esa-sceva/satcom-chunk-collection huggingface dataset:
# A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening
## 3 Experiment and Analysis  
In order to verify the effectiveness of the proposed method, simulation and real data verification experiments are performed, and the results of the four typical existing phase correlation methods are also compared. The proposed method is implemented with Python. The experiments are performed on a desktop with i7 CPU and 16 G RAM. The camera used in the experiment is a Medvision MVSUA133 GMT industrial camera. Some key parameters  
Figure 5: Left and right singular vector phase unwrapping, where (**a**) is the winding phase and unwrapping phase of \(\mathrm{u_{1}}\), and (**b**) is the winding phase and unwrapping phase of \(\mathrm{v_{1}}\).  
Figure 6: Straight-line fitting of left and right singular vectors, where (**a**) is the unwrapping phase and straight-line fitting result of left singular vector, and (**b**) is the unwrapping phase and straight-line fitting result of right singular vector.  
are shown in Table 1. The lens used in the experiments has a variable focal length and variable aperture, which are convenient for setting different experimental conditions.  
The simulation experiment uses images with different texture characteristics, artificially adding noise and simulated displacement; the real data verification experiment is carried out in a high-speed railway laboratory, and the track vibration during the experiment is captured by shooting the digital speckle pattern of the track plate.  
In this paper, we compare the performance of the proposed method to four typical phase correlation methods: (1) the local up-sampling based method, (2) the SVD-based method, (3) the RANSAC-based method, and (4) the SVD + RANSAC-based method. For the local up-sampling-based methods, we first obtain the integer pixel displacement by classical phase correlation methods, then up-sample the images locally, finally obtaining subpixel displacement by using the classical phase correlation methods again. For the SVD-based methods, we first acquire the cross-power spectrum matrix, then perform SVD decomposition on it, and, finally, perform straight-line fitting on the left and right unwrapped singular vectors to obtain the displacement. For the RANSAC-based method, we first acquire the cross-power spectrum matrix, and then perform RANSAC-based straight-line fitting on a row and column of it to obtain the displacement. For the SVD + RANSAC-based method, we first acquire the cross-power spectrum matrix, then perform SVD on it, and, finally, perform RANSAC-based straight-line fitting on the left and right unwrapped singular vectors to obtain the displacement.
## 3 Numerical Simulation
### Geometric Model, Mesh Division, and Observation Path Layout  
Finite element analysis is widely used in geotechnical engineering [25; 26; 27; 28; 29; 30], and the commercial finite element software COMSOL is used in this study. When conducting the three-dimensional modeling of the new pipe curtain freezing method, the width and height of the excavation area within the pipe curtain freezing pipe are 18.8 m \(\times\) 21 m. The odd-numbered pipes are solid top pipes filled with concrete and arranged with circular freezing pipes; the even-numbered pipes are freezing pipes with irregular freezing pipes arranged inside. The solid top pipes and empty top pipes are alternately arranged with spacing of 36 mm. The diameter of the curtain steel pipe is 1.62 m, and the diameter of the freezing pipe is 108 mm, with spacing of 791.6 mm between the curtain steel pipe and the freezing pipe. The thickness of the overlying soil is set to 5 m. According to the freezing range and considering the influence of the groundwater flow on the frozen soil curtain, the entire model dimensions (Y) \(\times\) width (X) \(\times\) height (Z) = 100 m \(\times\) 20 m \(\times\) 50 m. The model is meshed using a free tetrahedral model, defined with appropriate mesh sizes to ensure the convergence and accuracy of the computational model. The model boundaries use a larger mesh density to simplify the model calculations, while a smaller mesh density is used for the freezing zone of the freezing pipe. This study calculates a total of 899,432 grid cells for the final partition, with the smallest cell size being 0.1522. A schematic diagram of the model's mesh division is shown in Figure 3. Taking the smallest grid size computationally feasible as a reference, the smallest cell size is increased to 0.03926, resulting in a total of 535,172 grid cells for the final partition. A finite element model with two different grid sizes is computed, and the simulation outcomes show deformation values within a 5% range. The test results confirm the grid independence. The grid partition after increasing the smallest cell size is depicted in Figure 4. The new pipe curtain freezing model also sets the front side (y = 0 m) and back side (y = 100 m) positions in the model length direction as constant-temperature permeable boundaries, while the remaining model boundaries are adiabatic impermeable boundaries. The dimensions of the three-dimensional model are length (Y) \(\times\) width (X) \(\times\) height (Z) = 100 m \(\times\) 20 m \(\times\) 50 m. The model boundaries use a larger mesh density to simplify the model calculations, while a smaller mesh density is used for the freezing zone of the freezing pipe. A schematic diagram of the boundary settings for the new pipe curtain freezing model under seepage conditions is shown in Figure 5.  
