esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Expansion of previous work
### Asset Visualisation  
While GEMINI was adopted to illustrate the opportunities for automating metadata regardless of its ultimate use, it is perhaps worth considering the specific case of enabling resource discovery. GEMINI elements _Online resource_ and _Browse graphic_ are of specific interest here; the former enables pinpointing of the resource described, the latter is designed to support decisions based on whether the resource should indeed be pursued. A common approach to address these entries has been to provide a URL for the distributor's website while including a manually generated image snapshot of a subset of the data. The alternative proposed in the current work aims not only to facilitate improved metadata completion but also to provide for more effective visualisation with the potential for fast-tacking access to the data in question.  
Using OGC-compliant web portrayal services, datasets can be 'broadcast' for immediate use when unlicensed, or identified for subsequent purchase. Layers free of licensing restrictions may be served in accessible Web Feature Service (WFS) format whereas proprietary holdings may be visualised in Web Mapping Service (WMS) format prior to procurement. Service-specific Uniform Resource Locators (URLs) are subsequently written to their associated metadata record, coupling it with the portrayal service.
# NewSQL Databases Assessment: CockroachDB, MariaDB Xpand, and VoltDB
## 7 Conclusions and Future Work  
NewSQL databases are the most recent technology bridging the gap between SQL and NoSQL. Since there are many NewSQL solutions in the market, users have difficulties in choosing the most appropriate database for their needs without drastically changing their application.  
In this paper, we analyzed three popular NewSQL databases with the highest scores in the DB-Engines Ranking in the year 2022. Firstly, the OSSpal methodology was used, which showed that the NewSQL database with the best score was MariaDB Xpand, with 4.16 points, showing a higher score in several categories, followed by VoltDB with 4.10 and CockroachDB with 3.99. Using the Star Schema Benchmark, we experimentally evaluated performance and scalability, and we concluded that all databases demonstrated adequate scalability for inserting large volumes of data without much effort, with CockroachDB achieving the best results as it uses a fast bulk data loading. In the performance experiments, VoltDB showed better results in query execution time than CockroachDB and MariaDB Xpand, due to it being an in-memory database. On the other hand, CockroachDB showed lower consumption of CPU and RAM resources. The experiments conclude that with OSSpal methodology, the best database is MariaDB Xpand, although the benchmark performance results were poor. VoltDB had the best execution time performance, and CockroachDB had better scalability results.  
As future work, we intend to evaluate more NewSQL databases using the YCSB (Yahoo: Cloud Serving Benchmark) to assess their performance and scalability when facing different workload scenarios, as well as the use of TPC-DS, a Big Data-assisted decision support benchmark, to understand how these databases handle large volumes of data.  
Conceptualization, J.B. and F.S.; Methodology, E.P. and J.B.; Software, E.P.; Validation, E.P., F.S. and J.B.; Formal analysis, E.P., F.S. and J.B.; Investigation, E.P.; Resources, E.P.; Data curation, E.P.; Writing--original draft preparation, E.P.; Writing--review and editing, J.B. and F.S.; Supervision, J.B. and F.S.; Project administration, J.B. and F.S.; Funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Expected Water Quality Improvement Using Bioretention Cells  
Following the optimization process, 350 m\({}^{2}\) and 600 m\({}^{2}\) of bioretention cells are needed to reach the goals of 85% and 90% of the water quality achievement rate in the Jinggualiao stream. The relationship between the area of bioretention cells and the water achievement rate is shown in Figure 8. Notably, the slopes before and after 250 m\({}^{2}\) were different. After 250 m\({}^{2}\), the effectiveness of the bioretention cell was better than that at less than 250 m\({}^{2}\), which means that every 1 m\({}^{2}\) of bioretention cell resulted in a better water quality achievement rate. This is because the achievement rate is accounted for by a TP concentration of less than 20 \(\upmu\)g/L. When bioretention cells or other onsite runoff treatment facilities are built, the water quality is expected to improve in wet weather. The improved water quality concentration might not be as low as the required standard equally. When the bioretention cell area is sufficient, the quantity of improved water quality that is lower than the standard will increase nonlinearly. This implies that small control facilities might not perform as expected, but the gradual accumulation of work on runoff control is worthwhile and will have satisfactory results.  
