esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The main conclusion that can be drawn from previous studies on airfoil modification with leading-edge serrations is that this technique is a promising way to mitigate the noise output of airfoils. Leading-edge noise reduction strategies include more complex technical approaches, such as multi-material concepts, including serrated porous surface treatments [12; 13; 14; 15] and multi-ridge geometries [16; 17], which aim to enhance noise mitigation by manipulating local flow features or material responses. These approaches often introduce additional geometric or manufacturing complexity. In contrast, the present study explores an alternative strategy by introducing spanwise irregularities in serration amplitude within a single-material airfoil, offering a simpler yet effective approach to broadband noise reduction. Table 1 presents a compilation of studies on rod-airfoil configurations. Some studies were conducted to test the noise output of rod-airfoil setups and to produce benchmark data, as well as to perform numerical comparisons without modifying the airfoil [1; 2; 3; 5; 6; 7]. Remarkably, numerous studies have utilized serrations as a geometric modification to reduce noise [8; 9; 10; 11; 12; 13]. Most of these studies employed rod-airfoil configurations similar to those used in the present work. This study advances the field by testing irregular leading-edge serrations, representing a divergence from prior research. The effects of both regular and irregular leading-edge serrations on noise mitigation in a rod-airfoil configuration have been examined, utilizing a hybrid large eddy simulation (LES) and computational aeroacoustics (CAAs) approach. Notably, while previous studies have mainly addressed regular serrations with uniform amplitude, the aeroacoustic implications of spanwise  
irregularity in serration geometry have remained largely unexplored. This study fills this gap by introducing and systematically assessing irregular serration profiles, thereby yielding novel insights into their potential for broadband noise control. Furthermore, by employing a high-fidelity hybrid LES/CAA framework, the present work provides additional insights into the underlying flow-acoustic mechanisms.
## 3 Results and Discussion
### Performance Comparison
#### 3.1.2 Comparison with Supervised Deep CNN
YOLOv5 is the first YOLO model to be written in the PyTorch framework, and it is much more lightweight and easier to use. YOLO stands for 'You Only Look Once'. YOLO5 is a novel CNN that detects objects in real time (fastest) with great accuracy. This approach uses a single neural network to process the entire picture, then separates it into parts and predicts bounding boxes and probabilities for each component. These bounding boxes are weighted by the expected probability. Comparing Faster RCNN with YOLO5, we found that YOLO5 is better in accuracy for real time prediction whereas RCNN delivers better accuracy for static predictions such as video post processing as seen in Figures 8 and 9 and Table 1.  
The accuracy of the abovementioned deep CNN models was quite unsatisfactory compared to that of the proposed method. This deficiency was often due to the black-box nature of deep CNN architectures, which sometimes makes them difficult to trace, verify and debug. Additionally, while handling the image samples, we often noticed very thin (mostly vague) boundaries of the pores and fractures (\(\mathcal{H}_{i}\)). This characteristic is mainly due to negligible intensity differences between the bordering pixels and the pixels residing just outside \(\mathcal{H}_{i}\). Moreover, in many cases, we often observed negligible intensity differences among most pixels residing in a specific \(\mathcal{H}_{i}\) and the other pixels residing in several other parts of the same image sample, which makes it very difficult for deep CNN to extract pore- and fracture-specific shape information, possibly increasing the chance of misclassification [34; 37]. In contrast, models such as YOLO5 [34] and Faster RCNN [37; 39]seem to underperform because of the low quality of images and the irregular shape of pores and fractures, as detailed in the forthcoming sections. However, we also suspect the implementation strategy of CUSTOM Object CNN which was included in the current work for comparison with the performance of the proposed approach might be the other reason behind its poor performance. This can be rectified in future work, which will be a novel contribution in the field of identifying pores and fractures of the rock from the CT-scan images.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Active BCI
#### 4.3.1 System Commands  
The hybrid BCI system of [51] consists of four commands (one active and three reactive), while ref. [53] designed a system with two active commands. These commands allowed users to interact with the system efficiently, depending on the task complexity and the BCI paradigm employed.
## 4 Discussion
### Comparison of Systems under Prescribed Displacement and Inertial Load  
For the case of prescribed displacement and optimal load, the two systems (PEH/EMEH) have equivalent load power, which differs only in the relation to intrinsic electrical impedance. As the intrinsic impedance in an EMEH is in series with the load resistance, it should be minimized to maximize load power, and vice versa for the PEH.  
