esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Shooting and Bounce Ray Method for Calculating Echoes of Complex Underwater Targets
## 5 Acoustic Shooting and Bounce Ray Method  
In the Kirchhoff approximation problem of the meshing surface elements, the total target echo is solved on the basis of solving the acoustic scattering of each independent surface element. The key to the problem lies in the calculation of the integral of surface elements, and the judgment and treatment of the occlusion and reflection relationship of the complex surface of the target under the conditions of acoustic wave incidence [7; 11]. The bandwidth signal time domain calculation formula was deduced and accelerated the algorithm to realize efficiency for the large panel acoustic scattering problem to solve; in this paper, it is the GA PA bounce sound underwater target scattering characteristics of the acoustic radiation calculation method (ASBR, Acoustic Shooting and Bounce Ray) [12; 13].  
As shown in Figure 2, the beam aperture surface is defined in the front direction of the incoming acoustic wave of the target, and the beam pipeline is divided according to certain accuracy requirements. Each beam pipeline consists of four vertices and a tracking point. By dividing the subspace of the target surface and tracing the space propagation of each acoustic beam pipeline, the intersection and reflection relationship between the pipeline and the target surface element are calculated to complete the GA calculation. The PA sound field integration is also carried out on the surface element, where it meets the ejection condition, so as to realize the calculation of the acoustic scattering characteristics of underwater target combined with GA and PA [14].  
Based on the natural attribute, the parallel GA process on the exit surface yuan, according to Equation (9) for the time domain integral, and the PA process of the all beam line can be used in a fully parallel manner, the computing environment can select the nucleus, the blade array, as well as the GPU cluster. Thus, the high performance parallel platform and implementation of the parallel algorithm design and deployment are not discussed in this paper. In addition, the implementation of the ASBR method also needs to consider the following key issues about the acoustic beam pipeline tracking process, which have a direct impact on the performance of the algorithm
## 3 Results and Discussion
### Results of Photocatalytic Degradation under Different Conditions
#### 3.4.3 Effect of Wastewater pH Value on the Photocatalytic Effect  
Figure 6 shows the treatment of 250 mL MB dye wastewater at an initial concentration of 20 mg/L and different pH values (1.90, 3.98, 5.94, 7.98, and 10.02) after 2.5 h of treatment by 250 mg composite photocatalyst under ultraviolet light irradiation. The results show that the degradation rate was relatively high at pH = 5.94 and 7.98, while relatively low at pH = 1.90. When the pH value of the synthetic wastewater was 7.98, the degradation rate reached the peak, up to 94.1%. When the pH reached 10, the degradation rate began to decline significantly, which was still higher than the degradation rate at pH = 1.90. The pH value had a great influence on the stability of dye molecules, and the negative charges on the surface of GO. The GO surface was highly negatively charged in the pH range from 5.0 to 12.0, which might be ascribed to the deprotonation of anionic oxygenous functional groups on GO [23]. Highly negatively charged GO sheets in suspension could not only disperse homogeneously via electrostatic repulsion but also enhance the electrostatic attraction between them and cationic MB molecules, obviously improving the adsorption efficiency [24; 25; 26; 27]. However, when too many dye molecules are adsorbed on the surface of GO, the active sites on the surface of the catalyst will be over occupied, which will affect the degradation process of dye molecules. At the same time, the pH value affected the formation of free radicals, and the dispersion, activity, and surface charge of photocatalysts during the photocatalytic process [28], and was related to the \"isoelectric point\" of Ti. According to previous data [29], the isoelectric point of GO/TiO\({}_{2}\) composite was pH = 6.7. In the solution of pH > 6.7, the surface of GO/TiO\({}_{2}\) composite was negatively charged, and the surface of catalyst was positively charged. MB molecules are organic cations, which are negatively charged in alkaline medium. In contrast, GO/TiO\({}_{2}\) composite has stronger adsorptive capacity for MB molecules. Therefore, the degradation effect is better under mild alkaline conditions.  
Figure 5: Degradation results of MB synthetic wastewater treated with different amounts of composite photocatalyst added.
