esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of MS Circle Map in Digital Image Encryption
## 3 Results and Discussion
### NPCR and UACI Tests  
The NPCR (number of pixel change rate) and UACI (unified average changing intensity) are the most commonly used statistical standard test tools to analyze sensitivity and evaluate the resistance of encrypted images to differential attacks. The NPCR aims to calculate the number of pixels whose values differ from the total number of pixels in the image. In addition, this NPCR value serves as a reference to analyze whether small variations in pixel values in the original image can cause significant differences in the encrypted image. UACI measures the average intensity difference between two encrypted images where the original image has only one pixel value changed. In the context of image encryption, an ideal NPCR value is 100%, and an ideal UACI value is \(\geq\)33.4635%, which indicates that a single pixel change in the original image significantly impacts the encrypted image. The NPCR and UACI were calculated using Equations (10) and (11), respectively [26; 34].  
\[NPCR=\frac{\sum_{i,j}D(i,j)}{W\times H}\times 100\% \tag{10}\]  
\[UACI=\frac{1}{W\times H}\bigg{(}\sum_{i,j}\frac{|C_{1}(i,j)-C_{2}(i,j)|}{255} \bigg{)}\times 100\% \tag{11}\]  
If two encrypted images are denoted by \(C_{1}\) and \(C_{2}\), then their respective pixel values at coordinates \((i\), \(j)\) are represented by \(C_{1}(i\), \(j)\) and \(C_{2}(i\), \(j)\), with \(D(i\), \(j)\) = 1 if \(C_{1}(i\), \(j)=C_{2}(i\), \(j)\), and \(D(i\), \(j)\) = 0 if \(C_{1}(i\), \(j)\
eq C_{2}(i\), \(j)\). W and H are the width and length of the encrypted image in pixels.  
Table 12 shows the results of the NPCR and UACI calculations. All encrypted images have excellent NPCRs where the values are in the range of 99.59% to 99.62% and the overall average value is 99.61%. Furthermore, the UACI values for the 30 test images ranged from 28.67% to 40.40% and the average value is 33.14%. The average values of the NPCR and UACI show that both are close to their ideal values. Thus, it can be stated that there is a high level of change in the encrypted image when there is a slight modification in the original image. So, this means that the encrypted image using the MS Circle Map function provides strong resistance to differential attacks.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Test Data**} & **Entropy of Encrypted Color** & \multirow{2}{*}{**Test Data**} & **Entropy of Encrypted Grayscale** \\  & **Images** & & **Image** \\ \hline
7 & 7.9999 & 22 & 7.9999 \\ \hline
8 & 7.9999 & 23 & 7.9999 \\ \hline
9 & 7.9999 & 24 & 7.9999 \\ \hline
10 & 7.9993 & 25 & 7.9993 \\ \hline
11 & 7.9998 & 26 & 7.9998 \\ \hline
12 & 7.9999 & 27 & 7.9999 \\ \hline
13 & 7.9988 & 28 & 7.9986 \\ \hline
14 & 7.9997 & 29 & 7.9997 \\ \hline
15 & 7.9999 & 30 & 7.9999 \\ \hline Average & 7.9997 & Average & 7.9995 \\ \hline \end{tabular}
\end{table}
Table 11: _Cont._
## 4 Methods
### Preprocessing
#### 4.1.4 Fourier Transformation  
Incidence angle normalization adjusts the backscattering signal within the individual scenes but does not address the different backscatter distributions of the four orbits, caused by the varying influence of soil and plant parameters as well as crop row orientation on the incidence angle effect [73; 74; 75]. Therefore, the time series still contains backscatter changes unrelated to soil moisture, which need to be removed. Knowing the frequency of the signal caused by the changing sequence of incidence angles in the backscatter time series and determined by the Sentinel-1 revisit time of 12 days, the time series of \(\sigma_{0}^{norm}\) is transformed into a Fourier Series excluding these frequencies by:  
\[\sigma_{0}^{F}(t)\ \sim\ \frac{a_{0}}{2}+\sum_{k=1}^{n}(a_{k}\cos(k\omega t)+b_{ k}\sin(k\omega t)) \tag{2}\]  
with  
\[\omega=\frac{2\pi}{T} \tag{3}\]  
\[a_{0}=\frac{2}{T}\int_{-T/2}^{T/2}f(t)dt \tag{4}\]  
\[a_{k}=\frac{2}{T}\int_{-T/2}^{T/2}f(t)\cos(k\omega t)dt \tag{5}\]  
\[b_{k}=\frac{2}{T}\int_{-T/2}^{T/2}f(t)\sin(k\omega t)dt \tag{6}\]For each year, the combined backscattering time series was transformed into a Fourier Series, with \(T=365\) and \(n=24\), resulting in excluding signals with a frequency smaller than 15 days.
