esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, a combination of well-established algorithms and real-world data was implemented for the forward-looking problem of future vertiport network design in a large metropolitan city. The locations of vertiports were selected to operate urban air mobility (UAM) in the Seoul metropolitan area based on the population of commuters, and a noise priority route was created to minimize the number of people affected by noise using Aviation Environmental Design Tool (AEDT) software. Demand data were analyzed using survey data from the commuting population and were marked on a map using MATLAB. To cluster the data, the K-means algorithm function built in MATLAB was used to select the center of the cluster as the location of the vertiports, and the accuracy and reliability of the clustering were evaluated using silhouette techniques. The locations of the selected vertiports were also identified using satellite image maps to ensure that the location of the selected vertiports were suitable for the actual vertiport location, and if the location was not appropriate, final vertiports were selected through the repositioning process. A helicopter model was then used to analyze the amount of noise reduction achieved by the noise priority route, which is the route between the selected K-UAM vertiports compared to the shortest distance route. As a result, it was shown that the noise priority route that minimized the amount of noise exposure was more efficient than the business priority routes.  
UAM; ODM; vertiport; eVTOL; K-means clustering; AEDT; noise analysis +
Footnote †: journal: Applied Sciences  
18 June 2021  
11 June 2021
## 3 Methodology
### Spatial Location of Sugarcane Nodes Based on Binocular Vision
#### 3.3.3 Spatial Location of Sugarcane Nodes  
The centre point coordinates of the stem node in the left camera detected by the improved YOLOv4 algorithm are recorded as \(p_{l}(x_{l},y_{l})\). The corresponding point in the right picture was obtained by putting \(p_{l}(x_{l},y_{l})\) into Formula (5), then the parallax \(d=x_{l}-x_{r}\). Finally, the three-dimensional spatial coordinates of the stem node can be acquired by Formula (3). Because the most important index to evaluate the reliability of a binocular vision system is the accuracy of the coordinate in the \(Z\)-axis under spatial location, the left camera coordinate system was selected as the world coordinate system to verify the accuracy of the coordinates in the \(Z\)-axis under spatial location. Figure 12 shows the experimental platform of the spatial location of the binocular camera. The ZED2 binocular camera was used for image acquisition, and NVIDIA Jetson Nano was applied as an embedded chip for data processing, which communicate with each other through USB3.0. The overall platform was equipped with a camera-moving guide with a precise scale, as well as a power supply, screen and other related accessories.  
Figure 11: Feature point match map optimised based on RANSAC algorithm and epipolar constraint.  
Due to the difficulty in accurately measuring the distance from each node to the XOY plane of the left camera coordinate system, the relative distance was used to measure the location accuracy of the coordinate in the \(Z\)-axis. The experimental process was as follows:  
1. The binocular camera was fixed on the bracket guide rail with a scale, and the position of the guide rail where the camera was currently located was recorded. Subsequently, the sugarcane images in the field were collected and the spatial location experiment was carried out according to the method mentioned above. After sampling three times, the average \(XYZ\) coordinates of each node were recorded as the coordinates before moving. At the same time, the time spent in each location experiment was recorded.
2. The position of the binocular camera in the \(X\) and \(Y\) directions was kept unchanged, the camera was moved on the guide rail in the \(Z\) direction by D = 100 mm, the spatial location experiment was conducted again, and the average \(xyz\) coordinates of each node were recorded as the moved coordinates. In addition, the time required for each location experiment was recorded again.
3. The \(Z\) coordinate was subtracted before and after moving to get the average distance difference of two positions, and finally D was subtracted from the difference to get the location error in the \(Z\) coordinate.
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 3 System Model
### Energy Model  
In this work, all sensor nodes adopt a simplified energy consumption model [31]. In the process of data collection, the energy consumption mainly consists of the received power and the transmission power. Here, let \(\varepsilon_{amp}\) be the energy consumed in the transmission amplifier, \(E_{elc}\) be the basic energy consumed during transmitting and receiving per bit respectively, and \(d_{(vi,vj)}\) be the distance between node \(v_{i}\) and \(v_{j}\).  
