esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Support for Mission Critical Services
## 2 Status of Mission-Critical Solutions
The first complete 3 GPP standards describing the 5G network appeared in December 2018 [18], and the first implementation was in April 2019. At present, 5G networks are being deployed all over the world. The New Radio (NR) (5G RAN), uses new frequency bands, including 3.5-3.7, 12 and 28 GHz. The use of high bands enables the creation of much wider channels than LTE and provides an increased capacity of the base station in relation to the LTE network. Additional techniques, such as dynamic beam shaping and massive Multiple Input Multiple Output (MIMO) multi-antenna systems, improve the system's capacity. A 5G network can handle many virtualized mobile communication networks using a technique called Network Slicing (NS) [19]. The 5G backbone network, called 5G Core (5 GC), supports NS, system virtualization using the Network Function Virtualisation (NFV) paradigm and the creation of control plane applications using the publish-subscribe Service-Based Architecture (SBA) [18]. The system offers a programming interface for developers of service applications. The works on the usage of 5G for MCS have started; however, they are still at the preliminary phase [20]. The NS technology can be nicely used for the low-cost creation of permanent MCS that, despite sharing of common, commercial infrastructure is isolated. Such a solution can be used by police, etc. Unfortunately, the existing 3 GPP to NS approach gives no mechanisms to make reliable and disaster-prone NS. This makes it impossible to use 5G for disaster handling. Despite 5G SBA and NS making the solution a good candidate for MCS, some other 5G features, such as the use of high-frequency bands (which implies a small serving area of 5G cells) and higher complexity (which implies high energy consumption) makes LTE the solution of choice for broadband MCS, particularly for on-demand applications. The main advantages of 5G, which are high-capacity and high-throughput, have limited value for MCS. However, integration of 5G with Non-Terrestrial Network (NTN)--a topic that is actually under standardization by 3 GPP [21]--can be fully exploited for disaster handling by 5G. NTN can support terrestrial networks in areas where there is no terrestrial connectivity or such connectivity has been disturbed. NTN can Low Earth Orbit (LEO) satellite networks (500-2500 km and 4 ms propagation time), Medium Earth Orbit (MEO) satellite networks, High Altitude Platform System (HAPS). Issues with this approach are the lack of maturity of the solution and the lack of appropriate terminals for direct NTN communication. \begin{table}
\begin{tabular}{l c c} \hline \hline
**System** & **TETRA** & **LTE-MC** \\ \hline Spectral efficiency & Low & High \\ Push-to-Talk & Yes & Yes \\ Push-to-Video & No & Yes \\ Data Transmission capabilities & \(\sim\)20–150 Kpps & \(\sim\)1–100 Mbps \\ Direct Mode of operations & Yes & Yes \\ Group communication & Yes & Yes \\ Dedicated terminals & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of TETRA and LTE-MC.
## 3 Results and Discussion
### The Effect of Ultrasonication Pretreatment  
To examine the effect of ultrasonication, GL-Nic (5:1) used as 70% (_w/v_) aqueous solution, was mixed with dried powder of _M. oleifera_ leaves, shaken vigorously for a few seconds to form slurry, and then placed in the ultrasonication bath, at room temperature. Room temperature (\(23\pm 2\)\({}^{\circ}\)C) was preferred, because at lower temperatures, ultrasonication results in better cavitation, which is the main effect involved in the ultrasound-assisted extraction. At higher temperatures, bubbles formed through cavitation collapse less violently, and thus the ultrasonication effect is less effective [26]. Ultrasonication treatments were performed for 5, 10, 20, 30, and 40 min, followed by stirred-tank extraction. As can be seen in Figure 2, a regime of 30-min ultrasonication gave statistically higher Y\({}_{\text{TP}}\) (\(p<0.05\)), whereas extraction without ultrasonication pretreatment (0 min) was significantly less effective (\(p<0.05\)). Ultrasonication beyond 30 min resulted in decreased Y\({}_{\text{TP}}\), thus 30 min was chosen as the most appropriate pretreatment period.  
Figure 1: Plot showing the effect of R\({}_{\text{mol}}^{\text{D/A}}\) on the extraction effectiveness of the DES tested. Extractions were carried out at 50 \({}^{\circ}\)C, with \(C_{\text{DES}}=70\%\) (_w/v_), at 500 rpm, for 150 min. Assignment with \"\" denotes statistically different value.
