esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Metal Matrix Nanocomposites
## 2 Fabrication Methods
### Disintegrated Melt Deposition (DMD)  
This stir casting-derived process has extensively been used for manufacturing of Mg-based nanocomposites [33; 34; 35; 36; 37] where a composite slurry, based on stir casting principles, is produced first. The ensuing slurry is then passed through a pouring nozzle with an inert gas jet (i.e., argon), at a superheated temperature (i.e., 750 \({}^{\circ}\)C); finally, the slurry is deposited on a metallic substrate. The end product of the DMD process is an ingot which then can be hot extruded to obtain the desired shape (Figure 4). This process is mainly used to produce Mg nanocomposites as it circumvents the major drawbacks of stir casting namely high density of oxides in the nanocomposite, and withholding of the reinforcement particles, i.e., Al\({}_{2}\)O\({}_{3}\), SiC, Y\({}_{2}\)O\({}_{3}\), B\({}_{4}\)C, BN, ZrO\({}_{2}\), ZnO, CNT, in the crucible because of density differences between the particles and the Mg alloys [3]. For instance, [PERSON] et al. [38] produced an AZ31 + 1.5 vol% Al\({}_{2}\)O\({}_{3}\) nanocomposite with the alumina particles (Al\({}_{2}\)O\({}_{3}\)) of 50 nm size. Upon the DMD process, the ingot was extruded with extrusion ratio 20.25:1 at 350 \({}^{\circ}\)C to achieve 8-mm diameter bars; the overall grain size of the reinforced nanocomposite was much finer than the monolithic AZ31 alloy due to pinning effect of evenly distributed alumina nanoparticles.  
Figure 3: Engineering stress and engineering strain curves of (**a**) AZ31 alloy and AC-UST specimens, and (**b**) AZ31 alloy and Iso-UST specimens [32].  
Figure 2: Experimental setup for ultrasonic processing of nanocomposites with UST done (**a**) outside, and (**b**) inside the furnace [32].
# Flow Control, Active and Passive Applications
## 1 Introduction  
The Boundary Layer (BL) dynamic performance greatly affects the forces acting on any Bluff body. Ideally, the boundary layer should be attached to the surface but when separation occurs, the vortical structures and the dynamic forces' amplitude rapidly increase. In many aerodynamic applications, the Bluff bodies are shaped in such a way that the boundary separation is delayed as much as possible. Nevertheless, using novel technologies, it is possible to reattach the previously separated BL, or at least further delay its separation. One of the novel techniques which allows for the modification of the separation point of the BL is Active Flow Control (AFC). This consists of injecting/sucking fluid in pre-defined locations. In the vast majority of AFC applications, it is essential to perform an energy assessment in order to make sure that the energy saved by the reduction/increase of the forces due to the modification of the BL separation point is much larger than the energy employed for the actuation. In order to achieve this goal, it is essential to properly tune the five parameters associated to any AFC implementation, groove position, groove width, momentum coefficient, jet inclination angle and jet frequency. Such tuning can be carried out via a parametric optimization or using any optimizer. In other words, AFC is always associated with optimization methodologies; otherwise, the energy assessment cannot be successfully accomplished. The present book is based on a set of published articles that highlight some novel applications of flow control.
## 5 Simulation Results and Discussion
### SCN Design and Implementation  
We will consider the network topology shown in Figure 2. The parameters of the SCN are selected in conjunction with the synchrophasor application described in [30]. The deployment of the network involves the configuration of five networks described as follows. Network \(\mathcal{N}_{1}\) connects the PMU and PDC through a dedicated point-to-point channel with network address 191.168.1.0. The Internet is modelled using network \(\mathcal{N}_{2}\), where nodes \(v_{2}\), \(v_{3}\), \(v_{4}\), and \(v_{5}\) are intermediate routers that can be configured to mimic the behaviour of the Internet, such as background traffic, routing data, etc. To provide alternative routes to the PMU and PDC, we consider two more networks, \(\mathcal{N}_{3}\) and \(\mathcal{N}_{4}\). Network \(\mathcal{N}_{3}\) connects the PMU to the Internet using node \(v_{2}\), whereas the PDC is connected to the Internet using network \(\mathcal{N}_{4}\) with the help of node \(v_{5}\). The network addresses of networks \(\mathcal{N}_{3}\) and \(\mathcal{N}_{4}\) are 170.1.2.0 and 170.1.3.0, respectively. Furthermore, to model the background traffic over the Internet, network \(\mathcal{N}_{5}\) is constructed, which connects a node generating background traffic (corresponding to the _PDR_ data flow) to the Internet \(\mathcal{N}_{2}\). The network address for network \(\mathcal{N}_{5}\) is 170.1.1.0. It should be noted that all networks are designed in the class-B hierarchy. The design and implementation of the SCN in ns-3 are shown in Figure 9.
