esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Transcriptome Analysis  
RNA was extracted from leaves at 0, 12, 24, 48, and 96 h post-inoculation using Trizol reagent and quantified using a Nanodrop spectrophotometer (Thermo Fisher Scientific Inc., Wilmington, DE, USA) and a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Three biological replicates with sufficient RNA quality were used to construct RNA-seq libraries on the Illumina HiSeq platform (Illumina HiSeq X Ten) (Illumina, San Diego, CA, USA). Clean reads were mapped to the maize N531_R and N531 reference genomes using HISAT2 [34]. The alignment results were processed using StringTie v2.2.1 [35] software to assemble transcript isoforms and quantify expression values. Expression levels were calculated as fragments per kilobase of the exon model per million mapped reads (FPKMs) for both known and novel genes. Differential gene expression analysis was performed using the R package v4.4.0 DESeq2 to identify genes with a fold change (FC) \(\geq\) 2 and a false discovery rate (FDR) \(\leq\) 0.05. Hierarchical cluster analysis was performed on the differentially expressed genes (DEGs) to identify patterns of gene expression across different samples using Euclidean distance and complete linkage as the clustering method. Gene co-expression analysis was conducted to identify modules of co-expressed genes. Pairwise correlations between gene expression levels across samples were computed using Pearson's correlation. A weighted co-expression network was then constructed using the WGCNA package in R, allowing for the identification of gene modules with highly correlated expression patterns.
## 4 Proposed Scheme
### Emergency Location Extraction  
To ensure the basic location privacy of OLDs, the specific LC where it is located cannot be exposed in \(msg\). Otherwise, by connecting and analysing several messages it has sent, it is feasible for malicious parties to draw rough activity areas of one OLD. But when an emergency occurs, the control plane needs to roughly locate affected OLDs. By fully balancing affected and unaffected areas' densities of OLDs, the control plane is able to construct a most efficient multicast tree with average lowest source consumption \(Cst(T)\).  
When there are abnormal conditions, OLDs around will broadcast a message \(msg\) to report. After receiving those \(msgs\), the GC will perform the following computation to decide a rough location of LC.  
\[PID_{i,2} =(sk_{i}\cdot L_{i})\oplus H1(T_{t})\] \[=(sk_{i}\cdot\alpha\cdot NLC_{i}\cdot P)\oplus H1(T_{t})\] \[=(PK_{i}\cdot\alpha\cdot NLC_{i})\oplus H1(T_{t}) \tag{2}\] \[\Downarrow\] \[NLC_{i} =(PID_{i,2}\oplus H1(T_{t}))\cdot(PK_{i}\cdot\alpha)^{-1}\]  
By locating the LC, the GC will handle this area's packets preferentially to deal with emergencies.
# Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behaviors
###### Abstract  
Field investigation shows that most corrugations occur on the inner rail surface of curved tracks with a radius of less than 700 m. In order to explore the formation mechanism of metro corrugation, the relationship between wheel-rail stick-slip characteristics and rail corrugation is studied by combining single wheelset curving and a rigid-flexible coupling model. The numerical results illustrate that the lateral torsional stick-slip vibration of inner rail-inner wheel of the guiding wheelset on the small radius curve eventually leads to the generation of inner rail corrugation, and the lateral torsional stick-slip vibration of outer rail-outer wheel of the driven wheelset may also occur, but the intensity is weak and the probability is low. The lateral torsional stick-slip vibration of inner rail-inner wheel of the guiding wheelset on the large radius curve is also easy to cause inner rail corrugation, but the degree of inner rail corrugation is lower than that on the small radius curve; the outer rail on the large radius curve is still not easy to produce corrugation. The formation mechanism of rail corrugation on the straight track is different from that on the curve track, which is mainly related to the wheel-rail longitudinal torsional stick-slip vibration.  
metro; rail corrugation; curve passing; torsional stick-slip vibration; rigid-flexible coupling model +
Footnote †: journal: Applied  
Article Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behavior
# A Novel Structure of Rubber Ring for Hydraulic Buffer Seal Based on Numerical Simulation
## 4 Results
### Sealing Performance of Butterfly-Ring  
To compare the sealing performance of butterfly-ring and O-ring, the sealing simulations are carried out under assembly mode (hydraulic pressure 0 MPa) and working mode (hydraulic pressure 5 MPa). The maximum section diameter of the two types of seals is 7 mm, and the RA installation mode is adopted with the installation stroke of 9 mm. Considering that the pre-compression rate is the main factor affecting the sealing performance of the rubber ring, the pre-compression rates from 8% to 16%. Therefore, the experiment of butterfly-ring performance is composed of 18 simulation experiments of 2 groups.  
