esa-sceva/satcom-chunk-collection huggingface dataset:
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 4 Conclusions  
This paper used finite-difference forward simulations to generate microseismic records, and a modified Attention U-Net was used to successfully predict various complex subsurface velocity fields, including anticlines, synclines, and anomalous velocity models. Numerical tests demonstrated that the modified Attention U-Net effectively captures the hidden spatial correlations and features between microseismic records and velocity models, consistently yielding reliable velocity models for both single-event and coupled multi-event microseismic records. Additionally, the use of multi-event microseismic datasets demonstrates a greater capability for characterizing subsurface features while requiring less training time.  
Compared to traditional methods, DL algorithms reduce the reliance on manual feature extraction and data preprocessing, resulting in a more automated and efficient microseismic velocity modeling process. They require only a reasonable amount of training time, and once trained, the model can complete predictions in approximately one second. This addresses the pressing need for real-time decision-making in microseismic monitoring.  
Moving forward, our future research will focus on exploring methods to improve the model's noise resistance and investigating effective combinations of DL and traditional techniques such as FWI.  
Methodology, L.H. and P.Z.; Resources, J.Z. and X.S.; Software, Y.Z.; Validation, W.H.; Writing--Original Draft, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (No. 42130805, No. 42074154, No. 42374147, No. 42004106), the Natural Science Foundation of Jilin  
Figure 19: Noise robustness test for multi-layer random stratigraphic multi-event models. (**a–c**): True velocity models; (**d–f**): modeling results without added noise; (**g–i**): results with added gaussian noise at SNR = 15.  
Province (No. YDZJ202101 ZYTS020), and the Lift Project for Young Science and Technology Talents of Jilin Province (No. QT202116).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Using Graph Neural Networks for Social Recommendations
## 4 Experimental Evaluation
### Performance Metrics
#### 4.3.1 Root Mean Square Error (RMSE)  
First, we calculate RMSE over the entire set of predictions. RMSE is a metric that penalizes high differences between the predicted ratings \(\hat{r}_{i}\) and the ground truth rating \(r_{i}\) over all items \(i\) in the test (or validation) set. It is defined as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}(\hat{r}_{i}-r_{i})^{2}}{N}} \tag{16}\]  
where \(N\) is the total number of predicted ratings.  
RMSE is a good indicator of how well the system performs overall and is broadly used for validation (hyperparameter tuning, model selection, etc.). However, as it penalizes equally both high and low rating predictions, and is applied over the entirety of the validation/test data, it is not a good indicator of the accuracy of the top-\(n\) recommendations the system generates, compared to HR and NDCG.
## 2 Study Area and Dataset
The ground sample distance (GSD) of the orthophoto was 12 cm  
Figure 1: Location of the study area in Gongju province, South Korea. The red-green-blue (RGB) aerial orthophoto shows the spatial distribution of forests in the study area. in both the line and pixel. The lidar point cloud was acquired with an ALTM Gemini167 at an altitude of 1300 m (above sea level) in October 2015 with 2.5 points per square meter in the lowlands and 3-7 points per square meter in the mountain area. The difference in the point density occurred because the overlap rate of the lidar sensors is different between mountainous and non-mountainous areas. Points from terrain was classified from the Lidar point cloud, the DTM with the GSD of 1 m was created from the terrain-derived points. On the other hand, points from trees were extracted, and then the DSM with the GSD of 1 m was produced from the tree-derived points. The terrain- and tree-derived points were extracted by using the commercial software Global Mapper. The height difference map was generated by subtracting DTM from DSM. The height difference map is related with several tree parameters, including the height and density of trees, etc. That is, the pixel values in the height difference map are not tree height, but are closely related with tree height. Figure 2: Forest classification maps of the study area produced from the field survey. **(a)** Forest vertical structure; **(b)** dominant species’ leaf type; **(c)** natural and artificial forests.
# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 3 Higher Education Evaluation System
### Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)
#### 3.4.1 Principles of TOPSIS  
TOPSIS [25] is a common and comprehensive evaluation method which takes advantage of information from original data, and its results can accurately reflect pros and cons of evaluation schemes. The basic process is shown in Algorithm 2.  
```
0: Original dataset \(X=\{x_{1},x_{2}\cdots,x_{n}\}\)
0: The weight of each indicator \(W=\{w_{1},w_{2},\cdots,w_{m}\}\)
0: The higher education level of each research object
1: Normalizing indicator nature in the original dataset.
2: Constructing the normalized matrix \(F=\{f_{1},f_{2},\cdots,f_{n}\}\).
3:for\(F_{i}\), each column of \(F\)do
4: The \(i\)-th dimension of the worst plan \(F^{-}\leftarrow\) the maximum value of elements in \(F_{i}\).
5: The \(i\)-th dimension of the best plan \(F^{-}\leftarrow\) the maximum value of elements in \(F_{i}\).
6:endfor
7:for\(f_{i}\in F\)do
8: Determining the best plan \(f_{i}^{+}\).
9: Determining the worst plan \(f_{i}^{-}\).
10: Calculating \(C_{i}^{+}\): the closeness between \(f_{i}\) and \(f_{i}^{+}\).
11: Calculating \(C_{i}^{-}\): the closeness between \(f_{i}\) and \(f_{i}^{-}\).
12:endfor
13: Calculating the relative closeness of each evaluation object \(C_{i}^{*}\).
14: Sorting \(C_{i}^{*}\) according to the size of value.
```  
**Algorithm 2** Process of weighted TOPSIS.
## 4 Results and Discussion
### Results  
Based on the geometrical dimensions of the proppant crushing chamber and fracture conductivity chamber, the diameter of the proppant crushing chamber was 40.5 mm, the length of the proppant conductivity chamber was 177.8 mm, and the proppant placement concentration was set at 5 kg/m\({}^{2}\) (the fracture width was about 2.9 mm). The LZ sand crushing rate and the fracture conductivity were simulated and calculated. Figure 4 shows the simulation and pressure heat map of the fracture conductivity with the LZ sand. Figure 4 presents the results prior to the optimization of the LZ sand simulation, revealing that LZ sand particles did not fully occupy the entire fracture interval. Figure 4 shows the optimized outcomes of the LZ sand simulation, where the entire fracture space was filled with proppant particles as much as possible through optimization within the fracture interval. Figure 4 displays the stress distribution heatmap of the LZ sand under fracture pressure.  
The average crushing rate and fracture conductivity of LZ sand were derived from multiple computer simulations of the prop  
Figure 4: LZ sand simulation and mechanical relationship heat map.  
Figure 5: Crushing rate of LZ sand.  
Figure 6: Fracture conductivity of LZ sand.  
The crushing rate of the LZ sand continuously increased with increases in the closure pressure. The average error between the model values and the experimental values of the LZ sand crushing rate was 13.68%. The fracture conductivity of the LZ sand was inversely proportional to the closure pressure, and decreased with increases in the closure pressure. The average error between the model values and the experimental values of the LZ sand fracture conductivity was 11.39%.  
Following the same approach as for the LZ sand, simulations and calculations were conducted to assess the crushing rate and fracture conductivity of the XY ceramic, as illustrated in Figure 7.  
The average crushing rate and fracture conductivity of the XY ceramic were derived from multiple computer simulations of the proppant particle placement morphology at closure pressures of 10 to 60 MPa. The model values of the XY ceramic model were compared with the experimental values, as shown in Figures 8 and 9.  
The crushing rate of the XY ceramic continuously increased with increases in the closure pressure. The average error between the model values and the experimental values of XY ceramic crushing rate was 9.26%. The fracture conductivity of the XY ceramic was  
Figure 8: Crushing rate of XY ceramic.  
