esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 2 Image Denoising Method Based on Probability Statistics
### Modular Processing  
Data columns entering the statistics queue are first modulated. The modularization process is performed to take the remainder between the pixel block value and the preset minimum scale unit and subtract the remainder from the pixel block value, thus obtaining the output value. The details are shown in Figure 4.  
The purpose of modularization is to unify points with similar gray values in the image, reduce the impact of details and small changes on noise recognition, increase the recognition rate of effective pixel blocks, and thus improve the recognition of noise points. As shown in Figure 5, the gray values of pixel block 1 and pixel block 2 are the same after modularization. Therefore, by adjusting the minimum scale unit of modularization, we can reduce the loss of details and improve the ability to recognize noise points. When there are too many details in the image, the minimum scale unit can be adjusted higher. Most details can then be unified and will not be regarded as noise. When there are few details in the image, the minimum scale unit can be lowered. Highlighting the changes in the image can improve the ability to recognize noise.
## 3 Results and Discussion
### Velocity Analysis of the Surface Vessel  
Due to the regular harmonic motion of the surface vessel along with the waves, the velocity of the surface vessel also fluctuates regularly as shown in Figure 3. It can be seen from the comparison of the surface vessel velocity in the four connection types that the fluctuation of the forward speed of four connection methods is consistent, and they have little difference in the effect on the velocity of the surface vessel, the time when the velocity reaches a stable level is approximately the same. The multi-link and the elastic rod have the highest maximum horizontal velocity, which is about 1.6 m/s, the maximum horizontal velocity of the cable and the rigid rod are about 1.58 m/s. From the mean velocity as shown in Figure 4, it can be seen that the mean velocity of the four connection methods is not much differently, the mean velocity of the cable is slightly larger. However, on the whole, the maximum velocity of the four types of connection is not significantly different, and the surface vessel with cable has smaller velocity fluctuation and better performance.  
Figure 3: Velocity of surface vessel in four connection types. (**a**) Velocity response. (**b**) Enlarge velocity response curves.  
Figure 2: Displacements of surface vessel in four connection types. (**a**) Displacement response. (**b**) Enlarge displacement response curves.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Evaluation Metrics  
To systematically compare the performance of various models surveyed in the reviewed papers, two evaluation metrics are employed. These metrics were designed to capture different aspects of the model performance. The first metric, First Place Aggregation (FPA), measures the frequency with which a model is identified as the highest-performing or tied for the highest-performing model in the surveyed studies. FPA is calculated as:  
\[\text{FPA}=\frac{N_{\text{first}}}{N_{\text{total}}}\times 100\]  
Here, \(N_{\text{first}}\) represents the number of times the model achieves the top rank, while \(N_{\text{total}}\) is the total number of evaluations in which the model is included. FPA provides a straightforward measure of a model's dominance in performance comparisons. A key advantage of this metric is that it mitigates the influence of any poor performing models present within comparisons.  
The second metric utilizes a weighted rank aggregation (WRA) approach to account for the relative performance of a model across all comparisons, not just first-place rankings. The WRA score is calculated as:  
\[\text{WRA}=1-\frac{(N_{\text{rank}}-1)}{(N_{\text{total}}-1)}\]  
Here, \(N_{\text{rank}}\) denotes the model's rank in each comparison, with one representing the top rank. \(N_{\text{total}}\) is the total number of models in the comparison. This metric assigns a score of one to the top-performing model and zero to the lowest-performing model, offering a nuanced perspective that accounts for varying numbers of models across comparisons. WRA is particularly valuable in scenarios where the relative performance of models varies significantly, enabling a more comprehensive evaluation of their effectiveness.
## 4 Flow Field Analysis of Corner-Filled Goaf Area Using ANSYS Fluent
### Simulation Analysis of Upper and Lower Corner Fillings in the Working Face
The graph indicates that the oxygen concentration distribution in the goat roughly follows an \"S\" shape. A comparison of the three different filling conditions reveals a significant reduction in the width of the oxidation zone in the goat compared to the unfilled condition. Notably, when both the upper and lower corners are filled, the width of the oxidation zone experiences the greatest reduction at various positions in the goat, indicating the most effective control of the oxidation zone. Specifically, it reduces the width of the oxidation zone by 14.84% on the intake side, 21.07% in the middle, and 6.23% on the return side of the goat. The widths of the oxidation zone in the goat under different corner-filling conditions are summarized in Table 3.  
