esa-sceva/satcom-chunk-collection huggingface dataset:
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 5 Experimental Result
### Our Dataset  
To evaluate the performance of the proposed bug localization method, we utilized a dataset comprising six open-source software projects: Tomcat, AspectJ, Birt, Eclipse, JDT, and SWT. These projects were selected to ensure diversity in application domains, scale, and complexity, thereby allowing a comprehensive evaluation of the model across various real-world scenarios. The dataset included a total of 22,747 bug reports and 1276 source code files. Specifically, Tomcat contained 1056 bug reports and 155 source code files, representing a widely used web server environment with a focus on backend software. AspectJ, an aspect-oriented programming extension of Java, provided 593 bug reports and 194 source code files, which are particularly valuable for testing bug localization in modular programming contexts.  
Birt, a data visualization and reporting tool, included 4178 bug reports and 231 source code files, reflecting challenges associated with data-driven software. Eclipse, a prominent integrated development environment, contributed 6495 bug reports and 254 source code files, offering a diverse and extensive set of debugging scenarios encountered by software developers. JDT, the Java Development Tool within Eclipse, provided 6274 bug reports  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer (Type)** & **Output Shape** \\ \hline embedding (Embedding) & (None, 100, 100) \\ \hline LSTM & (None, 100, 128) \\ \hline attention\_with\_context & (None, 100, 128) \\ (AttentionWithContext) & (None, 128) \\ \hline addition (Addition) & (None, 128) \\ \hline dense (Dense) & (None, 976) \\ \hline \hline \end{tabular}
\end{table}
Table 3: A Summary of LSTM-Attention.  
and 222 source code files, focusing on issues related to Java programming. Finally, SWT, a graphical user interface library, included 4151 bug reports and 220 source code files, emphasizing UI-specific bug localization challenges.  
The selection of these projects was guided by their ability to represent diverse software domains and their availability of comprehensive bug reports and source code. The large number of bug reports ensured robust evaluation of the model, while the variety in project size and scope tested its adaptability and scalability by including projects of varying complexity, from smaller-scale systems such as Tomcat to extensive platforms such as Eclipse. The dataset allowed for a thorough analysis of the model's effectiveness across different software maintenance and debugging contexts.
## 5 Numerical Simulation
### Simulation Results  
#### 5.3.1 Case 1: Horizontal Contour  
In this case study, a horizontal desired contour is selected to verify the effectiveness of the proposed controller compared to the ESOC and the SMOC. In Figure 5a, it is observed that the proposed controller and the SMOC provide high accuracy contouring control performance within prescribed bounds while the ESOC cannot achieve that, which shows the superiority of the proposed SMO compared to the ESO. Similar results are described in Figure 5b,c for the tangential errors and contouring errors, respectively. However, only the proposed controller guarantees the prescribed performance in these error components since the model-based control with BLF is applied here compared to the PD control of the ESOC and SMOC. Anyways, the contour shapes are given in Figure 5d, which once again states the dominations of the proposed control algorithm with the other ones.  
For further analysis of these results, the disturbance estimation performances of the proposed SMO and ESO in terms of the boom, arm, and bucket are presented in Figure 6. One observes that the SMO in the proposed controller shows the fastest transient responses when abrupt disturbances occur compared to the SMO in the SMOC and the ESO, which explains the ability to keep the errors component inside the bounds of the proposed controller as mentioned above. In the steady-states, the ESO even shows better disturbance estimation compared to the other ones, but the tracking performance cannot be maintained since it already violates constraints at the last transient times.  
In addition, Figure 7 describes the high-accuracy velocity estimation performance of the proposed SMO during the operation, which partly contributes to the final tracking performance of the proposed control algorithm in Figure 5.  
Figure 5: Control performance in terms of (**a**) contouring error, (**b**) tangential error, (**c**) orientation error, and (**d**) contour shape in Case 1.  
