esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Sensitivity and Specificity  
The difference between FR types of methods (continuous or discrete) can be explained according to the objectives of the map produced. If a map is sought in which the values of greater susceptibility (landslide susceptibility index) define the unstable zones with greater accuracy (discarding zones without landslides for a better prediction for pixels without landslides, and minimising errors in the delimitation of high susceptibility zones) then the most appropriate methods are those that propose a greater density of classes in the high values of the continuous variables (CFR and RFR). However, if greater accuracy is sought for low susceptibility zones (marking the larger unstable zones for a better prediction for pixels with landslides), then conventional methods will be more appropriate even though they produce a costly excessive pessimism. The greater zoning of high slope values, when this variable is very significant in the study area, enables a better separation of unstable and stable zones, considering the varying cost of failure in the high susceptibility sections as opposed to the low susceptibility areas.  
Potentially unstable sections (susceptibility classes VH and H) appear to be quite effective in defining zones with potential landslides. In particular, RFR with GY offers a high and equal sensitivity and specificity (80%). The high specificity (precision in discarding landslide areas) is very important, as false positives can lead to expensive but unnecessary slope stabilisations. Sensitivity (precision in landslide areas) is also important to prevent the occupation of potentially dangerous areas that could result in many deaths and heavy economic losses.  
Finally, the ELSUS system is quite efficient with good results for all methods, but no differences can be seen. This is because it only considers sensitivity and so does not consider the improvement brought by continuous methods that also improve in specificity.
# GPU Accelerated PIC and SIC for OFDM-NOMA
## 3 CUDA Implementation  
The SIC and PIC techniques were implemented on both a central processing unit (CPU) and GPU, then their computational speeds were compared. The CUDA codes were compiled on a machine that ran Ubuntu OS with a 12-GB memory NVIDIA TITAN Xp graphics card that had 3840 CUDA cores and a clock rate of 1582 MHz. For computing, the NVCC compiler for the CUDA 9.2 platform and the GCC compiler for C++ object-oriented programming language were used. The CPU codes were compiled with an Intel Core i7 (four cores) 2.3-GHz, DDR3L 1600-MHz 16-GB memory machine that ran Mac OS Mojave. The results with CPU were obtained on MATLAB R2018b.  
Figure 3 summarizes the functions and kernels used for the implementation of SIC and PIC on GPU using CUDA. As for the FFT and IFFT tasks, the cuFFTlibrary functions with forward and inverse parameters were called. The functions had \(\mathcal{O}(nlogn)\) computation time complexity. One CUDA thread was assigned per subcarrier. A CUDA block of our GPU allowed calling up to 1024 threads per block. For SIC, the chain of operations for each user included FFT, demodulation, modulation, and IFFT with phase/amplitude adjustment (see Figure 1). For the FFT and IFFT computations, functions from the  
Figure 2: Block diagram of a typical parallel interference cancellation (PIC).  
cuFFT library with \(\mathcal{O}(nlogn)\) time complexity were used [18]. The CUDA kernels were developed for demodulation, modulation, and subtraction tasks. Due to the nature of the SIC receiver, the same chain of operations had to be repeated for each user and could not run in parallel. The entire SIC computational time on GPU, however, can still be decreased by processing demodulation, modulation, and subtraction operations per subcarrier, one after the other, but computing OFDM in parallel for each user using the divide and conquer algorithm [19]. Therefore, a grid of \(N_{1}\) blocks each having \(N_{2}\) threads was created so that the total number of parallel tasks for each kernel was equal to the number of subcarriers, i.e., \(N_{1}\times N_{2}=N\). These parallel tasks computed OFDM for demodulation, modulation, and subtraction, and this process constitutes one SIC iteration. Then, the process was repeated \(K\) times in order to decode the signal of each user.  
In the implementation of PIC on GPU, all the functions and kernels per subcarrier of each user can be executed in parallel. In this case, the number of threads per block was set equal to the number of users \(K\). Each thread handles the tasks per subcarrier for each user. The grid of blocks was then created with the number of blocks equal to the FFT size \(N\), which made the total number of parallel tasks \(K\times N\).
