications with different samples such as plane models and human face. Section 4 discusses this setup and the applications. Finally, conclusions are drawn in Section 5.
###### Abstract
A DOC coupled with DPF or CDPF has been widely applied for particulate matter clarification [17]. The oxidization process within DOC also affects NO, which can benefit the passive regeneration in downstream CDPF [18] and SCR. As the EAT thermal management largely focused on the DOC, the exhaust temperature and constituents of NOx, mainly NO and NOx, will also affect the NH3-NOx reactions of downstream SCR [19]. [PERSON] et al. [20] suggested that, compared to other NOx2/NOx ratios, the efficiency of the SCR reaction can be promoted to the greatest extent when the NOx2/NOx ratio reaches 50%. Based on the inherent characteristics of high NO emission (more than 90%) of diesel engines, a feasible solution to improve De-NOx efficiency and decrease urea water solution consumption is increasing the SCR inlet NO/NO2 ratio to 1:1 [21]. [PERSON] et al. [22] proposed that the optimal ratio of NOx2 to NOx in SCR may be 1:2, at which the nitrogen removal efficiency and the amount of N2 generated are maximized, with relatively less N2O produced. By increasing the ratio of NOx2 to NOx, the NO content at the outlet gradually decreases, while the N2 content first increases and then decreases and the N2O content increases. The relative location of the DOC, whether upstream (scheme A in Figure 1) or downstream (scheme B in Figure 1) of LO-SCR, also requires consideration. A DOC deployed upstream provides the possibility to partially oxidize NO into NOx and increase the proportion of fast reactions. However, at lower exhaust temperatures, a higher NO\({}_{2}\) concentration will increase N\({}_{2}\)O generation, [23], while, at higher exhaust temperatures, possible NO\({}_{2}\) surplus caused by an increased generation rate in the DOC will further hinder the NH\({}_{3}\)-NO\({}_{x}\) reactions in SCR. Another problem for the upstream DOC scheme is its effect on exhaust temperature; at cold start conditions, the cold DOC upstream will delay the catalyst temperature increase of LO-SCR [4]. Therefore, to achieve ULN emission control at mid to low load, the EAT thermal management strategy requires detailed analysis. This work focused on the NH\({}_{3}\)-NO\({}_{x}\) reactions in LO-SCR and related thermal management strategy optimization. Two major aspects were evaluated in this work: the effect of thermal management strategy on exhaust parameters before SCR and the effect of thermal management strategy on LO-SCR performance at ULN emission control. The former was analyzed through a diesel engine EAT bench test, focused on exhaust temperature increase with an electric heater and NO oxidization in a DOC. The latter was evaluated through simulation, mainly for the comparison of different EAT system schemes, and the final Lo-SCR performance with the optimized thermal management strategy. This paper can be a reference for ULN emission control at mid- to low-load working conditions.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
### Problem Statement  
The development of simulations plays a vital role in Mobile Ad Hoc Network (MANET)-related research. All aspects of the simulation need to be as realistic as possible for it to provide valid and meaningful data. Any aspect of the simulation model will lead to significant research questions. This research does not consider the following: radio propagation models, the implementation of protocols, power consumption, or security issues. There are significant differences in the performance results of simulations due to variations in the world models used. Many MANET protocol studies still employ basic simulation models with little or no relevance to the common, real-world applications cited for mobile ad hoc networks. In order to facilitate transferability to the real world, it is necessary to employ robust and realistic simulations of real-world scenarios for the evaluation of protocols. Realistic MANET simulations add soundness and credibility to evaluations of MANET routing protocols ([PERSON] et al., 2004, [17]; [PERSON] et al., 2010, [18]); they produce vastly different results compared to commonly used simplistic models ([PERSON] et al., 2007, [19]) and will lead to real deployments ([PERSON] and [PERSON], 2008, [20]). This research article addresses three important aspects for the development of a world model: simulation time, node count, and movement aspects of realistic models. They form the bases for the measures used for the evaluation of the model. The techniques and models presented here could be applied to research areas where mobility models are necessary, though issues addressed in this research are closely related to MANET. The main contribution of this work is to provide researchers access to models that facilitate easy construction of rigorous MANET simulation scenarios. The input to our models consists of desired values for the three previously mentioned measures. Subsequently, our models output parameters for a simulation scenario that approximately meets the benchmarks. Our first MANET model is designed based on a human movement mobility model (e.g., [PERSON] and [PERSON], 2006, [21]; [PERSON] et al., 2012, [22]). The second model is a shipping model based on maritime movements in the Irish Sea. To reiterate, our models enable researchers to test MANET routing protocols in a more realistic manner, thereby improving the credibility of their MANET simulation studies.