# OTFS-IM Modulation Based on Four-Dimensional Spherical Code in Air-to-Ground Communication
## 1 Introduction
However, most of the existing methods obtain a better detection performance by increasing the index dimensions, which increases the number of modulation steps and makes the transmitter design more complex. Another major feature of A2G communication in the UAV scenario is the limited energy of the UAV. Due to its small size, there are restrictions on payload and energy storage, which prevent it from performing remote operations over a long time [24]. Therefore, low-power communication research is still of great significance for an A2G network. Currently, most of the work on OTFS and OTFS-IM systems is based on two-dimensional modulation, such as quadrature amplitude modulation (QAM). In fact, it is possible to utilize multiple resource symbols jointly to increase the modulation dimension. For example, we can achieve four dimensions by combining two symbols together. According to the classical detection theory, a higher modulation dimension could have a larger minimum Euclidean distance between constellation points if it is elegantly designed, which confers better anti-interference and detection performance [25]. Given that the traditional OTFS-IM modulation could be improved and there are resource constraints on the UAV communication, it is critically important to design an optimal and practical OTFS-IM system for A2G communication. To this end, this paper creatively introduces four-dimensional spherical code into the OTFS-IM system, denoted as OTFS-IM4, where the spherical code is similar to the sphere packing problem [26]. Correspondingly, the conventional two-dimensional modulation is denoted as OTFS-IM2. The main contributions of this paper are as follows:  
* To enhance the endurance of UAVs, an OTFS-IM system based on four-dimensional spherical code is proposed in this paper, which allows the OTFS-IM system to have good signal detection performance under low signal-to-noise ratio. The design and implementation of this system are elaborately described in the paper;* In addition, this paper analyzes the proposed system, both analytically and numerically, to derive the approximate value of the bit error rate (BER) and the upper bound of the peak-to-average-power ratio (PAPR), which has general guiding significance. The rest of the paper is organized as follows: Section 2 introduces the basic model of the OTFS-IM system with the high-dimensional spherical code. Section 3 analyzes the performance of BER and PAPR in our proposed scheme. Section 4 verifies the effectiveness of the high-dimensional modulation and the practical significance of the theoretical analysis in Section 3. Finally, Section 5 concludes the paper.
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
## 6 Conclusions  
In premixed turbulent flames, flame surface density is defined as the ratio of _surface-to-volume_ and provides a model for evaluating turbulent burning velocity and establishing turbulent combustion models. However, in experiments, it is usually approximated with its two-dimensional value from 2D laser measurements, leading to an underestimation of the flame surface area due to the lack of information on flame wrinkles in the direction perpendicular to the measurement plane. This work describes different models for estimating 3D flame surface density from its original definition and investigates the effect of pressure on the robustness of the models using a CH\({}_{4}\)/air premixed turbulent Bunsen flame. The flame was operated at the high pressure up to 1.0 MPa. The flame front was indicated by the sharp increase of OH distribution, as detected by the OH-PLIF technique. Results show that the models considering local flame wrinkles are more reasonable and reliable. Moreover, the flame front wrinkles are closer to being isotropic at high pressure due to having smaller scales, resulting an underestimation of the global burning rate. The possible reasons are low flame speed at high pressures, requiring finer resolution in terms of flame thickness, and more accurate analysis in terms of the normal vector analysis. Further steps towards improving FSD at high pressures would refine FSD analysis, allowing one to obtain information on the third direction and verify the assumptions through DNS results.  
M.Z.; resources, writing--review and editing, J.W.; supervision, Z.H.; funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program (2021 YFA0716202) and National Natural Science Foundation of China (No. 52176130).  
Not applicable.  
