esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities
## 2 Related Work
### Refactoring Documentation  
[PERSON] and [PERSON] (1998) searched words stemming from the verb _refactor_, such as _refactoring_ or _refactored_, to identify refactoring-related commits. [PERSON] et al. (2016, 2017) also used a similar keyword-based approach to detect refactoring activity between a pair of program versions in order to identify whether a transformation contains refactoring. The authors identified refactorings based on a set of keywords detected in commit messages and focused on the following 13 terms in their search approach: _refactor_, _restruct_, _clean_, _not used_, _unused_, _reformat_, _import_, _remove_, _replace_, _split_, _reorg_, _rename_, _and move_. Later, [PERSON] et al. (2018) replicated [PERSON] experiment in two open source systems using [PERSON]'s 13 keywords. They concluded that commit messages in version histories are unreliable indicators of refactoring activities. This is due to the fact that developers do not consistently document refactoring activities in the commit messages. In another study, [PERSON] et al. (2019) compared and evaluated three approaches, namely manual analysis, commit message, and dynamic analysis, in order to analyze refactorings in open source repositories in terms of behavioral preservation. The authors found, in their experiment, that manual analysis achieves the best results in this comparative study and is considered as the most reliable approach in detecting behavior-preserving transformations. In another study, [PERSON] et al. (2020) surveyed 328 professional software engineers at Microsoft to investigate when and how they conduct refactoring. They first identified refactoring branches and then asked developers about the keywords that are usually used to mark refactoring events in commit messages. When surveyed, the developers mentioned several keywords to mark refactoring activities. [PERSON] et al. matched the top ten refactoring-related keywords identified from the survey (_refactor_, _clean-up_, _rewrite_, _restructure_, _redesign_, _move_, _extract_, _improve_, _split_, _reorganize_, _and_ _rename_) against the commit messages to identify refactoring commits from version histories. By using this approach, they found 94.29% of commits do not have any of the keywords, and only 5.76% of commits included refactoring-related keywords. Prior work (2011, 2012, 2013, 2014, 2015, 2016) has explored how developers document their refactoring activities in commit messages; this activity is called Self-Admitted Refactoring or Self-Affirmed Refactoring (SAR). In particular, SAR indicates developers' explicit documentation of refactoring operations intentionally introduced during a code change.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Security Challenges of UIoT
#### 3.3.4 Localization Techniques  
In UIoT networks, node management is necessary to protect the nodes from physical damages and security attacks. In this case, it is necessary to adapt localization techniques to UIoT nodes to identify the location of each node underwater. However, due to heavyweight and environmental limitations, the localization mechanism in terrestrial networks cannot be applied directly to the UIoT environment [41].
## 2 Related Work
### Relief Extraction Using Machine Learning  
Among the machine learning models with acceptable performance in the extraction of reliefs is the SVM-based approach [24]. The main idea of this method involves the initial extraction of relief candidate segments which includes actual reliefs and relief-like noise (cracks/dents). Then, various geometrical features are extracted from the candidate segments, and, lastly, the SVM model is trained to carefully determine if each candidate is actual relief or non-relief. The relief candidate segments are extracted from the stele using the modified curvature-based method [18]. The geometrical features extracted from the segments consist of appearance, cross-section, and local extrema information.  
The size and positions of relief candidate segment are important parameters for describing the appearance of objects. While the number of vertices in the segment determines the area of the segments, the statistical values of the surface points and their corresponding normalized surface points of the relief segments provide useful information relating to the positions of the surface points that are identified within the candidate segments. To obtain the cross-section features, 2D skeleton information of the segments are derived from the \(x\)- and \(y\)-coordinates of the surface points within the segments. The width and depth of the cross section and the magnitude of the curvatures are extracted as the cross-section features. For the local extrema information, the location of the maximum depth point along the skeleton of the segments is considered. This local feature relies on the depth value and the distance separation between the point of interest and the skeleton.  
The numbers of features extracted for the appearance, cross-section, and local extrema are 31, 25, and 23, respectively. Altogether, these features are used to train a binary SVM classifier with a third-order polynomial kernel.
# Effect of Track Irregularities on the Response of Two-Way Railway Tracks
## 1 Introduction
In this study, a model comprising three-dimensional soil domain and two-way track to investigate the effect of radiating waves from one track to the nearby track is developed. This model also considers the nonlinear train-track interaction and the rail-sleeper-ballast-soil interaction. Moreover, Lysmer boundary is considered to avoid the reflection of the wave from the soil domain. Finally, the results of this numerical simulation and the effect of different classes of irregularities on tracks at different speeds are discussed.
# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
## 2 Related Work  
Image classification has become an active research topic in computer vision. CNN-based image classification methods play an important role in image classification. In addition, with the development of graphics processing unit (GPU)-based hardware and open source libraries such as Tensorflow [19] and Keras [20], CNN-based image classification learning algorithms have become prominent [11]. Some of the CNN-based architectures used for image classification are AlexNet [21], SqueezeNet [22], and VGG16 [23] as convolutional neural network (CNN) algorithms.  
The accuracy performances of these DL algorithms may be different. In order to train a DL network for image classification, a dataset with a large number of images should be introduced to the network [9]. Some libraries such as IMAGENET [6] and CIFAR10 [7] provide picture datasets to train the network. IMAGENET and CIFAR10 libraries consist of labeled image data that are directly obtained from real picture data by photographing. This library has been used in some studies in the literature to solve some image classification problems [6; 24]. Due to the fact that datasets have a limited number of classes, they might not offer solutions to all problems with a desired accuracy. To train a class that is not available in libraries, it is necessary to create a new dataset. In such cases, a dataset can be created by taking many object photographs with a camera or by generating synthetic images with a game engine [25; 26] according to the problem. CNN-based algorithms are frequently used to train DL networks for image classification [27; 28; 29]. There are some studies in the literature which make use of CNN-based image classification using pictures of real objects or synthetic objects [30].  
Several studies have used synthetic object image data to identify computer-aided design (CAD) model files provided from the CAD model library [31]. Some studies also use a video and image dataset created with the rendering tools of game engines [32; 33; 34] to integrate AI into the games. The common purpose of machine learning studies using synthetic data in addition to real data is to increase the number of samples of datasets where the number of samples is low or limited [32]. In cases where the number of samples is limited and a CNN-based machine learning algorithm is used, the success of the network is low. To increase the performance of the network, many samples are needed [35]. To increase the number of samples, synthetic images can be generated by using 3D models and game engines. Using a game engine's rendering tools, a 3D model can be photographed from different angles and distances. This flexibility provided by the game engine allows the creation of many samples and removes the constraints on the dataset. In the literature, this method is used to diversify real datasets and increase the number of data. In this study, we aimed to create a dataset from fully synthetic data.
## 6 Conclusions  
This study involved the analysis of a complicated large-scale landslide in a hydro-power project area in China, and presented the application of multi-factor SVRM methods in landslide deformation prediction. Single-factor, two-factor, and three-factor SVRM models were built, and the parameters of these models were determined using a grid-research method. In comparison, we found the prediction accuracy of the models to be superior in terms of landslide development prediction. The accuracies of the two- and three-factor models were slightly higher than that of the single-factor model, and a two-factor model (displacement rate and rainfall) had the highest accuracy.  
In this regard, the consideration of multiple factors is helpful for improving the accuracy of landslide prediction models because models that include more factors have a higher prediction accuracy than a single-factor model. However, more factors might not produce a higher accuracy model [6; 43], depending on whether the factors involved in the model are key factors affecting the development process of a landslide [48]. Thus, it is crucial to determine the key controlling factors that influence accurate prediction of landslide development.  
[PERSON] was responsible for the conceptualization, investigation, data processing, model output, and original draft. [PERSON] was responsible for investigation and model validation. Both authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{1}{c}{**Single Factor**} & \multicolumn{1}{c}{**Two Factors**} & \multicolumn{1}{c}{**Three Factors**} \\ \cline{2-5}  & **Displacement Rate** & **Displacement Rate** & **Displacement Rate** & **Displacement Rate** & **Rainfall** \\  & **Rainfall** & **Reservoir Water Level** & **and Reservoir Water Level** \\ \hline \(c\) & 0.5 & 11.31371 & 1 & 11.31370 \\ \(\gamma\) & 2.82840 & 0.06250 & 0.70711 & 0.06250 \\ RMSE & 0.01644 & 0.00614 & 0.01063 & 0.00739 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters and prediction accuracy of the models.  
**Funding:** This study was supported by the National Natural Science Foundation of China (No. 41772386), the Strategic Leading Science and Technology Project of Chinese Academy of Sciences (Class A) (No. XDA23090203), and the National Key Research and Development Plan of China (No. YS2018 YGFH000001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are not publicly available due to privacy or ethical restrictions.  
