esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physical Characteristics of the ZnO Nanords and Nested Nanotubes
(**f**) Cross-section revealing an average nanorod height of \(\sim\)750 nm. Figure 8: Energy-dispersive X-ray spectroscopy (EDS) for elemental chemical analysis of ALD ZnO film on Si substrate showing only Zn, O and Si signals, with no other detectable contaminants. Figure 7: (**a**) X-ray diffraction (XRD) results of a 400 nm ALD ZnO film synthesized by ALD technology, which results in a significant increase of the (002) peak, establishing preferred growth along the c-axis orientation of the hexagonal wurtzite structure. (**b**) XRD 2 \(\theta\) scans as a function of the number of ALD growth cycles of ZnO, demonstrating that the ZnO film quality and the full width at half maximum (FWHM) are improving with increasing ALD ZnO film thickness. Figure 9: (**a**) Transmission electron microscopy (TEM) of detached ALD ZnO nanotubes revealing the polycrystalline structure of the sensing film. (**b**) High-resolution HR-TEM micrograph providing details of the individual grains and grain boundaries of an ALD ZnO nanotube wall.
## 3 Results and Discussion
### Results on the ANOVA
Via an approximate correlation between predicted and adjusted values, the model stability was validated. Hence, the model can be capably used to estimate the SLM product hardness, especially for any sort of processing parameters within this experimental research. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sum of** & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Mean Square**} & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} \\  & **Squares** & & & & \\ \hline Model & 20,831.78 & 3 & 6943.93 & 21.03 & \textless{}0.0001 & significant \\ A-laser power & 9029.59 & 1 & 9029.59 & 27.35 & 0.0002 & \\ B-scan speed & 4054.14 & 1 & 4054.14 & 12.28 & 0.0043 & \\ AB & 5778.18 & 1 & 5778.18 & 17.50 & 0.0013 & \\ Residual & 3961.39 & 12 & 330.12 & & \\ Cor Total & 24,793.17 & 15 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ANOVA table for output. Figure 6: Predicted values vs. actual values of hardness. The tight correlation between the adjusted and anticipated values supported the stability of the model. The model may thus be effectively used to predict the hardness of SLM products for any set of processing parameters that fall within the scope of this experimental investigation. Figure 7 represents the influence of controlling parameters on the microhardness of the SLM-built samples. When the laser power and scan speed was elevated, the hardness was at its highest. The laser power had a much bigger impact on the hardness than the scanning speed. It was observed that, even at the lower laser power, the hardness that was closer to the maximum hardness was obtained.
# Research on a Low Melting Point Al-Si-Cu (Ni) Filler Metal for 6063 Aluminum Alloy Brazing
###### Abstract  
A new type of low melting point Al-Si-Cu (Ni) filler metal for brazed 6063 aluminum alloy was designed, and the microstructure and properties of the filler metal were systematically studied. The results show that when the content of Cu in the Al-Si-Cu filler metal increased from 10 wt.% to 20 wt.%, the liquidus temperature of the filler metal decreased from 587.8 \({}^{\circ}\)C to 533.4 \({}^{\circ}\)C. Its microstructures were mainly composed of the \(\alpha\)-Al phase, a primary Si phase, and a 0(Al\({}_{2}\)Cu) phase. After a proper amount of Ni was added to the Al-Si-20 Cu filler metal, its melting range was narrowed, the spreading wettability was improved, and the microstructure was refined. Its microstructure mainly includes \(\alpha\)-Al solid solution, Si particles, and 0(Al\({}_{2}\)Cu) and \(\delta\)(Al\({}_{3}\)Ni\({}_{2}\)) intermetallic compounds. The results of the shear strength test indicate that the shear strength of the brazed joint with Al-6.5 Si-20 Cu-20 Ni filler metal was 150.4 MPa, which was 28.32% higher than that of the brazed joint with Al-6.5 Si-20 Cu filler metal.  
6063 aluminum alloy; Al-Si-Cu (Ni) filler metals; microstructure; shear strength +
Footnote †: journal: Applied Sciences
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 5 Results and Discussion
### Feature Selection Results  
With the first examination of the features complete, the feature selection algorithm as constructed in Section 3.2 was applied. The model was implemented in python and a normalized plot of feature importance was generated. Early stopping was applied using the validation set of the data to prevent overfitting. At iteration 408, the best result was achieved according to the model. The top 15 important ranked normalized features are given in Figure 12.  
