esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Age and Gender Recognition Using Ensemble Learning
## 3 Dataset and Methods
### Feature Extraction  
Extracting relevant features from the speech signal is one of the most critical steps that affects the performance of recognition systems. As a result of this process, speech signals are converted from their natural waveforms into a more compact parametric representation [7]. Various features can be used to recognize the age and gender of a speaker from the speech signal. However, mel-frequency cepstral coefficients (MFCCs) are the most effective speech feature type and were also used in this study. The feature extraction process of MFCCs includes many steps. The first two steps are framing and windowing. In the framing, the signal is divided into short segments that are assumed to be stationary. At this stage, an overlap area is created between two frames to prevent information loss between adjacent frames. The typical window length is 20-30 ms, and the overlap rate is 50%. In the windowing step, a window function is applied to each frame to reduce the discontinuity at the edges of the frames. In speech processing studies, the Hamming or Hamming window function is generally used. In the next step, after calculating the magnitude spectrum of the signal by applying DFT to each frame, it was converted into a mel spectrum by passing it  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Class ID** & **Class Label** & **Class Definition** & **Age Range** & **Gender** \\ \hline \#1 & F20s & Twenties female & 20–29 & Female \\ \hline \#2 & M20s & Twenties male & 20–29 & Male \\ \hline \#3 & F30s & Thirties female & 30–39 & Female \\ \hline \#4 & M30s & Thirties male & 30–39 & Male \\ \hline \#5 & F40s & Forties female & 40–49 & Female \\ \hline \#6 & M40s & Forties male & 40–49 & Male \\ \hline \#7 & F50s & Fifties female & 50–59 & Female \\ \hline \#8 & M50s & Fifties male & 50–59 & Male \\ \hline \#9 & F60s & Sixties female & 60–69 & Female \\ \hline \#10 & M60s & Sixties male & 60–69 & Male \\ \hline \#11 & F70s & Seventies female & 70–79 & Female \\ \hline \#12 & F80s & Eighties female & 80–89 & Female \\ \hline \hline \end{tabular}
\end{table}
Table 1: The class definitions used in the study.  
through a filter bank based on the mel scale, which represents the frequency perceived by the human ear [24]. Finally, after taking the logarithm of the mel spectrogram, the inverse cosine transformation was applied to obtain MFCCs. Thus, an acoustic vector representing each speech signal was obtained. In this study, Python's \"Librosa\" package was used with the following parameters to calculate MFCC features: number of MFCCs: 40, sampling rate: 2050, window length: 2048, window function: Hanning, and hop length: 512.
## 6 State of Art in Control and Automation in Distribution Networks
### Control and Automation at the Low-Voltage Level (LV)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Type** & **Implementation** & **Impacts** & **Limitations** \\ \hline \multirow{4}{*}{[121]} & Power flow management & Employment of series voltage source converters for load balancing in medium voltage distribution network. The control is based on a modified synchronous reference frame, and it can employed to mitigate congestion (Test distribution feeders). & Increases the capacity in feeders without grid reconfiguration, and more DG can be employed to mitigate congestion (Test bench and simulation). & \\  & & Grid-forming converters are utilized where reactive power is injected at affected nodes to regulate voltage within nominal values. This control is made of two layers, the cybernetic and its adaptable to the physical layer. The model is implemented in Typhoon HIL control) & Higgate voltage fluctuation caused by increased DER, and it is adaptable to implementation on any distribution network (Simulation). & IEC61850 protocol not considered \\ \cline{2-5}  & Modular multilevel & types of faults: inter-pole faults and single grounding faults. & This new model has been demonstrated to be dependable and conservative and can assist in grid planning and equipment selection (Simulation). & Complex multi-terminal DC distribution networks were not investigated. This method can flexibly transform the network topology and has a much faster calculation speed than simulation \\ \hline \multirow{4}{*}{[122]} & [PERSON]’s maximum spanning tree algorithm (optimal feeder reconfiguration) & [PERSON]’s maximum spanning tree algorithm & [PERSON]’s algorithm is employed to compute, obtain, or derive an optimal radial network (optimal maximal spanning tree) that provides improved voltage stability and the highest loss & It can quickly compute the optimal radial configuration that mitigates power losses and provides supreme voltage stability with load fluctuations (Simulation). & It is slow to converge when the topology changes, which could introduce erratic choices. \\  & & & \\ \cline{2-5}  & Power system status estimation (PSSE) using & This utilizes the combination of PMU and PSSE by initially combining data before and after the fault with PSSE. The goal of incorporating the PMU in the PSSE problem is to estimate the voltage and current amounts of the branch point and across the whole network once a failure occurs. For each network portion, branch node quantities are computed using the PMU and the governing equations of the line model, and the problematic part is identified based on a comparison of the obtained values. & The benefits are simplicity and step-by-step implementation, applied to different faults such as short circuits (Simulation). & Power system state estimation is heavily subject to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Control, automation, impacts, and limitations.
