esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Control of Radial Inlet Chamber and Downstream Effects on a Centrifugal Compressor Stage
## 2 Methodology
### Numerical Method
#### 2.2.2 Computational Models  
In order to investigate the flow characteristics inside the radial inlet chamber and its effects on the first stage of the compressor in the downstream, two sets of computational models were employed in the present study. For the flow analysis of the inlet chamber itself, the computational model included a radial inlet chamber (with or without guide vanes), a straight inlet pipe and an outlet pipe, as shown in Figure 2a. All the patches were modeled with data from the previous experimental research [36; 37]. Meanwhile, as shown in Figure 2b, a computational model of the radial inlet compressor stage was built for the study on the performance improvement and downstream effects of the radial inlet chamber.  
The boundary conditions for the CFD models were imposed according to the actual operating data. For the flow analysis model, the uniformly distributed total pressure and total temperature were adopted as the inlet boundary conditions at the entrance of the inlet pipe with a flow direction along the pipe axis, and the mass flow rate as the outlet boundary condition was set at the end of the outlet pipe using the velocity scaling method. For the downstream effect model, specific profiles extracted from the fully developed flow were provided at the entrance of the radial inlet chamber (i.e., _in_ in Figure 1), and the mass flow rate was imposed at the outlet of the first compressor stage (i.e., _out_ in Figure 1). All the solid walls were set as adiabatic and non-slip boundaries with hydraulically smooth surfaces.  
In order to achieve the final convergent solutions for the flow analysis, both the residual convergence and the mass flow convergence were taken into consideration. When the global residuals stayed lower than \(10^{-6}\) and the deviation between the inlet and outlet mass flow rates was less than 0.5%, the numerical solutions were considered to be converged.
## 3 Materials and Methods
### Data Collection  
Data were collected in the static way over several days on the roof of the Polytechnic of Bari in the headquarters of Taranto, in South Italy (Figure 1a). The location on the roof of the building allowed observations to be made in open sky conditions. For data collection, the smartphones were positioned in the old vertex of a permanent GNSS network called FATA [23] (Figure 1b). Therefore, the FATA point is a place of known coordinates, stable and free of any external electromagnetic disturbances, as it has been monitored for several years.  
The GNSS antenna of the smartphone is located in its upper left corner, as can be deduced from the manufacturer's data sheet. For this reason, it was decided to position the smartphone in such a way as to obtain better acquisition of satellite data. The antenna of the permanent TARA GNSS station (described in Section 3.1) is located about 22 m  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **Xiaomi Mi 10** & **Oppo Reno4 z 5G** & **Huawei P20 Lite** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Chipset \\ Operative System \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Snapdragon 865 Qualcomm \\ SDM865 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Dimensity 800 MediaTek \\ MT6873 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Huawei HiSilicon Kirin 659 \\ Android 8.0 Emotion UI 8.0 \\ Orego \\ GPS, GLONASS, BeiDou, Galileo, \\ QZSS \\ Frequency \\ Observations \\ Weight (g) \\ Dimensions (mm) \\ Cost (EUR) \\ \end{tabular} } & \begin{tabular}{c} Snapdragon 865 Qualcomm \\ SDM865 \\ Android 10 MIU111 \\ QZSS \\ L1/L5 \\ Code, Carrier Phase \\ 208 \\ 162.58 \(\times\) 74.8 \(\times\) 8.96 \\ under 800 \\ \end{tabular} & \
\begin{tabular}{c} Dimensity 800 MediaTek \\ MT6873 \\ Android 10 ColorOS 7.2 \\ GPS, GLONASS, BeiDou, \\ Galileo, QZSS \\ L1/L5 \\ Code, Carrier Phase \\ 184 \\ 163.8 \(\times\) 75.5 \(\times\) 8.1 \\ Under 300 \\ \end{tabular} &
\begin{tabular}{c} Huawei HiSilicon Kirin 659 \\ Android 8.0 Emotion UI 8.0 \\ Oreo \\ GPS/ GLONASS \\ L1 \\ Code, Carrier Phase \\ 145 \\ 148.6 \(\times\) 71.2 \(\times\) 7.4 \\ Under 300 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main features of the smartphones used in the experimentation.  
