esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study Case and Method  
### Taimali Landslide  
On 9 August 2009, at 17:31 local time (UTC + 8), a large-scale landslide took place in the upstream area of the Taimali River in southeastern Taiwan (120.81\({}^{\circ}\) E, 22.55\({}^{\circ}\) N) (Figure 1). The main trigger of the landslide was a violent rainstorm brought by Typhoon Morakot, which induced cumulative rainfall exceeding 2000 mm in three days [19]. Despite the numerous landslides of the preceding decades, the Taimali landslide was one of the largest landslides ever recorded in Taiwan. It generated clear and adequate seismic signals for the seismic and waveform inversion analyses.  
The Taimali landslide is located on the Eocene Bilusan Formation composed of phyllite and slate with sandstone interpbeds [20]. Folds and cleavages are also well developed in this region and hence lead to the fractured geologic condition of the Taimali landslide. According to the Central Geological Survey, [21], a region-scale anticlinorium lies to the west of the Taimali landslide and is seven kilometers from the landslide. The source area of the Taimali landslide was on a dip slope and prone to collapse during heavy rainfall.  
Pre- and post-event digital elevation models (DEMs) with resolutions of five meters were used to map the disturbed area of the landslide and to calculate the variance in topography (Figure 2). The main source area (Zone 1) was about \(9.3\times 10^{5}\) m\({}^{2}\), and its total volume was about \(7.5\times 10^{7}\) m\({}^{3}\). The maximum depth, roughly at the center of the landslide block, was 220 m. Based on the variance in topography, two deposit areas (Zone 2 and 3) could be recognized. The thickness of the landslide deposit in Zone 2 was thicker than that in Zone 3 (Table 1). This difference indicated that Zone 2 might be the main deposit area for this event. A relatively smaller landslide (\(3\times 10^{5}\) m\({}^{2}\)) occurred and provided some amount of debris to the deposit area (Zone 2 and Zone 3). Because the volume of the smaller source was not considered, the sum of the volume of the two major deposit areas is greater than the volume of the only major source area. Based on the average density of 2700 kg/m\({}^{3}\) for phyllite, the mass of the Taimali landslide was estimated at \(2\times 10^{11}\) kg, and therefore it generated high-energy seismic signals.  
Figure 1: The location of the study area. (**a**) The location of the Taimali landslide and the distribution of 15 seismic stations adopted in the waveform inversion. (**b**) The post-event satellite image of the Taimali landslide. The blue line denotes the boundary of the source area.
## 5 Publicly Available Datasets and Benchmarks
### UIIUC Sports-8  
The UIUC dataset contains 1572 color images distributed over eight distinct categories covering various scenes of sports events [81], with 130 to 250 images per category. These are high-resolution images, i.e., from 800 \(\times\) 600 to thousands of pixels per dimension; 70 images are randomly sampled for training with the remaining images used for testing each category.
## 5 Conclusions  
The effect of DBD plasma on the film cooling effectiveness of a 30-degree slot was experimentally investigated. The PSP technique was used to measure the film cooling effectiveness. Two input voltages (6 and 7 kV), two blowing ratios (0.5 and 1.0), and two exposed electrode locations were considered. Based on the measured results, the following conclusions were reached:  
1. Regardless of plasma operation, the film cooling effectiveness of the slot was higher for the 1.0 blowing ratio case than for the 0.5 blowing ratio case.
2. The case with the higher input voltage (7 kV) showed greater film cooling effectiveness than the case with the lower input voltage (6 kV).
3. The improvement of the film cooling effectiveness due to the DBD plasma was more significant when the coolant had less momentum, i.e., the case with the lower blowing ratio case or the cases with the exposed electrode located downstream from the slot.  
