esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Satellite Data  
The MODIS sensors onboard Aqua and Terra satellites have high temporal resolution and rich spectral bands, which can capture abundant spectral information and data of burned areas. The MODIS Level 1B and geolocation products were acquired from Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) [35] and our Remote Sensing Laboratory (location in Hefei, China), which can receive the daily MODIS data as well as subsequent data-processing. In addition, the Operational Land Imager (OLI) sensor onboard Landsat 8 satellite was employed as an independent reference data source to verify the accuracy of the burned area map derived from MODIS data (see Section 2.4 for the details). The OLI data over the study area was downloaded from the United States Geological Survey (USGS) EarthExplorer website [36]. The pre-fire and post-fire OLI scenes of the wildfire cases were used to generate the reference burned area maps that were utilized to validate the accuracy of the proposed method. The characteristics of the spectral bands used of MODIS and OLI sensors are shown in Table 1.
## 3 Routing Metrics Design
### Energy Consumption of Network Nodes
In order to comprehensively consider the current energy loss and the subsequent energy loss trend of network nodes, this paper proposes an energy consumption evaluation index of network nodes based on node degree balance, which can be expressed as:  
\[\overline{C_{i}} =\ln(d_{i}+1)\times e^{\overline{C_{i}}} \tag{5}\] \[=\ln(d_{i}+1)\times e^{\overline{C_{i}}}\]  
where \(d_{i}\) represents the node degree of node \(i\), whose value is equal to the number of one-hop neighbors around node \(i\), and \(C_{i}\) denotes the energy loss rate of node \(i\). In order to balance the energy consumption of each node in the network as much as possible, it is necessary to prevent nodes with large energy consumption from participating in excessive data forwarding services as relay nodes. Therefore, the energy consumption evaluation index of network nodes based on node degree balance \(\overline{C_{i}}\) proposed in this paper comprehensively considers the two factors of node degree and node energy loss, and emphasizes energy loss as the primary influencing factor through exponential amplification, while node degree is treated as the secondary influencing factor via logarithmic reduction. If the value of \(\overline{C_{i}}\) is larger, the cost for node i to maintain the current data forwarding status is greater and the lifetime is shorter. Therefore, it is necessary to avoid this node as much as possible in subsequent data forwarding.
# Development of an Intelligent Voltage Control System for Bulk Power Systems
## 4 Conclusions  
In this paper, a general-purpose intelligent voltage control system for bulk power systems was proposed. The proposed system consists of a numerical analysis module and a main expert system. The derivation of the sensitivity matrix and state-space generation, along with the searching strategy, was discussed first. Additionally, an effective performance index was proposed. Furthermore, as the sensitivity is the linearized value, a comparative study was performed to identify the prediction errors using the PSS/E simulation program. Based on our results, we have confirmed that the sensitivity errors are within 1% through various case studies.  
As a result, the proposed system showed a promising performance for practical applications, and the pure processing time, except for the communication delay and SCADA scanning time delay, which depend on the year of manufacture and communication environment of the country, was found to be within one second for most cases using a personal computer system. The error due to linearization was confirmed to be within 1%. Moreover, the proposed structure strongly implies further extensive application to reactive power planning of bulk power systems.  
