esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
## 4 Results and Discussion
### Raman Analysis for NDG Powder  
In this study, three measures with a fixed integration time of 60 s were averaged for the Raman mapping signals. As shown in Figure 9, The peaks of the spectrum can be explained as follows. The G-band is the main characteristic peak of graphene, which is caused by the in-plane vibration of carbon atoms. The G-band of NDG in this study appeared around 1580 cm\({}^{-1}\). The G-band can effectively reflect the number of graphene layers and is also easily affected by doping. The D-band is generally considered to be a disordered vibration, which is used to characterize the defects or edges in graphene. The D band of NDG was located at about 1353 cm\({}^{-1}\). The intensity of the D-band is proportional to the defects in graphene or NDG. In the study by [PERSON] et al. [23], it was pointed out that the intensity ratio of the D- and G-bands (i.e., (\(I_{\rm{D}}\)/\(I_{G}\) ratio) could be used to estimate the defects of NDG samples where a higher ratio produced more defects on NDG). The \(I_{\rm{D}}\)/\(I_{G}\) ratio of the NDG used in [23] was 1.02, and the corresponding result of the NDG in this study was 0.729  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Vertical Distance: 1.277 (\(nm\))**} \\ \hline
**Roughness** & **Value** \\ \hline \(\rm{R_{max}}\) & 4.884 (\(nm\)) \\ \(\rm{R_{z}}\) & 2.637 (\(nm\)) \\ \(\rm{R_{q}}\) & 0.900 (\(nm\)) \\ \(\rm{R_{a}}\) & 0.722 (\(nm\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Roughness measurement results.  
Figure 8: AFM inspection for NDG. (**a**) 2D image for NDG powder. (**b**) 3D morphology for NDG powder. (**c**) Roughness measurement for NDG powder.  
(436/597.9). This shows that the NDG used in this study had less defects than that of the NDG in [23]. In 2010, the \(I_{D}\)/\(I_{G}\) ratio was studied in detail by [PERSON] et al. [24]. More complex formulae were developed to estimate the \(I_{D}\)/\(I_{G}\) ratio in [24]. Related to the 2D-band, also known as the G'-band, it is a two-phonon resonance Raman peak that appears near 2718 cm\({}^{-1}\). The 2D-band can also be used to determine the number of layers for graphene or NDG. Compared to the result by [PERSON] el al. [25], the layer number of the NDG in this study was more than 10. Graphene with dozens of layers is sometimes called multi-layer or graphite-like graphene and the NDG used in this study belongs to the multi-layer graphene type.
# Server Operation and Virtualization to Save Energy and Cost in Future Sustainable Computing
## 2 Background
### NFV Standardization  
The standardization of NFV technology involves the definition of the structure of NFV and block of the main functions by ETSI (European Telecommunications Standards Institute, Sophia-Antipolis, France) and promotion of the standard on the open networking technology that operates by connecting the block interface with the standardized interface.  
The ETSI NFV working group is divided into 6 groups, and its main objective is to create an industrial (European Telecommunications Standards Institute, Sophia-Antipolis, France) standard on NFV (Network Function Virtualization). All documents prepared under the ISG group are not enacted as an official ESTI standard but regarded as a supplementary specification [11].  
Meanwhile, OPNFV (Open Platform for Network Function Virtualization, San Francisco, CA, USA) is a project that supplies open-source software platforms to distribute the NFV solution based on the equipment/application developers and solution vendors. OPNFV designs the framework to have wide interoperability related to the NFV use model that satisfies the requirements of platform providers, application developers, and users [12]. In other words, OPNFV applies the cloud computing-based NFV and existing network appliance hardware equipment to VNF with software for provision as a business model according to the cost reduction effect by CAPEX and OPEX in terms of cost to business operators such as service providers and cloud infrastructure vendors [13].  
Figure 2 shows a project on VIMs based on OpenStack, an open source to the Bare Metal system. \"Ceph\" was used to apply the KVM hypervisor to the computer and to apply the Linux (The Linux Foundation, San Francisco, CA, USA) distribution file system of P byte size to the storage. For network virtualization, OVS (Open VSwitch, The Linux Foundation, San Francisco, CA, USA) was applied as a software switch run inside the hypervisor. As for the SDN platform, the open source ODL (Open Day-light), which was used as a platform to control the SDN software switch, was applied.  
Figure 1: Vision of Network Function Virtualization.  
OPNFV is based on the NFV basic structure for systematic framework development; the project has been carried out by classifying it into Requirements, Integration & Testing, Collaborative Development, and Documentation.