In this study, the enhancement of the film cooling effectiveness using DBD plasma was observed. However, the improvement was not significant. For further improvement in film cooling effectiveness, the DBD plasma-induced flow and the dragging down of the coolant must be increased. A follow-up study aimed at optimizing the configuration of the DBD plasma actuator is currently in progress, and the results will be presented in the near future.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. NRF-2015R1A2A2A0100236), and the fundamental research of the Defense Acquisition Program Administration and Agency for Defense Development (2015-05-042).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Compressed Earth Blocks Characterisation
#### 2.3.2 Thermal Resistance, Conductivity, and Diffusivity  
The thermal resistance was measured under steady-state conditions using the guarded hot box method, according to ASTM C1363-11:2011 [26] and ISO 9869-1:2014 [27]. Each block was placed between two sensors in a temperature-controlled enclosure, while a known temperature difference was applied between the two block faces. The sensors measured the heat flux passing through, and the R-value is estimated through Equation (4), Table 3. Steady-state conditions are assumed to be verified when the percentage change in heat flux throughout the sample is \(\leq\)5% at 24-h intervals and after a minimum of 72 h of testing. A more detailed description of the test setup and method can be found in [28]. Thermal conductivity and diffusivity were then deduced analytically according to Equations (5) and (6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Equations** & **Units** & **Symbols** \\ \hline Bulk density & \(\gamma\) = \(m/V\) & (1) & [kg/m\({}^{3}\)] & \(m\) and \(V\) are the mass and the volume of the block \\ Open porosity & \(\varphi\) = \(I-(\gamma_{d}/\gamma_{s})\) & (2) & [\%] & \(\gamma_{d}\) is the dry bulk density, \(\gamma_{s}\) and is the soil particle \\ Natural moisture content & \(\omega\) = \((\gamma-\gamma_{d})/\gamma\) & (3) & [\%] & \(\gamma\) is the bulk density at the natural moisture \\  & & & & content, \(\gamma_{d}\) is the dry bulk density \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical attributes considered.  
Figure 3: Freshly compressed earth blocks (R1 type).
## 4 Performance Analysis
### Analysis of Measured Magnetic Target Signals  
The data used in this paper were collected from the on-site monitoring results of a highway near Tangxun Lake in Wuhan City. This section has fewer vehicles, street lamps, and residential areas. It is identified as a complex noise environment by the detection. In this experiment, the target-induced disturbance was detected by an inductive magnetic sensor, and the appearance of the target (i.e., the vehicle) was calibrated by a fluxgate sensor's geomagnetic measurements. The positions of the target and the magnetometer are shown in the schematic diagram, shown in Figure 5. The induction magnetometer  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mother Wavelet** & **SNR = 0 db** & **SNR = 5 db** & **SNR = 10 db** & **SNR = 15 db** \\ \hline Gauss & 3.2458 & 3.1455 & 2.9525 & 2.8574 \\ Cmhat & 3.1192 & 3.0858 & 2.6851 & 2.4790 \\ Morlet & 2.9851 & 2.8913 & 2.3524 & 2.1724 \\ Bump & 2.8939 & 2.7960 & 1.9572 & 1.6572 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Rayleigh entropy when SSWT is applied with different mother wavelets.  
Figure 4: Comparison of contour lines of STFT, CWT, and SSWT. (**a–c**) are time frequency contour plots of STFT, CWT and SSWT respectively.  
for the measurement of the alternating magnetic field and the calibration for the fluxgate magnetometer were placed in parallel at 20 cm.  
The vehicle was traveling at a uniform speed and vertically passed the CPA point along the connecting line from the magnetometer to the CPA point, and the average passing speed of the target was 5 km/s.  
The 50 Hz power frequency magnetic disturbance signal collected by the induction magnetometer used in this paper when the target (i.e., the vehicle) was passing the CPA point is shown in Figure 6:  
Figure 5: Schematic diagram of relative position between sensor and target track.  
Figure 6: 50 Hz signal with target from 3\(-\)axis inductive sensor.
