esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 3 Integrated Prediction Method Based on the LSTM-SVR Model  
### Dynamic Weight Coefficient of the Integration Model  
The specific prediction framework of the integration method is shown in Figure 2. Herein, w is the dynamic weight, and Y(t) is the result of the respective prediction results of the LSTM model and SVR model added by multiplying weights [39].  
To achieve model integration, the optimized weight coefficient must be calculated. In this study, the weight coefficient takes a total of 11 values between 0 and 1.0. Figure 3 shows the process of integrating the two models. First, the weight coefficient is selected from 0.0 each time the prediction results of the two models are multiplied by their respective weights; then, the results of the two prediction models calculated by the weight coefficient are added together to obtain the weighted prediction results; finally, the first step is repeated until each weight has been calculated to obtain the corresponding predicted value.  
Figure 3: Flow of the LSTM model and SVR fusion algorithm.  
Figure 2: Integrated prediction box of the LSTM and SVR models.  
Using the calculation shown in Figure 3, 11 sets of weight coefficients and corresponding prediction data as well as the determination coefficient \(R^{2}\) corresponding to the integration method are obtained. We display the obtained \(R^{2}\) in the manner shown in Figure 4. After comparing the \(R^{2}\) of each group, the weight corresponding to the prediction data group with the largest \(R^{2}\) is selected as the final coefficient of the prediction model of the integrated method in this paper.  
Figure 4 shows that, when the weight coefficient is 0.5, the correlation between the fitting result of the fusion method and the actual value is the largest; that is, the integrated model has the highest prediction accuracy at this time. Thus, w = 0.5 is selected as the weight coefficient of the integrated prediction method.
## 4 Conclusions and Loose Ends
### Security-Proofing--Concerns on IoT Network Reliance  
Integrating communication relays into IoT networks brings to light several security challenges that are paramount to maintaining the integrity, confidentiality, and safety of data traversing these networks, as well as services dependent on them. A multi-faceted approach is necessary, including encryption, authentication, regular updates, proactive monitoring, and compliance with established security standards.  
Such a strategy ensures the integrity and reliability of data transmission across the interconnected expanse of IoT devices and platforms, especially when unanticipated protocol translation systems and new devices are integrated within the networks. As these relays facilitate data transmission across different network layers and protocols, they inherently increase the system's attack surface, presenting potential vulnerabilities for exploitation that must be addressed from the beginning [43; 44].  
Implementing security measures can help in maintaining secure communications involving IoT relays. They help ensure safe and reliable transmission of data across diverse IoT ecosystems. This not only protects the network from potential threats, but also builds trust among users and stakeholders, fostering the growth and development of secure, interconnected IoT applications. Security measures like the following must urgently be taken into consideration when implementing systems like this project's Relay [45; 46]:
## 4 Topology and Control Strategy for Proposed Power Converter
### Augmented-State Feedback Linearization (AFL) Control for Nonlinear Systems  
The nonlinear equation system formed by Equations (5) and (6) describes the PV system dynamic behavior under all operating conditions. The traditional PI controller is designed as a linear system. In general, the engineers linearize the nonlinear systems using small-signal modeling methods and adjust the parameters of PI in the linear systems. However, in this method, we need a steady-state point that is to be fixed as the converter working state. In other words, in the fast changing irradiance cases under partial shadow conditions, a linear method such as small-signal modeling cannot satisfy the requirements.  
As can be seen from Figure 3, in the PV array under the local shadow conditions there is a significant shift in the static operating point: the voltage at the maximum power point is changed at a large range.  
Figure 6: Topology of DC-DC boost converter in PV system.  
The state variable \(z_{1}\) is determined by feedback coefficient matrix \(\mathbf{K}=[k_{1},k_{2},k_{3}]\) in the third-order differential equation, and the effect of PV output voltage \(V_{apv}\) given by ANN tracking reference \(V_{m}\) is determined as well.  
