esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Energy Management Systems in Microgrids
## 2 Energy Management Systems in Microgrids
### Classical Method-Based Energy Management Systems  
The classical methods used in microgrid energy management are based on linear and non-linear programming techniques. Microgrid energy management systems employ centralized controller control architecture and linear or non-linear programming techniques, with an emphasis on microgrid energy resource optimization [19].  
The rule-based and dynamic programming-based microgrid energy management techniques are among the classical methods. These techniques mostly concentrate on trading energy with the main grid and optimizing energy resources. Models of energy management based on a central rule are being developed for the microgrid operating in both the islanded and grid-connected modes. In terms of the microgrid system's voltage and frequency stability, a seamless transition between these two modes is guaranteed [20].  
In research on microgrid control, where a controller manages voltage and frequency, dynamic modeling is necessary to evaluate how control algorithms affect the performance of individual microgrid components. Additionally, a dynamic model is useful in demonstrating regulatory compliance with specific standards, such as Institute of Electrical and  
Figure 3: Different approaches are applied to energy management systems in a microgrid.  
Figure 2: A diagrammatic summary of a microgrid energy management system.  
Electronics Engineer (IEEE) Standard 1547 or local grid code. Software packages are typically used in dynamic modeling to either select pre-made component models or generate unique models from a diverse range of software packages.
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 5 Conclusions  
This paper proposes a 6G V2X multi-user scheduling algorithm based on BD precoding for ultra-massive MIMO systems. Aiming at the high-speed movement and millimeter wave characteristics of the vehicle in the 6G V2X scheme, a time-varying geometric channel model is established. On this basis, the BD precoding is sensitive to channel correlation, and the Pearson coefficient after matrix vectorization is used to measure the channel correlation between users, comprehensively consider the two factors of channel correlation and channel conditions, and ensure that the channel correlation is minimized while ensuring the selection of high-quality channels to achieve multi-user scheduling. BD precoding is then used to eliminate data stream interference. The system simulation results show that the proposed algorithm can effectively reduce the bit error rate and improve the system spectrum efficiency and is suitable for 6G V2X communication. In future work, we will put the proposed algorithm into actual conditions of automatic driving vehicle communication for verification to observe the stability of the algorithm and better evaluate the support ability of the proposed algorithm in 6G networks to the real automatic driving conditions.  
Conceptualization, Y.L.; methodology, J.D., Y.L.; software, J.D.; validation, J.D.; formal analysis, J.D.; investigation, J.D.; data curation, J.D.; writing--original draft preparation, J.D.; writing--review and editing, Y.L., J.D.; visualization, J.D.; supervision, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61501066) and Natural Science Foundation of Chongqing, China (No. cstc2019 cyj-msxmX0017).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
Figure 7: The influence of different vehicle speeds on spectrum efficiency.
## 5 Numerical Tests
### Checking the Linear-Dependence Issue and Stability  
To checking the linear dependence issue, a series examples are given. First, we need to check whether the exact solution is a continue function \(f=1\) on interval \([0,10]\). All the physical patches are not cut. As shown in Figure 4, as the number of physical patches is increased, and the coefficient matrix has a proper rank when the physical patches are not cut. The physical patch numbers are \(2,5,10,20,50,80,160\), respectively. The number of physical patches is equal to that of mathematical patches when no cutting is involved.  
As shown in Figure 5, the artificial discontinuities are added at position points of \(1,2,3,4\), which are only used to cut the physical patches, but the exact solution is still continuous. PPs is equal to rank with the increasing of PPs and unknowns, thus artificial discontinuities will not produce LD problem.  
The next example is to demonstrate the ability of NMM to approximate the discontinuous function which is discontinuous at \(3.5,5,7.5\), as expressed below:  
\[f(x)=\begin{cases}1&x\in[0,2,5)\cup[5,7.5)\\ \frac{1}{2}&x\in[2.5,5)\cup[7.5,10]\end{cases} \tag{17}\]The numbers of initial mathematical patches are \(2,16,32\), and the analytical solution is given in Figure 6. In Figure 6a, all physical patches are not cut, and in Figure 6b, the physical patches are only cut at \(2.5\), and that of Figure 6c are cut at both \(2.5\) and \(5\), and that of Figure 6d are cut at \(2.5,5,7,5\), showing that the NMM approximation has guaranteed the exact solution. We can see if the physical patches are not be cut, only locals are influenced. In Figure 7, the initial MPs is \(2,16,32\), it shows that although the physical patch is not be cut, the number of physical patches is equal to the rank, with no linear dependence incurred. So, the robustness is very well.  
