esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multilevel Switched Capacitor Inverter with Reduced Components and Self-Balance
## 1 Introduction
Only twelve transistors are employed and there are five complementary switch pairs, which simplifies the control strategy;
3. The capacitor voltage is self-balancing without the need for extra loops;
4. The proposed SCMLI is capable of extending to acquire more voltage levels and a higher boost factor;
5. A low-value cost function (CF) can be obtained in the topology. The remaining sections are organized as follows: Section 2 gives the structure of the proposed 13-level switched capacitor boost inverter, and its working states at different levels are also introduced. Section 3 analyzes the power losses and gives the parameter calculation of the multilevel inverter. Section 4 introduces the topology extension and comprehensive results of comparisons with other MLIs. Section 5 presents the simulation and experimental platform to verify the validity of the 13-level inverter. In the end, conclusions are given in Section 6.
# A Study of Order Modification for Reactive Power Support by Wind Farm during Communication Failure
## 3 Case Studies
### Simulation Results
#### 3.2.4 Summary of Results  
From the simulation results, we can observe improvements in response time in the event of communication failure. Table 4 shows the numerical results. The non-applied cases show a constant reactive power supply. In particular, since case A includes feedback control, the reactive power supply quantity corresponds exactly with the TSO order. Although the reactive power supply quantities from the modification processes are not equal to case A, the wind farm is able to increase the quantity, without additional compensation devices.  
Within a small number of iteration processes, the self-modification method finalizes the reactive power supply quantity, and handles the PCC voltage in the rated condition.
## 3 Results
### Climate Factor Response Analysis
#### 3.4.2 Interannual Variations in Climatic Factor Correlations  
Using the 16-day scale GPP data and climatic factor data, correlation calculations were performed for each year from 2001 to 2020. These correlations were then arranged along the elevation intervals. To better illustrate the interannual variations, a 3D plot was employed for visualization.  
Figure 9a shows the distribution of precipitation correlation with elevation for the years 2001 to 2020. Firstly, from an overall perspective, the trend of precipitation correlation across different years with respect to elevation changes is generally similar: it initially increases with elevation, then experiences a certain degree of decline, and subsequently rises again. This pattern is consistent with the trend of precipitation correlation calculated using the entire 16-day scale dataset, as described above. Secondly, in terms of interannual variation, the precipitation correlation with elevation in most years remains at a relatively high level, particularly after 2009, with only slight differences at peak values. In some years, the correlation is at a lower level, which could be related to the precipitation conditions and plant growth in those years. Between 2001 and 2009, the correlation exhibits greater  
Figure 8: Correlation between GPP and climatic variables at different temporal scales (including 16-day scale, seasonal scale, and annual scale). (**a**) Correlation between GPP and precipitation. (**b**) Correlation between GPP and temperature.  
fluctuations, which could be associated with local ecological management and changes in climatic conditions.  
Figure 9b illustrates the distribution of temperature correlation with elevation for the years 2001 to 2020. Firstly, from an overall perspective, the trend of temperature correlation across different years with respect to elevation changes is generally similar: it increases with elevation, reaches a peak, and then stabilizes gradually. It reaches a first low value around 1300 m, rises again, reaches a second low value around 2800 m, and then attains a second peak. This pattern aligns with the trend of precipitation correlation calculated using the entire 16-day scale dataset, as described above. Secondly, in terms of interannual variation, the correlation in most years remains at a relatively high level, with minimal differences between years, indicating a relatively stable state. The correlation in 2007 is lower than in other years, which is possibly due to the significant temperature deviations in that year compared to others. Overall, the temperature correlation shows little variation between years and consistently maintains a high level.  
Figure 9: Interannual variation of the correlation between GPP and climatic variables from 2001 to 2020 in northwestern Hubei (where the \(x\)-axis represents the years, the \(y\)-axis represents the elevation, and the \(z\)-axis represents the mean correlation). (**a**) Correlation with precipitation. (**b**) Correlation with temperature.