Figure 7: XY ceramic simulation and mechanical relationship heat map (**(a)** presents the results before the optimization of the XY ceramic simulation, **(b)** displays the optimized outcomes of the XY ceramic simulation, **(c)** shows the stress distribution heatmap of XY ceramic under fracture pressure).  
inversely proportional to the closure pressure, and decreased with increases in the closure pressure. The average error between the model values and the experimental values of the XY ceramic fracture conductivity was 9.08%.
## 4 Discussion  
### Error Analysis  
Structure similarity (SSIM) is used for evaluating the effect of encryption system. The equation of SSIM is shown as follows  
\[\text{SSIM}=\left(\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2}+\mu_{y} ^{2}+C_{1}}\right)\left(\frac{2\sigma_{x}\sigma_{y}+C_{2}}{\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}}\right)\left(\frac{\sigma_{xy}+C_{3}}{\sigma_{x}\sigma_{y }+C_{3}}\right) \tag{15}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean of the original image and the decrypted image, respectively, \(\sigma_{x}\) and \(\sigma_{y}\) are the variance of the original image and the decrypted image, respectively, \(\sigma_{xy}\) is the covariance between the original image and the decrypted image, \(C_{1}\), \(C_{2}\), \(C_{3}\) are small constants. According to Equation (7), when the array beam loaded with the images is transformed by the Fourier lens, the phase distribution is consistent with the amplitude transmittance function of the CDG on the focal plane behind the Fourier transform lens, and the beam field distribution can be regarded as a virtual grating. By multiplying the matched CDG, the two-phase cancellation can obtain the beam field with uniform distribution. However, because the phase distribution of the beam field on the back focal plane is not visible, the virtual grating and the CDG must be aligned strictly in order to effectively eliminate the phase fluctuation of the combined beam field. Therefore, it is necessary to calculate the encryption effect when the CDG is translated along the grating period direction. Figure 7a shows the SSIM between the decrypted image and the original image when the CDG is shifted \(x\) grating cycles along the horizontal direction. At the same time, the phase value of the PP will also affect the decryption effect. For this, we add random phase errors in the range of 0 to \(\alpha\pi\) for the PP value in the simulation, where \(0<\alpha<0.5\). Figure 7b shows the SSIM between the decrypted image and the original image when the PP value is associated with errors. As shown in Figure 7b, with the increase of the PP value error, the quality of decrypted image becomes worse. It shows that when the PP value error is small, the lossless image decryption can be achieved.
# Guidelines towards Information-Driven Mobility Management
## 4 Mobility in ICN
### Consumer Mobility
#### 4.5.2 CN as Consumer  
In this second example, illustrated in Figure 3, the CN entity is the consumer, while MN is a producer of information. In terms of consumer mobility, the situation is similar to the one described in the previous section: CN as a consumer expresses interest (sends an Interest packet, represented by II) carrying a Name Prefix for specific content, which in our example is being produced by MN. Meanwhile, and either before receiving data or after receiving some data packets, CN moves to a new location, performing reattachment to NDN router B. The receiver-driven design of ICN implies that once CN reconnects to a new node (in our example, router B), it starts sending Interest packets to get the desired content, based on the respective application requirements and settings. Therefore, the pull-based receiver-driven nature of ICN is beneficial for the case of consumer mobility, independently of the entity that is moving. In other words, for the case of consumer mobility, there is no need to distinguish between a MN and a CN entity, in future mobility management solutions.  
Figure 3: CN as Consumer. CN expresses interest by sending packet II and then moves. On the new location, CN again emits II. Therefore, subsequent data packets reach CN at the visited network.  
