esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experimental Results and Discussion
### ML Models Prediction Accuracy
#### 4.3.2 Prediction Accuracy of ML Models
The CNN exhibits a slightly faster convergence rate, surpassing a prediction accuracy ratio of 90% after 20 epochs and achieving 95% within 30 epochs. In comparison, the MLP reached 90% accuracy at 32 epochs and 95% by 45 epochs. The fact that we used categorial ML models for our wind prediction involves an inherent loss of information. To evaluate the loss of the combined model using the numerical model in conjunction with the ML model, we decategorized the combined forecast output by using the median velocity in each category and direction (U10 and V10) versus the measured wind velocity during that forecast. Figure 9 illustrates the decategorized predicted wind velocity and direction versus the observations for our CNN ML model. The forecast of both wind velocity and wind direction is significantly improved. The wind velocity \(r^{2}\) improved from 0.46 to 0.88 by using the ML postprocessing, and the wind direction further improved from \(r^{2}=0.0\) to \(r^{2}=0.52\). When looking at the wind direction, the inherent data loss from categorizing continuous quantities is most visible. Further improvement can be achieved by ignoring the lowest category in both U10 and V10 forecasts (lowest line of predictions on the plot) out of the 25 forecasted wind directions (5 \(\times\) 5 categories for U and V, respectively). In this case, the slope is further improved from 0.04 to 0.74. This observation highlights an inherent challenge in the postprocessing method we developed; it may not be the most suitable approach for predicting extreme weather events. The fact that many of the forecasted wind direction points are scattered around 150\({}^{\circ}\) indicates that the CNN model was able to identify typical morning conditions and forecast the local morning sea breeze, a typical Kinect phenomenon that was not resolved by the numerical forecast. The wind direction forecast is accurate even when focusing  
Figure 8: ML model training and validation performance analysis for the low resolution and V10 wind component: (**a**) CNN confusion matrix, (**b**) MLP confusion matrix, and (**c**) training accuracy with respect to the number of epochs. The green circles and diamonds represent a 90% and 95% threshold, respectively. on the afternoon predictions (Figure 9b, yellow points). Most of the sailing competitions in Lake Kineret are conducted during the strong westerly afternoon winds, and the fact that the combined forecast was able to predict wind direction within the narrow-angle of afternoon wind directions indicates that the combined forecast can provide sailors much more effective means for winning races.
## 3 Transmitter Hardware Impairments or 'RF Features' Overview
### DAC Models  
Based on [43], the DAC model is given by  
\[y(t)=x(t)+x^{HQ}(t)+x^{CM}(t)+x^{VQ}(t) \tag{8}\]  
where \(y(t)\) is the output continuous-time signal, \(x(t)\) is the input continuous-time signal and the corresponding discrete-time form is \(x[n]\), \(x^{HQ}(t)\) is the horizontal quantization additive effect, \(x^{CM}(t)\) is the clock additive effect, and \(x^{VQ}(t)\) is the vertical quantization additive effect. The horizontal quantization additive effect \(x^{HQ}(t)\) is given by  
\[x^{HQ}(t)=\!\!\sum_{n=-\infty}^{\infty}\!x[n]g\!\left(\frac{t-nT_{g}}{T_{g}} \right)-x(t) \tag{9}\]  
where \(T_{g}\) is a constant generation period, \(g(t)\) is a unitary pulse function:  
\[g(t)=\begin{cases}1,&0\leq t\leq 1\\ 0,&\text{elsewhere}\end{cases} \tag{10}\]  
The clock additive effect \(x^{CM}(t)\) is:  
\[x^{CM}(t)=\!\!\sum_{n=-\infty}^{\infty}\!x[n]h_{n}(t-nT_{g}) \tag{11}\]  
where \(h_{n}(t)\) yields to:  
\[h_{n}(t)=-\text{sign}(\Delta_{n})g\!\left(\frac{t-nT_{g}}{\Delta_{n}}\right) +\text{sign}(\Delta_{n+1})g\!\left(\frac{t-(n+1)T_{g}}{\Delta_{n+1}}\right) \tag{12}\]  
where \(\Delta_{n}\) is a time amount. For example, based on (10), \(g\!\left(\frac{t}{\Delta_{n}}\right)\) has a rising edge at time instant zero and a falling edge at time instant \(\Delta_{n}\). By assuming the nearest voltage level that DAC could provide for \(x[n]\) is \(\tilde{x}[n]\), the vertical quantisation additive effect is:  
\[x^{VQ}(t)=\!\!\sum_{n=-\infty}^{\infty}\!\left\{g\!\left[n\right]-x[n]\right\} \cdot\left[g\!\left(\frac{t-nT_{g}}{T_{g}}\right)+h_{n}(t-nT_{g})\right] \tag{13}\]  
Here, we demonstrate two examples in Figure 4(a),b to illustrate the effect of DAC in different transmitters. These two examples are given for the in-phase components of the signal. Clearly, the distortions existing in spoofer DAC are heavier than that in a genuine GNSS transmitter.  