Figure 13 presents a comparative schematic diagram illustrating the width of the oxidation zone on the intake side, in the middle, and on the return side of the goat under four different filling conditions. The diagram emphasizes that corner filling, as an effective measure to reduce the void ratio in the corner areas and seal the airflow leakage from the working face, significantly alters the internal flow field within the goat, thereby exerting a notable impact on the distribution of the oxidation zone. Filling only the lower corner exhibits a limited inhibitory effect on the width of the oxidation zone on the return side of the goat. Similarly, filling only the upper corner demonstrates poor inhibitory effects on the width of the oxidation zone on the intake side and middle of the goat. However, the simultaneous filling of both the upper and lower corners proves to be an effective approach to shorten the width of the oxidation zone in the goat. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Location within the** & **Spontaneous** & **Width of the** \\  & **Goaf** & **Combustion Area** & **Oxidation Zone** \\ \hline \multirow{3}{*}{Unfilled} & Inlet air side & 146.68–237.8 m & 91.12 m \\  & Middle & 97.62–209.26 m & 111.64 m \\  & Return air side & 27.53–188.24 m & 160.71 m \\ \hline \multirow{2}{*}{Filling of the upper corner} & Inlet air side & 159.7–238.3 m & 78.6 m \\  & Middle & 116.65–210.76 m & 94.11 m \\  & Return air side & 29.54–186.73 m & 157.19 m \\ \hline \multirow{2}{*}{Filling of the lower corner} & Inlet air side & 158.2–238.8 m & 80.6 m \\  & Middle & 116.65–211.76 m & 95.11 m \\  & Return air side & 37.05–188.73 m & 151.68 m \\ \hline \multirow{3}{*}{Filled} & Inlet air side & 155.69–233.29 m & 77.6 m \\  & Middle & 114.14–202.26 m & 88.12 m \\ \cline{1-1}  & Return air side & 29.54–180.23 m & 150.69 m \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distribution of the oxidation zone in the goat under different corner-filling conditions.
## 3 The Proposed Method
### Random Encryption  
The secret key \(\mathbf{K}\) is generated by the SHA-512 hashing algorithm [15] with user's key input. We use the secret key \(\mathbf{K}\) to compute a set of random numbers \(\mathbf{G}\mathbf{n}_{i}\) by the Gaussian distribution [16; 17] as shown in Equation (6):  
\[\mathbf{G}\mathbf{n}_{i}=G_{P}(\mathbf{K})=\left\{g_{i,j}\big{|}j\in[1,| \mathbf{G}\mathbf{n}_{i}|\right\} \tag{6}\]  
Therein, \(|\mathbf{G}\mathbf{n}_{i}|=|\mathbf{P}_{i}|\), \(g_{i,j}\) is calculated by the Gaussian function as shown in Equation (7), and \(\boldsymbol{x}\) is the value of \(\mathbf{K}\):  
\[g_{i,j}=\frac{i\times j}{|\where \(|\textbf{E}_{i}|=|\textbf{P}_{i}|\) and \(e_{i,j}\) is computed as follows:  
\[e_{i,j}=b\
u_{i,j}\times g_{i,j}=b\
u_{i,j}\times\frac{i\times j}{|\textbf{P}_{i }|}\times\frac{1}{\sqrt{2\pi}}e^{\frac{-\
u^{2}}{2}}\quad\forall\ j\in[1,| \textbf{P}_{i}|] \tag{11}\]  
Figure 4 shows the random encryption process for the backbone object \(\textbf{B}_{i}\) when the value of \(|\textbf{B}_{i}|>\frac{1}{s}\) (Figure 4a with \(|\textbf{B}_{i}|=17\)) and when \(|\textbf{B}_{i}|\leq\frac{1}{s}\) (Figure 4b with \(|\textbf{B}_{i}|=4\)). When the value of \(|\textbf{B}_{i}|>4\), the feature vertices of \(\textbf{B}_{i}\) will be encrypted by the cipher function \(C_{P}(.)\). Then, the randomization process is performed as shown in Figure 4a. In case \(|\textbf{B}_{i}|=4\), the backbone object will be directly encrypted by the cipher function \(C_{P}(.)\) and then randomized by a set of Gaussian numbers, as shown in Figure 4b.  
