esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulations and Results
### Medically Fit for Discharge  
Figure 15 shows a comparison of actual (black) data compared to ARIMA (green), Prophet (yellow) and GRNN (purple) predictions for the Medically Fit for Discharge indicator. This indicator, unlike Unallocated Patients with DTA presents a very uniform seasonality and is thus a candidate for higher accuracy and reliability predictions. The reason is that doctors typically discharge patients at the same fixed times every day and this creates a very uniform lag factor, that is important to all auto-regressive processes. However, once again in the red square, it can be observed that GRNN is the only prediction model that can quickly adapt and provide reliable predictions during the lockdown period. Finally, there are some obvious outliers in the data, especially in February, but investigation has shown that these are due to data quality issues rather than unusual occurrences.  
Once again, the residual charts in Figures 16-18 show very small residuals for all three models, apart from the lockdown period, where GRNN is the only model that can adapt rapidly. This clear accuracy advantage is also highlighted by the red squares in Figures 3, 7, 11 and 15. This may be dependent on the training, especially the size of the input layer, which, although the same for all models (8), by being relatively small may benefit GRNN, when the data change behaviour, and allow it a more agile response.  
Finally, in the prediction chart of Figure 18, a few GRNN predictions are missing, and this is due to the model being sensitive to
# Method of Suppressing Rayleigh Waves Based on the Technology of Time-Domain Differential Detection
## 3 Numerical Validation of the Differential Detection Method  
The effects of applying the time-domain differential method are illustrated through numerical simulations. Figures 3 and 4 depict the finite element method results from the forward modeling of the wave field for the two-layer medium model described previously [32]. Specifically, these figures represent the Vz and Vx component shot gather records at the surface, with wave trains displayed using inter-trace energy balancing. In the forward simulation, the source wavelet employed is a Ricker wavelet with a dominant frequency of 60 Hz, a trace spacing of 0.5 m, and a sampling interval of 50 \(\upmu\)s. The physical model and parameters are depicted in Figure 1.  
This study uses the adjacent trace differential detection method to present the results after surface wave suppression in Figure 4a,b for the Vz and Vx components, respectively. Two selected traces are emphasized for a detailed explanation to accurately display the processing effects. Figure 5 illustrates the extracted records for traces 11 and 12 Vz components and the new records post-surface wave suppression. Figures 3-5 show that applying common shot time-domain differential detection technology effectively suppresses Rayleigh waves while retaining significant body wave information, including reflected longitudinal waves. Furthermore, when the differential parameters are optimally matched, the black-wrapped reflected wave signals depicted in Figure 5b can be compared to the pre-differential signals (in color). This comparison highlights that the differential processing not only suppresses Rayleigh waves but also significantly diminishes other converted waves with velocities comparable to those of the Rayleigh waves, yielding beneficial results.  
Figure 6 illustrates the simulated split-spread geometry of common shot gather records before and after processing, demonstrating the improvement achieved by the differential detection method in attenuating Rayleigh waves.  
Given the real-world context of seismic wavefield diffusion and the resulting changes in differential parameters, the practical application of differential detection necessitates further exploration into the mechanisms of related parameter evolution and comprehensive effectiveness. In engineering applications, it is crucial to balance the preservation of reflected wave information while more effectively suppressing Rayleigh waves. Once suppression is achieved, the efficient and faithful restoration of reflected longitudinal waves warrants further investigation. Moreover, in engineering practice, as opposed to ideal conditions, systematic and refined strategic guidance is necessary for selecting related equipment performance, designing device parameters, and implementing technology based on various seismic exploration background conditions.  
Figure 4: Gun set records after time-domain differential processing. (**a**) Vz component. (**b**) Vx component.  
