esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Detection System of Safety Hazards in Industry 5.0
## 5 Experiments and Results
### AI Model Training on Real-World and Synthetic Datasets  
To assess the efficacy of our synthetic dataset SYN_HV, we utilize it to train a YOLOv5s model. The performance of this model is compared with the performance of YOLOv5s trained on a real-world dataset, CHV. To achieve a fair comparison, a common test set composed of real images of CHV is used. The performance of each trained model is quantified by precision, recall, AP, and mAP metrics.  
At each training round, we adopt a learning rate of 0.01, combined with a momentum of 0.937. During each iteration, the model processed batches of 32 images, all resized to 640 \(\times\) 640 pixels. To regulate the adjustment of the model weights, we apply a weight decay of 0.0005 and employ the Stochastic Gradient Descent (SGD) as our optimizer. Part of our training strategy is the use of early stopping. This precautionary measure ensured that if the model's performance did not improve over five consecutive epochs, the training would be terminated. Such an approach not only helps conserve computational resources but also acts as a deterrent to potential overfitting.  
Table 3 provides a comparison of performance metrics of object detectors trained on a real-world dataset, CHV, as well as on our synthetic dataset, SYN_HV, that contains only synthetic images. Both of those models are evaluated on the same test set of real images. It is observed that the AI model trained on real images demonstrates superior performance across all metrics than that trained solely on synthetic datasets. The superior performance of the trained model on real data can be explained by its familiarity with the real-world distributions and complexities encapsulated in the test set. On the other hand, the model trained on synthetic images may have difficulties understanding the real images and predicting the correct bounding boxes. Hence, the discrepancy in performance underscores the importance of training data that closely aligns with the conditions under which the AI model will be evaluated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trained Dataset** & **Precision** & **Recall** & **AP Vest** & **AP Helmet** & **mAP** \\ \hline CHV & 89.6\% & 84.8\% & 86.4\% & 91.4\% & 88.9\% \\ SYN\_HV & 77.5\% & 67.8\% & 67.6\% & 75.5\% & 71.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics comparison for trained AI models on CHV and SYN_HV.  
Figure 7: Number of instances per class for real-world and synthetic datasets.
## 3 Results
### Applying RF Model
Finally, the GESM by the RF model was prepared in ArcGIS 10.5 and divided into five classes from very low to very high (Figure 5a), using a natural break classification [8]. According to the results, of the entire study area (847.87 km\({}^{2}\)), 525.97 km\({}^{2}\) (62.03%) are located in the very low susceptibility class, 148.28 km\({}^{2}\) (17.49%) in the low susceptibility, 79.42 km\({}^{2}\) (9.37%) in the moderate class, 56.34 km\({}^{2}\) (6.64%) in the high class, and 37.88 km\({}^{2}\) (4.47%) are located in the very high susceptibility class. Of the total area of GE (0.729 km\({}^{2}\)) in the study area, 0.86% (0.01 km\({}^{2}\)) are located in the very low susceptibility class, \begin{table}
\begin{tabular}{c c c c} \hline  & **0** & **1** & **Class Error** \\ \hline
0 & 2487 & 256 & 0.0933 \\
1 & 66 & 2677 & 0.0240 \\ \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix from the random forest (RF) model (0 = no guilty, 1 = guilty). Figure 4: Relative influence of effective conditioning factors in the random forest (RF) model. \begin{table}
\begin{tabular}{l l} \hline
**Conditioning Factors** & **Weight** \\ \hline Distance from road & 381.67 \\ Elevation & 335.06 \\ Lithology & 234.21 \\ Slope degree & 153.85 \\ Drinage density & 126.72 \\ Distance from river & 106.84 \\ NDVI & 105.26 \\ Convergence index & 73.97 \\ Slope aspect & 72.41 \\ TWI & 71.3 \\ Plan curvature & 42.43 \\ LU & 25.38 \\ \hline \end{tabular}
\end{table} 
Table 5: Relative influence of effective conditioning factors in the RF model. 5.67% (0.04 km\({}^{2}\)) in the low susceptibility, 14.80% (0.11 km\({}^{2}\)) in the moderate susceptibility, 21.95% (0.16 km\({}^{2}\)) in the high susceptibility, and 56.72% (0.41 km\({}^{2}\)) in the very high susceptibility classes.
