esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Gas Foil Bearings in China
## 3 Challenges and Prospects
### Thermal Management of Gas Foil Bearing  
During high speed operation, a large amount of heat is generated in the foil bearing due to viscous dissipation or high-temperature working environments in some special cases, such as with micro gas turbines. The heat needs to be dissipated or discharged in time, otherwise it will cause a gas foil bearing failure. Therefore, various measures have been taken to transfer heat to prevent heat accumulation, such as processing multi-layer gap foils, adding holes, and adding heat dissipation channels on the bearing housing thatcan be used to guide cooling air. Apart from this, the development of phase coatings and supporting materials with a high heat capacity is also very beneficial for gas foil bearing thermal management. At present, a great deal of research on the thermal management of foil bearings has been carried out in China. Efforts are being made to increase the maximum operating temperature of foil bearings to 650 \({}^{\circ}\)C.
## 3 A Short Overview of Selected Examples of Measuring and Monitoring Systems on Sea Ships  
Measurement methods in marine technology have passed from the model of direct and indirect measurement into the system measurement phase. The most important characteristics of the system measurement are:  
* Measurement must be treated together with the measured object,
* Measurement is accompanied by instrumentation, usually called a measuring and/or monitoring system, whose role is to implement a set of operations, among others:  
* Detection of signals from the measured object;
* Analog-to-digital conversion of signals, which consists of sampling, quantization and digital coding;
* Operational processing of signals, mainly digital, carried out using computer technology, consisting of forming signals carrying information about measured quantities; usually we deal with multi-path processing.  
A short overview of selected examples presented below is based on the selection criteria concerning firstly a vital role of the considered system for safe and effective ship's operation and secondly well documented contribution of Gdynia Maritime University in the development of the state-of-the-art in the analyzed area of measurement.
###### Abstract
[22] proposed a two-stage damage propagation model for determining the delamination growth area in glass-fiber-reinforced polymer (GFRP) due to low-velocity impacts. The authors used an optical transmission scanning (OTS) method to examine the delamination growth due to repeated low velocity impacts. The damage area was found to increase rapidly in the first few impacts and slow down beyond a particular threshold. Therefore, the damage propagation was divided into two regions. Paris model was used to represent both the regions with different set of parameters without changing the underlying failure mechanism. The particle filter framework was used for predicting the future degradation state. The slope difference between the last two predicted values was evaluated, and the jump energy was determined to be at the instant where the slope difference was maximum. Therefore, the prediction accuracy primarily depends on the initial distribution of the jump energy, and a minimum of seven measurements were needed to deduce the jump energy. The model proved to be inaccurate in the first region, and the prediction accuracy improved only in the second region, with more measurement data being available to capture the degradation trend better. Since the methodology proposed in Refs. [22; 23] is a promising approach for long-term reliability studies and real-time applications, we have adopted their methodology and intend to further improve upon it. The aim of this paper is therefore to perform an online prognostic study on the GFRP laminate delamination growth. We propose a prognostic framework wherein the jump energy is deduced on the go and also the initial distribution for the damage state is varied depending on the measurement data available from the sensor. The novelty of the proposed prognostic framework lies in its ability to deduce the point of inflection from rapid to slow crack propagation on the fly. This approach provides an advantage for damage prognosis of composite materials wherein multiple failure mechanisms coexist and very limited knowledge of the underlying failure mechanism is available for the purpose of remaining fatigue life estimation. We intend to compare our prediction results with that of Ref. [22] and validate the effectiveness of our proposed method. The paper is organized as follows: Section 2 describes the degradation data used in this study along with an appropriate damage propagation model which best represents the degradation data. A particle-filter-based prognostic framework is also explained along with the proposed online prognostic framework adopted in this study. Section 3 compares the prediction results obtained from the optimized Paris-Paris model to that of the conventional Paris model using the proposed methodology. Section 4 compares the prediction results between the proposed method and the results from Ref. [22]. Lastly, the final section of the study provides some concluding remarks along with possible suggestions for future work.
