esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A nonlinear observer for a remotely operated vehicle (ROV) is investigated, and a four-degree-of-freedom nonlinear sliding state observer is designed in this study. An ocean current model and a simplified umbilical cable disturbing force model of ROVs were set up; the simplified cable force model characterized the cable disturbing force. The velocity information and the cable force were observed and estimated both online and in real time. We proved that the observation error was uniformly ultimately bounded. The modeling of the disturbing force and the compensation for the observer was an effective method to improve the observation precision and to reduce the chattering of the observer outputs.  
remotely operated vehicle; ocean current; cable disturbance modeling; lumped parameter method; sliding mode observer
# A VHF Band Small CRLH Antenna Using Double-Sided Meander Lines
## 2 Design of the CRLH Unit Cell and Antenna
parameter is \(f_{s}\) = 28.5 mm, \(f_{w}\) = 1.2 mm, \(ML_{s}\) = 1.5 mm, \(ML_{L}\) = 32.25 mm, \(via_{r}\) = 0.5 mm in Figure 4.  
Figure 5a,b,c show the bottom of the antennas composed of 3-, 5-, and 7-unit cells, respectively. The top of the antenna is the same as that from Figure 2b. As can be seen in Figure 5, the proposed antenna was designed to increase the length of the meander line at the bottom as the unit cells increase. As the unit cell increases, the length of the meander line becomes longer than one unit cell, so the zeroth-order resonant frequency of \begin{table}
\begin{tabular}{c c c} \hline \hline  & **With Full Ground at Bottom** & **With Meander Line at Bottom** \\  & **(Figure 2a)** & **(Figure 2b)** \\ \hline Size & \multicolumn{2}{c}{\(40.8\times 58.65\times 1.016\) mm\({}^{3}\)} \\ \hline Resonant frequency & 131.6 MHz & 90.2 MHz \\ \hline C\({}_{R}\) & 20.6 pF & 19.45 pF \\ (Parallel Capacitance) & 71 nH & 160 nH \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the parallel element values according to the CRLH TL unit cell structure. Figure 4: The unit cell of CRLH transmission line: (**a**) top view; (**b**) bottom view. Figure 3: Dispersion diagram of the CRLH TL unit cell: (**a**) dispersion diagram of Figure 2a; (**b**) dispersion diagram of Figure 2b. the proposed antenna moves to a lower side. The characteristics of each antenna according to the number of cells are shown in Table 2. In the case of the 3-cell antenna, the electrical size of the antenna has become very small. However, due to the low gain of the 3-cell antenna, it is not suitable for use in military applications. The 7-cell antenna has an overall size of 0.087\(\times\) 0.02\(\lambda\)\(\times\) 0.0003\(\lambda\) (308.1 \(\times\) 70.6 \(\times\) 1 mm\({}^{3}\)) 308.1 \(\times\) 70.6 \(\times\) 1 mm\({}^{3}\) and operates at 85 MHz. The bandwidth is 1.96% (84.2-85.87 MHz) based on VSWR 3.5:1, and has a realized gain of \(-\)22.6 dBi in simulation. Figure 6 shows the normalized radiation pattern of the 7-cell antenna, which confirms that the proposed antenna has an omnidirectional radiation pattern at the zeroth-order resonant frequency. Therefore, the proposed antenna may be used as an antenna for receiving military antennas [26; 27].
# Simulative and Experimental Investigation of Vibration Transfer Path at Gearboxes
## 6 Conclusions  
The effects of the amplitude modulation, such as the gravitation and sidebands, can be observed by analyzing the original signal from the rotating sensor. Hence, the post-processing of the original signal is useful to reduce the influence of the modulation so that the condition monitoring can be more accurate.  
The influence of the rotational speed on the peak-to-mean ratio at different sensor positions has been investigated. The result shows that the peak-to-mean ratio of the signal from the rotating sensor is large when the rotational speed is slow. Hence, the rotating sensor provides better performance at slow rotational speeds than the housing sensor. The influence of external torque on the peak-to-mean ratio has also been investigated, but their relationship is more complex because the torque changes the stiffness of each component and, hence, the vibration transmission of the transfer path. More speed and torque, especially with particular damages, will be investigated in the future to show the capabilities of rotating sensors in condition monitoring. Damage-related frequencies can be masked by external excitation, which raises the importance of the integrated sensors.  
