esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 2 UAV Technology in Wireless Communication
### UAV Communication Scenarios
#### 2.4.2 Normal User  
Due to obstacles in the coordinate LoS path, the signal to and from the BS for a terrestrial UE is regularly deflected or diffracted. As a result, the UE's gained signal quality will be significantly reduced. BSs are typically located at high elevations, such as cell towers or building tops. The likelihood of obstacles obstructing the LoS path dramatically decreases as the UE ascends to a higher altitude, as in the case of a hovering UAV. The signal quality improves as the path loss decreases since signal propagation through the sky is close to free-space propagation. The UAV can have LoS access to a number of nearby non-serving BSs. The increased likelihood of LoS paths to numerous non-serving cells will increase the UAV's obstacles since the cells share the same radio assets. The signal-to-interference-plus-noise ratio (SINR) may be low due to the high number of obstacles, making it difficult for the roaming UE to quickly receive and translate adaptable management-related signals (for instance, HO commands). Figure 5 presents the normal user scenario of UAVs in wireless communication.
###### Abstract  
Spores from _Bacillus horneckiae_ SBP3 (SBP3) of shallow hydrothermal vent origin have recently been reported to survive extreme conditions more often than their close phylogenetic relatives _B. horneckiae_ DSM 23495T (BHO) and _B. subtilis_ 168 (BSU) used in biodosimetry and the space microbiology model. To investigate the structures of unheated spores, Fourier-transform infrared spectroscopy (FTIR) analysis was used. The FTIR spectra of the spores from the strains SBP3, BHO and BSU mainly differed in the region that referred to lipids and amino acids or polypeptides, indicating that the SBP3 spores were richer in saturated fatty acids, and the protein structures of SBP3 and BHO spores were more aggregated and complex than those of BSU. SBP3 spores were more resistant (LD\({}_{00}\) = 4.2 \(\pm\) 0.3 min) to wet heat treatment (98 \({}^{\circ}\)C) than BHO (LD\({}_{00}\) = 1.8 \(\pm\) 0.2 min) and BSU (LD\({}_{90}\) = 2.9 \(\pm\) 0.5 min) spores were. In comparison to the untreated spores, the Raman spectra of the wet-heat-treated SBP3 spores showed minor variations in the bands that referred to proteins, whereas major changes were observed in the bands that referred to lipids and amide I in the heated BSU spores and to both lipids and proteins bands in the treated BHO spores. These results suggest that the major stability of SBP3 spore proteins could explain their greater resistance to wet heat compared to BHO and BSU. Our findings provide basic information for further comparative studies into spore responses to natural and laboratory stresses, which are useful in several different fields, such as astrobiology.  
Bacillus spores; Raman; FTIR; spore resistance; heat resistance; extremophiles +
Footnote †: journal: Applied Sciences
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 5 Performance Evaluation
### Performance Evaluation of the Proposed Protocol at Y-Junction  
The Y-junction is another three-leg road intersection scenario where the same size of angles exists between each two adjacent legs. The performance of this road scenario is close to the T-junction scenario. Figure 6 graphically illustrates the performance of the proposed protocol at all-ways stop signs and the main road crossing a secondary road. As we can see from Figure 6c,d, the proposed protocol succeeds in increasing the braking distance of the stopping vehicles in both tested scenarios by 25% on average. This enhances the safety conditions, as discussed earlier. In the highly contested road scenario, the stopping distance is decreased to meet the average stopping distance of vehicles when the assistance protocol is absent.  
The proposed driving assistant protocol eliminated accidents in the tested road scenarios. Figure 6e,f graphically show the accident rates at the tested road intersections. The effects of the proposed protocol are higher in the congested road scenarios than in the low-traffic-density scenarios. In the latter scenario, the accident rate is originally low (i.e., 1%). Thus, eliminating it is a lower effect than in highly congested road scenarios since the accident rate is high (i.e., up to 6%) without using the assistance protocol.  
Finally, from Figure 6e,f, we can see that the proposed protocol increases the throughput of the investigated road Y-junctions by 10% on average. From these figures (i.e., e and f) we can also infer that a higher increase in the throughput is obtained at higher traffic densities. Moreover, the throughput of the main road intersection is higher than the throughput of the all-ways stop sign scenario.  
