esa-sceva/satcom-chunk-collection huggingface dataset:
# EduChat: An AI-Based Chatbot for University-Related Information Using a Hybrid Approach
## 1 Introduction
Figure 1 depicts a snippet of a conversation from EduChat. Figure 1: A conversation example with EduChat. (User: Hi EduChat: Hello! I am an AI-based support staff member at the university. How can I assist you? User: Can I ask if the schedule for semester 1 is available yet? EduChat: You can access the portal at the following link: [[https://portal.huflit.edu.vn/](https://portal.huflit.edu.vn/)]([https://portal.huflit.edu.vn/](https://portal.huflit.edu.vn/)) (accessed on 1 October 2023). Please log in and use the ’View Schedule’ function. Make sure to select the correct academic year and semester you want to view. User: But I forgot my password. EduChat: You can click on ‘Forgot Password’ on the portal to reset your password.). The present paper is to explore the following issues:  
1. What effective method to build a chatbot to support a specific domain such as issues related to universities? 2. For languages with limited resources such as Vietnamese, does the combination of various methods without relying on deep learning bring effectiveness? The main contributions of this paper are as follows:  
* With only a small dataset in the low-resource Vietnamese language, we have proposed an efficient hybrid method to construct a robust chatbot system for the field of education. * Within EduChat, for managing dialogues within the specialized domain, we have established an effective set of rules using DCG and introduced the improved random forest algorithm to enhance text classification accuracy. * The model presented in this paper can be applied to various systems, across different contexts and languages. The remaining sections of this paper are organized as follows: Section 2 provides an overview of related work to contextualize our research. In Section 3, we introduce the proposed model. Sections 4 and 5 detail the components of EduChat. Experimental results will be discussed in Section 6. Finally, this paper concludes in Section 7, summarizing key findings and proposing future directions.
## 2 Materials and Methods
### Microstructural and Mechanical Characterization  
The phase composition was determined using X-ray diffraction (PANalytyal, Almelo, Netherlands). The diffraction data were collected over a 20 range of 20-44\({}^{\circ}\) with a step with of 0.02\({}^{\circ}\) and Cu-K\(\alpha\) (\(\lambda\) = 1.5405981 A) radiation. Diffractometer was anode supplied with 60 kV and a current of 30 mA.  
Bulk densities of the obtained samples were determined by the Archimedes method in distilled water. Prior to the microstructure study by scanning electron microscopy (SEM; VEGA 3 LMH, Tescan, Brno, Czech Republic), the samples were polished to a 1 \(\upmu\)m finish. The Vickers hardness (_HV_) and fracture toughness (\(K_{Ic}\)) were calculated using the indenter load 19.6 N and 10 indents of each sample. The diagonal sizes of corresponding indentations and indentation-induced crack lengths were determined by SEM. A more detailed description of the methods used in this research has been presented in previous works [11; 40]. The Young's modulus values were calculated from indentation load-displacement curves after micro-indentation obtained with the Anton Paar Micro Indentation Tester (Graz, Austria). The fracture toughness was evaluated according to the method proposed by [PERSON] and [PERSON] [41].  
In obtain the average matrix grain size, the SPS samples were thermally etched for 60 min at a temperature of 1600 \({}^{\circ}\)C.  
The biaxial flexural strength (\(\sigma\)) was measured using the piston-on-3-ball method. The obtained specimens were placed, with the polished surface down, on 3 balls located 120\({}^{\circ}\) apart on a circle with a diameter of 10 mm as the tensile side. A piston positioned above the center of the 3-ball support directly applied the load to the unpolished side, producing a biaxial flexural loading condition. Twelve specimens were tested at room temperature with a 5 kN testing machine applying a piston speed of 1 mm/min until failure occurred.  
Figure 1: The temperature-time diagram recorded in the spark plasma sintering (SPS) apparatus during the process.
# Power Dissipation and Wear Modeling in Wheel-Rail Contact
## 2 Formulation of Wheel-Rail Contact Problem
### Wear Evaluation Models
#### 2.4.2 Wear Depth Evaluation  
Nowadays, [PERSON]'s wear law [12] seems to be the most frequently used wear law. According to it, the volume of the worn material is dependent on the wear coefficient, normal force and sliding velocity divided by the hardness of the softer contact material. The wear coefficient is calculated experimentally. In the literature can be found different estimations of wear coefficients. In the USFD wear model, the wear rate is a function of the \(T\gamma\) index. The wear rate is linear and constant for small and medium values of this index, respectively, and is rapidly increasing for high values of this index.  
