esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge and Cloud Computing in Smart Cities
## 3 Enabling Technologies
### Comparative Analysis of Enabling Technologies
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technology** & **Functionality** & **Benefits** & **Limitations** & **Influence on Edge-Cloud Computing** \\ \hline Advanced communication networks [78, 79, 80, 81, 82, 83, 84, 85, 86] & High-speed data transmission, low-latency networking, real-time routing. & Enhances responsiveness, minimizes throughput. & High deployment costs, spectrum allocation complexity, interference management. & Ensures fast and reliable connectivity between edge and cloud layers. \\ \hline AI and ML [87, 88, 89, 90, 91, 92] & Intelligent workload distribution, predictive analytics, real-time optimizations. & Enhances efficiency, automates decision-making, and reduces task execution time. & Computational overhead, real-time inference complexity, data privacy concerns. & Reduces latency, optimizes task execution, and improves adaptability in dynamic environments. \\ \hline Blockchain and secure transmission authentication, integrity verification. & Decentralized security, attempting verification. & Ensures tamper-proof data transactions and eliminates reliance on centralized authorities. & High computational power demand, increased verification latency, and scalability challenges. & Strengthens trust and reliability in multi-node environments but introduces verification delays. \\ \hline Edge virtualization and resource optimization optimization & Dynamic workload allocation, multi-tenant computing, containerized execution. & Improves system elasticity, enhances load balancing, and minimizes operational costs. & Complexity in orchestration, potential security vulnerabilities, resource contention. & Enables adaptive workload migration, optimizes resource distribution, and balances processing loads. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of enabling technologies in edge–cloud computing.
# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 2 Related Works  
### Object Detection of a Car  
You only look once (YOLO) [10] is a machine learning (ML) framework for object detection, which attempts to detect any object depending on what is used to train the detection model. YOLO object detection can detect the location and the class of the object in the input image by carrying out one calculation of convolutional neural networks. In the first place, the input images are divided into S \(\times\) S squares, which are called grid cells. As shown in Figure 2, there are 6 \(\times\) 6 grid cells in Figure 2. Secondly, the prediction of the bounding box must be applied after the grid cells in the image are obtained. Each grid cell must process a fixed amount of prediction of the bounding box and confidence score. There are five values in each bounding box, including x, y, w, h, and confidence. The x, y value is the center of the bounding box, and w and h are the width and height, respectively, of the bounding box. The confidence is the value of intersection over union (IOU). The confidence score represents the probability of the bounding box that may contain the object. The confidence score will be zero if the bounding box does not contain an object. After predicting the bounding box, it will abandon redundant bounding boxes by the threshold method and the non-maximum suppression (NMS) method. The above operation is the method used to detect the location of the object in the image. Lastly, the class of the bounding box must be predicted. In this process, the class probability map must be used (Figure 3).  
Figure 3: The probability map of specific classes.  
Figure 2: The input image is divided into 6 \(\times\) 6 grid cells.  
In this map, each color region represents one specific class. Using the blue region as an example, the blue region can be regarded as the car class in this class probability map. The class of the bounding box can then be predicted. The predicted bounding box is used in the second step. Then it can be compared with the class probability map. Figure 4 shows how to decide the class of the bounding box. Because the center of the bounding box is within the yellow grid cell, the yellow grid cell has to predict the bounding box. Thus, the only thing that must be done is to compare the left image to the right class map. Note that the yellow grid cell is inside the blue region, and we can assume that its class is a car. Finally, we can say that the red bounding box contains the car object. The object detection process is thus ended.  
There are various improvements and extensions in YOLOv2 [11], while it also still predicts objects based on the above process. There are many crucial factors to enhance the accuracy of the model. YOLOv2 uses batch normalization, anchor boxes, and k-means, and removes two fully connected layers.  
By adding these features into the model, the accuracy is increased and the detecting time is decreased.
## 2 Methodology  
Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority under Emergency Conditions  
In emergency observation, there are many factors that need to be considered to maximize a total reward of the observed targets, such as the priority level of missions and the total benefit gained from acquiring images. Furthermore, in contrast to daily scheduling, the resource allocation for real-time missions as an important feature of this problem also need to be addressed. In the context of a large number of point target imaging missions with different priority levels and newly arrived missions,we develop a multi-satellite dynamic mission scheduling model based on the mission priority under emergency conditions. The algorithm includes the following steps: (1) analysing the factors that influence the mission priority and calculating the priority for emergency missions; (2) analysing the constraints of satellite scheduling and building an optimization objective function; (3) obtaining the initial scheduling plan with the hybrid genetic tabu search algorithm; (4) using dynamic scheduling algorithm tries to insert newly arrived missions to the initial scheduling plan; and (5) evaluating the scheduling plan results. The flow chart of this model is shown in Figure 1.