# Numerical Simulation of Piles in a Liquefied Slope Using a Modified Soil-Pile Interface Model
## 4 Parametric Study
### Effect of the Input Amplitude  
The excess pore pressure in the ground is induced by cyclic shear load from seismic motion. Therefore, the study analyzed the effect of input motion amplitude by varying it from 0.05 g to 0.25 g. The input motions were created by adjusting the motion used in the experiment.  
Figure 15 displays the excess pore pressure profile after the end of shaking based on the input amplitude. Increasing the input amplitude from 0.05 g to 0.15 g significantly raised the excess pore pressure in the ground. The 0.15 g input amplitude resulted in complete liquefaction at the center of the slope, but it did not lead to full liquefaction at the crest of the slope. No further changes in the failure behavior of the liquefied slope were observed by the 0.25 g input amplitude since both the slope center and crest had already experienced complete liquefaction due to the 0.2 g input amplitude.  
Figure 16 demonstrates the lateral displacement profile of the ground after the end of shaking according to the input amplitude. The profile caused by motions with an amplitude of less than 0.15 g showed a linear shape regardless of the slope location. However, input motions with an amplitude greater than 0.2 g made the profile shape change depending on the slope location. These motions caused the sliding of the soil block from the ground surface to a depth of 10D in the center of the slope.  
The lateral displacement of the ground due to input motion induced a residual bending moment in the piles. Figure 17 shows the residual bending moment profile of the group piles according to the input amplitude. Input motions weaker than 0.1 g failed to cause liquefaction of the ground at deep depths, resulting in the maximum bending moment occurring not at the pile toe but within the ground.  
The residual bending moment of the pile varied significantly depending on the excess pore pressure at the slope crest. The 0.2 g amplitude, which could induce additional excess pore pressure increase at the slope crest compared with the 0.15 g amplitude resulted in a greater failure of the liquefied slope. This suggests that the input amplitude affected the failure of the liquefied slope, as well as the soil liquefaction. Furthermore, the maximum residual bending moments at the pile toe caused by the input amplitudes of 0.2 g and 0.25 g were 2.2 MN\(\cdot\)m and 2.3 MN\(\cdot\)m, respectively. It is worth noting that the increase in the moment (5%) was relatively small compared with the magnitude of the increase in the input amplitude (25%). This implies that input amplitude beyond a certain level did not induce a further increase in the bending moment of the piles.  
Figure 15: Excess pore pressure profile after the end of shaking at the slope (**a**) center and (**b**) crest.  
Figure 16: Depth profiles of the residual lateral displacement of soil at the slope (**a**) toe, (**b**) center, and (**c**) crest.
## 4 Results
### Effect of Cloud Point Density on Center Coordinates and Diameter of the Tires  
Additionally, to this study's main goals, an analysis of the influence of gradually decreasing density (number) of points on the carrier tires on their centers' derivation was performed. This is important for the correct statement about the geometry of the longitudinal axis. Trimble Realworks\({}^{\circledR}\) software was used for spatial sampling of the point clouds obtained by the procedure described in Chapter 4.1. Separate objects (point-groups) were created for every reduced point cloud. For each object, a cylindrical shape was fitted. No additional cylinder parameters, for example, position or radius, were specified. The distances between points and the number of points selected are given in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point Cloud Resolution [m]** & **Tire A [Points]** & **Tire B [Points]** & **Tire C [Points]** \\ \hline
0.005 & 15,895 & 75,821 & 3573 \\ \hline
0.010 & 15,073 & 47,725 & 3573 \\ \hline
0.020 & 9245 & 17,523 & 3573 \\ \hline
0.030 & 5288 & 9134 & 2350  
On the graphs (Figures 18-20), the center's coordinates and the diameter of the tires A, B, and C, which are determined by the gradual fitting of the selected point cloud in the Trimble Realworks(r) software environment, are separately plotted.  
Figure 19: Tire B—comparison of X, Y, and Z coordinates of the tire centers and their radiuses depending on the density of point cloud.  
Figure 20: Tire C—comparison of X, Y, and Z coordinates of the tire centers and their radiuses depending on the density of point cloud.  
