esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Discussion
### 1-D Elastic Wave Propagation Problem  
The effectiveness of the DGFEM to solve the problem of generalized [PERSON]'s theory was verified in articles [13; 16]. In order to prove the good performance of MDGFEM in cement concrete medium, we first consider the propagation of one-dimensional stress waves in a cement-concrete column with a length of 0.9 m and a cross-sectional area of 1 m\({}^{2}\). As shown in Figure 1, one end of the column is fixed, and the other end is subjected to the axial force impulse \(F(t)\), the magnitude of which is  
\[F(t)=\left\{\begin{matrix}1.0\times 10^{3}N&(0\leq t\leq 45\;\upmu\text{s})\\ 0&(t>45\;\upmu\text{s})\end{matrix}\right. \tag{30}\]  
The cement concrete column is divided into 90 elements along the length, that is, each element is 0.01 m. Figure 2 presents the stress solutions at difference time levels of \(t_{1}=60\;\upmu\text{s}\), \(t_{2}=180\;\upmu\text{s}\) produced by [PERSON]'s method, and MDGFEM with time step size \(\Delta t=1.0\times 10^{-6}\;\upmu\text{s}\). Compared with the polyline obtained by the [PERSON] method, the smooth curve obtained by MDGFEM shows the superiority of the algorithm in filtering out severe numerical oscillations in wave after wave.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Cement Concrete Payement** & **Semi-Rigid Base** & **Natural Soil** \\ \hline [PERSON] permeability coefficient \(k\) (m/s) & \(1.0\times 10^{-6}\) & \(1.0\times 10^{-5}\) & \(1.0\times 10^{-6}\) \\ Biot parameter \(\alpha\) & 1.0 & 1.0 & 1.0 \\ Porosity \(n\) & 0.05 & 0.04 & 0.4 \\ Young’s module (GPa) & 25 & 2.1 & 1.0 \\ shear module of the solid (GPa) & 10.42 & 0.84 & 0.38 \\ bulk module of the fluid \(K_{f}\) (GPa) & 0.02 & 0.02 & 0.02 \\ bulk module of the solid \(K_{s}\) (GPa) & 13.89 & 1.40 & 0.83 \\ densities of the fluid \(\rho_{f}\) (kg/m\({}^{3}\)) & 1000 & 1000 & 1000 \\ densities of the solid \(\rho_{s}\) (kg/m\({}^{3}\)) & 2400 & 2100 & 1000 \\ [PERSON]’s ratios & 0.20 & 0.25 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of relevant parameters for calculations.  
Figure 1: An elastic cement concrete column bearing an impulse load (\(90\times 1\) element).
## 5 Results and Analysis
### Experimental Testbed  
The platform used for the experiments is a part of the 230 TFLOPS Aziz supercomputer (ranked number 360 in June 2015 among the Top500 machines). It consists of 496 nodes and 11,904 cores in total. Each node contains two Intel Xeon E5-2695v2 processors (Intel Corporation, Santa Clara, CA, USA), each with 12 Cores (2.4 GHz). In addition, 380 of these nodes have 96 GB Random Access Memory, while each of the rest 112 nodes contain 256 GB. This sums up to a total of 66 TB memory. It also has two Nvidia K20X GPU (Kepler) accelerator nodes and two Intel Phi 5110P MIC accelerator nodes. It is controlled by 64-bit Red Hat Enterprise Linux v6.5 (Red Hat, Riyadh, Saudi Arabia). We used the K20 nodes to execute our code. We used CUDA toolkit 9.0 with C++ for the compilation of our code. The code was compiled using \(nvcc-O3-arch=sm\_35\). We compare our scheme with the following seven SpMV storage and computation techniques:  
1. The best CSR-based SpMV [58] from cuSPARSE v9.0 and CUSP v0.5.1 [52].
2. The best HYB [58] from the above two libraries.
3. BSR format from cuSPARSE. The block size for a given matrix was obtained from the experiments based on the performance. The best performing block size was selected for each matrix.
4. The CSR5 storage technique (see [79] for details of the scheme).
5. The SELL-P [74] sparse storage scheme, which is a padded version of SELL-C-\(\sigma\) scheme [75]. The implementation of the SELL-P scheme that we have used in this paper is from the MAGMA library [83; 84]. The best block sizes for the scheme were selected from our experiments based on the best performance.
