esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Examples
### Advection of Density Perturbation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Schemes** & \(\mathbf{h}\) & \(\mathbf{L_{1}}\)**Error** & \(\mathbf{L_{1}}\)**Order** & \(\mathbf{L_{2}}\)**Error** & \(\mathbf{L_{2}}\)**Order** & \(\mathbf{L_{\infty}}\)**Error** & \(\mathbf{L_{\infty}}\)**Order** \\ \hline \multirow{4}{*}{WENO5-LF} & 1/5 & \(1.20\times 10^{-2}\) & - & \(1.29\times 10^{-2}\) & - & \(1.69\times 10^{-2}\) & - \\  & 1/10 & \(6.27\times 10^{-4}\) & 4.265 & \(6.85\times 10^{-4}\) & 4.238 & \(9.80\times 10^{-4}\) & 4.107 \\  & 1/20 & \(1.96\times 10^{-5}\) & 5.001 & \(2.24\times 10^{-5}\) & 4.933 & \(3.61\times 10^{-5}\) & 4.764 \\  & 1/40 & \(6.05\times 10^{-7}\) & 5.014 & \(6.84\times 10^{-7}\) & 5.034 & \(1.14\times 10^{-6}\) & 4.981 \\ \hline \multirow{4}{*}{WENO5-ROE} & 1/5 & \(1.12\times 10^{-2}\) & - & \(1.19\times 10^{-2}\) & - & \(1.61\times 10^{-2}\) & - \\  & 1/10 & \(5.76\times 10^{-4}\) & 4.276 & \(6.32\times 10^{-4}\) & 4.240 & \(9.08\times 10^{-4}\) & 4.145 \\  & 1/20 & \(1.79\times 10^{-5}\) & 5.010 & \(2.05\times 10^{-5}\) & 4.943 & \(3.36\times 10^{-5}\) & 4.754 \\  & 1/40 & \(5.57\times 10^{-7}\) & 5.005 & \(6.31\times 10^{-7}\) & 5.025 & \(1.07\times 10^{-6}\) & 4.975 \\ \hline \multirow{4}{*}{WENO5-LBFS} & 1/5 & \(2.30\times 10^{-2}\) & - & \(2.49\times 10^{-2}\) & - & \(3.81\times 10^{-2}\) & - \\  & 1/10 & \(1.16\times 10^{-3}\) & 4.307 & \(1.31\times 10^{-3}\) & 4.250 & \(1.95\times 10^{-3}\) & 4.290 \\ \cline{1-1}  & 1/20 & \(3.52\times 10^{-5}\) & 5.046 & \(4.08\times 10^{-5}\) & 5.003 & \(7.19\times 10^{-5}\) & 4.762 \\ \cline{1-1}  & 1/40 & \(1.08\times 10^{-6}\) & 5.030 & \(1.21\times 10^{-6}\) & 5.070 & \(2.15\times 10^{-6}\) & 5.065 \\ \hline \multirow{4}{*}{WENO5-URLBFS} & 1/5 & \(1.00\times 10^{-2}\) & - & \(1.07\times 10^{-2}\) & - & \(1.56\times 10^{-2}\) & - \\ \cline{1-1}  & 1/10 & \(4.58\times 10^{-4}\) & 4.448 & \(5.10\times 10^{-4}\) & 4.393 & \(7.50\times 10^{-4}\) & 4.374 \\ \cline{1-1}  & 1/20 & \(1.33\times 10^{-5}\) & 5.110 & \(1.52\times 10^{-5}\) & 5.066 & \(2.48\times 10^{-5}\) & 4.918 \\ \cline{1-1}  & 1/40 & \(3.49\times 10^{-7}\) & 5.249 & \(3.92\times 10^{-7}\) & 5.277 & \(6.00\times 10^{-7}\) & 5.369 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy order and error of different flux solvers based on the WENO5 scheme at different grid resolutions. Figure 7: Advection of density perturbation problem. Figure (**a**) shows the accuracy of WENO5-LF, WENO5-ROE, WENO5-LBFS, and WENO5-URLBFS schemes; Figure (**b**) shows the CPU time and \(L_{2}\) error comparison of WENO5-LF, WENO5-ROE, WENO5-LBFS and WENO5-URLBFS schemes.
