esa-sceva/satcom-chunk-collection huggingface dataset:
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 2 Dynamic Weighted Protein Network Construction
### Construction of the Adjacency Matrix
#### 2.1.2 Protein Subnetwork Construction  
Let \(O=\{O_{1},O_{2},\ldots,O_{n}\}\) represent the set of n proteins with the same active period. We construct protein subnetworks by analyzing the interactions among proteins within the same active period. The key is to determine whether there is a connection relationship between different proteins within the same time point. To this end, we assess the interactions between different proteins from two perspectives: the number of connection differences and the number of common neighbors, proposing the connection probability (CP). If the CP exceeds a certain threshold, it is assumed that a connection exists between them. Specifically, if there are more common neighbors between two proteins, it indicates a higher interaction probability. The number of connection differences, which is the ratio of the number of directly connected edges between two proteins to the minimum value of the degree of the node, can also indirectly measure the interaction relationship between proteins. The definition of the CP between protein \(O_{u}\) and protein \(O_{v}\) is as follows:  
\[CS(O_{u},O_{v})=\frac{1}{1+\mathrm{e}^{-\left(\frac{|Nb(O_{u})|+|Nb(O_{v})|}{| Nb(O_{u})|+|Nb(O_{v})|}+\frac{d_{v}}{\min(nd_{u},nd_{v})}\right)}}, \tag{5}\]  
where \(Nb(O_{u})\) is the set of neighboring nodes of protein \(O_{u}\), \(dc(uv)\) is the number of edges directly connected between protein \(O_{u}\) and protein \(O_{v}\), \(nd_{u}\) is the degree of protein \(O_{u}\). The \(\frac{1}{1+\mathrm{e}^{-(\cdot)}}\) is the sigmoid function. For a given protein, at time point \(i\), if \(CS(O_{u},O_{v})\geq\delta\), it is considered that there is an interaction between protein \(O_{u}\) and protein \(O_{v}\), and an edge is added between them. The threshold \(\delta\) is selected based on prior knowledge as 0.9.  
A set of protein subnetworks at different time points is obtained at the end of the calculation. The adjacency matrices \(A=\{A_{1},A_{2},\ldots,A_{n}\}\) corresponding to the multiple protein subnetworks within this collection serve as inputs for the subsequent VGAE model. All the adjacency matrices required for multi-level embedding have been constructed. In the following, for the convenience of model description, we use \(A\) to replace the adjacency matrix of the protein subnetwork that needs to be input each time, namely \(A_{1},A_{2},\ldots,A_{n}\). The feature, attribute, and low-dimensional embedding matrices are represented similarly.
# A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method
## 2 Model of an Inverter  
The main circuit of a full bridge AC/DC inverter studied in this work is shown in Figure 1. In this system, it is assumed that the system is balanced, and the DC voltage of the inverter is stable. The general mathematical model of this inverter can be obtained as [22]  
\[\left\{\begin{array}{l}L\frac{d_{ie}}{dt}+Ri_{a}+e_{a}=U_{DC}(S_{a}-\frac{S_ {a}+S_{b}+S_{c}}{3})\\ L\frac{d_{ie}}{dt}+Ri_{b}+e_{b}=U_{DC}(S_{b}-\frac{S_{a}+S_{b}+S_{c}}{3})\\ L\frac{d_{ie}}{dt}+Ri_{c}+e_{c}=U_{DC}(S_{c}-\frac{S_{a}+S_{b}+S_{c}}{3})\\ \end{array}\right. \tag{1}\]  
where \(L\) and \(R\) are the filter inductance and resistance respectively. The \(S_{a}\), \(S_{b}\) and \(S_{c}\) are the switching state variables of the switching devices. The \(e_{a}\), \(e_{b}\), \(e_{c}\) are the phase voltages of the grid. This three-phase model can be also presented in a two-phase stationary coordinate system (\(\alpha\beta\)coordinate system). The transformation expression of the variables between these two coordinate systems is shown as following:\[\left[\begin{array}{c}\alpha\\ \beta\end{array}\right]=\frac{2}{3}\left[\begin{array}{ccc}1&-0.5&-0.5\\ 0&\frac{\sqrt{3}}{2}&-\frac{\sqrt{5}}{2}\end{array}\right]\left[\begin{array}[ ]{c}a\\ b\\ c\end{array}\right] \tag{2}\]  