# Investigation of Earthquake-Induced Pipe Damage in Liquefiable Soils
## 4 Evaluation of Pipeline Damage Caused by Earthquake in the Iskenderun Region
### Discussion of Results with Numerical Analysis of 3115 Station Data for Study Area 1
For a burial depth of H = 1.2D in the 120 m wide model, uplifts of 0.245 m and 0.30 m were calculated for pipes of \(\O\) 1500 mm and \(\O\) 2000 mm, respectively (Figure 16d). With reference to the photograph in Figure 12, the actual occurrence of liquefaction-induced broken pipe joints observed in the field support the pipe damage due to uplift as obtained in the analyses. The pipe uplift demonstrated by the analysis results presented in Figure 17b indicates that the uplift behavior observed and measured up to 40 cm in the caisson foundations shown in Figure 9b was the result of liquefaction. The settlement behavior observed in areas far from the pipe in the analyses represents the actual settlements observed near the port of Iskenderun in alluvial soils and artificial fill layers (Figure 10a). On the other hand, the pipe uplift occurrence in the analyses was supported by the observed pipe uplift up to 70 cm as shown in Figure 10b. When the boundaries in the analyses were extended to 120 m, the maximum downward displacement (settlement) dropped from about 2 m to 1.70 m (Figure 17b). The effects of the boundary conditions on the settlements around the pipe were reduced. In order to investigate the effect of the width of the analysis model on pipe displacements, various model widths were used. The total uplift initially calculated as 17 cm for the DC \(\O\) 2000 mm steel pipe (Figure 16c) increased to 30 cm when wider model boundaries were adopted (Figure 16d). These results indicate that in seismic analyses where significant displacements occur, increasing the model width is crucial to mitigate the misleading effect of boundary conditions on pipe behavior. Figure 16: (**a**) Station 3115 N (PGA = 287 cm/s\({}^{2}\) Vs30 = 424 m/s) earthquake motion during Pazarcı earthquake (Mw 7.7); (**b**) Ru value on the invert level for far field; (**c**) vertical displacement for DN \(\mathcal{O}\) 1500 mm and DN \(\mathcal{O}\) 2000 mm steel pipe H = 1.2D; (**d**) vertical displacement for DN \(\mathcal{O}\) 1500 mm and DN \(\mathcal{O}\) 2000 m steel pipe H = 1.2D with Station 3115 during earthquake motion in Pazarcı earthquake (Mw 7.7). In Figure 18, the change in Ru value after the earthquake is given in the 120 m wide cross section containing a \(\mathcal{O}\) 2000 mm pipe, where the red areas indicate Ru = 1. The PM4 Sand model was not used for the conglomerate layer beneath the liquefiable layer. The HSS model was used for this layer, and therefore Ru values were not calculated (Figure 18). Figure 17: Vertical displacement for DN \(\mathcal{O}\) 1500 mm steel pipe with Station 3115 during earthquake motion in Pazarck earthquake (Mw 7.7) (**a**) 50 m width model; (**b**) 120 m width model.
# Effect of Topography Truncation on Experimental Simulation of Flow over Complex Terrain
## 3 Mean wind Characteristics
### Mean Velocity
Therefore, these results indicate that the flow behavior in front  
Figure 5: East-West cutting planes of measurement locations: (A1) location A1; (A2) location A2; (A3) location A3; (A4) location A4; (A5) location A5. of the target locations should be completely simulated to improve the accuracy in wind tunnel simulation. The velocity profiles at the valley region, including the measurement locations of A4, A5, and A6, at the bottom of the valley, show virtually no distinction because these locations are considerably affected by recirculation flow in the enveloping separation bubble. The cutting planes of these three locations are shown in Figure 5. From the cutting planes, when the terrain model was truncated, most topographic features at the front of the measurement locations were retained, and only the ridge ahead of location A6 was truncated. Thus, few differences can be found in the velocity profiles. The velocity profiles at the valley region, including the measurement locations of A4 and A5 at the bottom of the valley, show virtually no distinction because these locations are considerably affected by recirculation flow in the enveloping separation bubble. The cutting planes of these two locations are shown in Figure 5. From the cutting planes, when the terrain model has been truncated, most topography features at the front of measurement locations are retained, particularly the mountain ridge. Thus, there are few differences in the velocity profiles. According to the above analysis, the velocity profiles of measurement locations at windward and leeward regions are deeply influenced by topographic truncation. Implementation with transition sections could not solve the problem, but accelerated the flow after it and brought inaccuracy to the location near the model edge. The measurement locations in the valley region are slightly affected by truncated terrain because the upwind flow behaviors are little changed. This means that the truncating the terrain causes a change in topographic characteristics, leading to different flow behaviors upstream, which influence the velocity characteristics downstream. Hence, repeating upwind flow behavior is essential for simulating velocity characteristics.