## 2 Data and Methods  
### Study Sites and Flux Measurements  
The carbon flux observed data (net ecosystem exchange (NEE) and ecosystem respiration (Re)) and meteorological data (air temperature and photosynthetically active radiation (PAR)) for 2003-2005 were downloaded from the ChinaFLUX website [4,16]. The measured GPP (gC/m\({}^{2}\)/d) was then given by subtracting Re from NEE as: GPP = NEE - Re. The daily data were integrated based on the filled half-hourly values [4,16]. More information about original observed half-hour values can be found at [PERSON] _et al._[4] and [PERSON] _et al._[5]. Finally, eight-day flux observed data from calculations of daily data were used for calibrating and validating GPP models. In this study, eight sites in five different land cover types were used: croplands (YuCheng (YC) in Shandong province), grasslands (XiLinGuoL (XLGL) in Inner Mongolia autonomous region, HaiBei (HB) in Qinghai province, and DangXiong (DX) in Tibet autonomous region), mixed forests (ChangBaiShan (CBS) in Jilin province), evergreen needleleaf forests (QianYanZhou (QYZ) in Jiangxi province), and evergreen broadleaf forests (DingHuShan (DHS) in Guangdong province and XiShuangBanNa (XSBN) in Yunnan province). A brief description of these sites is presented in Table 1 and Figure 1. All effective measurements were divided into two groups: one for model calibration, including 70% of all the data (randomly chosen), and the remainder for validation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sites ID** & **Lat(\({}^{\circ}\)N)** & **Lon(\({}^{\circ}\)E)** & **Data Range** & **Land Cover** \\ \hline CBS & 42.40 & 128.10 & 2003–2005 & MF \\ DHS & 23.17 & 112.53 & 2003–2005 & EBF \\ QYZ & 26.75 & 115.67 & 2003–2005 & ENF \\ XSBN & 21.90 & 101.27 & 2003–2005 & EBF \\ YC & 36.95 & 116.6 & 2003–2005 & Croplands \\ HB & 37.62 & 101.32 & 2003–2005 & Grasslands \\ XLGL & 43.63 & 116.70 & 2003–2004 & Grasslands \\ DX & 30.83 & 91.12 & 2003–2004 & Grasslands \\ \hline \end{tabular}
\end{table}
Table 1: A brief description of eight flux sites in this study.
## 3 Results and Discussions  
### Non-Dispersive Water  
In order to verify the validity of the modified model, the mKZK equation was used to predict the sound field distributions generated from the transducer. In this case, the media in the direction of propagation was degassed water, and the surface pressure of the transducer was set to be 0.4 MPa. Then, the results were compared with those obtained from experimental measurements as well as from the original KZK model. The results are presented in Figure 2 for comparison. Due to the axial-symmetry of the sound field, only the sound field distribution in the axial direction was studied. Also, since the major concern of this paper is to investigate how the focal-shift could be accurately predicted, the pressure profiles are all presented in a normalized way, so that the focal-shift effect is more intuitive and easy to observe. For the total pressure distributions presented in Figure 2a, the axial distribution of acoustic pressure seems identical for the modified and original KZK models, which provides reliable proof that the current theoretical modification did not compromise the accuracy of the sound field prediction in non-dispersive media. With the help of fast Fourier transform (FFT) algorithm, further analysis was then carried out by decomposing the total sound pressure into the superposition of linear and nonlinear components. In Figure 2b-d, all the components exhibited good agreement between the results calculated from the modified and the original KZK models, showing that both models are applicable for sound field prediction under the experimental conditions mentioned above. It should also be noted that, in Figure 2 the locations of the pressure peaks from the two models were exactly the same for all components, although the measured axial beam-widths seem a bit narrower than both theoretical predictions, especially for harmonic components. Since the linear fields were found to be almost identical for the measured and predicted results, we speculate that some far-field attenuation factors such as dissolved oxygen in water, or bubbles might exist. However, this does not affect the conclusion on the location of the maximum pressure.  
