esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Coflow Propane--Air Laminar Diffusion Flames at Subatmospheric Pressures
## 2 Experimental Apparatus
### Pressure Combustion Chamber  
A stainless-steel pressure chamber controlled by a pump and a PLC (Programmable Logic Controller) system was employed. The working pressure ranged from 30 kPa to 101 kPa. Figure 2 shows the schematic of the pressure chamber from the front. The inner diameter and the inner height of the chamber were 240 mm and 600 mm, respectively. There were three observation ports with the diameter of 100 mm at the 0\({}^{\circ}\), 90\({}^{\circ}\) and 180\({}^{\circ}\) locations. The 0\({}^{\circ}\) and 180\({}^{\circ}\) ports were used for the LII measurements while the 90\({}^{\circ}\) port was used for image-taking.  
The dimensions of the chamber and the observation ports used here were very similar to those described by [PERSON] et al. [21]. The dimensions of the chamber were determined by the air exchange required for the combustion process. The position of the observation ports was determined by the visible flame height, which may vary with pressure. A vacuum pump was used to pump air from the chamber, and an air channel at the bottom of the flange was used to supply air to maintain scheduled pressure dynamically.  
A coflow burner was mounted in the middle of the chamber 170 mm above the chamber floor. A 3-mm capillary stainless-steel pipe was used to supply the fuel. The coaxial coflow air inlet had a diameter of 25 mm. Both the fuel and air inlet tubes were filled with sintered copper to stabilize the flow and create a top hat exit velocity profile. The fuel nozzle was tapered to stabilize the flow around the nozzle [19]. The detailed draft of the coflow burner is shown in Figure 3.  
The fuel was propane of 99% purity in the volume fraction. The fuel flow rate was set to 0.93 mg/s to ensure that the soot could be completely oxidized at all the pressures considered. The coflow air flow rates of 40 mg/s and 20 mg/s were used to achieve the air-to-fuel velocity ratios of \(r_{v(fuel/air)}\)= 1 and 0.5, respectively. Thus, the total carbon released from the nozzle remained the same at all the pressures. The Froude numbers of the tested flames were in the range from 0.15 to ca. 1.73, with the fuel nozzle diameter as the characteristic dimension. Therefore, jet diffusion was still in the buoyancy-dominated mode. The accuracy of pressure measurements could reach \(\pm\)3%, and the mass flowmeter had a maximum error of 2%.  
Figure 2: A schematic of the low-pressure combustion chamber.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
###### Abstract  
Simultaneous localization and mapping (SLAM), as an important research topic in robotics, is useful but challenging to estimate robot pose and reconstruct a 3-D map of the surrounding environment. Despite recent success of several deep neural networks for visual SLAM, those methods cannot achieve robust results in complex industrial scenarios for constructing accurate and real-time maps due to the weak texture and complex geometric structure. This paper presents a novel and efficient visual SLAM system based on point-line-aware heterogeneous graph attention network, which combines points and line segments to solve the problem of the insufficient number of reliable features in traditional approaches. Firstly, a simultaneous feature extraction network is constructed based on the geometric relationships between points and points and lines. To further improve the efficiency and accuracy of the geometric association features of key regions, we design the point-line-aware attention module to guide the network to pay attention to the trivial features of both points and lines in images. Moreover, the network model is optimized by a transfer-aware knowledge distillation strategy to further improve the system's real-time performance. Secondly, to improve the accuracy of the point-line matching, we design a point-line heterogeneous graph attention network, which combines an edge aggregation graph attention module and a cross-heterogeneous graph iteration module to conduct learning on the intragraph and intergraph. Finally, the point-line matching process is transformed into an optimal transport problem, and a near-iterative method based on a greedy strategy is presented to solve the optimization problem. The experiments on the KITTI dataset and a self-made dataset demonstrate the better effectiveness, accuracy, and adaptability of our method than those of the state of the art in visual SLAM.  
