esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
## 3 Artificial Intelligence-Based Stroke Disease Prediction System Using EMG  
In this paper, we propose a new AI-based stroke disease system using EMG bio-signals from everyday life as shown in Figure 1. The first module executes offline processing and includes the functions of machine learning and the deep learning-based learning model generation and management of EMG data. The second module performs an online processing function as well as early detection and prediction of stroke based on EMG bio-signals collected in real-time from everyday life. In the offline module, the EMG bio-signals data measured in real-time during daily walking is updated in the repository according to the cycle set by the system. Preprocessing of the collected EMG bio-signals is performed, and a learning model is generated using machine learning and deep learning algorithms. Attribute subset selection from the preprocessed EMG is used to generate learning models with machine learning algorithms for early detection and prediction of stroke diseases. Machine learning LSTM-based learning models are developed and sent for online processing to the second module before being used in real-time stroke predictions. The online module measures and collects real-time EMG bio-signals in daily life at the request of the system or user. The \"preprocessing and normalization\" block removes missing or incomplete data from the collected EMG data. As the minimum and the maximum values are different for each attribute, a normalization process is performed depending on the measurement unit. In the attribute subset selection block, the prediction accuracy and the analysis speed are improved by selecting the EMG attribute subset defined in this paper. In addition, it is possible to guarantee optimal performance of the predicted model learned and provide analytical information. The 'Real-Time Stroke Prediction' block is mounted with pre-trained machine learning and LSTM-based prediction models. Real-time prediction and semantic analysis based on machine learning are performed using the selected EMG optimal attributes subset. At this time, the deep-learning LSTM model implements early detection and prediction of stroke in real-time based on EMG bio-signals that went through preprocessing blocks. These predictions and semantic analysis information are then transmitted to the medical staff or hospital. Finally, based on the medical doctor's diagnosis for the risk of stroke, the patients are provided assistance with receiving medical examinations and treatment services with emergency alarms and quick hospital visits as appropriate.
## 2 Preparation of IL-Based Gel
### Classification of Preparation Methods
#### 2.1.1 Sol-Gel Method
demonstrated a one-step sol-gel method to design a biometric ant-nest IL-based gel electrolyte from N-propyl-N-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide (PyT\({}_{13}\)TFSI) containing LiTFSI and 3- methacryloxypryptrimethooxysilane in formic acid [31]. The IL was immobilized within a biomimetic ant-nest SiO\({}_{2}\) matrix (X-SiO\({}_{2}\)) by in situ sol-gel of the silane coupling agent. The IL-based gel with 2 M LiTFSI displayed higher ionic conductivity compared to that of the neat IL electrolyte. By contrast, the SiO\({}_{2}\)/IL electrolyte showed much lower ionic conductivity than that of the IL electrolyte. By adapting the acid-catalyzed sol-gel method, [PERSON]'s research group successfully prepared a silica-based nanocomposite IL-based gel electrolyte from TEOS precursor and 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide (BMPTFSI) IL containing LiTFSI in water and 1-methoxy-2-propanol mixed solvent [32]. This study showed that with the increase in volume fraction (\(x\)) of IL electrolyte in the nanocomposite gels, the ionic conductivity exceeded that of the pure IL itself by more than 200% for the highest \(x\) values (\(x\) = 2.0). Beside SiO\({}_{2}\)-based IL-based gel, in 2016, [PERSON] and his team reported the non-aqueous sol-gel synthesis of IL-based gel offering ionic conductivity of about 1 mS cm\({}^{-1}\) based on a titanium alkoxide precursor, tetrabutyl titanate (TBDT), and solubilized in EMIMTFSI IL electrolyte [25]. The sol-gel procedure is beneficial for creating IL-based gels because it allows for the use of both the variety of the sol-gel process and IL chemistry. The sol-gel approach provides access to a broad range of supports, from pure metal oxides to organic-inorganic silica-derived hybrids, as well as the unique benefit of ILs with adjustable characteristics for particular requirements.
## 2 Materials and Methods
### Cell Cycle Analysis  
The effects of puerarin on the cell cycle were determined using a Muse Cell Cycle kit (MCH100106, Luminex Corporation, Austin, TX, USA). HaCaT cells were seeded at a density of 5 \(\times\) 10\({}^{5}\) cells/well on six-well plates. After 24 h, the cells were incubated with DEX (100 nM) and either puerarin (1, 10 uM) or _Centella asaatica_ extract (100 ug/mL) for 48 h. The cells were detached using trypsin-EDTA, collected by centrifuging, and fixed with 70% ethanol (1 mL/sample) for 3 h at \(-\)20 \({}^{\circ}\)C. The cells were then incubated with Muse Cell Cycle reagent (200 uL) for 30 min at room temperature, protected from the light. The proportion of cells arrested in the G0/G1, S, and G2/M phases was evaluated using a Muse Cell Analyzer (Merck KGaA, Darmstadt, Germany).
