esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Multivariate Metabolite Analysis  
Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to identify differences in metabolites among rice varieties. All pairwise comparisons: Meixiangzhan 2 vs. Daohuaxiang 2, Meixiangzhan 2 vs. Huanghuazhan, Meixiangzhan 2 vs. Yanfeng 47, Daohuaxiang 2 vs. Huanghuazhan, Daohuaxiang 2 vs. Yanfeng 47, Yexiangyoulisi vs. Daohuaxiang 2, Yexiangyoulisi vs. Huanghuazhan, Yexiangyoulisi vs. Meixiangzhan 2, and Yexiangyoulisi vs. Yanfeng 47. PLS-DA is a supervised statistical method in which partial least squares regressions are used to establish a relationship model between metabolite expression and sample category in order to predict sample category based on metabolite expression [34]. A PLS-DA model was established for each compared group, and the model evaluation parameters (R2 and Q2) were obtained using a seven-fold cross validation. The closer R2 and Q2 are to 1, the more stable and reliable the model is [11]. In addition, when Q2 was less than R2 and the y-intercept of Q2 was less than 0, the model was not over-fitted and the model was reliable. The variable importance in the projection (VIP) value of the first principal component of the PLS-DA model was used to represent the relative contribution of metabolite differences among groups. Fold change (FC), which was equivalent to the ratio of the mean quantitative values of the metabolites in the two compared groups, combined with the \(p\)-value of the \(t\)-test were employed to screen the differentially expressed metabolites and reduce the possibility of false positives. The threshold values used identify the differentially expressed metabolites were VIP \(>\) 1.0 and FC \(>\) 1.5 or FC \(<\) 0.667 with a \(p\)-value \(<\) 0.05. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database, which is the most well-known public pathway database, was used to determine the most important metabolic pathways associated with the differential metabolites among varieties.
# Height Control System for Wind Turbines Based on Critical Wind Speed Calculation
## 3 Proposed Materials and Methods
Specifically, the Onipko rotor chosen for this study has the following characteristics [26]: a rotor diameter of 2 m, corresponding to a radius of 1 m. The air density through which the rotor operates is 1.225 kg/m\({}^{3}\), and the rotor's drag coefficient is 1. The associated generator is capable of producing up to 10 kW of power. The turbine is designed to operate at wind speeds ranging from 0.1 to 5.6 m/s, which encompasses the wind conditions found across much of the Earth's surface. Additionally, the rotor's spiral design creates an air cushion around it, significantly increasing its operational efficiency. These parameters, as detailed in previous research, provide a solid foundation for exploring and optimizing the rotor-lowering system in this study. To configure the control system for optimal parameters, it is necessary to determine the critical wind speed. The critical wind speed is the threshold speed at which the wind turbine may be damaged or operate unstably due to excessive loads. An energy approach  
Figure 2: General PI controller-based control scheme. has been used to calculate the critical wind speed value. The wind power \(P_{wind}\) can be expressed through the kinetic energy of the airflow [27]:  
\[P_{wind}=\frac{1}{2}\rho A\vartheta^{3}, \tag{1}\]  
where \(A\) is the cross-sectional area of the rotor; \(\rho\) is the air density; and \(\vartheta\) is the wind speed. The generator power \(P_{gen}\) is related to the wind power through the power utilization coefficient \(C_{p}\), which can be calculated by the following [27]:  
\[P_{gen}=C_{p}P_{wind}, \tag{2}\]  
The wind force \(F\) can be represented by the following [28]:  
\[F=\frac{1}{2}p\vartheta^{2}AC_{d} \tag{3}\]  
Based on the energy approach and the characteristics of the wind turbine, calculations were performed to determine the critical wind force and speed (see Table 1). Given the generator power of 10 kW and the power utilization coefficient \(C_{p}\approx 0.4\), the wind power incident on the rotor is 25 kW. For this wind power, the calculated wind speed is approximately 23.5 m/s. At this speed, the wind force acting on the rotor is approximately 1058.65 N. Therefore, the critical wind force at which the rotor-lowering system should be activated is around 1058.65 N, which is close to the required value of 1200 N, considering errors and additional factors. This confirms that the target critical wind speed of around 25 m/s corresponds to the specified parameters of the wind turbine protection system.
