esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 2 5G Technologies beyond Release 15
### Multi-Sim Support  
Vehicles traverse areas covered by multiple public land mobile networks (PLMNs) and transit between two PLMN coverage areas (e.g., cross-border). In the multi-PLMN scenario, the vehicle's home PLMN (original serving network) may have areas with poor QoS or ineffective coverage due to overloading, network failure, etc. In order to ensure vehicle-to-network (V2N) connectivity, the ability of the vehicle to seamlessly switch to or simultaneously utilise a visited PLMN enables safer operation of the vehicle regardless of instantaneous network conditions. The vehicle connectivity to multiple PLMNs would typically require onboard units (OBUs) with several subscriber identity modules (SIMs), one for attaching to each PLMN. Devices ready to host multiple SIMs (multi-SIM) are widely available in the market, mostly using proprietary solutions through device vendors without any support from 3 GPP specifications. However, 3 GPP has approved a working item in Rel-17 to provide standardised support for multi-SIM UE [17], which can prove beneficial from a reliability perspective.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Time Stretching Performance  
The classification results for Time Stretching augmentation are shown in Table 4. The use of Time Stretching for data augmentation yields improved recognition performance compared to not using any data augmentation techniques. The recognition performance varied with the stretching range of Time Stretching, with accuracy increasing when the stretching factor changed from [0.80, 1.20] to [0.88, 1.12], and decreasing after [0.88, 1.12]. The highest accuracy can reach 0.933, and the macro F1 score can reach 0.935 within the [0.88, 1.12] stretching range. Additionally, online data augmentation using only spectrogram masking achieved an accuracy of 0.889, which is an improvement compared to not using any data augmentation techniques. When combining spectrogram masking with Time Stretching, there was a further improvement in recognition performance compared to  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Background Noise Ranges** & **Acc** & **Pre** & **Rec** & **Spc** & **F1** \\ \hline no noise (Baseline) & 86.81 & 78.44 & 71.71 & 67.58 & 74.92 \\  & 88.89 & 82.21 & 76.40 & 68.63 & 79.19 \\ \hline \multirow{2}{*}{\(-10\)} & 87.19 & 86.91 & 86.60 & 72.63 & 86.75 \\  & 86.26 & 85.63 & 85.35 & 71.42 & 85.49 \\ \hline \multirow{2}{*}{\(-5\)} & 88.13 & 88.50 & 87.94 & 74.82 & 88.22 \\  & 90.32 & 90.12 & 90.09 & 78.37 & 90.15 \\ \hline \multirow{2}{*}{0} & 90.60 & 90.75 & 90.93 & 81.29 & 90.84 \\  & 91.46 & 91.60 & 91.44 & 83.84 & 91.52 \\ \hline \multirow{2}{*}{5} & 92.88 & 91.81 & 91.39 & 83.67 & 91.60 \\  & 92.02 & 92.19 & 91.77 & 84.82 & 91.98 \\ \hline \multirow{2}{*}{10} & 92.88 & 92.02 & 91.71 & 85.37 & 91.86 \\  & 93.44 & 92.38 & 92.33 & 86.25 & 92.36 \\ \hline \multirow{2}{*}{15} & 92.59 & 92.47 & 92.73 & 85.48 & 92.59 \\  & 93.14 & 92.42 & 92.88 & 85.93 & 92.67 \\ \hline \multirow{2}{*}{20} & 92.58 & 92.47 & 92.73 & 86.02 & 92.59 \\  & 92.46 & 92.62 & 92.58 & 85.74 & 92.60 \\ \hline \end{tabular}
\end{table}
Table 6: The classification results (%) for background noise augmentation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Combination Type** & **Acc** & **Pre** & **Rec** & **Spc** & **F1** \\ \hline No Aug & 86.81 & 78.44 & 71.71 & 67.58 & 74.92 \\ \hline Online Aug & 88.89 & 82.21 & 76.39 & 68.63 & 79.19 \\ \hline BN [10] and PS [\(-\)5, 5] & 93.21 & 93.29 & 93.24 & 78.36 & 93.27 \\  & 91.28 & 91.38 & 92.33 & 77.86 & 91.86 \\ \hline TS [0.88, 1.12] and PS [\(-\)5, 5] & 95.81 & 95.87 & 95.85 & 83.41 & 95.86 \\  & 96.01 & 96.10 & 96.03 & 86.42 & 96.06 \\ \hline TS [0.88, 1.12] and BN [10] & 96.43 & 96.45 & 96.44 & 88.52 & 96.45 \\  & 97.45 & 97.16 & 97.09 & 90.61 & 97.12 \\ \hline \end{tabular}
\end{table}
Table 7: The classification results (%) for combined augmentation.  
