esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 1 Introduction
To solve the above problems, a cooperative multi-objective optimization model of a DC multi-microgrid system (MMGS) including RESs, EVs, and DC/AC converters is established. The goal of the model is to obtain the optimal MMGS economic cost and the network loss of the distribution network. The main contributions of this paper are as follows:  
1. A grid-connected MMGS containing RESs and EVs is constructed, where RESs, EVs, MGs and distribution networks are combined, bidirectional V2G technology is used and the across-time-and-space energy transmission (ATSET) of EVs is thoroughly considered. The effect of the across-time-and-space energy transmission on MMGS economic operation is analyzed to state the potential benefits of cooperative multi-objective optimization. 2. A cooperative multi-objective optimization model is established, including the dynamic economic dispatch of RESs, EVs, DC/AC converters, and the reactive power optimization of DC/AC converters in MMGS. The cooperative multi-objective optimization model consists of two loops. The inner-loop model uses the active power output of RESs, EVs, DC/AC converters as variables, and the daily operating cost of MMGS is used as the optimization objective. The outer-loop model uses the reactive power output of the DC/AC converters as the variable to optimize the network loss of the distribution network, thereby reducing network loss cost and carbon emissionscost. The ultimate goal of the cooperative multi-objective is to obtain the optimal daily economic cost. 3. The concepts of carbon neutrality and carbon peaking are combined. Through the cooperative multi-objective optimization model, the carbon emissions generated by the operation of the MMGS and the distribution network are effectively reduced. The cooperative multi-objective optimization model not only improves the economy but also reduces the total carbon emissions of MMGS and the distribution network.
## 3 Results and Discussion
### Sorption Experiments  
The obtained MIPs underwent a study of the sorption parameters in the case of the sorption of silver. Figure 9 shows the residual concentration of silver in the solution and  
Figure 8: SEM image of MIP(DVB).  
Figure 7: SEM image of MIP(DEGDMA).  
the concentration of silver accumulated in the MIPs. According to the results, it is observed that sorption occurs throughout the entire experiment, and the curves lie too close to each other. After one hour, the MIP(DEGDMA) starts to sorb a slightly higher concentration of silver, but at 150 min, the value becomes constant, and its final concentration is less than is shown for MIP(DVB).  
Based on the residual concentration data (Figure 9), the sorption parameters (sorption degree, sorption capacity) of MIP(DEGDMA) and MIP(DVB) were calculated.  
The sorption degree and sorption capacity of the MIPs are shown in Figures 10 and 11, respectively. The parameters increase with time, and more intense sorption of silver is observed in the case with MIP(DEGDMA). In the first 15 min, 6.39% of silver is sorbed by MIP(DEGDMA) and 6.62% of silver is sorbed by MIP(DVB). After that time, the characteristics of the sorption change, and MIP(DEGDMA) extracts more metal than MIP(DVB) up to 150 min (the difference is approximately 4-14%). The biggest difference is observed at 105 min: 48.56% vs. 34.59%. After 150 min, MIP(DEGDMA) reaches the equilibrium state, while MIP(DVB) continues the sorption of silver up to 180 min, after which equilibrium is reached. The values of the equilibrium sorption degree for MIP(DEGDMA) and MIP(DVB) are presented in Table 4, from which it is seen that after 180 min, equilibrium is reached (control measurements were obtained at 24 h). From the experimental data, it is seen that the sorption efficiency of MIP(DVB) is higher compared with MIP(DEGDMA). It can be supposed that MIP(DVB) has more stable cavities and higher binding capacity with silver molecules, while it has a looser structure. The same patterns are observed for the sorption capacity of MIPs. Also, it can be concluded that the saturation of MIP(DEGDMA) is faster than for MIP(DVB). Thus, the average sorption degree is approximately 72% and the average sorption capacity is 3.6 g/g, which is considered to be quite a productive result for MIPs.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **MIP(DEGDMA)** & **MIP(DVB)** \\ \hline Sorption degree, \% & 66.08 \(\pm\) 0.26 & 78.35 \(\pm\) 0.14 \\ Sorption capacity, g/g & 3.31 \(\pm\) 0.01 & 3.92 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sorption parameters of MIP(DEGDMA) and MIP(DVB) after 24 h.  
