esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Active Learning
As the learning process progresses, the model selectively queries the points where it is uncertain, allowing it to improve its accuracy without needing to train over the entire dataset. This process highlights the efficiency of active learning in reducing the overall computational effort while maintaining high predictive performance. Initially, a random subset of 50 instances was selected for training the active learner, which was constructed using a Random Forest classifier with 100 estimators, as shown in Figure 2. The initial performance of the learner across the entire dataset yields an accuracy of 80.6%. At this stage, the learner is tasked with querying data points where its confidence \(\gamma\) is less than 0.6. Figure 3A shows the 310 queries issued by the active learner. These queries are concentrated in regions where the learner has low confidence, as indicated by the confidence plot in Figure 2B. The newly acquired data points are subsequently added to the training dataset, and the active learner is retrained to improve its classification over the entire dataset. After this, the learner's accuracy increases significantly to 94.9  
Figure 1: A dataset of 800 instances available from scikit-learn [24]. Figure 3: (**A**) The 310 queries issued by the learner; (**B**) confidence plot after including queries in the training dataset (accuracy: 94.9%). Figure 2: (**A**) Fifty labeled instances randomly selected for initial training of active learner; (**B**) confidence plot after initial training of active learner (accuracy: 80.6%). This case study highlights the efficiency of active learning in boosting model performance by querying selectively, thus avoiding the need to label the entire dataset.
## 3 Mathematical Model
### Optimization
#### 3.2.3 Differential Evolution (DE)  
Differential evolution (DE) is another optimization algorithm that, similar to GAs, is based on an evolutionary process. DE has gained in popularity in recent years for its success in optimization problems in different fields of science and engineering. The algorithm was first introduced by [PERSON] et al. [40] in 1995 to optimize the non-differentiable and non-linear continuous function. DE algorithms are based on stochastic population-driven evolution methods similar to the other evolution methods. This approach uses a population set and searches the whole design space to find the optimal solution using crossover, mutation, and selection operators. One of the differences between DE and the other evolution methods is the mutation strategy used by DE, which applies to each point and explores the whole design space based on other individuals' solutions. [PERSON] et al. [41] presented several DE algorithms based on different crossover and mutation strategies. There are three controlling factors in a DE algorithm: the crossover rate, the population size, and scaling factors, which all must be controlled to get the best performance [42]. Figure 4 outlines the functioning of a DE algorithm. From mutation to the selection, all steps are repeated until the termination criteria are reached. Our study considers the crossover and the scaling factor to be 0.8 and 0.6, respectively.
# Intelligent Diagnosis of Compound Faults of Gearboxes Based on Periodical Group Sparse Model
## 2 Materials and Methods  
### Theoretical Methods  
#### 2.1.1 Performance Differences and Sparse Effect of Different Models  
In actual working conditions, there is a force transmission between several components in the transmission chain of machinery and equipment. The sparse model using only the 11 norm cannot better capture the clustering trend among the sparse coefficients. This leads to its inability to effectively extract multiple fault feature information, while the 11 parametric number also has the problem that the signal amplitude is underestimated, which will reduce the reconstruction accuracy of the signal.  
Figure 1: General technology roadmap.  
The performance of different sparse models and their sparse effects are different, and the expressions of the related sparse models are shown below for the Lasso model, group Lasso model and group sparse model, respectively.  
