esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
## 1 Introduction
In view of the above problems, this paper proposes a transformer fault diagnosis method called Correlation Coefficient's Density-Based Spatial Clustering of Applications with Noise (CCDBSACN), which applies a correlation coefficient to DBSCAN, constructs a partition coefficient characterized by a correlation coefficient, and enlarges the fault characteristics of dissolved gas data in oil with an amplification coefficient, successfully realizing the application of DBSCAN in DGA. The remaining sections of this paper are organized as follows: Section 2 gives an overview of definition and clustering principle of DBSCAN. Section 3 introduces the correlation coefficient through an analysis of the defects of the traditional DBSCAN and proposes CCDBSACN. In Section 4, the proposed method is used to cluster and diagnose the DGA data, and the effectiveness and advantages of this method are compared and analyzed. Some conclusions are presented in Section 5.
# Reconfigurable Intelligent Surface in Wireless-Powered Interference-Limited Communication Networks
## 1 Introduction  
### Background Information  
Today, reconfigurable intelligent surfaces (RIS) have emerged as a new innovative technology in the research community due to its great potential for reconfiguring propagation environments and enhancing the quality of signal reception [1]. RIS is expected to play a key role in improving the coverage area, energy efficiency and data rates of future wireless networks such as fifth-generation (5G) and beyond [2]. Compared with conventional systems, it requires no additional power supply, complex encoding or decoding operation to enhance the system's performance [3, 4]. In this case, the RIS concept is based on the utilization of large number of small, low-cost and passive reflecting elements to effectively control the propagation characteristics of the desired incident signal through the adjustable amplitude and phase shift of each reflecting element with no signal processing [4]. Practically, an RIS can be fabricated to conform to any infrastructural surfaces such as walls, ceiling and buildings for easy signal reflection [5]. As a result of this, an RIS eliminates the blockage and fading effects that are usually experienced by large-scale antenna wireless systems due to the existence of trees, buildings and humans [3], and also reduces the system's complexity and cost [6].  
Recently, wireless energy harvesting (EH) has been suggested as one promising technique to prolong the lifetime of energy-constrained wireless devices [7]. It involves the conversion of received RF energy to electrical power to boost the power of wireless devices. Generally, EH can be classified as wireless-powered communication networks (WPCNs) and simultaneous wireless information and power transfer (SWIPT). The formal technique involves the AP scavenging RF energy from a dedicated PB and employing the energy to send its information to the system destination. The latter technique usually employs energy-constrained relay nodes to harvest RF energy from the AP and uses the energy to transmit information to the system destination [8,9]. In both cases, there are three EH protocols, and this includes the time switching (TS) protocol, power splitting (PS), and ideal relaying protocol which are detailed in [10,11]. Thus, the performance of both approaches has been studied in literature in the context of cooperative relaying systems.
## 4 Comparison with Measured Surfaces  
Samples taken from the production process of a commercial corrugated board plant were measured with optical triangulation sensors. The shape of the surface was then approximated based on the proposed model, where the displacement functions were limited to quadratic polynomials in machine direction and cubic polynomials in cross-direction. Since the polynomial parameters describing the displacement functions need to be determined from the measurement data, the aim is to choose the lowest possible order without seriously reducing the quality of the fit. Needlessly increasing the number of degrees of freedom could lead to overfitting and increase the impact of measurement noise on the result; moreover, the number of measurement points required would be increased. Therefore, the entire model presented in this article is focused on reducing the number of parameters.  
Two examples (size of the boards 1884 \(\times\) 770 mm and 1430 \(\times\) 1040 mm, respectively) from ongoing research are presented in Figure 5. While boards were obtained from a commercial production plant, they would have been considered waste due to the amount of warp present. The surface defined by the model (displayed as continuous, colored surface in the plots) shows the same shape as the measured data points (black circles in the plots) on a larger scale, while smoothing local variations. The interpolation capability of the model is shown by the fact that only a 6 \(\times\) 6 grid of measured data points (highlighted as red crosses in Figure 5) was used as input to the fitting algorithm, while the other data points were reserved to verify the results. Some slight systematic deviation from the data can be seen near the edges of the board, which are most likely caused by the numerical solver for the differential Equation (16). The histograms in Figure 5 show the deviation between the measured points and the surface defined by the proposed model, with most of the data points only deviating by a few millimeters (95% of the measured data points are within 2.6 mm and 2.9 mm, respectively, of the reconstructed surfaces in the examples) from the model surface. Since, as seen in the 3D plots, the model surface reproduces the large-scale deformation of the corrugated board--in other words, the warp--the remaining deviations can be seen as being part of the noise when analyzing or controlling warp.
