esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling of Broken Rock Masses Based on the Coupled DFN-DEM Approach
### Statistics of Structural Plane Geometric Parameters  
To acquire the authentic geometric parameters of the fractured surrounding rock in the main powerhouse of the Liyang pumped storage power station, we conducted an exhaustive statistical analysis using on-site geological survey data. The data sources encompassed the \"Geological Sketch Display of the Roof Arch of the Main Powerhouse\", \"Geological Sketch Display of the Upstream Wall of the Main Powerhouse\", \"Geological Sketch Display of the Downstream Wall of the Main Powerhouse\", and \"Geological Sketch Display of the North End Wall and South End Wall of the Main Powerhouse\". Employing the statistical window method allowed for a comprehensive statistical analysis. Figure 7a-c show the placement of three geological statistical windows situated on the roof arch, upstream sidewall, and downstream sidewall of the main powerhouse (excluding large faults during this process).  
A custom program called \"Geological Statistical Window Analysis\" is developed based on the quantitative relationship between the geological geometric parameters of structural surfaces within the statistical windows. By importing a DXF file into the \"Geological Statistical Window Analysis\" program, one can obtain geometric parameters such as the count, dip angle, trace length, line density, areal density, and volume density of structural surfaces within the statistical windows.  
Figure 7: Structural plane statistical windows in main powerhouse area: (**a**) roof arch statistical window; (**b**) upstream sidewall statistics window; (**c**) downstream sidewall statistics window.  
Upon organizing the result files, the actual geometric parameters of structural surfaces within the rock masses of the arch roof, upstream sidewall, and downstream sidewall of the main powerhouse are collected, as presented in Table 2. The following can be observed from the table: (1) In comparison to the upstream and downstream sidewalls, the fractures in the arch roof rock mass exhibit the maximum length, averaging 4.99 m. However, they have the fewest fractures per unit area of rock mass (i.e., 0.15 fractures per m\({}^{2}\)). (2) When contrasted with the arch roof and downstream sidewall, the fractures in the upstream wall rock mass display a more evenly developed length (variance of trace length equals 2.85 m\({}^{2}\)), and the highest number of fractures per unit length of rock mass (0.87 fractures per meter). (3) The average trace length and trace length variance of structural surfaces in the surrounding rock of the main plant building are 4.30 m and 3.45 m\({}^{2}\), respectively. The average dip and dip variance of structural surfaces are 51.42\({}^{\circ}\) and (27.93\({}^{\circ}\))\({}^{2}\), respectively. The number and length of fractures per unit area of rock mass are 0.23 and 0.97 m\({}^{-1}\), respectively. The number of fractures per unit length of rock mass is 0.58 m\({}^{-1}\), and the fracture area per unit rock mass is 1.87 m\({}^{-1}\).
## 4 Results and Analysis
### Analysis of ARIMA Model Construction and Prediction Results
#### 4.3.3 Prediction Results  
Using the static rolling forecast approach, predictions were made for the area affected by forest fires in Yunnan Province, as illustrated in Figure 12a,b. Significant deviations between the predicted values and actual data were observed in 1999, 2008, 2011, and 2017, while the predictions from 1995 to 1998 and 2000 to 2007 showed a higher degree of conformity with the actual situation. Despite discrepancies in the predicted areas affected by fires in 2003, 2008, 2011, and 2019, the predictions for other years demonstrated high accuracy. The root mean square error (RMSE) for the number of fires was 109, and the affected area was 890 hectares, indicating an average deviation of 87 occurrences and 890 hectares between the predicted and actual values. Given that the areas affected by forest fires are influenced by multiple factors such as climate change, topography, human activities, land management strategies, and local government fire prevention measures, the ARIMA model used in this study has shown its potent potential and applicability in multivariate predictions.  
