esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Approach  
The approach followed in this research included four main parts, as outlined in Figure 1. First, a literature review was undertaken to examine and identify the advantages and shortcomings of existing inertial loading systems used in shake table experiments on large-scale structural specimens. Second, a new inertial system was conceived and analytically assessed by determining the associated dynamic equation of motions of the system. Third, the proposed system was experimentally and numerically evaluated to study the seismic performance when incorporating large-scale bridge column specimens. Finally, non-linear modeling of the proposed system was used to compare performance to the other prominent inertial loading setups.
## 3 Methodology
### Spatial Transformer  
We employ a self-attention layer to model the dynamic spatial dependencies. Furthermore, we adopt and improve the GWNN to replace the GCN to model the fixed spatial dependencies. The spatial transformer consists of the self-attention layer and improved GWNN. As illustrated in Figure 2, the input features of the spatial transformer will be mapped to a latent high-dimensional subspace by a \(1\times 1\) convolution layer and then be utilized by the self-attention layer and the improved GWNN as the basis for computation.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 5 The Proposed Adaptive Data Rate Model
By incorporating these techniques, the proposed SSFIR-ADR algorithm demonstrates its ability to effectively reduce energy consumption and mitigate interference while maintaining satisfactory packet delivery performance in LoRaWAN networks. ```
0:\(SF=[7,12]\), \(TP=[2,14]\), \(SNR_{\text{thresh}}\), \(SNR_{\text{req}}\), \(\rho_{SF}\)
0:\(TP_{\text{new}}\) and \(SF_{\text{new}}\) for each ED
1: ReceivedPacketList \(\leftarrow\) number of packets recently received
2:\(SNR_{\text{avg}}\leftarrow\) average SNR value for ED
3:\(SNR_{\text{thresh}}\leftarrow[-7.5,-10,-12.5,-15,-17.5,-20]\)
4:\(D_{margin}\gets 10\)
5:\(\rho\gets rand()/RAND_{MAX}\)
```  
**Algorithm 1** The Proposed SNR-based SF Collision and Interference Reduced ADR Model  
```
6:for each ED in the network do
7:\(SNR_{avg}\gets avg(\text{ReceivedPacketList})\)
8:\(SNR_{margin}\gets SNR_{avg}-SNR_{thresh}-D_{margin}\)
9:Steps \(\gets floor(SNR_{margin}/3)\)
10:\(SF\gets maxSF\)
11:whileSteps \(>\) 0 and \(SF>minSF\)do
12:\(SF\gets SF-1\)
13:Steps \(\gets Steps-1\)
14:endwhile
15:whileSteps \(>\) 0 and \(TP>minTP\)do
16:\(TP\gets TP-3\)
17:\(Steps\gets Steps-1\)
18:endwhile
19:Update UsageIndex
20:for each SF in SFvalue do
21:\(switch(SF)\)
22:if\(SNR_{avg}>SNR_{thresh}\)then
23:if\(\rho>\rho_{SF}\)then
24:\(SF\gets SF-1\)
25:endif
26:endif
27:endfor
28:\(TP_{new}\gets TP\)
29:\(SF_{new}\gets SF\)
30:endfor
31:return\(TP_{new},SF_{new}\)