## 3 Experimental Methods and Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{4}{c}{**Stars**} & \multicolumn{2}{c}{**Observed Object**} \\ \hline  & & & & & Calibrated magnitude: 7.06 \\ \hline Object & 19 Aql & 64 Ser & 21 Aql & 4 Aql & Starlink-1349 \\ \hline Measured & \(-\)3.8655 & \(-\)3.4995 & \(-\)3.7081 & \(-\)4.1174 & \(-\)2.7039 \\ \hline Real & 5.22 & 5.55 & 5.15 & 5.02 & \\ \hline Calibration & 9.08 & 9.05 & 8.86 & 9.14 & Mean: 9.07 \\   constant & & & & & Calibrated magnitude: 6.36 \\ \hline Object & 2 Her & 4 Her & 11 CrB & 53 Boo & Thor Ablestar \\ \hline Measured & \(-\)3.8668 & \(-\)3.4335 & \(-\)4.4487 & \(-\)4.1375 & \(-\)2.2233 \\ \hline Real & \(5.38\) & 5.74 & 4.8 & 4.98 & \\ \hline Calibration & 9.25 & 9.17 & 9.25 & 9.12 & Mean: 9.21 \\   constant & & & & & Calibrated magnitude: 6.99 \\ \hline Object & 17 Com & 16 Com & 14 Com & 12 Com & ISS \\ \hline Measured & \(-\)3.3755 & \(-\)3.5522 & \(-\)3.5886 & \(-\)3.7410 & \(-\)5.3928 \\   magnitude & 6.62 & 4.96 & 4.92 & 4.81 & \\ \hline Calibration & 9.99 & 8.51 & 8.51 & 8.55 & Mean: 8.53 \\   constant & & & & & Calibrated magnitude: 3.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Furthermore, the solar-phase angle was determined by the location of the Sun and satellite in Horizontal Coordinates at the time of observation using (12) and (13). The satellite coordinates information was provided by the telemetry code of [PERSON] et al. [24] using the TLE information, and the Sun ephemerides were taken at JPL Solar System Dynamics [32]. Therefore, all necessary information was available, and the Starlink estimated sizes were computed and presented in Table 4.  
According to [PERSON] et al. [33], Starlinks can be reasonably described as a sphere of 3 m in diameter. Although obtained with a rough method, our estimates presented in Table 4 were within the same order of magnitude. For a better size estimation, multiple observations of the same satellite, accurate albedos, good knowledge of the phase function, and a detailed study of the statistical errors associated with the observations and the photometric measurements are required. This will be an object of future work.
## 2 Materials and Methods  
### Configuration of Cold-Chain Insulated Container  
As shown in Figure 1, the cold-chain insulated container is comprised of an insulator base, an insulator lid, and double-layer panels filled with PCM. The seamless and leak-proof insulator base was made with all-in-one foaming technology and has a high insulation performance. The external dimension of the base is 540 \(\times\) 420 \(\times\) 480 mm and the internal 420 \(\times\) 300 \(\times\) 360 mm. The insulation material has a sandwich structure, which consists of 10 mm polyurethane (PU), 10 mm vacuum insulation panel (VIP), and 10 mm polyurethane (PU), namely the VIP layer is enclosed by PU material. The thermal conductivity of the PU and VIP are 0.022 W/(m\(\cdot\)K) and 0.005 W/(m\(\cdot\)K), respectively.  
Two layers of the PCM panels are inserted into the insulator base. The panels are made of high-density polyethylene (HDPE) and have an external dimension of 350 \(\times\) 290 \(\times\) 15 mm. Different preconditioning methods have been determined with the construction of the double-layer PCM panels, which contributes to a temperature-controlled transportation more than 72 h both at extreme temperatures and at alternating temperature.  
Water and an alkane mixture named OPSE manufactured by Ruhr New Material Technology Co., Ltd. in Hangzhou in China have been used as PCM in this paper. Their phase change temperature and enthalpy were analyzed with a three-layer calorimeter. Figures 2 and 3 present the stored heat of water and of OP5E as a function of temperature in the case of heating/cooling respectively. The bars in the figures display the partial enthalpy of the material in a temperature interval of 1 K. The temperature, at which the maximal partial enthalpy appears, corresponds to the melting/freezing point of the material. From Figure 2 it can be seen that water has a melting/freezing peak at 0 \({}^{\circ}\)C and the total enthalpy is 340 kJ/kg between \(-\)2 \({}^{\circ}\)C and 4 \({}^{\circ}\)C, while OP5E has a melting/freezing temperature at 5-6 \({}^{\circ}\)C, and the total enthalpy is 235 kJ/kg between 2 \({}^{\circ}\)C and 8 \({}^{\circ}\)C (see Figure 3). Their technical parameters and the total mass used in the container are given in Table 1. The enthalpy and the total mass of water and OP5E were used for the heat transfer calculation in Section 2.2.  
