esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Results of Experiments
### Image Processing of Prediction Image  
Although the boundary line could be detected, the authors still obtained the output as shown in Figure 13 because they made predictions based on an unknown weld image, pixel by pixel. Thus, an image processing method was developed to find the weld line with fewer errors. The sequence is shown in Figure 14. First, the left and right blocks are detected, and then the largest blocks on one side. The upper position of the center of gravity is used as the detection line. At last, find the overlap of each block, and the red line is the center of the overlap as the weld line. The yellow line is the average position of the weld line obtained for three consecutive images to stabilize the control in tracking welding. Specifically, the average brightness is first calculated. Secondly, binarization is performed using a fixed threshold (200-255) to extract high-brightness target regions. The noise is then removed, and the connectivity of the target regions is enhanced by morphological open operations and expansion operations. Following this, connectivity region analysis is performed to calculate the area and center of mass of all the target regions. The left and right detection regions are then divided based on the column coordinates, from which the target with the largest area is extracted to reduce the detection error. To ensure the consistency of the target features, a reference row is defined as an alignment reference. The exact location of the detection point is further determined by calculating the intersection point between the left and right target regions and this reference row. The corresponding detection line is drawn to represent the relative position of the target. Finally, the midpoint column coordinates of the left and right intersection points are calculated, and the detection data are smoothed using the sliding window averaging method to reduce the impact of short-term fluctuations on the detection results. This method can effectively reduce noise interference and improve the robustness of target localization.  
Figure 12: The result images of prediction with U-Net. (**a**) Weld boundary line under normal conditions; (**b**) Weld boundary line deviates from the weld line.  
Figure 13: One of the resulting images from U-Net.
## 4 Discussion
In contact with NaOCl, radiopacifer bismuth oxide changes color from yellow to black [15; 26], whereas zirconium oxide and calcium tungstate are unaffected [15]. Materials that do not contain bismuth oxide but instead tantalum oxide or zirconium oxide do not exhibit significant changes in color [40]. The MTA samples were darker (\(\Delta\)E > 30) than Portland cement (\(\Delta\)E < 10) after 24 h [26]. ProRoot MTA and Biodentine exhibited clinically perceptible discoloration when immersed in NaOCl [27; 41]. ProRoot MTA showed a significantly higher \(\Delta\)E (\(\Delta\)E > 15) value when compared with Biodentine (\(\Delta\)E < 10) after contact with NaOCl after 24 h [27]. Similar results were obtained in the present study for ProRoot MTA \(\Delta\)E = 41 and Biodentine \(\Delta\)E = 6 after 24 h. Furthermore, it has been previously demonstrated that ProRoot MTA immersed in 2.5% NaOCl shows a significant change in color, but no significant differences were observed in color after 1, 2, and 4 months after immersion [41]. In contrast, in the present study, significant differences for immersion durations of 15 min, 1 h, and 24 h were observed. After immersion in CHX, the most distinct change in color was observed for Biodentine (\(\Delta\)E = 23 after 24 h). It has been demonstrated by other authors that Biodentine shows a significantly higher \(\Delta\)E value after contact with CHX (\(\Delta\)E > 10) when compared to NaOCl (\(\Delta\)E < 10) [27]. CHX caused pronounced color changes in Biodentine as well as in white MTA [42]. In the present study, significant color differences were also observed after EDTA immersion. However, these changes were less severe than those upon immersion in other liquids and were not visible to the naked eye. In the literature, white MTA shows a significant change in color upon contact with EDTA when compared to that in saline [42]. This experiment had some limitations. Firstly, we attempted to simulate a clinical situation; however, there was a difference in environment, and materials were not put into teeth. Secondly, materials structures were not assessed. Thus, further studies that analyze the surface areas using EDX, SEM, or micro-hardness test for the evaluation of actual disintegration must be conducted.
# Magnetic Nanoparticles as Mediators for Magnetic Hyperthermia Therapy Applications: A Status Review
## 3 Results and Discussion  
The synthesized MNPs were characterized by various characteristic methods. We focused on and compared the results of X-ray diffraction analysis (XRD), thermogravimetric analysis (TGA/SDTA), modified thermogravimetric analysis, transmission electron microscopy (TEM), and magnetic and calorimetric measurements.
