esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### The Relationship between Landslides and Influencing Factors
Moreover, water from the river infiltrates the soil, increasing the soil's moisture content and saturation, which in turn increases the soil weight, reduces the soil's shear strength, and weakens the slope's stability [23]. The relationship of distance to rivers and landslides indicates that within the range of 0 to 2400 m, both the landslide number and LND decrease with increasing distance from a river. In the \(<\)400 m region, the LND is highest, with 48 landslides, accounting for 29.81% of all landslides. Within the range of 2000 to 2400 m, the LND reaches its minimum, with only three landslides, accounting for 1.86% of all landslides. In the \(>\)2400 m range, there is a slight increase in LND. Overall, landslides gradually decrease with increasing distance from the river, demonstrating a pronounced negative correlation (Figure 9). Figure 8: The relationship between landslide distribution and geological unit map. Figure 9: The relationship between landslide distribution and distance to rivers. The effect of rainfall on landslides is similar to the distance to rivers, i.e., an increase in soil moisture content, leading to an increase in soil pore pressure and a decrease in soil shear strength. When the pore pressure exceeds the soil's shear strength, shear failure occurs and a landslide occurs [24]. The relationship between landslides and rainfall is shown in Figure 10, and the results indicate that the highest number of landslides occur with rainfall ranging from 700 to 800 mm and 800 to 900 mm, at 82 and 54, respectively, accounting for 84.47% of all landslides. The relationship between LND and rainfall shows that LND increases with increasing rainfall, indicating that the greater the rainfall, the higher the likelihood of landslides occurring. When the average rainfall reaches 1000 mm, the maximum LND value reaches 0.04/km\({}^{2}\).
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Global Navigation Satellite System (GNSS)
#### 3.1.2 Indoor GNSS Satellites
_Grin-Locs_, shown in Figure 13b, are characterised by their ability to carry out absolute positioning by only utilizing the carrier phase measurements. The Grin-Locs transmitters are not time synchronized, and two or three transmitters are required for the 2D or 3D space, respectively. Each Grin-Loc transmitter consists of two antennas, located one wavelength (\(\lambda\)) apart and are fed by synchronized signals, each containing a specific code on the same carrier frequency generated locally. The one wavelength is used for obtaining a non-ambiguous measurement at the receiving node, and the two Grin-Locs emit on two frequencies are used for eliminating the near-far problem. The Grin-Loc positioning principle lies on the differences in phase of the measurements for every Grin-Loc. The AoA of the signal on the receiver's antenna and the hyperbola on which the receiver is lying are used for the position estimation. For the AoA, two antennas at the unknown receiving node's side are required, and an approximation is made by assuming that the angle in the middle of the two antennas is identical to the angles from each antenna towards the receiving node. This assumption is exact when the receiving node is located an infinite distance away from the two antennas and satisfactory when the distance between the antennas and the receiving node is greater than 10 times the spacing between the antennas (typically 10\(\lambda\), thus, 2 m). The system in [76] was evaluated using standard receivers and aiming to estimate the distributions of the error in the measurement of the phase of the carrier. The distributions were then used to predict the precision of the positioning. The guided and the free space setup were evaluated. The receiving node was connected using a coaxial cable to the signal simulator in the guided setup, whereas in the free space setup, the receiving node was equipped with a receiving antenna connected through free space propagation to the signal simulator. The guided setup was shown to outperform the free space setup [76].
