esa-sceva/satcom-chunk-collection huggingface dataset:
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 5 Conclusions
\begin{tabular}{l l l l l} \hline \hline
**Female** & & & & \\ \hline
**Parameter** & **Features** & **Mel Spectrogram** & **log-Mel Spectrogram** & **MFCCs** & **MFCCs + \(\Delta\)MFCCs** \\ \hline Model 1: ResNet-50 & & & & \\ \hline Learning rate & 0.0001 & 0.0005 & 0.0007 & 0.002 \\ \hline Batch size & 64 & 128 & 64 & 128 \\ \hline Epochs & 100 & 70 & 100 & 50 \\ \hline Optimizer & Adam & Adam & Adam & Adam \\ \hline Model 3: 2D-CNN & & & & \\ \hline Learning rate & 0.001 & 0.0001 & 0.0005 & 0.001 \\ \hline Batch size & \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Female** & & & & & \\ \hline \hline
**Deep Learning Model** & **Input Feature** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{6}{*}{ResNet-50} & Mel spectrogram & 56.28 & 55.57 & 55.24 & 55.31 \\ \cline{2-6}  & log-Mel spectrogram & 79.64 & 78.63 & 78.68 & 78.45 \\ \cline{2-6}  & **MFCCs** & **80.34** & **79.90** & **79.87** & **79.64** \\ \cline{2-6}  & MFCCs + AMFCCs & 78.70 & 78.42 & 78.39 & 78.19 \\ \hline \multirow{6}{*}{1D-CNN} & Mel spectrogram & 48.18 & 49.19 & 47.66 & 48.47 \\ \cline{2-6}  & log-Mel spectrogram & 71.30 & 71.20 & 70.86 & 70.89 \\ \cline{2-6}  & MFCCs & 80.84 & 81.02 & 80.84 & 80.78 \\ \cline{2-6}  & **MFCCs + AMFCCs** & **81.92** & **82.17** & **81.85** & **81.95** \\ \hline \multirow{6}{*}{2D-CNN} & Mel spectrogram & **81.92** & **82.17** & **81.85** & **81.95** \\ \cline{2-6}  & Mel spectrogram & 39.51 & 32.68 & 30.18 & 32.15 \\ \cline{2-6}  & log-Mel spectrogram & 78.50 & 78.11 & 78.08 & 77.91 \\ \cline{2-6}  & **MFCCs** & **81.35** & **81.26** & **81.18** & **81.07** \\ \cline{2-6}  & MFCCs + AMFCCs & 79.92 & 79.60 & 79.35 & 79.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Age prediction performance of different input features by model in the female dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Female** & & & & \\ \hline
**Input Feature** & **Deep Learning Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{6}{*}{MFCCs} & ResNet-50 & 80.34 & 79.90 & 79.87 & 79.64 \\ \cline{2-5}  & 1D-CNN & 80.84 & 81.02 & 80.84 & 80.78 \\ \cline{2-5}  & **2D-CNN** & **81.35** & **81.26** & **81.18** & **81.07** \\ \cline{2-5}  & ViT & 78.53 & 78.67 & 78.41 & 78.43 \\ \hline \multirow{6}{*}{MFCCs} & ResNet-50 & 78.70 & 78.42 & 78.39 & 78.19 \\ \cline{2-5}  & **1D-CNN** & **81.92** & **82.17** & **81.85** & **81.95** \\ \cline{2-5}  & 2D-CNN & 79.92 & 79.60 & 79.35 & 79.33 \\ \cline{2-5}  & ViT & 77.74 & 78.05 & 77.74 & 77.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 2 AI-Based QoT and Impairment Modeling
### AI-Based Impairment Modeling
#### 2.3.1 Nonlinear Noise  
For the nonlinear effect modeling, sophisticated analytical models such as the SSFM [6] can provide accurate estimations. However, these methods also result in a long computation time. Although approximate models can calculate much more quickly [53], they cannot guarantee the accuracy in all scenarios, thus leading to a high design margin and an inefficient utilization of network resources [3]. In [5], a combination of analytical models and ML methods is proposed to reach a higher accuracy for nonlinear noise estimation.
