esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels
### Analysis of the Influencing Factors of Earthquake Disasters in Portal Sections of Highway Tunnels
#### 3.3.4 Dislocating Inverted Arch Cracking and Inverted Arch  
Inverted arch cracking and inverted arch dislocation are caused by damage caused by the lower pressure exceeding the ultimate bearing capacity of the inverted arch structure due to the seismic force, and these two types of damage usually occur together. The inverted arch structure is subjected to a large surrounding rock stress under the action of an earthquake. Under the action of mountain stress and gravity, the concrete on both sides of the sidewall and the arch foot is squeezed to the axis of the tunnel, resulting in the upward extrusion of the inverted arch concrete, which results in cracking and dislocation [27]. During the Wenchuan Earthquake, the inverted arch and the floor of Longxi Tunnel were uplifted at many sites and were accompanied by longitudinal and transverse tension cracks. The maximum uplift height was 1.2 m, and the maximum cracking of the inverted arch of Longdongzi Tunnel reached 10 cm. The reason for the damage of the inverted arch of the two tunnels is that the large extrusion pressure on both sides of the tunnel indirectly acts on the inverted arch during the earthquake, resulting in the uplift and cracking of the inverted arch. The section of Zipingpu Tunnel is a large-span section, the external force on the inverted arch is greater than that on the ordinary section and insufficient seismic design and a lack of seismic measures cause cracking.  
The main reasons for inverted arch cracking and inverted arch dislocation are the insufficient concrete strength of the inverted arch, the large section span and the lack of seismic measures, which cannot effectively bear the seismic load and are tunnel structural factors.
# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 5 Experiment and Analysis  
### Data Sources  
The experimental data chosen in this paper are the gearbox fault diagnosis dataset from Southeast University of China [30]. The gearbox fault dataset of Southeast University is the vibration signal collected by drivetrain dynamic simulator (DDS). DDS is manufactured by Changxing Shenyang Technology Co., Ltd., Huzhou, China. The transmission system dynamics simulator mainly encompasses a motor, a motor controller, a planetary gearbox, a parallel gearbox, a brake and a brake controller. The picture of the test rig can be found in the reference [30]. In the test rig, the gears are straight cylindrical gears, the number of gear teeth in the reduction gearbox are 32, 80, 72 and 40, the module is 2, the transmission ratio of the reduction gearbox is 4.5, the brake used for gearbox loading is the magnetic powder brake, whose power is 3 KW, the data acquisition card used in measurement equipment is VQ-8, the sampling frequency is 51.2 kHz, the vibration sensor used in the measurement equipment is TES001V, and the sensitivity is 100 mv/g. The experimental platform can simulate five gear states under two working conditions. The two working conditions are (1) motor speed of 1200 rpm, brake load of 0 Nm; and (2) motor speed of 1800 rpm, brake load of 7.32 Nm. In the experiment, the sampling frequency of the data acquisition system is 51.2 kHz, and the duration of each fault sampling is 20 s. Table 2 lists the specific fault types.
## 3 Proposed Method
### Data Preprocess
#### 3.1.2 Data Normalization  
Since different sensing unit data often use different units of measure, the range of values can vary. If raw data are used directly as input to the model, it may result in data items with large values that sway the model's classification effect. Additionally, fluctuating unprocessed data may affect the model's performance [43]. Therefore, we need to normalize raw data by scaling it to fall within an interval of \(-1\) to \(1\). It eliminates differences in the range between different sensor channel types. Data normalization also speeds up model convergence and improves its training rate and accuracy.  