Figure 11: Residual sequence correlation test.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Variable** & **Coefficient** & **Std. Error** & **t-Statistic** & **Prob** \\ \hline AR(1) & \(-1.235444\) & \(0.171205\) & \(-7.216162\) & \(0.0000\) \\ AR(2) & \(-0.498097\) & \(0.269856\) & \(-1.845786\) & \(0.0806\) \\ AR(3) & \(-0.036110\) & \(0.168062\) & \(-0.214864\) & \(0.8322\) \\ MA(1) & \(0.519134\) & \(0.105983\) & \(4.898262\) & \(0.0001\) \\ MA(2) & \(-0.434994\) & \(0.110360\) & \(-3.941593\) & \(0.0009\) \\ MA(3) & \(-0.445270\) & \(0.107620\) & \(-4.137436\) & \(0.0006\) \\ MA(4) & \(0.457297\) & \(0.087562\) & \(5.222544\) & \(0.0000\) \\ MA(5) & \(0.947795\) & \(0.058639\) & \(16.16312\) & \(0.0000\) \\ R-squared & \(0.851229\) & Mean dependent var & \(-9.00000\) \\ Adjusted R-squared & \(0.796418\) & S.D. dependent var & \(291.3324\) \\ S.E. of regression & \(128.8941\) & [PERSON] info criterion & \(12.38147\) \\ Sum squared resid & \(315660.2\) & Schwarz criterion & \(12.76858\) \\ Log likelihood & \(-164.7602\) & Hamann–Quinn criter & \(12.49294\) \\ Durbin–Watson stat & \(1.392144\) & Durbin–Watson stat & \(1.392144\) \\ \hline \end{tabular}
\end{table}
Table 8: Parametric results of ARIMA (3,1,5) model.  
To enhance the accuracy of the forecasts, the static forecasting method for rolling prediction was utilized via the software EViews 8.0 [59], generating predictions for the number of forest fires and affected areas from 2022 to 2033. The results are depicted in Figure 13. The figure reveals that the predicted number of forest fires during 2024-2033 fluctuates significantly, with the years 2026 and 2030-2033 showing relatively higher frequencies of fires. In contrast, the predicted area affected by fires demonstrates an overall trend of increasing and decreasing, particularly from 2028 to 2033, where the rate of increase in the affected area becomes more pronounced.
# Fast Optimization of the Installation Position of 5G-R Antenna on the Train Roof
###### Abstract  
In this paper, a prediction model of the coupling coefficient based on a multi-module neural network (MMNN) is developed to quickly optimize the installation position of the roof antenna of the 5G-Railway (5G-R) communication system, so as to improve the anti-interference performance of the roof antenna. Firstly, a simulation model of the coupling coefficient between the pantograph arcing and the roof antenna (a monopole antenna operating frequency of 2 GHz) was established to construct the dataset. It is also verified that the influence of the electromagnetic interference (EMI) of pantograph arcing can be significantly reduced by predicting the new installation position (minimum coupling coefficient), and the installation position optimization of roof antenna can be realized. Then, the mind evolutionary algorithm of the back propagation neural network (MEA-BP) algorithm and particle swarm optimization--extreme learning machine (PSO-ELM) algorithm were adopted, respectively. The extreme learning machine algorithm constructed a different prediction model. And, by setting the integrated strategy of piecewise prediction, the prediction results are optimized and the accuracy of the prediction model based on the MMNN is further improved. Finally, the prediction model is proven to be able to replace the complicated electromagnetic simulation work accurately and efficiently by a variety of prediction performance indices, which provides an effective prediction method for the rapid optimization of the installation position of the roof antenna.  
coupling coefficient; pantograph arcing; machine learning; 5G-R
## 4 Simulation Results
This strategy can therefore minimize the slots used for resolving the collided tags and provide a better performance than other algorithms. Figure 11 demonstrates the effect of various numbers of tags ranging between 500 and 4000 when PER was set to 0.3. Figure 10(a) shows that CAQT used the fewest slots to identify all tags and also had the fewest idle slots show in Figure 10(b) during tag identification procedure. Thus, the CAQT had superior performance compared with QT, DFSA, and ERE-ABS. The reasons were as the same as discussed in Figure 10. In addition, as shown in Figure 10(a), the more tags existed in the reader's coverage, the more slots were required to identify all tags by all algorithms. Figure 11(a) demonstrates the estimated PER during the tag identification progress while the number of tags was set to 500 and PER was set to 0.3. Since ERE-ABS only calculated the PER before the tag resolution process, the estimate PER would keep a value during the whole tag identification  
Figure 11: The effect of number of tags. procedure. Therefore, the max and min estimated PER during the 100 times identification process is used to compare the deviation. However, the CAQT could dynamically adjust PER according to the current channel condition, and then thus the estimated PER would slightly oscillate and approached the real PER, which was 0.3. Figure 11(b) also presents that the number of idle slots of CAQT was fewer than that of ERE-ABS during the whole tag identification procedure because CAQT could accurately estimate the PER while ERE-ABS could not.
# Chaos-Based Underwater Communication With Arbitrary Transducers and Bandwidth
## 3 Simulation Results and Performance Analysis  
In this section, the BER performance of the proposed method is analyzed in both the AWGN channel and the underwater acoustic channel. Moreover, we analyze the BER performance versus the spreading factor in the underwater acoustic channel. The proposed method is also compared with some existing methods to show its superiority.
