esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Research
### Development
#### 4.3.2 Implementation of a Mobile Application for Self-Diagnosis of Learning Progress  
As shown in Figure 4, a mobile application was developed according to the menu structure presented in Figure 3. The mobile application screen consists of a total of 7 screens. Figure 4a shows the menu that appears when you run the mobile application for the first time, and you can log in or connect to the membership registration menu. The remaining six screens are connected to the main screen. Figure 4b shows a screen with the membership registration menu. Students are asked to register by entering their department, student number, name, and password. Teachers do not need to sign up separately because the admin account and password are entered into the database in advance. Figure 4c is a list where learners can select a question item. When selecting a question item, the students are connected to Figure 4d, and the teacher is connected to Figure 4e. Figure 4d shows that the learner reads the question item selected in Figure 4c and clicks 'Yes' if the learner understood the learning content or performed the practice, or 'No' if the learner did not understand the learning content or did not perform the practice. In Figure 4e, the teacher can view the number of students who selected 'Yes' or 'No' for the question item selected. In this way, the teacher can check whether or not he or she has taught well for each question item. Additionally, the teacher can consider how to improve the teaching method for each question item.  
Figure 3: Learning contents for Arduino class, (**a**) is making an Arduino circuit in Tinkercad and Arduino kits, (**b**) is programming practice learning in the Arduino IDE, (**c**) is completing the circuit diagram by adding a Bluetooth module, (**d**) is making an IoT system prototype in App Inventor.  
corresponding question item. When the learner answers the question in Figure 4d, it becomes connected to the list shown in Figure 4f. Students can view their own learning progress status in Figure 4f and perform their own self-diagnostic through the results. If the teacher clicks the 'Submit' button after reviewing a question item as shown in Figure 4e, it becomes connected to the full student list as shown in Figure 4g, and the information of all students can be viewed. Teachers can then click on a specific student to view that student's learning progress. In other words, the order in which learners use the application is shown in Figure 4a-f. The order in which instructors use the application is shown in Figure 4a,c,e,g.  
Figure 4: Development of the mobile application, (**a**) is the first screen to run in the mobile application, and functions to connect to the main page, login, and member registration. (**b**) is the membership registration menu. (**c**) is a menu for selecting an item. (**d**) is a menu that students check for questions. (**e**) is a menu for the teacher to view statistics for a specific question. (**f**) is a menu in which learners self-check all items. (**g**) is a menu that allows the teacher to view all students and the progress of a specific student.
## 3 Case Studies
### Case Study 2: Emergency Scenario (Emergency Management)
#### 3.2.2 Case Study 2: Algorithms  
As described above (cf. description of Figure 3), in an emergency scenario the Algorithm 2; Algorithm 3 are used. In particular, Algorithm 2 is a clustering NN, which was used in this study to detect environmental/structural alarm from the above matrix and has the following characteristics: (i) Input size = 63; (ii) Output size = 64; (iii) Hidden layers = 55; and (iv) Batch size = 50.  
Furthermore, Algorithm 2 uses the Stochastic Gradient Descent (SGD) as an optimizer function (i.e., an iterative method for optimizing an objective function with suitable smoothness properties). Meanwhile, the activation function REctified Linear Unit (relu) is used for hidden layers (relu is the most commonly used activation function in neural networks, especially in NNs). The output layer uses the Softmax function, which takes as input a vector of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers. The outputs of the algorithm are the nodes in which the alarms are not active. Using this information, the definition of the graph \(G\) is modified accordingly, thus eliminating the unsafe nodes and the possibility of passing through a damaged infrastructure, creating a simplified graph \(G^{*}\).  
Moreover, data from Algorithm 1 are also used to update the weights of the graph connections. If an environmental or structural alarm is detected by Algorithm 2, the Algorithm 3 is used to define an Emergency plan, using the graph \(G^{*}\) as inputs. In this case, the \"digraph\" function [46] is automatically activated to derive the fastest and safest paths that the people near to each node must follow to reach an exit (if available) or an assembly point (if the exits are closed).