For the collected dataset \(D=\{d_{1},d_{2},d_{3},\ldots,d_{n}\}\), each data sample contains multi-featured sensor data \(d_{i}=\{x_{1},x_{2},\ldots,x_{K}\}\), where \(K\) represents the number of features. To  
Figure 2: Overview of HMA Conv-LSTM. Input layer (**a**) reads the windowed data from the segmented sensor sequence; Sensor Feature Selection (SFS) (**b**) performs feature selection on input data based on attention mechanism; Hierarchical Multi-scale Convolution (HMC) (**c**) performs finer-grained feature extraction on the spatial information of the features; Multi-scale Conv (**d**) uses different scale convolution kernels to extract features from different hierarchical levels of data; Adaptive channel selection (ACS) (**e**) improves the discrimination and sensitivity of the model to the features of each channel; Adaptive Channel Feature Fusion (ACFF) (**f**) can retain important information and improve model efficiency; dynamic channel-selection-LSTM (DCS-LSTM) (**g**) can establish the linkage between feature vectors at different timesteps; Softmax Layer (**h**) obtains the probability distribution of the predicted values of each category, and finally takes the category with the largest predicted value as the classification result.  
determine the maximum and minimum values for all features in the dataset, we form the vectors \(x_{max}\) and \(x_{min}\). And then, we perform the normalization operation:  
\[x_{i}=2\times\frac{|x_{i}-x_{min}|}{|x_{max}-x_{min}|}-1 \tag{2}\]
## 6 Numerical Examples  
In this section, representative numerical examples are given to test the proposed algorithm. To eliminate any accuracy loss due to insufficient numerical precision, the computation of Sections 6.1 and 6.2 are performed by using the symbolic software Maple 12 with a precision of 32 decimal digits (roughly equivalent to quadruple precision in Fortran language on a 32-bit PC) to guarantee the results faithfully reflect the developed theory. To show pure numerical implementation, Section 6.3 is computed by a Fortran 90 code with Gaussian quadrature and SOLVEBLOCK [21] package employed. In the practical computation, various tolerances \(T_{l}\) ranging from \(10^{-3}\) to \(10^{-6}\) are adopted, and the linear (\(m=1\)) and cubic (\(m=3\)) reduced elements are employed for the presentation although other degreed elements have also been tested. For all the examples, the initial element size is taken as \(h_{0}=0.5\).
## 3 Results
### Analysis of the Results  
Statistical analysis of the results regarding the performance evaluation of the diagnostic model was carried out using the comparative table found in Figure 11 from Appendix A. The purpose of the analysis was to determine whether there was a significant difference in the mean CLR development stage diagnosed with a visual inspection and using the proposed technological integration. The outcome was relevant to get the necessary statistical support for answering the research question.  
The comparative table contained 202 observations with the corresponding diagnosed development stage for both treatments. Figure 13 shows the box plot chart describing the measurements. The \(x\)-axis contains the two treatments (\"visual inspection\" and \"technological integration\"), whereas the CLR development stage is presented on the \(y\)-axis. The graphical similarity of the data distribution of each treatment suggested a possible similarity to the means of the response variable. To assess this condition and make a decision based on the hypothesis, an ANOVA was executed.  
The results of the ANOVA can be seen in Table 6. The obtained \(p\)-value for the treatments factor was 0.231. This value was greater than the set significance (\(\alpha=0.1\)), which meant that there was not sufficient evidence for rejecting the null hypothesis. Thus, it was concluded, with 90% confidence, that there was no statistically significant difference between the diagnosis of the CLR development stage made by using visual inspection and the technological integration. This result indicated that both methods were significantly similar to diagnose the disease.  
This research demonstrated the feasibility of diagnosing the CLR development stage in the Colombian Caturra variety, with significant performance, through the integration of RS, WSN, and DL. The analysis of the results allowed obtaining statistical evidence for supporting the research hypothesis. In that sense, the outcome suggested that a technological integration could contribute to the protection  
Figure 13: Data distribution of the observations for both treatments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & \(F_{1}\)**-score (Test Set)** \\ \hline Sensor Data (JSON) & 0.570 \\ RGB & 0.920 \\ RGN & 0.946 \\ RE & 0.944 \\
**Composite** & **0.775** \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(F_{1}\)-score reached by the individual submodels and the composite model.  
of the phytosanitary status of coffee crops since it showed potential for complementing the traditional visual inspections towards the diagnosis of the most economically limiting disease for Colombian coffee production.
