esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification
### Fault Analysis of the DFIG-Based Wind Farm with LVRT Capability
#### 2.3.2 The Phase-to-Phase Short Circuit Fault Case  
For the similar wind farm with LVRT capability, the topology is shown in Figure 4, but the voltage level of the transmission lines are 110 kV and the transformer T\({}_{3}\) is out of operation. The wind power system details are presented in Appendix B. Figure 6 shows the real waveform records when a phase-A-to-phase-B fault occurs on Line 2.  
Figure 6: The real fault waveform records when a phase-A-to-phase-B fault occurs on the transmission line connected to a DFIG-based wind farm with LVRT capability: (**a**) voltages of the wind farm side, (**b**) currents of the wind farm side, (**c**) phase A voltage and current of the wind farm side, and (**d**) phase A voltage frequency and current frequency of the wind farm side.  
The fault occurs at 0.05 s and the fault duration is about 180 ms; then, the distance protection initiates and the circuit breaker trips. As shown in Figure 6a, the phase A voltage is approximately equal to the phase B voltage after the fault. It can be seen From Figure 6b that the fault currents increased after fault, fault current consists of a decaying DC component, a decaying AC component at rotor speed frequency, and a forced AC component at synchronous frequency according to Equation (11). The natural components of fault currents gradually decrease. At last, the forced component remains and the value is small than load current because of voltage sags. For the phase-to-phase short circuit fault, DFIG-based wind farms only provide positive and negative sequence currents, the amplitudes of which are small, and no zero-sequence current exists. Therefore, the frequency deviation is obvious. Figure 6c shows the wind farm side voltage and the current waveforms of phase A, which is extracted from Figure 6a,b. The frequency of the phase A voltage remains 50 Hz because of the system voltage support. At the beginning of the fault, the frequency of the phase A current is obviously greater than 50 Hz due to the existence of decaying AC component at rotor speed frequency, as shown in Figure 6d.  
Distance Protection and Its Improvement Method for the Transmission Line Connected to DFIG-Based Wind Farms
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 2 Swarm Intelligence
bees communicate the location of food sources through the waggle dance, influencing the colony's foraging patterns, and this behavior has led to the development of the bee algorithm, optimizing search processes. A real-world example of natural SI can be seen in deploying autonomous drones for agricultural monitoring [38]. Inspired by the behaviors of ant colonies and bee foraging, these drones use algorithms like ACO and the bee algorithm to cover large fields efficiently, identify areas needing attention, and optimize their flight paths for data collection. The drones communicate and coordinate with each other by sharing information about detected issues, such as pest infestations or soil moisture levels, much like ants laying pheromone trails or bees performing the waggle dance. This decentralized and self-organizing approach allows drones to adapt to dynamic field conditions and ensure comprehensive coverage without central control, enhancing the efficiency and effectiveness of agricultural management. SI represents a fascinating and effective approach to solving complex problems through simple, local interactions among agents [28]. SI algorithms, such as ACO [17], PSO [18], Artificial Bee Colony (ABC) [19], Firefly Algorithm (FA) [57], Cuckoo Search (CS) [24], and Bat Algorithm (BA) [25], have demonstrated significant success in various applications by drawing inspiration from nature. These algorithms capitalize on decentralization, self-organization, and emergence principles, offering robust, flexible, and scalable solutions. Table 2 compares these SI algorithms across different criteria, such as convergence speed, scalability, complexity, and suitability for dynamic environments. The details of these algorithms are discussed in the following section. Convergence Speed: Describes how quickly the algorithm approaches an optimal or near-optimal solution. Algorithms that converge too quickly may risk getting stuck in local optima, while slower convergence can lead to more thorough exploration. Scalability: Refers to the algorithm's ability to maintain performance as the problem size or dimensionality increases. Complexity: Relates to the computational demands and ease of algorithm implementation. High complexity may require more resources and be challenging to implement, especially in real-time systems.
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 1 Introduction
It contributes to the Arabic sentiment analysis (ASA) research community, by using the latest cutting-edge techniques to perform new experiments with the above relatively new, unexplored and extensive Arabic dialect dataset. This study thus answers the following research question (RQ):  
RQ: Is it possible to predict the customer churn of telecommunication companies in Saudi Arabia by analysing customers' tweets? This paper starts with reviewing the related research, then explaining the used methodology, finally discusses the results.