A modeling of the gearboxes is also introduced, and the time-varying gear mesh stiffness and bearing stiffness are also considered in this simulation. The peaks at the gear mesh frequency and Outer Race Defect frequency can be observed in the frequency spectrum. The vibration transmission through different components of the gearbox has been investigated using simulations, and the results show that the greatest reduction in the vibration intensity happens during transmission through the tapered roller bearing. Hence, the sensor at the rotating component for condition monitoring can provide good performance.  
Conceptualization, E.K. and C.C.; methodology, E.K., C.C. and M.O.; software, C.C.; supervision, M.O. and K.S.; writing, E.K. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) through the SIZA (Sensor-Integrated Gear, Sensor-Integrierendes Zahnrad) project (grant number 466653706).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
monocytogenes_ IC50 maintenance after IDe digestion (Figure 3c) despite the overgrowth observed because of gastric fluid stimulation (Figure 2c,g). The IC50 values for _S. enterica_ improved after digestion, with this sensitivity being linked to the presence of procyanidins, epicatechingallate, and quercetin-3-glucoside (Figure 3c). This rational analysis, in conjunction with the data presented in Tables 1 and 2, suggests that while digestion reduced the overall polyphenol content, especially of procyanidins and quercetin glycosides, no clear decrease in antimicrobial efficacy was observed, especially against Gram-negative bacteria. This implies that lower polyphenol levels might still be enough for bactericidal effects, with procyanidins, epicatechingallate, and quercetin glucosides likely contributing most significantly. The persistence of the antimicrobial activity despite compositional changes supports the hypothesis that polyphenol efficacy is strongly influenced by environmental factors like digestion and solubility, and suggests that these compounds may act synergistically with digestive fluids to offer a safe environment for the host. This has strong evolutionary support, since the human body, whose digestive system we are simulating in this study, has evolved in the presence of a polyphenol-rich diet. Although no previous evaluation of the variations in the antimicrobial activity of a polyphenol-rich grape marc extract after gastrointestinal digestion had been performed before this research, the effect of digestion on antimicrobial stability was previously assessed for two other natural extracts. [PERSON] et al. [49] and [PERSON] et al. [50] described in their studies the impact of in vitro gastrointestinal digestion on coffee pulp and propolis extracts, respectively. Both teams observed a reduction in both the phytochemical and antimicrobial activity, and a maintenance of the prebiotic potential. However, both authors employed the agar diffusion technique for the antimicrobial determination, which is a semi-quantitative method not appropriate for determining minimum inhibitory concentrations when using complex substances [51], because the molecules present in such extracts may present different solubilities, affecting their capability for diffusion and producing false-negative results [52]. Considering the limitations of this in vitro research, the complexity of natural extracts, and the active response of gut microbiota to prebiotics and pathogenic bacteria, future studies should include both bioaccessible and non-soluble extract fractions to better reflect in vivo conditions. Additionally, the role of polyphenol-derived metabolites in modulating microbial metabolic pathways and biofilm development inhibition effects should be further investigated.