A third-order system could be considered as a product of a first-order system and a second-order system. The system can be reduced to a reasonable order according to the system distribution of the roots, which means by designing the dominant pole placement. The system is designed as a second-order system in this paper, that is, the distance between the roots and the imaginary axis in the first-order system is larger than three times the distance between the roots and the imaginary axis in the second-order system. The state variables \(z_{1,1}\), \(z_{1,2}\), and \(z_{1,3}\)could be written as \(z_{1,1}=\omega_{n}\), \(z_{1,2}=-\xi\omega_{n}+\omega_{n}\sqrt{\xi^{2}-1}\), and \(z_{1,3}=-\xi\omega_{n}-\omega_{n}\sqrt{\xi^{2}-1}\). \(\xi\) is the second-order system damping ratio that affects the system dynamic performance. In order to be an overdamped system, \(\xi\) should be greater than or equal to 3. \(\omega_{n}\) is the system natural frequency that affects the steady-state precision and corresponding velocity, and \(\omega_{n}\) is supposed to be \(k\) times the \(LC_{in}\) circuit resonant frequency. The control parameters of the system could be written as (11):  
\[\left\{\begin{array}{c}k_{1}=\omega_{n}^{3}=k(1/\sqrt{LC_{in}})^{3}\\ k_{2}=(2\xi+1)\omega_{n}^{2}\\ k_{3}=(2\xi+1)\omega_{n}\end{array}\right.. \tag{11}\]  
The augmented state-space feedback linearization control strategy is presented in Figure 7.
# Space Charge Accumulation and Decay in Dielectric Materials with Dual Discrete Traps
## 3 Bipolar Charge Transport Model
### Numerical Techniques  
The charge advection-reaction equation is split into two parts, namely, the charge advection equation and the charge reaction equation, by the Strang splitting method. The charge reaction equations for free charges and trapped charges are resolved by the NSFD scheme [29]. The charge advection equation is solved by the weighted essentially non-oscillatory method (WENO), which can provide highly stable and accurate numerical charge distribution results [33].  
[PERSON]'s equation is solved by the Boundary Element Method (BEM) with Dirichlet boundary condition [34]. The BEM method is discretized spatially in integral forms by utilizing functional analysis. The advantages of BEM method are that they can simultaneously calculate the potentials and electric fields in the dielectric material and can obtain the electric field at every point of the discretization grid, particularly on the interfaces between electrodes and dielectric material.
###### Abstract  
Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.  
net load forecasting; phase space reconstruction; deep neural network +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Theoretical Study  
The solar irradiance on a horizontal surface, \(I_{bh}\) and \(I_{dh}\), necessary to calculate the different components of the incident solar irradiance on the \(PV\) field can be determined from a theoretical point of view. These components are determined below:  
1. Determination of \(I_{bh}\). As this was a theoretical study, the Hottel model [52] was used to determine the \(I_{bh}\). The Hottel model is a model of clear skies. [PERSON] proposes Equation (18) to determine the beam horizontal solar irradiance in clear atmospheres [52]: \[I_{bh}(n,T)=I_{0}(n)\cdot\cos\theta_{z}(n,T)\cdot\tau_{b}(n,T)\] (18) where \(I_{0}\) is the extraterrestrial solar irradiance measured in the plane normal to the Sun's rays (W/m\({}^{2}\)), and where \(\tau_{b}\) is the atmospheric transmittance for beam solar irradiance (_dimensionless_); \(\tau_{b}\) can be calculated using the following equation [52]: \[\tau_{b}=a_{0}+a_{1}\cdot e^{-k/\cos\theta_{z}}\] (19) where \(a_{0}\), \(a_{1}\), and \(k\) are empirical constants. If the standard atmosphere has 23 (km) of visibility, they can be calculated using the following equations: \[a_{0} =0.4237-0.00821(6-A)^{2}\] (20) \[a_{1} =0.5051-0.0059(6.5-A)^{2}\] (21) \[k =0.2711-0.01858(2.5-A)^{2}\] (22) where \(A\) is the height above sea level of the location (km). For altitudes lower than 2.5 (km), the empirical constants (\(a_{0}\), \(a_{1}\), \(k\)) have to be multiplied by their corresponding correction factors (\(r_{0}\), \(r_{1}\), \(r_{k}\)). These correction factors were tabulated for four different types of climate [52].
