esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset
## 1 Introduction
[PERSON] [18] and others, in order to improve the denoising effect, combined CEEMD and an improved wavelet threshold denoising method for adaptive noise reduction; the acoustic emission signal was first decomposed via CEEMD and then an improved threshold denoising method was used to decompose the IMF component for noise reduction processing, and they verified the effectiveness of the proposed method in mechanical seal acoustic emission experiments. [PERSON] et al. [19] proposed a prediction model combining CEEMDAN, Long Short-Term Memory (LSTM), and Improved Particle Swarm Optimization (IPSO) in the study of the vibration of hydropower units. [PERSON] et al. [20] combined VMD and Akaike information criteria (AIC); a germplasm denoising method for microseismic signal denoising was designed, and its performance in MS signal processing at Shuangjiang Water Station was demonstrated. [PERSON] et al. [21] designed a noise reduction method combining VMD and fuzzy entropy to remove non-linear noise in coal mine monitoring systems. [PERSON] et al. [22] combined CEEMD, Auto-Regressive Integrated Moving Average (ARIMA), and SVM to design a wind speed prediction model. The model predicted and summarized the CEEMD component and the reconstructed SVM decomposition component of the signal, respectively, with the predicted wind speed. The CEEMDAN-wavelet packet noise reduction algorithm proposed by [PERSON] et al. [23] had the same feature as the most current improvements based on the EMD algorithm [24; 25; 26], which provided the \"selective processing of decomposed IMF components\" without making innovations in the decomposition process. Although the traditional CEEMD noise reduction algorithm has good performance in a large number of acoustic emission studies, it is affected by the addition of random white noise; the noise reduction effect will fluctuate and even have poor noise reduction results. In this paper, an improved CEEMD-WPT noise reduction algorithm is proposed to solve the above problems. The experimental results show that, compared with the traditional CEEMD and CEEMDAN noise reduction algorithms, it has the best noise reduction effect and greatly improves the stability of noise reduction. The algorithm is expected to provide a cleaner signal for the subsequent recognition process, and is of great significance for improving the performance of the SHM system. In addition, this paper designed a \"principal interval coefficient\" to solve the problem of there being no reliable quantitative evaluation index for the actual signal noise reduction effect at present, and proved its reliability through experimentation. The proposed index makes it easier for future researchers to quantify noise.
# Secured Real-Time Machine Communication Protocol
## 2 Existing Protocols  
### Application Layer Protocols  
Modbus TCP is an extension of the Modbus protocol that uses TCP/IP for communication Table 1. It operates at the application layer of the TCP/IP model. The protocol is simple to implement and widely supported, making it easy to integrate with a variety of devices and systems [7]. It is suitable for communication over Ethernet networks, facilitating remote monitoring and control [7]. While Modbus TCP can utilize TLS sockets for encryption and authentication, it remains vulnerable to certain types of cyber-attacks, such as replay attacks, denial of service attacks, and unauthorized device access with forged certificates [8]. These vulnerabilities stem from the protocol's lack of native mechanisms to validate message integrity and prevent session hijacking. Furthermore, Modbus TCP is not ideal for large and complex data transfers due to its simplicity [7].  
The OPC Unified Architecture (OPC UA) is a machine-to-machine communication protocol for industrial automation developed by the OPC Foundation. It operates at the application layer. The OPC UA is platform-independent and ensures interoperability across various systems and devices [9]. It provides robust security features, including encryption, authentication, and auditing, to protect data integrity and confidentiality [10]. However, the complexity of implementation due to its comprehensive feature set [9] and higher computational resource requirements can be a constraint for systems with limited processing power [11].
# Coherence Enhancement Based on Recursive Anisotropic Scale-Space with Adaptive Kernels
## 6 Conclusions  
We presented an algorithm for detecting curvilinear features present in images. Linear features are detected in the image representations obtained by a recursive anisotropic diffusion process that was developed to improve the characterization power of linear features while suppressing insignificant details. The results demonstrate that our algorithm is a useful characterization scheme, especially for the detection of curvilinear features. The local geometrical image features are analyzed using the eigenvectors and corresponding eigenvalues of the structure tensor obtained in an anisotropic scale-space. The image features due to undesirable noise are suppressed while significant geometrical image features such as edges and corners are preserved across the scale-space in the proposed anisotropic scale-space. The structure tensor, which takes local geometrical features into account, is further enhanced for the analysis of local image structures by using anisotropic diffusion kernels that are recursively determined based on the eigenvalues of the structure tensor. The effectiveness and robustness of the recursive anisotropic diffusion process in the detection of curvilinear features was demonstrated. The quantitative and qualitative results obtained using satellite and medical images indicate that the proposed algorithm outperforms conventional anisotropic diffusion algorithms and provides better accuracy.  