## 2 Theory and Methods
### Pseudorange Multipath Combination Model  
To eliminate the influence of the geometry and ionosphere delay, the pseudo-range multipath (MP) combination model based on the pseudo-range and dual-frequency carrier phase observation is used. The concrete MP combination can be expressed as follows:  
\[MP_{j}=P_{j}-\frac{f_{j}^{2}+{f_{i}}^{2}}{f_{j}^{2}-f_{i}^{2}}\lambda_{i} \varphi_{j}+\frac{2{f_{i}}^{2}}{f^{2}-f_{i}^{2}}\lambda_{i}\varphi_{i}-B_{ij} \tag{4}\]  
where the subscripts \(i\) and \(j\) (\(i\
eq j\)) indicate different frequencies; \(MP_{j}\) is the pseudo-range multipath combination in unit with meter; \(\lambda_{i}\) and \(\lambda_{i}\) are wavelengths; \(f_{i}\) and \(f_{i}\) are frequencies; \(\varphi_{i}\) and \(\varphi_{j}\) are carrier phase observations; \(P_{i}\) and \(P_{j}\) are pseudo-range observations; \(B_{ij}\) is composed of the ambiguity term and hardware delay bias. Thus, it can be considered to be constant if no cycle slip occurs on the carrier phase observation. The pseudo-range multipath combination can cancel out atmospheric (ionospheric and tropospheric) delays as well as all geometric errors, but the ambiguities and differential hardware bias still remains in \(B_{ij}\). Thus, to remove the constant, a moving average filter method is used, which is expressed as follows:  
\[\overset{\rightarrow}{MP_{j}}=MP_{j}-\overline{MP_{j}} \tag{5}\]  
where \(MP_{j}\) is the pseudo-range multipath combination in units of meters, which is calculated by Equation (4). \(\overline{MP_{j}}\) is the averageof \(MP_{j}\); \(\overset{\rightarrow}{MP_{j}}\) are the pseudo-range multipath residuals.  
After removing the constant term \(B_{ij}\) by subtracting the average value of the multipath combination series for each continuous ambiguity block, the residual series are dominated by the pseudo-range multipath and tracking noise of the pseudo-range observation. More information is detailed in [17; 20].
## 4 Experimental Evaluation
### Surrounding Vehicle Localization
#### 4.2.2 Performance Evaluation
Position Error**} & \multicolumn{2}{c}{**Radar Lon. Position Error**} & \multicolumn{2}{c}{**V2X Lon. Position Error**} & \multicolumn{2}{c}{**Fusion Lon. Position Error**} \\ \cline{2-7}
**(m)** & **RMSE (m)** & **SD (m)** & **RMSE (m)** & **SD (m)** & **RMSE (m)** & **SD (m)** & **RMSE (m)** & **SD (m)** \\ \hline
0–10 & 0.36 & 0.24 & 0.39 & 0.18 & 0.25 & 0.22 & 0.13 & 0.13 \\
10–20 & 0.19 & 0.15 & 0.51 & 0.21 & 0.31 & 0.28 & 0.12 & 0.11 \\
20–30 & 0.33 & 0.23 & 0.54 & 0.15 & 0.38 & 0.37 & 0.24 & 0.09 \\
30–Figure 11: Relative distance to the remote vehicle during the normal driving scenario 2. (**a**) Ground truth longitudinal distance; (**b**) ground truth lateral distance; (**c**) absolute error for longitudinal distance estimates; (**d**) absolute error for lateral distance estimates. The performance of the individual remote sensing systems and the proposed cooperative environment perception approach is evaluated by computing the RMSE and the standard deviation of the error at different relative distances as shown in Tables 9 and 10. Similar to the previous scenario, the results are grouped in separate 10-m bins (in the longitudinal direction) to determine the positioning accuracy at varying relative distances for the driving scenario with the remote vehicle driving in the adjacent lane. Taking into account all of the position estimates that fall between 0 and 70 m in the longitudinal direction, the longitudinal and lateral localization accuracies of the proposed method in terms of the RMSE are found to be 0.33 m and 0.09 m, respectively. Figure 12 shows the relative positioning accuracy in the longitudinal and lateral directions for the combined sets of the measurements from the two normal driving scenarios. In the longitudinal direction, the total RMSE of the relative position estimated with the proposed fusion approach is 0.27 m, which corresponds to an improvement of 86% and 52% compared to the total RMSE of the relative position estimated with the camera and radar sensors, respectively. In the lateral direction, the total RMSE of the relative position estimated with the proposed fusion approach is 0.12 m, which corresponds to an improvement of 27% and 56% compared to the total RMSE of the relative position estimated with the camera and radar sensors, respectively.
