esa-sceva/satcom-chunk-collection huggingface dataset:
# Helicopter Blade-Vortex Interaction Airload and Noise Prediction Using Coupling CFD/VWM Method
## 3 Results and Discussions
### Sensitivity Study of BVI Noise
#### 3.3.3 Tip Mach Number  
By holding the advance ratio, thrust coefficient, and tip-path-plane angle constant, the tip Mach number increases from 0.55 to 0.725 to analyze the effects on the BVI noise characteristics.  
The locations of BVI events on the rotor plane and the miss-distance on the axial plane are less sensitive to \(M_{tip}\) because the wake skew angle and the basic epi-cycloid are not affected by \(M_{tip}\). Figure 18 illustrates the sound pressure at #7, #3, and #9 at different tip Mach numbers. With each increasing tip Mach number, the impulse waveform seems unaffected, while the amplitude increases at each observation location. When \(M_{tip}\) increases to 0.7, the local Mach number on the advancing side reaches transonic speed, and interactions #A1 and #A2 are more influential which accounts for the stronger noise impulse characteristics. However, it should be noted that the calculation of BVI noise in this paper treats Equations (13) and (14) as linear equations which are nonlinear at transonic speed.  
Figure 16: Predicted acoustic pressure at #3 variance with the thrust coefficients.  
Figure 17: Max BVISPL variance with the thrust coefficient.  
Figure 19 shows the contour plots of SPL for three tip Mach numbers. As \(M_{tip}\) increases, interactions #A1 and #A2 become more influential (Figure 19b), and two peaks of sound pressure appear. When the tip Mach number reaches 0.725, one can observe that the BVI noise largely from #A1 and #A2 become stronger; on the other hand, another strong noise which radiates to the rotor plane appears in space.  
Figure 20 presents variations in peak noise levels as functions of the tip Mach number. It is observed that 5 dB increases in the maximum BVI noise occur when the tip Mach number varies from 0.55 to 0.725, demonstrating that the tip Mach number is one of the most sensitive governing parameters of BVI noise.  
Figure 19: Contour plots of SPL on the sphere surface for different tip Mach numbers. (**a**) \(M_{tip}=0.6\); (**b**) \(M_{tip}=0.7\); (**c**) \(M_{tip}=0.725\).  
Figure 18: Predicted acoustic pressure at #7, #3, and #9 variance with the tip Mach number.  
Figure 20: Max BVISPL variance with the tip Mach number.
# Evaluation of the GPS Precise Orbit and Clock Corrections from MADOCA Real-Time Products
## 5 SISRE Computation
As for the SISRE performance, the orbit-error-only SISRE of each satellite achieves a level of 5 cm and the SISRE of each satellite is roughly 10 cm. These results indicate that the MADOCA real-time products achieve JAXA's goal for real-time orbit and clock accuracy and can meet the centimeter accuracy demands of RTPPP users. In order to reveal the long-term characteristic of the quality of MADOCA real-time products further, the daily RMS of orbit-error-only SISRE and SISRE values are calculated from the whole year 2018 for all the available GPS satellites. The time series of daily RMS of SISRE are shown in Figure 8.  
In Figure 8, we can see that the orbit-error-only SISRE values demonstrate a consistency with short-term values and can basically maintain good stability over the whole year comparison except for a few jumps. Due to the variation in the accuracy of the clock, SISRE values of all available satellites experience relatively large fluctuations, yet most of them are still less than 10 cm. In addition, it is worth mentioning that the reason for the interruption of the graphics in the figure is that the MADOCA raw data is defective or does not exist during these time periods. As such, the raw data in these time periods is considered to be unavailable. Figure 7: Seven-day signal-in-space range errors of MADOCA real-time products for all available GPS satellites relative to IGS final products from 11 November 2018 to 17 November 2018.
## 5 Evaluation
### Discussion  
A comparison of previous studies relating to LDP using the proposed method is presented in Table 6. We show the results of the robustness against poisoning attacks in terms of the frequency and mean gains. The frequency and value gains for PrivKV and PrivKVM without defensive measures were 2.5 and 2.5, respectively, and 10.5 and 30.2, respectively. However, the frequency and value gains of the proposed method, which applies OT for defense and EM for estimation, were 0.4 and 2.9, respectively.  