2. Determination of \(I_{dh}\). As this was a theoretical study, the Liu-Jordan model [53] was used to determine the \(I_{dh}\). The Liu-Jordan model is a model of clear skies. [PERSON] and [PERSON] proposed Equation (23), to determine the diffuse horizontal solar irradiance in clear atmospheres [53]: \[I_{dh}(n,T)=I_{0}(n)\cdot\cos\theta_{z}(n,T)\cdot\tau_{d}(n,T)\] (23) where \(I_{0}\) is the extraterrestrial solar irradiance measured in the plane normal to the Sun's rays (W/m\({}^{2}\)), and where \(\tau_{d}\) is the atmospheric transmittance for diffuse solar irradiance (_dimensionless_); \(\tau_{d}\) can be calculated using the following equation [53]: \[\tau_{d}=0.271-0.294\cdot\tau_{b}\] (24)
# Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
## 4 Results and Discussion
### Ablation Study  
In this study, ablation experiments were conducted on STN, filter networks, and residual connections. A comprehensive performance was compared in terms of FPS, AASR, MACs, PARAMs, and convergence speed, as shown in Table 1. R (T0, 13, 23) represents the model using three residual connections simultaneously, from position T to 0, from position 1 to 3, and from position 2 to 3, and others are also similar. Considering that CAPTCHA recognizers as part of a crawler usually run on personal computers, all FPS were measured on a CPU platform. In both datasets, the number of model parameters using the CRNN was reduced by almost 41% compared to the PARAMs using FC layers, while the recognition accuracy was improved by more than 12%. This shows that both on M-CAPTCHA with a priori statistical distributions and complex backgrounds and on the simple and low-noise P-CAPTCHA, the CRNN has a clear advantage in terms of accuracy and complexity.  
Figure 20: AASR confusion matrix of Adaptive CAPTCHA on M–dataset.  
Although the STN can theoretically be used to correct the geometric deformation of characters, the experimental results are not ideal when only promoting a little AASR on the M-CAPTCHA but increasing the number of parameters. The reason is that there are many irregular and non-linear deformations across a single CAPTCHA, making the STN difficult to calibrate. The performance improvement of the STN for the P-CAPTCHA is noticeable from around 85% to 95%, which is due to the low noise and the regular geometrical deformation of character strokes. However, the 95% ASR of the STN on simple datasets is still not as good as the 99% ASR of the CRNN, not to mention the fact that the STN is ineffective on complex datasets, and therefore the STN is not the best choice for text CAPTCHA breaking.  
Adaptive CAPTCHA converges much faster than Deep CAPTCHA on both datasets. Among all possible residual connections in the experimental results, the model with residual connection T1 had a convergence speed of 40 epochs on the M-dataset and 20 epochs on the P-dataset while maintaining the highest accuracy and the minimum number of parameters. From the table, for a low-complexity network such as Adaptive CAPTCHA, too many residual connections may destroy the gradient propagation instead of decreasing the convergence speed.  
The table also shows that Deep CAPTCHA had advantages in FPS and MACs, as shown in the table, which indicates there was a lower computational complexity. However, for real-time data collection tasks, the FPS value of 72 for Adaptive CAPTCHA was sufficient. For the Deep CAPTCHA, there was an obvious shortcoming in PARAMs, which was about 70% higher than the Adaptive CAPTCHA. The proposed model, Adaptive CAPTCHA, performed well in terms of AASR, PARAMs, and convergence speed while maintaining proper performance in FPS and MACs, which can meet the requirements of web crawlers and security vulnerability assessment.
# Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition
## 2 Methods
### Learning Crucial Facial Patches  
Only a few NMPs play key roles in micro-expression recognition [5], because each active patch has a different importance for micro-expression recognition. For example, the eye area and the mouth area are highly distinguishable for people to express their emotion. Therefore, we should set different weights for each active patches so as to find out the NMPs and improve the subsequent recognition accuracy.  
In this paper, the Entropy-Weight method is used to calculate the weights of each active patch and select the NMPs essential for micro-expression recognition. Information entropy can represent the information content of an image, and express the richness of the image texture. When an image is divided into many sub-patches, the local information entropy can partly reflect the quantity of information for each patch. We therefore can calculate the contribution of the texture feature of each patch based on local information entropy. The weight of the histogram of the patches is given by information entropy, which can absolutely embody the importance of each patch.  
Information entropy of the local patch indicates the information contained in the pixel. The greater the amount of information, the more abundant texture information of the patch. Considering the strong discriminating ability of texture features to the expression details, our paper introduces the concept of entropy by using the entropy weight to express the NMPs weight.  
