esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Method  
In simulation of this research, Tabu Search (TS) is used for optimal operation of DC smart house. TS was proposed in 1986 by Glover. More details about TS can be found in [17,18]. Minimization of the operational cost for the DC smart house is the goal of this research. Therefore, the objective function is assumed to be the operational cost in this simulation. The objective function is minimized at each time point, so the obtained solutions are assumed to be the hourly amount of charge/discharge of the battery. The objective function and constraint condition for simulation are as follows.  
Objective function:  
\[C_{day}=\sum_{i=1}^{24}(P_{BUYi}-P_{SALEi})C_{pi} \tag{10}\]  
\[P_{BUYi}=\eta_{inv}(P_{PLi}+P_{HPi}+\eta_{bat}P_{DCi}) \tag{11}\]  
\(P_{SALEi}\) =  
\[\left\{\begin{array}{l}\eta_{inv}(P_{PVi}+\eta_{bat}P_{DCi}-P_{PLi}-P_{HPi}) \\ (P_{DC}\leq 0)\\ \eta_{inv}(P_{PVi}-\eta_{bat}P_{DCi}-P_{PLi}-P_{HPi})\\ (P_{DC}>0)\end{array}\right. \tag{12}\]  
In the above objective function, \(C_{day}\) [YEN] is the operational cost per day, \(C_{p}\) is electric power rate. This is equal to 11.46 YEN/kWh at nighttime (23:00-07:00), 26.22 YEN/kWh during living hours (07:00-10:00 and 17:00-23:00), and 35.04 YEN/kWh during daytime hours (10:00-17:00) [19]. \(P_{BUY}\) [kWh] is purchasing electric power consumption, \(P_{SALE}\) [kWh] is selling electric power, \(P_{PL}\) [kWh] is power consumption of load, \(P_{HP}\) [kWh] is power consumption of heat pump system, \(P_{DC}\) [kWh] is charge/discharge power of battery. According to feed-in-tariffs for PV systems in Japan, the selling electric power rate is set to 39 YEN/kWh in this paper. If \(P_{DC}\) is positive, the battery will charge electric power.  
Constraint condition:  
\[20\%\leq\eta_{C_{B}}\leq 100\% \tag{13}\]  
\[\eta_{C_{B}}=\frac{C_{B}}{C_{Bmax}}\times 100 \tag{14}\]  
\[|P_{I}|\leq 6\;\mathrm{kW} \tag{15}\]  
\[P_{BUYi-1}\times 0.8\leq P_{BUYi}\leq P_{BUYi-1}\times 1.2 \tag{16}\]  
\[P_{SALEi-1}\times 0.8\leq P_{SALEi}\leq P_{SALEi-1}\times 1.2 \tag{17}\]  
In the above constraint conditions, \(C_{B}\) [kWh] is the total charge/discharge power of battery, \(P_{I}\) is the inverter capacity (=6 kW). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output of PV. Additionally, the optimal reference fixes the combination output of PV and battery to be constant from 10:00 to 17:00 in order for the power fluctuation in the grid point to be stable.
# In Situ and Partial In Situ Synthesis of Cellulose Magnetic/Maghemite Composites
## 3 Results
### Structural Characterization
#### 3.1.5 Magnetic Properties Study  
The magnetic properties of the composites (CM\({}_{0}\), CM\({}_{15}\)) were monitored by measuring the hysteresis loops (magnetization curves) at room temperature (Figure 6).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **Degradation** & **T\({}_{\text{onset}}\)** & **T\({}_{\text{peak}}\)** & **W** & **T\({}_{\text{10}}\)** & **T\({}_{\text{20}}\)** & **T\({}_{\text{peak}}\)** \\  & **Stage** & \({}^{\circ}\)**C** & **DTG**, \({}^{\circ}\)**C** & **wt\%** & \({}^{\circ}\)**C** & \({}^{\circ}\)**C** & **DTA**, \({}^{\circ}\)**C** \\ \hline \multirow{2}{*}{C} & I & \multirow{2}{*}{312} & \multirow{2}{*}{341.1} & 95.57 & \multirow{2}{*}{314} & \multirow{2}{*}{325} & \multirow{2}{*}{341.9} \\  & residue & & & & & & \\ \hline \multirow{4}{*}{CM\({}_{0}\)} & I & 40.4 & \multirow{4}{*}{346.6} & \multirow{4}{*}{259} & \multirow{4}{*}{309} & \multirow{4}{*}{379.3} \\  & I & 210.6 & & & & & \\ \cline{1-1} \cline{5-10}  & residue & 255 & & & & & \\ \cline{1-1} \cline{5-10}  & & 258.5 & & & & & \\ \hline \multirow{4}{*}{CM\({}_{15}\)} & I & 40.7 & \multirow{4}{*}{351} & \multirow{4}{*}{67.21} & \multirow{4}{*}{254} & \multirow{4}{*}{309} & \multirow{4}{*}{389.3} \\  & I & 56.4 & & & & & \\ \cline{1-1} \cline{5-10}  & residue & 207 & & & & & \\ \cline{1-1} \cline{5-10}  & & 262 & & & & & \\ \cline{1-1} \cline{5-10}  & & 265.1 & & & & & \\ \hline \hline \end{tabular} Where T\({}_{\text{onset}}\)—the temperature at which the thermal decomposition begins; T\({}_{\text{peak}}\)—the temperature at which the degradation rate is maximum; W—weight loss; T\({}_{10}\) and T\({}_{20}\)—the temperatures corresponding to 10% and 20% respectively, weight loss.  
