esa-sceva/satcom-chunk-collection huggingface dataset:
# Heterogeneous Graph-Convolution-Network-Based Short-Text Classification
## 3 Model Description
### The Feature Learning Module  
In most existing studies, researchers only obtain information from the corpus. However, the characteristics of the short text make it challenging to obtain sufficient semantic information from short texts. This paper fully considers the external entity information and establishes a heterogeneous graph \(G=(v,\varepsilon)\), in which \(v\) is the node set and \(\varepsilon\) is the edge set. \(v=D\cup E\cup W\) denotes that \(it\) consists of three parts: the document node set \(D=\{d_{1},d_{2},\ldots,d_{n}\}\), the entity node set \(E=\{e_{1},e_{2},\ldots,e_{n}\}\), and the word node set \(W=\{w_{1},w_{2},\ldots,w_{n}\}\). \(\varepsilon\) indicates the relationship between nodes.  
The edge between a document node and a word node is determined by the frequency of the word in the document. That is, term frequency-inverse document frequency (TF-IDF) is used as the weight of the edge between a document node and a word node. The value of the edge between two word nodes is determined by the word co-occurrence information of the whole corpus, which uses the point mutual information (PMI) between words [26] to evaluate the degree of correlation between words as the weight of the edge between the two word nodes. The value of the edge between the document nodes and the entity nodes is determined by the confidence of the words in the document mapped to Wikipedia entities. For node \(i\) and node \(j\) in \(G\), the adjacency matrix \(A_{ij}\) is defined as follows:  
\[A_{ij}=\left\{\begin{array}{ccc}TF-IDF_{ij}&\text{ \  
the semantic correlation between two words. The value of PMI is positively correlated with the semantic correlation of words in the corpus.  
The feature matrix of the document-entity-word node is defined as \(X\in R^{c\times d}\). For the one-layer GNN network, the features of the K-dimensional document-entity-word node can be expressed as follows:  
\[L^{(1)}\in\rho(\widetilde{A}XW_{0}) \tag{3}\]  
where \(\widetilde{A}=D^{-1/2}AD^{1/2}\) is a regularized and normalized adjacency matrix, \(A\) is the adjacency matrix of the text heterogeneous graph, \(D\) is the degree matrix, \(W_{0}\) is the weight matrix, and \(\rho(\cdot)\) is the activation function. The multi-layer GNN aggregates the information of neighbor nodes of different orders, and the output characteristics can be expressed as follows:  
\[L^{(j+1)}\in\rho(\widetilde{A}L^{(j)}W_{j}) \tag{4}\]  
where \(j\) is the number of layers and \(L^{(0)}=X\). In this study, we use the two-layer graph convolution neural network to learn the node features and obtain the feature representation of document, entity, and word nodes.
# Coordination of Macro Base Stations for 5G Network with User Clustering
## 2 Energy Management Model of 5G Macro Base Station Network
### Optimization Step 1: Energy Management of the 5G Communication Equipment
Maximum transmit power constraints of BSs: \[P_{tr}^{m,t}=\sum\limits_{u_{t}\in\mathbf{N}_{m}}a_{t}^{m,u_{t}}P_{tr,t}^{m,u_{t}}\] (15) \[P_{tr}^{m,t}\leq P_{tr}^{\max,m}\] (16) where \(P_{tr}^{m,t}\) denotes the total transmit power of AAU in BS \(m\); and \(P_{tr}^{max,m}\) denotes the maximum transmit power of BS \(m\). The maximum data transmission rate constraints (14) and the maximum transmit power constraints of BSs (16) ensure that all users' QoS requirement can be satisfied. Because Equations (8) and (9) are nonlinear constraints, the big-M method is applied to linearize them, as shown by Equations (17)-(24). \[P_{AAU}^{m,t}=P_{AAU,Ac}^{m,t}+P_{AAU,Sl}^{m,t}\] (17) \[P_{AAU,Ac}^{m,t}\geq-M(1-U_{bs}^{m,t})+\frac{1}{\delta_{PA}^{m}}P_{tr}^{m,t}+ P_{AAU,Ac}^{m,t}\] (18) \[P_{AAU,Sl}^{m,t}\geq-M\cdot U_{bs}^{m,t}+P_{AAU,Sl}^{m,c}\] (19) \[a_{t}^{m,u_{t}}\leq U_{bs}^{m,t},m\in\mathbf{N}_{u_{t}}\]0 (20) \[a_{t}^{m,u_{t}}\leq U_{bs}^{m,t},m\in\mathbf{N}_{u_{t}}\]1 (21) \[a_{t}^{m,u_{t}}\leq U_{bs}^{m,t},m\in\mathbf{N}_{u_{t}}\]2 (22) \[a_{t}^{m,u_{t}}\leq U_{bs}^{m,t},m\in\mathbf{N}_{u_{t}}\]3 (23) \[a_{t}^{m,u_{t}}\leq U_{bs}^{m,t},m\in\mathbf{N}_{u_{t}}\]4 (24)  