The steps of determining the weight by the Entropy-Weight method are as follows:  
Suppose that there are \(m\) objects to be evaluated and \(n\) evaluation indexes, the original data matrix of the image is as follows:  
\[\left[\begin{array}{cccc}x_{11}&x_{12}&\cdots&x_{1n}\\ x_{21}&x_{22}&\cdots&x_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ x_{m1}&x_{m2}&\cdots&x_{mn}\end{array}\right]_{m\times n}\]  
1. Step 1: Standardization of the original matrix, thus the normalized matrix is obtained. where \(\mathrm{R}=\left(r_{ij}\right)_{m\times n}\) is the Establishing evaluation matrix. \[r_{ij}=\frac{x_{ij}-min\{x_{ij}\}}{max\{x_{ij}\}-min\{x_{ij}\}}\]  
where the formula is the standard value of the \(j\)th evaluation index on the \(i\)th evaluation object.
2. Step 2: Calculating the proportion \(f_{ij}\) of index value of the \(i\)-th object the \(j\)-th index. \[f_{ij}=\frac{r_{ij}}{\sum_{i=1}^{m}r_{ij}}\]  
Figure 5: Different radius and numbers of neighboring points on three planes. (**a**) XY orthogonal planes; (**b**) XT orthogonal planes; (**c**) YT orthogonal planes.  
Step 3: Determining information entropy. In the case of \(m\) objects and \(n\) indexes, the information entropy of the \(j\)th index is defined as follows:  
\[H_{j}=-k\sum_{i=1}^{m}f_{ij}\ln f_{ij}\]  
where, \(k=\frac{1}{\ln m}\).  
Step 4: Defining entropy weight. The entropy weight of the \(j\)th index is defined as follows:  
\[w_{j}=\frac{1-H_{j}}{m-\sum_{j=1}^{n}H_{j}}\]  
where, \(0\leq w_{j}\leq 1,\sum_{j=1}^{n}w_{j}=1\).
###### Abstract  
We investigated the effect of ten-sessions (with or without receiving modalities) of training and 4-weeks of detraining in individuals with anterior knee pain (AKP). Thirty patients with AKP (19 males; 11 females; pain duration: 46 months) were randomly assigned to either a rehabilitation program (with or without pre-exercise modality). Quadriceps (strength, activation, power, and endurance) and self-reported functions (pain perception and functional outcomes) were recorded pre- and post-rehabilitation and detraining (4-weeks). The application of electro-cryotherapy did not produce a summative effect on quadriceps or self-reported functions (condition \(\times\) time: F\({}_{2,56}<\) 2.27, \(p>\) 0.11 for all tests). Regardless of the modality application (time effect), quadriceps (strength: 12%, \(p=\) 0.06; power: 20%, \(p=\) 0.006; endurance: 13%, \(p=\) 0.0002) and self-reported (pain perception during daily activities: \(-\)70%, \(p<\) 0.0001; at least: \(-\)44%, \(p=\) 0.04; at worst: \(-\)43%, \(p<\) 0.0001; functional outcome scores: 13%, \(p=\) 0.0003) functions improved after ten sessions of rehabilitation. The improved values were maintained after 4-weeks of detraining, but functional outcome scores showed an 8% further increase (\(p=\) 0.02). Regardless of electro-cryotherapy application prior to voluntary exercises at each session, quadriceps and self-reported function improved after ten sessions of rehabilitation for AKP. Improved functioning lasted throughout a 4-week detraining period.  
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brings additional benefits, relative to the adoption of strengthening exercises alone. In 1985, [PERSON] introduced an objective and optimal method of progression in the external load of voluntary exercise--the daily adjustable progressive resistance exercise (DAPRE) technique [15]. Since many reports [13; 16; 17; 18] using this approach have demonstrated strength development in patients with AKP [17] and knee osteoarthritis [13; 16] the DAPRE technique would be suitable for use as a basis for the progression of strengthening exercises.  
