esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanisms of Fluoride Toxicity: From Enzymes to Underlying Integrative Networks
## 3 Mechanisms of Fluoride Toxicity on Cellular Level
### What Do the Studies of Fluoride Effects on Enzymes In Vitro Tell Us?
#### 3.5.2 Aluminum-fluoride Complexes  
In the 1980s, [PERSON] noticed that the activation of G protein by fluoride depends on trace amounts of free aluminum ions (Al\({}^{3+}\)), a contaminant in glass test tubes, and chemicals used in AC assays [67]. The hypothesis that the activating species is aluminum tetrafluoride (AlF\({}_{4}\)-), which acted as an analog of a \(\gamma\) phosphate moiety in G\(\alpha\) GTP, was confirmed by \({}^{19}\)F NMR (nuclear magnetic resonance) titration experiments [67; 80; 85]. X-ray crystallography also provided evidence that AlF\({}_{4}\)- seems to be the active site species [86]. Further modeling of the equilibria complexes predicted that at physiological pH, in millimolar fluoride concentrations, the ternary complex AlF\({}_{3}\)(OH)- and neutral AlF\({}_{3}\) predominate [87; 88; 89]. These discoveries heralded a new field of research of aluminofluide complexes (AlFx), which mimicked the phosphate group (PO\({}_{4}\)3-) in phosphate transfer reactions [89]. However, the exact structure of the activating species in solution is still being debated [20; 82]. Using the \({}^{19}\)F NMR analysis and computational chemistry, [PERSON] et al. recently showed that AlF\({}_{4}\)- is the primary species, which mimics the transition state of the reaction [20].  
The widespread use of AlFx as a general activator of heterotrimeric G proteins provide evidence that AlFx is a molecule giving false messages, which are amplified by processes of signal transduction (Figure 3).  
A small false signal of AlFx can activate various G\(\alpha\) and effector proteins, such as AC or phospholipase C. In consequence, this generates cAMP, inositol 1,4,5-trisphosphate, and diacylglycerol. The increased level of second messenger-molecules activates protein kinase A, which phosphorylates a great number of proteins. AlFx acts again as the analog of phosphate. Protein phosphorylation constitutes one of the major posttranslational mechanisms employed in the signaling cascades. Phosphoryl transfer reactions are also involved in processes such as regulation of cell metabolism, energy transduction, cytoskeletal protein assembly, regulation of cell differentiation and growth, aging, and apoptosis. Considering that all these reactions are fundamental for nearly all biological processes, the common denominator of which is the transfer of a phosphoryl group, we can conclude that fluoride represents the dangerous hidden toxin for all living organisms.
## 3 ANN Drag Model and Training Strategy
### The Construction of ANN
#### 3.1.2 The Activation Function  
Different activation functions have various ranges of influence on the prediction accuracy of the ANN; thus, it is necessary to discuss the performance of commonly used activation functions, including Sigmoid, Tanh, Leaky-LU, and Gauss. The predicted results of ANN with a single hidden layer under three independent experiments are illustrated in Table 4.  
There is an obvious difference in computational accuracy among the four activation functions. Specifically, the Leaky-LU function demonstrates the lowest prediction accuracy compared to the Tanh function and the Sigmoid function. The prediction results of the Gauss function for the three independent experiments show significant differences. Considering the characteristics of the Gauss activation function, this may be caused by its local response property [45]. Therefore, it is suggested to avoid using it to build the polydisperse drag force model. Moreover, the Sigmoid and Tanh activation functions have a similar prediction accuracy, and both belong to the so-called \"S-function\". Because the Tanh function shows a symmetry property with the origin point in the Cartesian coordinate system, it will be beneficial to be utilized for preserving the original properties of the parameters [45]. Consequently, it is more reasonable to use the Tanh function as the activation function in modeling the drag force.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Fanet
#### 2.1.2 Main Applications  
FANETs can be applied to some specific situations, for example, UAV network to provide an internet access infrastructure, UAV network for localization and attack for military purposes and UAV network for remote sensing or scanning. In the first case, the UAV network is supposed to be used as an internet access point. This type of network requires slow movement of the UAV. The communication infrastructure must be stable and capable of supporting a high throughput and access [36]. When used for localization and attack for military purposes, UAV networks must be designed so that the node is capable of recognizing a target and executing the attack, with the possibility of leaving the area immediately after the task execution; or performing self-detonation to eliminate the enemy. The goal is to search for a target in a way that is not necessary to complete scan the area as a whole [37]. When UAV networks are designed for remote sensing and scanning specific areas, using computer vision techniques makes it possible to scan the complete area under monitoring [38].