\[\hat{\psi}_{LASSO}=\underset{\psi}{\text{argmin}}\|y-\text{X}\psi\|_{2}^{2}+ \lambda_{1}\|\Psi\|_{1} \tag{1}\]  
\[\underset{\omega_{1},\omega_{2}}{\text{argmin}}\|y-\Phi_{1}\omega_{1}-\Phi_{2} \omega_{2}\|_{2}^{2}+\sum_{j=1}^{j_{1}+1}\lambda_{1,j}\|\omega_{1,j}\|_{1}+ \sum_{j=1}^{j_{2}+1}\lambda_{2,j}\|\omega_{2,j}\|_{1} \tag{2}\]  
\[\hat{\psi}_{Group-LASSO}=\underset{\psi}{\text{argmin}}\|y-\text{X}\psi\|_{2}^ {2}+\lambda_{2}\|\Psi\|_{2} \tag{3}\]  
\[\hat{\psi}_{SGL}=\underset{\psi}{\text{argmin}}\|y-\text{X}\psi\|_{2}^{2}+ \lambda_{1}\|\Psi\|_{1}+\lambda_{2}\|\Psi\|_{2} \tag{4}\]  
where \(\text{X}\in\mathbb{R}^{N\times Q}\) is the dictionary matrix, \(y\in\mathbb{R}^{N}\) is the test signal, and \(\psi\in\mathbb{R}^{Q}\) is the corresponding sparse coefficient vector.\(\lambda_{1}\) and \(\lambda_{2}\) are the adjustment parameters. The sparse effect of each model is shown in Figure 2. From the figure, it can be seen that the Lasso model, as a typical representative of the parametric-based sparse model, is based on the principle of optimizing the selection of each group of original signals so that each group of data maintains good sparsity. The RSSD objective function shown in Equation (2) is a typical Lasso model. The Group-Lasso model mainly groups the original signals and eliminates the relevant intra-group data, so that each group of data between the signals maintains good sparsity, but the processed data still have more redundant information. The group sparse model further optimizes the redundant data within groups by combining the advantages of both models, which can reduce the influence of redundant information on the signal to a greater extent and keep a small amount of data to reveal the essential information in the signal. It is a good balance of the inter-group sparsity and intra-group sparsity of signal. Therefore, this paper selects the group sparsity-based model to deal with compound faults.
# Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation
## 4 Experiments
### Parameter Sensitivity Analysis  
For our method, there is one hyper-parameter to be adjusted, \(\lambda\) indicates the weight of fuzzy graph learning. We run FGLSF varying with values of parameter \(\lambda\) which are sampled from {0, 0.001, 0.01, 0.1, 0.2} on three tasks, e.g., \(C\to W\),\(W\to C\) and \(P\to C\). If \(\lambda\) is too small, the model would fail to align distributions of two domains. And as it getting larger, the problem of representation shrinkage is emerging. The experimental results shown on Figure 3 are consistent with the analysis, the accuracy first rises and then falls as \(\lambda\) gets bigger.
# Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming
###### Abstract  
The meaningful problem of improving crane safety, reliability, and efficiency is extensively studied in the literature and targeted via various model-based control approaches. In recent years, crane data-driven modeling has attracted much attention compared to physics-based models, particularly due to its potential in real-time crane control applications, specifically in model predictive control. This paper proposes grammar-guided genetic programming with sparse regression (G3P-SR) to identify the nonlinear dynamics of an underactuated crane system. G3P-SR uses grammars to bias the search space and produces a fixed number of candidate model terms, while a local search method based on an \(l_{0}\)-regularized regression results in a sparse solution, thereby also reducing model complexity as well as reducing the probability of overfitting. Identification is performed on experimental data obtained from a laboratory-scale overhead crane. The proposed method is compared with multi-gene genetic programming (MGGP), NARX neural network, and Takagi-Sugeno fuzzy (TSF) ARX models in terms of model complexity, prediction accuracy, and sensitivity. The G3P-SR algorithm evolved a model with a maximum mean square error (MSE) of crane velocity and sway prediction of \(1.1860\times 10^{-4}\) and \(4.8531\times 10^{-4}\), respectively, in simulations for different testing data sets, showing better accuracy than the TSF ARX and MGGP models. Only the NARX neural network model with velocity and sway maximum MSEs of \(1.4595\times 10^{-4}\) and \(4.8571\times 10^{-4}\) achieves a similar accuracy or an even better one in some testing scenarios, but at the cost of increasing the total number of parameters to be estimated by over 300% and the number of output lags compared to the G3P-SR model. Moreover, the G3P-SR model is proven to be notably less sensitive, exhibiting the least deviation from the nominal trajectory for deviations in the payload mass by approximately a factor of 10.  
crane dynamics; data-driven identification; genetic programming: sparse regression Article
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 5 Research Methodology
### CFS-RF Hybrid Method
#### 5.5.2 Classifier for Modified SVM  
The dataset's weights for each XiBest feature are updated. The steps for creating the support vector, categorizing the datasets, and applying the improved SVM classifier are as follows:  
\[f(x)=b+(x,w) \tag{6}\]  
where weight (\(w\)) and bias (\(b\)) are two terms.  
