esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Software Quality with AI: A Transformer-Based Approach for Code Smell Detection
## 1 Introduction
These proposed models offer enhanced decision making on code semantics, a factor that enhances the performance of the detection. This paper introduces a novel Relation-Aware Transformer model (RABERT) for code smell detection, specifically targeting the large class smell. The key contributions of this research are as follows:  
Novel transformer-based approach: We propose Relation-Aware BERT (RABERT), which integrates relational embeddings into the transformer architecture to capture interdependencies between software metrics. This enhances the detection of structural software issues. Empirical performance comparison: We conduct a comprehensive evaluation of classical machine-learning models (gradient boosting, decision trees, SVM) and transformer-based models (Feature-Aware BERT, Hierarchical BERT, RABERT). Results show that RABERT achieves the highest accuracy (90.0%) and precision (91.0%) but requires further optimization to improve recall. Benchmark dataset for code smell detection: We curate and analyze a dataset of 1000 Java version 23 based classes from GitHub 3.15 Repository open-source projects, labeled with 20 software complexity metrics. Our dataset is constructed from 50+ open-source Java projects on GitHub, following filtering criteria (e.g., active development and minimum star rating) like those adopted in [5; 6]. This approach ensures that the selected samples have sufficient quality and diversity for the analysis of code smells. Computational trade-off analysis: we compare the training and inference times of classical and transformer-based models, highlighting the computational overhead of transformers and discussing practical deployment considerations. These contributions bridge the gap between traditional machine-learning approaches and modern deep-learning models for software quality assessment. By incorporating relational embeddings, we demonstrate how transformers can be effectively leveraged for automated software quality assurance, opening new directions for research in AI-driven software maintenance. The remainder of this paper is structured as follows: Section 2 presents related work on code smell detection employing machine learning and transformer-based models; Section 3 describes the methodologies used for data preparation and application design; Section 4 outlines the experiment and computes the result; Section 5 discusses and analyzes the result; Section 6 provides the conclusion and several directions for future work.
# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
###### Abstract
Actigraph calculates count values based on activity intensity, and the hip is the best location to calculate the intensity of PA, but its ability to detect body posture is inadequate in a single worn accelerometer [23]. The commonly used inclinometer measures the angle between the gravity direction and the acceleration vector, but it can detect the posture only with 70% accuracy at best [24; 25]. In the case of thigh-worn accelerometers, which measure the thigh inclination angle, it can differentiate between sitting and standing with over 90% accuracy. However, differentiation of sitting from laying is difficult because of different orientations for both postures [26; 27; 28]. A combination of the angle of rotation and inclination for thigh has shown promising results to differentiate sitting from laying [29]. Multiple accelerometer-based solutions have reached over 98% accuracy in identifying body posture [30; 31]. However, in the case of a large-scale population, less complex (single-worn) and high feasibility systems are of primary importance besides the ability to provide information on light intensity behavior to the researchers. Obviously, one has to face a trade-off between simple systems and more burdensome and complex systems offering higher accuracy [32]. Variance is an important parameter for classifying activities. Poincare plot-based activity classification is an investigation aimed to develop a simple variance-based classification method that may be less complex in terms of computational complexity than a deep learning-based system. To the best of our knowledge, there is no research on variance-based activity classification, so this paper was warranted for such investigation. Poincare plot can be a powerful tool for analyzing the variability of the angle derived from a single waist- and chest-mounted accelerometer. A 2D plot can be constructed by plotting consecutive points of angle in time intervals into series on a Cartesian plane. It is usually used for qualitative visualization of heart rate (HR) interval because of its ability to measure the short-term and long-term HR variability. In the determination of physical activity intensity, low (like short-term) and high (like long-term) angle variability analyzing property can be useful, the same as the Actigraph 'count' value, to classify activities. In this paper, we propose a single sensor-based activity classification method where Poincare plots have been introduced to analyze the variance of angle calculated from the raw accelerometer data.
## 3 System Model  
This section introduces the proposed framework (Figure 1) in detail.The main research idea of the paper is to jointly optimize the feature extraction and similarity calculation links and construct a similarity matrix to utilize the spatial information of the image to improve the detection performance in complex and changing scenes. The query and reference image pairs are taken as the input of our MetricNet. After the feature extraction network processing, we get the generated high-level visual representations. To achieve distinguishability in image descriptions, a self-attention component called the Channel Weighting Module is designed to extract the high-level features for regions of interest. Then the Similarity Measurement Module computes the patch-by-patch similarity matrix and obtains a value between 0 and 1 as the similarity of the pair of images to determine whether the two images are collected from the same location.  
Figure 1: The pipeline of the proposed MetricNet.  
