esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Features Extraction and Encoding  
The feature extraction step has been largely dealt with in the field of image analysis applications. It is the process of acquiring relevant information such as shape, color and texture. The extraction and characterization of particular regions in a given image is one of the preliminary steps in many image processing procedures. To accomplish this objective, it is crucial to analyze these images by extracting important details (patterns) and providing an accurate description of them. If relevant and representative features are well extracted, then the corresponding images will be interpreted better and then classified correctly. It is known that the majority of classification and/or categorization methods follow a common approach that is based on extracting visual features and applying a feature quantization step (Bag of Words (BoW) model) and/or a soft assignment with mixture models. However, these attempts give little importance to feature encoding and therefore result in lossy step, as shown in [25; 26]. In order to overcome this shortcoming (i.e., reduce the quantization error), we propose here to apply a robust encoding step based on Fisher kernel [25; 27; 28], which is defined as an extension of the BoW model. The resulting Fisher Vector, which is determined by the gradient with respect to the model's parameters, contains both zero order statistics and higher order statistics of the developed infinite mixtures of the features. Thus, the obtained vector is computed by concatenating the gradient for all components of the infinite mixture model (inMGNMM). After that, we proceed with a power normalization step on the feature vector in order to obtain an invariant vector of descriptors, as in [28]. The last step consists of introducing the obtained descriptors into a linear SVM classifier to perform the classification or the categorization task. Thus, the proposed pipeline is summarized as follows (see also Figure 1):  
1. Extracting visual features from the training images;  
Figure 1: The pipeline of the proposed method. First, robust invariant visual features are extracted from each image. Then, these features are modelled using an infinite multivariate generalized Normal mixture model. The next step consists of encoding them using Fisher vectors to obtain effective descriptors. Finally, an SVM classifier is applied for categorization and classification applications.  
Encoding extracted features into robust descriptors based on the proposed infinite mixture model and a Fisher kernel to encode the higher order statistics of the infinite mixtures of the extracted features;  
Classifying and/or categorizing image descriptors using the SVM classifier.
## 1 Introduction
### Contribution  
This paper considers both the performance and the robustness of the box-shaped solution space simultaneously. The performance of the box-shaped solution space is defined as the worst value of the performance function within it. For instance, the cost of the box-shaped solution space is the maximum value of the cost function within this solution space. Usually, optimal performance and maximum volume are conflicting objectives. To this end, a multi-objective optimization problem is formulated to simultaneously optimize the performance and the volume of the box-shaped solution space, indicating a trade-off between performance and robustness.  
The proposed optimization problem involves evaluating the performance of the box-shaped solution space and addressing multiple conflicting objectives. Evaluating this performance metric is essentially a box-constrained global optimization problem. The DIRECT algorithm is a well-known partitioning-based method for efficiently finding the global optimizer. Evolutionary algorithms are popular for generating Pareto-optimal solutions in multi-objective optimization problems. Among these, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most widely used and competitive. Hence, this paper proposes a novel DIRECT-SGA-II method that combines the DIRECT algorithm with the NSGA-II algorithm. Since the DIRECT algorithm requires only function evaluation at sampling points and the NSGA-II algorithm does not utilize gradients, our approach is suitable for black-box performance functions.  
The set of Pareto-optimal solution spaces identified by the DIRECT-SGA-II method includes not only a box-shaped solution space with the largest volume but also one with optimal performance and various trade-offs between performance and robustness. Based on this Pareto-optimal set, engineering designers can select their preferred solution space. For example, if performance is the primary concern, designers can prioritize solution spaces with high performance even if they have lower robustness. Conversely, if robustness is crucial, designers can prioritize solutions with high robustness even if they have lower performance. Finally, the effectiveness of the proposed approach is demonstrated through two case studies from real-world applications.
## 6 Results and Discussion  
In this paper, four HSPMSMs with different rotor topologies for fuel-cell electric-vehicle application were designed, analyzed, and compared in terms of electromagnetism, mechanology, rotor dynamics, and thermocids. For a more intuitive conclusion, the effective performance of these HSPMSMs was compared by using a weighted-average method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Topology** & **Torque Reduction at Maximal Temperature** & **Torque Reduction after Returning 20 \({}^{\circ}\)C** \\ \hline M1 & 15.80\% & 2.45\% \\ M2 & 7.56\% & 1.66\% \\ M3 & 8.71\% & 0.72\% \\ M4 & 16.45\% & 1.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Influence of temperature variation on torque performance.  
