esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Simulation Results  
### Field Monitoring and Numerical Validation  
In order to verify the accuracy of the finite element model, the measured and simulated values of the horizontal displacement of pile 3 and the stress on waist beam 1 at a temperature of 10 and 25 \({}^{\circ}\)C were extracted and compared, as shown in Figure 20a,b, respectively.  
Figure 19: The flow chart of the modeling and calculations.  
Figure 20a demonstrates that after the temperature increased by 15 \({}^{\circ}\)C, the measured horizontal displacement of the pile crown changed from \(-\)1.78 to 0.84 mm, which increased by 2.63 mm. The simulated horizontal displacement of the pile crown varied from \(-\)1.04 to 0.60 mm, resulting in an increment of 1.64 mm. Thus, the measured values were larger than the simulated ones, which might be due to the impact of continuous precipitation during monitoring on the displacement of the retaining piles. Additionally, the groundwater factor was not considered in the simulation, which led to the difference between the two; however, the deviation value was not large.  
Figure 20b reveals that the simulated values were all larger than the experimental ones, and the stress on points E1 and E3 was quite different. This may be because the waist beam and the retaining pile were bolted during the test, and they might be dislocated during the excavation of the foundation pit, thereby releasing certain stress. The simulation used binding contact, which fundamentally eliminated the influence of mutual dislocation, so the simulation values were slightly larger than the experimental ones. The measured and simulated stress on point E1 increased by 10.57 and 7.98 MPa, respectively, after the temperature rose by 15 \({}^{\circ}\)C; the simulated values were lower than the measured ones. The analysis showed that it took a long time for the temperature to rise from 10 to 25 \({}^{\circ}\)C during the test. Although the stress monitoring was performed one month later to reduce the influence of the soil creep characteristics, it was not removed completely. However, the numerical simulation was a transient process, which only considered the impact of the change in the temperature, so the simulated increment in the stress was smaller than the actual one.  
Although there was a certain difference between the measured values and the simulated ones, it was within an acceptable range, and the trends of the measured and simulated deformation were basically consistent. Therefore, as long as the selected parameters are suitable, it is feasible to analyze the impact of the thermal stress on the supporting structure of the foundation pit by the finite element method, which can provide a frame of reference for future practical projects.
# Contactless Ultrasonic Wavefield Imaging to Visualize Near-Surface Damage in Concrete Elements
## 3 Results
### Ultrasonic Wavefield Data  
An example time domain ultrasonic surface wave signal collected from the pristine concrete sample case is shown in Figure 3, which reveals suitably high signal-to-noise level. Measured ultrasonic time-space domain surface wave data for all the sample cases are shown in Figure 4. The total time required to acquire each wavefield data set was 5.6 min when using the following data acquisition parameters: 25 time averages per signal and an 80 ms pulse repetition interval. Although not presented nor discussed in this paper, the wavefield data acquisition time can be significantly reduced using a compressed sensing approach [18; 20]. Wavefield snapshots at four time instances are shown for each wavefield data set, and the wavefield images each represent a 250 mm \(\times\) 49 mm scan area. The locations of impact damage points are indicated as markers ('X') within the scan area for each sample case. In the figure, propagation of incident surface waves and scattered wavefields nearby impact damage locations are seen. In Case 0 (pristine concrete) seen in Figure 4a, forward propagating surface waves are the dominant wave components. Cases 1 to 4, seen in Figure 4b-e, show disruption to the forward propagating wavefield nearby the impact damage locations.  
