esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Crowd Participation Features on Mobile Edge Computing
## 2 Opportunistic Caching
### Cache Size: How Big Is the Buffer Space?
Based on the disease-diffusion algorithm, the source node \(s\) first transmits multiple copies of a packet \(m\) to neighbors. The newly infected nodes then repeat this process until one or more connected nodes belonging to the giant component receive \(m\), as shown in Figure 3a. At the end of this phase, one connected node and several disconnected nodes consist of the source expanding tree, \(SET\). (2) Shortest path phase: In this case, a connected path for each source-destination pair exists with a high probability in the giant cluster; the packets therefore can be transmitted along this path. In other words, if there is a packet at time slot \(t-1\) at node \(u\), node \(u\) will instantaneously send the packet to the next hop along the shortest path at the next time slot \(t\), only if the edge between them is active. Thus, the packets will quickly reach the node \(v\) as shown in Figure 3b. (3) Destination flooding phase: After the node \(v\) receives the packet \(m\), it floods \(m\) among the disconnected nodes until one of the infected nodes encounters the destination node \(d\). Finally, all infected nodes in this phase constitute the destination expanding tree, \(DET\) as shown in Figure 3c. From the above discussion, we can infer that the buffer occupation in the disconnected nodes is low. The reason behind this is that there exists a giant cluster, leading to a finite size of \(SET\) and \(DET\) in each time slot. Hence, the disconnected nodes only need to cache packets coming from nearby sources or going to nearby destinations. We can obtain the expected waiting time before the links become active through the property of the edge-Markov chain and derive the expectation of the buffer size for a random selected node. Based on the edge-Markov model, it can be inferred that the time when the packet is first sent from the source node into the connected nodes does not exceed \(logn/pM\), and the expectation of the buffer occupation of node \(u\) will be the sum of the above two parts: \(E(B_{u}(t))=c_{1}rT_{d}(\log\!n/pM)\) where \(M\) is the size of the giant cluster and \(c_{1}\) is determined by \(S_{n}\) and \(D_{n}\), which are two parameters that denote the number of infected nodes in the source-flooding stage and destination-flooding stage, respectively.
## 7 Changes in Section 4.3: Test Results  
The results referring to Figure 10 should be updated:  
\"The mean deviation from the specified flight path was 21 mm, with a maximum error of 54 mm. The standard deviation of the roll angle error was 0.4\({}^{\circ}\), and its mean was very close to zero, around 0.025\({}^{\circ}\). Only the roll angle is shown here, as it had the largest values since the UAV moved almost exclusively in the \(yz\)-plane in the simulation.\" should be changed to \"The mean deviation from the specified flight path was 19 mm, with a maximum error of 61 mm. The standard deviation of the pitch angle error was 0.6\({}^{\circ}\), and its mean was very close to zero, around \(-\)0.076\({}^{\circ}\). Only the pitch angle is shown here as the yaw angle is of similar quality.\"  
Figure 11 should be replaced by:  
In the original publication, there was a mistake in \"Figure 12. Path and roll angle errors, poor shielding: \(\sigma\) = 8 uT.\" as published. The corrected \"Figure 12. Path and pitch angle errors, poor shielding: \(\sigma\) = 8 uT.\" appears below.  
Figure 12 should be replaced by:  
Figure 12. Path and pitch angle errors, poor shielding: \(\sigma\) = 8 uT.  
\"Figure 12 shows the deviation from the specified flight path and the specified roll angle for each waypoint. The mean deviation from the specified flight path was 41 mm, where the maximum error was 221 mm. The mean deviation from the specified roll angle was \(-0.22^{\circ}\), where the maximum error was \(12.3^{\circ}\). The standard deviation of the roll angle error was \(2.96^{\circ}\).\" should be changed to \"Figure 12 shows the deviation from the specified flight path and the specified pitch angle for each waypoint. The mean deviation from the specified flight path was 29 mm, where the maximum error was 204 mm. The mean deviation from the specified pitch angle was \(0.15^{\circ}\), where the maximum error was \(28.4^{\circ}\). The standard deviation of the pitch angle error was \(4.4^{\circ}\).\"  
Figure 13 should be replaced by:  
The results referring to Figure 13 should be updated:  
\"The mean deviation from the specified flight path was 91 mm. In areas far from the transmission lines, as in this case, very strong outliers could occur occasionally, so that the maximum error was now about 2260 mm.\" should be changed to \"The mean deviation from the specified flight path was 34 mm. In areas far from the transmission lines, as in this case, very strong outliers could occur occasionally, so that the maximum error was now about 2202 mm.\"  
Figure 14 should be replaced by:  
Figure 14: Localization quality close to the transmission lines and far away, three sensors, \(\sigma=8\) uT.  