where \(P_{AAU,Ac}^{m,t}\) and \(P_{AAU,Sl}^{m,t}\) are the powers of AAU \(m\) when BS \(m\) is in active and sleep mode, respectively; \(P_{BBU,Ac}^{m,t}\) and \(P_{BBU,Sl}^{m,t}\) are the powers of BBU \(m\) when BS \(m\) is in active and sleep mode, respectively; and \(M\) is a sufficiently large number. In summary, the optimization of the energy management of communications equipment in the 5G macro Bs network is shown as (\(\textbf{P}_{1}^{t}\)). \[a_{t}^{m,u_{t}}\leq U_{bs}^{m,t},m\in\mathbf{N}_{u_{t}}\]5 \[a_{t}^{m,u_{t}}\leq U_{bs}^{m,t},m\in\mathbf{N}_{u_{t}}\]6  
It can be observed that (\(\textbf{P}_{1}^{t}\)) is a single-step optimization problem. Solving (\(P_{1}^{t}\)) for each time step \(t\in\textbf{T}\), we obtain the optimal on/off state \(\left\{\hat{U}_{bs}^{m,t}\right\}\), user association method \(\left\{\hat{a}_{t}^{m,m^{\prime}}\right\}\), and power profile \(\left\{P_{com}^{m,t^{\prime}}\right\}\) of the communications equipment in each 5G BS.
# PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network
## 3 Wavelet Transform
These interference sources will introduce additional noise, affecting the accuracy of the signal and the analysis of the data. In the experiment, due to the inability to achieve complete darkness, the photomultiplier tube will still output small electrical signals when not exposed to pulsed xenon lamps. After passing through the operational amplifier circuit, it will output static noise as shown in Figure 5. After calculation, the standard deviation of static noise is 4404.932, and the average absolute deviation is 3533.375. This will have a significant impact on the accuracy of subsequent experiments. Six wavelet algorithms were selected for denoising, and the results are shown in Table 1 below. Finally, the sym8 wavelet was selected for 5-layer decomposition. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Wavelet** & **Decomposition** & **Evaluation Index** & **Hard Threshold** & **Soft Threshold** & **Improvement** \\ \hline db10 & 4 & SNR & 37.68 & 39.58 & 40.29 \\ \cline{3-6}  & & Mean standard deviation & 3451 & 3102 & 2993 \\ \hline sym8 & 5 & SNR & 36.65 & 42.68 & 44.26 \\ \cline{3-6}  & & Mean standard deviation & 2864 & 2685 & 2697 \\ \hline coif5 & 4 & SNR & 38.61 & 39.31 & 39.98 \\ \cline{3-6}  & & Mean standard deviation & 3504 & 3220 & 3302 \\ \hline bior5.5 & 5 & SNR & 39.41 & 40.24 & 41.11 \\ \cline{3-6}  & & Mean standard deviation & 2845 & 2811 & 2774 \\ \hline ribo6.8 & 5 & SNR & 34.43 & 36.68 & 37.40 \\ \cline{3-6}  & & Mean standard deviation & 3102 & 2992 & 2864 \\ \hline fk22 & 5 & SNR & 38.86 & 40.24 & 40.82 \\ \cline{3-6}  & & Mean standard deviation & 3305 & 3105 & 3102 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of wavelet algorithms. Figure 5: Static noise of photomultiplier tubes. The denoising effect and signal fidelity can be determined by performing fast Fourier transform (FFT) analysis on the processed signal. The FFT image after denoising is shown in Figure 8. The results indicate that noise reduction processing effectively reduces noise interference without causing serious signal distortion. The advantages of wavelet transform in time-frequency localization and frequency band segmentation can effectively distinguish between effective signals and noisy signals. Its excellent zoom performance enables the method based on multi-resolution analysis and wavelet transform to have fine scale division ability, which can effectively improve the detection accuracy [30] of this experiment. Embedding the above MATLAB code in Quartus and Keil can achieve real-time data processing without the need to import it into a PC for analysis, thus simplifying the experimental process.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 3 Proposed Method
### Overview  
To address the problem of 6D pose estimation from RGB-D images, we propose RSCS6D, a keypoint extraction-based method that includes the RSCS keypoint extraction algorithm, as shown in Figure 2. Specifically, we first obtain the mask of the RGB image using a semantic segmentation algorithm (PIDNet in this work) and combine it with the depth map to acquire the point cloud of the target object.  
