esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Seismic Performance Evaluation
### Demand/Capacity Evaluation  
The Demand/Capacity Ratios (DCR) from the response spectrum (RSA), equivalent static method (ESA) and nonlinear time history (NTHA) analyses of the model bridge subjected to near- and far-field ground motions are presented in Tables 6 and 7. The results show that the longer piers tend to be more vulnerable to nonlinear interactions between the axial forces and the biaxial bending moment. Maximum axial-bending DCR values of up to 0.92 and 0.96 were found for pier P3 when the bridge was subjected to the Kobe-Takatori and Northbridge-Downey ground motions, respectively, thus representing both near-field and far-field earthquakes. In addition, the DCR values of less than one and the minor differences between the DCR values of the piers obtained from various analyses (ESA, RSA and NTHA) confirm that sufficient ductility, strength, geometries and reinforcement have been provided in the structure designed for this study. It is also possible to conclude that near-field ground motion tends to result in higher DCR values than far-field earthquakes, with the difference between the two being as much as 10%. There is a direct relationship between the PGA and DCR of a bridge under near-field ground motion; consequently, in most cases an earthquake with a higher PGA also is likely to result in larger DCR values. The same relationship does not appear to hold for far-field motions, however. To better understand the DCR distribution for RC arch bridges, Figure 13 presents the DCR for pier and arch ribs of a bridge subjected to the Erzican earthquake. This suggests that nonlinear interactions between axial forces, in-plane and out-of-plane bending for longer piers are more critical. Here, the maximum DCR values obtained for arch ribs were around 0.350, but these are not shown due to space limitations.  
Figure 12: Distribution of the average internal forces in the RC arch due to near-field earthquakes. (**a**) Axial and shear forces; (**b**) Bending moments.  
The fraction of the input energy dissipated by mechanisms such as modal damping and hysteretic dissipation is considered a useful indicator of the damage level of a structure [35]. The percentage of the input energy lost to modal damping and hysteretic energy dissipated is shown in Figure 13. Here, approximately 53% and 57% of the input energy have been dissipated through modal damping when the bridge is subjected to near-field and far-field earthquakes, respectively, associated with the Imperial Valley at EC Country Center and Northbridge at La Puente station.
## 2 General Situation of the Engineering and Analysis of In Situ Stress
The three test points are, respectively, the middle parking lot of the first mining area in the east, the transportation roadway of the third mining area in the middle, and the main west transportation roadway. The measurement point locations are depicted in Figure 4.  
The in situ stress values at the three measurement points were obtained from the data of the hollow inclusion stress meter. The specific results are presented in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stress Component**} & **No.** & **1** & **2** & **3** \\ \cline{2-5}  & **H/m** & **743.6** & **839.3** & **844.2** \\ \hline \multirow{2}{*}{Maximum principal stress (\(\sigma_{1}\))} & Magnitude/MPa & 24.1 & 47.0 & 54.1 \\  & Dip Direction/\({}^{\circ}\) & 265.7 & 106.6 & 95.6 \\  & Dip Angle/\({}^{\circ}\) & 9.37 & 1.4 & \(-\)3.5 \\ \hline \multirow{2}{*}{Intermediate Principal Stress (\(\sigma_{2}\))} & Magnitude/MPa & 13.8 & 22.4 & 27.8 \\  & Dip Direction/\({}^{\circ}\) & 15.9 & \(-\)17.0 & 1.2 \\  & Dip Angle/\({}^{\circ}\) & 64.4 & 87.5 & \(-\)51.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stress magnitude and direction at monitoring points. Figure 4: Layout of initial rock stress monitoring points. (a) Point 1. (b) Point 2. (c) Point 3. After analyzing the magnitudes and directions of the stresses at each point in Table 1, it can be discovered that in situ stress in Ronghua No. 1 Mine exhibits the following laws:  
1. Angles of the \(\sigma_{1}\) of the three points are 265.72\({}^{\circ}\), 106.57\({}^{\circ}\), and 95.62\({}^{\circ}\), respectively. As the mining area is relatively large, point 1 is located on the east side of the mining area, and points 2 and 3 are located on the west side. Consequently, the maximum horizontal principal stress (\(\sigma_{\rm H}\)) in the east side of the mining area is inclined to be nearly north-south, while the \(\sigma_{\rm H}\) in the west side of the mining area is generally inclined to be south-north. 2. The \(\sigma_{1}\) is predominantly concentrated in the horizontal direction, varying approximately between 1.81 and 2.14 times the magnitude of the vertical stress, indicating that the in situ stress field within this region is predominantly influenced by horizontal tectonic stresses. 3. The \(\sigma_{1}\) of the three measurement points, ranked in ascending order, are as follows: point 1 is 24.1 MPa; point 2 is 47.0 MPa; and point 3 is 54.1 MPa. The \(\sigma_{\rm v}\), ranked in ascending order, are as follows: point 1 is 13.3 MPa; point 2 is 22.4 MPa; and point 3 is 25.3 MPa. The \(\sigma_{3}\), ranked in ascending order, are as follows: point 1 is 8.8 MPa; point 2 is 14.9 MPa; and point 3 is 21.2 MPa. 4. In the depth range of the three measuring points, the stress gradients reveal values of 0.03 MPa/m for the \(\sigma_{\rm H}\), 0.012 MPa/m for the \(\sigma_{\rm V}\), and 0.012 MPa/m for the \(\sigma_{\rm h}\).
# Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
## 3 Results
### Volume Calculation Results
The same lack of occlusions is visible in the CHC algorithm results (Figure 9b), where it can be observed that the cluster segmented in the arms is thinner than the cluster segmented in the trunk. In both cases, the TLS point cloud underestimates the exterior surface of the VSAO and, consequently, the volume that these two algorithms estimate. In the same manner, the occlusion of the lower arms is also noticed in the point cloud captured with the MMS equipment (Figure 10a), affecting both the position of the skeleton (not centered) and the determination of the radius of the segmented cylinders (lower than the real value). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scanner**} & **Volume Calculation** & **VSAO 1 A** & **VSAO B** & **Absolute Error** & **Relative Error** \\  & **Algorithm** & **(dm3)** & **(dm3)** & **(dm3)** & **(\%)** \\ \hline TLS 2 & OctoMap & 2.329 & 1.666 & 0.793 & 28.41 \\ TLS & Convex hull cluster & 2.807 & 2.450 & 0.179 & 6.40 \\ MMS 3 & VCTS 4  & segment median & 2.868 & 2.341 & 0.264 & 9.44 \\ MMS & VCTS segment mean & 2.863 & 2.665 & 0.099 & 3.55 \\ MMS & VCTS line string median & 2.843 & 2.128 & 0.358 & 12.81 \\ MMS & VCTS line string mean & 2.916 & 2.779 & 0.069 & 2.46 \\  & Real volume & 2.790 & 2.790 & & \\ \hline \hline \end{tabular}  
* Vine-shaped artificial object. 2 Terrestrial laser scanner. 3 Mobile mapping system. 4 Volume calculation from the trunk skeleton.  
\end{table} 
Table 10: Volume calculation results and errors committed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**OctoMap Parameters** & **TLS 1 Test Area** & **MMS 2 Test Area** & **MMS Real Case** \\ \hline Class to use & 13 & - & - \\ Compute free voxels & False & - & - \\ Voxel resolution & 0.01 & - & - \\ \hline
**CHC 3 Parameters** & **TLS Test Area** & **MMS Test Area** & **MMS Real Case** \\ \hline Class to use & 13 & - & - \\ Color weight & 0 & - & - \\ Normal weight & 1 & - & - \\ Spatial weight & 1 & - & - \\ Seed resolution & 0.15 & - & - \\ Voxel resolution & 0.01 & - & - \\ \hline
**VCTS 4 Parameters** & **TLS Test Area** & **MMS Test Area** & **MMS Real Case** \\ \hline Class to use & - & 13 & 13 \\ Minimum outlier threshold & - & 0.01 & 0.01 \\ \hline \hline \end{tabular}  
* Terrestrial laser scanner. 2 Mobile mapping system. 3 Convex hull cluster. 4 Volume calculation from the trunk skeleton.  
\end{table} 
Table 9: List of the utilized parameters in the volume calculation algorithms.
## 6 Conclusions  
In this paper, we proposed three RNN-based hybrid models, namely CNN-LSTM, GRU-CNN, and ensemble models, to make one-time-step and multi-time-step predictions of the closing price of three stock market indices in different financial markets. We evaluated and compared the performance of the proposed models with conventional benchmarks (i.e., RNN, LSTM, GRU, and WaveNet) over three different periods: a long period of more than 15 years and two short periods of three years before and after the COVID-19 pandemic. The proposed models significantly outperformed the benchmark models by achieving high predictive performance for various sizes of look-back and look-ahead periods in terms of MSE and MAE. Moreover, we found that the proposed ensemble model was comparable to the GRU, which performed well among benchmarks and outperformed the benchmarks in many cases.  
