esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Results and Analysis
#### 4.2.2 Feature Detection and Matching
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Matching** & \multicolumn{4}{c}{**Number of Matched Point Pairs\(\uparrow\)**} \\ \cline{2-5}
**Algorithm** & **Statue1** & **Statue2** & **Archi1** & **Archi2** \\ \hline SIFT & 5050 & **2138** & 2568 & 3209 \\ SURF & 69 & 229 & 44 & 65 \\ MS-HLMO & 719 & 424 & 151 & 312 \\ Ours & **9136** & 1782 & **4090** & **3382** \\ \hline \hline \end{tabular}  
* The arrow indicates that a higher number of matched point pairs is preferable; the bolded data represents the optimal results for each section.  
\end{table} 
Table 5: The number of matched point pairs across four sets of self-collected data using various matching algorithms. demonstrating high precision and robustness. On the other hand, the MS-HLMO algorithm performs inconsistently in terms of matching accuracy, especially underperforming in the Archil dataset, even though it outperforms SIFT and SURF in terms of the number of matched point pairs. The specific comparison of the results is illustrated in Figure 15. It can be clearly observed from Figure 15 that across all datasets, our algorithm and MS-HLMO consistently outperform SIFT and SURF algorithms, particularly when it comes to matching accuracy. Consequently, our algorithm not only matches more point pairs when processing self-collected datasets but also achieves a higher matching accuracy when taking into account both the number of matched point pairs and matching accuracy, highlighting its superiority and potential in real-world applications. In summary, to address the issue of insufficient point cloud density in existing multi-view image 3D reconstruction algorithms, this study proposes a solution framework based on feature-matching optimization. By integrating the scale invariance of SIFT feature descriptors with the high-dimensional search efficiency of KD-Tree (resulting in a 68.5% reduction in matching time) alongside a self-designed dynamic threshold RANSAC optimization strategy (which enhances inlier selection accuracy by 23.6%), the experimental results demonstrate the algorithm's exceptional performance across four datasets. This accomplishment demonstrates the synergistic efficacy of the adaptive RANSAC filtering mechanism and SIFT + KD-Tree feature space optimization, which effectively alleviates the issue of low model point cloud density caused by insufficient matching points during multi-view image reconstruction and provides a larger number of matching point pairs for subsequent multi-view geometric computations.
# Sigma Routing Metric for RPL Protocol
## 3 Related Works
An energy-based RPL objective function is proposed [32], which is based on the remaining energy of each node. Other research in [33] describes the problems of RPL when implemented for mostly downward traffic, referring to it as an upward-oriented protocol. A modified protocol called DT-RPL is proposed for diverse traffic requirements, including both upward and downward traffic. The protocol exploits downward packets to update the ETX metric values in the child nodes transmitted in the RPL messages. Other research for Downward routing in RPL is proposed by [PERSON] et al. [34], where they classify the different causes of packet loss and present a set of reliability mechanisms that eliminate or mitigate all causes of packet losses. [PERSON] et al. in [35] introduces a new OF based on a Non-Linear Length (NL-OF), which takes into account any number of metrics and constraints for QoS routing. NL-OF ensures that each path in the DODAG respects the input constraints. The NL-OF can be used to meet the requirements of sensible applications, such as real-time applications. Other research in [36] proposes a Queue Utilization-based RPL (QU-RPL) that improves the load balancing and congestion traffic in LLN networks. QU-RPL is designed for each node to select its parent node considering the queue utilization of its neighbor nodes as well as their hop distances to an LLN border router (LBR). Another protocol is described in [37] where the authors evaluate the performance of the P2P-RPL variant and compare it with normal RPL. P2P-RPL differs from RPL, in that the traffic flows in pre-calculated DAGs in the latter, whereas in P2P-RPL, the best-quality route is calculated for any source-destination node pair. The authors create a simulated P2P-RPL environment in NS3 and find that the Packet Delivery Ratio in P2P-RPL is higher than on RPL, due to the specific routes selected for the particular source-destination pairs, instead of always going through the root of the DODAG as in normal RPL. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Target Performance** \\ \hline HC & Communication latency and energy consumption reduction (indirectly) \\ ETX & Reduction of required frame retransmissions \\ RE & Expansion of network lifetime \\ PFI & Reduction of network-layer losses \\ ETT & Assignment of weights to the individual links \\ WCETT & Focus on fixed nodes of local networks \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Metrics. In [20], [PERSON] et al. propose a new metric for the RPL protocol called Per Hop ETX (PH-ETX). PH-ETX calculates the best routing path, taking the average of the ETX values between each node along the path to avoid the problem of a long, single hop. To improve the quality of service in LLN networks, one of the most widely used techniques is the ETX metric, which focuses on connection states at the network layer level. Among measurements previously mentioned, some of them are specific for a best parent selection in each node. However, PH-ETX defers the responsibility of best parent choosing to ETX, and then uses it to select the best path for routing.