The rubber rings at the piston rod side and at groove side are required to have good sealing performance, especially at the groove side which is used to be found leakage in hydraulic buffer of articulated landing gear. The sealing performance is compared under installation mode and working mode as Figure 11 shown.  
It can be found that the contact peak pressure of butterfly-ring is greater than that of O-ring at the maximum compression rate. When the pre-compression rate is 16%, the contact peak pressure at the groove side of butterfly-ring increases by 14%. And this law holds for both piston rod side and groove side. Moreover, it is worth noting that the proposed butterfly-ring improves the contact peak pressure at groove side and keeps the contact pressure at piston rod side close to that of the O-ring. Specifically, when the pre-compression rate is 8%, the contact peak pressure of butterfly-ring is 0.15 MPa lower than that of O-ring. When the pre-compression rate is 16%, the contact peak pressure of butterfly-ring is 0.45 MPa higher than that of O-ring. When the pre-compression rate is 14%, the contact peak pressure of butterfly-ring and O-ring is basically the same. As a whole, the contact peak pressure of butterfly-ring is close to that of O-ring at piston rod side, as shown in Figure 11a.  
At the groove side, the contact peak pressure of butterfly-ring is always higher than that of O-ring, as shown in Figure 11b. Compared with O-ring, the contact peak pressure of butterfly-ring at groove side is increased about 30% under working status.  
Figure 11: Verification of the proposed butterfly-ring. The comparisons of contact peak pressure between O-ring and butterfly-ring are made at piston rod side in (**a**) and at groove side in (**b**), with nine different pre-compression rates. Moreover, the contact peak pressure of O-ring and butterfly-ring under assemble status and working status are compared, respectively.
# A Memetic Algorithm for an External Depot Production Routing Problem
## 2 External Depot Production Routing Problem (EDPRP)  
The EDPRP is an extension of the classic case of the PRP in which deterministic demands are met by a homogeneous fleet of vehicles over a discrete (multi-period) and finite planning horizon. In the EDPRP, the geographical position of a plant without storage capacity is different from that of the depot. The depot is supplied by the plant and the customer demand is only satisfied from the products stored at the depot. All vehicles start and end their trips at the depot. The collection of products from the plant to supply the depot is carried out either by vehicles that leave the depot and then collect a quantity of products directly from the plant and return to the depot, or by vehicles after satisfying the demand of one or more customers. In this extension of the PRP, products manufactured at the plant are not delivered directly to customers. The products are first stored at the depot before being delivered to customers. It is also important to note here that the quantities of products received by the depot in period are not distributed in the same period. These products must first be stored before distribution. Only the ML policy has been implemented in this work as a replenishment policy.  
To describe the problem, the following elements have been defined: \(G=(N,A)\) is a complete graph in which \(N\) represents the set of nodes formed by the plant, the depot and the customers with the index \(i\in\{0\ldots\,n+1\}\) and \(A(N)=\{(i,j):\,i,j\in N,i\
eq j\}\) all the arcs in \(G\). The plant is represented by \(n+1\), the depot is indexed by 0 and all customers are represented by \(N_{c}=\{1,\ldots,n\}\) is the set of customers. \(N_{dc}=\{0,\ldots,n\}\) is the set made up by the depot and the customers. \(N_{cu}=\{1,\ldots,n+1\}\) is the set made up by the customers and the plant. \(N=\{0,\ldots,n+1\}\) is the set consisting of the depot, the customers, and the plant. \(T=\{1,\ldots,l\}\) is the set of periods (days) of the planning horizon and \(K=\{1,\ldots,m\}\) is the set of vehicles. See [19] for settings, variables and the MIP linking settings and variables. The product collection and distribution network in the EDPRP can be summarized in Figure 1. The authors. proposed a TPDH to solve the problem. In this work, we develop an MA to compare its results with those of the TPDH in the resolution of the EDPRP. The details of this MA are presented in the following section.