\end{table}
Table 7: Thermo-gravimetric data.  
Figure 6: Magnetization curves for ferromagnetic composite CM\({}_{0}\) and CM\({}_{15}\).  
The total in situ composite CM\({}_{0}\)'s saturation magnetization is 8.7 emu/g. Moreover, 9.9 emu/g for the partial in situ composite CM\({}_{15}\) was obtained. The magnetization of the two composites differs because magnetite generally has a higher saturation magnetization than maghemite [7], with CM\({}_{15}\) containing a higher proportion of magnetite. The magnetization curve and the demagnetization curve are the same, and the hysteresis cycle of the two composites describes a single curve. This indicates that superparamagnetism (SP) is present in CM\({}_{0}\) and CM\({}_{15}\)[28, 29]. The difference is provided by the quantitative ratio between magnetite and maghemite. Magnetic usually has a higher saturation magnetization than maghemite [7]. It makes sense that the magnetization would be higher in the case of CM\({}_{15}\), which contains more magnetite than the CM\({}_{0}\) composite.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 3 Results
### Local Accuracies in the Profiles  
The local error and the displacement of each cloud relative to the reference cloud were evaluated in all three coordinate axes (X--transverse; Y--longitudinal; Z--height) on three profiles (see Figure 7). Only ICP-transformed clouds were evaluated.  
Figure 14 presents the local behavior of the clouds after individual transformations on a detail of the central profile. In the Figure 14a, the difference between transformations is minimal, while in the Figure 14b, the difference is clearly visible, and the error remains high even after the ICP transformation.  
Figure 10: The RMSEs of the Leica RTC 360 (**a**) after transformation to spherical targets (RMSE\({}_{\text{OVERALL}}\) = 1.2 mm); and (**b**) after ICP transformation (RMSE\({}_{\text{ICP-ALL}}\) = 1.0 mm).  
Figure 11: The RMSEs of the Emesent Hovermap ST-X (**a**) after transformation to spherical targets (RMSE\({}_{\text{OVERALL}}\) = 7.3 mm); and (**b**) after ICP transformation (RMSE\({}_{\text{ICP-ALL}}\) = 5.8 mm).  
Figure 12: The RMSEs of the GeoSLAM ZEB Horizon RT (**a**) after transformation to spherical targets (RMSE\({}_{\text{OVERALL}}\) = 35.3 mm); and (**b**) after ICP transformation (RMSE\({}_{\text{ICP-ALL}}\) = 21.9 mm).