## 2 Numerical Approach
### Artificial Neural Network  
With the recent developments in the machine learning area, the techniques related to machine learning has been applied in many fields such as image processing, data regression and classification,  
Figure 2: The data obtained from the experiment, XFOIL and CFD are compared with the drag coefficient in (**a**) and the lift coefficient in (**b**) at the angles of attack from \(-15^{\circ}\) to \(15^{\circ}\).  
and optimization [36; 37; 38; 39]. Among the many techniques in machine learning, an ANN is popularly used in constructing a surrogate model because an ANN can predict output values with good accuracy, as shown in [PERSON] et al. [40]. To make use of this advantage, an ANN is used for surrogate model construction in the current paper, and its accuracy is investigated.  
A loss function \(J\), which is used to train the ANN in a reconstructing surrogate model by minimizing it, can be defined as  
\[J=\frac{1}{N}\sum_{i=1}^{N}(\phi_{i}-\hat{\phi}_{i})^{2}+\lambda||\mathbf{w}|| _{2}^{2}, \tag{5}\]  
where \(||\cdot||_{2}\) indicates \(L_{2}\) norm, \(\lambda\) is the regularization parameter, and \(\mathbf{w}\) is a set of weights of the ANN in vector form. The \(\phi_{i}\) and \(\hat{\phi}_{i}\) are the values of the output variables at the \(i\)-th test design obtained from simulation (in this study using XFOIL) and the ANN, respectively.  
In Equation (5), the first term on the right hand side computes the discrepancy between the values computed from simulation and ANN in mean square form. The second term on the right hand side measures the complexity of the ANN to prevent the ANN from overfitting the output values of the given datasets, which is called \(L_{2}\) regularization. The ANN in the present paper is implemented using the open-source software TensorFlow [41]. The constructed ANN has two hidden layers, and 64 neurons are used at each hidden layer. The rectified linear unit (ReLu) function is used as the ANN's activation function. The Xavier initialization initializes the weights in the ANN, and the Proximal Adagrad algorithm is used for an optimizer [42]. The learning rate and regularization parameter are set to 0.01 and \(10^{-4}\), respectively. The ANN training process is repeated until the maximum number of iterations (\(10^{5}\)) is reached. Although the values of the ANN parameters defined in the current study are not thoroughly optimized, when the change of prediction accuracy of the ANN-computed surrogate models is investigated by varying the number of neurons and magnitude of the learning rate, the settings used in this study show good accuracy in most cases. Some test cases are provided in Table 1.
## 4 Numerical Result Analysis and Discussion  
### Design Working Condition  
The comparison between the predicted performance obtained from the three-dimensional simulation and the designed data is listed in Table 6. It is seen that the prediction of mass flow rate, output power, and total-to-static pressure agree well with the designed data in the one-dimensional optimization design. The predictions are slightly higher than the designed data, with a maximum difference of 1.33% for the mass flow rate. The deviation of the total-to-static efficiency is only 0.47%.  
Figure 5 represents the streamlines in the nozzle and rotor blade passages. It is observed that the velocity varies gradually along the flow passage, and the streamlines distribute uniformly. There is no obvious swirl flow and separation, which demonstrates a good flow property for the current radial inflow turbine at the design working condition.  
Figure 4: Mesh independence study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gas Type** & **Resolution** & **Total-to-Static Efficiency** \\ \hline Perfect gas & / & 82.82\% \\  & \(101\times 101\) & 85.78\% \\ Real gas & \(201\times 201\) & 85.77\% \\  & \(301\times 301\) & 85.77\% \\ \hline \end{tabular}
\end{table}
Table 5: Table resolution test of the thermal-physical properties.  
As presented in Figure 6, the Mach number at 50% blade span gradually increases along the nozzle passage, and the maximum value of 1.26 occurs at the nozzle exit. Although the transonic flow exists in the nozzle passage, the maximum is still within the limits of the Mach number in Table 2.  
The static pressure and temperature distribution at 50% blade span are shown in Figure 7. The static pressure and temperature drop gradually inside the turbine. The distributions of the static pressured drop and the temperature drop are reasonable with no abrupt changes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Design** & **Prediction** & **Error (\%)** \\ \hline Total-to-static expansion ratio & 2.896 & 2.896 & 0.00 \\ Mass flow rate (kg/s) & 16 & 16.213 & 1.33 \\ Output power (MW) & 2.118 & 2.135 & 0.80 \\ Total-to-static efficiency (\%) & 85.30 & 85.77 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evolution of the one-dimensional design.  
Figure 5: Streamlines in the nozzle and rotor passages.  
