esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Dynamic Stall in Yawed Condition between the Two Motions  
The 30% span of the Phase VI blade is investigated due to the manifest dynamic stall on the inboard blade. The wind speed is 10 m/s, and the yaw angle is 10\({}^{\circ}\) and 30\({}^{\circ}\), respectively (the cases of S1000100 and S1000300 in NREL UAE Phase VI [30]). The B-L dynamic stall model incorporated with [PERSON]'s rotational stall delay model shows a good agreement with the experimental data for the yaw angle of 10\({}^{\circ}\), as shown in Figure 15. A noticeable difference in both the lift coefficient and drag coefficient between the 2D and 3D results is observed, manifesting the 3D rotational effect.  
Figure 16 shows the aerodynamic force coefficients and pitching moment coefficient varying with azimuthal angle at the yaw angles of 10\({}^{\circ}\) and 30\({}^{\circ}\), respectively. The calculated results are generally in a good agreement with the experimental data. When the yaw angle increases, the 3D dynamic stall becomes more intense with the high lift coefficient, but trends of air load variations are consistent. The difference between PO and OF is moderate. As aforementioned, in 2D cases, the aerodynamic coefficients under PO are higher during upstroke and lower during downstroke. A slight improvement can be observed in the prediction under OF; the possible reason is that dynamic stall under yawed inflow is essentially a velocity-varying problem without pitch motion and fore-aft motion. Notice that prediction of the pitching moment coefficients is quite poor in both amplitudes and trends. Given that the pitching moment depends not merely on the integrated normal force but also on the location of the center of pressure, it is somewhat not surprising that the predicted pitching moment is unsatisfactory under the dynamic condition, because 3D effects have an influence on the normal force and on the movement of the center of pressure, which depends on appropriate coupling of the dynamic stall and the 3D rotational stall delay models. This problem may be solved only if deep understanding and accurate modeling of both the dynamic stall and the 3D stall delay are achieved. Nevertheless,  
Figure 15: Validation of the corrected Beddoes-Leishman (B-L) dynamic stall model in lift coefficient and drag coefficient. The wind speed is 10 m/s, the yaw angle is 10\({}^{\circ}\), and radial location is 30% span. **(a)** The lift coefficient; **(b)** The drag coefficient.  
the results from the B-L dynamic stall model show a different effect between PO and OF when their different additional camber effects are included.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 2 Problem Statement
### Problem Formulation  
As assumed above, with equal and orthogonal resource allocation, the ergodic rate of user \(m\) served by BS \(k\) on carrier band \(n\) should be denoted as  
\[r_{k,m}^{n}=\psi_{k,m}^{n}\ \frac{C_{k,m}^{n}}{L_{k}^{n}}, \tag{7}\]  
which is depending on not only the scheduling indicator but also the traffic load of the scheduled service supplier for each user.  
In the traditional cellular network, to bring the best performance, users are always connected to the BSs who would provide with the best Signal to Interference plus Noise Ratio (SINR) performances. In the two-layer HetNets, due to the different power levels between the macro Bs and the FemtoBSs, it is always the Macro BS that could serve the users under such cell association policies. Thereby, the traffic load would be extremely uneven among different layers causing the over loaded macrocell and lightly loaded femtocells, which has prevented the heterogeneous network from achieving better performances.  
To balance the traffic load and maximize the network utility, cell association schemes are designed that aim to determine the optimal user scheduling policy  
\[\begin{array}{ll}\underset{\boldsymbol{\Psi},\boldsymbol{L}}{\max}&\sum \limits_{n=1}^{N}\sum\limits_{k=1}^{K}\sum\limits_{m=1}^{M}r_{k,m}^{n}\\ \text{s.t.}&\sum\limits_{k=1}^{K}\sum\limits_{n=1}^{N}\psi_{k,m}^{n}=1,\ \ \forall m,\\ &L_{k}^{n}=\sum\limits_{m=1}^{M}\psi_{k,m}^{n}\leq M,\ \ \forall k,n,\\ &\psi_{k,m}^{n}\in\{0,1\},\ \forall k,n,m,\end{array} \tag{8}\]  
where \(\boldsymbol{\Psi}=(\psi_{k,m}^{n})_{k\in\mathcal{K},m\in\mathcal{M}}^{n\in \mathcal{N}}\), \(\boldsymbol{L}=(L_{k}^{n})_{k\in\mathcal{K}}^{n\in\mathcal{N}}\) and the network utility function is defined as the sum rate of the whole network.  