visual SLAM; point-line aware; knowledge distillation; heterogeneous graph attention network +
Footnote †: journal: Applied  
2023
## 4 Simulation and Performance Evaluation
### Comparison with VGDRA  
In this section, we compare the performance of proposed strategy with VGDRA [24]. VGDRA also adopts virtual grid structure and mobile sink, but sink's trajectory is fixed. We compare the two algorithms from three parts. Part 1: The source nodes are distributed unevenly in a small area of network, as we can see in Figure 19a; Part 2: The source nodes are evenly distributed in the network, as we can see in Figure 19b; Part 3: Only the energy consumption of updating routes is considered. In the Figure 19, the red lines indicate the routing path. The dots with green border are source nodes. The red star is the mobile sink.
# A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching
## 1 Introduction
A novel feature collection method, MA-HS, was developed to achieve this goal [38]. The MA-HS was employed to enhance feature selection performance by improving the search space and fitness function. The experimental results showed that it outperformed other algorithms such as the genetic algorithm (GA), binary dragonfly algorithm (BDA), binary salp swarm algorithm (BSSA), and whale optimization algorithm (WOA) [38]. Work was conducted to employ the initial center frequency-guided filter (ICFGF) approach to detect bearing faults through a two-phase process [39]. In the first phase, energy spectrum distributions were assessed using a variation analysis scale. In the second phase, a modified Mayfly optimization method (MMA) was used to determine the optimal resonance demodulation frequency. Employing the MMA in the ICFGF was found to be effective in detecting faults with high accuracy, as evidenced by results from [39]. The study also compared ICFGF to other techniques such as conditional variation selection and fast kurtogram, demonstrating its superior performance. The Mayfly method was also used to optimize the model of combined cooling heat and power (CCHP) systems [40]. The authors in [40] were able to obtain the optimal size of the components and minimum fuel consumption in the system. It was noted that the Mayfly Algorithm was more effective in providing the required solution in a shorter time. Researchers carried out a study assessing the major contribution of implementing the MA to conduct optimization of the performance of solar photovoltaic thermal collectors PVTC that are integrated with an electric hydrogen generation system [41]. To achieve the study goal, a solar (PVTC) with a hydrogen generation system has been modeled for predicting several factors related to the performance of the system using artificial intelligence and the Mayfly Algorithm. The MA has been used to improve the forecasting accuracy in the model. The aim of this research is to utilize the Mayfly Algorithm to determine the ideal circuit parameters that result in minimal rise time in Case I. In Case II, the goal is to use the same algorithm to find the optimal circuit parameters that produce symmetrical fall and rise times for the inverter. In Case III, the focus will be on finding the optimal circuit parameters that lead to an output waveform with symmetrical rise and fall times, as well as a symmetrical propagation delay time. The rest of this paper is organized as follows: the MA is briefly described in Section 2; the switching characteristics of the CMOS inverter are presented in Section 3; Section 4 explains the formulation of the problem; results and Spice simulations are provided in Section 5; finally, Section 6 concludes the paper.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 4 Results
### Validation of the Effectiveness of the Water Index  
In order to analyze the accuracy of the NDWI and SWI, the contrast value derived from the mean values of NDWI or SWI is calculated to evaluate the separation between water bodies and nonwater bodies. The mean values and contrast values of the water bodies and nonwater bodies for the NDWI and SWI in each study area are summarized in Table 3. The mean value of the SWI in terms of water bodies is higher than that of the NDWI and the difference between the mean NDWI value and the mean SWI value in terms of nonwater bodies is not significant. Therefore, the contrast values of SWI are higher than those of NDWI in each study area, which suggests that the SWI is a more robust and the more effective water index for enhancing water information features in Sentinel-2 imagery.  
