esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Coherent Noise Removal of Source Array in the NSST Domain
## 2 Theory and Method
### The Denoising Method in NSST Domain  
The NSST abandons the subsampling operation and combines the non-subsampled Laplacian pyramid transform (NSLP) with the non-subsampled directional filter. After the NSST, the size of each direction sub-band at each scale is the same as the original image, which greatly improves the image redundancy. The enhancement of the image coefficient redundancy makes the NSST have the translation invariance and avoid the spectrum aliasing and the pseudo-Gibbs phenomenon. Therefore, the NSST has a stronger data processing performance. The NSST has a more delicate representation of the scale direction and the physical significance of the transformed coefficients is clearer, which is more conducive to the processing of the coefficients. The NSST can transform the time-space domain signal \(f(x)\) into the NSST domain (\(j\), \(l\), \(k\)), so the different signals can be easily separated in the NSST domain. Each NSST coefficient has its own specific frequency, direction, and position. The scale parameter \(j\) reflects the frequency difference; the direction parameter \(l\) reflects the direction difference, that is, mainly reflects the apparent velocity difference; and the position parameter \(k\) reflects the spatial location difference and the amplitude difference.  
Figure 2: The decomposition process of the NSST.  
As shown in Figure 3, The specific steps of the coherent noise removal method of the vibrator source array seismic data in the NSST domain in this article are as follows:  
In this article, we choose the \(L_{2}\) norm to construct the threshold function.  
The \(L_{2}\) norm of the NSST domain coefficient is:  
\[e_{j,l}=\sqrt{\sum C_{j,l}^{2}} \tag{4}\]  
In Equation (4), \(e_{j,l}\) is the \(L_{2}\) norm of the NSST coefficient in the \(j\) scale \(l\) direction.  
The threshold function is the adaptive threshold changing with scale and direction, which is expressed as follows:  
\[T_{j,l}=\lambda_{j}\frac{\sigma\sqrt{2\ln N}\lg(j+1)}{\lg\left(e_{j,l}-\min e _{j,l}+10\right)} \tag{5}\]  
In Equation (5), \(T_{j,l}\) is the threshold function; \(N\) is the total number of sampling points for the seismic data; \(e_{j,l}\) is the \(L_{2}\) norm of the NSST coefficient in the \(j\) scale \(l\) direction, \(j=1,2,\ldots,J\), \(J\) is the total scale of the decomposition; \(\sigma=\frac{\mathrm{median}(\mathrm{[C]})}{0.6745}\), \(\sigma\) is the noise standard deviation, median( ) is a median value for all the elements in the data matrix; \(\min(\ )\) is minimum value function; and \(\lambda_{j}\) is the test constant.  
Figure 3: The flowchart of seismic coherent noise removal method in the NSST domain.
## 3 Full-Scale PCS Model Development and Validation
### Full-Scale PCS Testing  
Figure 8a depicts the onsite paving procedure for the full-scale PCS roadbed. Figure 8b illustrates the stress measurement locations on the cantilever beams. Six critical measurement points are identified in Figure 8c as follows: the two beam ends, the junction between varying and constant cross-sections, and the midpoint of the varying cross-section. Stress concentrations are anticipated at the beam ends and at the junction between varying and constant cross-sections. Moreover, notable bending and shear stresses may develop at the midpoint of the varying cross-section due to the paving loads.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 3 Results and Discussion
### Evaluation of Near Infrared LEDs Wavelength and Arrangement
The radiation distribution at the region of interest after being irradiated with an NIR light also depends on the features of the LEDs. The optical power emitted by the LEDs and irradiation time for this experiment in both ring LED and square LED arrangements is set to be the same. If the optical powers of the light are not the same, it will manipulate the effectiveness of penetration depth [13]. However, in this evaluation, all of the features are controlled.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 2 Evolution and Enablers of Industrial Internet of Things
### Industry 4.0-Building Blocks
#### 2.5.8 Edge Computing  
Introduction of enormous smart devices making an industrial revolution in all domains, causes extensive data processing, computation and burden of traffic on a single server either a cloud server or cloudlet. This motivates researchers to develop a new computing technology named as edge computing. The idea is to develop embedded automation controllers on devices named as edge-node (e-node) in the literature. This device is intelligent, with low processing power, better hardware security. Edge computing is an extending form of previous fog and cloud computing technologies. It comprises peer-to-peer networking, self-organizing network, and remotely manageable server. It gives following advantages:  
* Encourages real-time connectivity.