# Application of Virtual Reality in Learning Quantum Mechanics
## Appendix A Achievement Test (18 multiple-choice questions)
(D) The nucleus is mainly composed of protons and neutrons. (E) The spectrum of hydrogen atoms is discontinuous because light has the characteristics of particles. 14. Which of the following experiments established an atomic structure model in which electrons orbit the nucleus? (A) Thomson charge-to-mass ratio experiment  
(B) Blackbody radiation experiment  
(C) Photoelectric effect experiment  
(D) Rutherford gold foil experiment  
(E) X-ray crystal diffraction experiment  
15. Which of the following is true about Milikan oil drop experiment? (A) All oil droplets sprayed out in the experiment will fall into the electric field. (B) A very small number of oil droplets will enter the electric field through the small hole of the positive plate. (C) All oil droplets in the experiment will be suspended above the positive plate. (D) All oil droplets will gather into one large oil droplet and fall into the electric field. (E) All oil droplets will adhere to the wall of the container. 16. What kind of physical quantity is the main purpose of Milikan oil drop experiment? (A) mass of electrons  
(B) number of electrons  
(C) size of electrons  
(D) the charge-to-mass ratio of electrons  
(E) electricity of electrons  
17. In the electron diffraction experiment, which of the following patterns will be generated on the photographic film by the electrons emitted from the electron gun? (A) square  
(B) triangle  
(C) concentric circles  
(D) solid circles  
(E) no pattern generation  
18. In the electron diffraction experiment, the electrons emitted by the electron gun pass through which of the following to produce patterns on the photographic film? (A) metal foil board  
(B) white paper  
(C) plastic board  
(D) glass board  
(E) wood board
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 4 Technical Discussion
### 4.1. Note Specifications and Applications
#### 4.1.3 Applications  
Most of the recent sensor nodes reviewed in this paper are targeted for environmental monitoring and various IoT applications. A notable number of the research nodes are designed for multiple applications to allow for flexibility. It can be observed from Table 2 that, of the selected commercial notes, only MICAz is targeted for multiple applications. LOTUS integrates the desirable features and characteristics of IRIS, TelosB and Imote2 motes onto a single board and can be used for applications demanding high processing speed thanks to the presence of the low-power ARM Cortex M3 CPU. Energy consumption by the sensor nodes also depends on the WSN applications. In some applications, it is possible to turn off some motes while other applications require all the motes to be always active.  
Several applications and scenarios, including remote sensing, forest fire detection and smart parking systems, require sensor nodes capable of operating under ultra-low power consumption. In some of these applications, the motes are deployed in remote and inaccessible areas where it is difficult to replace or recharge depleted batteries. The Nanolcarus mote [35], Waspmote and the sensor node proposed by [25] are notable examples of nodes that are mostly optimized for power consumption preservation. For applications demanding high communication requirements, there is a need for motes that utilize efficient, long-range and low-energy-consumption communication modules. Applications that require highly accurate sensor measurements such as air quality monitoring and structural health monitoring need motes that have reliable hardware architecture and a communication system that has error correction/detection capabilities.  