6. The WPK1 and WPK2 storage techniques by [PERSON] et al. [78].
# The Effect of Energy Loss Straggling on SEUs Induced by Low-Energy Protons in 28 nm FDSOI SRAMs
## 4 Discussion
due to TID effects during performing experiments. A relevant threshold LET was estimated to be 0.9 MeV-cm\({}^{2}\)/mg through TCAD simulation. The estimated value was considerably large than the biggest LET of proton in silicon, which is 0.54 MeV-cm\({}^{2}\)/mg. Conversely, SEUs caused by low-energy protons indeed appeared at each energy point. In the future, it is advised that energy loss straggling should be taken into consideration to evaluate the influence of protons on SEUs. After all, space is a proton rich environment and a more conservative evaluation merely based on LET can bring about serious consequences.
## 6 Discussions on Future Research Directions and Challenges  
In the previous sections, it was shown that PLS has received significant research interest within the fields of information-theoretic fundamentals and signal processing. However, future wireless networks will become increasingly heterogeneous and will have new features and more complex network environments. Even though the research community has made great efforts in the existing works, many technical challenges still exist, namely the basic assumptions regarding channel types, eavesdropper models, the availability of CSI and application scenarios. Most of the existing assumptions that are used in PLS may be insufficient or unreliable for future wireless communication in practical security systems. Hence, there are still many technical challenges that need to be discussed in order to facilitate the integration of PLS into practical design systems. In the following section, we discuss some future directions and notable challenges, which are based on the knowledge that has been gained from the existing literature. It should be noted that only a few research directions are discussed in this section because the future work regarding PLS is a pervasive field of study.
###### Abstract  
This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1-3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.  
Keywords:transformer; transmission line; discrete wavelet transform; mother wavelet; fault classification +
Footnote †: journal: Applied Sciences
## 2 FPAPr Design and Fabrication  
### Basic Principles of Phased Array Probe  
The basic principles of the PAUT originate from [PERSON]'s principle, which is a theory on the wave nature of light. When applied to non-destructive inspections, [PERSON]'s principle mainly supports the description of ultrasound refraction, reflection, and diffraction. When electric pulses are exerted with a time lag on an arrayed element, the element becomes a wave source that generates spherical waves. The spherical waves interfere with each other due to the time lag and form a wavefront shape associated with this interference.  
Figure 1: Piping and fitting with various curvature structures: (**a**) elbow; (**b**) flange and reducer.  
Figure 2: Images of the geometry of the accessibility limitation and non-contact by standard wedge PA probes on elbow curvature of pipe: (**a**) non-contact image by solid wedge fastening to conventional smallest PA probe in flange–elbow curved pipe; (**b**) difficulty in surface contact as the size of the rigid wedge mounted to the PA probe increases.  
The time delay refers to the time lag implemented on the element. In the PAUT, the time delay is electronically adjusted for exerting electric pulses to the element, which could electronically regulate ultrasonic beam steering, focus position, and injection angle. Figure 3 illustrates the beam focus position and direction according to the time lag for an ultrasonic beam at a perpendicular incidence. The associated equation is expressed as Equation (1), where \(t_{i}\) indicates the time delay implemented on the element, \(c\) is the ultrasonic pulse velocity, \(a\) is the distance between elements, and \(\mu\) is the distance between the probe and focus [20; 21; 22; 23].  
\[\Delta t_{i}=\frac{1}{c}\bigg{(}\sqrt{a^{2}+\mu^{2}}-\mu\bigg{)} \tag{1}\]  
The focusing performance of the PA probe is determined by various parameters, thus requiring a distinct definition of each parameter feature. Figure 4 summarizes the probe design parameters. Generally, beam focusing with phased array ultrasound occurs within the near-field, and the Fresnel Region of the phased array ultrasound probe is determined by the probe aperture and wavelength (\(\lambda\)), as shown in Equation (2). Therefore, the larger the aperture and the larger the number of piezoelectric elements, the larger the Fresnel Region (\(N\)), which, in turn, enhances the focusing performance.  
\[N=\frac{A^{2}}{4\lambda} \tag{2}\]  
Additionally, the range of angle of dispersion (\(\theta\)), which can be acquired from the frequency and the probe aperture, widens as the size decreases and wavelength increases, which is the opposite of the Fresnel Region equation. The angle of dispersion of the ultrasonic probe is calculated as shown in Equation (3) [24; 25; 26].  
\[sin\theta=0.5\frac{\lambda}{A} \tag{3}\]  
Figure 4: Phased array probe parameters.  