Conceptualization, H.L. and W.Y.; methodology, H.L.; validation, H.K. and J.K.; formal analysis, H.L.; investigation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, J.O.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1F1A1076667) and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20174010201620). The present research was also conducted under the Research Grant of Kwangwoon University in 2019.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bus Number** & **PSS/E** & **Linear Prediction** & **\% Error** \\ \hline
1020 & 0.9616 & 0.9636 & 0.2080 \\
1525 & 0.9781 & 0.9846 & 0.6688 \\
1545 & 0.9697 & 0.9769 & 0.7497 \\
1565 & 0.9724 & 0.9804 & 0.8308 \\
1575 & 0.9704 & 0.9783 & 0.8232 \\
1585 & 0.9711 & 0.9798 & 0.8987 \\
1590 & 0.9641 & 0.9721 & 0.8322 \\
1595 & 0.9730 & 0.9800 & 0.7295 \\
1630 & 0.9775 & 0.9840 & 0.6706 \\
1720 & 0.9771 & 0.9826 & 0.5696 \\
1740 & 0.9750 & 0.9805 & 0.5658 \\
1746 & 0.9741 & 0.9796 & 0.5682 \\
1770 & 0.9734 & 0.9799 & 0.6746 \\
1786 & 0.9735 & 0.9800 & 0.6725 \\
1790 & 0.9732 & 0.9798 & 0.6854 \\
1985 & 0.9747 & 0.9802 & 0.5734 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the linear prediction error.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 3 Experiments and Analysis
### Experiment Result
#### 3.2.3 Experiment Results on Xiongan Dataset  
Figure 9 shows the visually comparison of different methods on the Xiongan dataset. It can be found that the classification map obtained by CTAFNet keeps highly consistent with the ground truth, and there is little noise in the classification map. Compared with CTAFNet, the other three transformer models (SegFormer, Swin-UNet and FSSANet) produce more noises and misclassifications. As can be seen in the enlarged area, CTAFNet can completely classify the Grass (pink block in the middle) without any omissions or errors, which indicates that CTAFNet can effectively integrate the advantages of CNN and Transformer.  
Table 7 shows the experimental results of different methods in the Xiongan test set. The experimental results demonstrate that the proposed CTAFNet achieves the best performance with 96.17% pixel accuracy and 86.84% mIoU. Specifically, the pixel accuracy and mIoU of CTAFNet is 1.80% and 3.98% higher than the second-best model ENLFCN, respectively. SegFormer has the worst performance, achieving only 66.58% pixel accuracy and 42.37% mIoU. In terms of classification accuracy of each class, CTAFNet obtained the best score in 10 of the 18 classes. Among them, its IoU scores on the 3 th, 12 th, 13 th, 16 th, and 17 th categories of ground objects far exceeded values for the second-best model, which were 6.43%, 6.93%, 11.04%, 4.29% and 14.70% higher, respectively.  
Figure 9: Visually comparison of different methods on the Xiongan dataset.
## Materials and Methods  
### Reagents and Materials  
The standard liquid pesticides \(\alpha\)-HCH (CAS number: 319-84-6), \(\beta\)-HCH (CAS number: 319-85-7), \(\gamma\)-HCH (CAS number: 58-89-9), \(\delta\)-HCH (CAS number: 319-86-8), p,p\({}^{\prime}\)-DDD (CAS number: 72-54-8), o,p-DDE (CAS number: 3424-82-6), p,p\({}^{\prime}\)-DDE (CAS number: 72-55-9), o,p-DDT (CAS number: 789-02-6), and p,p\({}^{\prime}\)-DDT (CAS number: 50-29-3) were obtained from the Agro-Environment Protection Institute, Ministry of Agriculture (Tianjin, China) at concentrations of 1000 mg L\({}^{-1}\). A standard mixture containing 20 mg L\({}^{-1}\) of each of the nine OCPs was prepared in methanol and stored at \(-\)20 \({}^{\circ}\)C in the dark. High-performance liquid chromatography grade acetonitrile, methanol, and n-hexane were purchased from Sigma-Aldrich (St. Louis, MO, USA). Anhydrous sodium sulfate was supplied by Agilent (CA, California, USA). The MWCNTs (8-15 nm inner diameter (id), 10-30 \(\upmu\)m long, 95% purity), analytical grade ferric chloride hexahydrate (FeCl\({}_{3}\bullet\)6H\({}_{2}\)O), ferrous chloride tetrahydrate (FeCl\({}_{2}\bullet\)4H\({}_{2}\)O), 2-methylimidazole, cobalt nitrate hexahydrate (Co(NO\({}_{3}\))\({}_{2}\bullet\)6H\({}_{2}\)O), and ammonium hydroxide (mass fraction 28%) were provided by Aladdin Co. (Shanghai, China). Ethanol and all of the other reagents were of analytical grade and acquired from the Beijing Chemical Reagents Co. (Beijing, China).
