esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To improve the accuracy and reliability of precipitation estimation, numerous models based on machine learning technology have been developed for integrating data from multiple sources. However, little attention has been paid to extracting the spatiotemporal correlation patterns between satellite products and rain gauge observations during the merging process. This paper focuses on this issue by proposing an integrated framework to generate an accurate and reliable spatiotemporal estimation of precipitation. The proposed framework integrates Funk-Singular Value Decomposition (F-SVD) in the recommender system to achieve the accurate spatial distribution of precipitation based on the spatiotemporal interpolation of rain gauge observations and Convolutional Long Short-Term Memory (ConvLSTM) to merge precipitation data from interpolation results and satellite observation through exploiting the spatiotemporal correlation pattern between them. The framework (FS-ConvLSTM) is utilized to obtain hourly precipitation merging data with a resolution of \(0.1^{\circ}\) in Jianxi Basin, southeast of China, from both rain gauge data and Global Precipitation Measurement (GPM) from 2006 to 2018. The LSTM and Inverse Distance Weighting (IDW) are constructed for comparison purposes. The results demonstrate that the framework could not only provide more accurate precipitation distribution but also achieve better stability and reliability. Compared with other models, it performs better in variation process description and rainfall capture capability, and the root mean square error (RSME) and probability of detection (POD) are improved by 63.6% and 22.9% from the original GPM, respectively. In addition, the merged precipitation combines the strength of different data while mitigating their weaknesses and has good agreement with observed precipitation in terms of magnitude and spatial distribution. Consequently, the proposed framework provides a valuable tool to improve the accuracy of precipitation estimation, which can have important implications for water resource management and natural disaster preparedness.  
spatiotemporal fusion; machine learning; multi-source precipitation; ConvLSTM; F-SVD 28 April 2023  
10 June 2023  
15 June 2023
## 4 Analysis of Factors Affecting the Reflection Coefficient
### Effects of Frequency on Reflection Coefficients  
A map of the reflection coefficients is shown in Figure 5, mapped versus incident angle and frequency. The other parameters are the same as the model in Section 3. Obviously, the reflection coefficients approach 1 at 1-100 Hz. With the increase in frequency, there is a more rapid change in reflection coefficient. It is noted that frequency has an apparent effect on reflection coefficients, which decrease at first and then increase at small incident  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Fluid Viscosity** &
\begin{tabular}{c} **Young’s Modulus** \\ **(Pa)** \\ \end{tabular} & **Layer Depth (m)** & **Shear Modulus (Pa)** & **Poisson’s Ratio** \\ \hline Layer 1 & 0.001 & 7.24 \(\times\) 10\({}^{9}\) & 0.5 & 2.74 \(\times\) 10\({}^{9}\) & 0.32 \\ Layer 2 & 0.001 & 7.98 \(\times\) 10\({}^{9}\) & 0.5 & 3.05 \(\times\) 10\({}^{9}\) & 0.32 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the porous ice.  
Figure 4: Comparison of reflection coefficients at different frequencies between the EM and the ITM. (**a**) Curves of the reflection coefficient at 500 Hz. (**b**) Curves of the reflection coefficient at 1000 Hz. (**c**) Curves of the reflection coefficient at 1500 Hz. (**d**) Curves of the reflection coefficient at 2000 Hz.  
angles. As for larger incident angles, the reflection coefficient decreases with the increase in frequency and even reaches 1 between 82\({}^{\circ}\) and 90\({}^{\circ}\). It can be explained that total reflection easily happens at those larger incident angles, in which case frequency is not the dominant factor. Moreover, the acoustic wavelength at low frequency is larger than the thickness of ice, so the wave is not trapped in pores without energy loss. Nevertheless, the wavelength is small at higher frequency and, thus, trapped in pores, which causes the loss of the incoming energy. Regarding the area in Figure 5 where the reflection coefficients approach 1 at 1000 Hz, it might be caused by other dominant factors, which needs further exploration.