Under the assumption of \(\gamma=1\), we can extract an optimal load resistance from Equation (22) as:  
\[\begin{array}{ll}\text{EMEH}:&\quad R_{L\_PD\_opt}=\sqrt{\left(\omega L \right)^{2}+\left(R_{W}\right)^{2}}\\ \text{PEH}:&\quad R_{L\_PD\_opt}=\
icefrac{{1}}{{\sqrt{\left(\omega C_{P} \right)^{2}+\left(\frac{1}{k_{E}}\right)^{2}}}}\end{array} \tag{26}\]  
which shows that power optimization is achieved by impedance matching in this case. We compare this to the corresponding case of inertial load (see Equation (17)), which gives:  
\[\begin{array}{ll}\text{EMEH}:&\quad R_{L\_II\_opt}=\sqrt{\left(\omega L \right)^{2}+\left(\frac{\theta^{2}}{\xi_{M}}+R_{W}\right)^{2}}\\ \text{PEH}:&\quad R_{L\_II\_opt}=\
icefrac{{1}}{{\sqrt{\left(\omega C_{P} \right)^{2}+\left(\frac{1}{\xi_{M}}+\frac{1}{k_{E}}\right)^{2}}}}\end{array} \tag{27}\]The above expression for optimal load resistance for an EMEH, under inertial load, can also be found in [6,14], under the assumption of negligible effect from inductance (\(\beta\approx 0\)). The term \(\theta^{2}/C_{M}\) in the optimal load for an EMEH, is in [14] defined as the electrical analogue of the mechanical damping. The same term appears in the optimal load expression for a PEH. In this case, the electrical analogue is equivalent to a resistance in parallel with the impedance, and thus becomes \(C_{M}/\theta^{2}\) for a PEH. Although the electrical analogues are inverted between the systems, the series vs. parallel nature means the ratio \(\theta^{2}/C_{M}\) should in both cases be minimized to achieve the highest possible power. This must be weighed against the negative effects of reducing the coupling factor, \(\theta\), or increasing the mechanical damping.  
Intuitively, the difference in optimal load between the case of prescribed displacement and inertial load lies only in the electrical analogue to mechanical damping, which is only present in the case of inertial load. The comparison between the cases of inertial load and prescribed displacement thus supports the argument made by [14] that simple impedance matching does not provide the optimal load in the case of an energy harvester under inertial load.  
Our results also show that applying the same resonant frequencies for the case of inertial load to the case of prescribed displacement generally leads to significantly larger dimensionless power output. This indicates that it may be beneficial to create a forced vibration of the proof mass if possible (e.g., by fixating to a surface that is static relative the vibrating surface). For the corresponding anti-resonant frequencies, this only holds for sufficiently small values of \(k\) and \(\xi_{E}\).
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Routing Table Management  
We explain how GCS manages routing tables of drones and GCS, and Algorithms 1 and 2 describe GCS-routing operations.  
```
1:Drone geographic information and flight schedules
2:Initialization:
3:InitLinkCost (Drone geographic information)
4:tree \(T\leftarrow\)GenerateRoute (GCS)
5:GCS\(\leftarrow\)applyRouteInfo (\(T\)) Distribution:
6:T:Shortest paths tree for drones from GCS
7:M: Aggregated route message
8:m:Route message for one drone
9:for each subtree \(t\) of \(T\)do
10:for each child drone \(i\) of the subtree \(t\)do
11:\(m_{i}=\)GenerateRoute (\(i\))
12:\(M_{t}\gets M_{t}+m_{i}\)
13:endfor
14:a root of the subtree \(t\leftarrow\)sendRouteInfo (\(M_{t}\))
15:endfor Maintenance:
16:whiledo
17:if deployment changes or failure is reported then
18:updateLinkCost (changed-links)
19:tree \(T\leftarrow\)GenerateRoute (GCS)
20:for only for drones whose route has been changed do
21:Go **Distribution** process
22:endfor
23:endif
24:endwhile
```  
**Algorithm 1** GCS-routing operations in GCS.  
### Routing Table Management  
We explain how GCS manages routing tables of drones and GCS, and Algorithms 1 and 2 describe GCS-routing operations.  