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 2 Method  
### Overview of Proposed Method  
This section shows the overview architecture of the designed Transformer-based classification method. As shown in Figure 1, a specially designed CNN model is used to extract the low-level semantic feature maps of GCI. The convolution operation is highly localized so it is beneficial to learn the local feature information of images. Then, the feature maps are fed into Transformer to learn the feature relationships between sequences to obtain the global feature representation of images. Finally, a linear classifier is used to complete the classification. In addition, a loss function is supplemented for enhanced supervised feature learning. The specific parameter information of the model is described in Table 1, where the number in parentheses after the convolutional layer or module indicates the kernel size, and the number in parentheses after the block indicates the number of times the feature map is downsampled. The details of each part are described below.
## 5 Discussion
### Engagement Level of the AR App for Displaying Sculptures in Public Spaces  
A high engagement level (5.88 of 7; interest--6.25; usability--5.50) with the virtual artwork within the AR application in public spaces supported the first hypothesis, demonstrating that AR technology can effectively engage users with virtual art in real public environments. This insight highlighted the potential of AR technologies as tools for presenting and evaluating artworks in public spaces, particularly from the perspective of user engagement with AR content.  
However, since the participant group consisted of students familiar with both art and technology, their higher engagement levels may be attributed to their age and greater technological familiarity compared to the general population. Although the study aimed to minimize bias regarding the digital or digitized nature of the sculptures, the participants' prior knowledge of technology and art may have influenced their perceptions and evaluations. To address this limitation, future research could include a broader audience, particularly non-expert users, to provide more diverse perspectives and enhance the reliability of the findings.  
On the positive side, the use of experienced users eliminated the novelty effect associated with encountering an AR application and digital artwork for the first time, potentially allowing for a more objective assessment of engagement in the activity.  
The research contributes to theoretical models of art perception and digital interaction by highlighting how user interest and immersion are influenced by the nature of the artwork--whether digital or digitized. The analysis of the AR questionnaire for sculptures provides a new tool for measuring engagement and immersion in AR environments, offering opportunities for further theoretical exploration. Practically, the study demonstrates that AR technologies can enhance public spaces by increasing interest in art.  
Nevertheless, challenges persist, particularly when integrating AR with architectural objects or other large-scale structures. One key obstacle is the limited screen size of mobile or tablet devices, which requires a significant distance between the viewer and the object, making it impractical in urban settings. On the other hand, as demonstrated in this study, AR technologies are more suitable for viewing artworks, as they are typically smaller in scale and thus easier to visualize within these constraints. This approach aligns with similar experiences in fields such as gaming [54; 55] and the fashion industry [56; 57], where the scale is comparable to the sculptures examined in this research.  
By adopting this approach, the transformative potential of AR technology can be emphasized, showcasing its ability to enrich public spaces, enhance emotional engagement, and support participatory evaluation of art in urban environments.