Figure 9: Concentration of silver nitrate during sorption in solution (**a**) and in the MIPs (**b**).
## 7 Conclusions  
Low-powered sensor nodes are deployed in various applications due to their cost-effective and easy management. However, resource restriction on the part of sensors harms the performance of real-time applications. Such impacts degrade network delivery and lifetime in the large-scale applications. In addition, due to ad-hoc and insecure communication media, sensor-based networks are open to various attackers by disclosing node data and impacting network openness. This paper  
Figure 5: Impact of the nodes and energy consumption.  
Figure 6: Impact of the nodes and data breaches.  
proposes energy-aware graph clustering and intelligent routing using a supervised system for WSNs. The EGCIR protocol makes the use of graph clustering, which is a machine-learning technique that learns the decisions based on diverse conditions. Moreover, the analysis of the residual energy and time edges to compute the transmission latency improves the route lifetime and energy consumption in data transmission. Furthermore, the secure distribution of keys in the hierarchy, i.e., from the BS to cluster heads and from cluster heads to sensor nodes, leads to avoiding the chances for malicious threats with nominal overheads for large scale networks. The simulation experiments demonstrate that the EGCIR protocol outperforms the existing solutions in terms of various routing and security measurements. However, the EGCIR protocol needs to train the sensor nodes with a neural network-based algorithm to further decrease the processing overheads and improve the energy efficiency.  
Conceptualization, K.H. and S.M.F.; methodology, K.H.; software, A.A. ([PERSON]); validation, I.U.D.; T.S. and A.A. ([PERSON]); formal analysis, A.A. ([PERSON]); investigation, T.S.; resources, K.H.; data curation, S.M.F.; writing--original draft preparation, T.S.; writing--review and editing, I.U.D.; visualization, T.S.; supervision, I.U.D.; project administration, A.A. ([PERSON]); funding acquisition, A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project number RSF-2020/184.  
The authors declare no conflict of interest.
# Statistical Analysis and Modeling for Optical Networks
## 5 Statistical Analysis and Modeling for Optical Networks
### Characterization of Optical Networks
#### 5.1.2 Scale-Free Network  
The degree distribution in scale-free networks follows a power law, meaning that the probability of a node having a specific degree decreases proportionally to a power of that degree. This power law distribution of nodal degrees resembles a rectangular hyperbola. In a scale-free network, the number of connections (degree) a node has is not evenly distributed. Instead, a small number of nodes have a very high degree (acting as \"central nodes\"), while the majority of nodes have a relatively low degree. While the Internet serves as a classic example, optical networks, due to their survivability requirements, deviate from a strict scale-free structure. However, their nodal degree distributions often approximate a power law, leading to their classification as scale-free-like (SF-like). In Figure 3, we show the nodal degree distribution of Germany, which is an SF-like network.  
Scale-free networks have a few central nodes (also known as hubs), which have a disproportionately high number of connections, while most nodes have only a few. This  
Figure 3: Nodal degree distribution of Germany optical network (histogram) is fitted with a power law distribution [87]. It shows the ‘SF-like’ characteristic of this network.  
Figure 2: Nodal degree distribution of vBNS Network (histogram) is fitted with [PERSON]’s distribution [77]. It shows the ‘Random-like’ characteristic of this network.  
uneven connectivity pattern leads to resilience against random failures, as the majority of low-degree nodes can be removed without significantly disrupting the network. However, scale-free networks are highly vulnerable to targeted attacks on hubs, which can fragment the system. Their self-organizing nature and preferential attachment mechanism, where new nodes are more likely to connect to highly connected nodes, drive their formation. This structure enhances efficiency in information flow and robustness in many real-world systems, including the internet, social networks, biological networks, and citation graphs. The presence of hubs allows for rapid communication and efficient resource distribution but also introduces hierarchical organization. Scale-free networks are a fundamental framework in complex systems, shaping dynamics in both natural and engineered networks.