## 1 Introduction
As mentioned above, the dynamic stall characteristics of rotor airfoil under an actual working environment of a helicopter are not well studied. Previous research seldom focused on the characteristics of a dynamic stall vortex under coupled freestream velocity/pitching oscillation conditions directly. Therefore, the purpose of this study is to explore the effects of OFV on dynamic stall characteristics of rotor airfoil. In this research, the moving-embedded grid method [26; 27] is adopted to simulate the oscillating velocity and pitching of an airfoil. The unsteady RANS equations coupling with third-order Roe-MUSCL spatial discretization scheme are chosen as the governing equations to predict the unsteady flowfield of an airfoil, and the highly-efficient implicit scheme of lower-upper symmetric Gauss-Seidel (LU-SGS) is adopted for temporal discretization. To capture the separated airflow and vortex more accurately, the SST k - \(\omega\) turbulence model is employed in the Computational Fluid Dynamics (CFD) code. Based on these methods, the dynamic stall characteristics of airfoil are researched under coupled conditions. It is indicated from the simulated results that the dynamic stall characteristics of airfoil associate with a critical value of Cp peak. By studying the vortices characteristics, it is illustrated that the dissipation rate of the LEV presents as exponent characteristics. However, this dissipation rate is not sensitive to oscillating velocity.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
###### Abstract  
IEEE 802.11p based Dedicated Short-Range Communication (DSRC) is considered a potential wireless technology to enable transportation safety and traffic efficiency. A major challenge in the development of IEEE 802.11p technology is ensuring communication reliability in highly dynamic Vehicle-to-Vehicle (V2V) environments. The design of IEEE 802.11p does not have a sufficient number of training symbols in the time domain and pilot carriers in the frequency domain to enable accurate estimation of rapidly varying V2V channels. The channel estimation of IEEE 802.11p is preamble based, which cannot guarantee a suitable equalization in urban and highway scenarios, especially for longer length data packets. This limitation has been investigated by some research works, which suggest that one major challenge is determining an accurate means of updating channel estimate over the course of packet length while adhering to the standard. The motivation behind this article is to overcome this challenge. We have proposed an improved Constructed Data Pilot (iCDP) scheme which adheres to the standard and constructs data pilots by considering the correlation characteristics between adjacent data symbols in time domain and adjacent subcarriers in frequency domain. It is in contrast to previous schemes which considered the correlation in the time domain. The results have shown that the proposed scheme performs better than previous schemes in terms of bit error rate (BER) and root-mean-square error (RMSE).  
IEEE 802.11p; Vehicular Networks; Intelligent Transportation Systems; constructed data pilots; vehicle-to-vehicle +
Footnote †: journal: Accepted: 21 December 2018; Published: 28 December 2018
## 2 Standard Nondestructive Testing and Evaluation Techniques
### Radiography Testing  
Another type of NDT technique usually used for crack detection is radiography testing. This technique involves utilizing ionizing radiation such as gamma rays or X-rays to penetrate the component of interest, with the resulting shadow image being captured on a film or digital detector. A diagram demonstrating X-ray radiography is shown in Figure 5.  
[PERSON] et al. [36] utilized such an approach and developed an innovative automatic vision system to inspect and detect welding cracks in gas pipelines from their corresponding radiographic images. The presented system would process radiographic images and employ diverse computer vision and image processing algorithms. The goal was to identify welding defects and compute essential parameters, including dimensions such as the area, width, length, and perimeter of the detected cracks [36]. The advantages of their system included its low cost, high accuracy in detecting welding defects, and the elimination of the need for skilled inspectors to interpret the images [36].  
Further, the authors in [37] introduced an approach for characterizing pipeline defects by capturing a limited number of radiographs from various angles around the pipe. The results of this approach were accurate to within \(\pm 3\) mm for lateral and axial dimensions. The experimental results in this approach were compared against simulation examples. The types of inspected defects included inner wall flat-bottomed holes and inner wall corrosion patches.  
It is worth noting that, while radiography testing offers the advantage of providing in-depth visuals of the internal composition of materials, it is associated with potential health risks and environmental implications due to ionizing radiation exposure.  
Figure 4: A demonstration of the MPT principle in NDT. After the specimen is magnetized, magnetic particles are applied. These particles will aggregate at the defect location with a magnetic flux leakage.