Figure 3: Beam steering according to time delay in PAUT.
## 2 POMDP Modeling
### POMDP Specification
* \(O\): represents the joint observation space of agents in the problem, which is composed of the observation spaces of each agent, \(O=O^{1}\times O^{2}\times\cdots\times O^{|N|}\). At time \(t\), the observation information of agent \(i\) is \(o_{t}^{i}\in O^{i}\). The observation information of each agent includes its inventory information and demand information. In addition, the observation information for the main manufacturer agent includes its logistics information, and for the supplier agent includes its production information. * The inventory observation information represents the proportion of various materials of the agent. At time \(t\), for agent \(i\) it is: \(inv_{t}^{i}=[inv_{t}^{i,1},inv_{t}^{i,2},...,inv_{t}^{i,M^{i}}]\). * The demand observation information represents the order demand information for various materials of the agent. At time \(t\), the material \(j\) demand observation information of agent \(i\) is \(\mathit{ord}_{t}^{i,j}=[\mathit{ord}_{t}^{i,j,0},\mathit{ord}_{t}^{i,j,1},..., \mathit{ord}_{t}^{i,j,w}]\), where \(w\) is the maximum waiting time for the material \(j\) and \(\mathit{ord}_{t}^{i,j,k}\) is the ratio of the absolute number of material \(j\) demand with remaining order duration time \(k\) to the maximum inventory limit of material \(j\) for agent \(i\) at time \(t\). * The logistics observation information represents the transportation information for various materials of the main manufacturer.At time \(t\), the logistics observation information of material \(j\) of the main manufacturer is \(\mathit{trn}_{t}^{j}=[\mathit{trn}_{t}^{i,0},\mathit{trn}_{t}^{i,1},\ldots, \mathit{trn}_{t}^{i,j}]\), where \(l\) is the maximum transportation time for the material and \(\mathit{trn}_{t}^{i}\) is the ratio of the absolute number of material \(j\) transportation with remaining transportation time \(k\) to the maximum inventory limit of material \(j\) for the main manufacturer at time \(t\). * The production observation information represents the production information for the corresponding material of the supplier.At time \(t\), the production observation information of material \(j\) of supplier \(i\) is \(\mathit{pdf}_{t}^{i,j}=[\mathit{pdf}_{t}^{i,j,0},\mathit{pdf}_{t}^{i,j,1}, \ldots,\mathit{pdf}_{t}^{i,j,w}]\), where \(u\) is the maximum production time for the material and \(\mathit{pdf}_{t}^{i,j,k}\) is the ratio of the absolute number of material \(j\) production with remaining production time \(k\) to the maximum inventory limit of material \(j\) for supplier \(i\) at time \(t\). * \(\gamma\in[0,1]\) is the discount factor. It represents the importance of future rewards compared to immediate rewards for the agents.
# Investigation of 2 DOF PID Controller for Physio-Therapeutic Application for Elbow Rehabilitation
Since this prototype has already been investigated, the 2 DOF PID controller is considered here in order to examine the performance of the system in terms of its flexion and extension movements with the controller. Currently, the majority of research in this field is devoted to neurorehabilitation and neuropsychological rehabilitation. While neurorehabilitation focuses on manipulating the joint and training the patient's central nervous system, orthopedic rehabilitation focuses on the total body's rehabilitation. The posttraumatic elbow joint, for example, is only capable of small-angle flexion/extension movements during the early stages of therapy. These movements must be permitted and, to a limited extent, promoted in order to counteract muscular stiffening. Patients face difficulties during upper limb arm rehabilitation, including the inability to perform daily activities, such as using phones and tablets, picking up and organizing objects, and so on. The wounded hand's brain is connected to the brain, which provides noteworthy traits, such as speed and precision. For patients undergoing physical therapy, a variety of practical and effective treatments and rehabilitation technologies are used to support the injured hands and to improve therapeutic outcomes. However, the most critical role that a rehabilitation robot can accomplish is to preserve the health of the muscles and joints. As a result, the recommended physiotherapy aims to attain a fully functional hand throughout the recovery process. Our technological contributions are summarized below:  
1. Develop a robotic system for rehabilitation of elbow flexion. 2. Create a two-degree-of-freedom PID torque controller for the elbow robotic rehabilitation system.