Figure 1: Configuration of the cold-chain insulated container with Phase Change Material (PCM).  
Figure 2: Partial enthalpy distribution of water.
# Effect of Strain Hardening on Increase in Collapse Pressure during the Manufacture of ERW Pipe
## 1 Introduction
In the case of UOE pipes, which are manufactured by the sequential processes of U-forming, O-forming and expansion, compression could be used instead of expansion to accomplish the strain hardening effect at the end of the manufacture process [4; 13]. _Offshore Standard DNV-OS-F101_[11] also mentions the possibility that collapse pressure may increase when external cold compression is applied, but how much the collapse pressure could be increased is unclear. [PERSON] et al. [3] conducted three-dimensional finite element analysis to simulate the actual manufacture of an ERW pipe and used the sizing portion of the process to evaluate the possibility of improving the collapse pressure. That study confirmed that improving the out-of-roundness of the cross-section via the sizing process leads to an increase in the collapse pressure. The roundness was affected by the compression rate during sizing, which is called the sizing ratio. However, the effect of residual stress was minor. In that study, a finite element model was established using a shell element for the initial part of the manufacture process and a solid element was used for the sizing portion of the process. Thus, deformation and residual stresses in the shell model were transferred to the solid element. Stress conditions and a deformed shape at the middle section of the shell model were mapped to the solid model. This led to a conservative evaluation of the collapse pressure. The advantages of the external cold compression by sizing process on the collapse pressure of the ERW pipe was evaluated. To reflect the continuity of the ERW pipe manufacture process, the entire process was simulated using a single finite element model with a solid element. The analysis model incorporated the material model that included both the Bauschinger effect and strain hardening. Actual shapes of the rollers and the contact conditions between the roll and the steel plate were considered. That simulation resulted in modifications to the compressive stress-strain curve in the circumferential direction to accommodate various compression rates in the sizing process. Collapse pressure was evaluated by focusing on changes to the circumferential compressive stress-strain curve. In particular, the relationship between the collapse pressure and the sizing process was investigated with respect to the slenderness of the pipe. Moreover, the possibility of increasing the collapse strength of the ERW pipe by using the recommended current design standards was discussed.
## 8 Economic Impact of 5G on Smart Cities  
The impact of 5G on the deployment of smart cities, the technological renovation of urban metropolitan areas, and the quality of life of the citizens, as well as economic growth and development, is no longer a theoretical issue. Already entering its third year of global launching, 5G has offered tangible examples of how a city benefits not only from the technological and qualitative aspects but also in terms of economics [33; 34; 35].  
Smart city solutions applied to the management of electrical grids and vehicle traffic have resulted in substantial financial gains through a reduction in energy and fuel consumption [33]. 5G solutions have enabled cities to accomplish significant smart grid efficiencies [33].  
In addition to providing increased bandwidth and data rates, 5G cells have allowed the ubiquitous connection of an even larger number of devices within the network infrastructure. This has led not only to a significant increase in the pool of physical users benefiting from existing services and applications but also in investment towards novel services with emphasis on AR/VR and shifting the paradigm from quality-of-service (QoS) to quality-of-experience (QoE) [34].  
It has also been showcased that the economic impact of 5G-enabled smart cities is not limited to the city finances but also to budget investment for further city development and job openings. Thus, the GDP increases, and the job openings create more career choices for the citizens towards a more \"digitally aware\" professional environment [35].