Figure 6: Estimation performance of (**a**) force disturbance in the x-direction, (**b**) force disturbance in the y-direction, and (**c**) torque disturbance in Case 1.  
Figure 7: Velocity estimation performance of (**a**) boom, (**b**) arm, and (**c**) bucket in Case 1.  
Finally, the control signals of both controllers are shown in Figure 8. Overall, at the steady state, there is not so much difference in the control signals of the SMO-based controllers and the ESOC. However, in the transient response, since the ESOC reacts slower with changes in disturbance and provides the worst tracking errors, it generates the highest control signal compared to the remaining ones.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 5 Development Trend of Water Quality Monitoring Based on UV-Visible Spectroscopy
### Combination of UV-Vis Spectroscopy and Wireless Communication Network Technology  
Since 5G networks are expected to massive expand today's Internet of Things (IoT), humankind has entered the era of big data through the rapid development of IoT technology, With the help of the IoT, key information of target objects can be easily obtained and relevant data stored in cloud space [75]. Through the combination of UV-Vis spectrum technology and data communication technology, real-time monitoring and early warning water quality parameter data can be obtained, which is one of the main development trends for water quality monitoring based on UV-Vis spectroscopy in the future [76; 77; 78]. Water quality monitoring in the form of an early warning system was shown in Figure 6.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 4 Discussion  
### Performance Comparison of Winter Wheat Yield Prediction Models  
We compared commonly used machine learning models with the ensemble voting model constructed in this study. The results show that the R\({}^{2}\) values for different algorithm models range from 0.03 to 0.90, demonstrating significant performance differences. Among these models, the ensemble voting model exhibited the highest R\({}^{2}\) and the lowest RMSE and MAE for both the full growth cycle and individual growth cycles. This model integrates ridge, gradient boosting, and random forest using a weighted approach during training, where the results of weak learners compensate for the errors of individual learners [49; 50], enhancing the flexibility of reducible error [51] and showcasing the substantial advantages of ensemble learning in overall prediction performance. When selecting the best ensemble method for a given problem, it is important to consider the suitability of the setup (such as class imbalance and high dimensionality) as well as computational costs [52]. Due to its advantages of fast training speed and low computational cost, ensemble learning methods are widely used in various fields, including short-term power load forecasting, cost estimation, and plasma reaction dynamics modeling [53; 54; 55].
###### Abstract  
With the rapid development of unmanned aerial vehicle (UAV) manufacturing technology, large-scale UAV swarm ad hoc networks are becoming widely used in military and civilian spheres. UAV swarms equipped with ad hoc networks and satellite networks are being developed for 6G heterogeneous networks, especially in offshore and remote areas. A key operational aspect in large-scale UAV swarm networks is slot allocation for large capacity and a low probability of conflict. Traditional methods typically form coalitions among UAVs that are in close spatial proximity to reduce internal network interference, thereby achieving greater throughput. However, significant internal interference still persists. Given that UAV networks are required to transmit a substantial amount of safety-related control information, any packet loss due to internal interference can easily pose potential risks. In this paper, we propose a distributed time coalition formation game algorithm that ensures the absence of internal interference and collisions while sharing time slot resources, thereby enhancing the network's throughput performance. Instead of forming a coalition from UAVs within a contiguous block area as used in prior studies, UAV nodes with no interference from each other form a coalition that can be called a time coalition. UAVs belonging to one coalition share their transmitting slots with each other, and thus, every UAV node achieves the whole transmitting slots of coalition members. They can transmit data packets simultaneously with no interference. In addition, a distributed coalition formation game-based TDMA (DCFG-TDMA) protocol based on the distributed time coalition formation algorithm is designed for UAV swarm ad hoc networks. Our simulation results verify that the proposed algorithm can significantly improve the UAV throughput compared with that of the conventional TDMA protocol.  