By transforming all the variables to the two-phase stationary coordinate system, the mathematical model can be expressed as  
\[\left\{\begin{array}{l}L\frac{di_{i}}{dt}+Ri_{a}+e_{\alpha}=\frac{2}{3}U_{ DC}S_{\alpha}\\ L\frac{di_{g}}{dt}+Ri_{\beta}+e_{\beta}=\frac{2}{3}U_{DC}S_{\beta}\end{array}\right. \tag{3}\]  
where \(S_{\alpha}\) and \(S_{\beta}\) are the switching functions in two-phase stationary coordinate. In this modeling expression, the switching variables, current and voltage are still of AC system. The model can be transformed to the two-phase rotating coordinate system. The transformation expression between these two systems is defined as  
\[\left[\begin{array}{c}d\\ q\end{array}\right]=\left[\begin{array}{ccc}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{array}\right]\left[\begin{array}{c}\alpha\\ \beta\end{array}\right] \tag{4}\]  
By taking (4) into (3), the mathematical model of this inverter in two-phase rotating coordinate system is shown as followings:  
\[\left\{\begin{array}{l}L\frac{di_{i}}{dt}+R_{Id}=\omega Li_{q}-e_{d}+\frac{2 }{3}U_{DC}S_{d}\\ L\frac{di_{q}}{dt}+Ri_{q}=-\omega Li_{d}-e_{q}+\frac{2}{3}U_{DC}S_{q}\end{array}\right. \tag{5}\]  
where \(S_{d}\) and \(S_{q}\) are the switching functions in two-phase stationary coordinate. In this expression, all of the variables (e.g., current, voltage and switching states) are of DC system. The orientation of these systems are shown in Figure 2.  
Figure 1: Structure of a three-phase inverter.  
Figure 2: The orientation of \(abc\) and \(dq\) coordinate systems.
## 2 Models and Methods
### Numerical Model
#### 2.2.2 Discretization  
The influences of computational domain discretization were investigated by [PERSON] [17] and [PERSON] et al. [18]. Comparing three types of grid elements, namely tetrahedral, hexahedral, and polyhedral meshes, a hexahedral mesh was found to be the least numerical diffusive. Furthermore, using this type of element helps with shorter convergence time. For this reason the computational domain was discretized using a hexahedron mesh that was manually implemented using ANSYS ICEM software (Figure 2). The number of elements depending on air flow velocity ranged from 0.5 to 5.1 million. The boundary layer consisted of 15 elements in height, where the height of the first element as a nondimentional coefficient y+ for 97% of elements was below 1 (17).  
\[y^{+}=\frac{\rho_{p}u_{\tau}\Delta s}{\mu_{p}} \tag{17}\]  
where:  
\[u_{\tau}=\sqrt{\frac{\tau_{wall}}{\rho_{p}}},\quad\tau_{wall}=\frac{C_{f}\rho_ {p}u_{\infty}^{2}}{2},\quad C_{f}=\frac{0.026}{Re^{1/7}},\quad Re=\frac{\rho_{ p}u_{\infty}L}{\mu_{p}}\]  
\(u_{\tau}\)--friction velocity  
\(\Delta s\)--wall spacing  
\(\tau_{wall}\)--wall shear stress  
\(C_{f}\)--coefficient  
\(Re\)--Reynolds number.  
The cell Courant number calculated from Equation (18) for 99.9997% of elements was below 1. The minimum orthogonal quality was 0.323. Additionally, between the boundary layer and the domain a transitional layer was applied. The transitional layer consisted of 15 elements in height. The growth ratio between the elements in both layers was 1.15, while the aspect ratio did not exceed 60.  
\[C=\frac{u_{\infty}\Delta t}{\Delta x}[-] \tag{18}\]  
where:  
\(u_{\infty}\)--free stream velocity  
\(\Delta t\)--time step  
\(\Delta x\)--cell size.  
Figure 2: 3D hexahedral mesh around the [PERSON] body. Grid for 5.1 million elements.