## 6 Numerical Results
### Discussion and Future Research: Model Modifications  
Expanding this model to encompass additional slices results in a stochastic process characterized by states \((m_{1},m_{2},\ldots,m_{K-1},n_{1},n_{2},\ldots,n_{K})\), with \(K\) denoting the total number of slices. However, this expansion necessitates the derivation of the optimal policy to manage resources effectively. The efficiency of resource allocation thus becomes a critical concern. The formulation of the optimal resource allocation strategy, represented by \((m_{1},m_{2},\ldots,m_{K-1})\), is essential to enhancing system performance. As the number of slices grows, so does the complexity of the state space and the array of potential policies, potentially leading to increased computational demands and iteration counts to reach a solution. Moreover, selecting an appropriate initial policy becomes more challenging. It is crucial to acknowledge that with a rising number of service providers, the foundational algorithm previously discussed cannot be directly implemented. But the idea from paper [50] could be used.  
Addressing the limitations of our system model, we incorporate elastic traffic, which assumes that data volume requirements are parameters, whereas real-time streaming traffic relies on user-determined service durations. In such a case, the transition rate matrix, \(q(\mathbf{s}^{\prime}|\mathbf{s},a)\), would require modification. Regarding IoT services, the applicability of our model is contingent upon the specific service type. For instance, if the service involves sensors transmitting data where volume--not transmission time--is important, then our model is applicable.  
Beyond the Poisson arrival process, alternative processes such as MAP and BMAP offer more complex structures that enable queuing systems to adjust to variable traffic  
Figure 11: Resource utilization coefficient. (**A**) Baseline algorithm. (**B**) Optimal policy.  
Figure 12: Metrics for baseline algorithm. (**A**) Probability that if a signal arrives, this results in resource reallocation. (**B**) Resource utilization coefficient.  
loads dynamically. It is pertinent to note that while Poisson processes represent a simpler flow type, MAP can be viewed as a subset of BMAP; BMAP allows for multiple simultaneous arrivals.  
This model can also be examined under dynamic conditions within a stochastic environment. For example, considering a controller's operational status--functional or non-functional--within the framework of an unreliable system is pertinent. To account for factors such as communication channel conditions or the distance between a base station and a user, one must employ resource queuing system methodologies.
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 2 Background  
### Review Methodology  
As mentioned in the introduction, we used a keyword-based search and selected work published mostly during the past twenty years, from 2000 to 2020. We performed the search of the most used scientific research databases which cover research from the field interesting for this review: Scopus, IEEE Xplore, Web of Science (WoS), and Google Scholar. We used keyword-based searches to identify and filter relevant articles based on appropriately selected keywords (traffic congestion; multisource data fusion; data fusion, traffic flow modeling; congestion estimation; traffic state estimation). Some older sources were also mentioned to provide the information of the previously developed methods. The main part of this research includes 30 papers which are thoroughly discussed, and their methods and results are summarized in the following sections.
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 4 Results
### Batch Size  
The number of samples that are processed during one training iteration is determined by the hyperparameter known as batch size. The method will process the first 16 photos (from the first to the 16 th) if the batch size is 16 samples, for instance. The algorithm will then obtain further 16 images (from positions 17 to 32), and the second iteration will continue with the same process until all of the images are processed for a particular epoch operation. A performance comparison between the different batches is shown in Table 5. A batch size of 16 for the 3000 training images and 600 testing images produces accuracy of 86.09%, which is higher than that of 84.01% for 2200 and 1200 training and testing images.
## 4 Recent Progress in Microencapsulation Techniques for Natural Bioactive Compounds
### Ionic Gelation  
It is a process of the formation of complexes while mixing a charged biopolymer with anionic salt. The process of gelation can be external or internal [7] and both do not have a major influence on encapsulation efficiency. The external gelation forms when core material and charged biopolymer drops are filled into an anionic salt solution with the help of extrusion, a syringe needle, or electrospinningning [7; 128; 98]. The process can also be accomplished by first incorporating an ionic solution into the core materials and a charged biopolymer dispersed in a water-in-oil emulsion [7]. Under the outer gelation, the outer layer of the encapsulate comprising droplets of core materials is hardened by an anionic agent, which provides protection to the inner core materials by forming cross-links. In the case of internal gelation, the core materials, encapsulants, and anionic salt solutions are mixed together and continuously mixed with an emulsion to obtain the desired size of the encapsulates [7].  
