esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Palladium Nanoparticles  
Recent studies have shown that palladium nanoparticles (Pd-NPs) have significant antimicrobial, electrical, catalytic, and photosensitive properties. Pd-NPs applied to nanocomposite scaffolds with reduced graphene oxide functionalized with polypyrrole have prevented the development of microbial films on these scaffolds. Nanocomposite scaffolds also promoted the proliferation of the SaOS-2 cell line [116]  
On the other hand, [PERSON] et al. reported that Mg-HA-Collagen type I scaffolds functionalized with golden nanorods (Au-NRs) and Pd-NPs inhibited the growth of adipose-derived MSCs up to 89% and 94%, respectively. Analysis of matrix mineralization density (by Alizarin Red staining) and the expression of ON (specific for bone mineralization) and the OC (specific for mature osteoblast) led to comparable results. Variants with Au-NRs and Pd-NPs also showed decreasing cell differentiation (20% lower) than the control. These results support other studies that found cytotoxic effects of Pd-NPs [117]. Recently, [PERSON] et al. presented the production and characterization of HAp/ZnO 0.05 wt%/Pd 0.1 wt% nanocomposite scaffolds. The initial materials were prepared using sol-gel and precipitation methods. The biocompatibility of scaffolds was tested in simulated body fluid (SBF) and with the interaction of dental pulp stem cells (DPSCs). In comparison to the HAp scaffold, HAp/ZnO/Pd scaffolds showed lower cell proliferation. These results could be caused due to the amount of deposited and released Zn\({}^{2+}\) ions having an inhibitory effect on cells. HAp/ZnO/Pd scaffolds coated with different concentrations of CS had lower density and microhardness and higher compressive strength and toughness than the pure HAp. However, HAp/ZnO/Pd scaffolds coated with CS exhibited antibacterial properties, which were examined in this study using _Pseudomonas aeruginosa_[118].  
[PERSON] et al. used synthetic porous Au/Pd alloy nanoparticles in bone regeneration during photothermal therapy. Pd-NPs were used as hyperthermia agents. The irradiation with a near-infrared laser caused mild localized heat (40-43 \({}^{\circ}\)C) produced by Au/Pd nanoparticles, which accelerated cell proliferation and bone regeneration. After 6 weeks, a new bone was formed in nearly 97% of the cranial defected area. Results of RNA sequencing showed that the Wnt signaling pathway was influenced by the mild localized heat [119].
## 2 System Model  
Figure 1 shows the typical architecture of connected vehicular networks using the NR V2X sidelink communication. Each vehicle broadcasts the basic safety message periodically.  
The message contains the instantaneous states of the vehicles, which helps a vehicle to generate the neighborhood map, which eventually helps the vehicle for its informed motion planning. In this work, we want to study the latency and reliability performance of V2X applications in a multi-lane highway scenario using 3 GPP's NR system without support from a base station.  
Figure 2 shows a summary of advanced V2X applications supported by the 3 GPP NR V2X system.  
Figure 1: System architecture of connected vehicular networks.  
Figure 2: NR-V2X-supported advanced applications and network features.  
It also highlights important features that are added by the 3 GPP working group. These advanced applications require high reliability (up to 99.99%), ultra-low latency (as low as 3 [ms]), and high bandwidth (as high as an order of terabytes (TB)/hour) [22]. A brief description of important features is given below.  
* **Automated driving**: A vehicle can sense the surrounding environment and can make lateral and longitudinal decisions by itself or by taking very little input from humans.
* **Cooperative perception**: A vehicle can increase its field of view and perception of neighbors by exchanging onboard sensory information through V2V and V2I communication.
* **Platooning**: A platoon of vehicles will route from the source to the destination in close proximity following the leader vehicle. The follower vehicles should not need any input from humans.
* **Infotainment**: Information (e.g., road maintenance updates, weather updates, etc.) and entertainment (e.g., live traffic updates, news, music, etc.) can be disseminated from one vehicle to other vehicles through V2V and/or I2V communication in a very short duration (within the order of milliseconds).  