## 1 Introduction  
The Hall thruster is a spacecraft propulsion device that generates thrust via electrostatic acceleration of plasma ions. Figure 1 shows a Hall thruster operating in the High-Power Electric Propulsion Laboratory at the Georgia Institute of Technology.  
Figure 1: Pratt & Whitney T-140 Hall thruster operating with xenon propellant.  
Performance varies across design and power class, but typical thrusters have a demonstrated lifespan on the order of 10,000 h and produce 0.1-1 N of thrust with a specific impulse (total imparted momentum per unit weight of propellant) of 1000-3000 s [1; 2; 3; 4; 5; 6; 7; 8]. These characteristics have made the Hall thruster a leading propulsion option for both satellite station-keeping and deep-space exploration. Hall thrusters have flown on hundreds of spacecraft since the 1971 launch of the Russian METEOR-18; they presently provide orbit corrections for the SpaceX Starlink constellation and are scheduled to serve as primary propulsion elements for both the NASA Psyche and Artemis missions [9; 10; 11].  
The most common variant of the Hall thruster, called the stationary plasma thruster (SPT), ignites and sustains plasma within a discharge chamber composed of ceramic walls. Hall thruster erosion is the gradual removal of ceramic material from the discharge chamber walls by plasma sputtering. Total removal of the ceramic results in exposure of magnetic circuitry components to plasma and effectively ends thruster life [2; 5; 12; 13]. Wall material removal also impacts thruster performance well before the end of life is reached. Thruster life tests have shown that thrust and specific impulse can decrease by 5-8% over the first 1000 h of operation, when the erosion rate is highest [14; 15; 16; 17; 18; 19; 20].  
Erosion is a plasma-material interaction that forms a coupled feedback loop; sputtering alters wall surface properties, which leads to changes in plasma boundary conditions that, in turn, adjust the discharge sputtering rate. Further complications arise due to the high sensitivity of sputtering to both surface and plasma conditions. Despite substantial efforts to experimentally measure and computationally model the process, many aspects are not well understood. The sputtering rate of the ceramic discharge chamber walls has not been adequately quantified, the connection between erosion and performance degradation requires further study, and an explanation of the ubiquitous presence of so-called \"anomalous\" erosion ridges in thruster life tests has remained elusive.  
The purpose of this review is to introduce the reader to the unique challenges presented by plasma-induced Hall thruster erosion and to overview experimental and computational studies on the topic. Section 2 introduces erosion physics, Sections 3 and 4 highlight experimental and modeling efforts, respectively, and Section 5 discusses erosion mitigation concepts. Unless otherwise noted, Hall thruster is used synonymously with SPT in this review.
## 1 Introduction  
In recent years, image-guided percutaneous tumor ablation has become more and more widespread in clinical applications because of its minimally invasive characteristics [1; 2; 3]. Three imaging modalities are commonly used for guidance of intra-operative tumor ablation: computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound imaging (US). CT and MRI provide large or adequate fields of view, and good contrast and resolution, but they are limited by costs, the need to adapt equipment for a magnetic field, availability, and radiation doses in clinical applications. US can provide the unique advantages of low cost, real-time monitoring, and a lack of ionizing radiation, but lacks a large field of view and adequate resolution at increasing lesion depth [4; 5; 6]. It is clear that no single modality is superior in all areas, and in fact, the modalities are often complementary to each other. However, because image-guided tumor ablation is usually performed using physical tools, such as forceps, it would be ideal for the imaging system to clearly and accurately image those tools as they move inside the patient. US can accomplish this task, but other modalities with poor real-time imaging are not as effective.  
Conventional two-dimensional (2D) US is extensively used for a variety of clinical applications in tumor ablation surgery. Although conventional 2D US is a highly flexible imaging modality, its images represent only 2D thin planes of the patient's three-dimensional (3D) anatomy, and the physician must mentally integrate many 2D images to form an impression of the 3D anatomy and pathology. This practice is inefficient, and may lead to inconsistency and incorrect diagnoses and treatment. This problem can be solved by using 3D US. Through 3D US, arbitrarily oriented planes within the patient, including planes inaccessible by 2D US, can be viewed. In addition, 3D US also allows a 3D volume-rendering or surface-rendering view and 3D segmentations of the VOI (Volume of Interest). In recent years, many researchers have attempted to reconstruct 3D volumes or surfaces from conventional 2D ultrasound data. Among the several currently developed 3D ultrasound techniques, freehand 3D ultrasound may easily gain widespread clinical acceptance because it allows physicians to move the ultrasound probe unrestrictedly [7; 8; 9].
