esa-sceva/satcom-chunk-collection huggingface dataset:
# Sample, Scalable Route to Produce Transparent Superhydrophobic/Hydrophilic Film Surfaces
## 2 Experimental Section
### Preparation of Superhydrophobic/Superhydrophilic Coatings  
Sol-gel preparation  
The sol-gel was synthesized using the hydration reaction of TEOS. To create silanol monomers, hydrolysis of TEOS in an ethanol/ammonia solution replaces the ethoxyl groups (-Si-OEt) with silanol groups (-Si-OH). First, 60 mL of EtOH, 30 mL of deionized water, and 2.5 mL of diluted (5 vol%) nitric were added to 10 mL of TEOS. The mixture was stirred vigorously for 20 min at room temperature and 420 rpm to obtain a uniform solution.  
Formulation preparation  
The nanoparticle suspensions were prepared by ultrasonication of 1 g of nanoparticle in 100 mL of ethanol (EtOH) using a probe sonicator for 10 min. The composite sol-gel/nanocomposite was prepared by stirring 70 mL of nanomaterial-EtOH suspension with 30 mL of sol-gel for 10 min.  
Deposition procedure  
The glass substrates, cleaned using an ultrasonication bath in Acetone for 2 min and IPA for 5 min, were sprayed using Sono-Tek Exacta Coat, an ultrasonic spray system with AccuMist nozzle. The ultrasound frequency of the nozzle was kept at 60 kHz, and the generator power was set to 1.5 W. The suspension was sonicated for 10 min prior to the spraying. The volumetric flow rate was 2 mL/min, while the path speed, i.e., the speed at which the nozzle moves during the spraying procedure, was kept at 100 mm/min. Further spraying parameters, such as the distance between the nozzle and the substrate in mm, shaped air pressure, i.e., pressurized air used to guide the sprayed droplets towards the substrate, in psi and area spacing in mm.  
Characterization methods  
An ultraviolet-visible spectrophotometer (UV-Vis) (Perkin Elmer, model Lambda 1050 spectrometer) was used to study the percentage transmittance within the visible wavelength range (350 nm-800 nm) of the suspensions and the films. The Litesizer (Anton Paar Co., model 500, Austria) was used to determine the particle size of the nanoparticles within the suspension. The analysis was run in a 1 cm optical cuvette and the refractive indexes were set at 1.54, 1.46, and 1.3577 for HNT, Silica, and ethanol, respectively. A PHI VersaProbe 5000 scanning X -ray photoelectron spectrometer was used to conduct the X-ray photoelectron spectroscopy (XPS) analysis to assess the chemical composition of the as-deposited layer. A monochromated Al X-ray source (1486.6 eV), which is part of the system, was employed as a probe for the tests. A dual-beam system, Scios 2 (Thermofisher Scientific) was used for the scanning electron microscopy (SEM) investigation for particles distribution and size measurements. Transmission electron microscopy (TEM) investigation was performed using a 300 kV Titan system (Thermofisher Scientific) to determine the dimensions of raw materials. The water contact angle (WCA) was determined using a Goniometer (Rame-hart Instrument Co., model 590) using 15 \(\upmu\)L drops of deionized water.