Figure 3: Grid division diagram of new pipe curtain freezing method.
## 2 Main Concepts
### Multi-Frame Reconstruction  
Reconstructing an HDR image from multiple frames involves capturing several images of the same scene, which are then combined to produce an image with a wider dynamic range than would be possible with a single frame. This approach allows additional luminance information to be collected from different exposures, which is essential for dealing with scenes with high contrast between light and dark areas.  
On the other hand, this technique faces major challenges, such as the alignment of objects in relation to the reference image, noise in low-exposure images, and overexposure in high-exposure images. In order to overcome these problems, some authors have proposed specific networks for frame alignment tasks and image restoration networks that deal with natural degradations. These networks can be based on CNNs [14; 48], GANs [10; 10], Transformers [17; 49; 50], or diffusion architectures [3; 28; 51].  
Multi-frame methods can also be classified into different subcategories. The following subsections deal with some of the main ones: burst, bracketing, and multi-exposure.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 1 Introduction  
Recent years have witnessed extensive research in topology-based methods to analyze and study data [1; 2]. The popularity of topology-based techniques comes from the generality and the robustness of the techniques and their applicability to a wide range of areas. The Reeb graph [3] has been one of the most successful topological tools in data analysis and data understanding. The Reeb graph is a data structure that is associated with a scalar function defined on a manifold. It gives an efficient topological summary for the manifold by encoding the evolution of the connectivity of its level sets. Reeb graphs, and their loop-less version, contour trees [4], are of fundamental importance in computational topology, geometric processing, image processing, computer graphics, and, more recently, data analysis and visualization. Examples of Reeb graph applications include quadrangulation [5], shape understanding [6], surface understanding and data simplification [7], parametrization [8; 9], segmentation [10], animation [11], feature detection [12], data reduction and simplification [13; 14], image processing [15], visualization of isosurfaces [16], and many others.  
The past decade has witnessed an increase of large geometric data on which a scalar field is defined. This has yielded several challenges for the time efficiency of computing topological structures on such data. The parallelization of the utilized algorithms is a natural direction one should take in order to improve the computational-time efficiency. In this article, we introduce an efficient shared memory parallel algorithm in order to compute the Reeb graph from a scalar function defined on a triangulated surface with or without a boundary. In addition, our algorithm provides a fast method for retrieving the surface data from the constructed Reeb graph. The data consists of the Reeb graph as well as the map that goes from the Reeb graph back to the manifold, called the augmented Reeb graph [17]. For this purpose, we define an explicit map that associates the Reeb graph data to itscorresponding data on the manifold. As an application, we show how the Reeb graph can be used to identify and calculate curves with certain homological properties on a surface. Finally, we show how the data retrieval aspect of our algorithm, along with the curves that are extracted from the Reeb graph structure, can be used for mesh segmentation and mesh parameterization.