Figure 3: Numerical simulation of gun set records. (**a**) Vz component. (**b**) Vx component.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 3 Case Study  
### Overview of the Study Area  
Nanchang City is located in the southern part of the northern part of Jiangxi Province, with geographic coordinates of 28\({}^{\circ}\)41\({}^{\prime}\)-29\({}^{\circ}\)11\({}^{\prime}\) N latitude and 115\({}^{\circ}\)27\({}^{\prime}\)-116\({}^{\circ}\)35\({}^{\prime}\) E longitude, which belongs to a typical subtropical humid monsoon climate zone. The climate of this region is characterized by a remarkable average annual precipitation of about 1611.8 mm, frequent heavy rainfall, and an average number of days of heavy rainfall of 5.7 days per year, which is mainly affected by the Jianghuai Meiyu Frontal rainstorms [27]. The JAC Meiyu Feng rainstorms usually occur from May to July, with long duration and high rainfall intensity, often accompanied by thunderstorms and short-term heavy precipitation, which is very likely to cause flooding. In this paper, Honggutan District of Nanchang City is selected as the research object, which is located on the right bank of Ganjiang River and belongs to one of the central urban areas of Nanchang City, which is an important area for the rapid development of the city in recent years. The catchment area of Honggutan District is 10 square kilometers, and the surface elevation ranges from 15.6 m to 29.5 m. As shown in Figure 2. The terrain is generally gently sloping, and there are low-lying areas in some areas, which tend to form waterlogged areas during heavy rainfall. In addition, the high density of development in the county and the extensive distribution of roads, buildings, and other impervious surfaces further exacerbate the rapid accumulation of surface runoff and the risk of waterlogging.
## 3 Multi-Modal Distribution of RSSI
### Variation in Carrier Allocation  
The second cause of the multi-modal RSSI distribution is the variation of the amount of subcarriers. Driven by the high throughput requirement, multi-carrier modulation has gradually become the dominant modulation scheme in modern communication systems. Orthogonal frequency division multiplexing (OFDM) is undoubtedly the most popular multi-carrier modulation technique. Similar to single-carrier modulations, an OFDM symbol is transmitted for a certain amount of time; however, it has an extra degree of freedom: each OFDM symbol may occupy different amounts of subcarriers. The amount of occupied subcarriers in an OFDM symbol corresponds to the number of null subcarriers at the input of the inverse fast Fourier transform (iFFT), after which a cyclic prefix is added to form the complete OFDM symbol. The change in the quantity of occupied subcarriers causes the signal to be stronger in one OFDM symbol time than the other. An illustration of the OFDM signal in time domain and the corresponding PDF are shown in Figure 2. The example RSSI plot has two levels of average signal strength, namely \(K3\) and \(K4\); as a result, the PDF exhibits a bimodal distribution. Intuitively, the OFDM symbol containing more subcarriers would have a relatively stronger signal strength. This assumption is validated experimentally in the next section.  
### Variation in Carrier Allocation  
The second cause of the multi-modal RSSI distribution is the variation of the amount of subcarriers. Driven by the high throughput requirement, multi-carrier modulation has gradually become the dominant modulation scheme in modern communication systems. Orthogonal frequency division multiplexing (OFDM) is undoubtedly the most popular multi-carrier modulation technique. Similar to single-carrier modulations, an OFDM symbol is transmitted for a certain amount of time; however, it has an extra degree of freedom: each OFDM symbol may occupy different amounts of subcarriers. The amount of occupied subcarriers in an OFDM symbol corresponds to the number of null subcarriers at the input of the inverse fast Fourier transform (iFFT), after which a cyclic prefix is added to form the complete OFDM symbol. The change in the quantity of occupied subcarriers causes the signal to be stronger in one OFDM symbol time than the other. An illustration of the OFDM signal in time domain and the corresponding PDF are shown in Figure 2. The example RSSI plot has two levels of average signal strength, namely \(K3\) and \(K4\); as a result, the PDF exhibits a bimodal distribution. Intuitively, the OFDM symbol containing more subcarriers would have a relatively stronger signal strength. This assumption is validated experimentally in the next section.