To better understand the scattered wavefields set up by impact damage, _f-k_ domain signal analyses were carried out. The 3-D FTs of the ultrasonic wavefield data sets were computed using Equation (1). The _f-k_ domain magnitude spectrum for each sample case was then normalized with respect to its maximum spectral magnitude. Figure 5 shows normalized _f-k\({}_{x}\)-k\({}_{y}\)_ magnitude spectra for the wavefield data seen in Figure 4, where _k\({}_{x}\)_ and _k\({}_{y}\)_ are wavenumbers in horizontal and vertical directions, respectively. For visualization purposes, only spectra at the center frequency of excitation (43 kHz) are shown. The pristine concrete sample case (Case 0) seen in Figure 5a, mostly shows forward propagating surface wave components (indicated as 'FP') on the positive _k\({}_{x}\)_ side. The damaged concrete samples cases (Cases 1 to 4 seen in Figure 5b-e) exhibit broadened magnitude spectra, with higher spectral energy outside the major (forward propagating) wavenumber band than for the pristine concrete case. The ultrasonic wavefield data and the corresponding _f-k_ magnitude spectra demonstrate that impact damage in concrete sets up wavefields that exhibit wavenumber broadening. We presume that the wavenumber broadening is caused by surface wave scatter set up by the cracked damaged regions in the concrete.  
Figure 4: Measured time-space domain wavefield data: (**a**) Case 0 (pristine), (**b**) Case 1 (D1 only), (**c**) Case 2 (D1 and D2), (**d**) Case 3 (D1 to D3) and (**e**) Case 4 (D1 to D4). The wavefield images each represent a 250 mm (horizontal) \(\times\) 49 mm (vertical) scan area. ‘X’ indicates an impact damage location.  
Figure 3: Example time domain surface wave signal collected from the pristine sample (Case 0).
## 6 Conclusions  
This article focuses on the analysis and design of the multi-output inverter, aiming to solve the problems of increasing the number of inverters, system costs, and volume caused by traditional single-output inverters in multi-load WPT scenarios. Firstly, the topology structure and output extension method of the proposed inverter were introduced in detail, and its operating principle is analyzed. Subsequently, the operating characteristics of the proposed inverter were analyzed, and the expressions for its output voltages were calculated. Based on these calculation results, a comparison was made with existing multi-output inverters, and the parameters of the proposed inverter were designed. Then, a comparative analysis was conducted on various resonant compensation networks, and two suitable resonant compensation networks were selected based on the actual application requirements of multi-load WPT systems. The analysis and calculation were carried out to verify their performance and determine their output power, ensuring efficient collaboration between the resonant compensation networks and the proposed inverter. Finally, in the simulation and experimental verification section, a simulation model of the proposed inverter was first constructed to simulate its performance, and then an experimental setup was set up for experimental verification. By analyzing the simulation and experimental results of inverter output voltage and current, load voltage and current, system dynamic  
Figure 21: Experimental waveforms of the load switching. (**a**) LCC-S; (**b**) S-S.  
Figure 22: Inverter efficiency and loss distribution. (**a**) Efficiency. (**b**) Loss distribution.  
performance, and inverter conversion efficiency, it can be concluded that the total power provided by the system to the four loads can reached about 65 W, and the inverter efficiency can reach over 92%, proving the feasibility and superiority of the proposed multi-output inverter. In addition, the constant voltage and constant current characteristics of two compensation topologies were verified. The research results of this paper provide an efficient and economical solution for multi-load WPT systems.  
Conceptualization, X.G.; methodology, X.G.; validation, Y.W. and M.T.; investigation, X.G. and Y.W.; resources, X.G.; data curation, Y.W. and M.T.; writing--original draft preparation, X.G., Y.W. and D.R.; writing--review and editing, X.G., D.R. and L.W.; visualization, X.G., Y.W. and D.R.; supervision, X.G.; project administration, X.G. and L.W.; funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jiangsu Provincial University (Natural Science) Foundation and Talent Startup Funding of Wuxi University, grant number 24 KJB470027 and 2021r044, respectively.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 3 Results and Discussion
### Energy Optimization
When we prune weights, we only need to prune the weights of the \(L_{1}\)-\(L_{4}\), because the weights of the \(L_{5}\)-\(L_{8}\) is the transposition of the \(L_{1}\)-\(L_{4}\) weights. In order to explore the efficiency of pruning the weights of the different layers on the performance of the model, we separately prune different rates of the weights of each layer. We use the data of node 7 to train and test the model. During the test, we record the average value of \(PRD\), \(QS\) and reconstruction error of each mini-batch of the test set. We use \(W_{i}\) to represent the weights of the \(i_{th}\) layer. Figure 15 shows the compression performance of the model under different prune rates of each layer. When the prune rate is below 20%, pruning the weight respectively of any layer will not lead to the decline of the performance of the model. This means that there are redundant data in the weights of our model, which cannot affect the performance of the model. On the whole, the compression performance decreases with the increasing the prune rate. Removing too many parameters from the model will affect the data learning and fitting ability of the model. If we seek to increase the prune rate while maintaining the performance of the model, we need to choose an appropriate prune rate according to the model compression performance change. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Parameters Number** & **Storage (Byte)** & **Proportion (\%)** \\ \hline \(L_{1}\) & 12,100 & 48,400 & 32.20 \\ \(L_{2}\) & 5050 & 20,200 & 13.44 \\ \(L_{3}\) & 1275 & 5100 & 3.39 \\ \(L_{4}\) & 312 & 1248 & 0.83 \\ \(L_{5}\) & 325 & 1300 & 0.86 \\ \(L_{6}\) & 1300 & 5200 & 3.50 \\ \(L_{7}\) & 5100 & 20,400 & 13.57 \\ \(L_{8}\) & 12,120 & 48,480 & 32.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The distribution of the Stacked RBM-AE model parameters.
