esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Methods
#### 2.3.2 Revised Universal Soil Loss Equation (RUSLE) Model  
In order to compute soil erosion in the Loess Plateau's forest farming subregion, the RUSLE model was utilized, and the research area's features and data were used to develop the methodology for each factor. The RUSLE model's equations are as follows:  
\[A=R\times K\times LS\times C\times P \tag{1}\]  
where \(A\) is the annual quantity of soil erosion (t/ha per year), \(R\) is the precipitation erosivity factor, \(K\) is the soil erodibility factor, \(L\) is the slope length factor, \(S\) is the surface vegetation cover and management factor, and \(P\) is the factor for soil and water conservation methods.
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 3 Proposed Method
### Ca-Cgnet  
In this work, we design a deep neural network named the \"Component-aware Capsule Graph Neural Network\" (CA-CGNet) for shape correspondence based on the functional maps framework. We believe that the capsule graph network allows a powerful understanding of the mesh's positions and directions features. Since existing routing algorithms do not fully consider the potential features between capsules, a component-aware graph capsule network is proposed to further extract deeper semantic information by building graph relationships between capsules. Then, in order to get more precise shape correspondence, the functional map with component constraint and the loss function with semantic constraint as the regularization terms are designed. The basic pipeline can be described by the following steps: firstly, the mesh is over-segmented to form several patches, and the patches with similar features are aggregated together to form components through a dynamic clustering algorithm. Secondly, in order to fully learn the relationships between capsules, we consider capsules as nodes of the graph neural network and further enhance the capsules through a component-aware graph routing algorithm with a knowledge distillation strategy to increase the convergence speed by decreasing the number of parameters while maintaining network accuracy. Finally, the component-constrained functional map is represented with Equation (11), and the loss function with semantic constraint is derived from Equation (17). The entire network architecture is given in Figure 2 in detail.  
Figure 2: The architecture of our proposed network CA-CGNet. For the mesh component, dynamic clustering (DC) is applied to form the patches generated by over-segmentation. To further improve the quality of feature acquisition, the component-aware graph routing algorithm is constructed to learn the relationship between the capsules in the same layer through a component-aware graph neural network (CA-GNN). The component constrained functional map is represented by Equation (11) and the component-based semantic loss is derived from Equation (17).
## 3 Analysis
### Analysis of Vertical Bearing Characteristics of Bored Piles  
#### 3.4.1 Test Analysis of Pile Side Friction  
Figure 6 depicts the P-S curve of the static load test of bored piles with empty bottom piles with a varying center distance of the pile. According to the standard [3], the load value at the starting point of the steep drop section of the P-S curve is the vertical ultimate bearing capacity of a single pile. It is known that the P-S curve of an empty bored pile bottom pile with a varying center distance of pile contains a steep drop section that can be used to assess the ultimate bearing capacity of a single pile. When the center distance of the pile was 2.0D, the ultimate bearing capacity of the two test piles was 3300 kN, and the pile top settlement was 42.36 and 51.36 mm, respectively. Similarly, when the spacing between piles was 2.5D, the ultimate bearing capacity of a single pile was 3000 kN, and the total settlement of the pile top was 51.78 and 43.29 mm, respectively. Since there is no bottom resistance at the end of the empty bottom pile of the bored piles, the ultimate bearing capacity stated above is the total side friction of the pile. The average values of the ultimate positive side friction of the pile in zones 2D and 2.5D were 105.1 and 95.5 kPa, respectively. The final positive side friction of the bored piles decreased by 9.13% when the center distance of the pile was increased from 2D to 2.5D.  
Using the same test procedure and the same criterion of ultimate bearing capacity. [PERSON] et al. [3] determined the ultimate positive friction resistance of a non-squeezed pile on a loess base to be 64.3 kPa. When the center distance of the pile was 2D and 2.5D, the comparative study revealed that the squeezing effect increased the positive side friction of the pile by 48.5% and 63.5%, respectively. This is because the squeezing soil effect during the formation of the bored piles compacts the soil, increases the pile-soil interface stress, brings the pile-soil interface into closer contact, and significantly increases the positive-sidefriction of the pile. When the center distance of the pile decreased, the compaction effect became more pronounced, and the friction resistance increased.