# Usability Test for an Over-Ground Walking Assistance Robotic Device Based on the Mecanum Wheel
## 2 Methods
### Usability Test  
This study evaluated usability via a satisfaction survey and one-on-one in-depth interviews. The satisfaction survey was conducted after the participants walked freely for 100 m using the COWAR and OWAR-MW devices. To ensure comparability, the same satisfaction questionnaire was administered to both stroke patients and physical therapists for each device. The satisfaction questionnaire consisted of 21 items distributed across three domains: safety, operability and functionality, and convenience. The safety domain included seven items addressing operational safety and fall risk during device use. The operability and functionality domains comprised seven items evaluating ease of control and functional performance. The convenience domain also contained seven items that assessed overall user comfort during use. Each item was rated on a 5-point Likert scale (1 = very dissatisfied, 5 = very satisfied). A satisfaction questionnaire was developed by modifying and supplementing the usability test guidelines for a walking assistive device, provided by the Support Center for Senior-Friendly Industry of the Korea Health Industry [19]. According to these guidelines, average satisfaction scores were interpreted as follows: \(\geq\)4.5 indicates adequate, 4.0-4.4 indicates improvement required as needed, 3.0-3.9 indicates improvement recommended, 2.0-2.9 indicates improvement required, and \(\leq\)1.9 indicates improvement essential.  
In-depth interviews were used to derive specific improvement requirements for the OWAR-MW device compared to the commercial system (COWAR). Each in-depth interview was conducted within 30 min per participant.
# Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network
## 4 Experimental Results and Discussion
### Cost Effectiveness Analysis  
In this paper, an effective adaptive values acquisition is developed by multilayer perceptron network training, which simplifies and lowers the time required for image processing. The most crucial point is that HE, CLAHE, and Imadjust are not targeted and use a single processing mode, whereas our method can perform advisable contrast enhancement based on the specific features of each light field image. The results indicate that the proposed method can improve the overall contrast of the image effectively. From a cost-benefit viewpoint, our adaptive enhancement algorithm utilizes an MLP network to predict parameter implementation in real time. The architecture is devised with a light structure, which reduces the cost waste, and it can precisely obtain the parameters. The light field image batch processing is realized, which greatly shortens the image processing time.
## 3 Results and Discussion
### Results for Other Face Datasets
#### 3.2.1 Results for EYB and EYB-Dark Face Datasets
These results can be seen in Figure S5. While using the five levels of decomposition and _sym2_ wavelet for MRA-DWT/IDWT, we varied the wavelet family for DWT/IDWT-IF for Experiment 6, i.e., _haar_, _sym2_, _db2_, and _bior 3.3_. The best results (see Figure S6) were also produced using the _db2_ wavelet. The same outcome was previously obtained from the AT&T Face Dataset. Against the challenges of dark images, our proposed method was proven to produce a high result of 96.1% accuracy. This result can be compared against the study in [59] using the same subset of the dataset, producing an accuracy of 95.4% but with the help of the contrast-limited adaptive histogram equalization (CLAHE) method inside the recognition system. Our proposed method performed better compared with the system using a contrast adjustment method.
# An Efficient Parallelization of Microscopic Traffic Simulation
## 2 Architecture and Processes
### Parallel Execution Model  
GPUs are able to execute a vast number of threads in parallel. The software library PyCUDA 2025.1 is utilized to implement the simulator. PyCUDA is a Python 3.10.0 interface to the CUDA API that facilitates low-level and direct memory management of the GPU as well as the programming and execution of the GPU code, called _kernel_[24]. With the abstract CUDA concept, threads are grouped in CUDA blocks. The threads in each block are executed by Streaming Multiprocessors (SMs), which consist of multiple CUDA cores. The SM follows the Single Instruction Multiple Data (SIMD) principle [25]. The CUDA Core is the elementary processor that operates on a single data element, and recent GPUs integrate a large number (typically thousands) of them. Each thread can simulate, for example, a single vehicle or a single TLL.  