The adaboosting method is used in the first stage of Algorithm 3 with respect to every altered classifier (Figure 3). The value of each Classifier's weight is determined by:  
\[(M_{i})=\sum_{j=1}^{d}err(Xj)\times wj \tag{7}\]  
where \(err(Xj)\) is the error for each classifier, and \(wj\) is its weight. Afterward, by calculating the weight using and validating the error rate:  
\[log\frac{(1-ErrorRate(M_{i}))}{ErrorRate(M_{i})} \tag{8}\]  
Figure 3: RF modified to function as AdaBoosting.  
As a result, performance is improved and variation is reduced. The second step uses the bagging algorithm's basic idea to make these classifiers function as bootstraps. A shallower tree (during each phase of splitting), a sample of the variables, and a new dataset are utilized in the system to minimize overfitting, creating a composite model with reduced bias.  
\[\text{Voting average}=\sum_{i=1}^{1}pci\left(\frac{w_{i}}{x}\right) \tag{9}\]  
The average voting method is calculated using Equation (9).
# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 2 Principle of FPM
### Evaluation Indicators
#### 2.4.2 Structural Similarity  
Although traditional evaluation methods such as PSNR can objectively measure the difference between the original image and the reconstructed image, there is often a certain gap between these evaluation indicators and the subjective evaluation of people, and it is easy for it to appear that the image evaluation index with good subjective quality is low. Structure similarity (SSIM) is a full-reference image quality evaluation index. It measures the similarity of images from three aspects: brightness, contrast, and structure, which is more in line with a human subjective evaluation index. The input of SSIM is two images, one is an uncompressed undistorted image (grand truth), and the other is a restored image. The calculation is as follows:  
\[\begin{array}{c}SSIM=[l(I_{1},I_{2})]^{\alpha}[c(I_{1},I_{2})]^{\beta}[s(I_{1 },I_{2})]^{\gamma},\\ \\ l(I_{1},I_{2})=\frac{2u_{1}u_{2}+C_{1}}{\mu_{1}^{2}+\mu_{2}^{2}+C_{1}},\\ \\ c(I_{1},I_{2})=\frac{2\gamma_{1}\alpha_{2}+C_{2}}{\sigma_{1}^{2}+\sigma_{2}^{2} +C_{2}}\\ \\ s(I_{1},I_{2})=\frac{\sigma_{1}+C_{2}}{\sigma_{1}\sigma_{2}+C_{3}}\#(13)\end{array} \tag{13}\]  
Among them is the brightness comparison of two images; it is the contrast comparison of two images; and it is the structure comparison of two images. \(\alpha\), \(\beta\), and \(\gamma\) are weight parameters, \(\alpha\), \(\beta\), \(\gamma>0\); \(\mu_{1}\) and \(\mu_{2}\) are the average values of the two images; \(\sigma_{1}\) and \(\sigma_{2}\) are the standard deviation of two images. \(\sigma_{12}\) is the covariance of two images. and \(C_{1}\), \(C_{2}\), and \(C_{3}\) are constants used to maintain the stability of the calculation. In practical application, \(\alpha=\beta=\gamma=1\) and \(c_{3}=\frac{C_{2}}{2}\), and the updated SSIM expression is:  
\[SSIM=\frac{\left(2\mu_{1}\mu_{2}+C_{1}\right)\left(2\sigma_{12}+C_{2}\right)} {\left(\mu_{1}^{2}+\mu_{2}^{2}+C_{1}\right)\left(\sigma_{1}^{2}+\sigma_{2}^{2} +C_{2}\right)} \tag{14}\]  
where \(C_{1}=\left(0.01*L\right)^{2}\), \(C_{2}=\left(0.03*L\right)^{2}\), and L is the dynamic range of pixel value. The value of SSIM is generally 0-1. The larger the value of SSIM, the smaller the gap between the output image and the undistorted image. When the two images are consistent, SSIM = 1. At the same time, SSIM has symmetry, that is, SSIM(\(x\), \(y\)) = SSIM(\(y\), x).  