# Fracturing Fluids and Their Application in the Republic of Croatia
## 5 Fracturing Fluids--Composition and Additives
### Classification of Fracturing Fluids
#### 5.1.5 Foam-Based Fracturing Fluids  
Foam-based fracturing fluids include water-based foams, acid-based foams, alcohol-based foams and CO\({}_{2}\)-based foams (see Table 8).  
Foam-based fracturing fluids are two-phase fluids produced when a big part of the inner phase capacity (normally more than half of the whole volume) is spread as tiny disperse units through a continuous liquid form [53]. The viscosity of foam-based fluid strongly relies on the foam grade (the gas portion of the overall mixture) and foam texture (the consistency of bubbles in the mixture). Owing to its viscous and less dense properties, foams are very special and flexible.  
[PERSON] et al. (2018) conducted fracturing experiments on a high pressure triaxial apparatus. Silitstone specimens were fractured with water and foam (N\({}_{2}\) + water) to investigate the effect of fracturing fluid on rock permeability and the fracture pattern. To examine the fracture pattern and the fracture surface of the fractured specimens, they used computerized tomography (CT) scanning and 3-D scanning technology. They showed that fracturing with foam can induce a complex twisted fracture with a greater surface area and an increased permeability of about 5 \(\times\) 10\({}^{5}\) times greater than those obtained in intact and water-fractured specimens [54].
# Big-Delay Estimation for Speech Separation in Assisted Living Environments
## 1 Introduction
Underdetermined speech demixing occurs when the number of sources exceeds the number of microphones. With an increased inter-microphone gap between the RM and a far-end microphone, depicted in Figure 1b, phase wraparound becomes a challenge in near-field settings, leading to two issues: scaling and permutation ambiguities, which complicate underdetermined speech demixing problems. As a result, in the TF domain, a source may exhibit erroneous phases and gains across different TF-bins [50], especially when the frequencies are above \(f_{\text{aliasing}}\). This complicates relative delay estimation and consequently source demixing, as clustering-based techniques struggle to assign the TF bins to their correct sources. The authors of [46; 49; 51] demonstrated that if the IPDs of the low-frequency components that are less than \(f_{\text{aliasing}}\), are estimated accurately, they can be used to align the higher frequency TF bins, where phase wraparound occurs to the correct sources. We address the problem of achieving speech separation in competing-speaker assisted living environments, which are characterized by large delays--and consequently phase wraparound--due to the significant physical separation of microphones. In this paper, we propose two methods of adapting the Degenerate Unmixing Estimation Technique (DUET)-based mask demixing function to operate effectively in anechoic scenarios with large relative delays, of up to 200 samples. These proposed methods are called Elevato-DUET and Elevato-AdRess. Numerical evaluations on real-speech mixtures demonstrate that Elevato-AdRess outperforms Elevato-DUET in most speech separation quality metrics. Furthermore, the techniques introduced in this paper are not affected by the permutation or scaling problems commonly encountered in scenarios involving large relative delays. This paper is organized as follows. In Section 2, we introduce the mixing model. Section 3 describes the Elevatogram, originally introduced in [5]. Section 4 outlines the contributions of this paper--namely, the Elevato-AdRess and Elevato-DUET algorithms. Finally, Section 5 evaluates the performance of these algorithms in the task of separating the target source, \(s_{j}[n]\), from a mixture of sources \(J=4\).
## 5 Conclusions  
Quantitatively assessing the spatiotemporal variation of WUEc and its primary driving factors is crucial for understanding the impact of environmental changes on the carbon balance of agricultural ecosystems. This study employed remote sensing data and geographic information system (GIS) techniques to analyze the spatiotemporal variation and major drivers of WUEc in Xinjiang from 2002 to 2022. The results reveal an overall decline in WUEc, accompanied by marked spatial heterogeneity between southern and northern Xinjiang. Excessive evaporative loss, which leads to persistently low WUEc levels, constitutes a major constraint on agricultural development in the region. To address this issue, we recommend the implementation of soil and water conservation measures--such as building irrigation infrastructure and establishing windbreaks--and the promotion of advanced irrigation techniques, including subsurface drip and infiltration irrigation, to reduce water loss and enhance WUEc. The findings also offer critical insights and a scientific foundation for future research on carbon-water interactions, as well as for regional water resource management and land-use planning in Xinjiang.  
