esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Implications, Limitations, and Future Research  
The goal of this study is to identify the factors that motivate individuals' intention to use BBCA technology from the perspective of PMT, so that BBCA vendors can be better informed about how to develop more user-friendly tools that are more in tune with users' requirements. Our research adds to the existing body of knowledge by incorporating the constructs of privacy concerns as predictors of coping and threat appraisal processes, as well as innovativeness as a predictor of intention to use. Specifically, this research revealsthe significance of privacy concerns when the Protection Motivation Theory is applied in the field of digital privacy. Regarding maladaptive coping behaviors, we provide an alternative explanation why protective information security behaviors have yet to be adopted, as users hesitate to use BBCA technologies when they feel vulnerable to potential misuse of their personal data by the same technologies that aim to protect their privacy and security. Even when people are motivated to protect themselves, the sense of vulnerability generated by the threat of an improper monitoring tool outweighs the fear of privacy threats.  
Regarding practice, our findings benefit developers of BBCA technologies in developing tools that better satisfy the needs of users. Our research provides useful insights to them, revealing the need for reliable and trustworthy BBCA tools that assure users that their behavioral biometric data are safe. Most significantly, BBCA vendors need to design and promote comprehensive standards to build trust with users and ensure that BBCA tools development complies with these requirements.  
While this study's results are insightful, they must be viewed in the context of certain limitations. First, since the questionary was filled out online, the study environment was less regulated. Background noise or the presence of other people in the session, for example, may have disrupted or affected participants. This implies that actions may have been influenced by factors other than the manipulated variables. It is also conceivable that some of the measurement items in the study questionnaire, as well as their wordings, might have been misinterpreted by the respondents, resulting in biased responses. Further to the above, a major drawback of the use of MTurk for academic research is the threat to external validity [16].  
However, the external validity of data collected through MTurk appears to be a benefit rather than a concern, as MTurk offers a unique range of diversity in the United States and globally, which we took advantage of.  
Finally, although our PMT-based model is fairly effective in predicting behavior, further research could be conducted to investigate other factors, such as ethical, social, and convenience factors, to increase its predictive power. Another approach could include situational considerations, such as current legal regulations and user's previous experience of security breaches.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 4 Simulation Results
### Communication Stability Performance Evaluation  
In this paper, the handover failure probability and BER are used as performance indicators, and the DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) [20] is used as the comparison scheme for the BER experiment. And the handover failure probability equation is as follows:  
\[F_{\mathrm{HO}}=\frac{\sum_{r=1}^{N_{r}}\frac{p(r)^{B}-p(r)^{B+1}}{1-P(r)^{B+1}}} {N_{r}} \tag{8}\]  
where \(F_{\mathrm{HO}}\) is the handover failure probability of the specified scheme, \(P(r)\) is the utilization of the RF communication uplink server after \(r\) iterations, and \(B\) is the maximum queue length of the RF communication uplink.
## 3 Distributed Jamming System Model for LEO Constellation Downlink
### Jamming Evaluation Metrics
#### 3.1.3 Frequency Compensation  
Due to the high operational speeds of low-Earth-orbit satellites, the Doppler effect introduced by the relayed deceptive signals and genuine satellite signals reaching the receiver can result in deviations. Therefore, during the forwarding process, frequency compensation is necessary for the deceptive signals to ensure that the velocity measurements at the target-receiving device align with the actual values.  