Figure 5: The performance evaluation of the driving assistance protocol at T-junction.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 5 Discussion
The spatial resolution (2 km) of the Himawari-8 data is still not adequate enough, and this may introduce challenges to the detection of mixed pixels with different degrees of combustion. Unfortunately, at present, there is no officially reliable Himawari-8 burned area product. In this study, the sum of the active fire data was used to get the burned area data. The active fire detection algorithm is based on certain thresholds, which may not be robust for different regions. Therefore, in order to better apply the Himawari-8 data to fire research, it is necessary to develop a reliable product of burned extend for Himawari-8 fire. The second important limitation of H8-FSR is that it estimates an overall distribution of FSR by computing the rate of burned area centroid movement in a windy climate. The rate of fire center movement can well represent overall FSR when all the pixels have a similar direction of movement at every time step. However, small-scale wind has a substantial direction variation [80; 81], which means the burned pixels in varying locations have different directions when the wind speed is very small. As a result, the movement of a fire center cannot actually reflect the fire spread, and thus affects the effectiveness of the H8-FSR.
## 3 Loss Calculation of IM Driven FESS
### Mechanical Losses  
#### 3.3.1 Windage Loss  
The windage losses in a FESS are due to the friction between the rotating surfaces and the surrounding medium, which is air in most cases. The windage loss increases with increase of the flywheel speed and is proportional to the cube of the flywheel angular velocity [67]. The windage losses of the FESS housed in a containment for the pressure of \(>\)100 Pa is given by Equation (18).  
\[P_{w}=\frac{1}{64}C_{m}\omega^{3}\rho\left(D_{r0}^{5}-D_{r1}^{5}\right) \tag{18}\]  
where \(C_{m}=\frac{3.87}{\sqrt{R_{e}}}\) is the torque coefficient, \(R_{e}\) is the Reynolds number and is given by \(R_{e}=\sqrt{\frac{\rho\omega D_{r0}^{2}}{4\mu}}\), \(\rho\) is the coolant density and \(\mu\) is the dynamic viscosity of air [3].  
For the high-speed rotor flywheels operating in low air pressure environments (0.1 Pa to 10 Pa), windage losses of the FESS is given by Equation (19) [68].  
\[P_{w}=\frac{P}{2}\sqrt{\frac{2\pi}{RT}}\omega^{2}a^{4} \tag{19}\]  
where, \(P\) is the gas pressure, \(a\) is radius of the rotor disk and \(R\) is the gas constant. By substituting values of parameters (\(R_{e}\) and \(C_{m}\)) and coefficients from Table 2 in Equations (18) and (19). The simplified Equations for windage losses can be written as:  
\[\left\{\begin{array}{ll}P_{w}=4.5\times 10^{-7}\omega^{2.5}&P\geq 100\\ P_{w}=6.6\times 10^{-6}\omega^{2}&P\leq 10\end{array}\right. \tag{20}\]
## 2 Structure of Proposed AOC  
Figure 1 shows the structure of the proposed AOC. By utilizing a 0.5-mm-pitch electrical connector, which has a 38-pin, and high-density optical assembly technique, the size of the proposed form-factor is 5.2 cm\({}^{3}\) (10.9 \(\times\) 56.6 \(\times\) 8.4 mm), which is 55% smaller than that of the standard form-factor of \"QSFP28\" (12.5 cm\({}^{3}\): 18.4 \(\times\) 80.3 \(\times\) 8.5 mm) [8], and 45% smaller than \"\(\upmu\)QSFP\" (9.4 cm\({}^{3}\): 13.8 \(\times\) 64.9 \(\times\) 10.6 mm) [9]. A cross-sectional view of the proposed AOC is shown in Figure 1b. In the AOC, a 4-ch vertical cavity surface emitting laser (VCSEL), a 4-ch photodiode (PD), a VCSEL driver integrated circuit (IC), and a transimpedance amplifier (TIA) IC are mounted on the backside of a multi-layer organic substrate. To control the VCSEL driver and the TIA, a micro control unit and an electrically erasable programmable read-only memory (EEPROM) are mounted on the topside of the substrate. An optical connector is placed under the VCSEL and the PD. Thermal vias are fabricated through the substrate to the bottom of the ICs.  
In the case of a conventional AOC, the ICs (the VCSEL driver and the TIA) are mounted on the top side of the substrate [3; 4; 5; 6]. On the contrary, the ICs of the proposed AOC are mounted on the bottom side of the substrate. In the latter structure, as shown in Figure 1b, heat directly dissipates to the topside of the case through the thermal vias. Therefore, compared with the conventional AOCs, the proposed AOC structure can suppress deteriorations in the characteristics of the optical devices in a high-temperature environment.
