esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty-Based Optimization Design for the Multilayer TPS of Hypersonic Wing
### Uncertainty Analysis for the Transient Heat Transfer of the Multilayer TPS  
Based on the established deterministic transient heat transfer analysis model, the uncertainty analysis of the multilayer TPS is further conducted. In the work, the material thermophysical properties, including heat conductivity \(\lambda\), specific heat \(c\), and emissivity \(\varepsilon\) are served as uncertain-but-bounded parameters with the deviation factor of 0.05, while the other parameters are assumed to be constant. Meanwhile, \(T_{1\_\max}\), \(T_{2\_\max}\), \(T_{3\_\max}\) and \(T_{4\_\max}\) are selected to be the output responses.  
Considering that the nesting of the uncertainty analysis and transient heat transfer analysis over the whole trajectory would lead to an unaffordable computational amount, an efficient uncertainty analysis method based on the surrogate model is proposed.  
(1) Firstly, we take the design variables and uncertain parameters as design factors, and utilize the Latin Hypercube sampling method to obtain the design matrix. To guarantee the accuracy of the surrogate model, 500 sample points are calculated as source data, and another 50 sample points are calculated to conduct the error test.  
(2) Then, to choose the approximate surrogate model, the Response surface model (RSM), Radial basis function (RBF), Orthogonal polynomial model (OPM), and Kriging model (KM) are compared. The R-square errors of all the surrogate models are listed in Table 5. We can clearly see that the RBF model possesses the highest approximation quality. Therefore, the RBF model is chosen for the later uncertainty analysis and optimization design.  
(3) After that, by taking advantage of the high computational efficiency of the RBF surrogate model, the uncertainty propagation analysis of the heat transfer characteristics for the multilayer TPS is conducted by utilizing the Monte Carol Simulation (MCS). Then, the uncertain responses can be easily calculated as \(T_{1\_\max}^{l}=[1361.3,1401.4]\)K, \(T_{2\_\max}^{l}=[1360.7,1404.7]\)K, \(T_{3\_\max}^{l}=[593.2,733.2]\)K, \(T_{4\_\max}^{l}=[420.0,472.2]\)K.
## 1 Introduction  
A large number of wireless sensor networks (WSN)-based applications typically employ numerous inexpensive tiny nodes with on-board sensor(s). They have very limited memory space, energy, and computational power and are interconnected with simplex radio where each node itself acts as a router.  
The communication is generally limited between a node (source) and the cluster-head/base-station (sink). Since the nodes operate at very low radio power, typically, the transmitted power being a few milli-watts, and the communication range being limited to tens of meters, they are extremely vulnerable to jamming attacks at the physical and data link layers. However, this extreme vulnerability of the WSN to jamming is only one side of the coin. The other side is that, because it is so vulnerable, it is malleable too, and can take the imprint of the jammed area on itself, owing to its high density, spread, and synergy. WSN are, therefore, very suitable for hunting jammers, _i.e._, detecting, localizing and tracking the jammers for the latter's eventual liquidation in an information war, which otherwise, is a very costly and difficult task.
## 2 Materials and Methods
### Numerical Implementation of the SCE Model  
In the present study, a standard sphere was adopted in the 2D SCE model, as shown in Figure 1a, in which a 10-\(\upmu\)m-radius cell with a 5-nm plasma membrane was surrounded by 200-\(\upmu\)m-length intracellular media. It was worth mentioning that a parameter \(\theta\) was used to indicate the differential of arc, \(dl\), with a different position on the plasma membrane. The numerical implementation of this SCE model was conducted using the commercial finite element software (COMSOL Multiphysics 5.5, COMSOL Inc., Burlington, MA, USA).  
The SCE model was solved in conjunction with the transient state solution in the electric currents module for calculating the space potential and the ordinary differential equations (ODE) module for pore density and pore radius. The transmembrane potential (TMP) varies with \(\theta\) such that a set variable containing a series of \(\Delta\psi\) along the circumference was defined for the ODE calculation. In this study, we applied the linear extrusion operator to define the electric potential difference between the inner and outer membrane. Then, we adopted the general extrusion operator to extend its definition into the whole plasma membrane area. In this way, we can obtain such a set variable.  
By assigning the plasm membrane domain the dynamic conductivity, the electric currents module was coupled with the ODE module to construct an interaction loop for pore evolution and electric field distribution.  