Figure 1: Positioning of the smartphone on the rooftop of the building and approximate location of smartphones and TARA GNSS Permanent Station: (**a**) image extracted from Google Earth; (**b**) panoramic photo of the smartphone on the test site.  
(baseline) from the FATA station where the smartphone is positioned. The coordinates of the TARA and FATA stations are reported in Table 2.
## 3 System Model and Problem Formulation
### Problem Formulation  
In this paper, we investigate the relay selection problem by jointly considering the LQoS and the NFC. Specifically, the relay selection problem is formulated as the following multi-objective optimization problem.  
\[\begin{split}\max\limits_{I,Q}E_{j}&=I_{a,j}Q_{j,b }\left(\tau_{m}\right)\\ \text{s.t.C}1.1&:P_{a,j}\geq\delta\\ \text{C}1.2&:0\leq T_{a,b}\leq M_{th},\end{split} \tag{9}\]  
where \(\delta\) represents the minimum successful transmission probability and \(M_{th}\) represents the message TTL.  
The objective of (9) is to improve the network performance comprehensively. In (9), maximization \(I_{a,j}\) can effectively ensure the user QoS, while maximization \(Q_{j,b}\) can improve the possibility of RNs encountering the DN, so as to successfully forward messages. In addition, C1.1 guarantees the transmission rate from the SN \(N_{a}\) to the neighbor node \(N_{j}\) and C1.2 ensures the validity of the message.
# Actuating Performance Analysis of a New Smart Aggregate Using Piezoceramic Stack
## 4 Conclusions  
A new type of smart aggregate using piezoceramic stack (SAPS) was developed in this research for improved output, as compared with a conventional smart aggregate with  
Figure 17: The impedance signatures of SAPSs with 4 and 6 PZT discs: (a) a SAPS with 4 PZT discs; (b) a SAPS with 6 PZT discs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Resonance Frequency (kHz)**} & \multicolumn{3}{c}{**Anti-Resonance Frequency (kHz)**} \\  & **Measured** & **Theoretical** & **Relative Error** & **Measured** & **Theoretical** & **Relative Error** \\ \hline
4 PZT discs & 202.8 & 201.29 & 0.74 & 222.6 & 221.7 & 0.40 \\
6 PZT discs & 156.3 & 152.61 & 2.36 & 173.8 & 168.32 & 3.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Theoretical and experimental resonance and anti-resonance frequencies of the two SAPSs.  
a single piezoceramic patch. The proposed smart aggregate is preferred for applications with a strong stress wave attenuating effect. To investigate its characteristics, the proposed SAPS was simplified to a one-dimensional axial model. Based on the simplified model, the influences of the structural parameters, including the number of the PZT discs, the dimension of the PZT disc, protective shield, and copper lids, and the elastic modulus of the epoxy on the electromechanical and displacement output performance were discussed by using numerical method. The results indicate that the number and thickness of the PZT discs have a greater effect on the first resonance frequency and the first electromechanical coupling factor of SAPSs than the dimensions of the protective shell and copper lids. Moreover, the results also show that the number of the PZT discs and the outer diameter of the protective shell have a much greater influence on the displacement output than other parameters. The results also demonstrate that the first resonance frequency, the first electromechanical coupling factor, and the displacement output of SAPSs change less than 1.79% when the elastic modulus of the epoxy changes from 1.28 GPa to 5.12 GPa, which demonstrates that the elastic modulus of the epoxy has a very limited influence on the property of SAPSs, and it will be helpful in the design and fabrication of SAPSs. Lastly, an experimental setup was designed, and experimental results validate the proposed model.  
This study provides an approach to increasing the output of SAs and also presents a method to design SAPSs. Future research will involve SAPS's sensing performance study, and it will also involve damage detection by using SAPSs.  
Conceptualization, methodology, formal analysis, G.L.; validation, Q.W. and Z.L.; writing--original draft preparation, G.L. and H.S.; writing--review and editing, T.W.; supervision, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant Number 51808417.  
Not applicable.  
The authors declare that they have no conflict of interest.