###### Abstract  
Background/Objectives: Aging increases the risk of metabolic diseases, such as diabetes, hypertension, dyslipidemia, and obesity, which may contribute to cataract development. This study examined whether metabolic diseases mediate the relationship between the dietary inflammatory index (DII) and cataract prevalence using structural equation modeling (SEM). Methods: Data were obtained from 9260 individuals aged 40 and older who participated in the 8 th Korea National Health and Nutrition Examination Survey (KNHANES 2019-2021). DII scores, calculated from 24 h dietary recall, reflect the inflammatory potential of the diet, with higher scores indicating a more pro-inflammatory profile. SEM was applied to assess mediation effects, and analyses were conducted using SPSS 29.0 and AMOS 29.0. Results: Among the participants, 1853 individuals (20.0%) were diagnosed with cataracts, and the mean DII was 1.5 \(\pm\) 2.8. In the high DII group, the risk of hypertension (\(\beta\) = 0.077, \(p\) < 0.001) and dyslipidemia (\(\beta\) = 0.033, \(p\) = 0.001) increased significantly. Hypertension (\(\beta\) = 0.187, \(p\) < 0.05), diabetes mellitus (\(\beta\) = 0.132, \(p\) < 0.05), dyslipidemia (\(\beta\) = 0.108, \(p\) < 0.05), and obesity (\(\beta\) = 0.025, \(p\) < 0.05) had direct effects on cataract prevalence. Diabetes 0.002 (\(p\) < 0.05), hypertension 0.014 (\(p\) < 0.05), and dyslipidemia 0.004 (\(p\) < 0.05) also showed significant indirect effects. Conclusions: Metabolic diseases mediate the relationship between DII and cataracts. Managing inflammation and metabolic health may reduce cataract risk.  
cataract; inflammation; metabolic diseases; Korea
## 6 Experimental Results
### Use Cases
#### 6.2.3 Plate Recognition  
Automatic license plate recognition (ALPR) concerns the identification of vehicle license plate from an image or video [17]. Applications of ALPR range from electronic toll collection on pay-per-use roads to speed limit enforcement and traffic control.  
To identify a plate in a given image we:  
1. Perform plate localization to discover the plate in the image;
2. (Possibly) rotate the image to correct the possible skewing of the plate;
3. Segment characters within the plate to detect them;
4. Recognize the extracted characters using Optical Character Recognition (OCR) techniques;
5. If the recognized plate is included in the knowledge base of \"known\" plates, the alarm is raised.  
Examples of plate recognition are shown in Figure 10.  
In our implementation, we exploited the open source C++ library OpenALPR, ([[http://www.openalpr.com/](http://www.openalpr.com/)]([http://www.openalpr.com/](http://www.openalpr.com/)), accessed on 3 December 2021) that uses OpenCV ([[https://opencv.org/](https://opencv.org/)]([https://opencv.org/](https://opencv.org/)), accessed on 3 December 2021) for image processing and Tesseract for OCR. Experiments were performed on the OpenALPR benchmark, ([[https://github.com/openalpr/benchmarks/tree/master/endtoend/](https://github.com/openalpr/benchmarks/tree/master/endtoend/)]([https://github.com/openalpr/benchmarks/tree/master/endtoend/](https://github.com/openalpr/benchmarks/tree/master/endtoend/)), accessed on 3 December 2021) including pictures of cars taken under different light conditions with resolution ranging between 640 \(\times\) 480, 800 \(\times\) 600, and 1792 \(\times\) 1312 pixels.  
Table 4 reports experiments performed using this specific use case (average image size is about 340 kB). In this case, an additional test was performed to stress the network, feeding images with size of about 1 MB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Framework** & **Throughput (Images/s)** & **Throughput (KB/s)** \\ \hline Spark & 6.5 & 396.5 \\ Storm & 7.2 & 439.2 \\ Flink & 5.4 & 329.4 \\ Samza & 7.4 & 451.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison between the four considered frameworks for the text recognition use case.
## 4 Simulation Results
### Open Sky with Three SVs
In summary, when the number of visible satellites drops to three, the TCAPLL can still navigate even with low quality MEMS inertial sensors. In this case, the navigation solution quality degrades as it will depend more on the INS. This degradation will converge with time depending on the satellites' geometry, receiver dynamics and the quality of the inertial sensors. Moreover, it was also shown that the position uncertainty is more affected than velocity, and the frequency aiding uncertainty increases slightly for visible satellites, and exponentially for non-visible satellites.
# Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis
###### Abstract  
Due to the noise accompanied with rolling element bearing fault signal, it can reduce the accuracy of faulty diagnoses. In order to improve the robustness of a faulty diagnosis, this study proposed a fault diagnosis model based on modified local linear embedding (M-LLE) algorithm. Aiming at the characteristics of rolling element bearing fault data, the vibration signal was first analyzed in time domain and frequency domain to construct high dimension eigenvectors. Next, the high-dimensional eigenvectors can be reduced to low-dimensional eigenvectors by M-LLE algorithm. In the M-LLE algorithm, the Mahalanobis distance (MD) metric is adopted to replace Euclidean distance in traditional neighborhood construction and L1-norm is used to standardize weight matrix, which can enhance the anti-noise ability of the Local Linear Embedding (LLE) algorithm. Finally, fault diagnosis results can be obtained when low-dimensional rolling element bearing fault data is classified by K-Nearest Neighbor (KNN) classifier. By simulating the noisy artificial data sets in different degrees, the proposed algorithm can get the perfect local structure of manifolds. The effectiveness of M-LLE algorithm can be proved. In addition, experimental results of real rolling element bearing data which provided by the University of Cincinnati show that the accuracies of all kinds of faults can reach 100%. It can be deemed that the proposed fault diagnosis model can effectively improve the accuracy of fault diagnosis.  
fault diagnosis; rolling element bearing; MD; L1-norm; LLE algorithm Article
## 3 Vulnerability Detection Methods  
A vulnerability is a term, also known as a weakness, that refers to a situation in which the protection methods and techniques of a system or application are weak or incomplete. These vulnerabilities can allow malicious actors to gain unauthorized access to the system or unintended use of the service. Security problems can be caused by many different factors, including the following:  
- Software bugs;  
- Design flaws;  
- Weak encryption methods;  
- Poor configuration;  
- Lack of updates and patches;  
- User errors;  
- Social engineering;  
- Factors such as malware infiltration, which can contribute to the emergence of vulnerabilities [22].  
Likewise, vulnerabilities can be detected by different methods such as vulnerability scans, penetration tests, source-code analyses, log analyses, social engineering tests and audits. All these methods are basically characterized as vulnerability analyses. To detect security problems, the following three basic features must be present in the scanning process [23]:  
- Discovery: The process of providing a snapshot or continuous view of the assets within the target system and application.  
- Assessment: The detection of anomalies or known vulnerabilities registered in the CVE database in the target system and application.  
- Prioritization: The prioritization of assets within the target system and application using CVE data, data science or threat intelligence to resolve vulnerability results.  
Almost all applications developed recently provide web-based services. Finding vulnerabilities in web applications is a difficult and comprehensive process. Modern web applications are created with a wide network of third-party frameworks, services and APIs. All of this scope expansion can lead to the discovery of new vulnerabilities or exacerbate existing vulnerabilities, making vulnerability detection even more difficult. At the same time, the development of comprehensive web applications using a development process with many developers has popularized Agile and DevOps approaches [24]. As a result, rapid version releases and enhancements have increased, and unintended security vulnerabilities have also emerged through these approaches.
# A Watermarking Method for 3D Printing Based on Menger Curvature and K-Mean Clustering
## 3 The Proposed Algorithm
### Watermark Extracting  
The watermark extraction process is similar to the embedding process. Firstly, we also extract facets from the scanned 3D triangle mesh \(\mathbf{M}^{\prime}\) to compute the Menger facet curvatures. After that,  
Figure 4: Watermark bit embedding by changing the mean Menger curvature.  
we classify them into groups by the K-mean clustering algorithm based on the value of Menger curvatures. The watermark key is re-used for the clustering process. For each group \(m_{g}\), we find the maximum Menger curvature \(K_{max}^{m_{g}^{\prime}}\) and the minimum Menger curvature \(K_{min}^{m_{g}^{\prime}}\) and calculate the mean Menger curvature \(K_{mean}^{m_{g}^{\prime}}\) similar to Equation (3). \(\Delta_{mg}^{\prime}=(K_{min}^{m_{g}^{\prime}}+K_{max}^{m_{g}^{\prime}})/2\) is the average value of \(K_{min}^{m_{g}^{\prime}}\) and \(K_{max}^{m_{g}^{\prime}}\). Finally, the watermark bit \(\omega_{g}\) can be extracted by comparing the mean Menger curvature \(K_{mean}^{m_{g}^{\prime}}\) with the average value \(\Delta_{mg}^{\prime}\) as described in Equation (10).  
\[\omega_{g}=\left\{\begin{array}{l}1\text{ if }K_{mean}^{m_{g}^{\prime}} \geq\Delta_{mg}^{\prime}\\ 0\text{ if }K_{mean}^{m_{g}^{\prime}}<\Delta_{mg}^{\prime}\end{array}\right. \tag{10}\]
## 2 Study Area and Data
### Data  
The data used in this study are divided into two categories: satellite data and site validation data. To ensure an adequate number of validation sites and the temporal integrity of satellite data, the study period was selected to span from 2017 to 2019. The detailed data are listed in Tables 1 and 2.
# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 2 Materials and Methods
### Cochlagrams of the Speech Signal  
Figure 7 shows a cochleagram for a normal male voice. Figure 8 shows a cochleagram for substitution voicing after total laryngectomy and TEP implantation. They were generated from sound data for a frequency range of 30-5000 Hz using a 128-channel filter bank. A cochleagram is a time-frequency representation of a sound signal generated by a filter bank. It divides the sound into different frequencies based on a model that mimics the internal structure and composition of the human ear (outer and middle ear, membrane, cochlea, and hair cells) [55]. A comparative visual analysis of cochlegrams reveals the detectable features of cochleograms that reflect high irregularity, frequency shifts, aperiodicity, and phonatory interruptions of substitution voicing.  
In terms of classification performance, the cochleagram can outperform the spectrogram [56]. We consider cochlegrams to be a useful addition to the domain of audio analysis, which can be used in conjunction with traditional acoustic features provided by Fast Fourier Transformation (FFT) and work well with deep learning audio analysis applications [57; 58].  
Figure 6: Statistical distribution of voice signal SNR in datasets. The difference between the German and Italian datasets is statistically significant (\(p<0.001\)). The difference between the German and Lithuanian datasets is statistically significant (\(p<0.01\)). The difference between the Italian and Lithuanian datasets is statistically significant (\(p<0.001\)).
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 2 System Description  
In this section, the OFDM system model and the conventional frequency offset estimation methods are reviewed in Sections 2.1 and 2.2, respectively.
## 3 Audio Characterization and Recognition Tests
### Data Analysis
#### 3.1.3 Frequency Band Energy Share and Harmonic Component
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Feature Name** & **Formula** \\
**Number** & & \\ \hline
1 &
\begin{tabular}{c} Short-time \\ over-zero rate \\ \end{tabular} & \(d_{1}=\sum_{n=2}^{N}|sgn[y(n)]-sgn[y(n-1)]|,sgn[y]=\begin{cases}1,y\geq 0 \\ 0,y<0 \\ \end{cases}\) \\
2 &
\begin{tabular}{c} Skewness \\ \end{tabular} & \(d_{2}=\frac{E[(y(n-f)^{2})]}{\mu^{2}}\) \\
3 &
\begin{tabular}{c} Kurtosis \\ \end{tabular} & \(d_{3}=\frac{E[(y^{2}-\mu)^{4}]}{\mu^{2}}\) \\
4 &
\begin{tabular}{c} Peak factor \\ \end{tabular} & \(d_{4}=\frac{max(y(n))}{\sqrt{\frac{1}{N}\sum_{i=1}^{N}y(n)^{2}}}\) \\
5 &
\begin{tabular}{c} Correlation \\ coefficient \\ \end{tabular} & \(d_{5}=\frac{y,(n)\times y,(n)}{[y_{i}(n)]\times[y_{j}(n)]},1\leq i\leq j\leq 10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selected time domain features and formulas. similar percentage of energy in the frequency band for the three types of audio. Therefore, it was necessary to combine the above features, and the combination of features proposed in this paper is notated as \(TF=(d_{5},v_{1},v_{2},v_{3},v_{4},v_{5})\).
