esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Data Collection  
Data were collected during four field trips in October and November 2018, and January and March 2019. These periods were selected because floods are the most severe from October to November, and the spring rice is the most impacted by floods. Flooded areas were detected (Figure 4). Four focus group discussions (FGDs) were organized during the field trips with the involvement of the research team and five local authorities to determine flood hazard factors. After FGDs, 10 factors were selected and are included in a questionnaire sent to the committee members.  
Maps of flood hazard factors were established with a resolution of \(30\times 30\) m. Maps of slopes and terrain elevation were generated by the ArcGIS software using a Digital Elevation Model (DEM) supported by the Ministry of Natural Resources and Environment (MONRE) Vietnam in 2019. Maps of the Creek-bottom terrain, high tide-induced flooding areas, and water storage by transportation corridors are based on the information collected during field trips. A map of the soil types was extracted from the soil map of Tuy Phuoc district at a scale of 1/50,000 supported by the Ministry of Agriculture and Rural Development (MARD) Vietnam in 2019. Maps of stream density, drainage channel density, and distance to water body were realized using the ArcGIS software and based on a water surface map of the Tuy Phuoc district at a scale of 1/50,000 provided by MONRE in 2019. A map of land use and land cover was obtained from MONRE in 2019 (Figure 5).  
Figure 4: Flooding sites detected during field trips: (**a**) Floods cover the national road along the Phuoc Thang commune (October 2018); (**b**) Floods cover build-up areas in the Phuoc Thang commune (November 2018); (**c**) Flooded rice fields in the Thanh Quang hamlet (January 2019); (**d**) Flooded rice fields in the Pho Dong hamlet (January 2019) (photos by the first author [PERSON], H.X., 2018–2019).  
Figure 5: _Cont_.  
Data for weighting criteria and prioritizing alternative sites in the Phuoc Thang commune were collected using a questionnaire with the committee of decision makers (DMs), including five village heads, who have lived for a long time in the local villages and are knowledgeable about various issues related to the local flood hazards. The questionnaire consists of three parts: (i) selecting factors affecting the likelihood of flood hazards and flood levels locally with \"yes-no\" questions, and then explaining the selections (Table 1); (ii) comparing 10 criteria by scoring the information of the respondents on the AHP's comparison matrix; and (iii) selecting alternatives using a Likert's 5-scale of agreement (1 = strongly disagree; 2 = disagree; 3 = undecided; 4 = agree; 5 = strongly agree). Respondents took approximately 30 min to complete the questionnaire.
# Design and Construction of a Low-Cost Test Bench for Testing Agricultural Spray Nozzles
## 2 Materials and Methods  
### Test Bench Design Guidelines  
The test bench, applied on a movable trolley, was designed and built at the Section of Mechanics and Mechanization of the Di3A (University of Catania, Catania, Italy) based on the ISO 5682-1 standard, dealing with Equipment for crop protection-Spraying equipment-Test methods for sprayer nozzles. In addition, two main aspects were taken into consideration: the low cost of the whole equipment and test conditions similar to those present in standard commercial sprayers. With respect to the first aspect, the cost of mechanical structure and hardware components was less than 5000 \(\mathbf{\upepsilon}\), whereas the cost of the image acquisition system (a camera with a macro lens that can also be used independently for other applications) was about 1200 \(\mathbf{\upepsilon}\). Globally, the cost of the whole equipment was much lower than that of PDPA systems, though these systems allow for measuring drop velocity also. With regard to the second aspect, the test bench allows for testing agricultural spray nozzles under ordinary work conditions in a 1:1 scale for water flux, pressure, nozzle speed, and distance from the target.  
According to the ISO 5682-1 reference, the nozzle under test should move above a row of Petri dishes with equal surface areas, spraying a test liquid (clean water with the addition of a soluble coloring agent, free from solids in suspension). Each Petri dish receives some of the droplets from the jet. All the droplets in each Petri dish should be measured and classified by size so that the cumulative volumetric curves and all the usual distribution parameters can be calculated. The accuracy of the equipment in measuring the drop diameter should be within 10 \(\upmu\)m.  
The selected nozzle speed should allow for a sufficient number of droplets to be collected (at least 2000 droplets should be collected to make a representative sample), while avoiding the merging of the droplets. The maximum speed fixed by the ISO 5682-1 standard is 3 m/s. The distance between the nozzle and Petri dishes should correspond to the normal distance between the nozzles and the crop. Finally, the test has to be carried out by letting the spraying nozzle pass once over the row of Petri dishes.  