## 3 Results and Discussion
### DPPH Free Radical Scavenging Effect and the Reduction of ROS Production by the Samples
Compared to these results, ROS production was remarkably reduced to 55.40 \(\pm\) 4.95% after treatment with 0.3% of the AF, and a 53.79 \(\pm\) 2.69% reduction was even observed in the presence of 0.5% of the AF. These results definitely confirm that the AF protects against oxidative stress in mitochondria because of its strong antioxidant effects that incorporate synergistic effects with quercetin. Moreover, the AF protects against mitochondrial dysfunction induced by UVb light, which can inhibit skin aging by reversing mitochondrial dysfunction caused by exogenous stresses. This hypothesis is supported by recent studies showing that quercetin prevents mitochondrial damage induced by several exogenously and endogenously produced cellular stressors and oxidative stress [41]. As shown in Figure 5, quercetin itself and quercetin present in the AW and AF prevented intracellular ROS generation induced by  
Figure 5: ROS production in UVb-irradiated human skin fibroblasts treated with 10 ug/mL quercetin and various concentrations of the two aloe extract samples. AW: hot water extraction of _Aloe vera_ leaf skin at 100 \({}^{\circ}\)C for 24 h. AF: _L. plantarum_ fermentation of _Aloe vera_ leaf skin at 37 \({}^{\circ}\)C for 3 days and extraction with an ultrasonicator at 40 kHz for 1 h at room temperature. The mean values \(\pm\) SD for separate triplicate experiments are shown, and error bars indicate the SD. The significance of the difference was set to \({}^{\ast}\)_p_ < 0.05 and ***_p_ < 0.001 compared with the control (UV\(-\)) group. UVb light. Therefore, as shown in Figure 5, the reduction in ROS production by damaged skin fibroblast cells treated with the AF suggests that the AF mediates the skin antiaging process because quercetin and its derivatives were proven to attenuate the skin healing process [37] effectively. However, a more exact effect of the fermented aloe leaf on mitochondrial protection should be correlated by measuring mitochondrial ROS generations after being exposed with UVb light.
## 5 Plenary Lectures--Trends in Analytical Techniques  
Instrumental Analysis for Metals in Biology: Ultratraces, Isotopes, Species, and Images R. Lobinski 1,2  
1 Institute of Analytical and Physical Chemistry for the Environment and Materials, (IPREM-UMR 5254), CNRS, E2S UPPA, 64000 Pau, France; ryszard.lobinski@univ-pau.fr
2 Chair of Analytical Chemistry, Warsaw University of Technology, 00-664 Warszawa, Poland  
The concentrations of the individual metals in biological systems are tightly controlled by the physiology and biochemistry of an organism. Biologically mediated processes lead to the isotopic fractionation and sub-cellular distribution. The metal-induced gene expression results in the synthesis of a myriad of biological ligands on the proteome and metabolome levels implied in the uptake, trafficking, accumulation, and excretion of trace elements. There is increasing evidence that biochemical and physiological processes can contribute to the fractionation of mineral elements on the isotopic level, as it has been well established for oxygen, nitrogen, or sulfur. Consequently, information on the metal concentrations, speciation, isotopic composition, and distribution is essential for the understanding of the role and fate of metals in a biological system.  
The status of a mineral element in a biological sample can be defined by: (i) concentration (possibly isotopic composition), (ii) chemical speciation, and (iii) localization. The control of all the three is essential for the cellular metal homeostasis. Therefore, there is a need for analytical methods able to provide the necessary information for all the elements, for the maximum of species and with a maximum spatial resolution at an adequate time scale.  
The lecture discusses the state of the art of the development of such a versatile \"toolbox\", its recent advances, and evolution trends. Particular attention will be given to global approaches aimed at a simultaneous comprehensive speciation analysis of one or several elements (metallomics [1]). Recent trends in ultratrace analysis by ICP MS, precise isotope ratio measurements by multicollector ICP MS, high resolution (Fourier Transform) electrospray mass spectrometry, and nanoSIMS imaging will be highlighted.