The RSCS keypoint extraction algorithm extracts keypoints from the point cloud in two steps:  
Figure 2: The pipeline of RSCS6D.  
Edge Keypoint Extraction: Convert the RGB image to grayscale and use the Sobel edge detection operator to identify 2D edge keypoints. These are then combined with the depth map to derive the point cloud edge keypoints.
2. Internal Keypoint Extraction: Compute the curvature matrix from the depth map using PCA-based plane fitting and apply the Sobel operator to the curvature matrix to identify keypoints with significant curvature changes.  
The union of these two sets of keypoints forms the final keypoint cloud. These keypoints are fed into a CNN-based pose estimation network to predict the translation of the target object. Finally, a 3D tight bounding box is generated from the keypoint cloud, and the Kabsch algorithm is used to predict the rotation of the target object. This approach maintains interpretability while achieving higher accuracy and faster convergence compared to traditional methods.
## 6 Experiments and Results
### Machine Learning Model Comparison  
We experimented with several machine learning models, including logistic regression, support vector machines (SVM), and K-means clustering, to evaluate their classification performance on the dataset and, in particular, their ability to accurately identify and classify portscan attacks (normal or malicious, a binary classification). We compared the performance of these models before and after applying graph theory representation. SVM is a well-known supervised machine learning algorithm used for classification tasks, which identifies the optimal hyperplane separating different classes. Similarly, logistic regression is a statistical model used in machine learning for binary classification problems (which is the case in our study), predicting the probability of a certain class, whereas K-means is an unsupervised machine learning algorithm used for clustering, partitioning a dataset into 'K' different clusters based on feature similarity, where \(K=2\) in our case, given the two classes.  
The machine learning models were implemented using standard parameters commonly used within the community. For logistic regression, we used L2 regularization with a default C value of 1.0. For SVM, we employed a radial basis function (RBF) kernel with a gamma value of'scale' and a C value of 1.0. Lastly, the K-means clustering algorithm was applied with the number of clusters set to the number of unique classes in the dataset, employing the K-means++ initialization method.
# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 3 Methods
### Evaluation Metrics  
The evaluation metrics used in this paper include PSNR-\(\mu\), SSIM-\(\mu\), PSNR-L, SSIM-L, and HDR-VDP-2 [51]. PSNR-\(\mu\), SSIM-\(\mu\), PSNR-L, and SSIM-L can show the quality of the HDR image reconstructed after alignment of multi-exposure LDR images. Specifically, PSNR-\(\mu\) and SSIM-\(\mu\) are used to evaluate the quality of the HDR images after tone mapping using \(\mu\)-law. PSNR-L and SSIM-L are used to evaluate the quality of the HDR images in the linear space. HDR-VDP-2 [51] is used to evaluate the difference between the HDR image reconstructed by the human eye and the ground truth image. The formula of PSNR is expressed as follows:  
\[PSNR=10\text{log}_{10}(\frac{MAX_{I}^{2}}{\text{MSE}}) \tag{2}\]  
The \(MAX_{I}\) is the maximum value of image pixel coloration. The \(MSE\) is the mean squared error that is expressed as follows:  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\left\|I(i,j)-K(i,j)\right\|^{2} \tag{3}\]  
where \(m\) and \(n\) represent the height and width of the image, respectively. \(I\) and \(K\) represent the reconstructed HDR image and the ground truth HDR image, respectively. The SSIM is expressed as follows:  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}{}^{2}+\mu_{y}{}^{2}+c_{1}\right)\left(\sigma_{x}{}^{2} +\sigma_{y}{}^{2}+c_{2}\right)} \tag{4}\]  
where \(x\) and \(y\) represent the reconstructed HDR image and the ground truth HDR image, \(\mu_{x}\) and \(\mu_{y}\) represent the mean values of \(x\) and \(y\), \(\sigma_{x}{}^{2}\) and \(\sigma_{y}{}^{2}\) represent the variance of \(x\) and \(y\), and \(\sigma_{xy}\) represent the covariance of \(x\) and \(y\), respectively.