# Enhancement of Optical Chirality Using Metasurfaces for Enantiomer-Selective Molecular Sensing
## 3 Results and Discussion
The phase difference remains constant with subtle oscillations less than the angle of 5 degrees, whereas it shows significant variations of scattering power as the separation is tuned. This implies that the nature of chirality, i.e., the state of CPL, is preserved even when the separation varies, and the magnitude of electric and magnetic fields caused by the scattering power dominantly affects the C enhancement. The electromagnetic field and relative phase for the two representative cases of red dots are visually presented in Figure 5b-g. Figure 5b,c shows the electric and magnetic fields at the separations for maximum scattering of 355 nm, and Figure 5e,f shows the fields for minimum scattering of 610 nm, respectively, which closely matches the separation conditions of the C enhancement in Figure 4c. There is a clear difference in the amplitude of the fields that the substantially enhanced fields are observed at 355 nm compared to the suppressed fields at 610 nm. In contrast, the relative phase of the two examples shows similar profiles owing to the preservation of CPL regardless of the effect of super- or sub-radiant conditions (Figure 5d,g). This implies that the C enhancement is dominantly affected by the change of the field's magnitudes rather than the distortion of CPL. Finally, we analyze the simulation in which the designed structures were arranged periodically for sensing the molecules distributed in a large area. For this study, an array structure is simulated by setting the periodic boundary condition in a lateral direction in the FDTD simulation method. To evaluate and optimize the performance, we analyze the C enhancement for the various periods of array (Figure 6a). Several diffraction orders appear in the panel as the period increases, and the area below the first diffraction order is the subwavelength condition. It is observed that the strongly enhanced optical chirality appears near the boundary of each diffraction order. This is explained by the effect of radiation generated by every single nanostructure that induces grating coupling. Figure 6b shows the change in C enhancement at the resonance wavelength of 475 nm. It is concluded from the figure that averaged C enhancement values up to 50 are shown near the boundary of each diffraction order, including the period that induces multiple grating coupling in the two-dimensional array. Figure 5: **(a)** The scattering efficiency and phase difference as the separation is tuned. **(b)** The electric field, **(c)** the magnetic field, and **(d)** the profile of \(\cos(\beta_{i\mathbf{E,~{}H}})\) at the separations of 355 nm **(b–d)** and 610 nm **(e–f)**. The peak and dip of scattering efficiency marked as red circles in **(a)**.
## 2 Materials and Methods
### Models  
The propeller model used in this work resembles a generic propeller blade close to the geometry of an off-the-shelf turboprop propeller. It is a five-bladed propeller with variable blade pitch operated under constant rotational speed. The basic geometry of the blade is shown in Figure 4 by means of chord and twist distribution. The resulting aspect ratio according to [5] is 6.87. The twist is the geometric twist, which is added on top of the blade pitch angle resulting from the trim analysis. All radial sections are positioned to form a straight 50% chord line. A lift curve slope (\(C_{ls}\)) of 6.5894 is used for all sections (when applicable in the method).  
Figure 5 shows the resulting model geometry together with the global coordinate system. The global x-axis points in the direction of flight, while the y- and z-axes span the propeller plane. The global reference frame (to which all loads in this paper are referenced) stays fixed and does not move or tilt with the forced motion.  
Figure 4: Chord and twist distributions of the blade (R = 1.25 m).  
For the whirl flutter stability analysis, the propeller is coupled to the simplified pylon model shown in Figure 1. The structural properties of the system are listed in Table 2 and resemble a typical turbopro engine-pylon combination. The pitch stiffness and yaw stiffness are varied for parameter sweeps and derived from uncoupled (non-rotating) pitch or yaw frequency using the equation given in Table 2. According to this model, the distance between pivot point and the propeller plane (\(a\)), therefore, scales the force terms in the transfer matrices in comparison to the moment terms. For a short pylon (small \(a\)), the moment terms dominate the generalized propeller loads, while for long pylons, the force terms have a higher impact. Three different pylon lengths are, therefore, considered in this paper (see Table 2), called \"short\", \"medium\" and \"long\".  
Whirl flutter stability results are compared for three different operating points in this paper, which are listed in Table 3. They differ in the trim target (i.e., thrust coefficient (\(C_{T}\))) and the advance ratio (i.e., airspeed (\(V\))).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Property** & **Symbol** & **Value** \\ \hline Pitch/yaw inertia & \(l_{\theta}/l_{\psi}\) & 97.42 kg m\({}^{2}\) \\ Total polar inertia & & \\ of rotating parts & \(l_{\phi}\) & 6.69 kg m\({}^{2}\) \\ Pitch stiffness & \(K_{\phi}\) & \(l_{\psi}*\omega_{\phi}^{2}\) \\ Yaw stiffness & \(K_{\psi}\) & \(l_{\psi}*\omega_{\psi}^{2}\) \\ Pylon length & \(a\) & 0.425 m, 0.85 m and 1.7 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the pylon system about the pivoting point.  
