esa-sceva/satcom-chunk-collection huggingface dataset:
# Direct Fabrication of Ultrahydrophobic Laser-Induced Graphene for Strain Sensors
## 3 Results and Discussion
### Superhydrophobic Property
On the other hand, the circuit resistance of the SH-LIG prepared on the composite material stays almost constant, with a negligible variation of less than 1%. This further confirms the remarkable stability of the fabricated SH-LIG under complete immersion conditions. Figure 8: Dynamic contact angle measurements on the SH-LIG modified area and electrical resistance measurements performed on a SH-LIG conduction line circuit (green line) at different time intervals up to 25 h.  
Figure 9: Depiction of highly reflective plastron (thin air film) layer over the superhydrophobic LIG on silicon surface (**a**) before immersion (**b**) after immersion. High-speed camera images in Figure 10a,b, show the droplet bounce behavior before and after water immersion for 24 h. Indeed, the droplet bounce characteristics provide a direct means to examine the surface's ultrahydroophibc property due to the energy exchange involved on surface impact. The contact time of the droplet (6 \(\upmu\)L) on the superhydrophobic surface from impact (1.0 m/s) to lift-off is considered as the analysis parameter in this study. In coherence with the droplet bounce behavior on artificial ultrahydrophobic surfaces, the drop spreads like a thin sheet to a maximum diameter, recoils, and leaves the surface for both the tested surfaces [60; 61]. For the freshly prepared SH-LIG, the droplet lifts off within 11.2 ms; for the immersed SH-LIG, the droplet lift-off occurs within 12.1 ms. The contact time is similar for both surfaces and relates to the droplet bounce behavior on conventional biomimetic ultrahydrophobic surfaces. Such surfaces with reduced contact time have the potential for pagophobic applications [62]. The SH-LIG surfaces fabricated in this study demonstrate stable water-repellence properties, bolstering the applicational prospects of substrate-independent laser-based graphene oxide devices.
# Seismological and Engineering Demand Misfits for Evaluating Simulated Ground Motion Records
## 2 Study Area
### Simulated Records of the 1992 Erzincan (Turkey) (Mw = 6.6) Earthquake  
The NAFZ, with an active right-lateral strike-slip fault mechanism, is situated in the northern part of Turkey. In the eastern segments of the NAFZ, the 1992 Erzincan earthquake with the moment magnitude of 6.6 occurred. This earthquake led to at least five hundred mortalities besides an economical damage cost of five to ten trillion Turkish Liras [50]. Erzincan is an area placed on a deep alluvial basin with softer soil conditions [47]. The region is formed at the conjunction of three active faults with strike-slip mechanisms: the left-lateral North East Anatolian, the right-lateral North Anatolian and the left-lateral Ovacik faults. In spite of the intense seismicity of the region, only three stations recorded the 1992 Erzincan event. Thus, as the first case study, simulations are evaluated for the 1992 Erzincan event that occurred on a tectonically complex zone. Due to sparse seismic networks and limited number of recorded motions, simulations have been calibrated using the data at only three stations [46]. Figure 1 shows the epicenter, fault plane and location of the existing stations during the 1992 Erzincan event. Table 1 summarizes the information corresponding to the selected three stations.  