## 6 Experimental Results
### Experimental Setup  
The experimental setup is shown in Figure 7 and uses a 3-DOF FARA-AT2 robot manipulator. This robot manipulator has 6-DOF, but for these experiments, joints 4, 5, and 6 were blocked. The 3-DOF FARA-AT2 robot had a CSMP series motor at each joint. The CSMP-02 BB driver (Samsung, Namdong-gu, Incheon, Korea) was used for joints 1 and 2, while the CSMP-01 BB driver was used for joint 3. The gear box at each joint was 120:1,120:1, and 100:1 at joints 1, 2, and 3, respectively. The encoder at each joint was a 2048 line count incremental encoder. The controller was run on Labview-FPGA, NI-PXI-8110 and NI-PXI-7842R PXI cards (National Instruments, Austin, TX, USA) with the frequency control set at 500 Hz. The NI-PXI-8110 was run on a Windows operating system.  
The desired trajectory at each joint is given as:  
\[q_{id}(t)=\frac{\pi}{6}\sin(\frac{\pi t}{1600})(i=1,2,3) \tag{39}\]  
The related parameters were chosen to be \(\alpha_{1}=8,\alpha_{2}=28,\alpha_{3}=7\) and \(\varepsilon=0.01\). The [23]+ESO is given as:  
\[\tau_{[23]+ESO}=\tau_{0}+\tau_{smc}+\tau_{ob} \tag{40}\]  
Figure 6: Tracking error at each joint for AFTC S-TSMC1 and AFTC S-TSMC2.  
Figure 7: 3-DOF FARA-AT2 robot manipulator.  
where \(\tau_{eq}=\mathbf{M}(q)(\ddot{q}_{d}+c\Lambda|\epsilon|^{\Lambda-I}\cdot\dot{e})+ \mathbf{H}(q,\dot{q})\), \(\tau_{o}=\mathbf{M}(q)K_{1}sign(S)\) and \(\tau_{ob}=-\mathbf{M}(q)\hat{\phi}\), where \(K_{1}=diag(k_{1i})\in\Re^{m\alpha n}\). The sliding mode surface was selected as:  
\[S=\dot{e}+c[e]^{\Lambda} \tag{41}\]  
The parameters for the [23]+ESO were chosen as \(c=diag(7;7;7),K_{1}=diag(80;80;110)\), \(u_{s}=10\) and \(\Lambda=diag(0.5;0.5;0.5)\). The parameters for AFTC S-TSMC1 were chosen as \(\psi_{1}=\psi_{2}=\psi_{3}=2\), \(\alpha=diag(1;1;1)\), \(\beta=diag(0.5;0.5;0.5)\), \(\Lambda=diag(0.5;0.5;0.5)\), \(u_{s}=3\), \(\Gamma=diag(7;7;7)\) and \(K_{1}=diag(80;80;110)\). The parameters for AFTC S-TSMC2 were chosen as \(\psi_{1}=\psi_{2}=\psi_{3}=2\), \(\Lambda=diag(0.5;0.5;0.5)\), \(c=diag(7;7;7)\), \(u_{s}=3\) and \(K_{1}=diag(80;80;110)\).  
To avoid a singularity, the terms containing power \(\Lambda-I\) in Equations (16), (27) and (35) are replaced with the saturation function.  
\[sat(u_{f},u_{s})=\left\{\begin{array}{ll}u_{s}&if\;u_{f}\geq u_{s}\\ u_{f}&if\;u_{f}<u_{s}\end{array}\right. \tag{42}\]  
where \(u_{s}\) is a positive constant, and \(u_{f}=\Lambda|x|^{\Lambda-I}\cdot\dot{x}\) with \(x=e,\varepsilon\) and E.  
To avoid chattering, the signum functions in Equations (16), (27) and (35) are replaced with the saturation function.  
\[sat(s)=\left\{\begin{array}{ll}sgn(s)&if|s|\geq\lambda\\ \frac{s}{\lambda}&if|s|<\lambda\end{array}\right. \tag{43}\]  
where \(\lambda=1.7\).
## 2 Materials and Methods
### Statistical Indicators Used for Evaluation of the ANFIS and ANFIS-GA Models  
The ANFIS and ANFIS-GA models can be validated using different statistical methods. The most acknowledged statistical error indicators are the mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination \(R^{2}\). However, in this research, we only focused on the RMSE and \(R^{2}\).  