## 3 Quadcopter Control  
### Controller Framework  
A quadcopter is an underactuated system, which means that six degrees of freedom in space are controlled by just four motors. Hence, controllers in such vehicles must be designed for a subset of four degrees of freedom. Furthermore, the fact must be taken into account that the control of the x and y positions in space is influenced by changes in the pitch and roll angles. Having considered the aforementioned relationships, the control of a quadrotor is normally designed for two independent subsets of coordinates. The necessity for a swashplate mechanism is eliminated with four separate rotors. The swashplate mechanism was necessary to give the helicopter more degrees of freedom, but the same level of control can be achieved by simply adding two more rotors, as implemented in the structure of quadcopters. Despite the fact that the command is for three position coordinates (x, y, z) plus yaw angle, the control algorithm employs both roll and pitch orientation controllers. In the inertial coordinate system, the control signals of three position controllers define a force vector (thrust). The setpoints (\(u1_{x},u1_{y}\)) transmitted to the roll and pitch controls are considered as the orientation of the vector. The stated architecture, as well as the elements of our proposed controllers, are depicted in Figure 2, which will be explored in the next sections.  
The altitude controller and the attitude controller are the two main parts of the control architecture. The altitude controller, as shown in Figure 2, maintains the altitude of the aerial robot at the required level. In most commercial aerial robots, the altitude controller is a PID controller with fixed control gain values. In this research, a proposed control architecture consisting of a MPC controller and a gravity compensator is proposed as a replacement for conventional PID controllers. The scaling factor of the compensator is adaptively adjusted during the operation of the robot, using actions generated by the reinforcement learning agent. The robot attitude controller is the second major controlling component of the system. The controller is made up of two distinct PID controller blocks. The difference between the desired x and y position and the actual x and y location in space is measured and defined as the position error in 2D. The x-position and y-position PID controllers in the outer loop were designed to minimize the error. The control commands of the position controllers are transformed to appropriate roll and pitch setpoints. The inner loop PID controllers use the resulting roll and pitch setpoints as reference inputs.  
Figure 2: The elements of the proposed control architecture of the quadcopter.  
The control gain values of the inner loop PID controllers are constant in our proposed control architecture, whereas the control gain values of the outer loop PID controllers are adaptively adjusted using trained policy from the RL-based adaptation algorithm.
## 3 Evaluation and Application of PWV Derived from CPRAP
### Application of CPRAP-Derived PWV
#### 3.2.2 Application of CPRAP for Rainfall Monitoring  
Apart from drought monitoring, the established CPRAP is also applied for rainfall monitoring. Previous studies have proven that the atmospheric water vapor changes sharply before precipitation [56], which provides useful indicator for rainfall monitoring and forecasting. GNSS-PWV time series have been widely used in heavy precipitation prediction [57]. Therefore, one GNSS stations (ZJXC, ZHOS) in Zhejiang province was selected to investigate the relationship between PWV and rainfall. This station was determined because it is near the ocean and the evident water vapor change can be observed during the rainfall period. The hourly PWV derived from the CPRAP was first obtained at the ZJXC and ZHOS station and the corresponding rainfall was also obtained from the collocated meteorological station. Figures 10(a) and 10(a) gives the time series of PWV and rainfall over the period of 19 February to 10 March 2015, Figure 10(b),c provide the hourly PWV and rainfall on 26 February and 7 March respectively. Figure 10(b),c provide the hourly PWV and rainfall on 26 February and 7 March, respectively. It can be observed that the PWV showed an increasing trend several hours before the rainfall event and sharply decreased after it. The statistical results also reveal that the PWV increment before the rainfall lasted a few hours and reached more than 20 mm. Therefore, such evident PWV change before rainfall is a very important signal for rainfall forecasting, and the potential of using PWV estimates obtained from the established CPRAP for rainfall monitoring and forecasting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stations** & \(\mathbf{SPCI_{ERA5}}\) & \(\mathbf{SPCI_{GNSS}}\) \\ \hline Mean. & 0.875 & 0.865 \\ HLFY & 0.88 & 0.86 \\ HNMY & 0.90 & 0.90 \\ JLYJ & 0.88 & 0.87 \\ JLCL & 0.84 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Correlation coefficients between SPEI and \(\mathbf{SPCI_{ERA5}}\)/\(\mathbf{SPCI_{GNSS}}\) at HLFY, HNMY, JLYJ and JLCL stations (\(p<0.05\)) over the period of 2012 to 2018.  
