esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
In transportation networks [13], the paralysis of critical nodes can have significant impacts on the connectivity, access efficiency, and capacity allocation of the entire transportation network. Existing algorithms can be categorized into local, semi-local, and global algorithms based on the characteristics of the nodes in the network. [PERSON] et al. proposed an algorithm named Weight LeaderRank [14], which assigns degree-related weights to links associated with nodes. This algorithm outperforms the PageRank [15] algorithm in identifying important nodes, is more tolerant of noisy data, and is more robust against attacks. [PERSON] et al. proposed a local propagation probability (LPP) [16] model to identify key nodes by recursively calculating the local propagation probability of each node, respectively seeking the propagation influence score between neighboring nodes of the same and different orders, and separating the nodes layer by layer based on the hierarchical structure score of the nodes. [PERSON] et al. measure the global importance of each node (GIN) [17], considering the influence of the node itself and its neighbors, which enables a more accurate identification. The k-shell algorithm identifies important nodes by decomposing the network layer by layer to examine their relative positions. However, it suffers from coarse granularity in identification. Local algorithms have a low time complexity but limited granularity, whereas global algorithms achieve higher accuracy at the cost of computational efficiency. Consequently, semi-local algorithms are often the superior choice. Due to the property of nodes to both propagate and receive information, information is repeatedly propagated in restricted areas where nodes are closely connected. Based on this, this paper proposes a semi-local algorithm called LBIA, which accurately identifies key nodes in the restricted region while maintaining a low time complexity. The main contributions of LBIA are outlined below.
# A Fast Approach to Optimize Trad Pattern Shape for Tire Noise Reduction
## Results and Conclusions
### The Validation of the Tire Noise Prediction Method
#### 5.3.2 The Objective Function  
The objective function plays a crucial role in determining the optimization result. According to the literature [52], any reduction in \(H_{ij}(f)\) can reduce the radiated noise. An exciting force between the tread pattern and the road that is around its mean value will lead to similar acoustic pressure variations inside the vehicle. According to the literature [28; 42; 53], a simple measure to quantify this change is the standard deviation; the standard deviation of the tread impact force function is considered to be a measure of the sound pressure level inside the vehicle:  
\[\widetilde{F}_{STD}=\sqrt{\int_{0}^{2\pi}\left(\widetilde{F}(\varphi)- \widetilde{F}_{mean}\right)^{2}} \tag{15}\]  
where \(F_{mean}\) is the average value of the exciting force. Furthermore, during the collision, the tread block impacts the road and pulsive noise is generated due to the deformation of the tread surface in the radial direction. Acoustic sources located near the contact path are then amplified by the tire-pavement geometry, resulting in far-field noise, which is then perceived by humans. The sound pressure level (SPL) experienced by pedestrians is important [54]. Here, we define the \(1/3\)-octave sound pressure level curve, as shown in Figure 13.  
Figure 12: The shapes of the leading edge are described by the parabola equation.  
\(f_{\rm s}\) and \(f_{\rm e}\) are the start and end frequencies, and \(S\) is the difference between the area of the original curve and that of the optimized curve. The objective function can be defined as:  
\[\widetilde{F}_{spl}=\max(S) \tag{16}\]  
When taking both objectives into account, the problem transforms into a multi-objective optimization problem. Generally, two methods can be used to solve multi-objective optimization problems: the Pareto method and the scalarization method [55]. In this study, NSGA-II was applied [56].
# BELMKN: Bayesian Extreme Learning Machines Kohonen Network
## 1 Introduction
The BELMKN framework consists of three levels, namely, feature learning, cluster prediction, and partitional clustering. In the first level, ELM-based feature learning utilizes transformations of data to extract useful features from the original data [28]. An elegant selection of features can greatly decrease the workload and simplify the subsequent design process. Generally, ideal features should be of use in distinguishing patterns belonging to different clusters; this helps to overcome the dataset that is prone to noise for better extraction and interpretation. In the next level, the model selection technique such as Bayesian Information Criterion (BIC) [29] can be used to predict the number of clusters from the ELM feature-extracted information. In the final level, the number of clusters predicted and the ELM feature-extracted data is given to the Kohonen Network to perform the clustering task. The performance of the proposed BELMKN framework is compared with the four clustering methods, namely, k-means [14], Self-Organizing Maps (SOM) [16], EM algorithm [18], and US-ELM [28]. The performance of the clustering algorithms is compared by applying 3 synthetic datasets and 10 standard benchmark datasets obtained from the UCI repository ([[https://archive.ics.uci.edu/ml/index.php](https://archive.ics.uci.edu/ml/index.php)]([https://archive.ics.uci.edu/ml/index.php](https://archive.ics.uci.edu/ml/index.php))). The rest of the paper is organized as follows. Section 2 presents the architecture diagram of the proposed BELMKN framework with a high-level description of the pseudo code. Section 3 discusses the illustrative examples (3 synthetic datasets) that are applied to the different clustering algorithms for comparison. Section 4 presents the results and discussion of various clustering methods by applying them to the ten benchmark datasets. Further, the effect of various parameters on clustering accuracy is discussed, and the paper is concluded in Section 5.
# Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
## 3 Experimental Setup
### LSTM Neural Network  
#### 3.4.1 Training Data  
To train the network, the mobile robot was operated in the proposed simulated dynamic environments and the velocities and sensor data labels were recorded at each time instance, which in this case, was every 0.1 s. Two additional parameters were added: the desired target point location in \((x,\,y)\) for the mobile robot \(tb3\_0\) and its odometry pose in \((x,\ y)\) coming from the odometry topic of the ROS. The target point was fixed, while the odometry pose of the robot varied every time instance \((0.1\ \mathrm{s})\).  
The trajectory planning and navigation examples were provided by the user to the network to be trained using the teleop command, which consisted of operating the mobile robot in the terminal window with specific keyboard keys that control the linear velocity in \(x\) and the angular velocity in \(z\) of the mobile robot \(tb3\_0\).  
The data was stored in a.csv file, where all the columns represent the 90 features coming from the LiDAR, and the target point and odometry pose location, while the rows represent each laser scanned packed with those features at each timestamp. The last two columns included are the linear velocity in \(x\) and the angular velocity in \(z\) recorded at each timestamp. It is crucial to mention that if a value is marked as infinite, it means that it was further away from the LiDAR reference frame, which had an operating range from 12 cm to 350 cm, so those values were replaced by the maximum range value of the sensor. That way it could be guaranteed that the proposed network would converge at some point. The roslaunch tool was implemented again to start two ROS nodes: the one with the obstacle controller algorithm, \"speed_controller\", and the one with the training data and.csv storage program, \"get_values\".
## 5 Results
### RQ1. Results of the Text-Image Experiment  
We first conducted the experiment that compared the Text Only model with the multimodal model that combined text and image data of issue reports. Table 3 shows the result. In Table 3, the \"Project\" column represents the target projects for the experiments, and \"Model\" indicates the experimental models. The \"Precision\", \"Recall\", and \"F1-score\" columns represent the performance evaluation metrics for each experimental model, with all values expressed in percentage (%) and rounded to two decimal places. Additionally, we highlighted the highest scores for each project in bold. The first row per each project in Table 3 corresponds to the Precision, Recall, and F1-score values of the Text Only model. The second row per each project in Table 3 corresponds to the Precision, Recall, and F1-score values of the the multimodal model MulDIC\({}_{TI}\) which combined text and image data.  
With regard to the Precision values that are shown in the third column of Table 3, the Text Only model yielded a Precision of 68.45% and the MulDIC\({}_{TI}\) model a Precision of 79.60% for the VS Code project. The difference was 11.15%. In the case of Kubernetes, the Text Only model yielded a Precision of 66.91%, while the MulDIC\({}_{TI}\) model a Precision of 68.35%. The difference was 1.44%. For Flutter, the Text Only model yielded a Precision of 67.70%, while the MulDIC\({}_{TI}\) model yielded a Precision of 69.79%. The difference was 2.09%. For Roslyn, the Text Only model yielded a Precision of 62.92%, while the MulDIC\({}_{TI}\) model yielded a Precision of 66.13%. The difference was 3.21%. The MulDIC\({}_{TI}\) model outperformed the Text Only model across four different projects in terms of Precision.  
With regard to the Recall values that are shown in the fourth column of Table 3, the Text Only model achieved a Recall of 68.27% for VS Code, 66.10% for Kubernetes, 67.66% for Flutter, and 62.86% for Roslyn. In comparison, the MulDIC\({}_{TI}\) model achieved a Recall of 77.92% for VS Code, 68.33% for Kubernetes, 69.41% for Flutter, and 66.04% for Roslyn. The MulDIC\({}_{TI}\) model made improvements in Recall of 9.65%, 2.23%, 1.75%, and 3.18% for VS Code, Kubernetes, Flutter, and Roslyn, when compared to the Text Only model.  