# Research on Narrow Linewidth External Cavity Semiconductor Lasers
## 3 Waveguide Structure of NLECSLs
### Silicon-Based Waveguide Structure of NLECSLs
[38] demonstrated the hybrid integration of an InP-based laser and amplifier array PIC and high-quality factor silicon nitride microring resonator. Laser emission based on the gain of the interference combination amplifier array in the external cavity was formed by the feedback from the silicon nitride micro resonator chip; the linewidth was reduced to 3 kHz, and the average output power was 37.9 mW. Table 4 shows the performances of silicon-based waveguide structures of NLECSLs. Silicon-based waveguide structures of NLECSLs have excellent characteristics, such as compact structure, low cost, mass production, integrated packaging, small size, etc., and have a wide tuning range, while achieving a narrow linewidth. At present, the technical difficulty of silicon-based waveguide structures of NLECSL involves how to improve the coupling efficiency and reduce the reflectivity at the coupling. In addition, the heat accumulation and dissipation during the thermo-optic effect tuning process takes a certain amount of time, which affects the high-speed tuning. How to further improve the modulation speed is a big challenge.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 3 Materials and Methods
### Kalman Filter Algorithm
In summary, the KF algorithm has the following characteristics:  
1. The object of the KF algorithm research is a stochastic process, with sequential data. 2. The goal of filtering is to predict all random processes even with useless noise. 3. Differing from the least squares method, the white noise existing in the dynamic system or the observation error existing in the observation data does not need to be filtered. The statistical characteristics of this noise information will be used by the model in the prediction process. 4. The KF algorithm uses a recursive algorithm, and spatial state representation equations are used to construct time-domain filters for prediction of multidimensional random variables (the predicted system state consists of multiple features). 5. Compared to the ARIMA model, the time series data used for prediction can be smooth or not. 6. The prediction process only considers the process noise, the noise generated by the observation method and the statistical characteristics of the system at the current time point. Besides, the model calculation is small, which is very suitable for real-time prediction. Based on the characteristics of the Kalman Filter algorithm, this paper constructs the KF algorithm on the edge device (Raspberry Pi) to predict the concentration of various air pollutants in real time. Although there are certain measurement errors in low-cost sensors and processing noises in the model, the KF can improve the accuracy of sensors from the algorithm level by combining the errored observation data. Moreover, the predicted value of the next moment can be obtained from the data of the previous moment, so that the system has predictability for various pollutants and improves the decision-making efficiency. As shown in Figure 3, the KF is used in the edge computing environment of this paper.
## 3 Numerical Modelling
From experimental evidence, i.e., on the surface of the prosthetic, woven layers inclined \(\pm 45^{\circ}\) are visible. Hence, it is assumed to have woven layers throughout the entire component. Thus, the in-plane Young moduli (\(E_{1}\) and \(E_{2}\)) are assumed equal. As consequence, also \(v_{13}\) and \(v_{23}\) and \(G_{13}\) and \(G_{23}\) are considered equal [47; 48]. Table 6 outlines the orthotropic material properties that best describe the dynamic behaviour of the component after an iterative tuning phase. The first six mode shapes (orthotropic material FEA) are displayed in Figure 9, while corresponding natural frequencies are compared to EMA ones in Table 7, which shows the MAC values computed for each mode shape as well. The mode shapes depicted in Figure 9 seem to match those observed in the experimental counterparts (in Figure 4). Specifically, the first, third, and fifth modes correspond to the first, second, and third bending moments in the XZ plane. The second and sixth modes affect the torsion of the distal area of the prosthetic, while the fourth mode affects the torsion of the socket area. The good agreement between the experimental and numerical mode shapes is also demonstrated by the evaluation of the MAC matrix. Indeed, as visible in Figure 8b, high diagonal values result in the MAC matrix of FEA and EMA comparison, as already obtained by the authors for other mechanical systems [51; 52]. MAC index of the sixth mode shape falls below the above-mentioned threshold of 70%, probably because the simulation is not able to capture some major displacements in the toe area (see Figure 4f). Even natural frequencies are almost the same between EMA and FEA referring to the iso-frequency line of Figure 8. The real components turn out to be slightly stiffer than the orthotropic material model starting from the third mode shape. \begin{table}
\begin{tabular}{l l} \hline \hline
**Property** & **Value** \\ \hline \(E_{1}=E_{2}\) [GPa] & 11.0 \\ \(E_{3}\) [GPa] & 42.3 \\ \(G_{12}\) [GPa] & 32.0 \\ \(G_{23}=G_{13}\) [GPa] & 3.0 \\ \(v_{12}\) & 0.6 \\ \(v_{13}=v_{23}\) & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Orthotropic material properties of the 3D model.