```  
**Algorithm 1**_Cont_. Figure 3: The SF Map – Algorithm SSFIR-ADR2.
# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 2 Materials and Methods
### Deepfake Video Detection
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Content**} & \multicolumn{4}{c}{**Source**} & \multicolumn{4}{c}{**Dataset/ACC–EE–AUC**} \\ \cline{3-14}  & & **Feature** & **Face** & **Image** & **Image** & **Video** & **Sound** & **FF-DF** & **UADFV** & **Celeb-DF** & **TF-TIMIT** & **DFD** & **DFDC** & **Other** \\ \hline Eye blinking [23] & LRCN (LSTM + CNN) & ✓ & ✓ & & ✓ & & & & & & 0.99 \\ \hline Using space-temporal temporal properties [25] & RCN (CNN + RNN) & ✓ & & ✓ & ✓ & & 96.9 & & & & & \\ \hline In-frame and temporal inconsistent [26] & CNN + RNN & ✓ & & ✓ & & & & & & & 97.1 \\ \hline Face warping artifacts [27] & CNN & ✓ & & ✓ & ✓ & 93.0 & 97.7 & 64.6 & 99.9 & 93.0 & 75.5 \\ \hline MesoNet [6] & CNN & & & ✓ & & ✓ & 95.23 & 82.1 & 53.6 & 87.8 & & \\ \hline Capsule consensus [28] & Capsule consensus & ✓ & & ✓ & ✓ & & 99.33 & & & & & \\ \hline Head poses [29] & SVM & ✓ & ✓ & ✓ & ✓ & 47.3 & 89.0 & 54.6 & 53.2 & 55.9 & \\ \hline Face manipulation [30] & CNN & ✓ & ✓ & ✓ & ✓ & & & & & & 98.4 \\ \hline Multi-task learning [31] & CNN + DE & & ✓ & ✓ & ✓ & & & 36.5 & 62.2 & & \\ \hline Voice inconsistency [32] & LSTM + DNN & ✓ & & & ✓ & ✓ & & & & & 24.74 \\ \hline Video inconsistency [33] & LSTM + DNN & ✓ & & & ✓ & ✓ & & & & 17.6 \\ \hline Audio features [34] & DNN & ✓ & & & ✓ & ✓ & & & & 1.26 \\ \hline Video and audio features [35] & LSTM & ✓ & & ✓ & ✓ & ✓ & 100 & 99.6 & & & \\ \hline XceptionNet [36] & CNN & & & & & & & 99.26 & 38.7 & 56.7 & & \\ \hline Forgery detection by body analysis [37] & DNN-LSTM & & ✓ & ✓ & & & & & & 94.39 \\ \hline Head pose estimation [24] & LBP + CNN & & ✓ & ✓ & & & & & 91.70 \\ \hline Time and space analysis [25] & CNN + RNN & & ✓ & & ✓ & & 96.90 & & & \\ \hline Using a f2f counterfeiting technique [38] & CNN + LSTM & ✓ & ✓ & ✓ & ✓ & & 95 & & \\ \hline Use of visual artifacts [39] & MLP & ✓ & ✓ & ✓ & ✓ & & & 84 & 84 \\ \hline Optical flow [40] & CNN & & ✓ & & ✓ & & & & 81.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of deepfake detection methods in the literature. In addition, with the developments in deepfake detection in recent years, many comprehensive datasets have emerged; today, we can easily access Celeb-DF, FaceForensics++, UADVF, DeeperForensics, DFTIMIT, and many private datasets as well [7; 41; 42; 43]. With the development of these datasets, model training has become healthier and stronger. The datasets used for deepfake detection are highly effective for model training, and if we can train the model well, we can then make detections with high accuracy by utilizing the successful program.
## 4 High-Resolution Images in Camera-Based WMSNs  
The resolution and field of view of camera sensor nodes in WMSNs are low, while most applications of WMSNs need a high field of view with high quality. According to the literature, the previous methods solved this problem by using a hierarchy of cameras which contains stronger and more expensive cameras in higher tiers of it. But, the presented method uses image stitching to produce high quality images which cover wide areas by utilizing low cost camera sensor nodes just in lowest tier. The image stitching is the process of combining multiple images in order to produce an image with higher resolution and wider field of view.  
Due to camera sensor nodes limitations and the complexity of image stitching process, in order to reduce the volume of data transmission and prolonging network lifetime, process of creating high-resolution images with maximum field of view consists of these stages. By the first request from Sink to the desired cluster head, the cluster head gathers the low-resolution images of all camera nodes in its cluster and sends all of them to Sink. Sink stitches these images to produce a high-resolution image as the result of the first query. A lot of efforts in image stitching process are taken to find transformation matrices. Hence, having these matrices beforehand makes stitching process really less complicated. So, Sink calculates these matrices and sends them to the cluster head to perform stitching in the subsequent queries (Details are explained in subsection 4.1). Also, based on stitched image a Coverage Data Structure is created by Sink (Details are explained in subsection 4.2.1). By the next queries, using this Coverage Data Structure and running Fair Camera Node Selection algorithm, the cluster head selects just a subset of camera nodes and gathers their images (subsection 4.2.2). Because the cluster head has transformation matrices, a simple procedure is adequate to stitch images of this selected subset before sending them to Sink.  
Figure 3: Symbolic figure of proposed two-tier camera-based WMSN topology.
