esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### BBE Estimated by Different Thermal Infrared Bands  
Th emissivity of the land surface is mainly determined by its thermo-physical characteristics. Since different satellites may have different thermal infrared band widths, it is better to integrate the narrow thermal infrared band emissivity into broad band emissivity (BBE) with different satellite products. Then, the TR of the MODIS LST is calculated by its BBE; the ASTER and the ETM+ should also estimate their BBE in the same wavelength.  
\begin{table}
\begin{tabular}{c c c c} \hline
**RSI** & **RSI Calculation** & **LST-RSI Relationship** & **Studies** \\ \hline NDVI & \(NDVI=\frac{\rho_{\text{NIR}}-\rho_{\text{RDI}}}{\rho_{\text{NIR}}+\rho_{ \text{RDI}}}\) & \(LST=\text{a}_{1}NDVI+\text{b}_{1}\) & [PERSON] et al. [(26)]; [PERSON] et al. [(11)] \\ \(f_{0}\) & \(f_{v}=1-(\frac{NDVI_{\text{max}}-NDVI}{NDVI_{\text{max}}-NDVI_{\text{min}}})^{ 0.625}\) & \(LST=\text{a}_{2}fv+\text{b}_{2}\) & [PERSON] et al. [(10)] \\ NDBI & \(NDVI=\frac{\rho_{\text{NIR}}-\rho_{\text{NIR}}}{\rho_{\text{NIR}}+\rho_{ \text{NIR}}}\) & \(LST=\text{a}_{3}NDBI+\text{b}_{3}\) & [PERSON] et al. [(9)]; [PERSON] et al. [(36)] \\ ISA & \(ISA=F_{\text{high\_albedo}}+F_{\text{low\_albedo}}\) & \(LST=\text{a}_{4}ISA+\text{b}_{4}\) & [PERSON] et al. [(9)] \\ \(F_{i}\) & \(\rho_{b}=\sum\limits_{i=1}^{N}f_{i}\rho_{i,b}+e_{b}\) & \(LST=\sum\limits_{i=1}^{n}F_{i}T_{i}+e\) & [PERSON] and [PERSON] [(7)] \\  & Subject to: \(\sum\limits_{i=1}^{N}f_{i}=1\) and \(f_{i}\geq 0\) & & \\ \hline \end{tabular}
\end{table}
Table 1: Some LST downscaling method with VNIR data as assistance.  
The BBE estimation methods of the ASTER and ETM+ are different because the ASTER has five thermal bands, and the ETM+ has only one thermal band.  
[PERSON] et al. [8] gave the BBE estimation method of the MODIS and the ASTER with their narrow thermal band emissivity.  
\[\varepsilon_{bb\_mod}=0.095+0.329\varepsilon_{29}+0.572\varepsilon_{31} \tag{14}\]  
\[\varepsilon_{bb\_ast}=0.197+0.025\varepsilon_{10}+0.057\varepsilon_{11}+0.237 \varepsilon_{12}+0.333\varepsilon_{13}+0.146\varepsilon_{14} \tag{15}\]  
where \(\varepsilon_{bb\_mod}\) is the BBE of the MODIS, and \(\varepsilon_{29}\) and \(\varepsilon_{31}\) are, respectively, MODIS thermal band emissivity values of 29 and 31 from the MODIS emissivity product (MOD11A1). \(\varepsilon_{bb\_ast}\) is the BBE of the ASTER, and \(\varepsilon_{10}\) to \(\varepsilon_{14}\) are the ASTER thermal narrowband emissivity values from the ASTER emissivity product (AST_05).  
[PERSON] et al. [2] made an overview about the emissivity retrieval method and gave a method to estimate the emissivity using a flat pixel composed of \(N\) endmembers with a given spectral domain, as follows:  
\[\varepsilon_{bb\_ETM}=\sum_{k=1}^{N}F_{i}\varepsilon_{endmember,k} \tag{16}\]  
where \(\varepsilon_{bb\_ETM}\) is the BBE of ETM+, \(F_{i}\) is the fraction of the endmember in a pixel, and \(\varepsilon_{endmember,k}\) is the BBE of the typical endmembers given in Table 2 [46].
