esa-sceva/satcom-chunk-collection huggingface dataset:
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 2 The Proposed Methodology
### 2d-Ssa
#### 2.2.2 Singular Value Decomposition (SVD)  
After obtaining the trajectory matrix \(\mathfrak{Z}^{b}\), SVD is applied to determine the eigenvalues \(\left(\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{M_{x}M_{y}}\right)\), and the corresponding eigenvectors \(\left(U_{1},U_{2},\cdots,U_{M_{x}M_{y}}\right)\) of \(\left(\mathfrak{Z}^{b}\left(\mathfrak{Z}^{b}\left(\mathfrak{Z}^{b}\right)^{T}\right)\). It is possible to rewrite \(\mathfrak{Z}^{b}\) as follows:  
\[\mathfrak{Z}^{b}=\mathfrak{Z}^{b}_{1}+\mathfrak{Z}^{b}_{2}+\cdots+\mathfrak{Z }^{b}_{M_{x}M_{y}} \tag{10}\]  
where the \(i\)th elementary matrix is \(\mathfrak{Z}^{b}_{i}=\sqrt{\lambda_{i}}U_{i}V_{i}^{T}\) and its Principal Components (PCs) are defined as \(V_{i}=\frac{(\mathfrak{Z}^{b})^{T}U_{i}}{\sqrt{\lambda_{i}}}\)
## 5 Experimental Setup, Analysis and Evaluations
### Triggering Controller Addition during Network Migration  
Once the location of the master controller was identified during the network migration, overloading of the master controller, while increasing the number of SDN switches in the network was continuously monitored. Figure 13 presents the traffic load on the master controllers. Taking the reference from [31], we assumed a number of OpenFlow requests (\(\gamma\)) in the range of 0.05-0.105 million per second by an SDN switch, the request-handling capacity of controller (\(\sigma\)) as 1.1 million requests per second, and 0.01-0.02 million iBGP peering session requests per second by iBGP (gateways) routers formed during migration. Figure 13a-d shows the number of requests made by switches and iBGP-peers (gateways) formed during migration in Random, Abilene, Xeex, and GEANT2009 networks, respectively. The horizontal axis of Figure 13 shows the sum of the number of switches migrated and iBGP gateways formed during migration. Switches were set for incremental migration by one, while the number of gateways formed during migration increased or decreased based on the switch migration. Hence, in some steps of the migration, the same number of nodes migrated due to dynamically changing iBPG peers. We ran the experiment with two other networks (viz. Xeex and GEANT2009) to measure the controller overloading situation during migration. The Random network has six switches and four gateways. After migration, in total, 10 nodes generated control/iBGP traffic to the controller. Similarly, the Abilene network has 11 nodes and 4 gateways, and hence a total of 15 nodes generate control/iBGP traffic to the controller at the end of migration. Similarly, the same applies to Xeex and GEANT2009 networks. The decision to add a new controller is taken only if the number of requests by switches and gateways exceeds the controller capacity. For example, in Figure 13c,d, the number of requests made by switches and gateways to the controller exceeded the controller capacity after a certain number of routers were migrated.  
Figure 12: Cumulative latency plot with and without BFR during migration for: (**a**) Abilene and (**b**) GEANT2009 networks.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 6 Performance Evaluation
### Cost Comparison  
We show the analysis results of the computational and communication costs of the proposed protocol.
# Method for Resonant Frequency Attenuation in Dynamic Audio Equalizer
###### Abstract  
The attenuation of resonant frequencies across the entire spectrum of an audio signal is important because it helps to eliminate the harshness, sibilance, clear muddiness, boominess, and proximity effect of any sound source. This paper presents a method for the attenuation of resonant frequencies across the entire spectrum of an audio signal. A spectrum obtained by the Fast Fourier Transform is segmented into bands--one-third octave bands and Equivalent Rectangular Bandwidth-scale bands--in order to obtain the maximum value per band. Additionally, a curve representing the general shape of the spectrum is generated using the standard deviation to create a threshold curve for detecting resonant frequencies. The array with maximum values per bands and the array with the threshold curve are used to detect the resonant frequencies and calculate the attenuation for each filter. Subsequently, the coefficients of a second-order section of IIR-Peak filters are calculated for processing the input signal. Twenty audio files from different sources are utilized to test the algorithm. The output produced is then compared to that produced by the commercially available Soothe2 and RESO plug-ins. The Root Mean Square Level and the Loudness Units Full Scale integrated metrics are reported. The proposed plug-in output is more attenuated than the output from commercial plug-ins under factory conditions. The average RMS attenuation is \(-2.32\) dBFS, while Soothe2 and RESO exhibit \(-1.27\) dBFS and \(-1.10\) dBFS, respectively. The attenuation per octave band over time is calculated using the Wavelet Transform. Finally, an annotator agreement used as a subjective result is made with 40 people related to audio and music in order to verify if the attenuation generated by the present work at resonant frequencies agrees with subjective opinion. The octave band analysis and annotator agreement show that the proposed plug-in performs better on audio from vocal, percussion, and guitar ensembles.  
