esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of Tonal Trailing-Edge Noise Radiated by Low Reynolds Number Airfoils
## 4 Results of the Parametric AFL Sensitivity Study  
### Linear Stability Analysis  
The growth rates of flow disturbances within a BL or shear layer flows may be predicted using linear stability theory (LST), solving the linearized form of the Navier-Stokes equations (e.g., [PERSON] and Reed [22]). The disturbance solution vector \(\phi\) includes pressure, velocities, and temperature fluctuations,  
\[\phi=\left(p^{\prime},u^{\prime},v^{\prime},w^{\prime},T^{\prime}\right)^{T} \tag{5}\]  
It is assumed that the gradients in the mean flow are significantly smaller in the streamwise direction than in the wall-normal direction. Therefore, the flow at any given location along the BL is assumed to be quasi-parallel. The disturbance modal shape is thus assumed to have the following form,  
\[X(x,y)=\psi(y)e^{iax} \tag{6}\]  
where \(\psi\) is an amplitude function. Substituting Equation (6) into the disturbance vector yields Equation (7),  
\[\phi(x,y,z,t)=\psi(y)e^{i(ax+\beta z-\omega t)} \tag{7}\]  
where \(\omega\) is the perturbation frequency, and \(\beta\) and \(\alpha\) are the spanwise and streamwise wave number, respectively. In the analysis of the spatial evolution of the modes, \(\omega\) and \(\beta\) are typically specified by the user, and \(\alpha\) is the variable of interest in the numerical solution. Assuming a known set of mean flow profiles from numerical simulations or experiment, the linearized Navier-Stokes equations are solved in order to obtain information about the disturbance evolution in the flow. For the current application, a spatial analysis is conducted in order to determine the disturbance growth rate at a given location on the airfoil surface. Therefore, such analysis predicts the values of  
\[\alpha=\alpha(\omega,\beta)=\alpha_{r}+i\alpha_{i} \tag{8}\]  
for a known set of frequencies (e.g., corresponding to the prominent tones in the acoustic and surface pressure spectra), where \(\alpha_{r}\) is the real spatial wavenumber and \(-\alpha_{i}\) determines the instantaneous growth rate at a given streamwise location. Once the growth rates are acquired at each location, they are integrated along the instability's convection path to obtain the total growth factor \(N\) for a disturbance at a given frequency defined by  
\[N=\ln\left(\frac{A}{A_{0}}\right)=\int_{x_{0}}^{x}-\alpha_{i}dx \tag{9}\]  
where \(A_{0}\) is the initial disturbance amplitude, and \(A\) is the amplitude of the disturbance at a given streamwise location. Similarly, \(x_{0}\) and \(x\) denote the initial disturbance location and the final location, respectively. The current work employs a linear stability code LASTRAC (Chang [23]) to investigate the instability dynamics within the NACA-0012 airfoil transitional BL. The adopted coordinate system is body-fitted, where \(x,y,\) and \(z\) represent the streamwise, wall-normal, and spanwise direction, respectively.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 3 Proposed Methods  
To overcome the problems described in Section 2.3, we used the following methods to improve YOLOX:  
* We added the ECA modules [37] to the backbone network of YOLOX and connected the ASFF structure [38] after the neck network. This can enhance the feature extraction and fusion of the network, thereby improving the accuracy of the model in detecting small objects.
* We replaced the BCE function with the VFL function [39], which not only balances the numbers of positive and negative samples, but also highlights the key samples in the positive samples.
* In the detection of large-size images, we used the SAHI framework [40], which can cooperate with the target detection model to achieve the high-precision detection of large-size images.