# Tire Bubble Defect Detection Using Incremental Learning
## 4 Conclusions  
This paper proposes a semi-automated tire defect detection process using a dataset of speckle images provided by the largest tire manufacturer in Taiwan, and labeled by professionals into six defect categories. To supplement the dataset, we have enhanced images of defects that are difficult to detect and added them to the dataset during training to improve accuracy. Using the training sample, we obtained a detection rate of 92.54% with the initial model in the first stage. During the second stage, we implemented an incremental YOLO architecture upon the architecture in the first stage, which improved the detection rate to 98.56%, reduced the detection speed, and provided better detection rates than previous studies for the hard-to-detect bubbles. During the quality control process in tire manufacturing, the model is likely to encounter new defects that were not present in the training data. We can incrementally train the model to recognize new defects with the proposed incremental YOLO architecture, which significantly improves the accuracy compared to the original model. For defect recognition, the original and incremental models had misjudged rates of 2.3% and 7.7%, respectively. The misjudgments are primarily due to less visible defects in the speckle images. As a result, more research may be required to further improve the detection rate.  
Conceptualization, C.-Y.C. and Y.-D.S.; methodology, C.-Y.C.; software, W.-Y.L.; validation, C.-Y.C. and Y.-D.S.; investigation, W.-Y.L.; writing--original draft preparation, W.-Y.L. and Y.-D.S.; writing--review and editing, C.-Y.C. and Y.-D.S.; visualization, W.-Y.L.; supervision, C.-Y.C. and Y.-D.S.; project administration, C.-Y.C.; funding acquisition, C.-Y.C. All authors have read and agreed to the published version of the manuscript. This research was funded by Intelligent Recognition Industry Service Center, a Featured Research Center of the Taiwan Ministry of Education's Higher Education SPROUT Project. And the APC was funded by Ministry of Education, Taiwan.  
Not applicable. Informed Consent StatementNot applicable. Conflicts of InterestThe authors declare no conflict of interest.
# Biomechanical Performance of the Cemented Hip Stem with Different Surface Finish
## 5 Discussion
### Effects of Interface Conditions of Stress
In essence, the shearing strength was affected by the frictional effect of surface asperities as well as adhesion between the cement and the metal. These characteristics were also incorporated into the finite element simulation model through the proposed elastic spring layer model. The results obtained by the proposed model obviously showed that stems with different surface roughness induced different stress levels at the cement-stem interfaces. Survival of artificial hip prosthesis is highly dependent on the integrity of the bonded interfaces between the bone cement and the metallic prosthesis. Accordingly, in this study, the mechanical effect derived from the use of roughened or smooth stems was further assessed by failure analysis of the cemented interface. In addition, deterioration of the interface was usually initiated under tensile loading from the pores or defects that could be formed at the bonding surface during cement polymerization [11; 30; 51]. Thus, tensile failure of the cemented interface was regarded as the primary mechanism for failure of the femoral prosthesis. As indicated in Section 4.3, for a cemented prosthetic femur with roughened stem, more cement-stem interfaces could be disrupted by higher tensile stress, as compared to the case with polished stems. The obtained results do not reflect the clinically observed long-term failure of cemented prostheses. However, this analysis based on the static failure criteria provided information regarding the short-term mechanical effects of the stem surface finish, which is in accordance with the results of clinical studies [20; 52; 53]. Based on 4- to 8-year follow-up with radiographic examinations, [PERSON] et al. [20] concluded that a femoral stem with roughened surface (Ra \(\geq\) 2.0 \(\upmu\)m) was more likely to result in mechanical loosening than with satin surface (Ra = 0.5 \(\upmu\)m). [PERSON] and [PERSON] [52] monitored the clinical outcomes of total hip replacements with grit-blasted (Ra = 2.1 \(\upmu\)m) and polished (Ra = 0.1 \(\upmu\)m) components during short-term follow-up after surgery and found that the grit-blasted components had a lower survival rate (91%) than polished components(100%). They also found that the femoral components in this examination had the same geometry and were implanted using modern cementing techniques under the same surgical procedures, but with different surface finishes (polished and roughened surfaces). These findings describing the effects of stem surface finish on the postoperative performance of the cemented hip prostheses are similar to the results obtained in this study.