Figure 13: Electromagnetic torque with temperature variation.  
Multiple objectives were converted into single objective by using the weighted-average method. The weight mean of finite multiset of objectives \([x_{1},x_{2},\ldots,x_{n}]\) with corresponding non-negative weights \([\omega_{1},\omega_{2},\ldots,\omega_{n}]\) can be expressed as follows:  
\[\overline{m}\ =\ \frac{\sum\limits_{i\ =\ 1}^{n}x_{i}\omega_{i}}{\sum\limits_{i\ =\ 1}^{n}\omega_{i}}, \tag{16}\]  
when the weights were normalized, such that they summed up to 1, Equation (16) was simplified as:  
\[\overline{m}\ =\ \sum\limits_{i\ =\ 1}^{n}x_{i}\omega_{i}^{\prime}, \tag{17}\]  
In this section, all results are quantified and assigned weighted coefficients in which the relative importance of each target was determined on the basis of the above FEA results. The score of the weighted average represents the relative importance of different targets and is more descriptive than a simple average. This smooths out the data and improves result accuracy. The weight coefficient is determined by the target application; for different applications, the coefficient is quite different. Therefore, for a determined application, a set of weight coefficient can be assigned, and the final score can then be obtained. For example, to drive an HS compressor for fuel cell, electromagnetic performance is assigned with a larger coefficient. However, for turbocharger driving applications, thermal performance attracts more attention. Taking the application of compressor for fuel cells as an example, the score of the optimal result at each viewpoint was 100. Other results were quantified on the basis of the above method. Figure 14a shows the quantitative comparison from the viewpoint of torque ripple, PM usage, thermal performance, etc. The numbers of the figure are their corresponding weights. Figure 14b shows the overall comparison of weighted means considering the corresponding weights. The merits of different HSPMSMs are shown via the quantitative results, and the best topology could then be determined for the corresponding applications.  
Figure 14: Comparison of different HSPMSMs. (**a**) Quantitative comparison considering single target; (**b**) overall comparison.
# Analytical Investigation of Viscoelastic Stagnation-Point Flows with Regard to Their Singularity
## 3 Analytical Solutions of a Wall-Free Stagnation-Point Flow and Their Singularities  
For the velocity field of a two-dimensional steady stagnation-point flow, there exists a similarity solution described as  
\[\mathbf{u}=(xf^{\prime}(y),-f(y)), \tag{12}\]  
where \(f(y)\) is an arbitrary function depending only on \(y\)[5, 6]. In the case of a wall-free stagnation-point flow, the flow is a potential flow, which means \(\
abla\times\mathbf{u}=0\). This leads to \(f^{\prime\prime}(y)=0\), and further \(f(y)=ay\). The parameter \(a\) stands for the constant rate of the strain and is employed in the non-dimensionalization, as shown in (10). Hence, the dimensionless velocity field is given by  
\[\mathbf{u}=(x,-y). \tag{13}\]  
The velocity field is symmetrical for both the \(x\) and the \(y\) axis, as shown in Figure 1, so we only need to consider the case with \(x,y>0\) in the following analysis.  
The stress tensor is symmetric and takes a two-dimensional form of:  
\[\mathbf{T}=\begin{pmatrix}T_{11}&T_{12}\\ T_{12}&T_{22}\end{pmatrix}. \tag{14}\]  
Figure 1: Sketch of the velocity field in a wall-free stagnation-point flow.  
