esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)
## 5 Results and Discussion
### Results and Discussion according to Varying Number of Vehicles
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **No of** & **LAKA-** & **NCAS-** & **TPDA-** & **BDTC-** \\  & **Vehicle** & **IOD** & **IOD** & **IOV** & **SIODTs** \\ \hline \multirow{6}{*}{Energy Efficiency (\%)} & 25 & 7.246 & 15.246 & 25.475 & 38.248 \\  & 50 & 15.847 & 25.337 & 29.465 & 45.854 \\  & 75 & 22.116 & 32.213 & 35.422 & 58.571 \\  & 100 & 35.229 & 49.241 & 53.647 & 67.756 \\  & 125 & 42.764 & 53.249 & 58.457 & 75.349 \\  & 150 & 58.743 & 68.761 & 71.359 & 85.249 \\  & 175 & 61.794 & 76.473 & 79.246 & 89.545 \\  & 200 & 78.467 & 85.179 & 88.407 & 95.175 \\ \hline \multirow{6}{*}{Packet Delivery Ratio (\%)} & 25 & 77.247 & 89.335 & 92.347 & 96.224 \\  & 50 & 82.476 & 88.176 & 90.223 & 98.124 \\ \cline{1-1}  & 75 & 83.247 & 87.462 & 91.276 & 95.316 \\ \cline{1-1}  & 100 & 79.264 & 85.176 & 88.214 & 97.224 \\ \cline{1-1}  & 125 & 78.348 & 87.394 & 89.316 & 96.347 \\ \cline{1-1}  & 150 & 82.467 & 88.279 & 90.462 & 97.249 \\ \cline{1-1}  & 175 & 81.278 & 89.175 & 92.476 & 95.328 \\ \cline{1-1}  & 200 & 82.476 & 88.176 & 91.467 & 96.475 \\ \hline \multirow{6}{*}{Routing Overhead (PACs)} & 25 & 1027 & 546 & 394 & 245 \\ \cline{1-1}  & 50 & 1576 & 847 & 637 & 384 \\ \cline{1-1}  & 75 & 2045 & 1078 & 814 & 425 \\ \cline{1-1}  & 100 & 2546 & 1347 & 1057 & 598 \\ \cline{1-1}  & 125 & 2908 & 1647 & 1437 & 785 \\ \cline{1-1}  & 150 & 3564 & 2067 & 1854 & 964 \\ \cline{1-1}  & 175 & 3964 & 2247 & 2014 & 1025 \\ \cline{1-1}  & 200 & 4276 & 2816 & 2576 & 1256 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The values of Energy Efficiency, Packet Delivery Ratio, and Routing Overhead concerning vehicles. According to the number of vehicles, the packet loss measured by the proposed BDTC-SIOVAs is 282 packets, and for the earlier works, it is LAKA-IOD (457 packets), NCAS-IOD (361 packets), and TPDA-IOV (346 packets). So, it is understood that the proposed BDTC-SIOVAs produce around 140 packets to 270 packets lower loss of packets when compared with the earlier works. In terms of the number of vehicles, the throughput measured by the proposed BDTC-SIOVAs is 782 Kbps, and for the earlier works, it is LAKA-IOD (354 Kbps), NCAS-IOD (502 Kbps), and TPDA-IOV (542 Kbps). So, it is understood that the proposed BDTC-SIOVAs achieve around 240 Kbps to 400 Kbps better throughput when compared with the earlier works. Throughput of the proposed BDTC-SIOVAs is high and it is achieved using the concept of enhanced trust with hybrid optimization during transmission.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 1 Introduction
### Organization  
In Figure 3, we see the overall outline of the article. Section II provides an overview of the preliminaries of NTNs while highlighting the significance of UAVs and FANETs. Moreover, the applications and use cases of FANETs are described in Section III. Next, Section IV presents an overview of the state-of-the-art FANET in terms of standards, physical layer advancements, network management, and routing protocols, with the major  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  
focus on DTN routing protocols due to the nature of intermittent connections in FANET. Then, a DTN-assisted FANET framework is explained and evaluated in Section V. After that, Section VI explores the applications of AI/ML/DL in FANET. Section VII elaborates on the challenges and future research directions. Finally, Section VIII concludes the paper.