# Dynamic Frequency Support from a DFIG-Based Wind Turbine Generator via Virtual Inertia Control
## 4 Proposed Virtual Inertia Control Method of the DFIG-based WTG
Figure 7 represents the topology model of the IEEEG1 steam governor, and Table 3 displays its coefficients, as in [28]. Three simulation scenarios on wind speed conditions of 10.0 m/s, 8.0 m/s, and 13.0 m/s are carried out to investigate the performance of virtual inertia control strategies. As a disturbance, SG6--which generates 90 MW load--trips out from the grid at 10.0 s. Further, the system frequency is calculated by using a phase-locked loop. To verify the effectiveness of the suggested virtual inertia control method, frequency nadir, maximum _df/dt_, nadir-based frequency response, and released kinetic energy from the DFIG-based WTG of the proposed strategy are compared to the conventional strategy. Moreover, \(C\) is set to 100 to obtain the better performances on improving the maximum _df/dt_ and the frequency deviation. For the conventional strategy, \(K\) is set to 10, as in [15]. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Item** & **Values** & **Units** \\ \hline Nominal Stator Voltage & 2.3 & kV \\ \hline Nominal Apparent Power & 5.5 & MVA \\ \hline Nominal Active Power & 5.0 & MW \\ \hline Magnetizing Reactance & 2.9 & p.u. \\ \hline Stator Leakage Reactance & 0.18 & p.u. \\ \hline Rotor Resistance & 0.016 & p.u. \\ \hline Rotor Leakage Reactance & 0.16 & p.u. \\ \hline Stator Resistance & 0.023 & p.u. \\ \hline Inertia Constant & 5.0 & S \\ \hline Stable Operating Range of \(\omega_{r}\) & 0.70–1.25 & p.u. \\ \hline Rated, Cut-in, and Cut-out Speeds & 11, 4, and 25 & m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the DFIG-based WTG. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(K\) & \(K_{1}\) & \(K_{2}\) & \(K_{3}\) & \(T_{1}\) & \(T_{2}\) & \(T_{3}\) \\ \hline
20 & 0.3 & 0.4 & 0.3 & 0.1 & 1.0 & 0.25 \\ \hline \(\mathbf{T_{4}}\) & \(T_{5}\) & \(T_{6}\) & \(\mathbf{U_{o}}\) & \(\mathbf{U_{c}}\) & \(P_{max}\) & \(P_{min}\) \\ \hline
0.3 & 10 & 0.4 & 0.1 & –0.2 & 1 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of the IEEEG1 steam governor model. Figure 7: IEEEG1 model (type B). As in [29], the nadir-based frequency response (NBFR)--which is a metric for assessing the rigidness of an electric power grid--can be expressed as:  
\[\text{NBFR}=\frac{P_{loss}}{f_{nom}-f_{nadir}}, \tag{14}\]  
where \(P_{loss}\) denotes the loss of active power generation and is 90.0 MW in this research. \(f_{nadir}\) is the minimum system frequency during a disturbance.
## 2 Materials and Methods
### Preparation, Analytical Characterisation, and Mechanical Testing  
The MT was initially dried and submitted to a thorough de-flocculation process, by hand, upon arrival at the laboratory (Figure 3). The FA was received with a water content lower than 3% and was thus considered ready for use. Preparation of the pastes and mortars started with the dry mixing of the solid components (FA+MT or FA+MT+GS) for 5 min in a Hobart counter mixer.  
The activator was a combination of sodium hydroxide (SH) and sodium silicate (SS), with different SS/SH weight ratios of 0.5, 0.75, 1.00, and 1.25 (Table 2). The sodium hydroxide was supplied in pellets, with a specific gravity of 2.13 at 20 \({}^{\circ}\)C (99 wt.%), and was then dissolved in deionised water to previously defined concentrations of 7.5, 10.0, 12.5, and 15.0 molal. The SS presented a unit weight of 1.464 g/cm\({}^{3}\), a SiO\({}_{2}\)/Na\({}_{2}\)O weight ratio of 2.0 (molar oxide ratio of 2.063), and a Na\({}_{2}\)O concentration in the solution of 13.0%. Four to six hours after mixing the SH and the SS, when the temperature of the resulting activator reached 20 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C, it was mixed with the solid components, and additional mixing followed to guarantee homogenisation.  
A crucial aspect of the preparation of the pastes was the definition of their optimal compaction properties, namely, the dry unit weight and its equivalent water content, which was achieved through a standard Proctor test. This Proctor test was performed on the paste with 20% FA and 80% MT and on the mortar with 20% FA, 30% MT, and 50% GS. A dry unit weight of \(\gamma_{\rm d}\) = 1.974 g/cm\({}^{3}\) and a corresponding water content of \(\omega\) = 13.0% were obtained for the paste, while values of \(\gamma_{\rm d}\) = 2.064 g/cm\({}^{3}\) and \(\omega\) = 9.0% were obtained for the mortar. The compositions of the pastes and mortars tested  
Figure 3: Tailings as received in the laboratory (**a**); after most of the water was removed (**b**); after drying in the oven (**c**); after breaking the clods (**d**).  
are presented in Tables 2 and 3, respectively. Note that the slight variations in the FA/MT ratios in groups P2 and P3 were necessary to maintain the activator/ash ratio (Ac/FA) at a constant value of 0.75.  
Each mixture was statically compacted inside a cylindrical mould 37 mm in diameter and with a height of 70 mm (EN 13286-53 [42]). After demoulding, they were stored under 85 \({}^{\circ}\)C and 20% relative humidity and tested after 24 h. The uniaxial compressive strengh (UCS) tests were carried out under monotonic displacement control at a rate of 0.2 mm/min. The results represent the average of three tested specimens.  
The original FA and MT, as well as selected pastes, were characterised using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The FA and MT were also submitted to thermogravimetric analysis (TGA) by exposure to gradually increasing temperatures.