# Factors Affecting Cybersecurity Awareness among University Students
## 4 Results
### Description of the Independent Variable (X) Used
#### 4.2.6 Hypothesis Test  
ANOVA Test (F-Test)  
The following is an F-test to see whether the independent variable has a simultaneous effect on the dependent variable [65]. The hypothesis in this test is as follows:  
**Hypothesis 0.**  
1. _Password Security (_X\({}_{1}\)_), Browser Security (_X\({}_{2}\)_), and Social Media Activities (_X\({}_{3}\)_) together are not significantly related to Cybersecurity Awareness (_Y_).
2. _Password Security (_X\({}_{1}\)_), Browser Security (_X\({}_{2}\)_), and Social Media Activities (_X\({}_{3}\)_) together are significantly related to Cybersecurity Awareness (_Y_).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Variable** & **Regression Coefficient (\(\beta\))** \\ \hline Intercept & 4.301 \\ Password Security (X\({}_{1}\)) & 0.147 \\ Browser Security (X\({}_{2}\)) & 0.188 \\ Media Social Activities (X\({}_{3}\)) & 0.241 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Multiple linear regression coefficient.  
Figure 3: Heteroskedasticity Test.  
Table 13 shows the results of the \(p\)-value (0.000) \(<\) 0.05, so it can be concluded that Password Security (X\({}_{1}\)), Browser Security (X\({}_{2}\)), and Social Media Activities (X\({}_{3}\)) together have a significant effect on Cybersecurity Awareness (Y).  
\begin{tabular}{c c c} \hline \hline
**Model** & **F** & **Sig (\(p\)-Value)** \\ \hline Regression & 38.666 & 0.000 \\ \hline \hline \end{tabular}  
Partial Test (\(t\)-Test)  
A partial test [66] using the \(t\)-test is used to determine the significant effect of each independent variable on the dependent variable. The hypothesis in the partial test is as follows:  
**Hypothesis 1.**  
1. _Password Security (X\({}_{1}\)) is not significantly related to Cybersecurity Awareness (Y)._
2. _Password Security (X\({}_{1}\)) is significantly related to Cybersecurity Awareness (Y)._  
**Hypothesis 2.**  
1. _Browser Security (X\({}_{2}\)) is not significantly related to Cybersecurity Awareness (Y)._
2. _Browser Security is (X\({}_{2}\)) is significantly related to Cybersecurity Awareness (Y)._  
**Hypothesis 3.**  
1. _Social Media Activities (X\({}_{3}\)) is not significantly related to the Cybersecurity Awareness (Y)._
2. _Social Media Activities (X\({}_{3}\)) is significantly related to Cybersecurity Awareness (Y)._  
Table 14 shows the results of partial hypothesis testing (\(t\)-test). Based on these results, the conclusions are:  
1. The Password Security variable (X\({}_{1}\)) has a \(p\)-value (0.0001) \(<\) (0.05), so it can be concluded that Password Security (X\({}_{1}\)) has a significant effect on Cybersecurity Awareness (Y).
2. The Browser Security variable (X\({}_{2}\)) has a \(p\)-value (0.0001) \(<\) (0.05), so it can be concluded that Browser Security (X\({}_{2}\)) has a significant effect on Cybersecurity Awareness (Y).
3. The Social Media Activities (X\({}_{3}\)) variable has a \(p\)-value (0.0001) \(<\) (0.05), so it can be concluded that Social Media Activities (X\({}_{3}\)) have a significant effect on Cybersecurity Awareness (Y).