## 1 Introduction
It designs an underwater multi-mobile node electric field communication protocol and channel allocation technique and validates the effectiveness of the protocol through simulation analysis. This research aims to develop communication strategies for underwater electric field multi-node networking, and focuses on the following aspects: conducting simulation parameter design and calculation for electric field communication based on the characteristics of underwater electric field transmission; constructing an NS-3.31 underwater electric field multi-node networking communication network model and setting up simulation models for various layers of the multi-node communication network; conducting simulation program analysis for multi-node networking, comparing the impact of OLSR and AODV protocols on the communication performance of multi-node networking, and evaluating the performance of the simulation communication results; and finally, conducting dynamic communication experiments for the underwater electric field communication system based on AUV, and further conducting dynamic backoff multi-node communication experiments based on AUV using CSMA/CA, to verify the effectiveness of the underwater electric field communication protocol, thus achieving collision-free underwater electric field multi-node communication. The schematic diagram of AUV multi-node network communication based on underwater electric field is shown in Figure 1.  
Figure 1: Schematic diagram of AUV multi-node network communication based on the underwater electric field. The remaining parts of this article are organized as follows: In Section 2, the setup of the underwater electric field communication simulation model is presented. In Section 3, an analysis of the simulation program based on underwater electric field communication is conducted. In Section 4, the design and experimental verification of the underwater electric field communication channel backoff protocol are completed. Section 5 presents the main conclusions of this article.
# Smart Deployable Scissor Lift Brace to Mitigate Earthquake Risks of Soft-Story Buildings
### Earthquake Early Warning Systems Around the World  
In recent years, the Earthquake Early Warning (EEW) system has emerged as a promising technology that attempts to mitigate earthquake risks by providing warning messages to the public and industries. An EEW uses a system of accelerometers, seismometers, communication, computers, and alarms that is devised for notifying adjoining regions of a substantial earthquake while it is in progress. EEW does not predict earthquakes, it can only detect earthquakes and give warnings. EEW consists of the rapid detection of an earthquake that is underway and can make predictions about the expected ground shaking within seconds so that a warning can be broadcast to those in harm's way. Currently, such systems are implemented in Mexico [36] and Japan [37] and are being tested in other parts of the Americas, Asia, and Europe. The principle behind EEW is based on the fact that a seismic P-wave travels faster (1.73-1.85 times) than the more destructive S-wave [38]. When a P-wave is detected, the approximate location of the epicenter is estimated, and a warning signal is broadcasted to the affected areas before the S-wave arrives. In Japan, EEW provides earthquake warnings in the order of seconds, allowing for the reaction time to mitigate larger earthquake disasters such as shutting down certain industrial operations, reducing train speeds, and evacuating personnel [39]. Figure 2 illustrates the concepts behind an EEW system, schematically.  
Damage and collapse due to the soft-story effect are most often observed during and after an earthquake. The traditional methods such as adding infill walls and energy dissipation braces by retrofitting soft-story buildings tend to obstruct the very main purpose of soft stories. These methods of retrofitting soft-story buildings have not proven to be effective at addressing modern-day requirements of open first floors for parking and commercial activities. Keeping in mind the functional requirements of the open first story of buildings, smart devices can be developed which can be deployed instantly during an earthquake using signals provided by the EEW system. At other times, the smart structure can remain in its undeployed state without affecting the functionality of the  
Figure 2: Schematic diagram of an earthquake early warning system.  
building. The system can be made to fully deploy automatically and retract itself after each ground motion.
# Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City
## 4 Use Cases
### Basic Intelligent Services  
In the SG scenario, which is shown in Figure 7a, the information about energy can be treated as a property of a device. The properties of all devices that are used, can be integrated, scheduled and optimized. The users' requirements for electricity are analyzed to make a reasonable plan. The objective is that the data value of energy consumption can be reduced. Then the energy service will be implemented. In the ITS scenario, which is shown in Figure 7b, moving vehicles can be seen as objects accessed in the Internet of Vehicles (IoV), which is one special type of IoT. The users' destinations, location, speed and direction of these vehicles are uploaded to the management terminal server, and the reasonable scheduling schemes (vehicles' routings) are distributed to every vehicle. Then, a better traffic environment can be guaranteed. In the IMD scenario, which is shown in Figure 7c, patients can record their own health-related data through a wearable device or other measuring equipment. The wearable device can measure some real-time data, such as the heart rate, exercise data and sleep-activity status. The other non-wearable device can periodically measure some relatively stable data, such as body weight, blood pressure, blood sugar and blood fat. In addition, patients can also take some necessary pictures and upload these pictures to the medical system through the Internet. The patients' information represents the physiological characteristics, which can be used for disease recognition. The doctors can implement remote diagnosis for their patients according to the health-related data. In the SRS scenario, which is shown in Figure 7d, all the products in the supermarket are labeled by the RFID tags on the products. The product shelves are equipped with RFID readers. The RFID readers record the basic information and location of these products. When people go to the supermarket, their shopping habits are recorded and their requirements will be confirmed. In the management system of the supermarket, the recorded data will be analyzed to predict what customers need. Then, some specific products may be recommended to customers, and customers are guided to the corresponding product shelves by a robot. In addition to the SRS, there are other multiple-recommender systems. For example, diet recommendations can promote the appropriate food for a patient according to his/her health-related data. Known people's preferences and characteristics, entertainment recommendations can introduce an appropriate place for people to relax.  
Figure 7: Processes of basic intelligent services.
###### Abstract  
Ionospheric scintillation is a pressing issue in space weather studies due to its diverse effects on positioning, navigation, and timing (PNT) systems. Developing an accurate and timely prediction model for this event is crucial. In this work, we developed two machine learning models for the prediction of ionospheric scintillation events at the equatorial anomaly during the maximum and minimum phases of solar cycle 24. The models developed in this study are the Random Forest (RF) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm. The models take inputs based on the solar wind parameters obtained from the OMNI Web database from the years 2010-2017 and Pc5 wave power obtained from the Bear Island (BJN) magnetometer station. We retrieved data from the Scintillation Network and Decision Aid (SCINDA) receiver in Egypt from which the S4 index was computed to quantify amplitude scintillations that were utilized as the target in the model development. Out-of-sample model testing was performed to evaluate the prediction accuracy of the models on unseen data after training. The similarity between the observed and predicted scintillation events, quantified by the \(R^{2}\) score, was 0.66 and 0.74 for the RF and XGBoost models, respectively. The corresponding Root Mean Square Errors (RMSEs) associated with the models were 0.01 and 0.01 for the RF and XGBoost models, respectively. The similarity in error shows that the XGBoost model is a good and preferred choice for the prediction of ionospheric scintillation events at the equatorial anomaly. With these results, we recommend the use of ensemble learning techniques for the study of the ionospheric scintillation phenomenon.  
machine learning: ensemble learning techniques; ionospheric scintillation; Random Forest algorithm; eXtreme Gradient Boosting algorithm 2024 15 1213. [[https://doi.org/10.3390/atmos1510213](https://doi.org/10.3390/atmos1510213)]([https://doi.org/10.3390/atmos1510213](https://doi.org/10.3390/atmos1510213)) 1224 15 1213 1
## 3 Results and Discussion
### Interlayer Interference Time and Backflow Gas Volume
Also, there is an extension observed in the duration of backflow and interlayer interference time. The results presented in Figure 7a demonstrate that a larger pressure difference between layers during commingled two-layer production leads to an  
Figure 6: (**a**) Pressure change and (**b**) accumulated gas production curves for the commingled production of HS + BX layers. increase in backflow time, ratio of backflow time to total gas discharge time, and ratio of backflow gas discharge time to total gas discharge time, respectively. Such an observed pattern remains distinguishably in the commingled three-layer production depicted in Figure 7b. The time statistics for experiments with commingled production for two, three, and four layers are listed in Table 2, from which it is evident that, as the pressure differential increases under the commingled production from the same layer, there is a significant prolongation of interlayer interference time that also constitutes a higher proportion of total gas production time. Figure 7: Backflow time, backflow time/gas production time, and backflow gas discharge time/gas production time curves under different interlayer pressure differences in the (**a**) commingled two-layer production and (**b**) commingled three-layer production. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Ratio of**} & \multirow{2}{*}{**Ratio of**} & \multirow{2}{*}{**Ratio of**} & \multirow{2}{*}{**Index of**} & \multirow{2}{*  
The interference time also increases with a significant interlayer pressure difference as the number of commingled layers increases. The increase in the number of commingled layers leads to an increase in both gas backflow and the maximum instantaneous backflow rate, as well as an extended duration and volume of backflow, when the interlayer pressure ratio remains constant or similar. The larger the pressure ratio between layers, the more significant the observed increase.
