esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Ultimate Service Value
#### 1.1.7 Scientific Research and Education Value  
The value of scientific research and education was calculated by using the number of scientific-research publications. Based on the results of [PERSON] [27], the average number of annual papers published in the last three years was considered as the newest research achievement. In this study, papers with Liao estuarine wetland and Panjin wetland keywords were searched in the database of Chinese periodical literature from 2013 to 2015, and the number of published papers was counted.  
Intermediate Service Value  
(1) Groundwater Supplementation Value  
In the process of determining the value of supplementing groundwater, only the value supplemented by need wetland to the groundwater was considered. River infiltration, canal-system leakage, irrigation-water leakage, and underground lateral runoff were not calculated. In this wetland, the reed is submerged by water for six months, from May to October each year. When the infiltration amount of surface water into the groundwater was calculated, the surface-water infiltration coefficient was determined by the reed-wetland infiltration coefficient [27], determining groundwater energy.  
(2) Water-Conservation Value  
The value of water conservation was determined by measuring the rainfall intercepted by reeds aboveground and calculating the reed area of the Liaohe estuarine wetland based on the method of water conservation of forest ecological systems. The rainfall intercepted by reeds aboveground was obtained from a field survey.  
(3) Nutrient-Recycling Value  
There are two methods of determining the value of nutrient recycling: maintaining nutrients in the bio bank, and maintaining nutrients in the soil reservoir. This method aims to determine the ratio between net primary productivity and the amount of nutrients. In this study, the method of maintaining nutrients in the bio bank was adopted. N/P content in the net primary productivity of reeds was used to calculate the value of nutrient recycling.  
(4) Biodiversity Value  
At present, there are 253 species of birds living in the Liaohe estuarine wetland. The value of providing the habitat of this island for birds was used to evaluate the biodiversity value because maintaining bird-population quantity and quality, especially endangered birds, is the most important value of the Liaohe estuarine wetland.  
From the above, the total amount of ecosystem services (g) was converted into energy (J), then converted into energy through energy transformity (sej/J or sej/g) to calculate each energy of all wetland server values. Finally, energy was converted into monetary value through the energy currency-conversion rate. In this study, the related published survey reports and papers were referred [27; 28; 29; 30], and the indices of all energy referred to the research results by [PERSON] [31]. The energy currency -onversion rate was 1.21 \(\times\) 10\({}^{13}\) sej/$ [32].
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
###### Abstract  
In unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs), a UAV is employed as a mobile sink to gather data from sensor nodes. Incorporating UAV helps prolong the network lifetime and avoid the energy-hole problem faced by sensor networks. In emergency applications, timely data collection from sensor nodes and transferal of the data to the base station (BS) is a prime requisite. The timely and safe path of UAV is one of the fundamental premises for effective UWSN operations. It is essential and challenging to identify a suitable path in an environment comprising various obstacles and to ensure that the path can efficiently reach the target point. This paper proposes a hybrid path planning (HPP) algorithm for efficient data collection by assuring the shortest collision-free path for UAV in emergency environments. In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment. Our simulation results show that the proposed HPP outperforms the PRM and conventional ABC schemes significantly in terms of flight time, energy consumption, convergence time, and flight path.  