```
1:Drone geographic information and flight schedules
2:Initialization:
3:InitLinkCost (Drone geographic information)
4:tree \(T\leftarrow\)GenerateRoute (GCS)
5:GCS\(\leftarrow\)applyRouteInfo (\(T\)) Distribution:
6:T:Shortest paths tree for drones from GCS
7:M: Aggregated route message
8:m:Route message for one drone
9:for each subtree \(t\) of \(T\)do
10:for each child drone \(i\) of the subtree \(t\)do
11:\(m_{i}=\)GenerateRoute (\(i\))
12:\(M_{t}\gets M_{t}+m_{i}\)
13:endfor
14:a root of the subtree \(t\leftarrow\)sendRouteInfo (\(M_{t}\))
15:endfor Maintenance:
16:whiledo
17:if deployment changes or failure is reported then
18:updateLinkCost (changed-links)
19:tree \(T\leftarrow\)GenerateRoute (GCS)
20:for only for drones whose route has been changed do
22:Go **Distribution** process
23:endfor
24:endif
25:endwhile
```  
**Algorithm 2** GCS-routing operations in GCS.  
Figure 2: GCS-routing architecture.
## 1 Introduction
(2019) on a DU 91-W2-250 airfoil near stall conditions showed a substantial increase in the lift coefficient due to VG implementation. [PERSON] et al. (2019) observed that with different VG geometries, a significant reduction in the total separation area was achieved in a normal shock boundary layer (BL) control. Similarly, shock-induced detachment was succesfully delayed after the installation of sub-boundary-layer Vortex Generators as shown in [PERSON] et al. (2019, 2020). According to two different investigations at zero and adverse streamwise pressure gradient, a primary vortex was satisfactorily characterized and predicted by Reynolds average Navier-Stokes (RANS)-based simulations. Recently, similar studies carried out by [PERSON] et al. (2019) and [PERSON] et al. (2020), made a comparison between RANS-based numerical simulations and the wind tunnel results of the EU project AVATAR. A reasonably good agreement was found between the simulations and the experiments in both senses, qualitatively and quantitatively. As a result, we can conclude that RANS-based simulations are able to capture the large scale effects of the streamwise vortexes, see also [PERSON] et al. (2020). [PERSON] (2019) and [PERSON] (2019) compared on 1 MW and 2.5 MW wind turbines the resultant power values with and without VGs, respectively. Both studies showed that VGs increased the wind turbine output power for mostly all wind speeds. Furthermore, the blade rlement momentum (BEM)-based computations carried out by [PERSON] et al. (2020) on a 5 MW wind turbine showed an increase of the average power output between 3% to 9% due to another type of passive flow control devices, such as Gurney flaps and VG rows implementation on the blades. Some interesting advantages are shown by VGs, such as their small size which allows distributing a big number of them along the profile. They can also be installed at any time after the construction of the blade or replaced without any substantial cost. The principal aim of this study was to characterize the generated primary vortex by means of computational techniques and, in this way, obtain the VG size and orientation fitting for their application in blades and airfoils. In order to develop a parametric study that covers a big range of cases, numerical simulations were carried out evaluating four different vane incident angles, \(\beta=10^{\circ}\), \(\beta=15^{\circ}\), \(\beta=18^{\circ}\), and \(\beta=20^{\circ}\) and six different VG heights of 0.2H, 0.4H, 0.6H, 0.8H, H, and 1.2H. The CFD simulations in this study were carried out with OpenFOAM, see [PERSON] et al. [29], open-source code; the vortex size was computed for all these cases. Section 2 explains the numerical setup; Section 3 shows the results and their discussion divided in four subsections, the primary vortex center path, vortex size, the wall shear stress, and the vortex strength; and finally, in Section 4, conclusions are presented.
## 5 Experimental Results
### Beam Measurements  
Power patterns were obtained by transmitting a continuous-wave (CW) signal at \(\mathbf{f}_{\mathrm{RF}}=5.71\) GHz. The LO signal frequency \(\mathbf{f}_{\mathrm{LO}}\) determines the IF as \(\mathbf{f}_{\mathrm{RF}}-\mathbf{f}_{\mathrm{LO}}\). For the measurements in this study, \(\mathbf{f}_{\mathrm{LO}}\) was set to 5.7 GHz, resulting in an IF of 10 MHz, with the downconverted outputs digitized at \(\mathbf{f}_{\mathrm{CLK}}=200\) MHz.  