Figure 13: Localization quality, three sensors, \(\sigma=8\) uT.
## 2 Materials and Methods
### Red Fox Optimization  
The Red Fox Optimization (RFO) algorithm is used to optimize a selected objective function, combining global and local search phases to explore the solution design space. To briefly illustrate how this algorithm works, a schematic dataflow of the key aspects in a single optimization process is presented in Algorithm 1.  
```
Input: Number individuals, Max iterations Output: best solution, associated design variables Initialize, randomly the population For each iteration do  For each individual do  Compute objective function via FEM \(\overline{x}_{best}\leftarrow\) maximum value of objective function # Global search phase \(d\Big{(}(\overline{x}_{i})^{t},(\overline{x}_{best})^{t}\Big{)}\leftarrow\sqrt{ \Big{|}\big{|}(\overline{x}_{i})^{t}-(\overline{x}_{best})^{t}\Big{|}\Big{|}}\) \((\overline{x}_{i})^{t}\leftarrow(\overline{x}_{i})^{t}+\alpha\ sign\Big{(}( \overline{x}_{best})^{t}-(\overline{x}_{i})^{t}\Big{)}\) \(\alpha\leftarrow\text{random}\Big{(}0,\ d\Big{(}(\overline{x}_{i})^{t},(\overline {x}_{best})^{t}\Big{)}\Big{)}\) # Local search phase  Initialize, random parameters: \(\mu,a,\phi_{0}\)  If \(\mu>0.75\), then \(x_{new}^{n-1}\gets a\ rsin(\phi_{1})+a\ rsin(\phi_{2})+\cdots+a\ rsin( \phi_{n-1})+x_{actual}^{n-1}\)  End If # Dynamic control of population  Initialize random parameter: \(\kappa\)  If \(\kappa\geq 0.45\), then  Initialize new individuals  Else \(\Big{(}\overline{x}^{\text{(reproduced)}}\Big{)}^{t}\leftarrow\kappa\ \frac{\big{(}\overline{x}^{\text{(1)}}\big{)}^{t}+\big{(}\overline{x}^{\text{ (2)}}\big{)}^{t}}{2}\)  End If  End For
```  
**Algorithm 1** Red fox algorithm. Schematic representation  
The schematic dataflow comprises the population initialization, where each individual (red fox) is randomly generated within the design space. Loops are then initiated until convergence criteria are met. Within these loops, the fitness assessment of each individual  
Figure 1: Quadratic beam element and its degrees of freedom.  
is carried out considering the selected objective function. Then, the global search (exploration) starts, performing wider searches. The local, detailed, search (exploitation) is then carried out in promising regions, using smaller movements. The population control is implemented in a probabilistic basis, considering a random parameter that will define if the worst individuals will be removed. The global and local searches allow us to adapt the technique approach to the population evolution, avoiding the process to get stuck in a local optimum, and refining near-optimal solutions. Additionally, a continuous improvement of the solutions is achieved by retaining high-quality solutions while ensuring population diversity. Further details about this algorithm can be found in [8]. Unless otherwise stated, in this work, a population of 30 individuals progressing during 100 iterations was used for each optimization process, in this way balancing computational cost and solution accuracy, while observing Central Limit Theorem ([36; 37]).
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 2 Design, Materials, and Methods
### Format Output  
ZRLE compresses high-bit data produced by delta coding and zero separation. Zero separation splits delta data with a selected resolution that must be experimentally identified. Figure 2 shows a flow chart where the low-bit and high-bit streams are the compressed results of continuous, real-time ECG data. Zero separation directly transfers the low-bit portion of the rotated delta data into the low-bit stream without compression. The maximal CR is then determined by the high-bit resolution and limited (low-bit resolution + high-bit resolution)/(low-bit resolution). Figure 6 shows the encoded data format of a fixed-block size, where the M value is determined by ZRLE, and should be smaller than N, the block size.