## 2 Mathematical Model for Centroid
### Mathematical Modeling for Centroid Calculation
#### 2.2.3 Centroid Mathematical Model of Each Section  
According to the structural characteristics of the end milling cutter, the centroid position of the chip groove section varies with the change of the helix angle \(\beta_{i}\) and cutting edge length \(H\). Taking the first quadrant helical flute as an example, when the centroid of the helical flute on the end section rotates with the helix at an angle of \(\alpha_{i}\), the coordinate of the centroid position is changed to:  
\[\left[\begin{array}{c}x_{\beta_{1}}\\ y_{\beta_{1}}\end{array}\right]=\left[\begin{array}{cc}\cos\alpha_{1}&-\sin \alpha_{1}\\ \sin\alpha_{1}&\cos\alpha_{1}\end{array}\right].\left[\begin{array}{c}x_{1} \\ y_{1}\end{array}\right] \tag{12}\]  
Similarly, if there are \(n\) helical flutes in the end milling cutter, the rotation angle of the centroid of the helical flute at any height, \(h\), of different cross section is \(\alpha_{i}\)\(i=1,2...n\). The centroid coordinates of the end section of the nth helical flute are \((x_{n},y_{n})\), then the centroid coordinates of the helical flute at any height \(h\) can be expressed as:  
\[\left\{\begin{array}{c}\left[\begin{array}{c}x_{\beta_{n}}\\ y_{\beta_{n}}\end{array}\right]=\left[\begin{array}{cc}\cos\alpha_{i}&-\sin \alpha_{i}\\ \sin\alpha_{i}&\cos\alpha_{i}\end{array}\right].\left[\begin{array}{c}x_{n} \\ y_{n}\end{array}\right],i=1,2\ldots n,n=1,2\ldots\right. \tag{13}\]  
From the centroid formula, the centroid coordinate and eccentricity expression of arbitrary height section can be obtained as follows:  
\[\left\{\begin{array}{c}x_{0}=-\frac{\overset{s\overset{n}{\underset{i=1}{ \sum}}}x_{\beta_{i}}}{\pi\cdot R^{2}\cdot R^{2}\overset{n}{\underset{i=1}{ \sum}}}x_{\beta_{i}}\\ y_{0}=-\frac{\overset{s\overset{n}{\underset{i=1}{\sum}}}{\pi\cdot R^{2}\cdot R ^{2}\overset{n}{\underset{i=1}{\sum}}}x_{\beta_{i}}}{\pi\cdot R^{2}\cdot R^{2} \overset{n}{\underset{i=1}{\sum}}}x_{\beta_{i}}\\ e=\sqrt{{x_{0}}^{2}+{y_{0}}^{2}}=\sqrt{\frac{\overset{S^{2}(\underset{i=1}{ \sum}}x_{\beta_{i}})^{2}+\overset{n}{\underset{i=1}{\sum}}y_{\beta_{i}})^{2} )}{\left(\pi R^{2}-nS\right)^{2}}}\end{array}\right. \tag{14}\]  
It should be noted is that if the density of the tool material is uniform, the center of mass of any section coincides with its geometry center position, that is, the centroid coordinates coincide with the geometry center coordinates.
## 5 Future Prospects  
### The Development of AIoT (Artificial Intelligence of Things)  
The Internet of Things (IoT) for Hydrogen Systems is a layered architecture that integrates AI and the Internet of Things (IoT) to improve the efficiency and efficacy of hydrogen energy systems. Each layer helps to optimize hydrogen production, distribution, and utilization. Table 5 describes in detail the four basic layers of this design. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Layer** & **Description \& Features** \\ \hline \multirow{7}{*}{1} & & The AI layer is the AIoT system’s brain, responsible for data processing, decision-making, and improving hydrogen-related processes. Advanced machine learning, deep learning, and AI algorithms are included. The AI layer improves the system’s intelligence by enabling data-driven decision-making, predictive capabilities, and continual development. \\  & & Features: \\  & & a. Data Analysis: \\  & & AI algorithms evaluate data from IoT sensors and devices to find patterns, anomalies, and trends in hydrogen production, storage, and consumption. \\  & & b. Decision-Making: \\  & & AI models employ sensor data to make smart decisions in real-time, such as altering hydrogen production rates, maximizing energy use, or initiating safety routines. \\  & & c. Predictive Maintenance: \\  & & AI anticipates when equipment and infrastructure need maintenance, decreasing downtime and ensuring system reliability. \\  & & d. Energy Optimization: \\  & & AI optimizes energy use and production by matching hydrogen production to demand to reduce costs and environmental impact. \\  & & This layer offers a framework for efficiently connecting and managing the AI and IoT components. Cloud platforms, communication protocols, and networking infrastructure are all part of it. The platform and network layer provide for seamless data flow across the AI and IoT levels while also providing data security, scalability, and accessibility. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Architecture of AIoT for Hydrogen Energy Systems. By integrating these four layers, AIoT for Hydrogen Systems increases the efficiency, safety, and sustainability of hydrogen energy operations, making them more adaptive to dynamic conditions and contributing to the spread of clean hydrogen technologies (Figure 7).