Figure 4: (**a**) Backbone object encryption, and (**b**) feature vertices encryption.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 3 Results and Discussion
### Denoising Characterization
#### 3.2.3 WPD Analysis  
The structure diagram of an n-layer wavelet packet analysis is shown in Figure 11.  
After n-layer wavelet packet decomposition of the original signal U(0,0), 2n components are obtained, where U(i,j) corresponds to the \(j\)th component of the \(i\)th layer. \(g(k)\) and \(h(k)\) represent the low-pass and high-pass filter coefficients, respectively, as shown in Equation (12).  
\[g(x)=(-1)^{k}h(1-k) \tag{12}\]  
\(\psi(x)\) and \(\varphi(x)\) denoted the wavelet function and the orthogonal scaling function, respectively, satisfying Equation (13).  
\[\left\{\begin{array}{ll}\varphi(x)=\sqrt{2}\sum\limits_{k\in Z}h(k)\phi(2x- k)\\ \psi(x)=\sqrt{2}\sum\limits_{k\in Z}g(k)\phi(2x-k)\end{array}\right. \tag{13}\]  
Figure 11: Structure of n-layer wavelet packet analysis.  
Let \(w_{0}(x)=\psi(x)\) and \(w_{1}(x)=\varphi(x)\), and the extension of Equation (13) yields Equation (14).  
\[\left\{\begin{array}{l}w_{2n}(x)=\sqrt{2}\sum\limits_{k\in Z}h(k)w_{n}(2x-k)\\ w_{2n+1}(x)=\sqrt{2}\sum\limits_{k\in Z}g(k)w_{n}(2x-k)\end{array}\right. \tag{14}\]  
The reconstruction formula for the wavelet packets is shown in Equation (15).  
\[w_{n}(x)=2\sum\limits_{k\in Z}h(x-2k)w_{2n}(k)+2\sum\limits_{k\in Z}g(x-2k)w_{ 2n+1}(k) \tag{15}\]  
The general steps for wavelet packet threshold denoising are as follows [40]:  
1. Determine the number of decomposition levels, choose wavelet basis functions, and perform wavelet packet decomposition.
2. Select appropriate thresholds and threshold functions.
3. Reconstruct the wavelet packets.
# Additive Manufacturing for Surgical Planning and Education: A Review
## 2 Workflow
### Data Segmentation and Mesh Generation  
Most medical imaging systems produce data as a series of 2D images, most commonly in DICOM file format. Special software is needed to transform this medical data into 3D files that are suitable for 3D-printing machines to construct the objects [11].  
Figure 1: A schematic of the workflow for a medical part.  
Figure 2: An MRI image from a patient’s spine from different views to locate the cancer tumor.  
To convert medical-imaging data into 3D-printable data, there exist, today, numerous software options, ranging from open-source licensing, such as PostDICOM, Horos, RadiAnt, Navegatium, Pro Surgical 3D, MicroDicom, 3 Dimviewer, Mango, Escape EMV, RTPotter, InVesalius, DICOM4 QUI, and many more [[[https://www.postdicom.com/en/blog/top-25-free-dicom-viewers](https://www.postdicom.com/en/blog/top-25-free-dicom-viewers)]([https://www.postdicom.com/en/blog/top-25-free-dicom-viewers](https://www.postdicom.com/en/blog/top-25-free-dicom-viewers))] (18 February 2024) to relatively expensive specialized medical CAD software such as Materials Mimics, 3-matic, 3D Slicer, Simpleware, Virtual Surgical Planning, D2P, and others, each offering unique features and applications [12].  