To describe the resting potential of the cell before electroporation, the initial conditions of the domain outside and inside the plasma membrane were set as:  
\[\left\{\begin{aligned} \psi_{i}(0^{-})=V_{rest}\\ \psi_{o}(0^{-})=0\end{aligned}\right.. \tag{12}\]  
One of a pair of plate electrodes was set to the H-FIRE protocols, while the other was set to ground, which is for creating an equivalent parallel electric field.  
All other external domain boundaries that did not contact another domain were set as electrical insulation.  
A free triangular mesh was generated in the model except for the plasma membrane domain, which was constructed by the mapped mesh, as shown in Figure 1c.  
The simulations required approximately 15 min to solve for each protocol on an Intel i7-8550U processor with 8 GB of RAM.
# Artificial-Intelligence-Based Charger Deployment in Wireless Reshargeable Sensor Networks
## 4 Proposed Scheme
### Layoff Algorithm  
Although metaheuristic algorithms can find the better solutions, they still have some defects. For example, they will require a lot of computational time to converge. As metaheuristic algorithms will continue to repeatedly match all of the solutions to find the best solution, this process will waste computation. The solution has a chance of falling into a local optimum when using a metaheuristic algorithm. As the deployment problem is a continuity problem, the candidate chargers which are deployed in the beginning can cover a greater area and have fewer overlapping areas. The coverage area will be smaller when the candidate charger continues to be deployed. This phenomenon will apparently occur when the sensor nodes are increased in number, so we propose using the layoff algorithm (LA) to solve it.  
The concept of the LA has two parts. The first part involves removing useless candidate chargers to reduce the computational time. The second part is to ensure that the sensor nodes will be covered in each iteration. According to this concept, not only can the quality of the solution be promised, but it can also ensure that the lost-covered-sensor problem will not happen. We define fitness function as follows:  
In the LA, the first step is randomly firing the candidate chargers and updating the CCL. If the CCL complies with the standard, the \(S_{overlapping}\) is calculated. If the CCL does not comply with the standard, it will randomly choose new C\({}_{i}\). The next step is to calculate \(f(x)\). It can evaluate the quality of the solution.  
\[f(x)=\frac{1}{S_{overlapping}}, \tag{10}\]  
In the layoff algorithm, all the sensor nodes will be covered in each iteration. The value of \(S_{covered}\) can be simplified to 1 to decrease the computational time. All the candidate chargers will be deployed in the beginning. We can lay off the useless positions of candidate chargers in each iteration. Notice that the layoff algorithm is just a screening scheme for the selection and deletion of candidate chargers. It cannot work alone but can be combined with any metaheuristic algorithm. We used pseudocode to represent the layoff algorithm. The first step is to create the initial solution \(N_{c}\) and the CCL, and then calculate the initial fitness function, as shown in lines 1-2 of Algorithm 5. In the next step, the position of the candidate charger \(C_{i}\) is randomly laid off. If the solution is better, \(C_{i}\) will be laid off, and vice versa, shown in lines 4-7 of Algorithm 5. The detailed method of design is presented in the next part.  
```
Input: \(V_{c},V_{s}\) Output: \(V_{E}\)
1 Create the initial solution \(N_{c}\) and CCL
2 Calculate initial Fitness Function
3 While the termination criterion is not met
4 Choice C\({}_{i}\) by metaheuristic algorithm
5 Fitness Function (\(N_{c},C_{i}\))
6 If quality is not better
7 Recovery the C\({}_{i}\)
8 End If
9 End While
```  
**Algorithm 5** Layoff algorithm
# PLORC: A Pipelined Lossless Reference-Free Compression Architecture for FASTQ Files
## 2 Proposed Compressor
### The FASTQ Splitter Module  
The FASTQ Splitter module is responsible for the following:  
* Dividing the input data stream into three output streams. The three compressors receive the streams and perform compression processes, respectively.
* Generating the header and _'ID_info'_ signal, as shown in Figure 3. The FASTQ Splitter extracts (1) the length of the first ID, (2) the content of the first ID, and (3) the positions, type, and amount of the special characters other than the numbers and English letters in the first ID. The information is transformed into the header and _'ID_info'_.
* Handling some exceptional situations and requesting user intervention. The module detects the exceptional situations and sets the corresponding bit as one in _'user_req'_ to call for user intervention. The information about _'user_req'_ is listed below:
* The _'user_req[0]'_ notation represents the occurrence of ambiguous bases other than 'A', 'C', 'G', 'T', and 'N'.