## 4 Quality Assessment Methodology  
### Uncertainty Requirements  
The accuracy assessment results were analyzed against predefined uncertainty levels based on a review of the existing user requirements from the Global Climate Observing System (GCOS), the World Meteorological Organization (WMO) and the key performance indicators of C3S.  
In the last update of the GCOS requirements [64], there is a distinction between the products targeted for \"adaptation\" and \"modelling\" applications that results in different needs for the horizontal resolution. Modelling requirements (i.e., uncertainty Max [5%; 0.0025], see Table 6) are the focus of this study since modelling is the main application targeted. Other requirements come from the WMO [65], which aids in the setting of the priorities to be agreed upon by WMO members and their space agencies for enhancing the space-based GCOS system. The WMO specifies the requirements for the SA for climatologic applications at three quality levels (Table 6): Threshold (minimum requirement), breakthrough (significant improvement) and goal (optimum, no further improvement required). The stated \"goal\" WMO uncertainty requirement of 5% is, thus, equivalent to the GCOS requirement in relative terms. Apart from the GCOS and WMO, the Key Performance Indicator (KPI) of the maximum accuracy being between 10% and 0.01 was defined in the C3S program [66].  
Based on the existing requirements, three different levels (i.e., optimal, target and threshold) are predefined in this study (Table 7) with the aim to verify whether the results fit the purpose (Table 6). The optimal level (Max [5%, 0.0025]) was selected according to the GCOS uncertainty requirement and is equivalent to the WMO goal. The target level (Max [10%, 0.01]) is equivalent to the C3S KPI and partly equivalent to the WMO breakthrough level. Finally, the threshold level (Max [20%, 0.02]) was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GCOS** & **WMO** & **C3S KPI** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Surface Albedo** \\ **requirements** \\ \end{tabular} } & \multicolumn{3}{c}{Goal: 5\%} \\  & Max (5\%; 0.0025) & Breakthrough: 10\% & Max (10\%; 0.01) \\ \hline \hline \end{tabular}
\end{table}
Table 6: GCOS, WMO and C3S SA uncertainty requirements.  
Figure 2: Sentinel-2 surface reflectance (Band 8A) images (3 km \(\times\) 3 km) at a spatial resolution of 10 m centered over a selection of 4 GBOV sites (**a**) GER_HAI, (**b**) AUS_TMB, (**c**) ITA_REN, (**d**) USA_PSU). STscore and RAWscore, indicators of the spatial representativeness, are displayed in the plots.  
adopted from the WMO. Poor performances of the product correspond to values above the threshold level (minimum requirement). Figure 3 displays the selected uncertainty levels as a function of the product values.