The recorded raw time histories at the selected stations are taken from the Turkish strong ground motion database [42]. For simulation of the time histories at the selected three stations the validated parameters corresponding to the source, propagation and site effects proposed by [PERSON] et al. [46] are employed. In that study, calibration of the simulation parameters corresponding to the 1992 Erzincan event has been performed using the recorded time histories at the existing three stations. The simulated  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Code**} & **Latitude** & **Longitude** & **Site Class** & **R\({}_{\text{pri}}\)** & **PGA-EW** & **PGA-NS** & **PGA-Sim** & **PGV-EW** & **PGV-NS** & **PGV-Sim** \\  & & (*) & (*) & (*) & (**NEHKP) [51] & **(****km)** & **(****km)** & **(****km)** & **(****km)** & **(****km)** & **(****km)** & **(****km)** & **(****km)** \\ \hline Erzincan-Merker & ERC & 39.752 & 39.487 & D & 12.83 & 430.66 & 509.20 & 445.81 & 56.80 & 79.84 & 33.25 \\ Refahive & REF & 39.899 & 38.768 & C & 76.45 & 75.26 & 66.78 & 39.36 & 3.67 & 3.93 & 2.30 \\ Teren & TER & 39.777 & 40.391 & D & 65.62 & 25.56 & 37.90 & 35.95 & 4.30 & 2.86 & 3.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on the selected stations corresponding to the 1992 Erzincan (Turkey) event with Mw = 6.6.  
Figure 1: Selected stations along with the epicenter of the 1992 Erzincan event where the rectangular box shows the fault projection.  
Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) values at the selected stations in both North-South (NS) and East-West (EW) directions are provided in Table 1.
# Power Control and Channel Allocation Algorithm for Energy Harvesting D2D Communications
## 3 Optimal Joint Power Control and Channel Allocation Algorithm  
### Optimization Formulation  
In this section, we analyze the optimal transmission power and energy harvesting time of D2D users, the optimal channel matching between cellular users and D2D users, taking the minimum QoS demands of cellular users and the limited transmission power into consideration, the optimization problem (P1) is established as  
\[\max\limits_{\{p_{j,i}^{d},\theta_{j,i},x_{ji}\}}C_{D2D}=\sum\limits_{i=1}^{M} \sum\limits_{j=1}^{K}x_{ji}C_{j,i}^{d} \tag{6}\]  
Subject to:  
\[SINR_{i}^{B}\geq rth_{c} \tag{7}\]  
\[0<\theta_{j,i}<1 \tag{8}\]  
\[(1-\theta_{j,i})P_{j,i}^{d}\leq\xi_{j}\theta_{j,i}P_{BS}h_{Bj} \tag{9}\]  
\[P_{i}^{c}\leq P_{\max}^{c},\ P_{j,i}^{d}\leq P_{\max}^{d} \tag{10}\]  
\[x_{ji}\in\{0,1\},\ \sum\limits_{i}x_{ji}\leq 1 \tag{11}\]  
where \(x_{ji}\) is the channel reuse indicator of the D2D user, \(x_{ji}=1\) means the D2D user \(j\) reuses the channel of cellular user \(i\), otherwise, it does not reuse. \(rth_{c}\) represents the minimum SINR threshold of the cellular users, \(P_{\max}^{c}\) and \(P_{\max}^{d}\) represent the maximum transmit power of cellular users and D2D users, respectively. Equation (6) is the objective function of maximizing the total capacity of D2D users. Equation (7) guarantees the QoS requirement of cellular users. Equation (8) is the energy harvesting time constraint. Equation (9) is the energy causality constraint and means that the consumed energy for transmitting signals cannot exceed its harvested energy. Equation (10) indicates the limit transmission power of the cellular user and the D2D users. Equation (11) represents that one D2D user can reuse at most one channel.  
The optimization problem P1 is a mixed-integer nonlinear programming problem, which is NP-hard and cannot be solved by standard convex optimization methods [15]. Therefore, we cluster D2D users according to interference weight graph in the first step, and those in the same cluster can share the same channel. Then, a joint iteration optimization method is used to determine the optimal transmission power and energy harvesting time. Last, we allocate channel for D2D users with the KM algorithm to maximize the total capacity of D2D users.
## 3 The FQS Algorithm
### Preprocessing Phase  
In the preprocessing phase, FQS needs to determine three elements: (1) The maximal expected shift position (\(pos\)) for pattern \(P\) using Equation (4); (2) a shift table for pattern \(P\) using the QS algorithm; and (3) a shift table for \(P[0,...,pos-1]\), the prefix of \(P\), using the QS algorithm. The maximal expected shift position (\(pos\), from Equation (4)) is the maximal expected shift distance using the bad character rule. \(pos\) is calculated from pattern \(P\) in the preprocessing phase.