## 5 Evaluation of Results  
The evaluation of synthetic datasets typically revolves around two primary criteria: fidelity and utility [30; 31]. Fidelity [35] describes the relevance of the generated synthetic data. For 2D synthetic images, fidelity can be understood as the similarity between the synthetic images and the real images, achieved by the proposed workflow for realistic scene generation and rendering. Utility [36], on the other hand, measures how the synthesized dataset benefits fundamental computer vision tasks during ML model training. As fidelity is not straightforward to quantify, the evaluation focuses mainly on utility. To validate the utility of the generated synthetic ballast image dataset, several experiments were designed and conducted. The UIUC HAL Cluster provided all the computing resources necessary for training and testing the deep learning models used in these experiments [37].
# A Framework for Real-Time 3D Freeform Manipulation of Facial Video
## 2 Facial Manipulation Framework  
As shown in Figure 2, the proposed framework consists of three modules: registration, manipulation, and rendering. In the registration module, facial landmarks are detected from the video frame, and states of the 3D face model (position, orientation, shape, and expression) are estimated. At the same time, in the manipulation module, the mean face model in the neutral state is deformed by the user's input, and the deformation is applied to the current state of the face model. Finally, in the rendering module, the deformed model of the current state is rendered on the video frame.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 4 Quantum Key Distribution Categories  
The fundamental of QKD is to use the non-cloning of the non-orthogonal single quantum state to accomplish the key distribution. Due to the usage of [PERSON]'s uncertainty principle in the prepare-and-measure protocol, the measurement of the system's quantum state is hardly attainable without interrupting the quantum state. As discussed in the no-cloning principle [86], the possibilities to amplified or copy the qubit without disturbing them are impossible. Consequently, the QKD system can identify the adversaries by measuring the error parameter when transmitting the photons.  
Figure 2 shows the classification of QKD protocol that can be divided into three categories based on their physical characteristics.
## 6 Conclusions  
Aiming to address the issue of noise interference, such as shot noise and dark current noise from the instrument, negatively affecting the localization and identification of spectral characteristic peaks, this paper proposes a new Raman spectral denoising method. VMD is utilized to decompose the original sequence into several sub-sequences and a residual sequence, followed by further decomposition of the residual sequence using EWT. The zero-crossing rate and correlation coefficient are used to categorize spectral and noise signals, and an EBiLSTM network architecture is constructed for spectral feature extraction and noise identification. Through different experimental validations, the proposed new method outperforms comparative algorithms.  
1. Using the VMD-EWT decomposition algorithm achieves higher denoising accuracy compared to other modal decomposition methods, with the MSE reduced by 23-56% and the SNR improved by 10-66%.
2. The constructed neural network Raman spectral denoising model demonstrates 13.38-72% enhanced denoising performance over state-of-the-art neural networks, reflecting the more stable denoising capabilities of the VMD-EWT-EBiLSTM network structure.  
* Future improvements to the network model should focus on optimizing the computational efficiency and processing speed.  
Conceptualization, Y.B. and X.Z.; methodology, X.Z. and P.H.; software, X.Z., Y.B. and Y.T.; validation, X.Z. and Y.M.; formal analysis, X.Z. and P.H.; investigation, Y.B. and X.Z.; resources, Y.T. and X.L.; data curation, X.L.; writing--original draft preparation, X.Z.; writing--review and editing, Y.B. and X.Z.; visualization, X.Z.; supervision, Y.B. and Y.M.; project administration, X.Z. and Y.B.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 3 Methods
### Segmentation of the Lung Anatomical Structures
#### 3.1.2 Gray-Level Thresholding  
Several researchers have proposed different segmentation methods for lung anatomical structures. We applied gray-level thresholding due to its quick and straightforward procedure. To segment the region of interest (ROI), thresholding replaces an image pixel with a black one if it has a gray level of less than a predefined threshold value.  
\[\text{If }g\big{(}P_{x,y}\big{)}<\ T,\text{ then }P_{x,y}\ =\ 0,\text{ else1}, \tag{1}\]  
where \(g\big{(}P_{x,y}\big{)}\) is the gray value of the image pixel \(P_{x,y}\) and \(T\) is the threshold gray value.  