Problems such as the above are nonlinear and non-convex combinatorial optimization problems for which it is unpractical to search for a global solution [36]. In this paper, two load balanced cell association schemes are involved on the basis of carrier aggregation techniques for the two-tier HetNets. In an earlier work of ours, a distributed stochastic control based [37] user scheduling scheme is proposed for single flow carrier aggregation where users can only associate with no more than one BS on one carrier. In this paper, the problem is reformulated to the circumstances where multiple flows are allowed for carrier aggregation to achieve a joint solution across the HetNets.
## 3 Simulation of Tube-Cap Welding
### Simulation Process  
In this study, the change in shape according to the temperature of the welding part and plastic deformation in electrical resistance welding was simulated by analysis, and for this study, an electro-thermal-mechanical combination model was applied as shown in Figure 2. In addition, transient simulation was applied to calculate each coupled model according to the detailed time step to derive changes in the finely changing physical properties and results.  
The process that proceeds as the interpretation is coupled is as follows. First of all, in the initial stage, the end plug and cladding tube are contacted by pressure during squeeze time, so only the mechanical model is applied to calculate the speed field and overall stress of the end plug and cladding tube. However, after contact between the two objects is made, three models are combined to perform a resistance welding analysis. The main coupling process of the three models is as follows. The electrical model allows the input current value to calculate the distribution of the current density, and the thermal model calculates the resistance heat generation obtained from the electrical model and the resulting temperature distribution. The mechanical model calculates the material property value change and plastic deformation depending on the temperature distribution of the welding part calculated in the thermal model. The contact area between objects obtained from the mechanical model is applied again as an input value of current density, and the coupling process for the three models is repeated until the analysis is completed. In this study, the time step of each iteration was applied to 10 us or less so that the analysis result of the resistance welding applying the AC welding current of 60 Hz could be well converged [11; 12; 13; 14; 15; 16].  
\[\int\limits_{V}\overline{\sigma}\delta\tilde{\underline{e}}dV+K\int\limits_{V} \tilde{\varepsilon}_{ii}\delta\tilde{\varepsilon}_{jj}dV-\int\limits_{S_{t}}t_{ i}\delta u_{j}dS+\delta\Pi_{C}=0 \tag{2}\]  
Figure 1: Tube-cap welding diagram for fuel rod.  
Figure 2: Process of simulation for welding.  
\[\int\limits_{V}\Phi_{j}\delta\Phi_{j}dV-\int\limits_{S_{t}}\Phi_{n}dS+\delta\Pi_{ \Phi}=0 \tag{3}\]  
\[\int\limits_{V}kT_{j}\delta T_{j}dV+\int\limits_{V}\rho_{m}c_{m}\dot{T}\delta TdV -\int\limits_{V}\dot{q}_{V}\delta TdV-\int\limits_{\tilde{S}_{t}}\dot{q}_{S}dS+ \delta\Pi_{T}=0 \tag{4}\]  
The governing equation for the mechanical model is shown in Equation (2) and is mainly used to calculate shape changes due to plastic deformation. The governing equation can be used to calculate the contact area, internal stress, plastic deformation energy, friction, and heat generation. The electrical potential (\(\Phi\)) is an important variable in the electrical model and its governing equation is expressed as a Laplace equation in Equation (3). The thermal model follows the Galerkin heat transfer equation and is expressed in Equation (4). Table 1 shows the nomenclature of symbols used in governing equations.
## 2 Study Areas and Survey Conditions
### Greenland Areas  
The other four sites (Green1-4) were located approximately 30 km northeast of Sisimiut in the Kangerluarsuk Tulleq area, West Greenland (Figure 1), near the Sisimiut hydropower plant. In contrast with the Svalbard campaigns, the Greenland winter campaign was preceded by 15 mm of water equivalent snowfall, which, along with low speed winds (2 to 3 m s\({}^{-1}\)), resulted in much smoother and more featureless snow surface (Figure 2d,e). However, areas Green3 and Green4 were subject to moderate winds on the night prior to the surveys, which produced light snow ripples on the surface.  
