esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Numerical Simulation of VIV of Deep-Sea Flexible Risers
## 2 Numerical Simulation Method of VIV of Riser
### Method of Fluid-Structure Interaction (FSI)  
Fluid-structure interaction (FSI) occurs due to the coupling between a fluid and a solid structure. The solid structure experiences motion response or deformation under the action of flow field force, while the deformation or motion reaction of the solid structure affects the fluid, causing a change in fluid force at the next moment.  
The vortex-induced vibration (VIV) problem is a classical FSI problem that requires a comprehensive consideration of the interaction between flow field motion parameters and structural motion parameters during analysis. On one hand, the cylinder in the flow field experiences fluid force, causing parameters such as amplitude, velocity and acceleration to change. On the other hand, the cylinder parameters affect the flow field, leading to changes in the flow field motion parameters.  
For quasi-3D FSI solutions based on the slice method, we followed these steps, as shown in Figure 4:  
1. At the initial moment, the riser structure was fixed, and the flow field was calculated. Each flow field slice simultaneously solved the governing equation of the fluid to obtain the fluid force of each slice node;
2. The self-compiled UDF [32] program cylinder.c called the Computer_Force_And_Moment macro to extract the fluid force on the slice node at the current moment and to simplify the fluid force into uniformly distributed loads acting on the finite elements represented by each slice;
3. Uniformly distributed loads acting on the elements were converted into node loads by an equivalent load method. We solved the dynamic equation of the riser structure using the Newmark-\(\beta\) method to obtain the displacement, velocity and acceleration of the finite element nodes at the current moment;
4. The cylinder.c program called the DEFINE_CG_MOTION macro to transmit the displacement and speed of finite element nodes at the slice calculated in the previous step back to Fluent to update the displacement and speed of the cylinder in the slice. We then realized the mesh update in the fluid domain by combining the overset mesh technology;
5. We entered the next time step and repeated until the set total time was reached and the calculation was completed.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 2 Electrical Subsystems in Satellites  
### Unregulated and Regulated Bus Systems for Satellite EPSs and EPS Architectures  
Satellite EPSs can be classified into unregulated bus and regulated bus systems. Figure 5a,b show the two types of bus systems [37]. To reduce the number of components, conversion stages, and cost, and to improve the reliability of the regulated bus system, unidirectional and bidirectional converters are sometimes integrated into a single unit, resulting in a three-port integrated converter. A comparison of a conventional two-port converter and an integrated three-port converter for satellite EPSs was discussed in [38]. The power transfer from the source to the batteries and loads uses either the Direct Energy Transfer (DET) or Maximum Power-Point Tracking (MPPT) architectures [39; 40; 41; 42; 43; 44; 45; 46; 23; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69;  
its reliability. The MPPT architecture is a series bus transfer method that uses a power converter in series between the PV and loads to maintain bus voltage stability. For MPPT, the algorithms used in stand-alone terrestrial applications, such as the Perturb and Observe (P&O), Incremental Conductance (IC), and Ripple Correlation Control (RCC) methods, may be used. Compared to the IC and RCC methods, the P&O oscillates more and draws less power. More power is extracted using the IC method than the RCC method. In contrast to the IC method, the RCC method is smoother and exhibits fewer oscillations [36]. A detailed comparison of the MPPT algorithms for space applications is provided in [56; 57]. S3R and MPPT each offer distinct advantages, so their trade-offs must be carefully considered to make the optimal choice. Moreover, techniques combining the two approaches to leverage their specific advantages are also being studied [52].
# Experimental Detection of Nonlinear Dynamics Using a Laser Profilometer
## 3 Results and Discussion
### Post-Processing Methodology  
The acquired time histories have been post-processed with a time-frequency analysis, using both Fourier transform and Continuous Wavelet transform with the aim of detecting frequency variations in the time evolution, which can be addressed to nonlinear phenomena.
# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 2 Related Work
### Helmet Detection  
Automatic helmet detection is urgently needed in construction engineering and safety driving monitoring. The traditional helmet detection methods focus on the design of the artificial features to lead the classification towards appropriately discriminating helmet from non-helmet targets. The well-known image descriptors such as the local binary pattern (LBP), local variance (LV) and histogram of oriented gradient (HOG) are used to enhance the feature extraction step, and they achieve promising accuracy through a supporting vector machine [22]. The circular Hough transform (CHT) accompanied with HOG descriptor are applied to extract the helmet attributes, and the multilayer perceptron (MLP) classifier is used to perform the final helmet classification [23]. The method combining multi-feature fusion and a support vector machine (SVM) is used to detect and track the helmet in a factory environment to keep an eye on safety production [24]. However, the choosing of manual features is labor-consuming and poor in generalization, which prevents their application. Nowadays, thanks to the development of deep learning and CNN, a large number of modern helmet detection approaches have been proposed. The CNN-based multi-task learning model has been designed for tracking individual motorcycles through identifying helmets [25]. The faster region-based convolutional neural network (Faster RCNN) is utilized to detect both motorcyclists and helmets [26]. The faster RCNN equipped with the multi-scale training and increasing anchors strategies has proved to be capable of detecting helmets on different scales [27]. Taking the processing speed into consideration, YOLO is even more popular. An improved YOLOv3 model has been applied to detect helmets and successfully increased the average accuracy [28]. Replacing the traditional YOLOv3 backbone of darknet-53 with a deep separable convolution structure, the performance of helmet detection has been further improved [29]. Nevertheless, all these models ask for quality stable input images, which is reachable in training set acquisition but difficult to reach in practical terms. In other words, if there is no assurance about input quality, the detection performance will decrease quickly. To the best of our knowledge, few methods have focused on solving the helmet detection problem with poor quality input images.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
###### Abstract  
With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user's requirements based on service, we propose an efficient service placement system named fuzzy-analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.  
distributed cloud computing; service placement; fuzzy system; energy efficiency; resource management; Internet of Things +
Footnote †: journal: Applied Soft
## 4 Algorithms for TSOAP
### The ITLB Algorithm  
The ITLB algorithm builds from the fact that in any schedule, there is a task 'bottleneck' that defines the optimal value achievable for the problem instance. This bottleneck is most likely to be between tasks whose pairwise _gcd_'s are low; for example, a subset of tasks with co-prime periods. The ITLB algorithm works as follows; an initial incumbent solution \(b\) is found using SWAPFIT, and the tasks are then sorted according to non-decreasing harmonic period; tasks with low harmonic period are the most difficult to separate. Then, starting with low \(i\), the first \(i\) tasks are solved to optimality, with backstepping guided by \(b\). The optimal value \(l\) found by this process constitutes a lower bound on \(z\); as the number of tasks is increased above \(i\), it is clear to see that \(l\) cannot possibly decrease. Given the bound \(l\), and a set of offsets _O'_ for the first \(i\) tasks, the algorithm then attempts to schedule the remaining \(n\)-\(i\) tasks using SWAPFIT, such that the optimal value does not increase. If this can be achieved, then a provably optimal schedule has been found; if it cannot, \(i\) is incremented to deepen the search, and \(b\) and \(l\) are updated if a better solution or tighter lower bound have been found, and the process repeated. The ITLB algorithm is shown in Figure 8.  
The call to Solve() on line 8 finds the best solution to the first \(i\) tasks; while the call to Fathom() on line 9 applies the heuristic to schedule the remaining tasks. It can be seen that with every iteration the lower bound is non-decreasing, and the incumbent is potentially improved; the iterations repeat until optimality is proved. In the worst case, every possible offset combination is enumerated and tested, and it is clear to see that the problem is solved exactly. However, it is also clear that the algorithm may be terminated at any point, and the best know solution returned; in addition, the deviation from this solution from the best known lower bound can also be returned. Thus, unlike regular backsteppingalgorithms for this and similar problems, ITLB constitutes a _complete anytime_ algorithm for TSOAP. The following Section presents the performance data for the described algorithms.
## 3 The Results of SBR Latex Mortar Strength Tests
### Compressive Strength of SBR Latex Mortar  
The compressive strength of the mortar specimens mixed with SBR latex at different glass transition temperatures was measured at 7 and 28 days to observe the development of strength over time. The mix inflowing ratios for the SBR latex were varied, and the results are presented in Figure 5.  
As displayed in Figure 5, the mortar mixed with 7% SBR latex showed higher compressive strength at 28 days compared to the non-mixed mortar. However, the mortar mixed with 5% and 9% SBR latex demonstrated lower compressive strength than the non-mixed mortar, indicating that the strength improvement effect was not observed for these proportions. Particularly, the mortar mixed with 9% SBR latex exhibited significantly lower compressive strength due to excessive flexibility.  
The compressive strength of the mortar mixed with Tg +4 \({}^{\circ}\)C SBR latex at a 7% incorporation rate was observed to be the highest, indicating that this mixture ratio achieves high strength. Conversely, the compressive strength of the mortar mixed with Tg \(-16\)\({}^{\circ}\)C SBR latex was found to be lower than that of the non-mixed mortar.  