A 1 m\({}^{2}\) bioretention cell can reduce TP loads by 55 g/year according to the results of the watershed-scale model; however, this value is not recommended for use at each tea farm site. In the Jinggualiao watershed, the conditions of tea farms are quite different. Tea farms are located on either flat land or sloping fields. The slope and water routes are very different on different tea farms; therefore, the effectiveness of bioretention cells is not equal at each site. For this reason, a site-scale model is required to determine the optimal control size for the tea farms. For example, the results of the tea farm case study (Table 6) show that a 1 m\({}^{2}\) bioretention cell reduces TP loads by approximately 20.5 g/year, which is much less than the average value resulting from the whole watershed. To provide detailed information for each tea farm, the site-scale model is helpful for assessing the water collection route, the location of the control facility, and the optimal size for reaching the management goal.
## 1 Introduction
The use of C&DW ultrafines as SCMs in new cements is an innovative line of work undertaken in recent years by a number of authors, whose interest has focused primarily on the mechanical performance of pastes and/or mortars bearing those materials as partial (3% to 30%) cement replacements. The consensus conclusion reached [14, 15, 16, 17, 18] is that GRC contains SiO2, Al2O3 and Fe2O3 (44%\(\leq\) SiO2 + Al2O3 + Fe2O3 \(\leq\) 59%), along with CaO (42% to 16%) and exhibits low pozzolancity and hydraulicity. Its filler effect likewise-identified would favour hydration, providing nucleation sites that would densify the cement matrix microstructure. Masonry and mixed C&DW ultrafines [19, 20, 21, 22] comprise essentially SiO2, Al2O3 and Fe2O3 (SiO2 + Al2O3 + Fe2O3 \(\geq\) 84%), with pozzolancity at least as high as fly ash and similar to that of other masonry materials, such as masonry [4,23] and fired clay sanitary ware [24] rubble. Further to research on mechanical behaviour, GRC has no adverse effect on new eco-cement performance at low replacement ratios (\(\leq\)7%), whereas masonry ultrafines induce no significant decline in compressive or flexural strength at percentages of up to 25%. Another line of research that has evoked considerable interest, as noted earlier, is the valorisation of agroforestry waste as an SCM. Two types of such waste are distinguished, depending on the origin [25]: laboratory-calcined (500 \({}^{\circ}\)C \(\leq\) T \(\leq\) 700 \({}^{\circ}\)C) biomass agroforestry waste (primarily bagasse, rice husk and to a lesser extent bamboo ash) [26, 27, 28]; and biomass or bottom ash, BA, from power and/or heat plants [29, 30, 31, 32], which generate around 10 Mt of BA yearly [33]. SiO2, Al2O3 and Fe2O3 (SiO2 + Al2O3 + Fe2O3 \(\geq\) 70%) are the majority compounds in such waste, which exhibits high early and late age lime fixation capacity (=pozzolanicity). No common pattern has been identified in the effect of including such SCMs on end product mechanical performance, however, which has on the contrary been observed to depend on the origin and nature of the waste and the replacement ratio [30; 34; 35; 36]. In that regard, no prior study has been published in the international literature assessing the durability of ternary or quaternary eco-cements, simultaneously bearing mixed C&DW ultrafines (U\({}_{\text{C\&DW}}\)) and BA or both with silica fume (U\({}_{\text{C\&DW}}\) + BA + SF). The research described hereunder used the Koch and Steinegger method [37] to analyse the seawater-, chloride- and sulphate-resistance of cement pastes bearing 30% or 45% SCMs (U\({}_{\text{C\&DW}}\) + BA or U\({}_{\text{C\&DW}}\) + BA + SF). Secondarily, the processes governing cement decay were determined on the grounds of XRD-identified mineralogical variations and the morphological changes observed in the exposed materials with scanning electron microscopy. The variations in pore size distribution detected with mercury porosimetry were also explored.
