esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of the VFPEH  
In this paper, a vortex-induced and flutter coupled excitation piezoelectric energy harvester (VFPEH) with symmetrical airfoil is proposed, which is composed of a piezoelectric cantilever beam module, a cylindrical bluff body (hollow structure), a long shaft, two bearings (Nedel XUDZ, Yokohama, Japan) and two airfoil components, as shown in Figure 1. The piezoelectric cantilever beam module is composed of a metal substrate and a piezoelectric ceramic sheet. The piezoelectric ceramic sheet is fixed at a position 10 mm away from the fixed end of the substrate, the cylindrical bluff body is fixed at the free end of the cantilever beam, and the two airfoil parts are symmetrically fixed at both ends of the long axis placed on the same axis with the cylinder. The standard airfoil used is NACA 0012; the interface pattern is symmetrical to the airfoil chord. The airfoil is connected to the long axis at the airfoil chord and at the maximum thickness of the airfoil, according to the relevant definition of the airfoil parameters, the position of the maximum thickness is at 30% of the chord length from the leading edge.  
When the harvester system VFPEH is placed in the incoming airflow, the cylindrical bluff body and airfoil take experience a vortex-induced vibration and flutter, respectively. When the wind velocity is lower than the flutter onset of velocity, a vortex-induced vibration in the VFPEH takes place. On the contrary, it produces flutter and drives the cylindrical bluff body to swing with a certain amplitude and increases the deformation of the piezoelectric cantilever beam. In this process, the piezoelectric cantilever beam not only experiences a bending deformation, but also a torsional deformation due to the existence of airfoil, which increases the deformation of the cantilever and improves the energy harvesting performance.  
Figure 1: Schematic diagram of the VFPEH.
# Helicopter Blade-Vortex Interaction Airload and Noise Prediction Using Coupling CFD/VWM Method
## 3 Results and Discussions
### Sensitivity Study of BVI Noise
#### 3.3.4 Advance Ratio  
To study the effect of the advance ratio, the BVI noise at three BVI states (\(\mu=0.164,\ 0.194,\ 0.224\)) are simulated with a tip-path-plane, thrust coefficient, and tip Mach number that are all constant.  
When the advance ratio changes, the relative motion between the wake and rotor disk dramatically impacts the locations of BVI events and interaction angles; Figure 21 shows the locations of BVI events on the rotor disk at \(\mu=0.194,\ 0.224\). Compared with the results in Figure 5a, the interaction locations undoubtedly move backward with increasing \(\mu\), and interaction #A2 changes into a parallel interaction.  
Figure 22 presents the impact of the advance ratio on the predicted sound pressure at #7, #3, and #9. As #7 is located between the noise level peaks from interactions #A2 and #A3, a negative impulse from #A3 dominates the sound pressure signature at \(\mu=0.164\). With increasing \(\mu\), the impulse characteristics increase because interaction #A2 changes to a parallel interaction. However, at the same time the noise radiation direction will move down the rotor disk (Figure 23), which is why the sound pressure at #3 and #9 reach their maximum values at \(\mu=0.194\) rather than \(\mu=0.224\). The conclusion we have drawn from these analyses is that the sound pressure signature at one observation location is closely associated with the dominant interaction in BVI, which generates stronger impulse noise.  
A change in the interaction angle makes a difference in noise directivity. At \(\mu=0.194\) (Figure 23a), interactions #A2 and #A3 dominate the BVI noise propagation as compared with the radiation direction at \(\mu=0.164\). However, when the advance ratio increases to 0.224, the noise from #A2 becomes dominant because the interaction #A2 changes to a parallel interaction. We also observed that the radiation direction of the BVI noise moves down the rotor disk. This agrees with the conclusions of the rotor/isolated vortex line interference instances in which an interaction changes to a parallel interaction, and then the noise radiation direction from it moves down the rotor disk [(28)].  
