esa-sceva/satcom-chunk-collection huggingface dataset:
# A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
## 3 Discussion of the Analysis Results
### Comparative Analysis of Intelligent Seismic Inversion and Other Inversion Methods  
The JNET trained for 200 rounds was selected to generate seismic inversion, and a small piece of the seismic inversion body in the well-dense area was obtained by using the method of write-by-track inversion. The 1450-1485 ms sections of well A8 and well A14 in the well density area were selected, and the traditional seismic inversion method (SMI) and AI were used to compare the sand prediction ability of these two methods. It  
Figure 5: Single well curve analysis for different training rounds. The green line is the well Vsh curve, the blue line is the seismic curve, and the red line is the artificial intelligence Vsh curve: **(a)** Partial single-well curves after one round of training; **(b)** Partial single-well curves after 50 rounds of training; **(c)** Partial single-well curves after 100 rounds of training; **(d)** Partial single-well curves after 200 rounds of training.  
can be seen from Figure 6 that for the thick sand body in the lower half, both AI seismic inversion and SMI inversion can identify the sand body, but the resolution of AI seismic inversion is higher, clearly depicting the distribution and stack of the sand body in the thick sand body. For the strata with complex geological conditions in the upper part of the formation, SMI inversion is almost difficult to identify, and only large sections of fuzzy sand layer can be seen. However, AI seismic inversion can identify thinner sand layers and predict and characterize their morphological distribution. In general, the sand body obtained by SMI inversion can only show rough rules, the characterization of sand body interface is not clear, the recognition and processing ability of complex strata is weak, and the prediction accuracy reached 62.7%. While the recognition of thin sand obtained by AI seismic inversion is quite accurate and detailed, with strong correspondence with lithology, a high coincidence rate with the VSH curve, and the clear characterization of the sand body boundary, the prediction accuracy reached 81.3%.  
By comparing the AI seismic inversion results of Figure 6a,b, the prediction accuracy for sand bodies in well A14 and well A8 is 72.1% and 85.5% respectively. It is found that the prediction accuracy for sand bodies in well A14 is slightly lower than that in well A8, while the prediction accuracy of the traditional inversion method (SMI) was 63.4% and 62.1%, respectively, because the number of AI seismic inversion training datasets is not large enough and the feature learning of well A14 is not complete, indicating that the AI seismic inversion results have some instability.
###### Abstract  
In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.  
empirical wavelet transform; maximum correlated kurtosis deconvolution; rolling element bearing; noise condition +
Footnote †: journal: Applied Sciences
# Propagation of Optical Coherence Vortex Lattices in Turbulent Atmosphere
## 4 Conclusions  
We have formulated the propagation of the OCVLs in turbulent atmosphere by using the convolution approach, and we have explored propagation dynamics, such as the spectral density, the normalized \(M^{2}\)-factor, and the beam wander of the OCVLs in turbulent atmosphere. We have found that the beam distribution of the OCVLs changes from a single doughnut beam distribution to hollow array distribution on propagation, and finally becomes the Gaussian distribution in the far field. We also have found that the OCVLs with large topological charge, large beam array order, large relative distance, and small coherence length can mitigate the influence of atmospheric turbulence. Our findings are useful for free-space optical communications and information transfer.  
Data curation, Y.H.; writing (original draft), Y.H.; methodology, Y.H.; writing (review and editing), Y.Y., F.W., X.L., J.Z., J.Y., and Y.C.; formal analysis, X.L., J.Z., and J.Y.; supervision, Y.Y., F.W., L.L., and Y.C.; project administration, Y.C.  
This research was funded by the National Natural Science Fund for Distinguished Young Scholar [grant number 11525418], the National Natural Science Foundation of China [grant numbers 91750201 and 11774251 and 11874046 and 11804198], the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Qing Lan Project of Jiangsu Province.  
The authors declare no conflict of interest.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 3 Background  
An ad-hoc network is a transitory network that is created by combining base stations with wireless devices in the absence of any defined system and service administration. The VANET is cutting-edge technology that provides wireless networks to next-generation cars. It is important for study because it offers the potential for a dramatic transformation in the transport network through ITS. The method's main goal is to provide vehicles with better communication, resulting in a more stable and safe transport network. The establishment of the VANET was motivated by the need to transmit data on the roads among vehicles in order to avert disasters and, hence, improve vehicle and driver security. Based on the conditions, all the data from sensor devices can be shown to the driver, communicated to an on-road unit (RSU), or relayed into neighboring vehicles. With the exception of road safety information, a range of different uses for automobile networks are discussed, such as gaming, travel/tourism, entertainment, internet connectivity, and so on.