## 3 Results and Discussion  
### Temperature during Processing  
The coalescence instability of pork fat when subjected to different degrees of comminution and subsequent heating is a known fact. When pork fatty tissue is disintegrated and friction-heated during the production of minced meat, it can be found in roughly two forms. First, the fat remains in its natural fat cells as a single cell or in aggregates. Second, the fat can be squeezed out of the cell and dispersed into the surrounding meat batter in the form of small droplets or larger fat pools. The latter phenomenon is known as \"fat smearing\" [23]. Smeared fat may trigger all kinds of problems in minced meat. First, the texture deteriorates as otherwise buttery and savory fat becomes distorted because it was heated and \"broken\" into liquid, leaving a dry, \"sandy\" consistency behind [24]. With an increased amount of fat smearing, the energy applied during comminution dissipates more and more as viscous energy (heat), leading to a less efficient mincing of the that-far non-disintegrated fat tissue [23]. It also adversely affects the efficiency and effectiveness of cleaning and disinfection procedures established for meat mincing equipment.  
When minced pork, in our investigation, was produced exclusively from fresh meat chilled at 3 \({}^{\circ}\)C, the meat temperature at the end of the first mincing cycle remained almost unaffected (Figure 2a). However, during the second mincing cycle, additional energy dissipated into heat increased the end meat temperature to 5.4 \({}^{\circ}\)C (Figure 2b). This led to a small to moderate appearance of fat smearing on the surface of minced pork (Scheme 1a). When the frozen to fresh meat ratio was altered, by the gradual addition of frozen meat, this made the batter easier to handle as it became more rigid, while remaining pliable. By adding just 10% of frozen meat, the end-product temperature was decreased to 1.5 \({}^{\circ}\)C, and the 30/70 frozen to fresh meat ratio resulted in a harder and more resistant to smearing fat tissue (Scheme 1b) because its temperature by the end of the second mixing cycle was well below 0 \({}^{\circ}\)C (Figure 2b). Meat was also comminuted more cleanly and regularly, with virtually no connective tissue wrapping itself around the cutting heads. However, by tempering the final temperature of minced pork to \(-1\) \({}^{\circ}\)C, and with the addition of 50% of frozen meat, it appeared that it has lost a bit of its flexibility and became slightly brittle, as it shattered into smaller fragments when comminuted (Scheme 1c). No fat smearing or drips formed at cut surfaces, were observed when the highest 50/50 frozen to fresh meat ratio was utilized, since the below zero temperatures immediately re-froze any exude almost immediately.  
Figure 2: Temperature of 18 mm-minced meat after the first mincing step (**a**) and 2.8 mm-minced meat after the second mincing step (**b**) (n = 30) (mean \(\pm\) standard deviation) of minced meat with varying frozen to fresh meat ratios. Different letters indicate significant differences (\(p<0.05\)).
## 3 Results
#### Time History Plot Comparison and Forecast Errors Analysis

Table 13: Error statistics resume for the 100 performance test periods. Figure 11: Time history (**a**) and error (**b**) comparison between observed data and model results in performance test dataset, i.e., between 28 May 2010 and 22 August 2010. The normal distribution of the errors is a plausible hypothesis for the two hybrid models: the kurtosis and symmetry indices deviate insignificantly from zero. The assumption of normality of the errors can also be positively evaluated thanks to the histograms shown in Figure 12.  
In addition, also the results of the Jarque-Bera test, shown in Table 14, do not allow for the rejection of the normal distribution assumption of errors for the two hybrid models. The normal distribution assumption of errors for the DD-TSA model is less strong but must not be abandoned, as shown by both the kurtosis and symmetry indices not being excessively high. The behavior of the error autocorrelation function during the performance test phase has been evaluated. If the error dataset still contains high residual autocorrelation, the adopted models were probably not able to extract all the information content due to temporal dependencies in the series. In Figure 13, it can be noted that both the DD-TSA model and the DD+ANN (_t_-1) model forecast errors have a weekly autocorrelation (a lag equal to 7) that is statistically significant. However, this autocorrelation is much lower compared to that present in the original series for the same lag, which was equal to about 0.7. However, the DD+ANN (_t_ 7) model has shown to have no significant residual autocorrelation in the dataset of forecast errors. Even the values of the autocorrelation error function for important lags, such as 7 and 14 periods, are very low, as shown in Table 15.  