## 3 Results
### Validation with Altimetry Observations  
We adjusted the CryoSat-2 and IceBridge ATM observations to fit the coverage of the TanDEM-X SAR acquisitions, to validate the constructed DEM (Figure 8a,b). Significantly more CryoSat-2 altimetry measurements than ATM observations were collected. The trajectory of the three ATM observations flow along the glacier surface are shown in Figure 8b.  
We show the scatter plots between the constructed TanDEM-X DEM and the two altimetry observations in the same manner as the comparison to the other DEMs (Figure 9). Before plotting, the suspicious CryoSat-2 observations around the rough and steep terrain area were set as outliers. The coefficients of determination of the CryoSat-2 and ATM were high values of 0.998 and 0.995, respectively. The calculated offset of the CryoSat-2 case was just \(-6.5\) m, but a \(-12.5\) m offset was found for the ATM observation, which was slightly larger than that of the CryoSat-2.  
Figure 7: Scatter plots between the constructed DEM and the other two DEMS of (**a**) the global TanDEM-X DEM and (**b**) the GIMP DEM. We assumed that there was only an offset between two observations as \(Y=X+offset\). They are well correlated (\(R^{2}=0.998\)) and the calculated offsets were \(-3.1\) m and \(-7.0\) m, respectively. (**c**) Residuals of TanDEM height with respect to GIMP DEM, (**d**) residuals of TanDEM height with respect to Global DEM.  
The scatter plots along the six ATM flight paths are shown in Figure 10 to examine the cause of the larger offset. We noticed that the larger offsets were found at both the bedrock and the upper part of the glacier (Figure 10a,d,e,f)). The offsets along the yellow and green flight lines were just \(-1.3\) m and \(-5.1\) m, respectively (Figure 10b,c). The ATM observations in the sea-ice region were excluded as outliers in the scatter plot. The constructed DEM along the primary tributary of the glacier had a good correlation with the ATM measurements (Figure 10b). We suspect that the poor \(R^{2}\) of \(-0.844\) (only \(0.001\) in the regression model with linear) as shown in Figure 10c resulted from a significant topographic variation during the approximately two and half months' time span between the two observations at the terminus of the glacier.  
Figure 8: **(a)** The location of the CryoSat-2 altimetry measurements collected for two months from January 22, 2015 to March 19, 2015 as shown in Table 2, and **(b)** the IceBridge Airborne Topographic Mapper (ATM) observations acquired on May 5, 2015. Most of the ATM observations were collected along the path of the Petermann Glacier flow. Each color in **(b)** represents six flight trajectories collecting the ATM data in Table 2.  
Figure 9: Scatter plots between the constructed DEM and altimeter observations of **(a)** the CryoSat-2 and **(b)** the IceBridge ATM. We also assumed that there was only an offset between two observations as \(Y=X+offset\). They show a very good correlation coefficient, \(R^{2}\) of \(0.995\), and the calculated offset of the CryoSat-2 and ATM altimetry observations were \(-6.5\) m and \(-12.5\) m, respectively.
## 8 Conclusions  
Effective mobility management is crucial for ensuring the expected QoE and QoS in 5G HetNet. The HO process plays a critical role in mobility management, and failures during this process can have adverse effects on the reliability, stability, and connectivity of 5G communication networks. This survey paper reviewed various techniques to address mobility and HO challenges in 5G HetNet. We provided a detailed background of HO and mobility and explained how QoS, connectivity, and data rates are affected in 5G UDPNets. We also discussed the architecture of 5G and various 5G technologies, including HetNet, mm-Wave, massive MIMO, full duplex, etc. We further investigated the HO process and mobility management in 5G UD-HetNet, covering various events, conditions, decisions, control parameters, and KPIs. After discussing the HO and mobility management in 5G UD-HetNet, we compared recent structures that mitigate HO and mobility management issues based on KPIs, HODPs, and HCPs while highlighting their limitations. Finally, we examined the future-focused challenges in 5G UD-HetNet associated with the HO process and mobility management, including security and privacy, ICI, load balancing, signaling overhead, and high speed. In the last section, we provided possible efficient and optimized future directions and recommendations. By addressing the challenges discussed in this paper and implementing future directions and recommendations, we can ensure a seamless and reliable 5G HetNet with improved QoE and QoS, providing users with an enhanced communication experience.  
