esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Service Delivery in 5G-Enabled D2D Communications Network
### D2D Communications Process
#### 3.3.1 D2D Communications Network Assisted  
D2D connectivity is initiated by gNB via SMF/AMF. When data packets reach the SMF, data packets in the form of Internet Protocol (IP) or CCN can be analyzed by the 5 GC whether the link can be considered as a D2D link or not. This process starts from Step 1. It is assumed that a UE can discover other UEs in proximity, hence a UE can also initiate D2D connectivity and start the process from Step 3.  
The generic and concise D2D connectivity process in Figure 2 is explained in the following steps:  
1. When the SMF receives a session request message, it checks if the transmitter and receiver are within the same cell and the channel condition can support D2D communications. Then, it initiates the D2D connectivity with the help of gNB. The request messages include UE IDs and data name.
2. Then, gNB starts a strategy list of communication patterns, push uplink shared channel, physical uplink control channel, power indicator, scan spectrum and time. After analyzing the request messages from SMF, it sends the message to \(UE_{1}\).
3. \(UE_{1}\), hoping to make D2D connectivity with \(UE_{2}\), sends regular information in the physical radio access channel with binary code, UE ID, data name and the UE can also detect received information from other UEs.
4. After matching the targeted \(UE_{2}\), the initiating \(UE_{1}\) sends the D2D connectivity request to gNB.
5. Then, gNB analyzes the D2D connectivity request and chooses the best communication pattern for the D2D pair based on the conditions of the channel and cell resource utilization.  
Sometimes the D2D connectivity request fails; the normal cellular communication mode is adapted and the communication between the UEs goes back to the conventional cellular mode.
* Conversely, if the D2D mode request including orthogonal or reused pattern is permitted, gNB establishes a strategy list including communication patterns, power indicator, scan spectrum and scan time and sends it to \(UE_{1}\).
* After completing the channel measurement such as normalized interference intervals, \(UE_{1}\) provides an update to gNB.
* gNB allocates spectrum resource to \(UE_{1}\) and lets \(UE_{2}\) recognize the same channel. Training sequences at the allocated channel are sent by the \(UE_{1}\) to help \(UE_{2}\) to get the link quality. Then, \(UE_{2}\) sends a confirmation message back to \(UE_{1}\) after checking that the link quality is suitable and \(UE_{1}\) confirms it to gNB.
# Comparative Analysis of the Methods for Fiber Bragg Structures Spectrum Modeling
## 3 Results and Discussion
### Results of a \(\pi\)-Phase-Shifted FBG Modeling
If we compare the refractive index profiles of the grating (Figure 10b,c), we can see that in the case of the interval \(\Delta z\) = 0.12 \(\times\)\(\Lambda\) (Figure 10b), the partitioning of the refractive index profile near the phase shift is asymmetrical relative to the center of the shift, while the partitioning with the interval \(\Delta z\) = 0.13 \(\times\)\(\Lambda\) gives symmetrical profile relative to the phase shift center. The same effect was observed at all of the partition intervals indicated in Figure 9, at which the RMSD\({}_{\text{LS}}\) exceeded 0.02. Therefore, based on the analysis of the refractive index profile of the grating model, one can conclude that the asymmetry of the refractive index profile partition near the phase shift relative to its center causes the asymmetry of the spectral response of the simulated grating, which results in the significant increase of the RMSD\({}_{\text{LS}}\) values at certain partition intervals, as it was reported in Figure 9. In the next sub-section of the paper, the authors propose a solution to eliminate the discussed negative effect in phase-shifted FBG simulation.
# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 6 Discussion  
The operational rating outcomes of the main test were managed among 0 and 2, with the aggregate rating in the upper range as well as the standard deviation within of 0.5. The Table 4 Word Error Rate (WER) is relative to the different localities; the transitional technical rating outcomes were all dispersed somewhere around 1 and 3, with the aggregate rating around 2.25 and the standard deviation within 0.5. All of the data demonstrated that the practical rating outcomes satisfied the standards of the subject rating, and the ratings were generally focused and consistent. When matched to the subjective scoring findings, the  
Figure 6: Speak Correct errors for each locality.  
Figure 7: Comparison between males and females according to locality.  
quantitative approach of the technical rating data revealed a high level of association with the subjective rating, evidence that supports the research's hypothesis. This hypothesis, moreover, requires more evidence; therefore, in this segment, different components of the manual as well as technical rating outcomes are evaluated and contrasted one by one to demonstrate the study hypothesis that speech evaluation technology could indeed accomplish the rating of Speak Correct post-listening repeating concerns.  