Figure 5: Propeller model and coordinate system used in this study.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Name** & **Airspeed** & **Shaft Speed** & **Advance Ratio** & **Thrust Coefficient** & **Air Density** \\  & (\(V\), m s\({}^{-1}\)) & (\(\Omega\), rpm) & (\(J\), -) & (\(C_{T}\), -) & (\(\rho\), kg m\({}^{-3}\)) \\ \hline \(C_{T}=0\), \(V=V_{D}\) & 142 & 1600 & 2.13 & 0.0 & 1.225 \\ \(
## 4 Results
### Factors Controlling Acoustic Velocity  
#### 4.6.1 Porosity, Permeability, Texture, and Mineralogy  
The compressional acoustic velocities (V\({}_{\rm P}\)) of the measured samples range from 3 to 6.5 km/s (Appendix A). The velocities display an inverse relation with both porosity and permeability (Figure 10). However, a wide scatter of velocities at the comparable porosity and permeability values can be observed (i.e., at 15% porosity, and at 0.6 mD permeability). Samples with mud-dominated texture (mudstone and wackestone) show higher porosity  
Figure 9: **(A)** Porosity–permeability relation with texture superimposed. The mud-supported samples show higher poro-perm values relative to grain-supported samples. **(B)** Porosity–permeability data with burrow filling material superimposed. Samples with coarse-filled material have higher porosity–permeability values.  
and permeability values (Section 4.5), and thus, lower velocity values (\(\mu\) = 4.58 \(\pm\) 0.77 km/s) than samples with grain-dominated texture (packstone and grainstone) (\(\mu\) = 5.02 \(\pm\) 0.62 km/s), as shown in Figure 11A. However, some samples with grain-dominated texture have high porosity and permeability values (up to \(\sim\)16.3% and 1.1 mD), and low velocities (Figure 11A).  
Figure 10: (**A**) Porosity and (**B**) Permeability inverse relation with compressional velocity (V\({}_{\rm P}\)). Velocity is more controlled by porosity (R2 = 0.7) than permeability (R2 = 0.5).  
Based on XRD data (Section 4.2), the majority of the samples are dominated by calcite. However, quartz is common throughout the studied section. In this study, the 10% quartz content limit is used to differentiate between quartz-rich and quartz-poor samples. Quartz-rich samples have lower velocity values relative to quartz-poor samples at any given porosity (Figure 11B).
## 2 The Phytochemistry of _Artemisia annua_ L. (Qinghao)
### Sesquiterpenenoids
#### 2.6.1 Farnesane Sesquiterpenes  
The farnesanes are the structurally simplest group of sesquiterpenes. This acyclic group of natural products is produced by ionization of the pyrophosphate group in the 15-carbon precursor, FPP **(378)**, followed either by quenching with water or loss of a proton, which results in the variety of structures shown in Table 29. Alternatively, FPP can undergo further cyclization to mono-cyclic sesquiterpenes (Section 2.6.2), bicyclic sesquiterpenes (Section 2.6.3) or tricyclic sesquiterpenes (Section 2.6.4), according to enzymatically-catalysed mechanisms which bear close analogies with the biosynthesis of the cyclized monoterepenoids (Sections 2.5.3 and 2.5.4).  
\(\beta\)-Farnesene **(382)** is the most abundant member of this class of sesquiterpenes [22, 43], constituting up to 10% of the essential oil [19, 27]. A cDNA clone encoding (_E_)-\(\beta\)-farnesene synthase, which catalyzes the formation of \(\beta\)-farnesene **(382)** from FPP **(378)**, has recently been isolated from _A. annua_[133]. Interestingly, the enzyme amorpha-4,11-diene synthase, which catalyses the formation of the bicyclic sesquiterpene amorpha-4,11-diene, (Section 2.6.3) also produces small amounts of acyclic sesquiterpenes such as _trans_-nerolidol **(380)** and (_E_)-1,3(15),6,10-farnesatetraene **(382)**, both of which are reported as natural products from _A. annua_[134] (Table 29).  
\begin{table}
\begin{tabular}{|l|l|l|l|} \hline
**Name** & **Alternative Name(s)** & **CAS Number** & **References** \\ \hline Farnesol **(376)** & 2,6,10-Farnesutrient-1-ol & [4602-84-0] & [23, 27, 135] \\  & 3,7,11-Trimethyl-2,6,10-dodecetrien-1-ol & [106-28-5] & \\ \hline \multirow{3}{*}{_2,6,10-Famesetrien-1-ol acetate (377)_} & \multirow{3}{*}{_2,6,10-Pamestrien-1-ol acetate (377)_} & \multirow{3}{*}{[27]} \\  & & & \\ \cline{1-1}  
The six-membered ring in the bisabolane sesquiterpenes from _A. annua_ (Table 30) is formed by cyclization of the C\({}_{15}\) precursor, FPP (**378**), in much the same way that the _p_-menthane monoterepenes (Section 2.5.3) arise from the corresponding C\({}_{10}\) precursor. Alternative cyclizations of FPP can lead to the ten-membered germacrane sesquiterpenes, which are described in Table 31, and the eleven-membered humulane sesquiterpenes in Table 33. The bicyclic caryophyllanes, which are formed by a further cyclization of a humulane precursor, have also been included in Table 33.