# NN-Based 8 FSK Demodulator for the Covert Channel
## 1 Introduction
In summary, to the best of the authors' knowledge, there are neither studies on high or-dered FSK modulation as a covert modulation nor comprehensive study regarding its non-perfect demodulation (in a presence of non-perfect SIC). Therefore, the purpose of this article is to fill this gap, as follows:  
* To ascertain the pros and cons of using 8 FSK modulation we have compared the performances of the main modulation schemes used to create a covert channel;
* We examined the imperfect SIC operation on 8 FSK demodulation;
* We proposed both soft- and hard-decision NN-based demodulator for covert channel and compared it with correlator-based FSK detector. The rest of the paper is organized as follows. Section 2 briefly presents the covert channel model and receiver based on SIC operation. This section also justifies the use of 8 FSK as a covert modulation. Section 3 presents sources of imperfect SIC and its impact on covert modulation reception. Section 4 presents the NN-based 8 FSK demodulator and test scheme used for its evaluation. Section 5 presents the simulation results for the NN-based 8 FSK demodulator and compares it with the traditional correlator-based 8 FSK demodulator. Finally, the conclusions are presented in Section 6.
# Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
## 2 Large Eddy Simulation of Turbulent Mixing Layer and Dynamic Mode Decomposition
### Large Eddy Simulation Method  
#### 2.2.1 Computational Domain and the Original Grid  
The large eddy simulation is conducted in both 2D and 3D computational domain. The 2D computational domain extends from \(0\sim 350\delta_{\omega}(0)\) in the streamwise \(x\)-direction, and from \(-300\delta_{\omega}(0)\sim 300\delta_{\omega}(0)\) in the crosswise \(y\)-direction. In this computational domain, the streamwise range \(0\sim 200\delta_{\omega}(0)\) is the physical computational domain, the downstream flow range \(200\delta_{\omega}(0)\sim 350\delta_{\omega}(0)\) is an additional buffer to avoid the pollution of the flow field caused by the reflected wave generated by the exit boundary at \(x=350\delta_{\omega}(0)\)[56]. The 3D computational domain will be discussed in detail in Section 2.2.4.  
The original grid points in the \(x\) and \(y\) directions are 576 and 575, totaling about 300,000 grid points. The grid is stretched from the origin along the \(y\)-direction and symmetric with respect to the centerline of the computational domain. The grid stretching ratio in the \(y\)-direction is 1.01. In the physical computational domain, the number of grid points in the \(x\)-direction is 500, grid uniform distribution and grid point spacing \(\Delta x=0.4\delta_{\omega}(0)\). In the buffer zone, the grid points in \(x\)-direction grid is 77, along the \(x\)-direction grid stretch, stretch ratio of 1.04. This grid is called original grid.  
The computational domain and the original grid as well as the freestream parameters in Section 2.1 are the same as those in the studies by [PERSON] et al. [56] and [PERSON] [57], making it easy to compare the numerical simulation results.
## 5 User Evaluation Results
### Age and Gender Effect
#### 5.2.2 Age and Gender Effect in \"Mute\" Condition  
In regards to \"Mute\" condition, we computed [PERSON]'s rank-order correlation to investigate the relation between age and errors for both males and females separately. Correlations indicates a moderate, positive, significant correlation between errors and age in both males and females (R = 0.53, \(p<0.001\) each). Accordingly, we can state that as age increases, the number of errors increases for both males and females (Figure 11).  
Focusing on gender differences, moreover, Figure 11 suggest that confidence intervals do not overlap in the \(\approx\)22-40 age interval. Therefore, we can state there is a significant difference at a confidence level of 95% between males and females in terms of errors in that age interval.  
We further analyzed gender differences by comparing the number of errors between males and females in the different age groups (Figure 12). To begin with, in 18-25 age group, males make fewer mistakes than females (Medians: 1 vs. 6), and the difference is significant (\(p<0.001\)). Moreover, in 26-40 age group, males also make fewer error than females (Medians: 2 vs. 6.5), and the difference is significant (\(p=0.022\)). In the 41+ age group, no significant differences were found.  