The GPU has three types of memory: a _shared memory_ that is fast and can only be accessed by a single SM, a _global memory_ that is slower but can be seen and accessed by all  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Variable** & **Value** \\ \hline Maximum acceleration & \(a\) & 2.5 \\ Desired deceleration & \(b\) & 1.8 \\ Target time headway & \(\tau\) & 1.5 \\ Minimum spacing & \(s_{0}\) & 3.0 \\ Length of the vehicle & \(l\) & 5.0 \\ Acceleration exponent & \(\delta\) & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Used variables for the IDM (according to [19]).  
SMs, and each thread has private _local memory_, but this memory has not been used in the current state of development.  
Important software design aspects are how to distribute the data and how to organize and synchronize the processes such that simulation time is minimized while respecting limitations in terms of memory size, memory bandwidth, and the number of available SMs. These aspects are further detailed below.  
The simulator written in PyCUDA has been integrated into the Python-based HybridPy traffic simulation platform [8], which can already interface with SUMO and MATLAB. This means that existing traffic scenarios can be used for testing and evaluation. Figure 2 gives an overview of the main processes of ParSim as well as the data synchronization process between the CPU and the parallel execution on the GPU. The use of the global memory, thread synchronization issues, and data extraction are further explained below.
# RPKI Defense Capability Simulation Method Based on Container Virtualization
## 1 Introduction
To overcome the existing shortcomings in current containerization technology simulation methods, a network simulation method based on OpenStack and Docker containers, named the SOD-BGP, is proposed. The innovation of this method lies in the introduction of cloud computing platforms to achieve a large-scale network simulation across multiple hosts. This method also uses Docker container technology to achieve the comprehensive simulation of BGP networks and the RPKI while supporting simulation for both IPv4 and IPv6 scenarios. By employing our self-developed simulation node deployment and configuration technology, we have enabled flexible configurations and the automated loading of BGP malicious attack simulation scenarios. Additionally, our custom data collection strategy gathers data from both the control plane and the data plane, ensuring an accurate evaluation of the simulation results. In summary, our main contributions are as follows:  
1. A simulation platform that integrates hardware resources and deploys virtual routing nodes on a large scale is proposed, which combines cloud computing technology and virtualized container technology. 2. The complete framework simulation of the BGP and the RPKI is implemented on the proposed simulation platform, which includes the automatic configuration of dynamic routing protocols and the automatic release of RPKI certificate resources and encryption information. 3. On the basis of malicious BGP prefix hijacking attacks, a data collection and performance evaluation technique is proposed to assess the defensive capability and effectiveness of the RPKI against malicious attacks from both the control and data planes in an experimental environment. The remainder of this article is organized as follows. In Section 2, several related works are introduced. Section 3 describes the framework structure of the simulation method proposed in this article. Section 4 presents an exhaustive description of the framework and technical implementation. Section 5 introduces the data collection and analysis techniques for this experiment. Section 6 presents specific experiments and analyses to validate the simulation method. Finally, Section 7 summarizes the full text and describes future research.
## 3 System Model
### Packet Arrival Model  
In this packet arrival model, a renewal reward process is employed as defined in [33]. Every packet possesses a time of arrival, a UE, priority and deadline, and a packet size. The parameter \(K_{i}\) is taken to be the data packet arriving in the cell at time \(t_{i}\), while \(B_{i}\) is taken to be the time of inter-arrival of the reward renewal process \(\mathcal{B}\), \(t_{i}=t_{i-1}+B_{i}=\sum_{i}^{j}B_{j}\). The size of the packet is denoted as \(S_{i}\), and the deadline is denoted as \(D_{i}\). The time \(t_{i}\) is calculated from the time of arrival to the end of when a packet is successfully delivered. The priority of the packet \(K_{i}\) is represented as \(P_{i}\), and upon a successful packet delivery, the priority becomes the reward, while \(C_{i}\) is the designated UE. The n-tuple representing a single packet with its random parameter is taken as \(K_{i}=(b_{i},\ s_{i},\ d_{i},\ p_{i},\ q_{i},\ r_{i})\). Let \(b_{i}\), \(s_{i}\), \(d_{i}\), and \(p_{i}\) be the realizations of \(B_{i}\), \(S_{i}\), \(D_{i}\), and \(P_{i}\), and let \(q_{i}=t_{i}+d_{i}\), which is considered as the reward. Let \(\hat{f}_{t}^{r}\) represent the set of already processed tasks by the policy \(\sigma\) up to the time \(t\).  