# CFD and Experimental Comparison for Micro-Pump Performance in Space Applications: A Case Study
###### Abstract  
This paper presents a case study comparing CFD predictions with experimental measurements for micropumps, with the goal of evaluating the accuracy and limitations of CFD methods in complex microscale geometries. A fast design and evaluation methodology was developed, integrating linear design, 3D fully viscous CFD-based optimization, and rapid prototyping and testing. The main problem at this scale and configuration of pumps is the combination of Reynolds and Taylor numbers. Their impact on labyrinth performance prediction and therefore volumetric efficiency dominates the losses at this scale. Multiple CFD simulations were conducted using various turbulence models and solver settings, and results were compared against experimental data. The labyrinth region was simulated both independently and as part of the full pump assembly, with RANS and LES used for the former and RANS for the latter. Precision 3D-printed rotors and solutes were tested, and performance maps were obtained. Significant discrepancies between CFD and experiments were observed, which were reconciled using two empirical scaling coefficients for pressure and mass flow. These collapsed the CFD predictions onto the experimental data across all available speedlines. While the generalizability of these coefficients remains uncertain, the concept of using corrected scales, rather than other methods, seems to capture the macroscopic discrepancies between CFD and experiments.  
Keywords:closed impeller; centrifugal pump; additive manufacturing; CFD; hydraulic efficiency +
Footnote †: journal: Applied sciences
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 3 Problem Statement  
* In energy-efficient routing, the key problem is losing connectivity during data transmission while utilizing energy control mechanisms [21].
* Data loss occurs as a result of a defective network. As a result, conflicts arise in effective data transmission from the clustering nodes [24].
* Due to insufficient power being given to the nodes, the advancing of numerous subjects and node energy consumption are important concerns in WSNs, as they show higher redundancy and energy consumption [23].
## 5 Discussion
### Geoelectric Strike Direction Dependent of the Periods
#### 5.1.2 Origin and Possible Mechanism Inducing Geoelectric Strike in a Rift Context  
Asal rift results from the westward propagation of Aden ridge and has an approximate extension rate of 17 mm yr\({}^{-1}\) in the north-east direction, with many fissures and normal faults striking to the N120\({}^{\circ}\)E direction [13; 17]. It can be argued that geoelectric strike estimated in this study and the strike determined from others studies [4; 15; 19] may have a common origin. It is likely safe to suggest that at the short periods, the geoelectric strike is the result of conductive shear zones which form as echelon structures and are perhaps cracks or fissures filled by fluids at shallow to intermediate depth corresponding to the upper and middle crust. Those cracks or fissures are mainly accommodated by the extension rate resulting from the horizontal deformation beneath Asal-Ghoubbet into the north-east direction (\(\sim\)N43\({}^{\circ}\)E). Additionally, the geoelectric strike estimated for the long periods is more related to the vertical deformation of the Asal rift (at lower lithosphere and/or upper mantle scale) due to the predominant activity of dyke/magna intrusion acting as a highly conductive medium. Then, dyke intrusion triggers the evolution and development of dipping active normal faults acting as conductive zones between the top of the dyke (the maximum depth reached by the magma pressure starting from the great depth to shallow depth) and the faults locations at the surface. This latter mechanism contributes to the subsidence of the volcanic rocks and may explain that both tectonic and magmatic activity controls the rift deformation and the rifting process at different depth scale. A geoelectric strike can be used to reveal structural hints at depth, lateral variation of shallow or deep geological structures, shear zones and can be used to understand the mantle flow. In a similar geological rifting context of Asal rift such as the Kenya rift located in the East African Rift system, a geoelectric strike that varies with periods and with site location has revealed hidden structures at depth where the geoelectric strike of north-south for short periods is interpreted as conductive sediments and the northwest-southeast striking for the long periods was interpreted as conductive lineaments [11]. Moreover, in an extensional area of Great Basin-Colorado, geoelectric strike determined from the MT phase tensor was interpreted as a faulting trends in the upper-middle crustal basement controlling the Paleozoic and Mesozoic uplifts [42]. That latter authors found a good correlation between the direction of induction vectors and phase tensor strike to justify the presence of dominant 2D structure as in the case of this study (Figure 5), which is an interesting similarity between the two extensional areas.