Assuming that the velocities of the \(i\)th satellite and the \(j\)th jammer are represented by the vectors \(\mathbf{V}_{i}^{L}\) and \(\mathbf{V}_{j}^{Z}\), respectively, and that the satellite's emission frequency is \(f_{0}\), then the Doppler frequency shift of the \(i\)th satellite and the \(j\)th jammer can be expressed as follows:  
\[f_{ij}^{Z}=f_{0}\frac{V_{ij}^{Z}}{c}=\frac{f_{0}}{c}.\frac{\left(\mathbf{r}_{i}^{L }-\mathbf{r}_{j}^{Z}\right)\cdot\left(\mathbf{V}_{i}^{L}-\mathbf{V}_{j}^{Z}\right)}{\left| \mathbf{r}_{i}^{L}-\mathbf{r}_{j}^{Z}\right|}. \tag{6}\]  
Similarly, for the relative velocity and Doppler frequency shift between the \(i\)th satellite and the \(k\)th target receiver, we have  
\[f_{ik}^{T}=f_{0}\frac{V_{ik}^{T}}{c}=\frac{f_{0}}{c}.\frac{\left(\mathbf{r}_{i}^{L }-\mathbf{r}_{k}^{T}\right)\cdot\left(\mathbf{V}_{i}^{L}-\mathbf{V}_{k}^{T}\right)}{\left| \mathbf{r}_{i}^{L}-\mathbf{r}_{k}^{T}\right|}. \tag{7}\]  
Therefore, the compensatory Doppler frequency shift is  
\[\Delta f_{ijk}=f_{ik}^{T}-f_{ij}^{Z}. \tag{8}\]
## 6 Conclusions  
Global path planning is the foundation of the UAV logistics and transportation system; in order to plan an efficient flight path, this paper firstly establishes two three-dimensional models of mountainous area and city, and constructs a multi-evaluation objective function by combining constraints and mission requirements. Secondly, the standard AVOA principle is analyzed, and then the improvement strategies of SAVOA in the initial, exploration, and exploitation phases are presented. Finally, SAVOA is compared with five algorithms in simulation experiments in four environments, and the results show that SAVOA not only outperforms the standard AVOA in various indexes, but also outperforms the traditional algorithms ABC and GA, as well as the new intelligent algorithms SSA and WOA which have emerged in recent years, and demonstrates a better optimization ability and stronger robustness.  
However, only the flight path of a single UAV is considered in this paper. In subsequent studies, how to ensure efficient collaboration among multiple UAVs to accomplish the delivery task will be investigated in depth, which not only requires optimization of the algorithm to avoid mutual collisions among UAVs but also considers the recognition and adaptive adjustment of obstacles in various dynamic environments.  
Conceptualization, H.J.; Methodology, W.Z.; Software, W.Z.; Investigation, W.Z.; Writing--original draft, W.Z.; Writing--review and editing, X.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available from the corresponding authors.  
The authors declare no conflict of interest.
## 3 Experimental Procedure  
This study commenced with a thorough optical characterization of SiO\({}_{2}\)- and Si\({}_{3}\)N\({}_{4}\)-based SLARCs. To achieve this, a series of 18 SLARC samples were fabricated by depositing thin films of each material onto n-type (100) silicon wafers with a resistivity of less than 0.003 ohms-cm, doped with arsenic.  
SiO\({}_{2}\) SLARCs were produced through thermal oxidation at 1100 \({}^{\circ}\)C, resulting in thicknesses of 90, 120, and 150 nm (three samples for each thickness). Precise thickness measurements were obtained using a reflectometer, yielding values of 98.1 \(\pm\) 0.3 nm, 129.6 \(\pm\) 0.3 nm, and 147.0 \(\pm\) 0.4 nm, respectively. Conversely, Si\({}_{3}\)N\({}_{4}\) SLARCs were fabricated via plasma-enhanced chemical vapor deposition at 770 \({}^{\circ}\)C and 250 mTorr, targeting thicknesses of 90, 120, and 150 nm (three samples for each thickness). The measured thicknesses for these samples were 92.5 \(\pm\) 1.1 nm, 117.6 \(\pm\) 1.3 nm, and 145.7 \(\pm\) 1.5 nm.  
The optical properties of the SLARCs were determined by measuring reflectivity and refractive index using a UV-VIS spectrophotometer and ellipsometer over a spectral range of 350 to 1000 nm. To complete the optical characterization, the optical properties of NQDs and LiF films, essential components for subsequent MLARC configurations, were sourced from previous studies [13; 14]. Notably, NQDs (CdSe/ZnS) were integrated into SLARCs via a mist deposition technique, while LiF was incorporated through thermal evaporation. The optical properties of the silicon substrate were referenced from [PERSON] and [PERSON] [15; 16].  
Building upon the experimentally measured optical properties of SLARCs, the second phase of the study involved integrating both SLARCs and MLARCs (SiO\({}_{2}\)/NQDs/LiF and Si\({}_{3}\)N\({}_{4}\)/NQDs/LiF) into Si-SCs. To gain insight into light propagation and interaction within these complex structures, both continuous and patterned MLARC configurations were subjected to rigorous modeling and simulation using Ansys-Lumerical FDTD software.  
A comprehensive analysis of the simulated structures yielded valuable optical and electrical properties, including optical reflectivity, internal and external quantum efficiencies, open-circuit voltage, short-circuit current, fill factor, and ultimately, power conversion efficiency.