The overall workflow of converting DICOM files into 3D-printable files consists of, in a slice of the model, identifying the areas of interest such as bone and/or soft tissue by selecting different color ranges, or manually drawing around regions of interest, and then extrapolating the selected data through subsequent slices to create the 3D model. This can sometimes require manually selecting data in multiple regions of the model. Once the 3D model has been produced, it often requires digital post-processing to clean it up, smooth it or sharpen it, adjust colors and textures, etc. [12]. Figure 3 presents the final stage of preparation of a 3D model for printing in Magics.
# A Novel Approach for the Estimation of Doubly Spread Acoustic Channels Based on Wavelet Transform
## 3 WT-Based Sparse Channel Estimation
### WT-Based Algorithm
**Initialization:** Set the residual signal vector \(\mathbf{r_{e}}=\mathbf{r}\); iteration index \(i=1\);  
**Iterate:**  
(1) Calculate and save the energy rate of \(\mathbf{r_{e}}\) as: \(E=||\mathbf{r_{e}}||_{2}^{2}/||\mathbf{r}||_{2}^{2}\). (2) Use \(\mathbf{S^{+}}\), \(\mathbf{S^{-}}\) as mother waves and do WT with \(\mathbf{r_{e}}\), respectively. Select the maximum peaks and get corresponding time shifts \(b_{\max}^{+}\), \(b_{\max}^{-}\). (3) Calculate the path parameters:  
\[\hat{a}_{l}=\frac{2t_{0}}{2t_{0}-(b_{\max}{}^{-}-b_{\max}{}^{+})} \tag{20}\]  
\[\hat{\tau}_{l}=(b_{\max}{}^{+}+b_{\max}{}^{-})/2 \tag{21}\]  
where \(l\) represents the \(l\)th path, and the path amplitude \(\hat{A}_{l}\) can be calculated as (19). (4) Update residual signal:  
\[x(t)=\cos(-2\pi k\ln(1-\frac{t}{t_{0}}))+\cos(2\pi k\ln(1+\frac{t}{t_{0}})) \tag{13}\]0  
where \(N\) is the length of \(\mathbf{r_{e}}\). (5) If \(E-||\mathbf{r_{e}}||_{2}^{2}/||\mathbf{r}||_{2}^{2}<\varepsilon\), stop the iteration, else, set \(i=i+1\) and go to step 1). **Output:** The estimated channel parameters \(\{\hat{A}_{i},\hat{a}_{i},\hat{\tau}_{i}\}_{i=1}^{L}\). Note: since the number of paths \(L\) is unknown, we use \(\varepsilon\) as a threshold to determine the termination of the algorithm. \(\varepsilon\) represents the normalized decrease of residual signal, and when all paths have been detected, the decrease will be small and \(\varepsilon\) can be set as a small positive number, such as 0.01 in this paper.
## 3 Results  
We can divide the results of the research into two groups, the technological and the practical part. Within the technological part, several tests of the created application were carried out from the point of view of its correct functionality. That is, whether the application works correctly, whether its control is in accordance with the requirements, or whether the control using the controller and the like works properly.
# A Systematic Literature Review on Military Software Defined Networks
## 4 Identified Significant Research Contributions
### Control Systems
Additionally, [PERSON] et al. [51] proposed a transmission framework for Software Defined-Airborn Tactical Networks with the aim of providing a fundamental infrastructure for improving the communications capability between the control and data planes. Furthermore, the authors proposed a dedicated communication protocol, aiming at transmitting the non-elastic C/D information in both a reliable and timely manner. The authors conducted a simulation for the purpose of illustrating the performance of their scheme. They built a simplified avionics network in EXata 5.1 for every aircraft, and regarded an avionics network as a node of the SD-ATN. The simplified avionics network, which is considered as the common node, includes two devices that implement the functions of the platform controller and SD-ATN transmission system, respectively. The avionics network that represents the active control node includes one more device that implements the functions of the SD-ATN controller, while the devices within an aircraft are connected through wired links. Finally, [PERSON] [50] explored how SDN can be incorporated, both physically and logically, within heterogeneous tactical networks. Being a Master's thesis, this work provides a comprehensive introduction into the related concepts and underlying requirements, while the main contribution is focused towards the collection of network information for the establishment of topology mapping. The author evaluated three conceptual models and proposed an approach for topology mapping through local legacy routers.