## 3 Materials and Methods
### Research Methodology
#### 3.2.2 Experimental Design  
The data acquired from the case study outlined in Section 3.2.1 were adopted to set up an experimental design to thoroughly evaluate the effectiveness of the proposed approach in  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Mean delay/advance [days] & 11.4 & 35.9 & \(-9.0\) & 1.8 & 21.3 & \(-6.5\) & 5.9 & \(-11.0\) \\ Delay/advance standard deviation & 19.6 & 31.4 & 19.9 & 31.9 & 12.5 & 31.9 & 41.2 & 22.3 \\ Unitary purchasing cost [\%] & 80.9\% & 97.7\% & 91.6\% & 84.6\% & 103.0\% & 94.7\% & 97.7\% & 110.2\% \\ Overall capacity [\%] & 41.3\% & 84.1\% & 26.2\% & 98.7\% & 24.7\% & 74.1\% & 76.2\% & 87.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Case study second supplier data.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Mean demand [pieces] & 1091 & 329 & 1049 & 342 & 937 & 1346 & 1275 & 1595 \\ Demand standard deviation & 10.8 & 13.5 & 8.7 & 12.6 & 8.3 & 10.1 & 10.8 & 12.1 \\ Unitary holding cost [\%] & 51.1\% & 176.4\% & 87.4\% & 140.7\% & 173.0\% & 134.4\% & 29.5\% & 163.5\% \\ Unitary shortage cost [\%] & 184.6\% & 84.8\% & 28.7\% & 196.2\% & 119.7\% & 144.3\% & 163.3\% & 93.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Case study component data.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Mean delay/advance [days] & 9.8 & \(-4.7\) & 10.8 & \(-31.4\) & \(-13.4\) & \(-38.7\) & \(-11.6\) & \(-22.5\) \\ Delay/advance standard deviation & 22.7 & 21.2 & 22.2 & 22.3 & 23.5 & 27.9 & 47.3 & 28.9 \\ Unitary purchasing cost [\%] & 3079 & 4768 & 3271 & 2743 & 2872 & 2302 & 684 & 3928 \\ Overall capacity [\%] & 95.9\% & 18.4\% & 85.8\% & 7.7\% & 85.9\% & 69.6\% & 67.6\% & 75.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Case study first supplier data.  
contrast to benchmarks. Section Benchmarks delineates the benchmarks against which the proposed approach was assessed. Subsequently, Section Evaluation Metrics enumerates the metrics chosen for the comparative analysis. Finally, Section Experiment Setup elucidates the experimental configurations employed for each evaluation.
## 3 Noise Analysis
### Selection of Routes and Vertiports  
It is assumed that the operation is based on the mission profile, as shown in Figure 11. On AEDT, we assumed that there would be no significant difference in noise analysis by combining vertical take-off, vertical landing, and transitions into one process, so we performed noise analysis assuming these three as one process.  
A noise priority scenario is a scenario in which most of the paths are set to fly along the least populated area to reduce their impact on noise as much as possible. On the other hand, a business priority scenario is a scenario that heads to a destination at the shortest distance, ignoring the impact of noise. In the case of a single, straight-line business priority  
Figure 11: K-UAM mission profile.  