The high pressure experiment was performed by [PERSON] at Institute of Fluid Science, Tohoku University, Japan under the support of the Japan Society for the Promotion of Science for a Postdoctoral Fellowship (JSPS) grant. The authors acknowledged the group members contributed to the experiments.  
The authors declare no conflict of interest.  
Samples of the compounds are available from the authors.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PSD & Flame Surface Density \\ PLIF & Planar Laser Induced Florescence \\ \end{tabular}
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 5 Technological Infrastructure
### 5G Communication Technology  
Fifth-generation (5G) communication technology represents a significant leap in mobile communications, offering faster data speeds, lower latency, and more excellent connectivity compared to previous generations [141].  
With theoretical speeds of up to 10 Gbps and latency as low as 1 millisecond, 5G enables a wide range of applications that require real-time data transmission and high bandwidth. These include enhanced mobile broadband, ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) [142]. Industries such as healthcare, automotive, and entertainment are poised to benefit immensely from 5G. For example, 5G supports telemedicine and remote surgeries in healthcare by providing reliable, high-speed connections necessary for transmitting high-definition video and large medical data files in real-time.  
Beyond 5G, the focus is on developing technologies like 6G, which aims to provide even higher speeds, lower latency, and more extensive connectivity [59]. 6G is expected to integrate advanced technologies such as artificial intelligence, machine learning, and blockchain to enhance network management, security, and efficiency. Potential applications of 6G include holographic communications, advanced AR and VR, and ubiquitous IoT connectivity, enabling smart environments and autonomous systems to operate seamlessly [143]. Research and development in 6G technology are exploring new spectrum bands, such as terahertz frequencies, to achieve these ambitious goals. As society movestoward 6G, the focus will also be on sustainable and energy-efficient solutions to support the ever-growing demand for data and connectivity.  
Figure 7 shows a 5G network architecture, integrating various components and technologies to create a robust communication system. The core is a massive multiple-input multiple-output (MIMO) network that connects to resources and wireless and wired links [144]. The cloud provides extensive data processing and storage capabilities, connected to the core network and servers via wired links. The internet is crucial, linking the core network and servers to broader online resources. The mobile small cell network, including 5G-enabled devices, ensures coverage and connectivity for mobile users. The edge network includes sensors, IoT devices, and connected appliances, receiving and sending data through wireless links. A residential setup is also included, demonstrating the network's ability to provide high-speed internet access to homes.
## 3 Structure Design and Optimization
### Thermal Plates  
The module is characterized by many thermal plates (Figure 7). They are necessary to allow thermal communication between stacked structures. Their functional surface is perpendicular to the wall, hence they must be fabricated horizontally. The junction point wall is \(25\times 11\times 1\) mm\({}^{3}\) and requires a support structure inaccessible for removal in the post-processing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Lattice Dimensions**} & \multicolumn{3}{c}{**Minimum**} & **Total Wall** \\ \cline{2-7}  & **x** & **y** & **z** & **Beam** & **Overhanging Angle** & **Thickness** \\ \hline
**Type** & **[mm]** & **[mm]** & **[mm]** & **[mm]** & [\({}^{\circ}\)] & **[mm]** \\ \hline A & 5 & 5 & 8 & 0.4 & 48.5 & 5 \\ B & 10 & 10 & 16.5 & 0.85 & 49.4 & 11 \\ C & 20 & 10 & 25 & 1 & 48.2 & 21 \\ D & 11 & 11 & 16.5 & 0.9 & 46.7 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lattice cell geometry.  
Figure 6: Manufacturability verification carried out on investigated cells: \(5\times 5\times 8\) (A), \(10\times 10\times 16.5\) (B), \(20\times 10\times 25\) (C), \(11\times 11\times 16.5\) (D) and prototype (E).  
Figure 7: Thermal plates for allow the heat transfer between stacked structures.  
To avoid this problem, the thermal plates were integrated in the lattice below designing the grow structure reported in Figure 8.