Figure 5: Examples of DAC characteristics at the transmitter, for a genuine (**a**) and a spoofer (**b**) transmitter.
# Solar Power Prediction Modeling Based on Artificial Neural Networks under Partial Shading
###### Abstract  
Photovoltaic systems are emerging as an important device to address the environmental pollution generated from conventional energy production. The objectives of this study are to accurately predict the power of photovoltaic systems under partial shading conditions and to model high-efficiency photovoltaic systems. First, the power loss under partial shading conditions was addressed using a bypass diode. In previous studies, for the power prediction, one or two parameters were trained through artificial neural networks. In this study, we employ five main parameters to improve the accuracy: the photo-current (\(I_{ph}\)), diode saturation current (\(I_{0}\) ), diode idealization factor (\(n\)), series resistance (\(R_{s}\)), and shunt resistance (\(R_{sh}\)). Compared to the results of previous studies, the proposed model yielded consistent results. As a result, more accurate power predictions are possible with variations in temperature and irradiation.  
photovoltaic systems; partial shading; bypass diode; artificial neural network +
Footnote †: journal: Applied
# Possibilities of Evaluating the Effectiveness of Noise Barriers in Slovakia
## 2 Materials and Methods
### Theory of Sound Energy Propagation Through a Noise Barrier
#### 2.1.2 Sound Insulation  
Airborne sound insulation is the ability of a planar separating structure to transmit acoustic power propagating in air from a source into an adjacent space in an attenuated extent and is characterized by the sound insulation index \(SI\) (dB). This index is frequency dependent and increases with the area density of the separating structure. It is given in the third-octave bands in the frequency range from 100 to 3150 Hz [25]. Noise barriers must have an average sound insulation that is sufficiently great so that the sound passing directly through them is negligible compared to the sound propagating around their upper edge.  
A single number rating of sound insulation determined in the laboratory is used to assess airborne sound insulation and is given by the following equation [23]:  
\[DL_{SI,G}=-10\cdot log\left(\frac{\sum_{i=1}^{18}10^{0.1L_{i}}\cdot 10^{-0.1S_{i} }}{\sum_{i=1}^{18}10^{0.1L_{i}}}\right) \tag{2}\]  
where:  
\(\text{SI}_{i}\)--sound insulation index in the \(i\)-th one-third octave band; and  
\(L_{i}\)--sound pressure level (A) of the normalized traffic noise spectrum in the \(i\)-th third-octave band (dB).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & \(DL_{\alpha}\) **(dB)** \\ \hline A0 & Not specified \\ A1 & \textless{}4 \\ A2 & 4–7 \\ A3 & 8–11 \\ A4 & \textgreater{}11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sound absorption categories [24].
## 2 Materials and Methods  
Ti6 Al4V titanium alloy in the state after plastic working and 3D printing was selected for this study. The tests were carried out on 24 samples in the form of disks with a diameter of 14 mm, which were printed on a 3D printer (DMLS samples), and 24 samples from a solid bar with a diameter of 10 mm (PW samples).  