## 4 Experimental Tests
### Verification of Displacement Estimation Using Model Structures
Since the relative standard deviations of radial distortion parameter on the \(Y\) axis, and tangential distortion on the \(X\) and \(Y\) axes show relatively large, as shown in Figure 5, the weights on the three distortion parameters are set to be 2.5 to enlarge the searching range. Table 2 shows the translational and rotational displacement results using the camera-intrinsic parameter adjusted by applying GA in the calculation of the 6-DOF displacement. The experimental test without GA has been performed with intrinsic parameters calculated by 40 captured images, using a checkerboard shown in Figure 4f. The table includes error of 6-DOF displacements  
Figure 6: Experimental setup (**a**) port facilities for displacement measurement; (**b**) model structures and the motion stage. Figure 7: Graphic User Interface for estimating 6-DOF displacement. calculated based on ten different GA parameters and actual movement. The results show that the estimated displacements with the compensated camera-intrinsic parameters show better performance in both the translational and rotational displacements estimation. In the design standard for port and harbor structures [31; 32; 33], the maximum allowable horizontal displacement at the functional performance level is 100 mm. Considering the acceptable measurement tolerance, the proposed method with the RMSE of less than 3 mm and 1\({}^{\circ}\) for translational and rotation displacements, respectively, can be applied to the port structures to monitor the structural condition.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 3 Experimental Results  
In this experiment, the Butterworth filter and Chebyshev Type I filter were used as design prototypes to optimize low-pass, high-pass, and band-pass filters that meet the characteristics, and white noise was randomly generated and filtered through the filter. After calculating the SNR before and after filtering, it is confirmed that the design parameters optimized through the MOPSO algorithm can effectively improve the filter performance.  
Figure 2: Design system process diagram of MOPSO algorithm applied to IIR Filter.  
Results  
Figures 3 and 4 are three filter types designed based on Butterworth and Chebyshev Type I characteristics. One has no passband oscillation, and the other has passband oscillation. However, with the filter, practical considerations will cause the passband to os-climate between 0 and 1 (dB) and speed up the roll-off. Figure 5 shows Chebyshev Type I Filter Passband Ripple Enlarged View.  
Figure 4: (**a**) Design of Chebyshev Type I low–pass filter. (**b**) Design of Chebyshev Type I high–pass filter. (**c**) Design of Chebyshev Type I band–pass filter.  
The difference in the responses can be seen in Figure 6; although the low-pass filters designed using the same design parameters look similar in the filter transition bandwidth, the roll-off decreases the speed. The descent speed of Upper Chebyshev I is slightly faster than Butterworth's. To make the roll-off speed faster, one can also re-adjust the parameters of the particle swarm to achieve a faster roll-off, but this will cause the problem of large passband oscillation. In practical applications, most people still hope to have a smooth and smooth behavior.  
Figure 5: **(a)** Chebyshev Type I low–pass filter passband ripple enlarged view. **(b)** Chebyshev Type I high–pass filter passband ripple enlarged view. **(c)** Chebyshev Type I band–pass filter passband ripple enlarged view.  
stable passband, especially when processing audio. They hope the passband will be stable to maintain signal stability and complete noise filtering.  
Table 2 randomly generates a section of white noise and filters it through the designed filter. The signal-to-noise ratio is calculated before and after filtering to ensure the designed filter can effectively filter.