## 3 The Proposed Coyote Optimization Algorithm
### Improved Coyote Optimization Algorithm
#### 3.2.1 The First Modification  
In the first modification, we focus on the function of Equation (15) with intent to produce new solutions with higher quality. As seen from Equation (15), COA has employed a center solution \(Sol_{center,p}\) for each pack \(p\) to update new solutions because in natural behavior of coyote, a center tendency or a center social condition of all coyotes can lead to a better new social condition for each coyote in the flock. Each coyote can find new social conditions more effectively by using the center social condition from the whole population. However, center variables are impossible to form a promising solution. Furthermore, the use of the center variables, which are in charge of producing new solutions in Equation (15), is completely ineffective. Center variables included in one solution, and a center social condition are totally different in the nature phenomenon and in math. However, the method was successful in [56] in dealing with benchmark optimization functions, because many functions have optimal solutions with \"0 value\" variables. It should be noted that the \"0 value\" solutions are in the middle of the lower bound and upper bound of variables. Unintentionally, center solution is very useful for finding \"0 value\" solutions. Unlikely, the manner is not useful for the ORPD problem, because the optimal solutions of the ORPD problem are not around the center ones. Consequently, we suggest the model in Equation (15) should be changed by replacing center solution with a global best solution, which has been found so far. The search is performed as follows:  
\[Sol_{d,p}^{new}=Sol_{d,p}+\mu_{1}.(Sol_{best,p}-Sol_{r1,p})+\mu_{2}.(Sol_{ Gbest}-Sol_{r2,p}) \tag{23}\]  
The equation can take advantage of the information exchange between current solutions and the so-far best solution. By using the modification, COA can take several advantages as follows:  
1. Reduce determination of \(N_{pack}\) center solutions for \(N_{pack}\): The task of finding center solutions uses high simulation time, because it is comprised of many steps such as ranking each variable for the whole population and determining center variables from result of rank. The advantage is highly significant for large scale problem such as the ORPD problem due to high number of control variables and high population size.
2. Many new solutions with high quality are found: The superiority can reduce the number of iterations, reducing the number of iterations, but the best optimal solution is still much more effective than that found by origin COA method.  
The performance of the first modification is investigated by comparing the original COA and COA with the first modification, called COA1.
# Reliability Analysis of Small-Sample Failure Data for Random Truncation High-Voltage Relay
## 2 Principle of Random Truncation Small-Sample Reliability Analysis
### Model Hypothesis Testing
#### 2.4.2 D-Test  
To test the Weibull distribution hypothesis and the fitting effect of the distribution, the d-test is required. The d-test, also known as the Kolmogorov test, arranges \(p\) fault data in ascending order, calculates the distribution function \(\hat{F}(t_{i})\) corresponding to each data \(t_{i}\) based on parameter estimation, and compares \(\hat{F}(t_{i})\) with the empirical distribution function \(M(t_{i})\). The d-test statistic is as follows:  
\[D_{p}=\sup_{-\infty<x<+\infty}\left|\hat{F}(t_{i})-M(t_{i})\right|=\max\{d_{i}\} \tag{10}\]  
We compare \(D_{p}\) with the critical value \(D_{p,a}\) and accept the original hypothesis if \(D_{p}<D_{p,a}\). Otherwise, we reject the original hypothesis [23]. \(D_{p,a}\) can be obtained via the d-test statistical table.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 3 Cloud Storage System Model Based on Erasure Code
### Data Encoding Storage and Recovery
#### 3.2.1 Source Matrix (Cauchy Matrix) \(C\)  
The definition of a Cauchy matrix is as follows.  