## 3 Model Validation  
### Numerical Stability  
The accuracy of our mathematical model solution depends on the number of interior nodal points and the number of time intervals required to carry the computation until the end. This accuracy increases with a decreasing \(\Delta t\). The chosen length for each control volume is \(\Delta x=\frac{2t}{5}\), with \(L=1\,\mathrm{mm}\), the typical thickness for a Gd parallel plate of a magnetocaloric regenerator [37,38]. For each \(\Delta x\), the value of \(\Delta t\) has to be set to meet prescribed stability requirements [39]. The \(\Delta t\) value must be maintained below a specific limit, which requires that the nodal coefficients at the previous time instants must be greater than or equal to zero:  
1. \(\left[1-\frac{2k_{12}\Delta t}{\rho_{1}C_{1}\Delta x}-\frac{2\hbar\Delta t}{ \rho_{1}C_{1}\Delta x}\right]\geq 0\), for \(T_{1}^{n}\) coefficient;
2. \(\left[1-\frac{k_{12}\Delta t}{\rho_{2}C_{1}\Delta x^{2}}-\frac{k_{23}\Delta t}{ \rho_{2}C_{2}\Delta x^{2}}\right]\geq 0\), for \(T_{2}^{n}\) coefficient;
3. \(\left[1-\frac{k_{23}\Delta t}{\rho_{3}C_{3}\Delta x^{2}}\right]\geq 0\), for \(T_{3}^{n}\) coefficient.  
A \(\Delta t\) value of \(1\,\mathrm{ms}\) meets the stability criterion for every considered scenario.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 4 Deep-Learning Techniques  
Deep learning techniques showed their effectiveness in a multitude of applications in real life, such as power systems [40], autonomous vehicles [41], and neurobiology [42]. Multiple works experimented with the use of deep-learning techniques to recognize Arabic handwritten text. The majority of these techniques are based on encoder-decoder architectures, involving different combinations of convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), and connectionist temporal classification (CTC). Other works employed Transformer architectures, and some utilized generative adversarial networks (GANs).