# Fairness and Trust in Virtual Environments: The Effects of Reputation
## 5 Results
### Manipulation Check
#### 5.2.2 Hypothesis 2  
To understand whether Receivers' behaviors changed after the introduction of an evaluation on Observers, we carried out a Generalized Linear Mixed Models (GLMM) analysis. The final models about the reputation capability to influence the Receiver's decision making are reported in Table 5.  
The decision about asking for a suggestion resulted influenced by the interaction between the Reputation score and the Age of the participants. The adolescents payed more attention to the reputation of their partners, avoiding asking untrustworthy Observers. As regards the use of the reputational information to decide about the Donor's deals (i.e., coherence of acceptance) we observed different patterns of compliance. Our participants relied on their partners' reputation more often if this was positive, whereas when interacting with an illreputed Observer our participants trusted his/her suggestions significantly less. Finally, the tendency to leave a feedback resulted influenced by the Reputation level and by Gender. Observer with an ambiguous reputation were less frequently being evaluated. Furthermore, the males showed themselves more inclined to feedback their partner.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**General Models**} \\ \hline
**Target** & **Akaike**1  & **F** & **df(1)** & **df(2)** & **Precision** \\ \hline Donation 2  & 65.002 & 52.01 *** & 5 & 3151 & 55.7\% \\ Requested 3  & 66.209 & 4.11 *** & 5 & 3155 & 54.3\% \\ Fairness 4  & 65.658 & 6.63 *** & 5 & 3155 & 54.0\% \\ \hline \hline \multicolumn{5}{c}{**Fixed Effects and Parameters-Donation \({}^{d}\)**} \\ \hline
**Parameter** & **F** & **B**2  & **Student t** \\ \hline Age (A) & 109.5 *** & \(-\)0.601 & \(-\)10.64 *** \\ Gender (M) & 42.94 *** & \(-\)0.273 & \(-\)4.41 *** \\ Age(A)*Gender (M) & 28.99 *** & 0.639 & 9.71 *** \\ Condition(CC)*Gender (M) & 42.94 *** & 0.337 & 5.38 *** \\ \hline \hline \multicolumn{5}{c}{**Fixed Effects and Parameters-Requested \({}^{d}\)**} \\ \hline
**Parameter** & **F** & **B**2  & **Student t** \\ \hline Age (A) & 3.43 * & 0.356 & 2.32 * \\ Condition (CC) & 4.52 *** & 0.526 & 3.18 *** \\ Condition(CC)*Age (A) & 11.46 *** & \(-\)0.601 & \(-\)3.38 *** \\ \hline \hline \multicolumn{5}{c}{**Fixed Effects and Parameters-Fairness \({}^{d}\)**} \\ \hline
**Parameter** & **F** & **B**2  & **Student t** \\ \hline Age (U) & 15.97 *** & 0.662 & 4.66 *** \\ Condition (RT) & 19.70 *** & 0.670 & 4.38 *** \\ Condition(RT)*Age (A) & 13.32 *** & \(-\)0.599 & \(-\)3.65 *** \\ \hline \hline \end{tabular}
\end{table}
Table 4: GLMM—D Donor’s behaviours: Donation and Requested amounts and Fairness of the deals (CC: Control condition; RT: Reputation treatment; A: Adolescent; U: University Student; M: Male).
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 4 Opportunistic Routing Protocol for a WBAN Based on a Time-Varying Link
### Opportunistic Routing Algorithm for the WBAN  
When selecting candidate nodes, the neighbor nodes that can minimize the expected transmission cost between node \(v_{i}\) and sink constitute the optimal candidate set of \(v_{i}\). Namely:  
\[Dl_{i}=\min_{Ca\in Nv_{i}}|de_{iCa}+Cf_{iCa}| \tag{13}\]  
where \(Nv_{i}\) is the set of neighbor nodes of \(v_{i}\). Since the objective function is composed of multiple nodes, we designed the following candidate set construction algorithm (Algorithm 1). On the basis of the Bellman-Ford algorithm, queue optimization was added to reduce redundant slack operations. This is an efficient shortest path algorithm that finds the shortest path from a source vertex to all other vertices. Use the array to record the estimated value of the minimum transmission cost for each vertex.  