Conceptualization, B.-S.S. and B.-W.H.; methodology, B.-W.H.; software, N.S.; validation, N.S., B.-S.S. and B.-W.H.; formal analysis, B.-W.H.; investigation, B.-S.S. and B.-W.H.; resources, B.-W.H.; data curation, B.-S.S.; writing-original draft preparation, B.-W.H.; writing-review and editing, B.-S.S. and B.-W.H.; visualization, N.S.; supervision, B.-S.S. and B.-W.H.; project administration, B.-W.H.; funding acquisition, B.-W.H. All authors have read and agreed to the published version of the manuscript. This research was supported by the Chung-Ang University Young Scientist Scholarship in 2015 and the National Research Foundation of Korea NRF-2018R1A4A1059731. The authors declare no conflict of interest.
## Experimental
### Chemical analysis and identification of volatile compounds  
The chemical analysis of volatile compounds absorbed by the fiber (SPME) and synthetic 2-methyl-6-vinylpyrazine (injection volume 1 \(\upmu\)L), took place in a GC/MSD. The oven starting temperature was 60\({}^{\circ}\)C, increasing by 30\({}^{\circ}\)C/min until reaching 200 \({}^{\circ}\)C, a temperature maintained for 2 min. Helium was used as a carrier gas at a constant flow of 1 mL/min. The injection temperature was 250 \({}^{\circ}\)C and the auxiliary temperature was 280 \({}^{\circ}\)C. The injector worked in splitless mode during 0.30 min. The MSD used electronic ionization (70 EV), in SCAN mode and with a 35 to 550 UMA, provided by a spectral library (Wiley175, NIST). Compounds were identified considering retention times, retention index, mass spectral evaluation and comparing with the spectral library.  
_2-Methyl-6-vinylpyrazine synthesis:_ A suspension of 2,6-dimethylpyrazine (0.32 g, 3 mmol), dimethylamine hydrochloride (0.24 g, 3 mmol), paraformaldehyde (0.105 g, 1.16 mmol), and water (2 mL) was placed in a 10 mL glass microwave reaction vessel containing a stir bar. The reaction vessel was sealed with a cap and then placed into the microwave cavity. The microwave unit was programmed to heat the reaction mixture at 100 \({}^{\circ}\)C with 100 Watts power for 2 h. After the reaction was completed and the vessel was cooled to below 50 \({}^{\circ}\)C using a flow of compressed air, water (5 mL) was added and the crude material was extracted with ether (10 mL x 5), and the organic phase was dried with magnesium sulfate. The resulting material was filtered and concentrated in a rotavapor, producing 280 mg of reaction crude, which was then purified by column chromatography using ether as solvent, to afford 0.14 g (38% yield) as a colorless oil. \({}^{1}\)H-NMR (200 MHz, CDCl\({}_{3}\)) \(\delta\) 2.56 (s, 3H), 5.60 (dd, \(J=1.2\) and 11.0 Hz, 1H), 6.33 (dd, \(J=1.2\) and 17.4 Hz, 1H), 6.79 (dd, \(J=17.4\) and 11 Hz, 1H), 8.30 (s, 1H), 8.40 (s, 1H); \({}^{13}\)C-NMR (50 MHz, CDCl\({}_{3}\)) \(\delta\) 21.7, 120.5, 133.7, 139.7, 141.3, 150.0, 153.3. The synthesized standard (1 \(\mu\)g/mL) was analyzed with chromatographic-spectrometric analysis. The mass spectral data were (m/z): 39 (21%), 52 (48%), 54 (18%) 94 (13%), 119 (33%), 120 (100%) (Figure 3).  
Figure 3: Mass spectrum of 2-methyl-6-vinylpyrazine.  