## 3 Multi-Criteria Decision Making (MCDM) Method  
### Analytic Hierarchy Process (AHP)  
The AHP [18] is one of MCDM methods that was originally developed by Prof. [PERSON]. The AHP is supposed to calculate the weights of the multiple criteria according to its relative importance to optimize decision making considering qualitative, quantitative and conflicting factors. The main benefits of using AHP are as given below  
* In AHP, the importance of each criterion becomes clear because the objective of decision is organized into a hierarchical structure.
* The inconsistency between alternatives can be checked through AHP.
* There is no bias in the decision making.  
Our protocol adopts the AHP approach to calculate the weight of proposed metrics. In order to determine the weights of them, the AHP uses the judgments of decision makers to form a decomposition of criteria into hierarchies.  
Figure 3 shows the AHP hierarchy model. The objective of decision making is represented at the top of the hierarchy model. The below level of hierarchy model consists of multiple criteria taken into account as decision factors. The bottom level includes the set of neighbor nodes as decision alternatives.  
Figure 3: Analytic Hierarchy Process (AHP) hierarchy model.  
Once the hierarchy model is constructed, the decision maker has to construct a pairwise comparison matrix in order to determine the relative importance among the decision factors. The pairwise comparison matrix is represented as a square matrix \(C\) given by [18],  
\[C=C_{ij}n*n=\begin{pmatrix}C_{11}&C_{12}&C_{13}\\ C_{21}&C_{22}&C_{23}\\ C_{31}&C_{32}&C_{33}\end{pmatrix} \tag{1}\]  
where \(C_{ij}\) indicates the relative importance between decision factor \(i\) and \(j\). The decision maker determines the relative importance according to Table 1[18]. For example, the row 1 column 2 of the matrix is filled with 5 if decision factor 1 is more strongly important than decision factor 2. In the opposite case, the row 1 column 2 of the matrix is filled with 1/5.
## 3 Datasets
### Gridded Precipitation Products (GPPs)
#### 3.1.5 Integrated Multi-Satellite Retrieval for GPM (IMERG)  
Integrated Multi-Satellite Retrieval for the GPM (IMERG) project intended to combine all satellite microwave precipitation data, geosynchronous satellite Infrared data, and gauge observations to generate precise precipitation estimates at a fine spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) in half-hourly scales [77]. To this end, first, precipitation estimates from passive microwave satellites are integrated using the morphing technique [78]. Subsequently, the microwave passive precipitation estimates are supplemented with Infrared precipitation estimates using the Kalman filtering approach for the regions where the passive precipitations estimates are sparse [79]. In the final step, monthly gauge-based precipitation data from the Global Precipitation Climatology Center (GPCC) are included to regionalizeand correct the inherent biases of satellite estimates. See [PERSON] et al. [77] for further detail on the IMERG satellite precipitation products. In this study, gauge-adjusted IMERG precipitation data between 2000 and 2020 was employed for evaluation.
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 2 Compression and Decompression Algorithms Based on Hybrid Strategies
### Strategies
#### 2.3.2 Run-Length Coding  
Run-length encoding stands as a testament to the efficiency of image data compression. It is particularly adept at condensing sequences of identical components into a more compact form, significantly optimizing both storage and transmission of image data. This  
Figure 3: The process of predictive coding.  
encoding method exploits a common characteristic of images where sequences of repeating components are prevalent, compressing these sequences into a pair that contains a component value and a run-length count. The efficacy of this approach lies in its simplicity and the drastic reduction in data size that it can achieve for images with substantial uniform areas. In the specific implementation at hand in Figure 4, run-length encoding is initiated by encoding the first pixel component in a series of consecutive identical components, which is executed using either predictive or non-coding modes. The encoding mode is selected based on the context and neighborhood of the pixel component, ensuring the most efficient encoding strategy is applied. The subsequent identical pixel components in the sequence are represented by a series of binary 1s, culminating with a binary 0, denoting the end of the run. To illustrate, if a sequence of n identical pixel components is encountered, the initial component is encoded in the selected mode, followed by the run-length encoding flag '00B'. The count of n - 1 identical components is then denoted by n - 2 binary 1s ('1B') succeeded by a single terminating binary 0 ('0B').  
During the decompression phase, the algorithm looks for the '00B' flag to signify the beginning of a run-length-encoded sequence. Subsequent bits are read in a continuous stream until a '0B' is reached. The number of '1 Bs' encountered prior to the '0B' determines the count n, and the pixel component preceding the '00B' flag is then replicated n + 1 times to accurately reconstruct the original sequence of pixel components. This process ensures that the decompression faithfully restores the image to its original state.  
