esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Shape Design of Grommet through Analysis of Physical Properties of EPDM Materials
## 2 Experimental Analysis Based on Molding Process Conditions
### Elongation according to Time and Gas Removal Conditions  
Figure 2 shows the plot of the major factors that determine elongation. Time and temperature affect elongation, and gas removal has a minor effect on elongation. Figure 2b shows the diagram of the interactions. It can be confirmed that temperature and time affect each other.  
Table 3 shows the ANOVA results for the analysis of the factors that affect elongation. Based on the analysis of tensile strength according to process parameters, temperature and time affect tensile strength and elongation, while degassing does not. However, a few specimens without degassing did exhibit pores. We set the degassing parameter in additional experiments. The regression Equation (2) was derived through ANOVA analysis. The R\({}^{2}\) value of the regression equation is 96.69% and the adj. R\({}^{2}\) value is 95.86%.  
\[\textit{elongation}=\ 2213-7.436\textit{Temp}-1.437\textit{Time}-0.00702 \textit{Temp}\times\textit{Time} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Source** & **DF** & **Adj SS** & **Adj MS** & **\(F\)-Value** & **\(p\)-Value** \\ \hline Model & 3 & 15.95 & 5.31661 & 69.95 & 0.000 \\ Linear & 2 & 11.91 & 5.95374 & 78.33 & 0.000 \\ Temp (\({}^{\text{C}}\)) & 1 & 5.380 & 5.38007 & 70.78 & 0.000 \\ Time (s) & 1 & 6.5274 & 6.52741 & 85.88 & 0.000 \\
2-way interactions & 1 & 4.0423 & 4.04234 & 53.18 & 0.000 \\ T (\({}^{\text{C}}\)) \(\times\) Time (s) & 1 & 4.0423 & 4.04234 & 53.18 & 0.000 \\ Error & 12 & 0.9121 & 0.0760 & & \\ Total & 15 & 16.8619 & & & \\ \hline \end{tabular}
\end{table}
Table 2: ANOVA result for tensile strength.  
Figure 1: Main effects plot and interaction plot for tensile strength. (**a**) Main effects plot for tensile strength; and (**b**) Interaction plot for tensile strength.
## 3 Metabolic Responses to Different Antibiotic Classes
### Aminoglycos and Redox Response  
Aminoglycos, including gentamicin and tobramycin, rely on an active uptake mechanism dependent on the membrane electrochemical gradient (proton motive force), which is required to cross the cytoplasmic membrane and reach their ribosomal target within the bacterial cell [44].  
Under conditions that compromise this gradient--such as anoxia, medium acidification, or the concomitant use of respiratory chain inhibitors--the bactericidal efficacy of aminoglycos may be significantly reduced [44; 45].  
Their effectiveness is therefore linked to the respiratory state of the cell.  
Several studies have shown that metabolic downshifting toward fermentation or microaerophilic conditions induces functional tolerance to aminoglycos, reducing mitochondrial ROS production and lethal cellular damage [6; 7; 12].  
In _Pseudomonas aeruginosa_, the activation of compensatory redox pathways (e.g., glutathione biosynthesis, the NADPH pathway) is associated with a persistent phenotype tolerant to aminoglycos, even in the absence of genetic resistance mechanisms [46].
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 2 Background and Related Work
### Architectures for Securing ITS Networks  
This section introduces the security protocols implemented in this work. As seen, the standard approach relies on PKI in both European and USA security architectures. Table 2 lists the ETSI specifications related to our C-ITS PKI research. Meanwhile, the C-ITS PKI solution offers a high level of security, but it has certain limitations. As previously discussed, a major drawback of PKI is its inefficiency, a point that has also been emphasised in the literature [7; 13; 14; 31]. These constraints have motivated the research community to explore alternative security protocols. The DLAPP [13] and MFSPV [14] protocols were chosen to be implemented in this study; therefore, they will be briefly described. Our selection criteria for these protocols include their significance within the research community.