**Author Contributions:** In this survey article, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the conceptualization of the study. [PERSON] prepared the original draft of the manuscript and visualized the findings. [PERSON] and [PERSON] supervised the study and provided critical feedback on the manuscript. The manuscript has been reviewed and edited by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported and funded by a Telekom Malaysia Research & Development grant, RDTC: 211034, MMUE/220006, TM, Malaysia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the data are available in the paper.  
**Conflicts of Interest:** All authors declare no conflict of interest.
## 3 Results  
### Microstructure  
#### 3.1.1 Laser-Hardened Austempeered Gray Cast Iron (LHAGI)  
Figure 4 shows the microstructure of LHAGI with various austempering temperatures. There were four diverse zones produced under the laser-hardened surface. First, Zone 4 was the substrate of AGI, which indicates that the original pearlitic structure has been converted into acicular ferrite and stable austenite with high carbon content after receiving the austempering heat treatment and is called ausferrite when produced in austempeered ductile iron (ADI) [22; 23; 24]. The ferrite plates grew from needle-like into feather-like with increasing austempering temperature from 232 \({}^{\circ}\)C to 343 \({}^{\circ}\)C, since more carbon atoms diffused with a high diffusion rate [25]. Then, Zone 1 was the central laser-hardened region, which contained the ledeburite. In Zone 1, the graphite flakes were dissolved during the laser-hardening process due to the high local temperature. The morphology of ledeburite had no differences among LHAGI specimens with various austempering temperatures. In addition to Zone 1, there were two heat-affected zones, which were caused by the thermal radiation from the central laser-hardened region. In the matrix of the upper heat-affected zone (Zone 2), because the heating temperature was above the eutectoid temperature during the laser-hardening process, pearlite was transformed into unstable austenite, and austenitized regions were quickly cooled by air and the neighboring substrate. Therefore, the main component in Zone 2 was martensite, which was coarse near Zone 1 and thin near Zone 3 due to the decrease in temperature. In addition, the heating temperature in the lower heat-affected zone (Zone 3) was much lower than that in Zone 2, which resulted in tempering of acicular ferrite and the carbon saturated austenite. Table 4 displays the dimensions of laser-hardened regions and heat-affected regions of LHAGI with various austempering temperatures. The width and depth of Zone 1, Zone 2, and Zone 3 were independent of the austempering temperature.  
Figure 4: Microstructure of Difference Zones in the Matrix of Laser-Hardened Austempeered Gray Cast Iron (LHAGI) Specimens with Various Austempering Temperatures (**a**) 232 \({}^{\circ}\)C; (**b**) 288 \({}^{\circ}\)C; (**c**) 343 \({}^{\circ}\)C.
In addition, the extracts obtained using these methods often require additional concentration and purification, thus extending the duration of analysis [12]. Among the emerging extraction techniques, ultrasound-assisted extraction (UAE) is accepted as an environmentally friendly option due to its high performance, lower solvent consumption and reduced duration, as well as its suitability for use with temperature-sensitive compounds [14]. UAE uses sound waves at a frequency (>20 kHz) able to generate collapsing cavitation bubbles, creating extreme local conditions. These phenomena cause fragmentation, erosion and sonoporation in plant tissues, which favors the release of bioactive compounds. Cavitation improves the absorption and diffusion of the solvent and, thus, increases the extraction yield. UAE also induces turbulence and shear force, causing cell walls to rupture and accelerating mass transfer. Together, these mechanisms contribute to the overall effectiveness of the process [11; 14]. This work aimed to valorize white and red SGP, which are considered waste by-products of Grappa production. Antioxidant bioactive compounds were extracted from these materials using a water bath (WB), a water bath + ultrasound (WUS) and UAE, and the method that yielded the best results in terms of the total phenolic content (TPC) and ferric reducing antioxidant potential (FRAP) was optimized. Then, grapeseed oil was fortified with the extracts from grape pomace (EGPs), obtained under the optimum conditions, to delay its oxidation.