dynamic equalizer; frequency attenuation; resonant frequencies; audio plug-in; IIR-Peak filter
# Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
###### Abstract  
Shingled photovoltaic (PV) modules with increased output have attracted growing interest compared to conventional PV modules. However, the area per unit solar cell of shingled PV modules is smaller because these modules are manufactured by dividing and bonding solar cells, which means that shingled PV modules can easily have inferior shading characteristics. Therefore, analysis of the extent to which the shadow affects the output loss is essential, and the circuit needs to be designed accordingly. In this study, the loss resulting from the shading of the shingled string used to manufacture the shingled module was analyzed using simulation. A divided cell was modeled using a double-diode model, and a shingled string was formed by connecting the cell in series. The shading pattern was simulated according to the shading ratio of the vertical and horizontal patterns, and in the case of the shingled string, greater losses occurred in the vertical direction than the horizontal direction. In addition, it was modularized and compared with a conventional PV module and a shingled PV module. The results confirmed that the shingled PV module delivered higher shading output than the conventional PV module in less shade, and the result of the shading characteristic simulation of the shingled PV module was confirmed to be accurate within an error of 1%.  
simulation; photovoltaic; shingled; shading; PSpice +
Footnote †: journal: Applied Sciences
## 3 Experimental Results and Discussions
### Pinching Effect  
Individual loops of the three bent specimens at the peak lateral displacement of \(\delta\) = 25, 50, and 75 mm are compared in Figure 13 to present the pinching effect. The so-called \"pinching\" occurs when, after unloading from a previous cycle, the initial reloading exhibits relatively low stiffness during the recovery of the residual displacement, and then the stiffness suddenly increases. For two-column bent specimens, the generation of the pinching effect is associated with the failure mechanisms of the plastic hinge zones at both the top and bottom of the columns.  
Figure 12: Residual drifts of the three bent specimens.  
For the cast-in-place bent B2-CIP, the pinching effect primarily originates from the cracking and crushing of concrete in the plastic hinge zones at both the top and bottom of the columns, along with bond-slip behavior between longitudinal rebar and surrounding concrete/grouted mortar. Upon reloading the bent specimen, a delay is observed in the closure of the cracks in the concrete of the compression zone, which only reseal and restore stiffness after loading reaches a certain extent.  
In the two precast bents, since both the grouted splice sleeves and grouted central tenons were embedded within the cap beams above the cap-column seams, the failure modes in the plastic hinge zones at the top of the columns resemble those of the bent B2-CIP. For the bent B2-PGS, an opening emerges at the column-footing interface, and the cover concrete at the bottom of the column gradually becomes crushed during loading. This leads to a scenario during initial loading where the opening angles remain unclosed, thereby inducing the pinching effect. However, for the bent B2-PGT, the presence of grouted central tenons restricts the opening of the column-footing joints, consequently rendering the pinching effect less pronounced than in B2-PGS, particularly at lower displacement amplitudes.  
In this study, all the two-column bent specimens exhibit, overall, similar pinching behavior. This is primarily because, in the B2-PGS and B2-PGT specimens, the damage at the top of the columns is notably more severe than at the bottom, significantly contributing to the overall pinching effect.  
Figure 13: Pinching effects of tested bents illustrated with individual hysteretic loops.