## 1 Introduction
To sum up, this current research area needs to be improved in the following aspects:  
1. For research on LE, the spatiotemporal lag is not fully considered, and the results deviate from reality. The impact of related factors on LE should be analyzed by combining time lag and spatial lag;
2. In terms of research on the relationship between the ecological environment and health, research on the ecological environment and LE of China's land area in the B&R has not yet been reported;
3. There are few studies on the ecological environment indicators of China's land area in the B&R and few studies on the comprehensive ecological indicators. In view of the shortcomings of the current research, this paper has the following innovations and contributions:  
1. A time lag spatial crosscorrelation analysis method for the ecological environment and LE of the B&R is proposed for the first time. In this paper, the influence of the ecological environment on the time lag and spatial lag of LE is considered;
2. This paper reveals the spatial and temporal patterns and laws of the ecological environment and LE of the B&R for the first time. At present, there are not many reports on cities along the B&R in China, and there are few studies on LE in the cities along China's land area around the B&R. Most of them only focus on the construction of a healthy B&R;
3. This paper provides a new objective assessment method for the ecological assessment of the B&R area. A remote-sensing comprehensive ecological index (RSCEI) is proposed. Five indicators, i.e., land surface temperature, gross primary productivity, leaf area index, fractional vegetation cover, and ground humidity, were selected to be used for the principal component analysis (PCA) to regress and integrate the comprehensive indicators of the ecological environment of the B&R region during 2010, 2015, and 2020 for an ecological quality assessment, which is more advantageous than a single ecological index.
###### Abstract
* Sufficient data storage capacity to manage the substantial volume of collected data: The implementation of multi-threading techniques enables simultaneous data storage on both the end nodes and the gateway's local micro SD cards. This ensures the availability of ample data storage capacity to manage the substantial volume of collected data. * Precise time-alignment method for acceleration time history from wireless sensor nodes: This study underscores the significance of employing Dynamic Time Warping (DTW) for achieving precise time alignment in the context of Structural Health Monitoring (SHM) applications. DTW, originally introduced as dynamic programming by [PERSON] and [PERSON] in 1978 [47], serves as a powerful technique for time optimization in time series data. In conclusion, the establishment of a resilient infrastructure encompassing essential functions is vital in tackling the intricate challenges discussed. The present research endeavors to address these gaps by integrating and extending insights from previous studies, providing comprehensive solutions that effectively overcome identified limitations and facilitate further advancements in the field. By elucidating the methodology and guidelines outlined in this study, researchers and developers can leverage this platform to construct their own synchronized sensing systems. This approach effectively addresses the challenges associated with deploying low-cost vibration-based SHM sensor networks.
## 3 Methods
### Prediction and Validation  
Since crop harvesting is conducted yearly, our prediction and validation were also conducted on a yearly basis. Leave-one-year-out cross-validation was conducted to examine the accuracy of corn and soybean yield predictions [61; 62; 63; 64; 65]. The hindcasting models were built by training with the nine-year dataset, excluding the target year, and the predicted yields for the target year were validated using the hidden, true observation values of the target year. We conducted 10 rounds of training and validation, and the number of datasets for each round was approximately 3,200 for training and 350 for validation. The mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the correlation coefficient between the observed and predicted yields during 2006-2015 were calculated for the validation statistics.
## 2 Related Works
### MPC-Based SNNI  
MPC is a cryptographic technique that enables two or more parties to jointly compute a function \(f\) over their respective secret inputs without revealing their secret inputs to each other. SNNI is a special case of MPC, i.e., secure two-party computation (2 PC), where the client and the server jointly evaluate the prediction function on private data (held by the client) and the NN's parameters (held by the server). GC is an MPC protocol commonly used in MPC-based SNNI frameworks. In the context of SNNI, when the bit length of the numbers in use is fixed, all the operations in an NN can be represented using a boolean circuit. This means the entire NN can be translated into one (likely extensive) boolean circuit. The GC protocol [19] provides a method to evaluate such a boolean circuit securely. DeepSecure [43] was pioneered as the first SNNI framework that purely relied on GC. Following this, XONN [44] and GarbledNN [45] are other frameworks that follow this purely GC approach. Such purely GC frameworks suffer from three drawbacks. Firstly, the boolean circuits are unsuitable for performing arithmetic operations (used in NN). Hence, all these frameworks require some modification on NN to make them compatible with boolean circuits, e.g., XONN works on BNNs. This modification significantly drops the accuracy of the NN. Secondly, the purely GC-based approach is bulky and incurs a large overhead [46]. This is considered as the major hurdle of the purely GC-based approach. Thirdly, it leaks the entire NN architecture to the client, i.e., the number of layers, type of layers, and the number of neurons in each layer.  