The horizontal axis of the graph represents the selected distances between points in the cloud. The vertical axis shows the coordinates of the center and the diameter of each tire. In the direction of the \(X\)-axis (transversal direction), there are differences up to +/- 1 mm on all rings in the whole range of investigated values. This corresponds to an ideal circular shape of the ring, which we expected. The same applies to the values of \(Z\)-axis and the values of the radius. The values of X and \(Z\)-axes are essential for the determination of deformations, rectification values, and, therefore, correct operation of the kiln.  
The filtration of points for the previous analysis was done in Trimble RealWorks software using the function Spatial Sampling--Random method. The points are then selected without taking into account their stationing. Therefore, the \(Y\)-axis coordinates of the ring centers have a scatter of up to 20 mm from the value of about 100 mm of the mutual distance of the points. The Y coordinate is only informative--it changes during the kiln operation; also, the differences in coordinates may arise due to the random selection of point throughout the ring width.  
The graph shows that the decreasing density of the points has a negligible effect on the center's resulting coordinates in the transverse direction or diameter of the tire. The red line shows the value of the center or radius of the tire, determined as a weighted average of the respective values.
## 4 Use Case Evaluation
### Semantic Reasoning with CYPHER Queries
#### 4.3.2 Co-Work Social Relationships  
To identify _co-work_ relationships between sensors, we use the _computeBy_ property of the _Entity_ nodes. If two entities compute the same _Service_, they are considered to be co-working. Here, is an example of the Cypher query used to identify _co-work_ relationships:  
```
MATCH(s1: Service)-[COMPUTES_BY]->(e1:Entity) MATCH(s2: Service)-[COMPUTES_BY]->(e2:Entity) WHEREe1<e2 AND s1=s2 CREATE(e1)-[CO_WORK]->(e2)
```  
This query matches pairs of _Service_ nodes (s1, s2) that _computesBy Entity_ nodes (e1, e2) that have the same _Service_ value. The query then creates a CO_WORK relationship between the two entities.  
The identified social relationships can be leveraged to develop a more comprehensive understanding of the environment in which the Green Route scenario operates. This understanding can be used to optimize various aspects of the scenario, such as identifying the most suitable sensors for measuring environmental parameters, refining the criteria for selecting the most appropriate web services for processing sensor data, and enhancing the overall accuracy of route recommendations. Moreover, the insights gained from this analysis can be utilized to develop more advanced decision-making frameworks for managing similar scenarios in other contexts. Overall, the use of Cypher queries for semantic reasoning offers a powerful approach to extract valuable insights from complex RDF data, and to help decision makers make more informed choices in complex and dynamic environments.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 2 Related Work  
In this section, we review the related works on cloud detection in EO data and onboard processing (Section 2.1), and domain adaptation in remote sensing applications is also surveyed (Section 2.2).
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
###### Abstract
We demonstrated that the proposed strategy can reduce the energy consumption of a MEC system while maintaining the length of the queue below a predetermined threshold even under different system workloads, the number of MECSs, and the ranges of neighborhoods of these MECSs. We summarize our main contributions as follows. * We address the energy-saving problem in a MEC system. To resolve the problem, we propose a MECS sleep control method by adopting a bio-inspired approach. By mimicking the cell differentiation process in a biological system, our method enables each MECS to make a sleep decision in a distributed manner. * We analyze our sleep control method, which is composed of a sleep decision module, a task transfer module, and a MECS activation module. We show that the worst-case time complexity of our sleep control method is \(O(|N_{i}|)\). Therefore, our method scales linearly with the number of neighboring MECSs. * We compare the performance of our method with that of a conventional method through simulation studies. We show that compared with a conventional method, our method can reduce the energy consumption of a MEC system while providing a comparable task service delay. We also show that our method can provide a more stable service delay by reducing the variation in the length of the task queue in a MECS. The remainder of this paper is organized as follows. In Section 2, we discuss energy-saving issues in a MEC system. We explain the system model in Section 3 and present our bio-inspired MECS sleep-control strategy in Section 4. In Section 5, we verify our method through simulations. We conclude the study by discussing future research directions in Section 6. Before we proceed, in Table 1, we present the notations used in this paper.