In this research, an empirical dataset was employed to perform experimental studies on speech quality assessment techniques. The Speak Correct computerized interface introduced in this paper was initially contrasted with specialist scores for correlation experiments; then, the evaluation process in this paper was contrasted with another established speech fluency assessment technique; and, finally, the rating effectiveness of the fluency evaluation procedure in this study was analyzed. Furthermore, this research integrated the phonetic pronunciation bias speech network to recognize phonetic mispronunciation, and the phoneme margin of error of every fluency standard was collected to evaluate the system's erroneous corrective feedback performance. The findings indicate that the high accuracy of the Speak Correct computerized interface described in this work, based on a composite of attributes, has a strong correlation with the real ratings of human operators. The empirical findings reveal that the feature-based combinatorial assessment approach surpasses other fluency assessment methods. This is mostly due to the fact that the approach provided in this work includes information from both the phoneme length and the phoneme acoustical score characteristics, and calculates the most efficient assessment methods by optimizing the regression analysis.
# Viscoelastic Strains of Palaeozoic Shales under the Burger's Model Description
## 7 Linear Creep Limit in Paleozoic Shales  
The determination of the linear creep limit is significant with regard to describing the loading range, in which the Boltzmann superposition is valid and linear viscoelastic relationships can be used.  
The linear creep limit can be determined by analyzing creep velocity at various loading levels [39]. Creep velocity which is the tangent of the angle of inclination of the creep curve is constant in the secondary creep range (Figures 3 and 11). Velocity increases proportionally (linearly) depending on the loading level. The linear limit is determined by the value of loading, for which the relationship of creep velocity loses its linear character.  
Figure 11: Secondary creep range and determination of the value of creep velocity [39].  
Figure 10: (**a**) Shear \(\gamma_{Qit}\) (green) and volumetric change \(\varepsilon_{Qit}\) (blue) during laboratory creep tests for shale sample W260A at different loading levels. (**b**) Axial creep of sample W260A at various levels of loading described by [PERSON]’s model with volumetric strain (red, Equation (22)) and without volumetric strain (blue, Equation (24)).  
Values of creep velocity at different loading levels for shales from different areas are compiled in Figure 12.  
Based on the performed analyses (Figure 12) it can be assumed that the linear creep limit for the analyzed Paleozoic shales is 0.7 (\(\sigma_{1}-\sigma_{3}\))\({}_{max}\). It should be assumed that beyond this loading, creep in shale will be non-linear, and its description will exceed the Boltzmann superposition rule. Such creep can be described by the plasticity theory [48] or by failure mechanics [49] depending on the type of failure. However, in this case various creep tests should be made at loadings close to instantaneous strengths in order to recognize the failure mechanism accompanying creep.
# An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute
## 4 Use-Cases
### Use-Case 3: Locating Link Failure  
While the SDN concept introduces issues mainly arising from software development problems, it still retains errors induced by hardware failures. This use-case serves as a simple demonstration of a hardware failure in the network.  
The results of turning the link between host \(h3\) and switch _sw4_ to down are shown in Figure 14. The link connecting the host with its switch is correctly identified as being the most problematic one. Other identified links come from the same reason as mentioned in Section 4.1. This use-case shows an example for a situation where the SDN traceroute tool would not be able to detect any problem on its own. Using only Felix, we might be able to see that something is amiss with packet forwarding between host \(h3\) and all its peers, but we would not be able to tell where on the path the error occurs. Again, the combination of different tools takes us closer to locating the issue.  
Figure 14: Our framework’s display of detected links that are affected by issues in the link failure use-case. The thick line between \(h3\) and _sw4_ indicates that the connection is the most likely to have problems.  
Figure 13: The framework successfully identified switch _sw1_ as the first point where issues occur in the configuration error use-case. Marking host \(h1\) as the most problematic one (see thick dashed line) helps in later analysis at switch _sw1_.
## 4 Application of the Multi-Objective AutoML Model  
To assess the applicability of the proposed multi-objective AutoML framework, three uniaxial compression tests were performed on rock-like specimens, and their stress-strain responses were recorded. Cylindrical samples (50 mm diameter \(\times\) 100 mm height) were cast from gypsum plaster (CaSO\({}_{4}\) :2H\({}_{2}\)O hemihydrate) using a resin template. After demolding at 24 h, specimens were dried at 50 \({}^{\circ}\)C for 72 h. Three plaster-to-water ratios (P/W = 2.0, 2.7, and 3.5 by weight) produced specimens SI, SII, and SIII, respectively (Table 3). As the P/W increased, the specimen density (\(\rho\)), uniaxial compressive strength (\(\sigma_{c}\)), and [PERSON]'s modulus (\(E\)) rose from 1.27 to 1.91 g/cm\({}^{3}\), 21.63 to 63.22 MPa, and 3.32 to 6.87 GPa, respectively, while the [PERSON]'s ratio (\(\
u\)) remained near 0.24. The experimental stress-strain curves are shown in Figure 8.  