# Designing an Interactive Visual Analytics System for
## 5 Interactive Precipitation Data Analysis System
### Multi-Site Analysis Interface
#### 5.2.1 Precipitation Trend Analysis  
Precipitation trend analysis on multiple weather stations is important for identifying statistical trend differences between stations. Figure 10 shows the performed trend analysis on the two weather stations (i.e., IAD and DCA) from the period of 1997 to 2023. All precipitation events are represented as colored circles. Three statistical methods, linear, polynomial, and exponential regression, are supported to find trend lines on the per-station precipitation data. The Mann-Kendall test is also performed to determine the significance of the trend. The measured statistics are displayed next to each station name. Figure 10A shows the trend analysis with linear regression. It reveals a gradual increasing trend (IAD: \(R^{2}=0.022,p<0.01,\tau=0.099\) and \(DCA:R^{2}=0.040,p<0.01,\tau=0.094\)). Figure 10B displays the trend analysis results using polynomial regression while hiding all precipitation events (IAD: \(R^{2}=0.023,p<0.01,\tau=0.099\) and \(DCA:R^{2}=0.042,p<0.01,\)\(\tau=0.094\)). The option to hide precipitation events enhances the visibility of trend lines. The y-axis scale automatically adjusts by evaluating all visible data in the visualization. Thus, when precipitation events are hidden, the y-axis scale is adjusted to the range of trend line values, resulting in the change of the y-axis scale from \(0\sim 15.0\) to \(0\sim 1.2\). Currently, trend analysis visualization only supports the analysis of up to six stations due to possible causes of visual cluttering and computational overhead of identifying trends for all stations.
## 2 Data Acquisition and Model Configuration
### Data Visualization and Trend Analysis  
In this study, the input variables are mostly the textural properties of the erbium- and iron-modified TiO\({}_{2}\) photocatalyst. The texture properties examined include the specific surface area (S\({}_{\text{BET}}\)), the pore volume (V\({}_{\text{p}}\)), the pore diameter (d\({}_{\text{p}}\)), and the crystallite size. The target variable was measured as a function of the photodegradation of the BPA. Three-dimensional plots shown in Figure 2 were employed to visualize the relationship between the input variables and the target variables. It can be seen in Figure 2a that there is a nonlinear interaction between the photocatalyst-specific surface area, the crystallite size, and the BPA degradation. A high BPA degradation potential can be observed at an S\({}_{\text{BET}}\) range of 120-140 m\({}^{2}\)/g and crystallite size of 4-8 nm, which is consistent with that reported in the literature [46]. Besides using TiO\({}_{2}\)-based photocatalysts, photocatalysts such as cobalt ferrite have been reported to photodegrade BPA in water at a high rate [47]. In addition, Figure 2b also illustrates the visualization of the interaction between the photocatalyst pore diameter, the crystallite size, and the BPA degradation. Also, a nonlinear interaction exists between the photocatalyst pore diameter, the crystallite size, and the BPA degradation, resulting in a high BPA degradation at a pore diameter range of 5-7 nm and crystallite size of 4-8 nm. Figure 2c depicts the visualization of the interaction relationship between the photocatalyst pore diameter, crystallite size, and high BPA degradation. It can be seen that a nonlinear interaction relationship also exists between the photocatalyst pore diameter and crystallite size, resulting in high BPA degradation. In Figure 2d, a nonlinear interaction relationship can be observed between the photocatalyst pore diameter, the pore volume, and the BPA degradation, which also resulted in a high BPA degradation. The three-dimensional plots show