* Overall network traffic reduces, as some computation is done on the edge of the network.
* Enhances security by encryption of data near to the network core.
* Optimize the resource usage.  
IIoT applications have critical communication requirements. Cloud computing, fog computing, and edge computing platforms need to be optimized for better, efficient results. Cloud computing can be used where there is no high requirement of real-time connections, privacy, and security. On a local area network, fog computing uses a centralized system which interacts between the network and cloud server, whereas edge computing does computation on embedded systems of the network. Edge computing has direct interaction with sensors and actuators. The need for cloud, fog and edge computing architectures is increased with the growth of the IIoT application. To increase the use of IIoT smart devices, researchers are focusing on fog or edge computing paradigms which results in industrial development.
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
## 7 Conclusions  
In this paper, the experimental results of the receiver prototype designed for Cubesat applications have been reported. The tests have been executed both at a component-level and at board-level. Regarding the component-level tests, an example of the characterization of a custom low-pass filter has  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Requirement** & **Test Result** \\ \hline Sensitivity range: \(-115\)—\(70\) dBm & \(\begin{array}{c}\text{P}_{\text{IN}}=-115\text{ dBm}\rightarrow\text{P}_{\text{OUT}}=-0.93\text{ dBm}\\ \text{P}_{\text{IN}}=-70\text{ dBm}\rightarrow\text{P}_{\text{OUT}}=+0.25 \text{ dBm}\end{array}\) \\ Noise figure: \(<\)3 dB @ \(-115\) dBm & \(\begin{array}{c}\text{NF}=2.34\text{ dB}\\ \text{IF}=390\text{ MHz}\end{array}\) \\ IF output power: \(0\text{ dBm}\pm 1\text{ dB}\) & \(\begin{array}{c}-0.93\text{ dBm}\leq\text{P}_{\text{OUT}}\leq+0.25\text{ dBm}\end{array}\) \\ DC power absorption: \(<\)3.5 W & \(\begin{array}{c}\text{P}_{\text{A}}=4.86\text{ W}\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compliance of the receiver test results with the high-level requirements.  
been reported. The characterization has been performed measuring the S\({}_{21}\) parameter with a network analyzer connected to the input and output port of the component by means of the RF test points. The measured S\({}_{21}\) parameter agrees very well with the simulated one, exhibiting a 4.14 dB in-band attenuation at 390 MHz frequency, which is exactly equal to the attenuation obtained by simulations. Such a result has shown that the design approach allows to test the components individually after they are mounted on the board, which could be used to perform radiation tests and to discover the behavior of the COTS RF components for debugging purposes. Performing the component-level radiation tests through a unique board-level radiation test is extremely attractive to reduce design costs and avoid the purchase of expensive evaluation boards.  
Regarding the board-level tests, compliance with high-level requirements has been successfully verified. A CW signal with a power level within \(-0.93\)+\(0.25\) dBm has been measured at the IF output port at a 390 MHz frequency for a sensitivity range of \(-115\)--\(70\) dBm. Moreover, a 2.34 dB noise figure and a 4.86 W DC power absorption have been measured.  
[PERSON] performed the experimental tests and drafted the manuscript. [PERSON]; [PERSON] supervised and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
The Ph.D. [PERSON] has benefited from a PhD MIUR fellowships for the 2018/2019 academic year, course XXXII, awarded within the framework of the \"Programma Operativo Nazionale Ricerca e Innovazione\" (PON RI 2014/2020) Axis I \"Investments in Human Capital\" - Action I.1- \"Innovative PhDs with industrial characterization.\" Funding FSE-FESR.  
The authors declare no conflict of interest.
# Estimation of the Ultimate Strength of FRP Strips-to-Masonry Substrates Bond
## 3 Prediction with New Models
### The ANN Model with K-Fold Cross-Validation (KFCV)  
This section used the Feed-Forward Back Propagation Neural Network (FFBPNN) to forecast the ultimate strength of FRP strips applied on masonry elements [2; 72; 73]. Firstly, all data should be arranged randomly into \(K\) folds. The _K-1_ folds were utilized in the training step from the data, and the last fold was maintained for the neural network test. Different values for the \(K\) parameter were examined (e.g., \(K\) = 2, 3, 4, 5) to obtain the best values. Finally, the performance of each neural network was analyzed using the percentage of corrected prediction for \(K\) folds. Finally, the correct classification factor (CCF) and the percentage of area under the CCF (AUCCF) for different ANN structures were utilized to determine the optimal epoch. For this problem, a neural network with three layers was chosen.  
