esa-sceva/satcom-chunk-collection huggingface dataset:
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 4 Simulation Systems and Results
### Simulation Results
#### 4.2.4 The MSE and Complexity Performance of Proposed Algorithm 1  
Figure 8 compares the MSE performance of Algorithm 1 and the conventional time-domain estimation method [4] (i.e., Equation (5)), where \(\epsilon\) is set to \(-0.3\) and \(0.1\), and \(N_{rb}\) is \(20\), \(30\), and \(40\). The parameter \(e_{max}\) in Algorithm 1 is set to \(0.5\), and \(M\) is set to \(60\), according to the simulation results in Figures 5 and 6. In the time-domain estimation method, the time-domain pilot signal with two repeated halves is constructed by setting the symbols with odd subcarrier indices to zeros. Since the half subcarriers of the pilot OFDM symbol transmit zeros (not data symbols), only one pilot OFDM symbol is configured to keep the same data rate with Algorithm 1.  
For the time-domain estimation method, Figure 8 shows that the MSE performance of \(\epsilon=-0.3\) is near to that of \(\epsilon=0.1\) for the given SNR and \(N_{rb}\). This is because the estimation accuracy of the time-domain method is not affected by the ICI, when the Doppler shift does not exceed the estimation range. Moreover, Algorithm 1 outperforms the time-domain estimation method at the same \(\epsilon\) and \(N_{rb}\), since Algorithm 1 only uses the received data of the pilot subcarriers to estimate the frequency offset, and is less affected by the noise of the non-pilot subcarriers.  
Table 3 demonstrates the mean numbers of complex-multiplex operations of Algorithm 1 and the time-domain estimation method in the simulations. According to Equation (5), the complexity of the time-domain estimation method is \(O(N)\); specifically, the number of complex-multiplex operations is \(N/2\) in the simulations. According to Table 2, the number of complex-multiplex operations of Algorithm 1 is \(2N_{s}(M+1)(L_{1}+L_{2}+1)+3N_{s}\), where \(N_{s}=12\times N_{rb}/2\) in the simulated radio frame structure. Compared to the time-domain estimation method, Algorithm 1 needs more computing resources for the additional frequency offset compensation and correlation operations.  
Figure 8: The MSE performance of proposed Algorithm 1 and the conventional time domain estimation.
# Stick-Slip Phenomena and Acoustic Emission in the Hertzian Linear Contact
###### Abstract  
AE detection and analysis usually requires a specific, costly platform due to its particular burst nature and high-frequency content. This experimental study investigates the relationship between low-demand acoustic emission parameters (AE) and the occurrence of stick-slip (SS) at the Hertzian linear contact. Hence, the correlation of basic AE characteristics (amplitude, energy, and evolution in time) with stick-slip characteristics (static and kinetic friction coefficients, amplitude, energy, and evolution in time) is pursued. Tribological tests were conducted on cylinder-plane specimens under dry friction conditions with different loads at different low driving speeds and Hertzian contact pressures at a constant stiffness. The AE, normal, and friction forces were recorded simultaneously on the experimental stand. At the cylinder-plane interface, the jumps specific to the stick-slip phenomenon (friction coefficient--COF) were followed after a few milliseconds by AE jump peaks. The results of the experiments show that the amplitude and energy generated by AE were sensitive to the occurrence of the stick-slip phenomenon, while the AE and COF energies in the stick and slip phases had the same law of variation based on the driving velocities. The results show that the amplitude and energy of the sampled low-frequency AE signals were enough to detect the friction in SS and demonstrate the potential of AE as a tool for detecting and monitoring the tribological behaviour of SS at the linear Hertzian contact.  
stick-slip; acoustic emission; linear Hertzian contact; friction coefficient +
Footnote †: journal: Applied Sciences
## 3 Numerical Configuration  
### Meshing Strategy  
#### 3.1.1 Mesh Configuration 3D  
Figure 2 shows the division between the existing compressor, in blue, and both parts of the mixer, in red, whose geometric optimization is the main objective of the present paper.  
