esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Motion Characteristics of Bionic Morphing Wing Based on Sarrus Linkages
## 2 Materials and Methods
### Virtual Prototype Folding and Unfolding Motion Simulation  
The underwater-aerial transmedia aircraft designed in this paper is unfolded in the air and folded in the water, so the aircraft needs to complete the folding action in the process of diving into the water.  
According to the design requirements, the aircraft should fly at least two meters above the water surface. If it falls into the water, according to the free fall formula \(\{S=\frac{1}{2}gt^{2}\}\), the falling time is 0.64 s. Therefore, the folding action of the morphing wing is required to be completed within 0.5 s. According to the movement time requirement of the morphing wing, the steering engine can rotate 90\({}^{\circ}\) in 0.5 s time. Therefore, Futaba S9001 was selected as the driving engine model, and its working speed was 0.22 s/60\({}^{\circ}\) at 4.8 V.  
We imported the 3D model created in SolidWorks into ADAMS, then set the corresponding simulation environment; added gravity, materials, and other basic attributes; created kinematic pairs for the simulation model according to the motion relationship; and made the following assumptions:  
1. The purpose of this simulation and experiment is to verify the feasibility of the folding scheme, so the resistance on the wing during the movement was not considered.
2. The role of skin is to form the whole wing and provide lift in the process of movement. In order to verify the kinematic characteristics, simulation verification was carried out in static condition in this paper, and the skin weight is light, so the skin structure was removed in the simulation model.
3. Without considering the elastic deformation of the component, it was regarded as a rigid body.
4. The frictional resistance between the hinges was ignored.
5. The virtual prototype model finally established is shown in Figure 8.  
The driving part of the virtual model rotated according to the assigned driving function, and the motion function STEP (time, 0, 0 d, 0.33, \(-\)90 d) +STEP (time, 2.67, 0 d, 3, 90 d) was added to the driving kinematic pair according to the steering engine data.  
The motion simulation of the entire virtual model can be completed by setting the simulation time and step length, and the motion posture of the model at different times can be observed, as shown in Figure 9. The kinematic simulation can obtain the value of each revolute pair force of the morphing wing. At the same time, the key physical parameters such as velocity; displacement; and acceleration of inner, middle, and outer wings can be obtained by motion simulation. Through these data, the design scheme adopted in this paper is fed back to see whether it meets the design requirements.
High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion  
[PERSON]  
1 School of Earth Sciences and Engineering, Sun Yat-sen University, Guangzhou 510275, China; [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (R.G.)  
[PERSON]  
2 State Key Laboratory of Coal Mine Disaster Dynamics and Control, School of Resource and Safety Engineering, Chongqing University, Chongqing 400044, China  
theoretical and picked arrival time of specified seismic phases. The ray tracing-based location methods have been widely used in the literature due to their simple theory and operation. Detailed reviews of related studies can refer to [4, 5]. However, ray tracing-based location methods are set up on the basis of geometrical ray approximation for high-frequency wave propagating, which have inherent difficulties in modeling multi-path effects and focusing and defocusing effects of realistic broadband wavefield propagation in complex velocity structure. Moreover, they could be easily affected by large arrival time picking errors. The above shortcomings increase difficulties in an accurate location with ray tracing-based location technique [6]. In the past few years, with the development of computer technologies and improvement of numerical modeling methods, wave equation-based location methods have been gradually introduced and applied to a MS location in the fields of mining and oil-gas exploration. Wave equation-based modeling can overcome the above shortcomings of simplified ray tracing-based location methods. In addition, the wave equation-based location can directly utilize the recording broadband waveform data, that is, it does not require the high-accuracy arrival time picking of specified seismic phase. Wave equation-based location methods usually fall into two categories, i.e., wavefield reverse-time migration-based location [7] and waveform inversion-based location [8]. The former takes the maximum energy spatial point of stacking backpropagated waveform from all sensors as the location result, and it only requires a small amount of simultaneous wavefield simulations. For example, [PERSON] and [PERSON] [9, 10] developed a full wavefield-based reverse time imaging method for locating microseismic events. The wavefield reverse-time migration-based location methods are conceptually simple and easy to perform, but the critical imaging conditions for location can be easily affected by noises [11]. As the ultimate means of geophysical inversion, waveform inversion-based location methods takes advantage of the fitting degree between synthetic waveforms and observed waveforms, which fully considers waveform complexity and can comprehensively constrain the source location.