Figure 6 compares the compressive strength of mortar specimens mixed with 4 \({}^{\circ}\)C SBR latex and \(-16\)\({}^{\circ}\)C SBR latex, both at a 5% mixture ratio. This comparison serves as a control group to examine the impact of the glass transition temperature on compressive strength.  
Figure 5: Compressive strength of mortar specimen mixed with different mixture ratios of SBR latex with two glass transition temperatures.  
Figure 6: Comparison of compressive strength of mortar with 4 \({}^{\circ}\)C SBR latex and \(-16\)\({}^{\circ}\)C SBR latex at 5% mixture ratio.  
The compressive strength of Tg \(-\)16 \({}^{\circ}\)C SBR latex was found to be lower by approximately 5 MPa at 7 days and by about 9 MPa at 28 days compared to Tg 4 \({}^{\circ}\)C SBR latex. The comparison indicates that the glass transition temperature of SBR latex significantly affects the compressive strength of the modified mortar. At a 5% mixture ratio, the 4 \({}^{\circ}\)C SBR latex provides better strength performance than the \(-\)16 \({}^{\circ}\)C SBR latex. This finding emphasizes the need to carefully select the glass transition temperature of SBR latex to match the desired mechanical properties of the mortar.
# A Vision-Based Approach for Autonomous Motion in Cluttered Environments
## 2 Problem Formulation
### Map Building Problem  
In this paper, we use the occupancy grid map to represent the environment; it is the most common method for robotic applications. A grid map discretizes the environment into cells and assigns a probability to each cell. The state of each cell is defined as a discrete random state, occupied or free according to its assigned probability.  
The map-building problem is to build the occupancy grid map of the environment, using the given noisy and uncertain sensor measurement data.  
**Problem 1** (Map-Building Problem): _Given noisy, uncertain sensor measurement data, we build the occupancy grid map of the environment. Each cell of the grid map should be defined as a discrete state of occupied or free to present the occupancy state of the environment._
## 1 Introduction  
### General Context  
Electrical distribution networks in medium- and low-voltage applications provide electricity to all end-users while observing quality, security, reliability, and efficiency criteria [1, 2]. These grids are typically operated at voltage levels lower or equal to 25 kV, mostly using alternating current (AC) technologies. Most of them are built with a tree structure (i.e., radial configuration) that helps to reduce investment costs and the complexity of coordinating protective devices [3, 4]. However, these radial configurations typically increase energy losses when compared to meshed topologies [5], and they deteriorate the voltage profiles (low voltage regulation) for end-users located far from the central substation nodes [6]. Distribution companies typically use shunt compensation methodologies or grid topology modifications to address these technical challenges. In the case of shunt compensation, active and reactive power injection devices are commonly used (dispersed generation [7], batteries [8], capacitors [9], or flexible AC transmission systems [10; 11], i.e., FACTS). In contrast, in the case of topology modifications, phase balancing or grid reconfiguration are common strategies for distribution companies. In the case of power loss reduction or voltage profile improvement, shunt reactive power compensation has demonstrated an adequate balance between saving operating costs regarding energy losses compared to the investment costs associated with compensation systems [9].  
On the other hand, given the transition of electrical distribution networks from passive to active operation schemes [12], the use of fixed compensation strategies [13], such as capacitor banks or grid topology variations, results in low economic savings in comparison with dynamic compensation strategies using FACTS [10].  
The use of FACTS compensation devices in electrical distribution networks has shown positive effects on the technical characteristics of electrical grids, especially in the case of energy losses minimization, with improvements of about 12.63% and 13.97% in the conventional IEEE 33- and 69-bus systems [10]. In addition, for distribution networks in the Colombian context, energy losses can oscillate between 14.5% and 21.8% of the total energy input [10]. This implies that the development of compensation technologies for reducing these losses is essential for distribution companies. In this regard, one of the advantages of reactive power compensation systems is that they are reliable [14] and economical [15], in addition to the fact that they have long useful lives [16].
## 5 Conclusions  
The formation tracking control problems of a group of low-cost underwater drones are subject to nonlinearity, parametric uncertainties, and external disturbances have been discussed and implemented. A complete scenario of the underwater drone operation has been simulated by integrating the formation tracking control term and the collision-obstacle avoidance term. In particular, the use of a neural network technique approximates unknown parameters of UUV, thereby designing controllers to compensate for these parameters. We emphasize here that this paper aims to take the first step for future research to develop an application of multiple low-cost underwater drones.  
Several important issues along this research are to be addressed in the future, such as performing experiments in a pool with image processing algorithms to determine the relative position of the UUV follower and UUV leader, and the integration of some low-cost sensors (i.e., a sonar, an underwater LiDAR) to increase accuracy in determining the distance between UUVs.  