Continuing our study of the \"Mute\" condition, we now focus on activation times. To do so, we computed [PERSON]'s rank-order correlation to investigate the relation  
Figure 11: Error versus Age in Sound condition: correlation for males and females displayed using a scatter plot with regression lines and 95% confidence interval. Significant correlations are marked with asterisk.  
Figure 12: Boxplots comparing age groups of males and females regarding errors in “Mute” condition. Significant differences are marked with asterisk.  
between age and times of activation for both males and females separately. Correlations indicate a moderate, positive, significant correlation between times of activation and age in males (R = 0.63, \(p<0.001\)). Moreover, the [PERSON]'s rank-order indicate a weak, positive, but _not significant_ correlation between times of activation and age in females (R = 0.21, \(p=0.16\)). Accordingly, we can state that as age increases, the times of activation increase for males. Females have actually a similar tendency, but the correlation is not significant (Figure 13).  
Focusing on gender differences, Figure 13 also suggests that confidence intervals do not overlap in the 18-28 age range. Therefore, we can state that there is a significant difference at a confidence level of 95% between males and females in terms of activation time in that age range.  
We further analyzed gender differences by comparing the activation times between males and females in the different age groups (Figure 14). To begin with, in 18-25 age group, males are faster than females (Medians: 3199 vs. 3785), and the difference is significant (\(p<0.001\)). Finally, no significant differences were found in the 26-40 and 41+ age groups.
# Global Transcriptomic Response of _Staphylococcus aureus_ to Viulent Bacteriophage Infection
## 3 Results
### Prophage Activation in the Late Stages of Infection  
GO enrichment analysis of down- and upregulated DEGs at 15 and 30 min revealed no statistically significant GO terms. However, the significant part of DEGs comprises the genes that were annotated as prophage genes (_n_ = 20 for 15 min; \(n\) = 28 for 30 min).  
Three regions corresponding to intact prophages were found in the SA515 genome assembly by [PERSON] (Table 1). These phage regions showed genetic similarity with _Staphylococcus phage_ phiJB, _S. phage_ Sa3, and _S. phage_ phi2958 PVL, belonging to the temperate staphylophages of the _Siphoviridae_ family. It is worth noting that during the infection, only two and five genes for phage region No. 1 and No. 2, respectively, were overexpressed at 30 min post-infection, while for phage region No. 3, a significant number of the genes (_n_ = 21) were upregulated at the same time point. The prophage from region No. 3 was identified as a Sa3 int phage, the typical sake-carrying phage.  
Figure 2: Gene Ontology (GO) enrichment analysis of host differentially expressed genes (DEGs) (up- and downregulated genes) in the early stage of infection (5 min). (**A**) biological processes; (**B**) molecular function; (**C**) cellular component. Enriched GO terms for DEGs colored by red and blue. Red bars indicate terms enriched in the upregulated DEGs. Blue bars represent terms enriched in the downregulated DEGs.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Reactive Routing Protocols  
In this type of routing protocol, the path between the nodes is formed when a node decides it wants to exchange its packets. As it moves from the source node to the destination node, this node now starts the process of route discovery in advance. The method is considered successful when a route is recognized by both nodes in the hypothetical situation. This type of data transfer is recorded in a table along with each potential path, starting at the source and remaining there until the route is no longer required. Each intermediate node is essential for maintaining the link's records between the source and the destination. These intermediary nodes will help send the replies back to the source after a request has been issued to the destination from the source node. Data traffic will start being transmitted to the destination as soon as the source node receives the response.
# Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique
## 3 Result and Discussion
### Correlation Analysis of Spectral Parameters and Oleic Acid Content  
In order to choose the best parameters as the argument model for the four types of hyperspectral parameters mentioned above, we conducted a correlation analysis for the oleic acid content of rapeseed with all the parameters and reported the results in Figure 2. As shown in those subplots, for the multifractal features, \(h(0)\) possessed the best correlation coefficient 0.7898 and is superior to other Hurst exponents. For the trilateral parameters, the best correlation coefficient 0.7751 comes from the area of the yellow edge. In subplot Figure 2c, the original reflectance (blue line) and first-order derivative reflectance (red line) shows different performance. The maximum of the correlation coefficient is 0.782 and the corresponding wavelength is at 574 nm. The correlation coefficients between the rapeseed oleic acid content and the three spectral indexes of RSI, NDSI, and DSI are shown in subplot Figure 2d-f, respectively. By comparison, NDSI brings the best result with correlation coefficient being 0.7950 and the corresponding optimal spectral index is NDSI (\(R_{575}\) and \(R_{576}\)).  