Figure 2: The normalized acoustic pressure distributions along the axis of the HIFU transducer without the phantom: (**a**) the overall pressure; (**b**) the fundamental component; (**c**) the second harmonic; (**d**) the third harmonic.
# Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
## 2 System Model  
We consider a jammer UAV (J) flying above the target area and emitting its jamming signal towards base stations on the ground. There are two base stations located outside the jamming region that may transmit and receive data from each other, as shown in Figure 1. Without loss of generality, we consider that the jammer UAV is moving in a three-dimensional Cartesian coordinates system with variable speed and constant acceleration. Constant acceleration is used in many works such as in [4] and non-acceleration model [27]. Non-acceleration models are commonly used in monocular SLAM systems [27]. The base stations are at fixed positions in the XY-plane, as shown in Figure 2. During a jamming attack, the network topology may change and nodes will be affected based on their locations and jammer's transmission power. Note that, the key parameters are defined in Table 1.  
Based on jamming signal and noise powers at the received signal by sensors, the nodes are classified into three different types [28], as shown in Figure 3. When jamming signal attacks nodes, the SNR decreases which result in increased Bit Error Rate (BER) and decreased Packet Delivery Ratio (PDR). Thus, the system fails to decode the signal properly and it requests the re-transmission of signals. Packets received with SNR below the system threshold value \((SNR<\gamma)\) are considered invalid data and require re-transmission of data. The sensors located inside the jamming region are called Jammed Nodes, \(N_{J}\), and have SNR less the than system threshold value (\(\gamma\)). Hence, they are completely isolated from the network and cannot send or receive data from their neighbors. On the other hand, the nodes located near the jamming region and have SNR larger than the system threshold are known as the Boundary Nodes, \(B\). These nodes lost their links with some of their neighbors. However, they might still have active links with unaffected sensors and can communicate with them. Lastly, the nodes outside the attacked area with SNR larger than the system threshold value are called Unaffected Nodes \(N\).  
Figure 1: Jammer Unmanned Aerial Vehicles (UAV) targeted two base stations on the ground where the blue base station represents unaffected nodes and the red base stations are the jammed nodes. The dot green line depicts the sensing range and the solid red line the jamming region.
## 5 Conclusions  
The results of this study showed that the appropriate increase of DO concentration could improve the power generation and pollutant removal rate of the _L. hexandra_ CW-MFC system. When the aeration rate was 6 L-min\({}^{-1}\), the DO concentration was 4.5 mg-L\({}^{-1}\)the maximum voltage of the system was 520 mV, and the maximum removal rates of COD and Cr (VI) were 93.73% and 97.77%, respectively. The higher DO concentration promoted an increase in Cr absorption by the substrate and _L. hexandra_, and enhanced the conversion of Cr (VI) to Cr (III). When the DO concentration was 4.5 mg:L\({}^{-1}\), Cr mainly existed in the residual state in the substrate and _L. hexandra_, and the increase of DO concentration promoted the transformation of Cr to low toxicity and low mobility. Aeration significantly increased the abundance of _Geobacter_ in the system. This positively affected the electricity production and Cr (VI) reduction in the system; therefore, this may be the key micro-organism to enhance system performance and Cr (VI) reduction. In summary, an appropriate increase of cathode aeration can help to improve the overall performance of CW-MFC, which will be of great significance for the practical application of CW-MFC in the treatment of Cr (VI) wastewater, and can provide a new direction for the application of _L. hexandra_ to the treatment of chromium wastewater in other countries.  
Investigation, S.Y.; data curation, G.T.; writing--original draft preparation, Y.S.; writing--review and editing, P.J.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of China [51868010], Guangxi Natural Science Foundation Program [2021 GXNSFBA196023], Guilin Science and Technology Development Program [20190219-3].  
Not applicable.  
This work was supported by the Collaborative Innovation Center for Exploration of Hidden Nonferrous Metal Deposits and Development of New Materials in Guangxi.  
The authors declare no conflict of interest.