The stability and structure of microstructures in ionic gelation are determined by the appropriate concentration of anionic salt and biopolymer, core load, extent of cross-linking, and drying conditions. The improper conditions during encapsulation may compromise the stability and structure of microstructures, such as being cracked, porous, rough, shrunken, etc., due to poor cross-linkages, resulting in imperfect microstructures with poor encapsulation efficiency [128; 129; 130]. This technology works on the cross-links of polyelectrolytes on the addition of multivalent cations formed during extrusion, gelation, or emulsification [88]. [PERSON] et al. [131] reported this method as the most commonly used technology to produce gel particles by taking aqueous polymer solution and dripping it through a syringe nozzle or into a gelling bath with calcium chloride. The alginates, konjac flour, chitosan, carboxymethyl cellulose, and pectin are some commonly used polymers used for the formation of cross-linked gelling systems, with alginate as the most commonly used, especially in the controlled release of active ingredients and enzyme immobilization, amongst all these due to its edible, biodegradable, and gelling attributes [96; 132]. The betalain-loaded microcapsules produced by using external gelation technology utilizing sodium alginate and bovine serum albumin as shell materials has been noticed to exert antioxidant effects after 25 days of storage [40]. Internal gelation or emulsification has a higher entrapment of active ingredients as compared to external gelation or extrusion.  
The main advantages of this technique are the low polydispersity and the high encapsulation efficiency [25].
## 4 Experiments and Results  
### Datasets and Training Settings  
Five public colonoscopy image datasets are used for the study, including CVC-ClinicDB [29], Kvasir [30], CVC-300 [31], CVC-ColonDB [32], and ETIS-LaribPolypDB [33]. In this study, the Kvasir dataset was divided into training, validation, and testing sets in an 8:1:1 ratio, while the CVC ClinicDB, CVC-300, CVC-ColonDB, and ETIS-LaribPolypDB datasets were used as the testing sets. Five representative models, UNet [3], ResUnet [34], PraNet [7], CaraNet [35], and Polyp-PVT [19], were selected and compared with the model proposed in this paper.  
The experimental framework utilized PyTorch 1.13.1 with GeForce GTX 3060 12 G GPU. The configuration standardized input images at 352 \(\times\) 352-pixel resolution, maintained a batch size of 8 samples per iteration, and implemented 100-epoch maximum training cycles with a multi-scale training strategy similar to PraNet. Initialization employed ImageNet pretrained weights for the backbone network, coupled with gradient clipping at 0.5 threshold to stabilize training dynamics. The AdamW optimizer operated with uniform hyperparameters: \(1\times 10^{-4}\) learning rate and \(1\times 10^{-4}\) weight decay coefficient.  
The image segmentation performance of models were evaluated through five complementary metrics: the Mean Dice Coefficient (mDice) quantifying spatial overlap accuracy  
Figure 4: Structure of CBAM.  