## 2 Long-Range Propagation Analysis of Air-to-Air Airborne Radars
### Abnormal Atmospheric Conditions and Propagation Characteristics
Figure 5a represents the \(32\times 32\) triangular array configuration of an airborne array antenna with array distances of 0.475 \(\lambda\) and 0.538 \(\lambda\) along the \(x\)- and \(y\)-axis. Each element pattern is assumed to be an ideal isotropic pattern, and then the total array pattern with 1204 array elements is calculated in the \(Az/El\) domain, as shown in Figure 5b. Figure 5c shows the directivity of the array antenna in the H-plane, which has a half-power beamwidth (HPBW) of 2.85\({}^{\circ}\) and a side lobe level (SLL) of \(-\)13.5 dB. Figure 6 illustrates comparisons of the AREPS simulation path loss results at \(h_{s}\) of 5 km when using the refractivity of the actual data, the quad-linear model, and the trilinear model. The path loss results using the actual data, and the quad-linear model exhibit similar trends in terms of the propagation characteristics in the abnormal atmospheric condition. On the other hand, the trilinear model shows significant deviations in relation to the path loss characteristics. Note that, it is possible that the path loss results can be more different according to the refractivity model, although the \(RMSD\) values are small. These AREPS simulation results were previously validated by the measurement in references [15; 16] of a long-range propagation in a ground-to-ground situation. Furthermore, to verify these results, we compare the path loss of the AREPS simulation  
Figure 3: Quad-linear model of the modified refractivity according to the altitude. with that of the ITU-R P.528 model [17] and the MATLAB PETOOL software [18]. Figure 7 represents the comparison of the path loss results as a function of range in accordance with the atmospheric conditions without the terrain effects. In the normal atmosphere, all results have a similar tendency of path loss according to the range. In the abnormal atmosphere, the AREPS and PETOOL results also show a similar fluctuation, and both results follow the ITU-R model trend in the long-range propagation. Therefore, the resulting propagation characteristics demonstrate that the quad-linear model of refractivity can provide a highly accurate wave propagation estimation for air-to-air airborne radar simulation in abnormal atmospheric conditions.
# FSI Application in the Parametric Analysis of Blade-Disk Connection of Turbojet Engine Compressor
## 1 Introduction
The main analysis steps in the algorithm, as shown in Figure 3, included the following: the development of a virtual model, including parametric blade-disk connection and discrete models for FEM and CFD analyses; performing numerical structural-flow analyses; and applying them to the analysis of the parametric model. In the structural analysis, mass forces resulting from the given rotational speed were assumed. In addition, the condition of the engine's overall dimensions being unchanged was imposed, so when defining the decision variables, parameters that could change the radial dimensions of the rotor stage were not considered. The last stage of the analysis included the introduction of aerodynamic loads to the structure. In this case, a one-way FSI analysis was used, which included first determining the pressure distribution on the blade and then implementing the obtained results for structural analysis.
## 7 Simulations and Results
### Scenario 2: HEMS with an ESS
#### 7.2.1 Cost Using RTP and CPP  
After obtaining the optimal scheduling through the GWO, GA, and the proposed technique HGWGA by considering the RTP tariff, the electricity consumption charges were 422.42 cents, 410.07 cents, and 399.34 cents, respectively, i.e., the optimal scheduling cost using the GWO, GA, and  
Figure 11: AWT without ESS or PV.  
Figure 12: Per-hour load with ESS.  
the proposed HGWGA was reduced by 20.03%, 22.36%, and 24.39%, respectively, in comparison with the unscheduled case. Similarly, when the CPP signal was applied, the electricity consumption costs for the GWO, GA, and the proposed technique HGWGA were 432.30 cents, 435.57 cents, and 409.36 cents, respectively, which demonstrated that consumption cost of the unscheduled case was reduced by 36.35%, 35.87%, and 39.73%, respectively, as shown in Table 7. The total electricity consumption costs for the unscheduled and scheduled cases in the RTP and CPP schemes are depicted in Figures 13 and 14, respectively.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 4 Partial Discharge Measurement Using Different Types of Sensors on the Power Transformer and GIS
### Mechanism in Chemical Sensors  
Chemical changes in the composition of insulating materials in transformers and GIS can be utilized for the chemical detection of PD signals. By observing these changes, it is possible to identify PD activity occurring within the equipment. The use of DGA or HPLC is used to analyze the chemical characteristics. The DGA study sought to determine the concentrations of several gases, including hydrogen, ethylene, acetylenes, carbon dioxide, and methane, in a sample of fluid from an oil-cooling transformer's container or gas from the GIS [126]. These levels must not be higher than those specified in the occurrence of oil insulation or insulation material malfunction. Unfortunately, no scientific or experimental link or calibration has been established between DGA readings, dissolved gas levels, and fault types. The HPLC test quantifies the by-products of transformer or GIS insulation breakdown. When a transformer's insulation fails, glucose is produced. Real-time monitoring of PD byproducts through the collection and analysis of sufficient quantities of insulation breakdown byproducts is time-consuming. Due to the lack of a consistent association between the glucose concentration emitted during insulation failure and the kind and severity of high voltage transformer malfunction, both HPLC and DGA tests are prone to the same degree of ambiguity. Inaccurate localization of the PD signal  
Figure 13: (**a**) The basic UHF monitoring system for PD and its guiding principle and (**b**) principle of a typical ultra high frequency [62].  