## 3 Hierarchical Attribute-Based Encryption
\begin{tabular}{l l} \hline \hline HABE Algorithm & \\ \hline Setup (\(K\)) \(\geq\)\(P_{K}\), \(M_{K0}\) & // security parameter \(K\) as input, private key \(P_{K}\) and central authority’s master key \(M_{K0}\) as output \\ \hline Delegate (\(P_{K}\), \(M_{Kl}\), \(S_{a}\)) \(\geq\)\(M_{Kl+1}\) & // private key \(P_{K}\), domain authority’s master key \(M_{Kl}\) for a set of \\ Delegate (\(P_{K}\), \(M_{Kl}\), \(S_{a}\)) \(\geq\)\(M_{Kl+1}\) & attributes \(S_{a}\), a set of attributes \(S_{a}\) where \(S_{a}\in S_{a}^{\prime}\), master key \(M_{Kl+1}\) of \\  & domain authority as output \\ \hline KeyGen (\(P_{K}\), \(M_{Kl}\), \(S_{a}\)) \(\geq\)\(S_{K}\) & // private key \(P_{K}\), domain authority’s master key \(M_{Kl}\) and \(S_{a}\) set of \\  & attributes as input, attribute secret key \(S_{K}\) as output. \\ \hline Encrypt (\(P_{K}\), \(m\), \(T\)) \(\geq\)\(C_{T}\) & // private key \(P_{K}\), a message \(m\) and access policy tree \(T\) as input, cipher text \\  & \(C_{T}\) as the outcome. \\ \hline Decrypt (\(T\), \(C_{T}\), \(S_{K}\)) \(\geq\)\(m\), & // cipher text \(C_{T}\), access tree policy \(T\), attribute secret key \(S_{K}\) as input, \\ if \(S_{a}\)\(\in\)\(T\)\(\bot\)_otherwise_ & a message \(m\) as output \\ \hline \hline \end{tabular}   
Though various domain owners can manage the identical attribute, it is complicated to execute in practice. Furthermore, HABE cannot proficiently aid compound attributes and it may degrade the support for multivalue tasks. Therefore, to overcome these limitations, a new BH-WABE approach is proposed in this paper.
## 4 Different Phases of Metabolic Engineering  
There is no particular distinct classification as such, but various phases can be defined depending on altered character and the consequential biochemical result. Broadly it can be classified into five stages (Figure 2). The initial stage of metabolic engineering deals with the innate pathways in its regular host. The aim here is to enhance the production of the target metabolite only by overexpression it and deleting the genes which hinder its production. The second phase deals with expressing a studied pathway into another relevant host strain. This has a very small effect on the total metabolic profile of the organism, but these changes will eventually enhance the targeted product by accelerating the kinetics or enhanced thermodynamic outline or usage of a highly viable co-factor, etc. For instance, in _E. coli_, acetoacetyl-CoA acetyltransferase (AtoB) was placed in the place of _Clostidium_ acetoacetyl-CoA thiolase (Thl), and also a group of genes like ldhA, adhE, and frd were knocked out, thus creating a NADH flux and consequently enhancing the n-butanol production [58; 77].  
These small changes, however, will not affect the main frame structure of the pathway, but will enhance the target metabolite production. The third approach is contrasting to the above approaches, where attempts are made to further construct the existing pathways. This strategy helps in creating new frontiers of metabolic engineering. With the help of this approach, a pathway is extended with a combination of enzymes which usually do not work together in nature. This integration is performed to achieve a specific metabolic work with an enhanced efficacy or sometimes a new functionality. Most of these approaches are not experimentally proven; they are merely software generated predictions. For instance, a pathway has been designed for total carbon conservation using non-oxidative glycolysis [78], the synthesis of propane from glucose [79; 80], etc. A complete pathway analysis is very much required for identifying the most favourable pathways out of various routes. These comprehensive analyses efficiently relate the candidates  
Figure 2: The five stages of metabolic engineering (adapted from [77]).  
