esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Real-World Use Cases/Project  
The below case study describes a threat scenario in the VANET environment. For MEC and edge caching, deployed entities such as vehicles and RSUs are fully furnished with computing resources and caching in the described scenario. The adversary in the scenario aims to access the vehicles and RSUs sensitive data illegally through eavesdropping by malware implanting, by initiating illegal sensitive data transmission, or by exploiting the vulnerabilities of the systems to reach the targeted RSUs or vehicles in the network to access devices private data without their knowledge. During the transmission of data from vehicle X to Z, there is the possibility that the adversaries may access private data such as in-car data transmission, V2X data transmission, and during the transmission of data between users. The deployed vehicles' inadequate storage and computing resources motivate them to transfer their data through V2V and V2R communications. During the data transmission process, there is also a chance that vehicles may unintentionally transfer their private data with other vehicles. During data sharing, the vehicle's private data may be hidden in standard data transmission, and this may cause severe physical damage.  
If we summarize the above scenario, the vehicular data leakage threats can be categorized into two parts: the data transferring from vehicles called uplink and the data caching from RSUs called the downlink. The following two challenges must be solved to mitigate data leakage in the VANET environment.  
* The process of raw data to save from leakage in other applications, such as data allocation and sharing, deprived of breaching the data usability, and only on the requests of other vehicles will the newly created sensitive data be shared.
* Without any precise tasks, the existing unusable data must be cached, and from the perspective of specific attack types detection of data, leakage must be identified. The data leakage may be because of unknown system vulnerabilities or unintentional behavior.
## 4 Results
### Experimental Results
#### 4.3.2 Qualitative Evaluation
Among the baseline models, UNetAttn shows improvements in capturing tumor structures but still exhibits misclassifications along the tumor boundaries. In contrast, the proposed model achieves more precise and comprehensive segmentation, closely aligning with the ground truth. This enhancement results in a more accurate delineation of tumor boundaries, reducing misclassifications and improving segmentation quality. Figure 11: Training progress plot for 3 DIRCADb dataset. Figure 12: A qualitative comparison of the output of different models. The first column (**a**) is ground truth, whose segmentation results by UNet, ResUNet, UNetAttn, Patch Attn + ResUNet, Tile Attn + ResUNet, and the proposed model are, respectively, displayed in columns (**b**–**g**).
## 5 Experiment and Analysis
### Field Trials
#### 5.2.2 Experiment and Analysis  
We used the following experimental method, since we only completed the electrification of the electric actuator and related hardware arrangement and did not make functional modifications to other modules. A person with rice harvesting experience drove the modified car to the test area and performed operations such as changing direction and stabilizing the height of the cutting platform while ensuring that changes in the harvester's feeding volume were mainly affected by traveling speed. The 4 LZ-3.2 Z full-feed grain combine  
Figure 16: Harvesting trial site.  
Figure 15: Simulation results of adaptive adjustment to the harvester’s traveling speed.  
harvester had a screen display function that displayed the traveling speed in real-time during operation. The industrial control computer read the harvester bridge's output voltage signal and the Hall-type speed sensor's pulse signal and substituted them into the power shaft of the tilting conveyor model to obtain the current feeding volume.  
The test was timed with a stopwatch. Rice was harvested in 130.43 s from the measurement area with no blockage in the machinery's operation process. Data starting at 0.43 s were excluded and rounded to 130 s to facilitate the subsequent data processing and analysis. The time domain diagram of the traveling speed is shown in Figure 17. The time domain diagram of the feeding volume is shown in Figure 18.  
The response accuracy, speed and stability of the control system were evaluated by analyzing the time-domain curve characteristics of the feeding volume. In Figure 18, the maximum deviation of the feed curve from the rated feed (3.2 kg/s) occurred at 71 s with a value of 3.038 kg/s, corresponding to a maximum error of 0.162, an error rate of 5.2%, and an average error rate of 3.4% for the whole process. These results indicate the system's high response accuracy. As seen in the curve change trend, most data points were between 3.17 and 3.23, indicating the system's good stability. Compared to Figure 17, the feeding volume and traveling speed changes during the operation were nearly matched, indicating the system's quick and effective response.  
