esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Use of Starlink Downlink Tones for Positioning
## 2 Signal Detection and Tracking  
### Range Dynamics and Visibility  
The current Starlink constellation consists of nearly 3000 satellites (in January 2023) placed in several low Earth orbit planes. The orbits are nearly circular and have three main inclinations: \(\sim\)53.2\({}^{\circ}\), 70\({}^{\circ}\), and 97.7\({}^{\circ}\). The first inclination is the most deployed today, which makes the satellites visibility better in equator and mid-latitude regions than near polar regions.  
In order to determine the signal dynamics which are needed to design the signal processing, we simulated the Starlink constellation based on orbit parameters that are published daily by NORAD in two-line element (TLE) files [27]. We used the Simplified General Perturbation Model, SGP-4 [28; 29], to compute the positions and velocities of the satellites. This allowed us, for a given location of the observer, to derive typical values for the dynamics of the distance between the satellite and the user, as well as the expected visibility of the satellites (Table 1).  
The Doppler shift and Doppler shift rate have high values resulting from the high speed of LEO satellites combined with the use of signals in the Ku band. The high visibility of the satellites is a geometric visibility (i.e., the number of satellites in view simultaneously over an elevation mask). Effective visibility, which is defined relative to the minimum received signal strength that allows tracking of the signals, will be much lower due to the low altitude of the satellite orbits. In addition, the low orbit altitude increases the satellite speed and reduces the time the satellite remains above a given elevation mask for near-ground observers. A satellite remains in view above an elevation angle of 25\({}^{\circ}\) for only a few minutes.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 5 Experiment and Evaluation
### Experiment Design
#### 5.3.3 Third Experiment: Aggregation Rate  
The third experiment measures the aggregation rate of the fog server and evaluates its capability in filtering data. iFogSim [25] is utilized to simulate the wide and dense geographical distribution of clients. First, we simulate the walking speed of young adults and elderly. Then, we simulate the speed of bike riding to test the aggregation rate of the fog server. In total, 1000 clients uploaded data in the speed of 760 bytes/s for 10 min. The fog server synchronizes the database with the cloud. Data being uploaded to the cloud are calculated to find the aggregation rate.
## 3 The Proposed Downloading Scheme
### The Minimum-Cost Maximum-Flow Algorithm  
This section will introduce the MCMF routing algorithm. As shown in Figure 4, the two minimum cost paths between (9, 12) are \(P(9,8,7,12)\) and \(P(9,14,13,12)\), which are identified by the blue arrows and lines. The maximum flow allowed between (9, 12) in Figure 4 includes four paths, and the other maximum flow paths with larger costs are identified by black arrows and lines. The traditional multi-data stream maximum flow linear programming problem [18] has the following three problems: 1. The possibility of data transmission on a shorter or longer path is the same, so the transmission cost cannot be minimized; 2. Because the transmission cost cannot be minimized, it may cause loopback; 3. On-board computing resources are limited, it is also difficult to solve the LPs problem in a timely manner even if in a limited network size.  
In order to solve the above-mentioned problems, the distance bandwidth product (BD) is proposed as the link cost, allowing shorter paths to be planned for larger traffic, minimizing transmission costs, and preventing loopback. Secondly, each data stream's computation is constrained by subgraph restrictions depending on energy and computational resources, thereby greatly reducing the computational complexity. Once a subgraph \(G^{c}\) is generated for every flow \(c\in C\), the Constrained MCMF algorithm including two LPs can be formulated.  
Figure 4: Multi-hop routing in a LEO satellite network. The number next to the node represents the ID of the satellite.
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 2 Background
### Common Vulnerability and Exposures  
As mentioned earlier, the Common Vulnerabilities and Exposures (CVE) program [14], developed by the MITRE Corporation, provides a standardized method for identifying and tracking specific cybersecurity vulnerabilities. It serves as a dictionary of publicly known vulnerabilities and exposures, assigning them unique identifiers known as CVE IDs for easy reference. The process of adding a new CVE to the database typically follows the following steps:  
1. A developer identifies a vulnerability while working on software or a system.
2. The developer informs MITRE by submitting a form ([[https://cveform.mitre.org/](https://cveform.mitre.org/)]([https://cveform.mitre.org/](https://cveform.mitre.org/))) (accessed on 10 March 2024) which includes details such as the vendor name, vulnerability type, affected products, and more.