Run-length encoding, thus, serves as a powerful tool in the domain of image compression, capitalizing on the redundancy within component sequences to achieve more efficient data storage and handling. It is particularly effective for images with large areas of uniform color, where it can significantly compress the data without loss of information.
# Semi-Analytical Planetary Landing Guidance with Constraint Equations Using Model Predictive Control
## 6 Conclusions  
This article presents a semi-analytical powered descent guidance algorithm for planetary landing. The proposed algorithm can rapidly determine whether the current state is controllable and can generate a near optimal descent trajectory in real time. This algorithm can also deal with early startup and unreachable scenarios. The proposed algorithm adopts a decoupling method and derives the fuel-optimal solution under this control mode. The detailed motion modes in three directions are investigated, and the reachability/controllability determination method is presented, which is beneficial for both online decision making and offline design. Solutions of the explicit constraint equations are in closed form so that the real-time computation is guaranteed. Numerical simulations demonstrate that although the proposed algorithm sacrifices certain optimality and control smoothness, it can give a rapid semi-analytical determination of the reachability/controllability and ensure high computational efficiency. The average computation time, including time for determining the reachability, time for judging the motion modes, and time for solving constraint equations, is less than 20 ms on MATLAB. The generation method of the controllability set is semi-analytical, and solutions of the equations involved are all in closed form. In addition, the MPC simulation shows that our algorithm can still maintain a good accuracy under external disturbances, indicating that the proposed algorithm has the capability of online decision making and trajectory planning.  
Conceptualization, X.M., L.C., Y.S., J.L. and S.G.; methodology, X.M., L.C., Y.S., J.L. and S.G.; software, X.M.; validation, X.M.; formal analysis, X.M.; investigation, X.M., L.C., Y.S., J.L. and S.G.; resources, X.M., L.C. and Y.S.; data curation, X.M.; writing--original draft preparation, X.M.; writing--review and editing, X.M. and S.G.; visualization, X.M.; supervision, S.G.; project administration, S.G.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No.11822205 and 11772167).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 13: Control profiles of Example 3.
## 3 Results and Discussion
### Comparison of Ultimate Bearing Capacity with Existing Theoretical Methods
As a result of the shear box tests, the internal friction angles of the sand soil were obtained as 42.8 for Dr = 10%, 45.4 for Dr = 40%, 49.4 for Dr = 65%, and 51.7 for Dr = 90%. In the model experiments, it was also observed that the theoretical and experimental data yielded closer results with increasing foundation size. This provides a more accurate representation of the soil and foundation interaction and shows that larger foundations yield results closer to real soil conditions. The literature also states that the bearing capacity can be estimated more accurately with increasing foundation size and that theoretical approaches and experimental results tend to converge [70]. This emphasizes that future studies should be conducted with larger foundation sizes to better understand the effects of foundation size and soil density on the bearing capacity. In conclusion, this study has shown that [PERSON]'s theory provides a better fit, especially in soils with a high relative density, while [PERSON] and [PERSON]'s theories provide more accurate results in soils with a low density. [PERSON]'s theory should not be considered because it provides excessively high results. It has also been observed that increasing the foundation size contributes to a closer approximation of the theoretical and experimental results. These findings indicate the limitations of existing theories and potential improvements for more accurate modeling of foundation design and soil interactions.
## Materials and Methods
### Glucose Sensor Performance Test  
The three-electrode system was composed of the glucose sensor, Ag/AgCl and platinum electrode (working electrode, reference electrode and counter electrode). The sensors included the NO-P sensor only coated with PU membrane, SGL-P sensor coated with unilaminar PU/PEG film and SHPM-OS sensor. The evaluation was carried out by chronoamperometry of electrochemical workstation (CHI760E, Shanghai Chenhua Instruments Limited, China) at a potential of +0.65 V (vs. Ag/AgCl). At room temperature, the glucose titration experiments were performed in a 50 mL PBS solution, which was constantly and gently stirred. After background current was stabilized, high concentration of glucose (2.5 M) was dropwise added into PBS so that the glucose concentration in measurement solution was enhanced by 2 mM for each step until the final concentration reached 20 mM. The added amount of 2.5-M glucose solution for each step was calculated according to the solution dilution formula to ensure the stepwise increase of 2 mM. The response step curves of the sensors were obtained. The current corresponding to analyte concentration was measured at steady state (the plateau of step curve) and then the sensitivity and linearity of sensors were obtained by using multiple linear fitting between response current and glucose concentration.  