\begin{table}
\begin{tabular}{c c c c} \hline
**MCU** & **Clock Freq.** & \multicolumn{2}{c}{**Power Consumption**} \\ \cline{2-4}  & **(MHz)** & **Active** & **Sleep** \\ \hline Atmega328p & 16 & 14 mA & 66 \(\mu\)A \\ \hline STM32L152 MCU & 32 & 6.24 mA & 4.6 \(\mu\)A \\ \hline JNS5139 microcontroller & 32 & 37 mA & 2.6 \(\mu\)A \\ \hline Atmega324 PA & 20 & 0.4 mA@1 MHz, 1.8 V & 0.6 \(\mu\)A (RTC on) \\ \hline ATMega88 & 20 & 0.3 mA & 0.8 \(\mu\)A (RTC on) \\ \hline MSP430 PR5969 & 16 & 103 \(\mu\)A/MHz & 0.25 \(\mu\)A (LPM3.5) \\ \hline STM32L76 RG & 80 & 100 \(\mu\)A/MHz & 420 nA _Stanally_ mode (RTC on) \\ \hline CC430P5137 & 20 & 160 \(\mu\)A/MHz & 2 \(\mu\)A \\ \hline Arm Cortex M0+ (SAMD21) & 48 & -7 mA & -12.8 \(\mu\)A \\ \hline ARM Cortex-M4 MCU & 80 & 229 mA & 250 \(\mu\)A (LPDS), 4 \(\mu\)A (hibernate) \\ \hline ATmega1281 & 8 & 500 \(\mu\)A/MHz & 0.1 \(\mu\)[EMAIL_ADDRESS] V \\ \hline ARM Cortex M3 CPU & 120 & 195 \(\mu\)A/MHz & 1.11 \(\mu\)A (RTC on) \\ \hline \end{tabular}
\end{table}
Table 3: Power consumption of different MCUs.
## 4 Results
### Interpretations  
Integrating the AMT and gravity data provided a more robust interpretation. The 2D inversion of the AMT data imaged the potential targets from the 2D resistivity inversions that were supported by the gravity-derived interpretations (e.g., low-density fracture/fault zones). The cross-sections showed the 2D-jointly interpreted AMT and gravity sections.  
Figure 5: Profile-4 cross-sections derived from (**A**) 2D modeling of AMT data (upper panel) and (**B**) density modelling (lower panel).  
These sections were selected to demonstrate the different structural patterns and the geometry and extension of high-heat granites.  
Figure 4 displays the 2D cross-section of Profile-2, extending along the east-west direction with a total length of 26 km. The section demonstrated the presence of low-resistivity subsurface anomalies (5-29 ohm.m), indicating a thick sedimentary cover (\(\sim\)3-3.5 km). On the other side, the 2D gravity model showed a similarity with the AMT section, and indicated that the relief of the basement was deeper towards the west.  
Figure 5 shows the cross-section of Profile-4, extending 48 km in length and aligned along almost an east-west direction. The section showed a highly resistive region (347-685 ohm.m) sandwiched by two low-resistive anomalies (40-97 ohm.m). This region was associated with  
Figure 6: Profile-13 cross-sections derived from (**A**) 2D modeling of AMT data (upper panel) and (**B**) density modelling (lower panel).  
granitic rocks and bounded by faults parallel to the Red Sea (shown by black lines in Figure 5). The 2D gravity model also showed a high-density massive granitic rock in the middle of the modeled density section. The basement rocks appeared with an irregular relief at a depth of around 3000 m, with its main massive body extending downward (>5000 m).  
Figure 6 shows the cross-section of Profile-13, extending north-south with a total length of 92 km. It shows a highly oriented resistive area (790-1935 ohm.m) associated with an intrusion of granitic rock into the sedimentary layer. Two east-west faults bounded this massive granitic body. These faults most probably belonged to the east-west transform faults that are common in the Arabian Shield. The gravity model showed that the basement was shallow to the south (right side) and deep to the north (left side). The high-density massive granitic body and its nearby low-density low-resistivity rocks was indicated well in the middle of the modeled density section.
# Computational Evaluation of Turbulent Supersonic Jet Impinging on Inclined Plate
## 3 Turbulence Modeling  
Two different turbulence modeling procedures were employed to assess the predictive capabilities of the present CFD model to simulate the jet flow impingement on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid** & **Overall Size** & **Surface Resolution \(N_{y}\times N_{z}\)** \\ \hline Coarse & 0.66 M & \(42\times 19\) \\ Medium & 2.7 M & \(79\times 35\) \\ Fine & 6.8 M & \(100\times 50\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Grid convergence test: spatial resolutions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid** & **P/P\({}_{c}\)** & **Relative Error** \\ \hline Coarse & 0.124 & 17.2\% \\ Medium & 0.136 & 9.9\% \\ Fine & 0.142 & 1.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Grid convergence test: relative error against experiment.  