# A GAN-Augmented Corrosion Prediction Model for Uncoated Steel Plates
## 1 Introduction
The current prediction methods have low accuracy, which is not enough for corrosion simulation. * The specificity of the environment and steel. Many methods are not generally applicable. They usually target specific environments and specific steels. In this paper, we use adversarial learning to simulate the generation of future corrosion on steel surfaces. We collect data from real samples. We choose GAN as our model since the goal of this research is to predict the corrosion of the next stage according to the current stage corrosion and identify the number of days of the current corrosive status. The input of the GAN model should be the current data, not the traditional random vector. To expand the diversity of the dataset, we use Gaussian noise and a generative adversarial network (GAN) to enrich the dataset, which can improve the accuracy of the system. Together with UNet [13] and MobileNetV2 [14] our system can be used to predict next stage corrosion (the output of the system) based on the current corrosive status (the input for the system). Our system can also be used to identify the stage of corrosion and how many days of the corrosion. Based on the experimental results, our model achieves high accuracy for predicting the corrosion surface on steel members. The main contributions of this research are listed as follows:  
* We propose a method for predicting the corrosion surface of uncoated steel plates. * We use Unet to simulate the corrosion surface, and verify its reliability for the simulation. * Our system can also predict the stage of corrosion and days of the stage based on the current corrosive status. Compared to traditional methods that usually depend on individuals' experience, our proposed method can achieve faster and more accurate prediction of the corrosion behavior, which is facilitated by the development of data science. Our method could greatly save the cost for assessment and maintenance of steel structures. The rest of this paper is organized as follows. Section 2 reviews research work related to prediction of the corrosion on steel members. Section 3 explains our proposed model and dataset. In Section 4, several comprehensive experiments are conducted to evaluate the effectiveness of the proposed model. Finally, in Section 5 we conclude the paper and provide future work.
## 2 Materials and Methods
### Correlation Analyses of Pearson and Spearman  
Correlation analysis quantitatively describes the strength of correlational dependencies through statistical indicators called correlation coefficients that carry this information [40]. Correlation coefficients can take values between \(-1\) and \(1\). The absolute value of the coefficient provides information for the strength of the dependence between two variables, \(X\) and \(Y\), and also, the sign of the correlation coefficient provides information for the direction of dependences. A positive number indicates positive relations between the variables, i.e., if one variable increases, the other is expected to also increase. A negative coefficient indicates negative relations between the variables. If one variable increases, the other is expected to decrease.  
The value of the correlation coefficient (r) determines the strength of the correlation. According to [41], when r = 0, there is no correlation. When r = 1, there is a perfect (so-called functional) correlation. When the absolute value of the correlation coefficient r is between the following:  
* 0.00 and 0.09, there is a negligible correlation;
* 0.10 and 0.39, there is a weak correlation;
* 0.40 and 0.69, there is a significant correlation;
* 0.70 and 0.89, there is a strong correlation;
* 0.90 and 1.00, there is a very strong correlation.  
Pearson's simple linear correlation coefficient is calculated by the following formula:  
\[r=\frac{p}{SxSy} \tag{3}\]  
where \(P\) is the moment of the products. \(Sx\) is the deviation of the variable \(X\). \(Sy\) is the corresponding standard deviation of the variable \(Y\). \(P\) is calculated by the following formula:  
\[P=\frac{\sum XY}{n-1}-\frac{\sum X\sum Y}{n(n-1)} \tag{4}\]  
where \(\sum X\) is the sum of the values of \(X\), \(\sum Y\) is the sum of the values of \(Y\), \(\sum XY\) is the sum of the \(X\) and \(Y\) products, and \(n\) is the sample size.  
[PERSON]'s rank correlation coefficient is calculated using the following formula:  
\[r_{s}=1-\frac{6\sum d^{2}}{n(n^{2}-1)} \tag{5}\]  
where \(d\) is the difference in the rank numbers of \(X\) and \(Y\) and \(n\) is the sample size.  
The two CAs were implemented over the sEMG data using the capabilities provided by Microsoft Excel (v. 2023).
