esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 2 Related Work  
### The Deep Q-Learning Network for Deep Reinforcement Learning  
The DQN model for DRL was utilized in this study to address the issues encountered in complex environments by substituting the Q-table used in Q-learning with neural network layers (Figure 2). Two crucial components of DRL are experience replay and fixed Q-targets. Experience replay is a replay mechanism that employs a replay memory to store the different strategies \((S_{t},a_{t},S_{t+1},r_{t})\) experienced in the same state during learning. In DRL, experience replay acts as a memory storage repository that controls the maximum number of entries in the replay memory, thereby avoiding the unbounded growth that can occur in the Q-table. During each DQN update, experiences from the replay memory can be randomly sampled for learning rather than sequential selection, which can lead to a high level of similarity among the chosen samples, potentially causing the DQN to converge to local optima during the learning process. Utilizing replay memory with random sampling breaks the correlations between data in the dataset. There is no need for frequent interactions with the environment; new parameters can be obtained through training using previous experiences (Figure 3). During training, neural networks are typically trained in batches, a process known as batch learning in machine learning. This approach offers advantages such as increased speed and accuracy in learning (Figure 4).  
Fixed Q-targets are primarily employed to address the target value instability within the Q-network. Unlike the Q-network, the target values in fixed Q-targets do not change with each update during training. Instead, they are updated at regular intervals or after a certain number of training steps. In the fixed Q-target update process, the Q-network's neural network parameters are copied to the fixed Q-targets to ensure that the parameters  
Figure 3: The DQN learning process diagram.  
Figure 2: The DQN network architecture diagram.  
of the fixed Q-targets remain relatively stable. During Q-learning, the action with the maximum initial value at each state is selected. However, the primary focus for neural networks is to optimize the loss function through the selection of appropriate parameters during training, which requires a training dataset with inputs (x) and labels (y). In Q-learning, the state corresponds to the input (x), and value (Q-value) learning serves as the label (y). The objective is to make the estimated Q-values approach the target Q-values, leading to the definition of a loss function (Equation (1)). This formulation allows the use of neural networks for training. A loss function is introduced to incorporate this process into the Q-learning update formula and convert it into the DQN update formula (Figure 5).  
\[Loss\ function=(\gamma\times\underset{a_{t+1}}{max}Q(s_{t+1},a_{t+1},w)-Q(s_{t}, a_{t},w))^{2} \tag{1}\]
## 4 Results
### Statistics and Visualization of Teaching Input in Classroom Teaching Videos
#### 4.4.1 Evaluation Method of Engagement Degree  
We recruited 22 students majoring in educational technology to score the engagement of seven main behaviors. If the scores of several raters for a certain kind of behavior are consistent, the engagement reflected by this behavior can be measured by a specific range of values. Firstly, the behavioral engagement \(E\) is divided into four grades, \(E\in\{0--3\}\). Single pictures of 7 kinds of behaviors were selected, with 5 pictures of each kind, and 5 pictures showed the same kind of behaviors differently, with a total of 35 pictures. After the disorder, 22 students scored according to the images. After screening the data through SPSS analysis, the \(\alpha\) value of 21 raters is 0.974. For each category, the \(\alpha\) value of raters is all above 0.9, which indicates that the scores of various behaviors have high reliability. Finally, the average engagement level of each kind of behavior is taken as the final engagement value, and 0-1 is defined as low engagement, 1-2 as medium engagement, and 2-3 as high engagement. For the convenience of drawing and scoring, the scores are normalized as shown in Table 11.  
Due to the space, this paper focuses on the exploration of S-T behavior recognition and only makes simple statistics on the automatic recognition data of classroom behavior. Data mining and detailed interpretation based on automatic recognition data of classroom behavior will be the next step of this research.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Behavior Categories** & **Raising Hands** & **Writing** & **Reading** & **Speaking** & **Discussion** & **Listening** & **Inattention Behavior** \\ \hline Engagement Score & 2.8 & 2.6 & 2.5 & 2.4 & 2.3 & 1.4 & 0.7 \\ \hline Normalized Score & 1 & 0.9 & 0.86 & 0.81 & 0.76 & 0.3 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Main behaviors and investment.  