## 3 Materials and Methods
### Data Analysis
#### 3.2.1 Dataset Overview  
The annotation process proved to be a resource-intensive task, primarily due to the challenges associated with securing time from busy medical professionals, including both practicing physicians and medical students. With their commitments ranging from extended working hours and on-call duties to direct patient care responsibilities and rigorous exam preparation, allocating time for annotation was constrained. As a result, only 209 abstracts were annotated for technicality and readability aspects. Each annotated abstract included both English and German versions, ensuring comprehensive coverage of the research literature across languages. The dataset encompassed various medical disciplines, creating a representative subset of medical research publications.
## 3 Results
### Quality of Life Questionnaires  
At Day 90, improvements in the SF-36 (Figure 4a) were found for energy/fatigue, general health, pain, physical functioning, and role limitations due to physical health (all \(p\leq 0.007\)). Energy/fatigue was increased by 5.8 \(\pm\) 12.5 at Day 30 (\(p\) = 0.020), and emotional wellbeing, energy/fatigue, general health, and role limitations due to physical health were significantly increased by 2.9 \(\pm\) 5.6, 5.0 \(\pm\) 12.7, 4.0 \(\pm\) 8.7, and 9.5 \(\pm\) 38.0, respectively, at Day 60 (all \(p\leq 0.027\)). There were no changes in emotional wellbeing, role limitations due to emotional problems, and social functioning (all \(p\geq 0.239\)) at Day 90.  
There was an increase in the aggregate score (\(p\) < 0.001) and each individual domain of the Life Habits Checklist, including the neurodegenerative prevention score, brain health promotion score, healthy body rhythm score, food quality and dietary habits score, and healthy shape and weight promotion score (\(p\leq 0.014\); Figure 4b) from screening to Day 90. At Day 30, the aggregate, healthy body rhythm, and healthy shape were different from  
Figure 4: The change (\(\Lambda\)) in the 36-Item Short Form Survey (SF-36; (**a**)) and the Life Habits Checklist (LHC; (**b**)) from screening to Day 90. Data are mean \(\pm\) standard error of the mean. SF-36 and LHC were assessed using a repeated measures mixed effects model with visit number as the fixed effect and participant as the random effect. * Significantly different from screening at Day 90 (\(p\) < 0.05).  
screening by 2.6 \(\pm\) 8.1, 3.9 \(\pm\) 11.7 and 2.6 \(\pm\) 8.1, respectively (all \(p\)\(\leq\) 0.044). At Day 60, the aggregate, brain, dietary, neurodegeneration, and healthy shape domains were increased from screening by 3.9 \(\pm\) 7.9, 4.0 \(\pm\) 8.0, 4.7 \(\pm\) 10.0, 4.1 \(\pm\) 9.0, and 3.9 \(\pm\) 7.9, respectively (all \(p\)\(\leq\) 0.013). The healthy body rhythm score increased by 3.5 \(\pm\) 10.5 from screening at Day 60 (\(p\) = 0.051).  
Daytime sleepiness throughout the day reduced from screening (5.6 \(\pm\) 3.5) at Day 90 (4.9 \(\pm\) 3.5; \(p\) = 0.005) (Figure 5a). There was no change in the Epworth Sleepiness scale at Day 30 (5.3 \(\pm\) 3.1; \(p\) = 0.124) or Day 60 (5.2 \(\pm\) 3.7; \(p\) = 0.133). The frequency and severity of a depressed mood or depression (Figure 5b) decreased from screening (2.7 \(\pm\) 3.3) at Day 90 (1.6 \(\pm\) 1.9; \(p\) = 0.003). There were no changes in PHQ-9 at Day 30 (2.4 \(\pm\) 2.5; \(p\) = 0.302) or Day 60 (2.1 \(\pm\) 2.0; \(p\) = 0.266).