Figure 10: Long-term change of SPEI, \(\mathbf{SPCI_{ERA5}}\) and \(\mathbf{SPCI_{GNSS}}\) at HLFY, HNMY, JLYJ and JLCL stations, respectively over the period of 2012 to 2018.
## 3 Results and Discussion
### Model Comparison Analysis
The Grad-CAM method proved to be an effective means of evaluating the reliability of the model, due to the distinctive features it exhibited. Figure 11: Improved YOLOv5s model: (**a**) _mAP50_ growth curve, (**b**) _Precision_ growth curve, and (**c**) _Recall_ growth curve in 1000 epochs. Figure 13 illustrates the detection results of the YOLOv5s-ShuffleNet-CBAM model for different degrees of maize Fusarium infection. It is evident that the model exhibits high precision in identifying healthy maize, and it is also capable of detecting minor fungal lesions that are often overlooked by human observers. Figure 12: Grad-CAM analysis: (**a–d**) “Sound”, (**e–h**) “Mild”, and (**i–l**) “Severe”. Figure 13: The detection of mold in maize: (0) “Sound”, (1) “Mild”, and (2) “Severe”.
## 3 Mathematical Modeling
### Linearization of the Model  
When optimizing a timetable, the train traffic dynamic model and passenger flow dynamic model are linearized to build a MILP model. From Equations (1) and (4)-(25), it can be found that Equations (10), (17), (22) and (23) are nonlinear constraints.  
Equation (10) can be rewritten in the following nonlinear constraint form:  
\[t_{i,s}^{a}-t_{i-1,s}^{d}\geq A_{i}^{s}\cdot A_{i-1}^{s}\cdot(I_{da}-I_{ta}-I_{dt}) +A_{i}^{s}\cdot I_{ta}+A_{i-1}^{s}\cdot I_{dt},\;\forall i\in I\backslash\{1\}, \forall s\in S \tag{26}\]  
Introducing a binary variable \(y_{i,i-1}^{s}=A_{i}^{s}\cdot A_{i-1}^{s}\) transforms Equation (26) into Equation (27):  
\[\left\{\begin{array}{l}y_{i,i-1}^{s}\leq A_{i}^{s},y_{i,i-1}^{s}\leq A_{i-1} ^{s}\\ y_{i,i-1}^{s}\geq A_{i}^{s}+A_{i-1}^{s}-1\\ t_{i,s}^{a}-t_{i-1,s}^{a}\geq y_{i,i-1}^{s}\cdot(I_{da}-I_{ta}-I_{dt})+A_{i}^{s }\cdot I_{ta}+A_{i-1}^{s}\cdot I_{dt}\end{array}\right. \tag{27}\]  
Similarly, the nonlinear Equation (17) can be transformed into the following linear constraint form:  
\[\left\{\begin{array}{l}0\leq Q_{i,sk}^{anv}\leq Q_{i,sk}^{all,w}\\ Q_{i,sk}^{anv}\leq A_{i}^{s}\cdot M\\ Q_{i,sk}^{anv}\geq Q_{i,sk}^{all,w}-(2-A_{i}^{k}-A_{i}^{s})\cdot M\end{array}\right. \tag{28}\]  
The total number of passengers boarding the train \(i\) at station \(s\) is calculated by Equation (22), and the binary variable \(\mu_{i,s}\) is introduced to represent the relative size relationship between \(Q_{i,s}^{r}\) and \(Q_{i,s}^{anv}\). If \(Q_{i,s}^{r}>Q_{i,s}^{anv}\), then \(\mu_{i,s}\) is equal to \(1\). Otherwise, \(\mu_{i,s}\) is equal to \(0\).  