## 3 Static Analysis
### Parameter Analysis
For dimensionless displacement \(x^{\prime}>0\), when the value of \(\alpha\) increases, the curve moves to the right overall, and the suspension system deviates from the quasi zero stiffness more and more. It also illustrates that the value of the parameter \(\alpha\) should not be too large. From Figure 7, the corresponding stiffness curves under different values are nearly symmetric at z = 0. When \(\gamma_{2}\geq 1.2\), the dynamic stiffness curve is a concave parabola shape, and the stiffness value at static equilibrium position is the smallest; when \(0.9\leq\gamma_{2}<1.2\), the stiffness value is unstable with varying between positive and negative. Therefore, the configuration of structural parameters needs to be reasonably selected to make the dynamic stiffness of the static equilibrium position zero or greater than zero. Additionally, when \(\gamma_{2}=1\) and \(\alpha=1.2\), its dynamic stiffness curve is an oblique line with a tiny angle, and the stiffness value can be considered to be \(K^{\prime}=-1\). This situation shows that the distance from the isolated equipment to the static equilibrium position can be infinite, and dynamic stiffness of the region above the line \(K^{\prime}=-1\) is always less than static stiffness. However, in the case of \(\gamma_{2}>1\), the infinite range, from the isolated equipment to the static equilibrium position, decreases with increases in the values of \(\gamma_{2}\). Figure 7 also shows several sets of stiffness curves corresponding to different values of \(\alpha\). Through a comparison, it can be concluded that when \(\alpha=0.5\) and \(\gamma_{2}\geq 1.2\), the dynamic stiffness of the system around the static equilibrium position is very close to zero, that is, near quasi-zero stiffness. This illustrates that the selection of parameters \(\alpha\) and \(\gamma_{2}\) has a certain impact on the stiffness characteristics of the system. Furthermore, the dynamic stiffness curves of the system under different variations of \(\alpha\) are shown in Figure 8. It can be seen that as the value of \(\alpha\) increases, the minimum value of the dimensionless stiffness curve decreases. When \(\alpha=1.2\) and \(\alpha=1.4\), the system is close to quasi-zero stiffness, but the concave parabola is closer to the center axis. The correctness of the above \(\gamma_{2}\geq 1.2\) analysis results is verified again. The larger the range of displacement changes for the suspension system, the better the vibration isolation effect. To sum up, the design parameters of the suspension system \(1.2\leq\gamma_{2}\leq 1.4\) and \(\alpha=0.5\) are the most ideal configuration parameters of the system. However, in the actual design process, it is difficult to obtain these accurate values because of errors in manufacturing and assembly, and \(\gamma_{2}<1.2\) may also appear, therefore, the quasi-zero stiffness should be satisfied as far as possible during the design.
# Deep Neural Network-Based Guidance Law Using Supervised Learning
## 5 Results
### Design of the Controller Model  
A controller model is designed for the control of the acceleration of a missile. The practical system model is expressed with a high-order system, but this study is defined as a first-order system with a time constant \(\tau\) for simple analysis. The first-order control system can be expressed mathematically with a first-order differential equation, and the characteristic of the transient response relies entirely on the time constant \(\tau\). Thus, acceleration commands which are intended to be controlled can be represented by the first-order system, such as in Equation (13). The system response is determined by the time constant \(\tau\).  
\[\tau\dot{a}_{m}+a_{m}=a_{cmd} \tag{13}\]  
where \(\tau\) is the time constant, \(a_{m}\) is the derived acceleration and \(a_{cmd}\) is the acceleration command.  
Figure 12 shows a schematic diagram of the missile simulator with a first-order system. The first-order system can be viewed as a linear time-invariant (LTI) input-output system. The missile is maneuvered by acceleration derived from the controller model. Thus, the trajectory simulation is executed with acceleration commands that have passed through the first-order system.  
The DNNG and DNNLG laws are trained without a controller model. These two guidance laws are trained as a state-control pair of the PNG with a navigation constant of 3. The simulation is performed for the time constant {0, 0.05, 0.07, 0.1, 0.5, 1} on 150 random initial conditions. The simulation results are shown in Table 5. The DNNG and DNNLG laws have a 100% hit rate under all initial conditions. The DNNG makes a difference from the PNG as the time constant grows, which appears to be the result of training without considering the controller model during the DNNG model's training. Thus, SL cannot achieve the same results entirely for the data other than the training data, while fully learning about training data. These results seem to reveal the limit of SL, and to make up for it, we need to explore other ways, such as building a very large database that takes into account all the factors, or using reinforcement learning (RL).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Guidance Law** & **Hit Rate (\%)** & **Energy** \\ \hline PNG & 100 & 1195.4409 \\ DNNG & 100 & 1195.6002 \\ DNNLG & 100 & 1252.1283 \\ RLG [16] & 100 & 1287.4488 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average energy for the 100 random initial conditions outside the range of the training data.  