## 2 Methodological Approach
### Dial Segmentation and Center Localization  
After gauge detection, the next step is to segment the dial region and determine its center. Therefore, two methods were analyzed for this purpose: the Hough transform and the region growing. Some relevant results of these tests are shown in Figure 6, which indicates that the region growing is sensitive to interferences such as reflections, shadows or even characters in the dial. On the contrary, the Hough transform performed better, especially in images with high interference, so this was the method applied in the algorithm to segment the dial, which also determines the center of the dial. To implement the Hough transform, the RoI needs to be pre-processed to increase its quality; therefore, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was used to enhance the  
Figure 4: CenterNet HourGlass104 model Training. (**a**) Loss functions, (**b**) performance graphs.  
Figure 3: Examples of the data augmentation applied to some dataset images.  
illumination. Then, to eliminate some of the noise, the bilateral filter was applied, which also facilitates the circle detection of by preserving object edges.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 6 Application
### Photocatalytic Hydrogen Production
In the composite material, 2.5% PtNi\({}_{\rm x}\)/g-C\({}_{3}\)N\({}_{4}\) (8456 \(\mu\)mol\(\cdot\)h\({}^{-1}\)g\({}^{-1}\)) shows the highest rate of photocatalytic hydrogen generation, which is about 16 times of the rate of pure g-C\({}_{3}\)N\({}_{4}\) (515 \(\mu\)mol\(\cdot\)h\({}^{-1}\)g\({}^{-1}\)) photocatalytic hydrogen. From Figure 9B, the emission peak of pure g-C\({}_{3}\)N\({}_{4}\) at about 470 nm can be observed, which is recombined in response to band gap electron-hole pairs. In addition, 2.5% PtNi\({}_{\rm x}\)/g-C\({}_{3}\)N\({}_{4}\) shows a weaker emission peak intensity relative to pure g-C3N4, which means that the photoexcitation electron  
Figure 8: **(A)** Electrocatalytic performance for Mo\({}_{2}\)TiC\({}_{2}\)T\({}_{\rm x}\)-PtSA and reference HeR catalysts. **(B)** Magnified HAADF–STEM image of Mo\({}_{2}\)TiC\({}_{2}\)T\({}_{\rm x}\)-PtSA and its corresponding simulated image, as well as an illustration of the structure of Mo\({}_{2}\)TiC\({}_{2}\)T\({}_{\rm x}\)-PtSA, showing the isolated Pt atoms (circles in c) [335]. and hole recombination rate in 2.5% PtNi\({}_{x}\)/g-C\({}_{3}\)N\({}_{4}\) is lower [337; 338]. In other words, after loading PtNi\({}_{x}\) onto g-C\({}_{3}\)N\({}_{4}\), it can effectively suppress the recombination of the photogenerated charge, thereby improving photocatalytic performance. [PERSON] et al. [339] prepared a Bi\({}_{3}\)TiNbO\({}_{9}\) and Cr/Nb co-doped Bi\({}_{3}\)Ti\({}_{1-2\times}\)Cr\({}_{x}\)Nb\({}_{1+\times}\)O\({}_{9}\) (x = 0.1, 0.2, 0.3) photocatalyst by the solid state reaction method. The doping of Cr/Nb can reduce the band gap of Bi\({}_{3}\)TiNbO\({}_{9}\) by 1 eV. Figure 10A shows the hydrogen evolution experiment of all samples under full range illumination. Continuous hydrogen generation was detected through the experiment, and Cr/Nb co-doped Bi\({}_{3}\)TiNbO\({}_{9}\) showed a higher hydrogen generation rate than pure Bi\({}_{3}\)TiNbO\({}_{9}\). In the doped sample, Bi\({}_{3}\)Ti\({}_{0.8}\)Cr\({}_{0.1}\)Nb\({}_{1,1}\)O\({}_{9}\) (x = 0.1) showed the highest hydrogen generation rate, which was about twice that of pure Bi\({}_{3}\)TiNbO\({}_{9}\). However, as the amount of Cr/Nb doping further increased, the hydrogen generation rate of the photocatalyst after doping decreased by 40%. The average photocatalytic hydrogen production rate under different irradiation conditions is given in Figure 10B.