wireless sensor network; data gathering; unmanned aerial vehicle; path planning; artificial bee colony; collision avoidance; delay minimization; probabilistic roadmap 20212839 20212839 20212839
## Abbreviations  
The following abbreviations are used in this manuscript:  
**Abbreviations**  
ECC & elliptic curve cryptography  
DES & data encryption standard  
AES & advance encryption standard  
CHHCS & compound homogeneous hyperchaotic system  
CST & chaotic shift transform  
ECIES & elliptic curve integrate encrypt scheme  
DHAES & Diffie-Hellman augmented encryption scheme  
LF-NCHM & logistic Feigenbaum non-linear cross-coupled hyperchaotic map  
NPCR & number of pixel change rate  
UACI & unified average changing intensity  
BACI & block average changing intensity  
AES-256-GCM & advanced encryption standard-256-Galois/counter mode  
ET & encryption throughput  
**Symbols**  
\(k_{ENC}\) & The encryption key in ECIES  
\(k_{MAC}\) & The MAC key in ECIES  
u & The sender's private key in ECIES  
U & The sender's public key in ECIES  
v & The receiver's private key in ECIES  
V & The receiver's public key in ECIES  
c & The intermediate encrypted message in ECIES  
tag & The production from the MAC function in ECIES  
\(x_{0}\), \(y_{0}\), \(\mu\), \(\lambda\), \(\delta\)  
\(\mu\), \(\lambda\), \(x_{0}\), \(y_{0}\)  
The initial conditions for the hyperchaotic system  
\(\mu^{{}^{\prime}}_{0}\), \(\lambda^{{}^{\prime}}_{0}\), \(x^{{}^{\prime}}_{0}\), \(y^{{}^{\prime}}_{0}\)  
M, N & The length and the width of the image in simulation experiment  
rowChao, colChao & The two obtained hyperchaotic sequences  
indexRow, indexCol & The number of the rows and the columns for the amount of the blocks  
row, col & The shifting distance for the rows and the columns  
PM & The image after permutation  
C & The cipher image after the permutation and diffusion  
PKT & The formed encryption data packet after whole process  
spec256k1 & The parameters of the elliptic curve used in Bitcoin's public-key  
cryptography  
C' & The image after the value of a random pixel in the cipher iamge has changed
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 5 Accuracy Validation  
### Deformation Monitoring Accuracy  
To verify the reliability of the predictions in this paper, external levelling data were collected in the study area; the actual implementation times of the leveling survey were 13 November 2015, 23 May 2016, 26 October 2016, and 23 February 2017, and the locations of the leveling points are shown as red triangles in Figure 11d [33]. The measured values were obtained by leveling and the accuracy met the requirements of fourth-order leveling. Figure 11a-c show the results of the comparison between the vertically oriented deformations predicted and back extrapolated by the InSAR-3D-CTPIM model for the periods of 13 November 2015 to 23 May 2016, 26 October 2016, and 23 February 2017, and the level measurements.  
Figure 11: Comparison of level positions and InSAR-3D-CTPIM predicted values with measured values (reference time: 13 November 2015).  
Figure 10: (**a–d**): SBAS-InSAR late image monitoring results; (**e–h**): difference between predicted deformation and SBAS-InSAR monitoring deformation.  
As can be seen from the line graphs and difference bar graphs in Figure 11, the vertical deformation results predicted by the InSAR-3D-CTPIM model are in good agreement with the leveling data. The RMSEs of the vertical deformation predicted by the InSAR-3D-CTPIM model and the leveling measurement results in these three periods were \(\pm\)1.6 mm, \(\pm\)4.3 mm, and \(\pm\)4.5 mm, respectively, showing that the InSAR-3D-CTPIM model can reasonably predict the dynamic three-dimensional deformation of the mine surface.
## 2 Numerical Simulation Calculation Methods and Model Validation
### Model Accuracy Verification
#### 2.2.3 Summary  
As can be seen from the data, the performance curve of the fan predicted by the SST k-\(\omega\) model has a specific error compared with the test value, as can be seen from the external characteristics curve (Figure 5), when the flow rate of 75,000-85,000 m\({}^{3}\)/h near the design flow rate numerical simulation results and the test values are almost the same. As the pressure decreases, the simulated flow rate at each operating point is lower than the test value. Still, the maximum error does not exceed 5%, which aligns with the engineering calculation error requirements. As shown in Table 1, the noise simulated by the FW-H method using LES combined with acoustic analogy theory is very close to the experimental noise, with a maximum error of about 1%. Therefore, the method is highly reliable and can reference similar fan calculations.