With regard to the F1-scores that are shown in the fifth column of Table 3, the Text Only model achieved an F1-score of 68.36% for VS Code, 66.50% for Kubernetes, 67.68% for Flutter, and 62.89% for Roslyn. In comparison, the MulDIC\({}_{TI}\) model achieved an F1-score of 78.75% for VS Code, 68.34% for Kubernetes, 69.60% for Flutter, and 66.08% for Roslyn. The MulDIC\({}_{TI}\) model made improvements in F1-score of 10.39%, 1.84%, 1.92%, and 3.19% for VS Code, Kubernetes, Flutter, and Roslyn, when compared to the Text Only model.  
These results indicate that the image data in issue reports are helpful in the issue classification task. This means that images are useful information for classifying issue reports. Therefore, when the model considers the text and image data of an issue report together, it gains a better understanding of the issue report.
# Simulation Analysis of Cyclone Separator for Separation of Cenospheres
## 1 Introduction
When the terminal settling velocity of the particles exceeds the airflow velocity, the heavier particles will sink against the flow; conversely, the lighter particles with terminal settling velocities lower than the airflow velocity will rise with the airflow to the top of the classifier [30,31]. A cyclone separator is a device that utilizes centrifugal force in gas-solid two-phase flow to achieve particle separation. It is widely used in industrial dust particle separation processes. With proper design adjustments, it can also be explored for hollow microsphere dry separation studies. A cyclone separator is mainly characterized by a simple structure, operation flexibility, high efficiency, easy management and maintenance, and low price, and it is widely used in the pharmaceutical industry, especially being suitable for coarse dust particles, dust concentration, and high-temperature and high-pressure conditions; it is also often used as a fluidized bed reactor within the separation device or as a pre-separator, and it is widely used in the industry of separation devices [32]. The cyclone separator will produce two internal and external cyclone fields when working; under the action of the two internal and external cyclone fields, the large-density particles will fall downward, and the small-density particles will escape from the upper outlet, realizing the separation of particles [33, 34, 35]. In this study, we have proposed a method for separating cencospheres from fly ash using a cyclone. We have considered the fact that cencospheres have a lower density than fly ash, a property that results in a greater centrifugal force in the cyclone, thereby increasing the likelihood of effective separation through the cyclone. Next, we used software to accurately model the cyclone separator and simulated it using Ansys 19.1 Fluent software. Through this simulation, we were able to analyze in detail the specific effects of model structural parameters and material incidence velocity on the separation efficiency. Based on the simulation results, we further selected the most optimized structural parameters to improve the design of the cyclone separator. This not only optimized the structural model but also provides a solid theoretical basis and guidance for the application of dry separation of cencospheres in industry.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Slice-Based QoS Manager Solution
#### 4.6.4 Per-Interface Policing  
We developed and implemented our slice-based QoS solution using traffic policing on the ingressing ports of a switch. Rules are set up to limit the transmission rate on a given interface by setting the ingress policing rate and ingress policing burst. This is a form of QoS that drops packets received over the configured maximum bandwidth. The implementation of policing is usually less accurate and less effective than egress QoS. For its functioning, it uses a token bucket approach, where the size of the bucket corresponds to ingress policing burst. Initially, the bucket is set as full. When a packet is received, its size is converted to tokens and compared to the number of tokens currently in the bucket. In the eventuality of the required number of tokens being available, they are removed, and the packet is forwarded. Otherwise, the packet is dropped. On the other hand, when the bucket is not full, it is refilled with tokens at the previously configured maximum rate.
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
## 1 Introduction  
Since the discovery of metallic glasses (MGs), especially bulk metallic glasses (BMGs), the mechanical behavior of MGs is attracting increasing attention for both potential structural applications and scientific interests [1]. A series of distinguished mechanical properties, including high compressive plasticity, hardness, ultimate strength, and fracture toughness have been reported [1; 2; 3; 4; 5; 6; 7; 8]. These excellent properties and the unusual deformation mechanism of viscous flow and shear band motion make MGs a special member in the family of structural materials.  
At room temperature, the deformation behaviors of MGs are controlled by shear bands, which are kinds of localized viscous flow [9]. The shear bands are very different from the slip bands formed via dislocations in crystalline alloys, and result in the special mechanical behaviors of MGs. The information about the topics of shear bands, mechanical properties, and fracture behaviors of MGs can be found in some review papers [9; 10; 11; 12]. However, a review on the topic of how and why MGs fall out of the scope of conventional fracture mechanics is still lacking. In this article, we attempt to summarize the up-to-date understanding on this issue from several aspects about the fracture behaviors and fracture mechanism. We focus on the main fracture behaviors of metallic glasses, including the mode I fracture, brittle fracture, super ductile fracture, impact toughness, and fatigue fracture behaviors. The complex fracture mechanism of metallic glasses are discussed from the perspectives of discontinuous stress/strain field,plastic zone, and fracture resistance, which deviate from the classic fracture mechanics in polycrystalline alloys. This work would help researchers and engineers in their scientific and engineering studies of metallic glasses.