# Investigating Load-Bearing Capabilities and Failure Mechanisms of Inflatable Air Ribs
## 2 The Load-Bearing Performance of Air Ribs with Different Rise-Span Ratios
### The Simulation Model of Air Ribs Under Load  
Ten air ribs with different rise-span ratios were designed. The air ribs are made of high-strength textile material, which was purchased from Zhongyuan Textile Co. (Suning, Cangzhou, China). The parameters and material properties of the air ribs are shown in Table 1. The span of a usual air rib is 8 m, and the rise is 4 m. The internal pressure of air ribs is 0.2 MPa to ensure stability. The cross-sectional diameter of each rib is 0.3 m. The air ribs are divided into three groups:  
1. Proportional ribs: These ribs maintain a consistent rise-span ratio, with both rise and span scaling proportionally. The rise-span ratio of the air ribs is 0.5, as shown in Figure 1b, and they are named 0.5-S/R and 0.5-mini.
2. Equal-span ribs: These ribs have a fixed span of 8 m, while the rise varies proportionally. The air ribs are classified based on their rise-span ratios, such as 0.333-S, 0.375-S, 0.417-S, and 0.458-S, as shown in Figure 1c.
3. Equal-rise ribs: These ribs have a fixed rise of 4 m, while the span varies proportionally. The air ribs are named according to their rise-span ratios, such as 0.532-R, 0.583-R, 0.625-R, and 0.666-R, as shown in Figure 1d.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Air Rib** & **Rise/(m)** & **Span/(m)** & **Thickness /(m)** & **Density /(Kg/m3)** & **Elastic Modu-** & **Poisson’s Ratio** & **Breaking Strength/(MPa)** \\ \hline
**0.333-S** & 2.67 & 8 & & & & & \\
**0.375-S** & 3 & 8 & & & & & \\
**0.417-S** & 3.33 & 8 & & & & & \\
**0.458-S** & 3.67 & 8 & & & & & \\
**0.55-R** & 4 & 8 & & & & & \\
**0.542-R** & 4 & 7.83 & & & & & \\
**0.583-R** & 4 & 6.86 & & & & & \\
**0.625-R** & 4 & 6.4 & & & & & \\
**0.667-R** & 4 & 6 & & & & & \\
**0.5-mini** & 3.5 & 7 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Air rib design parameters: rise–span ratio and material properties.  
The simulation model for calculating the bearing performance of the air ribs is illustrated in Figure 1e. The model consists primarily of air ribs and a rigid plate. The model is based on an implicit solution in ABAQUS 2021 while simplifying the membrane material properties and ignoring the effect of membrane anisotropy on structural statics. The rigid plate is constrained to move downward only at a speed of 0.05 m/s for 10 s. It is ensured that the load is applied in the negative y-axis direction, while the displacement of the rigid plate corresponds to the deformation at the top of the air rib. The hinge constraints are applied to the bottom of the air ribs to simulate realistic movements. The support force of the air rib can be monitored by a reference point on the rigid plate. There is no energy transfer between the rigid plate and the air rib.
## 1 Introduction
The DEM has been applied to simulate the motion of particles such as soil, grain, and small metals ([PERSON] et al. [17]; [PERSON] [18]). [PERSON] et al. [1] used the DEM to predict the cone penetration resistance and wear of a fine ripper acting in sand clay [1; 17; 18]. The particle-to-particle collision behavior and friction-inducing load are affected by the particle size, density, and contact state. In this study, the DEM is applied to the phenomenon of metal material wear through interaction with soil, and the appropriateness of the procedure is verified. The process of determining the particle size, material properties, and wear parameters for wear analysis using the DEM is presented. The most important factor in wear analysis is the reflection of the accumulated wear energy and determination of the particle size, which affects the relative behavior of the particles. This study focuses on practical methods to solve this problem. Numerous numerical models have been proposed to evaluate complex and nonlinear wear behavior. [PERSON] et al. [19] evaluated particle wear using the shear impact energy model (SIEM). This wear model connects the wear volume with the shear collision energy, and its accuracy has been verified by experiments in a laboratory-scale planetary ball mill. An accelerated wear test design method based on dissipation wear model entropy analysis under mixed lubrication was proposed by [PERSON] et al. [20]. However, most wear-related studies use the Archard wear law. A wear evaluation numerical model is presented in a way that defines a reasonable wear coefficient according to the type of load state and material. [PERSON] et al. [21] presented a wear coefficient suitable for the stress state as well as a model that reflected the thermal effect. This study applies [PERSON]'s wear law, the most widely used general law. Therefore, this study proposes a modeling method that can accurately reflect the transmitted wear load and particle behavior when metallic materials are worn. To verify the proposed model, the wear test experiment conducted by [PERSON] et al. [22] was reviewed. In the reference study, wear simulation using the DEM was also performed, but the results of calculating the wear shape were found to have poor accuracy, and the cause was environmental factors. However, this study proposes a particle modeling method for the wear simulation of metal materials, confirms through simulation that very similar wear shapes are calculated when compared with the experiment, and reviews the transmitted wear load generated in the simulation model.