## 2 Materials and Methods
### Preparation of the Total RNA and Microarray Analysis  
Total RNA was prepared from deeply frozen yeast cell pellet samples using the hot acidic phenol procedure [17]. The quantity and quality of the RNA preparations were tested with the 2100 Bioanalyzer expert assay RNA Nano (Agilent Technologies, Santa Clara, CA, USA). To identify changes that accumulated throughout the adaptive phase of the continuous culture, comparative microarray hybridizations were performed using the samples after 40 and after 240 generations. Each comparison of the transcriptomes was performed in three biological replicates, each with two technical replicates with a dye-swap. Yeast (v2) Gene Expression \(8\times 15\) K Microarray slides containing oligonucleotides representing almost all known _S. cerevisiae_ genes identified in the SGD were used (Agilent Technologies). Cy3 or Cy5 fluorescently labeled cRNA probes were synthesized using the Agilent Quick Amp Two-Color Labeling Kit according to the manufacturer's protocol, with total RNA samples as the templates. The probes were hybridized concurrently to microarrays, and the resulting fluorescence images were scanned with the Axon GenePix 4000B (Molecular Devices, San Jose, CA, USA) microarray scanner. Feature extraction was accomplished with GenePix Pro 6.1. Raw data were normalized, averaged, and subjected to statistical analysis with Acuity 4.0 software. Additional data manipulations were performed in Microsoft Excel. For every gene, the value of LogRatio expressing over or underrepresentation of its transcript in cells grown for 240 generations relative to 40 generations was calculated. A gene was considered to be differentially expressed between the evolved and original strains if its transcript level differed between the two strains by at least 1.87-fold (LogRatio \(\geq\) 0.9), and the probability of such a difference by chance was less than 0.05 (\(p<0.05\)). The resulting lists of differentially expressed genes (DEGs) (see also microarray analysis results in Table S3) were subjected to further bioinformatics analysis. Complete transcriptome analysis data are deposited in Gene Expression Omnibus ([[https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)]([https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/))) under accession number: GSE162203.
## 2 System Model
If the user is different in any dimension, the signal can  
Figure 1: Simplified model of a LEO satellite’s cellular beam layout. Figure 2: Frequency allocation of the MHSTFC-UD model. be easily separated. Therefore, the uplink signal received by the antenna pointing to the central area on the LEO satellite is the user's desired signal from the central area and the weak interference signal of the same frequency from the adjacent beam cell. The signal received by the antenna pointing to the edge area on the LEO satellite is the user's desired signal from the edge area and the weak interference signal of the same frequency from the central area of the adjacent beam cell.
# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
## 2 Cross-Media Communication Model
### Water-Air Interface  
A sound wave enters the air from the water. Reflection and transmission will occur at the interface due to the large difference in the characteristic impedance between the air and water. The reflection and transmission at the interface will cause a vibration at the water-air interface, and the particle interference at the incident point of the sound pressure that excites the WSAW propagates laterally on the water's surface.  
The incident sound pressure is \(p_{i}\), the incident angle is \(0^{\circ}\), and the angular frequency is \(\omega\); the initial position change of the free water surface can be expressed as [18]:  
\[\delta(x)=\frac{2p_{i}}{\omega\rho c_{w}}\cos(kx),(t=0,z=0) \tag{1}\]  
where \(\rho\) is the density of water; \(c_{w}\) represents the speed of sound propagation in the water; \(k\) is the wave number of the WSAW, which is obtained from the dispersion relationship; and \(\alpha\) is the attenuation coefficient in water, which can be expressed as [23]:  
\[\alpha=\frac{4\eta k^{2}\sqrt{kg+\sigma^{\prime}k^{3}/\rho}}{(g+3\sigma^{ \prime}k^{2}/\rho)} \tag{2}\]  
where \(\eta\) is the water viscosity of the water medium; \(\sigma^{\prime}=72.75\times 10^{-3}\) N/m is the water surface tension at 25 \({}^{\circ}\)C; and \(g=9.8066\) m/s\({}^{2}\) is the acceleration of gravity.  
The choice of the horizontal axis in Equation (1) does not affect the form of the harmonic of the WSAW. Therefore, a 3D mathematical model of the WSAW is obtained by extending Equation (1):  
\[\delta(x,y,t)=\frac{2p_{i}}{\omega\rho c_{w}}e^{-\kappa\sqrt{x^{2}+y^{2}}}\cos \left(k\sqrt{x^{2}+y^{2}}-\omega t\right) \tag{3}\]  
where \(x\) and \(y\) are two horizontal axes.