## 2 Peculiarities of ATR Measurements
### Feasibility Test
The intensities of the components are fitted with \(E_{s}^{2}=Amp_{s}\times\cos^{2}(\varphi+\Delta\psi_{s})\) and \(E_{p}^{2}=Amp_{p}\times\sin^{2}(\varphi+\Delta\psi_{p})\) with phase angles set \(\Delta\psi_{s}=\Delta\psi_{p}=10^{\circ}\). The value of the phase angle is larger than the possible alignment error of the polarisers, which is smaller than \(\pm 5^{\circ}\). Cumulative phase changes on the nine mirrors and the ATR prism (ten reflections) are the most probable cause of this phase change. Due to co-rotation of the polariser and analyser (on the IN and OUT ports, respectively) the change between s- and p-pol. becomes easily detected since such an alignment has the maximum transmission for the same polarisation, either s- or p-pol. Indeed, the fits plotted by the expressions given above miss the experimental results by a large margin (not shown in an already busy panel (c)). The solid lines in Figure 7c show a good fit when the \(\cos^{n}(\varphi+\Delta\psi_{s})\) and \(\sin^{n}(\varphi+\Delta\psi_{p})\) are used with \(n=8\), which corresponds to the number of reflections without two reflections from the flat M1 and M10 mirrors. Each reflection on the mirror contributes to the overall transmission \(T_{IN-OUT}\propto R_{m2}\times R_{m3}\times\ldots R_{m9}\propto R^{8}\). A perfect fit of the output signal (port OUT) can be obtained by the combined \(E_{s}^{2}\) and \(E_{p}^{2}\) intensity addition by Equation (4). The \(A_{s,p}\) are the arbitrary amplitudes accounting for both the absorption (losses) and changes in amplitude due to reflection; \(\Delta\psi_{s,p}\) are the phases for s- and p-polarisations, respectively. The angle \(\varphi\) defines the orientation of the linear polarisation in the incident beam, i.e., for \(\varphi=0^{\circ}\) only \(E_{s}\) polarisation is present and \(\varphi=90^{\circ}\) corresponds to pure \(E_{p}\). When pure \(E_{s}\) polarisation is incident on the sample and there are no changes in the phase of the reflected beam, the orientational output power will have a horizontally aligned figure-8; consequently, for the pure \(E_{p}\) case, it will have a vertical figure-8. Figure 7d shows the polarisation analysis at the OUT port when the IN port polariser is set to one of the local min or max orientation angles (Angles 1-4 shown in Figure 7c). Changes in the amplitude and phase angles at four settings of Angles 1-4 are colour coded in the legend in Figure 7d. This shows that Equation (4) can be used for the arbitrary orientation of polarisation at the IN port. Changes in amplitudes \(A_{s,p}\) due to absorption in the sample and phase angle changes \(\Delta\psi_{s,p}\) are due to refractive index contrast \(n_{2}/n_{1}\) and can be accounted for using a fixed polarisation for excitation then carrying out polarisation analysis at the output. It is also obvious that such analysis has to be carried out for each spectral point.
## 3 Materials and Methods
### Improved Region Growing Algorithm Based on Vehicle Trajectory Information
#### 3.3.3 Region Growing  
The region growing segmentation method was employed using supervoxels as the basic elements to extract the road surface point cloud. First, the geometric features and surface characteristics were computed for each complete supervoxel, including normal vectors and residuals. Because the complete road surface is the target of the segmentation algorithm in this study, each supervoxel was assumed to represent a small patch of the road surface, and all points within each complete supervoxel should approximately lie on a flat plane. The normal vectors of each supervoxel were computed using Principal Component Analysis (PCA) [43]. The normal vector is determined using the eigenvector corresponding to the smallest eigenvalue of the covariance matrix, \(\mathbf{M}\), as shown in Equation (1):  
\[\mathbf{M}=\frac{1}{k}\sum\limits_{i=1}^{n}{(p_{i}-\overline{p})(p_{i}- \overline{p})^{T}} \tag{1}\]  
where \(k\) is the number of points within the supervoxel, and \(\overline{p}=\frac{\sum\limits_{i=1}^{n}{p_{i}}}{m}\) is the center point \(p_{i}=(x_{i},y_{i},z_{i})\) of the supervoxel, also considered the centroid of the supervoxel.  