# Corrosion Behavior of Stainless Steel in Seawater in the Presence of Sulfide
## 1 Introduction
In this work, we combined the results of electrochemical measurements with the results of surface characterization, which included optical and SEM/EDS microscopy and 3D profilometry, to compare the corrosion behavior of 304L and 316L stainless steels in seawater at different temperatures and different concentrations of Na\({}_{2}\)S (10 and 40 ppm), to determine the form of corrosion attack, the depth of corrosion penetration into the material, and the elemental composition of corrosion products on the surface.
# Integration of ERT, IP and SP Methods in Hard Rock Engineering
## 4 Results and Discussion
### Groundwater Flow Modeling  
Most of the uncertainties in the subsurface geological model were reduced/removed by 2D ERT and IP models through the delineation of weathered and unweathered rock, detection of fractures/faults, and the distinction between water and clay. However, ERT and IP cannot map the groundwater flow in the investigated area. Groundwater flow mapping was performed by SP. SP anomalies suggest both positive and negative values ranging from \(-\)150 mV to 900 mV over the entire investigated area. The negative SP suggests the groundwater flow path. The 2D SP models were generated along the same ERT and IP profiles. The 2D SP models interpreted for the groundwater flow are shown in Figure 6. The integration of 2D SP models provides an overall view of the projected site with clear insights into the subsurface. The SP results suggest that the groundwater flow is mainly mapped along the main fractures/faults. The arrowheads in Figure 6 suggest the groundwater flow direction. Furthermore, the low SP (negative values) delineates the fractures/faults. Hence, in addition to the groundwater flow mapping, SP also supports ERT and IP for the delineation of fractures/faults, and hence the accuracy in the interpretation of  
Figure 5: Integration of 10 2D IP models along all profiles (with IP increasing from blue to red on a color bar) interpreted for the clay (shown by red color), and fractures/faults (dotted reds lines).  
the subsurface geological model is increased. The results obtained by different geophysical methods show a good correlation with each other and the hydrogeological information of the project site (Figures 2-6). Thus, the use of integrated geophysical methods reduces the geological model uncertainties caused by the limited data and provides more accuracy in the subsurface geological model for the successful construction of engineered structures.  
Based on the interpretation of the subsurface using three geophysical methods, a conceptual model of the investigated site was obtained as shown in Figure 7a. The conceptual model provides an overall picture of the study area for the delineation of four distinct layers (topsoil cover, weathered layer, weathered/unweathered transition zone and unweathered layer using ERT, the main faults using ERT, IP and SP, clay using ERT and IP, and groundwater flow path using SP.  
Figure 6: Integration of 2D SP models along all profiles (with SP increasing from blue to red on a color bar) interpreted for the groundwater flow modeling (shown by black arrows) and the fractures/faults (dotted reds lines).
## 2 Materials and Methods
### Cell Surface Marker Analysis  
THP-1 cells and primary monocytes were plasma-treated and incubated for 96 h. Subsequently, cells were washed, harvested with accutase (BioLegend, San Diego, CA, USA) and pooled into V-bottom 96-well plates (Corning, Corning, NY, USA). After washing of the well plate, the cells were stained with fluorochrome-conjugated antibodies (all Becton Dickinson, Franklin Lakes, NJ, USA) targeted against CD11b (APC-Cy7), CD11c (BV421), CD15s (PerCP-Cy5.5), CD33 (BV510), CD41 (Pacific Blue), CD45 RA (PE-Dazzle), CD49d (FITC), CD55 (PE-Cy5), CD63 (PE), CD66b (AF700), CD69 (BV650), CD80 (FITC) CD154 (APC-Cy7), CD163 (APC), CD206 (PE) CD271 (PE-Cy7) and HLA-ABC (APC). After two additional washing procedures, the mean fluorescence intensity (MFI) of each cell surface markers was analyzed by flow cytometry. The compensation of the 12-marker panel was carried out using compensation beads (Becton Dickinson, Franklin Lakes, NJ, USA) yielding a compensation matrix (Supplementary Figure S1b) calculated via the _CytExpert_ 2.2 flow cytometry acquisition software (Beckman-Coulter, Brea, CA, USA). The flow data were normalized to untreated controls (without PMA).