NR V2X has a number of advanced features that enable it to provide higher reliability, lower latency, and higher bandwidth [14]. These are supports for (a) flexible frame structure by providing flexible numerology, (b) multiple frequency ranges, including sub 6 GHz and millimeter wave bands (24.25-52.6 GHz), (c) mini-slot scheduling for time-critical services, (d) sidelink network architecture for vehicles outside of the communication range of a base station, (e) resource allocation by base station (mode 1) and by the vehicles autonomously (mode 2), and (f) sophisticated MAC and PHY layers.  
Although NR V2X supports working in high-frequency ranges, it imposes several challenges in terms of communication point of view, including high signal attenuation, communication range restriction, higher background noise, and difficulty in costly hardware implementation. Hence, it is imperative to implement and conduct a comprehensive study of the 3 GPP NR V2X stack in a widely accepted simulator (such as ns-3 [23]) under different realistic traffic scenarios.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 5 Simulation Results
### Average Residual Energy of Nodes  
The Average Residual Energy (ARE) of 500 nodes in a 100 m \(\times\) 100 m network is shown in Figure 19 and there are four mobile Sinks in this network for data gathering. It is shown that the residual energy decrease quickly when no optimization algorithms are carried out. At the time of 2 \(\times\) 10\({}^{4}\) seconds this value is already below the threshold, 0.7 J. Because in this case, all nodes are active and they collect data with their initial sensing radius which may lead to high energy consumption. Moreover, the leaders should upload a considerable number of redundant data to the mobile Sink that also shortens the network lifetime.  
While after sleep scheduling, the number of active nodes significantly decreases, so the value of ARE increases. Moreover, after sensing radius adjustment, network life in LDGM could be further prolonged. For example, at the time of 3 \(\times\) 10\({}^{4}\) s, the value of ARE is closed to 0.8 J which ensures that most of the nodes could still work for a long time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Value of \(\lambda_{2}\)** & **Number of Active Nodes** & \(R_{s}\)**(\(S_{j}\)) after Amended** & **Number of Nodes with Different \(R_{s}\)**(\(S_{j}\))** \\ \hline \multirow{2}{*}{6.4 \(\times\) 10\({}^{-5}\)} & 252 & 7 m & 155 \\  & (Initial Value of \(R_{s}\)(\(S_{i}\)) is 8 m) & 6 m & 51 \\  & & 5 m & 11 \\ \hline \multirow{2}{*}{4.9 \(\times\) 10\({}^{-5}\)} & 284 & 6 m & 179 \\  & (Initial Value of \(R_{s}\)(\(S_{i}\)) is 7 m) & 5 m & 28 \\  & (Initial Value of \(R_{s}\)(\(S_{i}\)) is 7 m) & 4 m & 3 \\ \hline \multirow{2}{*}{3.6 \(\times\) 10\({}^{-5}\)} & 317 & 5 m & 168 \\  & (Initial Value of \(R_{s}\)(\(S_{i}\)) is 6 m) & 4 m & 22 \\  & (Initial Value of \(R_{s}\)(\(S_{i}\)) is 6 m) & 3 m & 2 \\ \hline \multirow{2}{*}{2.5 \(\times\) 10\({}^{-5}\)} & 382 & 4 m & 182 \\  & (Initial Value of \(R_{s}\)(\(S_{i}\)) is 5 m) & 3 m & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lengths of the sensing radius after adjustment.  
Figure 19: Average residual energy of nodes (100 m \(\times\) 100 m, 500 nodes).
# The Effect of 3D TVs on Eye Movement and Motor Performance
## 5 Conclusions  
Three-dimensional TVs have been commercialized in recent years; however, the commercialization of them has faced difficulties on the market. The purpose of this study was mainly to investigate in depth the effects of 3D TV environments on eye movement and motor performance. We also discussed the effect of parallax and index of difficulty, since both variables could influence eye movement and motor performance.  
The results showed that the environment had significant effects on eye movement time, index of eye performance, eye fixation accuracy, number of fixations, time to first fixation, saccadic duration, revisited fixation duration, hand movement time, index of hand performance, and error rate. Participants were found to have longer eye movement time, lower index of eye performance, longer time to first fixation, longer saccade duration, longer revisited fixation duration, lower eye gaze accuracy, longer hand movement time, and lower index of hand performance when the target was presented in a 3D environment.  
