esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Electric Propulsion System Characterization
### Electric Propulsion System Comparison  
By comparing the EPUs selected on the basis of efficiency for individual EPU setups, it is possible to select the electric propulsion system parameters for a specific aircraft purpose. Figure 22 shows the overall efficiency of the selected EPU where it can be seen that the higher power EPUs have a higher degree of overall efficiency. Figure 23 shows the thrust force with respect to the rotor angular velocity for the selected EPU setups based on experimental measurements and specifications from the EPU manufacturer.  
Figure 21: Energy consumption of MN 4014 BLDC motor: (**a**) electric current with respect to thrust force; (**b**) overall efficiency.  
Figure 22: The overall efficiency of selected EPUs.  
Very important information is the total time that the aircraft can be in the air, which depends on the mission itself, i.e., on the required flight performance and the cargo that the aircraft carries. Based on the characteristic of EPUs, an estimate of the flight time was performed for selected series of multirotor configurations defined by the parameters of the propulsion and energy system. In particular, the basic case of stationary flight for conventional configurations was considered. The drop in battery voltage and the power consumption by the control system (also the equipment system) are ignored. The estimated flight time is defined by the following expression  
\[t_{est}\approx\frac{B_{cap}}{t_{dem}} \tag{20}\]  
where \(B_{cap}\) is the battery capacity in mAh, and \(i_{dem}\) is the electric current at the required thrust force determined according to the characteristics. The required thrust force to achieve a stationary flight depends on the overall multirotor mass. To that end, Table 2 shows the parameters for three different EPU setups with appropriate power source, considered for quadrotor, hexarotor, and octorotor configuration of multirotor UAV. Based on typical values of aircraft (\(m_{UAV}\)) and payload (\(m_{PL}\)) mass for each configuration, stationary flight time was estimated. An increase in the number of rotors increases available payload mass, but at a cost of higher battery capacity requirement, without a significant increase in flight time.
# Seismic Observations in Bucharest Area with a Raspberry Shake Citizen Science Network
## 4 Ambient Noise Observations
Following the methodology described by [PERSON] et al. (2020)[50], we computed the long-term evolution of the seismic noise for the citizen seismometer installed at the French School Anna de Noailles. To achieve this, we used the SeismoRMS software package ([PERSON] et al., 2020)[38], which is accessible to the general public. We employed 1800 s time frames that had a 50% overlap in order to determine the probabilistic power spectral density (PPSD) acceleration amplitudes for each day. After that, the PPSDs were converted into displacement spectral powers, and then, using Parseval's identity, they were converted once again to the displacement root mean square in the frequency domain of interest. The analyzed period includes several national holidays for students, such as the autumn, Great Union, and winter holidays. We found that there is around a fifty percent reduction in the noise level on all holidays. [PERSON] et al. (2021)[43] demonstrated a similar behavior of ASN for several professional stations belonging to the Romanian Seismic Network that were installed in schools and kindergartens in Bucharest. Based on their findings, they concluded that the decrease in the noise level is predominantly related to the reduced human mobility inside the buildings in which the stations are housed. A zoom in on the week of 31 October-6 November 2022 provides a clearer perspective of the 3 November M\({}_{\rm L}\) = 5.3 Vrancea earthquake (Figure 12). Figure 11: Spectrograms (**top**) and amplitude–time plots (**bottom**) showing noise level from Monday to Sunday (31 October–6 November 2022). The highlighted yellow line and the larger amplitude plot represent the 3 November 2022 earthquake from the Vrancea zone.
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 5 Results and Discussion
### Ablation Study  
We also perform an ablation test to evaluate the effectiveness of the two-stage training strategy and the incorporation of MFMVDR results. The results are shown in Table 6. The first row corresponds to a single TCN-DenseUNet model. The second row uses the MFMVDR as a post-processing module for the TCN-DenseUNet. The model in the third row only takes the noisy input and the enhanced speech from the first stage as inputs in the second training stage. The fourth row is the results of the proposed model.  
By comparing the first and fourth rows, we can observe that our chosen base network, TCN-DenseUNet, achieves strong performance, but there is still room for noticeable improvement by training it in two stages and fusing the MFMVDR information. When we compare the first and second rows, it can be seen that post-processing the output of the neural network with the MFMVDR filter alone does not work and even leads to a significant drop in all metrics of the processed speech. However, by adopting the two-stage training approach as shown in the first and third rows, we can achieve improvements in both enhancement and WER metrics. Furthermore, comparing the third and fourth rows, it becomes apparent that combining the information from the MFMVDR in the second training phase can further improve the performance of the system. This finding suggests that the results of the MFMVDR can also contribute to the overall system's performance improvement.