## 3 Results
### Sea Currents  
Taking into consideration the proximity of Areas A and B and the observation points, both these areas were considered as a single area for the purpose of sea currents analysis (Table 7). For this period, the predominant current was South-Southwest with a current intensity reaching 0.50 m per second. Area D showed great variability in the current direction along the observation period, going from North-Northwest to South-Southwest, with a current intensity reaching 0.36 m per second. Area E displayed great variability in the current direction as well, ranging from East-Northeast to West-Southwest with a current intensity reaching 0.75 m per second. The current maximum intensity was observed in depths ranging from 5 to 15 m and decreased gradually with increasing depth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sea State**} \\ \hline
**Areas** & **Hm0 (m)** & **Hm0 (m)** & **Hm0 (m)** & **Predominant** \\  & **Minimum** & **Maximum** & **Mean** & **Wave Direction** \\ \hline A, B and C & 0.30 & 9.70 & 2.10 & NW \\ \hline D & 0.28 & 9.79 & 1.72 & NW \\ \hline E & 0.14 & 6.97 & 0.96 & WSW \\ \hline \hline \end{tabular}
\end{table}
Table 6: Minimum, maximum, and mean of the significant wave height (Hm0) (m) and predominant wave direction in the areas studied.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Macroalgae**} & \multicolumn{2}{c}{**Temperature (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Phosphate (P)**} & \multirow{2}{*}{**Nitrogen Compounds (N)**} \\ \cline{2-2} \cline{4-6}  & **Minimum** & **Maximum** & **Ideal** & & & **Nitrogen Compounds (N)** \\ \hline \multirow{4}{*}{\begin{tabular}{l} Gracilaria \\ (_Gracilaria gracilis_) \\ \end{tabular} } & \multirow{4}{*}{10.0 \({}^{\circ}\)C [110]} & \multirow{4}{*}{28.0 \({}^{\circ}\)C [110]} & \multirow{4}{*}{28.0 \({}^{\circ}\)C [110]} & \multirow{4}{*}{28.0 \({}^{\circ}\)C [_Gracilaria_ sp.) [111]} & & \\  & & & & 25 \({}^{\circ}\)C (_Gracilaria_ & & \\  & & & _vertucos_) [112]} & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{l} Nori \\ (_Porphyna umbilicalis_) \\ \end{tabular} } & \multirow{4}{*}{3 \({}^{\circ}\)C (Porphyna [113])} & \multirow{4}{*}{20.0 \({}^{\circ}\)C [113]} & \multirow{4}{*}{10.0–15.0 \({}^{\circ}\)C [113]} & Fast growth: & Fast growth: & Fast growth: \\  & & & 19.50 \(\mu\)mol L\({}^{-1}\) & 35 \(\mu\)mol L\({}^{-1}\) \\ \cline{1-1} \cline{3-4}  & _haitantens_) [113]} & & & [116] & (Ammonia) [115] \\ \hline \multirow{4}{*}{
\begin{tabular}{l} Wakame \\ (_Indaria pinnatifida_) \\ \end{tabular} } & \multirow{4}{*}{5 \({}^{\circ}\)C [117]} & \multirow{4}{*}{25.0 \({}^{\circ}\)C [117]} & \multirow{4}{*}{25.0 \({}^{\circ}\)C [117]} & 12.0–13.0 \({}^{\circ}\)C [118]} & \multirow{4}{*}{-} & \multirow{4}{*}{-} \\  & & & 15.0 \({}^{\circ}\)C [117]} & & \\ \hline \multicolumn{6}{c}{- Data not found.} \\ \end{tabular}
\end{table}
Table 5: Minimum, maximum, and optimal temperature (\({}^{\circ}\)C), phosphate (\(\mu\)mol L\({}^{-1}\)) and nitrogen (\(\mu\)mol L\({}^{-1}\)) for the production of the selected potential macroalgae species to be produced in aquaculture in Portugal.
## 2 Materials and Methods
### Model Settings and Validation
#### 2.3.3 Greenhouse Model  
The computational model greenhouse has dimensions of length 12 m, width 10 m, and height 6 m. The greenhouse model has four circulating fans, four shelves of cultivating bed for tomato, and four CO\({}_{2}\) perforated tubes on each shelf (Figure 6). Leakage paths were managed in the door area and for tiny gaps across the greenhouse rib structure between the wall and the roof [31].  