Interestingly, no significant effects of environment were found on the number of fixations and error rate. Regarding ID, the results showed that there were significant main effects between index of difficulty and hand movement time, index of hand and eye performances, error rate, eye fixation accuracy, saccadic duration, and revisited fixation duration. Finally, no significant differences were found between different levels of depth on any independent variables, although bigger depth (210 cm) mostly had the best eye movement and motor performance compared with smaller depth (190 cm and 140 cm).  
This study is the first in-depth investigations of the effect of 3D TVs to eye movement and motor performance. The parameters could be beneficial for developers [35; 36] and virtual reality researchers [59; 60; 61; 62; 63] to enhance the human performance of 3D TVs.  
**Author Contributions:** Conceptualization, C.J.L. and R.W.; methodology, C.J.L. and R.W.; software, R.W.; validation, C.J.L. and Y.T.P.; formal analysis, C.J.L., R.W. and Y.T.P.; investigation, C.J.L., R.W. and Y.T.P.; resources, R.W.; writing--original draft preparation, R.W. and Y.T.P.; writing--review and editing, C.J.L.; supervision, C.J.L.; funding acquisition, Y.T.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology of Taiwan (MOST 103-2221-E-011-100-MY3) and Mapia University Directed Research for Innovation and Value Enhancement (DRIVE).  
The current study was conducted according to the ethical guidelines published by the National Taiwan University Research Ethics Committee.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The researchers would like to extend their deepest gratitude to all participants in this study.  
The authors declare no conflict of interest.
## 7 Conclusions and Future Work  
In conclusion, it is strongly believed that since context-aware middleware is estimated to become one of the prime research objectives within the Agriculture 4.0 approach, the proposed solution is considered to constitute a complete framework for supporting context awareness. It is in that regard that the introduced model, based on the integration of WSANs into the IoT, has the benefit of being effortlessly adaptable, modifiable, and extendable for any application in any PF system environment no matter how complex it is.  
Future work on the subject is intended to include an in-depth performance evaluation of the model through the integration of multiple PF environments with various cultivations and in distinct locations in order to improve the interoperability and standardization of the proposed framework with regard to some significant cutting-edge standards such as the \"ISO/IEC 30141\" provided by the ISO as well as \"OpenLS\" and \"Sensor Web Enablement\" provided by the OGC. For what is more, since the involvement of smart mobile devices and social networking was not taken into much consideration, these features are going to be included as part of the ongoing work. Finally, issues regarding reliability analysis and cybersecurity are also going to be thoroughly examined.  
Conceptualization, E.S. and K.A.; methodology, formal analysis, investigation, writing--original draft preparation, E.S.; validation, E.S., K.A. and D.P.; writing--review and editing, E.S. and D.P.; supervision, K.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Authors would like to thank the Agricultural University of Athens for providing the required resources to complete and present this research project.  
The authors declare no conflict of interest.
## 6 Discussion
### Methodology
In this study, the time span between the optical and SAR data was not bigger than three days, which can be considered as an optimal case. Furthermore, the reconstruction quality highly depends on the underlying land cover class and their affection to seasonal changes. For highly variable classes, e.g., agriculture, the reconstruction based on data from a similar season should be preferred. The algorithm is capable to incorporate very diverse data sources. This was demonstrated by the integration of Landsat TM 5, SPOT 4, ERS, ALOS PalSAR and TerraSAR-X data. Based on these results, the CFV technique, which can be regarded as a modification of the Closest Spectral Fit technique proposed by [PERSON] _et al._[35], is found to fulfill the requirements of a generic and sensor independent approach that is simple and efficient.
# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
###### Abstract  
The traditional approach for coastal monitoring consists in ground investigations that are burdensome both in terms of logistics and costs, on a national or even regional scale. Earth Observation (EO) techniques can represent a cost-effective alternative for a wide scale coastal monitoring. Thanks to the all-weather day/night radar imaging capability and to the nationwide acquisition plan named MapItaly, devised by the Italian Space Agency and active since 2010, COSMO-SkyMed (CSK) constellation is able to provide X-band images covering the Italian territory. However, any remote sensing approach must be accurately calibrated and corrected taking into account the marine conditions. Therefore, in situ data are essential for proper EO data selection, geocoding, tidal corrections and validation of EO products. A combined semi-automatic technique for coastal risk assessment and monitoring, named COSMO-Beach, is presented here, integrating ground truths with EO data, as well as its application on two different test sites in Apulia Region (South Italy). The research has shown that CSK data for coastal monitoring ensure a shoreline detection accuracy lower than image pixel resolution, and also providing several advantages: low-cost data, a short revisit period, operational continuity and a low computational time.  