Other solutions, such as those described by [PERSON] et al. [52], were discarded because they are based on the use of water sensitive papers. Calculation of spray spectrum parameters by using water sensitive papers (WSPs) requires knowledge of the spread factor as well as the overlap between stains to be made negligible. The spread factor is reported in WSP data sheets, but it is calculated at well-defined conditions (water temperature 20 \({}^{\circ}\)C, relative humidity 40%, droplets reaching the WSP at sedimentary velocity), which are different from real conditions and thus greater errors are expected.
# Enhancing Railway Network Safety by Reproducing Wheel-Rail Electrical Contact on a Laboratory Scale
###### Abstract  
Ensuring the safety of rail networks requires precise detection of a train's position on a track section. This is achieved using a \"track circuit\" system, in which the wheel-rail electrical contact is the key to maintaining the system's reliability. However, any degradation of this contact can lead to a track circuit malfunction known as \"deshunting\", creating a serious safety risk for the rail network and for passengers. This paper presents a refined approach to this concern by implementing a laboratory-scale test bench. The main objective is to reproduce the wheel-rail electrical contact under controlled conditions to better understand the various aspects of this contact. The criteria governing the dimensioning of the test bench at reduced scale are based primarily on mechanical considerations. In this study, a series of tests were carried out to investigate the behavior of the electrical resistance as a function of various parameters such as load, current and time. An original homemade salt spray system was designed and used for obtaining controlled rail oxidation. Our preliminary results highlight the impact of these factors on the electrical resistance, providing valuable insights for future advances in rail safety technology.  
health monitoring; failure; deshunting; track circuit; wheel-rail electrical contact; oxidation; test bench +
Footnote †: journal: Applied sciences
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 4 Performance Indicators  
The indicators identified for the proper performance analysis of the unlicensed coexistence scenarios are divided into three categories in terms of the quality of experience provided to the users, the background traffic user perceived quality and the level of fairness between WiFi and 3 GPP LBT downlink service provision. About this last aspect, it has to be noted that, following the general deployment considerations established by 3 GPP [26], coexisting deployments are expected to be provided by different operators and without any coordination between them besides the general collision avoidance mechanisms defined in the previous section.
# The Power System and Microgrid Protection--A Review
## 3 Protection Scheme of Microgrid
### Impedance-Based Protection Method
The authors of [88] improved the impedance-based fault location by using a short-distributed line model. The high penetration of DGs changes the short circuit current level. The low fault current in inverter-based microgrids is mainly due to the inverter's controller and not due to the power system's impedance. [PERSON] and [PERSON] [89] studied an impedance-based pilot protection scheme. Their proposed protection scheme employed communication between relays or, in general, between relays of the same protection zone to locate the fault. The relay feeder was equipped with an impedance element to detect a fault with a directional element that determines the direction of the fault. The communication-assisted impedance-based protection scheme was proposed for inverter-dominated microgrids. Consequently, the vast literature and extensive existing experiences in designing impedance and directional elements can be utilized in designing these protection elements [90; 91]. Different pilot protection logics can be used to determine the location of faults. [PERSON] and [PERSON] [92] investigated the fault location in an unbalanced DG system using an impedance-based method. [PERSON] [93] proposed an efficient protection scheme for a DC microgrid with high penetration of constant power loads (CPLs). This method exploits the fault location scheme strategy using the transient behavior of the first-order derivatives of fault current; in addition, it utilizes pre-fault data for fault location in the CPL line and for detecting high- and low-resistance faults in dc microgrids. In Reference [94], a microgrid protection and control scheme has been proposed in synchronous islanded mode using the PCC breaker relays, battery energy storage system (BESS) inverter controller, and remote input/output mirror bits based communications approach (85 RIO). Figure 12 shows the impedance-based protection scheme with various distributed resources.