Figure 4: Water bodies enhanced by the NDWI and SWI in the four study areas. ((**a**) Taihu Lake composite image (R: Band 8; G: Band 4; B: Band 3), (**b**) NDWI of Taihu Lake, (**c**) SWI of Taihu Lake, (**d**) Yangtze River composite image (R: Band 4; G: Band 3; B: Band 2), (**e**) NDWI of the Yangtze River, (**f**) SWI of the Yangtze River, (**g**) Chaka Salt Lake composite image (R: Band 2; G: Band 3; B: Band 4), (**h**) NDWI of Chaka Salt Lake, (**i**) SWI of Chaka Salt Lake, (**j**) Chain Lake composite image (R: Band 8; G: Band 4; B: Band 3), (**k**) NDWI of Chain Lake, and (**l**) SWI of Chain Lake).  
Figure 5 shows the box plots of the NDWI and SWI in terms of water bodies and nonwater bodies. Figure 5a-d are the study areas of Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake, respectively. The plots show that the NDWI and SWI can distinguish most of the water bodies and nonwater bodies; however, the maximum or minimum NDWI values for the nonwater areas almost overlap with the standard deviation of the NDWI values for the water bodies, while the maximum or minimum SWI values for the nonwater areas can be effectively distinguished from the SWI values for the water bodies based on the standard deviation. By performing analysis of the box plots, the results suggest that the NDWI will cause some misclassifications of water and nonwater bodies, while the SWI can effectively separate different water body types and nonwater bodies.
## 3 Results
### Case 2  
The patient was a 56-year-old female with right chronic otitis media. She used an AC-HA on the right, but was unable to continue using it due to otorhea and participated in this trial. Her air conduction hearing thresholds are 75 (125 Hz)-85 (250 Hz)-100 (500 Hz)-110 (1 kHz)-105 (2 kHz)-115 (4 kHz)-105 (8 kHz) dB HL on the right and 30 (125 Hz)-25 (250 Hz)-25 (500 Hz)-35 (1 kHz)-45 (2 kHz)-55 (4 kHz)-50 (8 kHz) dB HL on the left. Her bone conduction hearing thresholds are 30 (250 Hz)-35 (500 Hz)-50 (1 kHz)-65 (2 kHz)-60 (4 kHz) dB HL on the right and 20 (250 Hz)-20 (500 Hz)-30 (1 kHz)-50 (2 kHz)-50 (4 kHz) dB HL on the left. She could not attempt using ADHEAR because the device immediately peeled off from her skin. The hearing thresholds with Baha were almost the same at middle-to-high frequencies as the thresholds with CC-HA but were smaller at low frequencies. Regarding the sound localization test, RMS decreased with Baha (Figures 4 and 5). The patient refused the later sound localization test with CC-HA because she experienced severe difficulty when performing the test with Baha and she was discouraged to continue the test. GBI was higher in Baha than in CC-HA (Figure 6).  
Figure 6: Results of Glasgow Benefit Inventory (GBI).
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 1 Introduction
### Traditional Methods and Challenges  
Traditional methods for subsurface defect detection include visual inspection, ultrasonic testing, and X-ray imaging. While these methods have proven effective, they oftenrequire significant time, specialized equipment, and can sometimes be intrusive. Visual inspections are subjective and dependent on the inspector's experience. Ultrasonic and X-ray methods, although precise, involve complex equipment and safety concerns, particularly with radiation exposure [4].
# Dynamic Linked Data: A SPARQL Event Processing Architecture
## 4 Broker Design
### Subscription Processing Unit Manager  
This section describes the internal structure of the Subscription Processing Unit (SPU) manager that processes the subscriptions. The SPU manager architecture is shown in Figure 6.  
Figure 6: SPU manager architecture.  
Each subscription is processed by an SPU that is instantiated by the Creator module when a subscrib request is received. The Creator module, by analyzing the SPARQL query, may instantiate a different kind of SPU (i.e., implementing a different algorithm) or link the subscription with an existing SPU (i.e., the SPARQL query is the same [54]). An SPU is deallocated by the Destroyer module when an unsubscribe request is received. This may be issued directly by a client (see Section 3.2) or by the Dependability Manager (see Figure 4) if a client connection has been lost.  