Given two disjoint sets \(X=\{x_{1},x_{2},\ldots,x_{k+m}\}\) and \(Y=\{y_{1},y_{2},\ldots,y_{k}\}\), the role of \(X\) and \(Y\) is to generate encoding coefficients in the encoding system, and the element \(C_{i,j}\) of the Cauchy matrix \(C\) can be described by Equation (1):  
\[C_{i,j}=\frac{1}{x_{i}-y_{i}} \tag{1}\]  
In Equation (1), \(x_{i}\) and \(y_{i}\) are elements in the finite field \(GF(2^{w})\). During the encoding process, \(x_{i}\) is the ID of storage nodes in the cloud system, and \(y_{i}\) is the ID of the original data blocks, where \(x_{i}\
eq y_{i}\). The constructed source matrix \(C_{(k+m,k)}\) is shown in Equation (2):  
\[C_{(k+m,k)}=\left[\begin{array}{cccc}\frac{1}{x_{1}-y_{1}}&\frac{1}{x_{1}-y_ {2}}&\cdots&\frac{1}{x_{1}-y_{k}}\\ \frac{1}{x_{2}-y_{1}}&\frac{1}{x_{2}-y_{2}}&\cdots&\frac{1}{x_{2}-y_{k}}\\ \vdots&\vdots&\ddots&\vdots\\ \frac{1}{x_{k+m}-y_{1}}&\frac{1}{x_{k+m}-y_{2}}&\cdots&\frac{1}{x_{k+m}-y_{k}} \end{array}\right] \tag{2}\]  
An important property of the Cauchy matrix is that any submatrix is invertible, which makes it highly fault-tolerant in erasure codes.
## 3 Results  
### Material Contants of AAA  
To prove the reliability of the simulation, the experimental and simulation results were compared and validated, as shown in Figure 5. The material constants of the Holzapfel model used in this simulation were calculated based on the comparative study between the experimental and simulation results. [PERSON] et al. [21] performed a biaxial tensile test on NAA and AAA specimens at a strain rate of 12 mm/min, and [PERSON] et al. [22] tested AAA specimens at a strain rate of 3 mm/min. First, the ratio of the tensile stress and tensile stretch of the NAA and AAA tissues was calculated using the results of the biaxial tensile test under a 1:1 tension ratio conducted by [PERSON] et al. [21]. This computed ratio was then substituted for the results of the biaxial tensile test of the AAA tissue performed by [PERSON] et al. [22]. Consequently, the stress-stretch curves in the circumferential and axial directions of the NAA and AAA tissues under the same strain rates were derived and calculated from the simulation results, and were compared with the experimental results, as shown in Figure 5. The material constants used are reported in Table 1.  
The arterial tissue has various tensile characteristics depending on the loading conditions, because they are composed of three layers with different fiber directions according to the layer. Moreover, as the aneurysm progresses, the arteries harden; hence, the tensile stress tends to increase, and the stretch decreases. Consequently, the tensile stress value in the sample on the circumferentially oriented strip of AAA was 51.4 kPa, which was 63.8% and 58.4% higher than those in the samples on the axially oriented and circumferentially oriented strips of NAA, respectively. In contrast, the tensile stretch values in the sample  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{C_{10}}\) (MPa) & \(\mathbf{K_{1}}\) (MPa) & \(\mathbf{K_{2}}\) & \(\mathbf{\kappa}\) \\ \hline AAA & 0.03 & 150 & 250 & 0.261 \\ NAA & 0.1 & 20 & 61 & 0.226 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material constants.  
Figure 5: Stress–stretch curve of human abdominal aorta tissue from biaxial tensile test and simulations.  
on the AAA strip were both 1.15, which was 7.3% lower than in the sample on the NAA strip. Therefore, arterial tissue appears to have different tensile characteristics depending on the aneurysm progression and fiber orientation. In addition, the correlation between the simulation and experimental results demonstrated error rates (1\(-\) (experimental results/simulation results)) of 0.002 and 0.02 in the axial and circumferential directions in the AAA tissue, respectively. In the NAA tissue, the axial direction error rate was confirmed to be 0.02.