SSIM can also use the sliding window to divide the image into blocks. Let the total number of blocks be N. Considering the influence of window shape on the block, the mean, variance, and covariance of each window are calculated by Gaussian weighting. Then, the structural similarity SSIM of the corresponding block is calculated. Finally, using the average value as the structural similarity measure of the two images, the average structural similarity is MSSIM.  
\[MSSIM(X,Y)=\frac{1}{N}\sum_{K=1}^{N}SSIM(x_{k},y_{k}) \tag{15}\]
## 2 Materials and Methods
### Core Ontology of Phenotypes
The logical relations between the phenotype classes as well as range restrictions are represented in OWL by anonymous equivalent classes or general class axioms based on property restrictions. Let us consider an example study for people with hypertension or obesity as inclusion criteria. All relevant phenotype classes are modelled in a single PheSO based on the COP. First, the USiP classes \"Age\", \"Gender\", \"Weight\", \"Height\", \"Systolic_Blood_Pressure\", \"Myocardial_Infarction\", and \"Stroke\" are added (Figure 2A), including relevant annotations such as data types, units, and codes. Some example annotations of the class \"Systolic_Blood_Pressure\" are listed in Figure 2A1. Assuming men between 40 and 65 years (i.e., age in years \(\geq\)40 and \(<\)66) with high blood pressure are relevant for our example study, we insert the RSiP classes \"Male\", \"Age_40_66\", and \"Hypertension\" under the corresponding USiP classes (Figure 2A) and define the desired value ranges as property restrictions (Figure 2A2). Next, in order to define a phenotype class for obesity based on BMI, we specify the UDeP class \"BMI\" (Figure 2B) and annotate it with an appropriate formula (Figure 2B1). The USiP classes \"Height\" and \"Weight\" are used as variables in this formula. The RDeP class \"Obesity\" can now be added as a subclass of \"BMI\" with a respective value range (Figure 2B2). Finally, the UCoP class \"SBP_BMI\" (\"SPB\" stands for \"systolic blood pressure\") and its restricted subclass \"Hypertension_OR_Obesity\" are added (Figure 2C). The corresponding Boolean restriction is depicted in Figure 2C1. For an in-depth description of the COP as well as further examples and details on how PheSOs are composed and what information must be provided by domain experts, please refer to [23].
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 5 Performance Evaluation
### General Discussion
This enhancement underscores the value of ETX in refining decision-making, leading to more effective and reliable parent selection.
## 2 Related Works  
### The LEACH Protocol  
The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol [19] proposed by [PERSON] _et al._, is one of the basic clustering and routing protocols in WSNs and is used by many subsequent clustering and routing protocols. The main idea of LEACH is to select cluster heads by rotation and the high energy consumption for communicating with the BS is spreads among all the nodes.  
The operation of LEACH consists of rounds, and each round consists of two phases; the set-up phase and the steady-state phase. In the set-up phase the clusters are formed and in the steady-state phase data is delivered to the BS. In the set-up phase, each node decides to become a CH or not for the current round. The decision is based on the suggested percentage of CHs for the network and the times of being CH so far. The node generates a random number between 0 and 1, then it becomes a CH for the current round if the number is lower than the threshold _T_(_i_), as follows:  
\[T(i)=\begin{cases}\dfrac{P}{1-P(r\ mod\dfrac{1}{P})}\end{cases},\qquad\text{ if }n\in G \tag{1}\] \[0\qquad,otherwise\]  
where \(P\) is the desired percentage of CHs, \(r\) is the number of current round, and \(G\) is the set of nodes that have not been elected as CHs in the last _1/P_ rounds [16]. When a node is elected as CH, it broadcasts an advertisement packet. According to the received signal strength, other nodes decide which cluster they could join [20,21].