Within the figure, it can be seen that the most important feature is the zone and this can be further validated based on the data examined in Section 5.1 concerning the distribution of interventions within the zones. The next important feature is that of commercial activity and this again can be expected given the propensity of interventions being needed earlier when there are commercial activities as highlighted in Figure 11. The next feature on the list is that of the population density within the traffic zone showing how important it is to understand the density of people living near a particular road for the maintenance activities. Following these are the physical features of area and length, which definitely should be considered to be important as they represent information on the actual road's dimensions. It is important however to note that whilst the physical characteristics are important they are not the most important features, which is an important concluding result. The next series of features related to the traffic in the zone and on the type of persons living near a particular road.  
Figure 11: Distribution of maintenance interventions by the presence of commercial activities.  
It should be noted that these results are about the particular situation within the municipality under question and the same features could result in a different relationship in a different region. As such, it is critical to utilize results from the particular municipality and not rely on feature representations of other cities or municipalities. Therefore, what is important is the modelling process and also the ability to verify whether the results produced by the model can be explained considering the situation in real life within the area. The feature selection workflow is key to the work as it provides an ability to filter out the most important variables in attempting to make a prediction on future occurrences of the dependent variable. This feature selection can also be used on any database with more variables when available. This includes those that were not available in this study but could be available in other agencies or networks. This means the workflow is not isolated to this particular area. Once these features were computed, the next phase of the study was the use of these particular features within a model to predict maintenance interventions.
## 3 Proposed SPMIPv6 Protocol
### Operational Architecture of SPMIPv6  
Figure 2 shows the detail operational architecture of the SPMIPv6 which includes SLMA, SMAG and IP sensor nodes. And it shows how these entities communicate each other by different types of interfaces. The SMAG needs two or more interfaces for the communication with different access network such as SPMIPv6 based IP-WSN and external PMIPv6 network. It includes the network layer, adaptation layer and physical layer functionality. The network layer provides different functionality such as addressing, routing, neighbor discovery and the data structure for holding IP sensor node information. The most important layer is adaptation that ensures mesh routing, compression and decompression, and fragmentation and reassembly. The physical layer provides access to different  
physical interfaces. SLMA holds network related information such as binding cache entry, encapsulation and decapsulation. Binding cache entry provides data structure to hold different information such as new SPMIPv6 flag, link local address for each interface, home prefix, bi-directional tunnel interface identifier, access technology and time stamp. Finally, the IP sensor node works as a very smart tiny 6 LoWPAN based node. It holds a tiny TCP/IP protocol stack. Moreover it holds the basic functionality of adaptation layer. All the sensor nodes consist of IPv6 addresses for local and global communications.
## 3 Feature Extraction
The signal is further analyzed by extracting the frequency center (FC), root mean square frequency (RMSF), and root variance frequency (RVF), all related to the \(N\) frequency amplitudes \(f_{i}\), as shown in Table 2.  
