esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Finite Element Analysis Formulation for Shear in Reinforced Concrete Beams
## 2 Shear Solutions in One-Dimensional Beams  
Nodal displacements of a typical beam-column element are obtained by solving differential equations expressed in terms of the longitudinal coordinate. Therefore, the typical beam-column is often referred to as a one-dimensional element, although the nodal degrees of freedom can be defined in two- or three-dimensional spaces. The bending of a slender beam is accurately solved using the plane section assumption in the realization of a one-dimensional element, a.k.a. the Bernoulli-Euler beam [PERSON] and [PERSON] (1999); [PERSON] (1999). This simplification yields the stiffer behavior of a short beam, as it neglects shear deformation, which imposes additional rotation of the cross section. The Timoshenko beam suggests a solution to this problem. The equilibrium and strain-displacement equations of the Timoshenko beam can be derived as follows [PERSON] and [PERSON] (1999),  
\[n^{\prime}=p \tag{1a}\] \[\varepsilon=u^{\prime}\] (1b) \[m^{\prime\prime}=q\] (1c) \[\kappa=\theta\] (1d) \[v^{\prime}=-q \tag{1e}\]\[\gamma=w^{\prime}-\theta \tag{1f}\]  
where, \(n=\) axial force field; \(m=\) bending moment field; \(q\) and \(p=\) applied transverse and axial load per unit length, respectively; \(v=\) shear force field; \(\kappa=\) curvature field; \(\gamma=\) shear strain field; \(\varepsilon=\) axial strain field; \(w=\) transverse displacement field; \(\theta=\) rotation field; and \(u=\) axial displacement field. A prime denotes differentiation with respect to \(\xi\), which locates a position in the reference coordinate system, as depicted in Figure 1.  
The Timoshenko beam can accurately describe the shear behavior of homogeneous and linear beams. The sectional displacements, including axial and transverse displacements and rotation, can define the normal deformations of each point or layer of the cross section. However, as the required constitutive model to calculate the corresponding stresses is uniaxial, ad-hoc functions, such as shear functions, are required to define shear profiles over the cross section, mostly with parabolic shapes or constant ones. Moreover, the beam shear is uncoupled from the flexural responses, and the description of nonlinear shear evolution of the composite beams that are composed of nonlinear materials is hardly achievable. To resolve this problem, this study suggests a new element formulation to determine shear stress distributions over the cross section.
# Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series
## 3 Simulation Study  
To evaluate the performance of our model, we conducted a simulation study. We generated a dataset consisting of \(M=120\) documents, where each document is represented by a word time series of length \(T=100\). The vocabulary size was set to \(V=6\), and the documents were divided into \(C=2\) classes. Each word time series was governed by an underlying latent topic structure with \(J=3\) topics. Our model was evaluated using a six-fold cross-validation with \(M_{T}=100\) train and \(M_{T\!st}=20\) test documents. We conducted our analysis in Fortran 77. Given the observed word time series from \(M\) documents, we fitted the sDCTM model to estimate the model parameters, \(\mathbf{\beta}\), \(\mathbf{\Sigma}\), \(\mathbf{\Phi}\), and \(\mathbf{\eta}\). As the model parameters are matrices, we assessed the estimation error using the squared Frobenius norm. The squared Frobenius norm lies between two matrices, such as \(\mathbf{M}\) and \(\mathbf{N}\), and is defined as  
\[d(\mathbf{M},\mathbf{N})=\text{tr}\{(\mathbf{M}-\mathbf{N})^{{}^{\prime}}(\mathbf{M}-\mathbf{N})\} \tag{9}\]  
The average squared Frobenius norm (over the six-fold cross-validation) for each of the model parameters is shown in Table 2. The low values of the squared Frobenius norm on the model parameters suggest that we are able to estimate the matrices well.  