source and evaluation of insulation deterioration are also beyond the chemical approach's capabilities. As a result, the chemical technique cannot provide real-time online monitoring.  
PD Detection in Transformer  
Figure 14a illustrates a hydrogen-oil detector. A portable gas chromatograph is connected to a semipermeable membrane, which is then located in the transformer's oil tank. The portable gas chromatograph can test the concentration of hydrogen gas at predetermined time intervals. The fuel cell type detectors are used in the second profit-oriented version of the DGA testing for power transformers, as illustrated in Figure 14[127].
## 6 Analysis of the Algorithm
### Analysis of Extract-Phase and Voting-Phase of Algorithm Recover-Motif
Assume that Collision-Detection(\(S_{1},U_{1},S_{2},U_{2}\)) returns the result in \(t(n,||U_{1}||+||U_{2}||)\) time and the Point-Selection(\(S_{1},S_{2},L\))) selects \(s(n,L)\) positions in \(g(n,L)\) time. Assume that with a probability of at most \(\varphi(n)\), the function Initial-Boundaries() does not stop when \(L\leq|G|/4\), and \(||U_{S_{2i-1}^{\prime}}||+||U_{S_{j}^{\prime\prime}}||\) in the algorithm Recover-Motif is no more than \(f(n,|G|)\)._  
_Then, with a probability of at most \(k_{1}\varphi(n)\), the entire algorithm Recover-Motif does not stop in the time complexity \((O(k_{1}(\sum_{i=1}^{i_{0}}(t(n,s(n,\frac{n}{2^{i}n^{2/5}}))+g(n,\frac{n}{2^{ i0}n^{2/5}})))+k_{1}h^{2}\log n+k_{1}k_{2}t(n,f(n,|G|))+h^{2}\log n)+k_{1}k_{2}( \log n)(\log\log n)),O(k_{2}))\), where \(i_{0}\) is the largest \(j\), such that \(\frac{n}{2^{n/5}}\leq\min(n^{2/5},|G|)\) and \(h=\min(n^{2/5},|G|)\)._  
**Proof:** The function Initial-Boundaries()is executed \(k_{1}\) times. According to the condition that with a probability of at most \(\varphi(n)\), the function Initial-Boundaries(.) does not stop when \(L\leq|G|/4\), we have the fact that with a probability of at most \(k_{1}\varphi(n)\), one of those executions of Initial-Boundaries(.) does not stop when \(L\leq|G|/4\). In the rest of the proof, we assume that all executions of Initial-Boundaries(.) stops when \(L\leq|G|/4\). When \(L=O(h)\), we detect rough left and right motif boundaries and run Improve-Boundaries(), which takes \(O(h^{2}\log n)\) time by Lemma 21. It takes \(O(\sum_{i=1}^{i_{0}}(t(n,s(n,\frac{n}{2^{i}n^{2/5}}))+g(n,\frac{n}{2^{i}n^{2/5 }})+h^{2}\log n)\) time to run Initial-Boundaries(\(S_{2i-1}^{\prime},S_{2i}^{\prime}\)) one time for one pair (\(S_{2i-1}^{\prime},S_{2i}^{\prime}\)) in \(Z_{1}\). It takes \(O(k_{1}(\sum_{i=1}^{i_{0}}(t(n,s(n,\frac{n}{2^{i2/5}}))+g(n,\frac{n}{2^{i2/5}}) +k_{1}h^{2}\log n)\) time to run Initial-Boundaries(\(S_{2i-1}^{\prime},S_{2i}^{\prime}\)) one time