From the local wear analysis [17; 22], it follows that the local wear depth function \(w=w(x)\) can be estimated from the total dissipated energy:  
\[w=\alpha_{w}E_{f}. \tag{15}\]  
The local energy wear coefficient \(\alpha_{w}>0\), appearing in (15) as the proportionality coefficient, is determined experimentally and belongs to the rail material parameters [16]. This coefficient is usually set as equal to \(\alpha_{w}=\alpha_{f}\), especially for a unit contact patch [16]. From (13) and (15), we may estimate also the wear rate function \(dw\) as equal to  
\[dw=\alpha_{w}\mu\mid F_{2}\mid du_{1}. \tag{16}\]  
The wear rate law (16) relating the increment of the wear depth with longitudinal velocity \(du_{1}\) and normal pressure \(F_{2}\) represents the classical Archard wear law [16]. This function appears also in the condition (7) and can be called the inner variable. Since it updates the gap between the bodies in contact, it also updates the solution of the contact problem (5)-(8). The system (4)-(10) and (14)-(16), consisting of equations as well as equality- and inequality-type boundary conditions, governs the thermo-elasto-plastic wheel-rail contact problem in terms of displacement, generalized stresses and temperature. In order to estimate the dissipated power and wear, we shall solve it numerically. Finite element and finite difference methods have been used to obtain a finite-dimensional formulation of this system. The next section provides the details of the numerical solution method.
# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 5 Experiments
### Time Complexity Analysis  
EPEM mainly consists of three computing modules: individual feature projector, predictive expert constructor, and trustworthiness investor. In our approach, the most time-consuming part is acquiring the common and individual features of nodes and corresponding links. Compared to some graph neural network-based methods, the time complexity increases. However, when dealing with heterogeneous structural features and generating expert discriminators, the time complexity is more sensitive to the number of experts. It is directly proportional to both the number of links and the number of expert types. In our dataset, the number of experts is the same as the number of link types, so the actual time complexity is not high. The time complexity of individual feature projector is \(O(N^{2})\), the time complexity of predictive expert constructor is \(O(N*K^{2})\), and the time complexity of trustworthiness investor is \(O(N*K)\). Therefore, the total time complexity of module aggregation is \(O(N^{2}+N*(K^{2}+K))\).
## 8 Practical Considerations for Real-World Applications
### Optimizing DRL for Real-Time Processing in Robotics  
Real-time decision-making is a fundamental requirement in robotic applications, where delays in processing sensor data and executing control commands can significantly impact performance and safety. Traditional deep reinforcement learning (DRL) methods, while effective in simulation, often struggle with real-time execution due to computational overhead, sample inefficiency, and high-dimensional state-action spaces. Recent advance ments have focused on optimizing DRL for real-time robotic applications by improving computational efficiency, reducing latency, and enabling on-device processing.  
One approach to achieving real-time DRL execution is _on-device learning and optimization_. [PERSON] et al. [77] introduced the R\({}^{3}\) framework, which co-optimizes timing, memory, and algorithm performance for DRL on embedded robotic platforms. Their method dynamically adjusts batch sizes and replay buffer sizes to balance computational efficiency with learning stability, significantly reducing latency in real-time robotic control.  
_Parallel and asynchronous processing_ techniques have also been explored to improve DRL efficiency. [PERSON] et al. [78] proposed a non-blocking asynchronous DRL training architecture that decouples action execution from learning updates, allowing robots to make faster decisions while continuously refining policies in the background. Their approach significantly outperforms conventional blocking architectures, particularly in real-world robotic control tasks with variable communication delays.  
Another strategy is to leverage _lightweight neural architectures_ for real-time inference. [PERSON] et al. [79] discussed the use of compressed deep networks and network quantization techniques to accelerate DRL policy execution on robotic hardware. Their findings suggest that reducing model complexity without sacrificing policy performance is key to achieving real-time responsiveness.  
Additionally, _model-based reinforcement learning_ (MBRL) has been proposed as an alternative to traditional model-free DRL approaches. [PERSON] et al. [80] introduced Deep Model Predictive Optimization (DMPO), which integrates model predictive control (MPC) with reinforcement learning to improve planning efficiency in high-speed robotic tasks. By incorporating predictive models, DMPO enables robots to make faster and more accurate decisions, reducing the reliance on large sample collections.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 3 Challenge: Data Quality
### Active Learning  
In addition to semi-supervised learning, active learning (AL) is another category of algorithms used to deal with the scenarios when the annotated data are not sufficient. In active learning, the ML models are initially trained using the labeled data, and then adapted to select the unlabeled samples to query humans (oracle) for labeling. In the next iteration, the newly labeled data will be utilized for model retraining. Such iterations repeat until the model performance is satisfactory (convergence) or the labeling budget is depleted.  