## 3 The Proposed Approach  
Our approach consists of two steps: (1) navigating the master controller with the proposed arm movement pattern to create variation with the desired pattern, and (2) using a linear estimator--which, in this paper, is the Kalman filter--to minimize such variation. The Kalman filter [29] is a recursive-form optimal estimator for continuous time problems that can be used to smooth out variation, as it provides discrete-time solutions in a one-step-ahead fashion, which is useful in various applications. Although for real-time and online applications, some stochastic estimation methods can produce a better state estimation, one-step-ahead estimators, such as the Kalman filter, are preferred, as they are computationally efficient and reliable [30]. Furthermore, the Kalman filter has high prediction accuracy, requires low computational complexity, does not store history data, and provides the quality of the estimation (estimation variance) [31].
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 2 Background
There is a possibility that none of the desired sensors have the capability to communicate with the UAV because of limited resources, while on the contrary, the UAV may get many responses from activated ground sensors and be unable to handle them at the same time. To tackle both of these conditions, step-4 is introduced to select some reasonable amount of field sensors for further processing. We name \"shunting\" this process of pushing or pulling some nodes from the process to make sure they are in a reasonable range. Localization (step-5) is to find out sensor nodes installed in crop field with the UAV, and a special lightweight energy-efficient antenna is designed for this purpose. We have given full details about this virtual phase array antenna in a previous article [12]. The best node among all will be selected as CH in step-6. Many parameters like energy, antenna size, energy consumption rate and distance to the UAV are investigated before selecting a node as CH. The final step-7 is data collection, in which the CH collects data from all neighboring nodes and the aggregated data is transmitted to the UAV by using a point to point dedicated link. This lifecycle keeps on until the whole or the selected area of the crop field is scanned and data is harvested successfully. The whole process spans three dimensions: dynamic clustering, dynamic cluster head selection and localization of field sensors by the UAV.
# World-Models for Bitrate Streaming
## 3 World-Models
### Generative Models  
RNNs can recognize patterns with dynamic temporal behavior and, thus, serve as the foundation for many world-models. At each step \(t\), a simple recurrent neural network accepts an input \(x_{t}\), and outputs a value \(h_{t}\). The RNN passes information from one step of the network to the next. The left side of Figure 2 displays the recurrent nature of an RNN and the right displays an unfolded version of a simple RNN. More complex architectures are used in practice; [PERSON] et al. [25] introduced Long Short-Term Memory (LSTM), a variant of the RNN that was designed to handle long term dependencies in input sequences.  
The output of an RNN is deterministic; however, an RNN can output the parameters for probabilistic model to introduce stochasticity [26]. The output distribution is dependent on the hidden state in the RNN, a representation of previous inputs. Mixture Density Networks (MDNs) [27] are a class of models that output the parameters to a Gaussian Mixture Model (GMM), a probabilistic model that is composed of several Gaussian distributions with different weights. GMMs contain three parameters: \(\mu\), the centers of each component, \(\sigma\) the scales of each component, and \(\pi\), the weights of each component. Rather than performing a regression, this model maximizes the likelihood that a model gives rise to a set of data points. This model can, in theory, approximate any arbitrary continuous probability distribution. [PERSON] [26] combined a Mixture Density Network with an RNN (MDN-RNN), and used the network to achieve state-of-the-art results on handwriting generation. In a prediction network, the hidden state of the model passes through another layer, outputting the parameters to a mixture model \(y_{t}\), a distribution modeling the input at the next time step. During inference, a sample from the mixture model, \(P(x_{t}\mid y_{t-1})\) is then fed as the next input into the network. This structure allows for a RNN to generate entire sequences, where each output is conditioned on a representation of past states.