# Enhancing Efficiency of Electromagnetic Simulation in Time Domain with Transformation Optics
## 4 Convergence of Transmittance Spectrum with Uniform Grids  
The accuracy of the simulated spectrum depends on the cell resolution, and it should converge when the cell size approaches to zero. To test the convergence, the simulation in uniform grids with the similar system setup in Section 3 are performed to obtain the transmittance spectrum \(T(\lambda)\); the cell size is set to be constant in all directions, i.e., \(\Delta x=\Delta y=\Delta z=\Delta\). In this test, the metal is the built-in PEC. For each uniform case, we use the stability factor \(S=0.5\), as the default setting in Meep.  
Figure 4 shows the results yielded when \(\Delta\) are 10 nm, 5 nm, and 2.5 nm, respectively. As seen, while the profiles are similar, they converge with \(\Delta\). The wavelength at peak transmittance \(\lambda_{p}\) and the corresponding peak \(T(\lambda_{p})\) for each \(\Delta\) is listed in Table 1. The convergence for both \(\lambda_{p}\) and \(T(\lambda_{p})\) is almost linear. Suppose that they have the relationships \(\lambda_{p}=a\Delta+b\) and \(T(\lambda_{p})=c\Delta+d\), respectively, where \(a,b,c\), and \(d\) are constants. We obtain \(a=9.44\), \(b=632\), \(c=0.06\), and \(d=3.51\) when the curve fitting is utilized. In other words, if \(\Delta\) approaches to zero, it is expected that \(\lambda_{p}\) and \(T(\lambda_{p})\) will approximate to 632 nm and 3.51, respectively. To verify, the transmittance spectrum predicted in [33] is reproduced and shown in Figure 4. From the theory, we obtain \(\lambda_{p}=626\) nm and \(T(\lambda_{p})=3.49\), respectively, which are well agreed with our approximation.  
Besides \(\lambda_{p}\) and \(T(\lambda_{p})\), the rest parts of the spectrums in Figure 4 are also seen to converge linearly with the cell size at relative wavelengths, i.e., for the wavelength ratio \(r\), \(\lambda_{p}\) and \(T(r\lambda_{p})\) have similar linear relationships with \(\Delta\) to those when \(r=1\). With curve fitting for each \(r\) from 0.6 to 1.3, the approximated transmittance spectrum is shown in Figure 4. The result is close to that from the theoretical prediction. Therefore, it can be considered as the converged profile from the simulation in uniform grids.
# Machine Learning for Network Intrusion Detection--A Comparative Study
## 4 Background
### Algorithms and Techniques
#### 4.2.4 Logistic Regression  
Logistic regression (LR) is a binary classification method that uses a probability function to measure the probability of an event taking place. It uses the following formula to calculate the probability. Some of the advantages include that can perform rapid classification and is easy to extend to a multi-class problem. The main disadvantage is that nonlinear problems cannot be solved using LR.
###### Abstract  
This paper introduces the concept of targeted field investigation on the reliability of earth-retaining structures in an active state, which is implemented in a random finite element method (RFEM) framework. The open source RFEM software REARTH2D was used and modified suitably in order to accommodate the purposes of the present research. Soil properties are modeled as random fields, and measurements are modeled by sampling from different points of the field domain. Failure is considered to have occurred when the \"actual\" resultant earth pressure force on the retaining wall (calculated using the friction angle random field) is greater than the respective \"predicted\" force (calculated using an homogenous friction angle field characterized by the mean of the values sampled from the respective random field). Two sampling strategies are investigated, namely, sampling from a single point and sampling from a domain, through an extensive parametric analysis. As shown, the statistical uncertainty related to soil properties may be significant and can only be minimized by performing targeted field investigation. Among the main findings is that the optimal sampling location in the active state is immediately adjacent to the wall face. In addition, it is advisable that the entire wall height be considered in sampling. Finally, it was observed that the benefit from a targeted field investigation is much greater as compared to the benefit gained using characteristic values in a Load and Resistance Factor Design framework.  
probabilistic analysis; soil sampling location; random finite element method; spatial correlation length; active earth pressure; optimal sampling location; characteristic value +
Footnote †: journal: Applied Sciences
## 4 Augmented Performance Analysis  
The purpose of the LEO-assisted GNSS structure is to improve the sensitivity of GNSS signal acquisition and tracking. With the assistance of LEO, the influence of navigation bit reversal is eliminated. Then, the coherent integration time can be extended to improve the SNR of the GNSS signal. However, it can be seen from the above analysis that the extension of the coherent integration time is prevented by the oscillator noise. Therefore, it is necessary to analyze the SNR gain and the performance benefits of acquisition and tracking that can be obtained after the coherent integration extension with LEO assistance.  