## 3 Algorithm Design
### Hierarchical Structure Model  
In UASNs, the most important characteristics are node mobility, multipath propagation loss, time uncertainty, and low communication rate. In this paper, our two localization methods are based on hierarchical structure model.  
Figure 2 shows a hierarchical structure and projection model. Using the depth information of the mobile node, three task anchors \(n_{1}\), \(n_{2}\) and \(n_{3}\) will be projected to three positions \(n^{\prime}_{1}\), \(n^{\prime}_{2}\) and \(n^{\prime}_{3}\) in the plane where the mobile node locates, respectively.  
Note that the minimum communication angle \(\alpha_{ij}^{t}\in[0,\dfrac{\pi}{2}](j=1,2,3)\) can be obtained by the mobile node \(n_{i}\) based on the AOA measurement, and the depth difference \(k_{ij}^{t}(j=1,2,3)\) can be measured by the equipped pressure sensors. When the anchor \(n_{j}\) and the mobile node \(n_{i}\) are with the same depth, \(\alpha_{ij}^{t}=0\), \(k_{ij}^{t}=0\). When \(\alpha_{ij}^{t}=\dfrac{\pi}{2}\), the value of the minimum communication angle between \(n_{j}\) and \(n_{i}\) will be maximum. So at time instant \(t\) the geographic distance between the anchor \(n_{j}\) and the mobile node \(n_{i}\) can be calculated as:  
\[d^{t}(n_{i},n_{j})=k_{ij}^{t}/\sin\alpha_{ij}^{t} \tag{9}\]  
And at time instant \(t\), the distance \(p_{ij}^{t}\) between the mobile node \(n_{i}\) and the task anchor's projection \(n_{j}^{t}\) can be calculated as:  
\[p_{ij}^{t}=k_{ij}^{t}/\tan\alpha_{ij}^{t} \tag{10}\]
# A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering
## 5 Performance Evaluation
### Experimental Results  
Figure 6 shows the results of comparing the prediction accuracies of the EMDP, AutAI, and k-RRI algorithms for the training datasets \(M_{500}Givet_{10}\), \(M_{500}Givet_{20}\), and \(M_{500}Givet_{30}\).  
From the graph, we can observe that the \(MAE\) scores of EMDP and AutAI for the \(M_{500}Given_{10}\) dataset were 0.826 and 0.823, respectively, indicating that AutAI slightly outperformed EMDP. For the \(M_{500}Given_{10}\) dataset, the highest accuracy was shown by the proposed method k-RRI, outperforming EMDP by 11.3% and AutAI by 10.9%. On the other hand, the \(MAE\) scores of EMDP and AutAI for the \(M_{500}Given_{20}\) dataset were 0.776 and 0.836, respectively, indicating that EMDP outperformed AutAI. For \(M_{500}Given_{20}\), the highest accuracy was shown by the proposed method k-RRI, outperforming EMDP by 5.6% and AutAI by 13.8%. Lastly, the \(MAE\) scores of EMDP and AutAI for \(M_{500}Given_{30}\) dataset were 0.820 and 0.804, respectively, indicating that AutAI slightly outperformed EMDP. As in the previous two cases, the highest accuracy was shown by k-RRI, outperforming EMDP by 12.2% and AutAI by 10.0%.  