Figure 1: Location of the surveyed areas with the generated orthomosaics (both SDEMs and TDEMs). The dotted line overlapped in the TDEMs indicates the areal extent of the SDEMs. The orthophotos display the position of the ground control points (GCPs) distributed across the surface that were used for georeferencing each area. The co-ground control points (CGCPs) refer to the number of GCPs shared in the co-georeferencing process. Camera positions refer to the land-based Structure from Motion (SfM) mapping performed for some of the terrain topographies. The orthomosaics assist in providing visual information on the snow surface and terrain type.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Area Name**} & \multirow{2}{*}{**Coordinates**} & \multirow{2}{*}{**Survey Date (dd/mm/yyyy)**} & \multicolumn{2}{c}{**Luminance**} & \multirow{2}{*}{**Snow Surface Type**} & \multirow{2}{*}{**Physiology Descriptions**} & \multirow{2}{*}{**Number of CPCs**} & \multirow{2}{*}{**CGPs**} \\  & **(Lat/Lon in WGS 80)** & & & & & & & \\ \cline{3-10} \cline{5-10} \cline{Green1 is a northwest-facing soil terrain ridge at the edge of a glacier torrent with some rock debris and sparse vegetation. In contrast, Green2 faces south and has moderately steep relief that is mostly characterized by thick (10 to 30 cm) vegetation-covered topography near the slope base (Figure 2f). Green3 and Green4 are two relatively big areas in close proximity on the side of a glacier valley with a highly heterogeneous topography consisting of soil, boulders of variable sizes (up to 4 m), rock debris, and low vegetation (similar to Figure 2f). All surveys in Greenland were conducted on fair to sunny days with fast-moving clouds leading to variable lighting conditions (Figure 2d,e). The topographies of all areas are shown in the orthomosaics in Figure 1.
# Dynamic Response of Drill Floor Considering Propagation of Blast Pressure Subsequent to Blowout
## 5 Structural Analysis
### Implementation to Finite Element Analysis
#### 5.1.3 Strain Rate Effect  
The strain rate effect on the material behavior was accounted for by implementing the Cowper-Symonds relation into the material constitutive model in this study. This model is widely used for expressing material behavior under dynamic loading conditions, and in this model, an increase in the stress flow at a high strain rate can be defined by the following Equation (12):  
\[\frac{\sigma_{\text{D}}}{\sigma_{\text{S}}}=1+\left(\frac{\dot{\epsilon}}{ \text{D}}\right)^{\frac{1}{4}} \tag{12}\]  
where \(\sigma_{\text{D}}\) is the dynamic stress at a particular strain rate, \(\sigma_{\text{S}}\) is the static stress, \(\dot{\epsilon}\) is the uniaxial plastic strain rate, and D and q are constants for steel.  
The scale of increase in the yield strength at a high strain rate can be controlled by the determination constants D and q, which are commonly obtained from the dynamic tensile test results. The test result of S355 and S460 are available in \"Elevated temperature and high strain rate properties of offshore steel\". The increase in the yield strength of S235 is obtained from the equation of Cowper-Symonds, and D = 4000 and q = 5 as proposed by DNV-RP-C208.
###### Abstract  
Korea's first geostationary satellite, the \"Communication, Ocean, and Meteorological Satellite\" (COMS), has been operating since 2010. The Meteorological Imager (MI), an sensor on-board the COMS, has observed sea-surface radiances for the estimation of sea surface temperature (SST) in the western Pacific Ocean and eastern Indian Ocean. To derive the SST coefficients of COMS, quality-controlled surface drifting buoy data were collected for the period of April 2011 to March 2015. A collocation procedure between COMS/MI data and the surface drifter data produced a matchup database for 4 years from 2011 to 2015. The coefficients for the COMS/MI SST were derived by applying appropriate algorithms, i.e., the Multi-channel SST (MCSST) and Non-linear SST (NLSST) algorithms, for daytime and nighttime data using a regression method. Validation results suggest the possibility of the continuous use of the coefficients as representative SST coefficients of COMS. The estimated SSTs near the edge of a full disk with high satellite zenith angles over 60\({}^{\circ}\) revealed relatively large errors compared to drifter temperatures. Most of NLSST formulations exhibited overestimation of SSTs at low SSTs (<10 \({}^{\circ}\)C). This study suggests an approach by which SST can be measured accurately in order to contribute to tracking climate change.  