using Time Stretching alone, with the highest accuracy reaching 0.944 within the [0.88, 1.12] stretching range.
## 4 Proposed Robust Capon IOVP Beamforming
### Recursive Least-Squares Adaptation  
We derive an RLS version of the RCB-IOVP method. The steering vector and the columns of the rank reduction matrix are updated as:  
\[\tilde{\mathbf{a}}_{d}[i]=\left[\tilde{\mathbf{a}}_{d}[i]-\left(\mathbf{I}_{D}+\lambda_{ \text{\tiny{RCB}}}[i]\mathbf{R}_{D}^{-1}[i]\right)^{-1}\tilde{\mathbf{a}}_{d}[i]\right] \tag{39}\]  
\[\mathbf{s}_{d}=-\left(\tau_{d}[i]\mathbf{a}_{d}[i]\mathbf{a}_{d}^{H}[i]+\lambda_{\text{ \tiny{RCB}}}[i]\mathbf{\alpha}_{d}[i]\mathbf{\alpha}_{d}^{H}[i]\right)^{-1}\mathbf{a}_{d}[ i]\tilde{\mathbf{a}}_{d}^{H}[i]\mathbf{r}_{d}[i] \tag{40}\]  
\[\tilde{\mathbf{k}}[i+1]=\frac{\alpha^{-1}\mathbf{R}_{D}^{-1}[i]\tilde{\mathbf{x}}[i+1]}{1 +\alpha^{-1}\tilde{\mathbf{x}}^{H}[i+1]\mathbf{R}_{D}^{-1}[i]\tilde{\mathbf{x}}[i]} \tag{41}\]  
\[\mathbf{R}_{D}^{-1}[i+1]=\alpha^{-1}\mathbf{R}_{D}^{-1}[i]-\alpha^{-1}\tilde{\mathbf{k}}[ i+1]\tilde{\mathbf{x}}^{H}[i+1]\mathbf{R}_{D}^{-1}[i] \tag{42}\]  
where (39) to (42) need \(2D^{3}+7D^{2}+4D+3\) complex multiplications, and the projection operations need a complexity of \(MD\) complex multiplications. It is obvious that the complexity is significantly decreased if the selected rank \(D\ll M\). The proposed RCB-IOVP RLS algorithm employs (25) and (39) to (42). The key of the RCB-IOVP RLS algorithm is to update the assumed steering vector \(\tilde{\mathbf{a}}_{d}[i]\) with RLS iterations, and the updated beamformer \(\bar{\mathbf{\omega}}[i]\) is obtained by plugging (39) into (25) without significant extra complexity.  
Note that the complexity introduced by the pseudo-inverse operation can be removed if \(\mathbf{S}_{D}\) has orthogonal column vectors; this can be achieved by incorporating the Gram-Schmidt procedure in the calculation of \(\mathbf{S}_{D}\). Furthermore, an alternative recursive realization of the robust adaptive linear constrained beamforming method introduced by [19] can be used to further reduce the computational complexity requirement to obtain the diagonal loading terms.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 4 Conclusions  
The paper has presented a thorough comparison among the performance achieved by four low-latency algorithms for positive sequence synchrophasor, frequency and ROCOF estimation. Three of them are designed directly on the SV signal model, while the fourth one is the reference P-class method of the latest IEC/IEEE standard, which is used as a benchmark. The paper has shown how, depending on the actual conditions of the three-phase signals and on the specific assumptions of each method, results can differ noticeably. It is extremely important to notice that the algorithms are characterized by parameters that need to be finely tuned according to the desired performance target. It is also clear that techniques designed to track the synchrophasor dynamics better preserve the bandpass characteristics of the positive sequence synchrophasor, but at the expense of a lower immunity to disturbances. The paper has highlighted that, depending on the expected level of narrowband or wideband disturbances, some methods can be prone to errors. Countermeasures, such as different weighting functions have been also presented, while keeping in mind that a tradeoff between bandwidth and immunity, between fast response to abrupt changes and small artifacts in the response itself, is always to be sought. The presented algorithms have, in general, good performance, and peculiar characteristics, but some unexpected outcomes have been highlighted by the analysis (e.g., those about the RFE under phase modulation). The performed comparison allows understanding in detail the potentialities of these methods that are simple and lightweight and thus likely to be implemented in commercial PMUs for three-phase systems.  