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 6 Discussion  
This study demonstrates that the _MPGT-Align_ method has undergone comprehensive evaluation and exhibits superior performance compared to various baseline methods across  
Figure 5: The performance of _MPGT-Align_ with different Transformer layers \(L\).  
Figure 6: Performance of _MPGT-Align_ with different perturbation ratio and temperature on _ZH-EN_ dataset.  
multiple datasets. The advantages of _MPGT-Align_ can be attributed to two primary factors. First, it employs a relatively simple relation structure and contrastive learning mechanism, which despite being similar to those of most unsupervised methods, enables it to achieve effective alignment learning results. Second, _MPGT-Align_ utilizes Transformer networks with multi-hop feature acquisition conducted prior to model training, providing it with an edge over other unsupervised methods that often have more complex architectures.  
Despite its numerous advantages, the _MPGT-Align_ method does exhibit certain limitations. One notable limitation is its inconsistent performance across different datasets. For instance, while _MPGT-Align_ performs exceptionally well on datasets such as _ZH-EN_, _JA-EN_, and large-scale monolingual datasets like _DWY100K_, its performance improvements on the _FR-EN_ dataset are less significant. Another limitation lies in the simplicity of its relation structure and contrastive learning mechanism. Although this simplicity offers advantages in terms of faster training and lower algorithmic complexity, it may also result in reduced representational power. In scenarios where the KGs contains highly convoluted and non-linear relationships, the basic relation structure might not adequately capture these intricate patterns, leading to sub-optimal alignment outcomes. Furthermore, the reliance on Transformer networks with pre-training multi-hop feature acquisition, while proven effective, limits _MPGT-Align_'s adaptability to diverse types of knowledge graph data. Methods incorporating a wider variety of neural network architectures or feature extraction techniques might offer greater versatility in handling varied knowledge graph characteristics.  
In conclusion, the experimental results across various dimensions strongly support the effectiveness of the _MPGT-Align_ method and its individual components. The _MPGT-Align_ demonstrates significant advantages in performance, efficiency, scalability, and component effectiveness. These strengths, coupled with our novel contributions, position it as a highly competitive approach in the field of knowledge graph alignment, with substantial potential for further applications and developments. However, there are areas that warrant further exploration to optimize the method. Future research could focus on enhancing the model's performance in challenging scenarios and exploring its potential in more complex knowledge graph alignment tasks.
## 3 Results and Discussion  
### Chickpea Delhydration Kinetics  
Figure 1 summarizes the results regarding the dehydration kinetics of chickpeas.  
The water removal rate in food products is governed by phenomena of matter, i.e., water and heat convection at the air-product interface, resulting in heating and dehydration of the product surface with consequent water diffusion and heat conduction [18; 19]. Three distinct phases can be identified in the unit operation of food dehydration: (1) heating phase; (2) steady-state dehydration phase; and (3) slow-moving dehydration phase [18; 19].  
As shown in Figure 1, in the first interval, from 0 to 7 h, only 2.78% of water was removed. This clearly highlighted a long heating phase of chickpea mainly due to the reduced initial moisture content of chickpea (i.e., 10.5%) and the high thickness of the seed coat that limits and delays heat penetration, moisture diffusion, and water evaporation [11; 18; 19]. In the second interval, from 7 to 10 h, higher water loss was recorded (i.e., 5.08%). In this second interval, which corresponds to the steady-state dehydration phase, the heat supplied contributes only to the evaporation of water from the surface, creating a water concentration gradient between the surface and the interior of the product that causes progressive dehydration via diffusion from the core to the exterior [18; 19].  
In the last interval, from 10 to 12 h, the water content decreases via surface evaporation, and the amount of heat used to evaporate it also decreases resulting in an excess of heat at the surface of the product. Despite the result of zero percent reported in Figure 1, approximately 3.00% of bonded water remained in the product. This third interval pointed out the third phase in food dehydration, i.e., the slow-moving dehydration phase [18; 19]. Moreover, this results in a lower evaporation rate as well as in a reduction in the water removed from the product [18; 19]. As a confirmation of the obtained results, Figure 1 shows a change in the slope of the curve with time. Furthermore, a moisture loss of only 2.69% was recorded. Finally, to the best of authors' knowledge, this is the first work in the literature that clearly describes the chickpea dehydration kinetics, taking a step forward in the comprehension of the dehydration dynamics and the improvement in the unit operation of drying.