## 6 Performance of Retrofitted Column against Vehicle Impact
### Performance of Column with RC Jacket  
A concrete jacket, 40 mm thick, is provided up to the 1.3 m height of columns C300 and C400, as seen in Figure 17b. The jacket contains an additional amount of longitudinal and shear reinforcement, as indicated in Figure 17b. A varying amount of longitudinal reinforcement is provided in the jacket to find out the optimum amount of reinforcement required for specific car impact velocities (Table 3). This treatment of the column increases the shear and flexural capacity of the column along with the overall stiffness of the column. The shear capacity (\(Vn\)) of the axially loaded RC section can be calculated relatively accurately by the formula given by ACI 318-11 [41], as follows:  
\[V_{n}=V_{c}+V_{s}=0.166\bigg{(}1+\frac{P}{13.8A_{g}}\bigg{)}bd\sqrt{f_{c}}+ \frac{A_{s}f_{y}d}{S} \tag{7}\]  
where \(f_{c}\) is the cylinder compressive strength (MPa) of concrete, \(A_{g}\) is the gross concrete area, \(P\) is the applied axial force (N), \(d\) is the depth of the Column section, \(b\) is the width of the column, \(A_{s}\) is the area of shear reinforcement, \(f_{y}\) is the yield strength of shear reinforcement, and \(S\) is the spacing of shear reinforcement. The shear capacity of the column with the RC jacket can be calculated using following Equation (8):  
\[V_{n}=0.166\bigg{(}1+\frac{P}{13.8A_{g}}\bigg{)}bd\sqrt{f_{c}}+\frac{A_{s}f_{ y}d}{S}+\frac{A_{sj}f_{y}d_{j}}{S_{j}} \tag{8}\]  
where \(A_{sj},f_{yj}\), and \(S_{j}\) are the area, yield strength, and spacing of the shear reinforcement in the jacket, respectively, and \(d_{j}\) is the depth of the RC jacket. The material strength can be increased to consider the strain rate effect, following the formula described in [43]. If the calculated shear capacity, considering the strain rate effect, is equal to or higher than the impact force generated during car impact, the column will not suffer any damage. The damage level can be defined by the damage index (DI), which is the ratio between impact force and shear capacity, as given by Equation (9). \[DI=\frac{\text{Peak}\;impact\;force\;(PIF)}{\text{Shear}\;capacity} \tag{9}\]  
Figure 18: Displacement history of AFRP-retrofitted column C300: (**a**) car impact velocity of 40 km/h; (**b**) car impact velocity of 50 km/h. The calculated static shear capacity, PIFs, and damage index (DI) for different columns are given in Table 4. It can be seen from Table 4 that columns C300 and C400 (without RC jackets) suffer permanent deformation at a car impact velocity of 40 and 60 km/h, respectively, where DI values are more than 1.5. However, when a 40-mm-thick RC jacket is provided with minimum shear reinforcement (\(\upd\)10@150 mm spacing), the column is safe for the above-mentioned car-impact velocity. In these cases, the DI values are less than 1.5.
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 2 Methods
### Machine Learning Modeling  
The selected features from hypothesis testing were employed to establish a dataset for machine learning modeling. The dataset was split into training and testing datasets with an 80/20 ratio to avoid overfitting or underfitting [25]. Figure 5 depicts the ML modeling approach performed using the training dataset and evaluated using the testing dataset.  
The training dataset was utilized to tune the hyperparameters of the machine learning model in order to decide which parameter values are best for machine learning modeling. The grid search cross-validation technique is one such method for hyperparameter tuning. Validation set methods, such as leave-one-out cross-validation (LOOCV), 10-fold cross-validation, and 5-fold cross-validation, are types of cross-validation methods. In this paper, 5-fold cross-validation was used in the proposed method, as it has proven to be the most efficient approach with the lowest computing cost and the best outcome [26]. The 5-fold cross-validation approach divided the training datasets into 5 folds, with 80% of the data (4 folds) used as a training set and 20% of the data (1 fold) used to validate the ML model's accuracy (80/20 ratio) [26]. This procedure was repeated five times with different validation data. Each validation iteration yielded an accuracy and an error value. The mean or average of the accuracy and error values was used to calculate the 5-fold cross-validation results. Fivefold cross-validation was performed in order to evaluate hyperparameter variations. More hyperparameters needed more iterations to obtain the grid search cross-validation results. We utilized the machine learning parameters that yielded the best validation accuracy to train the optimal model by utilizing the whole training dataset [26]. As a result, the testing dataset was used to determine the best ML models. The Logistic Regression (LR), k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) algorithms are the most widely utilized ML algorithms in fault diagnosis due to their high accuracy outcomes [5,20]. Especially when training samples are limited, RF has a significant advantage, and when compared to ANN and SVM, RF performs significantly better on noisy data [27].  