Digital integrators at each ADFT bin output were used to compute the received energy over a fixed duration. Spatial beamforming occurs at a rate of 200 MHz, and the PSD is calculated in real time by squaring the magnitude of each frequency bin. This estimate is derived by averaging over 1024 temporal FFT computations performed at 200 MHz. The geometry of the measurement setup significantly influences the measured array factor of the beams. Ideally, the transmitter and receiver should be sufficiently far apart so that the waves incident on the receiver array can be approximated as plane waves. According to standard guidelines [102], the separation between the transmitter and receiver should exceed 20 m at 5.7 GHz to ensure that the receiver aperture is in the far field. The beams were measured on an open parking deck with a transmitter-receiver separation of approximately 20 m.  
Figure 7 illustrates the measured array factor patterns for beams 0 to 16. Due to the input of real-valued IF signals into the ADFT, the resulting beam patterns exhibit symmetry, producing mirror images for the remaining bins. Additionally, the presence of two mirror peaks is observed within bins 0 to 16. The measured beams conform closely to the expected patterns, maintaining a separation of more than 10 dB from the other beam. Each ADFT output bin corresponds to an RF far-field beam directed at a discrete angle given by  
Figure 8 depicts the measured temporal PSD distribution of respective beams at multiple frequencies when the signal source is positioned at a broadside angle of \(15^{\circ}\), \(35^{\circ}\), and \(50^{\circ}\). The corresponding experimental setup with relative source locations and RF frequencies being emitted for testing purposes is shown in Figure 9.  
Figure 7: Sixteen beams measured from the 5.7 GHz array (vertical axis is in decibel, and the horizontal axis is the azimuthal angle \([-90^{\circ},90^{\circ}]\)). Each subfigure contains the measured array factor patterns of the beam using the real inputs of the ADFT core. The imaginary component of each ADFT input is set to zero here, thus resulting in symmetrical main lobes as expected.  
Figure 8: Temporal PSD over the 1024 discrete frequency bins for the particular RF beam at multiple frequencies when the source is placed at 15\({}^{\circ}\), 35\({}^{\circ}\), and 50\({}^{\circ}\) broadside receiver angles.  
Figure 9: Illustration of the overall setup showing the transmitting antennas, receiving antenna array and the multibeam spectral sensor. The 32-element antenna array receiver captures signals from different directions, with the transmitters located 20 m away from the receiver.
# Simultaneous Determination of 12 Marker Components in Yeonkyopaedok-san Using HPLC-PDA and LC-MS/MS
## 2 Materials and Methods
### UPLC-MS/MS Apparatus and Conditions for Quantification of the 12 Marker Components  
The apparatus used in the UPLC-MS/MS system for the quantification of the 12 marker components in YPS included an ACQUITY UPLC H-Class system (Waters, Milford, MA, USA) with a Xevo TQ-S micro triple quadrupole mass spectrometer (Waters) equipped with an electrospray ionization (ESI) interface. The MassLynx software (version 4.2, Waters) was used to carry out the data acquisition and processing. The analytical column used for chromatographic separation was a Waters ACQUITY UPLC BEH C\({}_{18}\) column (2.1 mm \(\times\) 100 mm, 1.7 \(\upmu\)m). Two mobile phases were used: 0.1% (\(\
u\)/\(\
u\)) aqueous formic acid (solvent A) and acetonitrile (solvent B). The mobile phase was eluted at 0.3 mL/min. The gradient conditions used were as follows: 10% solvent B at initial to 1.0 min, 10-40% solvent B at 1.0 to 6.0 min, 40-95% solvent B at 6.0 to 10.0 min, 95% solvent B at 10.0 to 15.0 min, 95-10% solvent B at 15.0 to 15.1 min, and 10% solvent B at 15.1-18.0 min. The volume of autosampler injection for the quantification of all analytes was 2 \(\upmu\)L, and the temperature of the column and sample trap were maintained at 40 \({}^{\circ}\)C and 5 \({}^{\circ}\)C, respectively. The 12 marker components in a MS system were detected in positive and negative ion mode using an ESI interface. Quantification was conducted using the multiple reaction monitoring (MRM) mode. Capillary voltage, source temperature, desolvation temperature, desolvation gas flow, and cone gas flow were set at 1.2 kV, 150 \({}^{\circ}\)C, 450 \({}^{\circ}\)C, 800 L/h, and 50 L/h, respectively. Apart from these common MS parameters, other optimized analytical parameters, such as collision energy, cone voltage, and the transition required for the analysis of each analyte are displayed in Table S3.