## 3 Modeling of the Microgrid
### Wind Turbine and Permanent Magnet Synchronous Generator  
The instantaneous power delivered to the PMSG axis by the wind turbine, as in the system described in [8], can be represented by:  
\[P=\frac{1}{2}\rho AV^{3}C_{p}(\lambda,\beta), \tag{5}\]  
where \(\rho\) is the air density, \(A\) is the area swept by the turbine rotor blades, \(V\) is the instantaneous wind speed and \(C_{p}(\lambda,\beta)\) is the efficiency power conversion factor, which is a nonlinear function of the tip speed ratio \(\lambda=\omega_{t}R/V\) and the pitch blades angle \(\beta\). Since the manufacturers of wind turbines do not give information about power conversion factor characteristics, some typical empirical curves are often used for representing wind turbines in power system studies, such as the one presented in [8]:  
\[C_{p}(\lambda,\beta)=0.22\bigg{(}\frac{116}{\lambda_{i}}-0.4\beta-5\bigg{)}e^{ \frac{-12.5}{\lambda_{i}}}, \tag{6}\]  
where:  
\[\frac{1}{\lambda_{i}}=\frac{1}{\lambda+0.08\beta}-\frac{0.035}{\beta^{3}+1}. \tag{7}\]  
The PMSG was represented using the model in the \(dq\) reference frame rotating at a rotor electrical angular speed \(\omega_{r}=(P/2)\omega_{t}\):  
\[v_{sd}=R_{s}i_{sd}+\frac{d}{dt}\lambda_{sd}-\omega_{r}\lambda_{sq}\]  
\[v_{sq}=R_{s}i_{sq}+\frac{d}{dt}\lambda_{sq}+\omega_{r}\lambda_{sd}\ \, \tag{8}\]  
where \(v_{sd}\), \(v_{sq}\), \(i_{sd}\), \(i_{sq}\), \(\lambda_{sd}\), and \(\lambda_{sq}\) are the direct and quadrature axes components of the stator voltage, current, and flux space vectors, respectively. \(T_{e}\) and \(T_{m}\) are the electromagnetic and mechanical torque machines, and \(J\) and \(b\) are rotor inertia and viscous friction coefficients. The flux-current relations and the electromagnetic torque equation complete the model:  
\[\lambda_{sd}=L_{sd}i_{sd}+\Lambda\]  
\[\lambda_{sq}=L_{sq}i_{sq}\ \, \tag{9}\]  
\[T_{e}=\frac{3}{2}\frac{P}{2}\big{(}\lambda_{sd}i_{sq}-\lambda_{sq}i_{sd}\big{)} =\frac{3}{2}\frac{P}{2}\big{[}\Lambda i_{sq}+\big{(}L_{sd}-L_{sq}\big{)}i_{sd }i_{sq}\big{]}\]  
where \(\Lambda\) is the permanent magnet flux.
# Impact of HPC and Automated CFD Simulation Processes on Virtual Product Development--A Case Study
## 2 Materials and Methods
### CFD Workflow  
As shown in Figure 2, a CFD workflow implies setting up an initial design (CAD model), deriving an idealized and defeatured geometry (geometry), its discretization (mesh), the computation (solver) and a report to evaluate the simulation results. During a product's optimization, several of the model parameters (design variables) are usually varied systematically and thus a large number of simulations are generated. However, since changes to the geometry require new meshing, the simulation procedure described so far might be repeated for each design iteration. If the model is parametrized, the variation in design variables and the restart of the downstream process steps can be automated. Since an automation of the overall optimization process results in a multitude of simulations, it is appropriate to utilize HPC systems.  
The variation in the design variables can be applied on different levels: They can affect the mesh, the idealized geometry or the original CAD model. It is advantageous to apply design variables to the CAD model if the optimized design is to be available as a CAD model after completion of the optimized design. However, this implies a significantly higher computing effort per design iteration and an increasing amount of data to be transferred between a local host and the remote HPC system. Therefore, the scope of our article focuses on applying design variables only to the simulation model (geometry and mesh as depicted in Figure 2).  