## 3 Results and Discussion
### Volatile Compounds

Table 6: _Cont._According to previous reports, the percentages of terpenoids that exist in olive oils depend on both the olive variety and geographical origin [81]; actually, \(\alpha\)-farnesene was often reported to be the most representative terpenoid in virgin olive oils from different geographic locations [81; 82]. Other related compounds identified in unripe olive fruits in quite low amounts (<1%) were a-murolene, p-cymene, limonene, 1,8-cineole, \(\beta\)-caryophyllene, \(\alpha\)-pinene, and linalool. The role of terpenoids in the definition of flavor is not clear. In addition, hydrocarbons were the second most abundant chemical class in the WOF, with toluene (8.33%) and 4,8-dimethyl-(E)-nona-1,3,7-triene (8.09%) being the major volatile constituents. The last hydrocarbon has also been reported in black ripe table olives [83]. Regarding the aldehydes, which represented the most abundant volatile class in olive oils, being the products of the lipoxygenase pathway, (E)-2-hexenal (4.78%), followed by hexanal (3.80%), was the most abundant aldehyde identified in the WOF tested, which is responsible for green, apple-like, fatty, and grass sensory notes. These results agree with previous findings, suggesting that (E)-2-hexenal was the most prominent compound in two Tunisian wild oils [6]. Other minor aldehydes observed were nonanal, benzeneacetaldehyde, (E,E)-2,4-heptadienal, octanal, heptanal, decanal, and 2-methyl butanal. Of the eight identified alcohols, 2,3-butanediol, 1-hexanol, benzyl alcohol, and phenethyl alcohol were found in appreciable amounts. Generally, WOF is characterized by low levels of total alcohols [6]. The level of esters (0.38%), such as methyl hexanoate and isopropyl myristate, which are responsible for the floral sensory note [84], was found to be much lower compared to the levels of aldehydes and alcohols. Olive oil derived from wild _Olea europaea_ ssp. _europaea_ var. _sylvestris_ is characterized by a pungent taste due to the higher phenolic content when compared to the oil extracted from cultivars [6; 29].
## 3 Results and Discussion
### Effect of Maturity on the Macroelement and Microelement Contents of Yellow-Fleshed Cassava Roots
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **Ca** & **Mg** & **Na** & **K** & **P** & **S** \\ \hline Trial set 1 & & & & & & \\ Ca & 1.00 *** & & & & & \\ Mg & 0.291 *** & 1.00 *** & & & & \\ Na & 0.099 & 0.193 *** & 1.00 *** & & & \\ K & 0.274 *** & 0.074 & \(-0.271\) *** & 1.00 *** & & \\ P & 0.14 * & \(-0.062\) & \(-0.073\) & 0.513 *** & 1.00 *** & \\ S & 0.17 *** & 0.024 & \(-0.067\) & 0.397 *** & 0.276 *** & 1.00 *** \\ \hline
**Variables** & **Na** & **Ca** & **Mg** & **K** & **P** & **S** \\ \hline Trial set 2 & & & & & & \\ Na & 1.00 *** & & & & & \\ Ca & 0.341 *** & 1.00 *** & & & & \\ Mg & 0.214 *** & 0.605 *** & 1.00 *** & & & \\ K & \(-0.126\) & 0.502 *** & 0.606 *** & 1.00 *** & & \\ P & \(-0.052\) & 0.343 *** & 0.306 *** & 0.636 *** & 1.00 *** & \\ S & 0.054 & 0.097 & 0.382 *** & 0.23 *** & 0.216 *** & 1.00 *** \\ \hline \hline \end{tabular} ns, not significant at \(p<0.05\); *, significant \(p<0.05\); **, significant at \(p<0.01\); ***, significant at \(p<0.001\).  
\end{table} 
Table 8: Pearson correlation coefficients of the macromineral contents of yellow cassava roots (dataset 1 and 2). of the study also showed that there was a significant negative (\(p<0.05\)) correlation between the Fe and Ni contents (r = \(-0.22\)), and Al and Ni contents (r = \(-0.228\)). Further, a significant (\(p<0.05\)) but a weak correlation was observed between the B and Mo contents (r = \(-0.161\)), Cu and Al contents (r = \(-0.186\)), as well as the Zn and Al contents (r = \(-0.141\)). From the data set 2 trial, the results of the study showed a significant positive (\(p<0.001\)) correlation between the Mo and Co contents (r = 0.991), B and Mo contents (r = 0.789), B and Co contents (r = 0.783), Fe and B contents (r = 0.663), Mn and Zn contents (r = 0.618), Fe and Co contents (r = 0.625), Co and Al contents (r = 0.604), Mo and Al contents (r = 0.603), Fe and Al contents (r = 0.536), and B and Al contents (r = 0.532). However, the study also showed that there was no correlation between the Al and Ni (r = 0.089), Al and Zn (r = 0.069), and Ni and Cu (r = 0.142) contents. The observed strong positive correlation shows that those minerals could be bred together to increase their concentrations in yellow cassava roots. However, those minerals with a negative and significant correlation could be difficult to improve together in the breeding program as one will increase, and the other will decrease. Curiously, both for macro- and microminerals, r values \(>\)0.5 and lower \(p\)-values were found for set 2 than set 1, and the levels of minerals were higher than set 1.
# Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD
## 3 Optimization Techniques for the Spark Platform
### Optimization Methodology  
As we discussed in Sections 3.2 and 3.3, our optimization methods include (1) the configuration of the Spark JVM heap and (2) the experimental options of the RDD caching policy as follows:  
1. **Spark JVM heap configuration:** We investigated the effects of changing the capacity fraction ratios of the shuffle and storage spaces. The ratio of shuffle and storage space is 60%:30%, 50%:40%, and 20%:60%, respectively. The \"20%:60%\" of shuffle and storage ratio is the default value in the Spark setup. We choose \"60%:30%\" to contrast the result with sufficient shuffle space and configure \"50%:40%\" to show the performance in a balanced way.
2. **RDD caching policy:** We also examined the effects of different RDD caching policies. We compared the performance of various policies such as OFF_HEAP, MEMORY_ONLY, MEMORY_AND_DISK, and DISK_ONLY, where DISK denotes the SSD in this experiment.  
Table 3 shows total 12 different experimental configurations based on the RDD caching policies and Spark JVM capacity fraction ratios. In the experiment configurations labeled with \"_1\" (for example, \"N_1\"), we set 60% of the Spark JVM heap for shuffling and 30% for storage spaces. With those labeled \"_2\", we set 50% of the Spark JVM heap for shuffling and 40% for storage. Finally, for those labeled with \"_3\", we set 20% of the Spark JVM heap for shuffling and 60% for storage, as can be seen from the \"Option\", \"Shuffle\", and \"Storage\" columns in Table 3. Note that our testbed cluster's maximum memory size of the executor is 2.7 GB, i.e., each worker node has 2.7 GB as the Spark JVM heap size.  
In terms of the RDD caching policy, the \"N\" option is not to cache the RDD, \"M\" option is to cache the RDD on the memory only, \"M&S\" option is to cache the RDD on the memory and SSD together, and finally, \"S\" option is for caching the RDD on the SSD only.  
Through our experiments, we propose optimizing strategies that can achieve the best performance from the cluster which has insufficient memory amounts by carefully adjusting the Spark JVM heap configuration and employing an effective RDD caching policy, as we will see in Section 4.
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 1 Introduction
In contrast, this work presents the design, implementation, and experiment with the in the NAP that provides the CoAP services (e.g., CoAP Observe, delayed response, group communication) to IoT devices through the ICN core network. The CoAP and its extensions are implemented in the NAP so that it can support all communication scenarios discussed in [10]. This approach opens the direction of future developments of IoT services through the inherent features of ICN (e.g., request aggregation, ICN multicast). The remainder of this paper is organized as follows. In Section 2, we introduce the CoAP and CoAP extensions as well as the POINT architecture. In Section 3, we explain the problem statements and how our solution handles those. Section 4 presents the design and implementation of the CoAP Handler for the POINT architecture that provides the transparent CoAP services to the IoT devices through the ICN operator network. Then Section 5 discusses the experiments of our approach. Section 6 presents the related work and, finally, Section 7 concludes our paper.