Figure 6: The contour of relative Mach number at 50% blade span.  
The variations of the enthalpy and entropy in the radial inflow turbine are represented in Figure 8. It is seen that the enthalpy drops uniformly along the flow passage in Figure 8a. As shown in Figure 8b, the entropy reduction changes smoothly along most flow passages. The entropy varies from 2.667 to 2.856 kJ/(kg\(\cdot\)K). There is an abrupt increase of about 0.1 kJ/(kg\(\cdot\)K) at the interface between the stationary and rotational domains due to the irreversible shock wave energy at the nozzle exit.
## 2 Method
### Higher Frequency Region f2
#### 2.3.3 Optimal Listening Points  
So far, Equation (18) delivers \(V=11\) sets of \(L=128\) impulse responses for each key. The solutions only vary slightly, due to microphone misplacements, equipment and background noise, numerical errors and the spatial variations of the loudspeaker sound radiation characteristics. The solutions are valid inside the listening region. Outside the listening region synthesis errors occur, because loudspeaker signals interfere in an arbitrary manner. Outside an anechoic chamber, this will lead to unnatural reflections. Consequently, the ideal impulse response minimizes synthesis errors outside the listening region. This is achieved by selecting the impulse responses with the lowest signal energy  
\[R_{\Omega}(\mathbf{\Theta}_{l},\mathbf{\kappa}_{a},\omega)=\min_{v,\mathbf{\kappa}_{a}}\{R^ {{}^{\prime}}_{\Omega}(\mathbf{\Theta}_{l},\mathbf{\kappa}_{a},\omega)\}, \tag{19}\]  
where  
\[R^{{}^{\prime}}_{\Omega}(\mathbf{\Theta},\mathbf{\kappa}_{a},\omega)\propto\sum_{l=1} ^{L}\int_{t=0}^{\infty}R^{{}^{\prime}}_{\Omega}(\mathbf{\Theta}_{l},\mathbf{\mathrm{X} }_{v,j}\mathbf{\kappa}_{a},t)^{2}\mathrm{d}t. \tag{20}\]  
The signal energy is the sum of all \(L=128\) loudspeaker impulse responses. In Equation (20) the signal energy of each key \(\mathbf{\kappa}_{a}\) is calculated for each of the \(v=1,\dots,11\) microphone array positions. As defined in Equation (19), the final impulse response \(R_{\Omega}(\mathbf{\Theta},\mathbf{\kappa}_{a},\omega)\) for each specific key is the one where \(v\) creates minimal signal energy. This solution exhibits the most constructive interference inside the listening region. The solution is valid for the considered frequency region f2, i.e., between 1.5 and 4 kHz. For higher frequencies, a third method is ideal, as discussed in the following section.
## 4 Experimental Test Results
### Constant Amplitude Fatigue Loading
#### 4.1.3 Behavior of the Z-Jointed Precast FDDP under CAF  
For the tested specimen S5 under CAF loading, the NSC cylinders' average compressive and splitting tensile strengths were 37 MPa and 3.23 MPa, respectively. The tested cylinders for the UHPFRC, which were cast 10 days before the fatigue testing, resulted in an average compressive strength of about 127 Mpa. During the initiation of the fatigue pre-cracking procedure, at a static load of 220.5 kN, the flexural crack propagated from underneath the mid-point of the wheel footprint about 100 mm towards the middle shear pockets. When the applied load increased to 275.625 kN, the flexural crack propagated further another 300 mm. However, when the applied load reached 330.75 kN, the flexural crack propagated diagonally from the mid-point of the wheel footprint to the closest corner of the middle shear pocket. The maximum recorded flexural crack width at that point measured 80 um. No more flexural cracks were observed during the CAF test that lasted over 16 days.  
After each 500,000 cycles, the slab was subjected to monotonic loading to observe the change in slab flexural stiffness through deflection measurements. Figure 9(a) depicts the load-deflection relationship for the slab at the center of the footprint of the wheel load. It can be observed that the slope of the curves after each group of fatigue cycles appeared unchanged and stayed linear. After the 4 million fatigue cycles, the slab was subjected to increasing monotonic load to collapse. The precast FDDP failed due to punching shear at a jacking load of 931 kN, which corresponded to about 4.47 times the CHBDC factored design wheel load. Figure 9(b) depicts the load-deflection relationship for the tested slab under static loading to collapse. The deflection value was recorded at the mid-length of the free edge of the short slab, noted as a \"Free end\" that reached 1.78 mm at failure. On the other hand, deflections under the wheel footprint, denoted as \"Under load 1 and load  
Figure 9: Monotonic load-deflection curves for slab S3 during CAF loading and static load test to collapse.  