The residual is defined as the root mean square of the perpendicular distance from each point to the fitted surface, as shown in Equation (2):  
\[r=\sqrt{\frac{\sum\limits_{i=1}^{m}{d_{i}^{2}}}{n}} \tag{2}\]  
\[d_{i}=\frac{|Ax_{i}+By_{i}+Cz_{i}+D|}{\sqrt{A^{2}+B^{2}+C^{2}}} \tag{3}\]  
where \(d_{i}\) is the orthogonal distance from each point \(p_{i}\) to the fitted plane, which is defined by the normal vector \(\overrightarrow{N_{i}}=(A,B,C)\) and centroid. \(D\) is a constant that represents the intercept of the fitting plane.  
Finally, the supervoxel dataset was iteratively clustered using the input threshold values as constraints to complete the extraction of the road surface. During the extraction process, as the road surface is typically smooth and continuous, starting from the first extracted seed supervoxel, its adjacent supervoxels can be included in the current region if they satisfy three conditions: 1 deviation of the normal vectors, 2 residual, and 3 slope, as shown in Equation (4):  
\[\rho=\frac{\left|z_{S_{i}}-z_{S_{j}}\right|}{\sqrt{\left(x_{S_{i}}-x_{S_{j}} \right)^{2}+\left(y_{S_{i}}-y_{S_{j}}\right)}}\times 100\% \tag{4}\]  
where \(z_{S_{i}}\) and \(z_{S_{j}}\) is the elevation of adjacent supervoxels \(S_{i}\) and \(S_{j}\).
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 6 Results Using the Spoofing Detection Algorithm
### ECADLL vs. SQMT  
In this section, we compare the performance of the ECADLL vs. an SQMT as presented in [14]. Figure 16 shows the decision made by SQMT and ECADLL using TEXBAT ds3. We observe that ECADLL is able to detect the distortions at the same time as SQMT given the aid of the RM. ECADLL loses track of the residual signal for some time (160-180 s), and during this time, it is not able to detect the attack. We also observe how the region of detection of the SQMT is limited by the use of fixed correlators, while once the ECADLL recovers the tracking on Unit1, it is able to detect the attack for the duration of the test.  
Figure 16: A comparison of the impairment detection using GPS Satellite Number 6 for the static dataset, TEXBAT ds3. In red, the decision made by ECADLL is shown, and in blue, the one using the Signal Quality Monitoring Technique (SQMT).  
Figure 15: Amplitude estimation for satellite 13, using TEXBAT ds7. In blue, the in-phase amplitude estimation, and in red, the quadrature estimation are shown.  
In the case of a dynamic scenario, we used TEXBAT ds6, which is similar to ds3, but based on a dynamic dataset recorded by means of a moving vehicle in Austin, TX, and spoofing the position components of the receiver. The detection results, shown in Figure 17, demonstrate that ECADLL has a similar behavior in both static and dynamic scenarios. After 150 s into the test, the architecture reaches the steady state of the feedback and starts tracking the satellite signal on Unit1. The SQMT technique is also able to detect the spoofing attack, but only for a limited duration of the attack, between 170 s and 290 s, similar to what was observed in Figure 16. At around 50 s in Figure 17, we can observe that a small impairment was detected, and we believe this to be a multipath signal present in the dynamic scenarios of TEXBAT.  
Comparing the behavior of the two methods in Figures 16 and 17, we observe how ECADLL has similar detection capabilities to the SQMT when the LOS and spoofing signal are close to each other. The ECADLL is able to maintain the detection throughout the test, even when the two signals are well separated, differently from SQMT.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 3 Parent and PHY Selection Heuristic
### Motivation  
In our previous work, a GA was defined that acted as a centralized scheduler for multi-PHY TSCH networks by making parent, PHY and slots selections for each node in the network [7]. This GA allowed us to implement the slot bonding problem and find solutions in near-optimal fashion. As such, it was used for the analysis of the proposed multi-PHY TSCH slot bonding technique. Due to its time complexity and its centralized approach, it is not feasible to use it for real-time parent and PHY selections in dynamic TSCH networks.  
Therefore, in this work, a computationally efficient heuristic is presented that allows nodes to make parent and PHY selections in a multi-PHY 6 TiSCH network. The algorithm is a step towards a distributed approach that can be integrated in RPL OFs, taking into account the ETX and a PHY characteristic, that is, the data rate that is translated into the number of slots needed to bond together to transmit a packet.