U  
25 January 2025  
Revised: 12 February 2025  
24 February 2025  
Published: 28 February 2025  
**Citation:** [PERSON]; [PERSON] Slot Allocation Protocol for UAV Swarm Ad Hoc Networks: A Distributed Coalition Formation Game Approach,  
**Entropy 2025**, 27, 256. [[https://doi.org/10.3390/e27030256](https://doi.org/10.3390/e27030256)]([https://doi.org/10.3390/e27030256](https://doi.org/10.3390/e27030256))  
**Copyright:** 62 2025 by the authors.  
Unionsec MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods
### Characterization  
#### 2.3.1 X-ray Diffraction  
A Rigaku Smartlab (Rigaku Corporation, Tokyo, Japan) diffractometer was employed to take X-ray diffraction data, utilizing CuK\(\alpha\)l (\(\lambda\) = 1.5406 A) radiation. The generator radiation's acceleration voltage was set at 45 kV, while the emission current was 200 mA in this test. Room temperature diffractograms were acquired using parallel beam geometry, scanning 20 = 5\({}^{\circ}\) to 60\({}^{\circ}\) at increments of 0.02\({}^{\circ}\) in a continuous manner at a scanning pace of 4\({}^{\circ}\)/min. The Bragg equation was used to determine the interplanar distance (d) of PP in composites:  
\[\upmu\lambda=2\text{d}\,\sin\theta \tag{1}\]  
Here, n is the reflection order, \(\theta\) is the diffraction angle, \(\lambda\) is the wavelength of X-rays, and d indicates the distance between the crystal lattice planes, which cause the diffraction. The margin of error was \(\pm\)0.05%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **PP** & **MA** & **C** & **E** & **G** \\  & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** \\ \hline PP-25G & 75 & & - & - & 25 \\ PP-30G & 70 & & - & - & 30 \\ PP-25G-E & 52.5 & 2.5 & - & 20 & 25 \\ PP-25G-C & 66 & 2.5 & 6.5 & - & 25 \\ PP-25G-C-E & 46 & 2.5 & 6.5 & 20 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The PP composite formulations.  
Rigaku PDXL 2 data analysis software was used to obtain the crystallite sizes, height, intensity values and FWHM (full width at half-maximum intensity of the diffraction peak). Crystalline phases were identified according to the Powder Diffraction FileTM (PDF) of the International Centre for Diffraction Data (ICDD). Curve fitting applied to diffraction profiles was used to extract individual diffraction maxima. Using a peak deconvolution program (PDXL: integrated X-ray powder diffraction software), the amorphous phase was assumed to be represented by a broad peak at roughly 20 of 18\({}^{\circ}\). By reporting the area of the maximum diffraction of the crystalline phases to the total area (crystalline and amorphous phases) resulting from the elimination of the reference line, the crystallinity index (CI) was calculated with a margin of error of \(\pm\)2%. The orientation index (OI) was calculated with a margin of error of \(\pm\)5% using the relationship:  
\[\text{OI}=\text{I}_{040}/\text{I}_{110}-0.55 \tag{2}\]  
where I040 and I110 represent the height of the (040) diffraction peak and the height of the (110) diffraction peak, respectively, and 0.55 is the ratio of I040 and I110 determined for a perfect non-oriented PP. The mean size of the crystalline domains was determined based on the Scherrer equation:  
\[\text{B}(2\theta)=\frac{\text{K}\lambda}{\text{L}\cos\theta} \tag{3}\]  
where B(20) is the size of the ordered crystalline domains for the Bragg angle in question, K--a dimensionless shape factor (0.9), L--full width at half maximum (FWHM), \(\lambda\)--X-ray wavelength (1.54059A), and \(\theta\)--Bragg angle.