## Strengths  
1. **Sustainability:** Utilizes agricultural and industrial waste (e.g., coconut fiber, rice husk), reducing CO\({}_{2}\) emissions by up to 30% compared to traditional materials. Promotes a circular economy by repurposing waste streams.
2. **Acoustic Performance:** High sound absorption coefficients (e.g., 0.95 for flax-perlite panels, 0.7 NRC for foxtail millet husk composites). Enhanced sound insulation (e.g., 44.9 dB noise reduction at 10,000 Hz for jute-based composites).
3. **Versatility:** Applicable in building insulation, automotive components, and noise barriers. Compatible with 3D printing, producing lightweight panels with densities of 200-600 kg/m\({}^{3}\).
4. **Thermal and Mechanical Properties:** Low thermal conductivity (e.g., 0.15 W/m-K for arbolite). Compressive strengths of 1-10 MPa in materials like sawdust concrete.
5. **Innovative Design:** Air gaps and resonators improve low-frequency absorption (e.g., 0.5-0.9 absorption coefficients in the 100-500 Hz range). Hybrid designs (e.g., jute-E-glass composites) reduce noise by up to 44.9 dB.
## 4 Energy Management Systems
### Rule-based Energy Management Systems
#### 4.1.2 Equivalent Consumption Minimization Strategy (ECMS)  
The implementation principle of the cost function is the core of this EMS that effectively reduces fuel consumption [79]. The cost function, represented by Equation (2), operates within specified ranges for battery power (\(P_{battl}\)), SOC, and FC power (\(P_{fc}\)).  
\[F=\Big{(}P_{fc}+\alpha\;P_{batt}\Big{)}dt \tag{2}\]  
where \(\alpha\) is the equivalence factor represented by:  
\[\alpha=1-2\mu\frac{(\;SOC-0.5(SOC_{min}+SOC_{max}))}{SOC_{min}+SOC_{max}} \tag{3}\]  
where \(\mu\) is the \(SOC\) balance coefficient. The equality constraint is given by:  
\[P_{demand}=P_{fc}+P_{batt} \tag{4}\]  
Figure 15: FCHEV EMS Classification.  
To guarantee that the \(P_{fc}\), \(SOC\), and \(P_{batt}\) are all functioning within their limitations, the following boundary criteria must be met:  
\[\begin{array}{l}P_{fc,min}\ \leq\ P_{fc}\ \leq\ P_{fc,max}\\ P_{batt,min}\ \leq\ P_{batt}\ \leq\ P_{batt,max}\\ \\ 1.4\leq\alpha\leq\ 1.8\end{array} \tag{5}\]  
The above constraint on \(\alpha\) is derived from empirical studies and theoretical analyses to determine the optimal range for the equivalence factor to balance fuel efficiency and battery life based on the operational characteristics of the system, including the efficiency curves of the fuel cell and battery, as well as the overall system dynamics.  
The Simulink workspace incorporated the cost function through the utilization of a MATLAB 2020b S-Function block, as depicted in Figure 16b.
###### Abstract  
Due to the high amount of natural gas resources in Iran, the gas cycle as one of the main important power production system is used to produce electricity. The gas cycle has some disadvantages such as power consumption of air compressors, which is a major part of gas turbine electrical production and a considerable reduction in electrical power production by increasing the environment temperature due to a reduction in air density and constant volumetric airflow through a gas cycle. To overcome these weaknesses, several methods are applied such as cooling the inlet air of the system by different methods and integration heat recovery steam generator (HRSG) with the gas cycle. In this paper, using a heliostat solar receiver (HSR) in gas and combined cycles are investigated by energy, exergy, and economic analyses in Tehran city. The heliostat solar receiver is used to heat the pressurized exhaust air from the air compressor in gas and combined cycles. The key parameter of the three mentioned analyses was calculated and compared by writing computer code in MATLAB software. Results showed the use of HSR in gas and combined cycles increase the annual average energy efficiency from 28.4% and 48.5% to 44% and 76.5%, respectively. Additionally, for exergy efficiency, these increases are from 29.2% and 49.8% to 45.2% and 78.5%, respectively. However, from an economic point of view, adding the HRSG increases the payback period (PP) and it decreases the net present value (NPV) and internal rate of return (IRR).  