# Wind-Induced Dynamic Performance Evaluation of Tall Buildings Considering Future Wind Climate
## 3 Fragility Analysis for Human Occupant Comfort
### Fragility Analysis Results and Discussions
Figure 12: Probability density function of peak acceleration responses of the building for 50-year return period under: **(a)** historical; **(b)** mid-future; **(c)** late-future. Figure 13: Probability density function of peak acceleration responses of the building for 100-year return period under: (**a**) historical; (**b**) mid-future; (**c**) late-future. Table 9 also presents the failure probability of the building under different wind-loading scenarios. For the 10-year return period, the failure probability remains at 0% across all scenarios, indicating that the building is expected to perform adequately under frequent wind events. However, for the 50-year return period, we observe a significant increase in failure probability from 25.50% in historical conditions to 65% in the late-future scenario, highlighting a substantial risk as wind conditions become more severe. Likewise, for the 100-year return period, the failure probability escalates drastically from 58.50% historically to 95% in late-future conditions. The fragility curve against occupant comfort criteria obtained for the case study building is shown in Figure 14. The curve also includes the failure probability values under different wind scenarios, which shows that the failure probability increases significantly from historical to future under 50-year and 100-year wind. The mean values of peak acceleration and mean wind speed relationships, along with occupant comfort levels, are presented in Figure 15. These results highlight a clear upward trend in acceleration responses and associated failure probability under projected future wind conditions as future wind conditions become increasingly severe due to climate change. This suggests that buildings designed to meet current safety standards may become inadequate as wind speeds continue to rise due to climate change. Figure 14: Fragility curve for human occupant comfort. Figure 15: Mean maximum acceleration corresponding to mean wind speed.
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 3 Study of the Receiver Criticalities and Workflow for the Upgrading Feasibility Study
### Mitigation of the Self-Produced RFIs
#### 3.2.1 Mitigation of the RFIs Generated by the Electronic Control System
Concerning the L-band, the GAIA board emits signals at 1125 and 1375 MHz (see Figure 11b) with low level of amplitudes [53]. These signals do not represent a problem for the receiver, because they are located in the edge of the receiver bandwidth and, in the case of the signal at 1375 MHz, in a zone populated by other RFIs from Italian army radars. In Figure 11a,b, it is possible to see several signals from the surrounding area, such as FM radio signals (88-108 MHz), Radio TV broadcasting signals (470-828 MHz), mobile communications signals (940-960 MHz and 1810-1880 MHz [48; 49]), which have nothing to do with the GAIA board. In fact, these signals occur both when the board is powered on (blue curve) and when the board is powered off (red curve). On the basis of these results, it can be stated that the GAIA board is suitable for the remote control system of a sensitive radio astronomy receiver, such as our L-P band cryogenic receiver.
# Two-Parameter Probabilistic Model and Experimental Research on Micron Particle Deposition
## 3 Results and Discussion
### Simulation Results and Test Results  
#### 3.3.1 Particle Concentration Distribution of Simulation Results  
Figure 6 is the cloud map of particle concentration distribution in a symmetrical section of the gas transmission pipeline obtained by a simulation of the two-parameter particle probability deposition model when the wind speed is 12 m/s. When the particle size is 10 \(\upmu\)m, the particles fill the pipe under the action of airflow, and there is a high concentration area near the axis of the pipe, as shown in Figure 6a; when the particle size is 40 \(\upmu\)m, the particles show gravity settling and inertia settling, and the high concentration area of the particles gradually deviates from the axis of the pipe along the length of the pipe and tends to the lower wall area, as shown in Figure 6d.  
In order to better observe the variation in high particle concentration area along the pipe diameter direction, a vertical section extraction was carried out at 15 m (Y= 25 D) along the pipe length for Figure 6a and d, respectively. Among them, when the particle size is 10 \(\upmu\)m, the concentration distribution in the pipeline is mainly concentrated near the center of the cross-section, and the high-concentration area is approximately rectangular. Under the complex flow pattern near the wall, the particles are deviated by turbulence and irregular fluid motion inertia, and present a partial high-concentration distribution, as shown in Figure 6b. However, the particles with a particle size of 40 \(\upmu\)m are obviously affected by gravity, the particle concentration on the upper wall surface decreases obviously, the concentration distribution area on both side walls also tends to decrease gradually towards the lower wall surface with the increase in particle size, and the high-concentration area deviates from the center of the cross-section and moves downward, as shown in Figure 6d.  
