esa-sceva/satcom-chunk-collection huggingface dataset:
## Abstract  
It has been demonstrated that calcium alginate capsules can be used as an asphalt healing system by pre-placing rejuvenator (healing agent) into the asphalt mix and releasing the rejuvenator on demand (upon cracking). This healing mechanism relies on the properties of capsules which are determined by the capsule preparation process. In this study, to optimize the calcium alginate capsules, capsules are prepared using varying Alginate/Rejuvenator (A/R) ratios. Light microscope microscopy and Environmental Scanning Electron Microscope (ESEM) are employed to characterize the morphology and microstructure of these capsules. Thermal stability and mechanical property are investigated by thermogravimetric analysis (TGA) and compressive tests. The testing results indicate that higher alginate content results in smaller diameter and lower thermal resistance, but higher compressive strength. The optimum A/R ratio of calcium alginate capsules is found to be 30/70. To prove the effectiveness of the optimized capsules, the capsules are embedded in asphalt mortar beams and a bending and healing program is carried out. The effect of capsule shell material on the mechanical response of asphalt mixture is evaluated through three-point bending on the mortar beams embedded with blank capsules (without the healing agent). Aged mortar beams containing alginate capsules encapsulating rejuvenator demonstrate a higher strength recovery after bending tests, which indicates effective healing due to the release of the rejuvenators from the capsules.  
**Keywords:** asphalt; rejuvenation; alginate; capsules
# Landslide Prediction with Model Switching +
## 4 Accurate Early Warning System Design  
To ensure the switch-based neural networks prediction model can be precise in a changing environment, an accurate early warning system is designed, as shown in Figure 7. It is divided into two parts: A learning-based re-training flow, as described in Section 4.1, and a Prediction Horizon tuning flow, as shown in Section 4.2.
# Characteristic Analysis and Decision Model of Lane-Changing Game for Intelligent Connected Vehicles
## 3 Characteristics of Vehicle Lane-Changing Game  
Vehicle lane changes as a common microscopic driving behavior in road traffic has the characteristic of changing with driving intention, and vehicle lane changes can usually be divided into free lane changes and forced lane changes according to lane-change intention. The urban road is the main scenario of vehicle driving, with the driver generally pursuing a higher driving speed when the vehicle drives through the upstream intersection exit lane; therefore, the decision to change lanes freely is usually made.  
When approaching the intersection entrance lanes, conservative drivers tend to change to the target lane early, while drivers who are more confident in their driving skills or have an aggressive driving style tend to choose the lane with a higher speed. Left-turning vehicles choose to enter the left-turning lane early, while straight-traveling vehicles choose to stay in the straight lane, and then all vehicles move into the intersection inlet lanes in turn and wait for the green light to release. This paper assumes that vehicles are not driving aggressively and that left-turning vehicles should move into the left-turn lane early to comply with traffic rules; however, the lane-change conditions are usually restricted by the surrounding vehicles and road traffic, so the vehicle lane-change behavior at this time is usually a mandatory lane-change behavior. Figure 1 represents the lane-change game process for vehicles.  
In this paper, we assume that the LV (Lane-changing Vehicle) is the vehicle that needs to turn left and the RV (Rear Vehicle) is the vehicle driving in the left-turn lane. The vehicles in the model are assumed to be ICVs, which are equipped with onboard detectors and sensors, and the vehicles can transmit information with road facilities such as signals. Vehicles obtain decision combinations through the upper decision control system, and the system establishes a gain matrix for each vehicle and determines the optimal driving decision through evolutionary gaming.  
The intelligence of the ICVs is reflected in the ability to reduce the driving burden of the driver, accomplish the task of automatic driving, meet the expectations of the driver, and the vehicle can make human-like driving behavior in the face of a certain traffic environment. For example, the vehicle should not be too conservative and increase the driving time in the process of decision-making interaction with surrounding vehicles, and the vehicle should not be too aggressive and reduce the comfort of the driver. To simplify the game behavior and decision-making process, this paper decomposes the driving task of the ICVs, with the driving task of the vehicle in a certain period triggered by changes in the traffic environment, and the vehicle decision-making mechanism established by considering the expectations of passengers.  
Figure 1: Diagram of vehicle gaming lane changes.
## 2 Related Work
### Mass Classification as Benign or Malignant  
A whole mammogram image or the regions of interest (ROIs) are classified as benign or malignant, and there are many methods for this purpose.  