1. The mean absolute bias error can be defined as the average overall quantity of all the absolute bias errors determined when comparing the actual and predicted traffic volume. It is mathematically denoted as: \[\text{MABE}=\frac{1}{N}\sum_{i=1}^{N}\lvert(V_{i,P}-V_{i,M})\rvert\] 2. The MAPE is the mean absolute percentage difference that can be determined between predicted and actual traffic volumes. This is mathematically stated as: \[\text{MAPE}=\frac{1}{N}\sum_{i=1}^{N}\lvert\left(\frac{V_{i,P}-V_{i,M}}{V_{i, M}}\right)\rvert\times 100\] 3. The RMSE is determined by knowing the model's accuracy, which is determined by calculating the comparison between the predicted traffic volume and the actual traffic volume. The value is always positive and not negative. It is mathematically represented as: \[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(V_{i,P}-V_{i,M}\right)^{2}}\] 4. The \(R^{2}\) also known as the coefficient of determination, signifies the optimal relationship between both the predicted traffic volume of vehicles at the four signalized road intersections and the actual traffic volume. This is mathematically represented as: \[R^{2}=1-\frac{\frac{1}{N}\sum_{i=1}^{N}\left(V_{i,P}-V_{i,M}\right)^{2}}{\frac {1}{N}\sum_{i=1}^{x}\left(V_{i,P}-V_{M,avg}\right)^{2}}\] where \(V_{i,P}\) represents the predicted traffic volume of vehicles at the four signalized road intersections and \(V_{i,M}\) indicates the actual traffic volume of vehicles on these  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Population Size** & **Number of Iterations** & **Percentage of Crossover** & **Percentage of Mutation** & **Rate of Mutation** & **Selection Pressure** & **Selection Function** \\ \hline
100 & 500 & 0.8\% & 0.3\% & 0.02 & 8 & Roulette wheel \\ \hline \hline \end{tabular} Note. These tuning parameters can be found in the ANFIS-GA codes; changing these parameters has a significant impact on the ANFIS-GA model training and testing.  
\end{table}
Table 5: Tuning parameters used for the ANFIS-GA model.  
signalized road intersections. Figure 8 below shows the breakdown of the Off-peak and On-peak hours of traffic flow of vehicles.
## 4 Conclusions  
An FR4, substrate-based, two-port, loaded MIMO antenna has been proposed. Four types of design structures have been analyzed by cropping the patch region, loading the slits near the hexagonal-shaped patch, and adding/removing the slots in the ground area. The proposed design was analyzed in terms of peak gain, DG, reflection coefficient, total gain, electric field, directivity, and E-field pattern. The proposed structure provides an isolation of 35 dB, DG of 10 dB, and peak gain of 20.44 dB. The reflection response of \(-\)39.44 dB with the nine band responses having an S\({}_{11}\) less than \(-\)10 dB is attained in DS-3. The maximum electric field of 33.3 V/cm was attained in DS-3. Overall, the addition of slots around the patch area and DGS improves the performance of the antenna. The presented design targeted multiple wireless communication applications for the range of 1 GHz to 14 GHz. Therefore, the presented design structure has potential applications in satellite communication, vehicle speed monitoring, the military, and other 5G/6G applications requiring high data rate transmission.  
Conceptualization, M.A. and S.K.P.; methodology, M.A. and S.L.; software, S.L. and K.A.; validation, A.-A., S.K.P. and M.G.D.; writing--original draft preparation, all authors; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data will be made available upon reasonable request to the corresponding author.  
The authors declare no conflict of interest.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 2 The Establishment of the Cable Icing Data Set
### Experiment Preparation
#### 2.1.2 Experimental Conditions of Stay Cable Icing  
1. Environmental temperature: The minimum temperature of Wuhan in the past five years was \(-8\)\({}^{\circ}\)C, and the classic icing environment of the bridge is \(-4\)\({}^{\circ}\)C. Therefore, this paper assumes that the ambient temperature is \(-8\)\({}^{\circ}\)C and \(-4\)\({}^{\circ}\)C.
2. Wind speed: The dominant wind direction in winter is northwest wind. In this experiment, a fan was set in the direction the of northwest wind, and its wind speed was set to 2 m/s.
3. Cooling time: This experiment selected the cooling time from 3:00 a.m. to 6:00 a.m. (3 h). After cooling, the indoor temperature was adjusted to 2 \({}^{\circ}\)C until the ice was completely melted, and the complete icing and melting process was recorded.