## 2 Methods
### Evaluation Metric
#### 2.3.1 Image Similarity Evaluation Metrics  
The SSIM metric is a perception-based metric for measuring the similarity between two images that considers image degradation as a perceived change in structural information and incorporates important perceptual phenomena, such as luminance and contrast. SSIM is defined as the following:  
\[\text{SSIM}(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_ {2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+c_{2}\right)} \tag{4}\]  
where \(\mu_{x},\ \mu_{y}\) is the mean of \(x,y\), respectively; \(\sigma_{x}^{2},\ \sigma_{y}^{2}\) is the variance of \(x,y\), respectively; \(\sigma_{xy}\) is the covariance of \(x\) and \(y\); and \(c_{1},\ c_{2}\) are two variables to stabilize the division with weak denominator.  
The PSNR is the most widely used objective image quality metrics for evaluating the similarity between two images. The PSNR is defined as the following:  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{MAX_{I}^{2}}{\text{PSNR}}\right) \tag{5}\]  
where \(MAX_{I}^{2}\) is the maximum possible pixel value of images.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Modes Control
#### 3.2.1 Constant Current Charging  
In this mode of control, the charging station feeds the EV battery a steady current while it is being charged. During the initial phases of charging, when the battery's state of charge (SoC) remains relatively low, this mode control method is frequently employed [127].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Model** & **Type** & **Battery (KWh)** & **Range (Km)** & **Connector** \\ \hline \begin{tabular}{c} Plug-in Hybrid \\ (PHEV) \\ \end{tabular} & Chevrolet Volt & PHEV & 18.4 & 85 (battery) & Type 1 J1772 \\ \hline \begin{tabular}{c} Mitsubishi \\ Outlander \\ \end{tabular} & PHEV & 20 & 84 (battery) & CCS, Type 2 \\ \hline \begin{tabular}{c} Volvo XC40 \\ \end{tabular} & PHEV & 10.7 & 43 (battery) & CCS, Type \\ \hline \begin{tabular}{c} Toyota Prius Prime \\ \end{tabular} & PHEV & 8.8 & 40 (battery) & SAE J1772 \\ \hline \begin{tabular}{c} Nissan Leaf \\ \end{tabular} & PHEV & 64 & 480 & CHAdeMO, Type 2 \\ \hline \begin{tabular}{c} Electric Vehicle (BEV) \\ \end{tabular} & Tesla Model S & BEV & 100 & 620 & Supercharger \\ \hline \begin{tabular}{c} Tesla Model X \\ \end{tabular} & BEV & 100 & 500 & Supercharger \\ \hline \begin{tabular}{c} Tesla Model 3 \\ \end{tabular} & BEV & 82 & 580 & Supercharger \\ \hline \begin{tabular}{c} Fuel Cell Electric \\ Vehicle (FCEV) \\ \end{tabular} & Toyota Mirai & FCEV & \begin{tabular}{c} 1.6 (hydrogen \\ capacity) \\ \end{tabular} & 647 & N/A \\ \hline \begin{tabular}{c} Hyundai Nexo \\ \end{tabular} & FCEV & \begin{tabular}{c} 40 (hydrogen \\ capacity) \\ \end{tabular} & 570 & N/A \\ \hline \begin{tabular}{c} Honda Clarity \\ \end{tabular} & FCEV & \
\begin{tabular}{c} 25.5 (hydrogen \\ capacity) \\ \end{tabular} & 550 & N/A \\ \hline
\begin{tabular}{c} Extended Range \\ Electric Vehicle \\ (E-REV) \\ \end{tabular} & BYD Atto3 & E-REV & 60.4 & 420 (battery) & CCS, Type 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of commercial electric vehicles [26; 126; 128; 129; 130; 131; 132].  
Constant current charging facilitates faster charge rates, ensuring prompt replenishment of the battery's capacity.
## 5 Results
### Finding Trucks' Shortest Travel Paths without Colliding with the Obstacles  
The Informed-RRT* path planning algorithm is used to find the shortest path for each truck without colliding with the obstacles. Each iteration of Informed-RRT* brings the generated tree closer to the goal, and the bias towards the goal increases as the search radius is updated. Iteration in Informed-RRT* refers to a complete algorithm cycle that involves the following steps: constructing a random tree, and updating the search radius. These steps are repeated until a solution is obtained or a maximum number of iterations is reached. In our project, the maximum iteration number is considered 1000. The safety distance (sd) is an essential consideration in our method. If the sd selected is too small, the risk of trucks colliding with obstacles during their travel increases. On the other hand, if the sd selected is too large, the trucks may have longer unnecessary paths to reach their destinations. In our project, the sd is considered 30 cm.  