Furthermore, the AUCCF was measured until the optimal epoch was reached to optimize the neurons in the hidden layer. Therefore, the number of neurons changes from 2 to 13 in the hidden layers. Finally, by drawing the AUCCF, the structure with the best efficiency could be obtained. The AUCCF curve is displayed in Figure 8. It could be inferred that the 4-6-1 structure had the best performance (99.4%) in estimating the ultimate bond strength of FRP-to-masony joints. Furthermore, Figure 9 confirmed that the optimal epoch for the 4-6-1 structure was 3.  
Figure 8: AUCCF curve for different neural networks.  
Figure 7: Training state of ANN for 4-10-1.  
The R\({}^{2}\) values for the optimized structure (4-6-1) are presented in Figure 10 to show the performance of the ANN neural network with six hidden layers. This figure reveals that the correlation coefficient of the ANN neural network with six hidden layers is 0.9871. It depicts the high efficiency of the ANN structure with pattern (4-6-1).  
It should be noted that different activation functions (i.e., the Tansig and Pureline functions, respectively) were considered here for hidden and output layers.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 3 System and Sensor Models
### Definition of IMU Model  
An IMU, serving as a common sensor for mobile robots, encompasses state variables such as acceleration \(a\), angular velocity \(\omega\), observed acceleration \(\tilde{a}\), and observed angular velocity \(\tilde{\omega}\). These variables are typically defined as follows.  
\[\begin{split}\tilde{a}=a+w_{t}^{a},\ w_{t}^{a}\sim N(0,\Sigma^{a })\\ \tilde{\omega}=\omega+w_{t}^{\omega},w_{t}^{\omega}\sim N(0,\Sigma^{ \omega})\end{split} \tag{9}\]Based on observations of sensor data, the robot's state (rotation matrix \(R\), velocity \(V\), position \(P\)) can be dynamically represented as shown below.  
\[\begin{split}\dot{R}_{t}&=R_{t}(\hat{\omega}_{t}-w_{t }^{\omega})_{\times}\\ \dot{v}_{t}&=R_{t}(\hat{a}_{t}-w_{t}^{a})+g\\ \dot{p}_{t}&=v_{t}\end{split} \tag{10}\]
## 4 Experiments and Results
### Model Training  
Figure 4 illustrates the model's training performance. Against the simulation environment (Figure 3(a)), Figure 3(b) shows the changes in reward values during KDDPG and DDPG's training, where the reward values are sampled every 4000 steps to minimize the random variations. Towards the same goal point, both KDDPG and DDPG are trained 50 times to count their success rate (Figure 3(c)).  
The results demonstrate that KDDPG outperforms DDPG in terms of convergence speed and navigation performance. KDDPG converges at a success rate of 90% after approximately 20,000 steps, while DDPG achieves a 60% success rate after 75,000 steps, resulting in a four-fold longer training time.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Robot size (mm) & 306 \(\times\) 282.57 \(\times\) 142.35 \\ Max angle speed (rad/s) & 1.82 \\ Max speed (m/s) & 0.26 \\ Max payload (kg) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of mobile robot.  
Figure 4: Training performance comparison: (**a**) the Gazebo simulation environment; (**b**) reward comparison; (**c**) success rate comparison.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 3 Conceptual Design of a Ramjet Nozzle  
This section provides a comprehensive discussion of the operating principles of the ramjet component followed by a detailed study of the design requirement of the convergent-divergent nozzle.  
Firstly, the ramjet accomplishes compression by decelerating the oncoming airflow to low subsonic Mach numbers such that combustion can stably occur. The shocks are designed to be positioned in the intake system of the ramjet and the oncoming flow is decelerated from supersonic speeds through the shocks (2 < M < 5) and compressed. The compression takes place over several steps which include the airflow passing through one or more oblique shock waves generated by the fore body of the vehicle, finally ending with a normal shock after which the flow becomes subsonic. Then, subsonic diffusion takes place through a normal diffusion process. The process of supersonic compression followed by subsonic diffusion converts a major proportion of the kinetic energy of the oncoming flow to static pressure, which is fed into the combustion chamber (Figure 3) through stations 0 (free stream), 1 (inlet), 2 (combuster inlet), 3 (nozzle inlet), and 4 (nozzle exit).  