Eight characteristic (\(\
u\), \(y\)) pairs per specimen were extracted from the experimental curves as inputs for AutoML inversion of the mesoscopic parameters (\(E\), \(k_{s}/k_{n}\), and \(E_{d}\)) (Table 4). The inverted parameter values are listed in Table 5. To validate these results, PFC3D uniaxial compression simulations employing the linear parallel bond model were conducted; the numerical stress-strain curves closely match the experimental data (Figure 8). Such agreement indicates that the inverted mesoscopic parameters are physically reasonable. The multi-objective AutoML model reliably recovers the PFC3D contact model parameters \(E\), \(k_{s}/k_{n}\), and \(E_{d}\) and reacts the nonlinear behavior of the rock stress-strain curve. It should be noted that PFC3D's bonded-particle model cannot fully emulate the compaction-stage nonlinearity observed in laboratory tests, leading to an almost linear initial segment in all the numerical stress-strain curves (Figures 3 and 8). Since the developed inversion model was trained exclusively on PFC3D outputs, it inherently reproduced this linear behavior. Consequently, the current AutoML framework was designed for parameter inversion within the PFC3D numerical domain, rather than for the direct prediction of ex  
Figure 8: The experimental and numerical simulation stress-strain curves of three specimens: (**a**) SI; (**b**) SII; and (**c**) SIII.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Specimen** & **P/W Ratio** & \(\rho\) **(g/cm\({}^{3}\))** & \(\sigma_{c}\) **(MPa)** & \(E\) **(GPa)** & \(\
u\) \\ \hline SI & 2.0 & 1.27 & 21.63 & 3.32 & 0.24 \\ SII & 2.7 & 1.58 & 42.04 & 5.38 & 0.23 \\ SIII & 3.5 & 1.91 & 63.22 & 6.87 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic mechanical properties of tested specimens.  
perimental stress-strain responses. In future work, multiple data sources will be integrated, including calibrated PFC3D simulations matched to experimental curves, to develop an inversion model capable of capturing true material nonlinearity.
# Fuzzy Logic-Based Performance Enhancement of FSO Systems Under Adverse Weather Conditions
## 1 Introduction
### Main Contributions  
In this paper, we propose an adaptive FLC system to improve the performance of FSO communication under adverse weather conditions. The key contributions are as follows:  
1. FLC Development and Computational Efficiency:
2. We have developed a fuzzy control system that dynamically adjusts the BER threshold at the receiver in response to FSO link impairments (rain, fog, and distance).
3. We have conducted a complexity analysis of the FLC to demonstrate that its computational complexity is O(1). Therefore, it ensures constant-time execution.
4. We have implemented and tested the FLC using MATLAB (R2017b)'s fuzzy logic toolbox on an Intel Core i7-1165G7 (2.80 GHz) processor (UX435E Notebook PC, ASUSTeK Computer Inc., Taipei, Taiwan)
5. Experimental results confirm that the FLC executes under 4 ms per decision cycle, making it suitable for real-time applications.
6. FSO-OCDMA System Integration and Optimization:
7. We have integrated the FLC within an end-to-end FSO-OCDMA setup to enhance adaptability to varying atmospheric conditions.
8. We have analyzed the FSO system and optimized BER threshold levels to maintain reliable communication under rain and fog conditions.
9. Theoretical and Performance Analysis:* We have conducted theoretical performance and analysis of the FSO-FLC-OCDMA system using the Single Sigma Matrix (SSM) code.
* The system behavior is evaluated under different attenuation levels (dB/km) across varying distances from very-short-range (200 m) to long-range (3 km).
* Performance Gains and System Efficiency:
* In this work, we have demonstrated that the proposed system supports a higher number of users while extending transmission distances compared to conventional methods.
* In this work, it is ensured that optical transmission requirements were met with minimal effective transmitted source power.
## 3 Materials and Methods
### Adaptive Density Homogenization
#### 3.1.2 Calculating Neighborhood Circles for High-Density Trajectory Cell  
The identified high-density cells indicate that there are a large number of trajectories collected at these cells. However, the covering range of high-density trajectories at each high-density cell is uncertain in spite of having a fixed cell size. To determine the covering range of high-density trajectories, we compute a neighborhood circle for each high-density cell. As shown in Figure 3, for each \(HG_{j}\), we suppose its center point is \(c_{j}\). Then, the neighborhood circle of \(HG_{j}\) is calculated as follows.  