## 4 Discussion
### Morphological Variables  
The CrossFit training program did not significantly change body weight, height, BMI, fat mass, and fat-free mass assessments. Similar results were observed in a 4-week CrossFit training program, where a study assessing its effects in young adults reported no significant changes in body weight (\(p\) = 0.678) or body fat percentage (\(p\) = 0.082) after the intervention [30]. Similarly, another investigation found no significant effects on body weight or BMI after 3 months of CrossFit training in a similar population [31]. In contrast, evidence from other studies indicates that individuals with greater CrossFit experience tend to exhibit higher levels of fat-free mass and, notably, lower body fat percentages compared to those with less experience in this discipline [16; 32]. Given the current findings, future studies should focus on long-term interventions, such as increasing the number of training sessions or extending the intervention duration, to achieve more pronounced effects on body composition in adults [11].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**Novice Group (n = 10)**} & \multicolumn{3}{c}{**Advanced Group (n = 11)**} \\ \cline{2-7}  & **Mean** & **Responders** & **Eta2** & **Mean** & **Responders** & **Eta2** \\  & **Difference (SD)** & **(\%)** & **Pre vs. Post** & **Difference (SD)** & **(\%)** & **Pre vs. Post** \\ \hline Body weight (kg) & \(-\)0.45 (1.14) & 0 (0) & 0.15 \({}^{\circ}\) & 0.36 (1.95) & 0 (0) & 0.04 \({}^{\circ}\) \\ \hline BMI & \(-\)0.18 (0.41) & 0 (0) & 0.17 \({}^{\circ}\) & 0.10 (0.67) & 1 (9) & 0.03 \({}^{\circ}\) \\ \hline Fat mass (\%) & \(-\)1.25 (1.36) & 0 (0) & 0.49 \({}^{\circ}\) & \(-\)2.12 (4.58) & 1 (9) & 0.19 \({}^{\circ}\) \\ \hline Fat-free mass (\%) & 0.94 (1.36) & 0 (0) & 0.35 \({}^{\circ}\) & 2.31 (4.58) & 1 (9) & 0.22 \({}^{\circ}\) \\ \hline Back squat (kg) & 18.10 (18.78) & 4 (40) & 0.51 \(\dagger\) & 2.18 (7.11) & 0 (0) & 0.09 \({}^{\circ}\) \\ \hline Deadlift (kg) & 15.00 (17.75) & 2 (20) & 0.44 \({}^{\circ}\) & 0.91 (12.84) & 0 (0) & 0.01 \({}^{\circ}\) \\ \hline Military press (kg) & 2.60 (8.51) & 1 (10) & 0.09 \({}^{\circ}\) & 1.00 (3.10) & 0 (0) & 0.10 \({}^{\circ}\) \\ \hline CMJ (cm) & 1.51 (2.21) & 1 (10) & 0.34 \({}^{\circ}\) & \(-\)0.74 (3.87) & 0 (0) & 0.04 \({}^{\circ}\) \\ \hline Sit-ups (rep) & 6.90 (7.70) & 3 (30) & 0.47 \({}^{\circ}\) & 1.64 (4.37) & 2 (18) & 0.13 \({}^{\circ}\) \\ \hline Push-ups (rep) & 4.70 (4.97) & 3 (30) & 0.50 \(\dagger\) & 2.82 (4.92) & 2 (18) & 0.27 \({}^{\circ}\) \\ \hline
30 m sprint speed (s) & \(-\)0.26 (0.51) & 1 (10) & 0.22 \({}^{\circ}\) & \(-\)0.25 (0.53) & 1 (9) & 0.19 \({}^{\circ}\) \\ \hline \hline \end{tabular} SD = standard deviation; Eta2. \({}^{\circ}\) = trivial; \({}^{\circ}\) = small; \(\dagger\) = moderate; rep = repetitions.  
\end{table}
Table 4: Inter-individual variability of morphological variables and physical fitness in novice and advanced CrossFit practitioners.
# Innovating Statistics Education: The Design of a Novel App Using Design Thinking
## 2 Theoretical Background
### Related Work  
Our research explores the integration of mobile devices in educational settings, identifying both existing solutions and gaps in the current market. This analysis, grounded in rigorous systematic reviews and empirical studies, reveals how mobile technology significantly enhances teaching and learning outcomes [31; 32; 33].  
Firstly, a range of applications facilitate education without focusing on specific content areas. Examples include Kahoot [34] and Mentimeter [35], which are designed for general learning. In contrast, tools tailored explicitly to statistics education, such as Codap [36], Rossmanchance [37], Fathom [24], and ArtoffStat [38], offer functionalities for performing calculations and creating graphical representations.  