# Projection-Based Augmented Reality Assistance for Manual Electronic Component Assembly Processes
## 3 Proposed Method
### Projection Mapping
#### 3.6.2 Virtual Content Projection  
In the proposed projection mapping pipeline (Figure 5), as stated in the previous subsection, first, the virtual content is transferred to the camera image using the camera tracking data (\(T_{track}\), Section 3.3), which creates the view that is displayed in the screen. Then, this content, which is already referenced with respect to the camera image coordinate system, is again transformed using the camera-projector calibration (\(H_{calib}\), Section 3.6.1) to the projector image area that is subsequently projected. Thus, to project any content, we define its location in the reference 2D coordinate system of the board and then we apply the chain rule, which can be represented conceptually by \(T_{track}*H_{calib}\).  
In our application, we decided to project the following information (Figure 6), which answers three simple questions that are very useful for operators:  
* \"Where?\": The place where the operator has to assemble the current electronic component, which is highlighted with the projection of a white flicking rectangle.
* \"What?\": The reference number of the electronic components that must be assembled in the current step.
* \"How many?\": The number of the current electronic components that have already been assembled regarding the total number to be assembled. A fraction \"\(i/j\)\" is projected, where \(i\) is the number of current components already assembled from the total of \(j\).  
The projection of \"What?\" and \"How many?\" is located at the border frame (Figure 6), outside the electronic board, as this area is not used for anything and it offers good visibility. The projection of \"Where?\" on the other hand, is superimposed on the real position that corresponds to the inside the electronic board (Figure 6). This was not an appropriate area to get good contrast due to the material of the PCB and the limited power of the projector that was used, so we opted to flick the projection to capture the operator's visual attention, and, consequently, improve its visibility. This has been proven as a good solution, since the result of the usability test was positive (Section 4). A sample of the system performance can be seen in the Supplementary Video S1.
## 1 Introduction
Due to its advantages such as wide coverage, spatial continuity, and long time series, many studies have used remote sensing-derived vegetation index to extract phenological metrics on the TP [24; 25; 26]. However, to the best of our knowledge, no research has been conducted on combining remotely sensed phenology with the Biome-BGC model to improve its simulation performance in alpine regions, although many studies have shown that combining higher precision phenological data with process-based models can improve the performance of carbon flux simulation [27; 28; 29]. To this end, we first used the remotely sensed phenology to prescribe the phenological states within the Biome-BGC model. Then, we used the global sensitivity analysis method to identify those electrophysiological parameters that have a significant impact on the GPP simulation. Subsequently, we performed a calibration upon these specific parameters using the simulated annealing optimization algorithm in conjunction with the eddy covariance (EC)-derived GPP data. Finally, we used the Biome-BGC model after a phenology module modification and parameter calibration to simulate the GPP of alpine grasslands on the TP from 1982 to 2015. Our objectives were threefold: (1) to investigate whether the integration of remotely sensed phenology and the Biome-BGC model can improve the performance of carbon flux simulation of alpine grasslands on the TP; (2) to develop a practical parameter calibration method for complex process-based models; (3) to analyze the spatial and temporal variation of GPP of alpine grasslands on the TP from 1982 to 2015.
## 1 Introduction  
The advancement of Intelligent Traffic Systems (ITS) technology now provides functions to prevent accidents for vehicles and pedestrians as well as quickly find destinations. Furthermore, it also provides conveniences such as public transportation arrival alarms, among others. In ITS, V2X communication (composed of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), Vehicle-to-Nomadic devices (V2N), and In-Vehicle Networking (IVN) communication) provides convenient services such as collision prevention, traffic lane maintenance, and traffic information collection through mobile terminals [1; 2; 3; 4]. In order to provide convenience for the user, ITS is able to communicate between vehicles or between a vehicle and the infrastructure without network disconnection. For this communication, the Vehicular Ad hoc NETwork (VANET) protocol is used to minimize network disconnection caused by vehicle movement and to provide services to drivers or other users [5; 6; 7; 8]. In addition, in the VANET environment, cloud computing provides services through the VANET routing protocol. However, message forgery, modification, and extortion by man-in-the-middle attacks are possible, and there is the possibility that a black hole attack could cause a fatal traffic accident. Furthermore, the existing services provided by intelligent traffic systems are applied only inside the vehicle, and studies on services that use traffic infrastructure are insufficient. Thus, we need to simultaneously provide user-oriented active services and secure important data.  