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 2 System Model
Figure 2(b) shows the time-frequency matrix received in a real-world environment with the UAV in the same position. Linear spatial filtering is commonly employed for image enhancement and noise reduction and so is used here by considering a matrix as an image [27]. This involves convolution using a sliding filter template \(w\) with dimensions \(M\times O\), which gives  
\[\hat{y}_{jk}=w*\left[ \begin{array}{ccc}y_{jk}&\cdots&y_{j(k+O-1)}\\ \vdots&\ddots&\vdots\\ y_{(j+M-1)k}&\cdots&y_{(j+M-1)(k+O-1)}\end{array} \right] \tag{4}\]  
where \(w=[w_{1},\dots,w_{o},\dots,w_{O}]\), \(w_{o}=[w_{10},\dots,w_{Mo}]^{H}\), H denotes the transpose, \(*\) denotes convolution, and \(w=\left[ \begin{array}{cccc}0.0302&0.0446&0.591&0.0735&0.0345\\ 0.0543&0.0399&0.0254&0.0110&0\\ 0&0&0&0&0\end{array} \right]\). The time-frequency matrix is normalized so that  
\[\dot{y}_{jk}=\frac{\hat{y}_{jk}-\hat{y}_{\min}}{\hat{y}_{\max}-\hat{y}_{\min}} \tag{5}\]  
where \(\hat{y}_{\max}\) and \(\hat{y}_{\min}\) are the maximum and minimum values of the matrix, and \(\hat{y}_{jk},j=1,\dots,8/t_{a},k=1,\dots,(2.5-2.4)\times 10^{9}/f_{w}\). The time-frequency matrix after filtering and normalization is shown in Figure 2(c). This indicates that the signal consists of several carriers with a bandwidth of 9.47 MHz between 3.8 s and 4.9 s. The time-frequency matrix in a real-world environment with no UAV is shown in Figure 2(d) for comparison. Figure 3: _Cont._
## 2 Materials and Methods
### SSIP Equivalent Inversion Method
This factor is then substituted into Equation (9) to compute the updated model parameters for the subsequent inversion iteration. Following model updating, the it erative process continues until the mean squared error satisfies the predefined convergence criteria, ultimately yielding the final subsurface resistivity model \(m_{\eta}\). The workflow of the spread-spectrum induced polarization equivalent inversion algorithm is illustrated in Figure 3.  
The generalized linear inverse equation is solved using the conjugate gradient method to obtain \(\Delta w_{\
u}\), which is then substituted into \(\Delta w_{\
u}\). The Lagrange multiplier, also known as the damping factor or regularization parameter, plays a pivotal role in inversion. During the inversion process, it is assumed that the Lagrange multiplier (\(\lambda\)) selected for each iteration is reasonably chosen. This ensures an optimal trade-off between solution variance and resolution while guaranteeing that the error convergence curve exhibits steady descent, initially decreasing rapidly in early iterations before slowing down significantly, and the convergence criterion for iterative inversion is that the error convergence curve no longer decreases when the set number of iterations is reached. Consequently, an approximately ideal sequence of \(\lambda\) values can be constructed to mimic the desired error convergence behavior, allowing the inversion process to gradually relax constraints as the number of iterations increases. Figure 3: Flowchart of the equivalent inversion algorithm. The \(\lambda\) sequence is constructed using Equation (10):  
\[\lambda(k)=a\cdot k^{-2}+b \tag{10}\]  
The parameter k denotes the iteration index, while a and b are coefficients to be determined. Given the maximum number of inversion iterations (nmax) and an initial Lagrange multiplier \(\lambda_{\max}\) (the smoothing factor, typically selected between 0.1 and 1 based on the noise level in the data), the minimum Lagrange multiplier (\(\lambda_{\min}\), the background factor) can be set as \(\lambda_{\min}=\lambda_{\max}/10\) for simplicity. This allows the determination of a, b, and the intermediate Lagrange multipliers. In this inversion, we selected \(\lambda_{\max}=0.5\), \(\lambda_{\min}=0.05\), and nmax = 5, resulting in the following \(\lambda\) sequence: 0.5, 0.1529, 0.089, 0.066, 0.056, 0.05.