Substituting (13) and (14) into (11), we obtain the following three scalar constitutive equations:  
\[W_{1}x\frac{\partial T_{11}}{\partial x}-W_{1}y\frac{\partial T_{ 11}}{\partial y} =(k_{1}-1)T_{11}-k_{2}T_{22}-k_{3}+2, \tag{15}\] \[W_{1}x\frac{\partial T_{22}}{\partial x}-W_{1}y\frac{\partial T_ {22}}{\partial y} =k_{2}T_{11}-(k_{1}+1)T_{22}-k_{3}-2,\] (16) \[W_{1}x\frac{\partial T_{12}}{\partial x}-W_{1}y\frac{\partial T_ {12}}{\partial y} =-T_{12}, \tag{17}\]  
where \(k_{1}=2W_{3}-W_{2}-W_{4}\), \(k_{2}=W_{2}-W_{4}\) and \(k_{3}=4(W_{6}-W_{7})\). It is to notice that \(\overset{\circ}{\mathbf{D}}=0\) under the assumption of the velocity (13), so the Weissenberg number \(W_{5}\) has not any influence on the stress field.  
In the PDE system (15)-(17), Equation (17) for \(T_{12}\) is uncoupled with (15), (16), and thus can be solved independently. The coupling of (15) and (16) for \(T_{11}\) and \(T_{22}\) depends on the value of \(k_{2}\). If \(k_{2}=0\), both the equations are decoupled and can be solved separately for \(T_{11}\) and \(T_{22}\). If \(k_{2}\
eq 0\), Equations (15) and (16) constitute a coupled PDE system with non-constant coefficients. In addition, the solutions of the PDE system (15)-(17) must also satisfy the compatibility condition derived by substituting (4), (13) and (14) into the rotation of the momentum Equation (1):  
\[\frac{\partial^{2}T_{11}}{\partial x\partial y}-\frac{\partial^{2}T_{22}}{ \partial x\partial y}-\frac{\partial^{2}T_{12}}{\partial x^{2}}+\frac{ \partial^{2}T_{12}}{\partial y^{2}}=0. \tag{18}\]  
In the following, the analytic solutions of the constitutive Equations (15)-(17) with consideration of the compatibility condition (18) will be derived, respectively, for the two cases \(k_{2}=0\) and \(k_{2}\
eq 0\).
# Human-Like Walking with Heel Off and Toe Support for Biped Robot
## 3 Results
### Simulation  
To verify the effectiveness of the previously proposed control scheme, co-simulations are carried out in Adams and MATLAB. Adams is the most famous and widely used multibody dynamics software, and MATLAB has powerful functions in computing, programming, and control system designing. Co-simulations are able to utilize the advantages of both programs by combining the virtual mechanical system together with the complex control system. The virtual mechanical model of the biped robot is built in Adams, and the control scheme is programmed using the Simulink toolkit in MATLAB. With the Adams/Control plugin, information can be exchanged between Adams and MATLAB, in other words, outputting joint torques to the virtual model in Adams, and feeding joint motion information back to the control system in MATLAB.  
The main physical parameters of the biped robot model are shown in Table 1. The lengths of the links from the torso to the foot are 0.4, 0.37, 0.36 and 0.21 m, respectively. Their corresponding masses are 3, 1.8, 1.5 and 0.3 kg respectively. The planned duration of the heel support phase is 0.4 s, and the toe support phase is 0.3 s. In the implementation of the hybrid control scheme, the main work is to tune the parameters of the controllers by trial and error. If the parameters are selected appropriately, the desired walking pattern can be realized. Figure 7 shows the screenshots of the realized bipedal robotic walking in one cycle. Figure 8 presents the joint angles of the biped robot during walking. These angles have the same meanings as defined in Figure 2. From these two figures, it is clear to see that from the start point to 0.4 s, the sole of the stance foot is flat on the ground when the biped robot steps forward. After 0.4 s, the angle between the ground and the stance foot sole indicated by \(\theta_{1}\) begins to decrease, which means the stance heel lifts off the ground and rotates around the tiptoe. Thus, both the heel support phase and the toe support phase are realized, demonstrating that the proposed hybrid walking controller is effective.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Link** & **Length (m)** & **Mass (kg)** \\ \hline Torso & 0.4 & 3 \\ Thigh & 0.37 & 1.8 \\ Shank & 0.36 & 1.5 \\ Foot & 0.21 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main physical parameters of the biped robot model.  