As a last addition to the pool, the feature extraction process calculates the wavelet decomposition of the input 1-D signal using the Daubechies orthogonal wavelet [6]. This final tool, often used when extracting features from a signal, concerns both the time- and the frequency-domain localization of a signal. By choosing a waveform of reference (in  
\begin{table}
\begin{tabular}{l l} \hline \hline  & \(\text{AVG}=\mu_{x}=\frac{1}{N}\sum_{i=1}^{N}x_{i}\) & \(\text{STD}=\sigma=\left(\frac{1}{N-1}\sum_{i=1}^{N}(x_{i}-\mu_{x})^{2}\right)^{ 1/2}\) \\  & \(\text{MAX}=\text{max}(x)\) & \(\text{MIN}=\text{min}(x)\) \\  & \(\text{RMS}=\left(\frac{1}{N}\sum_{i=1}^{N}x_{i}^{2}\right)^{1/2}\) & \(\text{SRA}=\left(\frac{1}{N}\sum_{i=1}^{N}\sqrt{|x_{i}|}\right)^{2}\) \\  & \(\text{KV}=\frac{1}{N}\sum_{i=1}^{N}\left(\frac{x_{i}-\mu_{x}}{\sigma}\right)^{ 4}\) & \(\text{SV}=\frac{1}{N}\sum_{i=1}^{N}\left(\frac{x_{i}-\mu_{x}}{\sigma}\right)^{ 3}\) \\  & \(\text{PPV}=\text{max}(x)-\text{min}(x)\) & \(this case, Wavelet Daubechies 4 or \"db4\"), it is possible to use it as a window function on the signal, while applying different scalings on the wavelet, therefore obtaining a multi-resolution analysis. What is actually saved as additional features to the pool are only the energy percentages corresponding to the approximation (low frequency), and the ones of the details (higher frequencies) up to level 8: by adding these to the previous list, the total number of features in the pool amounts to 26. Before training a classifier, a feature ranking algorithm is used to associate an importance score to all the predictors fed to the classifier. In the presented work, the chosen ranking is operated by a Minimum Redundancy Maximum Relevance (MRMR) algorithm, which tries to decrease redundancy in the feature pool, while maximizing the relevance of the individual entries of the set.
## 2 Flywheel Theory and Structure
### Components of FESS
#### 2.2.4 Power Electronics Interface  
The electrical machine is connected to a power electronics converter interface in order to make power flow from the electrical grid to the FESS and back to the grid during the acceleration and deceleration of the flywheel. Bi-directional converters help exchange power between the grid and FESS by controlling the operation of the electrical machine. The power electronics converter, which is near to electrical machine, controls rotor magnetic flux and speed of the electrical machine with a suitable modulation technique to produce the gate pulses for the converter. An AC-DC converter (grid side converter) with suitable modulation technique is used to maintain a constant DC-link voltage by following grid side reference currents to control flow of active and reactive power [14]. The design of the converter and its switching technique are responsible for the power quality of the system considering that nearly sinusoidal stator currents, low total harmonic distortion THD (less than 5%) and stable DC-link voltage are the factors affecting the power quality in FESS [60]. The combination of AC-DC-AC power converters is called the back-to-back (BTB) configuration and is the most widely used topology in FESS applications [53]. [PERSON] in [61] has discussed implementation and control of AC-DC-AC in FESS. A new DC-DC boost inverter topology used for low speed flywheels is proposed in [62]. Different topologies of power electronics converters used in FESS are discussed in details in [25].
## 4 Implementation and Evaluation of the Mobile Social Music Karaoke System
### Experiment and Analysis
While transmission was conducted regularly with deviations not larger than 3 ms per 100 ms on average, deviations up to +/\(-\)25 ms per 100 ms occurred in reception. Although this fluctuation in reception is mainly due to the characteristics of UDP, data transmission is very fast and could support more devices without preparing a socket connection. The transmission/reception deviations did not increase very much even when the number of users in the group increased. In particular, even in the group of 4 members, the time did not change significantly. This is because packet transmission delay time was minimized by including the server and then the delay was no longer than 1 ms. Although the deviation of packet reception was higher than the transmission, the frustration was almost similar to the 2 groups of users described in the first experiment. On the other hand, we found that there were differences in performance according to the smartphone specifications. Deviations occurred a little more in the smartphones with low performance and not much deviation occurred in the recently released smartphones. Therefore, it could be seen that large performance degradation does not occur because one mobile device transmits packets, and the server transmits packets to individual smartphones of group members separately. The results of the experiments showed that the voice interface of voice recognition and TTS was effective in performing the desired functions while increasing accuracy based on the specified command set. Given the mobile and mobile environment, the role of a natural interface is important, and utilization to that end was possible. As for network-based music sharing, music packets could be quickly transmitted, relayed, received, and played so that music could be shared in real time and performance degradation did not occur even when the number of users increased. Figure 10: Measurement of packet transmission/reception between group members.