## 3 Results
### Individual Phenolic Composition
monopetulum_ (Su) extract was rich in caffeic acid (**12**; 6.79 mg g\({}^{-1}\) extract DW), gallic acid (**1**; 3.79 mg g\({}^{-1}\) extract DW), gentisic acid (**4**; 3.79 mg g\({}^{-1}\) extract DW), chlorogenic acid (**10**; 2.12 mg g\({}^{-1}\) extract DW), 3-hydroxybenzoic acid (**8**; 1.24 mg g\({}^{-1}\) extract DW), and 4-O-caffeoylquinic acid (**11**; 1.76 mg g\({}^{-1}\) extract DW). Interestingly, certain compounds displayed species-specific occurrences. For instance, 4-\(O\)-caffeoylquinic acid (**11**) was unique to _L. monopetulum_, while morin (**30**) and chrysin (**32**) were exclusively detected in _H. italicum picardii_ samples. Conversely, luteo-lin-7-\(O\)-glucoside (**24**), epicatechin (**15**), caffeic acid (**12**), and gallic acid (**1**) were common across all species, albeit in varying concentrations. These findings highlight the distinct phenolic profiles of each species, offering opportunities for their targeted exploitation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Rt (min)** & **No.** & **Compound** & _P. lentiiscus_ & **H. italicum_** & _C. mariscus_ & _P. coronopus_ & _L. monopetulum_ \\
13.9 & 15 & Epicatechin & 25.59 & 2.38 & 0.88 & 0.51 & 0.57 \\
14.9 & 16 & Ourateacatechin & 34.72 & 1.76 & n.d. & 0.41 & 0.95 \\
15.2 & 17 & Umbellifore & & 0.15 & n.d. & 0.41 & 0.05 \\
15.9 & 18 & Couramic acid & \textless{}0.01 & 0.16 & \textless{}0.01 & 1.19 & 0.40 \\
16.8 & 19 & Taxifolin & & & n.d. & & 0.36 \\
16.9 & 20 & Couramir & & 0.01 & n.d. & & \\
17.0 & 21 & Ferulic acid & & 0.51 & 1.38 & 0.81 & 0.06 \\
17.6 & 22 & Salicylic acid & & 2.59 & 1.64 & 5.36 & 0.48 \\
19.1 & 23 & Naringen-7-glucoside & 2.78 & 0.82 & 0.01 & 1.18 & \\
20.3 & 24 & Luteolin-7-O-glucose & 5.47 & 3.58 & 0.46 & 5.36 & 0.23 \\
21.3 & 25 & Rosmarinic acid & 0.60 & & & 0.12 & 0.58 \\
21.4 & 26 & Rutin & 0.54 & & & 0.09 & \\
22.2 & 27 & Ellagic acid & 0.24 & & & 0.26 & \textless{}0.01 \\
22.6 & 28 & Cinnamic acid & 0.21 & 0.38 & n.d. & 0.26 & 0.34 \\
25.9 & 29 & Quercetin & & 0.06 & 0.03 & 0.07 & \\
28.8 & 30 & Morin & & 0.03 & n.d. & & \\
31.7 & 31 & Flavone & & & & \\
32.6 & 32 & Chrysin & & 0.12 & n.d. & & \\ \hline  & & \(\Sigma\)Phenolics & **138.91** & **27.91** & **9.22** & **23.03** & **25.43** \\ \hline  & & & & & & & \\ \end{tabular} \(\dagger\): Data published in [25]; retention times may differ for some compounds.  
\end{table} 
Table 1: _Cont._
## 5 Performance Analysis  
Tables 2 and 3 demonstrate the performance of the considered feature selection methods on different classifier models in terms of mean ACC and mean MCC, respectively. From the table, it can be observed that the proposed method, DFG-A-DFC, has achieved the best results for more than 50%. The runner-up is DSFFC in terms of overall best classification accuracy. Further, Figure 2 illustrates the visual comparison of the feature-selection methods in the form of a bar chart. In the figure, the x-axis corresponds to the considered methods, while the y-axis depicts the number of times the best value is reported by a method on both parameters, i.e., ACC and MCC. It is clearly envisaged that DFG-A-DFC is best as it reports the best value for the maximum number of times among the considered methods. Further, Figure 3a depicts the comparison of the considered feature-selection methods with different classifiers on the number of datasets on which each has reported the best value for the ACC parameter. From the figure, it is visible that the proposed method, DFG-A-DFC, with SVM classifier has outperformed other methods on 87% of the considered datasets. Similarly, DFG-A-DFC with KNN and Naive Baye was superior on 50% of the datasets. Moreover, the proposed method shows competitive performance with the Adaboost classifier. Further, the same comparison was conducted for the MCC parameter in Figure 3b. Here, DFG-A-DFC with SVM classifier has attained the best value on 5 out of  
Figure 2: Bar chart for the number of times the best value is reported by considered methods on ACC and MCC. 6 datasets, while it is better on 50% of the datasets with KNN and Naive Baye. However, FCFS fails to report a best value on any dataset for both parameters.