As we discussed, the lung anatomical structures are allocated in a particular gray-level range on a CT scan. They appear in a range between black and white; for instance, the air appears black, the fluid appears gray, soft tissues appear various shades of gray, bones appear dense white, and vessels appear white. However, every pixel of each specific region represents an individual gray value that is related to the mean radiographic density of the lung anatomical structures [19]. The relationship between the gray value of a pixel and its associated density can be expressed by  
\[g\big{(}P_{x,y}\big{)}=\ \frac{HUI-b}{m}, \tag{2}\]  
where \(HUI\) is the radiographic density, \(b\) is the rescale intercept, and \(m\) is the rescale slope of the CT image. Using this equation, we convert the HU values of the lung structures into gray values to set a proper threshold.
## 6 Results of Uncertainty Simulations for Multiple Cases of Use of Radiation Thermometry
### Case 3: Measurement of Real Objects as Grey Bodies with Emissivity Uncertainty  
Case 3 is the measurement of real objects. Any spectral emissivity \(\varepsilon(\lambda)\) of real objects can easily be summarized as a scalar value with the corresponding uncertainty interval \(\varepsilon(\lambda)=\varepsilon\pm u_{\varepsilon}\). A chart of expected measurement uncertainty of various spectral emissivity ranges would provide radiation thermometer users a suitable reference for assessing the uncertainty of their measurements in real-world conditions. The use of a general-purpose instrument is considered in this case, using the sensitivity characteristic of a thermal camera with a vanadium oxide (VOx) microbolometer detector in 8 \(\upmu\)m-14 \(\upmu\)m range.  
In Table 12, input parameter values and standard uncertainties are listed. The last column lists standard uncertainty contributions in the case of measuring the human skin temperature. Emissivity of the human skin is simulated as 0.98 \(\pm\) 0.01 (boundary interval).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Value and Standard** & **Probability** &
\begin{tabular}{c} **Standard Uncertainty** \\ **Contributions for** \\ **Skin at 35 °C** \\ \end{tabular} \\ \hline \multirow{3}{*}{Emissivity} & (0.10–0.95) \(\pm\) 0.0058 & \multirow{3}{*}{Uniform} & \multirow{3}{*}{0.068 °C} \\  & (\(\pm\) 0.01/\(\sqrt{3}\)) & & \\ \cline{1-1}  & (0.10–0.95) \(\pm\) 0.058 & & \\ \cline{1-1} Reflected temperature & (23 \(\pm\) 1 °C) & Normal & 0.019 °C \\ \cline{1-1} \cline{1-1} Air temperature & (23 \(\pm\) 1) °C & Normal & 0.0014 °C \\ \cline{1-1} Relative humidity & (40 \(\pm\) 5)\% RH & Normal & 0.00094 °C \\ \cline{1-1} Transfer path distance & (1 \(  
Partial uncertainty contributions in a case of measuring skin at 35 \({}^{\circ}\)C are compared in Figure 22. The combined standard measurement uncertainty of measuring the human skin accounted for 0.070 \({}^{\circ}\)C.  
Figure 23 displays calculated measurement uncertainties of measuring grey body emissivities with \(\pm\)0.10 uniform uncertainty interval boundaries.
## 2 Real-Time Evaluation Method of a Diesel Vehicle SCR System
### Analysis of the NOx Conversion Process for a Real-Road Vehicle Test
Thus, the diesel SCR system has such a control strategy that the SCR system does not inject urea once the exhaust temperature is lower than the light-off temperature, i.e., the limit of the temperature window (generally \(\leq\)200 \({}^{\circ}\)C), and the SCR catalyst can only catalytically reduce NOx through the remaining NH\({}_{3}\) adsorbed on its catalyst surface. Therefore, as for the operation condition of lower exhaust temperatures, the SCR conversion efficiency decreases because of the worse decomposition rate of urea, limited urea injection, and poor catalyst activity. For the real-road diesel vehicle driving process, the SCR system undergoes three operation states, i.e., state I: the SCR normal state, state II: the SCR ammonia storage state, and state III: the SCR non-working state, as shown in Figure 1.  
State I: SCR normal state. Both the exhaust temperature and the NOx conversion efficiency remain high;  
State II: SCR ammonia storage state. As the exhaust gas temperature decreases, the de-NOx efficiency declines;  
State III: SCR non-operation state. The exhaust temperature is the lowest, and the NOx conversion efficiency is close to zero. Given the above, the three operation states of the diesel SCR system occur during the real-road vehicle test according to the exhaust gas temperature. Based on this relation between temperature and NOx conversion efficiency, the operation state of the SCR system can be identified once the exhaust temperature and the NOx emission before/after the SCR catalyst carrier are tested. Thus, according to the real-time exhaust temperature, the operation state of the SCR system can be confirmed. Furthermore, the corresponding theoretical NOx conversion efficiency should be deduced based on the real-time SCR operation state.