Several turbulence models were tested to adopt suitable turbulence models in the greenhouse [5; 32]. Natural ventilation of the greenhouse simulation was validated by different mesh sizes and different turbulence models to determine the accuracy of CFD simulation [20]. This study tested different turbulence models to simulate CO\({}_{2}\) distribution while considering CO\({}_{2}\) absorption by the photosynthesis process and found that the optimum convergence was achieved in the standard \(k-\varepsilon\) turbulence model. Accordingly, the standard \(k-\varepsilon\) turbulence model was applied in the greenhouse simulation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Symbol** & **Unit** & **Value** \\ \hline Canopy photosynthesis rate & P\({}_{\text{cg}}\) & g CO\({}_{2}\) h\({}^{-1}\) m\({}^{-2}\)ground area & \\ \hline CO\({}_{2}\) density & Cc & g m\({}^{-3}\) & 1839 \\ \hline Conductance of CO\({}_{2}\) & \(\tau_{\text{c}}\) & m s\({}^{-1}\) & 12.168 \(\times\) 10\({}^{-4}\) \\ \hline Crop respiration & R\({}^{\prime}\) & g h\({}^{-1}\)m\({}^{-2}\) & 2.84 \(\times\) 10\({}^{-2}\) \\ \hline Initial CO\({}_{2}\) & - & ppm & 450 \\ \hline Leaf area density & LAD/\(\alpha\) & m\({}^{2}\)leaf m\({}^{-3}\)row & 2.67 \\ \hline Leaf area index & LAI & m\({}^{2}\) m\({}^{-2}\) & 4 \\ \hline The light use efficiency of the plant canopy & \(\alpha_{\text{c}}\) & g CO\({}_{2}\) J\({}^{-1}\) & 3.705 \(\times\) 10\({}^{-6}\) \\ \hline The incident light flux PAR & Jo & W m\({}^{-2}\)leaf & 379.9 \\ \hline \end{tabular}
\end{table}
Table 1: Computational conditions used for the chamber model.  
Figure 6: Greenhouse model: (the squares represent fan circulator, rectangular shapes represent the plants, the circles represent CO\({}_{2}\) perforated tube, and the thick lines and dots represent outlet).
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 1 Introduction
The benefits of combining AI and DTs in agriculture are manifold. AI can leverage virtual models created by DTs to simulate future scenarios, predict issues like drought or crop diseases, and provide targeted solutions [19]. This approach would enhance operational efficiency, reduce resource waste, and allow farmers to make more informed and timely decisions. Furthermore, AI can help optimize the use of fertilizers and pesticides, reducing environmental impact and increasing the sustainability of agricultural practices [20]. In summary, AI, when used in conjunction with DTs, can offer innovative solutions to address the complex challenges of modern agriculture, bringing significant benefits in terms of productivity, efficiency, and sustainability. Although various studies have explored DT applications in broader agricultural contexts and covered general technical aspects, supply chain performance, precision agriculture, and smart farming frameworks [19; 21; 22; 23; 24; 25; 26; 27], they often fail to specifically address the critical aspect of water management. Furthermore, the existing literature frequently overlooks the maturity levels of DT technology in agricultural water management. This lack of targeted research leaves a gap in understanding how DTs can optimize water use, thereby enhancing crop yields and sustainability. To address this research gap, this review aims to provide a comprehensive analysis of the development and application of DTs specifically in the context of agricultural water management. By meticulously screening and synthesizing the relevant scientific literature, this study seeks to encompass a wide range of crucial aspects. These include the specific tools and techniques employed in DT systems, the types of farms and environments where these systems are applied, and the maturity levels of the developed technologies. This thorough examination will offer a detailed understanding of the current state of DT technology in agricultural water management and identify potential areas for future research and development. After the Introduction, Section 2 describes the background. Section 3 covers the research methodology, which consists of the research questions and eligibility criteria applied to find studies in the literature for review. Section 4 shows the results of the applied methodology, the types of DTs, the integration of AI, the available tools, the maturity level of DT technology in agriculture water, key concepts, and a discussion. Sections 5 and 6 provide the discussion and conclusion, respectively.