Some recent methods classify mammogram images containing masses as benign or malignant. [PERSON] et al. [11] developed a method using two mammography views, MLO and CC, for both breasts, extracting spatial and frequency domain features. They utilized particle swarm optimization (PSO) and support vector machine (SVM) for feature selection and classification, achieving an AUC-ROC of 0.79 for two-view and 0.75 for four-view images. [PERSON] et al. [13] implemented adaptive contrast enhancement in mammogram images, followed by segmentation and artificial neural network classification, resulting in a high accuracy of 97.2%. [PERSON] et al. [12] introduced a multi-dilated CNN that integrates multiple views and optimizes classification accuracy by modifying the cross-entropy cost function, achieving accuracies of 82.02% and 63.06%, respectively. [PERSON] et al. [14] employed bi-dimensional empirical mode decomposition and GLCM for feature extraction, leading to AUC-ROC values of 0.9 and 0.96. [PERSON] et al. [26] presented a novel model employing a transformer for feature extraction combined with transfer learning, tackling the issue of imbalanced data and achieving near-perfect classification accuracy. [PERSON], [PERSON] et al. [27] developed VGG19-DF with a dRVFL classifier, showing an average AUC of 0.93 and an accuracy of 81.71%.
# Suitability of Active Noise Barriers for Construction Sites
## 5 Discussion
### Target Area: Shadow Zone of the Facade  
The same method is used to define the suitable regions of the transducers when the target area is the shadow zone on the facade. As shown in Table 2, the optimal positions for the secondary sourcesare in the noise source incident side and the error sensors are in the shadow side. This table also illustrates that when the shadow zone on the facade of the building is considered as the target area, the active noise control is as effective as the passive noise barrier to reduce the noise level. The trend of insertion loss is rather similar to the results obtained when the street area was considered as the target area.  
Generally speaking, Figure 5 demonstrates that the suitable regions for the control sources and error sensors are below the line that connects the noise source to the barrier edge at the incident and at the shadow side, respectively. These regions closely match the locations previously proposed for the street area. Locating the transducers above this line adversely affects the performance of the active noise barrier. Results of ref. [45] also confirm that the error microphones placed in the shadow zone of the barrier could create a large quiet zone in the shadow area.  
Comparing the reductions achieved for these two target areas presented in Tables 1 and 2 and Figure 4-ii and Figure 5-ii propose a unique configuration for the positions of the control means.  
Figure 6a,b illustrates the insertion loss in the street area when the noise source is at \(d_{pr}=7\) m and the transducers are located in the positions presented in Tables 1 and 2, respectively. This figure shows approximately the same noise level reduction in the street area regardless of the location of the control zones. On the other hand, Figure 7 shows the insertion loss (dB) in the shadow zone of the facade for the same condition. This figure reveals that the active noise barrier achieves more reduction on the building facade when this area is considered as the control zone. Comparing these two figures reveals that the active noise barrier achieves better noise reduction in both areas when the shadow zone on the facade is considered as the target area.  
Figure 6: Insertion loss (dB) in the street area when the noise source is at \(d_{pr}=~{}7\) m and the control zone is (**a**) the street area ((\(X_{s}\), \(Z_{s}\)) = (\(-0.5\), \(1.8\)), (\(X_{e}\), \(Z_{e}\)) = (0.4,0.2)) and (**b**) the shadow zone on the facade ((\(X_{s}\), \(Z_{s}\)) = (\(-0.5\), \(2.3\)), (\(X_{e}\), \(Z_{e}\)) = (0.3,0.4)).
## 4 Discussions  
In this study, we derive the stiffness matrix of a TTI medium with a single group of inclined fractures using the linear slip theory and the projection decomposition method. The resulting stiffness matrix consists of two components: one representing the stiffness of the background isotropic medium and the other capturing the stiffness changes induced by the inclined fractures. By applying the projection decomposition approach, the stiffness variation due to the fractures is further separated into two parts. The first component \(C_{f}^{H}\sin\xi\) reflects the stiffness change in the horizontal direction, where vertical forces are less resisted, making the medium more prone to deformation. The second component \(C_{f}^{V}\cos\xi\) represents the stiffness variation in the vertical direction, where horizontal forces lead to greater susceptibility to deformation.  