The samples were printed on an EOS M100 (EOS GmbH, Krailling, Germany) using the DMLS method according to the parameters and standards found on the material sheet [33]. Each sample had two contours, 100% fill (with the maximum laser beam power being 200W, parameter set as Ti64 20 \(\upmu\)m FlexLine 1.x, layer thickness of 20 \(\upmu\)m, and volume rate of 1.68 mm\({}^{3}\)/s [33; 34; 35; 36]). Details of the process parameters are proprietary to EOS.  
In turn, the bar was cut on a Struers Discotom-10 cutting machine (STRUERS GmbH, Willich, Germany)--Figure 1.  
The samples prepared in this way were sandblasted in a \"VARIObasic\" cabin sandblaster from Renfert. The material used for sandblasting was glass beads, with sizes ranging from 90 to 150 \(\upmu\)m. The working pressure was set to 6 bar. Each sample was sandblasted for 120 s. Each time, care was taken to carry out the process in the same way for all samples, maintaining the same distance from the nozzle and an angle of approximately 45 degrees.  
The prepared specimens were then machined using a Struers Tegramin-30 automatic grinder-polisher. The surfaces were ground with P400, P500, P1200, P2000 and P4000 abrasive foils. The final processing step involved a mechanical polishing process on a polishing cloth using silicon oxide. The quenching was then carried out on selected samples in a high-vacuum furnace with the following process parameters:  
* Heating of the furnace to a temperature of 820 \(\pm\) 10 \({}^{\circ}\)C;
* Annealing of the samples for 120 min;
* Tempering the material to 500 \({}^{\circ}\)C with spontaneous cooling of the furnace from 820 \({}^{\circ}\)C to room temperature \(\pm\) 20 \({}^{\circ}\)C.  
In the final step of sample preparation, medical sterilization was carried out in a _Mocom Basic Plus_ autoclave at a steam temperature of T = 134 \({}^{\circ}\)C, at a pressure of 2.10 bar for 12 min. The study samples were divided into eight subgroups, as shown in Table 1.  
Figure 1: (a) Samples cut from the bar after plastic working, (b) DMLS-printed samples.
# Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method
## 4 Case Study
### Comparative Study  
We further compared the proposed method to other traditional classification methods including SVM, BP Neural Network (BPNN), and K-Nearest Neighbor (KNN) using the same dataset and accuracy indicator.  
The SVM parameter with the cost coefficient and kernel function coefficient is 1 and \(1/n_{feature}\) initially, where \(n_{feature}\) is the number of the features. The BPNN has one input layer, one hidden layer, and one output layer. The numbers of input neurons, hidden neurons, and the output neurons are \(N\times D\), \(1K\), and \(N\times 4\) respectively, wherein \(N\) represents the number of the images and \(D\) is the characteristics of the image. There are 10 neighbors in the KNN. Table 6 shows detailed information gathered in this comparative analysis, where the modified AlexNet structure shows the best average accuracy.  
Table 9 shows that the average accuracy of the modified AlexNet structure for spare part classes A, B, C, and D is 0.90, 0.95, 1.0, and 0.99 respectively; the proposed method outperforms all other methods tested here in terms of average accuracy. The SVM algorithm produced relatively close results at 0.88, 0.90, 0.84, and 0.98 for classes A, B, C, and D, respectively. As discussed above, class A had the fewest samples while class C and D had more samples. However, the performance of the modified AlexNet structure is consistent for all four classes. This suggests that the proposed approach is effective regardless of the number of available datasets.  
Figure 13: Results of \(r\) and \(p\) for semi-supervised learning.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **TP** & **FP** & **TN** & **FN** & **Accuracy** \\ \hline A & 26 & 3 & 0 & 0 & 89.7\% \\ B & 42 & 0 & 0 & 2 & 95.5\% \\ C & 70 & 0 & 0 & 0 & 100\% \\ D & 179 & 2 & 0 & 0 & 98.9\% \\ \hline \end{tabular}
\end{table}
Table 8: Semi-supervised learning results.  