Figure 6: The reference Zero Moment Point (ZMP) trajectory of the biped robot. In the heel support phase, the ZMP moves from the heel to the toe of the stance foot. In the toe support phase, the ZMP is kept constant at the tiptoe. In the impact phase, the ZMP transfers from the tiptoe of the stance foot to the heel of the swing foot.
# Virtualization Airborne Trusted General Computing Technology
## 4 Experimental Results and Analysis
### Analysis of the Experimental Results  
According to the results of application hosting function verification, the VAGCSP provides high-performance processing capacity with its four-core eight-thread processor and mass storage capacity with 4 TB SSD, integrated one 100 Mbps ARINC664P7 interface, and six gigabit Ethernet interfaces. VAGCSP supports simultaneous residence of avionics system applications with low safety levels, such as airborne information systems, airborne maintenance systems, and cabin management systems, which can provide a secure and isolated operating environment for applications in each VM.  
The results of the platform performance verification show that compared to the existing platform, the VAGCSP VMs have greatly improved performance in terms of integer calculation, floating-point calculation, and process overheads. Regarding the file access performance, the VAGCSP performed well in file creation and file handle serial processing; meanwhile, regarding the file deletion performance, the two platforms exhibited similar performance parameters, since both of them adopt SSDs. Moreover, little differences were noted in the network transfer performance data between the two platforms, since both of them use gigabit Ethernet interfaces, with the VAGCSP VMs slightly outperforming the other platform. While the weight and power consumption of the VAGCSP are slightly less than those of AGCSP devices, the computing performance of the VAGCSP is improved by over one-fold.  
Figure 6: Results of the unauthorized network access test.  
As revealed by the verification of its network security protection capabilities, the VAGCSP can offer trusted loading of VMs and provide them with security protection covering east-west traffic access control, intrusion detection and defense, malicious code detection, etc. With a high-security hardware unit, VM security middleware unit, and virtualization security gateway, the VAGCSP provides a secure and trusted computing environment.  
Based on these experiment results, VAGCSP completely achieved the design objectives.
## 3 Results and Discussion
### Neutron Bragg Edge Imaging at HZB  
Neutron Bragg edge imaging of fresh and worn, charged and discharged batteries revealed several interesting features. In comparison to the neutron diffraction results in Section 3.4, this method combines imaging with spectral information. The spectral plots of the macroscopic attenuation cross section, \(\Sigma\), versus wavelength are generated from  
\[\Sigma=\ln\frac{\text{open beam}-\text{closed beam}}{\text{sample}-\text{ closed beam}} \tag{8}\]  
but an additional correction was needed to account for a change in reactor power between measurement of the open beam and the sample measurement. A reference area was chosen in the images and a correction applied to the discharged data as  
\[\Sigma^{\prime}(\text{discharged})=\Sigma(\text{discharged})+[\Sigma(\text{ charged(reference)})-\Sigma(\text{discharged(reference)})] \tag{9}\]  
The attenuation signal through the batteries was always higher for the discharged battery versus the charged battery except for the region near 0.34 nm. The strongest peak around 0.32 nm is likely a result of Li\({}_{x}\)CoO\({}_{2}\), where \(x=1\) for a charged battery and \(x=0.5\) for a discharged battery [42]. If the lithium is becoming inactive in the chemical process, this could explain the large difference between charged and discharged states. There are two possible explanations as to why the signal is mostly higher in the fresh battery than the worn battery. First, with inactive Li not getting involved in migration from one electrode to another, this results in a larger difference between the charged and discharged states. Second, a remaining graphite, LiC\({}_{x}\), or LiCoO\({}_{2}\) Bragg peak above 0.42 nm is also possible as the samples become opaque at very large wavelengths. The feature from 0.36-0.38 nm in the worn battery was expected due to LiC\({}_{6}\) depletion as the battery is worn [25]. A strong LiC\({}_{6}\) peak from the 111 plane is located at 0.365 nm and in combination with peaks from  
Figure 4: Dark-field images of charged (**left**) and discharged (**right**) 15 mAh Li-po batteries. In each image, the battery on the left is the fresh battery and the battery on the right is the worn battery. The autocorrelation length \(\xi=1.97\) μm and \(\lambda=0.35\) nm. The color bar represents the dark-field signal acquired through each battery.  
large carbon compounds like C\({}_{60}\) at 0.377 nm, there is overlap from several Bragg edges (Figure 5).