The received power by node \(v_{j}\) that receives \(B\)-bit data is described as follows:  
\[E_{R}(v_{j})=E_{elc}B \tag{1}\]  
The transmission power during node \(v_{i}\) transmits \(B\)-bit data to node \(v_{j}\) is described as follows:  
\[E_{T}(v_{i},v_{j})=(E_{elc}+\varepsilon_{amp}d^{2}_{(v_{i},v_{j})})B \tag{2}\]
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 3 Graph Databases  
Graph databases are a type of NoSQL database in which data is represented as graphs with nodes and edges. Nodes represent entities in a database and can have associated properties and labels. A graph database is based on a graph data model of properties or labels that provide internal structures with nodes and edges [15]. This type of graph provides additional features to make the graph easier to understand, where nodes can have one or more labels, and relationships between nodes can contain properties. The edges represent relationships between different nodes, and a relationship is a link between a source node and a destination node. Graph databases are characterized by user interactivity, ease of understanding, and flexibility of the data schema. This database has gained significant notoriety in several fields, namely biology, chemistry, fraud detection, and military research, but especially in social networks. The three main characteristics of graph databases are:  
* Performance: In relational databases, queries run on them become slower as the number and depth of relationships increase. In graph databases, however, performance does not change drastically as data grows.  
* Flexibility: The structure and schema of a graph model adapts itself to the needs of the application, and it is possible to add new data without compromising its functionality.
* Agility: The continuous evolution of graph databases is consistent with today's agile development practices, allowing the data to evolve as users' needs change.  
In the following, we describe the main characteristics of the four analyzed graph databases and present their main strengths and weaknesses.
## 3 Results and Discussion  
### Single-Factor Experimental Result Analysis  
1. [label=(0)]
2. Analysis of Gas-Liquid Ratio Effect on CO\({}_{2}\) Concentration in MNB Water  
Figure 2 shows the change curve for the CO\({}_{2}\) content in the MNB water under different gas-liquid ratios. As shown in Figure 2, the CO\({}_{2}\) content in the MNB water gradually increased with the gas-liquid ratio. The CO\({}_{2}\) content in the MNB water tended to be stable when the gas-liquid ratio reached 2.7%, which showed that the CO\({}_{2}\) in the water has reached the saturation level.
3. Impact of Generator Running Time on CO\({}_{2}\) content in MNB Water  
As the running time of the generator increased, the quantity of CO\({}_{2}\) MNBs in the reaction vessel gradually increased, which became a suspension of the CO\({}_{2}\) MNBs. Therefore, the generator cycle running time is an essential factor influencing the CO\({}_{2}\) content in the MNB water. Figure 3 shows the resulting change in the CO\({}_{2}\) content in the MNB water under a fixed gas-liquid ratio and inlet water temperature. The results show that the CO\({}_{2}\) content gradually increased with the generator cycle running time. In particular, the trend of the changes in the CO\({}_{2}\) content became smaller when the time reached 30 min. When the running time was 40 min, the CO\({}_{2}\) content was at its maximum value and subsequently decreased as a result of some MNBs bursting and escaping from the water. Therefore, the optimal cycle running time for CO\({}_{2}\) MNB water is 30-40 min. The experimental results are shown in Figure 3.  
(3)  
The solubility of gas has an impact on the gas-liquid equilibrium balance inside the reaction vessel. Furthermore, temperature is an important factor influencing gas solubility [26; 27]. As a result, an investigation of the effect of the inlet water temperature was required. Figure 4 depicts the effect of the inlet water temperature on the CO\({}_{2}\) content of the MNB water. The amount of CO\({}_{2}\) dissolved in the water gradually decreased as the incoming water temperature increased, as seen in the results.  
Figure 3: Line graph of the CO\({}_{2}\) content changes at different running times.  
Figure 2: Line chart of the CO\({}_{2}\) content variation at different gas–liquid ratios.