**Step1:** Build the buffer region for center \(c_{j}\) by an increasing radius \(\epsilon_{i}=\epsilon_{0}+i\)*\(s\), where the initial radius \(\epsilon_{0}\) and the step length \(s\) are both set as 10 m and \(i\) denotes the iterative number.  
**Step2:** Count the ratio of trajectory points in two adjacent buffer regions as Formula (3), where \(Num(\epsilon_{i})\) is the number of trajectory points falling in the \(\epsilon_{i}\)-buffer region.  
\[R_{\epsilon_{i}}=\frac{Num(\epsilon_{i})}{Num(\epsilon_{i-1})} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Time** & **Car ID** & **Longitude** & **Latitude** & **Speed** \\ \hline
1 & 1 October 2018 & 07:23:55 & 210-197-99-205-62-145-201-224-247-111-102-55-62-36-220-4 & 113.018163 & 28.214653 & 27.0 \\ \hline
2 & 1 October 2018 & 07:24:10 & 210-197-99-205-62-145-201-224-247-111-102-55-62-36-220-4 & 113.014310 & 28.212013 & 0.0 \\ \hline \end{tabular}
\end{table}
Table 1: The main information in raw trajectory data.  
**Step3:** Construct the neighborhood circle for \(HG_{j}\) using \(c_{j}\) and the circle radius \(r_{j}\) defined as Formula (4), where \(N_{circle}\) is a given threshold and the maximum buffer radius is set as 200 m because of the maximum road intersection size.  
\[r_{j}=First\big{\{}\varepsilon_{i}|R_{\varepsilon_{i}}>N_{circle}\ \&\ R_{ \varepsilon_{i}}>R_{\varepsilon_{i-1}}\ \&\ R_{\varepsilon_{i}}>R_{\varepsilon_{i+1}},i=2\dots\big{\}} \tag{4}\]  
where \(First\{\varepsilon_{i}\dots\}\) means the first element in the set \(\{\varepsilon_{i}\dots\}\).
# Natural Hydrocarbon Samples Classification by Topological Analysis Method
## 2 Materials and Methods
### Metrics for Comparing Diagrams
#### 2.4.3. Heat Kernel Distance  
Let us provide a definition of kernel function taken from [57]. Given a set \(\mathcal{X}\), a function \(k:\mathcal{X}\times\mathcal{X}\to\mathbb{R}\) is a kernel if there exists a Hilbert space \(\mathcal{H}\), called feature space, and a map \(\Phi:\mathcal{X}\to\mathcal{H}\), called feature map, such that \(k(x,y)=\left\langle\Phi(x),\Phi(y)\right\rangle_{\mathcal{H}}\ \ \forall x,y\in \mathcal{X}\). Equivalently, \(k\) is a kernel if it is symmetric and positive definite. Kernels allow for the application of machine learning algorithms operating on a Hilbert space to be applied to more general settings, such as strings, graphs, or, in our case, persistence diagrams. A kernel induces a pseudometric \(d_{k}(x,y)=(k(x,x)+k(y,y)-2k(x,y))^{1/2}\) on \(\mathcal{X}\), which is the distance \(||\Phi(x)-\Phi(y)||_{\mathcal{H}}\) in the feature space. We call the kernel \(k\) stable w.r.t. a metric \(d\) on \(\mathcal{X}\) if there is a constant \(C>0\) such that \(d_{k}(x,y)\leq Cd(x,y)\ \ \forall x,y\in\mathcal{X}\).  