Figure 10: (**a**) Virtual noise width of eVTOL passing through the actual city; (**b**) virtual noise width of eVTOL passing through a virtual city modeled in an ideal space.  
scenario, it is also necessary to run a step-by-step flight through the node, as is the case with the noise priority scenario in the practice analysis.  
The two scenarios are applied to the routes of COEX in Gangnam-gu from Gimpo Airport on the AEDT among the locations of vertiports selected above, as shown in Figure 12. Administrative districts to be included in the districts affected by noise expected from the two routes are Gangseo-gu, Yangcheon-gu, Yeongdeungpo-gu, Dongjak-gu, Seocho-gu, Gangnam-gu, Songpa-gu, Gwanak-gu, Mapo-gu, and Yongsan-gu in Seoul, and Gwachenosi and Seongnam-si in Gyeonggi-do. Thus, reconstructing each administrative district in the same way as Figure 10 is shown in Figure 13, and Table 7 represents the characteristics of the reconstructed virtual space. Table 7 shows that the average error rate of the actual area and the virtual space area in each zone is 1.1%, and the maximum error rate is 2.7% of Gwanak-gu; thus, it is situated well.  
data, so in this paper, helicopters, which are currently in operation, were selected as noise operation models.  
The helicopters operating for civilian use and market demand in the United States [13] presented in the 2015 FAA Civil Aircraft Registry Database are as shown in Table 8. For this study, analysis was conducted using only a 3-5 seat helicopter type to match the SA-1, a five-seat eVTOL under development by Hyundai Motors.  
Meanwhile, the target noise of many eVTOL manufacturers is 62 to 63 dB. Therefore, the number of operations was adjusted after fixing it at 06:00-20:00, which includes commuting time, to implement the target noise on the AEDT. The noise map was constructed by increasing the number of operations by 100 from 200 to 500, and there was no significant difference in the take-off and landing sections; however, in the cruise section, 300-500 helicopters included 65 dB-70 dB noise, and 200 helicopters included 60 dB-65 dB, which is the main noise level. Therefore, the number of R44 operations in the AEDT was designated as 200, and the target noise was adjusted to 60-65 dB on cruise.
## 3 Reversible Watermarking Based on intDCT
### Information Hiding with DCT Coefficient Expansion [12]  
We introduce the hiding principle considering capacity and audio quality. The expansion technique is effective for reserving the hiding capacity [20; 22; 30; 34; 35; 36]. Suppose one bit of information \(b\in\{0,1\}\) is embedded into the \(i\)-th DCT coefficient \(H(i)\) of original data as  
\[S(i)=2H(i)+b, \tag{17}\]  
where \(S(i)\) is the \(i\)-th DCT coefficient of the stego data. Additionally, the original DCT coefficient \(H(i)\) and the embedded information \(b\) can be extracted from \(S(i)\) by  
\[H(i) = \lfloor S(i)/2\rfloor, \tag{18}\] \[b = S(i)-2H(i) \tag{19}\]  
where \(\lfloor\cdot\rfloor\) denotes the floor function.  
Generally, the embedding location affects the quality of stego data. In the work by [PERSON] et al. [12], when embedding \(K\) bits of information \(b_{1},\ldots,b_{K}\in\{0,1\}\) in the coefficients (\(K<N\)), the last coefficients corresponding to the highest frequencies are used, i.e.,  
\[S(i)=\left\{\begin{array}{cc}H(i)&i\leq N-K\\ 2H(i)+b_{K-N+i}&i>N-K\\ \end{array}\right. \tag{20}\]  
As described above, the previous work used the fixed part of the intDCT coefficients. This framework has two problems. The first is that using the highest frequency coefficient might not be optimal. The second is that embedding the information could lead to an overflow of the restored signal. Since the original signal is 16 bit PCM format, a sample value should be between \(-\)32,768 and 32,767. However, the embedding process does not ensure that the sample values of the stego signal are within this interval.