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 5 Conclusions  
In this paper, a novel closed-loop algorithm describing how to control the swimming efficiency of a robotic fish was proposed. We established the dynamic model and the hydrodynamic model of a two-joint robot fish. We formulated the problem of raising the propulsive efficiency as an algorithm designed in the joint space. Moreover, we characterized the mechanism of how the fish moves under the control and drive of the nerve and the muscles. Consequently, we abstracted a virtual musculoskeletal methodology from the nerve and muscle system and applied it in the joint space of the robotic fish. We evaluated the performance of the robotic fish controlled by two kinds of methods in the steady flow and Karman vortex. The results showed that the closed-loop control via virtual musculoskeletal methodology is more energy-efficient than the proportion control algorithm in both kinds of flow. The insight is that the virtual musculoskeletal methodology brings compliance to the muscles in the joints. Therefore, the robotic fish can adapt to the hydrodynamic effects of the water to reduce energy consumption when swimming.  
In our future work, we will build up a more general swimming model of the robotic fish by considering complex flow turbulence to adapt to the real environment. Additionally, we will take the task planning into the closed-loop control for the applications. Finally, we will build up a real platform and conduct experiments on it to evaluate the performance of the proposed algorithm.  
Conceptualization, D.X. and Y.Z.; methodology, Y.Z.; software, H.F.; validation, Y.Z. and H.F.; formal analysis, H.F.; investigation, Y.Z.; resources, D.X.; data curation, H.F.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; visualization, H.F.; supervision, D.X. and C.M.; project administration, D.X.; funding acquisition, D.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 62073017.  
Not applicable.  
The authors declare no conflicts of interest.
## 5 Results
### Supraharmonics Emission
#### 5.2.1 Case Study 1--CFL 20 W Lamp  
A CFL lamp with a non-PFC topology usually has a power factor of less than 0.6 [56]. Figure 5 shows the spectrum of the CFL 20 W lamp (PF = 0.6) within the frequency range of 9-150 kHz for various supply voltage levels. Each of the curves represents a different voltage supply level varying from 210 V to 250 V with a step of 10 V. The emission produced by EIT shifts slightly towards higher frequency bands as the supply voltage increases from 210 V to the nominal value of 230 V. While, for voltages above the nominal voltage value, the emission shifts noticeably towards lower frequency bands. Table 3 provides a numerical representation of the effects of voltage variations on frequency shifts and differences in emission levels. The tested CFL lamp generates the highest emission in the frequency ranges 9-15, 41-48, 83-94 and 126-146 kHz. The zooms for these spectra are shown in Figure 6. A level of emissions in the range of frequency 9-15 kHz (Figure 6a) cannot be attributed to the source of disturbances (EUT). The real source of these disturbances is the programmable power supply device used in the research. The relationship between the supply voltage level and disturbance generated by the device is shown in the frequency range of 83-94 kHz (Figure 6c). In the case when the supply voltage level is higher or lower than nominal, the peak value of emission in both cases is shifted in the frequency band towards lower frequencies (the maximum shift is equal to 3.4 kHz). The maximum value of the signal shift occurs for the signal corresponding to 250 V and 230 V for the dominant emission band between 126 and 146 kHz (Figure 6d) and is equal to 12.2 kHz. The value of supply voltage does not have a significant effect on the amplitude at the peak value of emission, but it can be observed that for most of the dominant emission bands, the amplitude slightly increased  
Figure 5: Supraharmonics spectrum of the CFL 20 W lamp for various supply voltage values [21]. as the supply voltage level decreased. At the nominal voltage supplying condition (230 V), the highest emission level generated by EUT is equal to 49.09 dBV. The obtained results do not exceed the permissible limits specified in EN55015 standard [21]. Figure 6: Zoom-ins on the dominant emission band of the CFL 20 W lamp supraharmonic spectrum for various supply voltage values: (**a**) frequency range 9–15 kHz, (**b**) frequency range 41–48 kHz, (**c**) frequency range 83–94 kHz, (**d**) frequency range 126–150 kHz [21].