## 3 Results and Discussion
### Ultrasonic Pulse Velocity Test  
The ultrasonic wave transmission measurement might be used to investigate the development of solid phases in the microstructure of cementitious materials. The results of the ultrasonic pulse velocity tests are presented in Figure 11. In the first hour, the measured values were stabilized at about 52 m/s. It might be because the specimens are somewhat fluid in this period and their stiffness is too low to be measured by the machine. After about 75 min, the p-wave velocity in the sample of mixture MIX-H showed nearly linear development with time in the studied time frame. The curves of mixtures MIX-L and MIX-M had a similar developing trend approximately 10 min later. Based on the linear fit of measuring results from 100 min to the end in Figure 11, mixture MIX-H demonstrates the highest gradient (about 3.424). Mixture MIX-M has a slightly higher slope than mixture MIX-L. The increase of p-wave velocity could reflect the growth of internal microstructure for cementitious materials. Therefore, this test could indicate that increasing the content of MK in calcined clay could accelerate the internal structure development in the first 4h.  
Figure 8: (**a**) The load and displacement curve of mixture MIX-L; (**b**) The load and displacement curve of mixture MIX-M; (**c**) The load and displacement curve of mixture MIX-H. (**d**) An example of a post-processed image obtained with the help of ImageJ.  
Figure 9: Mixture MIX-H samples in the green strength test. Photographs of demolded samples at different ages (first row); Photographs of damaged samples at different ages (second row).  
Figure 10: _Cont._  
Figure 10: (**a**) The average stress and strain curve of mixture MIX-L; (**b**) The average stress and strain curve of mixture MIX-M; (**c**) The average stress and strain curve of mixture MIX-H; (**d**) Comparison of green strength of mix designs at different ages. The relative standard deviation (RSD) of peak stresses is under 20% which is similar to the findings of [PERSON] et al. [30].
## 4 Results and Analysis
### Identifying Extreme Precipitation Events in the California Watershed  
The GNSS has great advantages in capturing instantaneous high-frequency signals due to its high temporal resolution, e.g., [PERSON] et al. successfully tracked the daily transient TWS caused by hurricane Harvey based on the vertical and horizontal GNSS displacements in Gulf Coast of the CONUS [33]. [PERSON] et al. confirmed that the GNSS could be used to track a stormwater event in Yunnan, China, which was weaker than the catastrophic storm surges [17].  
We investigate the TWS change caused by extreme precipitation in January 2019 in the California watershed. We obtain the spatial distribution of the monthly precipitation total, daily time series of precipitation, GNSS-TWS, and GLDAS-TWS in the California watershed in January 2019. As shown in Figure 10, precipitation is mainly concentrated around California's Central Valley and the western Pacific coastal area, the total of which is lower in the northeast and southeast inland areas of California. The maximum precipitation total is 460 mm in the northwest of California's Central Valley in January 2019. We calculate the daily precipitation time series of the grid points with a total precipitation greater than 100 mm, which shows that California experienced two extreme precipitation events in January 2019; the first extreme precipitation event lasted five days from 6 January to 10 January, with the maximum average precipitation reaching 30 mm on 7 January, and the second extreme precipitation event lasted six days from 16 January to 21 January, with the maximum average precipitation reaching 51 mm on January 17. In order to reduce the noise, we fit the daily GNSS-TWS time series with a 7 day median and a 3 day moving average filter, and the GNSS-TWS increased rapidly after the first extreme precipitation event. The GNSS-TWS reached the maximum immediately due to the continuous accumulation of precipitation after the second extreme precipitation event. The GNSS-TWS remained stable change for about 3 days after the two extreme precipitation events. The evolution of the daily GLDAS-TWS is consistent with that of the GNSS-TWS, while the amplitude of the GLDAS-TWS is smaller than the GNSS-TWS. The correlation coefficients of the CC-TWS, LC-TWS, SC-TWS, and GLDAS-TWS are 0.82, 0.78, and 0.82, respectively. This demonstrates that spatial constraint inversion can be an independent constraint strategy used to extract the TWS caused by extreme precipitation events.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**HUC-2**} & \multicolumn{4}{c}{**STD (mm)**} \\ \cline{2-4}  & **CC** & **LC** & **SC30060** \\ \hline
6 & 86 & 78 & 57 \\
7 & 57 & 56 & 54 \\
8 & 51 & 47 & 51 \\
9 & 45 & 54 & 54 \\
10 & 41 & 42 & 42 \\
11 & 48 & 53 & 47 \\
12 & 61 & 63 & 56 \\
13 & 47 & 83 & 87 \\
14 & 46 & 64 & 53 \\
15 & 43 & 82 & 64 \\
16 & 64 & 72 & 64 \\
17 & 54 & 55 & 54 \\
18 & 64 & 63 & 65 \\ CONUS & 37 & 37 & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._
# Impact of One Additional Substitution on Player Load and Coaching Tactics in Elite Football
## 2 Materials and Methods  
### Subjects and Design  
#### 2.1.1 Evaluation of TOS  
Due to a limit of maximum three substitutions allowed in regular time, comparability within the same playing level was limited. To achieve comparability, data from the highest German national youth league allowing four substitutions was included. Therefore, the TOS data of 306 German Bundesliga matches (\(n=1751\), \(M_{TOS}=71.25\), \(SD=14.85\)) were compared to the TOS of 182 German U19-Bundesliga West matches (\(n=1309\), \(M_{TOS}=67.03\), \(SD=15.94\)). Both collected datasets were post hoc from the 2015/16 season and obtained from the official match data [33; 34]. In line with [PERSON] and [PERSON] [31], both competitions are based around a program, where there are 6-7 days between competitive matches.  