sea surface temperature (SST); COMS/MI; MCSST; NLSST; SST coefficients 10.3390/rs10121916
## Abbreviation  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMDN & Active managed distribution network \\ APR & Automatic power restoration \\ AR & Automatic reclosing \\ ASAI & Average service availability index \\ ASUI & Average service unavailability index \\ ASS & Automatic stand-by switching on \\ AVR & Automatic voltage regulator \\ BES & Battery energy storage \\ BTPP & Biogas-turbine power plant \\ CAIDI & Customer average interruption duration index \\ CAIFI & Customer average interruption frequency index \\ DG & Distributed generation \\ DGU & Distributed generation unit \\ DNMG & Urban distribution network with connected microgrids \\ EDG & Engine-driven generator \\ EENS & Expected energy not supplied \\ EPS & Electric power system \\ ET & Electric traction substation \\ EXP & Exponential \\ FC & Fuel cell \\ FMECA & Failure mode effects and criticality analysis \\ GAM & Gamma \\ GT & Grounding transformer \\ GTPP & Gas-turbine power plant \\ GUM & Gumbel \\ HL & Hierarchical level \\ ICT & Information and communication technology \\ IEC & International Electrotechnical Commission \\ IP & Industrial park \\ IT & Information technology \\ LNOR & Log-normal \\ LTE & Long term evolution \\ LV & Low voltage \\ MG & Microgrid \\ MG(G) & Microgrid generation \\ MG(L) & Microgrid load \\ MT & Microturbine \\ MTBF & Mean time between failure \\ MTTR & Mean time to repair \\ MV & Medium voltage \\ MVUS & MV urban distribution substation \\ NOR & Normal \\ PAR & Pareto \\ PF & PowerFactory software \\ PFCB & PFC capacitor bank \\ PLC & Programmable logic controller \\ \end{tabular}
## 4 Conclusions  
The current study investigated four hypotheses regarding the co-treatment of \(\omega\)-3 FAs with various tea extracts using an in vitro digestion model system coupled with Caco-2 cells: (1) the digestive stability of \(\omega\)-3 FAs will be improved by preventing lipid oxidation induced by various tea extracts, (2) the digestive stability of \(\omega\)-3 FAs will be increased due to the stabilizing micelles induced by various tea extracts, (3) the intestinal absorption of \(\omega\)-3 FAs will be enhanced by increasing the integrity of the epithelial membrane induced by various tea extracts, and (4) the intestinal absorption of catechins will be improved by suppressing the expression of P-gp expression induced by \(\omega\)-3 FAs. The PV and DPPH radical scavenging assays demonstrated that the oxidation of \(\omega\)-3 FAs was prevented by the antioxidant effects of various tea extracts. The reduced deviation in the zeta potential of \(\omega\)-3 FAs with various tea extracts indicates that micelles of \(\omega\)-3 FAs were more stable, leading to enhanced digestive stability of \(\omega\)-3 FAs. The co-treatment of \(\omega\)-3 FAs with various tea extracts showed no impact on the TEER values, indicating that the epithelial integrity is preserved. The intake of GTE35 or Adiphenon(tm) alongside \(\omega\)-3 FAs resulted in no significant alteration in the P-gp expression, suggesting that \(\omega\)-3 FAs could contribute to the enhanced absorption of tea polyphenols, such as catechins, via the modulation of P-gp expression. The results from the current study suggest that the co-treatment with tea extracts can maximize the health benefits of \(\omega\)-3 FAs by enhancing digestive stability and intestinal absorption, which provides insight into potential dietary strategies in order to improve the bioavailability of \(\omega\)-3 FAs.  
Figure 6: Effect of the P-gp expression levels of \(\omega\)-3 FAs with and without various tea extracts in Caco-2 cells. Different letters indicate significant differences among groups (\(p<0.05\)). An asterisk (*) denotes significant differences between the \(\omega\)-3 FAs alone and those combined with each sample (\(p<0.05\)).  
**Author Contributions:** Conceptualization, I.-S.N., H.W.J., J.-O.C., B.-F.S., J.S. and S.-M.S.; Methodology, I.-S.N.; Formal analysis, I.-S.N.; Data curation, I.-S.N.; Writing--original draft, I.-S.N.; Writing--review & editing, H.W.J., J.-O.C., B.-F.S., J.S. and S.-M.S.; Visualization, S.-M.S.; Supervision, H.W.J., J.-O.C., B.-F.S., J.S. and S.-M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the AMOREPACIFIC R&I Center in 2024 (Grant No. R23S200001 and R24Z400009).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Experimental Setup
### Thermographic Measurement System  
The acquisition of the thermographic images is conducted with an infrared camera, typeImageIR 8300, from the manufacturer InfraTec. The actively cooled InSb focal plane array works with a global shutter (snap-shot detector), has a pixel size of \(15\,\mathrm{\SIUnitSymbolMicro m}\) at a full range resolution of \(640\,\mathrm{p}\mathrm{x}\times 512\,\mathrm{p}\mathrm{x}\) and a maximum frame rate of \(100\,\mathrm{Hz}\) with an integration time set to \(1600\,\mathrm{\SIUnitSymbolMicro s}\). The sensitivity is between \(2.0\) and \(5.0\,\mathrm{\SIUnitSymbolMicro m}\) and has a noise equivalent temperature difference (NETD) of less than \(25\,\mathrm{m}\mathrm{K}\oplus 30\,\mathrm{\SIUnitSymbolCelsius}\). The experimental setup is depicted in Figure 4. At a viewing distance of \(1.75\,\mathrm{m}\) and instantaneous field of view of \(0.15\,\mathrm{m}\mathrm{rad}\), a geometric resolution of \(0.26\,\mathrm{m}\mathrm{m}\) results on the surface per pixel. An image series of the static flow situation is acquired with 6000 images for the cylinder and 10,000 images for the airfoil measurements, respectively. The image processing of the thermographic images is conducted with the script language Python.  