On each update received by the broker, the SPU manager is activated (see Activate in Figure 6). The Activator module activates all the SPUs and waits for all of them to complete processing. Then, it signals to the main core thread (see Figure 5) the effective end-of-processing (EOP) so that the next update request can be processed. SPUs run in parallel, and each SPU may also run on a different machine in a distributed computing environment.  
An SPU implements the subscription algorithm and notifies its subscriber (or subscribers if the SPU is shared by multiple clients) of changes due to the last update (if any). As each subscription must be processed at any update, subscription processing shapes the scalability level. Here, multi-resolution approaches where a fast coarse-grained step filters out most unaffected subscriptions leaving the burden of detecting the need for notification to a few candidates, turn out to be particularly effective, as shown by the performance evaluation sections in [11; 22]. In particular, in [22], an algorithm is presented that speeds up the query processing and the results matching by (i) binding variables before sending the query to the SPARQL endpoint and (ii) performing a fast filtering stage based on look-up tables to reduce the amount of subscriptions that are candidates to produce notifications. Another option to optimize the subscription processing could be to implement a Rete network as described in [53]. Discussion on subscriptions processing optimization is out of the scope of this paper, but the reader can refer to [55] for a discussion on how performance can be evaluated.
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq4: What Are the Challenges of Utilizing AI in the Metaverse for Education?
#### 4.4.3 Ethical and Security Concerns  
The use of machine learning algorithms in educational contexts raises a number of ethical and security considerations [106; 108]. In fact, the lack of specific regulations and the need for extensive data collection for personalized experiences and the training of AI models raise privacy concerns [69; 107; 108]. For example, to create a personalized learning environment, metaverse education systems need to collect identity information and personal data, such as learning preferences and emotional responses [6]. In preschools, the risk is higher because children may not be aware of the volume of data they are providing. Thus, maintaining privacy is often at the discretion of platform owners [107]. It is therefore essential to give importance to the rights of young learners to protect their data when integrating AI technologies into the metaverse in educational environments. The study by [6] affirmed the importance of ensuring that AI decision-making maintains trust and integrity by aligning with ethical standards, highlighting two specific security concerns within the metaverse educational environment: firstly, the misuse of AI, such as exploiting AI to cheat on exams or plagiarize work, and secondly, the issue of data security and privacy when utilizing real-time data to develop digital twins, which necessitates high precision and the ability to identify and rectify errors. The study by [6] asserted the importance of ensuring that AI decision-making preserves trust and integrity by aligning with ethical standards. In this context, the authors highlight two specific security concerns within the metaverse educational environment. First, the misuse of AI, such as exploiting AI to cheat on exams or plagiarize assignments. Second, the issue of data security and privacy when using real-time data to develop digital twins. The study by [108] argued that certain educational topics, such as ethics and safety compliance, are difficult to address effectively within the metaverse. Therefore, the use of AI systems must be carefully adopted to handle these sensitive topics, which adds another layer of complexity to their implementation. Furthermore, privacy and security issues tend to arise due to the interactions between chatbots and users that may include sensitive information [56]. So, the use of chatbots in the metaverse for educational purposes requires stringent measures to protect against breaches of security. On the other hand, the security concerns pose significant barriers to developing AI techniques used as an assistive technology for students with specific learning difficulties [69].
# Target-Oriented Multi-Agent Coordination with Hierarchical Reinforcement Learning
## 3 Preliminaries  
### Reinforcement Learning  
A reinforcement learning problem can be modeled by a Markov decision process [48] (MDP), defined by the tuple \(\langle S,A,P,r,\gamma\rangle\), in which  
* \(S\subseteq R\) is a n-dimensional state space;
* \(A\subseteq R\) is a m-dimensional action space;
* \(P:S\times A\times S\to R_{+}\) is a transition probability function;
* \(r:S\to R\) is reward function;
* \(\gamma\in(0,1]\) is the discount f.  