# System for Evaluation and Compensation of Leg Length Discrepancy for Human Body Balancing
## 4 Human Body Balancing Algorithm
### Simulation Results
#### 4.1.1 Scenario 1: Human Body with LLD
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Iteration** & \(CoM_{x}\) & \(z_{L}\) & \(z_{R}\) & \(\Delta z\) & \(m_{L}\) & \(m_{R}\) & LASIS & RASIS \\  & [mm] & [mm] & [mm] & [mm] & [kg] & [kg] & [mm] & [mm] \\ \hline \(k=0\) & \(-19.35\) & 424.40 & 424.40 & 0 & 43.22 & 31.78 & 869.8 & 899.8 \\ \(k=1\) & \(-7.06\) & 443.75 & 424.40 & 19.35 & 39.53 & 35.47 & 889.1 & 899.8 \\ \(k=2\) & \(-2.42\) & 450.81 & 424.40 & 26.41 & 38.19 & 36.81 & 896.1 & 899.8 \\ \(k=3\) & \(-0.81\) & 453.23 & 424.40 & 28.83 & 37.73 & 37.27 & 898.5 & 899.8 \\ \(k=4\) & \(-0.27\) & 454.04 & 424.40 & 29.64 & 37.58 & 37.42 & 899.4 & 899.8 \\ \(k=5\) & \(-0.09\) & 454.31 & 424.40 & 29.91 & 37.53 & 37.47 & 899.7 & 899.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Scenario 1: Figure 11 response values at the end each iteration of the HBBA. Figure 10: Scenario 1: (**a**) Initial state of the human body with LLD (algorithm iteration \(k=0\)); (**b**) algorithm iteration \(k=1\); (**c**) algorithm iteration \(k=2\); (**d**) algorithm iteration \(k=3\); (**e**) algorithm iteration \(k=4\); and (**f**) the human body is balanced (algorithm iteration \(k=5\)). LLD evaluation and make the real-world subject feel restless. It should be mentioned that. if the human body model has only LLD, the HBBA will reach the same results as the LLD measuring method standing on blocks [8], but much more quickly and completely automatically.
## 2 Preliminaries
### Derivation of the Proposed sSMC-FCM Algorithm  
In this sub-section, we show the derivation of the proposed sSMC-FCM algorithm. Each membership \(U_{ik}\) can receive different fuzzification coefficients \(m_{ik}\). The supervision of element \(i\) to belong to cluster \(k\) is performed through adjusting \(m_{ik}\). A formula for \(m_{ik}\) can be given as follows, with exponential parameters \(M,\ M^{\prime}>1\):  
* \(m_{ij}=M\) for all \(1\leq j\leq C\), for unsupervised elements \(i\). * \(m_{ik}=M^{\prime}\), and \(m_{ij}=M\) for all \(j\
eq k\), for supervised elements \(i\) to belong to cluster \(k\). The approach to determine the exponential parameters \(M\) and \(M^{\prime}\) is discussed in Section 3.2. The objective function to be minimized in the sSMC-FCM algorithm is formulated as follows,  
\[J(U,V)=\sum_{i=1}^{N}\sum_{k=1}^{C}{U_{ik}}^{m_{ik}}{D_{ik}}^{2} \tag{7}\]  
where \(X=\{X_{1},X_{2},\ldots,X_{N}\}\) is the dataset, \(N\) is the number of elements, \(C\) is the number of clusters, \(U_{ik}\) is the membership grade of an element \(X_{i}\) in the cluster \(k\) with center \(V_{k}\), \(0\leq U_{ik}\leq 1\), \(1\leq i\leq N\), \(1\leq k\leq C\), \(\sum_{i=1}^{N}U_{ij}=1\), \(\sum_{i=1}^{N}U_{ik}>0\), \(m_{ik}>1\) is the fuzzification coefficient of \(X_{i}\) in cluster \(k\), and \({D_{ik}}^{2}=\left|\left|X_{i}-V_{k}\right|\right|^{2}\) is the distance between two vectors \(X_{i}\) and \(V_{k}\). Let \(Y\subset\{1,2,\ldots,N\}\times\{1,2,\ldots,C\}\) be the set of supervised elements, we have:  
\[m_{ij}=\left\{ \begin{array}{lcl}M&,&\forall k:(i,k)\
otin Y\\ M^{\prime}&,&(i,j)\in Y\\ M&,&\exists k:(i,k)\in Y\wedge j\
eq k\end{array} \right. \tag{8}\]  
To solve the optimization problem shown in Equation (7), we utilize the Lagrange multiplier method. Let  
\[L=\sum_{i=1}^{N}\sum_{k=1}^{C}{U_{ik}}^{m_{ik}}||X_{i}-V_{k}||^{2}-\sum_{i=1}^ {N}\lambda_{i}\left(\sum_{k=1}^{C}U_{ik}-1\right) \tag{9}\]  
Moreover, with  
\[ \begin{cases}\frac{\partial L_{k}}{\partial U_{ik}}=0,&1\leq i\leq N,1\leq k \leq C\\ \frac{\partial L}{\partial V_{k}}=0,&1\leq k\leq C\end{cases}
## 1 Introduction
In this paper, vibration data from Spectra Quest's Gearbox Prognostics Simulator (GPS) is tested using various fault detection approaches for both limited and unlimited input data. Another data source that can be considered is the real-scenario data, such as Reference [21]. However, the experiments are not compatible with the GPS dataset, and the measurement is not conducted thoroughly. Therefore, we only consider using the GPS dataset for the data augmentation in this study. Using GAN, we generate the broken signal to improve the training performance of the model. Using different approaches in both the experiment and test, we evaluated the generated data comprehensively and avoided misjudging during the data generation process for obtaining the final results. Our main contributions in this paper are as follows:  