# Fast Minimum Error Entropy for Linear Regression
###### Abstract  
The minimum error entropy (MEE) criterion finds extensive utility across diverse applications, particularly in contexts characterized by non-Gaussian noise. However, its computational demands are notable, and are primarily attributable to the double summation operation involved in calculating the probability density function (PDF) of the error. To address this, our study introduces a novel approach, termed the fast minimum error entropy (FMEE) algorithm, aimed at mitigating computational complexity through the utilization of polynomial expansions of the error PDF. Initially, the PDF approximation of a random variable is derived via the Gram-Charlier expansion. Subsequently, we proceed to ascertain and streamline the entropy of the random variable. Following this, the error entropy inherent to the linear regression model is delineated and expressed as a function of the regression coefficient vector. Lastly, leveraging the gradient descent algorithm, we compute the regression coefficient vector corresponding to the minimum error entropy. Theoretical scrutiny reveals that the time complexity of FMEE stands at \(O(n)\), in stark contrast to the \(O(n^{2})\) complexity associated with MEE. Experimentally, our findings underscore the remarkable efficiency gains afforded by FMEE, with time consumption registering less than 1% of that observed with MEE. Encouragingly, this efficiency leap is achieved without compromising accuracy, as evidenced by negligible differentials observed between the accuracies of FMEE and MEE. Furthermore, comprehensive regression experiments on real-world electric datasets in northwest China demonstrate that our FMEE outperforms baseline methods by a clear margin.  
m _algorithms_2024, 17, 341. [[https://doi.org/10.3390/a17080341](https://doi.org/10.3390/a17080341)]([https://doi.org/10.3390/a17080341](https://doi.org/10.3390/a17080341))
## 3 Technical Overview
### Filter Bank Implementation  
During the signal processing of the virtual input, the calculated coefficients are integrated after the \(BL\) adaptation algorithm. Therefore, a filter bank was realized that consists of seven non-uniform FIR filters [40], whose central frequencies match the central frequencies of the \(BL\) conversion matrix (see Figure 6). The seven filters were one lowpass, one highpass, and five bandpass filters. The Parks-McClellan [41] algorithm was used to implement the best possible FIR filter to provide the optimal frequency response according to Figure 6, and with the smallest number of coefficients. Table 1 presents the central frequency and filter band of each filter according to 50% filter overlapping. Computational constraints impose the use of a 7-band filter bank during the real-time simulation of the model.  
Table 1 presents the central frequency of each filter. The overlap of the filters is 50%. Computational constraints impose the use of a 7-band filter bank during the real-time simulation of the model.  
Realizing the appropriate filter types is crucial for the analysis and reconstruction of different types of sound sources. Currently, the model includes a simple implementation of a filter bank for the artificial sounds employed in the testing session. In a future development of the model, a dynamic system of filter banks could be used, which will depend on the type of virtual source (speech, music, and artificial sounds).  
Figure 5: Frequency band energy estimation controller.
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 4 Simulation and Results Discussion
### Coverage Holes Detection
#### 4.2.2 Results and Discussion  
Using the VD algorithm, the monitoring area was divided into several cells using the selected sensors for full coverage. Each sensor had its own cell that gave us a 40-cell area with no coverage hole found.  
The sensor indicated in Figure 8 was randomly chosen to exit the coverage network. The VD algorithm was reapplied to the remaining 39 sensors in the area to find the coverage holes introduced by the outgoing sensor. The result is shown in the Figure 9.  
It was assumed that, after a period of time (one hour), the base station updated the positions of the 40 sensors deployed and that one of the sensors came out of the network. Therefore, the next application started by randomly selecting a sensor to exit the network and then to apply the VD algorithm on the remaining 39 sensors in the network to discover the location of the coverage holes. The results of this application are shown in Figures 9 and 10.  
According to Figure 10a, it is noted that the sensor (13) that left the network was at the neighborhood of sensors (34), (25) and (28) so they then covered the space already covered by the sensor (13) but their sensing range was not enough to cover all the space. For this reason, we found a coverage hole between cell (27) and (33), as shown in Figure 10b.  
Figure 8: Voronoi cells of the 40 deployed sensors.  