## 2 Extension and Refinement of the Ocean Disturbance Simulation System Model
### Theoretical Basis
Some parameters are defined as follows: \(\theta\) represents the pitch angle; \(\varphi\) represents the yaw angle; \(W\) represents the weight of the underwater vehicle, typically measured in newtons (N); \(B\) represents the buoyancy force acting on the vehicle, measured in newtons (N); \(\mathbf{G(\eta)}\) represents the gravity and buoyancy force/moment vector as a function of the vehicle attitude \(\eta\); \(\eta=[x,y,\ z,\ \varphi,\ \theta,\ \psi]^{T}\) represents the pose vector, including position and attitude in six degrees of freedom. Then, we can express the matrix \(\mathbf{G(\eta)}\) as follows:  
\[\mathbf{G(\eta)}=\left[ \begin{array}{c}(W-B)sin\theta\\ -(W-B)cos\theta sinf\\ -(W-B)cos\theta cosf\\ (y_{g}W-y_{b}\,B)cos\theta cosf+(z_{g}W-z_{b}\,B)cos\theta sinf\\ (z_{g}W-z_{b}\,B)sin\theta+(x_{g}W-x_{b}\,B)cos\theta cosf\\ (x_{g}W-x_{b}\,B)cos\theta sinf+(y_{g}W-y_{b}\,B)sin\theta\end{array} \right], \tag{7}\]  
After the definition above, the Newton-Euler equation's general form is:  
\[\text{M\dot{v}}+\text{C(v)v}+\text{D(v)v}+\text{G(\eta)}=\tau, \tag{8}\]  
Several simplifying assumptions and physical constraints were adopted to facilitate the establishment of the dynamic model of the underwater inspection robot. First, the robot was modeled as a rigid body, and structural deformations were neglected during motion. Second, the surrounding fluid was considered an incompressible and homogeneous medium, allowing the application of classical hydrodynamic theories. Third, ocean currents were treated as external disturbances with constant velocity, and their influence was incorporated into the path planning and control framework. Finally, internal disturbances caused by the thrusters, including high-speed rotational effects on the fluid environment, were not considered in the current model to reduce computational complexity and focus on the primary dynamic behavior of the vehicle. In this simulation, the ROV was modeled as a rigid rectangular prism, consistent with common industrial designs for observation-class or inspection-class underwater vehicles. The simplified shape was used for hydrodynamic calculations and thrust distribution. The dimensions of the simulated ROV were approximately length 1.2 m, width 0.8 m, and height 0.6 m, based on the configuration in the source model. The vehicle was assumed to maintain a neutral buoyancy condition, and its center of mass was located near the geometric center shown in Figure 3.  
The ROV's orientation followed the standard right-hand coordinate system (Figure 2). Thrusters were symmetrically distributed on the ROV frame to enable overactuated motion in all six degrees of freedom. This layout allowed full control of the surge, sway, heave, roll, pitch, and yaw and facilitated realistic control and response testing under multi-directional flow conditions. Based on the theory above, we added three-dimensional ocean currents to the system.
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
###### Abstract  
Electron density (or electron concentration) is a critical metric for characterizing the ionosphere's mobility. Shortwave technologies, remote sensing systems, and satellite communications--all rely on precise estimations of electron density in the ionosphere. Using electron density profiles from FORMOSAT-3/COSMIC (Constellation Observation System for Meteorology, Ionosphere, and Climate) from 2006 to 2013, a four-dimensional physical grid model of ionospheric electron density was created in this study. The model, known as EDG-DNN, utilizes a DNN (deep neural network), and its output is the electron density displayed as a physical grid. The preprocessed electron density data are used to construct training, validation, and test sets. The International Reference Ionosphere model (IRI) was chosen as the reference model for the validation procedure since it predicts electron density well. This work used the IRI-2016 version. IRI-2016 produced more precise results of electron density when time and location parameters were input. This study compares the electron density provided by IRI-2016 to the EDG-DNN to assess the merits of the latter. The final results reveal that EDG-DNN has low-error and strong stability, can represent the global distribution structure of electron density, has some distinctive features of ionospheric electron density distribution, and predicts electron density well during quiet periods.  