2\", were recorded as 30.29 and 28.94 mm at failure, respectively. The deflection at the center of the long precast slab, denoted as \"Mid-span\", was recorded as 19.30 mm. The maximum deflection of the long precast slab at the mid-length of the edge joint, denoted as \"Fixed end\", was recorded as 2.65 mm at failure. Figure (e)e presents the top view of the slab showing punching shear failure at the footprint of the wheel load, while Figure (f)f presents the bottom view of the slab showing the crack pattern after failure.  
Figure 10: Monotonic load-deflection history for slab S5 under CAF loading and static load test to-collapse.
## 3 Results and Discussion
### Fabrication and Sensing Experiment of Uniformly Etched TMFBG
#### 3.3.2 Refractive Index-Sensing Characteristics
In order to overcome the interference from the environmental random factors, such as temperature and humidity, two TMFBGs can be cascaded similar to traditional FBG pairs to achieve the simultaneous measurement for different physical qualities, including temperature, humidity, and stress. The cross-sensitivity among the different parameters can be effectively eliminated by demodulating the contributions of the different parameters to the refractive index sensitivity of the whole TMFBG sensing system.
# 3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition
## 4 Experiments
### Results and Comparison Analysis
#### 4.3.2 Class-Wise Accuracy for Activity Recognition  
This section computes the class-wise accuracy for action recognition. We investigate the recognition accuracy of our method by making a confusion matrix and considering 10 action classes of the UCF Sports dataset. Table 3 demonstrates the accuracy of each action category, the x-axis denotes the predicted labels and the y-axis represents the ground truth labels. The intensity of the true score is high (diagonal) for each category, and our method achieves 94% for all 10 classes. It is interesting to note that some of categories with similar actions are more easily confused with each other, such as golf swing, kicking, running, swing bench and walking; these categories interfere with each other and yield low scores. A possible reason for this is the similarity of the features and representations among actions. In addition, the number of training samples is too small, so the result is confusing and misclassification occurs. The confusion matrix of HMDB51 dataset is shown in Figure 8, which is well diagonalized. However, some categories are easily misclassified; nonetheless, our proposed approach still performs well with most action categories.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fusion Method**} & \multirow{2}{*}{**UCF Sports**} & **Split 1** & **Split 2** & **Split 3** & **Average** \\  & & **(UCF 101)** & **(UCF 101)** & **(UCF 101)** & **(UCF 101)** \\ \hline Element-wise max & 91.8 & 90.5 & 89.9 & 89.6 & 90.0 \\ Element-wise sum & 92.1 & 90.8 & 90.1 & 89.9 & 90.2 \\ Concatenation & 92.8 & 91.2 & 90.5 & 91.0 & 90.9 \\ Linear weighted & 93.9 & 91.6 & 90.9 & 91.7 & 91.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of different earlier fusion methods on our model. The accuracy (%) is computed on a UCF Sports dataset, and all three splits and their average on UCF101.  
Figure 8: Confusion matrix on the HMDB51 dataset using our model.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Categories** & **Diving** & **Gold -Swing** & **Kicking** & **Lifting** & **Horse** & **Running** & **Skate** & **Swing** & **Swing** & **Swing-Side** & **Walking** \\ \hline Diving & 1.00 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Golf-Swing & 0 & 0.91 & 0.07 & 0 & 0 & 0 & 0 & 0.02 & 0 & 0 \\ Kicking & 0 & 0.06 & 0.94 & 0 & 0 & 0 & 0 & 0 & 0 \\ Lifting & 0 & 0 & 0 & 0.95 & 0 & 0 & 0 & 0 & 0 \\ Riding & 0 & 0 & 0 & 0 & 0.90 & 0 & 0 & 0 & 0 \\ Running & 0 & 0.06 & 0.01 & 0 & 0.01 & 0.91 & 0 & 0 & 0.01 \\ Stateboarding & 0 & 0 & 0 & 0 & 0 & 0.93 & 0 & 0 & 0 \\ Swing Bench & 0 & 0 & 0 & 0 & 0 & 0 & 1.00 & 0 & 0 \\ Swing Side & 0 & 0.01 & 0 & 0 & 0 & 0 & 0 & 0.99 & 0 \\ Walking & 0 & 0.07 & 0 & 0 & 0 & 0 & 0.04 & 0 & 0.89 \\ \hline \multicolumn{10}{c}{**Average**} & & & & & & & & \\ \multicolumn{10}{c}{**Accuracy**} & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matric of UCF sports dataset.