# Surface Quality of Staggered PCD End Mill in Milling of Carbon Fiber Reinforced Plastics
## 4 Effects of Fiber Orientation on the Machined Surface
### Slot Milling Defects
#### 4.2.2 Analysis of Machining Defects  
In the milling process, with the rotation of the end mill, the fiber cutting angle in milling is constantly varied. The fiber cutting angle is 90\({}^{\circ}\), and 0\({}^{\circ}\) is the demarcation point between milling along the fiber direction and reverse fiber direction. In addition, 45\({}^{\circ}\) and 135\({}^{\circ}\) are typical feature points of the two kinds of milling, so the surface topography at these intervals is very important. Figure 9 shows the fiber cutting angle when the angle between the feed direction and the fiber direction is \(\beta\) = 45\({}^{\circ}\). The surface defect of the slot milling under different fiber cutting angles is presented in Figure 10, which shows the slot topographies of four fiber directions and the corresponding fiber cutting angles.  
When \(\beta\) = 0\({}^{\circ}\), the edge of the left half of the slot is neat and without burrs, as shown in Figure 10a, in which \(\theta\) on the fiber cutting angle diagram is 180\({}^{\circ}\)-90\({}^{\circ}\), i.e., belongs to reverse fiber cutting. However,  
Figure 10: Slot milling topography of different fiber orientations. (**a**) \(\beta\) = 0\({}^{\circ}\); (**b**) \(\beta\) = 45\({}^{\circ}\); (**c**) \(\beta\) = 90\({}^{\circ}\); (**d**) \(\beta\) = 135\({}^{\circ}\).  
Figure 9: Diagram of slot milling cutting angle.  
the right half has a typical burr defect, in which \(\theta\) on the fiber cutting angle diagram is 90\({}^{\circ}\)-0\({}^{\circ}\), i.e., belongs to milling along the fiber direction. Tears and burrs are mainly located on the outer layer of the surface. They are the most common machining defects of CFRPs [29; 31]. Tears are caused by milling force and extend along the fiber direction of the outer layer.  
Through the analysis, when \(\beta\) is 45\({}^{\circ}\), 90\({}^{\circ}\) and 35\({}^{\circ}\), it is seen that burrs are more concentrated in the fiber cutting angle 0\({}^{\circ}\)-90\({}^{\circ}\) (cutting along the fiber direction), as shown in Figure 10b-d. For fiber cutting angle 90\({}^{\circ}\)-180\({}^{\circ}\) (reverse fiber milling), a smooth topography with no macroscopic defects is seen. However, through the observation of the digital super depth-of-field microscope, it can be seen that tears occur. When the tool is worn, there will be burr defects in the cutting area along the fiber direction, but in the reverse cutting area, even if the tool is badly worn, there will be no burr defects, as shown in Figure 10d. Thus, it can be concluded that in the milling process, by choosing a reasonable milling path and ensuring the surface fiber is in the inverse cutting state, a worn tool can be continuously used for milling so as to promote tool life and reduce production costs.
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 1 Introduction
### The Object Recognition Problem  
The object recognition problem is a key challenge in computer vision, with applications in several domains, such as robotics, surveillance, and augmented reality. Object recognition involves identifying and categorizing objects within digital images or video frames. This task is particularly challenging due to the variability in object appearance, including differences in viewpoint, scale, lighting conditions, occlusions, and so on.  
Addressing the object recognition problem typically involves developing algorithms and techniques capable of extracting discriminative features from visual data, followed by classification or matching processes to determine the identity or category of detected objects. Traditional approaches, such as SIFT, SURF, or ORB, were designed to capture distinctive characteristics of objects, as shown in Figure 1. However, these methods offer poor accuracy under complex visual variations and required extensive tuning for optimal performance across different scenarios [13; 14].
# A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading
## 3 Proposed Method
### Disease Classification  
The comprehensive features \(D\) (\(D_{DME}\) or \(D_{DR}\)) obtained through the feature fusion module are fed into the disease grading module to achieve the grading results. According to the grading task, DME outputs three types of labels, and DR outputs five types of labels. Figure 7 shows the detailed structure of the disease grading module. DR and DME have the same structure.  
The grading module shown in Figure 7 includes a global average pooling layer(GA), a fully connected layer (FC), relu and softmax activation functions, and a drop-out layer with a drop rate of 0.5.  