# Spatial Resolutions of On-Axis and Off-Axis Transmission Kikuchi Diffraction Methods
## 3 Results and Discussion
### Effects of the Accelerating Voltage and Specim Thickness on the On-Axis and Off-Axis TKDs
Nevertheless, the spatial resolution measured in this study is considerably lower for the samples with a similar thickness, mainly because of the improved filtering algorithm. Furthermore, the density of the ferritic steel is lower than that of the austenitic steel. According to [PERSON] et al., TKD is more effective in the analyses of less dense samples [23]. Additionally, [PERSON] et al. reported that the spatial resolution at a voltage of 20 kV and thickness of 91 nm could not be calculated owing to the low intensity. In contrast, we evaluated the resolution under the same conditions, because the current in the off-axis TKD was 2 nA, instead of 1.6 nA, as used by [PERSON] et al.
# Application of Gas Foil Bearings in China
###### Abstract
In addition, due to the interaction of the foil support structure and the nonlinearity, challenges still exist in accurate theoretical analyses and numerical predictions on foil bearing performance. The research on gas foil bearings has been carried out relatively early abroad, and mainly used in micro gas turbines [19; 20], turbo-expanders [21; 22], turbochargers [23; 24; 25], fuel cell air compressors machines [26; 27], air cycle machines [28], and other options [29]. In recent years, in order to catch up with advanced technology, gas foil bearing technology has been developing rapidly in China. In the 1970s, domestic scholars conducted research on double-row orifice hydrostatic gas journal bearings. Subsequently, a large number of theoretical and experimental studies were carried out in other forms of radial and thrust gas bearings. A series of in-depth studies were conducted in Xi'an Jiaotong University and other institutions, such as, Harbin Institute of Technology, and Beijing Precision Engineering Institute Aircraft industry, etc. [30] For example, a low-temperature turboexpander using rubber-stabilized hydrostatic gas bearings was successfully developed by Xi'an Jiaotong University in 1981. Currently, a series of theoretical and experimental research experiments on gas foil bearings has been conducted at Xi'an Jiaotong University [31],  
Figure 2: Typical gas foil journal bearings. (**a**) bump foil bearing; (**b**) multi-leaf foil bearing; (**c**) wire supporting foil bearing; (**d**) viscoelastic supporting gas foil bearing; (**e**) spring supported foil bearing; (**f**) protuberant foil bearing. Hunan University [32; 33], the Harbin Institute of Technology [34], Nanjing University of Aeronautics and Astronautics [35; 36; 37], the GREE company, and other institutions [38]. Although some progress has been achieved in foil bearings and their applications, a certain number of breakthroughs are still anticipated before large-scale application. In this paper, the progress of domestic gas foil bearings and their applications in high-speed turbo machinery are reviewed, which could provide a reference and guidance for further developments and applications of gas foil bearings.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 3 Multi-Objective Optimization Deployment Model  
### Perception Model  
The Boolean model overlooks the influence of real-world factors on wireless sensor nodes, and a simplified model may have some impact on the research. In actual monitoring environments, the sensing capability of sensor nodes can be disrupted by obstacles within the monitoring range, leading to a gradual decrease in the detection probability for monitoring targets beyond a certain range. In a probabilistic sensing model, the perception probability of sensor nodes for the monitoring area is set to be distance-dependent and follows an exponential relationship.  