# Determination of the Waterway Parameters as a Component of Safety Management System
## 4 Materials and Methods
### Research Results of Determining the Width of the Fairway by INSMS \"NEPTIN\" Application  
The safe fairway widths were determined at a confidence level of 0.95 as the maximum values of the safe manoeuvring areas for one type of \"maximum vessel\" two times per day (day and night) and for two positioning navigation systems provided for a given time of day. In Table 5 there is a list of the fairway widths of the safe manoeuvring areas for the \"maximum vessel\" at the seabed with a depth of 12.5 m for the current navigational markings at the confidence level (1 \(-\)\(\alpha\)) = 0.95. Figures 7-9 present the calculated widths of the fairway, so, in Figure 7, the fairway widths are estimated for restricted visibility (Vis \(<\) 2 NM) for two positioning systems, PNS and radar. As it is clearly visible in such a situation, the PNS system has an advantage over the positions taken by radar. In Figure 8, there are conditions with good visibility and, also, two positioning systems, but this time, instead of radar, the terrestrial method is used. In this case, using the PNS system is more favourable on the straight parts of the fairway, but on the turns, during perfect visibility, the terrestrial method gives better results. In Figure 9, night-time is taken into account, and the same positioning methods (PNS, terrestrial) as during good visibility and daytime were under consideration. As one could expect, using PNS gives the lowest fairway widths for a given vessel on the straight parts of the fairway. The terrestrial method again was better for the turns, but compared to the same method during the daytime, the widths are slightly bigger. Those calculations and results were to answer the question of whether it is possible to enter the port of Szczecin under the assumed conditions using various positioning systems and under which conditions the required widening of the fairway will be the smallest. Figure 7: Fairway widths of the safe manoeuvring areas for a bulk carrier going from Swinoujscie to Szczecin with restricted visibility and two positioning systems (PNS, radar).
## 8 Conclusions  
For the DFIGs integrated through the VSC-HVDC, the cascaded droop control is introduced to the DPF model for the coordinated regulation with the inertia of the VSC-HVDC and the DFIG, and the primary regulation of the SG and the DFIG, where the SGs'  
Figure 9: Comparison of dynamic power flows before and after optimization of \(H_{\text{DC}}\): (**a**) System frequency under low wind speed; (**b**) DC voltage under low wind speed; (**c**) System frequency under medium wind speed; (**d**) DC voltage under medium wind speed; (**e**) DC voltage under high wind speed; (**f**) DC voltage under high wind speed.  
output considers the governor delay, the inertia of the VSC-HVDC follows the DC voltage constraint, and the inertia of the DFIG changes with the kinetic energy. Some conclusions are yielded as follows:  
1. Ignorance of the governor delay of the SGs yields an optimistic estimation of the frequency oscillation and nadir. With the governor delay, the frequency nadir occurs at the early stage of the frequency regulation due to not being fully supportive of the SGs. With the larger delay, a longer time is needed to reach the steady state frequency.
2. With the virtual inertia control of VSC-HVDC, releasing the stored energy in the DC capacitor reduces the drop rate of the frequency at the early stage of regulation. Initialization of \(H_{\text{DC}}\) based on estimation of the frequency nadir provides more inertia support to the system frequency without violating the constraint of the DC voltage.
3. With the DFIG participating in frequency regulation through the VSC-HVDC, the transient drop rate and the steady-state deviation of frequency are further reduced. The rotor speed and kinetic energy of the DFIG vary during the frequency regulation, thus the equivalent inertia needed to be modified accordingly.
4. Optimization of \(H_{\text{DC}}\) based on the improved DPF model maximizes the virtual inertia of the VSC-HVDC for a better frequency regulation effect.  