# Modeling GPR Wave Propagation in Complex Underground Structures Using Conformal ADI-FDTD Algorithm
## 2 Methodology
### ADI-FDTD Algorithm
\begin{array}{l}a_{j}=-\frac{\Delta t}{2\mu\Delta y^{2}}.CB\ \ \ \ b_{j}=1+\frac{ \Delta t}{\mu\Delta y^{2}}.CB\ \ \ \ c_{j}=-\frac{\Delta t}{2\mu\Delta y^{2}}.CB\\ d_{j}=CA.E_{z}^{n+\frac{1}{2}}(i,j)-\frac{CB}{\Delta y}.\bigg{[}H_{x}^{n+\frac {1}{2}}(i,j+\frac{1}{2})-H_{x}^{n+\frac{1}{2}}(i,j-\frac{1}{2})\bigg{]}+\frac {CB}{\Delta x}.\bigg{[}H_{y}^{n+\frac{1}{2}}(i+\frac{1}{2},j)-H_{y}^{n+\frac{1} {2}}(i-\frac{1}{2},j)\bigg{]}.\end{array}  \tag{17}\]  
Then, Equation (16) can be rewritten as  
\[a_{j}E_{z}^{n+1}(i,j-1)+b_{j}E_{z}^{n+1}(i,j)+c_{j}E_{z}^{n+1}(i,j+1)=d_{j}, \tag{18}\]  
and Equation (17) can be written in the following matrix form:  
\[AX=Y, \tag{19}\]  
where  
\[X=\left[ \begin{array}{c}E_{z}^{n+1}(i,1)\\ \vdots\\ E_{z}^{n+1}(i,j)\\ \vdots\\ E_{z}^{n+1}(i,j_{max})\end{array} \right],Y=\left[ \begin{array}{c}d_{1}\\ \vdots\\ d_{j}\\ \vdots\\ d_{jmax}\end{array} \right],A=\left[ \begin{array}{cccc}b_{1}&c_{1}&0&0&0\\ a_{2}&b_{2}&c_{2}&0&0\\ 0&\ddots&\ddots&\ddots&0\\ 0&0&a_{jmax-1}&b_{jmax-1}&c_{jmax-1}\\ 0&0&0&a_{jmax}&b_{jmax}\end{array} \right]. \tag{20}\]  
Here, matrix \(A\) is a tridiagonal strip matrix, and the first-row elements (\(b_{1}\) and \(c_{1}\)) and the last-row elements (\(a_{jmax}\) and \(b_{jmax}\)) are given by absorption boundary conditions. In conclusion, the steps for calculating the 2D TM wave by ADI-FDTD are as follows:  
In the \(n\!\!\rightarrow\!\!n+1/2\) time step:  
(1) Calculate \(H_{x}^{n+1/2}\) by Equation (4);  
(2) Calculate \(E_{z}^{n+1/2}\) by Equation (10);  
(3) Calculate \(H_{y}^{n+1/2}\) by Equation (5). In the \(n+1/2\!\!\rightarrow\!\!n+1\) time step:  
(1) Calculate \(H_{y}^{n+1}\) by Equation (14);  
(2) Calculate \(E_{z}^{n+1}\) by Equation (18);  
(3) Calculate \(H_{x}^{n+1}\) by Equation (13).