Secret Sharing (SS) [47] and Boolean SS (also known as GMW) [48] are other common MPC protocols used in MPC-based SNNI. Many SS/GMW-based frameworks have been proposed to address SNNI, e.g., SecureML [49], ABY2 [50], QUOTIENT [51], Chameleon [52], ABY3 [20], SecureNN [53], FalconN [54], CrypTFlow [55], and Crypten [56]. These frameworks are highly efficient. Typically, they exhibit superior performance compared to existing SNNI approaches [46]. It is worth noting that many of them support private training (not only SNNI). Although SS/GMW-based frameworks offer many benefits, such as the fact that they can evaluate any function, including ReLU, and they are very efficient,they suffer from a serious security issue similar to the purely GC-based approach, namely, they leak the entire network architecture to the client.  
Overall, MPC-based SNNI frameworks provide a lower level of security than other SNNI approaches (including HeFUN), as they leak the entire NN's architecture.
## 3 Results
### Oil Spill Accident Calculation and Analysis  
Based on the reliability of the hydrodynamic simulation results, an oil spill approximating the Zhaojiabang oil spill accident in the upper reaches of the Taipu River was simulated and predicted according to the above-defined expansion, transport, and weathering processes. The calculated source intensity and related parameters of the simulated oil spill accident are shown in Table 1 and Figure 5.  
Figure 3: Flow chart of Taipu River oil spill model construction.  
Figure 4: Comparison of calculated and measured water levels in each section in March ((**a**). Taipu Gate; (**b**). Pingwang Bridge; (**c**). Liantang Bridge).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Accident Parameter** & **Design Information Record** \\ \hline Spill location & Zhaojiabang in the upper reaches of the Taipu River \\ Leakage time & 30 min \\ Oil type & Light diesel oil \\ Quantity of oil spill & 30 t \\ Calculation step length & 15 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculated source strength and parameters related to the oil spill accident.  
The simulated oil spill indicated that the maximum thickness of the oil film decreased from 1.94 mm to 0.48 mm from the location of the spill to the end of the Taipu River. The diffusion and drift processes of the oil film in the Taipu River are shown in Figure 6.  
Figure 5: Map of the oil spill location in the upper reaches of the Taipu River.  
Figure 6: Simulation results of an oil spill accident in Taipu River (The blue dots represent the oil film distribution. (**a**). Section of Beijing Hangzhou Grand Canal; (**b**). section of Fenhu Lake; (**c**). section of Xinao River).  
According to the calculation results, under the joint action of tidal currents and wind, the oil film formed by the oil spill generally drifts downstream in the direction of the coast. The above three local diagrams roughly illustrate the accident progression. The time taken by the oil film to reach the Beijing-Hangzhou Canal was 12 h, where the maximum thickness of the oil film was 1.05 mm; the time to reach the center of Fen Lake was 26 h, where the maximum local thickness was 0.64 mm; and the time to reach the lower boundary of the Taipu River was 48 h, where the maximum thickness was 0.48 mm. Oil spill evaporation mainly occurs within the first 10 h after an oil spill. At 0.2 h after the oil spill occurred, the oil spill evaporation rate was about 0.06%. In the 10 th hour, the evaporation rate of oil spill was about 0.11. Subsequently, the evaporation rate of the oil spill slowed down greatly, and the evaporation rate remained at about 13%. An oil spill emulsifies under the action of turbulence and wind, and the combination of water and oil increases the water content of the oil spill, but the water content does not exceed 80%.