To observe the low-velocity region of the corner of the square tube, the angle between the left side wall and the lower wall in Figure 6b,e is extracted by magnification. When particles with a particle size of 10 \(\upmu\)m migrate in the gas transmission pipeline, there are obvious low particle concentration areas at the corners of the pipe wall. This is mainly due to the difference in fluid viscosity and laminar velocity of the pipe wall affecting the lower-speed angular flow region. Due to small inertial force, particles have difficulty breaking through due to inertia. Turbulence in low-velocity region hinders entry into the corner low-velocity region, as shown in Figure 6c; particles with a particle size of 40 \(\upmu\)m settle down obviously from inertia, and some particles enter the corner low-velocity region by inertia force outside turbulence direction, as shown in Figure 6f.
## 1 Introduction  
### Carbon-Based Materials  
Carbon nanomaterials are classified as zero-dimensional (fullerene), one-dimensional (carbon nanotubes, CNTs) or two-dimensional (graphene). The main advantages of CNTs (their high stiffness, flexibility and tensile strength, along with their thermal and electrical conductivity) and graphene (which has extraordinary mechanical, electronic, and conductive properties coupled with a high surface area) make them highly promising for a wide range of applications from energy storage to optoelectronics to biomedical devices. Many of the distinctive properties of carbon nanomaterials derive from the fact that all carbon nanomaterials are made of sp\({}^{2}\) hybridized carbon [1].  
These carbon-based materials have attracted considerable interest as potential nanofillers for the fabrication of carbon-based nanocomposites. However, their prime drawback is their tendency to agglomerate, which makes them much less effective in nanocomposites [2]. Several methods have been put forward to achieve good, stable dispersion. One such technique is ultrasonically assisted polymerization in the presence of nanoparticles [3; 4; 5; 6; 7].
## 4 Southern Cluster's BUC, Products, and KPIs
### Description of the Business Use Case  
The goal of this Business Use Case is to prevent the power system from entering risky topological or operational states by improving management in extreme weather conditions through algorithms on predictive maintenance and increased predictions of storms and racing. Additionally, the objectives of this Business Use Case are:  
* Outage management and predictive maintenance. * Improved management of extreme weather conditions. * Optimization of outage management for better system performance. * Prompt notification of potentially dangerous power system topologies and regimes. * Preventing damage from extreme weather-related events. Units of distributed generation, the OHLs of the DSO, TSO, and microgrids will all be included in the F-channel application that will be created. To prevent the power system from entering risky topological or operational states, algorithms on predictive maintenance with improved forecasts in storms and icing will be created. Additionally, for the optimum outage management of the grid (DSOs or microgrids), better forecasts from increased spatial resolution and AI techniques together with the early warning on a potentially dangerous power system topology and prevention of damage brought on by extreme weather conditions will be implemented. The following are the primary anticipated advantages and features of this particular business case:  
* Predictive maintenance for storms and icing in local microgrids, DSO grids, and TSO grids. * The outage management of the grid (DSOs, TSOs, or micro-grids). In Tables 2 and 3, there are the KPIs for this BUC and the conditions/assumptions/ prerequisites for the BUC, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**SUC** & **Product/s Involved** \\ \hline DSO and TSO improved System adequacy & \\ (Processes for managing outages in the DSO grid and local microgrid and for doing storm and icing predictive maintenance). & Product to prevent or restore \\  & a severe system’s state \\ \hline DSO, DG and microgrid POI management & \\ (POIs updates, technical data, history data, & N/A \\  & data from forecasts, etc.). & \\ \hline Modified point of view simulations in different level & \\ of aggregation (Energy and system status forecasts & N/A \\  & for various DSO grid and local microgrid & \\  & aggregation levels). & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlation of System Use Cases (SUCs) and Products. Figure 9 shows an interaction diagram for the BUC, while Figure 10 depicts the diagram of the market process. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Objectives** \\ \hline Prediction error for ice appearance & \\ \hline Prediction error for storm appearance & \\ \hline Optimum operation of the power system & \\ because of optimization in the planned outages & \\ \hline Early notification of dangerous & \\ power system regimes & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key performance indicators for the BUC.