## 3 Results and Discussion
### Removal of FF and OA by Ozonation in Water Containing Br\({}^{-}\)  
The removal efficiency of FF and OA by ozonation was assessed in water containing 60 mg/L of bromide ion at pH 8. Figure 7a shows the removal of FF with 1 and 2 mg/L of ozone injection. Because of the lower reactivity between FF and the oxidants, ozone, and bromine (k\({}_{\text{O3,FF}}\) = 3.2 M\({}^{-1}\)s\({}^{-1}\) and k\({}_{\text{Bromine,FF}}\) = 3.5 M\({}^{-1}\)s\({}^{-1}\)), the removal of FF by ozonation in water containing bromide ion was only 4% and 8% within 20 min. In fact, it is difficult to remove FF from seawater by ozonation owing to the lower reactivity of FF with ozone and bromine.  
Figure 7 shows the effect of bromide ion on OA removal by ozonation, compared with OA removal in the absence of bromide ion. In the absence of bromide ion, ozone reacted rapidly with OA, and over 99% of OA was removed in 90 s, as shown in Figure 5. However, when bromide ion was present, the removal efficiency of OA was lower than that for ozonation without bromide ion, i.e., less than 10%. OA decreased rapidly within 15 s, and decreased slowly after 15 s. The primary mechanism of OA removal in the first phase of 15 s was trace ozone; subsequently OA was removed by the generated stable bromine. The measured reaction rates of OA with ozone and bromine at pH 8 were as \(2.4\times 10^{3}\) M\({}^{-1}\)s\({}^{-1}\) and \(4.0\times 10^{2}\) M\({}^{-1}\)s\({}^{-1}\), respectively. These results indicate that trace ozone is more important than bromine for OA removal owing to the reaction rate of ozone being the six times that of bromine. As mentioned in the introduction, both chlorine and ozone have been used in the chemical process for seawater treatment. In seawater chlorination, chlorine reacts rapidly with bromide ions to produce bromine. Ozone and chlorine (bromine) cannot be suggested for FF removal owing to their low reaction rates, thus other chemicals or treatment methods should be considered. To remove OA from seawater, ozonation process can be applied rather than bromination, owing to the rapid reaction rate of ozone with OA. For longer reaction times, both chlorine and bromine can be recommended for the treatment method.
## 1 Introduction
### System Model and Problem Formulation
#### 1.2.3 Uplink Scenario  
Next, we consider the uplink scenario where the UAVs send their own information to the BS using the LoRa. In the uplink scenario, the received signal at the BS and the corresponding SINR for the \(k\)-th UAV are, respectively, given by  
\[\mathbf{y}_{\mathrm{u}}[n]=\sum_{k=1}^{K}\sqrt{p_{k}[n]}\mathbf{h}_{\mathrm{u },k}[n]x_{k}[n]+\mathbf{z}[n], \tag{6}\]  
\[\mathrm{SINR}_{\mathrm{u},k}[n]=\frac{p_{k}[n]||\mathbf{h}_{\mathrm{u},k}[n]| |^{2}}{\sum_{i\
eq k}p_{i}[n]||\mathbf{h}_{\mathrm{u},i}[n]||^{2}+M\sigma^{2}} \tag{7}\]  
where \(p_{k}[n]\), \(\mathbf{h}_{\mathrm{u},k}[n]\in\mathbb{C}^{M\times 1}\), and \(\mathbf{z}[n]\) denote the transmit power of the \(k\)-th UAV, the uplink channel vector from the \(k\)-th UAV to the BS, and the noise vector at time slot \(n\), respectively.  
In the uplink scenario, for each time slot \(n\), we aim to minimize the total transmit power of the UAVs under the SINR constraints at the BS by optimizing the transmit power \(\{p_{k}[n]:k=1,\cdots,K\}\) of the UAVs, which is formulated as follows:  
\[\begin{split}\text{(P2)}:\min_{\{p[n]\}_{k=1}^{K}}& \sum_{k=1}^{K}p_{k}[n]\\ \text{s.t.}&\mathrm{SINR}_{\mathrm{u},k}[n]\geq \gamma_{\mathrm{u},k},\ \forall k.\end{split} \tag{8}\]  
where \(\gamma_{\mathrm{u},k}\) is the SINR threshold. Similarly to the downlink scenario, the problem (P2) for the uplink scenario is nonconvex as well.