We used the estimated model parameter \(\mathbf{\eta}\) and the variational parameter \(\mathbf{\gamma}_{d,t}\) for \(t=1,2,\ldots,T\) and \(d=1,2,\ldots,M_{T\!r}\) to predict the response associated with the training data containing \(M_{T\!r}=100\) documents as follows.  
\[c_{d}=\operatorname*{argmax}_{c\in\{1,\ldots C\}}\eta_{c}^{\top}\tilde{\tau}_{ d}, \tag{10}\]  
for \(d=1,2,\ldots,M_{T\!r}\) and \(\tilde{\tau}_{d}=\frac{T}{\sum_{l=1}^{T}}\gamma_{d,t}\). Table 3 shows the confusion matrix obtained on train data for one of the cross-validations. We were able to achieve a training accuracy of 100%, and the average training accuracy across all \(k=6\) cross-validation datasets was also 100%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Squared Frobenius Norm** \\ \hline \(\mathbf{\Phi}\) & 0.5270 \\ \hline \(\mathbf{\Sigma}\) & 0.3697 \\ \hline \(\mathbf{\beta}\) & 0.1622 \\ \hline \(\mathbf{\eta}\) & 0.2385 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average squared Frobenius norm between the true and estimated model parameters using the sDCTM model on the simulated dataset.
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 4 Experimental Analysis  
### Experimental Setup  
The FTP is one of the most commonly used stateful network protocols and is widely used for file transfer. The design concept of the TCP/IP protocol family has led to the malicious use of the vulnerability of the FTP. Therefore, the security of the FTP has become a research focus in the field of vulnerability discovery.  
According to statistics, nearly 4 million video surveillance equipment packages worldwide support a stateful network protocol, the Real Time Streaming Protocol (RTSP). However, the RTSP has been proven to have many medium- and high-risk buffer overflow vulnerabilities in its implementation. Hence, research on the security of the RTSP has theoretical significance and application value [31].  
This paper selected LightFTP [32], a lightweight server that supports the FTP, and Live555 [33], a streaming media server that supports the RTSP, as the test objects; their versions are 5980 ea1 and ceeb4f4, respectively. The hardware configuration includes an Intel(R) Xeon(R) Gold 6139 CPU @ 2.30 GHz, 376 of GB RAM, and the Ubuntu 18.04 server.  
For the above test objects, we compare SATFuzz with a baseline approach called AFLnet, a stateful-coverage-based fuzzer, and use the number of triggered crashes within the same time period as an indicator to evaluate the vulnerability discovery efficiency of each method. The higher the number of triggered crashes is, the higher the vulnerability discovery efficiency of the corresponding approach.  
We recorded message sequences for the most common usage scenarios of LightFTP and Live555, such as uploading a file and starting to stream a media source. These message sequences serve as the initial seed corpus for SATFuzz to use. The same is true for AFLnet and AFLnwe in the experiment. However, BoOFuzz started with a detailed model of the protocol, including the message templates and the state machine.
# Analysis of Ruggedness of 4H-SiC Power MOSFETs with Various Doping Parameters
## 1 Introduction  
SiC power metal-oxide-semiconductor field-effect transistors (MOSFETs), which have the advantages of a higher thermal conductivity, smaller device size, lower power dissipation, and faster switching speed than Si devices, are widely used in power applications. However, switching circuits such as traction inverters may lead to device instability and an abnormal operation when a circuit operates in the high-power condition for a long time. Additionally, device structural parameters such as the p-base, N+ source, and P+ source doping can influence the device robustness under unexpected operating conditions such as with unclamped inductive switching (UIS). It is, therefore, necessary to evaluate their ruggedness and to understand their failure mechanisms under abnormal operating conditions. In this work, their characteristics are analyzed through technology computer-aided design (TCAD) circuit simulations. By varying the doping concentration of the p-base, N+ source, and P+ source regions, the ruggedness of the device under the avalanche mode can be improved [1; 2; 3; 4; 5; 6; 7; 8; 9].
## 2 Methodology
### Evaluation Metrics  
Three different metrics are employed to evaluate the sensors' viewability of terrestrial snow: (1) viewed snow coverage percentage, (2) viewed snow classification coverage percentage, and (3) viewing repeat interval for each terrestrial snow class. These metrics help to quantitatively assess the sensor/constellation efficacy in observing snow across space and time, while also considering differences in regional snow climatology.