# The Effect of Curing Conditions on Selected Properties of Recycled Aggregate Concrete
## 3 Results and Discussion
### Sorption  
The sorption results of concretes made of CEM I and CEM III in relation to RCA content are presented in Figure 6. The wet conditions of hardening and the PEV way of curing were more suitable for concretes made of both CEM I and CEM III. In the case of CEM I as a binder, all conditions of hardening had more similar influence on sorption in comparison with CEM III. For CEM III used  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Cement** & **RCA** & \multirow{2}{*}{**Curing Conditions**} & \multirow{2}{*}{**Curing Conditions**} & \multirow{2}{*}{**\(\ell_{\text{c}}\) 90-d**} & \multicolumn{8}{c}{**Homogenous Groups**} \\ \cline{4-11}
**Type** & & & & & **(MPa)** & & & & & & & \\ \hline III & 100 & Dry & 39.4 & *** & & & & & & & \\ III & 0 & Dry & 41.8 & *** & *** & & & & & & \\ III & 50 & Dry & 41.8 & *** & *** & & & & & & \\ III & 50 & PEV & 42.3 & *** & *** & & & & & & \\ I & 50 & PEV & 45.2 & *** & *** & & & & & & \\ I & 0 & Dry & 46.1 & & *** & & & & & & \\ III & 0 & PEV & 46.9 & & & *** & *** & & & & \\ I & 100 & Dry & 47.5 & & & *** & *** & & & & \\ I & 50 & Dry & 47.6 & & & *** & *** & & & & \\ I & 0 & PEV & 48.5 & & & *** & & & & & \\ \hline III & 0 & **Wet** & 50.0 & & & & *** & & & & \\ III & 100 & **Wet** & 50.0 & & & & & & & & \\ III & 50 & **Wet** & 50.2 & & & & & & & & \\ III & 100 & PEV & 50.5 & & & & & & & & & \\ I & 0 & **Wet** & 53.7 & & & & & & & & & \\ I & 100 & PEV & 55.7 & & & & & & & & & \\ I & 50 & **Wet** & 57.3 & & & & & & & & & \\ I & 100 & **Wet** & 57.6 & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Tukey’s test results for 90-day compressive strength (\(\ell_{\text{c}}\) 90-d) of RAC series.  
Figure 5: Compressive strength increase between 28 and 90 days.  
as a binder, dry curing conditions were more hazardous compared to CEM I, but on the other hand wet curing conditions were slightly better for CEM III. The lower sorption for concrete made of blast-furnace slag cement followed its denser microstructure due to using just this binder, but only under the condition of proper curing (wet or PEV). The dry way of curing should be definitively excluded, which has been confirmed by [31].
## 4 The Third-Order Beam Theory  
### Preliminary Comments  
From the discussions presented in the previous sections, it is clear that the transverse shear stress distribution through the beam height, computed using the stress-strain relation \(\sigma_{xz}=2G\varepsilon_{xz}\), is either zero (in CBT) or constant (in TBT), although the actual variation of \(\sigma_{xz}(x,z)\) with \(z\), determined using the 3-D equations of equilibrium of linearized elasticity, is cubic. Therefore, it is necessary to have the displacement field (especially \(u_{1}\)) to be a cubic function of \(z\). In this section, a third-order beam theory which accounts for the von [PERSON] geometric nonlinearity, through thickness variation of the material and modified couple stress effect, is presented. The theory presented herein accounts for the vanishing of transverse shear stress on the bottom and top surfaces of the beam (see [4; 8; 38; 39]).
# Least-Squares Estimation of Tangent Space for a Triangular Mesh
## 3 Method
### Application of Texture Space Alignment  
Furthermore, the optimal alignment in texture space can be utilized to average tangent spaces in the previous methods [2; 3; 5]. To do this, we first reformulate Equation (3) by employing the texture coordinates \(\bar{\mathbf{p}}_{0}=(u_{0},v_{0},0)\), \(\bar{\mathbf{p}}_{1}=(u_{1},v_{1},0)\), and \(\bar{\mathbf{p}}_{2}=(u_{2},v_{2},0)\) of a triangle and the \(w\)-axis \(\mathbf{w}=(0,0,1)\) as follows:  
\[\Big{[}(\mathbf{p}_{1}-\mathbf{p}_{0})\big{|}(\mathbf{p}_{2}-\mathbf{p}_{0}) \big{|}\mathbf{n}\Big{]}=\Big{[}\mathbf{t}\big{|}\mathbf{b}\big{|}\mathbf{n} \Big{]}\Big{[}(\bar{\mathbf{p}}_{1}-\bar{\mathbf{p}}_{0})\big{|}(\bar{ \mathbf{p}}_{2}-\bar{\mathbf{p}}_{0})\big{|}\mathbf{w}\Big{]}. \tag{18}\]  
The tangent space of the triangle is then obtained by  
\[\Big{[}\mathbf{t}\big{|}\mathbf{b}\big{|}\mathbf{n}\Big{]}=\Big{[}(\mathbf{p }_{1}-\mathbf{p}_{0})\big{|}(\mathbf{p}_{2}-\mathbf{p}_{0})\big{|}\mathbf{n} \Big{]}\Big{[}(\bar{\mathbf{p}}_{1}-\bar{\mathbf{p}}_{0})\big{|}(\bar{ \mathbf{p}}_{2}-\bar{\mathbf{p}}_{0})\big{|}\mathbf{w}\Big{]}^{-1}, \tag{19}\]  
which corresponds to Equation (4).  