Keywords:integrated coastal zone management; coastal risk; shoreline erosion; Earth Observation; COSMO-SkyMed; ground truths +
Footnote †: journal: Sensors
## 3 Solution
### Master Stage
#### 3.2.8 Algorithmic Implementation of the DCVSA  
The implementation of the master optimization stage based on the new discrete-continuous version of the VSA is presented in Algorithm 1, which follows the structure proposed in [8].  
```
Data: Read the information of the AC distribution network to feed the optimization model (1)-(6).  Select the initial radius of the hyper-ellipse \(r_{0}\) and its initial center \(\mu_{0}\);  Generate the potential solutions \(w_{i}^{0}\) using (13);  Revise the upper and lower bounds for each \(w_{i}^{0}\) employing (15);  Solve the SAPF problem using the recursive Formula (9) for each \(w_{i}^{0}\);  Determine the value of the fitness function for each \(w_{i}^{0}\), i.e., \(A_{f}\big{(}w_{i}^{0}\big{)}\) using (16);  Determine the best current solution, i.e., \(w_{i,best}^{0}=\min_{i,t}\Big{\{}A_{f}\big{(}w_{i}^{0}\big{)}\Big{\}}\); for\(t=1:t_{\text{max}}\)do  Update the center of the hyper-ellipse \(\mu_{t}=w_{i,best}^{t-1}\);  Determine the new radius of the hyper-ellipse \(r_{t}\) with (17);  Generate the new potential solutions \(w_{i}^{t}\) using (13);  Revise the upper and lower bounds for each \(w_{i}^{t}\) employing (15);  Solve the SAPF problem using the recursive Formula (9) for each \(w_{i}^{t}\);  Determine the value of the fitness function for each \(w_{i}^{t}\), i.e., \(A_{f}\big{(}w_{i}^{t}\big{)}\) using (16);  Determine the best current solution, i.e., \(w_{i,best}^{t}=\min_{i,t}\Big{\{}A_{f}\big{(}w_{i}^{t}\big{)}\Big{\}}\); if\(\tau\geq\tau_{\text{max}}\)then  Reports as the solution of the problem \(\mu_{t}\); break;  end if  end for Result: Return the optimal solution found
```  
**Algorithm 1**Schematic implementation of the DCVSA to optimal allocation and sizing of D-STATCOMs in electric distribution networks.
## 3 Results and Discussion
### Adsorbents Characteristics
#### 3.1.2 Structure of Nano-Sized Materials by XRD and Small-Angle X-ray Scattering
The Porod constant was determined to ensure the asymptotic intensity decay proportional to q\({}^{-3}\) at higher angles according to the Porod law for homogeneous particles. A high degree of linearity in the Porod range may indicate a well-developed interphase boundary without the current phenomenon of blurring. The Porod plots calculated for all tested samples can be found in Figure 9. Sample C4 shows the lowest deviation from the linearity law among the tested carbon materials. The linearity range of the function I-q\({}^{3}\) = f(q) for the sample C4 covers q values from 0.125 A\({}^{-1}\) to almost the end of the curve. For this sample, the very last part of the curve even rises slightly upwards. Although such a positive deviation is small, it may indicate the presence of microporous regions within the interface that are responsible for additional scattering. The samples C1 and C3 show a very narrow linearity range (q from 0.13 to 0.23 A\({}^{-1}\)). For these samples, this linearity range is subject to a significant negative deviation from the linearity assumptions. It is visible as a reduction in scattering, particularly at high angles. Therefore, the values for the specific surface area may be distorted. Such a situation assumes that a quasi-two-phase system has a diffuse phase boundary or a transition zone (i.e., a boundary layer) of average thickness between two general phases. In the case of sample C2, the interface layers are expected to be highly blurred. In this case, the negative deviation from the linear curve is so large that the Porod constant cannot be determined with a reasonable value. The polymer samples P1 and P2 illustrate the scattering by a two-phase system with a sharp boundary between the electron densities in each phase. Comparing the Porod curves for polymers and carbon materials, the latter shows a significantly higher phase contrast. Figure 9: _Cont._  
The specific surface areas calculated from the Porod analysis agree well with the BET results (taking into account the general reciprocal relationship of the size of the interface). However, the SAXS values are slightly higher than the BET values. A possible reason for this is probably that SAXS can also detect closed pores and the nanostructured surface of the particles, which are not accessible to the BET method. For porous materials, SAXS therefore provides access to the closed porosity. The largest difference in area value obtained with these two methods concerns sample C4 and corresponds to \(\sim\)260 m\({}^{2}\)/g, indicating a rather significant closed porosity that may not be involved in the adsorption process. For the other samples, the closed porosity value is similar and amounts to approximately \(\sim\)150 m\({}^{2}\)/g-180 m\({}^{2}\)/g.