## 3 Multi-GNSS PPP/INS Mathematical Model
### Robust Kalman Filter Parameter Estimation Model  
To reduce the effects of measurement outliers on smartphones positioning, the robust Kalman filter method is used for parameter estimation to balance the contribution of normal and abnormal observations with equivalent variance matrix. Based on the nonlinearextended Kalman filter dynamic and observation models, the formula of robust Kalman gain matrix \(\overline{K}_{k}\) can be written as [39]:  
\[\overline{K}_{k}=P_{k}^{-}H_{k}^{\top}\left(H_{k}P_{k}^{-}H_{k}^{\top}+\overline {R}_{k}\right)^{-1} \tag{27}\]  
where \(P_{k}^{-}\) denotes the state covariance matrix, and \(H_{k}\) denotes the coefficient matrix. With the robust M estimation principle, the equivalent covariance matrix is \(\overline{R}_{k}\) instead of the original covariance \(R_{k}\), which can be expressed as [40]:  
\[\overline{R}_{k}=\frac{R_{k}}{\gamma_{i}} \tag{28}\]  
where \(\gamma_{i}\) denotes the variance inflation factor. Based on the institute of geodesy and geophysics (IGG) III weighting function, the reduction factor of the weight elements could be chosen as [41]:  
\[\gamma_{i}=\begin{cases}1&|\overline{v}_{i}|\leq k_{0}\\ \frac{k_{0}}{|\overline{v}_{i}|}\left(\frac{k_{1}-|\overline{v}_{i}|}{k_{1}-k _{0}}\right)^{2}&k_{0}<|\overline{v}_{i}|\leq k_{1}\\ 0&|\overline{v}_{i}|>k_{1}\end{cases} \tag{29}\]  
where \(k_{0}\) and \(k_{1}\) are thresholds commonly used as 1.5-3.0 and 3.0-8.0, respectively; \(\overline{v}_{i}\) denotes a standardized residual, as follows [42]:  
\[\overline{v}_{i}=\frac{v_{i}}{\sqrt{\phi_{0}^{2}Q_{vi}}} \tag{30}\]  
where \(v_{i}\) denotes the residual of measurements, and \(Q_{vi}\) denotes corresponding variance, \(\phi_{0}^{2}\) denotes the unit weight variance estimated by generalized least squares.  
\[\phi_{0}^{2}=\frac{\xi^{T}Q_{\xi}^{-1}\xi}{n} \tag{31}\]  
where \(\xi\) denotes predicted residual vector (innovations), \(Q_{\xi}\) denotes the corresponding covariance matrix as follows:  
\[Q_{\xi}=R_{k}+H_{k}P_{k}^{-}H_{k}^{\top} \tag{32}\]  
The updated state correction vector and error covariance of the robust Kalman filter can be expressed as:  
\[\hat{X}_{k}=\hat{X}_{\overline{k}}+\overline{K}_{k}(L_{k}-H_{k}\hat{X}_{ \overline{k}}) \tag{33}\]  
\[P_{k}=\left[I-\overline{K}_{k}H_{k}\right]P_{k}^{-}\left[I-\overline{K}_{k}H_ {k}\right]^{T}+\overline{K}_{k}\overline{R}_{k}\overline{K}_{k}^{T} \tag{34}\]
## 5 Conclusions  
This study demonstrated that thermal and hyperspectral imagery provides indices that display a close link with plant functioning, show potential for wheat yield screening and phenotyping under real field breeding trial conditions. High-resolution imagery was acquired with thermal and hyperspectral cameras installed on-board a manned aircraft that flew over a field trial for wheat phenotyping on two separate dates. The imagery enabled the calculation of vegetation and spectral indices related to canopy structure, photosynthetic pigment absorption, the quantification of chlorophyll fluorescence emission, as well as indices derived from canopy temperature. These physiological indices designed to minimize the sensitivity to structural changes while maximizing a rapid response to environmental conditions could be of paramount importance for plant breeding in variable climates such as the Mediterranean, and especially under rainfed conditions. Under the settings of this study, the thermal index CWSI demonstrated the best relationship with yield (R\({}^{2}\) = 0.53), the fluorescence emission quantification carried through the FLD method (R\({}^{2}\) = 0.47) and the xanthophyll index PRI (R\({}^{2}\) = 0.49). The multiple regression model built for the three indices explained 77% of the total variability in yield and was statistically significant (\(p\) < 0.001). On the contrary, NDVI, a standard vegetation index widely used in plant phenotyping, showed a weaker relationship for both durum and bread wheat (R\({}^{2}\) = 0.10). These results may be explained by the terminal stress that is usually observed in semi-arid regions, as crops displaying higher values of NDVI deplete soil reserves during vegetative growth and are more sensitive to water stress during yield formation. This study showed promising results on the use of new high-resolution remote sensing indices suitable for field-based phenotyping under rainfed conditions. The use of the remote sensing physiological indicators enables the evaluation of crop performance over wider areas, allowing high throughput phenotyping assessments while still taking spatial variability into account.  