In WSNs, assuming a set of wireless sensor nodes \(s=\{s_{1},s_{2},\cdots,s_{i},\cdots,s_{n}\}\), a set of grid points \(P=\{P_{1},P_{2},\cdots,P_{j},\cdots,P_{n}\}\), \((x_{i}y_{i})\) and \((x_{j},y_{j})\) corresponding to the 2D spatial coordinates in their respective sets of \(s_{i}\) and \(P_{j}\), the Euclidean distance between two nodes is given by  
\[d(s_{i},P_{j})=\sqrt{\left(x_{i}-x_{j}\right)^{2}}\ \ +\ \ \ \left(y_{i}-y_{j}\right)^{2} \tag{1}\]  
According to the definition of the probability perception model, the probability of monitoring point \(P_{j}\) being sensed by node \(s_{i}\) is given by  
\[P_{\text{cov}}\left(s_{i},P_{j}\right)=\left\{\begin{array}{cc}1&d(s_{i},P_{ j})\leq R-r\\ e^{-\lambda\alpha^{\beta}}&R-r<d(s_{i},P_{j})<\ \ R+r\\ 0&d(s_{i},P_{j})\geq R+r\end{array}\right. \tag{2}\]  
where \(R\) represents the sensing radius of the sensor node, and \(r\) is the reliability monitoring parameter of the sensor node, with the typical value being \(0<r<R\). The variables \(\alpha\) and \(\beta\) are the attenuation coefficients of the sensing capability of the sensor node, and \(\lambda\) is an input parameter derived by  
\[\lambda=d(s_{i},P_{j})-(R-r) \tag{3}\]
## 3 Results and Discussion
### Optimization of Pyr Loading Content  
It is important to obtain a high loading content in the pesticide delivery system. During the loading process, the solvent selection had a great effect on the loading content [39]. Acetonitrile, dichloromethane, and methanol were chosen to dissolve Pyr, respectively, to obtain the optimum solvent. In a preliminary experiment, M100 was used as the carriers and the mass ratio of Pyr to MSN was set as 2:1. As shown in Table 3, the loading content was as low as 5.86% and 4.25%, respectively, when acetonitrile and methanol was used. The highest loading content was obtained as 18.86% when dichloromethane was used to dissolve Pyr. As a result, dichloromethane was selected as the optimum solvent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nanoparticle** & **Specific Surface Area (m\({}^{2}\)/g)** & **Pore Volume (cm\({}^{3}\)/g)** & **Pore Diameter (nm)** \\ \hline M15 & 342.09 & 0.091 & 1.487 \\ M100 & 900.01 & 0.222 & 2.743 \\ M200 & 810.47 & 1.42 & 2.524 \\ Pyr@M15 & 63.12 & 0.00636 & 1.35 \\ Pyr@M100 & 452.05 & 0.0415 & 1.879 \\ Pyr@M200 & 182.90 & 0.120 & 2.117 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mesoporous Structure Characterization of Nanoparticles.  
Figure 4: Nitrogen adsorption—desorption isotherms (**a**) and pore size distribution (**b**) of nanoparticles.  
Figure 3: FTIR spectra of carbon quantum dots–MSN (**a**) and Pyr@M (**b**).  
The mesoporous structure, surface, and pore size of nanoparticles would have a great effect on pesticide loading. Pry was loaded to M15, M100, and M200, respectively, when the mass ratio of Pyr to MSN was set as 2:1 and dichloromethane was used as the solvent. It was seen in Table 3 that the loading content reduced when the size of the nanoparticles decreased. As was mentioned in Section 3.1, the specific surface area, pore volume, and pore diameter of carbon quantum dots-MSN decreased as the particle size decreased. In the pesticide loading process, the pesticide would occupy the mesoporous structure of carbon quantum dots-MSN. When the pore volume diameter became smaller, it would be more difficult to enter the pore for pesticide. Therefore, the highest loading content was obtained when M200 was used as the pesticide carriers.  
To reduce the influence of loading content on the study of pesticide uptake and translocation, similar loading contents of Pyr@M15, Pyr@M100, and Pyr@M200 were obtained, at about 23%, by changing the mass ratio of Pyr to MSN; shown in Table 3.
## 2 System Model and Channel Modeling Methods
### Channel Modeling Methods  
Currently, there are two main approaches to model VLC channels for V2I or V2V. One is to apply the Beer-Lambert formula [31; 41] based on the Lambert source assumption. In this method, the radiation of Tx is required to obey the Lambertian radiation model, and then the angular distribution of the radiation intensity is modeled using the generalized Lambertian radiation intensity [42]. Then, the channel DC gain (optical power gain) and system impulse response (CIR) expressions are further obtained. The other method of VLC channel modeling pursues more realistic results. Researchers create a simulation environment in software such as OpticStudio(r) or Zemax(r). Then, a type of non-sequential ray tracing is applied to the asymmetric light source to obtain data for calculating the CIR. Finally, these data are imported into Matlab(r) software to construct the CIR [43; 44]. However, the above two channel modeling approaches are mainly applied in indoor VLC systems and extended to outdoor VLC studies. The main difference between outdoor and indoor VLC is the propagation characteristics of light. Outdoor VLC is subject to sunlight, rain, fog, and AT [37; 45; 46]. Therefore, in [47; 48], the authors proposed an outdoor vehicular VLC channel modeling under the influence of AT. This channel model considers the effect of AT in the outdoor environment, which is more realistic than the two methods mentioned above and is used in this paper. Considering the short transmission distance of the proposed V2I-VLC, the effects of AT are modeled using a weak turbulence mechanism, i.e., the received signal at Rx is as follows:  