Figure 7 demonstrates the results of comparing the prediction accuracies of k-RRI as the number of \(k\) values varies. From the graph, we can observe that the highest accuracy can be obtained when the number of \(k\) values is set to \(k=8\) for the \(M_{500}Given_{10}\) and \(M_{500}Given_{20}\) datasets, and \(k=6\) for the \(M_{500}Given_{30}\) dataset. It is important to note that the prediction accuracy of k-RRI varied depending on the number of recursive steps with standard deviation (SD), which is a measure of the variation or dispersion of a dataset. The SD exhibited values of 0.0047, 0.0048, and 0.0022 at \(Given_{10}\), \(Given_{20}\), and \(Given_{30}\), respectively. The SD may be significantly high or negligibly low depending on the system's characteristics. From this perspective, \(k=2\) is recommended for a system less sensitive to prediction accuracy in terms of computational time. For a system sensitive to prediction accuracy, it is recommended that the value of \(k\) should be optimized according to the system under consideration.  
Figure 6: The prediction accuracy of missing data imputation algorithms.  
Figure 7: The prediction accuracy of k-RRI as number of k values varies.
## 4 Results and Discussion
### Pile-Wall Spacing Factor Analysis
#### 4.3.2 The Influence of the Pile-Wall Gap on the Bearing Characteristics of Retaining Walls  
(1) Horizontal displacement of the retaining wall  
Figure 15 shows the change rule of horizontal displacement of the retaining wall at different pile-wall spacing, and the horizontal displacement of the retaining wall decreases gradually with the increase in the pile-wall gap, and the interaction between the pile-wall weakens. When excavating at 1 m, the pile-wall gap expands from 1 m to 4 m, and the top displacement of the wall decreases from 4.78 mm to 2.12 mm, decreasing by 55.6%; when excavating at 2 m, the pile-wall gap increases from 1 m to 4 m, and the top displacement of the wall decreases from 9.95 mm to 4.28 mm, decreasing by 56.9%.  
Figure 13: Pile displacement at different pile–wall spacing values. (a) Excavation at 1 m. (b) Excavation at 2 m.  
(2) Distribution of soil pressure  
Figure 16 indicates the distribution rule of soil pressure at the back of the wall with different pile-wall gap values, and a larger pile-wall gap leads to smaller horizontal displacement of the retaining wall when excavating and unloading and greater soil pressure  
Figure 14: Pile stress at different pile-wall spacing values. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.  
Figure 15: Displacement of the retaining walls with different pile-wall spacing values. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.  
at the wall's back. With the increase in the excavation depth, the soil pressure of the retaining wall weakens, and the maximum soil pressure of the retaining wall is located at the wall's bottom. When excavating at 1 m, the pile-wall gap increases from 1 m to 4 m, and the maximum earth pressure at the wall's bottom increases from 33.1 kPa to 39.7 kPa; increasing by 16.6%; when excavating at 2 m, the pile-wall gap increases from 1 m to 4 m, and the maximum earth pressure at the wall's bottom increases from 32.5 kPa to 37.7 kPa, increasing by 16.0%.
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 2 Materials and Methods
### State of the Art
#### 2.2.3 Security Options for Swarms of Heterogeneous Autonomous Vehicles  
Establishing a cyber-security architecture for autonomous vehicles is highly recommendable, as vulnerabilities in autonomous vehicles can be damaging to quality of life and human safety [16]. However, it is difficult to secure solutions based on heterogeneous robots, since although different manufacturers offer particular solutions that focus on specific security aspects (e.g., DJI's Pilot PE app [17] or Parrot's FreeFlight 6 app [18], which ensure data privacy), there is a lack of global solutions that cover all areas of security and there is no universal solution valid for heterogeneous ecosystems.  