## 3 Numerical Algorithm
### The Physics-Based Preconditioners
#### 3.4.2. Based on the Crank-Nicolson Method
: _We remark that the preconditioning procedure that we are using in this paper is referred to as the physics-based or partial differential equation (PDE)-based method [20; 22; 29; 41]. A different physical process usually corresponds to a different class of PDE's. For example, the diffusion process is modeled by a parabolic type of PDE or a wave phenomenon described by a hyperbolic type of PDE. Some physical process, fluid flow for instance, can have a mixture of hyperbolic and parabolic characteristics. More importantly, some physical process can have multiple time-scale behavior resulting in less or more stiff terms in the governing equations. Depending on the stiffness measure, there exist well-established numerical techniques such as explicit algorithms (for non-stiff problems), implicit algorithms for stiff problems, or a semi-implicit algorithm for the mixed equation systems. We also note that stiff parts in the system are the source of the stringent time step stability restrictions. Therefore, having some physical insight regarding the model helps target specific stiff parts (diffusion terms in our case) to step over fine time scales resulting in the accelerated convergence of the linear algebra problem (Krylov step in our case). We remark that there exist matrix-based preconditioning technologies such as LU decomposition, incomplete LU and its variants, or other sorts of approaches [25; 29]. They can be successfully implemented to accelerate the convergence. However, the main disadvantage of almost all of them is that they all require some information about the Jacobian matrix of the nonlinear system. As noted earlier, forming the Jacobian matrix can be complicated and storing it can be expensive. Our matrix-free method (JFNK) does not require the formation or storage of the Jacobian matrix. Our preferred physics-based preconditioner also has the same characteristics and avoids the explicit formation of the preconditioning matrix and its inverse, but it rather computes the effect of the inverse of the preconditioning matrix multiplied by a Krylov vector. This basic structure of our preconditioning procedure provides a unique opportunity for flexible and straight forward code executions. The reliability and efficiency of the preconditioners are demonstrated in the following section.
# Machine Failure Prediction Using Survival Analysis
## 5 Results
### Survival and Cumulative Hazard Curves  
From the analysis of the survival and cumulative hazard curves, it is observed that the safe operation of the machines ranges from 0 to \(\sim\)200 min of operation. From this point onwards, the risk of machine failure increases rapidly. The same pattern is confirmed by the failure of machines after this time point, as shown in Figure 7. Furthermore, an examination was carried out on the machines in the only categorical variable contained in the dataset, which was the type of machine shown in Figure 8. This variable took on the values of high, medium, and low. From this examination, it appears that high-quality machines perform better than low- and medium-quality machines, although to a small extent, in the time range of 200-240 min. This may also be due to their relatively smaller population, as high-quality machines accounted for 20% of the total dataset. In addition, a noticeable superiority of medium-quality machines over low-quality machines is observed in the time range of 200-230 min, which evens out in the later time frames.  
Figure 6: Optimal number of features based on RFE accuracy score for different causes of machine failure.  
Figure 7: Cumulative hazard and survival curves.