esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simulation of the Densification Process of NdFeB Bulks by a Modified Drucker-Prager Cap Model
###### Abstract
It is especially suitable for describing unique non-uniform densification characteristics under spark plasma sintering loading conditions [15]. However, it cannot simulate spontaneous densification without external forces, such as expansion and contraction caused by sintering temperature. Moreover, the temperature difference between the edge and the center leads to differences in local shrinkage rates and deformation. Although the DPC model can load temperature fields, it does not consider the nonlinear effects of temperature on material parameters. Therefore, most studies have been based on the analysis results derived from pressing experiments. There have been very few studies on the densification of powder thermal processing processes, limiting a comprehensive understanding of the link between the sintering densification process of NdFeB powder and its microstructure. Modeling based on the DPC model is beneficial for assessing the area of stress generation before and after sintering to analyze the densification process of powders. In this work, sequentially coupled thermal stress analysis is adopted. In sintering process simulations, a segmented simulation method of \"pressing and sintering\" is employed. The density evolution variables from the DPC model quantify the densification process, while shear surface parameters and cap parameters are used as state variables to control the evolution of the yield surface [13]. Thermal-mechanical coupling variables are defined to link temperature evolution with the mechanical response. FEA modeling was used to simulate the sintering densification process of NdFeB and calculate the temperature field and stress field, revealing the effects of different sintering temperatures on the microstructure. The model was established in the FEA software ABAQUS (ABAQUS 2024). Moreover, microstructural SEM analysis, an X-ray residual stress tester, and additional experimental methods were used to validate the FEA results and enhance model accuracy.
###### Abstract  
Background: Previous studies have shown that epiretinal membranes (ERMs) may be associated with abnormal outer retinal anatomy. However, long-term morphological and functional results of pars plana vitrectomy (PPV) with ERM and internal limiting membrane (ILM) peeling in eyes with central bouquet (CB) alterations have not yet been investigated. Methods: In a retrospective, consecutive study all patients underwent best corrected visual acuity (BCVA) testing and spectral domain optical coherence tomography (SD-OCT) before and after a mean of 20 months (range 3-70 months) postoperatively. CB abnormalities and ERMs were classified according to [PERSON]'s staging systems. Results: Of the 67 eyes, 22 (34%) showed CB abnormalities at baseline. The mean BCVA increased from 0.42 at baseline to 0.20 LogMAR at final follow-up (\(p<0.001\)). Neither ERM stage (\(p=0.06\)) nor CB stage (\(p=0.039\)) at baseline were significant predictors of vision improvement following varying. Conclusions: Our results show that baseline BCVA, but not classification of CB changes and ERM at baseline, seems to be a useful predictor for functional outcomes following PPV with ERM and ILM peeling in the long-term.  
acquired vitelliform lesion; epiretinal membrane; foveal bouquet; membrane peeling; predictive factor; pars plana vitrectomy
## 8 The Effect of Alloying Elements on Electrical Steel
### Effect of Aluminium  
The addition of aluminium (and silicon) to steel results in increased electrical resistance, leading to reduced energy loss in electrical appliances. Moreover, there is a positive impact on narrowing the magnetic properties of the steel. However, the drawback of these additives is that they make a steel extremely brittle and are challenging to process through conventional thermal-mechanical methods. To address this issue, the authors of one study developed an innovative processing technique in which the rolling of the fragile steel sheet was avoided. This process involves hot dipping the solid steel in a pure aluminium bath, followed by the diffusion treatment of the steel [70].  
In the study conducted in [71], it was discovered that using silicon as a diffusant can effectively reduce power loss in non-oriented silicon iron. However, some issues were observed, such as an uneven distribution of aluminium on the surface, affecting both content and thickness penetration. The method of diffusion also had a slight influence on the anisotropy constants, potentially introducing the right amount of internal stress [72]. One significant drawback of this method is the creation of a permeable material on the steel's side. To address this concern and regulate the amount of diffusant permeating the steel sheet, several measures can be implemented. These measures include restricting the number of diffusants, adjusting the structure and depth of the paste, or creating a suitable paste to ensure that a sufficient excess of diffusant is always available. Additionally, controlling the resistivity gradient along the thickness of the sheet may further enhance the steel's performance under distorted magnetization conditions [72].  
Aluminium is often added to electrical steels to improve their magnetic properties. In one study, an increase in aluminium content resulted in improved electrical resistance and caused changes in the resulting material's properties. However, magnetic properties, such as magnetic core loss and magnetic permeability, were not significantly affected. Alloys with aluminium concentrations higher than 1% showed the best magnetic properties, with a core loss of about 3.5 W/kg (at 1.5 Tesla and 60 Hz) and permeability close to 4000 g/Oe (at 1.5 Tesla and 60 Hz) [73].  