Neither SVM nor KNN is an effective tool for practical spare parts management because of their \"black boxes\"; they cannot offer understandable explanations to decision-makers regarding the rule's assignment of criteria. The proposed method, conversely, makes the extracted criteria and hierarchical structure explicable and modifiable according to the expert knowledge or feedback based on the results. In the learning phase, the proposed method changes the traditional classification problem into an image classification problem which is helpful to the data visualization. It also outranks other methods tested here in terms of its accuracy and calculation speed.
## 3 Development and Validation of CFD/CSD Coupled Solvers
### Procedure of CFD/CSD Simulation  
The computational procedure of CFD/CSD simulation is shown in Figure 2. By taking the simulation of \(t\) time-step as an example, the aerodynamic load distribution on each fluid node on the elastic surface, corresponding to the dynamic boundary in the CFD fluid calculation domain, is obtained by the CFD solver first, and the load distribution is the input of the fluid/solid interpolation in the user-defined program. Through the fluid/solid load interpolation, the aerodynamic load distribution on the fluid node can be transformed to be the aerodynamic load on the FEM node of the structure model. With the state variables of each FEM node on the \(t\) time step and the aerodynamic load distribution on the structural node just obtained as the input of the CSD calculation module, the position and velocity of each structural FEM node in the \(t+1\) time step can be obtained.  
The displacements of the structural nodes are the input of the fluid/solid interpolation calculation, through which the displacements of each fluid node are obtained. Finally,  
Figure 2: Procedure of CFD/CSD simulation.  
according to the displacement of these aerodynamic fluid nodes, the fluid computational mesh on the \(t+1\) time step is updated, and the aerodynamic calculation and simulation of the next time step are carried out.
## 2 Materials and Methods
### Segmentation  
The modelling of the TEG enables the designer to optimize the design for better performance. From the Seebeck effect, the output voltage is proportional to the temperature gradient. It is desirable to  
Figure 1: Segmented thermoelectric generator module.  
operate a TEG over a wide range of temperature gradients. However, as TE materials are temperature dependent, it is challenging to find a single material that can operate efficiently over a wide range of temperatures. In order to achieve this, segmentation is called for. In a segmented TEG device, two or more TE materials can be arranged in sections across the length of the n-type and p-type legs of the TEG device. The figure of merit is the contribution of all the materials used. Segmentation, if done properly, results in a higher conversion efficiency. Factors to consider in segmentation are materials with a higher figure of merit and the compatibility factor \(s=\frac{\sqrt{1+ZT}-1}{\alpha T}\). The compatibility factor of the materials considered for segmentation should not differ by a factor greater than two. [PERSON] [45] further stated that maximum efficiency occurs when the compatibility factor (s) is equal to the relative current density given by \(u=\frac{J}{k\sqrt{T}}\). Another condition for maximum conversion efficiency is \(\frac{R_{L}}{R}=\sqrt{1+ZT_{av}}\)[44]. Figure 2 shows a simplified segmented unicouple of n-type Ba\({}_{0.1}\)Yb\({}_{0.2}\)Fe\({}_{0.1}\)Co\({}_{3.9}\)Sb\({}_{12}\)[3], n-type Bi\({}_{2}\)Te\({}_{3}\)[33], p-type Ce\({}_{0.5}\)Yb\({}_{0.5}\)Fe\({}_{3.25}\)Co\({}_{0.75}\)Sb\({}_{12}\)[32],and p-type Sb\({}_{2}\)Te\({}_{3}\)[34] materials. Skutterudites show high \(ZT\) at a mid-range temperature [3,32]. Bi\({}_{2}\)Te\({}_{3}\) and Sb\({}_{2}\)Te\({}_{3}\), however, generally undergo oxidation, volatility, and decomposition at a higher temperature. They show high \(ZT\) at a lower temperature around 100-150 \({}^{\circ}\)C [33,34]; as such, they are suitable for low temperature applications. Equations (8) and (9) give a typical figure of merit (\(k^{-1}\)) for two materials' segmentation and conversion efficiency.  