# IBP Based Caching Strategy in D2D
## 3 Method
In our model, within each community, devices are labeled in an ascending order according to their node importance \(P_{k}\), the device with highest node importance is labeled as first user, \(u_{1}\), i.e., first customer in IBP. By modeling the influence of other users on content selection, we define contribution degree \(\overline{I_{ik}}\) to indicate the contribution of caching content \(k\) to device \(u_{i}\) to other users within same community. The contribution degree is given by :  
\[\overline{I_{ik}}=\frac{1}{N-1}\sum_{ \begin{subarray}{c}j=1;\\ j\
eq k-j,\end{subarray} }^{N}p(z_{j,k}=1\mid\mathbf{z}_{-j,k}) \tag{3}\]  
where \(p(z_{j,k}=1\mid\mathbf{z}_{-j,k})\) is the conditional probability of user \(u_{j}\) requesting content \(k\) given by Equation (2). High contribution degree indicates content \(k\) will be requested by other devices with high probability. When BS receives a request sent by device \(u_{i}\), firstly checks whether the content is cached by other devices. If the D2D caching network cannot respond to this request, BS will serve the request; otherwise, BS locates content holder with highest physical closeness. BS calculates contribution degree \(\overline{I_{ik}}\) according to Equation (3) and makes caching decisions for device \(u_{i}\) by setting caching flag \(fq\) (\(fq\in\{0,1\}\)). If \(\overline{I_{ik}}>=I_{th}\), the content can be cached by device \(u_{i}\), i.e., \(fq=1\); otherwise, \(fq=0\), the content will not be cached by device \(u_{i}\), as described in Algorithm 1. The caching threshold \(I_{th}\) is a design number and ranges from 0 to 1. Input: G(V,E), \(w_{th}\), \(I_{th}\);  
Output: fq (caching flag, fq, indicates whether device can cache the content);  
Initialize: fq = 0;  
```
1:BS collects encounter and interaction information of devices within the cellular network, calculates closeness \(w_{ij}\), constructs reliable D2D communication network G(V,E) ;
2:grouping the devices into several social communities and calculating node importance to community of each device by Equation (1);
3:for each community do
4: labeling devices according to their node importance to community, i.e., \(u_{1},u_{2},.....,u_{N}\);
5:endfor
6:BS receives a request for content f sent by device \(u_{i}\)
7:BS calculates contribution degree \(\overline{I_{if}}\) according to Equation (3);
8:if\(\overline{I_{if}}>=I_{th}\)then
9: let \(fq=1\);
10:else
11: let \(fq=0\);
12:endif
13:if content \(f\) can be retrieved from other devices in the D2D caching network then
14: BS locates content holder with highest physical closeness and sends caching flag \(fq\) to the content holder;
15:establishing D2D communication to transmit content \(f\) with caching flag \(fq\);
16:else
17: BS servers the request directly with content \(f\) and caching flag \(fq\);
18:endif
19:When device \(u_{i}\) receives content f
20:if\(fq\
eq 0\)then
21: device \(u_{i}\) caches content \(f\);
22:endif
```  
**Algorithm 1** IBPSC strategy
# Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning
## 3 Digital Meter Detection and Identification
### Perspective Transformation  
The detection target area and the four corners of the detection target area are obtained from the polygon-YOLOv5 model. The homography matrix can be computed through the four corner points and the width and height of the LED digital meter. The LED digital meter area is corrected according to the perspective transformation. The perspective transformation formula is demonstrated below.  
\[\left[x^{\prime},y^{\prime},w^{\prime}\right]=\left[u,v,w\right] \begin{bmatrix}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\end{bmatrix}=\left[u,v,w\right]\begin{bmatrix}T_{1}&T_{2 }\\ T_{3}&a_{33}\end{bmatrix} \tag{4}\]  
where parameter \(w=1\), \((u,v)\) is the raw image coordinates, \((\frac{x^{\prime}}{w^{\prime}},\frac{y^{\prime}}{w^{\prime}})\) is the image coordinates obtained after perspective transformation, \(T_{1}=\begin{bmatrix}a_{11}&a_{12}\\ a_{21}&a_{22}\end{bmatrix}\) is the image linear  
Figure 7: Diagram of polygon boxes and rectangular boxes.  
transformation matrix, \(T_{2}=\begin{bmatrix}a_{13}&a_{23}\end{bmatrix}^{\mathrm{T}}\) is the image homography matrix, \(T_{3}=\begin{bmatrix}a_{31}&a_{32}\end{bmatrix}\) is the image shift matrix and parameter \(a_{33}=1\). Figure 8 shows the image after correction.