inclined flat plate, namely, unsteady RANS and DES. The compressible flow governing equations, based on the \(k\)-\(\omega\) formulation, can be formally written the same way for both approaches [27]. Specifically, the balance equations for mass, momentum, and energy read:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})=0\, \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}(\rho u _{i}u_{i})=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x_{j}}( t_{ji}+\rho\tau_{ji})\, \tag{2}\]  
\[\frac{\partial}{\partial t}\left[\rho\left(e+\frac{1}{2}u_{i}u_{i}+k\right) \right]+\frac{\partial}{\partial x_{j}}\left[\rho u_{j}\left(h+\frac{1}{2}u_{i }u_{i}+k\right)\right]=\]  
\[\frac{\partial}{\partial x_{j}}\left[u_{i}\left(t_{ij}+\rho\tau_{ij}\right)+ \left(\frac{\mu}{\mathrm{Pr}_{L}}+\frac{\mu_{T}}{\mathrm{Pr}_{T}}\right) \frac{\partial h}{\partial x_{j}}+\left(\mu+\sigma^{*}\frac{\rho k}{\omega} \right)\frac{\partial k}{\partial x_{j}}\right]\,\]  
where \(\rho\), \(\rho u_{i}\), and \(\rho E\) represent the balanced variables per unit volume. The energy Equation (3) ensures conservation of the total energy that is \(E=e+\frac{1}{2}u_{i}u_{i}+k\), including internal energy \(e\), kinetic-energy, and turbulent kinetic-energy. Note, that in the present formulation, the energy transport equation is based on the enthalpy variable \(h=e+p/\rho\) and, once this equation is solved, the temperature field can be directly estimated.  
In the above equations, \(t_{ij}\) and \(\tau_{ij}\) are the viscous and turbulent stresses, \(\mu_{T}\) the turbulent eddy-viscosity, \(Pr_{L}\) and \(Pr_{T}\) the molecular and turbulent Prandtl numbers. Here, according to the eddy-viscosity assumption, the unknown turbulent stresses are approximated as:  
\[\tau_{ij}=2\mu_{T}\bigg{(}S_{ij}-\frac{1}{3}S_{ll}\delta_{ij}\bigg{)}-\frac{2 }{3}\rho k\delta_{ij}\, \tag{4}\]  
where \(S_{ij}=\frac{1}{2}\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\right)\) is the resolved strain-rate tensor, and \(\delta_{ij}\) stands for the Kronecker delta, with \(\mu_{T}\) being determined in terms of the resolved turbulence variables.
# Net Ecosystem Exchange of CO\({}_{2}\) in Deciduous Pine Forest of Lower Western Himalaya, India
## Materials and Methods
### Geophysical Measurements
#### 2.3.3 Day-Time Net Canopy Photosynthesis (\(Ac\)) and Net Primary Productivity (NPP)  
Procedure and periodicity for hourly sampling of leaf-level photosynthesis of the over and understory species were similar as described above for night-time canopy respiration (Section 2.3.1). On each day of measurement, daytime (0600 to 1800 h LMT) net photosynthesis and respiration in the next night (1830 to 0530 h LMT) was measured hourly.  
Day-time leaf-level photosynthesis of both the overstory and understory species was measured using the LI-6400 XT photosynthesis system under prevailing environmental conditions. Hourly leaf-level photosynthesis data were upscaled to the canopy-level according to [PERSON] and [PERSON] [21] assuming single light assimilation responses in relation to all the leaves in the canopy and sunlit and shaded class based on canopy sub-division into the three (top, mid, and bottom) layers. Descriptions of upscaling and associated errors are described in [PERSON] et al. [13]. Thus, it represents day-time net canopy assimilation (\(Ac\)). In other words, it was the gross primary production (GPP) minus the day-time respiration (\(Rd_{day}\)). Mathematically, it can be written as:  
\[A_{c}=\mathrm{GPP-}Rd_{day} \tag{1}\]  
Given the strong inhibition of day-time respiration [8] in light conditions and lower level of day-time respiration with respect to night-time respiration [22; 23]; and thus, contribution of day-time respiration in total daily canopy respiration, \(Ac\) has been assumed equivalent to the gross primary productivity (\(Ac\approx\mathrm{GPP}\)). Daily net primary productivity (NPP) was computed by subtracting \(Rnc\) from \(Ac\)[24]. Mathematically, it can be expressed as:  
\[\mathrm{NPP}=A_{c}-R_{\mathrm{nc}} \tag{2}\]  
Therefore, daily net ecosystem exchange (NEE) becomes:  
\[\mathrm{NEE}=R_{\mathrm{s}}-\mathrm{NPP} \tag{3}\]  
where, \(R_{\mathrm{s}}\) is soil respiration (heterotrophic and root respiration).  