To evaluate the stability of the SHPM-OS sensor, sensitivities were measured twice per week in PBS with the concentration of glucose in the range of 0-20 mM. The response speed of the glucose sensor was evaluated by adding the high concentration of glucose (2.5 M), which rapidly increased the glucose concentration to 10 mM in 50 mL PBS. Anti-interference experiment was performed by in sequence adding 4 mM glucose, 0.48 mM uric acid (UA), 0.11 mM ascorbic acid (AA), 0.17 mM acetaminophen (AP) and 2 mM glucose [18; 27]. And the selectivity of glucose biosensor against interference was evaluated by the relative rate of current change, which was defined as the ratio of the present current to the initial current. The surface morphology images of the glucose sensor were characterized by the field emission scanning electron microscope (SEM, ZEISS MERLIN Compact, Carl Zeiss AG, Germany), and the size of pores on porous membrane were measured by ImageJ (National Institutes of Health). After each test, the sensor was cleaned and stored in a 0.01-M PBS solution. The storage solution was changed twice a week. The sensitivity change of the sensor with storage time was also tested.
## 3 Case Studies
### Case Study 2: Emergency Scenario (Emergency Management)  
The second case study refers to an emergency scenario, in which sensor alarms are active and the IoT system is envisioned as a tool to control the environmental and structural conditions of the areas and assets of the park and to automatically recognize several sensor alarm conditions and provide, in real-time, customized emergency plans that show safe paths for the park's visitors, who need to reach an exit or an assembly point from one of the six areas of the park because of the emergency.  
Figure 8 shows two examples of emergency plans, which contain: (i) a graph that indicates the connections among the nodes, exits, and assembly points; (ii) sensing nodes that are represented by red and green circles if an alarm is active or not, respectively; (iii) exits that are represented by yellow and green circles if the exit is safe or not, respectively; (iv) yellow triangles with a black exclamation point that indicate the point where the alarm is active; (v) green signals that indicate an available exit and red access-denied signals that point out an unavailable exit (closed); and (vi) white signals that show the assembly points.
## 2 Method
### Azimuth Offset
The amplitude of azimuth compressed data as a function of the azimuth offset is depicted in Figure 4. A maximum value on the \(x\)-axis origin indicates that the measured phase (from L0 data) matches with the predicted geometry from orbit data as seen for precise orbit (solid line). An azimuth shift occurs if the measured and predicted phases are shifted in time relative to each other. This could be caused by an inaccurate geometry (orbit or target) or imprecise timing information. Using a predicted orbit, which was available before the acquisition has been acquired, an azimuth offset of 4.8 m remains (dashed line in Figure 4). This offset corresponds to an orbit shift in time of about 700 us. In contrast to the well balanced case depicted in Figure 3 (bottom), the predicted orbit introduces a remaining linear phase ramp in this case. The matched filter output from azimuth is complex interpolated to improve the resolution and thus to increase the accuracy of the azimuth offset result derived for each dataset. Figure 4: Amplitude of azimuth compressed data for different orbit data; each circle represents an individual range line. A complex interpolation is applied to improve the resolution (lines). In case of precise geometry information (precise orbit data), the maximum matches at the origin with a low remaining azimuth offset of 7 cm. Using a predicted orbit, an azimuth offset of about 5 m occurs due to more inaccurate positions and timestamps of the spacecraft. Figure 3: Point target phase \(\phi\) (**top**) derived from SAR data (detected phase in blue) acquired on 25 August 2016 and predicted phase from geometry (black line); resulting phase differences after unwrapping the SAR data phase (**bottom**). Each (blue) pixel represents the phase of the target response (at the amplitude peak within the range line) from an individual pulse received by the SAR instrument.
## 4 Experimental Results and Discussion
### Evaluation of the Proposed ASFO and E-CERP Approach with Conventional Techniques  
The particle swarm optimization is utilized for the optimization problem based on fuzzy clustering. Still, it has some disadvantages, such as that it is easy to fall into local optima and the convergence rate is slow, while the HHO algorithm is simple, flexible, easy to implement, and has a high convergence rate. In fuzzy and artificial bee colony-based implementations of MAC, the clustering, routing, and data delivery (FABC-MACRD)-based cross-layered method was used for the clustering and data transmission, in which the energy consumption was high, including the energy consumed for 500 nodes. In contrast, the proposed CERP method consumed less energy (4.19 J), and the packet delivery ratio of FABC-MACRD was 79%, while the proposed CERP was 96%. Compared to FABC-MACRD, the proposed method extended the network's lifespan, and the proposed K-medoids with ASFO and E-CERP improve the reliability of the communication link and reduce packet delay.