Therefore, for secure Vehicular Cloud Computing (VCC), we propose the VCC Service-oriented Security Framework (VCC-SSF) in this paper. VCC-SSF provides the authentication, encryption, access control, and privacy protection required to protect private information, payment, and other important information inside the vehicle from security threats that may occur in the VCC environment. In addition, VCC-SSF includes a new active payment service that meets user requirements through the Payment as well as Accident Management Services to actively provide traffic accident prevention and accident response and management.  
This paper is organized as follows: In Section 2, we describe the VCC's architecture and security requirements as well as review previous studies. Section 3 proposes the VCC-SSF architecture and application, and, in Section 4, the conclusion is presented.
# Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR
###### Abstract  
In order to study the deformation of bored pile, it is necessary to monitor the strain of the pile. The distributed optical fiber sensing technology realizes the integration of sensing and transmission, which is incomparable with traditional point monitoring method. In this paper, the Brillouin optical time domain reflectometer (BOTDR) distributed optical fiber sensing technology is used to monitor the deformation of the bored pile. The raw data monitored by BOTDR is processed by the wavelet basis function, that can perform noise removal processing. Three different methods of noise removal are chosen. Through the processing, the db5 wavelet is used to decompose the deformation data of bored pile monitored by BOTDR into two layers. The decomposed high-frequency signal is denoised by the Stein-based unbiased risk threshold, rigrsure. The decomposed data is smoothed by the translational mean method, and the final data after denoising and smoothing processing is real and reliable. The results of this study will provide data support for the deformation characteristics of bored pile, and also show the advantages of distributed optical fiber sensing technology.  
bored pile; deformation of pile; BOTDR; optical fiber monitoring; data processing Article
## 4 Empirical Analysis
### Hypothesis Test Results  
The structural equation modeling technique was employed to estimate the hypothesized relationships, with the adequacy of the structural model confirmed by the good fit indices (\(\chi^{2}\)/d.f. = 2.177, TLI = 0.956, CFI = 0.962, RMR = 0.022, RMSEA = 0.062) that satisfied the recommended thresholds. Table 4 lists the parameter estimates for the hypothesized relationships.  
The standardized coefficient (\(\beta\)) of e-SQ on CS was 0.394 (\(p\) = 0.000), thus supporting H1 and indicating the significant and positive effect of e-SQ on CS. The coefficient of e-SQ on e-trust was 0.195 (\(p\) = 0.000), thus supporting H2 and revealing the significant and positive effect of e-SQ on e-trust. Similarly, the coefficient of e-SQ on CUI was 0.213 (\(p\) = 0.000), thus supporting H3 and revealing the significant and positive effect of e-SQ on CUI. Moreover, the coefficient of CS on e-trust was 0.579 (\(p\) = 0.000), thereby supporting H4 and indicating the significant and positive effect of CS on e-trust. Additionally, the coefficient of CS on CUI was 0.466 (\(p\) = 0.000), thus supporting H5 and highlighting the significant and positive effect of CS on CUI. Furthermore, the coefficient of e-trust on CUI was 0.194 (\(p\) = 0.005), thereby supporting H6 and demonstrating the significant and positive effect of e-trust on CUI. These findings underscore the need for further experiments to explore the mediating roles of CS and e-trust.  
The bootstrap method, developed for non-parametric resampling tests, was employed to identify the roles of CS and e-trust as mediators [65], with 500 bootstrapping procedures conducted at a 95% confidence level. Table 5 illustrates the indirect effects of the three paths between e-SQ and CUI. The indirect effects through CS (\(\beta\) = 0.183, \(p\) = 0.014) and e-trust (\(\beta\) = 0.038, \(p\) = 0.004) and through the sequential influences of CS and e-trust (\(\beta\) = 0.044, \(p\) = 0.004) were significant. Therefore, H7-H9 were supported. This result indicates that both CS and e-trust serve as significant mediating factors in the relationship between e-SQ and CUI within e-commerce marketplace platforms.