## 5 Verification Experiments and Analysis  
On the basis of models and mechanisms mentioned above, we implemented all these designs within a distributed simulator on Windows OS. All resources a swarm system covered are stored as entries in different resource tables within a MySQL database. Meanwhile, we employ Data Distribution Service (DDS) as the fundamental communication middleware. All other designs are implemented with \(C++\). Focusing on the topic of optimized task-service planning, the correctness and performance of proposed methods are mainly simulated and verified in special scenes. In the following, several typical experiments and the results are described in detail.
## 3 Results and Discussion
### Influence of Individual Flue Gas Components on Hg\({}^{0}\) Removal Performance  
Figure 8 shows the influences of CO\({}_{2}\), O\({}_{2}\), SO\({}_{2}\), NO, and H\({}_{2}\)O on the Hg\({}^{0}\) removal performance of 15%MnO\({}_{x}\)-15%CeO\({}_{2}\)/CNFs(OX) at 120 \({}^{\circ}\)C. The Hg\({}^{0}\) removal efficiency was 53.48% under a pure N\({}_{2}\) atmosphere, while it was 46.08% under a N\({}_{2}\) + 12%CO\({}_{2}\) atmosphere. This indicated that CO\({}_{2}\) had a slight inhibitory effect on Hg\({}^{0}\) removal performance, which may be caused by competitive adsorption of Hg\({}^{0}\) and CO\({}_{2}\) on the sorbent. The Hg\({}^{0}\) removal efficiency increased to 81.94% when the O\({}_{2}\) concentration was 3% (balanced N\({}_{2}\)), while it was 99.6% when the O\({}_{2}\) concentration increased to 6%. The observation indicated that O\({}_{2}\) played an important role in the Hg\({}^{0}\) removal process. In this study, a large amount of Ce was well dispersed on the support, which greatly facilitated the transport of O\({}_{2}\) in the flue gas to the sorbent surface. Moreover, the active oxygen content in CeO\({}_{2}\) favored the transformation of Mn\({}^{3+}\) to Mn\({}^{4+}\)[22], and the increase of the Mn\({}^{4+}\)/Mn\({}^{3+}\) ratio was beneficial to the removal of Hg\({}^{0}\)[32].  
When SO\({}_{2}\) was added into the N\({}_{2}\) flow, the Hg\({}^{0}\) removal efficiency decreased significantly. However, the negative influence of SO\({}_{2}\) on Hg\({}^{0}\) removal performance was relieved in the presence of O\({}_{2}\). As shown in Figure 7, the Hg\({}^{0}\) removal efficiency was 60.52% under the atmosphere of N\({}_{2}\) + 6%O\({}_{2}\) + 500 ppm SO\({}_{2}\). When the SO\({}_{2}\) concentration further increased to 1000 ppm and 1500 ppm, the Hg\({}^{0}\) removal efficiency decreased to 50.95% and 46.73%, respectively. The influence of SO\({}_{2}\) observed in the present study was consistent with that observed in previous studies [28]. The consumption of active oxygen and the reaction of Mn with SO\({}_{2}\) on the sorbent surface may account for the inhibition effects. The Hg\({}^{0}\) removal efficiency sharply reached 90.37% when 100 ppm NO was added into the N\({}_{2}\) atmosphere. Besides this, when the NO concentration was further increased to 200 ppm and 300 ppm, the Hg\({}^{0}\) removal efficiency increased to 97.33% and 98.17%, respectively. Furthermore, NO could  
Figure 7: The (**a**) O 1s, (**b**) Mn 2p, (**c**) Ce 3d, and (**d**) Hg 4f XPS spectra of the spent 15%MnO\({}_{x}\)–15%CeO\({}_{2}\)/CNFs(OX).  
promote the Hg\({}^{0}\) removal efficiency in the absence and presence of O\({}_{2}\). We believe that active oxygen on the sorbent surface promoted the conversion of NO to active nitrites, which were beneficial for Hg\({}^{0}\) oxidation [33]. Water vapor (H\({}_{2}\)O) exhibited only a slight inhibition effect on the Hg\({}^{0}\) removal performance, suggesting that the sorbent could be used in low-temperature coal-fired flue gas, which always contains H\({}_{2}\)O.