## 2 Methodology
### Experiments  
A validation study is needed to ensure mesh sensitivity and the selection of an appropriate turbulence model. Computational fluid dynamics (CFD) studies are usually validated with experimental results. Some experimental measurements were therefore obtained in the industrial plant. The airflow velocity was measured at the cross-section of inlet surface of injector channel. The cross-section has rectangle shape, which is 0.235 m in size for each edge, and airflow velocities were measured at 25 different points, as suitable for ASHRAE Standard-111 [18]. A pitot tube (\(\pm\)0.01 m/s), integrated with multifunctional data logger (TESTO 435-4, Testo, Seattle, WA, USA), was used for measuring airflow velocity. Thanks to these velocity measurements, the mean velocity of airflow at the inlet section was obtained. In the validation study simulations, the mean airflow velocity value at the inlet section was identified as the boundary condition. The validation study was carried out using temperature measurements of the fabric surface. In the last section of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Porosity—\(\beta\)** & **Injector Angle—\(\alpha\)** & **Airflow Velocity—V\({}_{\text{a}}\)** & **Fabric Velocity—V\({}_{\text{f}}\)** \\ \hline \hline
**(-)** & **(**\({}^{\circ}\)**)** & **(m/s)** & **(m/s)** \\ \hline
0.05 & 0 & 0.5 & 0.25 \\
0.1 & 10 & 1 & 0.3 \\
0.2 & 20 & 2 & 0.35 \\
0.3 & 30 & - & - \\
0.4 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric and operating conditions.  
Figure 2: Geometric details of injector channel of stenter machine.  
stenter machine, fabric surface temperatures were obtained by thermal camera. The temperature measurement points on the fabric surface are illustrated in Figure 3. An average temperature value was obtained by measuring the surface temperatures of the fabric before and after each injector channel by using a thermal camera (TESTO 870-2, \(\pm\)2% \({}^{\circ}\)C) from 10 different points on a line in Figure 3. The average temperatures obtained before each injector channel were defined as inlet into injector channels boundary condition of fabric. These temperature values were used in the validation study. The air temperature flowing in the injector channel was measured using thermocouple (\(\pm\)0.3 \({}^{\circ}\)C) integrated into the TESTO 435-4 multifunctional data logger.
## 4 Results
### Analysis of the Forested Areas' Exchange Status  
Understanding the dynamics of forested areas in relation to other land use types is crucial for effective forest management. Analyzing the exchange status between different land use types provides valuable insights into the factors influencing forestry areas. As displayed in Figure 5 and Table 3, the findings from the matrix transfer vary across different periods. Notably, the period from 2010 to 2015 indicated a significant transfer of agricultural land (592.6 km\({}^{2}\)) to forested areas (Figure 5b), while approximately 204.6 km\({}^{2}\) of forested areas was lost. This shift resulted in ecological enhancements in land use. Similarly, the exchange status between forested areas and artificial surfaces revealed a net transfer of 12.2 km\({}^{2}\) to forest areas compared to 22.6 km\({}^{2}\) from forest areas, resulting in a net loss of approximately \(-10.4\) km\({}^{2}\) of forested areas between 2005 and 2010. Thus, the inter-period indicates that agricultural land was the most critical land use type that impacted forested areas (Figure 5). Therefore, these findings indicate the importance of the responsible management of agricultural land. The research period spanning from 2005 to 2020 served as a comprehensive pivot for exploring the global intensity of the exchange between forested areas and other land use types that may influence their vulnerability. In the context of the interplay between agricultural land and forested areas, the analysis shows a net loss of forested areas (\(-78.8\) km\({}^{2}\)) with a transfer of approximately 370.5 km\({}^{2}\) into forested areas compared to a transfer out of 449.39 km\({}^{2}\). In addition, the nexus between ecological and bare land shows that forested areas were lost at about \(-127.8\) km\({}^{2}\). Or, artificial surfaces (\(-65.3\) km\({}^{2}\)) and grassland (\(-0.30\) km\({}^{2}\)), although causing a loss of forested areas, did not exceed the amount transferred out of agricultural land regarding forested areas. Therefore, agriculture and bare land emerged as the primary factors influencing the vulnerability of forested areas, with agriculture being the most significant contributor to forest loss (Figure 6). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Years/Values**} \\ \cline{2-10}  & \multicolumn{2}{c}{**2005**} & \multicolumn{2}{c}{**2010**} & \multicolumn{2}{c}{**2015**} & \multicolumn{2}{c}{**2020**} \\ \hline  & Km\({}^{2}\) & \% & Km\({}^{2}\) & \% & Km\({}^{2}\) & \% & Km\({}^{2}\) & \% \\ \hline Forested Areas & 944.66 & 14.16 & 678.94 & 10.18 & 1143.25 & 17.14 & 735.05 & 11.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The statistics of the forested areas during 2005, 2010, 2015, and 2020. Figure 4: Forested areas’ status in the Thüs Region during 2005, 2010, 2015, and 2020. Figure 5: **(a-c)** Indicate the exchange between other land use types and forested areas in 2005–2010, 2010–2015, and 2015–2020, respectively; **(d)** shows the study period (2005–2020).