## 3 Methodology
### Data Extraction  
Prior to data filtering, a process cleaned and normalized the claims' text by removing non-standard Unicode (UTF8) characters and converting them to lowercase. The Boolean search used domain-specific keywords to target hydrogen fuel use in aviation. The \"AND\" keyword set--[\"hydrogen\", \"fuel\"]--focused on core technologies. The \"OR\" keyword set--[\"aircraft\", \"aviation\", \"eVTOL\"]--narrowed the scope to aerospace applications. The search covered 2018 to 2024, marking the emergence and acceleration of hydrogen aviation patents. Table 1 lists the search commands and the hit counts.  
Figure 2 displays the GP results. The period includes pre-pandemic and post-pandemic years. Figure 2a shows the country-level patent totals; China and the USPTO dominate with 607 and 188 patents, respectively. The EPO, Japan, and South Korea follow. Figure 2b shows the annual growth, from just seven patents in 2018 to 271 in 2024. The R\({}^{2}\) value of 0.98 confirms a strong linear growth trend, predicting accelerating innovation in hydrogen-powered aviation.  
Figure 3a presents the WIPO results by the patent office.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Engine** & **Search Command** & **Hits** \\ \hline \multirow{3}{*}{Google} & CL = (hydrogen AND fuel AND (aircraft OR aviation OR eVTOL)) & \multirow{3}{*}{892} \\  & before:priority:20241231 & \\  & after:priority:20180101 status:GRANT language:ENGLISH type:PATENT & \\ \hline \multirow{3}{*}{WIPO} & EN\_CL:((hydrogen AND fuel) AND (aircraft OR aviation OR eVTOL)) AND & \multirow{3}{*}{965} \\  & DP:[2018 TO 2024] & \\ \hline \multirow{3}{*}{USPTO} & AND keywords = [’hydrogen’, ’fuel’] & 4159 \\  & OR keywords = [’aircraft’, ‘aviation’, ‘eVTOL’] & 257 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search engines, their tailored search commands, and the results.  
Figure 2: Google Patents results by (**a**) patent office country and (**b**) year with regression forecast.  
PCT represents international applications under the Patent Cooperation Treaty, while EPO refers to filings at the European Patent Office. WIPO data cover only countries that share their patent records. It fairly represents the USPTO, PCT, and EPO filings, but underrepresents countries like China. The USPTO hydrogen aviation patents accounted for more than 45% of those patents in the WIPO dataset.  
Figure 3b shows the USPTO assignee countries. The U.S. leads with 102 patents, followed by France (38) and the UK (15). The data revealed that more than 38% of the USPTO patents were assigned to non-U.S. entities, indicating strong international interest in protecting hydrogen aviation innovations in the U.S. market. This pattern suggests foreign applicants reserve their highest-value inventions for U.S. filings. As WIPO data emphasize USPTO coverage, and as USPTO data include many key foreign filings, the USPTO dataset serves as a strong proxy for understanding global patent trends in this domain. Therefore, the rest of the workflow focused on the USPTO claims data for subsequent classification, thematic, and quantitative analysis.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 2 Application Scenarios
### UAV-Mounted Base Stations  
An UAV-mounted BS is a flying vehicle equipped with one or more different types of payloads, aimed at providing radio access to ground users (Figure 1). Flying BSs can be used in non-accessibleareas or in all those situations where temporary, unexpected, and critical events require additional communication resources. UAV-based BSs with caching capabilities can also provide download links to ground users in content delivery applications, thus significantly decreasing terrestrial backhaul demands.  
The coverage area supported by an UAV-mounted BS is a function of many parameters [1; 39; 40; 41], such as, the type of RAN, the transmission power (depending on large- and small-scale fading of the channel), the antenna characteristics, the environment, the user density, and the UAV characteristics (i.e., flying altitude, battery lifetime, hovering time duration, storage size, and processing capabilities). In particular, _multi-tier_ U-RANs serving the same area by several cells of different sizes can be employed to accommodate large traffic volumes while keeping high quality of service (QoS) and good coverage [10; 42; 43].  