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 4 Performance Evaluation
### Simulation Environments  
We simulate and analyze the proposed scheme and other protocols on NS-3 simulator. Table 3 describes the detailed setup of our simulation. The nodes are placed in a 1000 \(\times\) 1000 m terrain. As shown in Figure 5, 100 nodes are placed in the form of a grid and the remaining 900 are randomly placed. Thus, the node density is determined arbitrarily, which can lead to the exceptions described in Section 3.4. Especially, as the black square in Figure 5 has a low node density, it adversely affects the performance. The provided results have considered these simulation environments. According to the MICA2 specification, the transmission and receiving power consumption of the sensor node are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Routing Protocol** & **Time Complexity** \\ \hline Multipath Routing & REER & \(O(N)\) \\  & RGMR & \(O(N\times P)\) \\ \hline Learning Algorithm & RRDLA & \(O(N\times K)\) \\ \hline Opportunistic Multipath Routing & Proposed Scheme & \(O(N\times P)\approx O(N)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time complexity of the proposed scheme and other protocols.
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 3 Method
### Methodology  
We design a triangular pyramid calibration target to complete the calibration of the camera and flash LiDAR system. The target is an arbitrary triangular pyramid with a chessboard pasted on its three planes, as shown in Figure 2a. Without knowing the size of the triangular pyramid, the only parameter to be determined is the size of the unit of the chessboard, hence the error of manufacture being ignored. In this section, the world coordinate system \(O_{W}-X_{W}Y_{W}Z_{W}\) is set up as follows. Origin \(O_{W}\) is the middle vertex of the triangular pyramid. Axis \(X_{W}\) is aligned with one edge of the triangular pyramid. Axis \(Y_{W}\) is on plane 1, perpendicular to axis \(X_{W}\). The direction of axis \(Z_{W}\) is determined by the right hand rule.
## 1 Introduction  
The advancement of artificial intelligence (AI) has significantly influenced numerous sectors, including education. Among the various AI technologies, ChatGPT, created by OpenAI, has attracted considerable attention for its sophisticated conversational abilities and its potential to enrich educational experiences. As an AI-driven chatbot, ChatGPT provides personalized, interactive support to students, offering solutions to complex questions, feedback on assignments, and enabling open-ended discussions [1]. Its versatility and usefulness make it a transformative tool in higher education, with the capability to redefine the traditional learning models.  
However, the adoption of ChatGPT in higher education is shaped by numerous factors that affect its acceptance and practical use among students and educators [2; 3; 4; 5]. Understanding these factors is essential for fully realizing the potential of ChatGPT and ensuring its smooth integration into educational environments. Empirical studies have investigated various aspects of ChatGPT adoption, including technological attributes, user perceptions, institutional policies, and broader educational contexts.  
This narrative review seeks to compile the findings from empirical studies regarding the factors affecting the adoption of ChatGPT in education. By concentrating on evidence-based research, the review aims to pinpoint the main factors that either encourage or impede the acceptance and use of ChatGPT among students and educators. The insights from this review will provide crucial guidance for educational institutions, policymakers, and technology developers in enhancing the deployment and utilization of ChatGPT in educational settings.  
The review is structured to initially present the theoretical frameworks frequently used in studies on ChatGPT adoption. It will then categorize and discuss the empirical findings under several themes: technological factors (e.g., perceived usefulness and ease of use), personal factors (e.g., attitudes, trust, and self-efficacy), social factors (e.g., social influence and peer usage), and organizational factors (e.g., institutional support and policy frameworks). This narrative review attempts to answer the following questions:  
* Which theoretical frameworks have been utilized in empirical studies examining user acceptance and adoption of ChatGPT in educational settings?
* What technological and personal factors influence the adoption of ChatGPT in higher education?  
This narrative review provides a comprehensive overview of these studies, aiming at the nature of ChatGPT adoption and offering actionable insights for fostering its effective integration in higher education.  
To achieve this goal, the following sections will be presented: first, an introduction to the narrative review methodology; next, the review results; followed by a discussion of the theoretical and practical implications and future research directions. The final section provides the conclusion.