Additionally, we introduced a novel feature, medium, which is the average of high and low prices, and evaluated the performance of the proposed models with four different features and two different optimizers. The results indicated that incorporating the novel feature improved model performance. Overall, our experiments verified that the proposed models outperformed the benchmark models in many cases and that incorporating the medium feature improved their performance.  
Conceptualization, H.S. and H.C.; methodology, H.S. and H.C.; software, H.S. and H.C.; validation, H.S. and H.C.; formal analysis, H.S. and H.C.; writing--original draft preparation, H.S. and H.C.; writing--review and editing, H.S.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT, Korea) (No. NRF-2020R1G1A1A01006808).  
Not applicable.  
Data Availability StatementData used in this study was obtained using the FinanceDataReader open-source library.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
Adam Adaptive Moment Estimation  
ANN Artificial Neural Network  
ARIMA Autoregressive Integrated Moving Average  
ARMA Autoregressive and Moving Average  
CNN Convolutional Neural Network  
DAX Deutscher Aktiennidex  
DOW Dow Jones Industrial Average  
GAN Generative Adversarial Network  
GRU Gated Recurrent Unit  
LSTM Long Short Term Memory  
MAE Mean Absolute Error  
MLP Multilayer Perceptron
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 7 Evaluation Platforms and Metrics  
In order to execute a cloud detector \(f(\cdot\,;\,\theta)\) on the XE1 (introduced in Section 6.2), the model format must be converted to the Ubotica neural network (UNN) format. This conversion involves 3 steps:  
1. Export the model to the ONNX format.
2. Convert the generated ONNX files to the OpenVINO Intermediate Representation format.
3. Convert the OpenVINO Intermediate Representation to the Ubotica Neural Network (UNN) format.  
Note that Step 2 also quantises \(\theta\) from FP32 to FP16, which is required to run inference on the Intel Myriad 2 VPU. The inference performance of \(f(\cdot\,;\,\theta)\) on a dataset \(\mathcal{D}=\{x_{m},y_{m}\}_{m=1}^{M}\) is evaluated based on two metrics:  
* Accuracy (ACC) of \(f(\cdot\,;\,\theta)\), \[\text{ACC}\triangleq\frac{1}{M}\sum_{m=1}^{M}\mathbb{I}(\arg\max\hat{y}_{m}= y_{m})\times 100\%,\] (13) where \(\mathbb{I}(\cdot)\) is the indicator function, and \(\hat{y}_{m}=f(x_{m}\,;\,\theta)\). The higher the test accuracy means the higher \(f(\cdot\,;\,\theta)\) predicts the correct class label. This helps in increasing the quality of each prediction, especially in challenging situations, e.g., clouds on ice or clouds on salt-lake.
* False positive (FP) rate of \(f(\cdot\,;\,\theta)\), \[\text{FP}\triangleq\frac{1}{M}\sum_{m=1}^{M}\mathbb{I}(\arg\max\hat{y}_{m}=1 \wedge y_{m}=0)\times 100\%.\] (14) The lower the FP rate means the less \(f(\cdot\,;\,\theta)\) incorrectly predicts non-cloudy data cubes as cloudy, which helps avoid discarding clear-sky data cubes.  
False negatives refer to the cases where cloudy images are misclassified as non-cloudy, resulting in downlinking cloudy images. As false negatives do not directly lead to data loss, whereas false positives do, false negatives are not as disruptive as false positives. Nevertheless, for the experiments on Ubotica CogniSAT-XE1 reported in Section 8.3, false negative rates are available through the confusion matrices in Appendix B.  
The inference performance of \(f(\cdot\,;\,\theta)\) on source and target datasets (Section 4.3) can be evaluated by substituting \(\mathcal{D}\) with \(\mathcal{D}^{*}_{\text{H}\text{T}\text{0}}\) and \(\mathcal{D}^{*}_{\text{H}\text{T}\text{0}}\) respectively. In principle, the inference performance is to be evaluated on the XE1 since it is the target device of interest. However, we found based on preliminary testing that there were minute differences in ACC and FP rate when we evaluated \(f(\cdot\,;\,\theta)\) with FP32 weights on a Linux desktop and FP16 weights on the XE1.  
The other evaluation metrics are the memory footprint and inference time per sample of the cloud detectors, as measured on the XE1 (see Figure 5a), as well as the execution time of the domain adaptation methods, as measured on the ARK-1123L (see Figure 5b).
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Data
#### 2.2.1 Sentinel Satellite Data  
Sentinel-1 satellite constellation offers C-band radar data, day and night, and under all weather conditions. At the study area, the two satellites provide images about every third day in three descending and two ascending paths. The applied Interferometric Wide (IW) swath mode covers a 250 km wide area and has \(5\times 20\) m spatial resolution. Level 1 Ground Range Detected (GRD) products containing both Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarizations were automatically downloaded for the selected time frame and preprocessed in several steps. For the validation of the research methodology, in total, 9 radar images were processed for 2016 and 21 for 2018 (Table 1).  