## 1 Introduction
### Related Work
#### 1.2.2 Ocean Data View  
Ocean data view (ODV) [10] is the most powerful software tool for the visualization of oceanographic data. It provides various graphical visualizations, stations maps and spatial interpolation. The interface shown in Figure 2 is based on context menus and requires some learning for new users due to the many options implemented.  
Despite its very good visualization capabilities, ODV is only a tool for visualizing a subset of data. The biggest disadvantages are:  
* Limited data filtering;
* Desktop tool with no collaboration possibilities;
* Changes and validations made in ODV can be exported to file, not committed to the database;
* Separate and detached visualizations of status and trend.  
Recently, Ocean Data View has also become available as a web version [11; 12]. The web interface is implemented using a websocket (QtWebSockets) to run web applications, or a combination of WebSocket and HTML5 Canvas. These types of solutions are graphical versions of the terminal approach to the server. Each client has its own isolated process on the server side and performs various operations over WebSocket. The main disadvantage of this approach is that we need some processing power on the server side for each client, and content caching on the browser side is not really used. The interface is the same as for the desktop version. With WebODV it is only possible to visualize prepared datasets available online. It is not possible to make any changes.  
Figure 2: ODV desktop application.
## 3 Results and Discussion
### Practical Applications and Future Research
The method proposed in this study is developed for the experimental field of the University of Sao Paulo and implemented in a particular geographical context. While the method demonstrates efficacy in the specific location where it was tested, its applicability to other locations warrants consideration. The soil characteristics, geological formations, and environmental conditions can vary significantly between different regions, potentially influencing the method's performance. Therefore, before extending the use of this method to other locations, further validation and adaptation may be necessary to ensure its suitability and reliability across diverse settings.
## 4 Discussion
### AgNP-BK510 Lys Characterization and Antimicrobial Efficiency
aeruginosa_ (Figure 3b), it decreased by 17 and 29%, respectively. This synergy has also been demonstrated with more complex systems such as peptides-AgNPs [11], endolysin-dendron-PEG or endolysin-dendron [6; 15; 27], bacteriophage-AgNPs [12; 13; 28], and endolysin-AgNPs [14]. [PERSON] et al. [14] studied the lytic activity of AgNPs, PlyB221 endolysin, and AgNPs-PlyB221 conjugate in _Bacillus cereus_ cultures. The AgNPs conjugated with PlyB221 (\(8.125\times 10^{-6}\) M) were more effective in reducing turbidity than the chloramphenicol (positive control), AgNPs, and PlyB221 endolysin, promoting an 80% reduction in turbidity. Any Gram-negative bacteria were tested in their study. [PERSON] et al. [16] studied spectrometrically by measuring the optical density changes of the bacterial culture at 600 nm, the effect of PEGylated and unmodified dendronized AgNPs combined with KP27 endolysin on wild-type _P. aeruginosa_ PAO1 and its LPS knockout mutant (1 wbpL). The synergic effect was seen in the presence of PEGylated and unPEGylated dend-AgNPs-KP27 at concentrations of 10, 20, and 50 \(\upmu\)g/mL. The greatest inhibitory effect occurred at the lower concentration (10 \(\upmu\)g/mL), as in the present study. A suggested mechanism that modulates enzymatic activity is the interaction of metal ions with specific amino acid residues in different domains of endolysins [23; 29]. According to [PERSON] et al. [30], the lytic activity of endolysin can be affected by divalent metal ions, while other metal ions may exert an inhibitory effect. Given the results obtained in the antibiograms, it is possible to obtain a stable solution that allows the inhibitory capacity to increase, compared to each one separately. Therefore, the ratio chosen to continue studying the conjugate was 2:1.
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 4 Experimental Evaluation and Results
### Experimental Settings
#### 4.1.2 Experimental Procedure  
In order to verify the feasibility of the four-channel FFT processor proposed in Section 3, we described the structure of the four-channel FFT processor with Verilog HDL, and synthesized and implemented it on Xilinx Vivado 2018.3. In Vivado 2018.3, we chose the xc7 vx690 fffg1761-2 board under the Virtex7 series as the hardware platform. First, the test data was generated by Matlab R2019b, and saved in a txt file. Then, Test Bench in Vivado read the data in the txt file and inputted it into the FFT processor. After that, Testbench received the processed results and saved the results to another txt file. Finally, Matlab read the txt file for the saved result, and adjusted the frequency domain order from bit reversed order to natural order.