that the photocatalyst's pore volume, the pore diameter, the pore volume, and the crystallite size significantly influence the BPA degradation. The effect of textural properties of an alumina-supported Ni catalyst for hydrogen production via methane decomposition has been reported by [PERSON] et al. [48]. The results revealed that the textural properties of Ni/Al2O3 significantly influence the yield of hydrogen produced from methane decomposition. Similarly, [PERSON] et al. [49] reported the effect of textual properties in Ni-Fe catalysts used for CO2 methanation. The study established that there is a relationship between the surface area and the rate of CO2 methanation.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 4 Dynamic Models
### Nearly Constant Turn (NCT) Model  
The dynamics of the NCT movement are captured through a nonlinear model, which is described by [5; 8; 20]  
\[\mathbf{x}_{k}=\mathbf{f}^{(j)}(\mathbf{x}_{k-1})+\mathbf{w}_{k-1}^{(j)}, \tag{31}\]  
where  
\[\mathbf{f}^{(j)}(\mathbf{x}_{k-1}):=\mathbf{A}^{(j)}(\mathbf{x}_{k-1}) \mathbf{x}_{k-1},\quad j=2,3, \tag{32}\]  
\[\mathbf{A}^{(j)}(\mathbf{x}_{k-1})=\begin{bmatrix}1&0&\frac{\sin(\omega_{k-1 }^{(j)}T)}{\omega_{k-1}^{(j)}}&-\frac{1-\cos(\omega_{k-1}^{(j)}T)}{\omega_{k-1 }^{(j)}}\\ 0&1&\frac{1-\cos(\omega_{k-1}^{(j)}T)}{\omega_{k-1}^{(j)}}&\frac{\sin(\omega_{ k-1}^{(j)}T)}{\omega_{k-1}^{(j)}}\\ 0&0&\cos(\omega_{k-1}^{(j)}T)&-\sin(\omega_{k-1}^{(j)}T)\\ 0&0&\sin(\omega_{k-1}^{(j)}T)&\cos(\omega_{k-1}^{(j)}T)\end{bmatrix},j=2,3. \tag{33}\]  
In (33), the angular velocity \(\omega_{k-1}^{(j)}\) at time \(t_{k-1}\) for model j is given by  
\[\omega_{k-1}^{(j)}=\kappa^{(j)}\frac{a_{m}}{\sqrt{\dot{x}_{k-1}^{2}+\dot{y}_{ k-1}^{2}}},\quad j=2,3, \tag{34}\]where \(a_{m}>0\) signifies a standard maneuvering acceleration, while \(\kappa^{(2)}\) and \(\kappa^{(3)}\) are indicators with values of \(1\) and \(-1\) for the NCT+ and NCT- maneuvers, respectively. The covariance matrices of \(\omega_{k-1}^{(2)}\) and \(\omega_{k-1}^{(3)}\) are defined as follows [20; 51]:  
\[\mathbf{Q}_{k-1}^{(j)}=q_{j}\begin{bmatrix}T^{3}/3&0&T^{2}/2&0\\ 0&T^{3}/3&0&T^{2}/2\\ T^{2}/2&0&T&0\\ 0&T^{2}/2&0&T\end{bmatrix},\quad j=2,3, \tag{35}\]  
where \(q_{2}\) and \(q_{3}\) denote the PSDs of the noise processes in models 2 and 3, respectively [3], with \(q_{2}=q_{3}\).
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for SAR System
#### 4.1.1 2D Fast Fourier Transform  
Firstly, the SAR raw data are a matrix of raw signals sampled over slow time in azimuth and fast time in the range frequencies. On the other hand, the Omega-k algorithm operates in the two-dimensional frequency domain; therefore, two-dimensional Fourier transform, that is, in range and azimuth, is applied to convert the data from the time domain to the frequency domain in the range and azimuth directions.
## 4 Experimental
### Simulation Experiments with MAC Protocols and the Wake-Up Modem
#### 4.3.3 Impact of Transmission Speed  
Depending on environmental conditions, nodes may need to lower their transmission speed to decrease the packet error rate. In this section, we are going to analyze how reducing the transmission speed by a factor of two affects the behavior of the protocols when not using acknowledgments.  
The first interesting result depicted in Figure 11(a) can be seen in the ALOHA-CS and FAMA x2 protocols. Reducing the transmission speed makes the data frames larger, and this leads to an increase in collisions when using ALOHA-CS. However, the difference between data frames and propagation delay is reduced, since FAMA x2 is much more efficient in channel utilization.  
There are also differences in terms of packet delay. Figure 11(b) shows FAMA x2 outperforming the other protocols. However, the delay when using ALOHA-CS increases substantially.  