## 4 Analysis of Experimental Results
### Experimental Results
The new dataset was substituted into the KNN-BILSTM model for training without considering the periodic influence of multiple components; the MAE and RMSE index values were 4.0442 and 4.6293, respectively, which were better than 7.14 and 9.69 found in the literature ([PERSON] et al., 2020). The error diagram is shown in Figure 9 shown. Another set of experiments was performed by putting the dataset in this paper into the AT-convLSTM model. The training results are shown in Figure 10. It can be seen that the training effect of the AT-convLSTM model for the dataset in this paper is relatively general. \begin{table}
\begin{tabular}{c c} \hline \hline
**K Value** & **Detection Point Number** \\ \hline Target point & 3310B \\ K = 1 & 3310B, 4465A \\ K = 2 & 3310B, 4465A, 4455A \\ K = 3 & 3310B, 4465A, 4455A, 4459A \\ K = 4 & 3310B, 4465A, 4455A, 4459A, 4451L \\ ⋮ & 3310B, 4465A, 4455A, 4459A, 4451L, 4451A, 4470A, 4461L, 4453L, 4475A, 4461M, 3310L, 3322L, 3319B, 3313B \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection point numbers under different combinations. Figure 9: New data prediction error. The results of other comparable models are shown in Figure 11 and Table 3. It can be seen that the model used in this study has the best predictive effect, and the SVR model has the worst fitting effect. The MAE index was compared with CNN-LSTM, KNN-LSTM, LSTM, GRU, SVR, and other models with improved performance of 13%, 22%, 19%, 18%, and 77%, respectively. In terms of running time, the SVR model takes the least amount of time, but its effect is the worst, indicating the shortcomings of SVR in predicting and processing high-dimensional data. Comparing CNN-LSTM, KNN-LSTM, and the model in this paper, although KNN-LSTM is superior to the model in this paper in running time and has fast convergence speed because it can only carry out one-way propagation in LSTM model training, the result is 22% different from the BILSTM model with 2-way propagation. Among them, the prediction effect of the LSTM model and the GRU model is close, and the GRU model is superior to the LSTM model in convergence speed and has fast convergence speed; however, the prediction performance of this model is still 18%, so the superior training speed can not make up for the performance gap, so the overall performance of this model is better than the listed comparison model.
## 5 Conclusions  
The ultrasonic rotation and transportation of non-spherical micro-particle by using sound field synthesis technique have been proposed and realized. The employed field synthesis method is based on the technique of mode matching and inverse filtering. A prototype device consisting of nine PZT transducers with megahertz frequency is designed to validate the approach, and the generated pressure field is measured by using a needle hydrophone. Within a circular region of 0.5\(\lambda\) radius, a localized elongated acoustic potential well is established and then rotated by modulating the excitation signal on each transducer. Correspondingly, a silica rod with 90 \(\upmu\)m length and 15 \(\upmu\)m diameter can be rotated clockwise or counter-clockwise. The actual angular position of the silica rod agrees with the theoretical predictions, although slight deviation appears in certain rotation steps. The maximum resolution of the current ultrasonic rotation setup is about 5\({}^{\circ}\), which may be further improved by more delicate transducer fabrication and device assembly. In addition, the transportation of particle along different trajectories is also achieved within the same device. The controllability and multi-functionality of the proposed method show great potential in biomedicine and micro-assembly applications. In the future, the more dexterous manipulation, where multi-particles can be independently driven to rotate and shift simultaneously within one microfluidic device, will be studied.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/1/73/s1](http://www.mdpi.com/2076-3417/8/1/73/s1)]([http://www.mdpi.com/2076-3417/8/1/73/s1](http://www.mdpi.com/2076-3417/8/1/73/s1)), Table S1 is corresponding amplitude and phase settings for the field rotation shown in Figure 3. Table S2 is corresponding amplitude and phase settings for the field movement shown in Figure 4.  