## 3 Methodology
### Detection of a Single Satellite  
The detection procedure for a single satellite includes two significant modules: finding the falling/rising moments and determining the valley periods.
## 2 Materials and Methods  
### Study Area  
Guangdong Province is located in the southernmost part of mainland China, spanning latitudes 20\({}^{\circ}\)09\({}^{\prime}\) to 25\({}^{\circ}\)31\({}^{\prime}\) North and longitudes 109\({}^{\circ}\)45\({}^{\prime}\) to 117\({}^{\circ}\)20\({}^{\prime}\) East (Figure 1). It has held the top position in total GDP in China for 32 consecutive years. Guangdong Province possesses the most abundant light, heat, and water resources in China. The primary grain crops are rice and corn. The terrain is higher in the north and lower in the south, featuring hills and mountains in the north and plains in the south. By the end of 2020, Guangdong Province had a permanent population of 126.01 million, an urbanization rate of 71.7%, and only 18.99 thousand km\({}^{2}\) of arable land, with per capita arable land representing less than one-fifth of the national average, making it one of the largest grain-consuming regions in the country. However, over the past 30 years, rapid urbanization and industrialization have caused considerable reduction of cropland by urban expansion in Guangdong Province. Additionally, the loss of rural labor has resulted in the abandonment of many croplands [27]. Therefore, selecting Guangdong Province, with its dense population, limited cropland, and frequent cropland use changes, as the study area is advantageous for analyzing the impact of the cropland use transition on grain production and regional food security. To better explore the spatial heterogeneity of cropland use transitions on food production, Guangdong Province is divided into four regions according to national land spatial planning [28]: the Pearl River Delta (PRD) region, which includes Guangzhou, Shenzhen, Zhuhai, Foshan, Dongguan, Zhongshan, Huizhou, Jiangmen, and Zhaoqing; the eastern Guangdong (EG) region, encompassing Shanwei, Jieyang, Shantou, and Chaozhou; the western Guangdong (WG) region, comprising Zhanjiang, Maoming, and Yangjiang; and the northern Guangdong (NG) region, which covers Meizhou, Heyuan, Shaoguan, Qingyuan, and Yunfu.
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 4 Experiments  
### Experimental Setup  
Experiments are conducted on several commonly used benchmarks as listed below.  
**FreiHAND**[41] was created to facilitate research on 3D hand pose estimation. The dataset includes 130,240 training images, each with corresponding 3D annotations for joint locations. And an additional 3960 evaluation samples are provided for performance evaluation.  
**HO-3D**[40] comprises color images of human hands interacting with objects. The dataset consists of 68 sequences, encompassing a total of 77,558 frames. The training set includes 66,034 images, while the test set comprises 11,524 images. This large-scale dataset provides a valuable resource for research in hand-object interaction and related computer vision tasks.  
**RHD**[13] contains 41,258 training samples and 2728 testing samples, each of which is a synthetic image of a hand in a specific pose. It provides information on keypoint visibility and occlusion/cropping, as well as camera parameters, segmentation maps, and depth maps for algorithm development.  
**STB**[18] provides both 2D and 3D annotations for 18,000 stereo pairs. For the evaluation process, 12,000 images captured using the Point Grey Bumblebee2 stereo camera from the \"B1 Counting\", \"B1 Random\", \"B2 Counting\", and \"B2 Random\" categories are utilized. This set of stereo image pairs includes both left and right images.  
To evaluate the effectiveness, we adopt a cross-dataset training and evaluation methodology. Models are trained on the HO-3D and FreiHAND official training datasets and assessed on the RHD and STB datasets. This approach allows us to assess generalization of the models across new datasets, considering the diverse sources of training and testing data.  
For evaluation, the STB dataset consists of samples from a single individual, while the RHD evaluation dataset includes samples from four distinct individuals, designated as RHD-A, RHD-B, RHD-C, and RHD-D, corresponding to individuals A, B, C, and D, and specifically covering hand poses numbered 923, 898, 979, and 981.  