Figure 11: Results without acknowledgment. (**a**) Normalized received packets; (**b**) delay; (**c**) energy per data packet—node; (**d**) energy per data packet—sink.  
Figure 11(c) shows the energy consumption per correctly received packet for the regular nodes. It can be seen how FAMA and FAMA x2 outperform the other protocols, since the overhead of the large RTS/CTS packets is reduced and collisions are more frequent for the other protocols.  
Finally, sink energy consumption per correctly received packet is shown in Figure 11(d). It can be seen that MACA produces the worst results and how the overhead of sending CTS packets in FAMA x2 is minimal when comparing to the energy spent by ALOHA-CS.
## 4 Conclusions and Future Prospect  
With significant growth in the CDI field over the past few decades, a tremendous research effort has provided various scientific and industrial breakthroughs in CDI technologies by exploring new electrode materials, as well as theoretical studies. Additionally, significant improvements have been made with the systematic advancement such as MCDI, HCDI, i-CDI, and FCDI. In particular, MC-MCDI opens various opportunities and shows many possibilities to overcome key limitations of the conventional CDI with a unique cell architecture using multichannel.  
Despite its compelling desalination performance, further investigations should be performed to investigate the practical use of MC-MCDI. For further success of MC-MCDI as the next-generation of CDI architecture, the following issues must be studied and resolved by future research: (1) A relatively complicated cell design is vulnerable to expand its desalination capacity by stacking the multiple-unit-cells, (2) it remains unclear if the ion removal mechanism originates from the electrosorption in nanopores of the carbon electrode or just from ionic movement through the  
Figure 9: (**A**) Schematic illustration and (**B**) continuous desalination operation of CDI assisted by redox couple reaction (Reprinted with permission from [34]. Copyright (2019) American Chemical Society).  
membrane due to the electrical field, and (3) its performance is still limited because the studies were exclusively evaluated with the porous carbonaceous materials. Moreover, it is important to conduct system optimization as well as various parameter studies of the MC-MCDI for practical use of the system. In conclusion, it is very promising that MC-MCDI is an innovative CDI system not only to enhance the desalination performance but also to expand the scope of the CDI system beyond the low salinity desalination.  
C.K. and N.K. conceived and organized the paper; C.K. and N.K. wrote the first draft; J.L., S.K., S.P.H., C.L., and J.Y. reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1G1A1003336) and by the research grant of the Waste to Energy Recycling Human Resource Development Project of Korea Ministry of Environment (ME).  
The authors declare no conflict of interest.
# A Multi-Level Optimization Method for Elastic Constants Identification of Composite Laminates
## 4 Elastic Constants Identification of Composite Laminate  
To demonstrate one of its applications, the proposed MLOM was used to identify the elastic constants of symmetric composite laminates, in which the composite layers had the same material properties, but may have had different fiber orientations. Herein, strains on the surface of the composite laminate were used to determine the four elastic constants (\(E_{1}\), \(E_{2}\), \(G_{12}\), and \(\
u_{12}\)) of the laminate.
## 2 Analysis of the State of the Art
### Related Standardization Work
#### The Video in the Web Activity  
Promoting the use of the video in the Web, the Video in the Web Activity [79] aims to build a solid architecture to enable users to create, navigate, search, link and distribute video, effectively making video part of the Web. This activity group is formed by three different working groups:  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Entity** & **Contribution** & \multicolumn{2}{c}{**Type**} \\ \cline{3-4}  & **Standard** & **Specification** \\ \hline User & UAProf & ✓ & \\  & Multimodal Architecture and Interfaces & ✓ & \\  & Extensible MultiModal Annotation markup language & ✓ & \\  & Ink Markup Language (InkML) & ✓ & \\  & Accessibility (All) & & ✓ \\  & Web Content Accessibility Guidelines (WCAG) & & ✓ \\  & Accessible Rich Internet Applications (WAI-ARIA) & & ✓ \\  & User Agent Accessibility Guidelines (UAAG) & & ✓ \\  & Authoring Tool Accessibility Guidelines (ATAG) & & ✓ \\  & Evaluation and Report Language (EARL) & & ✓ \\  & IndieUI & & ✓ \\ Device & CC/PP & ✓ & \\  & Introduction to Model-Based User Interfaces & & ✓ \\  & Model-Based User Interfaces Glossary & & ✓ \\  & Guidelines for writing device independent tests & & ✓ \\  & Delivery Context Overview for Device Independence & & ✓ \\  & Authoring Techniques for Device Independence & & ✓ \\  & Device Independence Principles & & ✓ \\  & Authoring Challenges for Device Independence & & ✓ \\  & Delivery Context Ontology & & ✓ \\  & Delivery Context: Client Interfaces (DCCI) & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 5: W3C standards and specifications for user and device modeling.  