between predictions and ground truth; the Mean Intersection over Union (mIoU) assessing pixel-level alignment precision; recall (sensitivity) evaluating true positive identification completeness; precision determining the reliability of positive predictions; and the F2-score prioritizing recall while maintaining precision-recall equilibrium to adaptively balance performance under diverse clinical scenarios
## 1 Introduction
Notably, many nonlinearities can be represented as varying parameters that depend on endogenous signals, for example, states and inputs [17], which broadens the applicability of the design methodology to nonlinear plants. Examples of successful applications of the LPV paradigm are--wind turbines [18], vehicles [19; 20] and drones [21]. As in Reference [13], the proposed approach is obtained using quadratic Lyapunov functions with constant matrices and, therefore, the results might be somehow conservative when compared to other types of Lyapunov functions, for example, parameter-dependent [22] or piecewise [23], which lead to more complex mathematical calculations and are beyond the scope of this paper. The final design procedure is developed using the theory of ellipsoidal invariant sets [24]. It consists on a linear matrix inequality (LMI)-based feasibility problem, which can be solved efficiently using available solvers (the reader is referred to Reference [25] for a tutorial on the application of LMIs to LPV analysis and design problems). This paper is structured as follows. In Section 2, the problem statement is introduced. In Section 3, the procedure for controller design with constant saturation is given. In Section 4, the proposed methodology is adapted to the case of time-varying input saturation. Section 5 presents an illustrative example with simulation results. Finally, Section 6 summarizes the main conclusions and discusses possible future work.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 3 Results
### Co-Citation Analysis  
#### 3.4.1 Reference Co-Citations Analysis  
The co-citation evaluation provides a detailed picture of authoritative figures often cited in other works, indicating their impact on the vehicle security research ecosystem [31]. Figure 8, generated using VOSviewer, shows the analysis of reference co-citations. It is also the case that [PERSON] dominates this dataset with a staggering 940 co-citations and 9640 total citations, significantly outpacing others' contributions, including [PERSON]'s, who follows at 4211 and [PERSON] with 4054, suggesting their pivotal contributions to such works indeed lie in security protocols within vehicular networks. Such numbers indicate that his research is one of the primary engines driving innovation in their industry. [PERSON] is also notable, as are [PERSON] and [PERSON], who each accumulate these impressive figures of over 600 co-citations, hinting at broad domains of influence. [PERSON] stands out with an unparalleled total citation count of 6101 paired with 613 co-citations, underscoring the dire importance of some of his single-authored publications. These authors often serve as a cornerstone of models, frameworks, or experimental works that are customarily cited. These researchers' strong ties in co-citation imply that they construct a focal point or center of gravity in the in-vehicle networks and cybersecurity paradigm, shaping ideas and practical interventions for connected car technologies.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Article Name** & **Journal/Conference Name** & **Author** & **Year** & **Number of Citations** \\ \hline IEEE 802.11 ad-Based Radar: An & _IEEE Transactions on Vehicular_ & _IEEE Transactions on Vehicular_ & [26] & 2018 & 556 \\ Approach to Joint Vehicular & _Technology_ & & [26] & 2018 & 494 \\ Communication-Radar System & _IEEE Transactions on Vehicular_ & & [19] & 2019 & 485 \\ Deep Reinforcement Learning for & _Technology_ & & [24] & 2018 & 396 \\ Offloading and Resource Allocation in & _IEEE Transactions on Vehicular_ & & [23] & 2018 & 336 \\ Vehicle Edge Computing and Networks & _Technology_ & & [28] & 2020 & 312 \\ Sensor technologies for intelligent & _Technology_ & & [25] & 2018 & 271 \\ Transportation systems & _Technology_ & & [29] & 2019 & 260 \\ Transportation Systems & _Technology_ & & [9] & 2020 & 240 \\ PA-CRT: Chinese Remainder & _IEEE Transactions on Dependable_ & & [30] & 2019 & 240 \\ Theorem-Based Conditional & _IEEE Transactions on Dependable_ & & [30] & 2019 & 240 \\ Privacy-Preserving Authentication & _Technology_ & & [30] & 2019 & 240 \\ Scheme in Vehicular Ad-Hoc Networks & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Highly cited publications.
# Influence of Right Triangular Prism Rough Beds on Hydraulic Jumps
## 3 Results and Analysis
### Effects of Right Triangular Prism Rough Beds on Jump Length Ratio  
The stilling basin's length depended on the jump length, which determined the channel bed's reinforcement range to prevent erosion during flood drainage work operations. Based  
Figure 8: (a) predicted versus numerical results of \(y_{2}/y_{1}\); (b) \(y_{2}/y_{1}\) versus \(Fr_{1}\) for rough beds.  
Figure 10: (a) \(y_{2}/y_{1}\) values versus \(Fr_{1}\) for this study and other works; (b) \(D\) values versus \(Fr_{1}\) for rough beds [1; 2; 4; 5; 7; 8; 9; 13].  