## 4 Approximate Prediction of Fast-Scale Instability and Adaptive Slope Compensation
### Fast-Scale Stability Analysis
Therefore, the cycle-by-cycle adapted slope \(m_{r}(nT)\) and amplitude \(V_{M}(nT)\), \(n\in\mathbb{N}\), of the compensating ramp signal \(v_{r}\) to guarantee a system free from fast-scale instability are  
\[m_{r}(nT)=R_{s}\frac{v_{01}(nT)}{2L}\qquad\text{and}\qquad V_{M}(nT)=R_{s}T \frac{v_{01}(nT)}{2L} \tag{22}\]  
The expression of the eigenvalue \(\lambda(\theta)\) given in (20) is plotted in Figure 11 in terms of the phase angle \(\theta\) with an adaptive slope compensation as given in (22). It can be observed that the eigenvalue \(\lambda(\theta)\) fulfills the condition \(-1<\lambda(\theta)<0\) for all values of phase angle \(\theta\in(0,2\pi)\) implying stability for all this range \(\theta\).
## 4 Result and Discussion
### Sensitivity Analysis  
SHAP (SHapley Additive exPlanations) is a commonly employed model interpretation technique rooted in the concept of Shapley values from game theory [61; 62; 63; 64]. It quantifies the contribution of input features to the model output through SHAP values. Analysis conducted with SHAP allows for the assessment of feature influence from both global and local perspectives, offering a more intuitive interpretation of model prediction outcomes. Furthermore, based on SHAP analysis results, adjustments to feature sizes can be made  
Figure 11: Taylor diagram of test set performance.  
more scientifically and effectively to achieve target output values, serving as a crucial reference for design purposes.  
The WAOA-ANN model input feature impact rankings obtained based on SHAP analysis are shown in Figure 12. For the prediction of \(\theta\), the ranking of the input feature's impact on the output is \(\sigma_{H}>c>P_{w}>v>E>r>\varphi>\sigma_{h}>\sigma_{v}\) and for the prediction of \(r_{b}/r\), the ranking is \(\sigma_{H}>P_{w}>c>\sigma_{v}>E>\varphi>r>\
u>\sigma_{h}\). The impact of input features on the output (promoting or reducing) changes with the value of the feature, and Figure 13 demonstrates the impact of different features on the WAOA-ANN output. From the figure, it can be seen that \(\theta\) is positively correlated with \(\sigma_{H},\
u,E,\sigma_{h}\), negatively correlated with \(c,P_{w},\varphi,\sigma_{v}\), and has a complex relationship with the \(r\). \(r_{b}/r\) is positively correlated with \(\sigma_{H},\sigma_{v}\), negatively correlated with \(P_{w},\epsilon,E,\varphi\), and has a complex relationship with the \(r,\
u,\sigma_{h}\).  
Figure 12: Average impact on model output of \(\theta\) and \(r_{b}/r\).  
Figure 13: Influence results of each parameter on \(\theta\) and \(r_{b}/r\) prediction.
## 3 Results
### Identification of Volatile Organic Compounds by HS-SPME-GC-Ms
#### 3.4.3 Key Volatile Components of Greengage Alcoholic Beverage by OAV Analysis  
The contribution of VOCs to the overall aroma of the GAB depended on their OAV, which was influenced by both their concentration and odor threshold [36]. Based on the concentration and odor threshold of the VOCs, a total of 13 aroma compounds with OAV \(\geq\) 1 were identified, including seven esters, two alcohols, two ketones, one acid, and one olefin.  
Among these compounds, combined with the VIP values, ethyl octanoate (OAV = 69.67-105.85) emerged not only as a potential marker for distinguishing aroma differences among the GAB samples fermented by different _S. cerevisiae_ strains, but also as a key contributor to the characteristic aroma of the GAB. Notably, \(\beta\)-Ionone exhibited an exceptionally high OAV (OAV = 982.86-1107.14), largely due to its low odor threshold (0.007 ug/L), making it a prominent contributor to the overall flavor of the GAB. In previous research, \(\beta\)-Ionone has been identified as a characteristic aroma compound in green tea beverages and citrus tea, offering a violet-like aroma [36; 37]. [PERSON] et al. highlighted \(\beta\)-Ionone as a unique aroma volatile in red wine [38]. Additionally, ethyl laurate was present above its threshold (OAV = 1.06) only in the SY fermentation group, which contributes floral and green apple aromas to the beverage as previously mentioned. Ethyl isobutyrate (OAV = 6.7-20.8) and ethyl 2-methylbutyrate (OAV = 6.05-8.7) were also major contributors to the GAB aroma, consistent with the findings by [PERSON] et al [30].  