* We briefly review the characteristic of the vibration signal data with different approaches in fault detection applications. These approaches are mainly used to verify that the generated data are high quality and suitable for fault detection applications. Figure 1: An example of Internet of Things (IoT) and artificial intelligence (AI) in fault detection application. We introduce GAN algorithms to generate a broken machine signal, which has high quality and is similar to the original signal. * The main contribution of this paper is the different approaches used to evaluate the generation data and to guarantee similarity with the original data. These approaches include different preprocessing processes and a variety of machine learning techniques in pattern recognition. The remainder of this paper is organized as follows. Section 2 describes the fault diagnosis input data and the Fast Fourier Transform (FFT), which provides the refined signal for further data processing and classification. Section 3 provides the working scheme with real data, which includes the full signal, principal component analysis (PCA) transform, and statistical analysis (SA). We also introduce different machine learning techniques for each approach and perform a comprehensive analysis. We discuss data generation using GAN and compare it with the original data in Section 4. With various preprocessing approaches and AI models, the generated data is evaluated carefully with high similarity with the real data. Conclusions for the data augmentation using GAN are presented in Section 5.
## 3 Proposed Architecture for Muti-Level 2-D DWT
### Proposed Multi-Level DWT Architecture
That is, the data onto LL and HH sub-bands should be multiplied with the factor \((\alpha\times\beta\times\gamma\times\delta/\text{K})^{2}\) and \((\alpha\times\beta\times\gamma\times\text{K})^{2}\), respectively. Moreover, the data onto LH and HL sub-bands should be multiplied with the factor \((\alpha\times\beta\times\gamma)^{2}\times\delta\). Figure 6: The 1-D DWT structure for the second level. Note: The items with * are the multiplication terms that should arrive ahead of time. Figure 7: The 1-D DWT structure for the third level. Note: The items with * are the multiplication terms that should arrive ahead of time. It should be noticed that, the proposed architecture can integrate each 3-level DWT system into a clock domain, so it can extend to higher levels, by dividing the entire multilevel DWT system into multiple 3-level DWT systems. For example, in practical applications, such as JPEG2000 image compression, 5-level 2-D DWT can reach the nearly ideal compression performance for full-resolution image [20]. Thus, the entire 5-level DWT system can be divided into two 3-level DWT systems. Namely, the first-level, second-level and third-level DWT constitute the first 3-level DWT system in the first clock domain, and their structures are as shown in the previous presentation. Then, the fourth-level and fifth-level DWT constitute the second 3-level DWT system in the second clock domain. And the fourth-level DWT has the same structure as the first-level DWT and the fifth-level DWT has the same structure as the second-level DWT. Moreover, the overall module design with a 2:1 parallelism ratio between the lower level and the above level always works. It is a fact that is preferable to analyze the scattering problems of the TD framework rather than in the frequency domain (FD) [21]. Hence, the proposed architecture has strong application value.
###### Abstract
The experimental findings demonstrated that the GLCM texture feature extraction method outperformed traditional image feature extraction methods in terms of obtaining more distinguishable features. Motivated by the efficiency of the GLCM texture features and the fuzzy system, we developed a novel GLCM-based fuzzy broad learning system. Our objective is to propose an effective method that offers athletes accurate and convenient screening for osteopenia and osteoporosis based on X-ray images. Initially, the texture characteristics of X-ray images were extracted using the GLCM. Subsequently, these extracted features were integrated with the fuzzy broad learning system to effectively mine crucial information and enhance the accuracy of predicting osteoporotic conditions. Our critical contributions are summarized and listed as follows:  
1. To the best of our knowledge, it is for the first time that the FBLS is utilized for the screening of osteopenia and osteoporosis for athletes based on X-ray images. With significant advantages in uncertain and non-linear modeling and rapid calculation ability, the FBLS is a potentially alternative approach for regular testing of osteopenia and osteoporosis for athletes. 2. A novel GLCM-based fuzzy broad learning system (GLCM-based FBLS) is first proposed for superior classification performance. Effective features are extracted through the use of gray-level co-occurrence matrix and then combined with the fuzzy systems. The feature extraction method with the GLCM can provide the fuzzy systems with detailed texture information. The fuzzy systems in the proposed model can handle uncertain or incomplete features in the learning process, contributing to higher screening accuracy. 3. We compare the proposed GLCM-based FBLS with three State-of-the-Art CNN models to analyze the advantages of using the proposed model in athletes' osteoporosis screening application. Based on deep learning, the CNN models have achieved significant progress in the field of osteopenia and osteoporosis automatic screening for athletes. This paper offers a new way to achieve better screening performance without numerous parameters and deep architecture. The following parts in the paper are organized as follows: In Section 2, we briefly introduce the related works and the proposed method including feature representation and we mainly describe the GLCM-based BLS. Experiment results and analysis are shown in Section 3. Lastly, the discussion is outlined in Section 4.