Based on the findings of [PERSON] et al. [32] on the progressive increase in fatigue, as well as the findings by [PERSON] et al. [35] on the linear increase of mean specific kinetic energy, the following load reduction calculations assumed a simplified linear increase in player load during a match. Therefore, the theoretical basis underlying the data evaluation started with 0% load at kick-off and linearly increased to 100% load at the end of the match (Figure 11). For the evaluation of the impact of an additional substitution on load reduction and the tactical influence of the coaches on the game, the actual share of the additional substitution in the substitution contingent (one out of four; 25%) was taken as a basis. Therefore, a relative increase of the substitution contingent higher than 25% would be considered beneficial as the additional substitution for tactical influence of coaches on the match. Furthermore, an increase of load reduction higher than the relative increase of the substitution contingent would characterise the additional substitution as beneficial for load reduction, because the gained advantage of the load reduction was greater than the cost of the additional substitution.
# Water Quality Evaluation and Prediction Based on a Combined Model
## 4 Results and Discussion
### Water Quality Evaluation Model
#### 4.1.3 Water Quality Evaluation of Lanzhou Xincheng Bridge Section  
The process of water quality evaluation is realized in MATLAB, and the confusion matrix and water quality evaluation chart are output, as shown in Figure 3[34]:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification** & **Class I** & **Class II** & **Class III** & **Class IV** & **Class V** & **Class Inferior V** \\ \hline DO/(mg\(\cdot\) L\({}^{-1}\)) \(\geq\) & 7.5 & 6.0 & 5.0 & 3.0 & 2.0 & rest \\ COD\({}_{\text{Mn}}\)/(mg\(\cdot\) L\({}^{-1}\)) \(\leq\) & 2.0 & 4.0 & 6.0 & 10 & 15 & rest \\ NH\({}_{3}\)-N/(mg\(\cdot\) L\({}^{-1}\)) \(\leq\) & 0.15 & 0.50 & 1.0 & 1.5 & 2.0 & rest \\ \hline \hline \end{tabular}
\end{table}
Table 1: Environmental quality standards for surface water (GB3838-2002).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Output Value** & **Water Quality Grade** \\ \hline
0.5 \(<y\leq\) 1.5 & Class I \\
1.5 \(<y\leq\) 2.5 & Class II \\
2.5 \(<y\leq\) 3.5 & Class III \\
3.5 \(<y\leq\) 4.5 & Class IV \\
4.5 \(<y\leq\) 5.5 & Class V \\
5.5 \(<y\leq\) 6.5 & Class inferior V \\ \hline \hline \end{tabular}
\end{table}
Table 2: Corresponding water quality grade of output values.  
Figure 3: Water quality grade confusion matrix of Lanzhou Xincheng Bridge section.  
In classification models, confusion matrices are often used to observe and judge the number of correct and incorrect classifications. As shown in Figure 3, among the 52 weeks of random sampling, correct in water quality evaluation was achieved for 47 weeks, and the total correct judgment rate reached 90.38%. The correct prediction rate of class II and class III water quality was 91.11% and 85.71%, respectively. Class II water quality was misjudged as class I for one week, class II water quality was misjudged class as III for three weeks, and class III water quality was misjudged class II for one week.  