## 2 Materials and Methods
### Model Evaluation  
The performance was measured considering the accuracy (ACC), sensitivity (SEN), and specificity (SPE), which vary between 0 and 1:  
\[Accuracy=((tp+tn)/(tp+tn+fn+fp)), \tag{1}\]  
\[Sensitivity=(tp/(tp+fn)), \tag{2}\]  
\[Specificity=(tn/(tn+fp)), \tag{3}\]  
where _tp_ indicates true positives, _tn_ indicates true negatives, _fn_ indicates false negatives, and _fp_ indicates false positives.  
The randomized cross-validation (RCV) and leave-one-patient-out (LOO) validation methods were compared to evaluate the performance of the model. In RCV, the data samples were randomly split into training and test sets using fivefold cross-validation. The performance metrics were computed separately for each patient in the test set, and the process was repeated five times. The average performance metrics were then reported [24]. On the other hand, in LOO validation, each patient's recordings were iteratively left out of the training set. The performance of the model was evaluated separately for each patient, and the results were compared for both data sets to assess the differences between the two validation strategies.
## 2 Preliminaries  
### Graph Theory and Notations  
For the convenience of the following discussion, some notations will be used throughout this paper. \(R^{n}\) denotes the \(n-\)dimensional Euclidean space. \(R^{m\times n}\) denotes an \(m\times n\) real matrix space. \(I_{n}\) and \(0_{n}\) denote an \(n-\)dimensional identity matrix and a zero matrix, respectively. The term \(1_{n}\) denotes an \(n-\)dimensional column vector with all ones. For any symmetric matrix \(P\), \(P<0\) means \(P\) is a negative definite matrix, and \(P>0\) means \(P\) is a positive definite matrix. The superscript '\(T\)' means the transpose. The Kronecker product of matrices \(X\) and \(Y\) is denoted as \(X\otimes Y\). In this paper, if not clearly stated, matrices are assumed to have compatible dimensions.  
Throughout this paper, the networked system topology is represented by a weighted directed graph \(\mathcal{G}=(\mathcal{V},\epsilon,\mathcal{A})\) with the set of nodes \(\mathcal{V}=\{v_{1},v_{2},\ldots,v_{N}\}\), the set of directed edges \(\epsilon\subseteq\mathcal{V}\times\mathcal{V}\), and a weighted adjacency matrix \(\mathcal{A}=(a_{ij})_{N\times N}\), whose entry \(a_{ij}\) denotes connection and weight between agents \(v_{i}\) and \(v_{j}\). If node \(v_{j}\) can receive information from node \(v_{i}\), then \(a_{ij}>0\); otherwise, \(a_{ij}=0\). The in-degree and out-degree of node \(i\) are defined as \(deg_{in}(i)=\sum_{j=1,j\
eq i}^{N}a_{ji}\) and \(deg_{out}(i)=\sum_{j=1,j\
eq i}^{N}a_{ij}\), respectively, and the Laplacian matrix \(L=(l_{ij})_{N\times N}\) is defined as \(l_{ij}=-a_{ij},i\
eq j,l_{ii}=\sum_{j=1,j\
eq i}^{N}a_{ij}\).  
In this paper, we will split \(N\) agents into \(r\) groups (\(r\geq 2\)). That means \(\mathcal{V}=\cup_{k=1}^{r}\mathcal{V}_{k}\) and \(\mathcal{V}_{p}\cap\mathcal{V}_{q}=0\) for different \(p\) and \(q\) (\(p\
eq q\)). Let the symbol \(\mathcal{V}_{k_{i}}\) denote the group to which the \(i\)th agent belongs. Further, the number of groups is the same as the number of virtual leaders.  
The network topology in this paper we consider to be directed and weakly connected. Firstly, we learn some concepts related to inter-act agents and intra-act agents. Agent \(i\) is said to be an inter-act agent if \(i\) belongs to \(\widetilde{\mathcal{V}}_{k_{i}}\), in which agent \(i\) can receive information from other groups. An agent that can only exchange information among agents in the group in which the agent belongs is said to be an intra-act agent. The set of intra-act agents is signed as \(\mathcal{V}_{k_{i}}\setminus\widetilde{\mathcal{V}}_{k_{i}}\).