for all pairs (\(S_{2i-1}^{\prime},S_{2i}^{\prime}\)) in \(Z_{1}\). It takes \(k_{2}(t(n,f(n,|G|))+h^{2}\log n)\) time to find the rough boundaries for all sequences in \(Z_{2}\) with a fixed sequence, \(S\), from \(Z_{1}\) by executing for the loop \"For each \(S_{j}^{\prime\prime}\in Z_{2}\)\" in the algorithm Recover-Motif. It takes \(k_{1}k_{2}(t(n,f(n,|G|))+h^{2}\log n)\) time to find the rough boundaries for all sequences in \(Z_{2}\) via all sequences, \(S^{\prime}_{2i-1}\), from \(Z_{1}\) through the loop \"For each \(S^{\prime\prime}_{j}\in Z_{2}\)\" in the algorithm Recover-Motif. Recall that parameters \(v\) and \(u_{1}\) are constants, and \(u_{2}\) is \(O(\log\log n)\). Calling Match(\(G_{l},G_{r},S^{\prime\prime}_{i}\)) takes \(O((v+u_{2})\log n)\) time for each \(S^{\prime\prime}_{i}\in Z_{2}\). The total times for calling Match(\(G_{l},G_{r},S^{\prime\prime}_{i}\)) is \(O(k_{1}k_{2}(v+u_{1})(v+u_{2})\log n)=O(k_{1}k_{2}(\log n)(\log\log n))\). The voting part takes \(O(k_{2})\) time for executing voting for recovering one character in the motif.
## 2 Modeling of Tethered UAV Dynamics
### Dynamiling of Tethered UAVs  
The dynamic model of the tethered UAV can be obtained from the dynamic equations of the UAV platform in Section 2.1, plus the tension of the tethered cable in Section 2.2. Substitute Equation (21) into Equation (1) to solve the dynamics model. The first-order derivatives of the system state quantities \(\mathbf{p}\) and \(\mathbf{q}\) are obtained as:  
\[\dot{\mathbf{q}}=\begin{pmatrix}uc\theta c\psi-v(c\varphi s\psi-s\theta s\varphi c \psi)+w(s\varphi s\psi-s\theta c\varphi c\psi)\\ us\psi c\theta+v(s\theta s\varphi s\psi+c\varphi c\psi)-w(s\varphi c\psi-s \theta s\varphi c\varphi)\\ -us\theta+vs\varphi c\theta+c\theta c\varphi\\ p+q\frac{s\theta s\varphi}{c\theta}+r\frac{s\theta c\varphi}{c\theta}\\ qc\varphi-rs\varphi\\ q\frac{s\varphi}{c\theta}+r\frac{c\varphi}{c\theta}\end{pmatrix} \tag{22}\]  
\[\dot{\mathbf{p}}=\begin{pmatrix}\frac{U_{1}(s\varphi s\psi+s\theta c\varphi c\psi )-mq\psi+mrv-T\cos\beta\sin\beta_{1}}{m}\\ \frac{-U_{1}(s\varphi c\psi-s\theta s\varphi c\varphi)+mv-mm-T\cos\beta\cos \beta_{1}}{m}\\ \frac{U_{1}c\theta c\varphi-mv-mv-m\cos\beta}{m}\\ \frac{U_{2}-1\varphi d\varphi+l_{2}\varphi}{U_{3}-l_{2}\varphi+l_{2}\varphi} \\ \frac{l_{x}}{l_{y}}\\ \frac{U_{4}-l_{2}\varphi q+l_{y}\varphi q}\end{pmatrix} \tag{23}\]  
Finally, Equations (22) and (23) are transformed into equations of state of the system. The complete dynamics model of a tethered UAV is described in the form of state space:  
\[\dot{\mathbf{X}}=f(\mathbf{X}) \tag{24}\]  
where \(\mathbf{X}=[\mathbf{q},\mathbf{p}]^{\mathrm{T}}=[x\,y\,z\,\varphi\,\theta\,\psi\,u\,v\,w\, p\,q\,r]\).