## 5 Simulation Results and Discussion
### Result Comparison of OECS with Fuzzy Logic Control  
To compare the performance and fuel efficacy of the proposed OECS with other controller, a flight controller based on Fuzzy logic control was used in this study. The developed OECS is a model-based method, which means that it requires the system identification process and identified model to design the control system. In contrast to the model-based method, Fuzzy logic control (FLC) is a knowledge-based method, which means that no model is constructed, and the design of the FLC requires some understanding of the plant's behavior and expert's experience. The details of the adopted FLC autopilot refer to the study in [25].  
Both of the climbing and descending performance of the target UAV without the presence of wind was evaluated. Since there is no autothrottle system in the used FLC, the throttle of the FLC was set to be constant and its value was 1300 PWM. The simulation results of climbing maneuver are shown in Figure 19. The initial altitude is 150 m and the commanded altitude is 158 m. During the aircraft climbing, the airspeed decreases dramatically about 0.8 m/s from initial airspeed. The settling time for the climbing maneuver is around 30 s, and the maximum change of pitch rate, \(\Delta q\), is about 1.5 rad/s. When the climbing maneuver is performed, the energy rates change dramatically. The simulation results of descending maneuver are shown in Figure 20. The results are similar to that of the climbing maneuver. In contrast to the OECS, the climbing and descending performance and fuel efficiency of the proposed OECS are better than that of FLC. The aircraft is maintained approximately at trim airspeed, and the required throttle is lower than that in FLC. Moreover, the energy rates are  
Figure 18: Simulation results of descending with the presence of wind.
## 3 Principles
### Delay Data Analysis  
The range of the scanning depth was 2 to 180 mm and the focusing delay data of 32 channels were calculated according to Equation (1). The delay focusing data of channels 1-32 update in real time with the change of the focal depth. The variation of focusing delay data relative to the focal depth \(F\) and channel \(j\) is shown in Figure 6. For the focal depth \(F\) and the channel \(j\), the variation is a monotonically decreasing function. The smaller the distance between two adjacent focal points, the more focusing points means that the quality of the image is higher. Whereas, field programmable gate array (FPGA) consumption also increases. The tradeoff between image quality and FPGA consumption, the distance between two adjacent focal points, was 0.45 mm. These features showed that the following focusing delay data could be stored to some extent that used less memory.  
Figure 5: Calculation of the focusing delay time \(\tau\) for the linear array transducer.  
The transmission and receiving of ultrasonic transducers were controlled by the 64 channel ultrasonic signal circuit and the beam synthesis algorithm was realized according to the focusing delay data. By adopting the sequential scanning method, the focus changed dynamically during the scanning process, so that the beam of the whole detection depth could converge well.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 5 Simulation  
To thoroughly evaluate the performance results of the KGTSA, extensive simulation experiments were conducted in this chapter. In Section 5.1, we introduce the relevant evaluation metrics, comparison algorithms, and experimental simulation parameter settings. In Section 5.2, we present the simulation experiment results of the KGTSA based on UAV and vehicle speed variations and provide a detailed analysis of these results. In Section 5.3, we evaluate the battery level variations of the three UAVs. In Section 5.4, we present the trajectories of the three UAVs and the vehicle. In Section 5.5, we assess the time consumption of the algorithm.  
It is important to emphasize that our study was primarily conducted through simulation. The following are the practical considerations for deploying the system in a real-world scenario. When using the system in a real-world scenario, several preparatory steps must be completed before the system is activated. First, the precise locations of all data collection points within the target area need to be determined. Subsequently, the vehicle and UAVs should be equipped with the necessary communication and data acquisition devices. The vehicle then transports the UAVs to the vicinity of the target area. After the system is activated, the central console uses a pre-designed algorithm to calculate the initial routes and task assignments for the vehicle and UAVs and sends these instructions accordingly.  