## 4 Results and Discussion
### Wavelength Requirements Analysis
To evaluate the accuracy of both prediction formulas, it would be useful to apply them to real-world optical networks and compare their estimation results with the wavelength requirements \(N_{\lambda}\) obtained through simulations using the algorithms presented in Section 3. These comparisons can be made using the following two mean relative errors:  
\[RE1(\%)=\frac{\left|N_{\lambda}-\hat{N}_{\lambda}\right|}{N_{\lambda}}\times 100 \tag{6}\]  
and  
\[RE2\ (\%)=\frac{\left|N_{\lambda}-\hat{N}_{\lambda min}\right|}{N_{\lambda}} \times 100 \tag{7}\]  
where \(\hat{N}_{\lambda}\) and \(\hat{N}_{\lambda min}\) represent the estimated wavelength requirements values obtained using Equations (4) and (5), respectively. Table 1 shows the 27 real-world networks considered in the analysis [21]. In these networks, the number of nodes varies between 7 (SANReN) and 60 (CONUS60), the number of links ranges from 7 (SANReN) to 88 (Germany50), the average node degree extends from 2 (SANReN) to 4.73 (COST239), and the algebraic connectivity varies between 0.05 (CONUS60) and 2.15 (COST239). The average values  
Figure 3: Wavelength requirements as a function of algebraic connectivity with the number of nodes and average node degree as parameters. obtained for \(RE1\) and \(RE2\) are 12.68% and 31.14%, respectively, showing that our prediction Formula (4) provides higher estimation accuracy than Formula (5). This is because Formula (5) was derived from random networks where wavelength usage was computed using a lower bound equation that sets a limit on the wavelength requirements. In contrast, Formula (4) was obtained by computing the number of wavelengths using a graph coloring technique, which yields more accurate results.
# Corrosion Behavior and Conductivity of TiNb and TiNb Coated Steel for Metallic Bipolar Plates
## 2 Materials and Methods  
### Preparation of Coatings  
The TiNb and TiNbN coatings were prepared by magnetron co-sputtering and reactive sputtering with a Process Equipment (KJLC, Kurt Company, US) using a radio frequency (RF) Ti target and a direct current (DC) Nb target. The substrate was 316L SS with a thickness of 0.2 mm. Before sputtering, the test specimens were ground with 800, 1500, and 2000 mesh sandpaper, polished with diamond polishing agents and ultrasonically cleaned in ethanol and acetone to remove the solid particles and grease. High purity (99.9999%) argon and nitrogen were used as working gases. The chamber was depressurized to below 7 mPa. During deposition, the substrate temperature was 523.15 K and the working pressure was in 0.5-0.8 Pa. To improve the adhesion between substrate and coating, a thin titanium layer was deposited at 7.7 W\(\cdot\)cm\({}^{-2}\) for 10 minutes followed by transition layers with different atomic ratios of Ti and Nb. Finally, the target powers of Ti and Nb were regulated to 4.4 W\(\cdot\)cm\({}^{-2}\) and held for 2 h to obtain the TiNb coating. The deposition parameters of the TiNbN coating were similar to that of TiNb with lower temperature (473.15 K). The gas flow volumetric ratio of reactive gas (nitrogen) and inert gas (argon) was set as 1:5. The thicknesses of the TiNb and the TiNbN coatings were about 660 and 2080 nm, respectively, according to coatings' deposition rates.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 4 Data Collection III: Moving Targets
### Experimental Results  
#### 4.3.1 Frequency Domain Response  
The frequency response from the captured moving vehicle data and drone data is shown in Figure 12 and Figure 13, respectively. The information displayed in the legends of Figures 12 and 13 is as follows:  
1. TX Copy: FFT of the captured analog signal created using RF hardware.
2. No Target: FFT of the captured experimental scene with no targets present.
3. \(+f_{D}\): FFT of the data captured from a target moving toward the radar.
4. \(-f_{D}\): FFT of data captured from a target moving away from the radar.