Figure 16 shows the trucks' shortest paths in each phase. In addition, the trucks' travel routes and distances between the starting and ending points are illustrated in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Operation Step** & **Travel Route** & **Distance (m)** \\ \hline \multirow{5}{*}{
\begin{tabular}{c} **RRT
## 3 Results
### Evaluation of Seasonal MODIS-Derived WUE
For HBGC, MODIS GPP was rather consistent with the observed one; however, ET was underestimated, and that caused the overvalued WUE in summer (Figure 2). In contrast, the overestimation of WUE for DX was mainly derived from the remarkable underestimation of ET in the growing season, with a bias of 47%, on average (Figure 2). The MODIS product has a significant low estimate of WUE for summer maize at the YC cropland site, compared to the observed WUE values estimated from eddy covariance systems. The bias of WUE is up to \(-2.16\) g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\(\mathrm{O}\) during the growing season of summer maize (from June-September). Both undervalued GPP and overvalued ET contributed to the big negative bias. The GPP of summer maize was 35%-66% undervalued, which played a more important role in the underestimation of WUE. Although the seasonal pattern of MODIS WUE was more consistent with the eddy covariance measurements for winter wheat than summer maize, we cannot ignore the big bias of GPP (28%-69%) and ET (33%-70%) for winter wheat (Figure 2). As important input remotely-sensed data in the algorithm of the MODIS ET product, MODIS LAI data are used to upscale the stomatal conductance at the leaf level to canopy conductance [31], the quality of which directly influences MODIS ET data. Therefore, we further compared the difference of LAI  
Figure 2: Comparison of MODIS- and eddy covariance-derived eight-day gross primary production (GPP) and evapotranspiration (ET) at eight ChinaFLUX sites. (**a**) CBS; (**b**) DHS; (**c**) QYZ; (**d**) XSBN; (**e**) YC; (**f**) HBGC; (**g**) NM; (**h**) DX. between MODIS LAI and observed one at three forest sites (_i.e._, CBS, DHS, and XSBN) and one cropland site, as shown in Figures 3 and 4. Due to missing and unreliable eight-day MODIS LAI data, these had been filled before using them to produce the MODIS ET dataset; here, we excluded eight-day MODIS LAI data with bad quality control values. For the CBS temperate deciduous forest, the MODIS product underestimated LAI in spring and autumn by 57%-67% in 2005 and 53%-66% in 2010 and overestimated the maximum value of LAI by 20% in 2005 and 19% in 2010. For the DHS subtropical evergreen forest, LAI was 56% and 22% overvalued by MODIS in 2005 and 2010. For the XSBN tropical evergreen forest, MODIS overestimated LAI by 21% in 2005 and 9% in 2010, on average. For the YC cropland ecosystem, LAI in growing seasons was significantly underestimated by the MODIS product (Figure 4). The mean bias of the LAI of winter wheat and summer maize was \(-\)1.47 and \(-\)0.74 m\({}^{2}\cdot\)m\({}^{-2}\) in 2005-2011. Since ET has a good linear relationship with LAI [21], high ET in forest and low ET in cropland were supposed to be associated with the overestimation of LAI for forest and the underestimation of LAI for cropland. Figure 3: Comparison of MODIS and observed leaf area index (LAI) at the (**a**,**b**) Changbaishan (CBS), (**c**,**d**) Dinghushan (DHS) and (**e**,**f**) Xishuangbanna (XSBN) forest sites.
# Three-Dimensional Inversion of the Time-Lapse Resistivity Method on the MPI Parallel Algorithm
## 2 Rationale and Theory
### Time-Lapse Function  
The time-lapse function ensures the continuity of the inversion results across different times. In the process of time-lapse inversion, the data at different times are synchronously  
Figure 1: The vertical contact band model.  
Figure 2: Apparent resistivity curve between analytical solution and finite difference method.  