If one of the vertices of the triangle is a duplicated vertex \(i_{j}\) of vertex \(i\) on a seam, the optimal alignment, \(\bar{\mathbf{R}}_{i_{j}}\), is employed to obtain the tangent space of the triangle aligned with respect to vertex \(i\):  
\[\Big{[}\mathbf{t}_{A}\big{|}\mathbf{b}_{A}\big{|}\mathbf{n}\Big{]}=\Big{[}( \mathbf{p}_{1}-\mathbf{p}_{0})\big{|}(\mathbf{p}_{2}-\mathbf{p}_{0})\big{|} \mathbf{n}\Big{]}\Big{(}\bar{\mathbf{R}}_{i_{j}}\Big{[}(\bar{\mathbf{p}}_{1} -\bar{\mathbf{p}}_{0})\big{|}(\bar{\mathbf{p}}_{2}-\bar{\mathbf{p}}_{0})\ \big{|}\ \mathbf{w} \Big{]}\Big{)}^{-1}. \tag{20}\]  
This is equivalent to computing the tangent space of the triangle using the aligned texture coordinates, as in Figure 4(b), instead of the original coordinates, as in Figure 4(a). The above equation can be further written as  
\[\Big{[}\mathbf{t}_{A}\big{|}\mathbf{b}_{A}\big{|}\mathbf{n}\Big{]}=\Big{[} \mathbf{t}\big{|}\mathbf{b}\big{|}\mathbf{n}\Big{]}\bar{\mathbf{R}}_{i_{j}}^{ \mathrm{T}}. \tag{21}\]  
Finally, the tangent space \([\mathbf{t}_{i}]\mathbf{b}_{i}\mathbf{n}_{i}]\) of vertex \(i\) is computed by averaging all the tangent spaces of the triangles aligned in this manner. The tangent space of the duplicated vertex \(i_{j}\) can also be directly obtained from \([\mathbf{t}_{i}]\mathbf{b}_{i}\mathbf{n}_{i}]\) as follows:  
\[\Big{[}\mathbf{t}_{i_{j}}\big{|}\mathbf{b}_{i_{j}}\big{|}\mathbf{n}_{i_{j}} \Big{]}=\Big{[}\mathbf{t}_{i}\big{|}\mathbf{b}_{i}\big{|}\mathbf{n}_{i}\Big{]} \bar{\mathbf{R}}_{i_{j}}. \tag{22}\]  
The derivation for the above simplification is similar to that employed for Equation (15).
## 3 Results
### Spatial Differences in Performances of the DNN Models  
Figures 5 and 6 show the spatial distribution of PE for different models. The spatial patterns of PE for both corn and soybean were similar among the 'EVI + Climate,' 'SIF + Climate,' and 'SIF + EVI + Climate' models. In particular, those models performed similarly well in the central part of the Corn Belt (e.g., Iowa). The magnitudes of PE were mostly smaller than \(\pm 2\%\). In contrast, PE was higher in the northern part of the Midwest states (i.e., North Dakota, northern South Dakota, and northwestern Minnesota), which indicates that models tended to underestimate yields of soybean in these regions (Figure 5b,d,f). The underestimation of corn and soybean yields by 'EVI', 'SIF', and 'SIF + EVI' models were also significant in the northwestern part of the study area (Figure 6b,d,f). In contrast, the PE values of models with only climate variables as inputs showed less spatial variability (Figure 6h).  