# Attosecond Time Delay in Photoionization of Noble-Gas and Halogen Atoms
## 2 Overview of Theoretical Methods
### Independent-Electron Approach  
We first choose the photon polarization along the \(z\) axis, and calculate the one-photon dipole transition amplitude \(\langle\psi_{\mathbf{k}}\left|z\right|\psi_{0}\rangle\) of the ionization from a bound state \(\psi_{0}\) to a continuum state \(\psi_{\mathbf{k}}\). The magnitude of the photoelectron momentum \(\mathbf{k}\) satisfies the energy conservation relation \(E\equiv k^{2}/2=\omega+\varepsilon_{0}\), where \(\omega\) is the photon energy and \(\varepsilon_{0}\) is energy of the initial state.  
In the spherical coordinates, one can separate the radial and angular parts of the initial and final states, using the partial-wave expansion:\[\psi_{0}(\mathbf{r}) =Y_{l_{0}m_{0}}(\Omega_{\mathbf{r}})R_{n_{0}l_{0}}(r), \tag{1}\] \[\psi_{\mathbf{k}}(\mathbf{r}) =k^{-1/2}\sum_{lm}\exp\left[-i\left(\sigma_{l}+\delta_{l}-\frac{ \pi}{2}l\right)\right]Y_{lm}^{*}(\Omega_{\mathbf{k}})R_{EI}(r)Y_{lm}(\Omega_{ \mathbf{r}}), \tag{2}\]  
where \(\sigma_{l}\equiv\arg\Gamma(l+1-i/k)\) is the Coulomb phase shift, and \(\delta_{l}\) is the short-range phase shift (with respect to Coulomb waves) due to the short-range part of the potential. Both the initial and final state wave functions are calculated using the LDA potential and a set of one-electron orbital equations  
\[\left[-\frac{1}{2}\
abla^{2}+V(\mathbf{r})\right]\psi_{i}( \mathbf{r})=\varepsilon_{i}\psi_{i}(\mathbf{r}), \tag{3}\] \[\rho(\mathbf{r})=\sum_{i}|\psi_{i}(\mathbf{r})|^{2},\] (4) \[V(\mathbf{r})=-\frac{Z}{r}+\int\frac{\rho(\mathbf{r}^{\prime})} {|\mathbf{r}-\mathbf{r}^{\prime}|}d\mathbf{r}^{\prime}+V_{XC}[\rho(\mathbf{r })], \tag{5}\]  
where index \(i\) runs through occupied orbitals, \(Z\) is the atomic number, \(\rho(\mathbf{r})\) is the charge density, and \(V_{XC}\) is the exchange-correlation functional. Since the exact \(V_{XC}\) is unknown, we approximate it with the \(X\alpha\) model potential \(V_{X\alpha}[\rho(\mathbf{r})]=-3\alpha[\frac{3}{8\pi}\rho(\mathbf{r})]^{1/3}\), in which the exchange parameter \(\alpha\) is adopted from Ref. [59]. Equations (3)-(5) are solved iteratively until a self-consistent result is reached.  
After integrating over the angular part depending on \(\Omega_{\mathbf{r}}\) and setting its value to \(A_{l}\), one can obtain the one-photon dipole transition amplitude as follow:  
\[\langle\psi_{\mathbf{k}}|z|\psi_{0}\rangle=k^{-1/2}\sum_{l=0\pm 1}A_{l}Y_{lm_{0} }^{*}(\Omega_{\mathbf{k}})\exp\left[-i\left(\sigma_{l}+\delta_{l}-\frac{\pi} {2}l\right)\right]\left\langle R_{EI}(r)|r|R_{n_{0}l_{0}}(r)\right\rangle. \tag{6}\]  
From here, we calculate cross sections and asymmetry parameter \(\beta\) (see Ref. [50] for detail).