# Research on Multi-Strategy Routing Protocol in Flying Ad Hoc Networks
## 3 Forwarding Policy Selection and Handover Design
### Forwarding Policy Selection  
In the RGF protocol, the source node must select and set the initial forwarding policy of the packet, as shown in Figure 5.  
Figure 4: RGF data packet header format.  
Figure 5: The process by which the source node selects the forwarding strategy.  
Suppose the source node does not have the effective location information of the destination node. In that case, it needs to start the reactive mechanism of the hybrid location service to build a reactive path and obtain the location information of the destination node, and then use the path to execute the reactive mechanism to forward the policy to transmit the data packet. If the source node has valid location information for the destination node, and there is also a reactive path available, the path will also be used first to transmit packets. Suppose the source node has valid location information for the destination node, but there is no available transmission path. In that case, the greedy forwarding strategy will be used to transmit the packets directly. In addition, if the source node finds that the location information of the destination node is about to be invalid, it actively obtains the location and motion information of the destination node through the reactive location service. At the same time, the current packet can still encapsulate the effective information of the destination node for transmission, thus avoiding the waiting delay caused by the need to retrieve the information of the destination node.  
The process of selecting the forwarding policy for intermediate nodes is shown in Figure 6.  
When an intermediate node forwards a packet, it first selects the forwarding policy of the packet according to the forwarding policy field in the packet header. Only when the handover conditions are met can the intermediate node change the forwarding policy of the packet. Otherwise, even if the intermediate node has a reactive path to the destination node, if the packet is currently greedy forwarding and the intermediate node is not the minimum local node, the intermediate node also needs to forward the packet with a greedy forwarding strategy.
###### Abstract  
The aim of the present study is to use a 3D finite element analysis to investigate and compare the transverse displacement and stress distribution between stainless steel miniscrews and titanium alloy miniscrews used for monocortical and bicortical anchorage during miniscrew-assisted rapid maxillary expansions. Skull models were constructed to depict expansion after and before midpalalat suture opening at varying miniscrew insertion depths in four clinical scenarios: monocortical, monocortical deepening, bicortical, and bicortical deepening. Finite element analyses of miniscrew properties, including transverse displacement and von Mises stress distribution, were performed for each clinical scenario. Peri-implant stress was lesser in both bicortical anchorage models compared to both monocortical models. Transverse displacement in the coronal and axial planes was also greater and more parallel in both bicortical models compared to both monocortical models. Transverse displacement and peri-implant stress did not significantly differ between monocortical and monocortical deepening models or between bicortical and bicortical deepening models. From a biomechanical perspective, the bicortical deepening miniscrew anchorage is preferable to monocortical and monocortical deepening anchorage, because bicortical anchorage induces less stress on the peri-implant bone. Consequently, bicortical deepening anchorage should be considered the preferred option in challenging clinical scenarios in which strong anchorage is required for maxillary skeletal expansion.  
b acortical anchorage; finite element analysis; monocortical anchorage; maxillary skeletal expansion; stainless steel miniscrew; titanium alloy miniscrew +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
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Footnote 0: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 2 Literature Review
### Crowdsensing  
Figure 1 shows the general structure of a crowdsensing system. It consists of an \(SP\) and users [10]. The users can be distinguished into data requesters and data providers. (1.1)(1.2) A data requester requests other users to provide sensing data through the \(SP\). A data provider is a user who can provide sensing data that meet the request of a data requester. (2) The data provider provides sensing data and (3) receives a reward. The \(SP\) manages the sensing data requests and the sensing data received from the data provider, transmits the sensing data to the data requester, and handles the reward data. However, the structure in which the \(SP\) performs sensing data processing and handles reward data poses a great risk of personal information exposure. In addition, if an attacker compromises only one \(SP\), the attacker can access sensitive personal information of all users.  