heat recovery steam generator (HRG); heliostat solar receiver (HSR); gas cycle; combined cycle; exergy; economic +
Footnote †: journal: Applied Sciences
## 3 Experiment and Analysis
### Analysis of the Results of the AMG Combined Cycle Slip Completeness Detection  
The cycle slips' detection and repair of the MW combination method assisted by an AR model are carried out on the same data of Experiment 2 in Section 3.1, and the effectiveness and reliability of this method are verified by comparison. In order to achieve a false detection rate of 5%, that is, \(\sigma_{\Delta N_{MW},M}\) should be less than 0.443 cycles, and the pseudorange multipath errors of the two frequency points should be less than 0.2 m.  
Firstly, a 500 epochs sliding-window with time series characteristics is constructed by using pseudorange data not contaminated by the multipath, and the AR model is used to predict pseudorange observations in a severe multipath environment according to the time-domain characteristics of the sliding-window. The predicted residuals are shown in Figure 8. The experimental results show that the predicted mean value of the pseudorange observations is 0.028 m, and the proportion of the predicted residuals less than 0.1 m is more than 96%.  
Figure 6: PRN38 cycle slips estimated value under multipath effects.  
Figure 7: PRN40 cycle slips estimated value under multipath effects.  
The AR model-assisted MW combined cycle slips' detection estimations are shown in Figure 9. Comparing Figure 4, the pseudorange multipath effect of the method used in this paper is significantly suppressed, and the cycle slips' estimations are all less than 0.2 cycles. As shown in Table 1, the detection estimation assisted by the AR model is 0.2919 cycles, which is close to 0.2895 cycles in the case of no cycle slips and significantly lower than 1.9042 cycles in the multipath environment, indicating that the cycle slip estimation is more accurate. At the same time, the false detection rate and miss detection rate of this method are less than 1%, so the AR+MW combination still maintains a powerful detection ability in a severe multipath environment.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 4 ANN-Based Placement Scheme Selection
### ANN Model Construction
The results in Figure 4 concerning the modelled ANNs prove that the loss function for all processes, i.e., training, validation, and testing, converges smoothly, obtaining constant loss values and reaching the global minimum in a short period. The acquired global minimum loss for the convergence during the testing phase, and the corresponding epoch values are listed in Table 3. According to Table 3, ANN with two hidden layers demonstrates the best performance among all the examined ANN techniques, providing the minimum loss score of 0.06. Furthermore, for each ANN layout, the training time is also recorded. Specifically, the training times for ANN with one, two, three, and four hidden layers are 0.81, 0.92, 1.4, and 1.6 seconds, respectively. Comparing the training time of the assessed ANNs models, it is evident that the training time depends directly on the applied layout structure. Finally, the conventional time complexity (TTC) for any ANN layout is \(O(n^{3})\)[37]. The TTC represents the standard theoretical asymptotic complexity, which takes into account only the training samples n. It only examines training samples, since the training phase is the most time-consuming operation in ML algorithms and occurs offline, and not in real-time scenarios. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(I_{h}\) & \(m_{h}\) & **Layout** & \begin{tabular}{c} **Converged** \\ **Epoch** \\ \end{tabular} & \
\begin{tabular}{c} **Minimum** \\ **Loss Score** \\ \end{tabular} &
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} \\ \hline
1 & 63 & \(\text{ANN}_{8-63-2}\) & 26 & 0.09 & 0.81 \\
2 & 32/31 & \(\text{ANN}_{8-32-31-2}\) & 37 & 0.06 & 0.92 \\
3 & 21/21/21 & \(\text{ANN}_{8-21-21-21-2}\) & 56 & 0.08 & 1.4 \\
4 & 16/16/16/15 & \(\text{ANN}_{8-16-16-16-15-2}\) & 58 & 0.15 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examined ANN layouts.