## 3 Finite Element Simulation and Verification
### Finite Element Model Simulation
#### 3.1.2 Material Properties  
The material constitutive models of the FEM were determined through the tests on the material characteristics presented in Section 2.1. An elastic material constitutive model was chosen for the concrete with a 29.5 GPa \(E_{\mathrm{c}}\), 24 kN/m\({}^{3}\) unit weight of concrete (\(\gamma_{\mathrm{c}}\)), and 0.25 Poisson's ratio of concrete (\(v_{\mathrm{c}}\)). A Mohr-Coulomb constitutive model was used for the sand with a 19 MPa elastic modulus of sand (\(E_{\mathrm{s}}\)), 19 kN/m\({}^{3}\) unit weight of sand (\(\gamma_{\mathrm{s}}\)), 35\({}^{\circ}\) internal friction angle of sand (\(\varphi_{\mathrm{s}}\)), 11.67\({}^{\circ}\) dilation angle of sand (\(\psi_{\mathrm{s}}\)), which is equal to 2\(\varphi_{\mathrm{s}}\)/3, 0.1 cohesion of sand (\(c_{\mathrm{s}}\)), and 0.3 Poisson's ratio of sand (\(v_{\mathrm{s}}\)) [54].
# Transcriptome Analysis of Egg Yolk Sialoglycoprotein on Osteogenic Activity in MC3T3-E1 Cells
## 3 Results
### KEGG Analysis  
KEGG enrichment analysis was performed for genes with different expression levels. Scatter plot is a graphical representation of KEGG enrichment analysis results. The 20 pathways with the most significant enrichment were selected (Figure 5). We found that gene expression in several pathways was significantly different after the treatment of EYG  
Figure 2: Effect of EYG on mineralized nodular of MC3T3-E1 cells. Morphology (**A**) and absorbance (**B**) at 570 nm after staining of MC3T3-E1 cells. MC3T3-E1 cells were treated with various concentrations of EYG. EYG promoted mineralized nodular of MC3T3-E1 cells. Data were expressed as mean \(\pm\) SEM and the number of replicates was 3 (\(n=3\)). Multiple comparisons were done using one way ANOVA analysis. \({}^{*}p<0.05\), \({}^{**}p<0.01\) versus control.  
including TNF signaling pathway, complement and coagulation cascades, ECM-receptor interaction, focal adhesion, protein digestion and absorption.  
Figure 4: GO analysis of DEGs between 50 \(\upmu\)g/mL concentrations of EYG group and control group. The ordinate was the GO term of enrichment, and the abscissa was the number of different genes in the term. Different colors were used to distinguish biological processes, cell components and molecular functions. * GO term was significantly enriched.  
Figure 3: Volcano plot of distribution trends for differentially expressed genes with 50 \(\upmu\)g/mL concentrations of EYG and control groups. Each dot represents one gene. Red dots represent upregulated genes and green dots represent downregulated genes. Blue dots represent genes with no differential expression. There are 123 differentially expressed genes in total, including 45 upregulated genes and 78 downregulated genes.  
We further explored the mechanism of several enriched pathways (Table 2) and found that EYG upregulated COL2A1, COL4A1, COL4A2 and other factors to enrich the signaling pathways. Besides, we also knew that EYG downregulated CCL2, CCL5, NOD2 and other factors to inhibit the inflammation-related pathways.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Pathway** & **ID** & **Gene Name** & **Corrected \(p\)-Value** \\ \hline UP & & & \\ \hline ECM–receptor interaction & mmu04512 & COL2A1 & 0.016550785 \\ \hline  & & COL4A2 & \\ \hline  & & COL4A1 & \\ \hline Protein digestion and absorption & mmu04974 & COL2A1 & 0.016550785 \\ \hline  & & COL4A2 & \\ \hline  & & COL4A1 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Enrichment of KEGG pathway for DEGs between treatment with 50 \(\upmu\)g/mL EYG and the control. \(p<0.05\) was considered statistically significant.  
Figure 5: KEGG enrichment \(p\) value result graph induced by DEGs between 50 \(\upmu\)g/mL concentrations of EYG group and control group. The ordinate was pathway name, and the abscissa was Gene Ratio. The size of dots represents the number of differentially expressed genes in this pathway, and the color of dots corresponds to different \(p\) value ranges.