Inspired by SAES [35], MetricNet focuses on the spatial patches of image information. In SAES, the visual inputs are directly split into four patches and fed into feature extraction networks. However, this results in the truncation of objects near the visual center. While the implications for localization tasks may not be immediately apparent, such changes can compromise the integrity of image information and lead to false loop detections. In contrast, the learning-based feature extraction is applied to the entire input image in our method, and then the high-dimensional features are segmented into four patches. MetricNet adeptly maintains the information integrity, specifically accounting for the data along the boundaries of these patches.  
The proposed method mainly incorporates three modules: Feature Extraction Network (FEN), Channel Weighting Module (CWM), and Similarity Measurement Module (SMM).
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 5 Optimizing Microstructure  
Disparate from nano-precipitates produced during solid-state phase transformation, in-situ nanoparticles directly formed in the liquid melt, which greatly affect solidification process of matrix. Through various advanced characterization methods, in-situ nanoparticles are verified to possess conspicuous effects on grain refinement, inhibiting segregation and optimizing inclusions.
# A Novel Device-Free Counting Method Based on Channel Status Information
## 3 Crowd Counting Based on CSI  
### Architecture  
Figure 1 shows the system architecture of this paper, and the system architecture consists of four stages: (1) data collection; (2) data preprocessing; (3) feature extraction and (4) learning counting method. To verify the validity of the architecture, we perform a series of experiments to examine the relationship between CSI changes and the number of moving people.  
First, we obtain CSI observations by different numbers of volunteers and, then, filter out both phase and amplitude noise from original CSI observations through wavelet denoising. It is worth noting that the phase information needs linear transformation.  
Second, following data preprocessing, we further extract the covariance matrix as the eigenvector from both the amplitude and phase information, respectively.  
Finally, since multiple transmit and receiver antennas combine into different streams, CSI consists of these streams, which constitute the spatial diversity of CSI. Meanwhile, the multipath effect exists indoors, which makes different subcarriers provide frequency diversity for CSI. This will also make the covariance characteristic vectors more reliable. Therefore, these works will enhance robustness, accuracy, and reliability of our method.
## 4 Simulation Results
### PV Energy Production Comparison and Efficiency Gains  
Figure 2 and Table 7 compare the annual PV energy production for the three MPPT methods and presents the percentage efficiency gains of the RL and ANN methods over the conventional method.  
It is seen that the RL-based MPPT method achieves the highest annual energy production, followed closely by the ANN-based method. The conventional method produces the least energy, with a significant performance gap compared to the advanced methods. The efficiency gains of the RL method over the traditional method range from 9% to 10%, depending on the month, with the highest gains observed during the summer months. The ANN method achieves efficiency gains of 5% to 6%, demonstrating its effectiveness in improving energy production. The obtained results confirm that advanced MPPT methods significantly enhance the efficiency of PV systems, with RL-based MPPT offering the greatest performance improvements.  
Figure 5: Daily power profiles (summer—1 July).  
Figure 6: Daily power profiles (winter—1 December).
## 5 Conclusions  
This study presents an automatic primary screening of glaucoma based on quantitative analysis of fundus images to assist ophthalmologists for glaucoma disease detection in a faster and inexpensive way. The proposed method consists of two main processing steps. OD segmentation is experimented on five different deep semantic algorithms then the extracted features from the cropped OD region are used as an input to training a classifier for prophesying the presence of glaucoma in the testing images. Three proposals are presented to classify glaucomatous images; (P1) pre-trained CNNs using transfer learning, (P2) pretrained CNNs features using SVM classifiers and (P3) the ensemble of methods in P1 and P2. The methods are evaluated on five datasets containing 2787 retinal images, namely REFUGE, ACRIMA, ORIGA, RIM-ONE and DRISHTI-GS1. The best option for OD segmentation was the combination of DeepLabv3+ and MobileNet, which achieved an accuracy of 99.7%, a dice coefficient of 91.73% and IoU of 84.89%. For glaucoma classification, the ensemble of the proposed methods performs well to find the best performance comparing to the conventional methods for RIM-ONE, ORIGA, DRISTI-GS1 and ACRIMA datasets with the accuracy of 97.37%, 90%, 86.84% and 99.53% and AUC of 100%, 92.06%, 91.67% and 99.98%, respectively, and achieve comparable result with CUHKMED, the top team in REFUGE challenging using REFUGE dataset with the accuracy of 95.59% and AUC of 95.10%.  