Sentinel-2 multispectral imaging satellites provide optical data with a five-day revisiting period (Table 1). From the available 13 spectral bands, covering the spectra from the visible part through near infrared to short-wave infrared, only 10 bands with 10 and 20 m spatial resolution were used in the analysis. In the first validation period in 2016, only the Sentinel-2A satellite was in orbit and just the Level 1C (L1C) data product was available for download. In 2018, also data from Sentinel-2B, at Level 2A (L2A, Bottom-of-Atmosphere (BoA) reflectance values) was accessible. In the Sentinel-2 granule system, the study area is fully covered by four \(100\times 100\) km tiles: 34 TDT, 34 TDS, 34 TET and 34 TES. Altogether, optical images from three different dates were processed in this research.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite/Sensor** & \begin{tabular}{c} **Dates of Data Using** \\ **in the Validation** \\ \end{tabular} & \
\begin{tabular}{c} **Polarization/Spectral** \\ **Bands** \\ \end{tabular} &
\begin{tabular}{c} **Spatial** \\ **Resolution/Coverage** \\ \end{tabular} \\ \hline Sentinel-1A/1B & 28 March 2018 & C-band (5405 GHz) & \(5\times 20\) m (resampled to \\  & 29 March 2018 & VV/VH & \(10\times 10\) m) \\  & 30 March 2018 & & 250 km width \\  & 31 March 2018 & & \\  & & B2: 492.4/492.1 nm & \\  & & B3: 559.8/559.0 nm & \\  & & B4: 664.6/664.9 nm & \\  & 18 March 2016 & B5: 704.1/703.8 nm & 10 m (B2, B3, B4, B8) \\ Sentinel-2A/2B & 28 March 2018 & B6: 740.5/739.1 nm & 20 m (B5, B6, B7, B8A, \\  & 31 March 2018 & B7: 782.8/779.7 nm & B11, B12) \\  & & B8: 832.8/832.9 nm & \(100\times 100\) km\({}^{2}\) \\  & & B8A: 864.7/864.0 nm & \\  & & B11: 1613.7/1610.4 nm & \\  & & B12: 220.4/2185.7 nm & \\  & & Blue: 450/520 nm & \\ SPOT 7 & 14 March 2016 & Green: 530/590 nm & 1.5 m \\  & & Red: 625/695 nm & 400 km\({}^{2}\) \\  & & Near Infrared: 760/890 nm & \\ PhaseOne P65+ & 28 March 2018 & RGB & \(10\) cm \\  & & & \(20\) km\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remotely sensed input data.
## 2 Problem Formulation
### Cooperation Relationships Modeling among Weapons Based on an Operation Loop
#### 2.1.3 Operation Loop Modeling  
Modern warfare theory, which is also called Observe Orient Decide Act (OODA) theory [20], argues that a combat operation is a cyclic process. First is target discovery (\(T\)\(\rightarrow\)\(S\)), then information is delivered to the node \(D\) (\(S\)\(\rightarrow\)\(D\)). Next, node \(D\) analyzes the battlefield situation and charges node \(I\) (\(D\)\(\rightarrow\)\(I\)) to attack node \(T\) (\(I\)\(\rightarrow\)\(T\)). The close loop (\(T\)\(\rightarrow\)\(S\)\(\rightarrow\)\(D\)\(\rightarrow\)\(I\)\(\rightarrow\)\(T\)) is called the operation loop [21]. During the operation, both parties build their own operation loops, and treat all the opponent's nodes as targets, as shown in Figure 3. Although only four classes of meta-functional nodes exist in one operation loop, each type of node can contain more than one weapon. Different weapons cooperate with others through loops. The number and technical performance of loops directly impact the combat effectiveness. The greater the number of loops, the stronger the threat capability. The fewer the nodes and edges in a loop, the better the threat effect.