It was observed that a higher aluminium content in the steel led to lower magnetic flux density (B50), while the B50/Bs ratio remained constant at about 0.84 for all aluminium fractions. The decrease in B50 was attributed in samples with high aluminium content, there was a reduction in the saturation magnetization density (Bs). The maximum permeability also decreased with increasing aluminium content, and the highest permeability appeared at higher external magnetic fields for aluminium proportions greater than 6.54% by weight. The average grain size decreased with increasing aluminium content, while the texture was not significantly affected [74].
# Blockchain and Smart Contracts for Digital Copyright Protection
## 5 Proposed Protocol
### Protection Protocol
thus obtaining \(\overline{\mathbb{E}_{pk^{X}}(X)}\), which is the watermarked and encrypted content to be sent to \(\mathcal{B}\) in the message \(m_{6}\). After sending \(\overline{\mathbb{E}_{pk^{X}}(X)}\) to \(\mathcal{B}\), \(\mathcal{CP}\) and \(\mathcal{B}\) can send the messages \(m_{7}\) and \(m_{8}\) to \(\mathcal{BC}\), which can automatically run the smart contract \(sm_{p}\). In fact, both messages return to \(\mathcal{BC}\) the tokens previously received in \(m_{4}\) and \(m_{5}\). This enables the smart contract to verify whether all the received data matches. If so, \(sm_{p}\) can make the payment and generate a new block in \(\mathcal{BC}\), which publishes the tokens uniquely identifying the current transaction: \(X_{d}\), \(T_{X}\), and \(SBlock\). \(sm_{p}\) ends by sending the message \(m_{9}\) to \(\mathcal{B}\). \(m_{9}\) contains the secret key \(sk^{X}\), which enables \(\mathcal{B}\) to decrypt \(\overline{\mathbb{E}_{pk^{X}}(X)}\), thus obtaining the final copyrighted content, as indicated by the following expressions:  
\[\overline{\mathbb{E}_{pk^{X}}(X)}=\mathbb{E}_{pk^{X}}(\bar{X}),\qquad\bar{X}= \mathbb{D}_{sk^{X}}(\overline{\mathbb{E}_{pk^{X}}(X)}) \tag{3}\]  
Figure 1: Protection protocol. The protocol ends with \(\mathcal{CP}\) saving a new entry in its databases. The entry contains the security tokens that characterize the concluded transaction: \(X_{d}\), \(T_{X}\), \(pk^{X}\), \(\mathbb{E}_{pk^{X}}(N)\), and \(SBlock\). It can be retrieved by \(\mathcal{CP}\) by using \(W_{\mathcal{CP}}\) as a search key. In fact, the saved tokens make it possible to recognize \(\mathcal{B}\) as the legitimate owner of the copyrighted content \(\bar{X}\) released by \(\mathcal{CP}\) through a transaction whose data are recorded in a block of \(\mathcal{BC}\).
## 4 Simulation Examples  
### Simulation of Half-Wave Rectifier Circuit  
The example is half-wave rectifier circuit. As shown in Figure 4, the excitation source was an ideal sinusoidal voltage source \(E(t)=100\sqrt{2}\sin(100\pi t)\). The value of the DC voltage source is 20 V. The first-order differential equation for the circuit is as follows:  
\[\frac{\mathrm{d}i}{\mathrm{d}t}=\frac{1}{L}(E(t)-d(t)-20-Ri) \tag{13}\]  
where \(i\) is the current, \(R\) is the resistance, and \(L\) is the inductance.  
A three-step L\({}_{2}\)MF method and an implicit trapezoidal integration method were used to solve Equation (13), and the integration step in this case was set to \(h=0.1\) ms. The simulation results are shown in Figure 5.  
As shown in Figure 5, since the three-step L\({}_{2}\)MF method was L-stable and had a strong damping characteristic, which could suppress the numerical oscillations of non-state variables when the diode D turned on. In contrast, the implicit trapezoidal integration method used in the style of EMTP produces  
Figure 4: Half-wave rectifier circuit. D represents the diode, which is an ideal switching device with voltage described as \(d(t)\). \(u_{RL}(t)\) denotes a load voltage.  
Figure 5: Load voltage calculated by the three-step low-order linear multistep formulae (L\({}_{2}\)MF) method and the implicit trapezoidal integration method(TR).  
a serious numerical oscillation when the network changed suddenly. It is worth mentioning that the implicit trapezoidal integration method directly used for solving Equation (13) did not produce numerical oscillations.
# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 4 Experimental Results and Discussion
### Comparison with Other State-of-the-Art Methods
The main reason is that the FiANets method uses multiple attention mechanisms to fuse spatio-temporal information, reducing redundant information and forming a channel balance between different features, enabling FiANets to achieve better prediction performance on the CamCAN dataset. On the NKI dataset, the FiANets method's MAE increased to 7.15. The main reason is that the dataset needs to be more extensive and significant for the FiANets method to learn valuable details. Compared with the FiANets and HRINets methods, the CNN-BiLSTM method achieved the worst prediction performance on both datasets. For example, on the CamCAN dataset, the MAE of CNN-BiLSTM is 3.76 and 3.41 higher than that of FiANets and HRINets, respectively. This shows that combining spatial and temporal features benefits age prediction. The feature information of different branches complements each other, making up for the shortcomings of single feature representation. Figure 3 shows the loss changes of different prediction models on these two datasets for intuitive display. On the CamCAN dataset, the validation loss of the \(GLTransformer\) method fluctuates wildly and is always at a significant loss value. This is consistent with the results in Table 1. This may be because the method has a deep network layer, which causes fitting during training and causes the network to fall into a local optimal state. The losses of other prediction methods are relatively stable, showing that these methods can effectively learn the brain information details in this dataset. In the NKI dataset, CNN-BiLSTM has the most significant fluctuation, and corresponding to Table 1, this method cannot learn practical long-distance dependencies on small-scale datasets. At the same time, its ability to model context and temporal global information is poor. In addition, on different datasets, the loss fluctuation range of the brain age prediction framework of the proposed LSTGINet is also the smallest (the color region floats up and down), which further illustrates that this method can effectively suppress the category imbalance problem in the datasets, and effectively solve the problems of gradient vanishing and gradient exploding that are prone to occur in the model during the training and verification stages.
# IgA Nephropathy Prediction in Children with Machine Learning Algorithms
###### Abstract  
Immunoglobulin A nephropathy (IgAN) is the most common primary glomerular disease all over the world and it is a major cause of renal failure. IgAN prediction in children with machine learning algorithms has been rarely studied. We retrospectively analyzed the electronic medical records from the Nanjing Eastern War Zone Hospital, chose eXtreme Gradient Boosting (XGBoost), random forest (RF), CatBoost, support vector machines (SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM) models in order to predict the probability that the patient would not reach or reach end-stage renal disease (ESRD) within five years, used the chi-square test to select the most relevant 16 features as the input of the model, and designed a decision-making system (DMS) of IgAN prediction in children that is based on XGBoost and Django framework. The receiver operating characteristic (ROC) curve was used in order to evaluate the performance of the models and XGBoost had the best performance by comparison. The AUC value, accuracy, precision, recall, and f1-score of XGBoost were 85.11%, 78.60%, 75.96%, 76.70%, and 76.33%, respectively. The XGBoost model is useful for physicians and pediatric patients in providing predictions regarding IgAN. As an advantage, a DMS can be designed based on the XGBoost model to assist a physician to effectively treat IgAN in children for preventing deterioration.  
IgAN nephropathy; machine learning; XGBoost; prediction; decision-making system Article
###### Abstract  
Pipes of various shapes constitute pipelines utilized in industrial sites. These pipes are coupled through welding, wherein complex curvatures such as a flange, an elbow, a reducer, and a branch pipe are often found. Using phased array ultrasonic testing (PAUT) to inspect weld zones with complex curvatures is faced with different challenges due to parts that are difficult to contact with probes, small-diameter pipes, spatial limitations due to adjacent pipes, nozzles, and sloped shapes. In this study, we developed a flexible PAUT probe (FPAPr) and a semi-automatic scanner that was improved to enable stable FPAPr scanning for securing its inspection data consistency and reproducibility. A mock-up test specimen was created for a flange, an elbow, a reducer, and a branch pipe. Artificial flaws were inserted into the specimen through notch and hole processing, and simulations and verification experiments were performed to verify the performance and field applicability of the FPAPr and semi-automatic scanner.  
on-destructive testing; plant; irregular curvature geometry; phased array ultrasonic testing; flexible phased array probe +
Footnote †: journal: Medical Engineering, Graduate School, Seoul National University of Science and Technology, Seoul 01811, Korea; [EMAIL_ADDRESS] (S.J.L.); [EMAIL_ADDRESS] (Y.L.K.)  
and surface, as shown in Figure 2, results in ultrasound beam distortion, sensitivity loss, and difficulty in quantitative flow analysis and assessment [5; 6; 7; 8]. Numerous institutions investigated enabling easy access for the PA probe on test specimens with a curvature [9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. However, previous studies were based on simple concave or convex curvatures, and most were verified through simulations rather than experiments. No precedents were found regarding experimental verifications performed for various complex curvatures found in actual pipelines.  