Figure 17: Time domain diagram of traveling speed.  
Figure 18: Time domain diagram of feeding volume.
# A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning
## 2 Related Works
### Evaluation of Student Performance for Grading  
In ref [38], the performance of 246 high school students in open-ended physics questions was graded by ML. The study was conducted on four physics questions, and they used the correlation coefficient method to feature selection and SMOT (synthetic minority over-sampling) to eliminate the imbalanced data problem. Bagging and AdaBoost.M1 (adaptive boosting) algorithms were applied as ensemble learning techniques (bagging and boosting) to define the grade (class) of the exam papers. The accuracy of the mentionedalgorithm ranged between 0.84-0.99 for Q1 (528 features), 0.65-0.84 for Q2 (1037 features), 0.85-0.91 for Q3 (724 features) and 0.87-0.98 for Q4 (316 features) for ten iterations. In ref [39], the scores of Portuguese high school students were predicted by a multilinear regression model (accuracy = 65.87), a random forest (accuracy = 94.42), a support vector machine (accuracy = 87.74), an artificial neural network (accuracy = 73.24), and an extreme gradient boosting machine (accuracy = 97.12). Optimal hyperparameters were found by the grid search and employed the lasso method to the features selection. Their datasets comprise the information of 362,261 high school students such as urban, income, age, employment, cultural level and grades. In the training process, the XGB has a better performance compared to the other algorithms. On the other hand, the SVR has a better performance on test data, which indicates XGB has an over-fit. After calculating the feature importance, the school size has a great impact on academic achievement. On the contrary, socioeconomic factors do not have a significant impact. The authors in this study [40] made a model for predicting middle school programming talent using ANN. The ANN is a satisfactory method for forecasting participants' skills, such as analytical thinking, problem-solving, and programming aptitude. They have used surveys to collect information that consists of four sections: demographic information, paper-folding problems, map sketching, and analytical thinking questions. The demographic information questions consist of gender, age, parents' educational levels, parents' jobs, grades in mathematics, homework time, the possession of an electronic device and time spent with the device, coding knowledge, and computer screen time. After the completion of the survey, the participants then took the 20-level Classic Maze course (CMC) on code.org. The participants' final scores in the CMC were calculated based on the level they completed and the lines of codes they wrote. Overall, 13 input features were provided as demographic information (five), grades in mathematics (one), paper-folding problems (three), map sketching (one), and analytical thinking questions (three). For the prediction phase, they employed ANN models consisting of one hidden layer, 13 inputs, and one target with accuracy = 91.26.
# New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions
## 4 The Beta RED Scheme
### Normalized Incomplete Beta Function  
The beta distribution function (or normalized incomplete beta function) \(I(\alpha,\beta)\) is given by the expression  
\[I_{z}(\alpha,\beta)=\frac{\mathfrak{B}(z;\alpha,\beta)}{\mathfrak{B}(1;\alpha, \beta)},\quad\mathfrak{B}(z;\alpha,\beta)=\int_{0}^{z}t^{\alpha-1}(1-t)^{ \beta-1}dt, \tag{3}\]  
with \(\alpha,\beta>0\) and \(z\in[0,1]\). By definition, \(I_{z}(\alpha,\beta)\) is strictly increasing, and is hence invertible. Its inverse, \(I_{z}^{-1}(\alpha,\beta)\), is also strictly increasing.  