In this study, we did not evaluate the frequency and mean gains with the defense method applying OCC. In our approach, random item selection in LDP is performed in collaboration between the user and server; hence, no malicious user sends fake data to manipulate the estimation. This is an ideal countermeasure under the assumption that the building blocks are secure. On the other hand, OCC is a probabilistic measure that allows small amounts of fake data to be sent in LDP. Therefore, we consider the proposed protocol to be more resilient than OCC.  
The experimental findings highlight the superior robustness of the emPrivKV scheme compared with other LDP schemes. Several factors contribute to this observation.  
First, PrivKV relies on MLE, where the single highest frequency determines the expected value of the perturbation. Consequently, the manipulation of the highest frequency can significantly impact the scheme. In contrast, the EM algorithm iteratively adjusts the probabilities based on all observed frequencies. Therefore, even if the highest frequency is manipulated, the involvement of other elements helps to mitigate the manipulation of frequency.  
Second, our estimation of the mean value incorporates not only positive statistics (\(v_{k}^{\prime}=1\)) but also both positive and negative statistics (\(v_{k}^{\prime}=1\) and 0). This renders the estimation more resilient to poisoning attacks, thereby explaining why emPrivKV exhibits smaller mean gains.  
Finally, by extrapolating our experimental gain results, we can gauge the overall robustness of the proposed protocol. According to Proposition 1, the M2 GA becomes irrelevant when a perturbation with the OT protocol is employed.
## 3 Related Work  
### Rateless Code for Video Streaming  
Prior usage of AL Raptor coding, especially that in various wireless standards [35], such as DVB-H and 3 rd Generation Partnership Project (3 GPP)'s Multimedia Broadcast Multicast Service (MBMS), has been multicast [36] without feedback, rather than unicast with feedback as herein. Moreover, unlike herein, it has been deployed in the standards with block-level symbols that can extend across packets rather than intra-packet as herein, resulting in longer repair latencies if and when the blocks extend over many packets.  
Data-partitioned video streams were first protected by a form of Raptor codes in [37], which paper from the current author also demonstrated that data partitioning has the least overhead of H.264/AVC error resilience methods. Since then, the idea was taken up in [38] providing an application to DVB-H and comparing with random linear codes (RLC). It was demonstrated that this form of AL-FEC is effective at lower data rates when compared to H.264/AVC without data partitioning. RLC were competitive with rateless coding in terms of error protection. However, as the information data length increases, the need to employ a Gaussian elimination (rather than belief propagation) decoding algorithm causes the computational complexity and, hence decoding latency, to increase as O(\(n^{3}\)) rather than O(\(n\)) for Raptor codes. In [39], RLC were applied in the same form as [37] had already used for Raptor codes, that is with expanded windows code (called Growth codes in [37]) to provide scalable channel coding. However, in [39], the protection was not applied to data-partitioned video but by prioritizing the different H.264/AVC slice types. Finally, there is a summary of the previous investigations of [38, 39] in [40]. The current contribution does not pursue the ideas originally developed by us in [37] because we seek herein to show that the complication of expanding window or growth codes may be unnecessary for data-partitioned video streaming.  
In our paper, adaptive provision of redundant rateless code results in reduced channel coding overhead. It is possible that if further advantage of cross-layer communication could be made then the overhead of rateless coding can be reduced still further. For example in [41, 42], the amount of rateless data was jointly determined by the modulation and coding scheme (MCS) at the PHY layer. It is possible that adaptation according to the wireless channel state could be combined with adaptation according to the PHY-layer MCS mode in a future cross-layer scheme.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 2 Structure of Filter Bank Multi-Carrier Systems
The structure of FIR filters consist of series of multipliers, delays and adders coupled together to give an output. This design makes these filters suitable for designing multirate filters. Multirate filtering constitutes the fundamental building block of FBMC systems. An overview of multirate filtering is presented in Section 2.1.
## 4 Results and Discussion
### Level 2: Inferential Statistics  
#### 4.2.1 \(t\)-Test  
To address RQ2.1 (Is there any particular distribution for fuel consumption in the city and the highway of vehicles in Canada?), a two-tailed \(T\)-test has been conducted to compare the means of fuel consumption in the city and on the highway for the same vehicle, with the following configurations.  
* Null Hypothesis (H0): mean of fuel consumption in the city = mean of fuel consumption on a highway;
* Alternative Hypothesis (Ha): mean of fuel consumption in a city \(\
eq\) mean of fuel consumption in highway;
* Chosen confidence level: 99%, which means \(\alpha\) = 0.01.  