\[\left\{\begin{array}{l}Q_{i,s}^{board}\leq Q_{i,s}^{anv}\\ Q_{i,s}^{board}\leq Q_{i,s}^{r}\\ Q_{i,s}^{board}\geq Q_{i,s}^{anv}-(1-\mu_{i,s})\cdot M\\ Q_{i,s}^{board}\geq Q_{i,s}^{anv}-\mu_{i,s}\cdot M\\ \mu_{i,s}\geq\frac{Q_{i,s}^{anv}-Q_{i,s}^{anv}}{M}\\ \mu_{i,s}\leq 1+\frac{Q_{i,s}^{anv}-Q_{i,s}^{anv}}{M}\\ \mu_{i,s}\in\{0,1\}\end{array}\right. \tag{29}\]  
Equation (23) can be converted to the following linear constraint form:  
\[\left\{\begin{array}{l}0\leq Q_{i,sk}^{board}\leq Q_{i,sk}^{anv}\\ Q_{i,sk}^{board}\geq Q_{i,sk}^{anv}-\delta_{i,sk}\cdot M\\ Q_{i,sk}^{board}\geq Q_{i,s}^{board}\cdot\beta_{i,sk}-(1-\delta_{i,sk})\cdot M \\ \delta_{i,sk}\geq\frac{Q_{i,sk}^{anv}-Q_{i,sk}^{board}\cdot\mathcal{J}_{i,sk}} {M}\\ \delta_{i,sk}\leq 1+\frac{Q_{i,sk}^{anv}-Q_{i,sk}^{band}\cdot\mathcal{J}_{i,sk}} {M}\\ \mathcal{J}_{i,sk}=Q_{i,sk}^{STD\_{anv}}/Q_{i,s}^{STD\_{anv}}\\ \delta_{i,sk}\in\{0,1\}\end{array}\right. \tag{30}\]  
where \(\mathcal{J}_{i,sk}\) is calculated when the train adopts the all-stop behavior as well as a fixed headway, dwell time, and running time between stations, i.e.,\(\mathcal{J}_{i,sk}=Q_{i,sk}^{STD\_{anv}}/Q_{i,s}^{STD\_{anv}}\), \(Q_{i,sk}^{STD\_{anv}}\) is the valid number of passengers waiting at station \(s\) who are eager to travel to station \(k\) under the all-stop behavior, and \(Q_{i,s}^{STD\_{anv}}\) is the total valid number of passengers waiting at station \(s\) under the all-stop behavior.
## 5 Concluding Remarks  
Damage from aged underground utilities in metropolitan areas was found to be the primary cause of ground subsidence in this study. The present paper developed a risk model to assess the probability of ground subsidence along railways surrounding the Seoul area of the Republic of Korea. The following conclusions and perspectives can be obtained.  
1. Between the two approaches (i.e., SVM and MLP) employed in this study to develop a risk model, the MLP approach was found to be more efficient by improving the classification accuracy of the presence of ground subsidence.
2. The number of hidden nodes played a significant role in determining overall accuracy of the risk model. Considering the number of input data for water and wastewater pipes (564 for water and 929 for wastewater pipes), it was deemed appropriate to have more hidden nodes than inputs to ensure the reliability of the risk model. A previous study recommended that the number of hidden nodes be 1.5 times the number of parameters in the input layer [17].