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nodes** & \(\mathbf{S_{R}}\) & \(\mathbf{D_{R}}\) & \(\mathbf{I_{R}}\) & \(\mathbf{S_{B}}\) & \(\mathbf{D_{B}}\) & \(\mathbf{I_{B}}\) \\ \hline \(S_{R}\) & \(S_{R}\)\(\rightarrow\)\(S_{R}\) & \(S_{R}\)\(\rightarrow\)\(D_{R}\) & & & \(S_{R}\)\(\rightarrow\)\(S_{B}\) & \\ \(D_{R}\) & \(D_{R}\)\(\rightarrow\)\(S_{R}\) & \(D_{R}\)\(\rightarrow\)\(D_{R}\) & \(D_{R}\)\(\rightarrow\)\(I_{R}\) & \(D_{R}\)\(\rightarrow\)\(S_{B}\) & \\ \(I_{R}\) & & & & & \(I_{R}\)\(\rightarrow\)\(S_{B}\) & \(I_{R}\)\(\rightarrow\)\(D_{B}\) & \(I_{R}\)\(\rightarrow\)\(I_{B}\) \\ \(S_{B}\) & \(S_{B}\)\(\rightarrow\)\(S_{R}\) & & & \(S_{B}\)\(\rightarrow\)\(S_{B}\) & \(S_{B}\)\(\rightarrow\)\(D_{B}\) & \\ \(D_{B}\) & \(D_{B}\)\(\rightarrow\)\(S_{R}\) & & & \(D_{B}\)\(\rightarrow\)\(S_{B}\) & \(D_{B}\)\(\rightarrow\)\(D_{B}\) & \(D_{B}\)\(\rightarrow\)\(I_{B}\) \\ \(I_{B}\) & \(I_{B}\)\(\rightarrow\)\(S_{R}\) & \(I_{B}\)\(\rightarrow\)\(D_{R}\) & \(I_{B}\)\(\rightarrow\)\(I_{R}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Meta-functional edge relation types.  
Figure 3: Schematic of an operation loop in battle.
## 3 Test with simulation
### RMS tracking errors  
Since the center of the simulated image pattern goes along a perfect circular path of radius \(r\) with center at the coordinate origin \(O\), the deviation of the distance between the tracked image pattern center \(o\) and the circle center \(O\) from the known radius is considered as a position error. The overall position error of tracking the image pattern in a group of \(K\) frames is quantified here with the root-mean-square (RMS) difference between the given and tracked radiuses, i.e.  
\[\delta_{r}=\sqrt{\frac{1}{K}\sum_{k=1}^{K}(r_{k}-r)^{2}}\qquad\text{for} \qquad r_{k}=\sqrt{x_{k}^{2}+y_{k}^{2}}. \tag{7}\]  
Wherein \((x_{k},y_{k})\) is the tracked image pattern position in each frame. In order to determine the instantaneous speed of the insect body part, the image pattern displacement is determined with a central difference for each frame as below.  
Figure 4: Synthetic image patterns of d\({}_{0}\)=6 pixels at 11 different positions.  
\[\Delta x_{k}=\frac{x_{k+1}-x_{k-1}}{2}\] \[\Delta y_{k}=\frac{y_{k+1}-y_{k-1}}{2} \tag{8}\] \[S_{k}=\sqrt{\Delta x_{k}^{2}+\Delta y_{k}^{2}}\]  
Wherein \(S_{k}\) is the tangential displacement along the circular path of the image pattern in the current tests, and the true value is a constant of ro\(\Delta\)t=0.2513 pixels. Here, \(\Delta\)t is the time interval between two successive frames. Thus the RMS error of the tangential displacement is determined as  
\[\delta_{s}=\sqrt{\frac{1}{K-2}\sum_{k=2}^{K-1}\bigl{(}S_{k}-r\omega\Delta t \bigr{)}^{2}} \tag{9}\]
## 2 Simulation Analysis of the Cubic FP Cavity with Reinforced Support
### Simulation Modelling  
In the analysis process, a 100 mm cubic FP cavity with reinforced-support was modelled firstly. The simulation models of the FP cavity are shown in Figure 1. In Figure 1a, the cube in the center of the structure was the FP cavity, and the outer skeletonized frame was the bracket. There were eight support screws on the bracket to fix the FP cavity. ULE (Ultra-Low Expansion) glass cavities and fused silica mirrors are the common materials for room-temperature FP cavities [22; 23; 24]. Polyimide is a commonly used resin engineering material. Materials such as Teflon and PEEK have also been used by researchers in previous studies [25]. In the simulation, a cylindrical shim made of polyimide was used where the screw contacts the FP cavity. The material of the FP cavity was set to be ULE glass and the mirrors were set to be fused silica. The compensation rings on the mirrors were also set to ULE glass. The support bracket and screws were set as titanium alloy. The simulation parameters for vibration sensitivity are shown in Table 1. When analyzing the vibration sensitivity, the support brackets and screws were removed to simplify the analysis process.  
When analyzing the zero-expansion temperature of the FP cavity, the complete structure as shown in Figure 1a was used.