Authors would acknowledge [PERSON], [PERSON], [PERSON], and [PERSON] for their technical support. Funding from Junta de Andalucia project P12-AGR-0482 and MINECO project AGL2012-40053-C03-01 is gratefully acknowledged.
## 5 Conclusions  
In addressing the complexity of spacecraft system structures and functions and the challenges posed by existing data-driven methods for anomaly detection, this paper proposed an innovative method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. Our primary work involved designing ontology concepts of the spacecraft equipment knowledge graph based on expert knowledge, and extracting feature data from the historical operation data of the spacecraft to build a rich spacecraft equipment knowledge graph. Furthermore, we constructed spacecraft anomaly event knowledge graphs based on various types of anomaly features. This method enabled anomaly device location and anomaly cause judgement during spacecraft operation by matching telemetry data with the feature data in the knowledge graph.  
Our experimental analysis has shown the potential of the proposed method not only to enhance anomaly detection accuracy and reduce false alarm rates in spacecraft compared to traditional threshold-based strategies, but also to provide a practical and effective means for analyzing anomaly causes and formulating treatment plans. However, its efficacy is contingent on the quality of data, the comprehensiveness of the knowledge graph, and the accuracy of the anomaly detection algorithm. Despite these limitations, the method offers scalability and flexibility, allowing for the incorporation of more entities and anomaly records, multidisciplinary knowledge integration, efficient data management, and quick abnormal state identification. Future work will explore the use of knowledge graph embedding models [46] to advance the processing of the knowledge graph, predicting missing relations, generating new knowledge, and supporting intelligent simulation testing and anomaly detection. While our method shows promise, it should be noted that our research is still in its nascent stages. To realize a truly efficient and effective system for spacecraft anomaly detection and diagnosis, there is much that needs to be explored and improved.  
**Author Contributions:** Conceptualization, X.Y. and P.H.; methodology, X.Y. and P.H.; software, P.H. and S.C.; validation, P.H. and S.C.; formal analysis, S.C.; investigation, X.Y. and P.H.; resources, X.Y.; data curation, P.H. and S.C.; writing--original draft preparation, P.H.; writing--review and editing, X.Y.; visualization, P.H. and S.C.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Pre-Research Project 50902060403 from the Equipment Development Department.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Due to confidentiality agreements, the data from this study cannot be publicly shared.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 4 Methods
### Point Cloud Semantic Segmentation  
#### 4.2.1 Projection of Point Cloud Spheres as the 2D Range Image  
Due to the disorderly nature of point clouds, deep learning networks cannot be used directly. At the same time, the current 3D Lidar (usually 16 or 32 laser beams) can obtain a large amount of point cloud data in one frame. The process of processing all these clouds is tedious and computationally intensive. To deal with the above problem, we obtain the pixel coordinates \((u,v)\) of the 2D image from the point cloud \(p_{i}=(x,y,z)\) by spherical projection, as follows:  
\[\begin{pmatrix}u\\ v\end{pmatrix}=\begin{pmatrix}[1-(\arcsin(z/R)+f_{up})f^{-1}]\times h\\ \frac{1}{2}[1+\arctan(y/x)\pi^{-1}]\times w\end{pmatrix} \tag{1}\]  
where \((u,v)\) represents the image coordinates, \((h,w)\) are the desired height and width of the distance image representation, and \(f=f_{up}+f_{down}\) denotes the vertical field-of-view of the Lidar. The variable \(r\) corresponds to the Euclidean distance from the original point in the point cloud to the laser source, specifically \(r=||p_{i}||_{2}\), representing the distance of each point. The point cloud projection process generates a pair of image coordinates \((u,v)\) for each \(p_{i}\). Using these indices, we extract the distance \(r\), as well as the x, y, and z coordinates, and reflectance for each \(p_{i}\), storing them in an image to create a \([5\times h\times w]\) tensor. We maintain the relationship between \(p_{i}\) and the (u, v) pairs in a list, facilitating the aggregation and cleaning of semantic information in the resulting point cloud. The schematic illustration of projecting the point cloud onto a distance image is depicted in Figure 2.  
Figure 1: Flowchart of the LO algorithm framework.