5. CFAR dets: cell averaging (CA)-constant false alarm rate (CFAR) detections (see Section 4.3.3).  
Figure 12: Frequency information from a vehicle moving toward (red) and away (yellow) from the FDA radar system with operational parameters \(f_{0}=2\;\mathrm{GHz}\), \(\Delta f=10\;\mathrm{kHz}\), and \(N=3\). Note that the CFAR detections are emphasized with a red diamond for visibility.
## 3 GAs for Multi-Objective Routing Optimization
### Representation and Encoding of Routes  
The representation and encoding of routes are crucial aspects of GAs for multi-objective message routing in EnFVs. The chosen representation directly impacts the algorithm's ability to explore diverse solutions within the search space. In message routing, efficient encoding is necessary to capture the sequence of waypoints, charging stations, and flight paths [27; 28].  
One commonly used representation is the chromosome-based approach, where each chromosome corresponds to a potential route. The chromosomes consist of a sequence of genes, with each gene representing a specific waypoint or charging station. The order of genes in the chromosome determines the sequence in which waypoints or charging stations are visited. For EVs, route encoding may include additional information like energy consumption and charging requirements. The chromosome can be expanded to include genes representing energy levels at each charging station or remaining battery capacity at waypoints. For FVs, encoding may incorporate flight restrictions, altitude limits, and airspace regulations. The genes within the chromosome can be expanded to include information related to flight paths, altitude ranges, and any specific requirements for each waypoint. The encoding scheme should balance representational capacity and computational complexity, allowing the exploration of diverse routes while ensuring feasible and efficient solutions. Various encoding schemes, such as binary encoding, permutation encoding, or integer encoding, can be explored based on specific routing problem requirements.  
Commonly used encoding schemes include binary encoding, suitable for discrete decision spaces; integer encoding, appropriate for finite options; permutation encoding, for cases where the order matters; real-valued encoding, enabling continuous representation; tree-based encoding, offering flexibility for complex relationships; and composite encoding, combining multiple schemes for tailored representation. The choice of encoding depends on the problem, decision variables, and objectives of the multi-objective GA. It is crucial to select an encoding scheme that effectively represents the problem domain, which facilitates exploration of the solution space, and enables accurate fitness evaluation during the optimization process.
# A Designed Calibration Approach for the Measurement -While-Drilling Instrument
###### Abstract  
The Measurement-While-Drilling (MWD) system, composed of a tri-axial magnetometer and a tri-axial accelerometer, is widely used in the Horizontal Directional Drilling machine in coal mines. This system can provide attitude information of each measuring point in the borehole, which will eventually allow the trajectory of the borehole to be drawn. The attitude information, however, showed a low-level accuracy, due to the sensor's imperfection and mounting errors. The accuracy worsened when low-cost sensors were employed, as they had higher random noise. Therefore, an exploration of ways to eliminate the sensor imperfection and mounting tolerance as well as to suppress the noise is needed. In this paper, a feasible calibration approach was designed to address these issues. This new approach combined three foundational calibration algorithms, including the ellipsoidal fitting method, the planar fitting method, and the inner product invariance method. The traditional ellipsoidal fitting method and planar fitting method were optimized by using the recursive least square criterion and omitting the steps of sample data acquisition, respectively. In addition, the noise suppression method was involved in our approach to improve the calibration accuracy. The numerical simulation results showed that the number of sampling points decreased significantly, but the accuracy of the azimuthal angle and the pitch angle fully met the engineering requirements. The experimental results showed that the pitch angle error was reduced by less than \(0.5^{\circ}\), and the azimuth error was also reduced by less than \(2.5^{\circ}\). It should be noted that this new approach could be implemented without the help of other expensive auxiliary equipment.  
Keywords:ellipsoid fitting method; MWD system; plane fitting method; recursive least square method; calibration approach +
Footnote †: journal: Applied Sciences
## 3 Methods
### Basic A* Algorithm  
The A* algorithm concerns finding the shortest path between the start and destination point by calculating the total cost of reaching the target with the following:  
\[\text{F(n)}=\text{G(n)}+\text{H(n)} \tag{2}\]  
where  
n--the next node on the path;  
F(n)--the total path cost;  
G(n)--the cost of reaching the analysed cell from the starting point;  
H(n)--the cost of reaching the target from the analysed cell.  