According to the above correlation analysis, we select the first two parameters with the best correlation in each type of hyperspectral feature, as listed in Table 3. The eight parameters with the higher correlation coefficients are greater than 0.7, which passes the correlation test under the 0.01 significance level. Table 4 shows the statistics of the eight  
Figure 2: Correlation analysis between four types of parameters and the oleic acid content. (**a–c**) are the correlation coefficient of the oleic acid content with respect to the multifractal feature, trilateral feature, and sensitive band, respectively. (**d–f**) represent the correlation coefficient between the oleic acid content and three spectral indexes, namely the ratio spectral index (RSI), normalized spectral index (NDSI), and difference spectral index (DSI).  
selected spectral feature. In the following, we use the above eight characteristic parameters to establish an estimation model for oleic acid content with BP neural network.
## 3 Results
### Problem Statement
#### 3.1.1 Proof of Principle with Fixed Lift-Off  
We demonstrated previously that identification of the object composition could be performed through analysis of the angular dependence of the inductive signal produced by the studied object [12]. The concept of angular scanning is important for understanding the results reported in this paper. Below, we summarize the essential elements of this concept and highlight aspects crucial to further analysis. Figure 3 is a plot of (a) amplitude and (b) phase of the rf signal measured over the centre of a \(38\times 38\,\mathrm{mm}^{2}\) plate made from copper (red diamonds), stainless steel (blue triangles), and ferrite (black squares) as each plate is tilted from \(\pm 30\)deg. Additionally, all recordings were done at an operational frequency of 6 kHz. The ferrite plate is 2 mm thick, while the others are all 0.5 mm. These measurements were done with a 3 mm lift-off and each point represents the mean of 30 readouts of amplitude and phase of the rf resonance (for a 1 s scan time) where the error bars represent one standard deviation. The particular choice of the material is done to illustrate different aspects of the secondary field generation. In particular, the presence of the angular dependence of the signal amplitude, Figure 3a, indicates that the inductive signal is produced in the vicinity of the object's surface. This is a direct consequence of the dissipative character of the eddy currents generation. We interpret the negligible angular dependence of the secondary field observed in the case of ferrite plate as a result of magnetisation created within the whole volume of the object, generating a field parallel to the primary field (i.e., invisible to the sensor) and partially uncoupled to object orientation. We refer to these two different aspects of the generated signal as surface (eddy currents) and volume (magnetisation) components of the secondary rf field. Consequently, the gradient of the angular dependence of the inductive signal amplitude represents a measure of the ratio between these two components (zero gradient, only volume component; non-zero gradient, non-zero surface component). It needs to be added that at low primary rf field frequencies, i.e., when the skin depth is significantly bigger than the object thickness eddy current generation has at least partially volume character. In summary, the signal observed at a particular angle primarily depends on the electrical conductivity of the object and the primary rf field frequency.
## 3 Study Area  
### Landslide Overview  
The Lishanyuan landslide is located in Xinhua County, Hunan Province, China (Figure 2). The longitudinal length of the landslide is 120 m, the horizontal width is 300 m, the average thickness is about 3 m, and the total volume is about 1.08 \(\times\) 10\({}^{5}\) m\({}^{3}\). The landslide is a shallow landslide with a main slide direction of 210\({}^{\circ}\). The middle and back edges of the slope are well covered with vegetation. There are several residential houses at the left foot of the slope. The area on the right side of the slope is poorly covered with vegetation. There is a village-level road and a small stream at the front edge of the landslide, and the foot of the slope has been washed by the river for a long time. Due to long-term river scouring at the foot of the slope, the landslide initially showed accelerated deformation characteristics in 1996. From then until 2012, it underwent a slow deformation trend. In 2013, the landslide accelerated again, with multiple cracks on the slope and subsidence of the village-level road. In April 2018, affected by heavy rainfall, the landslide had a local slip of about 600 m\({}^{3}\), and the sliding soil fell to the walls and windows of residential houses on the lower side of the slope, causing a direct loss of about 600,000 RMB. According to the on-site investigation, the landslide is a small and shallow traction landslide, which is very common and representative in Hunan Province, China.