Author contributions: Q.Z., conceptualization, formal analysis, investigation, methodology, supervision, visualization, writing--original draft; F.G., investigation, writing--original draft, data curation, and formal analysis; B.H., methodology, and visualization; H.L., Y.L., methodology; Y.X., R.L., investigation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the 2024 research project of \"Xinjiang Key Laboratory of Hydraulic Engineering Safety and Water Disaster Prevention and Control\" (ZDSYS-YJS-2024-24), the Xinjiang Uygur Autonomous Region Major Scientific and Technological Special Project--Research and Demonstration on the Development Model of Ecological Agriculture and Efficient Utilization of Soil and Water Resources in Modern Irrigation Areas (2023A02002-1).  
**Data Availability Statement:** The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
# Effect of PVA/PVAc Based Polymer Coating on Dust Reduction in Playground
## 2 Experimental Section  
### Materials and Sample Preparation  
Sandy loam soil and pumice sand were investigated in this study. Sandy loam is the most used playground soil in the Republic of Korea due to its low cost. However, sandy loam particles possess low permeability and cause dust due to human activity. Pumice sand is obtained from volcanic vitreous effusion rock and has light weight, porous, high water absorption, and anti-corrosive properties [27; 28; 29]. Sandy loam soil samples were obtained from a local company in Seoul, Republic of Korea and pumice sand was obtained from Asan, Republic of Korea. Figure 1 displays the particle size distribution of soil samples. Table 1 summarizes the chemical composition of the pumice sand and the sandy loam soil. Poly vinyl alcohol coating solution was used as coating material in this study. Polyvinyl Acetate (PVAc)-based gel polymer was added to the coating solution to improve the bonding efficiency of the coating solution [30]. Table 2 represents the physical and chemical properties of the PVA film.  
Figure 1: Particle size distribution of soil samples.
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 2 Materials and Methods
### Description of the Base Station Coordinator Node
#### 2.2.2 Control and Verification Console  
The coordinator node can be connected to a PC via USB or through a serial communication. This way, a human operator can verify the correct working of the system and intervene in the system if necessary. As presented in Figure 7, the PC runs a console application showing the messages that the coordinator module generates for each task. The messages sent to the remote nodes can correspond to the tasks, either generated from the web application or automatically generated for the periodical tasks implemented in the processor of the Arduino DUE system.  
The console shown in Figure 7 is a RealTerm serial terminal. This is a terminal program for engineers specially designed for capturing, controlling and debugging binary and other complex data streams. It is a free software development available in the SourceForge (SourceForge Media, La Jolla, CA, USA) open source community. In our case, it is used as a debugging tool to monitor the communication between the RICSs and the local control node.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 4 Analysis of Passenger Flow Characteristics and Influencing Factors of URT
### Dataset Source  
The dataset source is based on the swiping data of passengers entering and leaving the stations from Sigongli to Longtou Temple of Chongqing Rail Transit Line 3 in April 2021. The weather data of Chongqing during the same period is also combined. The passenger flow data comes from Chongqing Rail Transit Group. The mileage of Chongqing Rail Transit Line 3 is 67.09 km, with a total of 45 stations. Among them, there are 15 stations between Sigongli and Longtouisi, including Sigongli, Lianglukou, Niujaiotuo, Guanyinqiao, Chongqing North Station South Square, and other representative business districts and interchange hub stations. The weather data comes from the National Meteorological Science Data Sharing Platform, including some basic weather indicators such as temperature, weather conditions, wind power, and air quality index. These two types of data are processed, and the passenger flow data and weather data are matched by time.