In this study, we developed a flexible PAUT probe (FPAPr) that could effortlessly approach complex pipeline curvatures. Additionally, a semi-automatic scanner, which is an improvement of the existing low-profile scanner, was developed to enable stable scanning for securing inspection data consistency and reproducibility. A commonly used device (Olympus's Omniscan MX2 or Zetec's Topaz) with high field availability was used as an inspection device exclusively for acquiring signals, and mock-ups that incorporated a notch, hole, and flaws that could be generated during welding, were fabricated as test specimens for the experiment [19]. This work validated the performance and field applicability of the developed FPAPr and the semi-automatic scanner.
# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 4 Case Study
### Distributed Processing
The framework enables the task distribution and processing through computing-resources shared among available devices of the mobile environment. To configure the behavior of the system and the correct distribution of the tasks, the computing load must be defined in terms of the performance aspects for each t\({}_{\text{k}}\): response time and transfer rate for each device. The most suitable devices can be set up for each application depending on the performance aspects and costs estimations. This case study considers the distribution of tasks \(\mathbb{T}\) depicted by Figure 3b-d. These are only possible scenarios. Other distributions can be configured according to the complexity of the methods and the healthcare application, the available devices of the BAN and the amount of computing resources outside of the BAN. The suitable task distribution can be done after a calibration process of each scenario. The computational framework sets up the tasks that will be processed on each device and defines their efficient distribution according to the application needs, the available resources and the user preferences. The data flow switching between sensors and devices under the established task schedule gives an overall resilience for the mobile environment and allows the healthcare application meets its requirements. As shown in the figure, data acquisition (t\({}_{1}\): data acquisition) is always made through devices of BAN. The preprocessing stages (t\({}_{2}\): Noise reduction; t\({}_{3}\): Filtering) are serious candidate to be processed on the sensing devices and/or other devices of the BAN. In this way, the BAN supplies clean data instead of raw data. The next operations (t\({}_{4}\): Segmentation; t\({}_{5}\): Feature extraction; t\({}_{6}\): anomaly detection) need some computing power to be performed. If it is possible to be computed inside the BAN, it provides health information rather than a data signal. In addition, it may reduce the amount of data to be transmitted and the bandwidth is most efficiently used. There are recent research work aiming to reduce the computational cost of the ECG analysing techniques [105; 106] for enabling their processing in wearable devices. This is the scheme followed by Figure 3c. Finally, there exist tasks that could be done by several devices at the same time. For example, the data can be _visualized_ by the player's wearable, the medical staff's tablet and the mobile computer in order to inform all potential beneficiaries of the data.
# A Denoising Method for Seismic Data Based on SVD and Deep Learning
## 2 Method
### Denoising Method Based on SVD and Deep Learning  
Since the decomposed LSV and RSV have clear physical significance related to seismic data, the effective seismic signal and noise can be well distinguished using the decomposed RSV characteristics [12]. It is easy to manually distinguish a seismic signal or noise according to the RSV characteristics. However, the conventional mathematical or signal processing methods proposed by [PERSON] et al. [12] cannot reach the high-accuracy judgment requirement. By contrast, deep learning has significant advantages in dealing with such problems. Deep learning has developed mature classification abilities. Therefore, this paper uses the MobileNetV2 classification network [23] to predict and classify the right singular vector after SVD. The basic idea is that two-dimensional seismic data with different SNR sizes are subjected to SVD, and the obtained RSVs are transformed into graphs. The images are classified to make a dataset training network model. The trained deep learning model distinguishes the effective signal and noise corresponding to the RSV of seismic data after SVD. Then, the singular value corresponding to the noise is rejected, and the effective signal is retained and smoothed. Finally, denoised two-dimensional seismic data are obtained by SVD reconstruction, and the purpose of denoising is finally achieved (Figure 2).  
Figure 1: (**a**) Synthetic data; (**b**) the left singular vector diagram corresponding to the effective signal after SVD; (**c**) the right singular vector diagram corresponding to the effective signal.  
Figure 2: Improved SVD process for seismic data.
###### Abstract  
For accurate evaluation of high intensity focused ultrasound (HIFU) treatment effect, it is of great importance to effectively judge whether the sampled signal is the HIFU echo signal or the noise signal. In this paper, a judgment method based on an auto-regressive (AR) model and spectrum information entropy is proposed. In total, 188 groups of data are obtained while the HIFU source is on or off through experiments, and these sampled signals are judged by this method. The judgment results of this method are compared with empirical judgments. It is found that when the segment number for the power spectrum estimated by AR model is 14 to 17, the judgment results of this method have a higher consistency with empirical judgments, and Accuracy, Sensitivity and Specificity all have good values. Moreover, after comparing and analyzing this method with the classic power spectrum estimation method, it is found that the recognition rate of the two sampled signals of this method is higher than that of the classic power spectrum estimation method. Therefore, this method can effectively judge the different types of sampled signals.  
high intensity focused ultrasound (HIFU); auto-regressive (AR) model; spectrum information entropy; classic power spectrum +
Footnote †: journal: Applied Sciences