4. The inclination angle of the stay cable sleeve: The previous research data found that the ice thickness of the stay cable decreases with the increase in the inclination angle of the stay cable. Therefore, to make the network model more robust, the scaffold inclination angle was adjusted to 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\), and the stay cable sleeve was placed according to the adjusted angle.  
The experimental conditions are shown in Table 1.  
Figure 2: Stay cable ice experimental equipment.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Prohibited Zone with Omnidirectional Antenna
#### 4.1.2 Vertical Prohibited Zone  
The vertical prohibited zone is a crucial aspect that is often disregarded. However, a base station erected on a mountain has the potential to radiate to aircraft in the sky. To simplify the analysis, assuming the RA antenna gain is symmetrical, only the vertical plane needs to be studied.  
The 3D results of the 200 ft (60.96 m) scenario is depicted in Figure 13a,b. The X-axis represents the base station's \(X\)-coordinate, and the \(Y\)-axis signifies the height of the BSs. The \(Z\)-axis indicates the interference level at the aircraft position, depicted through color differentiation. Two-dimensional results are also provided in Figure 13d-f. The results are calculated within 1 km. Two-dimensional representations of 1000 ft (304.8 m) are provided in Figure 13g-i, and representation of 2000 ft (609.6 m) are provided in Figure 13j-l. The results are calculated within 5 km.
* The radius of the vertical prohibited zone varies with height. This is mainly due to the fact that the gain of the RA antenna varies at different angles. Its ability to receive interference is also different. At higher altitudes close to the aircraft's altitude AGL, the angle (\(\varphi_{\circ}\)) in Figure 10 is bigger, and the gain decreases accordingly.
* Similar to the horizontal prohibited zone, the largest vertical prohibited zone occurs with the RMa base station at 2000 ft. The magenta line represents the ICAO 6 dB margin, which increases the prohibited zone radius by 0.2-1.1 km. The path loss of NLOS and LOS is the most significant in the UMi scenario.  
Figure 13: Vertical prohibited zones of RMa, UMa, and UMi at \(AGL\) = 200 ft, 1000 ft, 2000 ft (ommidirectional antenna). (**a**) RMa (\(AGL=200\) ft, 3D). (**b**) UMa (\(AGL=200\) ft, 3D). (**c**) UMi (\(AGL=200\) ft, 3D). (**d**) RMa (\(AGL=200\) ft). (**e**) UMa (\(AGL=200\) ft). (**f**) UMi (\(AGL=200\) ft). (**g**) RMa (\(AGL=1000\) ft). (**h**) UMa (\(AGL=1000\) ft). (**i**) UMi (\(AGL=1000\) ft). (**j**) RMa (\(AGL=2000\) ft). (**k**) UMa (\(AGL=2000\) ft). (**l**) UMi (\(AGL=2000\) ft).
# PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration
## 2 Method
### Local Features  
To better capture the local geometric features of the point cloud while maintaining the invariance of the arrangement, PTRNet first constructs adjacent geometric graphs and then convolves the edges of the local graphs, collecting local features of the point cloud for subsequent registration [28]. The specific operations are as follows.  
We consider a point cloud with \(N\) points and \(F\) dimensions at each point, denoting the set. \(X=\{x_{1},x_{2},\ldots,x_{N}\}\subseteq\mathbb{R}^{F}\). When \(F=3\), each point contains three dimensional coordinates \(x_{i}=(x_{i},y_{i},z_{i})\). When \(F>3\), the information for each point contains additional data indicating colors, surface normals, and so on.  
We establish the KNN topology as shown in Figure 2. The graph \(G=\{V,E\}\) represents a point \(x_{i}\) and its \(k\) neighboring points \(\left\{x_{j_{1}},x_{j_{2}},\ldots,x_{j_{k}}\right\}\), where \(V=X\), \(E=<i,jk>\), and \(x_{i},x_{jk}\in V\). As a directed graph of the local point cloud structure, we define the edge feature as \(e_{ij}=h_{\Theta}(x_{i},x_{j})\), where \(h(\cdot):\mathbb{R}^{F}\times\mathbb{R}^{F}\rightarrow\mathbb{R}^{F}\) is a nonlinear function with learning parameter \(\Theta\) and \(F^{\prime}\) is the number of dimensions of the edge features. The \(K\) edge features are aggregated into \(\varsigma\) (i.e., accumulation or maximization) to obtain the output \(y_{i}\) corresponding to \(x_{i}\), which is defined as Formula (1).  