inverted. Assuming that \(\mathbf{m}_{\mathbf{\sigma},\mathbf{j}}\) is the conductivity model at a certain time, the time-lapse conductivity model \(\mathbf{M}_{\mathbf{\sigma}}\) can be defined as:  
\[\mathbf{M}_{\mathbf{\sigma}}=[\mathbf{m}_{\mathbf{\sigma},1}\ \mathbf{m}_{\mathbf{\sigma},2}\ \ldots\ \mathbf{m}_{\mathbf{ \sigma},\mathbf{nt}}]^{T}, \tag{5}\]  
where \(\mathbf{nt}\) is the number of observations. At present, there are two calculation methods for time-lapse function: L1 norm and L2 norm [14; 15]. The time-lapse function calculated by the L2 norm can better ensure the continuity of inversion results over time, whereas the time-lapse function calculated by the L1 norm can avoid the over-continuity of inversion results at different times with time and better reflect the local changes at different times [16]. In this study, we focus on the anti-interference effect of the time-lapse function on high noise, so the L2 norm method is used for calculation, and the calculation formula is as follows:  
\[\Phi_{t}(\mathbf{M}_{\mathbf{\sigma}})=\mathbf{M}_{\mathbf{\sigma}}^{T}\mathbf{C}\mathbf{M}_{\mathbf{ \sigma}}=\sum_{k=1}^{nt}\|\mathbf{m}_{\mathbf{\sigma},\mathbf{k}}-\mathbf{m}_{\mathbf{\sigma},\mathbf{k }+1}\|^{2}, \tag{6}\]  
\[\mathbf{C}=\begin{bmatrix}I_{1}&-&I_{2}&0&0&0\\ 0&I_{2}&-&I_{3}&0&0\\ 0&0&\ddots&\ddots&0&0\\ 0&0&0&I_{\text{nt}-1}&I_{\text{nt}}\end{bmatrix}, \tag{7}\]  
where \(\Phi_{t}(\mathbf{M}_{\mathbf{\sigma}})\) is a time-lapse function, which is calculated by the difference in the underground conductivity model at the adjacent time. By introducing the time-lapse function into the objective function, the difference in the inversion results at the adjacent time can be reduced by minimizing the objective function, to ensure that the underground physical property model changes continuously with time.
## 3 Test Data Analysis
### Frequency-Division Vibration Level Analysis
#### 3.3.3 Insertion Loss Comparison  
According to CJJ191-2012 \"Technical Specification for Floating Slab Track\" [30], the vibration damping effect of the steel-spring-floating slab track is mainly evaluated by comparison with the ordinary monolithic track. For the insertion loss analysis of the sub-frequency vibration level, \(\Delta L_{a}\) obtained by subtracting the sub-frequency vibration level of the ordinary monolithic track tunnel wall and the sub-frequency vibration level of the tunnel wall at the steel-spring-floating slab track in the corresponding frequency band under each line condition is mainly used as the evaluation index. Taking the maximum difference \(\Delta L_{max}\) of the frequency division vibration level as a reference, it is calculated according to the following formula [30]:  
\[\Delta L_{a}=10\lg\left(\sum_{i=1}^{n}10^{\frac{V_{L_{a}(i)}}{10}}\right)-10 \lg\left(\sum_{i=1}^{n}10^{\frac{V_{L_{a}(i)}}{10}}\right) \tag{2}\]  
Figure 17: Comparison of frequency-division vibration levels for each condition. (**a**) Straight line segment—ordinary monolithic track; (**b**) straight line segment—steel spring floating slab track; (**c**) curved segment—ordinary monolithic track; (**d**) curved segment—steel spring floating slab track.  
\[\Delta L_{\text{max}}=\max_{i=1\to n}\left[VL_{q}(i)-VL_{h}(i)\right] \tag{3}\] \[\Delta L_{\text{min}}=\min_{i=1\to n}\left[VL_{q}(i)-VL_{h}(i)\right] \tag{4}\]  
In the formula:  
\(VL_{q}(i)\)--select the segment where the steel-spring-floating slab track is not adopted as the reference object, and the vertical vibration acceleration of the tunnel wall measuring point is the frequency-division vibration level (dB) of the i-th center frequency in the 1/3- octave band; in this research, the ordinary monolithic track tunnel wall acceleration measurement point was selected;  
Figure 18: Comparison of frequency-division vibration levels of various rail components. (**a**) Rail; (**b**) track bed; (**c**) tunnel wall.  
\(V_{LH(i)}\)--the sub-frequency vibration level (dB) of the vertical vibration acceleration of the tunnel wall measuring point at the \(i\)-th center frequency of the 1/3-octave band in the segment where the steel-spring-floating slab track is taken.  
Therefore, the steel-spring-floating slab track frequency-division vibration level-insertion loss curve of the straight line and the curved segment is obtained, as shown in Figure 19. By comparing the frequency-division vibration level insertion loss curves of the straight line and the curved segment, it can be seen that the steel-spring-floating slab track insertion loss curve of the straight line and the curved segment is similar, and the insertion loss of the curved segment at high frequencies is greater than that of the straight line segment. The reason is that the high-frequency vibration response of the ordinary monolithic track tunnel wall in the curved segment is intensified, and the vibration damping effect is achieved by using the steel-spring-floating slab track.