Figure 6: As the number of cuts increases, the function approximation becomes more and more accurate. When the number of discontinuities is equal to the number of cuts, the function in the figure can be accurately approximated. The discontinuities of the function are at \(2.5,5\) and \(7.5\).  
Figure 7: As the number of cuts increases, the number of physical patches is always equal to the rank of the matrix, thus avoiding the problem of linear correlation causing equations that cannot be solved. Using the method in this article, the robustness will be better.
## 2 Literature Review
### Zigbee
#### 2.1.2 Applications of Zigbee  
Zigbee has various applications in different fields, in the medical field it can help to collect monitor the critical health parameters, such as body temperature, heart rate and blood pressure etc., using a wearable Zigbee device for at-home patients, as shown in Figure 2. In home automation, this technology can help to develop smart homes, to provide extreme comfort and security to the users, such as Wireless device control systems, smart alarms, smart refrigerators, and so on. WSNs can help to collect the weather information, traffic density, air quality, etc.
# On the Communication Performance of Airborne Distributed Coherent Radar
## 1 Introduction
Based on the above research status and analysis, this paper aims to build a channel model which is suitable for ADCR communication system and covers a wide range of factors. Meanwhile, this paper is also devoted to deriving the closed-form expression of OP under this channel model. Numerical results will verify the rationality of the theoretical expressions. The main contributions of this paper are as follows:  
1. A channel model suitable for ADCR communication systems under coherent conditions is constructed. Compared with the existing research on communication performance analysis, our proposed model features two innovations: First, the load of radar UAV and communication UAV is limited, which leads to the performance error of their power amplifiers. We use the model based on noncentral chi-squared distribution to express their transmission power error, and cascade it with other random variables in the scenario, such as multi-path fading, antenna pointing error, etc., which makes up for the shortcomings of the existing work. Second, considering the coherent detection of target by radar UAV, the communication signal can also be directly accumulated at the communication UAV. This performance analysis mode of radar-communication cooperative optimization is rarely seen in the existing research. 2. By approximating the Rician distribution and the noncentral chi-squared distribution, the analytic expression of OP at the ground base station is derived. Noncentral chi-squared distribution and Rician distribution are usually expressed in the form of modified Bessel function, which is not conducive to cascading with random variables such as multi-path fading. Therefore, we use an approximate expression to represent noncentral chi-squared distribution and Rician distribution respectively. 3. The rationality of the proposed closed-form expression is verified by numerical simulation. The rest of this paper is organized as follows: in Section 2, the communication system model based on ADCR is established, including the topology of the communication system, statistical model of errors and fading. On this basis, Section 2 derives the analytical expressions of the OP at the ground base station. The corresponding numerical simulation results are presented in Section 3, which proves the correctness of the theoretical derivation. Section 4 summarizes the full paper and analyzes the work in the future.
# Studying Semi-TCP and Its Application in Marine Internet
## 4 Experimental Simulation and Analysis  
Throughput and delay are important network performance indicators and are used as the main criteria in the performance testing of Semi-TCP. The average throughput of the network intuitively indicates the transmission performance of the network. Increasing the throughput is the most important for the network optimization. Packet delay refers to the time interval from when the packet is sent by the sending node to when the destination node receives the packet. It includes queue delay, transmission delay, and propagation delay. In the statistics of the EXata simulation, the above parameters will be directly given to facilitate observation and comparison. EXata is a network emulator that lets one evaluate on-the-move communication networks faster and with more realism than any other tool. It uses a software virtual network (SVN) to digitally represent the entire network, the various  
Figure 4: The processing flow of a node receiving a control frame that does not belong to itself when TET is off.  
Figure 5: The processing flow of receiving a control frame that does not belong to itself when TET is on.  
protocol layers, antennas, and devices. The system can interoperate at one or more protocol layers, with real radios and devices to provide hardware-in-the-loop capabilities [20].