3. MITRE assigns a new CVE ID to the reported vulnerability or delegates this task to an authorized organization if it falls within their expertise.
4. The organization responsible for assigning the CVE ID analyzes the vulnerability and generates a set of metrics used to calculate a final score, which determines its severity.
5. The CVE is officially added to the CVE database.
6. Software vendors and organizations utilize the CVE report and scores generated by MITRE to prioritize their patching process.  
Figure 3 provides an example of a CVE entry after it has been added to the official CVE list. The figure displays the CVE ID, description, authorized organization (CNA), generated score, severity level (indicated by color, with red representing critical), and metrics vector associated with the vulnerability.
# A Comprehensive Survey on Knowledge-Defined Networking
## 4 Management Plane
### Network Data Collection
#### 4.5.3 Data Representation Models  
Data models provide a standard and consistent way of modeling network data, which makes it easier to automate and manage networks. Yet Another Next Generation (YANG) is a modeling language used to describe the state data of network devices and their configurations. The NETCONF and YANG data formats are often used together to offer a standardized method for configuring and controlling network devices. Network administrators may use the YANG data model to define the data that are used to configure network devices and to make sure that the data are consistent between various devices and suppliers [244].  
Another data model called the Common Information Model (CIM) offers a uniform way to express data about network infrastructure and device services. A collection of standardized object classes and characteristics that define different facets of network infrastructure and services serves as the foundation for using the CIM data model in KDN. These object classes and properties provide a common language for different networking devices and systems to exchange information about their configuration, status, and performance [245].  
Table 4 summarizes management protocols, network monitoring frameworks, and data collection methods in the management plane.
## 3 Test Results and Analysis
### Stiffness Degradation  
The average of the secant stiffness in the positive and negative directions is used to measure specimen stiffness at each grade of lateral drift. The average secant stiffness \(K_{i}\) for each specimen at the \(i\)-th grade of lateral drift is calculated via formula (2):  
\[K_{i}=\sum_{j=1}^{m}\left(\left|+P_{i,j}\right|+\left|-P_{i,j}\right|\right)/ \sum_{j=1}^{m}\left(\left|+\Delta_{i,j}\right|+\left|-\Delta_{i,j}\right|\right) \tag{2}\]  
where \(m\) is the number of times that a certain grade of lateral drift is loaded, and \(\Delta_{i,j}\) and \(P_{i,j}\) are the corresponding maximum horizontal displacement and horizontal forces when the i-th grade of lateral drift is loaded for the \(j\)-th time.  
Figure 11 and Table 5 show the average secant stiffness corresponding to each lateral drift of each specimen from the start of loading at a 1/250 lateral drift to the end of loading. Figure 11 shows that all specimens essentially have a consistent trend of stiffness degradation: The stiffness  
Figure 10: Bar graph of bearing capacity.  
degradation is fast during the initial stage and slow during the later stage. The test results show that the horizontal stiffener has a small impact on the initial stiffness, whereas the vertical stiffener significantly improves the initial stiffness. Compared to specimen CFST-HS, specimen CSFT-3 has the following improvements: The initial stiffness improves by 19.53%. The core structure rebar slows the stiffness degradation. Compared to specimen CFST-VS, specimen CFST-SR has the following improvements: The initial stiffness improves by 6.42%. A comparison of specimen CFST-SR and specimen CFST-DT shows that the two have identical section steel ratios. For the double-skin circular CFST column, since the core concrete is under dual constraints, including an external steel tube and an internal steel tube, the section stiffness improves significantly. The stiffness degradation curve shows that the secant stiffness corresponding to each grade of lateral drift for specimen CFST-DT exceeds that for specimen CFST-SR. Specim CFST-MC has the highest section steel ratio and the slowest stiffness degradation.
### Appendix A.2  
In Table 11, the receiver configuration for the acquisition of the Galileo E5a and the GPS signals is detailed. Doppler prediction is not utilized. However, self-assistance to acquisition, from the primary to secondary band, is enabled. Therefore, once the primary band is successfully acquired, the receiver estimates the Doppler frequency in the secondary band. Subsequently, it initiates acquisition in the secondary band with a reduced Doppler search grid. For instance, a Doppler search space of \(\pm\)5000 Hz is applied for GPS L1 C/A signals, while a reduced Doppler search space of \(\pm\)500 Hz around the estimated Doppler frequency in the secondary band is employed for the GPS L5 and the Galileo E5a signals.  