# Research on Active Anti-Slip Control of High-Speed Trains Based on High-Order Sliding Mode
## 1 Introduction
The investigation of wheel-rail adhesion mechanisms has evolved significantly, with foundational studies employing non-Hertzian contact theory to characterize adhesion behavior through three-dimensional rolling contact models [11]. While these theoretical approaches provide valuable insights, their structural complexity and parameter acquisition challenges limit practical application, leading researchers to prioritize data-driven empirical models that better align with real-world operational data [12]. Recent advancements have explored the integration of adhesion dynamics into train control systems, including learning-based controllers and sliding mode tracking controllers relying on simplified linear adhesion assumptions [13; 14].Such controllers typically simplify wheel-rail adhesion forces into static linear creepage-dependent models, ignoring the dynamic transition characteristics between adhesion saturation zones and negative slope regions in practical operating scenarios. Building on the previous research, this paper proposes the design of an active anti-slip controller for trains based on higher-order sliding modes. The goal is to achieve accurate tracking of the train's target velocity-displacement curve while considering wheel-rail adhesion. Furthermore, the controller is designed to prevent wheelset slip under low-adhesion conditions between the wheels and rails. The key contributions of this paper, compared to existing work, are summarized as follows:1. The [PERSON] adhesion property model, which accounts for the influence of vehicle speed, is utilized to calculate the adhesion force between the wheels and rails. A more realistic train model is established by integrating the dynamics of the train body, electric motors, vector control, and air braking units. 2. A robust adhesion observer is designed, upon which an active anti-slip strategy is developed. This strategy, formulated from the perspective of controller output constraints, ensures the prevention of wheelset slip when the train encounters a low-adhesion track surface unexpectedly. 3. The train model, which considers wheel-rail adhesion, is transformed into a disturbed train model. Based on higher-order sliding mode theory and a saturation compensation control law, an output-constrained train controller is designed to achieve precise tracking of the target operating curve. This design ensures the controller's stability even when the anti-slip strategy limits the controller's output. Additionally, the controller's stability is theoretically proven. The remainder of this paper is organized as follows: Section 2 presents the train dynamics model, incorporating adhesion effects, and its transformation into a perturbed train model. Section 3 details the active anti-slip control law based on higher-order sliding modes, along with the stability proof of the closed-loop system. Section 4 evaluates the control performance of the proposed controller through Hardware-in-the-Loop simulations. Finally, Section 5 concludes the paper.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 7 Conclusions  
To improve the monitoring quality and energy utilization efficiency of WSNs in microgrids, this paper proposes the ATSAO algorithm to design the acquisition time and perform topology control of WSN. First, the CHs are selected according to the CHs selection algorithm, and then the acquisition time and sensor association are obtained by an iterative algorithm based on BCD technology. Three methods of different complexity are used to obtain the sensor association. Finally, a sub-optimal acquisition time and topology control strategy are obtained. The simulation results show that the ATSAO can effectively reduce the energy consumption of the WSN and improve the satisfaction of data acquisition, thereby ensuring the power supply stability of the microgrids.  
In the future, we will consider adding more practical constraints when formulating optimization problems to improve the performance of ATSAO. In addition, our current research is simulation-based, we will further implement real-time experiments.  