With respect to conventional RANs, the use of flying BSs allows one to better serve ground users with different requirements in terms of mobility, data rate, delay sensitivity, and reliability. Furthermore, they can be used to easily integrate recently developed communication technologies, such as free space optical (FSO) or millimeter-wave (mmWave) transmissions, which can achieve high data rate, assuring low spectrum costs [44; 45; 46].
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
## 4 Dead Zone Compensation Method of PV Inverter
### Modeling of the Compensation Voltage  
The photovoltaic inverter's output current amplitude is affected by light, temperature, and other factors. The required modulation voltage under various current amplitudes is also different. Harmonic suppression technology must be able to adapt to a change in environment. This section analyzes the harmonic emission level and the error voltage variation under different working conditions. This paper divides the segmentation interval with the measured data and proposes a segmentation adaptive harmonic suppression method.  
Figure 11 shows the actual measurement data curve of grid voltage and A-phase current waveform from a user's PV grid-connected inverter in Taian, Shandong Province on 10 August 2022. The data recorded the distribution range of photovoltaic current for a whole day. Data acquisition is completed with the help of the NI data acquisition board. The equipment records 25,600 data points every second.  
From the above figure, the photovoltaic works from 5:30 a.m. to 6:00 p.m. Its grid-connected current fluctuation range is mainly between 13 A and 41 A. Within this current range, the current trailing effect endures only for quite a short time, and the parasitic capacitor charges or discharges very quickly. The voltage error can be approximately equivalent to a square wave with a width of \(T_{\mathrm{d}}\). An important objective of this paper is to make the dead zone error voltage model adapt to more operating conditions especially, for  
Figure 11: Photovoltaic data curve in one day.  
small current working conditions in bad weather. Figure 12 shows the influence of current trailing to error voltage under different current conditions.  
From the above figures, the actual nonideal voltage is no longer a fixed error if the effect of current trailing is considered. The actual dead zone under \(I_{\mathrm{ac}}\) is exactly fifty percent of the set value. The relationship between error voltage and the grid-connected current of the PV inverter is positive. A larger current amplitude makes the error voltage closer to the square wave. Instead, a smaller current amplitude makes the error voltage closer to zero. Therefore, assuming a linear relationship between the discharge speed of parasitic capacitance and the size of the grid-connected current, the error voltage can be modeled as follows:  
\[\left\{\begin{array}{l}U_{\mathrm{dead}}=[U_{\mathrm{dc}}T_{\mathrm{d}}-f( I_{\mathrm{a}})]\mathrm{sign}(I_{\mathrm{a}})\\ f(I_{\mathrm{a}})=\left\{\begin{array}{ll}T_{\mathrm{e}}U_{\mathrm{dc}}&I_{ \mathrm{a}}>I_{\mathrm{ac}}\\ \frac{(2T_{\mathrm{d}}-K_{\mathrm{a}})I_{\mathrm{dc}}}{2}&I_{\mathrm{a}}<I_{ \mathrm{ac}}\\ T_{\mathrm{e}}=\frac{U_{\mathrm{dc}}}{KI_{\mathrm{a}}}\end{array}\right. \tag{7}\]  
where \(U_{\mathrm{dead}}\) represents actual error voltage caused by the dead zone, \(K\) represents slope coefficient, and \(T_{\mathrm{e}}\) represents the end time of the current trailing.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
###### Abstract  
The Internet of Things (IoT) and its applications in industrial settings are set to bring in the fourth industrial revolution. The industrial environment consisting of high profile manufacturing plants and a variety of equipment is inherently characterized by high reflectiveness, causing significant multi-path components that affect the propagation of wireless communications--a challenge among others that needs to be resolved. This paper provides a detailed insight into Narrow-Band IoT (NB-IoT), Industrial IoT (IIoT), and Wireless Sensor Networks (WSN) within the context of indoor industrial environments. It presents the applications of NB-IoT for industrial settings, such as the challenges associated with these applications. Furthermore, future research directions were put forth in the areas of NB-IoT network management using self-organizing network (SON) technology, edge computing for scalability enhancement, security in NB-IoT generated data, and proposing a suitable propagation model for reliable wireless communications.  
Narrow-Band IoT; Internet of Things; Industrial IoT; wireless communication; latency; self-organizing network (SON); edge computing; long term evolution (LTE) +
Footnote †: journal: Sensors