* The _'user_req[1]'_ notation determines that the number of quality scores is different from the bases in the current read.
* The _'user_req[2]'_ notation means a mismatch between the type or amount of special characters in the first read and the current read.
* The _'user_req[3]'_ notation means some of the four parts are missing in the current read.
## 2 Materials and Methods
### Mass and Center-of-Gravity Cordinates Calculation  
According to material-type identification results, the \(S_{L}\)-\(x\)-\(t\) thickness calibration surface function \(f_{t}\) (\(S_{L}\), \(x\)) of each material could be assigned. According to this function, the mass and center-of-gravity coordinates of the material could be approximately calculated, and the calculation results are very important for the variable gas-ejection method in this paper to control the falling points of materials.  
The shading of low-energy of all pixels in the material range can be used for approximate calculation of material mass, whose essence is to integrate the thickness \(t\) (expressed by function \(f_{t}\) (\(S_{L}\), \(x\))) of the material in the projection area of material (PAM) on the X-Y plane, and then multiply the integration evaluation by density and related constants of sorting system. The equation is as follows:  
\[m=\sum_{PAM}\rho P_{x}P_{y}f_{t}(S_{L},x) \tag{11}\]  
where \(P_{x}\) is X-direction resolution of image, \(P_{y}\) is Y-direction resolution of image, and \(\rho\) is density.  
Calculation accuracy of approximate center-of-gravity coordinates on the X-Y plane can avoid posture change caused by material overturning in the process of gas-ejection, making the falling point more difficult to control. The calculation equations of center-of-gravity coordinates are as follows:  
\[\left\{\begin{array}{l}x=\frac{\sum\limits_{PAM}xf_{t}(S_{L}x)}{\frac{\rho _{PAM}}{\rho_{AM}}f_{t}(S_{L}x)}\\ y=\frac{\sum\limits_{PAM}yf_{t}(S_{L}x)}{\frac{\rho_{PAM}}{\rho_{AM}}f_{t}(S_{ L}x)}\end{array}\right. \tag{12}\]
# Correlation between Acoustic Analysis and Psycho-Acoustic Evaluation of Violins
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Independent Variables** & **Wilks\({}^{\prime}\) Lambda** & **Partial Lambda** & **F-Remove** & \(p\)**-Level** & **Tolerance** & **1-Tolerance** \\ \hline Violin type & 0.721 & 0.972 & 1.484 & 0.208 & 0.350 & 0.650 \\ Mode frequency—free string excitation & 0.730 & 0.960 & 2.151 & 0.076 & 0.455 & 0.545 \\ Mode frequency—Pizzicato excitation & 0.707 & 0.991 & 0.473 & 0.755 & 0.545 & 0.455 \\ Mode frequency—musical part & 0.724 & 0.969 & 1.673 & 0.158 & 0.333 & 0.667 \\ Age & 0.715 & 0.980 & 1.028 & 0.394 & 0.411 & 0.589 \\
**Experience** & **0.784** & **0.894** & **6.085** & **0.000** & **0.411** & **0.589** \\
**Gender** & **0.745** & **0.940** & **3.267** & **0.013** & **0.977** & **0.023** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of the multifactor discriminant analysis regarding the equal sonority on all four strings. as being bright, ample, and equal on all strings. The frequency of 2800 Hz comes close to the acoustic quality in the respondents' preferences, but only for the clarity and brilliance of the sounds (Figure 11h). Figure 10: Histogram of total ranking categorized by gender (normal fitted). Figure 9: Contour plot, spline smoothed, of total ranking against mode frequency—free string, excitation and the musical experience. Figure 11: The correlation between the scores obtained for each criterion and signature modes: (**a**) mode A0; (**b**) mode CBR; (**c**) mode B1-; (**d**) mode A2; (**e**) dominant frequency; (**f**) mode frequency–free strings; (**g**) mode frequency–Pizzicato; (**h**) mode frequency–musical part. The authors in [32; 33; 34] mention a series of objective acoustic parameters (spectral centroid, attack time, spectral flow, spectral irregularity, energetic characteristics of the acoustic signal, and harmonic characteristics) on the basis of which the acoustic quality and timbre of violins can be assessed according to psycho-acoustic perceptions. In this stage of the study, the authors investigated the correlation between the main resonant frequencies extracted from the FFT analysis and the psycho-acoustic perceptions of the respondents.
# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 3 Methods  
### Basic Concepts  
In this part, we delineate the fundamental definitions and symbols pertaining to Convolutional Neural Networks (CNNs) that are pertinent to our proposed methodology. The CNN architecture comprises two primary components: data representation layers and parameter layers. Specifically, feature maps and latent vectors encapsulate the representational aspect of the data, whereas convolutional layers and fully-connected layers constitute the parameter layers, responsible for learning and optimizing the model parameters.  
In the context of a CNN, a feature map encapsulates data structured as a tensor \(\mathbf{M}\in\mathbb{R}^{C\times H\times W}\), where \(C\), \(H\), and \(W\), respectively, signify the dimensionality of channels, height, and width. For illustration, an RGB image input possesses a dimensionality of \(3\times H\times W\). In this paper, we adopt the notation \(\mathbf{M}_{t}\in\mathbb{R}^{C_{t}\times H_{t}\times W_{t}}\) to represent the \(t\)-th layer's feature map.  
_Convolution operation_. This fundamental mechanism within CNNs serves to abstract local patterns from the input data. For layer \(t\), convolution is achieved through the employment of filters denoted as \(\mathbf{Z}_{t}\in\mathbb{R}^{C_{t}\times C_{t-1}\times g_{t}\times s_{t}}\), where \(C_{t-1}\times s_{t}\times s_{t}\) encapsulates the dimensionality of each individual filter's parameters, and \(C_{t}\) denotes the count of such filters. Critically, the value of \(C_{t-1}\) must align with the channel count of the preceding feature map \(\mathbf{M}_{t-1}\), ensuring compatibility during the convolution process. Each filter systematically traverses \(\mathbf{M}_{t-1}\) in a dense fashion, applying itself to local neighborhoods, thereby generating a corresponding output map. These individual output maps, one per filter, are then concatenated along the channel dimension to produce the output feature map \(\mathbf{M}_{t}\in\mathbb{R}^{C_{t}\times H_{t}\times W_{t}}\).  
_Fully connected layer._ This layer constitutes a particular instantiation of the convolution operation, tailored for the global integration of information. We consider the feature map \(\mathbf{M}_{t+1}\in\mathbb{R}^{C_{t+1}\times H_{t+1}\times W_{t+1}}\) as the input to this layer. Instead of conventional convolutional filters, it employs a set of filters denoted as \(\mathbf{Z}\in\mathbb{R}^{D\times C_{t+1}\times H_{t+1}\times W_{t+1}}\), where each filter spans the entire spatial dimensions of the input feature map. In contrast to standard convolution, the layer outputs a condensed latent embedding, where the dimensions of height and width are reduced to unity, effectively collapsing the spatial information into a dense representation of the input data.
# Multi-Information Fusion Indoor Localization Using Smartphones
## 4 Fusion Localization Architecture
### Overview  
In this section, we show how the multi-information localization system operates. The motion model diagram of the target in the localization system is shown in Figure 2, which considers only the two dimensions. Therefore, the motion equation is expressed as follows:  
\[\begin{bmatrix}x_{m}\\ y_{m}\end{bmatrix}=\begin{bmatrix}x_{m-1}\\ y_{m-1}\end{bmatrix}+s_{m}*\begin{bmatrix}sin\beta_{m}\\ cos\beta_{m}\end{bmatrix} \tag{1}\]  
\[\beta_{m}=\beta_{meas}+\beta_{dh}+\beta_{static} \tag{2}\]  
where \((x_{m},\,y_{m})\) and \((x_{m-1},y_{m-1})\) denote the location of the pedestrian at times \(m\) and \(m-1\), \(s_{m}\) is the step length of the \(m\)-th step, and \(\beta_{m}\) denotes the heading direction of the \(m\)-th step after correction. \(\beta_{meas}\) is the data measured by the smartphone, \(\beta_{dh}\) is the correction angle for different devices, and \(\beta_{static}\) is the compensation error measured when the smartphone is stationary.  
The pedestrian location can be deduced using (1) and (2). The localization is built on an improved dynamic PDR method and CHAN estimation. The dynamic PDR localization method is primarily based on the pedestrian characteristics that are achieved from the acceleration, gyroscope, and magnetometer sensors during walking. The overall localization method is depicted in Algorithm 1.  
Figure 2: Motion model diagram of the target, where \(T_{i},\,\,i=1,\,2,\,3,\,\ldots\) is the trajectory of the target.