\[Z=\frac{\left(\alpha_{ph}+\alpha_{pc}+|\alpha_{th}|+\left|\alpha_{nc}\right| \right)^{2}}{\left\{[(K_{ph}+K_{pc})(P_{ph}+P_{pc})]\right\}^{\frac{1}{2}}+ \left[(K_{nh}+K_{nc})(P_{nh}+P_{nc})\right]^{\frac{1}{2}}\right\}^{2} \tag{8}\]  
\[\eta=\frac{P_{out}}{Q_{in}}=\frac{T_{H}-T_{C}}{T_{H}}\ \frac{\sqrt{1+ZT}-1}{ \sqrt{1+ZT}+\frac{T_{c}}{T_{H}}} \tag{9}\]  
where \(Z\) (\(k^{-1}\)) is the figure of merit in segmentation involving two materials, \(p_{ph}\), \(p_{pc}\), \(p_{nh}\), and \(pnc\) are the electrical resistivity of the hot and cold sides of the p-type and n-type TE materials, respectively. The same applied for the Seebeck coefficient and the thermal conductivity. \(T(k)\) is the average operating temperature, \(\eta\) is the TEG conversion efficiency.
## 3 Flexural Capacity Prediction Methods and Applicability Analysis
### Widely Used Methods for Predicting Flexural Capacity
#### 3.1.2 Bond-Reduction Method  
The bond-reduction method was introduced by [PERSON] and [PERSON] [39]. This method's basic idea was to take the strain increment of the un-bonded tendon as the product of the bond-reduction factor \(\Omega_{\mathrm{u}}\) and adjacent concrete fiber strain. The calculation for the un-bonded prestressed members can therefore be equivalent to that for the bonded prestressed ones. The pivotal factor \(\Omega_{\mathrm{u}}\) was obtained by the statistical analysis of the results of tests on monolithic beams prestressed with steel tendons [39]. This equation was adopted by the ACI 440.4R [48]. The expression of this equation is shown in Equation (3), in which \(\Omega_{\mathrm{u}}=1.5/(L/d_{\mathrm{p}})\) for one-point loading and \(\Omega_{\mathrm{u}}=3.0/(L/d_{\mathrm{p}})\) for two-point/uniform loading.  
\[\Delta\sigma_{\mathrm{p}}=\Omega_{\mathrm{u}}E_{\mathrm{p}}\epsilon_{\mathrm{ cu}}\big{(}d_{\mathrm{p}}/c_{\mathrm{u}}-1\big{)} \tag{3}\]  
Figure 11: Strain and stress distribution of the un-bonded prestressed beam.
## 3 Results and Discussion
### The Impact of Different Influencing Factors on Carbon Emissions in Wastewater Treatment
#### 3.4.3 The Impact of Influent Load Rate on Carbon Emissions  
Figure 7 depicts the carbon emission intensity of wastewater treatment plants having different load rates. The findings indicate that as the influent load rate rises, the emission intensity gradually diminishes. The emission intensity of WWTPs with a load rate of \(\leq\)60% stands at 0.640 kgCO\({}_{2}\)-eq/m\({}^{3}\). In contrast, the emission intensity of WWTPs with a load rate exceeding 100% is the lowest [39], at 0.390 kgCO\({}_{2}\)-eq/m\({}^{3}\). It is analyzed that high-load operation can improve equipment utilization efficiency and reduce the energy consumption and chemical dosage required per unit of treated water. At low load rates, the treatment equipment and facilities also need to be fully operational, and the number of equipment started is almost the same as that at high load, resulting in low operational efficiency and high electricity consumption per ton of water treated.  