After the test, the result showed that:  
* Statistic = 149.8128 (t-value);
* \(p\)-value = 0.0.  
It is clear that:  
\[p\text{-}value=0.0<\alpha/2=0.005. \tag{1}\]  
Therefore, the null hypothesis can be rejected. This means the mean of fuel consumption in a city and on a highway for the same individual has a significant difference.
## 4 Conclusions  
In this study, a quantum chemistry method based on the DFT was used to study the decomposition mechanism of N\({}_{2}\)O on a MgO (100) surface and the effect of SO\({}_{2}\) and CO on the catalytic N\({}_{2}\)O decomposition on the MgO (100) surface. The decomposition of N\({}_{2}\)O on the MgO (100) surface is a two-step reaction consisting of oxygen atom transfer and surface reduction. The activation energy of the oxygen transfer process is 1.601 eV, and  
Figure 8: Energy line diagram of MgO (100) surface reduced by CO.  
the surface reduction process can be carried out according to the ER or LH mechanisms, wherein its activation energies are 1.502 eV and 0.840 eV, respectively. By comparing the corresponding energy change process of ER mechanism and the LH mechanism, it can be concluded that the corresponding path of the LH mechanism is more likely to react. According to the adsorption of N\({}_{2}\)O molecules on the surface and the desorption of N\({}_{2}\) and O\({}_{2}\), the reaction energy barrier of N\({}_{2}\)O decomposition in MgO (100) is 1.601 eV, and its constant rate step is the transfer process of oxygen atoms, and its catalytic performance for N\({}_{2}\)O decomposition is weaker than that of the CaO (100) and CaS (100) surfaces. In addition, by comparing the adsorption energy, structure parameters, and charge transfer of N\({}_{2}\)O on the MgO (100) surface in Figures 3 and 7 and Tables 1 and 2, we can see that the adsorption energy, structure parameters, and charge transfer of N\({}_{2}\)O on the surface of N\({}_{2}\)O, the presence of SO\({}_{2}\) competes with N\({}_{2}\)O for the active oxygen sites on the surface of MgO (100), thus reducing the catalytic activity of MgO (100). However, CO can accelerate the reduction of the MgO (100) surface and, compared with the homogeneous reaction; the MgO (100) surface can catalyze the reduction of N\({}_{2}\)O by CO to a limited extent (wherein the activation energy decreases from 1.691 eV to 1.601 eV (According to the previous research, the barrier of homogeneous reduction of N\({}_{2}\)O by CO was determined from the energy distribution diagram [18]).  
Conceptualization, X.H.; Investigation, X.H., W.L. and L.W.; Writing--Original Draft, E.Z.; Writing--Review & Editing, E.Z., Y.Z. and H.Z.; Methodology, X.H., W.L. and L.W.; Validation, C.D. and H.Z.; Guidance, X.H. and C.D. All authors have read and agreed to the published version of the manuscript.  
We are thankful for the support of the National Natural Science Foundation of China (grant number 51776070) and the Special Fund for Basic Scientific Research Expenses of the Central University (grant number 2018 ZD08).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Charge Management of Electric Vehicles from Undesired Dynamics in Solar Photovoltaic Generation
## 4 Results
### Photovoltaic Power Fluctuations  
Figure 7a presents the rate of variation of the PV system power generation for the 28 th of September (the most critical day), while Figure 7b shows the behavior on the 6 th of September (average day). These plots allow us to better interpret the performance of the algorithm under study. The PV fluctuations on the most critical day exceed the limit close to 10.3%, reaching a maximum variation of 8.98 kW/min. With the application of the control technique, considering the same time response for the EV charging system and the sampling time of the PV system, the power fluctuations decrease by 8.8%. The ramp then reaches a maximum of 1.53 kW/min. This result shows a favorable response of the EMS to the dynamic behavior of PV generation.  
In the case of the average day, the PV generation shows a low level of power variations. The fluctuations over the limit ramp were 6.8%. The maximum rate reached 9.34 kW/min between 10:00 and 12:00. The power variations reduced by 4.8%, reaching a maximum ramp of 1.52 kW/min. This result indicates that this day will require less charge management from the EMS.  