Figure 9: Voronoi cells of remaining 39 sensors.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 2 Related Work
### Hypergraph Neural Networks  
Recently, a number of hypergraph neural networks have been proposed for various machine learning tasks, such as image classification, object detection, semantic segmentation, and human pose estimation. Depending on how they process hypergraphs, they can be divided into two categories. One is to map the hypergraph into a graph, then graph convolution methods can be applied to it. For example, in HyperGCN [25], each hyperedge is approximated by a set of pairs of edges, and then a GCN is executed on that graph. Line hypergraph convolution networks (LHCNs) [32] mapped the hypergraph to a weighted and attributed line graph and used GCN to learn the line graph. These methods increase the difficulty of the transformation and do not retain higher-order information well. Another kind of method is to design convolution operators directly on the hypergraph. For example, [PERSON] et al. [20] proposed a Hypergraph Neural Network (HGNN) framework in which hyperedge convolution is designed by using hypergraph Laplacian operators, so that the output vertex features are obtained by aggregating their associated hyperedge features. Considering the differences in the hypergraph structure on each layer, the Dynamic Hypergraph Neural Networks (DHGNNs) [22] further extended the idea of HGNN by adding a dynamic hypergraph building block and designing vertex convolution and hyperedge convolution to aggregate vertex and hyperedge features, respectively. [PERSON] et al. [26] proposed both hyperedge convolution and hypergraph attention operators, where hyperedge convolution is the same as HGNN, and the hypergraph attention operator utilized the attention mechanism to measure the connectivity between vertex and hyperedge. These methods can allow for a large degree of preservation of higher-order information while avoiding complex transformation processes. These works open up new perspectives and greatly broaden research ideas in the field of point cloud segmentation.
## 3 Materials and Methods
### The Proposed CLpCEwDCS Decoding Framework
#### 3.2.3 The DCS Decoding  
Essentially, CS is a GS decoding method with an additional degradation penalty term. When handling long texts, GS is prone to getting stuck in local optimal and generating duplicate tokens [32]. To overcome this anisotropy problem, a penalty term is added. It measures the similarity between the current candidate token and the previous tokens.  
Figure 2: The CLpCE-based model fine-tuning procedure. \(L_{CE}\) guides the supervised learning and \(L_{CL}\) directs the unsupervised learning, both parts contributing to the fine-tuning of pre-trained LLMs for accurate feature representation towards a specific task.  
However, CS considers only the word with the highest probability at the current time, and the generated text lacks diversity.  
The DCS decoding enriches the diversity, and meanwhile, it ensures the probability difference to the best token acceptable. Given the previously generated text \(x_{<t}\), the output of \(x_{t}\) at time \(t\) via DCS can be described as the token generation,  
\[\begin{split} x_{t}^{j}&=\{(1-\psi)\times p_{\theta }(v|x_{<t})\\ &-\psi\times(\max\{cos(h_{\
u},h_{\pi_{j}}):1\leq j\leq t-1\})\}. \end{split} \tag{9}\]  
To enhance the text generation diversity, DCS decoding is designed which uses tokens with the highest probabilities to form a candidate set. Firstly, the token \(x_{t}^{*}\) with the highest probability (\(p_{max}=p_{x_{t}^{*}}\)) at the current time is added into the candidate set. Then, the probabilities (\(p\)) of the remaining tokens are compared to the highest probability. If the difference of the probabilities between tokens is less than threshold \(\rho\), the token is added to the candidate set as well (Equation (10)).  
\[x_{t}^{m}\in\{(p_{max}-p_{x_{t}^{m}})\leq\rho\times p_{max}\} \tag{10}\]  
After that, selection of the candidate tokens will yield different outputs of token sequences as \(\{seq_{l}\}_{l=1}^{k}\) and \(seq_{l}=\{x_{<t},x_{t}^{j}\}\) for text generation. In the end, among the generated outputs of token sequences (\(\{seq_{l}\}_{l=1}^{k}\)), the final outcome is determined by the maxTRR values as  
\[out=\min\{maxTRR(seq_{l})\}_{l=1}^{k}. \tag{11}\]  
In Equations (9)-(11), \"\(\max\)\" and \"\(\min\)\" denote the operation of maximization and minimization, respectively. Since the parameter \(\rho\) dictates the quality of token generation, its value should be carefully defined. The metric maxTRR is defined in Equation (12), and it quantifies the token diversity in a candidate output of text summarization. In any case, when the token with \(p_{max}\) is selected, DCS is degenerated into the CS decoding strategy.