S.S., N.M., K.H. and K.Y.W. conceived this research study. S.S. and K.Y.W. implemented the experiments, curated the simulation and data analysis and drafted the study. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by King Mongkut's Institute of Technology Ladkrabang, Grant Number KREF146204.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Application to Real Samples
Based, also, on some uncertainty in the meantime [44], a ban was placed upon the use of BPA in polycarbonate infant feeding bottles based on the precautionary principle. Moreover, a specific migration limit (SML), expressed in mg of substance per Kg of food (mg Kg\({}^{-1}\)), was established for BPA at 0.05 mg of BPA per Kg of food (mg Kg\({}^{-1}\)) [45]. According to the above report, BPA levels, do not exceed in any case the above-mentioned migration level of food packaging materials. Even though octylphenol was detected in almost all analyzed samples, concentration levels were quite low in milk (<4.5 \(\mu\)g Kg\({}^{-1}\)) whereas in other dairy products and the infant ones were found to range from 169.1 to 301.4 \(\mu\)g Kg\({}^{-1}\). The opposite was observed for nonylphenol isomers that were detected mostly in milk samples (<428.7 \(\mu\)g Kg\({}^{-1}\)) while other dairy products (mostly yogurts) rarely contained NP residues. A possible explanation is the relation of these results to the industrial processes of these co-products from milk and yogurt and their packaging material. For example, milk samples were mostly in paper packaging, while other commodities were in aluminum or plastic ones. Figure S11 illustrates the positive detections according to the type of packaging material. Moreover, the classification of the samples according to the fat content and its relation to the detected residues would be of particular interest (Figure 5). Despite the strong lipophilic properties of NPs expressed by the logarithm of the octanol/water partition coefficient of 4.2, higher percent concentrations of NPs were found in low-fat dairy products, e.g., chocolate milk of 1% fat (428.7 \(\mu\)g Kg\({}^{-1}\) of 4 NP) contrary to unexpected low concentrations found in high-fat dairy foodstuffs, e.g. condensed milk of 7% fat (49.3 \(\mu\)g Kg\({}^{-1}\) of NP). This observation is in agreement with other studies where results showed that the concentrations of NPs did not correlate with the food fat content [34]. As far as the less lipophilic BPA (K\({}_{\rm{ow}}\) of 2.2-3.4) is concerned, it seems that it is equally detected in all food commodities, and its appearance is not related to the fat content of foodstuffs. The same pattern was observed in the case of 4-tert-OP.
## 3 Methodology
### Machine Learning Methods
#### 3.2.3 Random Forest (RF)  
The RF model was first proposed by [PERSON] [36]. The RF model is a nonlinear statistical ensemble bagging method that constructs and subsequently averages many randomized de-correlated decision trees for classification and regression purposes [49]. For a regression problem, RF is a flexible and practical method that has the following characteristics: (1) it is unexcelled in accuracy among the current algorithms, and runs efficiently on large databases; (2) it can handle thousands of input variables without variable deletion; (3) it generates an internal unbiased estimate of the generalization error as the forest building progresses; and (4) it features an effective method of estimating missing data and maintains accuracy when a large proportion of the data are missing. Based on the above advantages, the RF model has been widely used in the establishment of regression relations, and good prediction results have been obtained [50; 51].  
In regression, RF employs recursive partitioning to divide the data into many homogeneous subsets, and multivariate regression trees are built using a deterministic algorithm. The results of all the trees are then averaged. In each subset, each tree is independently grown to its maximum size based on a bootstrap sample from the training dataset, without any pruning, and the ensemble predicts the data that are not in the tree (the out-of-bag (OOB) data). The regression tree is built by selecting a random set of predictors (the dataset) and response variables (the target) by a set of decision rules. The rules are constructed based on recursively partitioning the input space into successively smaller regions, which are determined by binary splits. By calculating the difference in the mean-square error between the OOB data and the data used to grow the regression trees, the RF algorithm provides an error for the prediction called the OOB error of the estimate for each variable. The binary splits in the feature space are then selected by minimizing the difference in a cost function, between the response variable and the predicted response that would result from a specific split. The final output is the model in the form of a tree, with the branches corresponding to the splitting rules and terminal nodes corresponding to the mean response for a particular set of decision rules [49]. In our study, the RF model is implemented based on the package compiled with MATLAB and Visual C++ express edition, downloaded from Google code ([[https://code.google.com/archive/p/randomforest-matlab/downloads](https://code.google.com/archive/p/randomforest-matlab/downloads)]([https://code.google.com/archive/p/randomforest-matlab/downloads](https://code.google.com/archive/p/randomforest-matlab/downloads))).