Figure 12: Comparison of classroom teaching modes.  
In the image, head posture, hand posture, and body posture, as the main body of behavior, is the most important information basis for engagement rating. Although there is a high consistency among the raters for each behavior, there are still subtle differences within the behavior. For example, when the discussion takes place, the raters give different grades to the students who turn around, sit in, and take a positive posture, which shows that the granularity of behavior and posture division needs to be refined and more visual information such as eyes and expressions are also subconsciously concerned by the raters.
## 1 Introduction
To overcome the impact of source inaccuracy on inversion, source-independent inversion strategies were first proposed in frequency-domain FWI, including convolution-based inversion methods [29; 30] and deconvolution-based inversion methods [31; 32; 33; 34]. In the time domain, source-independent inversion methods are usually constructed based on convolved wavefields [35]. This method can obtain good inversion results under ideal circumstances, and the same is true for EFWI [36]. However, the convolved wavefield method may amplify low-frequency noise due to the convolution operation used. Moreover, when there are bad traces in the data, this method cannot avoid their impact on the final inversion results. In the time domain, the waveform inversion method based on the L2 norm objective function can still be used to invert the source wavelet function [37; 38]. This kind of source wavelet inversion method has been used in FWI and reverse time migration [39; 40; 41]. However, just like traditional FWI, this source wavelet inversion method will also be affected by many factors, among which the amplitude error is one of the key factors [42]. When there are amplitude errors or bad traces in the seismic data, inversion will be significantly affected. The above research is mainly focused on acoustic media. This paper proposes a source wavelet estimation method using multi-component seismic data in elastic media. In this paper, we first review the basic principles of EFWI and the source wavelet inversion method based on the L2 norm objective function. Aiming at solving the effects of bad traces, surface wave residuals, and amplitude errors on source wavelet inversion in elastic media, a source wavelet inversion method using multi-component seismic data based on the cross-correlation elastic waveform inversion method is proposed. Finally, numerical tests are conducted to verify the effectiveness of the proposed method.
## 3 Results and Discussion
### Determination of Intracellular Hydrogen Peroxide Level in Jurkat Cells  
Antioxidant activity of BGR in the Jurkat cells was determined whether it was contributed to its anticancer activity. The intracellular ROS level was presented as a bar graph in Figure 2a. Results showed that at 12 h H\({}_{2}\)O\({}_{2}\) (0.1 \(\upmu\)g/mL) significantly increased ROS level (121.17 \(\pm\) 7.36%) in Jurkat cells compared to the untreated cells (100 \(\pm\) 3.29%) (\(p\) \(<\) 0.05). BGR at 0.5 \(\times\) IC\({}_{50}\) (100 \(\upmu\)g/mL) and 1 \(\times\) IC\({}_{50}\) (200 \(\upmu\)g/mL) concentrations obtained from the cell viability assay were determined for their antioxidant activity in the Jurkat cells. BGR at 5-7, 10-15 and 20-25 days (100 \(\upmu\)g/mL) decreased ROS level in Jurkat cells to 67.03 \(\pm\) 1.70%, 51.38 \(\pm\) 4.51%, 45.93 \(\pm\) 1.79%, respectively. A greater reduction in ROS was found after BGR treatment at 200 \(\upmu\)g/mL at age of 5-7, 10-15 and 20-25 days to 39.13 \(\pm\) 3.36%, 28.66 \(\pm\) 2.17%, 22.76 \(\pm\) 1.37%, respectively. Different harvest ages of BGR showed different cellular antioxidant ability with concentration-and age-dependent manner (\(p\) \(<\) 0.05) (Figure 2a). BGR at 1 \(\times\) IC\({}_{50}\) concentration at the age of 20-25 days displayed the most effective antioxidant activity.