# Time-Domain Dynamic Modeling and Analysis of Complex Heavy-Duty Gearbox Considering Floating Effect
## 2 LTC Modeling Approach of Heavy-Duty gearbox
### Detailed Formulations
#### 2.2.4. System Dynamic Equations
\begin{array}{l}M_{P}\ddot{x}_{P}^{i}+k_{CPB}\cdot\left[x_{P}^{i}-x_{C}-R_{ P}\cdot\cos(\theta_{C}+\gamma_{CP0}^{i})\right]\\ -N_{PS}^{i}\cdot\left[\sin(\gamma_{SP}^{i}-\dot{a}_{SP}^{i})+\mu_{PS}\cos( \gamma_{SP}^{i}-\dot{a}_{SP}^{i})\right]-c_{PS}\frac{d_{\rm{PB}}^{i}(r_{R}+r_{ \rm{R}})}{d_{R}^{2}}\dot{d}_{PS}^{i}\cos(\gamma_{SP}^{i}-\dot{a}_{SP}^{i})\\ -N_{PR}^{i}\cdot\left[\sin(\gamma_{RP}^{i}+\dot{a}_{RP}^{i})+\mu_{PR}\cos( \gamma_{RP}^{i}+\dot{a}_{RP}^{i})\right]-c_{PR}\frac{d_{\rm{PB}}(r_{R}+r_{\rm {R}})}{d_{PB}^{2}}\dot{d}_{PR}^{i}\cos(\gamma_{RP}^{i}+\dot{a}_{RP}^{i})=0\\ M_{P}\ddot{y}_{P}^{i}+k_{CPB}\cdot\left[y_{P}^{i}-y_{C}-R_{P}\cdot\sin(\theta_{ C}+\gamma_{CP0}^{i})\right]\\ -N_{PS}^{i}\cdot\left[\cos(\gamma_{SP}^{i}-\dot{a}_{SP}^{i})+\mu_{PS}\sin( \dot{a}_{SP}^{i}-\gamma_{SP}^{i})\right]-c_{PS}\frac{d_{\rm{PB}}(r_{R}+r_{\rm {R}})}{d_{R}^{2}}\dot{d}_{PS}^{i}\cos(\alpha_{SP}^{i}-\gamma_{SP}^{i})\\ -N_{PR}^{i}\cdot\left[\cos(\gamma_{RP}^{i}+\dot{a}_{RP}^{i})+\mu_{PR}\sin(\dot{ a}_{RP}^{i}+\gamma_{RP}^{i})\right]+c_{PR}\frac{d_{\rm{PB}}(r_{R}+r_{R})}{d_{R} ^{2}}\dot{d}_{PR}^{i}\cos(\alpha_{RP}^{i}-\gamma_{RP}^{i})=G_{P}\\ \end{array} , \tag{35}\]  
\[ \begin{array}{l}\dot{l}_{P}^{i}\ddot{\theta}_{P}^{i}+c_{PB}(\dot{\theta}_{ P}^{i}-\dot{\theta}_{R})-N_{PS}^{i}\cdot\left[\tau_{Sb}-\mu_{PS}\frac{Z_{\rm{S}}}{Z_{S}+Z_{P}} \dot{d}_{PS}^{i}\sin\alpha_{PS}^{i}\right]+c_{PS}\frac{Z_{\rm{S}}d_{\rm{PB}}( r_{R}+r_{\rm{S}})}{(Z_{S}+Z_{P})d_{PS}^{i}}\sin\alpha_{PS}^{i}\cdot\dot{d}_{PS}^{i} \\ -N_{PR}^{i}\cdot\left[\tau_{Rb}+\mu_{PR}\frac{Z_{\rm{S}}}{Z_{R}+Z_{P}}\dot{d}_{ PR}^{i}\sin\alpha_{PR}^{i}\right]+c_{PR}\frac{Z_{\rm{S}}d_{\rm{PB}}(r_{R}+r_{R})}{(Z_{R}+Z_{P})d_{ PR}^{i}}\sin\alpha_{PR}^{i}\cdot\dot{d}_{PR}^{i}=0\\ \end{array} . \tag{36}\]  
For the planet carrier, similarly, dynamic equations can be expressed as  
\[ \begin{array}{l}M_{\rm{C}}\ddot{x}_{C}-k_{CPB}\cdot\sum\limits_{i=1}^{n}\left[x _{P}^{i}-x_{C}-R_{P}\cdot\cos(\theta_{C}+\gamma_{CP0}^{i})\right]=F_{Cx}\\ M_{\rm{C}}\ddot{y}_{C}-k_{CPB}\cdot\sum\limits_{i=1}^{n}\left[y_{P}^{i}-y_{C}-R_ {P}\cdot\sin(\theta_{C}+\gamma_{CP0}^{i})\right]=F_{Cy}+G_{P}\\ I_{c}\ddot{\theta}_{c}+c_{pb}(\dot{\theta}_{r}-i\dot{\theta}_{p})+k_{CPB}\cdot \sum\limits_{i=1}^{n}\left[x_{P}^{i}-x_{C}-R_{P}\cdot\cos(\theta_{C}+\gamma_{CP0 }^{i})\cdot(x_{P}^{i}-x_{C})\right]\\ -k_{CPB}\cdot\sum\limits_{i=1}^{n}\left[y_{P}^{i}-y_{C}-R_{P}\cdot\sin(\theta_{C} +\gamma_{CP0}^{i})\cdot(y_{P}^{i}-y_{C})\right]=T_{c}\end{array} . \tag{37}\]