The heuristic function H(n) is usually calculated using the Euclidean distance as follows:  
\[H(n)=\sqrt{\left(x_{goal}-x_{n}\right)^{2}+\left(y_{goal}-y_{n}\right)^{2}} \tag{3}\]  
Figure 2: A satellite photo of the Northern Port in Gdánsk and its grayscale representation used as a test environment with an indication of the port’s real part in the panorama available in [38]. The area marked with an orange circle on the satellite image corresponds to the real port area indicated by the arrow in the panorama.  
where (x\({}_{\text{goal}}\), y\({}_{\text{goal}}\)) represents the coordinates of a target point, and (x\({}_{\text{n}}\), y\({}_{\text{n}}\)) represents the coordinates of node n.  
A simplified workflow of the basic A* algorithm is shown in Figure 3. First, based on the occupancy grid, depending on the presence of obstacles, environmental map coordinates are saved to an open and closed list. In the main loop, the algorithm selects the node with the lowest total cost F(n) from the open list and evaluates its neighbours, updating their costs if a more optimal path is found. This process repeats until the goal node is selected, indicating that the shortest path has been found, or the open list is empty, signifying that no path exists.  
The disadvantage of this approach Is the number of calculations performed, which increases significantly for large areas of analysis or a large number of obstacles. Additionally, the algorithm ensures the determination of the shortest path, assuming the presence of known static obstacles, which makes it impossible to use it in its basic form in relation to a dynamic environment.
# Force Analysis of Circular Diaphragm Wall Based on Circular Cylindrical Shell Theory
## 8 Conclusions  
In view of the shortcomings of existing calculation methods for circular diaphragm walls, as well as their own structural characteristics and mechanical characteristics, an alternative calculation method was developed with clear concept based on the circular cylindrical shell theory and superposition principle in elasticity. Taking the north anchorage circular foundation pit of G3 Tongling Yangtze River Highway and Railway Bridge as a case study, the proposed CCSTCM in this study was used to calculate and analyze, and its applicability and correctness were also proven. The specific research content is as follows.  
1. Because the circular diaphragm wall of the north anchorage foundation pit was mainly subjected to horizontal loads in the construction process, the mechanical calculation model of the circular diaphragm wall under the axisymmetric normal loads was given according to the mechanical characteristics of the circular diaphragm wall.
2. The circular diaphragm wall of the north anchorage foundation pit was simplified into a circular cylindrical shell model. The calculation formulas of the internal force and deformation of the circular diaphragm wall under axisymmetric normal loads were derived by the circular cylindrical shell theory in elasticity, and the analytical solution and calculation steps based on the elastic superposition principle were provided.
3. The numerical model of the whole construction process of the north anchorage circular foundation pit was established, and the site monitoring of Section A was carried out.
4. The proposed CCSTCM and the FENSM were used to calculate the engineering case, and then the radial displacement, circumferential stress and vertical bending moment of the circular diaphragm wall under normal loads were obtained. All the obtained results were summarized regularly, and the numerical calculation model established was proven to be correct and reliable.  
5. The TCR and NSR were compared with the SMR. On the whole, the TCR values were slightly larger than the NSR values, but slightly smaller than and closer to the SMR. Moreover, the curves obtained from the CCSTCM were highly similar to those obtained from the FENSM and site monitoring. All these facts fully prove the rationality and correctness of the CCSTCM proposed.  
Conceptualization, L.W.; methodology, L.W. and G.S.; software, L.W.; formal analysis, L.W.; supervision, L.W.; investigation, L.W. and G.S.; resources, L.W.; validation, G.S.; visualization, L.W.; writing--original draft, L.W.; writing--review and editing L.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Nantong Science and Technology Plan Program (JC20220282) and Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX23_0000).  
Not applicable.  
Data Availability Statement: Data can be obtained from the corresponding author upon reasonable.  
The authors declare no conflict of interest.