Quantitative evaluations employ the following metrics:  
**MPJPE:** Mean Per Joint Position Error (MPJPE) is a metric that quantifies the error, measured in Euclidean distance (mm), between the predicted joint positions and the corresponding ground truth joint positions, with respect to the root-relative coordinates. The computational methodology aligns with [PERSON] et al. [13].  
**PA-MPJPE:** Procrustes Analysis Mean Per Joint Position Error (PA-MPJPE) is a variant of MPJPE that employs Procrustes analysis [42] to align the predicted and ground truth poses while ignoring global variation.  
**PCK:** Percentage of Correct Keypoints (PCK) quantifies the percentage of correctly predicted keypoints that fall within a specified threshold from the ground truth keypoints.  
**AUC:** Area under the curve (AUC) quantifies the overall accuracy by calculating the area under the curve of the Percentage of Correct Keypoints (PCK) versus error thresholds.
# Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction
## 5 Experiments and Analysis
### Face Clustering on Dynamic Face Sequence  
The experiments in this section are face clustering with complex conditions on the BU-4 DFE dataset [25]. The dataset has 101 subjects (the female/male ratio is 3/2), including different races. Each subject was required to complete six expressions (happy, surprise, sad, angry, fear and disgust). All 2D face images sequence and dynamic 3D face shapes of different subjects were collected simultaneously and some sample images with 2D feature tracks are shown in Figure 1. In this paper, we only used 2D face images sequences to test the proposed method, hence ASM was introduced to get 76 2D feature tracks for each face image. We randomly selected \(c=[2,3,5,8,10,20,30]\) subjects out of 101 persons to complete face clustering. For each subject, we took continuous 11 frames for each face expression, and totally 66 frames of 6 expressions are selected for each subject. Therefore, the data matrix for \(c\) subjects is \(X\in R^{132c\times 76}\). We repeated 30 times of tests, and the \(c\) subjects are selected randomly for each test. The optimal parameter settings were \(\lambda=0.01,\lambda_{1}=0.2,\lambda_{2}=0.001,\lambda_{3}=0.05\).  
Figure 1: Sample images with 2D feature tracks from the BU-4 DFE dataset.  
It is easy to observe from Table 1 that the proposed method OSC-NRSfM outperformed other methods, except for the case when the number of clusters increased. The results show that the modified methods improved the accuracy of clustering impressively if there were relatively more cluster centers.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ARAugmented Reality  
C-RANCloud Radio Access Networks  
CSCCloud Service Customer  
CSPCloud Service Provider  
IoTInternet of Things  
ITSIntelligent Transportation System  
MDSMaximum-Distance Separable  
MQTTMessage Queuing Telemetry Transport  
OSOperating System  
PIDProportional-Integral-Differential  
QoSQuality of Service  
SDCSoftware-Defined Cache  
VRVirtual Reality
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Performance Evaluation Metrics
#### 4.1.4 Average Routing Goodput  
Goodput is the value of throughput computed in the application layer. It considers only the Basic Safety Message (BSM) or the useful data for computation rather than all data as in the case of throughput. In VANETs, BSM are considered the most useful information.  
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Description** \\ \hline \(Th_{avg}\) & Average Throughput \\ \(P_{r}\) & Packet Received \\ \(P_{s}\) & Packet Sent \\ \(T_{l}\) & Time of last received packet \\ \(T_{f}\) & Time of first transmit packet \\ EED & End-to-End Delay \\ \(T_{delay}\) & Transmission Delay \\ \(P_{delay}\) & Propagation Delay \\ \(Proc_{delay}\) & Processing Delay \\ \(Q_{delay}\) & Queuing Delay \\ PDR & Packet Delivery Ratio \\ \(Goodput_{Agg}\) & Average Goodput \\ \(T_{Sim}\) & Total Simulation Time \\ \(MAC-PHY_{oh}\) & MAC-PHYsical Overhead \\ \(PHY_{s}\) & PHYsical layer packets send in bytes \\ \(APP_{s}\) & Application layer packets send in bytes \\ \(PHY_{d}\) & PHYsical layer packets delivered in bytes \\ \hline \end{tabular}
\end{table}
Table 2: Summary of notations.  