```
Input: Node number \(N\), node location (\(x_{i}\),\(y_{i}\)) and initial transmission probability \(p_{ij}^{0}\) Output: Candidate set
1. \(S=\omega\); \(S0=\omega\); \(Dl_{\text{slink}}=0\); \(Dl_{i}^{0}=+\infty\), \(1\leq i\leq N\); \(A=\{\text{sink}\}\), \(B=\omega\), \(h=1\);
2. for all \(v_{i}\in V\), do
3. for all \(n\in Nv(v_{i})\) do
4. if \(EX_{i}\) (\(ne\), sink) < \(ETX\) (\(v_{i}\), sink), then
5. \(S0_{i}=S0_{i}\cup ne\);
6. end if
7. end for
8. end for
9. while not all nodes \(i\) (\(1<=i<=N\)) satisfy \(Dl_{i}^{h}=Dl_{i}^{h-1}\), do
10. for \(1<=i<=N\) do
11. Determine whether the initial candidate set \(S0^{\prime}\) includes nodes in set \(A\)
12. Judge data priority
13. for \(1<=j<=1S0_{i}^{\prime}1\)
14. Based on Equation (4), calculate the transmission success probability
15. According to data priority and Equations (11) and (12), calculate the transmission cost
16. end
17. if the transmission cost of node \(v_{i}\) is reduced
18. \(B0=B0\cup v_{i}\)
19. Update the candidate set and transmission cost of \(v_{i}\)
20. end
21. end
22. end
23. \(S=S0\), \(A\gets B\), \(B=\omega\), \(h=h+1\)
24. Perform data transmission and update the link evaluation index matrix \(L\)
25. end
```  
**Algorithm 1**Candidate Set Construction Algorithm  
After the candidate node is determined, the transmission cost corresponding to each candidate node is compared. The smaller the transmission cost of a node, the higher its priority. The minimum value is selected as the forwarding node of the source node. Finally, the transmission path is determined.  
The studied algorithm is a centralized algorithm. Before the nodes start to transmit data, the centralized central controller coordinates and manages all nodes. It calculates the candidate set and forwarding priority of each node and then notifies each node of the calculation result. When nodes need to transmit data, they send messages to all candidate nodes, and the final forwarding node is determined according to the forwarding priority.
## 4 Experiment and Discussion  
To demonstrate the proposed method, we applied it to an electronic device manufacturing company in Busan, South Korea and verified the location awareness of the items in the MSR. The portable XCODE IU9030 RFID reader with a frequency of 900 MHz was used. The XCODE IU9030 has a recognition range of 2-3 m (tag dependent), and the recognition rate is over 10 tags/sec. The air interface protocol supports the EPC Class 1, Gen2, ISO18000-6B, and the corresponding tag is the passive type, operated at a 900 MHz band frequency. The proposed algorithm was implemented by Visual C++ programming language at Visual Studio 2015. We considered an MSR of 216 cm in width and 144 cm in length, consisting of two rows and three columns, and each zone was a \(72\times 72\) cm square. When we attached the reference tags to the center of each zone, the distance between the adjacent reference tags (\(T\)) could be calculated as 72 cm, which is equal to the width of the zone. The propagation angle of the RFID reader (\(\theta\)) was set at 125\({}^{\circ}\), with aluminum foil placed in front of the RFID reader to fix the propagation angle at this value. As can be seen from many studies, metal and aluminum foils can  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(I_{dj}\) & **Recognized Tag Information of** & **Number of Reference Tags** & \(R_{ij}\) \\  & **Stocks** & & \\ \hline \(J_{11}\) & **A**, A1, A2, A3, A4} & 1 & \(R_{11}\) \\ \(J_{21}\) & **A**, A1, A2, A3, A4, A5, **B** & 2 & \\ \(J_{31}\) & **A**, A2, A3, A4, A5, A6, **B** & 2 & \\ \(J_{41}\) & **B**, A3, A4, A5, A6, A7} & 1 & \(R_{21}\) \\ \(J_{51}\) & **B**, C, A4, A5, A6, A7, A8} & 2 & \\ \(J_{61}\) & **B**, C, A5, A6, A7, A8, A9} & 2 & \\ \(J_{71}\) & **C**, A6, A7, A8, A9, A10} & 1 & \(R_{31}\) \\ \(J_{81}\) & **C**, A7, A8, A9, A10, A11, **D** & 2 & \\ \(J_{91}\) & **C**, A8, A9, A10, A11, A12, D} & 2 & \\ \(J_{101}\) & **D**, A9, A10, A11, A12} & 1 & \(R_{41}\) \\ \(J_{111}\) & **D**, A10, A11, A12} & 1 & \\ \(J_{121}\) & **D**, A11, A12} & 1 & \\ \(J_{131}\) & - & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recognized tag information of stock items in each zone.  