\[y=\eta\sum_{j=1}^{2}h_{j}x+n, \tag{1}\]  
where \(\eta\) (in A/W) is the photoelectric conversion efficiency of the PD; \(h_{j}\) is the real-valued channel coefficient of the link between the \(j^{th}\) Tx and Rx, which can be modeled as follows:  
\[h_{j}=h_{a}h^{PL_{-}av}, \tag{2}\]  
where \(h^{PL_{-}av}\) denotes the average path loss. \(h_{a}\) is the channel coefficient due to AT, which is generally modeled as a lognormal distribution with variance \(\sigma_{l}\)2, and its probability density function (PDF) is as follows:  
Footnote 2: The \(\eta\)-dependent channel model is defined as \(\eta=\eta_{1}+\eta_{2}+\eta_{3}+\eta_{4}+\eta_{5}+\eta_{6}+\eta_{7}+\eta_{8}\), where \(\eta_{1}\) is the channel coefficient. \[f_{h_{a}}(h_{a})=\frac{1}{2h_{a}\sqrt{2\pi\sigma_{y}}^{2}}e^{-\frac{\left[\ln \left(h_{a}\right)+2\
u_{y}\right]^{2}}{8\
u_{y}}\frac{2^{2}}{2}}\text{ for }h_{a}>0, \tag{3}\]  
Figure 5: Schematic model of the V2I–VLC system in the NLOS scenario formed by \(RS\) reflection. where \({\sigma_{l}}^{2}=4{\sigma_{y}}^{2}\). In addition, \(n\) in (1) denotes zero-mean additive Gaussian white noise (AWGN) with variance \({\sigma_{o}}^{2}\).
## 4 Discussion
### Behavior Effects of Cr(VI), Independent of Age  
Our results indicate behavior effects at very low concentrations of Cr(VI) in drinking water, levels that are considered safe for widespread and chronic human consumption. These data are summarized in Supplementary Table S1. Considering the effects of Cr(VI) alone, we first observed behavioral changes in females after 8 weeks of exposure with significantly decreased distance traveled in the 0.05 mg Cr(VI)/L group. We also observed a slight decrease in rearing activity following 8 weeks of Cr(VI) exposure. Together, these data suggest Cr(VI) impairs locomotor function. After 9 weeks of Cr(VI) exposure, we observed a slight concentration-associated decrease in open-arm exploration in the elevated plus maze, suggesting Cr(VI) contributes to anxiogenic effects. After 11 weeks, we observed two changes in the 3-chamber assay. During the sociability trial, we observed a concentration-associated increase in empty cage interactions, which resulted in less discrimination between cagemates and empty cages, suggesting Cr(VI) decreased sociability. During the social preference trial, we observed slightly increased interactions with the stranger rat, suggesting Cr(VI) increased preference for social novelty.  
Here, we discuss sex differences in Cr(VI) behavior effects by comparing the effects we observed in females with effects we previously reported in males [44]. These data are summarized in Supplementary Table S2. While we first observed significant behavior effects in females after 8 weeks of exposure, we observed effects in males as early as week 2 of Cr(VI) exposure. In addition to differences in the temporal manifestation of Cr(VI) neurotoxicity, the behaviors impacted and the degree of change in behavior were distinct between the sexes. After 2 weeks of exposure in males, we observed decreased rearing behavior and distance traveled, though we did not observe this effect in females until 8 weeks of exposure. After 8 weeks, we observed decreased rearing and distance traveled in both sexes, but this effect was only statistically significant in 0.05 mg Cr(VI)/L exposed females. We observed a statistically significant impairment of spatial memory in males after 4 weeks, while female spatial memory was unaffected after 4 or 10 weeks. After 10 weeks, males became hypoactive, whereas female activity was unaffected. We observed slightly decreased sociability in males after 5 weeks, which we did not observe in females until 11 weeks. We observed that 11 weeks of exposure to 0.05 mg Cr(VI)/L in males resulted in no social novelty preference, but females exhibited a stronger preference for social novelty. Overall, we observed that locomotor effects were more prominent in Cr(VI)-exposed females, but Cr(VI)-exposed males exhibited greater impacts on memory, sociability, and social novelty preference. Though behavior effects manifested distinctly between males and females, we observed behavior changes in males 6 weeks earlier than in females, suggesting males may be more vulnerable to Cr(VI) neurotoxicity.