In parameter inversion, the background isotropic medium parameters are typically expressed as the ratio of the difference to the sum of the upper and lower values, with magnitudes often around \(10^{-2}\) and occasionally \(10^{-3}\). In contrast, fracture parameters are represented by the difference between the upper and lower values, with magnitudes generally on the order of \(10^{-4}\) to \(10^{-5}\) and sometimes as low as \(10^{-6}\). This significant difference, up to three or even four orders of magnitude, can lead to instability in the inversion process and reduce its accuracy. Therefore, it is crucial to normalize the inversion parameters prior to the inversion to mitigate these issues and improve stability and precision. In addition, the moving mesh method can also solve some of the stability problems of fracture parameter inversion ([PERSON] and [PERSON], 2022) [7]. Since the primary focus of our paper is not on the moving mesh method, we have not delved deeply into its intricacies.  
This study focuses on the TTI media model induced by a single group of inclined fractures, which is primarily applicable to inclined fracture reservoirs. Based on the paper of [PERSON] and [PERSON] (1993) [19], the difference between fracture weakness parameters \(\delta_{N}\) and \(\delta_{T}\) can serve as an indicator for fracture fluid type. Based on the findings of [PERSON] and [PERSON] (1998) [20], the presence of fluid influences the elastic properties of fractures, thereby altering the fracture pressure, which subsequently leads to changes in the fracture's state. However, due to time and resource constraints, this paper does not address the fluid indicator factors. Future research can explore the inversion of these fluid factors in more detail.
## Theoretical Framework
### Network Training
\begin{array}{ccc}\frac{ \partial^{2}\mathbf{E}}{\partial\mathbf{w}_{1}^{2}}&\frac{\partial^{2} \mathbf{E}}{\partial\mathbf{w}_{1}\partial\mathbf{w}_{2}}&\cdots&\frac{ \partial^{2}\mathbf{E}}{\partial\mathbf{w}_{1}\partial\mathbf{w}_{N_{w}}}\\ \frac{\partial^{2}\mathbf{E}}{\partial\mathbf{w}_{2}\partial\mathbf{w}_{1}}& \frac{\partial^{2}\mathbf{E}}{\partial\mathbf{w}_{2}^{2}}&\cdots&\frac{ \partial^{2}\mathbf{E}}{\partial\mathbf{w}_{2}\partial\mathbf{w}_{N_{w}}}\\ \vdots&\vdots&\ddots&\vdots\\ \frac{\partial^{2}\mathbf{E}}{\partial\mathbf{w}_{N_{w}}\partial\mathbf{w}_{1}} &\frac{\partial^{2}\mathbf{E}}{\partial\mathbf{w}_{N_{w}}\partial\mathbf{w}_{2}} &\cdots&\frac{\partial^{2}\mathbf{E}}{\partial\mathbf{w}_{N_{w}}^{2}}\end{array}  \right]^{-1}\left[ \begin{array}{c}g_{1}\\ \delta_{2}\\ \vdots\\ g_{N_{w}}\end{array} \right] \tag{11}\]The updating of the weights by Newton method is obtained with the following expression:  
\[\mathbf{w}+\Delta\mathbf{w}=\mathbf{w}-\mathbf{H}(\mathbf{w})^{-1}\mathbf{G}( \mathbf{w}). \tag{12}\]  
The Hessian matrix \(\mathbf{H}\) of second-order partial derivatives of total error function have to be calculated and it could be very complicated. In order to simplify the calculating process, a matrix of first-order partial derivatives is presented as a Jacobian matrix, which has a mathematical relation with a Gradient and Hessian matrix. By combining the error of Equation (4) and the definition of gradient (5), it is possible to determinate that:  
\[g_{i}=\frac{\partial\mathbf{E}}{\partial\mathbf{w}_{i}}=\frac{\partial\Big{(} \frac{1}{2}\sum_{p=1}^{N_{p}}\sum_{\phi=1}^{N_{o}}e_{p\phi}^{2}\Big{)}}{ \partial\mathbf{w}_{i}}=\sum_{p=1}^{N_{p}}\sum_{\phi=1}^{N_{o}}\Bigg{(}\frac{ \partial e_{p\phi}}{\partial\mathbf{w}_{i}}e_{p\phi}\Bigg{)}. \tag{13}\]  
The error \(\boldsymbol{e}\) expressed in vector form is:  
\[\boldsymbol{e}=\Big{[} \begin{array}{cccccccc}e_{1,1}&\cdots&e_{1,N_{o}}&e_{ 2,1}&\cdots&e_{N_{p},1}&\cdots&e_{N_{p},N_{o}}\\ \end{array} \Big{]}^{\mathsf{T}}, \tag{14}\]  
and the Jacobian of the error \(\boldsymbol{e}\):  
\[\mathbf{J}=\left[ \begin{array}{cccccccc}\frac{\partial e_{1,1}}{\partial \mathbf{w}_{1}}&\ldots&\frac{\partial e_{1,N_{0}}}{\partial\mathbf{w}_{1}}& \frac{\partial e_{2,1}}{\partial\mathbf{w}_{1}}&\ldots&\frac{\partial e_{N_{ p},1}}{\partial\mathbf{w}_{1}}&\ldots&\frac{\partial e_{N_{p},N_{o}}}{\partial \mathbf{w}_{1}}\\ \vdots&\ddots&\vdots&\vdots&\ddots&\vdots&\ddots&\vdots\\ \frac{\partial e_{1,1}}{\partial\mathbf{w}_{N_{0}}}&\ldots&\frac{\partial e_{ 1,N_{0}}}{\partial\mathbf{w}_{N_{0}}}&\frac{\partial e_{2,1}}{\partial \mathbf{w}_{N_{0}}}&\ldots&\frac{\partial e_{N_{p},1}}{\partial\mathbf{w}_{N _{0}}}&\ldots&\frac{\partial e_{N_{p},N_{o}}}{\partial\mathbf{w}_{N_{0}}}\\ \end{array}
# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
###### Abstract  
The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.  