## 3 Implementation of Proposed Method
### Single Feature Extraction Network
#### 3.2.2 Improved Depthwise Separable Convolution  
To extract features better, reduce the dependency between parameters, and alleviate the phenomenon of overfitting, the nonlinear activation function of Rectified Linear Unit (ReLU) [26], which is defined as Equation (8), is used after each depthwise convolution and pointwise convolution in the feature extraction network to strengthen the nonlinear  
Figure 3: Schematic diagram of standard convolution and depth separable convolution. (**a**) Standard convolution process. (**b**) Depthwise convolution process. (**c**) Pointwise convolution process.  
expression ability. At the same time, a Batch Normalization layer (BN) [27] is usually added before the activation function to accelerate the convergence of the network and prevent gradient explosion. It can also improve the accuracy of the model. The calculation formula of layer BN can be seen from Equation (9), where \(\sum_{i=1}^{m}x_{i}\) is the output of the convolution layer, \(\mu_{B}\) and \(\sigma_{B}^{2}\) are mean and variance respectively, and \(y_{i}\) is the output result after normalization.  
\[ReLU(x)=\max(x,0), \tag{8}\]  
\[\left\{\begin{array}{c}\mu_{B}\leftarrow\frac{1}{m}\sum_{i=1}^{m}x_{i}\\ \sigma_{B}^{2}\leftarrow\frac{1}{m}\sum_{i=1}^{m}\left(x_{i}-\mu_{B}\right)^{ 2},\\ \hat{x}_{i}=\left(x_{i}-\mu_{B}\right)/\sqrt{\sigma_{B}^{2}+\varepsilon}\\ y_{i}\leftarrow\gamma\hat{x}_{i}+\beta\end{array}\right. \tag{9}\]  
However, the use of ReLU activation function may leave part of the neural network in a \"dead\" state, resulting in the loss of information. Suppose that there is a large gradient in the forward conduction process of the network so that the weight of the neural network is greatly updated, a negative value will be given by neuron for all inputs. Then the output of this negative value becomes zero after ReLU, and the gradient flowing through this neuron will always be zero. In this case, the neuron remains inactive, and the weight cannot be updated so that the network will not be able to learn normally.  
In order to avoid the occurrence of this phenomenon, we improve the network activation function, and replace the ReLU activation function with linear activation function after pointwise convolution [28; 29]. The linear activation function is expressed as Equation (10), where \(x\) is the input, \(f(x)\) represents the output, \(W\) represents the weight of the function, and \(b\) is the bias.  
\[f(x)=Wx+b, \tag{10}\]  
Linear activation does not increase the computation amount of depthwise separable convolution compared with ReLU activation function, so it retains the advantage in computation amount. More importantly, the linear output can preserve the information of each channel and provide more reliable features for subsequent object classification.
_Article_  
**Construction and Numerical Realization of a Magnetization Model for a Magnetostrictive Actuator Based on a Free Energy Hysteresis Model**  
**[PERSON] 1,2,*()**, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2()** and [PERSON] 3  
1 Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China  
2 Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China  
3 Hubei Wuhan Tobacco Company Cigarette Logistics Distribution Center, Wuhan 430000, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-1532-7337-823  
Received: 22 July 2019; Accepted: 26 August 2019; Published: 5 September 2019  
**Featured Application: This study constructs and analyzes the constitutive model of the giant magnetostrictive material actuator, which provides a theoretical basis for the development of a new intelligent structure of giant magnetostrictive material.**  
**Abstract:** Giant magnetostrictive actuators (GMA) driven by giant magnetostrictive material (GMM) has some advantages such as a large strain, high precision, large driving force, fast response, high reliability, and so on, and it has become the research hotspot in the field of microdrives. Research shows there is a nonlinear, intrinsic relationship between the output signal and the input signal of giant magnetostrictive actuators because of the strong coupling characteristics between the machine, electromagnetic field, and heat. It is very complicated to construct its nonlinear eigenmodel, and it is the basis of the practical process of giant magnetostrictive material to construct its nonlinear eigenmodel. Aiming at the design of giant magnetostrictive actuators, the magnetization model based on a free-energy hysteresis model has been deeply researched, constructed, and put forward by [PERSON], which combines Helmholtz-Gibbs free energy and statistical distribution theory, to simulate the hysteresis model at medium or high driving strengths. Its main input and output parameters include magnetic field strength, magnetization, and mechanical strain. Then, numerical realization and verification of the magnetization model are done by the Gauss-Legendre integral discretization method. The results show that the magnetization model and its numerical method are correct, and the research results provide a theoretical basis for the engineering application of giant magnetostrictive material and optimized structure of giant magnetostrictive material actuators, which have an important practical application value.  