The total number of correct and incorrect judgments and the corresponding water quality grade can be determined according to the above analysis, but it is not clear in which week the incorrect judgment occurred. This information can be obtained from the water quality evaluation chart presented below.  
In Figure 4, the \(x\)-axis indicates the number of weeks; the \(y\)-axis indicates the water quality evaluation; and the green dot and the red dots represent the real water quality grade and the water quality grade determined by the model, respectively. Overlapping of the red and green dots indicates that the water quality level is correctly judged for that week, whereas divergence indicates an incorrect judgment. As shown in Figure 4, the water quality grade is concentrated in class II and class III, which is not accidental because during the whole time period from 2004 to 2015, the water quality grade is mainly class II and class III. Five of the fifty-two water quality grades were incorrectly judged; details are shown in Table 3.
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 3 Challenges of Subsurface Defect Characterization by Thermography  
### Sequential Data Processing  
Sequential thermal data are pivotal in thermography methods like lock-in and pulsed thermography for defect detection, offering a time-resolved perspective that facilitates the identification of material defects such as cracks, voids, and delamination through the analysis of thermal process evolution. The acquisition and analysis of these data are vital for enhancing the reliability, safety, and performance of materials and components [16; 17]. However, challenges such as data quality issues, the need for synchronization, high computational demands, and the complexity of data interpretation underscore the importance of advanced research.  
Recent efforts in thermography for subsurface defect characterization have increasingly turned towards innovative neural network architectures to address these challenges. These architectures aim to enhance data processing speeds, accuracy, and overall quality in defect detection [18; 19]. The Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) architecture, for instance, is effective in capturing long-term dependencies in sequential data without succumbing to the vanishing gradient problem. Researchers, including [PERSON] et al., have leveraged LSTM-RNN for automatic classification of common defects in materials such as honeycomb structures, achieving enhanced accuracy and reliable identification of defects by analyzing the thermal response over time [20].  
Parallel to LSTMs, Gated Recurrent Unit (GRU) networks simplify the model structure while maintaining the ability to handle long-term dependencies effectively. This results in faster training times and reduced risk of overfitting [21]. GRUs have outperformed traditional RNNs and backpropagation networks in various sequential data modeling tasks, including defect depth recognition and quantification. [PERSON] and [PERSON] demonstrated the effectiveness of GRUs in quantifying defect depths in composite materials using pulsed thermography, showcasing GRUs' capability to process and analyze complex thermal datasets for accurate defect detection [22]. Figure 1 shows the structural differences between the LSTM and GRU architecture.  
In three-dimensional data analysis, 3D neural networks excel in processing 3D data, such as images and videos, enhancing object recognition, segmentation, and temporal changes handling. The introduction of a 3D convolutional neural network (CNN) model by [PERSON] et al. [24], tailored for lock-in thermography in carbon fiber-reinforced polymer (CFRP), integrates spatial and temporal convolutional filters with batch-size independent group normalization, showcasing superior accuracy in detecting and estimating subsurface defect depths [25]. Figure 2 compares the architectures of 2D-CNN and 3D-CNN.
## 3 Results and Discussion
### ANOVA of SSCFC Performance  
Analysis of variance (ANOVA) was employed to investigate the significance of each factor's influence on SSCFC's performance, thereby complementing the statistical inference dimensions that were not covered by the range analysis. Table 10 presents the ANOVA results for both fluidity and compressive strength of SSCFC.  
The ANOVA results for fluidity show F-values of 0.961 (W/C ratio), 5.407 (SS replacement ratio), and 0.373 (ceramsite replacement ratio). With factor degrees of freedom (f) = 2 and error degrees of freedom (fe) = 6, the critical F-value at \(\alpha\) = 0.05 is 5.143. The ANOVA results for fluidity reveal that both Factor A and Factor C exhibit F-values below 5.143, indicating statistically insignificant effects on SSCFC's fluidity. In contrast, Factor B demonstrates an F-value exceeding 5.143, confirming its statistically significant influence on the fluidity characteristics. The ANOVA results for compressive strength reveal consistent significance patterns across 7d/14d/28d ages, leading to a focused analysis on 28d results. Factors B and C show F-values substantially below 5.143, indicating minimal effects on the compressive strength. Factor A exhibits F-values significantly exceeding 5.143, proving its dominant impact on strength. The final analytical conclusions remain consistent with the aforementioned range analysis findings.