For the sake of consistency, the geometry was divided in several sections. Thus, in order to determine the different reference systems for the movement, the same mesh size was imposed at the interfaces between the different sections. However, as it can be seen in Figure 4, the sections which englobed the rotor and the stator were refined with respect the rest of the geometry. As a result, a base mesh composed of polyhedral cells was used, with a base size of 0.1 mm in general and 0.001 mm in the most critical sections, close to the blades of the compressor. Moreover, in order to increase the resolution in the blades, the curvature of the surface mesh in the blades was highly refined, increasing considerably the number of elements in these zones, as shown in Figure 5. Prism layers were configured on the walls, achieving a mean y+ value in the chosen mesh of 1.7, close to the ideal of 1.
## 4 Application and Results
### CSE Corpus Building
#### 4.1.5 Dialogues Processing  
We used the text processing workflow illustrated in Figure 5 to prepare the collected CSE dialogues. At first, all dialogues were converted to use the UTF-8 encoding scheme. Slang and abbreviations were removed or substituted with corresponding phrases using open-source slang databases Using dictionaries and the Textblob library [27] we performed spelling correction and removed any emojs. Empty lines, specific stopwords and specific punctuation marks were removed using traditional NLP libraries like NLTK [28] and spaCy [29]. Moreover, all HTML or other programming code and URLs or paths were stored in a separate database and substituted with _code_,_url_, and _path_ placeholders in the dialogue. Any illegal characters were also stripped and all text transformed to lowercase.  
The standard Penn Treebank [30] tokenisation rules were utilised for sentence tokenisation, and finally, standardisation processes using regular expressions and lookup tables were applied to tune the CSE dialogues. Figure 5 depicts the dialogues processing workflow where each stage, along with the individual tasks below, is shown.  
At the end of this step, a corpus composed of CSE dialogues was formed. Table 2 presents the summary of the produced corpus, named _CSE corpus_.  
The CSE corpus is composed of realized and fictional CSE attack dialogues. The existence of fictional attack dialogues does not affect its quality and capability because, similarly, a social engineer does not always act spontaneously but frequently prepares a fictional scenario (pretext attack) to guide the conversation and unleash his attack. The same applies to the CSE corpus, which incorporates a combination of confirmed and fictional CSE attacks.  
Figure 4: Example of linguistic analysis.  
Figure 5: The CSE dialogues processing workflow.
## 4 Simulation Case and Analysis
The box line diagram of the successful rescue rate and the rescue time for \begin{table}
\begin{tabular}{c c} \hline
**Simulation Results** & **Value** \\ \hline Successful rescue rate & 91.1\% \\ Rescue time (h) & 10.62 \\ \hline \end{tabular}
\end{table} 
Table 2: The simulation result for 45 PDTs of the TPM-0.5. Figure 12: The TPM-0.5 of SRP for 45 PDTs. Figure 13: Rescue time of each stage for the TPM-0.5 with 45 PDTs. the TPM-0.5 is shown in Figure 14. The results demonstrate the adaptivity of TPM with different numbers \(\mathbf{s}\) of PDTs. The successful rescue rate decreases as the number of the PDTs increases, whereas the change in the rescue time follows the opposite trend. The average rescue times for each stage of the TPM-0.5 with 45 PDTs and 70 PDTs are shown in Figures 15 and 16. The meaning of \(T_{i}\) in Figures 15 and 16 is the same as that in Figure 13. The results show that the number of the amphibious aircraft transfer sorties increased with the number of the PDTs due to the limitation of the amphibious aircraft rescue capabilities. It can be seen that the percentage of time for rescuing the PDTs increased with the number of the PDTs. Therefore, the TPM has practical advantages for amphibious aircraft applications in medium/distant maritime rescue. To investigate the effect of the time factor on the effectiveness of the TPM, TPM-X with a delay time varying from 1 h to 8 h was simulated for 45 and 70 PDTs, additionally. To reduce the impact of random factors on the simulation results, the simulation of each TPM-X was conducted fifty times. The simulation results of the successful rescue rate and the rescue time for 45 PDTs are shown in Figure 17. The successful rescue rate and the rescue time for 70 PDTs are shown in Figure 18.  