Figure 3: System model architecture.  
The renewal reward process which is a recurrent event process with independent identically distributed interevent times is employed as a strategy to effectively analyze the system's performance. The cumulative rewards function is one of the simplest ways to correlate the performance of various algorithms.  
**Definition 1**.: _The cumulative rewards function for time \(t\) and policy \(\sigma\) can be defined as:_  
\[F_{t}^{\sigma}=\sum_{K_{i}\in I_{t}^{\sigma}}p_{i} \tag{1}\]  
\(Lett=f_{n}\) and \(\hat{t}=f_{n-1}\); then, the rewards difference function is computed as:  
\[\Delta F_{t}^{\sigma}=F_{t}^{\sigma}-F_{\hat{t}}^{\sigma} \tag{2}\]  
The objective is to locate the policy \(\sigma\) that tends to maximize the cumulative rewards function. If the reward is deterministic and \(P_{i}=1\), the cumulative reward function then calculates the number of tasks that were serviced.
## 3 Results  
### Ground Detection Experiment  
In the ground detection experiment, the spectrometer was mounted on the pod of the unmanned helicopter, which was parked on the ground with only the engine turned on without turning the propeller. A container of ammonia water was placed 0.5 m away from the spectrometer, and the ammonia gas volatilized in the air was detected using the spectrometer. We used the RPCA method to extract the absorption features of the measured spectra, and the result is shown in Figure 7. The result shows that the system can detect the chemical gas and correctly extract the characteristic peak at 963 cm\({}^{-1}\) and 930 cm\({}^{-1}\) when the UAV engine is started.
# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 4 PDDQN-HHVBF Protocol
### \"Store-Carry-Forward\" Mechanism  
In this part we will introduce the \"Store-Carry-Forward\" mechanism of AUV. After the AUV data are collected, the data packets will be forwarded hop-by-hop to the Sink node. However, during the transmission of data packets, ocean current movement may cause all candidate relay nodes within the AUV transmission range to drift out of the AUV transmission range, which will lead to failure of AUV data packets transmission. If the AUV continues to re-transmit data packets in this area until the data transmission is successful, it will lead to a large waste of AUV energy. Or, the performance of all candidate relay nodes is very poor. If AUV transmits data to the relay nodes with low Q values, it is very likely that routing hole will occur, which will eventually lead to data packets loss. Therefore, \"Store-Carry-Forward\" mechanism makes AUV not forward data temporarily when it cannot find a suitable relay node, but continue to store and carry the packet and continue to collect new data packets according to the track until it finds a suitable relay node. Then, the AUV forwards all stored data packets to the suitable relay node.  
As shown in Figure 10, the AUV will transmit the data packet1 to the Sink node after collecting the data at the Point A. However, there is no suitable relay node in the AUV transmission range. Then, the AUV activated the \"Store-Carry-Forward\" mechanism to store and carry the data packet 1 to point B. During the movement from Point A to Point B, the AUV continues to detect data, that is data packet 2. After reaching Point B, the AUV starts an operation to find the relay node. If the situation is the same as at point A, it will continue to carry the two data packets to the next forwarding location. But the AUV can find a suitable relay node at point B as Figure 10, the AUV will transmit the two data packets to the relay node together. Then the Sink node will receive the two data packets, data packet1 and data packet 2 sent by the AUV from Point B.