Figure 21: BVI events occur on the rotor plane at different advance ratios. (**a**) \(\mu=0.194\); (**b**) \(\mu=0.224\).  
Figure 22: Predicted acoustic pressure at #7, #3, and #9 variance with the advance ratio.  
The peak noise level varying with \(\alpha_{tpp}\) at different advance ratios is presented in Figure 24. At the same tip-path-plane angle, the greater the forward speed is, the stronger the BVI noise will be due to higher noise radiation efficiency on the advancing side. However, with an increasing backward tilt angle, an inflection point appears at \(\mu=0.224\); the explanation for this is that at larger values of \(\mu\), the vortex filament moves faster above the rotor disk, thus the BVI noise decreases earlier for the inverse miss-distance.
## 3 Results
### PlyB221 and PlyP32 Show a Bacteriolytic Spectrum Broader than That of Their Parental Phages
cereus_ group, none of the tested strains were sensitive to the phages, albeit PlyB221 and PlyP32 were able to lyse _Bacillus megaterium_, _Bacillus licheniformis_, and _Bacillus pumilus_. PlyB221 was highly active against _B. megaterium_ (i.e., 74% and 80% of inhibition for strains Si0003 and 10A-B3, respectively) but showed reduced activity for _B. licheniformis_ and _B. pumilus_ compared to members of the _B. cereus_ group (i.e., 23%, 7%, and 28% for strains HT00023, Si0279, and 10A-B5  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & **Deep-Blue** & & & **Deep-Purple** \\ \hline
**Strain** & **Species** & **Reference** & \(\Phi\)1  & **PlyB221** & **CBD** & **CBD** & **PlyP32** & **CBD** \\ \hline \multicolumn{8}{c}{**Strains of the _B. cereus_ group**} \\ VD021 & _B. cereus_ & [37] & S & \(74.9\pm 3.9\) & + & S & \(77.1\pm 2.4\) & + \\ ATCC 10987 & _B. cereus_ & [38] & I & \(72.6\pm 2.3\) & + & I & \(77.8\pm 9.9\) & + \\ F4810/72 & _B. cereus_ 1  & **B. cereus** & [39] & L & \(67.8\pm 10.7\) & + \\ H3081.97 & _B. cereus_ 1  & L & \(57.3\pm 5.9\) & + & I & \(32.7\pm 0.7\) & + \\ WSBC10202 & _B. reinhardtiiensis_ & [41] & S & \(61.1\pm 3.4\) & + & L & \(71.2\pm 8.4\) & + \\ WSBC10204 & _B. reinhardtiiensis_ & [41] & L & \(76.4\pm 9.1\) & + & L & \(68.6\pm 6.8\) & + \\ SiO239 & _B. reinhardtiiensis_ 1  & S & \(66.6\pm 4\) & + & L & \(79.3\pm 2.8\) & + \\ LH002 & _B. northeasternensis_ 1  & **B. ** & \(w\) [24] & I & \(69.3\pm 10.5\) & + & S & \(75.7\pm 5.7\) & + \\ GBJ001 & _B. thuringiensis_ 1  & L & \(76.2\pm 3\) & + & L & \(85.0\pm 0.4\) & + \\ HD4 & _B. thuringiensis_ & BGSC 1  & L & \(38.1\pm 0.9\) & + & S & \(73.4\pm 7\) & + \\ NVH 391-98 & B. cytotoxicity & [43] & I & \(74.2\pm 6\) & + & L & \(80.4\pm 4.7\) & + \\ BcA20 086 & _B. cytotoxicity_ 1  & L & \(46.6\pm 11\) & + & L & \(34.4\pm 14.2\) & + \\ KBS 2-12 & _B. mycoides_ & MIAE & L & \(62.1\pm 2.7\) & + & S & \(62\pm 4.2\) & + \\ KNC2-18 & _B. mycoides_ & MIAE & I & \(66\pm 23.3\) & + & I & \(15.4\pm 8.6\) & + \\ \multicolumn{8}{c}{**Other Gram-positive bacteria**} \\ Bs168 & _B. subtilis_ 1  & I & \(0.2\pm 0.4\) & - & I & \(0.0\pm 0\) & - \\ B1688 & _B. subtilis_ 1  & I & \(0.2\pm 0.4\) & - & I & \(0.1\pm 4.5\) & - \\ Sl0279 & _B. pumilus_ 1  & I & \(6.8\pm 0.9\) & - & I & \(21.8\pm 7.7\) & - \\
10A-B5 & _B. pumilus_ 1  & I & \(28.3\pm 1.5\) & - & I & \(26.3\pm 10.2\) & - \\ S10003 & _B. megaterium_ 1 respectively). Concerning PlyP32, the percentage of inhibition detected against the strains of _B. megaterium_, _B. licheniformis_, and _B. pumilus_ were around 35%, 40%, and 25%, respectively. No endolysin activity was detected on _Bacillus subtilis_, _L. ivanovii_, or _S. epidermidis_ (Table 3).