Despite these developments, our proposal identifies a significant gap. Currently, no application on the market combines general learning tools with advanced statistical functionalities. This integration represents a significant innovation with the potential to transform how statistics is taught and understood.  
Supporting this perspective, a recent patent survey using the displacement tool uncovered patent CN114969660A [39], entitled \"Online Interactive Dynamic Statistical Education Method (OIDSE), platform and device and storage medium\", was filed in 2022. This patent describes an interactive online platform that enables users to manipulate distribution parameters, generate data, and visualize it graphically, indicating a trend toward more dynamic and interactive educational solutions.  
Furthermore, mobile devices facilitate a myriad of educational enhancements [40; 41]. They allow students and educators to access content anytime and anywhere, overcoming traditional learning barriers and fostering a more adaptable learning environment. The interactive nature of mobile technology also makes learning more engaging, catering to a technologically adept generation. Through adaptive learning technologies, education can be personalized to meet individual student needs by monitoring performance and adjusting difficulty levels in real-time. This approach caters to diverse learning speeds and enhances academic performance [42].  
Moreover, mobile technology promotes collaborative learning through shared applications and platforms that support group work and discussions. This not only improves learning outcomes but also enhances social and communication skills. Finally, integrating mobile devices into education is crucial for developing digital literacy skills and preparing students for future challenges in a technology-oriented professional landscape [43; 44]. While various systems demonstrate the relevance of mobile devices in teaching, the proposed solution uniquely integrates general and specialized educational tools, setting a new standard for mobile learning in statistics.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
###### Abstract  
A knowledge graph can structure heterogeneous knowledge in the field of power faults, construct the correlation between different pieces of knowledge, and solve the diversification, complexity, and island of fault data. There are many kinds of entities in power fault defect text, the relationship between entities is complex, and the data are often mixed with noise. It is necessary to research how to effectively mine the target data and separate the salient knowledge from the noise. Moreover, the traditional entity and relationship extraction methods used in the construction of a power fault knowledge graph cannot fully understand the text semantics, and the response accuracy is low. The Log system usually contains all kinds of information related to faults and a log analysis helps us collect fault information and perform association analysis. Therefore, a Bidirectional Sliced GRU with Gated Attention mechanism (BiSGRU-GA) model is proposed to detect the anomalous logs in the power system, this enriches the fault knowledge base and provides a good data resource for the construction of the knowledge graph. A new Bidirectional GRU with Gated Attention mechanism and Conditional Random Fields and a BERT input layer (BBigGRU-GA-CRF) model is proposed by introducing a BERT layer and Attention Mechanism into the Bidirectional GRU (BiGRU) model to more fully understand the context information of fault sentences and improve the accuracy of entity recognition of fault sentences. Aiming to solve the problems of large calculation cost and propagation error which occur in the traditional relationship extraction model, an improved Bidirectional Gated Recurrent Unit neural network with fewer parameters and the Gated Attention Mechanism (BiGRU-GA) model is proposed. This new model introduces an improved Gated Attention Mechanism to achieve better effects in relationship extraction. Compared with Bidirectional Long Short-Term Memory with Attention Mechanism (BiLSTM-Attention), the accuracy, recall, and F-measure of the model were improved by 1.79%, 13.83%, and 0.30% respectively, and the time cost is reduced by about 16%. The experimental results show that the BiGRU-GA model can capture local features, reduce the training time cost, and improve the model recognition effect.  
power failure; knowledge graph; attention mechanism; GRU; BERT
# Autonomous Manipulator of a Mobile Robot Based on a Vision System
## 3 Results
### Testing the Positioning System on the Basis of the Vision System
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Position Reached in \(x\), \(y\), \(z\)**} & \multicolumn{3}{c}{**Absolute Error for the \(x\), \(y\), \(z\) Axes**} \\  & **mm** & & **mm** & \\ \hline
754.6 & \(-\)270.1 & 543 & 7.5 & 8 & 1.1 \\
758.1 & \(-\)269.1 & 541.2 & 11 & 7 & 2.9 \\
759.2 & \(-\)269.2 & 539 & 12.1 & 7.1 & 5.1 \\
756.2 & \(-\)267.8 & 540.7 & 9.1 & 5.7 & 3.