Figure 12: Conceptual diagram of the missile system with added first-order system.
# Innovation and Application of the Type III Secretion System Inhibitors in Plant Pathogenic Bacteria
## 2 Regulation of T3 SS in _D. dadantii_  
The expression of T3 SS in _D. dadantii_ is governed by the master regulator HrpL. This alternative sigma factor activates T3 SS gene expression by binding to the highly conserved _hrp_ box (GGAACC-N\({}_{15/16}\)-CCACNNA) in their promoter regions. Genes such as _hrpA_, _hrpN_, and _dspE_, which encode the T3 SS pilus protein, a harpin, and a virulence effector, respectively, are all transcribed in a HrpL-dependent manner [17; 18; 27; 28]. The regulation of HrpL is achieved via two independent regulatory cascades in _D. dadantii_ (Figure 1) [29; 30; 31; 32]. Transcriptionally, HrpS is a \(\sigma^{54}\) (RpoN) enhancer binding protein, which binds the \(\sigma^{54}\) (RpoN)-containing RNA polymerase holoenzyme and initiates the expression of _hrpL_. A two-component signal transduction system (TCSTS) HrpX/HrpY is responsible for the activation of _hrpS_[29; 33]. Post-transcriptionally, _hrpL_ is regulated by RsmA, a small RNA-binding protein, which binds the \(5^{\prime}\) untranslated region of _hrpL_ mRNA and facilitates its degradation [34]. RsmB is a non-coding RNA containing multiple RsmA binding sites that bind RsmA proteins to antagonize their negative effect on _hrpL_ mRNA, resulting in a positive effect on the downstream T3 SS gene expression [35; 33]. The GacS/GacA TCSTS has been shown to upregulate the production of RsmB RNA in _D. dadantii_[31].
## 2 Cooperative SCARA System
### RRT Path Planner  
RRT planning is a path planning method used to determine routes from an initial point to a target in a complex configuration space and is especially useful in high-dimensional environments with numerous obstacles. The RRT algorithm is based on the incremental construction of a tree that explores the configuration space by generating random points and extending the tree towards these points, thereby ensuring efficient and rapid coverage  
Figure 10: Process for moving an object between both robots: (**a**) Robots position themselves near the object and grasp it, and (**b**) robots transfer the object.  
Figure 9: Cooperative SCARA system with identification of initial points on the object to be transferred.  
of the space. This technique is notable for its ability to address trajectory planning problems in nonlinear contexts with significant spatial constraints.  
The RRT process begins with the definition of a root node \(\mathrm{x_{init}}\) at the robot's initial position, a goal node \(\mathrm{x_{goal}}\), which is the desired position for the robot, and a structure \(\mathrm{T}\) that contains nodes and edges, where each node represents a configuration of the robot, and each edge represents a feasible movement between configurations.  
When starting the planning, the root node is assigned to the structure.  
\[\mathrm{T}=\{\mathrm{x_{init}}\} \tag{17}\]  
From there, random points are generated within the configuration space.  
\[\mathrm{x_{rand}}\ \sim\ \mathrm{Uniform}\ (\mathrm{C}) \tag{18}\]  
Using a distance metric, the nearest node \(\mathrm{x_{nearest}}\) from the existing tree to the randomly generated point \(\mathrm{x_{rand}}\) is selected, which must belong to \(\mathrm{T}\).  
\[\mathrm{x_{nearest}}\ \sim\ \mathrm{argmin}\ \mathrm{dist}\ (\mathrm{x},\ \mathrm{x_{rand}}) \tag{19}\]  
\[\mathrm{dist}\ (\mathrm{x_{1}},\mathrm{x_{2}})=\sqrt{\sum_{i=1}^{n}\bigl{(} \mathrm{x_{1}^{i}-x_{2}^{i}}\bigr{)}^{2}} \tag{20}\]  
The tree is extended from \(\mathrm{x_{nearest}}\) towards \(\mathrm{x_{rand}}\), and a new node \(\mathrm{x_{new}}\) is created at a predetermined distance \(\Delta\mathrm{t}\).  
\[\mathrm{x_{new}}=\mathrm{x_{nearest}}+\frac{\mathrm{x_{rand}}-\mathrm{x_{ nearest}}}{\|\mathrm{x_{rand}}-\mathrm{x_{nearest}}\|}\Delta\mathrm{t} \tag{21}\]  
\[\|\mathrm{x_{rand}}-\mathrm{x_{nearest}}\|=\sqrt{\sum_{i=1}^{n}\bigl{(} \mathrm{x_{rand}^{i}-x_{nearest}^{i}}\bigr{)}^{2}} \tag{22}\]  
If the proposed trajectory does not collide with any obstacles or the other robot, it is added as a new node to the tree \(\mathrm{T}\) and connected to the nearest node via a collision-free path.  