## 2 Materials and Methods
### Experimental Procedure  
The driving simulations were carried out in two daily sessions, from 8:00 to 11:30 and 14:00 to 17:30, with three rotating participants per session to ensure optimal individual driving conditions. Participants were advised to comply with signs, lane markings, and traffic regulations and to avoid any non-driving-related activities. The study utilized a within-subjects experimental design to ensure that each participant experienced different conditions and combinations of variables and scenarios. This approach ensured the comparability of data and minimized the potential impact of individual differences on the results, thereby enhancing the internal validity of the research.  
The specific steps of the experiment were as follows:  
1. Participants' familiarization: participants were introduced to the driving tasks and the simulator's functionalities via a practice drive in a non-experimental environment.
2. Scenario selection and simulation environment setup: the research team randomly selected test road sections and configured the traffic environment. The test vehicle was positioned in the left lane, and participants were informed of the destination.
3. Experiment commencement and data collection: the formal experiment began, and the related data were recorded.
4. Post-test breaks: following the completion of each testing round, participants were allowed to take short breaks.  
This procedure was replicated across various experimental scenarios. After approximately 20-30 min or five scenarios, participants took a break while the next participant was tested, which also reduced the drivers' adaption for the specific scenarios. Each participant completed tests across all 75 sections, with TC (traffic volume) being randomly but uniformly assigned. Driving data were collected for the 1000 m range before each diverging point. A total of 1800 tunnel-interchange driving samples were collected. Among these, 1712 samples involved successful diverging, while the remaining 88 samples, which involved crashes or failure to enter the ramp, were categorized as failure samples. These will be subject to separate analysis in Section 3.4.
# MBHAN: Motif-Based Heterogeneous Graph Attention Network
## 1 Introduction
The MCN is also not scalable to heterogeneous graphs. MA-GCNN similarly pays no attention to the heterogeneity of nodes in the graph and can only be applied to subgraph classification tasks. In view of the above, and inspired by the Heterogeneous Graph Attention Network (HAN) [19], this paper proposes an end-to-end Motif-based Heterogeneous Attention Network, MBHAN, which employs a hierarchical attention mechanism that includes node-level and motif-level attention. MBHAN is able to focus on the degree of importance of both a node's neighbors and the motif subgraph where the node is located. The MBHAN approach presented in this paper offers the following contributions:  
It efficiently learns multi-perspective representations for all the nodes in a heterogeneous graph, without having to artificially establish any meta-paths (or subgraphs). It avoids the knowledge loss in the conversion of heterogeneous graphs to homogeneous graphs and can learn the global node information in heterogeneous graphs. It can capture the subtle effects of different node types on downstream tasks, leading to more accurate knowledge mining.
## 2 Data
### MODIS Land Cover and LAI  
Land cover (LC) data from the NASA Earth Observing System Data and Information System (EOSDIS) with a spatial resolution of 500 m during 2001-2021 were generated by the integrated decision tree algorithm [53] and classified into 17 types of land cover depending on the vegetation classification scheme of the International Geosphere Biosphere Program (IGBP), which is followed by the EC flux site.  
The LAI is defined as half of the total area of all leaves per unit area on the ground [54], which is a key parameter indicating the structure and function of vegetation and has an important role in the balance of the ecosystem, carbon cycle, and vegetation growth. The MODIS LAI product (MOD15A2H.061) provides eight-day LAI data from 2002 to 2020 with a spatial resolution of 500 m, which is highly appropriate for analyzing the carbon cycle and vegetation growth. In addition, the MODIS LAI is the common LAI resource of LUE models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Vegetation 1** & **CRO** & **DBF** & **ENF** & **GRA** & **MF** & **OSH** & **WSA** & **SAV** \\ \hline \(\varepsilon_{\rm{msu}}\) (g C M)\({}^{-1}\)) 2 & 1.3 & 0.796 & 1.21 & 0.683 & 0.923 & 0.607 & 0.903 & 1.05 \\ \(\varepsilon_{\rm{msu}}\) (g C M)\({}^{-1}\)) 2 & 3.25 & 3.09 & 3.02 & 2.97 & 2.99 & 2.18 & 3.19 & 3.46 \\ T\({}_{\rm{amin},min}\) (\({}^{\circ}\)C) & 12.02 & 9.94 & 8.31 & 12.02 & 9.5 & 8.8 & 11.39 & 11.39 \\ T\({}_{\rm{amin},max}\) (\({}^{\circ}\)C) & \(-8\) & \(-6\) & \(-8\) & \(-7\) & \(-8\) & \(-8\) & \(-8\) & \(-8\) \\ VPD\({}_{\rm{max}}\) (hPa) & 43 & 16.5 & 46 & 53 & 24 & 48 & 32 & 31 \\ VPD\({}_{\rm{min}}\) (hPa) & 6.5 & 6.5 & 6.5 & 6.5 & 6.5 & 6.5 & 6.5 & 6.5 \\ \(\alpha\) & 0.23 & 0.18 & 0.15 & 0.23 & 0.17 & 0.16 & 0.23 & 0.16 \\ \(\Omega\) 3 & 0.9 & 0.8 & 0.6 & 0.9 & 0.7 & 0.8 & 0.8 & 0.8 \\ \hline \hline \end{tabular}  
* CRO: croplands; DBF: deciduous broadleaf forest; ENF: evergreen needleleaf forest; GRA: grasslands; MF: mixed forest; OSH: open shrulands; WSA: woody savannas; SAV: savannas. 2 [36] These two parameters were estimated by observed data from global flux tower sites. 3 [46] This estimation of the clumping index is the default used in the TL-LUE model.  