## 4 Conclusions  
This study demonstrated that photo-chemical etching is a robust approach for the fabrication of biodegradable Zn stents. Cylindrical Zn stents with different patterns were made using this method and successfully tested for balloon expansion and mechanical compression. The stents with a rhombus design expanded smoothly and uniformly, both with and without a Parylene C coating. A Parylene C coating on the Zn stents served as a moderate corrosion barrier in vitro, thus slowing down the device degradation when exposed to a standard culture medium with serum. After in vivo implantation, segments of Zn ribbon samples showed a significantly slower rate of degradation than Mg ribbon samples. The Zn segments were also seen to be non-toxic to surrounding tissues, stimulating only a mild inflammatory response, and showed excellent compatibility with nerves and blood vessels. Tissue reactions to segments of the Zn ribbon were comparable to the reactions to AZ31 Mg samples. Therefore, both in vitro and in vivo studies conducted with pure Zn metal stent materials, made by photo-chemical etching, confirmed that Zn is a promising material for biodegradable stent applications. Figure 18: Histology of Mg and Zn ribbon pieces after in vivo implantation: (**a**,**b**) sections stained with H&E; (**c**) whole mount immunofluorescence; and (**d**,**e**) sections immunostined for axons (red), blood vessels (green) and nuclei (blue). Little to no capsule (**a**,**b**, open arrowheads) formed around the cavities which had contained ribbon pieces (stars) and there was little inflammation (black arrow, **b**). Blood vessels clustered extensively around the stents (**c–e**, white arrows). Immunostaining confirmed that (**d**,**e**) both blood vessels (solid white arrows) and nerves (open arrows) were found close to the ribbon pieces (location indicated by white rectangles). (Bar in **b** applies to **a** and bar in **e** applies to **d**). **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/21/4503/s1](http://www.mdpi.com/2076-3417/9/21/4503/s1)]([http://www.mdpi.com/2076-3417/9/21/4503/s1](http://www.mdpi.com/2076-3417/9/21/4503/s1)). Table S1: General criteria applied to biodegradable materials for use in stent applications [1]. Reproduced from [1] with permission from John Wiley and Sons; Figure S1: Radiopacity of three Zn stents, made via photo-chemical etching, after exposure to X-ray; Figure S2: SEM images of unexpanded Zn stents, after immersion corrosion, but before removal of surface corrosion deposits. (a) Significant corrosion is seen with a bare Zn stent; (b) Polymeric folds are observed on the surface of a Parylene C coated Zn stent; Figure S3: (a) Surface morphology revealed by SEM of an unexpanded, uncoated Zn stent after cleaning, at 2000x. (b) Surface morphology of the same stent at 5000x.
# Design of Autonomous Mobile Robot for Cleaning in the Environment with Obstacles
## 5 Experiment Results
### Experiment 2  
In this experiment, two obstacles were placed at (\(-1\), \(1.5\)) and (\(1.2\), \(0.75\)) coordinates, the virtual robot started from (\(0\), \(0\)) coordinates, and the real robot (cleaning robot) was placed at (\(-2\), \(1\)) coordinates. \(c_{1}\) was \(2\), and \(c_{2}\) was \(2\), obstacle radius r was \(0.23\) m, tu was \(0.4\) s, and R was \(0.7\) m. The total experiment was performed for \(120\) s. The linear velocity for the virtual robot was \(0.15\) m/s, whereas the angular velocity was \(-0.15\) rad/s. The persistent excitation was \(h(t,E_{y})=\phi(t)tanh(E_{y})\) where \(\phi(t)\) was a non-smooth pulse function of amplitude \(0.5\), \(4\) s period with the pulse width of \(80\) percent. Figure 6 shows the experimental results of this experiment. The scale of Figure 6a is \(0.5\) m for \(1\) unit of the \(x\)-axis and \(1\) m for \(1\) unit of the \(y\)-axis. Due to the scale, obstacles are not in a complete  
Figure 5: Experiment 1 results—(**a**) real and virtual robots path in \((x,y)\)-plane; (**b**) position errors in \(x\) direction as a function of time; (**c**) position errors in \(y\) direction as a function of time; (**d**) orientation errors as a function of time; (**e**) linear velocity control; (**f**) angular velocity control; (**g**) ‘freeze’ signal.  
circular shape. Figure 6a shows the paths of both the robots in the \(XY\) plot. The green color indicates the path of the virtual robot, which starts from (0, 0) coordinates and moves in a circle in the anti-clockwise direction. The robot starts from (0, 1), avoids the two obstacles in its way, and converges to the desired trajectory at the end. Figure 6b shows the error in the \(x\)-coordinates as a function of time. As time passes, the controller helps the robot to achieve near-zero error in the \(x\)-axis. Figure 6c shows the error in the \(y\)-coordinates as a function of time, which also shows that error in the \(y\)-axis converges near zero, due to the controller. The robot orientation error also tries to converge to zero, as shown in Figure 6d. The real-time control signal generated by the controller (linear velocity and angular velocity) for the robot can be shown in Figure 6e,f. However, in these plots, some high linear velocities and angular velocities are generated. In that case, the real robot only moves to its top speeds. Freeze signals for the robot can be seen in Figure 6g, where zero means no freeze signal is generated and one unit shows that the robot is frozen. These are generated by the controller when the robot is in the potential field near the obstacle.  
Figure 6: Experiment 2 results—(**a**) real and virtual robots path in \((x,y)\)-plane; (**b**) position errors in \(x\) direction as a function of time; (**c**) position errors in \(y\) direction as a function of time; (**d**) orientation errors as a function of time; (**e**) linear velocity control; (**f**) angular velocity control; (**g**) ‘freeze’ signal.