dynamic feasibility, toxicity, physiochemical properties, kinetic efficiency, hydrophobicity of the generated by-products, etc., with their metabolism [81]. The major advantage of this approach is that the pathways have novel extensions without evolutionary changes. To date, very few studies have applied these synthetic pathways with new catalytic changes. Two of these studies involve the artificial route for carbon dioxide fixation [82] and the synthetic pathway for the production of 1,4-butanediol [83]. The above-stated instances establish the potential of metabolic engineering to create new products and synthetic pathways. This strategy, if further applied, takes paces towards a fifth approach, a comprehensive \"synthetic metabolism\" that includes designed pathways with required products. Here, artificial pathways comprising designed enzymes are established to create a synthetic metabolism altogether.
## Priority-Based Protocol Extension Method
### Frame Type Extension  
The data frame structure and the command frame structure of GB/T 26865.2-2011 are shown in Figure 3. The data frame sent by the PMU contains only one data section DATA, which stores its own measured data, and the data frame sent by the PDC contains the number of DATA segments equal to the number of PMUs connected to it. The DATA segment contains bit-mapped flags STAT, phasor estimates PHASORS, frequency offset FREQ, frequency change rate DFREQ, ANALOG, and DIGITAL. It does not state whether there is a change in digital quantities or whether the analog quantity requires time synchronization. All data are sent together. The command frame is sent by the main station, PDC, and PMU. It only contains one DATA segment and stores the command word CMD. CMD is 2 bytes in length, where Bits 12-4 are reserved. It refers to DLT 1402-2015 \"Technical specification for application of synchronous phasor in power plant and substation\" to supplement the reserved 9 bits; define Bits 05-04 as 01 points and 10 points; define Bits 07-06 as 01 selection, 10 Execute, and 11 cancel; and define the last remaining Bits 12\(\sim\)08 as the information object address. The 5-bit binary number represents up to 31 digital quantities. For a single uMPMU, 31 digital quantities are sufficient, but one distribution network PDC can connect up to more than 20 uMPMUs. In remote control, the PDC should match each remote control number of each uMPMU to the corresponding unique remote control number of the main station, and 31 is far from meeting the mapping requirements, so this extension is not applicable on the uMPMU.  
The newly added frame type is named the application extension frame, and the frame structure is shown in Figure 4. The application extension frame is transmitted through the command pipeline; the frame synchronization word SYNC value is 0 xAA53; and the DATA segment content includes the type identifier TYPE, the variable structure qualifier VSQ, transmission reason RTNR, the public address (PMU address), and the information object address INF. TYPE defines the structure, type, and format of INF. VSQ defines the number of information objects contained in the INF, as well as the addressing method. INF contains the information object address, the information element, and the CP56 Time2a time stamp. After the application extension frame is used, the uMPMU can choose to transmit different types of data through the data frame or application extension frame, and also solve the mapping problem between the uMPMU remote control number and the remote control number of the main station, and the remote control function can be added to the uMPMU.
## 4 Results and Discussion
### Discussion  
This study compared the identification results between the SVM and the traditional method of manual identification (Figures 7-9). As shown in Figures 7-9, the SVM classification model successfully identified most of the subfacies. However, due to the paucity of logging data in the study area, its accuracy may not be very high. The author believes that with the increase of drilling quantity in the later exploration stage, the accuracy will increase. From this study, we can see that the SVM algorithm has unique advantages in small- and medium-sized sample statistical processing and is suitable for classification tasks with unbalanced numbers of samples. However, it should be noted that to realize the multi-classification task, the method of \"one against one\" was used. Any two sample sequences were combined to construct \(\text{C}_{N}^{2}\) vector machines, the \"vote\" was adopted to classify and perform the identification of N types of depositional subfacies in the intervals [11]. It means that though this method is good, when there are N categories, the number of models is \(\frac{N(N-1)}{2}\). Therefore, when using an SVM to realize multi-classification tasks, there should not be too many classification objectives. This is due to the fact that too many classification objectives will require a huge amount of computation and reduce the final classification accuracy.  
Figure 6: The confusion matrix of the support vector machine (SVM) classification model.  
Figure 7: Comparison of the subfacies identification results by the SVM and geologists in well ARY14.