## 2 System Model
### Antenna Model
#### 2.2.2 Array Pattern  
Implementing a large number of antenna elements in the array will allow high-gain transmission of power towards the served MT by concentrating the field intensity in the direction of transmission [29]. The interference of the transmitted electromagnetic fields will form points of high intensity in certain directions, while producing low intensity transmissions towards unwanted directions. The array pattern is determined according to the physical implementation of the antenna elements and the electrical steering of the main beam. The array's radiation pattern \(A_{A}\) is determined using the element radiation pattern \(A_{E}\) in Equation (6), and assuming a uniformly spaced rectangular array (URA) in the horizontal and vertical dimensions with identical element radiation pattern as in  
\[A_{A}(\theta,\phi)=A_{E}(\theta,\phi)+10\log_{10}\bigg{[}1+\rho\bigg{(}\left| \sum_{m=1}^{N_{H}}\sum_{n=1}^{N_{V}}w_{m,n}v_{m,n}\right|^{2}-1\bigg{)}\bigg{]}, \tag{7}\]  
where \(v_{m,n}\) is the phase shift due to the antenna element placement, \(w_{m,n}\) is the weighting factor which provides the electrical down-tilt and attenuation of the antenna's sidelobes, and \(N_{H}\) and \(N_{V}\) are the number of horizontal and vertical antenna elements respectively. As it is shown in Equation (9), \(w\) is dependent on the antenna steering angles, the electrical down-tilt steering angle \(\theta_{tilt}\) and the electrical horizontal steering angle \(\varphi_{escam}\), and since we assume that the main beam of the antenna is centered towards the receiving MT, the antenna array will have a different radiation pattern towards each MT depending on their location. The 2D slice of the antenna pattern on the azimuth plane for a \(16\times 16\) array can be presented as in Figure 1.  
\[v_{m,n}=\exp\left(2\pi i\!\!\left((n-1)\frac{d_{V}}{\lambda}\cos(\theta)+(m-1) \frac{d_{H}}{\lambda}\sin(\theta)\sin(\phi)\right)\right) \tag{8}\]  
\[\begin{split} w_{m,n}=\frac{1}{\sqrt{N_{H}N_{V}}}\exp\left(2\pi i \!\!\left((n-1)\frac{d_{V}}{\lambda}\sin(\theta_{etilt})-(m-1)\frac{d_{H}}{ \lambda}\cos(\theta_{etilt})\sin(\phi_{escam})\right)\right)\\ m=1,2,\ldots N_{H}\\ n=1,2,\ldots N_{V}\end{split} \tag{9}\]
## 4 Results and Discussion
### Power Consumption  
The power consumption of the azimuth-based and sensor-based systems through the day hours is shown in Figure 10. The power consumption of the control unit, the sensor-based tracking system, and the azimuth-based tracking system was also recorded throughout the sun hours. The results showed that the azimuth-based PV system has a higher power consumption throughout the day than the sensor-based PV system. As for the power consumption of the control unit, it is fair to say that it was steady throughout the day compared to the tracking systems' consumption. The maximum power consumption of the azimuth-based system was 6.3 W at 9:34 a.m.  