The values of the autocorrelation function in the error performance test dataset. The function is evaluated for two representative lags: 7 and 14 days. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Test Statistic** & \(df\) & \(p\)**-Value** \\ \hline DD-TSA & 17.1512 & 2 & 0.0001887 \\ DD+ANN (_t_-1) & 2.2952 & 2 & 0.3174 \\ DD+ANN (_t_-7) & 1.2787 & 2 & 0.5276 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Jarque–Bera normality test performed for the errors of the three models applied to the 100 performance test data. Figure 12: (**a**) A histogram of the errors of the DD-TSA model. (**b**) A histogram of the errors of the DD+ANN (_t_-1) model. (**c**) A histogram of the errors of the DD+ANN (_t_-7) model. All of them were tested on 100 performance test days. The MPE and CVE results of Table 16 indicate the good performance of the two hybrid models in terms of prediction distortion. Also, the error dispersion of the two models with ANNs in cascade is very low. On the other hand, the high value of the MPE index for the DD-TSA model shows that, by using this procedure and this calibration dataset, it failed to grasp the behavior of the performance test series and it provided an underestimation of the prediction of the observed real data.
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 3 Data and Methods
### Surface Water Mapping
#### 3.2.1 Optical Data Processing  
All Sentinel-2 and Landsat 8 imagery for the study period were acquired as Top-Of-Atmosphere (TOA) Level-1C orthoimage products. Further pre-processing was required to mask clouds, cloud shadow and snow. In the Sentinel-2 imagery, clouds were classified and removed using the s2 cloudless machine learning algorithm and snow was removed using Sen2 Cor Scene Classification (SCL) [64]. Cloud shadows were masked using information on the solar azimuth, known cloud locations and by applying a cloud shadow algorithm [65]. The Landsat-8 data was resampled to match the resolution of the Sentinels using the nearest neighbor method in order to preserve the input data values. Subsequently the Landsat data was masked for clouds, cloud shadows and snow using the bit-mapped values of The C Function of Mask (CFMask) algorithm [66; 67]. In mountainous terrain, Sentinel-2 and Landsat-8 both require topographic (aka terrain) cast shadows to be removed, which was achieved using the solar azimuth and the GLO-30 Digital Elevation Model (DEM) [68]. A final terrain mask was applied to remove pixels with low light illumination using a hill shadow algorithm.  
The Sentinel-2 time series data was aggregated into monthly composites using the best-available-pixel (BAP) by 'cloudiness', a conservative approach chosen to reduce inaccuracies from imperfect cloud masks. Landsat composites were derived by simply taking the mean of the clear observations. A range of spectral indices with demonstrated sensitivity to water were computed, including NDVI, NDWI, mNDWI and AWEI, and used for model training and prediction. Only the best performing indices were retained.
## 2 Basic Theory
### Skeleton Simulation in Single-Light-Source Scenario
#### 2.2.1. Theoretic Proof of the Extension Solution in a Single Light Source Scenario  
For every video sequence, the extension solution is based on two facts:  
Temporal Distinguished Relative Position between Light Source and Human Body  
In a sequence, the relative position between a moving human and a fixed light source keeps changing. In other words, temporal discrete shadows \(Sh_{t}\) are generated by the light sources from different relative positions \(\theta_{t}\) towards the human. The temporal neighboring human shadows \(Sh_{t}\) and \(Sh_{t+1}\) are distinguished from each other because of different relative positions between the light source \(S\) and the human body. For neighboring frames at time coordinates \(t\) and \(t+1\), it is clear that \(\theta_{t}\
eq\theta_{t+1}\) and \(Sh_{t}\
eq Sh_{t+1}\).  