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 3 Propagation Modeling
### Adjusted COST231  
The testing environment in this research consists of both indoor and outdoor propagation. Whereas the COST231 model is only intended for indoor modeling, as it has an optimistic space path loss prediction, it only accounts for the free space loss. Hence, we made an adjustment to the COST231 model to account for the outdoor propagation parameters. Since this adjustment is inspired by the log-distance model, we added a path loss exponent (\(n_{A231}\)) to the free space path loss (\(L_{FS}\)). This adjusted model is presented in Equation (3). All of the other parameters in Equation (3) are defined similar to Equation (2).  
\[PL=n_{A231}\times L_{FS}(d)+L_{C}+\sum\alpha_{j}k_{j} \tag{3}\]
# Functional Characterization of Mung Bean Meal Protein-Derived Antioxidant Peptides
## 3 Materials and Methods
### Determination of Antioxidant Properties  
#### 3.6.1 DPPH Radical Scavenging Activity  
The scavenging activity of peptide samples (0.6 mg/mL) against DPPH radical was carried out according to the method described by [PERSON] et al. [44]. Peptide samples were dissolved in 0.1 M sodium phosphate buffer, pH 7.0 containing 1% (\(w/v\)) Triton X-100. DPPH was dissolved in 95% ethanol to a final concentration of 100 uM. Peptide samples (100 uL) at various concentrations were mixed with 100 uL of the DPPH solution in a 96-well plate while the blank contained 100 uL buffer instead of sample. The reaction mixtures were subsequently incubated at room temperature in the dark for 30 min followed by measurement of absorbance values at 517 nm. Reduced glutathione (GSH) was used as a positive control and assayed the same way as samples and blank.  
The DPPH radical-scavenging activity was calculated using the following equation:  
\[\text{DPPH radical scavenging activity}\ (\%)=\frac{\text{A}_{\text{blank}}-\text{A}_{ \text{sample}}}{\text{A}_{\text{blank}}}\ \times 100 \tag{1}\]  
The effective peptide concentration that scavenged 50% of the DPPH radicals (EC\({}_{50}\)) was determined from the regression plot of concentration against percentage scavenging activity using GraphPad Prism version 6.0 (GraphPad Software, San Diego, CA, USA).
# Microbiota and Metabolite Profiling of Spoiled Spanish-Style Green Table Olives
## Results and Discussion
### Metabolite Composition in Brine Samples
Using an AHC analysis, 3 groups (or clusters) were identified (Figure S3). Group 1 was composed of the unspoiled control samples which were located in the third quadrant. The PCA loading plot (Figure 5b) showed that the metabolites mainly associated with this group were lactic acid and 3_A_-dimethylbenzaldehyde. Group 2 was composed of the samples GFL1, GFL2, MFL1, MFL2, GFC1, and GFC2, which were located in the second quadrant, and were mainly associated with the linear C\({}_{4}\)-C\({}_{7}\) fatty acids and linear C\({}_{4}\)-C\({}_{6}\) alcohols. Among these metabolites, butyric and valeric acids were predominant. Their concentrations in the samples of this group ranged from 440-770 mg L\({}^{-1}\) (butyric  
Figure 5: (a) The PCA score plot of metabolite data, (b) the PCA loading plot of metabolite data. The 3 groups identified by agglomerative hierarchical cluster (AHC) analysis are highlighted by ellipses. See Table 5 for the meanings of samples abbreviations. acid) and from 792-1560 mg L\({}^{-1}\) (valeric acid), which were much greater than their corresponding odor threshold in water (butyric acid, 1.40 mg L\({}^{-1}\); valeric acid, 0.28 mg L\({}^{-1}\)) [34]. Therefore, it seems likely that these acids would have a noticeable impact on the olive odor. Caproic and heptanoic acids could also contribute to the odor as their concentrations were above the reported thresholds in water (caproic acid, 1.8 mg L\({}^{-1}\); heptanoic acid, 0.64 mg L\({}^{-1}\)) [34; 35]. Group 3 was composed of the rest of the spoiled samples, which were all located in the positive part of PC1. Two subgroups can be clearly observed within this group. One of them (GCC1, GCC2, GCL1, GCL2, MCL1, MCL2) was located in the positive part of PC2 and was characterized by high levels of cyclohexanoic acid (281-841 mg L\({}^{-1}\)), which was highly correlated to methyl cyclohexanecarboxylate, ethyl cyclohexanecarboxylate, and methyl hydrocinnamate. As mentioned in the Introduction section, cyclohexanoic acid is usually chosen as the reference for the \"zapatera\" off-odor descriptor in the sensory assessment of table olives. Although the odor threshold of this acid was not available, it can be inferred that such a threshold would be higher than 1.9 mg L\({}^{-1}\) (concentration found in control M1, Table 3) but lower than 19.2 mg L\({}^{-1}\) (=0.15 mM cyclohexanoic acid, reference concentration used for anchor point of 4). The second subgroup, which was located in the negative part of PC2, was composed of samples MCC1, MCC2, MFC1, and MFC2, and was mainly related to propyl acetate, (\(Z\))-3-hexen-1-ol, and propyl propanoate. It is worth noting that these samples, except MCC1, had notable levels of cyclohexanoic acid (particularly sample MFC2 with 508 mg L\({}^{-1}\)) (Table 3).