As such, the purpose of this study was to determine how ten-sessions of the DAPRE technique-based strengthening exercises, either in conjunction with or without electro-cryotherapy applications, would improve quadriceps and self-reported function in patients with AKP. Secondarily, we were also interested in observing a detraining effect on both treatment conditions after ten-sessions of rehabilitation. We hypothesized that: (1) patients in both conditions would gain quadriceps function after the ten-session exercise rehabilitation program because the DAPRE technique is a notable method capable of improving quadriceps function from 4 to 6 weeks of rehabilitation [13; 17], and (2) due to the effectiveness of therapeutic modalities [12; 14], patients exposed to pre-exercise modalities (e.g., electro-cryotherapy) would show greater improvements in quadriceps and self-reported functions than would those performing exercises alone.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 5 A Taxonomy for Gait, Balance, RoM Analysis
### Validation against a Gold Standard  
Gold standard methods in this review are categorized into two types: non-instrumented and instrumented. The non-instrumented gold standard methods in gait, balance, and RoM analysis are composed of subjective assessments, such as the assessments mentioned in Section 5.2, whereby expert give a score to the subject based on subjective observation. On the other hand, instrumented gold standard methods in gait, balance, and RoM analysis are the goniometer, optoelectronic systems, and force platforms due to the capability of attaining objective data. Validation against gold standard was presented by some of the articles selected in this review to validate the accuracy and application of wearable sensors to quantify gait, balance, and RoM parameters. A total of 57.14% of the articles included in this systematic review validated their results against a gold standard, 26.79% validated against a non-instrumented gold standard method, and 47.86% validated against an instrumented gold standard system.
## 3 Methodology
### Supervised ML  
#### 3.4.1 Statistical Models  
The workflow evaluated several SML models for classifying documents as relevant or irrelevant. Each method offers unique strengths, hence comparing their performance determined the most effective approach for this task. These SML algorithms, applied to the labeled dataset, provide diverse approaches to classification, allowing a comprehensive comparison of their effectiveness and computational efficiency. The following are brief descriptions for each of the 10 algorithms evaluated:  
Gradient Boosting (GB): An ensemble method that builds decision trees sequentially, with each tree reducing the errors of its predecessor by minimizing a specified loss function. For predictions, the ensemble output is  
\[\hat{y}=\sum_{t=1}^{T}\eta f_{t}(x) \tag{9}\]  
where \(x\) is the set of features, \(f_{t}(x)\) is the \(t\)th decision tree, \(\eta\) is the learning rate, and \(T\) is the total number of trees.  
Extreme Gradient Boosting (XGB): A more efficient implementation of GB that incorporates regularization to prevent overfitting and parallel processing to handle large datasets effectively.  
Random Forest (RF): An ensemble method that builds multiple decision trees using random subsets of features and data. The final prediction is the majority vote for classification  
\[\hat{y}=\text{mode}\{f_{t}(x)\}_{t=1}^{T} \tag{10}\]  
where \(f_{t}(x)\) is the prediction from the \(t\)th tree.  
Naive Bayes (NB): A probabilistic classifier based on [PERSON] theorem, assuming feature independence. The posterior probability for class \(C_{k}\) is  
\[P(C_{k}|x)=\frac{P(x|C_{k})P(C_{k})}{P(x)} \tag{11}\]  
where \(P(x|C_{k})\) is the likelihood, \(P(C_{k})\) is the prior, and \(P(x)\) is the evidence.  
AdaBoost (ADB): An adaptive boosting method that combines weak classifiers sequentially, adjusting weights for misclassified examples to focus on difficult cases.  
k-Nearest Neighbor (kNN): A non-parametric algorithm that assigns a class to a data point based on the majority class of its \(k\) nearest neighbors in feature space.  
Decision Tree (DT): A tree structure where nodes split data based on feature thresholds to maximize information gain or minimize Gini impurity. Predictions are based on the majority class in a leaf node.  
Support Vector Machine (SVM): A linear classifier that finds the hyperplane maximizing the margin between two classes. For linear SVM, the decision boundary satisfies  
\[w^{T}x+b=0 \tag{12}\]  
where \(w\) is the weight vector and \(b\) is the bias term.  
Logistic Regression (LR): A probabilistic model that estimates the likelihood of a class using the sigmoid function  
\[P(y=1|x)=\frac{1}{1+e^{-(w^{T}x+b)}} \tag{13}\]  
where \(w\) is the weight vector and \(b\) is the bias term.  
Stochastic Gradient Descent (SGD): A linear SVM implemented with the hinge loss function  
\[\text{Loss}=\text{max}\Big{(}0.1-y\Big{(}w^{T}x+b\Big{)}\Big{)} \tag{14}\]  
where \(y\) is the true label and \(x\) is the feature vector. SGD iteratively updates the weights to minimize this loss.