fault diagnosis; IMF; complementary ensemble empirical mode decomposition; multi-scale sample entropy +
Footnote †: journal: Applied Sciences
## 2 Fault Type and Fault Feature Information of the Protection System
### Classification of Protection System Fault Types
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Fault Types** & **Fault Feature Information** \\ \hline \hline \(f_{1}\) & Main DSP module failure of merging unit & Sampling anomaly of merging unit, Synchronization anomaly of merging unit, SV alarm of merging unit/protection device, Protection locking, etc. \\ \hline \(f_{2}\) & Sampling DSP module failure of merging unit & Sampling anomaly of merging unit, Synchronization anomaly of merging unit, SV alarm of merging unit/protection device, SV alarm of merging unit/protection device, Protection locking, etc. \\ \hline \(f_{3}\) & 1/O module fault of merging unit (Merging unit \(\leftrightarrow\) Switch) & SV alarm of protection device, SV interruption of protection device/merging unit/measurement and control device, GOOSE interruption of merging unit \\ \hline \(f_{4}\) & 1/O module fault of merging unit (Bus merging unit \(\rightarrow\) Line merging unit) & SV alarm of protection device, SY interruption of protection device/merging unit/measurement and control device, GOOSE interruption of merging unit, SV/GOOSE total alarm, Protection locking, etc. \\ \hline \(f_{5}\) & 1/O plug – in fault of protection device (Protection device \(\leftrightarrow\) Intelligent terminal) & SV/GOOSE alarm of protection device, GOOSE alarm of intelligent terminal, GOOSE interruption of protection device, Protection locking, etc. \\ \hline \(f_{7}\) & 1/O plug – in fault of protection device (Merging unit \(\rightarrow\) Protection device) & Protection locking, Redosing device locking, etc. \\ \hline \(f_{8}\) & Longitudinal channel fault & Channel differential exit, Longitudinal channel abnormal, In/out communication interruption of protection device, etc. \\ \hline \(f_{9}\) & CPU plug-in fault of protection device & Protection device parameter error, CPU X exception, Memory \\  & & self-check error, Protection locking, etc. \\ \hline \(f_{10}\) & 1/O board fault of intelligent terminal (Intelligent terminal \(\leftrightarrow\) Switch) & GOOSE interruption of merging unit/measurement and control device/intelligent terminal/protection device, GOOSE alarm of merging unit/intelligent terminal/protection device, etc. \\ \hline \(f_{11}\) & 1/O board fault of intelligent terminal (Protection device \(\leftrightarrow\) Intelligent terminal) & SV/GOOSE alarm of protection device, GOOSE alarm of intelligent terminal, SV/GOOSE interruption of protection device, RecRecision locking, etc. \\ \hline \(f_{12}\) & GOOSE plug-in fault of protection device & SV/GOOSE alarm of protection device, GOOSE alarm of intelligent terminal, SV/GOOSE interruption of protection device, Protection locking, Redosing lock, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Complex fault types and corresponding main fault feature information.