block propagations. This study attached an aluminum conical structure to the RFID reader with an angle of 125\({}^{\circ}\), as shown in Figure 4, to control the propagation angle of the RFID reader.  
The experiment proceeded in two parts. First, we tested the accuracy of the reference tag recognition by checking the recognition of the tags in accordance with the change in measurement distance. In addition, we tested the recognition accuracy in terms of inventory location by changing the number of stock items to be loaded in each zone and the tag recognition interval of the RFID reader. Second, we evaluated and verified the accuracy of the location identification by comparing the locations of the stock items identified by the proposed method with the actual locations. In order to enhance the experiment, we have developed a prototype embedding the proposed algorithm.
## 2 Data and Methods
### Geomagnetic and Solar Conditions Data  
The space weather condition from August to October 2018 is shown in Figure 2, including the 10.7 cm solar radio flux index (F10.7), the Dst, and the Kp index. The red vertical lines represent the times of four strong EQs.  
The Dst and Kp indexes indicate that intense solar activities and geomagnetic perturbations occurred from 26-27 August, 10-11 September, 22 September, and 7-10 October. They provide us with opportunities to compare the differential performances of ionospheric disturbances caused by intense magnetic storms and strong EQs.  
Furthermore, some researchers proposed that an increase in solar activity could also trigger EQ occurrence [22], and this could be the case, for example, for the first EQ of 29 August 2018, but the study of a possible influence of solar activity on the seismicity is beyond the scope of this study.
## 3 Results
### Explanatory Analysis of the Model  
#### 3.2.1 Interpretability Analysis of Attention Mechanism  
During the extraction of neighborhood features using SNSCNN, the channel attention mechanism was employed to amplify the influential drivers and diminish the drivers with minimal impact. The significance of each driver in the model's prediction process was extracted and represented as a driver importance distribution layer using ArcGIS software, as depicted in Figure 9. The analysis reveals several observations:  
1. Different driving factors exhibit varying degrees of influence on urban land change, as also evident in Figure 10.
2. Spatial variations in the magnitudes and effects of driving factors on urban land change can be observed. For instance, elevation, distance to major roads, distance to major railroads, distance to airports, distance to town centers, nighttime light intensity, and shopping center point density display stronger importance trends at the edges and weaker trends at the center. Conversely, slope, distance to water bodies, distance to highway entrances and exits, distance to railway stations, distance to subway stations, hospital point density, school point density, and attraction point density exhibit weaker trends at the edges and stronger trends at the center.
3. The differences in importance among drivers with similar trends primarily occur at the urban edges. For example, the importance of shopping center point density versus town center point density varies, indicating that urban expansion tends to favor urban grids in edge areas, albeit with different impacts from each driver.  
Figure 9: Spatial heterogeneity of neighborhood characteristics. (**a**) Elevation influence degree; (**b**) slope data; (**c**) hospital density; (**d**) shopping center density; (**e**) school site density; (**f**) attraction density data; (**g**) distance to airport; (**h**) distance to town center; (**i**) distance to bus station; (**j**) distance to subway station; (**k**) distance to train station; (**l**) distance to railroad; (**m**) distance to road; (**n**) distance to water. (**o**) nighttime light intensity.  
The model proposed in this paper incorporates the spatial attention mechanism, which highlights areas with high information and downplays areas with low information within the neighborhood sampling window. This allows for a differential portrayal of the regional impacts of each raster transformation. By extracting the spatial attention weights and visualizing the regional disparities in two study regions (coordinates A: 800, 800 and B: 1200, 1200), the effects were analyzed.  
1. the overall influence of the raster around region A is weaker than that of region B.
2. the influence of region B is greater in the northeast and southwest and weakest in the southeast.