# Radiation Hardness of Oxide Thin Films Prepared by Magnetron Sputtering Deposition
###### Abstract  
Thin amorphous oxide films (a-SiO\({}_{2}\), a-Al\({}_{2}\)O\({}_{3}\), a-MgO) were prepared by magnetron sputtering deposition. Their response to high-energy heavy ion beams (23 MeV I, 18 MeV Cu, 2.5 MeV Cu) and gamma-ray (1.25 MeV) irradiation was studied by elastic recoil detection analysis and infrared spectroscopy. It was established that their high radiation hardness is due to a high level of disorder, already present in as-prepared samples, so the high-energy heavy ion irradiation cannot change their structure much. In the case of a-SiO\({}_{2}\), this resulted in a completely different response to high-energy heavy ion irradiation found previously in thermally grown a-SiO\({}_{2}\). In the case of a-MgO, only gamma-ray irradiation was found to induce significant changes.  
high-energy heavy ion; ion track; gamma irradiation; IR spectroscopy; ERDA; oxides 2025157067 Article
# Challenges and Implications of Virtual Reality in History Education: A Systematic Review
## 2 Methods
### Data Items and Synthesis Methods  
An inductive analysis was conducted with the aim of identifying the data items, building upon an initial preliminary classification extracted from the selected papers. This classification, structured around the research questions, was developed by the two researchers in different iterations to reach a consensus. By tabulating the characteristics of each study and comparing them against these predefined categories, we ensured a structured approach to synthesis selection. This method allowed us to identify recurring themes and patterns, aligning each study with the most relevant analytical framework. The data items (categories) analyzed in this report were as follows:  
* Ethical considerations: Issues related to privacy, informed consent, and the responsible use of VR in educational contexts.
* Psychological and social impact: Effects of VR on students' perception, motivation, and social interaction in learning history.
* Usability challenges (difficulty of use): Technical or cognitive barriers that hinder effective use of VR by teachers and students.
* Pedagogical aspects: Teaching strategies and methodological approaches that enhance the use of VR for history education.
* Technical issues (motion and visualization): Technical problems such as latency, graphics quality, or motion sickness that affect the user experience.
* Economic aspects (development and production costs): Costs associated with designing, implementing, and maintaining VR experiences for history education.
* Social and cultural acceptance: Students' and teachers' perception and willingness to integrate VR into history learning.
* Social interaction in virtual environments: Communication and collaboration dynamics among users in history learning experiences within virtual environments.
# FedDB: A Federated Learning Approach Using DBSCAN for DDoS Attack Detection
###### Abstract  
The rise of Distributed Denial of Service (DDoS) attacks on the internet has necessitated the development of robust and efficient detection mechanisms. DDoS attacks continue to present a significant threat, making it imperative to find efficient ways to detect and prevent these attacks promptly. Traditional machine learning approaches raise privacy concerns when handling sensitive data. In response, federated learning has emerged as a promising paradigm, allowing model training across decentralized devices without centralizing data. However, challenges such as the non-IID (Non-Independent and Identically Distributed) problem persist due to data distribution imbalances among devices. In this research, we propose personalized federated learning (PFL) as a solution for detecting DDoS attacks. PFL preserves data privacy by keeping sensitive information localized on individual devices during model training, thus addressing privacy concerns that are inherent in traditional approaches. In this paper, we propose federated learning with DBSCAN clustering (FedDB). By combining personalized training with model aggregation, our approach effectively mitigates the common challenge of non-IID data in federated learning setups. The integration of DBSCAN clustering further enhances our method by effectively handling data distribution imbalances and improving the overall detection accuracy. Results indicate that our proposed model improves performance, achieving relatively consistent accuracy across all clients, demonstrating that our method effectively overcomes the non-IID problem. Evaluation of our approach utilizes the CICDDOS2019 dataset. Through comprehensive experimentation, we demonstrate the efficacy of personalized federated learning in enhancing detection accuracy while safeguarding data privacy and mitigating non-IID concerns.  