Table 12 presents the tracking configuration parameters for the Galileo E5a and the GPS signals. Initially, the tracking process operates without synchronization to telemetry preambles and/or the pilot's secondary code, utilizing a CI time of 1 ms, equivalent to the duration of the primary PRN code. After achieving synchronization, the CI time is set to 20 ms to enhance the apparent signal-to-noise ratio. The 'early-late space chips' represents the spacing between the Early and Prompt, and between the Prompt and Late correlators before synchronization, while the 'early-late narrow space chips' denotes the spacing between the Early and Prompt, and between the Prompt and Late correlators after synchronization. Both parameters are normalized by the chip period. Additionally, the PLL filter bandwidth and the DLL filter bandwidth, along with their configurations with narrow correlator settings, indicate the bandwidths of the PLL and DLL low-pass filters before and after achieving synchronization, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{Acquisition GPS L1 C/A} & \multicolumn{2}{c}{CI time} & 1 ms \\  & Max NCI combinations & 4 \\  & Doppler max & 5000 Hz \\  & Doppler step & 250 Hz \\  & PFA & 0.1 \\  & Downsampling factor & 4 \\ \hline \multirow{6}{*}{Acquisition GPS L5} & \multicolumn{2}{c}{CI time} & 1 ms \\  & Max NCI combinations & 4 \\  & Assistance to secondary band & enabled \\  & Doppler max & 500 Hz \\  & Doppler step & 250 Hz \\  & PFA & 0.1 \\ \hline \multirow{6}{*}{Acquisition Galileo E5a} & \multicolumn{2}{c}{CI time} & 1 ms \\  & Max NCI combinations & 4 \\  & Assistance to secondary band & enabled \\ \cline{1-1}  & Doppler max & 500 Hz \\ \cline{1-1}  & Doppler step & 250 Hz \\ \cline{1-1}  & PFA & 0.1 \\ \cline{1-1}  & Downsampling factor & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Receiver acquisition configuration.
## 2 Materials and Methods
### Dielectric Properties  
The effectiveness of MW heating can be quantified if the dielectric properties of the food are known. They can be assessed according to the complex relative permittivity defined by the equation  
\[\varepsilon_{r}={\varepsilon_{r}}^{\prime}-j\varepsilon_{r}{}^{\prime\prime} \tag{1}\]  
where the dielectric constant \(\varepsilon_{r}{}^{\prime}\) and the dielectric loss factor \(\varepsilon_{r}{}^{\prime\prime}\) quantify the capability of the material to store and dissipate MW energy, respectively. Several studies pertaining to the dielectric properties of many foods, such as fruits and vegetables, meat and ham, starch and glucose solutions, various food proteins and some types of pasta, have been published [23; 39; 40]. However, there are less data on the dielectric properties of almonds. On the other hand, the development of equations to predict the dielectric properties of food would be very useful to gain insights into interactions between food and electromagnetic energy. Within this framework, we used literature data and empirical equation modeling of both the dielectric constant and loss factor versus the temperature \(T\) and moisture content \(M\)[41]  
\[\begin{split}\varepsilon_{r}{}^{\prime}=2.96-0.10T+0.05M+6.56 \times 10^{-3}TM+2.41\times 10^{-3}T^{2}-0.07M^{2}-2.31\times 10^{-4}T^{2}M+6.37 \times 10^{-4}TM^{2}-\\ 1.65\times 10^{-5}T^{3}+0.01M^{3}+3.72\times 10^{-6}T^{2}M^{2}+8.94 \times 10^{-7}T^{3}M-2.79\times 10^{-5}TM^{3}+3.79\times 10^{-8}T^{4}-3.12 \times 10^{-4}M^{4}\end{split} \tag{2}\]  
and  
\[\begin{split}\varepsilon_{r}{}^{\prime\prime}=11.54-0.09T-4.95M+0. 01 TM+1.65\times 10^{-3}T^{2}-0.72M^{2}-1.58\times 10^{-4}T^{2}M-5.29\times 10^{- 4}TM^{2}\\ -1.35\times 10^{-5}T^{3}-0.04M^{3}+3.36\times 10^{-6}T^{2}M^{2}+6.0 \times 10^{-7}T^{3}M+7.66\times 10^{-6}TM^{3}\end{split} \tag{3}\]  
Equations (2) and (3) refer to a frequency of 2.45 GHz; \(T\) and \(M\) can change from 20 to 90 \({}^{\circ}\)C and from 4.2% to 19.6%, respectively. Nevertheless, the food to be heated is generally a heterogeneous dielectric mixture consisting, in this case, of almonds embedded in a host material (air). Thus, to simplify the resulting complex and difficult electromagnetic problem we assumed a continuous effective medium having dielectric permittivity, given by  
\[\varepsilon_{r,\,eff}=f_{nl}\varepsilon_{r,\,nl}+(1-f_{nl})\varepsilon_{r,\, air} \tag{4}\]  
where \(\varepsilon_{r,\,el}\), \(f_{nl}\) and \(\varepsilon_{r,\,air}\) are the relative dielectric permittivity of almonds, given by Equations (2) and (3), the volumetric concentration of almonds and the relative dielectric permittivity of air, respectively.
# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 5 Simulation Results
### Trajectory Optimization  
The UAV trajectories obtained by the previous method and proposed method are respectively shown in Figures 15 and 16. In the previous method, the UAV's flight altitude is assumed as a constant, which is 100 m in Figure 15. The UAV needs to hover above each UE for data collection. This may increase the complexity of the trajectory, especially when the number of UEs increases. In contrast, the proposed method in this article, i.e., the three-dimensional trajectory, finishes the data collection with fewer hovering points by  
Figure 14: Minimum number of hovering points by K-means and GA.  
Figure 13: Snapshot of hovering point placement by GA.  
optimizing the flight altitude. The UAV covers and collects data from multiple UEs at the same location each time when hovering.  
The results of the completion time with varying numbers of UEs are shown in Table 3 and Figure 17, where hovering time is the time for data transmission. From Table 3, it is clear that flight time occupies a large proportion of the mission completion time. The completion time, especially the flight time, is reduced by the proposed method, with a reduction rate of nearly 0.2. The hovering time slightly increases because of the increase in the UAV's flight altitude, which lowers the data rate.  
Figure 16: Snapshot of UAV trajectory by proposed method.  
Figure 15: Snapshot of UAV trajectory by previous method.
# A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network
## 4 Experiment and Result Analysis  
This part mainly discusses the simulation results of the experiment. In this paper, six beacon nodes were randomly deployed in a \(100\times 100\) area, and the mobile node ran along a fixed trajectory shown in Figure 3 with a moving step of 100. The sampling period was \(\Delta t=0.5\) s and the mobile node initial state \(X_{j}(0)=\left[\begin{array}{cc}1\ \mathrm{m}&20\ \mathrm{m}&1\ \frac{\mathrm{m}}{\mathrm{s}}&0.5\ \frac{\mathrm{m}}{\mathrm{s}}\end{array}\right]^{T}\). The initial of the error covariance matrix was set to \(P_{j}(0)=I_{4}\), the Markov transition matrix initial value was \(p=\left[\begin{array}{cc}0.995&0.005\\ 0.005&0.995\end{array}\right]\), and the measurement noise covariance matrix was \(R_{j}^{*}(0)=\sigma_{m}^{2}I_{M}\). The tracking threshold probability \(P_{G}\) was 0.99, and the detection probability \(P_{d}\) was 0.9. In order to simulate an NLOS environment, a probability value was generated randomly. Comparing this probability value with the NLOS probability threshold, if the probability that was generated randomly was less than the NLOS probability, the mobile node and the corresponding beacon nodes were considered to be in the NLOS condition. The simulation experiments were carried out under the conditions of NLOS errors obeying Gaussian, uniform, and exponential distributions. In this paper, we compared the proposed algorithm with the EKF [34], IMM-EKF [30], and MPDA [35]. The simulation results were obtained using 1000 Monte Carlo runs, and the root mean square error (RMSE) and the error cumulative distribution function (CDF) of the average positioning errors were used as the performance indicators for the evaluation algorithm:  
\[RMSE=\sqrt{\frac{1}{\mathrm{MC}}\frac{1}{\mathrm{K}}\frac{1}{\mathrm{I}}\prod \limits_{j=1}^{\mathrm{MC}}\prod\limits_{k=1}^{K}\left(\left(\hat{x}_{j}(k)-x_ {j}(k)\right)^{2}+\left(\hat{y}_{j}(k)-y_{j}(k)\right)^{2}\right)}, \tag{70}\]  
\[ALE=\frac{1}{\mathrm{MC}\cdot\mathrm{K}}\sum\limits_{j=1}^{\mathrm{MC}}\sum \limits_{k=1}^{\mathrm{K}}\sqrt{\left(\hat{x}_{j}(k)-x_{j}(k)\right)^{2}+\left( \hat{y}_{j}(k)-y_{j}(k)\right)^{2}}, \tag{71}\]  
where ALE is the average localization error, which represents the average Euclidean distance between the position estimate and the true position. \(K=100\) is the number of moving steps, \(\mathrm{MC}=1000\) is the number of time the Monte Carlo simulation ran. \(\left(\hat{x}_{j}(k),\hat{y}_{j}(k)\right)\) is the position estimate of the interactive output in the _jth_ Monte Carlo run and \(\left(x_{j}(k),y_{j}(k)\right)\) is the true position of the mobile terminal during the _jth_ Monte Carlo run.