The relationships between the response time of the charging system, the reduction of PV fluctuations, and the number of EVs connected in the PCC are presented in Figure 8. According to the trend observed in the graph, when the response time increases, the proportion of PV fluctuations greater than the ramp-rate limit also increases, but it is observed that the addition of charging stations in the PCC does not show a significant effect on the smoothness of these fluctuations. Therefore, for a response time of 1 and 2 s, the group of five charging stations achieved 97.23% and 96.53% reduction in the rate of change, respectively. The group of 10 charging stations achieved 97.1% and 96.53% reductions, respectively.  
Figure 9 presents the final result of the mitigated PV fluctuations injected directly into the grid for both days. The power measured at the PCC demonstrates a significant reduction of the fluctuating dynamics of the PV generation. In Figure 9a, two different areas are enlarged in which the softening effect of the ramp-rate control strategy can be better appreciated. It can be seen negative values of the power injected in the PCC, which indicate that the power flow at this node has been reversed. The PV generation is not sufficient to supply the charging demand of the group of EVs. Therefore, the missing power comes from the external energy sources connected to the PCC.  
Figure 6: Photovoltaic power and charging power of an electric vehicle.  
Figure 8: Response time, photovoltaic power fluctuations, and number of charging stations.  
Figure 7: Rate of change of photovoltaic power and injected power. (**a**) most critical day and (**b**) average day.  
Moreover, Figure 9b includes the rate of change of \(\rm P_{\rm EV}\) for the average day. As described above, the dynamics of the charging power follow the behavior of the PV generation.
## 4 MTAPSP Model
### Multilayer LSTM Based on a Multi-Head Self-Attention Mechanism
#### 4.2.2 MHSA  
The self-attention mechanism enables the model to automatically learn and focus on the most relevant information within the input data [32]. A multi-head self-attention mechanism that can map a sequence into multiple specific spaces separately perform multiple attention computations, and finally stitch the results together to obtain richer contextual information.  
As shown in Figure 3, in MTAPSP, when the multi-head self-attention mechanism receives the output sequence from LSTM, the sequence is linearly transformed into vector matrices Q (Query), K (Key), and V (Value), facilitating multiple attention computations. The single self-attention is calculated as shown in Equation (7).  
\[\begin{split} Attention(Q,K,V)&=\text{{softmax}}( \frac{QK^{T}}{\sqrt{d_{k}}})V\\ d_{k}&=\frac{d_{\text{{model}}}}{h}\end{split} \tag{7}\]  
In the equation, \(d_{k}\) represents the dimension of the vectors in a single attention computation, \(softmax\) denotes the weight normalization function, and the product of the dimension \(d_{k}\) and the number of heads \(h\) in a single attention computation equals the model's dimension \(d_{\text{model}}\). The multi-head self-attention mechanism stitches together the  
Figure 3: Structure of the multi-head self-attention mechanism.  
results of each set of single self-attention calculations to perform a linear transformation and finally output the final result, which is calculated by the following formula:  
\[\begin{split} MultiHead(Q,K,V)=Concat(head_{1},\dots,head_{h})W^{o} \\ head_{i}=Attention(QW^{Q}_{i},KW^{K}_{i},VW^{V}_{i})\end{split} \tag{8}\]  
where \(W^{O}\), \(W^{Q}_{i}\), \(W^{K}_{i}\), \(W^{V}_{i}\in R^{d_{\text{model}}\times d_{\text{z}}}\) is the weight matrix.
# Refined Ptychographic Reconstruction of Attosecond Pulses
## 1 Introduction  
The capability to follow electronic and nuclear processes evolving on ultrafast time scales is essential to foster the comprehension of the physical and chemical properties of matter. Ultrashort light pulses proved to be a powerful tool to investigate such dynamics. While nuclei motion in molecules and crystals can be probed with femtosecond pulses [1], attosecond radiation is needed to track electronic motion in matter [2, 3]. As direct measurements with electronic devices are impossible in this time domain, one of the first priority tasks in attosecond science was to develop techniques capable to characterize the temporal properties of the attosecond pulses. Attosecond streaking [4, 5], in combination with iterative algorithms for phase reconstruction, was established as a powerful and versatile tool suited for the purpose. In addition, attosecond streaking experiments gives us access to ultrafast photoelectron dynamics in atoms [6, 7] and solid samples [8, 9, 10, 11]. The constant improvement of both theoretical models and experimental techniques has pushed the time resolution of attosecond experiments, thus driving the search for increasingly precise attosecond pulse reconstructionprocedures. As a result, several different pulse reconstruction techniques have been presented in the last years [12; 13]. Among them, FROG-CRAB (frequency resolved optical gating for the complete reconstruction of attosecond bursts) [14] proved to be the most widely used. Despite the simplicity and robustness of this method, some of the approximations, like the central momentum approximation (CMA), which are at the core of the most widely used reconstruction algorithms, limit its accuracy [15; 16; 17]. As an alternative, one can employ reconstruction algorithms which do not rely on such approximations [18; 19]. Nevertheless, those algorithms require a significantly higher computational effort, which in turns considerably increases the associated computational time. In this work we present a new approach which enables to combine the strengths of a fast and robust algorithm, based on the CMA, such as ePIE (iterative ptychographic engine) [20] with the accuracy of the Volkov transform generalized projection algorithm (VTGPA) [18]. We tested the novel method, dubbed rePIE (refined ePIE), with simulated attosecond streaking traces which do not rely on any limiting approximation. The model used to obtain the simulated traces is presented in Section 2, while the results of the reconstructions are presented in Section 3. A detailed comparison of rePIE with simple ePIE and VTGPA is reported in Section 4, while Section 5 contains the conclusions.