**Acknowledgments:** The work is supported by the Natural Science Foundation of China (Grant No. 51675480 and 51205351), Natural Science Foundation of Zhejiang province (Grant No. LZ14E050003) and the Foundation from State Key Laboratory of Fluid Power and Mechatronic Systems (GZKF-201616).  
[PERSON] and [PERSON] conceived and designed the models and methods presented; [PERSON] developed the experimental software tools; [PERSON], [PERSON] and [PERSON] helped in conception and experiments; [PERSON] helped in the simulations; [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] wrote this paper.  
The authors declare no conflict of interest.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 7 Adaptive Golomb Code and Context Adaptive Binary Arithmetic Coder
### Long-Term Adaptation  
In the case of an context adaptive binary arithmetic coder with each number of the context, a probability distribution consisting of only two values--\(p(0)\) and \(p(1)\)--is associated. In practice, the number of occurrences of zeros and ones denoted \(n(0)\) and \(n(1)\) respectively, then \(p(0)=n(0)/(n(0)+n(1))\), and \(p(1)=1-p(0)\). The current adaptation of the distribution consists in increasing (in a given context) by 1 counter of occurrences of the currently encoded bit. Additionally, if the total number of occurrences of zeros and ones in a given context exceeds the value of \(N_{\text{max}}\), then their counters \(n(0)\) and \(n(1)\) are divided by 2, which is the equivalent of the long-term forgetfulness method. For this reason, the designer of such a binary coder should determine both the \(N_{\max}\) value and the initial values of \(n(0)\) and \(n(1)\) within each context in such a way as to obtain the best fit to the encoded data.  
In the proposed solution, in all contexts of the \(u_{G}\) coder, \(N_{\max}=2^{9}\) was set, and the counters \(n(0)\) and \(n(1)\) were initialised with the value 1. In the case of the \(v_{G}\) coder, in all contexts, \(N_{\max}=2^{12}\) was set, and the counters were initialised as \(n(0)=64\) and \(n(1)=60\).
## 3 Features Selection and TCN-ATT-LSTM Transfer Learning
### Feature Extraction TCN-ATT-LSTM Pretrained Network  
#### 3.2.1 TCN-ATT-LSTM Network  
The output power of wind and photovoltaic clusters is a typical complex time-series structure, characterized by long temporal spans. In predicting the output power at each time step, only the power information from previous time steps should be considered. To address this, this paper introduces the Temporal Convolutional Network (TCN), which integrates dilated causal convolution layers and residual connections [21]. In designing the pre-trained network, the TCN layer is first used to extract the wind and solar output  
Figure 2: Flowchart for selecting sensitive meteorological features.  
features from the source domain. The structure of the dilated causal convolution in TCN is shown in Figure 3, with the dilated convolution calculated as shown in Equation (6) below:  
\[F(u)=(x\odot df)(u)=\sum_{i=0}^{\varphi-1}f(i)X_{s-d\,i} \tag{6}\]  
where \(x\) represents the input sequence of meteorological features, \(u\) is the position in the dilated convolution, \(f\) is the convolution kernel, \(\odot\) denotes the convolution operation, \(d\) is the dilation factor, and \(\varphi\) is the size of the convolution kernel.  
The self-attention mechanism can dynamically learn information between data points to update weights, allowing the model to focus more on important input information and improve predictive performance. To improve the model's robustness and accelerate convergence, this paper incorporates the attention mechanism into the TCN. The residual unit of the TCN model, enhanced by the attention mechanism, is illustrated in Figure 4.  
The modeling of wind and solar power is a time-series problem, where there is temporal correlation between adjacent time scales. The Long Short-Term Memory (LSTM) neural network can effectively handle long-period time series by preserving historically relevant information that aligns with the sequence characteristics [22]. Therefore, this paper integrates an LSTM layer into the pre-trained network model to capture and predict both  
Figure 4: Structure diagram of TCN with attention mechanism.  
Figure 3: Dilated causal convolution structure of TCN.  
short- and long-term dependencies within the time series. The LSTM structure is illustrated in Figure 5.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 4 Experimental Analysis
### Architecture and Experiment Description  
As briefly introduced at the beginning of this paper, a custom-made Spinert set-up with four output channels was employed to simulate a GPS L1 constellation affected by a continuous wave (CW) interference, which was placed at the central frequency of the L1 band, i.e., 1575.42 MHz, with a power level 70 dB greater than that of the GPS signals.  