# Shear Wave Velocity Estimation Using the Real-Time Curve Tracing Method in Ultrasound Elastography
## 3 Phantom Experiments
### Image Reconstruction of Shear Wave Velocity  
#### 3.3.1 Homogeneous Region  
According to the real-time curve tracing method proposed in this paper, the experimental data of a high-frame rate shear wave propagation video can be obtained in the homogeneous region of the 040 GSE ultrasound phantom shown in Figure 1a, with which the shear wave velocity image reconstruction is realized. The B-ultrasound image of the homogeneous area is shown in Figure 5a. In the B-ultrasound image, the thick red line at the top corresponds to the position of the probe, the white rectangle is the selected region of interest and the four red cross marks in the longitudinal direction correspond to the focus coordinates in Table 1. The real-time curve tracing method is utilized to reconstruct the shear wave velocity image of the selected region of interest in the homogeneous region of the 040 GSE ultrasound phantom. In order to facilitate the presentation of the reconstruction effect, the results in Figure 5b are selected for subsequent detailed analysis, with a transverse range of 20 \(\lambda\) to 60 \(\lambda\) and a longitudinal range of 30 \(\lambda\) to 80 \(\lambda\).
## 1 Introduction
In 2020, they studied the nonlinear characteristics induced by torsional guided waves and material nonlinearity in the low-frequency range [33]. [PERSON] investigated the propagation behavior of fundamental Lamb waves interacting with different materials in welded joints [34]. [PERSON] focused on the interaction between incident longitudinal modes and pipeline defects, studying the mode conversion process of axisymmetric modes [35]. Contrary to intuition, defects in buried pipelines are easier to detect than those in non-buried pipelines under certain conditions [36]. [PERSON] found that water-filled waveguides should be treated as elastic pipes, which have significantly different properties from air-filled waveguides that are usually treated as rigid pipes [37]. [PERSON] found that the geometric propagation of shear wave signals in the spatial domain can be corrected by weighting, reducing computational deviations caused by material properties [38]. If the pipeline is buried in an elastic solid or submerged in a liquid, the challenge is to capture the acoustic field in the surrounding (nominal infinite) domain [39]. [PERSON] et al. discovered that, in addition to the stiffness of pipe clamps, the shear stiffness of soil significantly affects the wave velocity inside the pipe when studying the influence of soil properties on the propagation of leakage noise in buried plastic water pipes [40]. [PERSON] conducted a detailed quantitative analysis of longitudinal guided wave propagation in fluid-filled pipes buried in saturated porous media, finding that phase velocity and displacement amplitude gradually decrease with increasing porosity [41]. This study proposes a numerically simulated method based on acoustic-solid coupling to solve the dispersion characteristics of sound waves in complex pipelines, such as double-layer fluid-filled pipes (DLLFP), in the fields of pressure acoustics, solid mechanics, and acoustic-solid coupling. The feasibility and correctness of applying the Timoshenko beam model to solve the dispersion characteristics of multi-layer fluid-filled pipes are verified by comparing numerical solutions with analytical solutions, providing a reference for selecting guided wave modes in ultrasonic guided wave detection applied to DLLFP.