\[y_{i}=\underset{j:(i,j)\in E}{\xi}h_{\Theta}(x_{i},x_{j}) \tag{1}\]  
During the convolution operation in the images, \(x_{i}\) denotes the central pixel and \(x_{j}\) the pixel block around \(x_{i}\). If the input of this layer is an F-dimensional point cloud of N points, it is output as \(F^{\prime}\)-dimensional edge features of N points.  
The choices of edge function and aggregation method are key to the KNN graph convolution network [28]. The edge function should consider the local characteristics of the point cloud, so we define the edge function as  
\[h_{\Theta}(x_{i},x_{j})=h_{\Theta}(x_{i},x_{i}-x_{j}) \tag{2}\]  
In (1), a feature is considered as a set of multiple patches. The global information of (2) is obtained by a function based on query point \(x_{i}\), with the local information is obtained by \(\left(x_{i}-x_{j}\right)\) to obtain stronger feature representations. Using new symbols, we define the resulting feature equation as  
\[e^{\prime}_{ij}=ReLU(\mu\cdot x_{i}+\lambda(x_{i}-x_{j})) \tag{3}\]  
The implementation could be seen as a multilayer perceptron (MLP), with the learning parameter defined as \(\Theta=\{\mu,\ \lambda\}\) and the activation function defined as \(ReLU\). In graph  
Figure 1: The structure of PTRNet.  
convolutions, max or mean functions are often chosen arbitrarily as aggregation functions. We adopt a max function for aggregation operations. Edge features are aggregated by (4):  
\[y^{\prime}_{i}=\max_{j:(i,j)\in\mathbb{E}}e^{\prime}_{ij} \tag{4}\]
## 1 Introduction
[18] provided a compressible device for the elution of the receiving end of swabs, which allows for a conditioning fluid to be flown into the elution zone from a blister along a transfer path. Piezoelectric methods [19] allow for an actuator to strike a flexible film at low frequency ranges between 10 and 100 Hz, but also require a flow of conditioning fluid across the swab. [PERSON] [20] used one or more test fluids to be delivered to the tip of the swab through the shank after being squeezed out of porous filter membranes with a hydrophobic sealant. Alternating magnetic fields [21] have also been used for extraction of analytes from samples by tagging antibodies with magnetic beads, which binds to antigens on bacteria and gets removed on the influence of the magnetic trap. Similar issues of low recovery are also encountered in sample processing for immunoassays in clinical diagnostics. [PERSON] et al. [3] confirmed the preference of vortexing (43.6%) over sonication (17.7%) as a better technique of elution of bacterial cells from pre-moistened cotton swabs. Higher recovery efficiencies upto 55% can be obtained with vortexing, while tangential shear forces with associated flow (piezoelectric, pressurized flow) can yield a maximum of 60% recovery. Different extraction techniques by vortexing, squeezing, pummelling, maceration, rolling, and differential extraction of semen from swabs were compared by [PERSON] et al. [22]. This study showed higher average elution times (-5 min) with 8000-13,000 RPM and -400 to 1000 \(\upmu\)L of eluate. Though chemical processes can provide higher elution efficiency (-78%), they require a complex combination of buffers, enzymes, and detergent solutions, and up to 2 h of incubation period, and it is also specific to the type of cells. In this paper, a mechanical technique of elution of cotton-tipped applicator swabs was developed that significantly improves the recovery efficiency. Here, we demonstrate the highest elution recovery procedure by inflicting physical impingement forces on the swab fibres by magnetic particles. The collection and release of polystyrene beads and _E. coli_ bacteria are compared and characterized with physical parameters, such as rotational speed (RPM), elution time, and concentration. The ability of the device to elute micro-organisms like _E. coli_ from artificial mucous samples has also been explored. Separate protocols for the quantification of particulates have been developed using microscopic pictures and fluorescent intensity measurements. It is expected that this method has broad applicability in sample elution of a variety of clinical and environmental samples that are collected using swabs.