Figure 10: Power consumption of the sensor-based system and the azimuth-based system.  
Figure 9: Power generation of the sensor-based system and the azimuth-based system.  
In contrast, the minimum power consumption was 0.13 W at 8:52 a.m. As for the sensor-based system, the maximum power consumption was 6.0 W at 9:05 a.m. On the other hand, the minimum power consumption was 0.04 W at 2:35 p.m. The leading difference between the systems' power consumption was 6.5 W which was recorded at 9:48 a.m., where the azimuth-based system had higher consumption. The mean power consumption of the sensor-based system and the azimuth-based systems were 1.4 W and 4.1 W, respectively, whereas the azimuth-based system had a higher power consumption of 192%.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 5 Future Directions  
When discussing the future challenges for power electronics in satellite applications, the main expected change is the increase in power [132]. This adds additional design constraints for power converters like the need for better heat dissipation equipment, availability of components that can tolerate high power, reduction of EMI, and reduction in weight and volume of magnetic components. The increase in power could be tackled by increasing the voltage of the main bus, hence limiting the current flow and reducing the cable weight [133]. Moreover, increased voltage levels demand a better selection of  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Converter** & **Ref.** & **Figure** & \
\begin{tabular}{c} **Type of** \\ **Thruster** \\ \end{tabular} & **Reliability** & **Redundancy** & **Modularity** &
\begin{tabular}{c} **Power** \\ **Density** \\ \end{tabular} & **Efficiency** \\ \hline Resonant LLC & [120] & Figure 14b & Hall thruster & Medium & No & No & High & \(>\)95 \\
4 parallel converters & [122] & - & Hall thruster & High & No & Yes & High & 96.1 \\ DAB & [124,127] & Figure 18 & Ion thruster & Medium & No & No & High & High \\ DAB with combined dual modulation & [125] & Figure 18 & - & High & No & No & High & - \\
4-switch interleaved buck-boost + LLC resonant & [126] & Figure 19 & Ion thruster & High & No & Yes & High & High \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of various DC-DC converters in PPU for EP.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Application** & **Figure** & **Reference** & **Large** & **Small** & **Mini** & **Micro** & **Nano** & **Pico** & **Femto** \\ \hline \hline \multirow{8}{*}{MPPT} & Figure 9a & [36,76] & & & & ✓ & ✓ & ✓ & \\  & Figure 9b & [36,77] & & & & ✓ & ✓ & ✓ & \\  & Figure 10a & [35,36] & ✓ & ✓ & & & & & \\  & Figure 10b & [35,36,81] & ✓ & ✓ & & & & & \\  & Figure 10c & [35,36,82] & ✓ & ✓ & & & & & \\  & Figure 10d & [35,36] & ✓ & ✓ & & & & & \\  & Figure 10e & [35,36] & ✓ & ✓ & & & & & \\  & Figure 10f & [35,84] & ✓ & ✓ & & & & & \\  & Figure 11a & [35,44] & & & & & & & \\  & Figure 11b & [35,87] & & & & & & & \\ \hline \multirow{8}{*}{Voltage Regulation} & Figure 10f & [35,84] & ✓ & ✓ & & & & & \\  & Figure 11b & [35,87] & & & & & & & \\  & Figure 11c & [35,89] & & & & & & & \\ \hline \multirow{8}{*}{Voltage Regulation} & Figure 9a & [36,76] & & & & ✓ & ✓ & ✓ & ✓ & \\  & Figure 9b & [36,77] & & & & & ✓ & ✓ & ✓ & \\  & Figure 10a & [35,36] & ✓ & ✓ & & & & & \\  & Figure 10b & [35,36,82] & ✓ & ✓ & & & & & \\  & Figure 10d & [35,36] & ✓ & ✓ & & & & & \\  & Figure 10e & [35,36] & ✓ & ✓ & & & & & \\  & Figure 10f & [35,84] & ✓ & ✓ & & & & & \\  & Figure 11a & [3 converter topology, power device packaging, and magnetic components. In summary, although increasing the bus voltage level is an effective solution with regard to increased power levels in satellites, several hurdles need to be overcome to achieve the same [130].
# Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain
## 4 Experiments and Analysis  
### Dataset and Evaluation Metrics  
We used the public Traffic dataset from the California Department of Transportation as experimental input data to our model. This dataset contains 17,544 records of road occupancy rates recorded by 862 sets of sensors between 1 July 2016 and 2 July 2018, recording every 1 h. Part of the dataset is shown in Figure 4.  
In the experiment, the Autoformer's experiment configuration [19] was followed; we used the first 70% (12,184 data elements) as a training set, 70-80% of the data as validation set, and the last 20% of the data as a test set to verify the model's performance. The performance of the model was measured using mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (CORR):  
\[\text{MSE}=\frac{1}{n}\sum_{t=1}^{n}\left(y_{t}-\hat{y}_{t}\right)^{2}\]  
\[\text{MAE}=\frac{1}{n}\sum_{t=1}^{n}\left|y_{t}-\hat{y}_{t}\right|\]  
\[\text{CORR}=\frac{cov(Y,\hat{Y})}{\sigma\gamma\sigma\hat{Y}}\]  
Figure 4: Part of the Traffic dataset used in our experiments.  
Lower MSE/MAE values and higher CORR values correspond to better model performance. We compared performance from our model with that associated with three baseline models: Auttoformer [19], StemGNN [18], and LSTM [21]. The LSTM model is a Seq2 Seq model with a hidden layer dimension of 256, a codec of 3 layers each, and a 20% dropout rate to avoid overfitting. Auttoformer [19] and StemGNN [18] were implemented using the default configuration provided by the author.
## 5 Conclusions and Future Work  
Results from the present study confirm that the partially-purified cultured broths from _P. dendritiformis_ UJA2219 show antibacterial activity in a model food system and induce selective changes in the food microbiota. This opens the way for future studies on the effects of preparations derived from strain UJA2219 in food preservation, either by direct application in other food systems or for the development of active coatings aimed at controlling food spoilage or pathogenic bacteria. Strain UJA2219 was selected because it produces antimicrobial substances against four relevant foodborne pathogens (_E. coli, S. enterica, S. aureus_ and _B. cereus_). The results obtained by culture-dependent methods could be broadened by doing challenge tests with strains or cocktails of strains belonging to these bacterial species, in order to determine the best conditions for application of the antibacterial extracts. One limitation of the present study is the use of partially-purified extracts (which may contain more than one antimicrobial substance, as suggested also by the preliminary data on the genome of the bacterium). The purification of the inhibitory substance(s) produced by strain UJA2219 is needed in order to evaluate its potential for application in food preservation and to establish the inhibitory spectrum and stability in food systems of the inhibitory substance(s) produced. Further studies are also needed to combine culture-dependent and culture-independent approaches in order to obtain more information on the effects of said antimicrobials on the dynamics of the bacterial populations of the food system and the influence of factors such as storage conditions and time.  