Although usually the family of probability beta functions are described according to the parameters \(\alpha\) and \(\beta\), here it will be much more convenient to describe them according to their mean \(\mu\) and standard deviation \(\sigma\), as we will show later. It is well known that the expected value and the variance of the beta distribution is given by:  
\[\mu=E[I(\alpha,\beta)]=\frac{\alpha}{\alpha+\beta},\qquad\sigma^{2}=Var[I( \alpha,\beta)]=\frac{\alpha\beta}{(\alpha+\beta)^{2}(\alpha+\beta+1)}. \tag{4}\]  
If we solve \(\alpha\) and \(\beta\) as a function of \(\mu\) and \(\sigma^{2}\) in the above equations we obtain:  
\[\alpha(\mu,\sigma)=\mu\bigg{(}\frac{\mu(1-\mu)}{\sigma^{2}}-1\bigg{)},\qquad \beta(\mu,\sigma)=(1-\mu)\bigg{(}\frac{\mu(1-\mu)}{\sigma^{2}}-1\bigg{)}. \tag{5}\]  
Thus, given values \(\mu\in(0,1)\) and \(\sigma<\sqrt{\mu(1-\mu)}\), we have univocally determined parameters \(\alpha\) and \(\beta\) such that the beta distribution \(I(\alpha,\beta)\) has mean \(\mu\) and standard deviation \(\sigma\). Henceforth we will consider the beta distribution \(\tilde{I}(\mu,\sigma)\) with respect to the parameters \(\mu\) and \(\sigma\), specifically:  
\[\tilde{I}_{z}(\mu,\sigma)=I_{z}(\alpha(\mu,\sigma),\beta(\mu,\sigma)) \tag{6}\]
## 5 Example Analysis and Discussion
### Reliability-Based Optimization of a Vehicle Disc Brake System
Parameters in the USGA algorithm are set as follows: \(Pop\_size=30\), \(P_{\text{c}}=0.8\), \(P_{\text{m}}=0.2\), and \(N=12\). In Figure 8, the iterative convergence process of back plate thickness \(h_{3}\) generated by URBDO (CEP), URBDO (USGA), and RBDO (RIA) method for \(R_{\text{U}}=0.9987\) is displayed. A detailed comparison of the optimal solutions of the three methods is summarized in Table 3. The number of function calls of constraint is expressed as F-evaluations. Furthermore, the CPU time is used to compare the efficiency of the CEP and USGA algorithms in processing the URBDO model. Figure 8: The design history of the brake system problem. Figure 7: The comparative objective value of disc brakes under different reliability levels. As shown in Figure 8, the optimal values of the three approaches can be obtained if the iterations reach 10 times. According to Table 3, the CEP method is more efficient than the USGA algorithm when both approaches are simultaneously used to solve the URBDO model. This engineering problem confirms that both CEP method and the USGA algorithm can obtain almost equal optimal results. Since the rank-sum test is a classical nonparametric test that is not limited by the type of population distribution [45], it is used to benchmark the impact of the genetic algorithm parameters on the performance of the USGA algorithm. In this work, different population sizes and mutation probabilities are used to test the performance of USGA. Assuming that the value of population size \(Pop\_size=20\), two sets of sample data are carried out based on the USGA algorithm with mutation probability \(P_{\text{m}}=0.01\) and \(P_{\text{m}}=0.1\), respectively. Similarly, supposing that the value of \(P_{\text{m}}\) is 0.2, another two sets of sample data are carried out based on the USGA algorithm with \(Pop\_size=15\) and \(Pop\_size=30\), respectively. The optimal results of back plate thickness corresponding to the aforementioned four sets at \(P_{\text{c}}=0.8\) are summarized in Table 4. The following null hypothesis is designed for this problem under the significance level \(\alpha=0.05\). As presented in Table 4, the sample size of each set is 8, and the rank-sum \(T\) for \(Pop\_size=20\) and \(P_{\text{m}}=0.2\) are 52 and 53.5, respectively. The function \"ranksum\" in the MATLAB is employed to calculate the \(p\)-value. Then, the \(P\)-values corresponding to \(Pop\_size=20\) and \(P_{\text{m}}=0.2\) are 0.0957 and 0.1383, respectively. Since both 0.0957 and 0.1383 are greater than 0.05, the null hypothesis H\({}_{0}\) is retained. Therefore, there is no significant difference in the back plate thickness optimized based on the USGA algorithm. In other words, the setting of genetic algorithm parameters has no significant effect on the performance of the proposed USGA algorithm.