In order to compare the extraction method used in this experiment with the one previously reported [2] a calibration curve was drawn using the reference standard at the following concentrations: 187, 375, 1126, 1500 and 3500 ng.  
Applying the equation:  
\[\mathrm{Y=a+bx;\,parameters,\,a=7.45667e+006,\,b=2.0026e+007.}\]
## 2 Materials and Methods
### RGB+D Data from Multi Stations
#### 2.1.2 Fusion of Laser Scan and Image Data from Multi Stations  
The resulting product from the fusion of laser scanner data an RGB image of the same scene is a generated RGB+D (Red, Green, and Blue + Distance) image. The D is the 4 th channel of the image that depicts distance information from the laser scanner as pixel values.  
It should be noted that the RGB+D images are also used in other fields of science, especially in applications of RGB+D cameras for computer 3D reconstruction of objects [19].  
This paper focuses on applying RGB+D images generated from MS precise measurements in displacement monitoring and deformation analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Trimble SX10 Specifications**} \\ \hline Angle measurement accuracy & 1\({}^{\circ}\) \\ Prismless distance measurement accuracy & 2 mm + 1.5 ppm \\ Scanning 3D position accuracy at 100 m & 2.5 mm \\ Scanning measurement rate & 26.6 kHz \\ Scanning point spacing & SUPERFINE-6.25 mm, FINE-12.5 mm, \\  & STANDARD-25 mm or COARSE-50 mm at 50 m \\ Camera & Telescopic, Primary or Overview \\ Image sensor & 3 \(\times\) 5 MP (2592 \(\times\) 1944 px) CMOS sensor \\ Primary camera pixel size & 4.4 mm at 50 m \\ Primary camera Field Of View (FOW) & 13.12\({}^{\circ}\)\(\times\) 9.86\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Trimble SX10 specifications [18].  
Figure 1: Trimble multi station SX10 (Source: Own compilation).  
The fusion of data from the laser scanner and camera data to RGB+D images combines the advantage of the two measuring methods. With laser scans and the resulting dense point clouds, distance changes in the line of sight can be easily detected. High-resolution image data, in contrast, are most sensitive to displacements perpendicular to the viewing direction of the camera [15].  
This process's biggest hurdle is defining the precise spatial and angle relationship between the instrument, theodolite, and integrated camera. Empirical tests conducted with SX10 showed that methods like direct linear transformation (DLT) [20] for determining camera offsets and parameters were not sufficiently precise for this use case. There is a high discrepancy between calculated parameters based on the test conditions, such as lighting, test target distance to the camera and measurement of 3D coordinates. For the DLT method, precise 3D coordinates of the photogrammetry targets are required. Precision for measuring 3D coordinates with the SX10 is around 2.5 mm, using prismless distance measurement [18]. With all the problems mentioned, precise calculation of the camera and instrument parameters by the user is not ideal. The ideal solution is to obtain the technical specification of the used MS camera directly from the manufacturer. This solution was used for data processing in this paper.
## 5 Conclusions  
This paper proposed a joint optimization framework designed to enhance the security of downlink UAV communication systems by integrating AN-GSDM. The proposed algorithm alternates between optimizing the precoding matrix and the power-control factor, effectively managing multi-beam transmissions and artificial noise (AN) to maximize the secrecy rate. Simulation results demonstrate the algorithm's superior performance in improving transmission security by both maximizing the secrecy rate and ensuring robust  
Figure 10: The secrecy rate achieved by the proposed algorithm under different iteration numbers.  
protection against eavesdropping. Specifically, the algorithm minimizes [PERSON]'s achievable rate to near zero, effectively rendering eavesdropping infeasible, while maintaining the achievable rate for Bob. Additionally, the proposed method successfully tackles the challenges posed by non-convex optimization and dynamic UAV communication environments, exhibiting strong convergence properties and offering a practical, efficient solution. The findings underscore the potential of this optimization framework to significantly enhance security in UAV communication systems, particularly in scenarios with increasing numbers of eavesdroppers or dynamic channel conditions. Future work will focus on refining the algorithm to improve scalability and performance in more complex multi-user environments, as well as exploring the integration of additional channel models and further optimization techniques.  