## 1 Introduction
In order to effectively detect and classify the types of PQDs, this study developed an identification method, as based on ENN with [PERSON] and [PERSON]'s theorem. The proposed ENN [23; 24], which has been widely applied to the field of fault diagnosis [25; 26; 27], combines the extension theory with an artificial neural network. The ENN proposed in this paper, replaces the Euclidean distance with the extension distance to calculate the geometric distance between the test samples and clusters for rapid and proper classification and identification. To prove the feasibility of the proposed method, the transient signals of five common power quality disturbances were analyzed, including power interruptions [28; 29], voltage surges [30], voltage sag [31], voltage flicker [32], and power harmonics [33]. First, the power transient signals were processed by DWT [34; 35] and [PERSON]'s theorem [36; 37] to extract the meaningful features and reduce the large data. The matter-element model of the power quality disturbances was established with the aim of generating the wavelet energy coefficients of each order as eigenvalues. The correlation degree of each eigenvalue of power transient signal to be measured was then calculated using the extension distance function through ENN, in order to achieve the highest accurate identification and shorten the computation time. The proposed method was then verified by numerical simulations and compared with different algorithms, including the back-propagation network (BPN), probabilistic neural network (PNN), learning vector quantization network (LVQ), and extension method in order to illustrate its advantages and effectiveness for potential engineering applications. In addition, this method can be realized by the system-on-chip (SoC) in the foreseeable future, due to its simple structure good expandability, and high accuracy. The remainder of this paper is organized as follows. Section 2 presents the proposed methods including DWT, Parseval's theorem, and ENN. Section 3 discusses simulation results obtained from the proposed method and compares with the results from other algorithms. Finally, Section 4 provides concluding remarks.
# From Modeling to Failure Prognosis of Permanent Magnet Synchronous Machine
###### Abstract  
Due to the accelerating pace of environmental concerns and fear of the depletion of conventional sources of energy, researchers are working on finding renewable energy sources of power for different axes of life. The transportation sector has intervened in this field and introduced hybrid electric vehicles. Many complaints have been mentioned concerning fault detection and identification in the vehicle to ensure its safety, reliability and availability. Diagnosis has not been able to overcome all these concerns, and research has shifted toward prognosis, where the manufacturing sector is urged to integrate fault prognosis in the vehicle's electrical powertrain. In this article, prognosis of the vehicle's electrical machine is treated using a hidden Markov model after modeling the electrical machine using the finite element method. Permanent magnet machines are preferable in this application. The modeling of the machine is a combination of the electromagnetic, thermal and vibration finite element models. The considered faults are demagnetization, turn-to-turn short circuit and eccentricity. A strategy for the calculation of the remaining useful life (RUL) is suggested when a turn-to-turn short circuit fault occurs.  
hybrid electric vehicle; permanent magnet machine; finite element model; prognosis; hidden Markov model; remaining useful life
# OTFS-Based Handover Triggering in UAV Networks
## 7 Numerical Analysis
### Studying the Effects of \(n^{\prime}\) and \(P_{th}\)  
In this part of the numerical analysis, we investigate the performances of the schemes involved in the comparisons against the critical parameters \(n^{\prime}\) and \(P_{th}\) while considering perfect DD channel estimation, i.e., \(MSE_{\mathcal{H}}=0\) in (38). Also, the HST speed is set to 500 Km/Hr, and six UAVs are used. As \(n^{\prime}\) decreases, i.e., decreasing the number of coherence periods, the DD channel will be highly time-varying, and vice versa. Similarly, as \(P_{th}\) decreases high number of handovers are expected, and vice versa.
# Main Issues in Big Data Security
## 3 Systematic Mapping Study
### Main Topics Found
#### 3.5.2 Data Privacy  
Data privacy is probably the topic about which ordinary people are most concerned, but it should also be one of the greatest concerns for the organisations that use Big Data techniques. A Big Data system usually contains an enormous amount of personal information that organisations use in order to obtain a benefit from that data. However, we should ask ourselves where the limit regarding the use of that information is.  