In this paper, we propose a method for preventing personal information disclosure in conventional crowdsensing systems by separating sensing data processing and reward data management. The \(SP\) handles the reward data, whereas the processing of the sensing data is delegated to a data processing node managed by a third user other than the data requester and the data provider. This defines a new user type: a data processing node. Each data processing node validates the sensing data and acts as an intermediary for transferring data to the data requester. As a data processing node is randomly selected by the \(SP\), it is possible to prevent a malicious user from obtaining personal information of a  
Figure 1: Structure of crowdsensing system.  
user participating in the pool of sensing data processing nodes. The data processing node transmits, to the \(SP\), the information about the reward to be paid to the data provider after the validation of the sensing data is completed. Then, the \(SP\) handles the reward data accordingly. Therefore, unlike in conventional crowdsensing systems, the \(SP\) cannot access personal information included in the sensing data. In addition, an attacker must attack the \(SP\) and all data processing nodes to obtain the personal information of a specific user.
## 9 Discussion  
### Finding 1: Assisted Learning Performance Through Multi-Modalities  
In this study, research question 1--learners' perspectives on assisted learning performance through multi-modalities in mobile learning applications--was explored both qualitatively and quantitatively. It was based on theory of the Zone of Proximal Development (ZPD) [28], which posits that higher mental functions are built upon lower mental functions and are embedded in the context of the sociocultural milieu; thus, only humans can react indirectly to the world by relying on mediating tools [21]. On the other hand, it was pointed out by [45], for [PERSON] and his followers, the definition of mediation also refers to using \"tools\". Tools, in this sense, mean anything that is utilized to help solve a problem or achieve a goal. Mobile technology is seen as a mediating tool that can help in the transformation and manipulation of learning activity. Therefore, if appropriate tools based on technology can be used, advanced learning performance can be assured.  
The analysis of learners' responses regarding their perspectives on assisted learning performance through multi-modalities in mobile learning applications indicated that students generally enjoyed using various modalities and perceived them as beneficial for enhancing their learning performance, which also agrees with the answers from the interviews. Among the five modalities, the visual modality was the most favored, while the aural modality was considered the most important for learning improvement. In terms of mean scores, the highest was recorded for the perceived importance of the aural modality (M = 4.13, S*D = 0.69), whereas the lowest scores were observed for appreciation the verbal modality (M = 3.99, S*D = 0.79) and the perceived importance of the writing modality (M = 3.99, S*D = 0.75). The overall mean score across all modalities was 4.04 (S*D = 0.74), indicating a generally positive perception of multi-modal learning. These findings suggest that, while students recognize the value of different learning modalities, their preferences and perceived effectiveness vary. The visual modality was the most well received, the aural modality was viewed as essential for learning enhancement, and the verbal and writing modalities were considered less enjoyable and less significant to assisting their learning performance, respectively. This finding also agreed with the second discovery from the interview analysis, which indicated that the visual modality was more appreciated by users in their most-often-used learning apps for many reasons, such as the vivid, attractive, colorful, and straightforward effects.
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 4 Simulation Results
### Complexity and Performance Analysis  
The D2D communication resource allocation algorithm of the proposed M21 GS algorithm is simulated by MATLAB simulation software, and the complexity and performance comparison is based on delay acceptance random resource allocation (DARA) algorithm [16] and optimal algorithm [19]. Since the resource allocation scheme based on the delay acceptance algorithm in reference [16] is established in the macro cellular network environment, in order to adapt the algorithm of the document [16] to the heterogeneous cellular network composed of macro cells and micro cells, this paper will match the D2D user and channel. The D2D user establishes a preference list based on the system capacity implemented on the channel. The channel establishes a list of preferences based on the size of the total system capacity achieved by different D2D communications. In addition, when the channel is first assigned to a D2D user, it is also necessary to check the communication service quality of the macro cell user and the micro cell user. This scheme enables the D2D user to find a suitable channel and obtain a stable match between the D2D user and the channel but only one channel is allowed to share resources with at most one D2D user. The random resource allocation algorithm only needs to satisfy the conditions of Equations (7)-(11) to obtain an allocation scheme.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Cell Radius & 1000 m \\ \hline Number of channels that D2D users can share & 2 \\ \hline Macrocell subscribers (number of channels) & 10\(\sim\)50 \\ \hline Microcell base station density & \(10^{-5}\)/m\({}^{2}\) \\ \hline Channel bandwidth & 0.18 MHz \\ \hline D2D user transmit power & 20 dBm \\ \hline Macro and micro cell user transmit power & 20 dBm \\ \hline Gaussian white noise density & \(-\)174 dBm/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.