Figure 4: Experimental setup in the wind tunnel. The thermographic camera ImageJR 8300 watches the cylinder measurement object through a CaF\({}_{2}\)-window. Additional camera systems observe the test section of the wind turbine.
## 4 Experimental Results and Discussion
It is worth noting that these values have been obtained by implementing the complete architecture on prototype PCB with discrete off-the-shelf commercial components and using a VCSEL with a relatively high threshold current. Nevertheless, the proposed optical pulsed UWB modulation technique implemented on-chip in a standard CMOS Si technology using VCSEL with lower threshold currents (e.g., Philips-ULM Photonics VCSEL-ULM850-10-TTN0101U with \(I_{\text{th}}=0.5\) mA [15]) could allow the reduction of the transmitter power consumption down to about 1 mW corresponding to a power efficiency of 7.8 pJ/bit. In this case, the estimated maximum total power consumption of the overall system is about 1.5 mW. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Data Rate** & \multirow{2}{*}{**Transmitter Power (mW)**} & **Power Efficiency** & \multirow{2}{*}{**BER (Bit Error Rate)**} \\ (Year) & **(Mbps)** & & & **(pJ/bit)** & \\ \hline
[9] (2015) & 67 & 2 & 30 & \(<\)10\({}^{-7}\) \\
[10] (2010) & 136 & 3 & 22 & \(<\)10\({}^{-3}\) \\
[11] (2004) & 80 & 45 & 562.5 & \(<\)10\({}^{-14}\) \\
[12] (2013) & 135 & 1.4 & 10 & - \\
[13] (2011) & 10 & 1.84 & 97.5 & - \\
[14] (2007) & 40 & 4.3 & 107.5 & \(<\)10\({}^{-5}\) \\
[15] (2014) & 100 & 2.1 & 21 & \(<\)10\({}^{-7}\) \\
[4] (2015) & 100 & 3.2 & 32 & - \\ \multirow{2}{*}{**This work**} & \multirow{2}{*}{128} & \multirow{2}{*}{\
\begin{tabular}{c} 4.6 (measured with a VCSEL \\ having \(I_{\text{th}}=2.2\) mA) \\ 1 (estimated with a VCSEL \\ having \(I_{\text{th}}=0.5\) mA) \\ \end{tabular} } & 35.9 & \multirow{2}{*}{
\begin{tabular}{c} \(<\)10\({}^{-9}\) \\ \(<\)10\({}^{-9}\) \\ \(<\)10\({}^{-9}\) \\ \(<\)10\({}^{-10}\) \\ \end{tabular} } \\  & & & & \\ \end{tabular}
\end{table} 
Table 1: Main performances of different biotelemetry links. Figure 7: Power spectrum of the transmitted pulsed signal \(U_{\text{T}}(t)\) (orange trace) and its average value (purple trace).
# 6G Enabled Industrial Internet of Everything: Towards a Theoretical Framework
## 3 Theoretical Foundation and Approach
### 6 GIIoE Foundations and Boundaries
#### 3.1.2 6G Challenges  
6G is not necessarily a single technology but a collection of mobile system technologies. A fundamental concept of the 6G future opportunity is the principle of 'New Services', from spectrum to infrastructure. Tiny cell network densification is the dominant theme of 6G wireless evolution. Densifying networks, while avoiding increasing inter-cell interference,is a key challenge. Additional key technical aspect of 6G networks will be the use of high-frequency spectrum, specifically the use of millimeter wave (mmW) spectrum with extreme low latency.  
6G cost modelling is a challenge that allows one to compare the difference between data traffic demand and network costs for different network scenarios. The costs of 6G infrastructure densification depend heavily on the required throughput density, periodic interest rate, and tiny highly dense base station infrastructure investment.  
6G considers the spatial rollout of telecommunications technologies over time. Often researchers focus on just one key aspect, such as modelling spatial viability. In mobile systems, there has been limited spatial modelling of the potential rollout of new technologies.  
Network densification and additional spectrum will play a key role in delivering 6G, which will have a significant influence on the delivery economics. There has been limited modelling of both the spatial and temporal dynamics of the rollout for 6G. While the current research on 6G networks provides insight into the vague technical specifications of various technologies, little emphasis has been placed on the rollout implications for different global infrastructure strategies.