All optimal models developed from these algorithms were assessed using the confusion matrix technique, which compared the actual and predicted conditions of the RPM to determine which ML algorithm performed the best in fault diagnostics for RPM equipment. One of the parameters commonly used to evaluate ML models is accuracy, which is the comparative value between the number of correct predictions (both true positives \(TN\) and true negatives \(TF\)) and the total number of predictions (including true positives \(TP\), true negatives \(TN\), false positives \(FP\), and false negatives \(FN\)). The formula is described in Equation (1).  
\[Accuracy=\frac{TP+FP}{TP+TN+FP+FN} \tag{1}\]
## 3 Computational-Fluid-Dynamics Analysis  
Motions of the quadrotor depend on the variable speed of each rotor, which makes the aerodynamic performance design more challenging. It is also necessary to consider other factors such as the balance of the moment, and the stability of the crosswind. Moreover, the aerodynamic interference between the rotors is complex, and it is difficult to analyze the aerodynamic performance through flight test or wind tunnel test only. Therefore, the CFD simulation is our best choice to optimize the performance with a large number of combinations.  
In order to study the mutual interaction between the rotors, various combinations with different spacing (1 _d_-2 _d_) and tilt angle (0-50 deg) are numerically simulated with ANSYS. The N-S equation model is applied to analyze the characteristics of the external flow field [29]. Also, mesh refinement is performed on four rotor regions with a large gradient of physical field flow. According to the airfoil geometry, the Reynolds number is approximately \(1.1\times 10^{5}\), so the fluid is treated as incompressible. Additionally, the finite volume method is used to discrete the differential equations. Considering the low Reynolds number environment, the Spalart-Allmaras model is selected to obtain the flow field of the non-planar rotor pairs. The pressure correction is performed using the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm, and the pressure interpolation is selected in the standard format. Momentum, energy equation, and turbulent viscosity are all in the first-order upwind discrete format for the initial simulation, then the second-order upwind is applied in the final simulations. The rotor speed is set at 2200 rpm. At last, the sliding grid is used to deal with the interaction between the rotating and the stationary regions, and then the steady state calculation result of the Multiple Reference Frame (MRF) method is applied as the initial condition for the slip mesh transient calculation. The mesh distribution is showed in Figure 5.
# Intelligent Target Design Based on Complex Target Simulation
## 1 Introduction
Alternative approaches to investigate the internal structure of objects using wideband acoustics [14, 15] have shown some promise, but their performance does not enable rapid and effective detection of the target, and false alarm rates remain prohibitively high. The question of resolution has been raised again by the advent of very high-resolution side scan, forward-look, and Synthetic Aperture Radar (SAR) systems [16]. However, these systems require more data for calculations and the existing computer configuration cannot meet the requirements. Therefore, this paper uses the angle reflector and Luneburg ball to conduct array simulation, which overcomes the aforementioned problem. A target to be simulated not only needs to meet the RCS value requirement, but also needs to satisfy the distribution characteristics of the corresponding angle of the simulated target. For most of the currently available targets, the inability to simulate the corresponding RCS distribution characteristics is a key problem that needs to be solved urgently. A smart target design based on complex target simulation is proposed in this paper. This design is combined with a UAV by designing a corner reflector device that simulates the distribution characteristics with a sharp RCS, and a Luneburg ball reflector device that simulates the distribution characteristics of RCS in a stationary space. The simulation of a certain type of fighter aircraft can be realized as needed, such as F-22, F-35, J-20, and other fifth-generation fighter aircrafts. The intelligent expansion or combination of an angle reflector and a Luneburg ball is realized by Microcontroller Unit (MCU) or Field Programmable Gate Array (FPGA), and the corresponding simulation requirements are completed. The rest of this paper is organized as follows: Section 2 introduces the principle of angle reflector and Luneburg ball reflector. Section 3 presents the design scheme and simulation verification of the angle reflector and Luneburg ball. Section 4 introduces the experimental verification of a fighter according to the RCS distribution characteristics. The paper is concluded in Section 5.
## 3 Results
### Spatiotemporal Variation Characteristics of NPP in Yangtze River Delta
#### 3.1.2 Temporal Variation Characteristics of NPP  
As shown in Figure 4, the average NPP showed an oscillating upward trend, at a rate of 3.3 g C m\({}^{-2}\) a\({}^{-1}\), in the research region during 2000-2019, with a peak value of 609.84 g C m\({}^{-2}\) a\({}^{-1}\) in 2014, followed by a decrease and then an increase. According to the piecewise regression model [6], the year of 2009 was extracted as the turning point of the NPP trend. From 2000 to 2009, the average NPP increased by 46.84 g C m\({}^{-2}\) a\({}^{-1}\) in the Yangtze River Delta, and the annual growth rate of NPP was 4.68 g C m\({}^{-2}\) a\({}^{-1}\). From 2010-2019, the growth rate of NPP in the Yangtze River Delta slowed down to 3.55 g C m\({}^{-2}\) a\({}^{-1}\), with an average NPP growth of 35.5 g C m\({}^{-2}\) a\({}^{-1}\) over the 10-year period.  