# A Numerical Study of Blast Resistance of Carbon Fiber Reinforced Aluminum Alloy Laminates
## 1 Introduction
[PERSON] et al. [27] studied the effect of four different surface treatments on the flexural properties of FML materials and analyzed them using scanning electron microscopy (SEM), optical microscopy (OM), and profilometry. Results showed that the mechanical abrasion and alkaline etching treatments were not found to be suitable for enhancing the flexural properties due to weak interfacial adhesive bonding. Forest Products Laboratory etching (FPL-etching) and anodizing treatments form micro-scale pores on the surface oxidation layer, which provides an appropriate position for polymer resin penetration. Through the mechanical interlocking mechanism, the strong interface bond is formed and the bending resistance is significantly improved. [PERSON] [28] investigated the effect of the type and content of SiO\({}_{2}\) and ZrO\({}_{2}\) nanoparticles in the epoxy matrix on the high-speed impact performance of carbon fiber reinforced aluminum laminate (CARALL). Typical field emission scanning electron microscopy (FESEM) was used to observe the toughening effect of nanoparticles on the surface of CARALL fractures after impact. The results show that the controlled addition of SiO\({}_{2}\)/ZrO\({}_{2}\) rigid nanoparticles in CARALL is a promising method to improve the energy absorption of CARALL during a high-speed impact. It can be seen from the impact response analysis of FMLs mentioned above that, in order to explore the explosive impact resistance of curved FMLs, it is necessary to carry out research on the metal fiber metal arrangement, fiber direction, and curvature radius of the composite structure of a multi-layer fiber metal curved surface. In this paper, many design parameters, failure models, energy absorption characteristics, and optimization methods are analyzed by studying the explosive impact resistance performance of curved FMLs. In this study, four kinds of CARALL with different curvature radii (30-120 mm) with a ladder distribution were designed. Each gradient adopts three kinds of laminating methods (2, 4, 8) and five kinds of fiber arrangement methods (D0-DX). The clamping boundary is adopted, and the finite element analysis software ABAQUS/Explicit was used to carry out analysis of the external explosion load on each sample.
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 4 System Analysis and Simulation Results
### BER and MSE with Low-Resolution ADCs  
Referring to the framework shown in Figure 4, we firstly implemented the timing synchronization under one-bit quantization and then fed the starting point \(d\) back to the frequency offset estimation module. After completing the above operations, we simulated the BER performance of the frequency  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subcarrier** & **1024 (1 K Model)** \\ \hline Modulation & QPSK \\ Quantization bits & 1/2/3/4/5 \\ Superframe length & 559 \\ FFT-points & 128 \\ OFDM symbol length & 128 \\ Guard interval & 32 \\ Training sequence length & 32/64 \\ SNR & 10 dB \\ Frequency offset \(\epsilon\) & 0.2 \\ Channel delay & 1 \\ Sampling point number & 8 \\ Roll-off factor & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation system parameters. QPSK—quadrature phase shift keying; FFT—fast Fourier transform; OFDM—orthogonal frequency division multiplexing; SNR—signal-to-noise ratio.  
Figure 5: Autocorrelation curve at low resolution.  
offset estimation module at different quantization resolutions. As shown in Figure 6, we found that the BER of the frequency offset estimation module was gradually reduced as the quantization resolution increased. When the resolution was larger than 3 bits (including 3 bits), the BER began approaching zero infinitely. At the same time, increasing the length of the training sequence could further improve the BER performance. Therefore, we could use the 3-bit quantized signal to estimate the frequency offset in the frequency offset estimation module. However, due to the nonlinearity of low-resolution quantization and the imperfection of quantization theory, we could only get some performance comparison analysis. It is very difficult to describe the relationship between BER and quantization resolution through an equation.  
Figure 7 is a diagram of the relationship between MSE of the frequency offset estimation and quantization resolution when the length of the training sequence was 32 bits. We can see from Figure 7 that MSE gradually decreased with the increase in quantization resolution. When the quantization resolution was 3 bits, the MSE reached \(10^{-4}\) order of magnitude. Moreover, the MSE under 3-bit quantization was not much different from that of the MSE under 4-bit quantization. Since the power consumption of ADC is proportional to \(2^{b}\), where \(b\) is the bit width of ADC [32], we wanted the quantization resolution to be as low as possible. In general, communication efficiency and power consumption are mutually constrained. Synthesizing the theoretical analysis and simulation results, we found that the efficiency and power consumption could be well balanced when the quantization resolution was 3 bits.