Organisations should not have total freedom to use that information without our knowledge, although they also need to gain some benefit from the use of that data. Several techniques and mechanisms with which to protect the privacy of the data, and also allow companies to still make a profit from it have therefore been developed, and attempt to solve this problem in various different ways. Figure 4 contains a graphic that shows the main ways in which this problem is dealt with, and the quantity of papers found for each specific topic.  
Cryptography  
The most frequently employed solution as regards securing data privacy in a Big Data system is cryptography. Cryptography has been used to protect data for a considerable amount of time. This tendency continues in the case of Big Data, but it has a few inherent characteristics that make the direct application of traditional cryptography techniques impossible.  
One example of the use of cryptography can be found in [37], in which the authors propose a bitmap encryption scheme that guarantees users' privacy. Other authors' research is focused on how to process data that is already encrypted. One paper, for example, explains a technique with which to analyse and programme transformations with _PigLatin_ in the case of encrypted data [38].
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 6 Applications of Machine Learning in FANET
### Network Security  
The security of each node is paramount to ensure the security of the network. As a result, as the number of nodes in any wireless network increases, it becomes more vulnerable to jamming, and network breaches, among many other threats. Moreover, it becomes more challenging to monitor and protect each node individually due to the centralized nature of the operation. Therefore, network security is another important domain in FANETs where AI techniques, specifically distributed and/or FL, can be employed to protect the network.  
The FL has shown great potential in FANETs to detect and combat jamming by demonstrating its capability as the number of nodes increases. For instance, the authors in [156] proposed an FL architecture to detect jamming with a prioritization technique, laying the foundation for FL in jamming detection. In addition, they developed an adaptive anti-jamming methodology that is based on federated Q-learning in [157]. Another work also proposed the use of federated Q-learning networks to achieve a frequency hopping strategy to combat periodic jamming [158].  
In addition, FL is capable of incorporating many algorithms that can better secure the communication channel between the different nodes and the cloud [159], which can be deployed in FANETs to combat eavesdropping attacks, while AI-enabled solutions for security purposes have demonstrated potential in wireless systems, there are still few solutions that are suitable for networks such as FANETs, thus they require more investigation.
## 4 Simulation Experiments
### Issue 2: Unknown Measurement Uncertainty  
The second issue considers the same setting as in Section 4.2 except the residual variance \(\sigma_{\text{c}}^{2}\) is assumed to be unknown. We assign a Beta prior to the parameter \(\sigma_{\text{c}}\), i.e., \(\sigma_{\text{c}}\sim\mathcal{B}(a_{0},b_{0})\). We assume that prior information about the measurement uncertainty \(\sigma_{\text{c}}\) is available from repeatability testing, and we specify the hyper-parameters of the Beta prior for \(\sigma_{\text{c}}\) accordingly to reflect prior knowledge of \(\sigma_{\text{c}}\approx 0.5^{\circ}\text{C}\pm 0.1^{\circ}\text{C}\), i.e., by specifying \(a_{0}=12,b_{0}=12\); cf. [67]. The resulting (non-normalized) joint posterior distribution of the unknown parameters \(\mathbf{\Theta}_{2}=(\mathbf{R}_{\text{th}},\mathbf{\vartheta}_{R_{\text{th}}},\sigma _{\text{c}})\) is extended to  
\[p(\mathbf{R}_{\text{th}},\mathbf{\vartheta}_{R_{\text{th}}},\sigma_{\text{c}}| \mathbf{y})\propto\prod_{i=1}^{n}p(y_{i}|R_{\text{th}_{i}},\mathbf{z}_{i}, \sigma_{\text{c}})\cdot\left(\prod_{i=1}^{n}p(R_{\text{th}_{i}}|\mu_{R_{\text{ th}}},\sigma_{R_{\text{th}}})\right)p(\mu_{R_{\text{th}}})\cdot p(\sigma_{R_{ \text{th}}})\cdot p(\sigma_{\text{c}}) \tag{17}\]  
with \(\mathbf{z}_{i}=(I_{f},V_{f},\eta_{h})\). Modifications of the MCMC sampling scheme are straightforward and include an additional block to sample the parameter \(\sigma_{\text{c}}\).