Distributed Denial of Service (DDoS); federated learning (FL); personalized federated learning (PFL); non-IID
# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 3 Experiments and Discussion  
### Comparison Methods and Evaluation Metrics  
In the experiments, we use LeNet, ResNet, GoogleNet, and DenseNet as baselines for comparison. To verify the effectiveness of the padding preprocessing step, we also conducted comparative experiments before and after preprocessing. During the training process, we trained the network for 50 epochs with a batch size of 16, using accuracy as the key metric to evaluate the model's performance.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{12}\]  
where \(TP\) represents the number of actual positive samples correctly predicted by the model, \(FP\) denotes the number of actual negative samples incorrectly predicted as positive by the model, \(TN\) represents the number of actual negative samples correctly predicted by the model, and \(FN\) denotes the number of actual positive samples incorrectly predicted as negative by the model.
# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 2 Materials and Methods
### Experimental Methodology  
The robotic platform used was a customized ENCore 2s System (MRSI, North Billerica, MA, USA) controlled with a Galil DMC-2413 controller. This controller was integrated with Python version 3.7.9 and gclib version 1.37.6. The system was connected to a computer (Windows 10, 16 GB RAM, NVIDIA GeForce RTX 3070). The robotic platform was enclosedwithin a wooden frame equipped with detachable black sheets designed to exclude external light sources. Within this frame, a platform covered in black felt serves as the designated placement area for the laptops. A low-cost webcam (Microsoft Lifecam 3000-HD) was positioned on the head of the Cartesian robot, which is capable of lateral and vertical movement across the laptop cover to capture images for experimental trials. The camera is surrounded by a skirt that houses 4 LEDs that can illuminate the surface underneath the camera. The robot's origin and the laptop's origin were offset with an 85 mm spacer in the X direction to ensure complete coverage via the webcam. Inside the wooden frame, two under-cabinet light strips were positioned above the platform, providing internal illumination. The operational area, where the laptop was positioned for trial testing, is depicted in Figure 1.  
Testing involved seven different used laptops, which were labeled according to their respective manufacturers and displayed in Figure 2. A test consisted of the robotic system making a certain number of passes at a set velocity over the laptop while the webcam continuously captured images. In addition to recording the image, the X and Y positions of the robot were logged at every recorded frame. For these trials, a laptop was always positioned so that its hinge was facing away from the robot. Figure 3 shows the movement pattern during the test. The camera started from the top right (the laptop's origin) and moved in the positive Y direction toward the hinge of the laptop. One scanning pass was defined as the robot moving toward the hinge, traveling a predefined distance parallel to the hinge in the positive X direction, and then moving away from the hinge. This pattern was repeated over the length of the entire laptop. The location and characteristics of the screws in each of the seven laptops were measured with a caliper that had a 12\({}^{\prime\prime}\) range, a 0.001\({}^{\prime\prime}\) graduation, and a 0.1\({}^{\prime\prime}\) displacement per revolution (MSC Industrial Supply).  
Figure 1: (**a**) Setup of the robotic system with a laptop from a front view and the robot at its home position. (**b**) Top down view of the workspace, showing a laptop and the spacer used to offset the laptops so that the home position aligns with the mounted camera.