## 3 System Hardware and Software Structure
### Hardware and Software for In-Vehicle Driver Behavior Recognition
#### 3.1.1 Driver's Head Posture Recognition  
By deploying a camera situated between the primary and co-driver seats, specific angles of the driver's head turn are captured and analyzed. The implementation process unfolds as follows: Initially, in the context of the interior images captured by the camera, the YOLOv5 object detection algorithm is employed to perform facial recognition [16]. At this stage, the detected faces may encompass both the driver and potentially other unrelated passengers in the rear view of the driver. To ensure that the detected face only corresponds to the driver, a maximum face filtering method and a minimum face size threshold are incorporated.  
In order to enhance the real-time capability of the entire system, our model simplifies the analytical approach to the driver's head observation angles. Specific attention to the precise gaze direction of the driver's eyes is omitted and, instead, we assume that the extension line through the centers of the driver's eyes serves as the central axis for the driver's head observation angles. Facial feature points are extracted from the identified regions of interest associated with the driver's face. The Dlib method is utilized for this purpose, extracting 68 feature point locations of the eyes, mouth, nose, and contours of the face [17]. Key points such as the eye, nose, and mouth position are selected. Then, the Perspective-n-Point (PnP) algorithm is applied to compute the pose transformation from the face coordinate to the world coordinate [18]. Hence, we can calculate the orientation angles of the driver's head, including yaw, pitch, and roll.  
As depicted in Figure 4, this process enables a detailed assessment of the driver's real-time head movements and posture, facilitating a comprehensive understanding of their attentiveness and focus during vehicle operation.  
Figure 4: The performance of the driver’s head pose recognition.
## 3 Results
### LED Optical Medical Device Literature Investigation
#### 3.1.1 Key Performance Points for Treatment Using LED Optical Medical Devices  
Phototherapy is a medical technique that has been used for thousands of years to treat various diseases. An example is sun therapy by [PERSON], the father of Western medicine [6]. In modern times, various treatments using light have evolved. A common treatment that uses LEDs is photobiomodulation (PBM). This was previously called low-level laser therapy (LLLT) and was later changed to low-level light therapy by [PERSON] due to the advent of the next-generation LED with higher photon intensity [7]. After the change, the name was again changed to PBM due to uncertainty about the exact meaning of low power [8]. PBM was proposed by [PERSON] (1903-1984), a medical expert who researched and published the biological effects of low-power lasers in 1967 [9]. According to the literature, PBM is a treatment that uses a low-power laser or LED in the 1 m to 50 mW range to induce tissue regeneration and relieve inflammation. Red or near-infrared light with a wavelength of 600 nm to 1000 nm is used, and unlike other laser treatments that destroy tissue, PBM is known for its photochemical effect similar to plant photosynthesis [3; 10]. The mechanism of PBM is not yet clearly elucidated; however, when tissue is irradiated with low-output IR light, it is absorbed by a chromophore that penetrates the skin surface and absorbs light in a specific wavelength band. In the body, at that time [11; 12], the absorbed light energy is converted into chemical energy that increases the synthesis of adenosine triphosphate (ATP), which is the energy source of the cells in the cell tissue, resulting in cell regeneration and rehabilitation as well as pain and inflammation relief [13; 14]. According to the literature related to LED-LLLT, the main performance issues for LED light sources used in PPM include wavelength, power density, and luminance. In terms of wavelength, the first law of light biology, the Grothes-Draper law, states that when the light of the appropriate wavelength is applied to the chromophore, the chromophore has no absorption or reaction. Power density refers to the output used per unit area, and the unit is W\(\cdot\)cm\({}^{-2}\). When the power density is insufficient, light absorption does not occur in the tissue, and when the power density is high, it is converted to excessive heat for the tissue, which could be harmful. Fluence (energy density) or \"dose\" refers to the amount of energy applied per unit area. The energy density is multiplied by the radiation time, and the unit is J\(\cdot\)cm\({}^{-2}\). If the power density is too low, the irradiation time must be extended to achieve the best power density, which is why the desired results cannot be obtained [7; 12].