## 4 Aerostatic Torsional Stability Robustness Evaluation
### Robustness Calibration of Aerostatic Torsional Stability
#### 4.3.3 Nansha Bridge  
The calculated reliability indices and return periods of Nansha Bridge at 0\({}^{\circ}\) initial wind attack angle and +3\({}^{\circ}\) wind attack angle are listed in Tables 14 and 15, respectively. At the initial wind attack angle of 0\({}^{\circ}\), the maximum failure probability of Nansha Bridge was 6.02 \(\times\) 10\({}^{-5}\), and the shortest return period of the reference wind speed that the bridge could resist was 16,624 years. Even at a relatively unfavorable initial wind attack angle of +3\({}^{\circ}\), the maximum failure probability was 1.29 \(\times\) 10\({}^{-4}\), and the shortest return period could still reach 7749 years, which was far beyond the design service life. Therefore, Nansha  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(\delta_{b}\)} & \multicolumn{2}{c}{\(\delta_{t}\) = 0.05} & \multicolumn{2}{c}{\(\delta_{t}\) = 0.075} & \multicolumn{2}{c}{\(\delta_{t}\) = 0.1} \\ \cline{2-7}  & \(\beta\) & \(T_{m}\) **(Year)** & \(\beta\) & \(T_{m}\) **(Year)** & \(\beta\) & \(T_{m}\) **(Year)** \\ \hline \(\delta_{b}=0.12\) & 3.4041 & 3013 & 3.2776 & 1910 & 3.1164 & 1092 \\ \(\delta_{b}=0.14\) & 3.3177 & 2204 & 3.2121 & 1518 & 3.0765 & 955 \\ \(\delta_{b}=0.16\) & 3.248 & 1721 & 3.1572 & 1256 & 3.04 & 845 \\ \(\delta_{b}=0.18\) & 3.1922 & 1416 & 3.1124 & 1078 & 3.009 & 763 \\ \(\delta_{b}=0.20\) & 3.1472 & 1213 & 3.0757 & 952 & 2.9827 & 700 \\ \hline \end{tabular}
\end{table}
Table 13: Results of aerostatic torsional stability robustness evaluation of Xihoumen Bridge at +3\({}^{\circ}\) initial wind attack angle.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(\delta_{b}\)} & \multicolumn{2}{c}{\(\delta_{t}\) = 0.05} & \multicolumn{2}{c}{\(\delta_{t}\) = 0.075} & \multicolumn{2}{c}{\(\delta_{t}\) = 0.1} \\ \cline{2-7}  & \(\beta\) & \(T_{m}\) **(Year)** & \(\beta\) & \(T_{m}\) **(Year)** & \(\beta\) & \(T_{m}\) **(Year)** \\ \hline \(\delta_{b}=0.12\) & 5.0324 & 4,129,013 & 4.8607 & 1,709,820 & 4.6472 & 594,405 \\ \(\delta_{b}=0.14\) & 4.8311 & 1,472,963 & 4.6852 & 715,086 & 4.5022 & 297,382 \\ \(\delta_{b}=0.16\) & 4.6707 & 666,278 & 4.5435 & 361,482 & 4.3829 & 170,780 \\ \(\delta_{b}=0.18\) & 4.5418 & 358,578 & 4.4286 & 210,866 & 4.2849 & 109,396 \\ \(\delta_{b}=0.20\) & 4.4331 & 215,314 & 4.3309 & 134,679 & 4.2007 & 75,162 \\ \hline \end{tabular}
\end{table}
Table 11: Results of aerostatic torsional stability robustness evaluation of Jiangyin Yangtze River Bridge at +3\({}^{\circ}\) initial wind attack angle.  