## 5 Experimental Results and Analysis
### Data Source and Preprocessing
#### 5.1.2 Section Traffic Flow Dataset  
Calculate the section traffic flow based on the ETC data after initial cleaning, dividing time slices into 1 h intervals. One time slice includes the entire time process from start time to end time. Calculate the number of vehicles passing through the ETC gantry, which is the original traffic flow. Use the vehicle conversion coefficient to calculate the converted number of vehicles and record it as the converted traffic flow. The comparison of traffic flow before and after conversion is shown in Figure 9, where QD1-4 refers to different sections in the Fu-xia section of the expressway.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Field Name** & **Description** & **Example** \\ \hline
1 & TRADEID & Vehicle identification & S0***1 \\
2 & TRADETIME & Transaction time & 1 May 2021 20:00:01 \\
3 & FLAGID & Gantry ID & 3502** \\
4 & OBUID & Device MAC & 66 AD40** \\
5 & ENTIME & Entrance time & 3 September 2020 7:48:39 \\
6 & ENSTATION & Entrance toll station & 46** \\
7 & VEHCLASS & Vehicle type & 1 \\ \hline \end{tabular}
\end{table}
Table 6: Attribute table of ETC transaction data.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Attribute Name** & **Description** & **Example** \\ \hline
1 & EnNodeID & Previous gantry no. & G001535*********=10010 \\
2 & ExNodeID & Next gantry no. & G001535*****=10011 \\
3 & Distance & Distance between & 5236 m \\  & & gantries & \\
4 & LNG & Longitude & 118.56** \\
5 & LAT & Latitude & 24.85*** \\ \hline \end{tabular}
\end{table}
Table 7: Gantry Distance Data attributes.  
Figure 8: Distribution of interval speed status within 24 h. (**a**) The 24 h section vehicle speed data of QD\({}_{340257-340259}\). (**b**) The 24 h section vehicle speed data of QD\({}_{340259-340258}\). (**c**) The 24 h section vehicle speed data of QD\({}_{340258-34025}\). (**d**) The 24 h section vehicle speed data of QD\({}_{340250-34025}\).
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
## 5 Conclusions  
We showed that PINV\({}_{\text{z}}\) outperformed SSRB in terms of resolution for off-axis sources and provided a more homogeneous resolution across the FOV than SSRB or FORE. Furthermore, pseudoinverse methods are fast and can be implemented in real time applications, while other methods such as FORE are not currently fast enough.  
Additionally, 2D-PINV reconstruction can take into account the physical and geometrical information in the SRM, which may result in improved resolution compared to analytical reconstructions, without any impact in the reconstruction time.  
Conceptualization, J.M.U. and J.L.H.; Funding acquisition, J.M.U. and J.L.H.; Investigation, A.L.-M., P.G., J.M.U. and J.L.H.; Methodology, J.L.H.; Resources, J.M.U. and J.L.H.; Software, A.L.-M. and P.G.; Supervision, J.M.U. and J.L.H.; Validation, A.L.-M., P.G., J.C.-G., J.J.V. and M.D.; Writing--original draft, A.L.-M. and J.L.H.; Writing--review & editing, A.L.-M., P.G., J.M.U., J.C.-G., J.J.V., M.D. and J.L.H. All authors have read and agreed to the published version of the manuscript. Part of the calculations in this work were performed in the \"Cluster de Calculo para Tecnicas Fisicas\" funded in part by UCM and in part by UE Regional Funds. We acknowledge the support from the Spanish Government (FPA2015-65035-P, RTC-2015-3772-2, and RTI2018-095800-A-I00), Comunidad de Madrid (S2013/MIT-3024 TOPUS-CM, B2017/BMD-3888 PRONTO-CM), and European Regional Funds. This work was also supported by the EU's H2020 under MediNet, a Networking Activity of ENSAR-2 (grant agreement 654002), and by a NIH R01 CA215700-2 grant. The CNIC is supported by the Ministerio de Ciencia, Innovacion y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). This is a contribution to the Moncloa Campus of International Excellence. We acknowledge [PERSON] (QIMP group, Medical University of Vienna) for his support with the acquisition of the IQ phantom.  