ionosphere modeling; electron density; EDG-DNN Article
## 4 Characterization of the Optical Memory Buffer
### Characterizing the Bandwidth of the Delay Channels  
In Figure 4, we measure the magnitude and phase response of the FBG channels in the optical memory buffer system to characterize their bandwidth [32]. The measurement configuration is shown in Figure 4a. We connect a swept-laser-based all-optical vector network analyzer between the ports of the concatenation of the FBGs. We characterize 12 out of the 100 wavelength delay channels, and the results are shown in Figure 4b,c. In Figure 4b, we observe that the 3 dB bandwidth of the FBG channels can at least span the frequency range from \(-\)22.7 to +21.7 GHz (i.e., the 3 dB bandwidth is at least \(\pm\)20 GHz). Also, the results of Figure 4c show that the phase deviation is between \(-\)1.67 and +1.32 rad over the \(\pm\)20 GHz bandwidth.  
We further extend the investigation and measure the RF system bandwidth using the VNA. We use the same setup of Figure 3a, where we set the VNA to measure the magnitude response for the optical memory buffer when the signal passes through (i) only the LC-FBG in Figure 5a, (ii) only the CS-FBG in Figure 5b, and (iii) the entire optical buffer system  
Figure 4: (**a**) Experimental configuration to characterize the magnitude and phase response of the optical memory buffer system using a swept-laser-based optical vector network analyzer (OVA). (**b**) Magnitude response of different delayed wavelength channels of the delay system. (**c**) Phase response of different wavelength channels of the delay system.  
composed of the concatenation of FBGs in Figure 5c. As a proof of concept, we show the results for the delayed channel at 1550.12 nm. As can be observed from these figures, using the LC-FBG causes the system to suffer from frequency nulls induced by the fading of chromatic dispersion, and the same behavior is observed from the CS-FBG RF magnitude response. Meanwhile, the combination of FBGs eliminates the added fading and provides a flat magnitude response for frequency ranges higher than 20 GHz, enabling the system to delay large bandwidth signals.
# Simulation Study of Dynamic Rotation and Deformation for Plasmonic Electric Field-Skyrmions
## 2 Simulation and Methods
In addition, according to the expression of \(\mathbf{E_{0}}\), the integration result of \(\mathbf{E_{0}}\) depends on the upper and lower limitations (the integration variable is \(\mathbf{\beta}\)). Figure 1: The schematic diagram of SPP waves generated by quadrilateral, triangle, and hexagonal linear slits is shown in the top view in (**a**–**c**). The schematic diagram using six and multiple red linear slits to equivalently replace the grey arc-shaped slits in the black dashed lines is shown, respectively, in (**d**,**e**). The simulation parameters are as follows: The wavelength of the Gaussian linearly polarized source is 632.8 nm, located at the Z-axis coordinate of \(-\)105 nm. The diameter of focusing spots is assumed to be 360 nm, and the multi-arc symmetric structure is vertically incident along the Z-axis, as shown in Figure 2a. The distance \(d\) from the inner side of each arc-shaped slit to the center of the structure is 0.95 \(\upmu\)m, and the distance \(D\) from the outer side of each arc-shaped slit to the center of the structure is 1.05 \(\upmu\)m. The central angle \(\theta\) of the arc-shaped slit is \((2/15)\upmu\), and the width of the slit (\(D\)-\(d\)) is 100 nm. The phase of the excitation light source at each position of the slit is independently controlled. The SPP field is generated on the surface of metal Ag with a thickness of 120 nm, and SiO\({}_{2}\) with a thickness of 100 nm is used as the substrate for the simulation experiment, in addition, boundary conditions are PML. Moreover, the two-dimensional frequency-domain field has a width of 1.4 \(\upmu\)m and power monitors have Z-axis coordinates of 0.5 \(\upmu\)m, and 0.2 \(\upmu\)m, respectively, which were used to present the full view of the SPP electric-field.