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 4 Experimental Details and Analysis  
### Experimental Settings  
#### 4.1.1 Datasets  
We conduct extensive experiments using eight high-resolution SVs. Seven SVs are obtained from the Jilin-1 satellite constellation launched by Chang Guang Satellite Technology Co., Ltd. (Changchun, China). Moreover, only the Vancouver dataset is acquired using a high-resolution iris camera installed on the ISS. These datasets are divided into two groups: those with rotation (i.e., Dubai, Muharraq, Hong Kong, and Boston) and those without rotation (i.e., San Diego, Vancouver, Minneapolis, and San Francisco) to demonstrate the tracking effectiveness of the proposed method for rotating and non-rotating objects. Tracked targets include cars, planes, trains, and ships. These objects are represented by rotating bounding boxes annotated with four corner coordinates. One region of each SV is cropped for clear visualization. Table 1 provides detailed information about the datasets. Figure 5 shows the first frames, cropped regions, and tracked objects.
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
## 3 Results
### Linear Distribution of Index  
For the graded-index AR coating with linear index distribution [18], the index distribution is in the form  
\[n(z)=n_{\text{min}}+\frac{n_{\text{max}}-n_{\text{min}}}{z_{tot}}z,z\leq z_{ tot} \tag{3}\]  
where, \(n_{\text{min}}\) and \(n_{\text{max}}\) are minimum index and maximum index of graded-index AR coating, respectively. \(z\) is coordinate of coating and \(z=0\) is the surface of coating. \(z_{tot}\) is thickness of the coating.  
Figure 1a shows transmittance of graded-index AR coatings with linear index distribution at different searching thickness \(d\) of divided thin layers (as mentioned in Section 2). We find that the best searching thickness is \(d=0.925\) nm as the number of divided thin layers is N = 450. Thus, the optimum matching thickness of entire coating is N \(\times\)\(d\) = 0.925 \(\times\) 450 nm.  
The transmittance at best searching thickness \(d\) are shown in Figure 1b. From Figure 1b, it can be found that the transmittance are all above 99.8% at working wavelengths from 200 nm to 1200 nm. In Figure 1c, the intensity of standing wave field at wavelengths of 355 nm, 510 nm and 1064 nm are presented. From Figure 1c, it can be found that the intensity of standing wave field decreases periodically from surface to substrate and the distributions are similar for different wavelengths. The maximum intensities of these standing wave fields are near the interface between air and the coating and the minimum intensities are near the interface between the coating and the substrate. Moreover, Figure 1c shows that the period of standing wave field is proportional to the incident wavelength.  
For real fabrication of this graded-index coating, one can redivide the obtained optimized matching thickness. Figure 1d is an example for re-division. In this re-division, the coating is equally divided into 45 layers. The thickness of one layer is about 9 nm and the minimum index contrast among layers is 0.01. In Figure 1d, refractive index distribution for the division with best searching thickness \(d\) is also presented. From Figure 1d, we can find that refractive index distribution for the division with best thickness \(d\) (green line) is nearly continuous and quite close to the description of Equation (3). The comparison of transmittance between division with best thickness \(d\) and the re-division with 45 layers is shown in Figure 1e. From Figure 1e, it can be seen that the transmittance curves for both divisions are quite close.  
Figure 1: \(Cont.\)  
Figure 1: _Cont._