## 3 Numerical Results and Analysis
### Calculation of Electron Energy Probability Function  
In this section, the collision reactions shown in Table 2 and the simulation parameters in Table 1 are applied to the expansion region model. According to Equations (2) and (3) [38], the electron energy probability function (\(EEPF\)) diagram shown in Figure 6 is calculated.  
\[EEDF(\varepsilon)=\frac{2}{kT}\sqrt{\frac{\varepsilon}{\pi kT}}\mathrm{e}^{- \frac{\varepsilon}{kT}},\,EEPF(\varepsilon)=EEDF(\varepsilon)/\sqrt{\varepsilon} \tag{2}\]  
\[lg\left[\frac{EEDF(\varepsilon)}{\sqrt{\varepsilon}}\right]=-\frac{1}{KTln10 }\varepsilon+\lg\left(\frac{2}{kT}\sqrt{\frac{1}{\pi kT}}\right) \tag{3}\]  
where \(\varepsilon\) is the electron energy (i.e., \(\frac{1}{2}mv^{2}\)). The distribution function of \(\varepsilon\) is set to \(EEDF(\varepsilon)\), and \(KT\) is the electron temperature. As shown in Equation (3), the ratio of the absolute inverse of the slope of the \(EEPF\) straight line to \(ln10\) is \(KT\). \(KT_{low}\) and \(KT_{high}\) represent the electron temperatures of low and high energy states, which are 0.6 eV and 6.4 eV, respectively. It can be seen from Figure 6 that the electron energy shows a bi-Maxwellian electron energy distribution function (\(EEDF\)[39]), that is, a low temperature followed by a higher temperature, which is in line with the typical characteristics of negative hydrogen ion source discharge. The electron temperature of the extraction surface is calculated to be about 3.2 eV, which is consistent with the typical value of 3 eV as measured by a negative hydrogen ion source with the same magnetic field structure in [31]. In addition, the calculated low energy range at low temperature is 0-8 eV, which is consistent with the low energy range measured by Optical Emission Spectroscopy (OES) method and Langmuir Probe [31]. Therefore, the expansion region model is reasonably established, and the collision reactions and types shown in Table 2 basically conform to the main electron collision reactions of the negative hydrogen ion source.  
Figure 6: Diagram of EEPF as a function of electron energy.
## 4 Performance Evaluation  
In this section, we evaluate the performance in terms of ergodic sum rate. Further, the system availability for the proposed method is also discussed. Considering the beamforming style adopted in the paper, we can derive the following assumptions.  
**Assumption 1**.: _Since the HS is a central fixed station, the feeder beam of the satellite could be aligned without gain loss. Hence, the uplink and downlink channel gains between the HS and the satellite can be, respectively, written as_  
\[|g_{0}|=|\mathbf{v}_{0,S}^{\mathrm{H}}\mathbf{g}_{0}|=\sqrt{E_{r}}l(d)\sqrt{ G_{t,0}} \tag{21}\]  
_and_  
\[|h_{0}|=|\mathbf{h}_{0}^{\mathrm{H}}\mathbf{w}_{0,S}|=|\rho_{0}|\sqrt{G_{r,0} }l(d)\sqrt{E_{t}}. \tag{22}\]  
Following the principles proposed in [27], we have the following results for the service beam.  
**Assumption 2**.: _Since the satellite generates one user beam, and UTs are randomly located in the coverage area, the M beams at UTs could not be all aligned to the service beam. Therefore, we can approximate the service uplink and downlink channel gains as_  
\[|g_{i}|=|\mathbf{v}_{ULS}^{\mathrm{H}}\mathbf{G}_{i}\mathbf{w}_{i}|=\sqrt{ \eta_{i}E_{r}}l(d)\sqrt{G_{t}} \tag{23}\]  
_and_  
\[|h_{i}|=|\mathbf{v}_{i}^{\mathrm{H}}\mathbf{H}_{i}\mathbf{w}_{ULS}|=|\rho_{i}| \sqrt{\kappa_{i}G_{r}}l(d)\sqrt{E_{t}}, \tag{24}\]_where \(\eta_{i}\in[0.5,1]\) and \(\kappa_{i}\in[0.5,1]\) (we set \(0.5\) as lower bound, as half power beam width is considered for the service beam) denote the beamforming gains of uplink and downlink channels, respectively._