To visualize the contribution of each VOC to the aroma in the different fermentation groups, a heatmap was generated (Figure 6) based on one-way ANOVA (\(p\) < 0.05) for compounds with OAV \(\geq\) 1. The results showed that in the SY fermentation group, \(\beta\)-Ionone, ethyl octanoate (fresh, fruity aroma), ethyl laurate, ethyl hexanoate (banana, cheese aroma), ethyl butyrate (apple, butter aroma), and octanoic acid (acidic, cheese, fatty aroma) made significant aroma contributions. In the EC-1118 group, irisone (floral aroma) was a prominent contributor. For the NM-8 group, ethyl isobutyrate (floral, fruity aroma) showed a relatively high aroma contribution, though the overall aroma was less pronounced. In the ET008-54 group, isopentanol (alcohol, cocoa, floral aroma), ionene (floral aroma), and ethyl decanoate (fruity, brandy-like aroma) dominated the aroma profile. Overall, the SY fermentation group produced the richest aroma, followed by ET008-54, and EC-1118, with the NM-8 group showing a comparatively mild aroma profile.
## 4 Experimental Results and Analysis
### Ablation Study  
The ablation study of LCANet is performed to further evaluate the effectiveness of channel shuffling and MHSA. In this experiment, we denote the LCANet model with channel shuffling and MHSA removed as CNN, the model with channel shuffling only removed as CNN+MHSA, and the model with MHSA only removed as CNN+Shuffle.  
The results of the ablation experiments are shown in Figure 11. Compared with CNN, CNN+Shuffle can improve accuracy with fewer epochs, but it does not speed up the process of complete recognition. This is reasonable as channel shuffle works to increase data interaction between channels to avoid model mislearning, rather than directly contributing to learning speed. In contrast to channel shuffling, the number of cycles required for model training is reduced after using MHSA. This is because the MHSA brings additional global information that makes the various categories of samples more distinguishable.  
Figure 11: Ablation study of LCANet.  
Figure 10: Accuracy of damaged area detection using LCANet and contrast models.  
The ablation experiments proved that both channel shuffling and MHSA in LACNet are effective, allowing for faster and more stable model training.
## 2 Materials and Methods
### Genetic Algorithm  
One of the two algorithms used was a Genetic Algorithm (GA). This type of algorithm is inspired by the selection genes in natural selection. Usually, the individuals with the best genes have the most offspring. Under this principle, the search for the coefficients is carried out, with the best genes representing the combinations of coefficients and exponents that generate a lower RMSE.  
This algorithm was chosen because it is the most extensive and widely used metaheuristic algorithm despite being a first-generation algorithm. Although there are multiple adaptations for its improvement, it is well known that all metaheuristic algorithms risk falling into local optima. This situation happens because they find a suitable solution and keep searching around that solution in the search space, but better solutions exist elsewhere in that space. The genetic algorithm is particularly prone to this because several specific  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Biometric Variables** & **Nomenclature** \\ \hline Height at Withers sensed by Kinect® & HWK \\ Rump Height sensed by Kinect® & RHK \\ Body Length sensed by Kinect® & BLK \\ Body Diagonal Length sensed by Kinect® & BDLK \\ Body Total Length sensed by Kinect® & STLK \\ Girth Semi-Circumference sensed by Kinect® & GSCK \\ Abdomen Semi-Circumference sensed by Kinect® & ASCK \\ Body Weight Estimated & BWE \\ \hline \hline \end{tabular}
\end{table}
Table 1: Biometric variables used as elements of the polynomial model.  
search parameters must be tuned correctly, such as mutation percentage and individuals that can reproduce.  
The version of this algorithm used is shown as pseudo-code in Algorithm 1. It goes through a stage of selection, one of mutation, and one of elitism. The complete search parameters used are shown in Table 2. These parameters were obtained through multiple runs based on the performance of the algorithm; however, it is important to note that there are various tuning methods such as CRS-Tuning [60], F-Race [61; 62], and REVAC [63], which can be used to optimize the algorithm and other optimization techniques [64]. In particular, the use of these methods with a larger sample is recommended to compare the error reduction in future works.  