## 4 Results and Discussion
### Ablation Experiments on Different Attention Mechanisms  
Through the design of ablation experiments on various attention mechanisms, this study aimed to explore the effects of different attention mechanisms on the task of cyberattack behavior detection and their impact on model performance. The experiments compared the performance of self-attention, spatial attention, channel attention, and the proposed synchronized attention mechanisms on the same dataset, including the metrics of Precision, Recall, and Accuracy, in order to verify the enhancement effects of different attention mechanisms on the model's capability to recognize cyberattack behaviors. These experimental results visually demonstrated the contribution of different attention mechanisms to improving model performance in the task of detecting cyberattack behaviors, providing theoretical justification and empirical support for selecting suitable attention mechanisms.  
From Table 5, it can be observed that different attention mechanisms have distinctly varied impacts on model performance. Specifically, the self-attention mechanism exhibited Precision, Recall, and Accuracy of 0.73, 0.70, and 0.71, respectively, the spatial attention mechanism 0.81, 0.76, and 0.78, the channel attention mechanism 0.89, 0.85, and 0.87, and the proposed synchronized attention mechanism the highest at 0.93, 0.89, and 0.91. This series of results clearly indicates that, as compared to self-attention, spatial attention, and channel attention mechanisms, the synchronized attention mechanism performed best on the task of cyberattack behavior detection. By calculating the relationships between elements within a sequence, the self-attention mechanism captures long-distance dependency information; however, it may overlook features in the spatial or channel dimensions. The spatial attention mechanism focuses on the spatial location information of feature maps, aiding the model in concentrating on important features at spatial locations, whereas the channel attention mechanism focuses on the importance distribution across feature channels, enhancing the model's discriminative ability on feature channels. In contrast, the synchronized attention mechanism considers the interrelationships within the sequence while fully utilizing supplementary information between different data sources or blocks through the synchronization mechanism, achieving a more comprehensive and profound understanding of cyberattack behavior features.
## 4 Experimental Results
### Engineering Application  
To further validate the performance of the stacking ensemble model in terms of real-world prediction and generalization capabilities, the model was applied to the Pi County area of Chengdu, China. In October 2023, it predicted the natural gas procurement volumes for this area from November 2023 to October 2024. When writing this paper in November 2024, we compared the predicted values with the actual procurement volumes during this period, with the specific details presented in Table 10.  
Table 10 clearly demonstrates that our stacking ensemble model has exhibited outstanding performance in predicting natural gas procurement volumes. In particular, in March 2024, the model's performance reached its peak, with the gap between the predicted and actual values being negligible, with an error rate of only 0.35%, which is almost imperceptible. However, in certain months, such as November 2023, and April, May, and October 2024, we noticed an increase in prediction errors, reaching 5.39%, 4.84%, 5.37%, and 5.03%, respectively. These deviations may have been due to the economic slowdown that began in 2024, which led to a reduction in commercial production and a decrease in consumer spending, thereby affecting fluctuations in natural gas demand [40]. Despite these challenges, our stacking model has maintained a high level of prediction accuracy and stability. The errors for all months were kept within 6%, a result that fully attests to the model's powerful capability and reliability in predicting natural gas purchase vol  
Figure 8: Comparison of actual and predicted gas purchase volumes using stacking.  
umes. Even in a volatile economic environment, our model continues to provide precise predictions, offering valuable reference data for decision-makers.
## 5 Discussion and Best Practice
### Co-registration  
The co-registration approach is based on a reference or base RGB orthomosaic; the spectral images (pushbroom or 2D imager) are co-registered to it through image matching procedures (e.g., SIFT features, feature matching, RANSAC, transformation based on matching points). The RGB orthomosaic can be generated using any of the georeferencing approaches described above. The advantage of this approach is that it reduces the requirements for GNSS/IMU or the machine vision integration of the spectral sensor. The disadvantage is that co-registration is not a straightforward process, and requires identification of sufficient matching features. Sufficient spectral and spatial similarity has to exist between the RGB orthomosaic and the spectral imagery. Additionally, high absolute accuracy still requires a GCP survey or high-accuracy on-board GNSS on the SfM UAV.  
Recent developments in relatively low-cost GNSS receiver boards (e.g., Tersus, Emlid, Piksi) allow for high-accuracy position solutions (<10 cm absolute accuracy) on-board UAVs for a fraction of the cost of earlier survey-grade GNSS receivers. This will open the way for low-cost lightweight direct georeferencing solutions at sub-decimeter accuracy in the future; however, rigorous testing needs to occur in order to determine the effects of single frequency carrier phase versus dual frequency GNSS processing for typical UAV flight dynamics.