It is noted that the TPM can significantly affect medium/distant maritime rescue. The effect of the time factor on the effectiveness of the TPM can be seen in the simulation results. The greater the delay time of the TPM, the lower the successful rescue rate and the longer the rescue time. The successful rescue rate for 70 PDTs decreased faster than that for 45 PDTs, with the delay time increasing. Moreover, the successful rescue rate and the rescue time of the TPM were affected by many factors, such as the drift speed of PDTs, the uncertain weather conditions, and the practical rescue experience of the amphibious aircraft. In addition, the accuracy of the trajectory prediction for the PDTs decreased over time. Therefore, it is necessary to rapidly respond and prepare to the distress, especially when the number of the PDTs is high. Based on the simulation results of the TPM, for medium/distant maritime rescue, a delay time which is less than 1 h is usually recommended for the amphibious aircraft to rescue PDTs by using TPM. Figure 17: Successful rescue rate and rescue time for 45 PDTs with delay time from 0.5 h to 8.0 h.  
Figure 18: Successful rescue rate and rescue time for 70 PDTs with delay time from 0.5 h to 8.0 h.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
###### Abstract  
The advancement in wireless communication applications encourages the use of effective and efficient channel estimation (CE) techniques because of the varying behaviour of the Rayleigh fading channel. In most cases, the emphasis of most proposed CE schemes is to improve the CE performance and complexity for ensuring quality signal reception and improved system throughput. Candidate waveforms whose designs are based on filter bank multi-carrier (FBMC) modulation techniques such as filter bank orthogonal frequency division multiplexing based on offset quadrature amplitude modulation (OFDM-OQAM), universal filtered multicarrier (UFMC) and generalised frequency division multiplexing based on offset quadrature amplitude modulation (GFDM-OQAM) are no exception to the use of these proposed CE techniques in the literature. These schemes are considered as potential waveform candidates for the physical/media access control layer of the emerging fifth generation (5G) networks. Therefore, pinpoint CE techniques represent an important requirement for these waveforms to attain their full potentials. In this regard, this paper reviews the concept of CE as applicable to these waveforms as well as other waveform candidates under consideration in the emerging 5G networks. Since the design of the majority of the waveform candidates is filter based, a review of the general filter design considerations is presented in this paper. Secondly, we review general CE techniques for candidate waveforms of next generation networks and classify some of the studied CE techniques. In particular, we classify the CE schemes used in filter bank OFDM-OQAM and GFDM-OQAM based transceivers and present a performance comparison of some of these CE schemes. Besides, the paper reviews the performances of two linear CE schemes and three adaptive based CE schemes for two FBMC based waveform candidates assuming near perfect reconstruction (NPR) and non-perfect reconstruction (Non-PR) filter designs over slow and fast frequency selective Rayleigh fading channels. The results obtained are documented through computer simulations, where the performances of the studied CE schemes in terms of the normalised mean square error (NMSE) are analysed. Lastly, we summarise the findings of this work and suggest possible research directions in order to improve the potentials of the studied candidate waveforms over Rayleigh fading channels.  
filter bank; Non-PR; NPR; channel estimation; next generation networks; 5G waveforms; channel fading +
Footnote †: journal: Electronics Letters
# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
## 4 Results
### Model Comparisons
Among the deep learning technology, DNN algorithm is not as good as integrated BiLSTM, LSTM and BiLSTM for predicting hourly ST. As mentioned above, the results of DNN are sometimes even worse than the RF, as can be seen from Table 4, the worst R\({}^{2}\) obtained by DNN is 0.613, while the worst R\({}^{2}\) of RF is 0.656. The statistic performance of each model for an estimation of the hourly ST is shown in Table 5, which are the average performance of each model after 10 runs. According to Table 5, the maximum R\({}^{2}\), and the minimum RMSE and MAE (the best results), with values of 0.923, 1.53 \({}^{\circ}\)C, and 1.22 \({}^{\circ}\)C, respectively, were obtained using the integrated BiLSTM. By contrast, the minimum R\({}^{2}\), and the maximum RMSE and MAE values, were found to be 0.518, 3.43 \({}^{\circ}\)C, and 2.76 \({}^{\circ}\)C when using the LR. The integrated BiLSTM, BiLSTM, and LSTM perform better than the DNN, RF, SVR, and LR. DNN is inferior to LSTM-based method in processing time series data. Among the deep learning methods, the integrated BiLSTM method achieves the best prediction results for the hourly ST, whereas within the range of traditional machine learning, the random forest model achieves the best performance.