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 3 CNNMVN Learning Algorithm and Error Backpropagation
### CNNMVN Error Backpropagation
#### 3.2.2 Simple CNNMVN with Two Convolutional Layers
On picture (**a**), we see a single possible kernel position where the \(x_{11}\) pixel is involved into a convolution. On picture (**b**), we can see that pixel \(x_{33}\) is participating in the convolution four times—every time in a different kernel position. Figure 2: Passing the flattened feature maps to the first fully connected layer (**a**) is analogous to the same process between the fully connected layers (**b**). Let \(T\) be the number of times when the \(ij^{th}\) pixel was involved in a convolution. Taking into account all the considerations above, the errors of the feature map pixels created by a single kernel in the first layer are:  
\[\delta^{11}_{ij}=\sum_{h=1}^{H}\sum_{t=1}^{T}\delta^{h2t}\left(w_{t}^{h2}\right) ^{-1},\ \ i,j=a_{1}-r_{1}+1, \tag{22}\]  
where \(\delta^{11}_{ij}\) is the error of the feature map pixel with coordinates \(i,j\) produced by a single kernel in the first convolutional layer, and \(\delta^{h2t}\) and \(w_{t}^{h2}\) are the error of the output and the weight of the \(h^{th}\) kernel in the second convolutional layer that processed \(ij^{th}\) pixel at the \(t^{th}\) convolution, respectively. It is important to note that there is no normalization factor in (22) because there is no further need for error backpropagation. But there is an easier way to backpropagate the errors between convolutional layers: the backward convolution process. This method was proposed for CNNMVN in [62], and it is structurally similar to the error-backpropagation process for real-valued CNNs. To perform backward convolution and backpropagate the errors from the second convolutional layer to the first one, we should add zero padding to the feature map errors of each feature map of the second convolutional layer. Then, we rotate the kernels of the second convolutional layer by \(180^{\circ}\) and apply convolution to these errors (Figure 4). This process can be represented as follows:  
\[\delta^{11}_{ij}=\sum_{h=1}^{H}\sum_{u=1}^{r_{2}}\sum_{v=1}^{r_{2}}\delta^{h, 2*}_{i+u-1,j+v-1}\Big{(}w_{r_{2}-u+1,r_{2}-v+1}^{h,2}\Big{)}^{-1}, \tag{23}\]  
where \(w^{h,2}\) is the weight of the \(h^{th}\) kernel in the second convolutional layer and \(\delta^{h,2*}\) are the zero padded errors of the feature map obtained by the \(h^{th}\) kernel in the second convolutional layer. The zero padding should be equal to the kernel size minus one, that is \(r_{2}-1\). Therefore, a feature map of the errors obtained according to (23) is equivalent to the errors obtained using (22).
## 3 Design of the Direct Yaw Moment Control System
### Longitudinal Force Distribution  
To generate the additional yaw moment on the actual electric vehicle, the differential traction forces must be exerted on the tires of a driven axle. With the advantage of an electric vehicle with in-wheel motors, the traction forces can be differentiated easily.  
Consider the case of a rear wheel driven electric vehicle with the assumption that the driving resistance is small. If the longitudinal acceleration given by driver command input to the accelerator input is given as \(a_{x}\), the governing equation of longitudinal dynamics Equation (1) can be expressed as  
\[ma_{x}=F_{x3}+F_{x4}. \tag{29}\]  
Figure 6: Feedback compensation parameters dependency on velocity and loading condition.  