Figure 2: MN as consumer node. The MN starts by expressing interest in content (Interest packet II). This packet reaches the CN, which replies with data packet D1. Meanwhile, the MN moves, and does not receive D1. Upon reattachment, the MN sends II again.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 4 Experimental Setup and Performance
### Dataset Descriptions
#### 4.1.1 The People Counting Dataset (PCDS)  
The People Counting dataset (PCDS) is a first RGB-D dataset which contains 4500 videos that were taken at the entrance and exit of the bus in both normal and cluttered scenes. The videos are captured using a Kinect V1 camera. The dataset videos present large variations in illumination, occlusion, clutter and noise. This dataset is publicly available at [41]. Figure 12 shows video frame examples of the PCDS dataset.
## 4 Performance of KF Used for Parameter Estimation in RTHS
### Parameter Study of KF  
This subsection investigates the influence of the measurement noise covariance \(R\) and the initial values of \(\mathbf{x}_{0}\) and \(\mathbf{P}_{0}\) on the proposed time-delay compensation method. To perform this analysis, a series of different values of \(R\), \(\mathbf{x}_{0}\), and \(\mathbf{P}_{0}\) are used. They are considered as \(Q=W_{\mathrm{f}}Q^{0}\), where \(Q\) represents \(R\), \(\mathbf{x}_{0}\), and \(\mathbf{P}_{0}\), \(Q^{0}\) represents the nominal values of \(R\), \(\mathbf{x}_{0}\), and \(\mathbf{P}_{0}\) that were determined in the last subsection, and \(W_{\mathrm{f}}\) is a factor varying from 0.7 to 1.3. The duration of the simulations is 10 s, and the stop frequencies of the swept signal are 10 Hz, 20 Hz, and 30 Hz, respectively.
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 5 Conclusions  
This manuscript presents a novel photonic multi-beam receiver. The working mode can be reconfigured by controlling the optical switch and choosing suitable splitters. Ac  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **This Study** & [13] & [14] & [16] \\ \hline transmitter/receiver & receiver & both & transmitter & receiver \\ \hline reconfigurable multi-beam & yes & no & no & no \\ \hline number of the beams & \(1,2,4\) & \(3\) & \(4\) & \(2\) \\ \hline technical feature & variable splitters \& optical switch arrays & Blass matrix & subarray partitioning & programmable photonic processor \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison to related works.  
Figure 7: The multi\(-\)beam pattern in amplitude and phase inconsistency with the variances are 0.5 dB and 0.5 ps. (**a**) single\(-\)beam mode; (**b**) dual\(-\)beam mode; (**c**) four\(-\)beam mode.  
cording to the communication scenarios, the optical signals are sent to the splitter, which has suitable ports to divide the signal into required numbers. Thus, the optical energy is focused on the desired beam, and the system energy efficiency is improved by controlling the optical switch to reconfigure the optical path. The structures DP and EP have been designed to realize the reconfigurable function. The parameters comparison proves that the structure EP has better performance than the structure DP. Simulation results show the proposed architecture increasing the power of the main lobe when the received beam number is less than the maximum received number. Furthermore, the amplitude and phase inconsistency caused by the reconfigurable structure will decline the quality of the multi-beam. When the value of inconsistency is in a limited range, the balance and compensation of the amplitude and phase should be taken into account when the system is fabricated and deployed.  
Future work may include the experimental verifying of the proposed structure and the demonstration of a transmitter architecture with a variable beam-count. We believe that combining these developments will help pave the way for future multi-beam communications.  
Conceptualization, Y.L., J.S. and Q.Q.; methodology, Y.L.; software, Y.L.; validation, Z.F., S.S. and Y.W.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L.; visualization, Y.L.; supervision, D.J., Y.C. and X.L.; project administration, Q.Q.; funding acquisition, Q.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number (No. 62201120 and 61971110), the Natural Science Foundation of Sichuan Province, and the Research Foundation (Y030212059003044).  
Not applicable.  
The authors declare no conflict of interest.