3. A series of field experimental programs using GPR, ER survey, and PCP tests exhibited that the correlations between measurements seemed to be promising in the assessment of the risk index with respect to ground subsidence in spite of limited field validations. It is strongly recommended that further investigation needs to be done to verify this finding in the near future. Ground penetrating radar survey is useful to visually detect underground utilities and underground homogeneity. Electrical resistance survey gives the variation of electrical resistance, which can be  
Figure 9: Relationship between field measurements; (**a**) \(N\)-value versus average electrical resistivity and (**b**) \(N\)-value versus risk index.  
quantified for the tested section. Pneumatic cone penetration testing provides bearing capacity of tested ground, which is associated with risk index and ER values. Consequently, the use of hazard map systems in conjunction with field investigation is recommended as a proactive approach to mitigate the progress of ground subsidence along railways.  
The authors confirm contributions to the paper as follows: Conceptualization, J.O. and H.L; Methodology, J.O. and H.L; Risk model, H.L.; Numerical analysis, J.O.; Field Investigation, J.O.; Writing-Original Draft Preparation, J.O. and H.L.; Writing-Review & Editing, J.O.; Supervision, J.O.; Project Administration, J.O.; Funding Acquisition, J.O.  
This research was funded by the Korean Rail Network Authority, grant number 2015-2-203-201501-03.  
The authors would like to thank the Korea Rail Network Authority for their financial and technical support.  
The authors declare no conflict of interest.
## 4 Discussion  
Runoff has an important influence on the drainage coefficient, whether the drainage coefficient is calculated using the average draining formula or the empirical formula. The difference is that the drainage coefficient calculated by the empirical formula varies with the change of catchment area. The empirical formula considers the impact of runoff, topography, and catchment shape on the waterlogged area, so it requires higher timeliness of parameters and the calculation results are more realistic. The drainage duration has a significant impact on the drainage coefficient calculated by the average draining formula. In our research, the channel storage coefficient was added to the average draining formula, which reflected the effect of area on the drainage coefficient.  
Whether the empirical formula or the average draining formula is used, the related parameters should be modified, such as the parameters of the empirical formula, the paddy field stagnant water depth in the average draining formula, cultivated land infiltration coefficient, etc. Further study is needed in the future because our research used the regional empirical parameters directly. These two methods do not systematically evaluate the impact of underlying surface changes on drainage flow, nor do they consider the flow change during a drainage process. A model will be used to link the discharge coefficient with the peak of the hydrograph.  
In our research, it was assumed that the size of the waterlogged area has an impact on the drainage coefficient calculated by the average draining formula. This assumption was used to calculate F\({}_{\rm A}\) for different rainfall durations and return periods. In reality, the area size has an impact on the catchment's runoff, which will change the drainage coefficient. Therefore, when the results in this paper are applied to other areas, the F\({}_{\rm A}\) needs to be further confirmed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**1-Day Rainfall**} & \multicolumn{3}{c}{**3-Day Rainfall**} \\ \cline{2-7}  & **10-Year** & **5-Year** & **3-Year** & **10-Year** & **5-Year** & **3-Year** \\ \hline
1991 & 203 & 897 & 2029 & 5408 & 12,077 & 24,646 \\
1995 & 201 & 887 & 2008 & 5353 & 11,957 & 24,407 \\
2009 & 199 & 874 & 1967 & 5426 & 12,188 & 27,762 \\
2010 & 199 & 873 & 1966 & 5429 & 12,196 & 27,789 \\
2014 & 197 & 860 & 1928 & 5502 & 13,617 & 43,612 \\
2015 & 196 & 857 & 1919 & 5521 & 13,686 & 49,080 \\
2016 & 194 & 845 & 1890 & 5518 & 13,724 & 55,405 \\
2018 & 173 & 734 & 1821 & 5482 & 15,034 & 55,883 \\ Annual change rate & \(-\)1.11 & \(-\)6.04 & \(-\)7.70 & 2.74 & 109.52 & 1156.93 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The area corresponding to the average draining formula under 1-day and 3-day rainfall with different return periods (\(F_{A}\), in km\({}^{2}\)).