Figure 9: \(y_{2}/y_{2}\) versus \(t/y_{1}\) and \(Ls/y_{1}\) for all rough bed models.  
on the water surface profiles and velocity field in all numerical simulation results, we could define the jump length \(L_{j}\) as illustrated in Figure 11 below:  
Through the jump length data described in Table 7 and combined with the MLR method and Equation (2), this study found Equation (13) with a high correlation coefficient of 0.89.  
\[\frac{L_{j}}{y_{1}}=3.385 Fr_{1}-2.564\frac{t}{y_{1}}-0.452\frac{L_{s}}{y_{1}}+7. 848,R^{2}=0.89 \tag{13}\]  
The predicted results of the \(L_{j}/y_{1}\) parameter by Equation (13) were compared with the numerical simulation results, as shown in Figure 12a. In this figure, the \(L_{j}/y_{1}\) values were predicted quite accurately (error \(<\) 10%) with the \(L_{j}/y_{1}\) numerical simulation values. On the other hand, from Equation (13), the jump length is most dependent on \(Fr_{1}\). It means the supercritical flow with a larger \(Fr_{1}\) needs more jump length to change from supercritical to subcritical flow (see Figure 12b). Meanwhile, the two geometry parameters (\(t/y_{1}\), \(L_{s}/y_{1}\)) have a negligible effect on reducing the jump length ratio, as shown in Figure 13.  
Figure 14a shows the dimensionless jump lengths \(L_{j}/y_{2^{*}}\) versus the upstream Froude number \(Fr_{1}\). This figure indicates that the right triangular prism rough beds strongly decrease the jump length compared with the smooth bed. More specifically, the \(L_{j}/y_{2^{*}}\) value was about 3.0 for initial Froude numbers greater than 6.8 and about 3.2 for initial Froude numbers smaller than 6.8. It means the jump length on various rough beds was approximately 50% shorter than on smooth ones (see Figure 14b). The findings of this study are in good agreement with those of [PERSON] and [PERSON] [1], [PERSON] [7], [PERSON] and [PERSON] [2], and [PERSON], [PERSON] et al. [4]. They are about 3, 4, 3.2, and 3, respectively.  
Figure 11: Describe the hydraulic jump length on rough beds.  
Figure 12: (**a**) predicted versus numerical results of \(L_{j}/y_{1}\), (**b**) \(L_{j}/y_{1}\) versus \(Fr_{1}\) for rough beds.  
Figure 14b also indicates that the M_III and VI models reduce the jump length better than the M_I and II models. M_I model has the lowest effective jump length reduction with a small \(Fr_{1}\).
## 3 Network Architectures and Communication Technologies  
[PERSON] et al. [3] provided a thorough discussion on network architectures and communication technologies for smart public lighting applications, showing their benefits and downsides. Starting with significant activity on research, implementation, and in-field testing, authors also outline the steps required for the deployment of smart public lighting infrastructure, each discussed in accordance with the network topology considered. Furthermore, some additional services that a smart public lighting infrastructure could support and the benefits that would arise from integration with the upcoming 5G cellular network were introduced.  
Regarding the technologies that can be applied, [PERSON] et al. [4] focused their attention on a method to select the optimal set of energy efficiency retrofitting interventions on public street lighting systems. Their methodology, applied in the city of Bari, defines a plan of retrofitting interventions which exploit the initial budget available for the investment as well as financial or environmental benefits and savings resulting from the implementation of energy efficiency measures. This study advanced the existing literature on public street lighting by addressing a new topic, namely the multi-period approach for the development of the optimal plan of energy retrofitting interventions in urban street lighting. Furthermore, authors plan to provide the city authorities in charge of energy efficiency and green energy for public lighting systems with a straightforward tool for defining the optimal plan of interventions, focusing on the lighting system. Indeed, the researchers declared that the merit of the developed approach was its generalizability to different types of optimization criteria. Whilst in the current model they selected the total energy savings as the optimization criterion, an interesting development of the research can be to apply the retrofit decision-making model to optimize other indicators commonly used for assessing the energy performance of road lighting and compare the insights that the model provides in these different scenarios. Their results found that input data frequently suffer from estimation uncertainties, and sometimes even very small errors could make the \"optimal\" retrofit solution irrelevant from the financial and environmental convenience perspective, and that public lighting systems are complex systems where retrofitting interventions produce a variety of consequences, related to economic, energy, environment, and quality of life factors. Authors proposed as further research, the analysis of this issue by extending the analysis to a multi-objective problem.