# A Power Based Analysis for a Transonic Transport Aircraft Configuration through 3D RANS Simulations
## 3 Results and Discussion
### The Analysis of Power Imbalance Error
#### 3.1.2 Impact Factors of Power Imbalance Error
A more efficient way to increase the mesh density was examined through the mesh adaptation technique. Based on the reference mesh, the mesh density was increased in the regions with the high gradient of wake power, \(\hat{\mathrm{E}}_{w}\), After mesh adaptation, the total cell number increased from 4 million to about 8 million. The results showed that the \(e_{domain}\) was reduced from 38.5% to 34.1% and \(e_{wing}\) was reduced from 22.3% to 17.6%. The mesh density \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mesh Type** & \(e_{domain}\) & \(e_{wing}\) \\ \hline Unstructured mesh & 43.0\% & 26.8\% \\ Multiblock structured mesh & 36.4\% & 25.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Power imbalance error for unstructured mesh and multiblock structured mesh. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Discretization Methods** & \(e_{domain}\) & \(e_{wing}\) \\ \hline Second-order upwind & 43.0\% & 26.8\% \\ Third-order MUSCL & 38.5\% & 22.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Power imbalance error for the discretization methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Turbulent Model** & \(e_{domain}\) & \(e_{wing}\) \\ \hline S-A model & 40.8\% & 23.0\% \\ standard k-\(\epsilon\) model & 40.9\% & 22.3\% \\ k-\(\omega\) SST model & 38.5\% & 22.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Power imbalance error for three turbulent models. was mainly increased in the outer region of the boundary layer of the wing surface and in the vicinity of the trailing edge. This effort led to a notable improvement compared with the results of the unstructured fine mesh. The mesh adaptation obtained the best result for \(e_{domain}\) (34.1%) in this study. In summary, the study of impact factors showed that a multiblock structured mesh obtained less total power imbalance error than the unstructured mesh, especially in the wake region. The high-order discretization method was beneficial in reducing the error. The impact of the turbulent model was marginal. Increasing the mesh density in the outer region of the wing boundary layer could reduce the power imbalance error. In the best scenario, \(e_{domain}\) was 34.1% and \(e_{wing}\) was 17.6% for the wing body configuration. These results are used in the following discussions.
## 4 MPPT Control Design
### Current Reference Estimator \(I_{mpp}\)  
The aim of this step is the determination of the reference current \(I_{mpp}\) for different operating conditions. According to Figure 7, it is clearly shown that the performance of the PEMFC is largely influenced by the variation made on hydrogen, oxygen, and temperature. Thus, the hydrogen operating pressure \(P_{\mathrm{H_{2}}}\) varied from 0.01 to 0.6 bar, and oxygen operating pressure \(P_{\mathrm{O_{2}}}\) varied from 0.00022 to 0.0022 bar, while the operating temperature \(T\) varied from 25 to 50 \({}^{\circ}\)C. When \(P_{\mathrm{H_{2}}}\) and \(P_{\mathrm{O_{2}}}\) are equal to 0.01 and 0.00022 bar, respectively, the power generated by the PEMFC is at the lowest value compared to the pressure of other gasses. On the other hand, once \(P_{\mathrm{H_{2}}}\) and \(P_{\mathrm{O_{2}}}\) increase to 0.6 and 0.0022 bar, respectively, the power produced by the PEMFC is at the highest value. Consequently, the efficiency of the PEMFC is improved by rising the partial pressures. Besides, the efficiency of the PEMFC may also be improved by raising its temperature. However, compared to the influence of the partial pressure, the PEMFC is not largely influenced by its temperature variations.  
Figure 6: Overview of the designed maximum power point tracking control.  