# Diverse Humanoid Robot Pose Estimation from Images Using Only Sparse Datasets
## 2 Related Work
### Marker-Based Human-Robot Interaction  
Extensive research has been conducted in the field of HRI through marker-based approaches [4]. [PERSON] et al. [37] propose a system that integrates interactive spatial augmented reality (ISAR) and head-mounted display (HMD) technologies to enable users to program workspaces even in the absence of a physical robot. This system allows users to perform programming tasks without the robot's presence, and the visualization of the program facilitates a more intuitive understanding of the tasks. [PERSON] et al. [38] focus on improving the accuracy of surgical assistants' instrument insertion and manipulation using augmented reality. The augmented reality-assisted system provides 3D visualization to enhance task accuracy, utilizing marker-based tracking to ensure precise alignment between the user's HMD and the instruments. [PERSON] et al. [39] examine how mixed reality (MR)-based communication interacts with cognitive load in users, proposing a method for overlaying virtual objects onto an MR environment using fiducial markers. [PERSON] et al. [40] describe a method for sharing spatial information between the user and the robot using visual markers, and propose an MR interface that allows the user to visually perceive the robot's workspace and intuitively manipulate objects. However, the reliance on markers can introduce various limitations when applied to general situations.
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 5 Discussion
### Limitations  
There are two research shortcomings in this study: First, the sample size needs to be increased [62]. The selection of cases in this study was based on research drawn from sources in the literature and from the Internet, which could have led to an incomplete sample set. Efforts should be made to increase the sample size in future studies to explore the issues more comprehensively. Second, the study only utilized objective empowerment, whereas a combination of subjective and objective empowerment would be more appropriate [63]. This approach may have neglected subjective factors such as expert opinions or practical situations. Objective data and subjective judgment should be combined for a more comprehensive assessment of the problem.
## 6 Discussion
### Implication and Future Work  
The implications of our study for researchers, developers, companies, or users are as follows. First, researchers can develop issue classification techniques based on the multimodal model proposed in this paper. For example, we applied the multimodal model to binary issue classification. We believe this approach can be extended to research trends involving multi-class and multi-label classification techniques. Second, developers can save time in reviewing issue reports by using automated issue classification techniques. Developers need to classify issues related to their software into related tasks for efficient maintenance. Automated issue classification can reduce developers' time spent in reading individual issue reports and help developers to avoid misclassification of software-related issues. To be practical, high performance of issue classification is crucial, and our multimodal model demonstrates its capability to improve the performance of automated issue classification. Finally, companies can employ automated techniques to classify issue reports, which can help identify systematic issue trends of the software systems. Our multimodal model can contribute to making the trends more accurate.  
In the future, we would like to explore state-of-the-art feature extraction techniques and fusion methods. First, while we used CNN\(-\)based channels to extract modality features for text, image, and code data in this paper, we observed that each modality has its own state-of-the-art feature extraction models. For example, for text data, we see that the state-of-the-art model is based on BERT, and for code data, we see that the state-of-the-art model is CodeBERT [44]. For images, we can consider computer vision models utilizing Transformers. By adopting state-of-the-art feature extraction models, we expect to improve the performance of issue classification. Second, we would like to experiment with several multimodal fusion methods, which merge the representation vectors of the three modalities. In this paper, we employed an entry-level multimodal fusion technique, namely, element-wise multiplication, which lacks interaction between the features of modalities. In the future, we will apply the latest methods based on bilinear pooling, such as Multimodal Compact Bilinear Pooling (MCB) [28], Multimodal Low-rank Bilinear Pooling (MLB) [29], Multimodal Tucker Union (MUTAN) [27], and Multimodal Factorized Bilinear Pooling (MFB) [45].These techniques aim to optimize feature interaction while minimizing computational complexity. We will examine whether the different fusion methods can contribute to the performance of issue classification.