Figure 6: Theoretical and measured clock Allan variances.  
Figure 7: Power attenuation caused by oscillator stability.
## 5 Pre-Test and Pre-Test Data Analysis
### Pre-Test Data Analysis
An interesting observation is that the bio-feedback data in level 1 seems to be significantly higher than level 2 data as shown in heart rate and temperature plots and the [PERSON]'s LSD method supports the same result. A possible cause of this phenomenon may be related to the experimental design where subjects never exposed to a VR environment may become more anxious during the initial ET session. As the experiment progresses, the subjects become acclimated to the VR environment so there is less tension and greater ease of use of the system. In order to prevent this problem from happening, a ten-minute static VR experience can be added to the experiment flow between pre-test questionnaire and VRET interface to stabilize the subject's bio-response before the experiment begins. Figure 10: Bio-data—Subject’s skin conductance during the experiment session. Figure 11: (**a**) One-way ANOVA interval plot of heart rate vs. level; (**b**) [PERSON]’s least significant difference (LSD) method for multiple comparisons of heart rate vs. level; (**c**) One-way ANOVA interval plot of temperature vs. level; (**d**) [PERSON]’s LSD method for multiple comparisons of temperature vs. level; (**e**) One-way ANOVA interval plot of respiration vs. level; (**f**) [PERSON]’s LSD method for multiple comparisons of respiration vs. level; (**g**) One-way ANOVA interval plot of skin conductance vs. level; (**h**) [PERSON]’s LSD method for multiple comparisons of heart rate vs. level; (**c**) One-way ANOVA interval plot of temperature vs. level; (**d**) [PERSON]’s LSD method for multiple comparisons of temperature vs. level; (**e**) One-way ANOVA interval plot of respiration vs. level; (**f**) [PERSON]’s LSD method for multiple comparisons of respiration vs. level; (**g**) One-way ANOVA interval plot of respiration vs. level; (**g**) One-way ANOVA interval plot of skin conductance vs. level; (**h**) [PERSON]’s LSD method for multiple comparisons of skin conductance vs. level. According to [PERSON]'s research, if the amplitude of skin conductance is larger than 0.1 us, then the skin conductance indicates a larger emotional response [25]. In this research, skin conductance data are converted into the form of amplitude data. A Shewhart control chart of amplitude data is presented in Figure 12. If four out of five data points exceed 0.1 us, it is marked in red, which means the subject has a larger emotional response at this point of time. Sixteen of the thirty-one subjects meet the above conditions and have data points marked in red. These data points frequently appeared for the 6 th, 21 st, 25 th, and 32 nd subjects. The heart rate data are standardized according to individual data distribution and a Shewhart control chart of the standardized data is presented in Figure 13. If four out of five points exceed two standard deviations, the data points are marked in red indicating the subject has experienced a larger emotional response at this point of time. Twenty-one of the thirty-one subjects met the above condition with their heart rate data points marked in red.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 3 Related Work-Coordination Analysis
### Uncoordinated VNE  
One approach for the VNM/VLM coordination is to solve each sub-problem in an isolated and independent manner. In this case, VNM should be dealt with primarily, in the sense that it provides the input to the VLM's function. This lack of coordination between the VNE subproblems is called uncoordinated VNE, and it was primarily proposed in [18], where the authors' main goal was to maximize the long-term revenue. A greedy algorithm is used to solve VNM by choosing a set of eligible substrate nodes for each virtual node and then assign the computational demands to the prominent one in every case based on its available resources.  