Figure 7 also shows that, for each operating temperature and pressure, the maximum power can be obtained using the MPP curve. The latter is constructed using the function given in Equation (19). The synoptic diagram of this function is shown in Figure 8. It calculates the corresponding \(I_{mpp}\) for each MPP value. Therefore, the blue curve that has been constructed using the fitting function \(I_{mpp}=f(P)\) is considered as the MPP reference current estimator.  
\[f(P)=a_{1}\times P^{4}+a_{2}\times P^{3}+a_{3}\times P^{2}+a_{4}\times P+a_{5} \tag{19}\]  
where \(a_{1}=-5041e^{-010}\), \(a_{2}=1191e^{-07}\), \(a_{3}=-103e^{-04}\), \(a_{4}=3943e^{-04}\), and \(a_{5}=8\).  
Figure 9 clarifies that, for any operating power point, after several projections, the PEMFCs will be managed to operate at the desired point \(I_{mpp}\) and then extract the maximum power from the PEMFC. For instance, suppose that the system works at \(P_{0}\). The projection of this power point onto the blue  
Figure 8: Synoptic diagram of the maximum power point tracking function.  
Figure 7: The effect of the operation pressure and temperature on the PI polarization curves.  
curve will change the operating current from \(I_{0}\) to \(I_{1}\). As a consequence, the system will be working at \(P_{1}\). By applying the same process on the operating power point \(P_{1}\), the system will be brought to operate at \(P_{2}\), which is closer to the MPP. After several projections, the operating power point will change repeatedly until it reaches the MPP where the reference current equals to \(I_{mpp}\). The main feature of this method is that we can keep the fuel cell working at its MPP with great performance.
## 3 Mathematical Model
### Mesh Division and Boundary Conditions  
The size of AH36 steel in the experiment is 300 \(\times\) 150 \(\times\) 5 mm. To simplify the model and speed up the calculation, the asymmetric model was used and part of it was taken for simulation. A model of size 15 \(\times\) 7 \(\times\) 7 mm was created, and the model is shown in Figure 2. The model is set up with an air layer of 2 mm thickness. A dense mesh was used in the near-weld area, and a loose mesh was used in other areas. The minimum mesh size is 0.125 mm, and the total number of meshes is 270,000.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline  & **Fe** & **C** & **Si** & **Mn** & **Mo** & **Cr** & **V** & **P** & **S** & **Cu** & **Ni** & **Nb** \\ \hline AH36 & 98.021 & 0.16 & 0.4 & 1.4 & - & 0 & 0 & 0.014 & 0.003 & 0 & 0 & 0.002 \\ JM-56 & 97.399 & 0.077 & 0.87 & 1.45 & 0.002 & 0.031 & 0.004 & 0.012 & 0.013 & 0.125 & 0.017 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: AH36 steel and JM-56 wire chemical composition (wt.%).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Values** & **Unit** \\ \hline Laser power & 7 & kW \\ Laser power & 7.5 & kW \\ Laser power & 8 & kW \\ Current & 200 & A \\ Welding speed & 41.7 & mm/s \\ Feed rate & 166.7 & mm/s \\ Defocus distance & 0 & mm \\ Interval & 0 & mm \\ Shielding gas flow rate & 333.3 & mL/s \\ Laser diameter & 0.2 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Welding experimental parameters.  
Boundary conditions are important factors affecting the simulation results, and the boundary conditions should be consistent with the actual welding situation. The melt drop volume, melt drop initial temperature, and melt drop initial velocity was set in the inlet position of the model. According to the measurement results of the high-speed camera during the experiment, the melt droplet body radius \(r_{a}\), the average heat absorption time of the melt droplet \(t_{a}\), and the initial velocity \(v_{a}\) were obtained.  
The molten droplet is simplified to a sphere and the volume formula is:  
\[\mathrm{V}_{0}=\frac{4}{3}\pi r_{a}{}^{3} \tag{1}\]  
The initial temperature of the molten droplet \(T_{0}\) is:  
\[T_{0}=\frac{3\eta Ult_{a}}{4\pi C\rho r_{a}{}^{3}}+T_{1} \tag{2}\]  
where \(C\) is the specific heat capacity of the wire material, \(\eta\) is the arc energy absorption efficiency of the wire, \(U\) is the voltage, \(I\) is current, \(\rho\) is the density of the wire in the molten state, and \(T_{1}\) is the room temperature.