Conceptualization, M.J.G.; methodology, M.J.G., L.M.; formal analysis, L.M., M.J.G. and A.G.; investigation, L.M. and M.J.G.; writing--review and editing, M.J.G., L.M. and A.G.; supervision, M.J.G.; project administration, M.J.G.; funding acquisition, M.J.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministerio de Ciencia e Innovacion (Union Europea, Agencia Estatal de Investigacion, Programa Ramon y Cajal), grant number RYC-2017-23077. [PERSON] received a research grant from University of Jaen (Ayudas Predoctorales para la Formacion de PersonalInvestigador del Plan de Apoyo a la Investigacion de la Universidad de Jaen 2019-2020), grant number PIPFF91. The APC was funded by University of Jaen (AGR230).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
This work was supported by the Ramon y Cajal Program, action co-financed by Ministry of Economy and Competitiveness (MINECO) and European Union and pre-doctoral grants for the training of research personnel under Action 9a of the Operational Plan for Research Support in the University of Jaen.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Multirobot Task Planning Method Based on the Energy Penalty Strategy
## 4 GA for Multirobot Task Planning
### Population Initialization and Fitness Function  
According to the given tasks and the number of robots, the initial population is formed by the random sequencing of task points and random selection of breakpoint value. All task points are numbered with a natural number of 1-\(m\), and task point 1 is set as the starting virtual task point. Then, the chromosome genes are randomly generated to form the task sequence of the robot.  
The selection of the fitness function in the GA will have a certain impact on the convergence speed of the algorithm and the search for the optimal solution. Therefore, based on the objective requirements of energy optimization and energy balance in the algorithm model, the total energy consumption and penalty energy as the sum of values are minimized as the fitness function, as shown in Equation (6).  
In each iterative calculation, when the fitness function is greater than the total energy consumption (\(\left(PE\right)_{i}>0\)), the penalty energy item can be regarded as a punishment for the energy consumption imbalance, in which the greater the penalty energy, the heavier the punishment will be. This can promote the robots' tendency to achieve an energy consumption balance on the basis of the optimal total energy consumption.
## 6 Methodology for MP Analysis
### Sample Pre-Treatment and MP Extraction for Analysis
It is also necessary to take into account that acidic solutions can decompose plastic, alkaline solutions can lead to surface degradation of plastic and peroxide can change the appearance of MP particles and affect the properties of certain polymers. In addition, the digestion process can stimulate the leaching or conversion of additives embedded in MP composition and thus change the chemical properties of the particles [57]. The efficiency of a particular chemical in removing organic matter from a sample depends on the type of sample, the type of plastic material and the concentration of the solution used. For example, 30% H\({}_{2}\)O\({}_{2}\) has an efficiency of 80-86% in sludge and 96-108% in soil, but it is not suitable for all polymer types [53]. Enzymatic degradation proved to be the most efficient (>97%), with minimal MP damage, but the method is expensive, degradation can last from 24 h to 30 days [53; 99] and it is not applicable for the analysis of biodegradable polymers [57]. When choosing chemical reagents for treating samples in the process of MP extraction, it is mandatory to consider the stability of MPs because the procedure itself can induce the fragmentation of MPs and affect the increase in the amount of particles in the sample [57]. Each of the above mentioned methods has certain disadvantages, and none of them has maximum MP extraction efficiency [14; 36; 53; 57; 76; 99]. There is an urgent need to establish and standardize an extraction method that will be fast, efficient and simultaneously applicable for different types of polymers with minimal impact on the samples [104].
## 4 Proposed Estimation Scheme  
### Methodology  
The main studies in [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25] deal with the enhancement of error performance or complexity reduction in the GS. The number of iterations is a very important parameter which should be decided accurately prior to signal transmission in the downlink system. The number of insufficient iterations causes error floor like results in [14; 15; 16; 17; 18; 19; 20; 21; 22; 24; 25]. Thus, this paper proposes an estimation scheme of the number of minimum iterations in the GS pre-coding for optimal error performance. According to the system requirements, the system should estimate the number of minimum iterations which is only satisfied with the target error performance despite of the error floor. However, this paper considers the number of minimum iterations for only optimal error performance without the error floor. For performance enhancement, the design of initial solution is proposed. Additionally, for low-complexity estimation, complex mathematical analysis is not considered. Finally, for performances evaluation of the proposed estimation scheme, the number of estimated iterations is compared with the number of optimal iterations which is calculated in advance with Monte-Carlo simulation based on the system in (2).