Conceptualization, X.L., Y.X. and J.T.; methodology, X.L. and Y.X.; software, X.L., Y.X. and J.T.; validation, X.L., Y.X., J.Z., J.T. and Y.L.; formal analysis, D.H.; investigation, X.L., Y.X. and J.T.; resources, X.L.; data curation, Y.L. and J.T.; writing--original draft preparation, X.L., Y.X., J.Z. and Q.D.; writing--review and editing, J.T.; visualization, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the CAEP Foundation (grants YZJJZL2024076 and YZJ)ZQ2023012), the National Natural Science Foundation of China under grant 62441111, and the Sichuan Science and Technology Program under grants 2024 NSFSC0476 and 2025 YFHZ0199.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Results and Discussion  
### Multivariate Optimization of the Working Parameters of the Portable APPJ System  
Based on the analysis of variance (ANOVA) test, it was found that the model describing the effect of the working parameters x\({}_{1}\) (the He flow rate, in L min\({}^{-1}\)), x\({}_{2}\) (the modulation frequency of the HV AC, in Hz), and x\({}_{3}\) (the duty cycle of this HV AC, in %) on the response of the system, i.e., the length of the sprouts (L, in mm) grown from the plasma-exposed mung bean seeds, was found statistically significant. The \(p\)-value for the model was 0.031 (\(<<\alpha=0.1\)), while the \(p\)-values for the linear and square terms included in the model were 0.112 and 0.023, respectively, with the \(p\)-values of the separate terms being 0.057 (x\({}_{1}\)), 0.372 (x\({}_{2}\)), 0.037 (x\({}_{1}\)\({}^{2}\)), and 0.057 (x\({}_{2}\)\({}^{2}\)). On the other hand, the \(p\)-value for the lack-of-fit test was 0.725 (\(>>\alpha=0.1\)); therefore, it could not be concluded that the model did not fit the data well; ergo, the model accurately fitted the data, and there was no need to transform the data and/or add the other terms. The determination coefficient (R\({}^{2}\)), indicating the goodness of fit of the measured L values with the response surface regression equation, was fairly good, i.e., 62.5%. The equation of the developed model was as follows: L = 309 \(-\) 116.4x\({}_{1}\) + 0.0443x\({}_{2}\) + 11.29x\({}_{1}\)\({}^{2}\) - 0.000200x\({}_{2}\)\({}^{2}\).  
Considering the above-presented response surface regression equation, it was evident that the working parameter x\({}_{3}\) related to the power of the AC supplied to the CAPP was statistically insignificant; hence, we decided to use the lowest possible value, i.e., 25%. The increase in the working parameter x\({}_{1}\) led to a decrease in the L (Figure 1), likely because of the lower production rate of the ROS and RNS under the conditions of the higher flow rates of He, e.g., because the supplied CAPP energy could be consumed to a greater extent to sustain the APPJ itself. In the case of the working parameter x\({}_{2}\), there was a maximum at 110 Hz, offering the highest L value. As such, for the following set of working parameters, x\({}_{1}\) = 4.5 L min\({}^{-1}\), x\({}_{2}\) = 110 Hz, and x\({}_{3}\) = 25%, the highest possible value of L = 16.2 mm was obtained according to the developed model. These working parameters were regarded as optimal as they did not impair plant growth and have been applied in all further experiments.
# Study on the Influence of Collision Scene on the Energy Dissipation Process for Train Collision
## 4 Analysis of Train Collision Response  
In this section, the differences in train collision response under various collision scenarios are analyzed from the perspectives of collision energy dissipation process, interface energy absorption and average acceleration so as to provide suggestions for the selection of train collision scenarios. The trains in collision scenarios 1, 2, 3 and 4 have the same marshaling form and relative collision speed. The initial speed and relative collision speed of the train under various collision scenarios are performed in Table 5. The positive value of speed indicates that the speed is along the x direction in Figure 3, and the negative value presents that the speed is along the x negative direction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle**} & \multirow{2}{*}{**Time/ms**} & \multicolumn{2}{c}{**Speed (km/h)**} & \multirow{2}{*}{**Difference/\%**} \\ \cline{3-3} \cline{5-5}  & & **Test** & & **Simulation** \\ \hline M1 & & 14.39 & 14.20 & 1.32 \\ M2 & & 14.87 & 14.14 & 4.91 \\ S1 & 524 & 14.35 & 14.30 & 0.34 \\ S2 & & 14.69 & 14.37 & 2.17 \\ S3 & & 14.47 & 14.43 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The speed difference at the end of the collision.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Vehicle**} & \multicolumn{2}{c}{**Spearman Correlation**} \\  & **Coefficient** & **The Value of \(p\)** \\ \hline M1 & 0.94 & \(1.24\times 10^{-19}\) \\ M2 & 0.98 & \(2.28\times 10^{-83}\) \\ S1 & 0.88 & \(1.84\times 10^{-56}\) \\ S2 & 0.98 & \(1.36\times 10^{-96}\) \\ S3 & 1.00 & \(5.71\times 10^{-78}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation between simulation results and test results.  