In [PERSON] decision process, the agent performs an action \(a_{t}\in A\) according to the current state \(s_{t}\in S\) and its policy \(\pi(a_{t}|s_{t})\) at each timestep. The objective of the agent is to learn an optimal policy \(\pi_{\theta^{*}}:=argmax_{\pi\theta}\Big{[}\sum_{i=0}^{T}\gamma^{i}r_{t+i}|s_ {t}=s\Big{]}\).  
Hierarchical reinforcement learning uses a multi-layer policy to interact with the environment. For the target-oriented multi-agent coordination problem, the joint policy \(\pi_{j}\) omit consists of a high-level policy \(\pi_{h}\) and a low-level policy \(\pi_{l}\). The high-level policy distributes targets \(g_{t}\) to coordinate the individual low-level agents. Then the low-level policy agents output the primitive action according to the received target assignment \(g_{t}\) and current state \(s_{t}\). Differently, the high-level policy is used to coordinate agents to maximize global rewards, while the low-level policy is used to maximize individual local rewards under the high-level policy target assignment.
## 5 Experimental Study
### Results
#### 5.2.2 Extended Terminal Set with Composite Features Determined by Frequency of Occurrence
On the other hand, nodes such as \(\frac{dd}{w}\), \(ma*w\) were the first to be removed, suggesting that they were the least informative. Table 8 shows the selection process carried out by FBS. We can see that in this case, the order in which features were removed from the terminal set differed from that of BSFS. The results in this case showed no improvement over the terminal obtained at the end of Phase 1. Since neither method achieved a positive result, we can conclude that creating new composite features based on their frequency of occurrence in well-functioning rules does not generally lead to better results. These nodes, therefore, offer no advantages over the original set of atomic features identified in Phase 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Expression** & **Occurrence Frequency** \\ \hline \(dd*dd\) & 212 \\ \(ma+pt\) & 127 \\ \(ma*w\) & 110 \\ \(\frac{dd}{w}\) & 99 \\ \(\frac{pt}{w}\) & 48 \\ \(\frac{pt}{w}\) & 40 \\ \(dd-pt\) & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Frequency of occurrence of most commonly appearing sub-expressions from Phase 1. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Iteration**} & \multicolumn{6}{c}{**Terminals**} \\ \cline{2-7}  & \(dd*dd\) & \(dd-pt\) & \(\frac{dd}{w}\) & \(ma+pt\) & \(\frac{pt}{ma}\) & \(\frac{pt}{w}\) & \(ma*w\) \\ \hline Phase 1 optimised & \multicolumn{6}{c}{13.69} \\ \hline All terminals & \multicolumn{6}{c}{13.86 (\(\approx\))} \\ \hline
1 & 13.84 (\(\approx\)) & 13.86 (\(\approx\)) & **13.83** (\(\approx\)) & 14.09 (\(-\)) & 13.92 (\(-\)) & 13.95 (\(-\)) & 13.94 (\(-\)) \\
2 & 14.07 (\(-\)) & 13.85 (\(\approx\)) & - & 13.83 (\(\approx\)) & 14.11 (\(-\)) & 13.96 (\(-\)) & **13.82** (\(\approx\)) \\
3 & 14.00 (\(-\)) & **13.67** (\(\approx\)) & - & 13.93 (\(-\)) & 13.81 (\(\approx\)) & 14.01 (\(-\)) & - \\
4 & 14.01 (\(-\)) & - & - & 13.80 (\(\approx\)) & 13.95 (\(-\)) & **13.79** (\(\approx\)) & - \\
5 & 13.86 (\(\approx\)) & - & - & 13.73 (\(\approx\)) & **13.66** (\(\approx\)) & - & - \\
6 & 13.83 (\(\approx\)) & - & - & **13.65** (\(\approx\)) & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results obtained by BSFS for the extended terminal set with features created based on their frequency of occurrence.