**Acknowledgments:** The authors wish to thank the anonymous reviewers for their constructive comments. Special thanks are given to [PERSON] for providing the landslide monitoring data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### SLR Observations of BDS Satellites  
With an increasing number of satellites tracked by ILRS, tracking new GNSS satellites such as BDS is becoming more challenging for laser stations. To cope with the growing demand for observations, SLR stations are constantly optimizing their tracking strategies. Currently, SLR stations can track more than 140 different satellites by adjusting their tracking strategies [21]. Moreover, the number of available SLR stations has been increasing annually along with the development of ILRS [22]. As of August 2022, 34 stations can receive the return laser from BDS satellites, the distribution of which can be seen in Figure 1. Additionally, Table 2 lists their monument code, detector type, location name, and country. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **MEO** & **GEO/IGSO** & **MEO** \\  & **(SHAO)** & **(SHAO)** & **(NCRIEO)** \\ \hline No. corner cube & 42 & 90 & 38 \\ Diameter of each cube & 33 mm & 33 mm & 33 mm \\ Height of each cube & 24.0 mm & 24.0 mm & 23.3 mm \\ Dihedral offset & 0.6\({}^{\prime\prime}\) & 0.5\({}^{\prime\prime}\) & 1.1\({}^{\prime\prime}\) \\ \multirow{3}{*}{Satellites (Tracking by ILRS only)} & COMPASS-M3 (PRN: C11) & COMPASS-M3 (PRN: C08) & BeiDou3-M2 (PRN: C20) \\  & BeiDou3-M9 (PRN: C29) & COMPASS-I5 (PRN: C10) & BeiDou3-M3 (PRN: C21) \\ \multirow{3}{*}{Satellite (Tracking by ILRS only)} & COMPASS-I6B (PRN: C13) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of LRAs equipped on the nine BDS satellites long-term tracked by ILRS. Figure 1: The map of the distribution and condition of worldwide SLR stations tracking BDS. SLR stations typically employ three detector types including Photo-Multiplier Tube (PMT), Micro-Channel Plate (MCP), and Compensated Single-Photon Avalanche Diode (CSPAD). CSPAD has been improved over SPAD and both are capable of detecting single returning photons, making them single-photon stations. MCP and PMT can simultaneously detect multiple returning photons, making them multi-photon stations. Variations in detector types installed at SLR stations can lead to differences in their performance, as demonstrated in studies of center-of-mass corrections for spherical geodetic satellites [23] and the signature effect of GNSS satellites [16; 17]. The number of monthly SLR observations of long-term tracked satellites in the period of February 2017 to March 2021 are presented in Figure 2. Please note that the GEO satellites are not prioritized due to meter-level variations in their orbits among the products released by ACs [24] On average, the number of monthly SLR observations of MEO and IGSO satellites are around 250 and 150, respectively, with maximum values of 568 and 360, respectively. Among them, the number of MEO and IGSO satellites observed by CSPAD, MCP, and PMT stations are around 140 and 120; 100 and 30; and 6 and 3, respectively.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 4 Results and Discussion
### Result Analysis with XAI Techniques
#### 4.3.3 ANCHOR Explanation  
Table 5 illustrates how the Anchor algorithm predicts a 'Moderate' level of adaptability in students when present, based on specific features. Network Type greater than 1.00 indicates that at least 3G or 4G connectivity is associated with a 'Moderate' adaptability prediction, reflecting the importance of reliable internet access for students' ability to adapt to contemporary educational demands. Age less than or equal to 1.00 corresponds to students in the age group '16-20', revealing that adaptability may be particularly complex during late adolescence. Gender greater than 0.00 suggests that female students are predicted to have 'Moderate' adaptability, indicating gender-specific educational experiences or socialization patterns that influence adaptability. Institution Type greater than 0.00 refers to students attending 'Non-Government' institutions, suggesting that these institutions may have different resource levels or pedagogical approaches. Financial Condition less than or equal to 1.00 captures 'Mid' or 'Poor' economic backgrounds, emphasizing the role of socioeconomic status in shaping a student's adaptability.  
The precision and coverage metrics accompanying these rules indicate a high level of reliability and relevance within the dataset, suggesting these findings are consistent and pertinent to a subset of the student population. By integrating these interpretive insights into the study, educators and policymakers can better understand the factors contributing to student adaptability levels and inform initiatives to upgrade digital infrastructures. The distinction between government and non-government institutions in adaptability predictions may reflect differential access to resources, student support, and extracurricular opportunities, prompting a re-evaluation of resource distribution to ensure all institutions can foster student adaptability.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Anchor Explanation Condition**} \\ \hline Feature & Condition \\ \hline Institution Type & \textgreater{}1.00 \\ \hline Age & \textless{}1.00 \\ \hline Gender & \textgreater{}0.00 \\ \hline Financial Condition & \textless{}1.00 \\ \hline Precision & 0.9732360097323601 \\ \hline Coverage & 0.0888 \\ \hline Predicted Class & Moderate \\ \hline \hline \end{tabular}
\end{table}
Table 5: (Local) Anchor explanation for a ‘Moderate’ adaptability prediction.
# Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks
## 6 Conclusions and Future Work  
This paper proposes GLO-MIA, the first label-only MIA against GNNs in graph classification tasks, revealing the vulnerability of GNNs to such threats. Based on the intuition that the target model's predictions on the training data are more stable than those on testing data, GLO-MIA can effectively infer the membership of given graph samples by comparing their robustness scores with a predefined threshold. Our empirical evaluation results on four popular GNN models (GCN, GAT, GraphSAGE and GIN) and three benchmark datasets show that the attack accuracy of GLO-MIA can reach up to 0.825 at best, with a maximum improvement of 8.5% compared with the gap attack, and the performance of GLO-MIA is close to that of probability-based MIAs. Furthermore, we analyze the impact of perturbation magnitude and the number of perturbed graphs on the attack performance. Finally, we discuss the impact of regularization techniques as a possible defense on the attack performance.  
However, GLO-MIA applies under the assumption that the shadow dataset and target dataset follow the same distribution, and it is currently applicable to small/medium-scale undirected attributed graphs. In future work, we will enhance GLO-MIA's adaptability to diverse graph types (e.g., large-scale graphs) and improve its attack generalization capability across different distribution scenarios. We will also study other label-only MIAs in graph classification tasks and defense methods against these MIAs.  
Writing--original draft preparation, Y.L.; writing--review and editing, J.D. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the Shanghai Natural Science Foundation under grant number 22 ZR1422600.  
**Institutional Review Board Statement:** Not applicable.  
Figure 6: Impact of regularization on GLO-MIA and model performance (red line: classification accuracy on training set; orange line: classification accuracy on testing set; blue line: GLO-MIA; x-axis: weight decay coefficient; y-axis: accuracy).
## 4 Case Verification
### Data Preprocessing  
The factor analysis algorithm is used to preprocess the ambient temperature, load current, and top oil temperature in the above data set. After the establishment of the factor model, the KMO test and the Bartlett test are first performed. According to KMO = 0.723, the \(p\) value of the Bartlett test is less than 0.001, so this group of data is suitable for factor analysis.  
Table 4 shows the variance and cumulative sum explained by each common factor. From the initial eigenvalues, the variance percentage of the first common factor is 81.688. It represents that 81.688% of the information of the original variable can be explained by the first common factor. The percentage of variance of the second common factor is 11.775, so the second common factor can explain 11.775% of the information in the original variable. The cumulative variance of the first two common factor explanations is 93.463%. These two common factors already contain most of the variability of the original data and effectively summarize the main features of the original data set. However, the subsequent common factors have little contribution to the interpretation of the original variables, so the first two common factors are extracted for analysis and prediction tasks. The square sum of extracted loads is the variance contribution information of the two common factors extracted without rotation. The values are the same as the first two rows of initial eigenvalues. The square sum of rotational loads is the variance contribution information of the new common factor obtained after rotation, which helps distinguish and explain different factors. Compared with the unrotated contribution information, the variance contribution rate of each common factor changes, but the final cumulative variance contribution rate remains unchanged at 93.463%.  
Figure 7: The transformer load data.  
1. The component matrix after rotation is shown in Table 5. Obviously, the first common factor corresponds to a larger value in terms of ambient temperature and top oil temperature, while the second common factor is associated with a higher value of load current compared to the other two factors, which better represents the characteristic parameter of load current. Therefore, the first common factor is known as temperature factor \(f_{1}\), and the second common factor is known as load factor \(f_{2}\). These factors primarily represent the temperature variables and load variables that impact the temperature rise characteristics of the transformer.  
Table 6 shows the component score coefficient matrix after the rotation factor. Factor scores corresponding to temperature factor f1 and load factor f2 are obtained. They are entered into the fully connected neural network as two new variables.  
\[\left\{\begin{array}{l}f_{1}=0.889\widehat{x_{1}}-0.576\widehat{x_{2}}+0.508 \widehat{x_{3}}\\ f_{2}=-0.503\widehat{x_{1}}+1.267\widehat{x_{2}}-0.021\widehat{x_{3}}\\ \end{array}\right. \tag{7}\]  
where \(\widehat{x_{1}}\), \(\widehat{x_{2}}\), and \(\widehat{x_{3}}\) are the three original variables after standardization.
## 3 Results
### Simulation Results  
#### 3.2.1 Performance Metrics  
Simulations were performed for two scenarios, (1) using an offline motion planer or open-loop trajectory generator (28) using desired azimuth/elevation trajectories, and (2) closed-loop trajectory generation where the motion planer employs real trajectories (27), for both controllers PID and A-PI. Tables 3 and 4 show four performance metrics over the point-to-point tracking phase, for closed-loop and open-loop motion planning, respectively. Notice that, in all cases, the A-PI outperforms the PID, and remarkably in some cases, the open-loop trajectory generation shows better metrics in comparison to its close-loop counterpart.