## 3 Results
### Elucidation of the MSP-RT-Abundant Compound Structure by Nuclear Magnetic Resonance Analysis  
To determine the structure of the main extraction product contained in an MSP-RT crude polysaccharide extract in solution, \({}^{1}\)H and \({}^{13}\)C NMR spectra have been measured (see Supplementary Materials) and revealed a 4-\(\alpha\)-rhamnopyranosyloxy-benzyl glucosinolate structure. The \({}^{1}\)H and \({}^{13}\)C NMR resonances were assigned unequivocally based on the combined information from the 1D and 2D NMR (gCOSY, gHSQC, and gHMBC) experiments. Coupling constants (\({}^{1}\)H-\({}^{1}\)H) were measured directly from resolution-enhanced 1D spectra and confirmed, when necessary, by homo-decoupling. gHSQC and gHMBC analysis allowed the assignment of the X regiochemistry. The chemical shift assignments in the proton/spectrum of the investigated compound were the starting point for the further analysis of the \({}^{1}\)H and \({}^{13}\)C NMR spectra. The proton \({}^{1}\)H NMR of MSP-RT in the water solution is shown /in Figure S1 (see Supplementary Materials). The signal 1.25 (\({}^{13}\)C: 18.11 ppm) originated from the methyl group, and the signal 4.13 ppm (\({}^{13}\)C: 38.88 ppm) from CH\({}_{2}\) residue. The signals in the proton spectrum lying in the 2-6 ppm (in \({}^{13}\)C: 70-100 ppm) range were caused by protons (and carbons) of pyranose rings. Other signals (1H: 7.18 and 7.39 ppm; The \({}^{13}\)C NMR of X is shown in Figure S2 (see Supplementary Materials). The signals 118.94 ppm, 130.84 ppm, 130.80 ppm, and 156.15 ppm were from the phenyl ring. The chemical shift of the carbon that came from the C=N group was 163.63 ppm. In the spectrum, there were also visible signals of unidentified compounds, but these were products occurring in small quantities. The structure of the abundant compound is presented in Figure 7.  
Figure 6: Effect of _Møringa_ seed polysaccharide obtained at room temperature (MSP-RT) on % confuency (**a**) and cell count (**b**).  
Figure 7: The structure of the main compound in MSP-RT extract: 4-\(\alpha\)-rhamnopyranosyloxy-benzyl glucosinolate (for NMR data, see Supplementary Materials).  
In the mass spectrum of MSP-RT, crude polysaccharide extract peaks at \(m/z\) (ESI TOF MS ES-) 570.0203 were observed (Supplementary Data). The NMR and mass analyses confirm that the main product was 4-\(\alpha\)-rhamnopyrananoyl-benzyl glucosinolate.
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 4 Implementation  
With an eye towards examining the suitability of the proposed ARIBC scheme, which is an amalgam of relatively new IBC schemes and well-known, time-tested IT-security technologies, to real-world applications, we developed a proof-of-concept implementation. By leveraging the algorithms in RFC6507, we developed code that implements (a) the creation of the key-pair (KSAK/KPAK) of the KMS; (b) the extraction of the key-pair (SSK/PVT) of a new user; (c) the signing of a message; and (d) the verification of the  
Figure 4: ARIBC encryption and decryption processes.  
signature. To this end, instead of using a rather complicated and insufficiently documented open-source implementation of the Sakai-Kasahara scheme that is available in [49], we chose to write our own code. The code makes use of cryptographic libraries provided by the \"Legion of the Bouncy Castle\" (www.bouncycastle.org (accessed on 8 January 2021)) with an MIT-type license. Our code is validated by setting the values of the cryptographic parameters equal to those of the test data specified in RFC6507 and comparing the results. Additionally, we followed the RFC6507 recommendation to use curves and base points defined in FIPS 186-4 [50].  
The main elements of our implementation are the following:  
* Setup of a custom Key Management Service (KMS).
* Definition of the accepted format for identifiers for the authority, and a custom method for deriving these from the users' public IDs.
* PC with the following specifications: Intel (r) Core (tm) i7-5600U CPU @ 2.60 Hz, RAM: 16 GB and OS: 64-bit Windows 10 Pro.
* Custom Java code that implements the algorithms in RFC6507.
* Security parameter n = 256 bits.
* The NIST P-256 elliptic curve, (p256r1 variant)
* The NIST-recommended generator \(G\).
* Hashing with the SHA-256 algorithm (as defined in FIPS 180-4 [48]).
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 1 Introduction
Another challenge of VCS, especially for the video reconstruction process is the complexity of the mathematical formulations handled by the reconstruction system. For the sake of simplicity, video recovery techniques can be classified into two main categories: Optimization based algorithms, categorized also into convex and greedy algorithms, and Deep Learning methods. Sections 2 and 4 introduces the main approaches used to reconstruct the main video scenes from the compressed measurements. On the one hand, we clearly notice that iterative based approaches have high complexity (from few seconds to few minutes to recover an image). However, these techniques are not applicable for real-time applications. On the other hand, Neural Networks (NN) are applied in our topic of interest: the optimization of the transmission and reconstruction of video signals in wireless sensor networks. Neural networks have shown excellent performances in terms of quality of image reconstruction and reconstruction processing time (in the order of milliseconds). This makes the NN approach a good candidate for real-time applications of video-monitoring in a smart city context. Thus, this paper aims at better characterizing and comparing existing state of the art NN reconstruction based methods. The remaining of the paper is organized as follows: Section 2 presents an overview of the principles of compressive sensing. In Section 3 we present different image compressive sensing architectures, whilst Section 4 discusses different video compressive sensing sampling and reconstruction architectures while classifying them based on their sampling strategy. In Section 5 we classify recent deep learning-based video compressive sensing algorithms according to their modulation strategy. In Section 6, we provide recent research results with an experimental study on several VCS approaches to compare their performances in terms of the quality of their output and the testing time. Section 7 discusses the future research challenges and opportunities of compressive sensing. Section 8 eventually concludes the paper by identifying open research challenges and pointing out future research directions.