\end{table}
Table 1: Parameters required for the TL-LUE model for different vegetation types.
## Materials and Methods
### Research Method
#### 2.2.2 Morphological Spatial Pattern Analysis (MSPA)  
MSPA is a classification technology that uses the principles of mathematical morphology to effectively identify key landscape elements that significantly influence the ecological interconnectivity of a particular research area. This method utilizes a tailored series of mathematical morphology operators designed to describe the geometric shapes and connectivity of image components [35]. The technique began at the pixel scale, employing principles of mathematical morphology to categorize binary image pixels into seven distinct MSPA groups, differentiated by aspects like landscape, spatial configurations, and their overall effect [36]. Connectivity details include core area, islet, perforation, edge, loop, bridge, and branch (Table 3). Therefore, through the input of ecological sensitivity analysis data, this study classified sensitivity grades 1-3 as prospect elements, assigning them 2 points; the sensitivity levels 4-5 were used as background elements, and a score of 1 was assigned to generate a binary image. The MSPA-based identification of seven landscape elements within the research zone was then conducted using the eight-neighborhood analysis method in GuidosToolbox 3.3 [37]. The MSPA system employed pixels to pinpoint key areas that greatly influence the ecological network landscape's connectivity, including core zones and bridge regions [38]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rating Factor**} & \multicolumn{5}{c}{**Sensitivity Grading**} \\ \cline{2-6}  & **Level I** & **Level II** & **Level III** & **Level IV** & **Level V** \\ \hline \multirow{3}{*}{Elevation (m)} & Accounts for 15\% & Accounts for 20\% & Accounts for 30\% & Accounts for 20\% & Accounts for 15\% \\  & of the relative & of the relative & of the relative & of the relative & of the relative \\  & elevation section & elevation section & elevation section & elevation section \\ \hline \multirow{2}{*}{Slope (\({}^{\circ}\))} & \(\leq\)5\({}^{\circ}\) & 5\({}^{\circ}\)–15\({}^{\circ}\) & 15\({}^{\circ}\)–25\({}^{\circ}\) & 25\({}^{\circ}\)–45\({}^{\circ}\) & \textgreater{}45\({}^{\circ}\) \\ \hline \multirow{2}{*}{Aspect of slope} & South & Southeast, southwest & East, west & Northeast, northwest & North \\ \hline \multirow{2}{*}{Water buffer} & \textless{20 m} & 20–50 m & 50–100 m & 100–200 m & \textgreater{}200 m \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Vegetation \\ coverage \\ \end{tabular} } & \(\leq\)10\% & 10–30\% & 30–50\% & 50–70\% & 70–100\% \\ \hline \multirow{2}{*}{Land use types} & Construction land, current road & Cultivated land, bare land & Grassland, shrub & Forest land, wetland & River flats, forest wetlands, waters \\ \hline \multirow{2}{*}{Rank assignment} & \multirow{2}{*}{1} & \multirow{2}{*}{2} & \multirow{2}{*}{3} & \multirow{2}{*}{4} & \multirow{2}{*}{5} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Criteria for assessing and grading ecological sensitivity based on a single factor.