## 3 Results and Discussion
### Comparisons of Unrecorded Locations  
To predict the amplification characteristics of unrecorded locations, five randomly selected site points, other than those shown in Figure 8, named A, B, C, D and E, were used to predict their amplification factors. With the earthquakes with magnitude M5.0 as an example, the seven trained CNN models with the best testing results, namely, CNN-FSPA, non-SOCS, non-PGMS, non-TAIS, non-FAIS, non-BMTS and non-LHES, were used to calculate the amplification factors of the five points. The prediction results of the amplification factors at these five unrecorded points by the seven CNN-models are shown in Figure 12.  
Figure 12 shows that the amplification factors varied in the order E > D > C > A > B. As shown in Figure 12, all the five points, A, B, C, D and E, exhibited a large amplification factor when the CNN-FSPA model was used for prediction. If only the CNN-PSPA model (which showed better training) was used for prediction, the prediction results of the non-FAIS and non-LHES models for the five points were similar.  
However, the predicted amplification factor using the CNN-FSPA model was still rather small compared to that of the CNN-FSPA model. The reason for this was its insufficient training quantity and incomplete learning. Therefore, the task of predicting the amplification factor of unknown points should be performed in a series of steps. These inspiring results could provide a reference for seismic intensity setting in practical projects.  
For discussing the differences in the prediction results, the relative error of two types of CNNs was used, as given by Equation (6):  
\[Error_{PSPA}=\frac{|Value_{\text{CNN-PSPA}}-Value_{\text{CNN-FSPA}}|}{|Value_{ \text{CNN-FSPA}}|}\times 100\% \tag{6}\]  
where \(Error_{PSPA}\) denotes the difference rate between the CNN-PSPA and the CNN-PSPA, and \(Value_{\text{CNN-FSPA}}\) are the predicted amplification factors of the CNN-PSPA model and CNN-FSPA model, respectively. The error values of the predicted  
Figure 12: Prediction of the amplification factors of unrecorded points by seven models.  
amplification factors of CNN-PSPA for the five points (A, B, C, D and E) for an earthquake with M5.0 are shown in Table 13.  
Compared with the CNN-FSPA model, the CNN-PSPA models clearly showed a high error rate at point A, indicating that more attention is required. In addition, for the predictions in Table 13, it can be seen that the amplification factors of points D and E showed good predictions and small fluctuations in each model. Therefore, the CNN-FSPA model could predict the amplification factor of an unrecorded location well.
## 2 Material and Methods
### Data Collection
The Famagusta street network data were provided and were given access by the municipality. The data include all city streets and routes, stored as line shapefiles. In addition to physical location and characteristics, information such as street names and transit route characteristics were also available. Both sets of data were current as of 2020. The study was carried out through three phases:  
* The first phase contained a historical review and spatial analysis of the urban growth of Famagusta based on previous studies. The roles that physical configuration and socio-economic activities based on the land use distributions have played in different periods were also sought through current literature as historical clues for the subsequent spatial analysis;
* The second phase involved a comprehensive spatial analysis containing a space syntax analysis on the morphological evolution of Famagusta. Integration and choice measures were automatically calculated in Depthmap for any defined radius used;
* The third phase involved a GIS-based methodology for determining the density of street networks, and the growth of the city from the traditional core was laid out. The kernel density estimator (KDE) was calculated with ArcGIS. The integration value can be viewed on the map with a variety of colors, from red for most integrated roads and blue for the most segregated streets. The integration indicators at radius-n of a line consider the n-steps required to cover all the lines in the entire system. Thus, the integration value calculated at a lesser radius described a local syntactic property than that computed at a higher radius. The metric radius refers to the metric distance from each segment across all available streets from that segment to the distance from the radius. For instance, the amount of segments needed in the network to reach all other segments without a radius restriction was supposed to be a measure with radius \"n\" (Rn). If a radius of 500 m was measured, segments were only considered within the radius area and no segments were estimated beyond that radius. This implies that only the local relations between segment elements were known within 500 m along the adjacent segment lines from each of them. Both systems were processed for axial analysis at varied topological radii (Rn) and angular segment analysis at varying metric radii (500, 800, 2000, 6000, n); measures of integration and choice were processed at all metric radii. This study led to considerate syntactic logic behind the street network evolution identifying which directions were the most accessible street network development. The outcomes of this study were based on the syntactical investigation of Famagusta city. This is why the result of this article or the analysis drawn from the morphological examination may only apply to Famagusta city.