## 2 Materials and Methods
### Performance Evaluation  
Several accuracy metrics are available to evaluate the performance of a deep learning model. In this study, the performance of the YOLO v8 algorithm was evaluated using  
Figure 4: The design of YOLOv8’s network architecture (adapted from [[https://github.com/ultralytics/ultralytics/issues/189](https://github.com/ultralytics/ultralytics/issues/189)]([https://github.com/ultralytics/ultralytics/issues/189](https://github.com/ultralytics/ultralytics/issues/189)) (accessed on 19 April 2024)).  
both the testing and validation datasets. The metrics used for this assessment included recall (R), accuracy (A), F1 score (F1), and precision (P). According to [28; 29], these metrics are commonly employed in deep learning applications. The metrics used to assess the performance of the YOLOv8 model are discussed as follows:  
1. Accuracy (ACC): Accuracy is a fundamental metric that quantifies the model's overall correctness in its predictions. It is defined as the ratio of correctly classified objects to the total number of objects. \[\text{ACC}=\frac{\text{{True}\,\,\text{Positives}}+\text{{True}\,\,\text{ Negatives}}}{\text{{Total}\,\,\text{Objects}}}\] (1)
2. Precision (PR): Precision gauges the model's ability to make correct positive predictions. It is calculated as the ratio of true positive predictions to the total number of positive predictions. \[\text{PR}=\frac{\text{{True}\,\,\text{Positives}}}{\text{{True}\,\,\text{ Positives}}+\text{{False}\,\,\text{Positives}}}\] (2)
3. Recall (RC): Recall, also known as sensitivity or true positive rate, measures the model's capability to identify all relevant instances. It is defined as the ratio of true positive predictions to the total number of actual positive instances. \[\text{RC}=\frac{\text{{True}\,\,\text{Positives}}}{\text{{True}\,\,\text{ Positives}}+\text{{False}\,\,\text{Negatives}}}\] (3)
4. F1 Score (F1): The F1 score balances precision and recall to provide a single metric that quantifies the model's accuracy in detecting and classifying positive instances. \[\text{F1}=2\cdot\frac{\text{{Precision}}.\,\,\,\text{{Recall}}}{\text{{ Precision}}+\text{{Recall}}}\] (4)
5. Precision Average (PR-AVG): The precision average is calculated as the arithmetic mean of precision values for each class or category in the classification problem: \[\text{Precision}\,\,\text{Average}\,\,(\text{PR-AVG})=\,\,\frac{1}{N}\text{{ }\sum}_{i=1}^{N}\,\text{{Precision}_{i}}\] (5) where \(N\) represents the number of classes.
6. Recall Average (RC-AVG): Similarly, the recall average is computed as the average of recall values for each class. \[\text{Recall}\,\,\text{Average}\,\,(\text{RC-AVG})=\frac{1}{N}\text{{ }\sum}_{i=1}^{N}\,\text{{Recall}_{i}}\] (6)  
For this study, precision, recall, and mean average precision (mAP) were employed to evaluate the performance of the model under different batch size conditions. mAP is derived from the precision-recall curve.
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 6 Simulation Results
### Single Round Identification
#### 6.1.3 Time Performance  
Finally, Figure 10 demonstrates the average time needed to identify one tag and the time efficiency. It should be noted that time efficiency is defined as the ratio of the time cost of a singleton slot to the average time needed to identify one tag. As one can observe in Figure 10a, the proposed ACDQT takes about 1.2 ms to identify a tag on average, whereas, MCT, DPPS and CwT take about 1.25 ms, 1.43 ms and 1.95 ms for one tag identification, respectively. Compared with MCT, DPPS and CwT, the time gain of ACDQT is around 4.2%, 19.2% and 62.5%, respectively. It also demonstrates that although ACDQT takes more idle slots than other algorithms, the time saved by the smaller number of collision slots greatly improves the identification performance. Therefore, ACDQT takes much less time than the comparable algorithms. Moreover, in Figure 10b, one can also observe that the proposed ACDQT algorithm always has the highest time efficiency, which is about 50%.  
To sum up, in single-round identification (i.e., a static situation), the proposed ACDQT algorithm is superior to comparable algorithms in terms of fewer number of collision slots, lower communication overhead and shorter identification delay.