Conceptualization, G.F., P.A.P. and S.T.; methodology, G.F., P.A.P. and S.T.; formal analysis, G.F., P.A.P. and S.T.; writing--original draft preparation, G.F., P.A.P. and S.T.; writing--review and editing, G.F., P.A.P. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ROCOF Rate Of Change Of Frequency  
PMU Phasor Measurement Unit  
UTC Coordinated Universal Time  
IEEE Institute of Electrical and Electronics Engineers  
IEC International Electrotechnical Commission  
TVE Total Vector Error  
FE Frequency Error  
RFE ROCOF Error  
DFT Discrete Fourier Transform  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Response Time [ms]**} \\ \cline{2-4}  & **TVE** & **Frequency** & **ROCOF** \\ \hline IEC-P & 26.4 & 39.8 & 40.0 \\ SV-F & 31.0 & 56.8 & 60.0 \\ SV-TF & 53.0 & 60.0 & 60.0 \\ SV-IpDFT & 34.6 & 51.0 & 56.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Response times for \(-10^{\circ}\) phase step test.
## 3 Methodology
### Data Pre-Processing
#### 3.3.3 Data Segmentation  
The detailed documentation of an exercise needs to be more extensive and directly presented as input to the classifier. Instead, the data are divided into smaller segments known as windows. These windows are created by segmenting the body movement data from the sensor device, while the exercise-related information obtained from the mobile application serves as class labels. Each section is labeled based on the predominant class mode. Previous studies [31, 32] explored various window lengths with overlapping. In our study, we used a window size of 2 s for activity recognition (as shown in Figure 4), resulting in 100 samples. The results are presented both with and without overlapping windows. Our investigation considered the window dimensions and the extent of overlap. The continuous nature of our exercise activity also contributes to the need for overlapping. This overlap ensures that each successive window retains some information from the preceding one.
## 4 Experimental Results and Discussion
### Comparison of Results with Previous Works  
The SEResNet-BiGRU model is compared against state-of-the-art DL approaches in the scope of S-HAR. In Table 7, two conventional machine learning approaches (SVM and KNN) and three hybrid DL approaches are compared to the proposed SEResNet-BiGRU network: LSTM-CNN [56], Inno-HAR [25], and HiHAR [40]. Five applicable models are applied in this section based on the descriptions provided in the related articles. The findings reveal that DL techniques surpass conventional machine learning methods significantly in overall accuracy. The proposed SEResNet-BiGRU model obtained the highest performance on both datasets, with 98.03% and 98.92%, respectively, nearly 2% higher than the prior analyses' hybrid DL models. These findings support our hypothesis that the local spatio-temporal and long-term context variables retrieved by our hybrid DL model provide a complete interpretation of sensor data, consequently increasing classification accuracy. The findings imply that deep residual models operate satisfactorily on raw signals. Nonetheless, SE and BiGRU modules enhance HAR effectiveness for transitional activity recognition. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Accuracy (\%) \(\pm\) SD (\%)**} \\ \cline{2-3}  & **HAPT Dataset** & **MobiAct Dataset** \\ \hline Basic DL models & & \\ CNN & 94.40 \(\pm\) 0.36 & 93.69 \(\pm\) 0.34 \\ LSTM & 91.02 \(\pm\) 1.50 & 83.38 \(\pm\) 2.95 \\ BiGRU & 94.40 \(\pm\) 0.36 & 90.43 \(\pm\) 1.48 \\ \hline Proposed model & & \\ SEResNet-BiGRU & 98.03 \(\pm\) 0.17 & 98.92 \(\pm\) 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental findings of different DL models. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Accuracy (\%) \(\pm\) SD (\%)**} \\ \cline{2-9}  & \multicolumn{3}{c}{**HAPT Dataset**} & \multicolumn{3}{c}{**MobiAct Dataset**} \\ \hline ML Models & & & & & & & \\ KNNs (k = 7) [40] & 75.62 & & 65.86 \\ SVM [40] & 89.26 & & 63.60 \\ Hybrid DL models & & & & & & & \\ LSTM-CNN [56] & 90.49 \(\pm\) 1.01 & & 91.15 \(\pm\) 0.79 \\ InnoHAR [25] & 95.09 \(\pm\) 0.49 & & 93.70 \(\pm\) 0.49 \\ HiHAR [40] & 97.98 \(\pm\) 0.24 & & 96.16 \(\pm\) 0.22 \\ \hline Proposed model & & & & & & \\ SEResNet-BiGRU & 98.03 \(\pm\) 0.17 & & 98.92 \(\pm\) 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison results of the proposed model and previous works.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 3 Development of Track Condition Monitoring System  