In the initial stage of train crashworthiness design, more attention is paid to the energy dissipation process, the energy absorption of the interface and the maximum average acceleration index to verify whether the parameter design of the energy-absorbing structure meets requirements. The energy dissipation process is used to analyze whether the energy-absorbing structure is stable and orderly during the crushing process so as to avoid the instability of the energy-absorbing structure. According to the energy absorption, the energy absorption capacity of the energy absorbing structure is judged to meet the demand of collision energy dissipation for the sake of avoiding the insufficient energy absorption capacity of the energy absorption system. The severity of the collision process could be achieved by analyzing the maximum average acceleration so that the crushing force of the energy-absorbing structure will not be too large to cause secondary damage to the occupant.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 30 cm Depth in Bangladesh
#### 5.2.1 One-Day Lead-Time Forecast  
Table 4 demonstrates five tree-based models' performance under various time windows and input scenarios on the testing dataset. The DT model achieved the best forecast with the MAX-MIN-MEAN input scenario under a five-day time window, showing the lowest average RMSE value (1.349 \({}^{\circ}\)C) and the highest average R value (0.950). Compared with DT, the RF achieved a slight improvement in terms of RMSE and R values; in particular, the use of a five-day time window achieved the lowest average RMSE (1.225 \({}^{\circ}\)C) and the highest average R (0.959). The forecast performance of GBT utilizing all meteorological parameters and the largest time window achieved the lowest average RMSE (1.193 \({}^{\circ}\)C) and the highest average R value (0.961). The hybrid models, DT-GBT and RF-GBT, consistently performed well using the combination of all meteorological factors as inputs, thereby achieving the lowest average RMSE and the highest average R values and demonstrating their effectiveness and complex relationship-capturing capabilities. The hybrid DT-GBT model achieved the lowest average RMSE (1.223 \({}^{\circ}\)C) and the highest average R value (0.959) when the size of the time window was set to four days. The hybrid RF-GBT, using the eighth input scenario and a five-day time window consistently resulted in the lowest average RMSE (1.188 \({}^{\circ}\)C) and the highest average R value (0.962).  
**Best combination for one-day lead-time forecast:** The combination of hybrid RF-GBT, eighth input scenario, and five-day time window achieved the best forecast performance with the lowest average RMSE (1.188 \({}^{\circ}\)C) and the highest average R (0.962). The second-best forecast combination adopted the four-day time window, followed by three-day, two-day, and one-day time windows, using the eighth input scenario and the hybrid RF-GBT. This highlights the superior forecast capability of the hybrid RF-GBT for soil temperature at 30 cm depth. Figure 6 demonstrates a strong agreement between optimal predictions and actual observations of ST30 at four observation stations. Compared with forecasting ST10, extremely high temperature was captured better in ST30.
## 3 Results
### Rq03  
Considering that \"no model is perfect\", the prediction models present at least some of the following shortcomings.  
* Non-inclusion of spatial heterogeneity within the zones of study;
* Information imbalance (the amount of useless data is greater than useful data) because most data are non-accident related;
* Insufficient capacities to process and analyze an enormous amount of data;
* Poor handling of long-scale datasets. It is not practical to work with huge amounts of raw data; therefore, it is necessary to select relevant features to be extracted. If this selection is not made adequately, the generated models will not work correctly;
* Not having enough related information to train and test the models (e.g., it is essential to have information about traffic accidents and normal traffic conditions from the same segment).