Within the case study, there are slight limitations regarding the degree of automation: Since the approximation of the geometry and fluid domain is crucial for the validity of the results, the initial meshing requires manual user interaction for an assessment of the mesh quality. Although there are well-established mesh quality metrics, which indicate an overall quality and show starting points for mesh improvement, no computer can say whether the quality is sufficient or not without having in-depth knowledge about the context of the simulation. Therefore, we assume that the initial mesh has already been verified and potential adaptive mesh refinement on the verified mesh does not negatively influence the obtained results.
## 4 Dynamic Iteration Method
### Proposed Dynamic Iterative Method
Figure 7: Sensitivity analysis of \(p_{\text{t}}\) and \(N_{\text{s}}\): (**a**) Average _MRE_ of \(q\) and \(u\) corresponding to different \(p_{\text{t}}\) when \(N_{\text{s}}\) = 50; (**b**) average _MRE_ of \(q\) and \(u\) corresponding to different \(N_{\text{s}}\) when \(p_{\text{t}}\) = 0.3; (**c**) composite scaled sensitivity (CSS) of \(p_{\text{t}}\) and \(N_{\text{s}}\). encounters complex numbers due to the large initial step size, and both the fourth-order Runge-Kutta method and Euler method exhibit severe numerical divergence. The dynamic iteration method, with a smaller initial step size, improves iteration efficiency by two orders of magnitude while ensuring computational accuracy. Taking the inversion of the CSUH model parameters for the Changhe dam rockfill in Section 5.3 as an example, the inversion process includes one isotropic compression test and four CD tests, totaling 414 data points. Based on the dynamic iterative method with segmented adjustment of the model iteration increment step size, this paper developed parameter inversion software using VBA language on the Excel platform (website: [[https://github.com/longbiscuit/ModelParaOpt2.0_Excel_STADE](https://github.com/longbiscuit/ModelParaOpt2.0_Excel_STADE)]([https://github.com/longbiscuit/ModelParaOpt2.0_Excel_STADE](https://github.com/longbiscuit/ModelParaOpt2.0_Excel_STADE)), accessed on 29 April 2025.). The software runs on a Dell Inspiron 13 7000 series laptop (Dell Inc., Austin, TX, USA). Experimental results show that when the number of iterations is 50, the computation time is about 7 min; when the number of iterations increases to 3000, the required time is 332 min. Compared with the traditional method, the computation time is reduced to 1/47 of the original, as shown in Figure 8.  
In conclusion, the proposed dynamic iterative method with segmented iterative increment step sizes significantly reduces the number of iterations while ensuring prediction accuracy, greatly enhancing the efficiency of parameter inversion.
# A Fast Sparse Coding Method for Image Classification
## 2 Methods
### Sparse Coding  
An efficient sparse coding algorithm [17] is employed as mid-level feature, which represents images as a set of codes according to its 128-dimensional SIFT descriptors that are extracted from locals of each image. This sparse coding algorithm has a dictionary learning step and an encoding step, and is based on iteratively solving two convex optimization problems: an L1-regularized least squares problem and an L2-constrained least squares problem. In our framework, the dictionary learning step is replaced by the proposed \(k\)-medoids dictionary learning method, and existing encoding step is employed to generate the image coding based on dictionary learned by our method.  
\begin{table}
\begin{tabular}{l} \hline \hline // Initialization \\  // Input all samples as \(\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{N}]\) \\  // Randomly select a sample to join the medoids set \(\mathbf{M}_{set}\) as the first medoid \(\mathbf{M}_{set}^{(1)}\) \\  **do**\(\mathbf{x}_{random}\Rightarrow\mathbf{M}_{set}\) \\  // Iteratively search for other medoids \\  **for**\(m=2:K\) \\  // Allocation cluster numbers of candidate samples \\  **for**\(j=1:N\) \\  **do**\(\tilde{p}_{j}=\arg\min\limits_{\mathbf{p}}\|\mathbf{x}_{j}-\mathbf{M}_{set}^{(p)}\|\) \\  **end** \\  // Calculating the selecting probability of the candidate samples \\  **for**\(j=1:N\) \\  **do**\(P_{j}=\frac{\|\mathbf{x}_{j}-\mathbf{M}_{set}^{(\tilde{p}_{j})}\|}{\sum\limits_{k=1}^{N}\| \mathbf{x}_{k}-\mathbf{M}_{set}^{(\tilde{p}_{k})}\|}\) \\  **end** \\  // Extracting a candidate sample as a medoid by probability \\  **do**\(\mathbf{x}_{random}\)\(j\)-P\({}_{j}\)\(\Rightarrow\)\(\mathbf{M}_{set}\) \\  **end** \\  // Learning dictionary \\  // Allocation cluster number \\  **for**\(i=1:N\) \\  **do**\(w_{i}=\arg\min\limits_{\mathbf{k}}\|\mathbf{x}_{i}-\mathbf{M}_{set}^{(\mathbf{k})}\|\) \\  **end** \\  // Update dictionary \\  **M**\({}_{oldset}=\mathbf{M}_{set}\) \\  **for**\(k=1:K\) \\  **do**\(\mathbf{M}_{set}^{(\mathbf{k})}=\arg\min\limits_{\mathbf{x}_{i}}\sum\limits_{j:w_{j}=k}\| \mathbf{x}_{i}-\mathbf{x}_{j}\|\) \\  **end** \\  // To determine whether convergence \\  **if**\(\mathbf{M}_{set}=\mathbf{M}_{oldset}\) \\  **do** “**Output**\(\mathbf{M}_{set}\) **as medoids”** \\  **else**\(\mathbf{Go}\) **to the step “// Allocation cluster number”** \\  **end** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The algorithm of fast dictionary learning.