# Stability Reinforcement of Slopes Using Vegetation Considering the Existence of Soft Rock
## 2 Materials and Methods
### Project Background and Site Description
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Properties** & **Unit** & \multicolumn{3}{c}{**Sandy Loess**} & \multicolumn{3}{c}{**Silt**} \\  & & **Max** & **Min** & **Avg** & **Max** & **Min** & **Avg** \\ \hline Natura moisture & \% & 16.900 & 4.200 & 10.068 & 22.900 & 18.000 & 20.600 \\ content & g/cm\({}^{3}\) & 1.840 & 1.430 & 1.570 & 1.890 & 1.720 & 1.820 \\ Natural density & g/cm\({}^{3}\) & 1.590 & 1.320 & 1.430 & 1.560 & 1.400 & 1.510 \\ Void ratio & \% & 1.049 & 0.694 & 0.892 & 0.929 & 0.729 & 0.791 \\ Water saturation & \% & 63.800 & 13.500 & 29.879 & 78.700 & 63.900 & 70.480 \\ Liquid limit & \% & 26.500 & 24.900 & 25.497 & 26.100 & 25.400 & 25.700 \\ Plastic limit & \% & 17.300 & 16.200 & 16.641 & 16.700 & 16.500 & 16.620 \\ Plastic index & - & 9.500 & 8.400 & 8.856 & 9.400 & 8.800 & 9.080 \\ Liquid index & - & 0.010 & \(-1.450\) & \(-0.744\) & 0.690 & 0.160 & 0.438 \\ Cohesion & kPa & 17.2 & 16.3 & 16.8 & 18.2 & 17.8 & 18.1 \\ Friction angle & degree & 21.6 & 19.4 & 20.7 & 20.6 & 18.8 & 19.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic properties of the sandy loess and silts. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Soil Layer** & **Dry Density** & **Young’s Modulus** & **Poisson Ratio** & **Shear Modulus** & **Cohesion** & **Friction Angle** \\ \hline Unit & kg/m\({}^{3}\) & kPa & / & kPa & kPa & degree \\ Sandy loess & 1430 & 11,500 & 0.3 & 4423 & 16.8 \({}^{\text{a}}\) & 16.3 \({}^{\text{b}}\) & 20.7 \({}^{\text{a}}\) & 19.4 \({}^{\text{b}}\) \\ Silt & 1510 & 10,800 & 0.35 & 4000 & 18.1 \({}^{\text{a}}\) & 17.8 \({}^{\text{b}}\) & 19.4 \({}^{\text{a}}\) & 18.8 \({}^{\text{b}}\) \\ Mudstone & 2250 & 411,030 & 0.23 & 167,085 & 112.6 & & 34.3 \\ \hline \hline \end{tabular} Note: superscripts a and b denote average value and minimum value, respectively.  
\end{table} 
Table 2: Mechanical parameters and values for the numerical model. Figure 2: Geological conditions of the site based on drilling-hole tests.
## 4 A Focus on Relevant Applications of Apta-POF-SPR Sensors
### Disease Biomarkers Detection
Zoom of the resonance sensing region with two examples of self-assembled monolayer of aptamers. minescence protocol. At the selected time, 50 nM of thrombin (Sigma) was incubated on surfaces (kept in PBS at 4\({}^{\circ}\) or \(-\)20 \({}^{\circ}\)C) in buffer (Tris 50 mM, EDTA 1 mM, MgCl\({}_{2}\) 1 mM, KCl 150 mM pH 7.4) for one hour on a slow orbital shaker and followed by a washing step in the same buffer. After a passivation step with 3% \(w/v\) BSA for 30 min, a primary mouse anti-thrombin antibody at 2.5 \(\upmu\)g/mL concentration was applied for 30 min. The excess antibodies were removed with three extensive washing steps, and the samples were then incubated for 30 min with an antimouse HRP-conjugated secondary antibody at 2 \(\upmu\)g/mL concentration. After washing three times in buffer, the chemiluminescence signal was developed (SuperSignal West Femto Chemiluminescence Substrate kit, Thermo Fisher) and acquired for 0.5 s using a standard imaging system ChemDoc-It (Bio-Rad). The signal measured with the standard imaging system was quantified using the ImageJ software [83]. Figure 5 reports the obtained results, highlighting that the conservation of the interface at \(-\)20 \({}^{\circ}\)C seems to preserve the aptamer's ability to recognize its target in an unaltered way up to 34 days. A different system was developed by [PERSON] and coworkers, who used a gold film-supported graphene sheet on a POF device to detect dopamine, a biomarker for neurological disorders [53]. The specific detection of dopamine was achieved, at sub-nanomolar concentration, taking advantage of the \(\pi\)-stacking interaction between the graphene sheet and aptamers, highlighting the high sensitivity of the system. Another application was recently proposed by [PERSON] et al. [55], based on a U-bend POF configuration coupled to an aptamer layer and connected to a smartphone camera for the detection of Plasmodium glutamic dehydrogenase (PfGDH), a potential malaria biomarker. A co-deposition of thiolated aptamer and mercapto-hexanol was used to derivatize the gold layer sputtered on the U-bend POF. They used the smartphone flash as the light source, and the camera was used as the detector. The high performance of the system allowed the PfGDH detection in the subnanomolar range, well low its physiologically relevant range, not only in buffer solution but also in 20 times diluted human serum. Moreover, a shelf life study reported, on the 20 th day, an efficiency of the probes equal to 91.38% of the initial output, suggesting the high platform stability.