The yaw moment control input from the controller is given as follows  
\[M_{DYC}=\frac{l_{w}}{2}(F_{x4}-F_{x3}) \tag{30}\]  
where \(l_{w}\) indicates the vehicle tread width. By rearranging Equations (29) and (30), the command traction force of each tire can be calculated as follows  
Rear left tire:  
\[F_{x3}=\frac{ma_{x}}{2}-\frac{M_{DYC}}{l_{w}}, \tag{31}\]  
Rear right tire:  
\[F_{x4}=\frac{ma_{x}}{2}+\frac{M_{DYC}}{l_{w}}. \tag{32}\]
## 2 Artificial Neural Network Models Background, and Fundamental Capabilities
The selected loss function is usually the sum of squared errors, R, over the elements of the training set, \(T=\{(X^{(i)},Y^{(i)})|i\in\{1,\ldots,N\}|\}\). The backpropagation algorithm consists of a loop with a maximum of S steps where forward and backwards actions are taken, and in each iteration, forward actions process the output from the training data set, and backwards actions update the weights \(\alpha_{i}^{m}\) and \(\beta_{m}\). Forward and backward steps are run, starting near a linear learning process with mean 0 and variance 1 in input variables and randomly selected values close to 0 in weight until the sum of squared errors (selected loss function) is optimized with two consecutive iterations differing by less than a predefined threshold or the maximum number of iterations is reached. Because of non-linearity of expression in Equation (2), a heuristic that guarantees a global minimum is required (for example, R software employees a quasi-Newton approach and Matlab implements the Levenberg-Marquardt method). Nevertheless, obtaining a global minimum can lead to overfitting and lack of generalization, so a regularized loss function (see Equation (3)) is used in practice. \[L=\left(\sum_{i=1}^{N}\left(Y^{(i)}-f(X^{(i)})\right)^{2}\right)+\lambda\left( \sum_{i=0}^{M}\beta_{i}^{2}+\sum_{i=0,j=0}^{i=P,j=M}\alpha_{i}^{j2}\right) \tag{3}\]  
It is important to highlight that in the cross-validation process followed to determine the optimal network architecture, the following assumptions are recommended:  
* Hidden network neuron number. \(\lambda\) hyperparameter in (Equation (3)) controls the strength of the weights, so that it is possible to train the neural network with a high number of neurons and discard those with less significant weights. * Initial weights are randomly selected and the algorithm is executed several times for each data set. * Activation function selection. Sigmoid functions are preferably selected for the hidden layer neurons, and identity for the output layer.
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 3 Coordinated Control Strategy  
In some cases, a significant reduction on vibrations is required, then a common solution is to place many actuators on the structure. In this way, more damping can be introduced in the system, as described in [14]. For this reason, in this work, three actuators have been used.  
Now, it is necessary to develop an automatic algorithm for the computation of the control gain for each actuator. Such an algorithm will be designed according to the coupling between each actuator and the controlled modes. The Efficient Modal Control (EMC) proposed in [13] represents a suitable solution for this purpose. In the EMC, a proper gain is assigned to any controlled mode in order to get a control action generated by each actuator which is coordinated with the others. In this way, the control action of each device will be focused only on the controllable modes, in particular on the ones related to the larger vibration amplitudes.  
In the first step of the algorithm, the additional modal damping that can be introduced by each actuator is evaluated. Then, the EMC will be explained and the modified version of such strategy will be presented.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 3 Overview and Operation of IEEE 802.11 and IEEE 802.15.4
### IEEE 802.15.4
#### 3.2.3 IEEE 802.15.4 Contention-Based Channel Access in Beacon-Enabled Mode  
In the case of IEEE 802.15.4 contention-based channel access, the two main variables that control the channel access algorithm are the number of backoff (NB) and the backoff exponent (BE). Here, NB controls the number of times the CSMA/CA algorithm must take a backoff for channel access, and BE defines the number of backoff periods for channel access. At the start, NB is initialized with a value equal to zero and the value of BE is selected as \(BE\)\(\epsilon\) (\(macMinBE\), \(macMaxBE\)) with an initial value of \(macMinBE\) (default value equal to 3). To avoid collisions, the contention algorithm uses a random wait time in the range of \([2^{BE}-1]\). Here, one backoff unit period is equal to \(20T_{s}\) (\(T_{s}=16\)\(\upmu\)s). After the random backoff time, the channel is sensed for a time interval equal to Clear Channel Assessment (CCA). If the channel is busy, then the contention algorithm increments the values of BE and NB by one, while checking that BE does not increase beyond \(macMaxBE\). If NB has a value of less than or equal to \(macMaxBE\), the backoff process is repeated or the packet is dropped. If the channel is idle after CCA, CSMA/CA will immediately start the data transmission process [38].
# Simulation of Wave Propagation Using Finite Differences in Oil Exploration
## 2 Materials and Methods
### Implementation of the Optimization Algorithm  
The optimization algorithm involves the following work sequences (Figure 1):  
Figure 1: Optimization algorithm employed.  