## 2 Materials and Methods
### Description of Experimental Program  
In order to enhance our comprehension of the performance of fiber-reinforced polymers in concrete beams, this research conducted an experimental program focusing on the ultimate load-bearing capacity; midspan deflection; average deformation at the top, middle, and bottom of concrete members under various load levels; and the failure modes of beams. The concrete specimens were mixed, cured, and tested in accordance with the EURO code. Six small beams measuring 80 \(\times\) 120 \(\times\) 1100 mm were employed in the study. These included two steel-reinforced beams (ABST-1 and ABST-2), two glass fiber-reinforced beams (ABGF-1 and ABGF-2), and two basalt fiber-reinforced beams (ABBF-1 and ABBF-2). The FRP-reinforced concrete beams were designed in accordance with the guidelines of ACI 440: 1R-06 standards [30]. The evaluation aimed to assess the mechanical characteristics and effectiveness of multiple reinforcing materials in enhancing the structural performance of concrete beams, as per the ACI 440.1R-06 standards [30]. All the beams were simply supported, with support points positioned 80 mm from the outer edge of the beams, resulting in span axes of 940 mm. Point loads are applied at 1/3 of the beam length on both sides. Throughout the test, the ultimate bending capacity of the element was determined, and various values were measured. These included deformations on the surface of the concrete member in the compressive, mid, and tensile zones, maximum deflection at midspan, and the development of cracks and failure modes of the beams. The beams underwent preloading with a 0 kN force. The force buildup occurred over 30 s per 2 kN, and the load was then held constant for approximately 30 s to record test readings. Subsequently, the force was incrementally increased by 2 kN until failure. The load application utilized a hydraulic cylinder, controlled from the control panel of the PZA machine. Deflection was gauged using inductive sensors from the Megatron-Munchen return spring, with specific locations designated for deflection measurements. Additionally, the progressive development of cracks and the shear capacity of the beams were meticulously recorded during the testing process. After each load phase until failure, every new crack was documented, and photographs were taken for the preparation of simulation drawings, as illustrated in Figure 2.  
Figure 1: Specimens’ reinforcement setup: (**a**) steel reinforcement; (**b**) FRP reinforcement.
# Hybrid Active-Passive Reconfigurable Intelligent Surface for Cooperative Transmission Systems
## 2 System Model
### Passive Mode  
In passive mode, Switch 1 is \"off\", Switch 2 is \"on\", and all HAPR REs are passive. The received signal at D is represented as  
\[\begin{split} y_{p}&=\sqrt{P_{S}}\sum\limits_{n=1}^ {N}h_{S}e^{i\varphi_{n}}a_{n}e^{i\theta_{n}}h_{D}e^{i\phi_{n}}x+z_{D}\\ &=\sqrt{P_{S}}\sum\limits_{n=1}^{N}a_{n}h_{S}h_{D}e^{i(\theta_{ n}+\varphi_{n}+\phi_{n})}x+z_{D}\end{split} \tag{11}\]  
The SNR at D is as follows when using the reflection coefficient \(a=1\) and the aforementioned ideal phase design to maximize the SNR,  
\[\gamma_{p}=\frac{P_{S}\Big{|}\sum\limits_{n=1}^{N}a_{n}h_{S}h_{D}e^{i(\theta_ {n}+\varphi_{n}+\phi_{n})}\Big{|}^{2}}{\sigma_{D}^{2}}=\frac{N^{2}P_{S}h_{S}^{ 2}h_{D}^{2}}{\sigma_{D}^{2}} \tag{12}\]