# Numerical Study on Tip Vortex Cavitation Inception on a Foil
## 1 Introduction
The Lagrangian bubble dynamics approach, which is a somewhat costly method because of the requirement of Lagrangian equations of bubble motions, can determine the cavitation inception point by considering the nuclei effects and the interaction of the tip vortex and nuclei [20; 24; 25; 26; 27]. In the present study, the inception of tip vortex cavitation for an elliptic planform hydrofoil has been estimated using a numerical simulation and validation tests. In addition, new scaling laws with variable scaling exponent have also been suggested for cavitation inception index. Numerical investigation of the fully wetted and cavitating flows around the hydrofoil was carried out. Reynolds averaged Navier-Stokes (RANS) approach along with an Eulerian cavitation model was used for the vortex cavitation flow simulations. The validation work evaluated the effects of the turbulence model and grid resolution on the tip vortex flows. Some past studies reported that the RANS methods have been known to be inadequate in predicting the tip vortex flow accurately [18; 28; 29; 30; 31]. This was because the one-equation and two-equation turbulence models used in the RANS simulations showed excessive numerical diffusion inside the tip vortex [18; 23; 24]. In this regard, the current study confirms that the RANS method using the Reynolds stress model (RSM) can achieve an accurate representation of this kind of tip vortex flows and can adequately explain the flow physics involved. In the scaling of tip vortex cavitation inception, the extrapolation of the cavitation index from the model tests to large scale conditions is generally overestimated when the classical scaling laws are used [24; 32]. The McCormick's law [22] and Arndt's law [5; 8] are based on the rule that the cavitation inception is proportional to the Reynolds number raised to an exponent, \(R_{\epsilon}^{7}\). The recent analytical studies using the [PERSON]'s law under limited Reynolds number conditions have suggested various values lower than the classical value of the exponent \(\gamma\)[24; 33; 34; 35]. However, the current cavitation scaling study suggests a new expression of the scaling exponent \(\gamma\) as a function of the Reynolds number and shows the variation of the suggested scaling exponent at higher Reynolds numbers for full scale applications. The current numerical results are overall divided into two parts. The first part is about the validation of the present numerical solutions for the wetted and cavitating flows. In the second part, the results of the tip vortex cavitation inception analysis on an elliptic planform foil are presented and discussed under the light of the present new scaling concept.
# Designed on 0.18 um CMOS Process Small Size Broadband Millimeter Wave Chip Antenna
## 2 Materials and Methods
### Parameter Analysis of Antenna Feed Line  
First, this paper analyzes the parameters of the feed line length L\({}_{3}\) for a single patch antenna. Figure 7 shows three different lengths of square monopole patch antennas, each 300 \(\upmu\)m, 325 \(\upmu\)m, and 350 \(\upmu\)m, respectively. The results are shown in Figure 8, with the reflection coefficient of \(-10\) dB as the bandwidth standard. When the feed line length L\({}_{3}\) is 300 \(\upmu\)m, the bandwidth range is 88.6-94.5 GHz. When L\({}_{3}\) is 325 \(\upmu\)m, the bandwidth range is 85.7-97.5 GHz. When the L\({}_{3}\) length is 350 \(\upmu\)m, the bandwidth range is 83.1-100 GHz. From this parameter simulation, it can be seen that when the feed length is 350 \(\upmu\)m, there is an optimal bandwidth range.  
Figure 8: Reflection coefficient of antenna feed line variation.  
Figure 6: Simulated antenna efficiency.  
Figure 7: Antenna parameter L\({}_{3}\) evolution diagram.