# Modeling and Control of an Articulated Multibody Aircraft
## 2 Development of the Multibody Equations of Motion
### Orientation and Transformation Matrices  
The numerical simulation framework will rely on the appropriate transformation matrices to ultimately yield the EoMs in the body-fixed coordinate system. The standard transformation matrices of a rotation angle \(\mu\), about \(x\), \(y\) and \(z\) axes of the coordinate systems are expressed as  
\[R_{\mu|_{x}}=\begin{bmatrix}1&0&0\\ 0&c\ \mu&s\ \mu\\ 0&-s\ \mu&c\mu\end{bmatrix}R_{\mu|_{y}}=\begin{bmatrix}c\ \mu&0&-s\ \mu\\ 0&1&0\\ s\ \mu&0&c\mu\end{bmatrix}R_{\mu|_{z}}=\begin{bmatrix}c\ \mu&s\ \mu&0\\ -s\ \mu&c\ \mu&0\\ 0&0&1\end{bmatrix} \tag{6}\]  
where \(s\ \mu\) and \(c\ \mu\) represent the sin and cosine functions of \(\mu\), respectively. Given any two arbitrary coordinate systems, the transformation matrices can be obtained using relationships between the rotation angles and axes of rotation in Equation (6).  
In this paper, the attitude of the central body with respect to the inertial frame is described by the Euler angles \(\psi\) (yaw), \(\theta\) (pitch) and \(\phi\) (roll). The transformation matrix from inertial to body coordinate system, \([R]^{BI}\), is obtained using the \(z\)-\(y\)-\(x\) rotational sequence of the Euler angles as in  
\[[R]^{BI}=R_{\psi|_{z}}R_{\theta|_{y}}R_{\phi|_{x}} \tag{7}\]  
The transformation matrix from body to inertial coordinate system is obtained as the transpose of the \([R]^{BI}\) matrix, \([R]^{IB}=[R^{\prime}]^{BI}\). Euler angles \(\psi_{T}\) (tail yaw), \(\theta_{T}\) (tail pitch) and \(\phi_{T}\) (tail roll) are used to specify the orientation of the abdomen relative to the central body. The transformation matrix from the body to tail coordinate system also follows the \(z\)-\(y\)-\(x\) rotational sequence as in  
\[[R]^{TB}=R_{\phi_{T}|_{z}}R_{\theta_{T|_{y}}}R_{\phi_{T}|_{x}} \tag{8}\]
# Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model
## 5 Experimental Evaluation
### Batch Size Evaluation  
Training on different batch sizes, 8, 16, 32 and 48 was evaluated. Figure 4 demonstrates the model accuracy versus training epoch. Here, the average accuracy over seven attribute was used. Data augment was used and other parameters were set the same as for the experiments presented in Section 5.2.  
As shown in the figure, all models converged after about 40 training epochs. Models trained with batchsize 32 obtained better performance, and outperformed other models by about 2%. _SqueezeNet_ was more stable and smooth during training, while \(MobileNet\) and \(ShuffleNet\) fluctuated more. It was reasonably found that for bigger batch sizes the gradient descent direction computation was more accurate, and was gentler during model training. Smaller batch sizes led to more randomness, and it was harder to achieve optimal performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**CNNs Model** & **Data Augment** & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{a_{4}}\) & \(\mathbf{a_{5}}\) & \(\mathbf{a_{6}}\) & \(\mathbf{a_{7}}\) & \(\mathbf{overall}\) \\ \hline \multirow{2}{*}{_MobileNet_} & yes & 0.785 & 0.829 & 0.778 & 0.786 & 0.883 & 0.817 & 0.885 & 0.822 \\  & no & 0.732 & 0.816 & 0.765 & 0.745 & 0.871 & 0.809 & 0.868 & 0.801 \\ \hline \multirow{2}{*}{\(ShuffleNet\)} & yes & 0.803 & 0.809 & 0.804 & 0.812 & 0.878 & 0.813 & 0.894 & 0.830 \\  & no & 0.731 & 0.798 & 0.789 & 0.810 & 0.848 & 0.805 & 0.865 & 0.807 \\ \hline \multirow{2}{*}{\(SqueezeNet\)} & yes & 0.68 & 0.837 & 0.825 & 0.774 & 0.858 & 0.758 & 0.887 & 0.803 \\  & no & 0.657 & 0.829 & 0.814 & 0.752 & 0.849 & 0.674 & 0.862 & 0.777 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition accuracy for seven attributes.  
Figure 4: Model accuracy versus training epoch on various batchsize: (**a**) result on MobileNet model; and (**b**,**c**) result on ShuffleNet and SqueezeNet models, respectively. Blue, orange, gray and yellow curves represent batchsizes equal 8, 16, 32 and 48, respectively.  
Figure 3: Demonstration of error classified samples. Red texts represent ground truth label, while yellow texts represent the predicted results.