The solution presented by the authors of this paper is based on DDS, which offers a real-time communication middleware able to establish a common connectivity framework for robots that have their own onboard SDK (Software Development Kit). However, the cost of a full-feature (i.e., able to provide security mechanisms) DDS commercial license  
Figure 1: Core components of a system to manage missions with a swarm of autonomous vehicles.  
is expensive (around 8000 EUR/year) for most farmers. Some well-known providers are RTI, eProsima or Twin Oaks. Moreover, there are two open-source DDS implementations available: (a) the ADLink DDS Community Edition [19] that supports the OMG DDSI2 standard and ensures interoperability with other DDS implementations but lacks security support and (b) Eclipse Cyclone DDS [20], a distribution compliant with the OMG standards for interoperability and security (which guarantees authentication, access control and encryption). The authors of this article tested both implementations. Tests with the Eclipse Cyclone implementation have not been satisfactory for the ecosystem deployed: at the time of writing this article, neither flexible configuration of DDS options (e.g., QoS parameters and automatic discovery) nor compatibility with embedded systems (e.g., lack of compatibility with Raspberry Pi, a common board for unmanned robots) is supported. Tests with the DDS Community Edition have been successful from the communication point of view, but as said before, the solution lacks security support mechanisms.
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.4 Soil Moisture Data  
The Climate Change Initiative (CCI) SM dataset, which provides surface soil moisture information, is a product of the European Space Agency (ESA, Paris, France) CCI [42; 43]. The latest release (v07.1) spans 43 years from November 1978 to December 2021 and is accessible via this link: [[https://catalogue.ceda.ac.uk/uuid/](https://catalogue.ceda.ac.uk/uuid/)]([https://catalogue.ceda.ac.uk/uuid/](https://catalogue.ceda.ac.uk/uuid/)), accessed on 1 August 2023. The SM CCI COMBINED dataset, which has a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a volumetric unit (m\({}^{3}\)/m\({}^{3}\)), was selected to assess drought conditions in the Inner Mongolia grassland from 2002 to 2020. The reliability of the dataset has been validated in various regions worldwide [44; 45; 46]. Previous studies have also indicated that CCI-SM data are suitable for the in situ SM data in Inner Mongolia [47; 48; 49]. We also tested the CCI-SM data with 17 in situ data of Inner Mongolia from 2000 to 2010. The results indicate that monthly CCI-SM data have a good consistency with the in situ data, and over 94% of stations (16/17) passed the 0.05 significance level test (Figure S1).
# Durability of Subsea Tunnels under the Coupled Action of Stress and Chloride Ions
## 2 Chloride Ion Transfer Equation under the Coupled Action of Convection and Diffusion  
In concrete, chloride ions transfer mainly by diffusion under the action of the concentration gradient, the capillary permeability under the effect of the humidity gradient, the permeability under the influence of the pressure gradient, and the ion migration under the action of the potential gradient [4]. Among them, permeability under the influence of the pressure gradient and the capillary permeability are called convection [4]. To determine the chloride ion transfer in both the inside and outside of tunnel lining, it is necessary to consider the coupled action of convection and diffusion.  
The one-dimensional equation of chloride ion transfer under the coupled action of convection and diffusion is shown in Equation (1) [21]:  
\[\frac{\partial C}{\partial t}=\frac{\partial}{\partial x}[D\frac{\partial C}{ \partial x}-C(v_{p}+v_{c})] \tag{1}\]  
The initial conditions and boundary conditions are provided in Equation (2):  
\[\begin{array}{l}C|_{t=0,x>0}=C_{0}\\ C|_{x=0}=C_{s}\\ C|_{x=+\infty}=C_{0}\end{array} \tag{2}\]where \(C\) is the concentration of chlorine; \(x\) is the calculating depth from one side the lining; \(D\) is the diffusion coefficient of the chloride ion; \(v_{p}\) and \(v_{c}\) refer to the flow velocity of pore fluid under the action of pressure permeability and capillary permeability, respectively, also called convection velocity; \(C_{s}\) is the concentration of the free chlorine ions on the surface of the concrete; and \(C_{0}\) is the initial concentration of free chloride ions in the concrete.