```
1:Begin
2: Generate initial population \(\mathcal{P}\) based on random vectors with coefficients and exponents
3:while fitness evaluations consumed limit <40,000 do
4: Evaluate fitness of \(\mathcal{P}\) members
5: Selection of best \(\mathcal{P}\) members based on their fitness
6: Crossover of best parents from \(\mathcal{P}\) based on a random single point
7: Mutation in a part of the individuals based on a probability value
8: Create new population P based on most-fit individuals and elitism
9:endwhile
10: Save the best vector
11:end
```  
**Algorithm 1** Genetic algorithm with elitism
## 4 Discussion  
### Current Tunnel Longitudinal Deformation Calculation Theory  
For the calculation of longitudinal deformation caused by tunnel excavation, many scholars have given a series of research results.  
[PERSON] normalized the tunnel monitoring data and obtained the empirical formula for calculating the longitudinal deformation of the tunnel [32]:  
\[\frac{u_{r}}{u_{\max}}=\frac{1}{2}[1-\tanh(\frac{1}{3}-\frac{x}{2R})] \tag{1}\]  
where \(u_{r}\) is the longitudinal deformation at the distance \(x\) from the excavation face; \(u_{\max}\) is the longitudinal deformation far enough from the excavation face; \(x\) is the axial distance from the excavation face; \(R\) is the excavation radius of the tunnel.  
Based on the elastic deformation theory of surrounding rock after excavation, [PERSON] obtained the deformation law of tunnel without support as follows [33]:  
\[\frac{u_{r}}{u_{\max}}=0.25+0.75[1-(\frac{0.75}{0.75+x/R})^{2}]\,x\geq 0 \tag{2}\]  
[PERSON] performed the best fitting based on the measured value of tunnel wall convergence, and the empirical formula for calculating the longitudinal deformation of the tunnel is obtained as follows [34]:  
\[\frac{u_{r}}{u_{\max}}=[1+\exp(\frac{-x/R}{1.10})]^{-1.7} \tag{3}\]  
According to the two-dimensional finite element theory, [PERSON] and [PERSON] obtained the calculation formula for longitudinal deformation of ideal elastic-plastic surrounding rock as follows [35]:  
\[\frac{u_{r}}{u_{\max}}=\begin{cases}1-(1-u_{0}^{*})\exp(-\frac{3x/R}{2R^{*}}) &,x\geq 0\\ u_{0}^{*}\exp(x/R)&,x<0\end{cases} \tag{4}\]  
\[u_{0}^{*}=\frac{u_{0}}{u_{\max}}=\frac{1}{3}\exp(-0.15R^{*}) \tag{5}\]  
where \(R^{*}=R_{p}/R\), \(Rp\) is the maximum plastic zone radius caused by tunnel excavation, which the following formula can calculate the following:  
\[R_{p}=R\exp[\frac{(\sigma_{2}u_{r}/\sigma_{ci}+s)^{1-a}-(p_{i}m_{b}/\sigma_{ ci}+s)^{1-a}}{m_{b}(1-a)}] \tag{6}\]  
Figure 6: Comparison of LDP curves under different water content conditions.  
where \(\sigma_{r2}\) is the radial stress at the junction of the elastoplastic zone; \(p_{i}\) is the supporting force; \(\sigma_{0}\) is the initial hydrostatic pressure of surrounding rock; \(m_{b}\), \(s\), \(\alpha\) are Hoek-Brown criterion parameters.  
[PERSON] et al. derived a theoretical formula based on [PERSON]'s result about the relationship between the displacement of the tunnel surrounding rock and the distance from the excavation face to the monitoring section as follows [36]:  
\[\frac{u_{r}}{u_{\max}}=\begin{cases}(1-\lambda_{0})(1-e^{-\frac{x}{\lambda}})+ \lambda_{0}&,x\geq 0\\ \lambda_{0}e^{\frac{x}{\lambda_{1}}}&,x<0\end{cases} \tag{7}\]  
\[X_{1}=\frac{\lambda_{0}}{1-\lambda_{0}}X \tag{8}\]  
where \(\lambda_{0}=u_{0}/u_{\max}\), \(u_{0}\) is the longitudinal deformation of the tunnel at the excavation face; \(X\) is a constant.