For corn, the PE values of models based only on SIF, EVI, and climate inputs ranged from \(-20.25\%\) to \(47.03\%\), from \(-26.11\%\) to \(46.09\%\), and from \(15.68\%\) to \(30.85\%\) (Table 2). It was also found that the combination of remote sensing and climate data made the range of PE much smaller, i.e., \(-12.46\%\) to \(21.90\) for the model based on SIF and climate, \(-12.28\%\) to \(15.93\%\) for the model based on EVI and climate, and \(-11.44\%\) to \(17.83\) for the model with SIF, EVI, and climate data as inputs. As for soybean, the PE ranges by individual models are similar to those of corn. In addition, the DNN models performed well at the county level for both crop types. For above \(70\%\) of counties, PE values of 'EVI + Climate' and 'SIF + EVI + Climate' models ranged from \(-3.48\%\) to \(3.75\%\) and from \(-3.55\%\) to \(3.39\%\) for corn. The corresponding values were from \(-4.37\%\) to \(4.65\%\) and from \(-3.63\%\) to \(4.27\%\) for soybean (Table 2).  
Figure 5: Spatial distribution of percentage error (PE) of county-level corn and soybean yields estimated by models using both satellite and climate predictors. Left panels are the maps for the corn, and right are for the soybean. The figure includes results from models that input both SIF and climate data (**a,b**), input both EVI and climate data (**c,d**), and input SIF, EVI, and climate data (**e,f**). In each plot, the density plot in the upright corner presents the overall distribution of PE. The red dashed lines and ‘M’ in the density plots represent the mean values of PE, and the black ones are the referenced lines located at zero. Positive values mean that estimated yields were lower than census data, vice versa.
# Enhancing Friction Models for Starting Up Water Installations Containing Trapped Air
## 2 Mathematical Model
The general expression of the gravity term is expressed as follows: \[\frac{\Delta z}{L_{f}}=\frac{\mp\sum_{i=1}^{b-1}L_{i}\text{sin}\,\theta_{i}\mp \left(L_{f}-\sum_{i=1}^{b-1}L_{i}\right)\text{sin}\,\theta_{b}}{L_{f}}\] (2) where \(b\) = the position of a water filling column. Equation (1) must be applied, considering the negative and positive values of the longitudinal slopes. For a negative longitudinal slope, the sign \"\(-\)\" must be used, and for a positive value, the sign \"\(+\)\" is used. For a single pipeline, the gravity term can be computed as: \[\frac{\Delta z_{1}}{L_{f}}=\mp\text{sin}\,\theta_{1}\] (3) 3. Friction factor: this is used for describing friction losses along a hydraulic installation. For a laminar regime (Re < 2000), the Hagen-Poiseuille [23; 24] equation can be used. \[f=\frac{64}{\text{Re}}\] (4a) For transition and turbulent zones, the Swamee-Jain equation [25] was utilized, which is applicable for Reynold numbers varying from 3 \(\times\) 10\({}^{3}\) to 3 \(\times\) 10\({}^{8}\), and for absolute roughness (\(k_{s}\)) in a range between 10\({}^{-6}\) and 2 \(\times\) 10\({}^{-2}\) mm. \[f=\frac{0.25}{\left[\text{log}\bigg{(}\frac{k_{s}}{3.7D}+\frac{5.74}{\text{ Re}^{0.9}}\bigg{)}\right]^{2}}\] (4b) 4. Piston-flow model: this formulation is used to describe the air-water interface, which is considered perpendicular to the main direction of a pipeline. This equation can be applied for small pipe diameters. \[\frac{dL_{f}}{dt}=v_{f}\] (5) 5. Polytropic law: the air pocket volume, which is produced by the water movement during the filling operation, changes over time. The air pocket starts at rest (at atmospheric condition, \(p_{atm}^{*}\)), but when the regulating valve is opened, the air pocket pressure increases in value. The pipeline must be sized by considering a suitable pipe resistance for the maximum pressure attained during a transient event. The polytropic law for describing the air pocket behavior is given by:  
\[p_{1}^{*}x^{k}=p_{1,0}^{*}x_{0}^{k}=\text{constant} \tag{6}\]  
where \(k=\) polytropic coefficient, \(x=\) air pocket size, and the subscript \(0\) refers to the initial condition of an air pocket. The air pocket size is computed as \(x=L_{T}-L_{f}\), where \(L_{T}=\) total pipe length. The numerical resolution of the differential-algebraic system composed by Equations (1), (2), (4a), or (4b)-(6) is solved to find the unknown variables (\(p_{1}^{*}\), \(v_{f}\), \(L_{f}\), \(x\), \(\Delta z\), and \(f\)). The numerical resolution was performed using the method ODE23s in Simulink of Matlab. The system begins at rest; then, the initial conditions are \(v_{f}(0)=0\), \(p_{1,0}^{*}=p_{attn}^{*}\), and \(x_{0}=L_{T}-L_{f,0}\). The numerical resolution of the main variables can be addressed using the flowchart shown in Figure 2.  
Figure 1:0 Flowchart for the application of the proposed model.