Succeeding the compression process, the flame holders process the normal combustion phenomenon, similar to conventional gas turbine engines. The lower, subsonic speeds lead to efficient combustion, after which the flow is allowed to mix up to allow a uniform flow profile of temperature and pressure which is fed into the convergent-divergent nozzle. This nozzle accelerates the high temperature subsonic gas into the atmosphere, essentially creating an exit jet and producing thrust.  
Figure 3: Thermodynamic stations of a ramjet.  
Figure 2: Concept hypersonic aircraft.  
The following section elaborates on the design of the propelling nozzle for a ramjet engine for larger thrust values required, as well as the effect the geometrical configurations have on the thrust values accompanying any other factors affecting the efficient functioning of the nozzle.
## 5 THG Efficiency in Dependence of SOD Coefficients and Phase Mismatching between Interacting Waves
### Dependence of the Crystal Length on \(\Delta_{21}k\) for Reaching Maximum THG Efficiency
If the incident pulse duration is decreased until \(100/s\), then the SOD role becomes more remarkable (Figure 15), and in this case, a longer distance is required for the achievement of the maximum THG energy at a positive phase mismatching \(\Delta_{21}k\). On the other hand, at a negative \(\Delta_{21}k\), the MI appears in rather small propagation distance. Nevertheless, it is also possible to achieve high efficiency of the energy conversion. The verification of these statements is shown in Figure 16.  
Figure 16: The frequency conversion efficiency along \(z\)-coordinate at \(\Delta_{21}k=-10\) (**a**), \(\Delta_{21}k=-12\) (**b**), \(\Delta_{21}k=-14\) (**c**), \(\Delta_{21}k=-20\) (**d**), \(\Delta_{21}k=-40\) (**e**), \(\Delta_{21}k=-80\) (**f**) for the parameters \(\gamma=1\), \(\Delta_{31}k=0\), \(D_{1}=-0.0032\), \(D_{2}=-0.0083\), \(D_{3}=-0.0199\). Solid line denotes FW, dash-dotted line denotes SH, dashed line denotes TH. Figure 15: Dependencies of maximum conversion efficiency (**a**) of SH (dash-dotted line) and TH (solid line), and required crystal length (**b**) on \(\Delta_{21}k\) for the parameters \(\gamma=1\), \(\Delta_{31}k=0\), \(D_{1}=-0.0032\), \(D_{2}=-0.0083\), \(D_{3}=-0.0199\). Inserts show changing of the corresponding dependencies near \(\Delta_{21}k=0\). Indeed, the MI develops in the section \(z=50\) at \(\Delta_{21}k=-10\) (Figure 16); meanwhile, there is not any MI at \(\Delta_{21}k=-12\) until the section \(z=75\) (Figure 16b). Essentially, the high-efficiency THG is achieved before the section of the MI develops. At \(\Delta_{21}k=-14\), the MI development starts only from the section \(z=95\) (Figure 16c). However, the maximum THG efficiency decreases because of the decreasing of the effective cubic nonlinear response and increasing of SOD influence. Another feature of interest, which is presented in Figure 15, is very long distance, required for the achievement of the high-frequency conversion efficiency if \(\Delta_{21}k<-40\). In fact, this distance is longer than \(z=500\) (\(50\)  cm), until which the computation was provided. One can see two modes of the wave interactions in Figure 16d-f. In the first one, the fast energy conversion from FW to TH occurs, but in the second one, the energy converts between waves very slowly. Moreover, a part of TH energy converts to FW after the achievement of the maximum THG efficiency at \(\Delta_{21}k=-20\) (Figure 16d) and \(-40\) (Figure 16e), and then the quasi-stable mode of the wave propagation occurs. If the phase mismatching between FW and SH is decreased until \(\Delta_{21}k=-80\) (Figure 16f), this quasi-stable mode appears before the maximum THG efficiency achievement due to domination of the SOD's influence over influence of the effective cubic nonlinearity on the pulse propagation. In the section \(z=500\), the THG efficiency is higher than its value reached in the section \(z=100\). However, their difference is tiny.
# Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
## 5 Conclusions  
In this paper, we propose a new unscented Kalman filter (UKF), namely the maximum correntropy unscented Kalman filter (MCUKF), which shows strong robustness against heavy-tailed non-Gaussian noises. The proposed algorithm is recast in the form of a non-linear regression model and makes use of the MCC to obtain the filter estimates. The MCUKF is then applied to the spacecraft relative state estimation, compared with EKF, HEKF, UKF and NRUKF. Simulation results confirm that, with a large kernel bandwidth, the MCUKF will behave like the original UKF. With a proper kernel bandwidth, the new filter can achieve better performance than other filters, especially when the measurement system is disturbed by some impulsive non-Gaussian noises.  