* The Timed Text Working Group [80], which mission is to provide a language that represents textual information that is associated with timing information. This group has released two versions of the Timed Text Markup Language (TTML). The first one was released in 2010. The last one during 2013.
* The Media Annotations Working Group [81], which main purpose is to provide an ontology and API to facilitate cross-community data integration of multimedia information in the Web. The group has published the following documents:
* A second version of Use Cases and Requirements for Ontology and API for Media Resource 1.0 on January 2010, as an input for the development of \"the Ontology and the API for Media Resource 1.0\" Specification.
* A W3C Recommendation version of the Ontology for Media Resource on February 2012.
* The group is now about to publish a Proposed Recommendation of the API for Media Resources.
* The Media Fragments Working Group [82], which successfully aimed to address temporal and spatial media fragments in the Web using URIs. The group was closed on December 2013 having successfully published two versions (basic and advanced) of the Media Fragments URI 1.0 specification as a Recommendation.  
The Video in the Web Activity members are still working over the issue of the video codec to be used in the W3C specifications (in particular HTML5).
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 3 Parameter Identification Method for the Hyper-Viscoelastic Model
### Different Loading Histories and Virtual Experiment Data  
Similarly, with the identification in the Section 2.2, it is necessary to incorporate different loading histories to guarantee the accuracy of the identified multi-parameters in Equation (17). Hence, the relaxation tests of uniaxial tension and the simple shear tests with different loading velocities are taken as examples in this study. Due to the lack of appropriate data for these experiments, we followed the method applied in [PERSON] et al. [24; 25; 26] and got the corresponding virtual experiment data by using ANSYS, in which we assume the hyperelastic parameters are the same with the last group in Table 1, and the viscoelastic part is characterized by the following Prony series,  
\[P(\xi)=0.4e^{-\left(t-\xi\right)/10}+0.1e^{-\left(t-\xi\right)/100} \tag{18}\]  
In the virtual experiment, the finite element (FE) model for the relaxation tests is identical with that in Figure 2a, and the model for the simple shear tests is illustrated in Figure 4. All the material constants are determined by the above discussions.  
Then, three groups of data for relaxation tests and four groups of simple shear experiment data with different loading velocities are obtained. As shown in Figure 5, the maximum stretch is 1.3 in the relaxation tests with the loading time being respectively 3 s, 6 s, and 30 s, and the hold time being  
Figure 4: Initial FE model for the simple shear test.  
150 s. For the simple shear tests, the maximum shear strain is 1.0 with the loading velocities being 0.5/s, 0.1/s, 0.01/s, and 0.001/s.
###### Abstract  
Ground roll noise suppression is a crucial step in processing deep pre-stack seismic data. Recently, supervised deep learning methods have gained popularity in this field due to their ability to adaptively learn and extract powerful features. However, these methods rely on a large amount of clean seismic records without ground roll noise as reference labels. Unfortunately, generating high-quality and realistic clean seismic records for training remains a challenge. To tackle this problem, an unsupervised learning method called WPI-SD (wavelet prior information for deep learning in seismic data) is proposed for ground roll noise suppression in deep pre-stack seismic data. This approach takes into account the distinct temporal, lateral, and frequency characteristics that differentiate ground roll noise from real reflected waves in deep pre-stack seismic records. By designing a ground roll suppression loss function, the deep learning network can learn the specific distribution characteristics of real reflected waves within seismic records containing ground roll noise, even without labeled data. This enables the extraction of effective reflection signals and subsequent suppression of ground roll noise. Applied to actual seismic data processing, this method effectively mitigates ground roll noise while preserving valuable reflection signals, proving its practical significance.  
deep learning prior information; unsupervised learning; deep pre-stack seismic data; ground roll suppression +
Footnote †: journal: Applied Sciences