Bridge possesses the ability to resist the maximum wind speed that is unforeseen or unusual. It can be known from the results listed in Tables 14 and 15 that in different combinations of the coefficients of variation of \(U_{td}\) and \(U_{b}\), the relative errors of the reliability index were within 15.59% at the initial wind attack angle of 0\({}^{\circ}\) and 15.07% at the initial wind attack angle of +3\({}^{\circ}\).
## 3 Results and Discussion
### Microstructural Characterization  
#### 3.4.1 Optical Microscopy  
Polarized light microscopy is a simple method to characterize the changes that some materials undergo, such as the conservation of an ordered state or crystallinity. Figure 4 shows the polarized light micrographs of the fruit bagasse fibrils (A,B) and leaf (C,D), as well as the ESEM micrographs of the microcrystalline cellulose obtained from its processing, in which sizes to the order of 2-10 \(\upmu\)m were observed.  
The particle size decreased marginally without the use of chemical methods. In Figure 4B,D, spherical and ellipsoidal morphologies without agglomerations can be observed, while polarized light micrographs show agglomerations and birefringence characteristic of the ordered state of incompletely hydrolyzed cellulose crystals. This technique is very useful for monitoring the loss or gain of crystallinity.  
Figure 3: Comparison of Raman spectra of FB, FBO, LB and LBO in various spectral regions: (**A**) 100–1000 cm\({}^{-1}\), (**B**) 100–2000 cm\({}^{-1}\), and (**C**) 2000–3500 cm\({}^{-1}\).  
The presence of amorphous and crystalline cellulose can be seen in both bagasse by means of the polarized light technique in Figure 4, as well as the presence of phloem with amorphous characteristics and xylem tissue mainly with crystalline structures [49]. It is possible the decrease in the crystallinity index in the leaf bagasse after the organosolv process allowed for obtaining crystalline microcellulose (CMC) of a homogeneous size (Figure 4B,D) and a greater quantity in the supernatant of the extraction system compared with that obtained from the fruit bagasse.
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 3 Bin Picking Algorithm
### Entanglement Detection  
Before beginning the whole pick and place process, it is stored the sensor measurement for the force on the z+ axis (pointing upwards) being exerted on the flange when no object is being held. This measurement is referred to as the reference value for the force, \(F_{z\_ref}\).  
After performing the grasping and lifting actions, the current force value on the \(z\) axis, \(F_{z}\), is measured to determine how many tubes were picked. This requires prior knowledge of the tubes' mass and assumes all tubes have approximately the same mass. There are three possible cases: no object is being held, a single object is held and multiple objects are being held. These three cases are distinguished by computing the expected additional force exerted on the flange when a single tube is being held (which corresponds to the tube's weight), as presented in Algorithm 1.  
```
0:\(F_{z\_ref}\) - Reference z force (N)
0:\(F_{z}\) - Actual z force (N)
0:Mass - Tube mass (kg)
0:\(F_{tol}\) - Tolerance value for the force (N)
0:Outcome - Either \(no\_objects\_held\), \(single\_object\_held\) or \(multiple\_objects\_held\)
1:\(Weight\gets Mass*Gravitational\_acceleration\)
2:\(Expected\_force\gets F_{z\_ref}-Weight\)
3:if\(F_{z}<Expected\_force-F_{tol}\)then
4:\(Outcome\leftarrowmultiple\_objects\_held\)
5:elseif\(F_{z}>Expected\_force+F_{tol}\)then
6:\(Outcome\gets no\_objects\_held\)
7:else
8:\(Outcome\leftarrowsingle\_object\_held\)
```  
**Algorithm 1** Entanglement detection algorithm.  
When a single object is detected, the robot follows the default routine and proceeds to placing the tube on the destination area. If no object is detected, the robot restarts the bin picking process, and rescans the scene. Finally, if multiple objects are detected, a resolution routine is performed in an attempt to solve the entanglement problem, as explained in Section 3.6.