3. the influence of the northwest and southeast of area A is greater, and the central part of the east-west line that crosses itself is less influential.
## 5 Experimental Results and Analysis
### Experiment on the Generalization of Network Application
In subsequent experiments, the JNR was set to 10, and when the iteration count reached 90 the Pearson correlation coefficient had exceeded 0.99. Therefore, the iteration count of the GAN was set to 90 in the subsequent experiments. Figure 5: Comparison between generated samples and original samples. Figure 6: Heatmap of Pearson correlation coefficients.
## 4 Methodology
### Model Operation
#### 4.2.1 ViT-Based Encoder-Decoder  
As mentioned in Section 3.1, the ViT is useful for capturing the global context dependencies for images, unlike CNNs which focus on local characteristics. The model utilizes the ViT-B_16 pre-trained model in the encoder part with its classification head dropped, which was designed originally for image classification. This allows the model to be used as a feature extractor, as shown in Figure 3. The ViT-B_16 receives the input frame and divides it into 16 \(\times\) 16 patches, which are subsequently linearly embedded into vectors. Then, positional encodings are employed to preserve the spatial information in the obtained vectors. In ViT-B_16, each patch is represented by a 768-dimensional vector. The ViT-B_16 model has twelve transformer encoder layers and each encoder has twelve attention heads. Thus, the input patches are processed through these multi-head self-attention mechanisms, followed by feed-forward networks that allow the ViT to focus on different parts of the input image simultaneously, capturing dependencies and relationships across the entire image. Finally, the multi-layer perceptron (MLP) head is utilized to aggregate the information from all patches. The obtained features are fed to the spatiotemporal attention model to capture the spatiotemporal relationships among objects in the video frames. Subsequently, the decoder part uses those features to reconstruct the input frames and minimize the reconstruction loss. The decoder consists of four CNN layers to allow fast reconstruction of the video frames with an adaptive pooling layer that provides an output reconstructed frame with the same size as the input.  
In the proposed framework, each video frame was encoded into a compact and informative representation using the ViT-B_16 model in an unsupervised manner for video anomaly detection. The network was trained on normal frames only to preserve their normal distribution, which enables the normal and anomaly samples to be distinguished. After that, the STRA model is applied to capture the spatiotemporal dependencies and relationships between objects among the input frames sequence to leverage the features of the normal frames. A CNN decoder then reconstructs the original video frames using the features obtained from STRA model. In this way, the transformer is capable of capturing global dependencies and intricate patterns within the frame, which is crucial for identifying anomalies that often appear as unusual patterns or objects in a video sequence.  
The proposed model configuration becomes efficient at reconstructing normal frames during testing but finds it difficult to rebuild anomalous frames, as it does not learn to cope with such patterns. Hence, the reconstruction error can then be used as a signal to detect anomalies.
## 3 Experimental Results
### Ultrasonic Wave Velocity  
Repeated ultrasonic velocity measurements with cycle time of 30 s were performed during the loading test, and were interpreted by elapsed time through the rock sample. Ultrasonic velocities had a relative relationship with rock damage (Figure 5). The damage of salt rock was mainly caused by the dislocation and slip among rock grains, this movement resulted in micro-cracks among salt grains. When a large number of micro-cracks were connected, a fatal main crack would form [30]. For the salt rock sample under axial stress, P-wave and S-wave velocities did not show an obvious change before the stabilized-growth cracks stage, because there was only minor dislocation and slip among rock grains in the beginning [30]. When the grain dislocation exceeded a certain limitation, a number of micro-cracks were generated and extended; then the S-wave velocity had a downward trend, while the P-wave velocity remained constant. At the accelerated-growth cracks stage, both S-wave and P-wave velocities began to decrease significantly; P-wave velocity reduced from 4444 m/s to 2011 m/s, and S-wave velocity reduced from 2898 m/s to 834 m/s. Figure 5 shows the P- and S-wave velocity attenuations in sample SA-2 under uniaxial loading. We observed that the attenuation of S-waves was larger than that for the P-waves. In the axial compression, the samples had circumferential expansion with a great many of micro-cracks paralleling to the axial direction. S-waves showed a more sensitive response to this change.