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 3 System Model
### Analysis of Probability Model  
D2D communication is susceptible to the influence of equipments working in the same frequency and base station, which will reduce the reliability of data transmission. Assume the bit error rate of communication between different devices is \(P_{b}\), the packet loss rate \(Q\) and the length of each transmitted packet is \(L/K\), we can obtain the successful transmission probability of a single packet as \(p\), as shown in Equation (4).  
\[p=(1-p_{b})^{L/K} \tag{4}\]  
The probability that each packet can be correctly received by the destination node is \(d\), as shown in Equation (5).  
\[d=(1-Q)(1-p_{b})^{L/K} \tag{5}\]  
During the video transmission process, each video frame is divided into \(K\) packets for transmission. The probability that a group of \(K\) packets can all reach the destination node and be correctly received is \(e\), as shown in Equation (6).  
\[e=[(1-Q)(1-p_{b})^{L/K}]^{K} \tag{6}\]  
During transmission under the traditional replication-based mode, the probability of each destination node successfully recovering the original video frame is \(e\). Then, when the redundancy is set at \(r\) times, the total number of transmitted packets is \((r+1)\times K\), then, the probability of each destination node successfully recovering the original video frame will become \(t_{1}\), as shown in Equation (7).  
\[t_{1}=(1-(1-d)^{(r+1)})^{K} \tag{7}\]  
In this process, the probability that one packet is not correctly received for \((r+1)\) times is \((1-d)^{(r+1)}\); the probability that at least one complete packet is successfully received is \((1-(1-d)^{(r+1)})\)Compared to the traditional scheme, in the RLNC-based transmission scheme, each packet after encoding is linearly independent with a high probability, so any packet received by each node is effective [10]. When the total number of encoded packets is \(A\), the number of redundant packets is \((A-K)\). Therefore, in the RLNC-based scheme, the probability of each node successfully recovering the original data is \(t_{2}\), as shown in Equation (8).  
\[t_{2}(K\leq h\leq A)=\sum_{h}\Bigg{(}\begin{array}{c}h\\ A\end{array}\Bigg{)}d^{h}(1-d)^{A-h} \tag{8}\]  
\(h\) stands for the number of received packets. The node only needs to receive \(K\) packets to decode and recover the original video frame.  
In the transmission scheme proposed in this paper, the re-encoding operation is conducted at other destination nodes. Assume there are \(n\) destination nodes, we can obtain the redundancy of \(R\), as shown in Equation (9).  
\[R=n[(1-Q)(1-p_{b})^{L/K}]^{K} \tag{9}\]  
Then, the probability for other destination nodes to obtain the original data is \(t_{3}\), as shown in Equation (10).  
\[t_{3}(K\leq h\leq K+R)=\sum_{h}\Bigg{(}\begin{array}{c}h\\ K+R\end{array}\Bigg{)}d^{h}(1-d)^{K+R-h} \tag{10}\]
# Laser Vision Correction for Regular Myopia and Supracor Presbyopia: A Comparison Study
## 3 Results
### Uncorrected Visual Acuity  
Uncorrected visual acuity (UCVA) was assessed before (decimal scale: \(0.31\pm 0.24\)) and after the treatment (decimal scale: \(0.88\pm 0.20\)) for the whole study population. In the myopia group UCVA was \(0.19\pm 0.16\) preoperatively, and \(1.01\pm 0.15\) postoperatively. In the presbyopia group UCVA was \(0.38\pm 0.25\) preoperatively, and \(0.78\pm 0.17\) postoperatively. A statistically significant improvement was observed in both groups (\(p<0.0001\)). In 4 eyes (4.3%, two in myopia and two in presbyopia group), UCVA remained unchanged, and for two eyes (2.2%, all in the presbyopia group) UCVA was slightly worse than before the treatment. For all other eyes (93.5%), UCVA improved (see Table 1). The failure to achieve the target refraction and UCVA could be corrected with a re-treatment.  
While the multifocal presbyopia treatments showed a small trend (R\({}^{2}\) = 0.01) towards greater corrections having a lower postoperative UCVA, this trend could not be observed (R\({}^{2}\) = \(1\times 10^{-5}\)) for the myopic corrections (see Figure 1).
## 1 Introduction
By numerical analysis, a coupling efficiency of more than 76 percent from the single emitter to the waveguide was achieved. By collecting the emission in the exciton line both from the quantum dot and the output coupler into a Hanbury Brown and Twiss (HBT) interferometer, the second-order cross correlation function g\({}^{(2)}\)(\(\tau\)) was measured to prove the single photon nature of our waveguide coupled on-chip photon source. Our approach provides an easy way to implement quantum photonic devices integrated with a single photon source for on-chip quantum information processing.