### System Overview  
Figure 1 presents an overview of the track condition monitoring system. The system in an in-service vehicle consists of a compact on-board sensing device for measuring car-body vibrations and diagnosis software for identifying track conditions from the data.  
Figure 1: Track condition monitoring system.  
The data are transmitted to a server via a wireless phone connection and examined by using track condition diagnosis software. The results of the diagnosis are fed back to the infrastructure providers where they are used to help create maintenance plans, select work locations, and assign work priorities.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 4 Numerical Application to Bridge
### Without Uncertainties  
An arbitrary load was applied vertically on the upper side of the structure and the strain was measured at a sampling rate of 100 Hz during 100 s. Figure 6a plots the strains measured by the 50 sensors. The application of the subspace identification method [4] to the measured strains provides the stabilization diagram shown in Figure 6b. The mode shape obtained when the system order was two is shown in Figure 6c. Here, the analytic solution was obtained directly from the mass and stiffness matrices of the structure. The mode shape obtained from the modal identification was seen to approach the analytic solution. The natural frequency for the mode at hand was 2.065 Hz and showed an error of merely 0.86% compared to the analytic solution (2.083 Hz).  
Figure 5: Numerical application: (**a**) shape of the bridge structure, (**b**) layout of the strain sensors, (**c**) segments of the structure, and (**d**) first mode shape of the structure.  
The elastic modulus of the model can be obtained by model updating using the eigenvalue and strain measurement mode shape provided by the modal identification. Figure 7a presents the iterative process shows fast convergence. Figure 7b compares the elastic moduli of the updated model with those of the structure and shows good agreement. Figure 7c plots the error ratio of the structure-updated model elastic moduli and reveals a maximum error of approximately 4%, which demonstrates that the model was fairly updated.  
In addition, the model updating using the eigenvalue and the strain measurement mode shape of the structure instead of the eigenvalue and the measurement mode shape obtained by modal identification resulted in elastic moduli identical to the exact solution of Figure 7a. This shows that the error observed in the model updating was generated during the modal identification process.  
Figure 6: Modal identification: (**a**) measured strains; (**b**) stabilization diagram; and (**c**) mode shape obtained by modal identification.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 5 Tool Wear Monitoring Method Based on Deep Convolutional Neural Network
### Network Model Training  
In the training process of the model, the selection of hyperparameters has a direct influence on the training speed and classification accuracy of the model. Therefore, it is necessary to try different hyperparameters before training, and constantly optimize and select a set of optimal hyperparameters to improve the learning performance and effect [21; 22].  
Figure 11: The CNN model structure proposed by [PERSON] et al. (drawn from the literature [125]).  
Figure 10: The CNN model structure proposed by [PERSON] et al. (drawn from the literature [116]).  