## 5 Results
### Evaluation of the Successful Delivery Ratio (Reliability)  
Figure 5 shows the simulation results of the successful delivery ratio, that is, the reliability for underwater wireless communications. To thoroughly investigate the reliability of UWSNs, we provide the detailed simulation results in Figure 5a-f. Specifically, Figure 5a-f are the results when the transmitter powers are set as 1 W, 2 W, 10 W, 20 W, 30 W, and 40 W, respectively. Each of the figures provides the delivery ratio when the transmission distances are equal to 100 m, 200 m, 300 m, 400 m, 500 m, 600 m, 700 m, 800 m, 900 m, and 1000 m.  
Figure 5: Simulation results of the successful delivery ratio when the channel models are applied to different transmitter powers (1–40 W) and distances (100–1000 m) (**a**) Transmitter power = 1 W; (**b**) Transmitter power = 2 W; (**c**) Transmitter power = 10 W; (**d**) Transmitter power = 20 W; (**e**) Transmitter power = 30 W; (**f**) Transmitter power = 40 W.  
In all figures, we can see that, when the transmission distance increases, the successful delivery ratio decreases. We explain this phenomenon as follows. When the transmission distance increases, the SNR decreases (as shown in Figure 3), and thus, the BER increases (as shown in Figure 4); more BER means more number of received bits that have been altered owing to noise, interference, distortion, or bit synchronization errors. Accordingly, when transmitting a whole packet in UWSNs, more BER would result in less successful delivery ratio. In summary, when the distance increases, the SNR decreases, the BER increases, and thereby the delivery ratio decreases. The phenomenon remains consistent in all the cases of different transmitter powers (i.e., 1 W, 2 W, 10 W, 20 W, 30 W, and 40 W) in Figure 5a-f. The simulation results confirm that our channel models are reasonable in each step (from the SNR, the BER, to the delivery ratio), and are applicable to different transmitter powers.  
When the transmitter power increases from Figure 5a-f, we can find that the successful delivery ratio increases gradually. We take the transmission distance of 500 m as an example. For 500 m, when the transmitter powers are 1 W, 2 W, 10 W, 20 W, 30 W and 40 W, the corresponding delivery ratios are about 0%, 5%, 57%, 75%, 82%, and 88%, respectively, as shown in Figure 5a-f. The explanation is as follows. When the transmitter power is higher, the SNR is higher (as shown in Figure 3), and thereby the BER is lower (as shown in Figure 4); lower BER means lower probability of being wrong during packet transmissions. In other words, lower BER means higher successful probability of delivering packets. Accordingly, higher transmitter power produces higher SNR, and lower BER, which eventually results in higher successful delivery ratio. Note that the phenomenon occurs not only in the case of 500 m, but also in all the cases of different distances. Because the results are consistent in all the cases, the simulation results confirm that when calculating the successful delivery ratio (i.e., the reliability), our channel models are applicable to different distances.
###### Abstract  
Device-to-device (D2D) communication is a technique for direct communication between devices without going through a base station or other infrastructure. D2D communication technology has the advantages of improving spectrum efficiency and reducing transmission delay and transmission power. In D2D communication systems, orthogonal frequency-division multiple access (OFDMA) is widely used to maintain similarities with cellular communication systems and to secure transmission distance. OFDMA allows flexible and efficient use of frequency resources by allocating sub-channels independent to each user. In this paper, we consider a D2D overlay system that uses different sub-channels for cellular and D2D communications. In theory, the signals on different sub-channels of an OFDMA system are orthogonal and not interfered with each other. However, in a D2D communication system, which operates in a distributed manner, there is non-negligible interference from other sub-channels because of in-band emissions. In this paper, we address the performance degradation resulting from the interference from other sub-channels for OFDMA-based D2D group-casting systems. We consider three different scenarios of D2D relay, and we find the relay position that minimizes the outage probability. The simulation and analytical results show that the optimal location of a relay can be considerably different according to the source location and the target scenario.  
D2D; Group-casting; OFDMA; Relay; In-band emission; Interference +
Footnote †: This paper is an extended version of our paper published in Proceedings of the Tenth International Conference on Ubiquitous and Future Networks (ICUFN), Prague, Czech Republic, 3-6 July 2018, “Relay Position Considering Interference from Other Sub-Channels in D2D Group-casting Systems”.