# Model-Based Design of Aircraft Landing Gear System
## 2 Preliminary Sizing Model
### Shock Strut Sizing  
The last component of the main landing gear is the shock strut. This element connects the wheel assembly to the rest of the airframe and includes the retraction mechanism and the shock absorber. The shock absorber has the main function of absorbing the impacts that the aircraft is subjected to when in contact with the ground [12]. The most critical impact is the touchdown during the landing phase. Therefore, it is fundamental to size such a component to cope with the heavy loads arising from this event.  
One of the most common technological solutions adopted for this role is the oleop-neumatic absorber, which can be visualized in Figure 3. It can be simplified as a cylinder with a sliding piston, which separates the cylinder onto two chambers, connected by an orifice on the piston itself. Inside, two fluids are present: a compressible gas that acts as a pneumatic spring and oil, which, when the absorber is actuated, tends to flow through the orifice, thereby acting as the damping agent.  
The first factor to estimate is the maximum stroke of the shock absorber. Multiple sources [4; 5] provide a similar formulation to estimate it:  
\[S_{shock}=\frac{V_{predt}^{2}}{2g}+S_{tire,rest}\frac{1-LF_{a}-LF_{lg}\eta_{tire}}{ L\underline{F_{lg}}\eta_{shock}-1+LF_{a}} \tag{25}\]  
where \(LF_{lg}\) is the landing gear loading factor, which is expressed as the ratio between the maximum applied load on the landing gear and the aircraft weight.  
Then, the piston area can be found as follows:  
\[A_{piston}=\frac{M_{land}\mathcal{G}}{N_{shock}P_{shock,s}} \tag{26}\]  
where \(N_{shock}\) is the number of shock struts (usually two), and \(P_{shock,s}\) is the shock pressure at rest, which can be assumed to be equal to 1500 psi. Additionally, the orifice area \(A_{or}\) may be assumed to be 2% of the piston area.  
Using the calculated piston area, it is then possible to estimate the volume of the main chamber of the shock absorber in the static, fully compressed and fully extended positions:  
\[V_{shock,s}=A_{piston}S_{shock,e}+V_{shock,c} \tag{27}\]  
\[V_{shock,c}=0.1A_{piston}S_{shock} \tag{28}\]  
\[V_{shock,e}=A_{piston}S_{shock}+V_{shock,c} \tag{29}\]  
Figure 3: Example of an oleopneumatic shock absorber of an aircraft [21].  
where \(S_{shock,e}\) is the shock travel from the fully extended to the rest position, which could be assumed as \(S_{shock}/3\).  
Finally, since the shock absorber acts as an elastic and damping element, the damping coefficient and elastic stiffness can be estimated. The damping coefficient can be computed from the heaviest impact on the shock, i.e., the touchdown, as follows:  
\[c_{shock}=\frac{\rho_{oil}}{2}\,A_{piston}\,V_{vert}\left(\frac{A_{piston}}{C_{ dis}A_{\sigma}}\right)^{2} \tag{30}\]  
while the elastic stiffness can be estimated from Boyle's law [22]:  
\[k_{shock}=A_{piston}\frac{P_{shock,e}V_{shock,e}}{V_{shock,e}S_{shock,e}} \tag{31}\]  
where \(P_{shock,e}\) is the shock pressure when fully extended, which can be assumed to be \(P_{shock,e}/4\)[5].