The gist of that methodology is to constantly utilize the resources of the most capable substrate node for the satisfaction of the most demanding virtual one. Depending on the functional capabilities of the underlying physical network, the VLM solution can be implemented as either a single path mapping using the k-shortest path algorithm (Greedy-kSP) [31] for increasing values of k or a multiple path mapping, by reduction from the MCF problem [32] that splits the BW demand of a virtual link among several physical paths in the SN (Greedy-PS).  
However, the absence of coordination between node and link mapping may result in disproportionally distancing the neighboring virtual nodes onto the substrate topology, thus, increasing the overall cost of the embedded paths and, consequently, resulting in low acceptance ratios, high network latencies and low long-term revenue.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Optimal Feature Selection
#### 3.2.2 Optimal Object-Based Feature Selection  
Similar to optimal pixel-based feature selection, optimal object-based feature selection was also performed before the object-based classification procedure. Figure 8 shows the weights of the 50 object-based feature variables derived by the RF method, and Figure 9 shows the relationship between the number of object-based feature variables and the overall accuracy.  
As shown in Figure 8, the weights of the first four features were greater than 1, especially the weight of dMSR, which was greater than 2; additionally, the weights of the middle 14 features were between 0.5 and 1, and the weights of the last 32 features were less than 0.5. As shown in Figure 9, as the number of feature variables increased, the overall classification accuracy of features 1-16 displayed an upwards trend with slight  
Figure 8: Feature weights of different features in the object-based approach.  
Figure 9: Relationship between the number of feature variables and the overall object-based classification accuracy.  
fluctuations. The overall classification accuracy of features 17-27 displayed a downwards trend, and that of features 28-50 exhibited an upwards trend and gradually stabilized. The overall accuracy reached a maximum of 96.95% when the number of features was 9 and 28. To reduce the redundancy of features and improve the simplicity of the model for use in practical applications, the first 9 feature variables were selected as the optimal feature subset: dMSR, NIR\({}_{1}\), BLUEt\({}_{1}\), EVIt\({}_{2}\), DIS, REDt\({}_{1}\), dNDVI, GREENt\({}_{1}\), and PCA1.
## 3 Experimental Setups
The pipe was excited with the vibration motor while also recording the pipe wall temperature using a DS18B20 temperature sensor from Maxim Integrated, which was taped directly to the pipe surface. Temperature measurements were important to adjust any temperature-dependent pipe wall parameters within the theoretical models when comparing with the experiment results. For the buried pipe trials, a 6 m long MDPE pipe (of the same diameter and pipe wall thickness as the exposed pipe) was buried at a depth of 0.8 m in a trench of 8 m length before backfilling the trench with well-graded sand (SW) as shown in Figure 4 (the length and depth of the trench were governed by other work). For the trials, the average gravimetric water content and bulk density of the surrounding soil (taken at 0.6 m depth) were measured as 4.1% and 2123 kg/m\({}^{3}\), respectively. \begin{table}
\begin{tabular}{c c} \hline \hline Pipe inner radius (m) & 0.0361 \\ Pipe wall thickness (m) & 0.0092 \\ Elastic modulus (N/m\({}^{2}\)) & \(1.6\times 10^{9}\) \\ Poisson’s ratio & 0.4 \\ Density (kg/m\({}^{3}\)) & 900 \\ Material loss factor & 0.06 \\ Longitudinal wave speed (m/s) & 1455 \\ Bulk modulus of water (N/m\({}^{2}\)) & \(2.25\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometric and material properties of the MDPE pipe. Figure 4: Buried plastic pipe before and after soil backfilling. One ERM vibration motor, placed at one end of the pipe, and two piezoelectric sensors (Smart Material, 2015) were also installed at 3 m and 5.6 m from the vibration motor. Although this setup was primarily designed to test the acoustic data communication system developed by [29], the acoustic signal amplitudes measured at each sensor were also useful for estimating the acoustic attenuation along the buried pipe.