## 1 Introduction
[PERSON] et al. [16] compared the performance of three convolutional neural networks (VGG16, VGG19, and ResNet50) on butterfly classification. The results showed that ResNet50 has the highest training accuracy rate, while VGG16 has the highest test accuracy rate. [PERSON] et al. [17] established a new butterfly dataset, which includes indoor photos and outdoor photos, and compared the performance of three convolutional neural networks (AlexNet, VGGNet, and ResNet) on butterfly classification. The results showed that ResNet has the highest recognition accuracy rate. [PERSON] et al. [18] used different convolutional neural networks (AlexNet VGG-F, VGG16, VGG19) to conduct butterfly recognition research. [PERSON] et al. [19] built a dataset of butterflies and moths, and used four models (VGG-19, Inception-v3, Resnet18, and Resnet34) for identification research. The authors of [20; 21] used AlexNet to calculate global CNN features and used SVM to classify butterfly specimen images. The author of [22] established a dataset composed of ecological butterfly images and butterfly images in monograph of Chinese. On this dataset, the authors of [22; 23] used Faster R-CNN for automatic recognition of butterflies. In this study, we focus on butterfly classification in ecological images. Compared with the specimen images, the ecological images have a complex background and different scales and different illuminations. The classification of butterflies in ecological images is a challenging task. We propose a deep-learning-based method for fine-grained butterfly classification in ecological images using squeeze-and-excitation (SE) and spatial attention (SA) modules. The attention modules are used to focus on the most discriminative area and enhance the meaningful features in both channel and spatial dimensions. The SA module can make better use of the long-range dependencies in the images, while the SE module takes advantage of global information to enhance useful information features and suppress less useful features. Experimental results show that the proposed method is superior to other state-of-the-art methods. The remainder of this paper is organized as follows. Section 2 provides a detailed description of our dataset and the proposed method. The experimental results are reported in Section 3. Section 4 provides the conclusions.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 3 Model Development
### Optimization Method  
The BOMSDM proposed in the previous section can be classified as an NP-hard problem. This section introduced the non-dominated sorting genetic algorithm-II (NSGA-II), which is a method widely recognized for its applicability to complex multi-objective optimization problems [70]. For its efficient search and computing ability, NSGA-II has been regarded as a state-of-the-art algorithm for solving multi-objective problems [71].  
To enhance the computational performance of our model, we incorporated constraints into the chromosome generation process of NSGA-II, in which a chromosome referred to one solution of BOMSDM. In Appendix A, we explain the design of the chromosome generation for BOMSDM. For the rest of the calculation process of NSGA-II, we adopt the strategies proposed by [PERSON] et al. [70]; the process includes initializing the population (randomly generated initial solutions, which can then be optimized through subsequent genetic operations), non-dominated sorting, crowding distance calculation, selection, crossover and mutation, merge parent and offspring populations, non-dominated sorting and crowding distance calculation (again), and the final population output. After each generation, non-dominated sorting and crowding distance calculations are applied to select the next population. The process continues until the stopping criteria are met, and the final population consists of a set of non-dominated solutions. The general process of NSGA-II can be referred to in references [72; 73] and others. The pseudocode for the improved NSGA-II algorithm used in this paper is provided in Appendix A.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Model**} & **Facility-Community** & **Incremental** & **Subject to the Constraints** \\  & **Allocation Strategies** & **Strategy** & **Set Given by:** \\ \hline \multirow{2}{*}{BOMSDM} & Dynamic average and & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{Equations (6)–(13)} \\  & random allocation & & \\ Model I & Random allocation & & \\ Model II & Random allocation & & \\ Model III & Average allocation & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison between the BOMSDM and Models I, II, and III.