# Two-View Mammogram Synthesis from Single-View Data Using Generative Adversarial Networks
## 3 Results
### Comparison of Images Synthesized by Different Training Methods  
As an example of a successful case, we selected the case of bi-directional training with the PG technique and \(L_{FM}\), which achieved the synthesis of CC views that were visually similar to the real CC views at 128 \(\times\) 128 resolution. Figure 6 compares the synthesized images and the similarity metrics for each training method in the successful case. We also selected another successful case at 256 \(\times\) 256 resolution, and compared the synthesized images and similarity metrics for the training methods, as shown in Figure 7. In most cases, including Figures 6 and 7, we observed CC views that were the most realistic and visually similar to real views when synthesized using the bi-directional method with PG technique and \(L_{FM}\). The values of the similarity metrics were higher than those obtained for other training methods. In the images synthesized by the method using only CR-GAN, we can see obvious differences from the real views, in terms of the mammary gland shapes or textures. The sharpness was noticeably deteriorated in the images synthesized by the method using the PG technique without \(L_{FM}\), although the similarity metrics tended to be slightly increased, compared to the results of the method using only CR-GAN, as shown in Table 3. Additionally, there were artifacts at locations outside the breast in some views synthesized by the method using only CR-GAN or unidirectional training, as indicated by yellow arrows in Figures 6 and 7.  
Figure 6: Comparison of CC views at 128 \(\times\) 128 resolution synthesized by training methods. Examples chosen of a successful case using bi-directional training with PG technique and feature matching loss. The yellow arrows indicate artifacts outside the breast.
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 4 Experiments and Results
### Comparison of Results on FWBD  
To comprehensively assess the performance of CA-ACTION-SlowFast in detecting manual labor behavior in the field, we select Slow Only, YOWO [39], and YOWOv2-Large [40] as comparative methods for the experiments in this study. Table 5 illustrates the outcomes of each methodology applied to our proprietary FWBD dataset, where [EMAIL_ADDRESS] denotes the mean Average Precision at an overlap area threshold (IoU) of 0.5 during computation.  
Three distinct methods were selected for comparison: Slow Only, YOWO, and YOWOv2-Large. These methods represent diverse behavior detection techniques and model architectures. Slow Only is a model centered on behavior recognition using solely slow frame sequences, emphasizing the capture of detailed features in high-quality images. However, it may not give sufficient attention to the temporal information of the action. Its [EMAIL_ADDRESS]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Comparison Model**} & \multicolumn{2}{c}{**Total Number of**} & \multirow{2}{*}{**FLOPs (G)**} \\  & **Parameters (Million)** & & \\ \hline SlowFast & 33.646793 & 40.82 \\ CA-SlowFast & 59.456073 & 41.48 \\ ACTION-SlowFast & 33.647206 & 40.87 \\ CA-ACTION-SlowFast & 59.456486 & 41.52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model performance comparison.  
value is 66.96%, indicating its performance in the absence of temporal dimension feature fusion. You Only Watch Once (YOWO) combines 2D and 3D convolutional networks to process both spatial and temporal information for behavior recognition in videos. Its [EMAIL_ADDRESS] value is 78.71%, showing a notable improvement over Slow Only. This suggests that combining spatial and temporal features is crucial for behavior recognition. YOWOv2-Large is an enhanced version of YOWO, utilizing a larger network to further improve performance. Its [EMAIL_ADDRESS] value is 83.79%, indicating that recognition accuracy can be further enhanced by increasing model complexity. Our proposed CA-ACTION-SlowFast model combines features from both slow and fast frame sequences and integrates both CA and ACTION modules. The CA module enhances the model's focus on key channel features, while the ACTION module not only emphasizes spatial features but also considers their temporal changes. This proves beneficial for behaviors in complex scenes, such as fieldwork. This combination allows the model to consider both image details and dynamic behavior features, providing more comprehensive information for behavior identification. The [EMAIL_ADDRESS] value of the CA-ACTION-SlowFast model is 91.86%, surpassing that of any of the classical methods mentioned above and highlighting its superior performance in capturing manual labor behavior in the field.