# Study on Influence of Multi-Parameter Variation of Bladed Disk System on Vibration Characteristics
## 4 The Influence of Detuning Parameters on Bladed Disk System Is Analyzed
### Design of Bladed Disk System Parameter Variation
Based on condition 2, deviation analysis was conducted between conditions 3-10 and condition 2, as shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Modal Frequency under Different Operating Conditions**} \\ \cline{2-11}
**Order** & & & **Bladed** & **Individual** & **Middle** & **Counter** & **Double** & **Double** & **Double** & **3 Times the** & **One Third** \\
**Time** & **Disk** & **Disk** & **Blackog Tip** & **Weight of** & **Weight of** & **Blades** & **Blackog** & **Blackog** & **Weight at** & **of the** \\
**System** & **System** & **Balancing** & **Single** & **Single** & **Single** & **Weight at** & **Weight at** & **Weight at** & **the Tip of a** & **Adjacent** \\
**System** & **System** & **Balancing** & **Blade** & **Blade** & **the Top** & **the Top** & **Single** & **Blade** \\
**1** & **89.00** & **65.52** & **65.50** & **65.51** & **65.51** & **65.48** & **65.49** & **65.48** & **65.46** & **68.08** \\
**2** & **89.00** & **65.54** & **65.52** & **65.53** & **65.53** & **65.52** & **65.50** & **65.52** &  
According to Figures 8 and 9, it can be seen that the vibration frequency of the bladed disk system decreases after 18 blades are added to the original disk. The effect of different positions on the single blade on the modal frequency of the bladed disk system is very small. The larger the weight at the same position, the smaller the frequency. When the blade falls off, the modal frequency of the disk system increases greatly. In the above study of different working conditions, the change of an order 6-9 th is more obvious than that of other order.
# Lightweight Micro-Expression Recognition on Composite Database
## 3 Methodology
### Data Pre-Processing
#### 3.1.2 Composite Dataset  
Get All Apex Frames  
Samples from CASME II and SAMM datasets in the composite dataset both include the annotation for the apex frames. However, SMIC dataset does not. We take the approach of [28] to approximate the apex frame positions for SMIC dataset as the middle frame in a video sample. After this, apex frames for all the samples in the composite dataset are gathered to be the input to the proposed model. The calculation of the apex frame locations given a _frameList_, which is an array of sorted frames from onset to offset frames in the sample video, is shown below.  
\[\begin{array}{l}\text{apexLocation}=\text{length(frameList)}/2\\ \text{apexFrame}=\text{frameList[apexLocation]}\end{array} \tag{1}\]  
At first, face detection was performed on the apex frame images with the goal of retrieving key points that will be used in the face alignment and cropping step. The key points are the right eye and left eye center points as well as the face boundary locations in the image extracted using MTCNN [34] as shown in Figure 2.  
Next, face alignment is performed to minimize bias due to the head movements as shown in Figure 3. This is done by rotating the image around its center point. The rotation angle is calculated using the inverse of tangent mathematical formula on the difference between the x and y coordinates of the eyes' center locations. The calculation is shown in Equation (2).  
\[\begin{array}{l}\text{delta\_x}=\text{right\_eye\_x}-\text{left\_eye\_x} \\ \text{delta\_y}=\text{right\_eye\_y}-\text{left\_eye\_y}\\ \text{rotation\_angle}=\text{(atan(delta\_y/delta\_x)*180)}/\pi\end{array} \tag{2}\]  
Afterwards, the image is cropped using the face boundary locations found in the initial step to eliminate unnecessary details unrelated to facial expression in the image. The cropped images are then resized to 224 \(\times\) 224 pixels while ensuring that the image is scaled appropriately. The image dimension (224 \(\times\) 224) was selected in accordance with the input requirement of the proposed model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Original Class** & **New Class** & **Total** \\ \hline Angry: 4953, Disgust: 547, Fear: 5121, Sad: 6077 & Negative & 16,698 \\ Happiness: 8989 & Positive & 8989 \\ Surprise: 4002 & Surprise & 4002 \\ Neutral: 6198 & N/A & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Labels and number of samples in FER 2013 database.  
Figure 2: Face and eye detection using MTCNN. Green box: detected face boundary. Red dots: detected eyes’ center points.  
Data Augmentation  
Simple image augmentations are applied to prevent overfitting which are horizontal flip and random brightness on random samples in the training dataset. The seed used for the transformation is a randomly generated number between 0 and 10.