Methodology, L.Z.; software, S.Z.; formal analysis, L.Z. and S.Z.; investigation, G.C.; resources, L.Z.; data curation, S.Z. and Y.Z.; writing--original draft preparation, G.C.; visualization, G.C.; supervision, Y.L.; project administration, Y.Z.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (62073301, 41772376).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 4 Path-Smoothing Method Based on an Improved Dynamic Programming Algorithm
### Improved Dynamic Programming Algorithm
This means there is a shorter SGP between AD that does not need to bypass the obstacle between AC, which contradicts the definition that the SGP should be the shortest eight-directional movement path. Similarly, if D is on the outside of line \(\mathbf{b}\), there is also a shorter SGP, leading to another contradiction. Therefore, A and D are not adjacent points, and **Property 1** holds. \(\square\)  
From Property 1, we can deduce the following. **Inference 1. **_For any two nodes \(p_{i}\) and \(p_{j}(1\leq i\leq j\leq r)\) in the candidate point set \(P=\{B,\ p_{1},p_{2},\ldots,p_{r},E\}\), where the set of pre-adjacent points of \(p_{i}\) is \(p_{i}^{u}\subseteq\{p_{u},p_{u+1},\ldots,p_{i-1}\}\) and the set of pre-adjacent points of \(p_{j}\) is \(p_{j}^{v}\subseteq\{p_{v},p_{v+1},\ldots,p_{j-1}\}\), if \(i<j\), then \(u\leq v\)._  
**Proof of Inference 1. ** Assume \(\mathrm{i}<\mathrm{j}\) and \(u>v\) According to **Property 1**, the set of adjacent points after \(p_{u}\) is \(\{p_{u+1},p_{u+2},\ldots,p_{k}\}(\mathrm{k}\geq\mathrm{i})\), and the set of adjacent points after \(p_{v}\) is \(\{p_{v+1},p_{v+2},\ldots p_{l}\}(\mathrm{l}\geq\mathrm{j})\). Since \(\mathrm{l}\geq\mathrm{j}\) and \(\mathrm{i}<\mathrm{j}\), it follows that \(\mathrm{l}>\mathrm{i}\), so \(p_{v}\) and \(p_{i}\) are adjacent points. Furthermore, since \(u>v\), \(\mathrm{p}_{i}^{u}\subseteq\{\mathrm{P}_{v},\mathrm{P}_{v+1},\ldots,\mathrm{P} _{i-1}\}\), which contradicts \(\mathrm{p}_{i}^{u}\subseteq\{\mathrm{P}_{u},\mathrm{P}_{u+1},\ldots,\mathrm{P} _{i-1}\}\). Therefore, Inference 1 holds. \(\square\)  
Figure 12: Replace the path array with an index array. **Inference 1** indicates that the boundary of the set of preceding adjacent points for a path point will shift backward as the point moves through the candidate point set. Therefore, a sliding window can be used to optimize dynamic programming algorithms, thereby enhancing their efficiency. The sliding window defines the range of the preceding adjacent points and gradually advances toward the endpoint during the loop. The flow chart of the optimized dynamic programming algorithm is shown in Figure 14.
# A Theoretical Method and Model for TBM Tunnelling Trajectory Adjustment
## 4 Model Modification
### Data Preparation  
Based on the sixth section of the Qingdao Metro Project, the bias angle and distance in the horizontal and vertical direction were recorded for a total of 57 rings. Positive and negative values represent biases in different directions. In the horizontal direction, positive values indicate bias to the right of the expected trajectory, while negative values indicate bias to the left. In the vertical direction, positive values indicate that the TBM is above the expect trajectory, while negative values indicate that it is below. These data are used to judge whether manual intervention is required for the tunnelling trajectory based on the proposed criteria. The bias angle and distance for the 57 rings are shown in Figures 8 and 9.  
Figure 8: The bias angle and distance in the horizontal direction.  
Figure 7: Reasonable trajectory adjustment scheme suitable for the example.  
Tunnelling trajectories requiring manual intervention must satisfy two criteria. First, their bias distance must exceed 40 mm, and second, their bias angle and distance must be towards the same direction. As observed in Figures 8 and 9, most samples in the horizontal direction have bias distances within the range of \(-40\) mm to 40 mm. Additionally, some remaining samples have opposite bias angles and distances, such as rings 286# and 292#, with bias angles of 5 mm/m and 8 mm/m and bias distances of \(-46\) mm and \(-42\) mm. These samples do not meet the proposed criteria. Therefore, only the horizontal trajectories of rings 136#, 142#, 189#, 232#, and 626# require human intervention and will be further studied using the proposed method and model in Section 4.2. Similarly, the vertical trajectories of samples 286#, 334#, 356#, and 363# are suitable for the method and model, resulting in a total of nine samples listed in Table 3.