The primary challenge of active learning is the query strategy that selects the samples to label, and the goal is to minimize the volume of data to be labeled while maintaining the trained model accuracy. The goal of the query strategies is to find the samples that the models are easily wrong about. However, the neural networks can normally not judge the correctness of their predictions. There are mainly two categories of query strategies for deep neural networks, i.e., uncertainty sampling and diversity sampling.
## 1 Introduction
In their research, the maximum mean discrepancy (MMD) [25] was used to compute the difference between the source and target domain. Taking the MMD as the regular term, the resulting mapping matrix can further eliminate the difference between the source domain and target domain while selecting features. More recently, [PERSON] [26] proposed a new feature selection method called FSTSL [26]. In FSTSL, MMD acted as regular term, and the PCA was used to reduce feature dimension. On the basis of [PERSON], [PERSON] [27] utilized MMD as the global difference, and Graph Embedding (GE) was used as the local difference to quantify the difference between the source and target domain. However, these methods all only used MMD as a regularization term, and only marginal feature distributions were considered, ignoring conditional feature distributions. The method proposed in this paper is named joint subspace transfer learning (JSTL). Discriminative MMD and graph embedding are both taken into account to act as regularization. However, compared with DSTL, which only focuses on marginal distribution, conditional distribution is also considered in the proposed method. First, the maximum mean discrepancy was constructed on the source and target domain to measure the divergence across databases and predict the pseudo labels of the target data, then inter-class and intra-class distance acted as regularization to ensure the feature separability. Furthermore, graph embedding also acted as a regularization to reduce the divergence between the source and target domain and preserve the local structural consistencies over labels. By minimizing the divergence across databases, a feature mapping matrix was calculated to transform the source and target domain into a common subspace domain. Then, two close low-dimensional feature spaces were obtained for the source and target database, respectively. Finally, a traditional machine learning classifier was trained on the labeled source database and applied to an unlabeled target database for pathological voice detection and classification. To summarize, the main contributions of our work are as follows:  
* A new cross-corpus pathological voice recognition framework is proposed in this paper. Our method effectively eliminates the discrepancy between the source domain and the target domain to increase the recognition rate of cross-corpus pathological voice experiments. * The aim of the JSTL is to find a projection matrix, which transforms the source domain samples and target domain samples into a common space. The MMD is used to quantify the distance between the source and target domains. Graph embedding and interclass and intraclass distance are used as regular terms. The experimental results show that in the case of regular term constraints, the mapped features have better separability. * Compared to other methods that do not account for differences in conditional distributions, pseudo labels are used to construct the MMD matrix and graph embedding matrix.
## 4 Bespoke Space System Design
### Payload Selection  
The key component of any spacecraft system is the payload, which must be selected or designed to meet the mission requirements. The function of the rest of the spacecraft, referred to as the spacecraft bus, is to ensure the successful operation of the payload. The potential variability in the payload, as a key variable in a system, is illustrated in a selection of existing multi-spectral imaging satellite systems with the required GSD (Table 2).  
The spacecraft system properties can be further expressed in the panchromatic GSD of each imager plotted against its swath width and the total mass of the host spacecraft (Figure 4). The mass of the spacecraft is indicative of the mission budget, as the launch cost, which often dominates the mission budget, will rise with increasing mass. In this respect, the SkySat 3-13 design is an attractive option as it offers high-resolution imagery for an extremely low spacecraft mass when compared to other systems. However, the corollary of this is that it has the smallest swath width of all the imagers considered, which will correspond to a longer revisit time as the ground covered on each orbit is concomitantly less.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spacecraft** & **Multi-Spectral GSD, m** & **Panchromatic GSD, m** & **Swath Width, km** & **Spacecraft Mass, kg** & **Altitude, km** \\ \hline Worldview-3 & 1.24 & 0.31 & 13.1 & 2800 & 617 \\ \hline SkySat 3-13 & 1 & 0.72 & 6.6 & 110 & 500 \\ \hline Pleides 1A/1B & 2 & 0.5 & 20 & 1015 & 694 \\ \hline DubaiSat-2 & 4 & 1 & 12 & 300 & 600 \\ \hline Spot 6/7 & 8 & 2 & 30 & 720 & 694 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications for existing high-resolution satellite systems [21; 27; 28; 29].  
Figure 4: Panchromatic spatial resolution versus swath width for existing high-resolution satellite systems. Bubble size indicates spacecraft mass.