In addition, the results of the Theil-Sen estimator for NPP, during the period of 2000-2019, indicate that the area of positive \(\beta\) values was significantly larger than the area of negative values. Specifically, 85.90% of the regions had increasing trends and 14.10% of the regions had decreasing trends, and the mean value of \(\beta\) was 1.63, which indicated that the NPP in the research region had been in an overall increasing trend in the past 20 years. We further performed the Mann-Kendall test on the NPP trends, and the results showed that there were significant spatial heterogeneity characteristics of NPP trends in the Yangtze River Delta (Figure 5). Among them, the percent of significant increase in the region accounted for 47.25%, and the percent of significant decrease was only 5.16%; no significant increase region accounted for 35.08%, and no significant decrease accounted for 12.51%. The percentage of regions with no change was extremely small and negligible. We observed that the significance of NPP in most of the research regions was in increasing trends (including significant increase and non-significant increase), and the significant decrease was sporadically distributed in the southern part of Zhejiang Province (e.g., Lishuicity, Wenzhou city, Taizhou city), southeastern Jiangsu Province (e.g., Suzhou city, Nan-tong city), and Shanghai city.
## 3 Case Study
### Finite-Element Model and Material Parameters  
A two-dimensional finite element model with the dam-foundation system is shown in Figure 1a. The width to the upstream or downstream and the depth of the foundation is two times of the dam height. The X-axis is pointing downstream, and the Y-axis is vertically upward. Rigid constraints were imposed on the three truncated boundaries of the foundation, and a massless foundation was adopted in the model. The upstream elevation of the dam foundation interface is 1190 m while the downstream elevation is 1180 m, and the corresponding dam heights are 144 m and 154 m, respectively. Except for the crest and foundation surface with normal concrete of C\({}_{\text{I}}\) and C\({}_{\text{II}}\), the rest of the dam is poured with R\({}_{\text{I}}\)-R\({}_{\text{V}}\) RCC (as seen in Figure 1b). The dynamic elastic modulus and dynamic strength are increased by 30% on the basis of the corresponding static parameters. The foundation is mainly composed of grade III\({}_{1}\) and grade III\({}_{2}\) rocks and contains a small amount of grade II, grade III\({}_{3}\), and grade IV rocks. The fracture and disturbed belt are N40\({}^{\circ}\)-50\({}^{\circ}\) E/NW\(\angle\)15\({}^{\circ}\)-20\({}^{\circ}\)(A), N30\({}^{\circ}\)-40\({}^{\circ}\)E/SE\(\angle\)20\({}^{\circ}\)-25\({}^{\circ}\)(B), N10\({}^{\circ}\)-35\({}^{\circ}\)W/NE\(\angle\)15\({}^{\circ}\)-20\({}^{\circ}\)(C) and N35\({}^{\circ}\) E/NW\(\angle\)25\({}^{\circ}\)-30\({}^{\circ}\)(D).  
Related material parameters are shown in Tables 1 and 2. The modulus and strength of the dam were increased by 30% to account for the effect of the strain rate. The Rayleigh damping method can be expressed as [30]:  
\[\mathbf{C}=\alpha\mathbf{M}+\beta\mathbf{K} \tag{18}\]  
Figure 1: Finite element model of Guandi concrete gravity dam: (**a**) dam-foundation system; (**b**) materials of the dam  
where \(\mathbf{M}\) is the mass matrix, \(\mathbf{K}\) the stiffness matrix, \(\alpha\) and \(\beta\) are the damping coefficients, and can be defined as,  
\[\alpha=\frac{2f_{1}f_{2}\xi}{f_{1}+f_{2}},\;\beta=\frac{2\xi}{f_{1}+f_{2}} \tag{19}\]  
where \(f_{1}\) is the first natural frequency, \(f_{2}\) is the second natural frequency, the damping ratio \(\xi\) is 5%.