# A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business
## Appendix B Model Record Data Management
### Theoretical Frameworks and Models in the Literature
#### 4.5.3 Behavioral Theories: Behavioral Determinants of BCT Adoption  
Several models, such as the unified theory of acceptance and use of technology (UTAUT), its extension UTAUT2 [100], and the technology acceptance model (TAM) [101], have been used to study users' intentions and factors influencing their behavioral intention to adopt blockchain technology. The essential variables identified in the research on behavioral intentions for blockchain technology adoption are listed in Table 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Category** & **References** & **Determinants** & **Facilitators** \\ \hline \multirow{4}{*}{Technological factors} & [93; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111] & Technological maturity, Confidentiality, Transaction speed, Scalability, Network attacks (threat of a 51\% attack), Security and privacy, Cost and Efficiency. & Decentralized management [107; 108]; Immutability [109; 110], Data security [111], Traceability [110; 112] and Enhanced transparency [93]. \\ \hline \multirow{4}{*}{Organizational factors} & [2; 113] & Availability of specific blockchain tools, Infrastructural facilities, IT highlighted use of blockchain technology as corporate social responsibility (CSR) & Availability of specific blockchain tools, Infrastructural facilities, IT knowledge of employees, and original cost for the infrastructure. Organizational readiness, Organizational readiness, Organization's complexity, and An organization’s strategic orientation. \\ \hline \multirow{4}{*}{Environmental factors} & [35; 108; 114; 115; 116; 117; 118] & Trading partner readiness, Environmental Responsibility & Government support [116; 107], Trading partner support and collaboration [117; 35], and customer support [118]. \\ \hline \multirow{4}{*}{Behavioral factors} & [49; 67; 102; 114; 119; 120; 121; 122; 123; 124] & \(\bullet\) Social influence \(\bullet\) Perceived ease of use \(\bullet\) Perceived usefulness (performance, cost, efficiency expectancy) & Managers’ technological curiosity or learning [124], Managers’ risk-taking behavior, and Corporate leaders and managers’ familiarity with the benefits of blockchain stakeholders \\ \hline \multirow{4}{*}{Moderating factors} & [125; 126] & Technological characteristics \(\bullet\) Privacy \(\bullet\) Transparency of information \(\bullet\) Security and Reliability \(\bullet\) & Perceived usefulness \(\bullet\) Perceived ease of use \(\bullet\) Trust \\ \hline \end{tabular}
\end{table}
Table 2: Description of determinants of BCT adoption in business.
### Related Works
A comprehensive overview of the cooperative D2D techniquesis presented in [44] along with future research directions. A brief discussion on different types of simulators including Vienna long term evolution-advanced is also provided. The performance of a D2D aided cellular downlink system is studied in [45], where a D2D user cooperatively relays the traffic of a cellular far user (FU) and transmits its signal to the D2D receiver simultaneously exploiting non-orthogonal multiple access. Two different types of D2D aided cooperative schemes for cellular uplink transmission system are proposed in [46], where D2D agents are selected randomly by the BS based on certain density. The performance of a cooperative cellular uplink network is investigated in [47], where a D2D transmitter acts as a cooperative relay for a cellular user and transmits its signal to the D2D receiver simultaneously. To maximize the achievable sum data rate, optimal power allocation coefficient and spectrum allocation indicator are also identified. Social aware cooperative D2D networks are investigated in [48]. Moreover, formation of social relationship, cooperative network evaluation based on social learning, and peer selection on the basis of social relationship are also discussed. Furthermore, from the literature [25; 45], it is apparent that each user is assigned a specific frequency band, time slot, or code in order to avoid inter-symbol or inter-user interference in orthogonal multiple access. In contrast, the main idea of non-orthogonal multiple access (NOMA) is that multiple users share the same physical resources (e.g., frequency, time, and code) by allocating different amounts of power to the different users. Therefore, it is clear that although same physical resources are shared by NOMA users, each user must be allocated a different amount of power to exploit successive interference cancellation at the receiving nodes to decode the corresponding symbols. Unlike the existing works, this study proposes a dual-hop D2D aided CRS exploiting SM, where two cellular users (e.g., a near user (NU) and a relay aided FU) and a pair of D2D transmitter (D\({}_{1}\))-receiver (D\({}_{2}\)) are served in two cooperative phases (e.g., phase-1 (\(P_{1}\)) and phase-2 (\(P_{2}\))). Each phase requires one time-slot. The BS transmits data bits for NU and FU at the same time exploiting SM in \(P_{1}\). FU, located at cell edge, has poor channel condition with low signal-to-noise ratio (SNR). D2D capable device D\({}_{1}\) forwards FU information along with its own data for D\({}_{2}\) using SM in \(P_{2}\). While keeping all the benefits of cooperative D2D communication, the proposed scheme make the system interference free because of the inherent characteristics of SM. Moreover, there will be no leasing of time slots or power allocation for both cellular and D2D communication.