# A Novel Device-Free Counting Method Based on Channel Status Information
## 5 Conclusions  
Crowd counting plays a key role in many applications, but existing counting methods usually require specialized hardware deployment and strict use of conditions which hinder their wide deployment. Therefore, a Device-Free Indoor people-counting Method based on Channel Status Information is proposed in this paper. The experimental results show that a monotonic relationship lies between the CSI variations and crowd size in indoor environments (i.e., laboratory, corridor and office). Meanwhile, from the similarities and differences of amplitude and phase changes that CSI phase has higher detection accuracy and sensitivity. Comparing the proposed method with FCC systems in the same experimental environment, it can be observed that the proposed method can detect more people and have higher counting accuracy, regardless of either the amplitude or phase covariance matrix as the feature vector. In the next step, a method should be proposed to accurately estimate the population in the special environment with more people. Moreover, the more accurate method will be studied when the target has different speeds.  
[PERSON] and [PERSON] conceived the idea, conducted the investigation. [PERSON] collected data, conducted the experiments and wrote the paper. [PERSON] provided supervision and guidance during the work and did the project management. [PERSON] helped [PERSON] to improve the quality of the work. [PERSON] validated the idea to the work.  
This work was supported by the National Key Research and Development Plan (No. 2017 YFB1402103), Natural Science Foundation of China (Nos. 61172018, 61771387), Scientific Research Program of Shaanxi Province(2016 KTZDNY01-06, 2018 HJCG-05), Project of Xi'an science and technology planning foundation (XALG035), CERNET Innovation Project (No.NGII20160704), and Xi'an BeiLin Science Research Plan (Nos. GX1626, GX1623).  
The authors are grateful for the anonymous reviewers who have made constructive comments.  
The authors declare no conflict of interest.
## 3 Experiments and Results  
### The Training of the Action Policy Selection Process in Marine Oil Spill Emergency Response  
The proposed methodology is intended to train the action policy selection process in emergency response by fully using historical marine oil spill cases to maximize the cumulative reward and reduce the risk of accidents. In this study, the policy selection method was trained based on information from 55 spills recorded since 1967. The data for these spills were mainly collected from ITOPF, Wikipedia and specific websites. The selected historical case names are listed in Appendix B.  
In our experiment, we assumed that 10 continuous emergency response actions should be taken in one epoch; that is, the policy provides recommended actions for 10 marine oil spill instances. The experimental results include the cumulative reward and accuracy of the training models at 300, 500, 900 total epochs. The training curves are shown in Figure 3.  
The experimental results show that with an increasing number of training epochs, the cumulative reward and accuracy of the model increase. Specifically, 300 and 500 training epochs are inadequate for training, but the reward and accuracy tend to be smooth and steady after 800 epochs. According to the  
Figure 3: Training curves: (**a**) shows the cumulative reward curve of the proposed method in different epochs, (**b**) shows the accuracy curve of the proposed method in different epochs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Category** & **Human Behavior** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Cleaning spilled oil} & Use of booms \\  & & Use of dispersants \\  & & Use of mechanical recycling and sorbent materials \\ \hline \multirow{6}{*}{2} & \multirow{6}{*}{Rescue} & Tow oil vessel (potentially over a wide area) \\  & & Hang signal lights and establish anchors \\  & & Firefighting and fire extinction \\  & & Stopping ship leaks \\  & & Rescue crew \\  & & Abandon vessel \\  & & Voluntary stranding \\ \hline \multirow{6}{*}{3} & \multirow{6}{*}{Protection of sensitive marine resources} & Shut down sensitive resources \\  & & Spontaneous recovery \\ \cline{1-1}  & & Biological recovery \\ \cline{1-1}  & & Construct artificial reefs \\ \cline{1-1}  & & Enhancement of fishery resources \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of human behaviors during a marine oil spill emergency.  
optimal response policy given by the trained model, the cumulative reward theoretically reaches 7.2. Based on the results of training, we hypothesize that the application of the hybrid DRL/CBR model can assist decision makers in determining the best marine oil spill emergency response by providing effective countermeasures.
## 1 Introduction
This allows networks to be grouped logically rather than by physical location, and it enables the segmentation of a network into virtual networks or virtual groups (a feature that is supported by most network switches). The main contributions of this paper can be summarized as follows:  
1. An extensive literature review is given, providing the reader with insight into the key contributions in the area of VPN and VLAN implementations in static and dynamic networks;
2. Several details about the protocols and signaling used in VPN/VLAN systems are given, providing the reader with detailed information about security management in the considered scenario;
3. Mobility issues are addressed, and some solutions (such as available software tools) are described in detail and suggested for some mobile environments;
4. Some command lines are also described, providing the reader with instructions on how to address some VPN security issues. The remainder of the paper is structured as follows: Section 2 addresses VPNs in static and dynamic networks, while Section 3 illustrates the main features of VLANs. Section 4 illustrates the best solution for mobility management in VPNs and VLANs, while Section 5 describes some real security implementations. Section 6 concludes the paper.