**Keywords:** giant magnetostrctive material; microactuator; free energy; hysteresis model; magnetization
# Optical Lensless-Camera Communications Aided by Neural Network
## 2 Proposed Scheme
### Image Decoding Algorithm
#### 2.1.2 Neural Network Training  
In this stage, our main goals were to create a neural network structure that met our functional requirements and to find the best settings for the stable training process [11]. After many experiments and adjustments, in the training process, this paper adopts the fastest descent method with adaptive learning rate and additional momentum factor in the weight update process, as shown in Equation (1); the equation can prevent the network from falling into oscillations and find the minimum point more easily. The learning rate has a huge impact on the performance of the network. In different stages of training, the value of the learning rate is different, so we used the adaptive learning rate and tried to find the best settings for the training process. We used an initial learning rate 0.01 and a momentum 0.9 as well as other parameters, as shown in Table 1.  
\[w(k+1)=w(k)+\alpha(k)[(1-\eta)D(k)+\eta D(k-1)] \tag{1}\]  
where \(w(k)\) is the weight of step \(k\), \(\alpha(k)\) is the learning rate of step \(k\), \(\eta\) is the momentum factor, and \(D(k)\)\(=-\partial E/\partial w(k)\) is the negative gradient of step \(k\); \(E\) is the error function.  
Figure 3: YCbCr components analysis of images in different environments: (**a**) “off” image of the illumination LED without ambient light interference; (**b**) “on” image of the illumination LED without ambient light interference; (**c**) “on” image of the blue LED without ambient light interference; (**d**) “on” image of the illumination LED with blue ambient light interference.  
To maintain good decoding performance in time-varying environments, our neural network was trained periodically; accordingly, the number of images in the training set was determined by the channel condition.
## 3 Results
Western blotting detected PrPres in all mice that died of prion disease in the untreated (Control) group, but not in the non-lumen and Eco mode group including the two mice that died within the 576 day period following injection with the treated brain tissue (Figure 6). These results indicated that all of the untreated mice (Control) had succumbed to prion disease. However, the two mice that died within the study period in the non-lumen and Eco mode group did not appear to have developed prion disease. Figure 5: Mice injected with scrapie prion following exposure to RENO-S130 survived much longer than those incubated with untreated scrapie prion. The mice were injected intracerebrally with Chandler prion that had been either exposed to the RENO-S130 device (blue line: non-lumen (N = 6); red line: Eco (N = 6)) or was untreated (dotted black line: Control (N = 6)). Survival curves were statistically compared by log-rank test (where \(p\) < 0.01 was defined as significant). The survival curve for Control mice was reported previously [30]. Figure 6: PrPres was detected in the brains of mice that died of prion disease. Samples of prion-infected mouse brain homogenate, either exposed to the RENO-S130 device (non-lumen or Eco modes) or untreated samples (Control), were intracerebrally injected into mice (six mice per group). Mouse brains were subsequently isolated either when the mice displayed clinical symptoms of prion disease, such as tremors and ataxia, or at 577 days (-). To detect PrPres, samples were treated with PK and then analyzed by Western blotting with an anti-PrP antibody (SAF83). Brain samples of untreated control mice were previously obtained [30]. Size markers (kDa) are shown on the right-hand side of the blot. Thus, treatment of the prion infected samples with a hydrogen peroxide gas plasma sterilizer prior to subsequent injection into mice prolonged survival and delayed the clinical onset of prion disease. These results suggest that scrapie prion can be inactivated by treatment with the RENO-S130 device using either the non-lumen or Eco mode of operation.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 3 Proposed Method  
Compared with speech signal, it has higher sampling rate for ADS-B signal. However, the length of ADS-B signal is short and fixed 120 \(\upmu\)s. It has similar sampling points with a currently used speech dataset. Thus, we improve the Conv-TasNet [18] framework used in the speech signal separation to construct a multi-scale Conv-TasNet (MConv-TasNet), as shown in Figure 2. From the Figure 2, it can be observed that the framework consists of three models. The encoder model is to extract the features from the ADS-B signal and the MCS network is to estimate the separation masks. The decoder is to separate the overlapped signals. In the following, the description of the model is detailed.