Temporal Discrete Shadows for Same Human Pose  
In order to categorize different frames based on the human poses, the 2D contour of human body captured by a monocular camera is regarded as the human pose \(P_{t}\) at the time coordinate \(t\). As shown in Figure 4b, same human pose \(P_{0}\) appears repeatedly during an activity sequence. Since each relative position between the light source and human body is different frame by frame, multiple frames sharing the same human pose \(P_{0}\) can be found. Each frame owns different shadows \(Sh_{t}\) and projection angles \(\delta b_{t}\). In an activity sequence, all the human shadows \(Sh_{t}\) sharing the same human pose \(P_{0}\) are categorized into the set \(\{Sh_{t}\}\). For different human shadows \(Sh_{t}\in\{Sh_{t}\}\), their corresponding relative position angles \(\theta_{t}\) are distinguished from each other. If multiple human shadows in \(\{Sh_{t}\}\) with different projection angles \(\theta_{t}\) are integrated in one single frame as shown in Figure 4b, condition (2) of launching the basic theory proposed in Section 2.1 is satisfied. Through applying translation transformations on each integrated frames to make the all human poses \(P_{t}\) spatially coincide with the central pose \(P_{i_{k}}\), an artificial multiple light source scenario satisfying conditions (1) and (2) is established as shown in Figure 4c.  
Figure 4: Demo of SSSE procedure in a single light source scenario. (**a**) Pose classification based on major joint positions; (**b**) Spatial–temporal discrete human poses belonging to same class; (**c**) Temporal–spatial aggregation; (**d**) Three-dimensional (3D) human skeletonization.  
The simulated scenario makes it feasible to recover the skeleton of the shared pose \(P_{o}\) in a single light source scenario based on the basic theory proposed in Section 2.1.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 4 Computational Structure  
The transmitter and receiver architectures are shown in Figures 1 and 3, respectively. The transmitter retains the conventional OFDM transmitter structure with the only difference in the assignment block that incorporates the VC in constructing the transmitted symbol. The receiver consists of five main stages. First, the OFDM demodulation is performed with the help of the DFT block. Second, the next stage involves the demapping of the OFDM symbol into data and pilot carriers used for channel estimation. Third, with the help of the VC demapping block, the processing is carried out to obtain the data vectors\(\delta q\) and the V2V channel submatrices \(\mathbf{\psi}_{i,j}\). Fourth, combining the different replicas, \(\mathbf{\delta}_{q}\) is applied to obtain the vector of data transmitted by the \(N_{D}\) active carriers. Finally, the QAM demodulation of the data is performed with the corresponding constellation.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 8 Conclusions  
A sink mobility model that is built on creating a bipartite graph of the current network was presented in this paper. The proposed mobility model was then addressed after the concept of a bipartite graph was introduced. Additionally, the NS-2 simulator was adopted to run several scenarios to examine the performance and effectiveness of the mobility model presented in this research. Furthermore, the end-to-end delay, packet delivery ratio, and throughput were used to analyze the performance of the mobility model. Because the network with 51 nodes offered stable and respectable performance, the results demonstrate that the proposed mobility model performed better and is appropriate for use in medium-sized networks with moderate mobile sink speeds. Future research can build upon the work presented in this paper by measuring additional performance indicators, such as energy efficiency, routing overhead, and jitters. Furthermore, the suggested mobility model's effectiveness can be investigated when employing routing protocols other than AODV. By examining these areas, researchers can gain a more comprehensive understanding of the system's performance and potential limitations, thus paving the way for further improvements and innovations in this field. Moreover, comprehensive mathematical modeling and analysis can be conducted for other research problems in the same study area, thus contributing to a broader understanding of the proposed methods' underlying mechanisms and potential applications.  
Conceptualization, A.A.T.; Methodology, A.A.T.; Software, A.A.T., Q.A.A.-H. and A.O.; Validation, A.A.T. and Q.A.A.-H.; Formal analysis, A.A.T. and Q.A.A.-H.; Investigation, A.A.T., Q.A.A.-H. and A.O.; Resources, A.A.T., Q.A.A.-H. and A.O.; Data curation, A.A.T.; Writing--original draft, A.A.T., Q.A.A.-H. and A.O.; Writing--review & editing, A.A.T., Q.A.A.-H. and A.O.; Visualization, A.A.T.; Funding acquisition, A.A.T., Q.A.A.-H. and A.O. All authors have read and agreed to the published version of the manuscript.  
This research receives no external funding.  
No external data is associated with this research.  
The authors declare no conflict of interest.