## 3 Results and Discussion
### Characteristics of Filter Residues
#### 3.6.3 Magnetic Properties Analysis of Filter Residues  
A detailed magnetic characterization was conducted to understand the influence of PVA on the magnetic evolution of ferrite precipitates. Figure 7 shows magnetic hysteresis loops of samples under different thermal treatment temperatures. Ferrite samples prepared without PVA exhibited relatively weak magnetism, with saturation magnetization of only 0.123 emu/g after drying at 100 \({}^{\circ}\)C (Figure 7a), slightly increasing to 0.189 emu/g after calcination at 650 \({}^{\circ}\)C (Figure 7b), indicating the difficulty in forming strong magnetic phases under mild conditions through simple ferrite formation. With PVA introduction, sample B1 showed saturation magnetization of 0.152 emu/g after drying at 100 \({}^{\circ}\)C (Figure 7c), comparable to PVA-free samples. However, its magnetic properties were significantly enhanced after calcination at 650 \({}^{\circ}\)C, with saturation magnetization increasing to 2.43 emu/g (Figure 7d). This substantial magnetic enhancement suggests PVA's influence on magnetic phase formation at high temperatures, with hysteresis loop shape changes indicating magnetic structure reorganization. Sample B2 demonstrated the most distinctive magnetic evolution characteristics. After drying at 100 \({}^{\circ}\)C, its saturation magnetization reached a remarkable 10.30 emu/g (Figure 7e), substantially higher than other samples. However, after calcination at 650 \({}^{\circ}\)C, its magnetism dramatically decreased to 0.679 emu/g  
Figure 6: Visual appearance comparison of filter residues under different treatment conditions (A1, B1, A2, and B2) at three stages: original solution state, after drying at 100 \({}^{\circ}\)C, and after calcination at 650 \({}^{\circ}\)C, showing distinct color changes and physical transformations.  
(Figure 7f). This anomalous magnetic deterioration likely results from phase transitions at high temperatures, suggesting the formation of a unique metastable magnetic phase in the initial B2 sample [39]. The magnetic properties achieved in our study significantly surpass those reported in similar research. [PERSON] et al. [37] reported a maximum saturation magnetization of 5.2 emu/g for PVA-coated Fe\({}_{3}\)O\({}_{4}\) nanoparticles, whereas our B2 sample exhibited 10.30 emu/g at 100 \({}^{\circ}\)C. This enhanced magnetic performance likely results from our H\({}_{2}\)O\({}_{2}\) pre-oxidation method, which alters the interaction between PVA and iron ions. Similarly, [PERSON] et al. [29] employed different coating strategies for ferrite nanoparticles but achieved lower magnetic performance. The unique metastable magnetic phase formed in our B2 sample demonstrates the potential for tailoring magnetic properties through controlled processing conditions.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 4 Experiments and Evaluation  
### Model Training Details  
A wide range of experiments were performed on different datasets to demonstrate the efficacy of our method. Our approach was evaluated with several evaluation criteria by comparing to the typical SLAM methods on the KITTI dataset [36]. We used the training set of Wireframe [37] with the ground truth to train our models and the other comparedmethods. The training process used data enhancement techniques such as random Gaussian noise, motion blur, and brightness level changes to improve the network's robustness ability for changes in lighting and viewing angles. For end-to-end training of the point-line matching network, our network was implemented in Pytorch [38] using the Adam [39] optimizer to train the network with an initial learning rate of \(1\times 10^{-5}\) and a decay of the learning rate by 20 at each epoch. We trained our model on a GeForce GTX2080 Ti GPU.