For DR and DME grading, we utilize weighted \(L_{DR}\) and \(L_{DME}\) as the total loss of the model, which can be given by Equation (10).  
\[L=\alpha L_{DR}+\beta L_{DME} \tag{10}\]  
where, \(L\) is the total loss of the model, \(L_{DR}\) is the loss of DR classification, \(L_{DME}\) is the loss of DME classification, and \(L_{DR}\) and \(L_{DME}\) are both cross-entropy loss functions.  
The cross-entropy loss function is calculated by Equation (11).  
\[L(y,\hat{y})=-\frac{1}{N}\sum_{n=1}^{N}\sum_{c=1}^{M}y_{n}^{c}\ log\ \hat{y}_{n}^{c} \tag{11}\]  
Here, \(y_{n}^{c}\) represents the image true label, \(\hat{y}_{n}^{c}\) represents the probability that the image predicted disease classification belongs to label \(c\), \(N\) is the number of images included in the training set, \(M\) is the number of disease classification label categories, for DR classification, \(M\) is 5; for DME classification, \(M\) is 3.
## 3 Results and Analysis
### Performance Evaluation of Modified Nano-SiO\({}_{2}\)
#### 3.2.3 Antiswelling Test  
The water expansion of bentonite in the rock channel reduced the effective diameter of the channel and increased the injection pressure. The antiswelling performance is also an important index affecting the influence of the modified nano-SiO\({}_{2}\) dispersion under pressure reduction and injection. The swelling volumes of bentonite in kerosene, nano-SiO\({}_{2}\) dispersion and water are, respectively, shown in Table 2. The swelling volume of bentonite in kerosene was 1 mL, the swelling volume of bentonite in nano-SiO\({}_{2}\) was 1.8 mL and the swelling volume of bentonite in water was 2.4 mL. According to Equation (1), the antiswelling rate of modified nano-SiO\({}_{2}\) was 42.9%, and the modified nano-SiO\({}_{2}\) had a good antiswelling effect. After analysis, given that the modified nano-SiO\({}_{2}\) was hydrophobic, the modified nano-SiO\({}_{2}\) was adsorbed onto the surface of bentonite, and a hydrophobic layer formed on the bentonite surface, blocking the contact between water and bentonite to achieve the effect of antiswelling.  
Figure 8: Dispersion state of modified nano-SiO\({}_{2}\) in NaOH-regulated Polyethylene glycol octylphenyl ether (TX-100) (**A**) and after 30 days of dispersion (**B**).
## 3 The ASAPP Approach for Single-Tier MHCP Network  
In interference-limited networks, the coverage probability is equivalent to the complementary cumulative distribution function (ccdf) of SIR. Recently, it has been observed that the coverage of cellular networks based on non-PPP models can be approximated by adjusting the SIR threshold of the corresponding network based on a PPP model with the same density. The adjusting coefficient is called SIR gain and the method is called ASAPP [13; 14; 15; 16; 17; 18; 19; 20]. Using this method, the SIR gain for the MHCP modelled network can be expressed as  
\[G_{\text{MHCP}}=\frac{\text{MISR}_{PPP}}{\text{MISR}_{\text{MHCP}}} \tag{4}\]  
where MISR is mean interference-to-signal ratio (MISR), \(\text{MISR}_{PPP}\) represents the MISR for PPP-modelled network, and \(\text{MISR}_{\text{MHCP}}\) represents the MISR for MHCP-modelled network. For a network with BSs located at \(\Phi\) and the serving BS located at \(\text{x}_{0}\), the MISR at the typical user is defined as [13; 14; 15; 16]  
\[\text{MISR}\triangleq\mathbb{E}\left\{\frac{\text{I}}{\mathbb{E}_{h}(S)} \right\}=\mathbb{E}\left\{\frac{\sum\limits_{\text{x}\in\Phi(\text{x}_{0})}P _{x}\|x\|^{-\alpha}}{P_{x_{0}}\|\lambda_{0}\|^{-\alpha}}\right\} \tag{5}\]  
where I is the sum of all interference powers, and \(\mathbb{E}_{h}(S)\) is the signal power average over the fading. In the case of equal transmit powers, the MISR of PPP is derived as [15; 16; 17]  
\[\text{MISR}_{PPP}=2/(\alpha-2) \tag{6}\]  
In addition, \(\text{MISR}_{\text{MHCP}}\) can be obtained by simulation and data fitting. Then, we can get the value of \(G_{\text{MHCP}}\).  
Figure 1: Base stations distribution modelled by the Poisson point process (PPP) and Matérn hard-core process (MHCP).