\[\mathrm{T}=\mathrm{T}\cup\{\mathrm{x_{new}}\} \tag{23}\]  
This iterative procedure is repeated until one of the tree nodes \(\mathrm{x_{new}}\) is within a predefined distance from the goal \(\mathrm{x_{goal}}\), thus forming a route from the start to the goal.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 6 Evaluation of Algorithm Performance
### Connectivity  
After executing the PCEB-MS protocol, the entire network coverage needs to be guaranteed to ensure normal communication between all sensor nodes and sink nodes. This section defines a parameter, known as average connectivity, which refers to the percentage of sensor nodes covered by the sink to all the sensor nodes. With an increase in the number of sinks, connectivity reaches 100% when the number reaches a certain value. All the sensor nodes are covered by the network. In addition, all the experimental results presented are the average values from 1000 repetitions.  
Figure 10 makes a comparison between the PCEB-MS protocol and the CNP algorithm, in terms of the change in the number of sinks and the average connectivity. With an increase in the number of sink nodes in the network, the average connectivity increases correspondingly; when the number of sink nodes reaches 12, the average connectivity is close to 100%. It is evident that the average connectivity obtained using the PCEB-MS protocol is better than that obtained using the CNP algorithm. For example, the initial connectivity derived using the PCEB-MS protocol is 72% when only one sink is present, and that using the CNP algorithm is only 65% for the same number of sinks. When the number of sinks increases to 6, the connectivity obtained using the PCEB-MS protocol is close to 96%, whereas that obtained using the CNP is only 86%. Ultimately, when there are 12 sinks, the average connectivity using the CNP algorithm is slightly lower than that using the PCEB-MS.
## 4 Performance Evaluation
### LTE Mode 3
Additionally, our premise is that because it is possible to establish V2X communications with different technologies, the high transmission rates, extremely low latencies and high reliability offered by 5G NR networks can be exploited for advanced use cases such as those shown in Table 2. The following section describes the need for the use of MR-DC architectures that combine different communication interfaces, radio access technologies and cross-border operation to guarantee the Quality of Service (QoS) of advanced use cases in multi-operator scenarios.
# Analysis of Distributed Measurement Method for Array Antenna Position
## 2 Scheme and Principle
### Error Analysis of Distributed POS Technology
#### 2.3.4 Error Model of Inertial Device  
The errors of inertial devices mainly include gyro drift error, accelerometer bias error, calibration coefficient error and calculation input error. These errors are usually colored noises. Among these errors, gyro drift error and accelerometer bias error have great influence on the navigation accuracy of the system. Therefore, the paper mainly introduces gyro drift error model and accelerometer zero drift error model.  
(a) Gyro drift error model  
The drift error of the gyroscope is equivalent to the random constant \(\varepsilon^{b}\), the random error \(\varepsilon^{r}\) of the first-order Markov process and the Gaussian white noise \(\omega_{g}\), and the formula is shown as follows:  
\[\varepsilon=\varepsilon^{b}+\varepsilon^{r}+\omega_{g} \tag{32}\]2. Zero drift error model of accelerometer \[\
abla=\
abla^{b}+\
abla^{r}+\omega_{a}\] (33) where, \(\
abla^{b}\) is the random constant bias of accelerometer, \(\
abla^{r}\) is the random noise of the first-order Markov process of accelerometer, \(\omega_{a}\) is Gaussian white noise.  
Thus, the gyro and acceleration error model in the navigation coordinate system can be expressed as follows:  
\[\left\{\begin{array}{l}\varepsilon^{n}=C_{b}^{n}\varepsilon^{b}+\varepsilon^ {r}+\omega_{g}\\ \
abla^{n}=C_{b}^{n}\
abla^{b}+\
abla^{r}+\omega_{a}\end{array}\right. \tag{34}\]  
Through the analysis above, we can find that the core of the measurement method designed in the paper is to use the FBG measurement technology to obtain the deflection of the wing in the dynamic environment and assist the distributed POS technology to improve the measurement accuracy, so as to achieve the purpose of measuring the phase center of the array antenna. In order to verify the feasibility of this method, further analysis should be carried out in a specific carrier environment. Therefore, based on the available aircraft, the paper designs the structure of the experimental device and checks the structural strength.