## 3 Methodology  
### Research Framework  
The research framework of this study consists of four main parts, as shown in Figure 1: (1) Data processing, which introduces the extraction of merging scenarios from the RounD dataset and the preprocessing of raw data to obtain basic information on interacting vehicle pairs and hazardous scenarios. (2) Data generation: this part describes the structure of the TimeGAN model and the method of data generation. (3) Data evaluation: the generated data are evaluated from three perspectives, and their respective metrics are proposed in this section. (4) Simulation testing: the last section utilizes the Simulation of Urban Mobility (SUMO) and CARLA platforms to establish a simulation scenario of a roundabout and tests the generated hazardous scenarios to verify the effectiveness of the dangerous trajectories.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 3 Development Status and Key Technologies of ISTNs  
In light of the accelerated growth in communication technologies, terrestrial wireless networks have achieved tremendous success in terms of communication rate and quality of service [61]. However, there are still some remote areas that remain unconnected due to economic and technical reasons [62]. Therefore, satellite-terrestrial integration will be the key research direction of future wireless networks to empower seamless coverage worldwide [63].
## 3 Methods
### Affective Interaction Encoder  
The affective interaction encoder is mainly responsible for encoding the affective interaction relationship information between speakers in conversation. In this paper, we use directed graphs to characterize the affective interaction relationship between speakers and use GCN to extract the characteristics of affective interaction between speakers.  
Specifically, we construct a directed graph \(G=(V,E,R,W)\) to characterize the dialogue interaction, \(V\) represents the set of nodes, and \(E\) represents the set of edges. Each utterance represents a vertex, \(v_{i}\in V\), \(i=[1,2,\ldots,K]\), \(K\) is the number of utterances. The initialization vector of the vertex \(v_{i}\) is an utterance representation that encodes the utterance through the ERNIE pre-training model. \(r_{ij}\in E\) represents the edge relationship between pairs of utterance \((v_{i},v_{j})\), and \(r\in R\) represents the type of edge, which relates to dependencies between speakers.  
This paper adopts the graph attention mechanism based on text similarity to set the weight of the edge, i.e., for each node, the sum of the weights of the input edges is 1. Considering the \(m\) sentences \(v_{i-1}\), \(v_{i-2}\),..., \(v_{i-m}\) before each node utterance and the \(n\) sentences \(v_{i+1}\), \(v_{i+2}\),..., \(v_{i+n}\) after each node utterance, the weight calculation between the node \(v_{i}\) and the node \(v_{j}\) is shown in Equation (9):  
\[\partial_{ij}=softmax\Big{(}g_{i}^{T}W[g_{i-m},\ldots,g_{i+n}]\Big{)},s=i-m, \ldots,i+n \tag{9}\]  
where \(\partial_{ij}\) is the attention weight between a node and neighboring nodes and \(W\) is the parameter to be learned during training.  
In customer service conversations, speakers are affected by each other's emotions. Therefore, we construct the affective interaction-directed graph by constructing different types of edge relations. For customer service conversation, the edge relationship is shown in Table 1.  
After constructing the directed graph structure, the GCN adopts a two-step convolution operation to fuse the local neighborhood nodes of each node and encodes the node feature vector \(g_{i}\), that is not related to the speaker, into feature vector representation \(h_{i}\), that is related to the speaker's emotional interaction information. The calculation process is as follows:  
\[h_{t}^{(1)}=\sigma\Bigg{(}\sum_{r\in R}\sum_{s\in Y}\frac{w_{ts}}{c_{t,r}}W_{ r}^{(1)}g_{s}+w_{tt}W_{0}^{(1)}g_{t}\Bigg{)},i=1,2,\ldots,Y \tag{10}\]  
\[h_{t}^{(2)}=\sigma\Bigg{(}\sum_{s\in Y}W^{(2)}h_{s}^{(1)}+W_{0}^{(2)}h_{t}^{( 1)}\Bigg{)},t=1,2,\ldots,Y \tag{11}\]where \(\sigma\) is an activation function; \(w_{0}^{(1)}\), \(w_{r}^{(1)}\), \(w_{0}^{(2)}\), and \(w^{(2)}\) are transformation parameters. Formulas (10) and (11) can effectively aggregate the affective interaction information of local domain speakers at each statement node in the directed graph, and the self-connected edges can also ensure the transformation of autocorrelation features.