1. Definition of the geological model: defining the geological model that describes the characteristics of the reservoir rock and fluids, as well as their spatial distributions. The model is represented as a three-dimensional mesh of discrete points.
2. Definition of the wave model: defining the wave model that describes the propagation of seismic waves in the reservoir. This model is discretized using the finite difference method to obtain a discretized wave equation.
3. Definition of boundary conditions: defining the boundary conditions that limit the simulation domain. These conditions include reflection conditions, absorption conditions, and other relevant boundary conditions.
4. Source signal generation: generating a source signal representing the seismic energy source used to excite seismic waves in the reservoir.
5. Definition of simulation parameters: specifying simulation parameters, such as mesh size, simulation duration, sampling frequency, and source signal parameters.
6. Simulation initialization: initializing pressure and velocity values at each grid point to zero at the initial time.
7. Simulation iteration: performing simulation iterations using the finite difference method. The discretized wave equation updates the pressure and velocity values at each mesh point during each iteration, while boundary conditions limit the simulation domain.
8. Solution evaluation: evaluating the simulation solution against the objectives of petroleum exploration. This evaluation may involve visualizing wave patterns, identifying zones of interest in the reservoir, and estimating reservoir properties, such as permeability and porosity.
9. Optimization of simulation parameters: optimizing simulation parameters using an optimization algorithm. Parameters that can be optimized include energy source position and orientation, source signal frequency, and mesh parameters.
10. Simulation re-run: repeating the simulation with newly optimized parameters. The new solution is evaluated and the optimization process is repeated iteratively until the desired objective is achieved.
## 3 Methodology
### Keypoint Detection with Boundary Removal
#### 3.1.2 Boundary Point Detector  
We project a point's neighboring points onto the point' tangent plane, as seen in Figure 2, and it can be observed that the majority of a boundary point's surrounding points projected on the tangent plane are located on one side of the boundary point. There will, therefore, be a notch in the line connecting the projection points and the boundary point when these surrounding points are projected onto the boundary point's tangent plane. One can ascertain if a point is a boundary point or not by measuring the angle of the notch.  
The following illustrates how to decide whether a point \(p_{i}\) in a point cloud \(P\) is a boundary point.  
* Find \(p_{i}\)'s surrounding points centered at \(p_{i}\) on a sphere with radius \(r\) and designate them as \(Nbhd(p_{i})\). \[Nbhd(p_{i})=\{p_{j}|p_{j}\in P,\|p_{j}-p_{i}\|\leq r\}\] (9)Project each point \(p_{j}\) in \(Nbhd(p_{i})\) to a tangent plane centered at \(p_{i}\) with the following formula: \[q_{j}=p_{j}-\left(n\cdot\overrightarrow{p_{i}p_{j}}\right)\times n\] (10) where \(n\) is the normal of \(p_{i}\), and designate these projection points as \(Nbhd(q_{j})\).
* Find the nearest point to \(p_{i}\), designate it as \(q_{u}\), and then construct a local reference frame \((p_{i},u,v,w)\) in which \(w\) axis is \(p_{i}\)'s normal \(n\), \(u\) axis is \(\overrightarrow{p_{i}q_{u}}/\left|\overrightarrow{p_{i}q_{u}}\right|\), and \(v\) axis is \(u\times w\).
* Find the included angle by calculating the angle between the vector \(\overrightarrow{p_{i}q_{j}}\) and \(u\) axis, respectively, along a clockwise manner. Then, designate the set of the included angle as \(S=(\alpha_{1},\alpha_{2},\ldots,\alpha_{k})\), where \(k\) represents the number of all the included angles.
* Assign \(S^{\prime}=\left(\alpha_{1}^{\prime},\alpha_{2}^{\prime},\ldots,\alpha_{k}^{ \prime}\right)\) after sorting \(S\) in ascending order and then compute the difference between adjacent included angles as follows: \[L_{i}=\alpha_{i+1}^{\prime}-\alpha_{i}^{\prime}\] (11)
* Find maximum difference \(L_{max}\) and determine \(p_{i}\) is a boundary point if \(L_{max}>L_{th}\). \(L_{th}\) is a threshold in this case, whose value is modifiable to determine whether a point is a boundary point or not.