Conceptualization, T.S.; Data curation, J.H.; Formal analysis, J.H.; Funding acquisition, T.S.; Investigation, T.S.; Methodology, T.S.; Project administration, J.H.; Software, T.S.; Validation, J.H.; Writing-original draft, T.S.; Writing-review and editing, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Nantong Science and Technology Plan Project, grant number JC2021107.  
Not applicable.  
The authors declare no conflict of interest.
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Satellite Missions for Inland Water Level Monitoring
#### 3.1.3 Other Satellite Missions
In this research four simple statistical models were used to explore the most effective model structure to predict daily river water levels and therefore to improve the temporal resolution of the Jason-2 mission. Figure 9: Number of applications per satellite mission and year.
## 4 Experimental Results and Analysis
### Experimental Environment  
The GPU used in this experiment was a GTX3050. The model was trained for 200 epochs per cycle with a batch size of 32. The dataset was split into training, validation, and testing sets in a 7:1:2 ratio. Specifically, the training set contains 1750 images, the validation set contains 250 images, and the testing set contains 500 images.
# Bounded-Error Parameter Estimation Using Integro-Differential Equations for Hindmarsh-Rose Model
## 2 Parameter Estimation Method  
### Differential Algebra  
Parameter estimation is done through relations linking the inputs, outputs and parameters of the system (1). These relations are obtained using the Rosenfeld-Groebner algorithm, implemented in the package DifferentialAlgebra of Maple [1]. This algorithm provides relations, called IO polynomials, from an elimination order, consisting of eliminating unobservable variables. The IO polynomials have the following form, for \(i\) from 1 to \(m\):  
\[P_{i}(y,u,\theta)=m_{0}^{i}(y,u)+\sum_{l=1}^{q_{i}}\gamma_{l}^{i}(\theta)m_{l, j}(y,u)=0 \tag{2}\]  
where \((\gamma_{l}^{i})_{1\leq l\leq q_{i}}\) are rational in \(\theta\), \(\gamma_{u}^{i}\
eq\gamma_{v}^{i}\) (\(u\
eq v\)) and \((m_{l,j})_{1\leq l\leq q_{i}}\) are differential polynomials, with respect to \(y\) and \(u\). \(m_{0}^{i}\
eq 0\) and \(i\) from 1 to \(m\). According to [16], the number of the relations is the number of observations. Afterwards, only one output is considered, and the index \(i\) is omitted to lighten the notations.
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 4 Conclusions  
Based on the close relationship between population size and building distribution, this paper divides urban areas at the fine-scale of subdistricts. To extract residential houses from building data, we construct an approach to classifying buildings into subdistricts, which uses multi-source data and the _TF-IDF_ method. This method greatly reduces the time required compared with assigning buildings manually. The building variables related to the population are calculated and selected. According to the different population densities of different height floors, residential buildings are divided into low-rise, middle-rise, and high-rise buildings. The random forest method is then used to analyze the importance values according to the area, perimeter, and number of floors. A linear multiple regression model is established based on the number of floors in buildings, geometric features, and populations according to the importance of the variables for buildings.  
The average error rate of the model is determined as 12.33%, which is better than other methods when using such as estimating the population based on night light remote sensing data. The fine-scale population estimation model developed in this study may have important applications related to the national economy and the livelihoods of people. There may be some possible limitations in this study. In this paper, the building is abstracted as a point and _TF-IDF_ is used to predict its category. In real life, a high-rise building may have multiple functions at the same time. In addition, due to the dependence on building data and _POI_ data, the model in this paper cannot be directly transferred to areas where relevant data is missing. Future research may focus on the following aspects: (1) establishing a spatio-temporal population estimation model by considering the decreases and increases in the numbers of buildings over time, and by introducing time variables; and (2) introducing night light data, remote sensing image data, and other data, and determining whether they improve the accuracy of the model.  
Conceptualization, S.W. and R.L.; methodology, S.W.; formal analysis, J.J.; data curation, Y.M.; writing--original draft preparation, S.W.; writing--review and editing, R.L.; supervision, J.J.; project administration, Y.M. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Proportion of buildings with different floor heights.  
**Funding:** This research was supported by the National Natural Science Foundation of China (Grant No. U20A2091, 41771426) and the National Key Research and Development Program of China (Grant No.2016 YFB0502301).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The part data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.