# E-Sail Three-Dimensional Interplanetary Transfer with Fixed Pitch Angle
## 1 Introduction
In this regard, as discussed in Section 4, the numerical results obtained by simulations indicate that this simplified (constrained) steering law provides a rather limited performance variation in the case where the reference propulsive acceleration of the E-sail is compatible with a medium performance propellantless propulsion system. However, a careful analysis of the generic interplanetary mission scenario indicates that the performance degradation obtained using a constrained guidance law is strictly related to the specific value of the pitch angle selected during the transfer. In this regard, Section 4 contains a parametric study of the problem where the total flight time is obtained, for a given mission scenario, as a function of the selected sail pitch angle. Such a parametric study allows the best value of the sail pitch angle to be obtained in order to minimize the performance degradation in an assigned mission scenario and for a given E-sail reference propulsive acceleration level. Finally, Section 5 concludes the paper.
## 2 Materials and Methods
### Search Strategy  
The search was carried out in May 2023 on PubMed (Medline), Scopus, and Web of Science. The electronic search combined Medical Subject Heading (MeSH) terms, text words (tw), and truncation terms. The Boolean operators \"AND\" and \"OR\" were used to create the search strategy (Table 1). No language or publication date restrictions were applied. Additionally, gray literature was investigated through OpenGrey, and a manual search of the _Journal of Endodontics_, _International Endodontic Journal_, and _Australian Endodontic Journal_ was performed to find any additional papers. Moreover, an additional search was conducted using the reference lists of all included papers. References from different databases were imported into the EndNote X9 software (Thomson Reuters, New York, NY, USA), which automatically removed duplicate records.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Search Strategy** & **Findings** \\ \hline \multirow{7}{*}{PubMed} & \#1 ((non-thermal plasma[Title/Abstract]) or (nonthermal plasma[Title/Abstract]) \\  & or (Plasma Gases[Title/Abstract]) or (plasma treatment[Title/Abstract]) or \\  & plasma[Title/Abstract] or (Plasma Gases[MeSH Terms]) or plasma[MeSH Terms]) \\ \cline{2-3}  & \#2 ((dental cements[MeSH Terms]) or (root canal sealants[MeSH Terms]) or (dental \\  & cement *[Title/Abstract]) or (root canal seal *[Title/Abstract]) or (endodontic seal \\  & *[Title/Abstract]) or (root canal fill *[Title/Abstract]) or (seal*[Title/Abstract]) \\ \cline{2-3}  & \#3 ((endodontic *[Title/Abstract]) or (root canal[Title/Abstract]) or (endodontic \\  & treatment[Title/Abstract]) or (root canal treatment[Title/Abstract]) or (Root Canal Therapy[Title/Abstract]) or (Root Canal Therapy[MeSH Terms]) or \\  & (Endodontics[MeSH Terms]) \\ \cline{2-3}  & \#1 and \#2 and \#3 & 96 \\ \hline \multirow{7}{*}{ Scopus} & \#1 TTLE-ABS-KEY(“non-thermal plasma” or “nonthermal plasma” or “Plasma Gases” or “plasma treatment” or plasma) \\  & \#2 TTLE-ABS-KEY(“dental cements” or “root canal sealants” or “root canal sealants” or “total cement ** or “root canal fill ** or “seal **) \\ \cline{2-3}  & \#3 TTLE-ABS-KEY(“endodontic ** or “root canal” or “root canal” or “endodontic treatment” or “Root canal Treatment” or “Root Canal Therapy”) \\ \cline{2-3}  & \#1 and \#2 and \#3 & 140 \\ \hline \multirow{7}{*}{Web of Science} & \#1 TS = (“non-thermal plasma” or “nonthermal plasma” or “Plasma Gases” or “plasma treatment” or plasma) \\  & \#2 TS = (“dental cements” or “root canal sealants” or “dental cement ** or “root canal seal ** or “endodontic seal ** or “root canal fill ** or “seal **) \\ \cline{2-3}  & \#3 TS = (“endodontic ** or “root canal” or “root canal” or “endodontic treatment” or “root canal treatment” or “Root Canal Therapy”) \\ \cline{2-3}  & \#1 and \#2 and \#3 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search strategy in different databases.