# The Application of Numerical Ductile Fracture Simulation in the LBB Evaluation of Nuclear Pipes
## 3 Numerical Ductile Fracture Simulation  
### Ductile Fracture Model  
In this study, the SMCS model, which simplifies the microvoid damage evolution process and assumes that critical fracture strain (\(\varepsilon_{f}\)) is only related to the stress triaxiality (\(\sigma_{m}/\sigma_{e}\), the ratio of mean normal stress and equivalent stress), is applied to simulate the crack growth behavior of SA312 TP304L stainless steel pipes:  
\[\varepsilon_{f}=A\ exp\left[-1.5\frac{\sigma_{m}}{\sigma_{e}}\right]+B \tag{6}\]  
where \(A\) and \(B\) are material constants, which can be determined by fitting the test data of smooth and notched bars. The accumulated damage is calculated as  
\[\omega=\sum\Delta\omega=\sum\frac{\Delta\varepsilon_{p}}{\varepsilon_{f}} \tag{7}\]  
where \(\Delta\varepsilon_{p}\) is the equivalent plastic strain increment. Usually, when the accumulated damage becomes unity, \(\omega=\sum\Delta\omega=1\), ductile failure is assumed. However, as crack-tip stress and strain in FE simulation show high dependence on the element size, to achieve valid simulation results, an element size of less than 0.2 mm is usually required, which will lead to numerical problems in the simulation of crack growth behaviors for full-scale pipes. Thus, an element-size-dependent critical damage parameter (\(\omega_{c}\)) is introduced, which ensures a larger element size can be used in the simulation. The value of \(\omega_{c}\) for specific element size can be calibrated by comparing the simulation and test results of CT specimens. When the damage value of an element reaches \(\omega_{c}\), the stress in the element is reduced sharply (a decrease of 5000 MPa) to a small plateau value (10 MPa), then the Young's modulus is decreased to 100 MPa to keep the numerical stability. The SMCS model is implemented in ABAQUS 6.14 through the user subroutines.
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacteriocins in Cancer Therapy  
Bacteriocins are ribosomally synthesized by bacteria and are generally described as peptides or proteins that could inhibit or kill other related or nonrelated bacterial strains. There are several large categories of bacteriocin that are categorized in several ways. Bacteriocins produced by gram-negative bacteria are classified according to their size, including microcins (less than 20 kDa in size), colicins (20 to 90 kDa in size), and tailocins (high molecular weight bacteriocins). Bacteriocins derived from gram-positive bacteria are typically classified into four different groups. Class I bacteriocins are small (less than 5 kDa) thermostable peptides that are post-translationally modified, also known as lentibiotics. These contain unusual amino acids such as lanthionine (Lan), methylanthionine (MeLan), dehydrodualanine (Dha), dehydrobutyrine (Dhb), and D-alanine (D-Ala). Some class I members include nisin, lactic, and mersacidin [39; 40]. Class II bacteriocins are small (\(<\)10 kDa) thermostable bacteriocins, having an amphiphilic helical structure that helps them penetrate into the membrane of the target cell. This class is subdivided into different subclasses including class IIa, IIb, and IIc. Pediocin PA-1 and sakacin A are examples of subclass IIa, subclass IIb bacteriocins include lactacin F and lactococci G, and Gassericin A, circularin A, and carnocyclin A are members of subclass IIc [9; 41; 42]. Class III bacteriocins are large (\(>\)30 kDa) heat-labile bacteriocins, which are subdivided into two subclasses including bacteriolysins (subclass IIIa) and nonlytic proteins (subclass IIIb). Subclass IIIa is able to lysis the cell via degradation of the cell wall, and lysostaphin is one of the examples of this group that hydrolyzes the cell walls of several Staphylococcus species. Subclass IIIb causes the death of target cells by disrupting the plasma membrane. Some members of this class include megacins (of _Bacillus megaterium_), klebicin (of _Klebisella pneumonia_), helveticin I (of _L. helveticus_), and enterolysin (of _E. faecalis_). Class IV is characterized as complex proteins containing lipid or carbohydrate moieties [2; 43]. The result of a recent study demonstrated that bacteriocins could be used in the food industry as a preservative to extend the shelf life of products, and are non-immunogenic and practical in infectious disease treatment, while having specific toxicities for the cancer cell and cancer therapy (Table 1).
# Determining Rotor Blade Multi-Mode Vibration Components +
Footnote †: The new algorithm presented in this paper determines the multi-mode blade vibration components when the time of blade arrival is known from an experiment. The validation of the algorithm is presented in a numerical simulation, which assumes the blade vibration parameters. This shows the accuracy of the calculated vibration velocity amplitude and phase, as well as the good agreement between the calculated and assumed velocities. The accuracy of the calculations increased with the number of rotations up to N = 50. Therefore, N = 50 was used in further calculations. SO-3 engine 1 st-stage compressor rotor blades were analyzed for the nominal 15,000 rpm and the non-nominal 12,130 rpm regimes using the proposed Least Squares algorithm over the tip-timing method/data collection/procedure. The 1 st-stage compressor rotor blades of SO-3 engine were analyzed using tip-timing and the Least Squares algorithm for nominal 15,000 rpm and non-nominal 12,130 rpm. Two sensors in the casing and a once-per-revolution sensor below were used. The rotor blade was found to vibrate predominantly with one-mode shapes, but the second mode was also visible