On the receiver side, the KrakenSDR, a low-cost SDR with capabilities to process up to five channels, was used. In this study, the four outputs generated by Spinert were captured and sampled using this SDR with a sampling frequency of 2.4 MHz, mimicking a uniform linear array (ULA) configuration respecting the inter-element separation of half-wavelength of the L1 band. To increase the levels of the received signals, four low noise amplifiers (LNAs) had to be connected at its input ports to guarantee an appropriate processing of the samples.  
After the simulation, each channel of the KrakenSDR results in a data file containing the signal received with its corresponding phase offset due to the ULA configuration.  
These four data files were the input for the beamforming module, where the PI and the PREW algorithms were applied independently. The resulting signal obtained after the application of each algorithm was introduced into the FGI-GSRx-v1.0.0 software receiver, which allowed the processing of the output samples, presenting the final positioning solution along with certain valuable metrics for the analysis, as it is the carrier-to-noise density ratio (C/N0) and the deviation from the real location of the user [18]. For the processing of the GPS signals, an integration time of 15 ms was used, along with the Least Squares Estimator (LSE) to solve the navigation equations and obtain the positioning solution, using only those satellites with an elevation above 5\({}^{\circ}\) and a C/N0 level higher than 30 dB-Hz.
## 4 Results
### Comparison with Other Models  
#### 4.3.1 Comparison with Grid-Unit-Based GWR (G-GWR)  
In order to verify the applicability of slope units in expressing spatial proximity of the GWR, the commonly used grid unit is adopted as a comparison. For the validity of contrast and simplicity of calculation, grid size is selected as \(200\times 200\) m, approximately equal to the slope unit, generating 80,264 grid units.  
The following procedure is the same as the process of the proposed S-GWR. The variance inflation factor (VIF) values of each influencing factor are calculated, with the maximum test result of 2.14 (as shown in Table 5). It indicates that the original G-GWR passes the global multicollinearity test. Based on the principles of selecting principal components, G-GWR also obtained four components, including geological factor, slope shape factor, hydrographic factor, and aspect factor. The establishment of kernel function of G-GWR is the same as S-GWR.  
The established G-GWR model is then applied to the landslide susceptibility assessment in Qingchuan County. The sample evaluation contingency table of the G-GWR model is shown in  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Predicted Groups (Model)**} \\ \cline{3-4}  & & **Stable** & **Unstable** \\ \hline Actual Groups & Stable & 11,881 (72.43\%) & 4523 (27.57\%) \\ (inventory) & Unstable & 36 (12.37\%) & 255 (87.63\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sample evaluation contingency table of the proposed S-GWR model.  
Figure 6: Validation of S-GWR model. (**a**) ROC curve; (**b**) success rate curve.  
Table 6. It shows that 61 points of the 291 actual landslide points are classified as stable points, with a missing rate of 20.96%, which is higher than that of the S-GWR model with 12.37%. The ROC curves and success rate curves of the two models are shown in Figure 7. The AUC values of ROC curve and success rate curve of G-GWR are 0.837 and 0.827 respectively, both lower than those of S-GWR. The comparative verification of the S-GWR model and the G-GWR model shows that the S-GWR model has a better performance in classification precision, thus can better identify susceptible areas to landslide occurrence. This comparison indicates that slope unit is more suitable for GWR model in the expression of spatial relationship than traditional grid unit, in particular for the purpose of landslide susceptibility assessment.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Stereo-DIC Overview
Performing triangulation on \(\hat{\mathbf{x}}^{o}_{s_{j}}\) and \(\hat{\mathbf{x}}^{d}_{s_{j}}\) results in \(\hat{\mathbf{x}}^{o}_{w}\) and \(\hat{\mathbf{x}}^{d}_{w}\) respectively. Finally, \(\hat{\mathbf{x}}^{o}_{w}\) is subtracted from \(\hat{\mathbf{x}}^{d}_{w}\) to determine the displacement experienced by the point on the specimen, in the x-, y-, and z-directions represented as \(\hat{u}_{w}\), \(\hat{v}_{w}\), and \(\hat{w}_{w}\) respectively, between the time stamps at which the reference and deformed image pairs were captured. Note that subscript \(w\) indicates coordinates or displacements in the world CS, whereas variable \(w\) indicates displacement in the z-direction. This is done for all subset pairs across all image pair combinations, analyzed by temporal matching, to determine the full-field metric displacements experienced by the specimen throughout the image set.