This work was supported by the 863 Program (No. 2015 AA015702) and the National Natural Science Foundation of China (No. 61371087). [PERSON] proposed the main idea, wrote the draft manuscript and presented the experiments; [PERSON] and [PERSON] polished the language and were in charge of technical checking; [PERSON] and [PERSON] analyzed the data and drew the figures and tables.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Filter** & **Computation Ratio** \\ \hline UKF & 1 \\ HEKF & 6.81 \\ NRUKF & 4.62 \\ MCUKF & 3.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computation time comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Filter** & \(\mathbf{TAMSD_{p}}\) & \(\mathbf{TAMSD_{v}}\) \\ \hline EKF & \(1.0680\times 10^{-4}\) & \(2.2751\times 10^{-10}\) \\ HEKF & \(3.2302\times 10^{-5}\) & \(7.5898\times 10^{-11}\) \\ UKF & \(4.2884\times 10^{-5}\) & \(8.4705\times 10^{-11}\) \\ NRUKF & \(2.5510\times 10^{-5}\) & \(5.7120\times 10^{-11}\) \\ MCUKF (\(\sigma=2.0\)) & \(2.4842\times 10^{-5}\) & \(5.6557\times 10^{-11}\) \\ MCUKF (\(\sigma=5.0\)) & \(3.1545\times 10^{-5}\) & \(6.6535\times 10^{-11}\) \\ MCUKF (\(\sigma=20\)) & \(4.1800\times 10^{-5}\) & \(8.3054\times 10^{-11}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(\mathbf{TAMSD_{p}}\) and \(\mathbf{TAMSD_{v}}\) in non-Gaussian noises.
## 3 Experimental Results
### Steel Beam
#### 3.2.2 Hysteresis and Skeleton Curves  
In this paper, the effect of different degrees of corrosion on the seismic performance of steel frame beams is considered. The low cyclic repeat loading test of steel frame beams is carried out, and the load (P) - displacement (\(\Delta\)) hysteresis curves of the specimens are obtained, as shown in Figure 8. As can be seen:  
1. All of the load-displacement hysteresis curves, which show a distinct fusiform shape, are relatively full without pinching. The area surrounded by the hysteresis loops is relatively large, which means that the steel beam has good energy consumption and ductility.
2. The conclusion is obtained by comparing the hysteresis curve of six specimens: Before the frame beam specimens reached the yield load, the slope of the loading curve changed little, the stiffness of specimen was basically unchanged, and it was in the elastic stage. As the displacement amplitude and the number of cycles increased, the specimens began to yield and the plastic deformation further increased, while the strength and stiffness deteriorated  
Figure 8: Loading-displacement curves of the specimens. (**a**) B-1; (**b**) B-2; (**c**) B-3; (**d**) B-4; (**e**) B-5; (**f**) B-6.  
Figure 9: Typical failure model of beam bottom.  
significantly. Under the large displacement amplitude, the repeated loading caused the stiffness to degrade significantly. In general, the longer the corrosion time, the more obvious the stiffness and strength degradation.  
The skeleton curves play a very important role in the elastoplastic analysis, which can reflect the yield displacement and load, peak displacement and load, ultimate displacement and capacity of the specimen. The skeleton curves of different degrees of corrosion are obtained by connecting the peak points of all the loops of the hysteresis curve in Figure 9 (starting at the unloading point), as shown in Figure 10.  
As can be seen:  
1. Based on the above analysis, the failure process of the steel beam specimens is divided into the elastic stage, the elastoplastic stage, and the plastic failure stage. At the initial stage of loading, the skeleton curves developed linearly, the beam specimens were in the elastic stage. With the increase of displacement loading continually, skeleton curves appeared at an inflection point which is the yield point, indicating that the specimen reached the yield. As the loading displacement increased, the bearing capacity showed a nonlinear growth trend, and the lateral stiffness of the specimen decreased, the specimens were in the elastoplastic stage. As the loading displacement increased further, the skeleton curve began to decline after reaching the horizontal peak load, until the specimen was destroyed.
2. The skeleton curve of the specimens with different degrees of corrosion basically overlapped at the initial stage of loading. However, with the increase of the continual loading displacement, the skeleton curve began to appear different. The greater the degree of corrosion, the more obvious the difference, such as the most severely corroded B-6 specimen, which were the most degraded.