During the operation, the UAVs continuously monitor and record the data collection status at each task point as well as their own flight status information, including position and remaining energy, and transmit this data back to the central console in real time. The central console optimizes the operation plans for the vehicle and UAVs based on the received information. If external factors such as weather cause changes in the speeds of the vehicle and UAVs, the central console will quickly generate new control instructions to ensure the efficient execution of data collection tasks. Additionally, in case of emergencies such as equipment failures, the central console can promptly adjust the task plan to ensure the normal operation of the system. Future work will focus on testing the scalability of the algorithm in real-world conditions, particularly for coordinating a single vehicle with multiple UAVs, and addressing potential limitations in terms of the number of UAVs being supervised and the challenges involved in the real-time monitoring of multiple UAVs.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Semantic Indexing  
This module interfaces with Delta Lake to provide ACID-compliant storage and supports both batch and streaming workloads. In large-scale deployments, selecting technologies for semantic indexing requires balancing trade-offs among performance, scalability, and infrastructure complexity. FAISS offers fast, memory-efficient indexing for on-premise environments but lacks native support for distributed execution or elastic scaling, necessitating custom orchestration in multi-node setups. CLIP enhances semantic precision through strong vision-language alignment but incurs high computational costs for embedding generation, often requiring GPU clusters or inference acceleration frameworks in large-scale or real-time applications. Pinecone, as a cloud-native vector database, provides built-in elasticity and managed scalability, streamlining deployment. However, it introduces potential operational overhead, reliance on cloud infrastructure, and limited flexibility for hybrid or on-premise scenarios. These trade-offs inform ViMindXAI's indexing architecture, enabling adaptation to varying workload demands, privacy constraints, and deployment environments.
# Observer-Based Distributed Fault Detection for Heterogeneous Multi-Agent Systems
## 2 Preliminaries and Problem Formulation
### Unknown Input Observer  
Consider the following system with unknown input:  
\[\begin{split}\dot{\xi}&=Q\xi+Wu+Yw\\ y&=M\xi\end{split} \tag{1}\]  
where \(\xi\in\mathbb{R}^{n}\) is the state. \(u\in\mathbb{R}^{r}\) and \(w\in\mathbb{R}^{s}\) are the known and unknown inputs, and \(W\) and \(Y\) represent their input channels, respectively. \(y\in\mathbb{R}^{m}\) is the measurement output, and \(H\) represents the measurement matrix.  
In order to estimate the actual state of System (1), the following observer is designed.  
\[\begin{split}\dot{z}&=Gz+TWu+Ry\\ \dot{\xi}&=z+Hy\end{split} \tag{2}\]where \(\hat{\xi}\in\mathbb{R}^{n}\) and \(z\in\mathbb{R}^{n}\) are the estimated state and observer's state, respectively. Parameter matrices of observer (2) need to be designed to make the state estimation error be not influenced by unknown input \(w\), where the design method is shown as follows:  
\[\begin{split} G=TQ-R_{1}M,T=I-HM,\\ (HM-I)Y=0,R=R_{1}+R_{2},R_{2}=GH\end{split} \tag{3}\]  
then the state estimation error dynamics is shown as follows:  
\[\dot{e}(t)=Ge(t) \tag{4}\]  
where \(e=\xi-\hat{\xi}\). If the designed \(R_{1}\) makes \(G\) Hurwitz stable, \(e\) will converge to zero asymptotically. Observer (2) is usually called unknown input observer (UIO). Lemma 2 gives the existence conditions of a UIO.  
**Lemma 2** ([16]).: _A UIO for System (1) exists if_  
1. \(rank(MY)=rank(Y)\)__
2. \(\begin{bmatrix}sI-Q&Y\\ M&0\end{bmatrix}\) _is of full column rank for_ \(\forall s\in\mathbb{C}\)_,_ \(Re(s)\geq 0\)_, i.e.,_ \((TQ,M)\) _is detectable._  
**Remark 1**.: _The above two conditions guarantee existence of \(H\) and \(R_{1}\) in (3), respectively, where \(R_{1}\) makes \(G\) Hurwitz stable._