Although the approach is distributed, due to its implementation on the ROV-1 platform, the calculations are still centralized in the implementation aspect. However, with ROV-2 being released, the computations will be distributed completely for each UUV. In addition, we have only studied a group of UUVs on three degrees of freedom (3-DOF). In the future, the research will be extended to 6-DOF, where the effect of depth control of UUVs will be investigated.  
Conceptualization, H.A.P., S.T. and V.H.N.; methodology, H.A.P., T.S. and V.H.N.; software, H.A.P., T.S.; validation, H.A.P., T.S., V.H.N. and V.G.; writing--original draft preparation, H.A.P. and T.S.; writing--review and editing, H.A.P., T.S. and V.H.N.; visualization, H.A.P. and T.S.; supervision, T.S.; project administration, T.S. funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of National Education France.  
Figure 7: An example of the evolution of three BlueROV-1 in robot operating system (ROS)/Gazebo.  
**Acknowledgments:** We would like to express our sincere appreciation for the insightful and valuable comments of the editors and reviewers. Those allow us to clarify and improve the presentation of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis  
Building upon the theoretical framework and resonant constellation design methodology discussed earlier, this study analyzes resonant configurations in three distinct regions--the near-Earth region, lunar region, and combined region--using a four-satellite constellation as an illustrative example. Additionally, taking the optimal configuration in the combined region as a case study, the spatiotemporal distribution characteristics of DOP values for the resonant constellation are systematically investigated.
## 1 Introduction
The introduction of superpixels can suppress the speckle noise while maintaining the statistical and texture features of the image and utilizing context information [23]. Considering that the SLIC algorithm can generate compact, nearly uniform superpixels and that the algorithm is simple and computationally efficient, superpixels have been successfully applied to the segmentation of PolSAR images [24], the classification of features [25], or to optimize the results of PolSAR classification [26]. Therefore, this paper proposes a novel building extraction approach from a PolSAR image with statistical texture and polarization features by using a convolutional neural network and superpixel. This method combines Pauli decomposition parameters and the G\({}^{0}\) statistical texture parameter as the input dataset of CNN, then makes use of the superpixel generated by SLIC to optimize pixel-based initial classification results to reduce the inaccuracy of building boundaries due to noise effects. This paper is organized as follows: Section 2 introduces this method from characteristics and methods, including PauliRGB, G\({}^{0}\) statistical texture parameters, convolutional neural networks, and the generation of superpixels. In Section 3, the accuracy of the proposed method is verified using three types of data, and five building extraction algorithms are compared, which include threshold, SVM, RVCNN, PFDCNN, and our method without superpixel constraint. Section 4 discusses the characteristics of the method and the influence of the parameters of the method. The conclusion is in Section 5.
## 3 Results and Discussion
### Analysis of the Mechanism of Curing Soft Soil
#### 3.4.3 FTIR Analysis  
Seawater-mixed GC, G4, and G7 samples after curing for 28 days were tested and analyzed to study the changes in functional groups when SWC stabilizes soft soil. This analysis aims to deepen the understanding of the stabilization mechanism in soft soil. The test results are shown in Figure 12.  
As shown in Figure 11, the absorption peak of quartz appears near the wavenumber of 779 cm\({}^{-1}\). The absorption peaks of kaolinite and montmorillonite are observed near the wavenumber of 3619 cm\({}^{-1}\). The reduction in the kaolinite and montmorillonite peaks in the stabilized soil indicates that the high alkalinity of DA promotes the dissolution and consumption of active components in soft soil minerals, contributing to the formation of hydration products [24]. The peak at the wavenumber of 3440 cm\({}^{-1}\) is associated with the absorption of -OH groups [29]. The peak near 1630 cm\({}^{-1}\) is due to the bending vibration of -Si-OH bonds in the hardened product and the bending vibration of H-O-H in mineral crystalline water [30]. The decrease in the adsorption water peak in the stabilized soil suggests that some of the water is consumed in the reaction. Compared to GC and G4, the absorption peak at 464 cm\({}^{-1}\) in G7 is attributed to the bending vibration of Si-O-Si bonds. The bending vibration of Si-O-Al bonds can be observed at a wavenumber of 538 cm\({}^{-1}\). The absorption peak around 1000 cm\({}^{-1}\) is primarily due to the asym  
Figure 11: Thermogravimetric curve (**a**) of soft soil, GC, and G7, and thermogravimetric differential of soft soil (**b**), GC (**c**), and G7 (**d**).  
Figure 12: FTIR analysis of cement soil and stabilized soil.  
of Si-O-X bonds (X = Si or Al), indicating the presence of more C-S-H and C-A-S-H gels in the hydration products [31].