Now let us get to the definition of the Heat Kernel distance. Let \(\Omega=\{x=(x_{1},x_{2})\in\mathbb{R}^{2}:x_{2}\geq x_{1}\}\) denote the space above the diagonal, and let \(\delta_{p}\) denote a Dirac delta centered at the point \(p\). For a given persistence diagram \(D\), we now consider the solution \(u:\Omega\times\mathbb{R}_{\geq 0}\to\mathbb{R},\ (x,t)\mapsto u(x,t)\) of the partial differential equation:  
\[\Delta_{x}u =\partial_{t}inu(x,t)\in\Omega\times\mathbb{R}_{\geq 0}\] \[u =0 \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\[k_{\sigma}(D_{1},D_{2})=\frac{1}{8\pi\sigma}\sum_{\begin{subarray}{c}p\in D_{1}\\ q\in D_{2}\end{subarray}}[e^{-\frac{||p-q||^{2}}{8\sigma}}-e^{-\frac{||p-\overline {q}||^{2}}{8\sigma}}], \tag{6}\]  
where \(\overline{p}\) and \(\overline{q}\) are points of diagrams \(D_{1}\) and \(D_{2}\) accordingly, mirrored at the diagonal. The additional scale parameter \(\sigma\) is the dispersion parameter in Gaussian kernel. Finally, for Heat Kernel distance we have:  
\[\text{dist}_{heat}(D_{1},D_{2})=(k_{\sigma}(D_{1},D_{1})+k_{\sigma}(D_{2},D_{2 })-2k_{\sigma}(D_{1},D_{2}))^{1/2}. \tag{7}\]
## 2 Overview of Time-Series RSS Observation Outlier Detection Methods
### Moving Average and Moving Median Method  
This method replaces each RSS observation with the average or median of surrounding RSS observations over a sliding window. Outliers are detected by comparing the original RSS observations to the moving average or median. Using the time-series RSS observationin Equation (1), the moving average of RSS observations within a window size \"\(m\)\" is calculated. Mathematically, the moving average at time \"\(t\)\" can be calculated as follows [26]:  
\[MA(t)=\frac{\mathit{rss}(t)+\mathit{rss}(t-1)+\cdots+\mathit{rss}(t-m+1)}{m} \tag{3}\]  
where \(\mathit{rss}(t),\ldots,\mathit{rss}(t-m+1)\) are the \(m\) consecutive RSS observations at time \(t\).  
Like the moving average, the moving median takes the median value of the set of RSS observations as opposed to averaging the RSS observations. The value that divides the observation into its upper and lower halves is known as the median. Since the moving median is unaffected by outliers or skewness, it can be used to smooth out time-series RSS observations that contains these elements.  
Given the RSS observations in Equation (1), the moving median at time \"\(t\)\" for a window size \"\(m\)\" is calculated as follows [26]:  
1. Sort the m consecutive RSS observations \(\mathit{rss}(t),\mathit{rss}(t-1),\ldots,\mathit{rss}(t-m+1)\) in ascending or descending order.
2. Take the middle RSS observation as the moving median if \(m\) is odd.
3. Take the average of the two middle RSS observations as the moving median if \(m\) is even.  
Both moving average and moving median methods are used to smooth out time-series RSS observations and make it easier to identify trends and patterns. However, the moving median is typically more robust to outliers and skewness in the observations compared to the moving average.
## 3 Experimental Test Procedure
### RGB+D Fusion
#### 3.3.4 Image and Point Cloud Data Fusion  
Data measured with the instrument, point cloud and measurements, are in the same coordinate system, defined through the instrument setup. Coordinates of the camera center and theodolite angles (\(Hz\) and \(V\)) of each pixel are in the same coordinate system. Spatial rays projected from the camera center pass through the point cloud and the matching pixel in the image, as shown in Figure 7. Rectified images of the test area overlap with the point cloud (highlighted yellow) of the same area, an example is shown in Figure 8.  
Figure 7: Spatial ray projection from the camera to the point cloud and image (Source: Own compilation).  
The next step is matching between the image and point cloud, based on minimal angle difference, to assign 3D distances to some of the pixels. The goal is to find a point from the point cloud that lays on the same spatial ray as a pixel in the image.  
Pixel position for a point in the point cloud can be defined as:  
\[Pix_{i}=\begin{bmatrix}Pix_{xi}\\ Pix_{yi}\end{bmatrix}=\begin{bmatrix}min\left(abs\left(Hz_{img}-Hz_{i}\right) \right)\\ min\left(abs\left(V_{img}-V_{i}\right)\right)\end{bmatrix} \tag{5}\]  
where \(Hz_{img}\) is a matrix with horizontal demodolite angles for each pixel, \(V_{img}\) is a matrix with vertical theodolite angles for each pixel. The function min in Matlab return se matrix index (row and column) of the minimum value in the matrix. The result are the pixel positions in image of each point in the point cloud.  
Based on the pixels with distance values, an external boundary of the scan data is created for the image, Region Of Interest (ROI). The boundary is used to prevent the interpolation of data to the pixels of the image that are not covered by any point cloud data.  
Because the number of pixels in the image is usually larger than the number of point cloud points, 3D distances for the empty pixels are calculated using a 2D interpolation algorithm [33].  
The result of this step is an RGB+D image for each epoch. For each image pixel, RGB values, as shown in Figure 9, \(Hz\) and \(V\) angles, and 3D distance are known, as shown in Figure 10; also, the image's ROI is defined. The data were stored within a 6-dimension matrix, where in addition to the aforementioned five matrices, the sixth matrix contained the distance data for each pixel.