Figure 6: The impact of operating scale on emission intensity.
# Mechanisms of Sweep on the Performance of Transonic Centrifugal Compressor Impellers
## 4 Performance Comparison
### Choke Mass Flow Rate  
The choke mass flow rate of cases with different sweep angle is illustrated in Figure 9. It is observed that both forward sweep and aft sweep tend to increase the flow capacity, and the variation of the choke mass flow rate is 0.8% of that of the datum case. The choke flow capacity is decided by the throat area and the blockage factor. Because of the complexity of the investigated case with twin splitters, the impeller throat area is strongly three-dimensional and its variation trend can be well captured through inviscid simulations. By comparing the choke prediction through viscid and inviscid methods, it can be concluded that the change of the throat area rather than the blockage is dominant for the trend of choke mass flow rate. When the sweep angle decreases from 0\({}^{\circ}\) to \(-\)25\({}^{\circ}\), the throat area will be generally enlarged due to the reduced space occupied by blades at upper spans. When the sweep angle increases from 0\({}^{\circ}\) to +25\({}^{\circ}\), the induced inlet lean angle plays a more important role that turns the throat surface more perpendicular to the axial direction, and thus increases the throat area.  
Figure 8: Comparison of (**a**) pressure ratio and (**b**) relative Mach number near the inlet of SRV2-O. EXP: experiments.
## 2 Background and Preliminaries  
### Translation-Invariant Signal Decomposition Using the Derived Wavelet Frames  
Transient features are important for identifying dynamic changes in tool wear and breakage. However, irrelevant low frequency vibrations and background noise often overwhelm them [42]. Fidelity signal decomposition is beneficial for improving the signal-to-noise ratio and reducing the subsequent computation of the model. Due to the lack of shift-invariance, the ability of traditional wavelet transform to mine repetitive shock vibrations in the raw signal is relatively weak [43]. In this section, we introduced derived wavelet frames to perform a nearly shift-invariant multiresolution analysis. Derived wavelet frames (DWFs) are based on dyadic doubletree complex wavelet packets and are supplemented by non-dyadic implicit wavelet packets. The latter enhances the ability of the algorithm to extract transition-band features.
# Dynamic Stall Mechanisms of Pitching Airfoil: IDDES Study Across Different Mach Numbers
###### Abstract  
This study investigates dynamic stall mechanisms of a pitching NACA 0012 airfoil through high-fidelity computational fluid dynamics (CFD) simulations. The improved delayed detached eddy simulation (IDDES) method based on a sliding mesh system is constructed and validated against experimental airload measurements. The results demonstrate a good agreement and the capability to capture three-dimensional flow structures. Comparative analyses at two Mach numbers of 0.283 and 0.5 reveal distinct stall physics. At the Mach number of 0.283, a notable 9.7\({}^{\circ}\) delay is observed between the static and dynamic stall. The airfoil experiences a leading-edge stall dominated by a strong adverse pressure gradient and generates rapid airload variations. In addition, trailing-edge vortex (TEV) and secondary leading-edge vortices (LEVs) induce distinct airload fluctuations. After the shedding of primary vortices, secondary vortices develop. In contrast, the airfoil at the Mach number of 0.5 presents a reduced stall delay of 6.4\({}^{\circ}\) and a shock-induced dynamic stall characterized by dispersed, smaller vortices, which results in mild airload variations during stall. Aerodynamic damping analysis identifies stall delay as a primary contributor to negative damping. Enhanced pitching stability at the higher Mach number correlates with reduced stall delay and different LEV development characteristics. Results across varying reduced frequencies show that increasing reduced frequency delays the aerodynamic response and stall onset. At \(Ma=0.283\), this increasement promotes a divergent tendency in pitching motion, whereas at \(Ma=0.5\), it induces greater oscillatory stability attributed to distinct stall characteristics.  
dynamic stall; NACA 0012 airfoil; IDDES; aerodynamic damping; shock-induced stall +
Footnote †: journal: Applied sciences