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 2 Phenology of Forests
### Integration of Sentinel-2 Data with Other Imagery
In order to remove such artefacts from the data several approaches to normalise Bi-directional Reflectance Distribution Function (BRDF) effects have been suggested in the literature [69; 70; 71; 72], and hence should be considered in phenological research.
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Reliability Allocation Methodology Design
#### 4.2.3 Automatic Generation of AADL-Based Mission Reliability Formulas  
In order to optimize the reliability allocation, the reliability of each assigned unit in the ship electronic information system needs to be finely designed and adjusted to achieve the optimal reliability allocation scheme. In this study, a vector of decision variables is designed that consists of the reliability values of each assigned unit in the system, where each element corresponds to one assigned unit. The mission is divided into three categories according to the different objects: sea-to-air mission, sea-to-underwater mission, and sea-to-land mission. Since the software itself does not generate failures, and software failures are caused by hardware, the software reliability is 1, and only the hardware equipment of the ship electronic information system is allocated the unit reliability. Considering that the ship aircraft does not belong to the unit of the ship electronic information system, there is also no need for reliability allocation. The flowchart of the finally designed mission reliability calculation algorithm is shown in Figure 7.  
After sorting and summarizing, the mission reliability formulas of the AADL model for the sea-to-air mission \(R_{m1}\), sea-to-underwater mission \(R_{m2}\), and sea-to-land mission \(R_{m3}\) are shown below.  
\[R_{m2}={R_{15}}^{2}(1-(1-R_{3})(1-R_{4}))^{3}(1-(1-R_{5})(1-R_{6}))^{2}(1-(1-R_{ 7})(1-R_{8}))^{2}\] \[(1-(1-R_{9})(1-R_{10})){R_{11}}(0.6{R_{16}}+0.4{R_{17}})\prod\limits \limits_{i=20}^{21}{R_{i}} \tag{14}\]  
\[R_{m1}=(1-(1-R_{1})(1-R_{2}))^{2}(1-(1-R_{3})(1-R_{4}))^{3}(1-(1-R_{5})(1-R_{6} ))^{2}\prod\limits_{i=20}^{21}{R_{i}} \tag{15}\]  
\[(1-(1-R_{7})(1-R_{8}))^{2}(1-(1-R_{9})(1-R_{10}))^{2}{R_{11}}(0.5{R_{10}}+0.3{R _{11}}+0.2{R_{12}})\]  
\[R_{m3}=(1-0.04(1-R_{2}))^{2}(1-(1-R_{3})(1-R_{4}))^{3}(1-(1-R_{5})(1-R_{6}))^{2}\]  
\[(1-(1-R_{7})(1-R_{8}))^{2}(1-(1-R_{9})(1-R_{10})){R_{11}}(0.4{R_{18}}+0.6{R_{19 }})\prod\limits_{i=20}^{21}{R_{i}} \tag{16}\]
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 3 Cluster Analysis
### Hierarchy-Based Clustering  
Hierarchy-based clustering is based on the fact that it is possible to know the distances between each data point right from the outset. In other words, if our goal is to create clusters containing points with the lowest relative distances, then it is possible to create a distance-based hierarchy of clusters from singular points to a single cluster. The final cluster encompasses all of the data points. This can be achieved by following two different directions, as discussed by [PERSON] [27]. Firstly, we can start with a single cluster and divide it into smaller clusters. This occurs over several iterations until each cluster contains only one point and is no longer divisible. Secondly, it is possible to start by assigning each point to its cluster and keep merging the clusters over several iterations until only one remains. This is an example of hierarchy-based clustering, which follows the second method:  
1. Calculate the distance matrix between every point.
2. Assign each data point into its cluster.
3. Connect the two nearest clusters based on distance.
4. Recalculate the matrix (hierarchy).
5. Repeat steps 2, 3, and 4 until only one cluster remains.  
The obvious advantage of using hierarchy-based clustering is that the hierarchy is calculated implicitly. There is no need for any input from a user, such as the number of clusters in the K-means algorithm. [PERSON] et al. [28] used this algorithm along with YOLO to detect and recognize elements of road traffic, yielding the mAP (mean Average Precision) of 90.45%. Perhaps the only real disadvantage is that the resulting hierarchy needs to be processed further to provide clear data, which may undermine the reasons to avoid implementations such as K-means in the first place.