The next step is to use sample data for training, the sample data are divided into training set data and validation set data [126]. Network model training refers to inputting the training set data into the network to train parameters.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
The purpose of network model training is to gradually reduce the loss by adjusting the network parameters, so that the network output is very close to the actual value. The training is guided by the loss function, driving the network parameters to gradually change in loss reduction. The loss function is used to measure the quality of a set of parameters. Before each iteration, it compares the calculated result with the true value to guide the correct the training direction in the current iteration.  
The essence of the tool wear neural network is a multi-classification problem. The most commonly used loss functions in neural network classification tasks are the mean square error function and the cross-entropy function.  
The mean square error represents the mean square error between the monitored value and the actual value. The cross-entropy represents the gap between two probability distributions [126].  
The chain derivation method is used to calculate the gradient of the loss function for each weight. The samples are selected to update the weights in batches, driving the predicted value to continuously approach the actual value.  
When training the network using the error backpropagation algorithm based on gradient descent. A large learning rate may cause network oscillations to not converge. A small learning rate may cause a slow convergence and increase training time. The initial learning rate should be set reasonably according to actual conditions.
## 1 Introduction
The magneto-piezoelectric and slap-force energy harvesting device are installed onto the mechanical object. As the mechanical object advances, its relative airflow is channeled through the wind-collecting hood (also called fairing), initiating the rotation of the attached magnet's rotating shaft (see Figure 4). The repulsive force generated by the magnet is then employed to set the fixed-free elastic steel sheet, fitted with the magnet, into vibration (refer to Figure 2). The vibrational movement of the elastic steel sheet causes its free end to impact a lever mechanism resembling a seesaw (the rigid plate lever), inducing a swinging motion. This swinging rigid plate lever, in turn, slaps the PZT installed at the root of the elastic steel sheet. This design allows the PZT to withstand both deformation and impact force simultaneously. Device schematic diagrams of slap-type VEH installed in forward and vertical flight (moving) vehicles are shown in Figure 4. Through this thoughtful design, we not only capture electrical energy through the conventional vibration energy harvesting system but also harness additional electric energy generated by the slapping force of the PZT. This approach maximizes electrical generation efficiency, ensuring the effective utilization of energy in the process. This study comprises the following sections. In Section 2, we derive the equation of motion for elastic beams, delving into an analysis of the vibration modes exhibited by the elastic steel sheet. Employing [PERSON]'s second law, [PERSON]'s angle transformation, and Taylor series expansions, we establish the equation for the two-dimensional nonlinear beam. In Section 3, the Method of Multiple Scales (MOMS) is utilized to examine vibrations associated with each mode, substantiating the system's frequency response through a fixed-point plot. In Section 4, we integrate the piezoelectric equation with the nonlinear beam equation for a comprehensive assessment of theoretical electrical generation efficiency. Section 5 introduces an experimental setup to validate the accuracy of the proposed theoretical model, assessing the experimental voltage outputs for different modes. Section 6 discusses the validity of the theoretical framework, ensuring mutual confirmation between theory and practice. The feasibility of our proposed model is affirmed through this comprehensive approach. Section 7 presents the findings and conclusions derived from the study. Figure 3: Slap-type VEH applications, (**a**) UAV, (**b**) detailed construction on UAV, (**c**) bicycle.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 5 Parametric Study: Finite Element Analysis of a Nailed-Soil Slope with Varying Soil Densities
### Vertical Stresses under Nailed-Soil Slope Mass  
Figures 16 and 17 present the influence of soil density on the vertical stresses under the nailed-soil mass at construction and overburden pressure stages. It is inferred from the plots that the influence of soil relative density on vertical stresses under the nailed-soil mass is directly related, i.e., the higher the soil density, the larger the vertical stresses under the soil mass. Increasing the relative density of the sand results in a rise of the vertical stresses under the nailed-soil mass up to 20.0% for the considered soil types. It is observed from the graph of vertical stresses under the nailed-soil mass with varying density that, when compared to construction stages, overburden pressure stages exhibit a higher increase in vertical stress with the increase in soil density.  
Figure 16: Vertical stresses under the soil mass with various soil densities. (**a**) Loose sand. (**b**) Medium dense sand. (**c**) Dense sand.  
Figure 15: Horizontal stresses at the slope face with various soil densities (at mid-height).