The authors declare no conflict of interest.
## 3 System Model and Architecture Design  
### System Overview  
The proposed system architecture integrates various IoT devices, including sensors, actuators, and user mobile devices that work together to form an integrated SBD2D communication infrastructure, as shown in Figure 1. As shown in Figure 1, the various devices can communicate directly without passing through the cellular base station.  
Figure 1: SBD2D system architecture.
## 4 Results
### GBDT Model Construction and Accuracy Analysis  
#### 4.3.1 GBDT Model Parameter Optimization  
During the parameter tuning process of the gradient boosting decision tree model, the primary focus was on adjusting the number of weak learners (ntree) and the learning rate (shrinkage). The learning rate determines the contribution of each decision tree to the final prediction and is closely related to the number of decision trees. In this study, the learning rate was evaluated within the range of [0.005, 0.010, 0.050, 0.001], while the number of decision trees (ntree) was varied from 100 to 800 with a step size of 100. In order to determine the optimal parameter combinations, a grid search method was used in this study, and the R\({}^{2}\) values on the test set were used as an error criterion for optimal parameter determination. The results demonstrated that the R\({}^{2}\) value increased progressively with the growth of ntree, plateauing once the optimal parameter combination was reached. Following the evaluation of the regression model, the optimal parameters were determined to be an ntree value of 300 and a learning rate of 0.01.  
Based on the optimal parameters, this study utilized the gradient boosting decision tree regression algorithm to construct the AGB estimation model, and the results of model training and validation are shown in Figure 6, with a model training accuracy of R\({}^{2}\) = 0.99 and RMSE = 247 g/m\({}^{2}\) and a model validation accuracy of R\({}^{2}\) = 0.96 and RMSE = 560 g/m\({}^{2}\), which showed that the overall results of the AGB estimation model are good.  
Figure 5: AGB sample set information (**left**) and validation result (**right**).  
Figure 6: Plots of model training (**left**) and validation (**right**) fitting results.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 1 Introduction  
A radar is a system that emits and receives electromagnetic waves (EMW), usually a pulse, and determines the object's distance, location, and size in an aerial environment by using [1]. Systems use techniques such as pulse-echo measurement, phase-difference measurement, and frequency-modulated continuous-wave (FMCW) to obtain those target signatures. The distance to the target usually uses the pulse-echo technique, which measures the time it takes EMW to reflect on the transmitter. By measuring the phase difference, the system can determine the direction of the reflecting object. FMCW is used to determine the range and speed of the target by measuring the frequency shift of the received echo [2]. Radar target tracking uses sensor data to actively acquire target measurements where target motion parameters are performed to estimate the underlying motion rule and predict the target's future trajectory and unknown states.  
Target tracking is a complex task requiring high precision and accuracy due to the dynamic nature of targets, environmental factors, the tracking system's complexity, and the various types of data from many/different sensors that must be processed at a time. Currently, target tracking research focuses on two key areas: enhancing filtering algorithms and accurately characterizing the target motion state. The filtering algorithm plays a vital role in improving the quality of the target state estimation. In contrast, accurately describing the target's motion state is critical for reliable trajectory prediction. Several types of filters are commonly used in nonlinear estimation methods when numerical measurements are available, mainly including the extended Kalman filter (EKF) [2; 3; 4; 5], the unscented Kalman filter (UKF) [6; 7; 8; 9; 10], Particle Filters (PFs) [8; 11; 12; 13; 14; 15], and the cubature Kalman filter (CKF) [4; 16; 17; 18]. Recent research in radar systems has focused on using more advanced signal processing techniques, such as beamforming, to improve the detection and localization of targets.