## 3 Method  
The cost of multi-beam LiDAR has always been a significant obstacle to the widespread adoption of autonomous driving and mobile intelligent robots. The point clouds captured by low-cost LiDAR are typically sparse and have low resolution, which limits the performance of deep learning models and makes data annotation more challenging. Furthermore, we consider that the collection of point clouds comes from moving autonomous vehicles or mobile intelligent robots, which means that the point cloud data with spatial information also possesses temporal information. Our objective is to generate stable and accurate dense point clouds of road scenes from consecutive sparse point clouds with temporal features. To achieve this, we propose a TSE-UNet model designed for upsampling the point cloud from low-cost LiDAR, specifically for the task of point cloud super-resolution.  
The entire pipeline of the proposed method is illustrated in Figure 1. Firstly, datasets for training and evaluating the model performance are collected on both the CARLA simulator and our real-world platform, including CARLA32-128 (with 128 laser beams, RS-128) and Ruby32-128 (with 128 laser beams, RS-128). Subsequently, we transform the point cloud super-resolution task from a 3D spatial interpolation problem to a 2D image super-resolution problem, reducing the computational complexity of the deep learning model and enabling real-time application. Specifically, based on coordinate mapping, we project 3D sparse point clouds onto 2D projection sequential images and then encode the 2D images into feature maps using transposed convolution layers. Following this,we process the feature maps using the proposed TSE-UNet model. This model, built upon the encoder-decoder structure of UNet, takes multiple consecutive frames as input, representing multiple consecutive feature maps. We combine feature maps from two adjacent frames, enhance important features temporally using channel attention modules, and reinforce spatial correlations between adjacent frames using spatial attention modules, providing more accurate object localization and semantic information. The encoder further increases spatial receptive fields and downsamples features through dilated convolution layers, progressively extracting high-level semantic features. The decoder starts from the highest-level semantic feature map of the encoder to restore low-level semantics, ultimately outputting the super-resolution prediction for the current frame. Finally, the obtained prediction results are mapped back to 3D space through reverse projection. Subsequent sections provide detailed explanations of each part of the pipeline.
# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
## 5 Conclusions  
This study indicated that groundwater level assessment is achievable by combining the seasonal rainfall outlook approach and hydrological model simulations. The model calibration revealed that both hydrographic and error indicators verified its good performance. The validation results confirmed that these parameters are practical for subsequent seasonal assessments and appropriate for in situ conditions. Thus, this study demonstrated a stable and good performance of the modeling results. It also indicated that the groundwater level can be accurately obtained as long as the rainfall inputs are accurate. Thus, the method proposed by this study was confirmed to be feasible. For example, the rainfall results yielded by WGEN in terms of the 25 th and 75 th percentiles of simulated rainfall of 2018 were within 2149 and 3056 mm at the Guanxi station and within 1227 and 1714 mm at the Xinpu station. The annual rainfall at the Guanxi station (2031 mm) hit the interval (1921-3285 mm),while that at the Xinpu station (1295 mm) was within the range of the 25 th and 75 th percentiles (1193-1852 mm), even though the accuracy rate for the 3 month seasonal forecast had slight errors. However, the discrepancies in terms of groundwater table were more extensive in the dry season than in the rainy season. The groundwater simulations revealed overestimated results because the amount of monthly rainfall was far lower than the average of the historical record in some dry season months. However, the amount of rainfall returned to normal during the wet seasons, which provided results corresponding with seasonal forecasts and observations. These discrepancies might have been due to anthropogenic activities, such as unregistered pumping. Overcoming this in future work will require more measurement data.  
[PERSON] conducted the simulation and implemented data analysis. [PERSON]-[PERSON] conceptualized and designed the study, implemented data analysis, plotted figures, and wrote the paper. All authors read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, under Grant no. MOST 108-3114-M-008-001.  
The authors would like to thank [PERSON] for providing the program to generate seasonal rainfall.  
The authors declare no conflict of interest.