They are measured in bps, Kbps, or Mbps. The more the Goodput, the better is the performance of the network.  
\[Goodput_{Agg}=TotalP_{r}*8/TotalT_{Sim}/1000 \tag{8}\]
## 2 Methodology
### Adaptive DOFs Condensation of Transient Heat-Conduction Problem  
The adaptive DOFs condensation of the transient heat-conduction problem needs to establish a set of initial meshes ranging from coarse to refined, and then the DOFs condense gradually on the basis of the refined mesh. The establishment process of a simple two-dimensional initial computational mesh is shown in Figure 1. The initial computational mesh is refined twice on the basis of the initial coarse mesh, and each refinement involves the edge of a low-level element divided into two edges, that is, one low-level element refined into four high-level elements. The process is preset by the user before the computation process, and the initial computational mesh is used in the first computation time step.  
For the transient heat-conduction problem, temperature is the scale variable, and each node corresponds to a temperature DOF, respectively. Then, the process of DOFs condensation with the computation time step is shown in Figure 1. The calculation process of ADOF-FEM is shown in the Figure 2.  
1. Establish the initial coarse mesh according to the practical problem of transient heat conduction.
2. Considering the requirements of the problem, the user presets the refinement process of the coarse mesh and establishes the initial computational mesh. Then, the program obtains the shape function components between the different levels of the elements.
3. Begin the transient temperature-field calculation using the initial computational mesh.
4. Calculate the global error of the current time step by error evaluation. When the global error is less than the prespecified tolerance value, judge whether the coarsening criterion is met. Define the DOFs that meet the coarsening criterion as slave DOFs, while the rest are master DOFs.
5. Assemble the transformation matrix according to the shape function component and the information of master and slave DOFs.
6. Calculate the new FEM basic equation according to the transformation matrix and solve for the unknown variable of master DOFs.
7. Solve for the unknown variable of slave DOFs using the transformation matrix according to the solution of the unknown variable of master DOFs.
8. Continue to compute the solution of the next time step.  
In addition, it is worth noting that when using ADOF-FEM, as the slave DOFs are condensed, the contributions of the slave DOFs to the conductivity matrix need to be accumulated onto the master DOFs according to the transformation matrix, which increase the bandwidth of the matrix. However, since the transformation relationship between master and slave degrees of freedom is local and only a small range of slave degrees of freedom contribute to the corresponding master degrees of freedom, the increase in bandwidth should also be limited and the whole conduction matrix remains a diagonal sparse matrix.  
Figure 1: Illustration of adaptive DOFs condensation: (**a**) Form the initial computational mesh. (**b**) Condense the DOFs with the computation time step.
## 2 Classification of LPBF Defects
### Geometrical/Dimensional Defects  
Geometrical and dimensional defects can be attributed to either user and/or machine errors. User errors are typically resolved as the user becomes accustomed to LPBF technology. However, machine errors are often inevitable, though some can be reduced.  
**User errors** include (1) unoptimized manual input process parameters, (2) improper machine configuration setup and preparation, and (3) faulty usage of support structures.  
Unoptimized manually input process parameters are those process parameters (i.e., laser power, hatch spacing, scan speed) that the user can directly control. Certain part defects can occur throughout the build by choosing improper process parameters. Improper machine configuration setup and preparation can include not inspecting the recoater blade and not correctly resetting the build plate. A common improper preparation step is exposing the powder particles to atmospheric conditions, which leads to oxidation [3].  
**Machine errors** include (1) insufficient resolution error, (2) laser positioning/intensity error, and (3) platform-movement error. The staircase effect is one of the main resolution errors contributing to dimensional defects. It is defined as a stepped approximation by layers of curved and inclined surfaces due to limited fabrication resolution [4]. Insufficient resolution can cause higher surface roughness for curved surfaces. Laser positioning errors are due to slight shifts of the laser beam before or during the build process. Laser intensity errors occur when the adjusted laser intensity does not match the actual intensity. Both laser positioning and intensity errors are often associated with the software and machinery. Similarly, platform-movement errors are caused by a defective motion in the vertical direction, preventing even recoating and causing damage to the recoater blade.