###### Abstract  
The convergence zone of the Eurasian (EURA) and North Africa plate (NUBIA) is primarily marked by the activity between the Betics in south of Spain and the Rif and Atlas in Morocco. This area, where the diffuse tectonics between these plates are currently converging in a NW-SE direction, presents several continuous fault zones, such as the Betic-Alboran-Rif shear zone. The Royal Institute and Observatory of the Spanish Navy (ROA) currently operates geodetic stations in various parts of North Africa, some in particularly interesting locations, such as the Alhucemas (ALHU) rock, and also in more stable areas within the Nubian plate, such as Tiouine (TIOU). For the first time, the displacement velocities of the ROA cGNSS stations have been estimated to provide additional geodynamic information in an area with few stations. The obtained velocities have been compared with other recent studies in this field that included data older than 10 years or episodic campaigns without continuous stations. PRIDE (3.1.2) and SARI (February, 2025) software were used for processing, and the velocities were obtained by the ROA for international stations (RABT, SFER, MALA, HUEL, LAGO, TARI, and ALME). These initial results confirm the convergence trend between Eurasia and Nubia of approximately 4 mm/year in the NW-SE direction. It is also evident that there is independent behavior among the Atlas stations and those in the Moroccan Meseta compared to those located in the Rif mountain range, which could indicate the separation of smaller tectonic domains within the continental plate convergence zone. Along the Rif coast in Al Hocema Bay, the faults are being approached; additionally, there is a slight clockwise displacement towards Melilla, which has also been demonstrated by stations in the Middle Atlas, such as TAZA. As for the stations in the Strait of Gibraltar, they exhibit a similar behavior until reaching the diffuse zone of the Guadalquivir basin where the diffuse convergence zone may exist. This may explain why stations to the north of the basin, such as LJIA or HUEL, change their behavior compared to nearby ones like SFER in the south. Furthermore, Alboran seems to follow the same displacement in direction and velocity as the other stations in North Africa and southern Spain.  
**Keywords:** CGNSS measurements; velocity field; Nubia-Eurasian plate boundary; active tectonics
# LabConcept--A New Mobile Healthcare Platform for Standardizing Patient Results in Telemedicine
## 3 Appling the LabConcept Telemedicine Platform
### The Main Menu  
The main menu is composed of the \"Load results\", \"View results\", \"Results chart\", \"Send results\" and the \"LIS settings\", as shown in Figure 4a. This menu is accessed after the account was created and the log-in process was successful. The button \"BACK\" is linked to execute the \"logon.m\" or \"EXIT\" for exiting the LabConcept application. Every button available is linked to different GUI's.  
* Load results--This is the submenu that links the \"load_results_m.m\" corresponding to the \"load_results_m.fig\" GUI, which allows the user to upload the results via the mobile device's internal camera by using an algorithm that detects and decodes the QR code from the assay bulletin. The recognized strings are imported into a domain-specific matrix containing the results, the comments and upload dates;
* View results--This submenu links the \"view_results.m\" corresponding to the \"load_results.fig\" GUI, which allows the user to access the stored results in a table and to select the automatic \"Prediag\" function;
* Result chart--The \"Result chart\" submenu links the \"result_chart.m\" corresponding to the \"result_chart.fig\" GUI, which allows the user to visually inspect the stored results on a predefined period of time and observe the comments;
* Send results--This submenu from the LabConcept application links the \"send_results.m\" corresponding to the \"send_results.fig\" GUI, which allows the user to send the storedresults for a specific domain or the entire database on a predefined period of time to the doctor's e-mail address;
* LIS settings--The \"LIS settings\" is the feature where the authors implement a device-specific communication port for the home or professional use blood assay devices, which have the LIS protocol function embedded. This feature is customizable, depending on the specific protocol of each medical equipment, which has a specific baud rate, data length, stop bit, flow control and communication negotiation protocol.