## 1 Introduction
Previous studies have shown that the sensitivity to the strain rate of TiNi alloys strongly depends on the deformation temperature and strain rate, regardless of the alloy composition (equiatomic composition or nickel-enriched alloys) [36; 37; 38; 39; 40; 41]. In materials with an increased parameter of strain rate sensitivity, the formation of a neck was not observed until fracture. Data on the change in the activation volume of the TiNi alloy, depending on the grain size, are currently not available in the literature. The austenitic intermetallic TiNi has an atomic-ordered B2 structure with a bcc type lattice; however, when cooled below M\({}_{\rm s}\) or under the influence of stresses at temperatures below M\({}_{\rm d}\), the B2 phase undergoes a martensitic transformation into phase B19\({}^{\prime}\) with a monoclinic distorted orthorhombic unit cell. The main goal of this paper is to elucidate the correlation between the microstructure, fracture, mechanical behavior, and the strain rate/temperature sensitivity of the TiNi alloy processed by ECAP and high pressure torsion (HPT), which have not been considered comprehensively.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
###### Abstract  
The paper presents the Mixed-Integer Non-linear Programming (MINLP) approach to the synthesis of trusses. The solution of continuous/discrete non-convex and non-linear optimization problems is discussed with respect to the simultaneous topology, shape and discrete sizing optimization of trusses. A truss MINLP superstructure of different topology and design alternatives has been generated, and a special MINLP model formulation for trusses has been developed. In the optimization model, a mass objective function of the structure has been defined and subjected to design, load and dimensioning constraints. The MINLP problems are solved using the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm. Multi-level MINLP strategies are introduced to accelerate the convergence of the algorithm. The Modified Two-Phase and the Sequential Two-Phase MINLP strategies are proposed in order to solve highly combinatorial topology, shape and discrete sizing optimization problems. The importance of local buckling constraints on topology optimization is also discussed. Some simple numerical examples are shown at the end of the paper to demonstrate the suitability and efficiency of the proposed method.  
structural synthesis; topology optimization; discrete sizing optimization; Mixed-Integer Non-linear Programming; MINLP; Modified OA/ER algorithm; multi-level MINLP strategies; steel structures; trusses +
Footnote †: journal: Applied sciences  
(MINLP) approach. The general MINLP model formulation for truss structures is presented. This discrete/continuous non-convex and non-linear optimization problem is proposed to be solved by the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm by [PERSON] and [PERSON] [4]. In addition, some multi-level MINLP strategies that significantly accelerate the convergence of the optimization are presented. The suitability and efficiency of the proposed method are shown through some numerical examples.
# A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks
## 2 Methodology  
### A Spatiotemporal Mann-Whitney Scan Statistic  
Likelihood-based Scan Statistics such as the Space Time Scan Statistic and Space Time Permutation Scan Statistic are sensitive to the distribution of the response variable and hence perform well only against specific alternatives. Thus, as discussed so far, the Mann-Whitney Scan Statistic becomes a unique clustering alternative due to its non-parametric nature. However, to date, the Mann-Whitney Scan Statistic has only been developed to capture spatial variations with no regard for the temporal aspect. This section comprehensively explains the improvement of the Mann-Whitney Scan Statistic to be used in the spatiotemporal context.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 3 Methodology  
Below are the detailed procedures and methodology used for implementing a communications network based on an IP telephony service established on a single-board computer. Additionally, the design and implementation of a power network that enables the UAV to fly without interruption are presented.
# Examining the Optimal Use of WBG Devices in Induction Cookers
###### Abstract  
Modern induction cookers have started to demand challenging features such as slim design, high power ratings, high performance, and silence. All those requirements are directly related to the power semiconductors used in power converters. Si (silicon)-based power semiconductors are not capable of answering those demands because of strict operating conditions, such as high ambient temperatures. Therefore, WBG (Wide Band Gap) power semiconductors have been getting attention. In this study, WBG power semiconductors will be compared with Si-based IGBT (Insulated Gate Bipolar Transistor) under different operating conditions. The best option to use WBG power semiconductors in modern induction cookers will be analyzed. The performance of a series-resonant half-bridge converter was evaluated under various operating conditions. Measurements were obtained from the real operating conditions of induction hobs. The switching frequency is changed from 20 kHz to 100 kHz, while the power rating is increased to 3.7 kW. In addition to traditional 4-zone induction cooktops, this discussion also provides a comprehensive analysis of high-segment, fully flexible induction cooktops. While the IGBT-based design exhibits 25.79 W power loss per device, the WBG device exhibits 6.87 W in the maximum power condition of conventional induction cooker operation. When it comes to high-frequency operation, the WBG power device exhibits 10.05 W at 95 kHz. Total power loss is still well below that of the IGBT-based conventional design. Appropriate usage of WBG power semiconductors in modern induction cookers can exploit many more benefits than Si-based designs.  
induction cooking; wide band gap devices; resonant converters +
Footnote †: journal: Applied Sciences  
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