# Dual-Mic Speech Enhancement Based on TF-GSC with Leakage Suppression and Signal Recovery
## 3 Proposed TF-GSC with Leakage Suppression and Signal Recovery
### Leakage Suppression  
Although the FBF and BM do not operate perfectly, the ratio of the FBF output \(Y_{FBF}\) and the BM output \(Y_{U}\) bears information on the input SNR. The leakage of the desired signal to the BM output would be more severe when the input SNR increases, and vice versa. Therefore, the leakage suppression module applies a spectral gain \(G_{U}(n,k)\) to the BM output \(Y_{U}(n,k)\) to produce the modified noise reference \(\hat{Y}_{U}(n,k)\):  
\[\hat{Y}_{U}(n,k)=G_{U}(n,k)Y_{U}(n,k) \tag{17}\]  
Figure 1: A block diagram of the conventional transfer function-generalized sidelobe canceller (TF-GSC) (black) and the proposed one with leakage suppression and signal recovery (black+red). FBF, fixed beamformer; BM, blocking matrix; ANC, adaptive noise canceller.  
in which \(G_{U}(n,k)\) has a form similar to the square-root Wiener filter:  
\[G_{U}(n,k)=\sqrt{\frac{\mid Y_{U}(n,k)\mid^{2}}{\alpha\mid Y_{FBF}(n,k)\mid^{2}+ \mid Y_{U}(n,k)\mid^{2}}} \tag{18}\]  
where \(\alpha<1\) is a tuning parameter representing the attenuation of the desired signal component in the BM output. The leakage suppression would attenuate the desired signal component from \(Y_{U}(n,k)\) and thus reduce the signal cancellation in the ANC.
## 3 Results
### Application to Water Samples  
The developed method was applied in the extraction of analytes from two rivers in Catalonia, northern Spain: the Osor River (a mountain river, the sampling point was in a forest area) and the Llemena River (the sampling point was close to an urban center but in an area of farms where animals are fattened). Aliquots of the sample were first tested to ensure the absence of the target analytes. Further aliquots were then spiked at 200 \(\upmu\)g L\({}^{-1}\) for CPS and AHTN, and at 2000 \(\upmu\)g L\({}^{-1}\) for TCS. In Figure 9, absolute areas were presented together with the peak areas obtained from the 0.01 M NaCl solution spiked at the same concentration level. This plot allows a qualitative evaluation of the matrix effect. As can be seen, for CPS and AHTN, the only significant differences were found in the case of Llemena water. TCS is the compound that presents the most differences due to a stronger matrix effect.  
Absolute recoveries (AR) were calculated in accordance with the validation guidelines using a single-level calibration [42]. The values, set out in Table 5, can be considered as being acceptable for the Osor River sample, considering the spiked level [43]. However, for the Llemena River, AR deviated from the accepted values, which shows the greater complexity of the matrix. These results suggest that the proposed technology can be used for studies exploring the binding of organic pollutants with water matrix components.  
Figure 8: Ion extracted chromatogram for a solution containing CPS: 50 \(\upmu\)g L\({}^{-1}\), TCS: 500 \(\upmu\)g L\({}^{-1}\) and AHTN 50 \(\upmu\)g L\({}^{-1}\): 1. AHTN, 2. CPS. 3. TCS.
# Performance Analysis of Hard-Switching Based Hybrid FSO/RF System over Turbulence Channels
## 3 Numerical Results
From such comparisons, it is evident that there is a good agreement in results. Figure 4: Bit error rate as a function of average SNR of FSO link.
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 4 Image Encryption and Decryption
### Decryption Scheme  
The procedure of obtaining the plain image from the encrypted image is a simple reverse process, starting with the extraction of the image with the same genesis block and then diffusion with the chaotic matrix \(W(m,n)\). An inverse Fourier transform is applied to reshuffle the blocks, and the inverse value of the edge intensity can be used to retain our watermarked image. Once the watermarked image is obtained by applying a discrete wavelet transform to the sub-bands with secret images, the secret image can be obtained.