## 2 Related Study  
This study analyzes four critical cyberattacks on energy infrastructure--Industroyer, Triton, NotPetya, and BlackEnergy3--using a systematic approach. Data collection follows a structured and comprehensive approach. In the first step, government reports for each attack are examined to establish a precise timeline of events and document primary impact indicators. The second step involves gathering malware behavior patterns from academic research, specifically concentrating on Indicators of Compromise (IoCs) and associated malware techniques. In the third stage, threat intelligence databases, such as MITRE ATT&CK, and VirusTotal, are utilized to collect real-time attack techniques, providing insights into specific protocols and exploit tools. Each data source serves a unique analytical purpose: government reports offer a baseline for operational impact, academic studies reveal the technical behavior of malware, and threat databases track current exploit trends to identify emerging attack patterns in SCADA and SIS vulnerabilities. Each source contributes uniquely: government reports provide operational insights and immediate impact metrics; academic papers offer detailed behavioral analysis of malware patterns; and threat databases yield current exploit trends, identifying specific techniques and tools. This comprehensive data approach supports a layered security framework, analyzing SCADA, ICS, and SIS system vulnerabilities systematically. Each attack is not only significant in terms of the damage caused but also reveals critical vulnerabilities across interconnected infrastructure systems, making them relevant to global critical industries, including power grids, industrial control systems (ICSs), and SCADA systems. These attacks are not isolated incidents, but rather, they reflect broader security weaknesses that could lead to severe consequences across various industries [26].  
While the five-layer model in this study provides a foundational framework for malware threat defense within energy infrastructure, each case analysis underscores the need to address human-driven risks, such as insider threats and social engineering. [PERSON]'s model [27] expands on this framework by integrating 'cyber personas' and predictive analytics to anticipate and mitigate these human factors, adding an essential behavioral component to traditional technical defenses. For instance, insider behavior patterns observed in cases such as Triton and Industroyer reveal vulnerabilities that [PERSON]'s model is designed to address systematically.  
[PERSON]'s model expands upon traditional defense layers by embedding 'cyber persona' factors, which analyze insider behavior patterns and vulnerabilities to social engineering, providing a critical pathway for understanding and mitigating human-induced cybersecurity risks.
## 1 Introduction
* The experiments on two well-known benchmark datasets validate that the proposed method improves the sentiment classification in terms of accuracy. All the acronyms used in this paper are listed in Table 1. The rest of the paper is organized as follows: Section 2 is about related work on SML, SSL, and Sentiment Lexicons for textual sentiment classification. The proposed unified SA framework (LeSSA) is briefly described in Section 3. The technical and implementation details of LeSSA are discussed in Section 4. Experimental results, evaluation and discussion are presented in Section 5. Finally, in Section 6, we conclude the paper by signaling future work. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Acronym** \\ \hline Sentiment Analysis & SA \\ Support Vector Machine & SVM \\ Naive Bayes & NB \\ Supervised Machine Learning & SML \\ Social Networks Sites & SNSs \\ Semi-Supervised Learning & SSL \\ Unstructured User-Generated Contents & UUGC \\ Text Pre-Processing & TPP \\ Sentiment Features Extraction and Selection & SFES \\ Multi-Model Sentiment Learning Layer & MMSLL \\ Wide Coverage Sentiment Lexicon-Based Sentiment Learner & VCSL-SL \\ Bag of Words & BOW \\ Minimum Redundancy Maximum Relevance & MRMR \\ Self-Training Based Sentiment Learner & ST-SL \\ Part-of-Speech Tag & POS \\ Feature Engineering & FE \\ Multi-Model Sentiment Learning & MMSL \\ Sentiment Classification & SC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of acronyms being used in this paper.
## 3 RPL Overview  
In this section we provide a RPL overview, with emphasis on the procedures for network creation. The section is organized in three parts. First, we present the mechanisms used by RPL to build a network. We then briefly review the routing mechanisms in RPL. Finally, we present the Trickle algorithm, which is used by RPL to disseminate control messages for network creation and maintenance.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Surface Model Generation
#### 2.2.7 Additional Notes  
In general, there exist workflows that can extract DSMs from Pleiades stereo or tri-stereo data. However, it is difficult to evaluate various approaches and to find the best solution for different applications. One good example is the Pleiades workshop held in Paris in November 2014 named _DEM computation from satellite images: Existing tools and developments_ where different solutions were benchmarked. The main challenge remains in finding the optimal parameters for image matching (like a custom-tailored cost function, appropriate occlusion detection and handling principle), while being computationally efficient (this includes memory optimizations, parallel processing on a multi-core central processing unit (CPU) and exhaustive usage of a graphical processing unit (GPU)). From an implementation point of view it would be beneficial to have an epipolar rectification method that can be applied for push-broom optical and SAR images.
# Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs
## 2 Related Work and Contributions
However, these works in the literature are normally based on quite specific assumptions, such as the fixed communication load, and the same priority of the sensing event. The current approaches have not considered the different parameters of WSNs, UAVs and other related constraints. The contributions of this paper are listed as follows. We propose a cloud-assisted UAV data collection framework for distributed WSNs. The approach is based on the correlation between the area of emerging events and the WSN surveillance region. According to the event correlation matrix, the collected data clusters sequences are computed in the cloud and transmitted to the UAV. Meanwhile, based on global information including GPS position and data size of the ground WSNs, and the UAV basic parameters in terms of the flying height, position, and hovering time are calculated. The performance of our proposal is evaluated through simulation. To test our approach, we implemented a practical application in the real world.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 4 Performance Evaluation and Discussion
### Evaluation of BER Characteristics with Respect to Temperature  
The demodulation method shown in Table 3 is used in this study. Additionally, the default parameter shown in Table 3 assumes an ambient temperature of \(25\,\mathrm{\SIUnitSymbolCelsiusCelsius}\) and the parameter for the volume of the transmitter molecule is set as \(r_{0}=1.0\)  
Figure 11: BER characteristics of demodulation method by the proposed MLSE method.  
Figure 10: Number of received molecules considering the volume of transmitted molecules.
# A Numerical Approach for the Efficient Concept Design of Laser-Based Hybrid Joints
###### Abstract
* How can the laser costs and joint strength be efficiently determined for the tailored microstructure concepts? * How can the methods and models be used to develop cost- and strength-optimized microstructure concepts? The main novelty of the presented approach is that tailored (strength, costs) microstructures can be fully generated based on simulation models. Iterative experiments throughout the design process are not required. The approach to answer these questions within this paper is structured as follows: In Section 2.1, a new model for automated efficient development of microstructure concepts while considering the laser manufacturing constraints is presented. For these concepts, a new model for the efficient strength estimation (cohesive zone model) of laser-based joints is presented in Section 2.2 and a model for the cost calculation is presented in Section 2.3. In Section 3, a combined optimization approach is presented that links all models to develop tailored requirements-optimized concepts regarding cost and strength. The approach and the models are validated using a demonstration example in Section 3. Finally, a conclusion and outlook are given in Section 4.
## 5 Results and Discussion
### Training Results  
The evaluation of DQN training results typically involves monitoring reward changes throughout the training process. Initially, with randomly initialized model parameters, the rewards are low. As the training advances, the model gradually learns improved strategies, leading to increasing rewards. Throughout the training, the model's loss change is monitored to ensure convergence.  
In the early stages of training, due to the random initialization of parameters, rewards may exhibit significant variation. However, as training progresses, reward variation gradually diminishes, ultimately converging. Subsequently, model performance is assessed using either a test set or real-world scenario tests. Successful performance validates the model's applicability to real-world situations.  
Figure 4 illustrates the average reward results of DQN training. Observing the graph, we note that during the initial 0-20 iterations, the average cumulative reward shows a slight increase, but with noticeable fluctuations. However, as the iteration number reaches 20-60 generations, the model enters a fast learning phase, leading to a significant increase in reward with a reduced fluctuation range. Beyond the 60 th iteration, fluctuations nearly vanish, and the reward stabilizes, indicating model convergence and improved training effectiveness.  
From Figures 5 and 6, it can be observed that as the number of iterations increases, the intersections' total delay and the average queue length in the training gradually decrease, and fluctuations diminish. This indicates that the model can be applied to real-time signal priority for self-driving buses and effectively reduce queues and delays.  
Figure 4: Average cumulative reward training analysis.