NEE is an indicator of net carbon balance of the ecosystem. Negative sign denotes a net carbon flux from atmosphere to ecosystem (i.e., carbon sink), and thus a net carbon uptake by the ecosystem. As it is a derived flux, thus, we computed the standard error (s.e.) associated with NEE to represent the magnitude of variations.  
\[\mathrm{SE}=\sqrt{\mathrm{SE_{NPP}}^{2}+\,\mathrm{SE_{R_{S}}}^{2}} \tag{4}\]where, \(\mathrm{SE_{NPP}}\) is the s.e. of NPP; while \(\mathrm{SE_{Rs}}\) is the s.e. of \(R\)s.  
Carbon use efficiency (CUE) was computed as the ratio of daily integrals of NPP to \(Ac\) (\(\approx\)GPP) that signifies the capacity of plants to transfer carbon from atmosphere to terrestrial biomass [25]. It is a dimensionless index of carbon flux efficiency from the atmosphere to the vegetation (range: 0 to 1). Values closer to 1 is indicative of a higher carbon flux efficiency from atmosphere to vegetation.
## 4 Proposed Lightweight Authentication Based on Multi-Factor Hash Chain with T-OTP in VMF
### Lightweight Authentication Processes Based on Hash Chain  
#### 4.2.1 Initialization and Registration Phases  
The processes proposed in this study were improved by applying the existing multi-factor hash chain and T-OTP authentication proposed by [PERSON] and [PERSON]. The one-time initialization and registration phases similar to the hash chain-based OTP method are depicted in Figure 2.  
1. [label=0]
2. Fetch user information: Execution of the initialization and registration phases.
3. Challenge/Reject: Initial settings of the range of the time slot length for the configuration of T-OTP values in the multi-factor hash chain structure by the administrator.
4. Key matching: Clock synchronization (periodic linkage of time between the user and administrator in CNR networks within a predetermined time-error range).
5. Establish session: Delivery of the seed hash value on T-OTP by the administrator to the user.  
The administrator was defined as the operation control authority or commander of the squad in any military operation, and the user was defined as a connected device or squad members that executed the operations under the received tactical messages. During the initialization and registration phase, the hash value of the hash chain was used as the T-OTP to execute the authentication phase. A user shared a bit-length seed to calculate the hash chain used as the OTP, after which the hash chain _h(x)_ was continued up to length \(i\) of the repetitive hash chain. The hash chain length was defined as \(2\times 10^{7}\) to \(2\times 10^{8}\) owing to the \(2^{114}\) or \(2^{224}\) based minimum security strengths and open multi-factor standard, and the length of the key used for certification was dynamically composed of values within the range of 130-224 bits because of protocol header storage limitations in VMF standards. In contrast to the Kogan method, a periodic clock synchronization process existed between the user and the administrator within a predetermined time-error range. In addition, for the authentication and verification phases that directly followed the initialization and registration phases, the user and administrator did not have to share secret keys, cryptographic hash functions, or digital signatures during the T-OTP authentication process based on the hash value of the hash chain. They had to share only pre-built abstract hash chain structures in wireless military terminals at the time of creation of the VMF-based CNR networks.
## 5 Materials and Methods
### Performance Comparison Methodology  
To assess the effectiveness of our proposed approach, we conduct a comparative evaluation between different algorithmic configurations. This evaluation focuses on analyzing the performance of the standard genetic algorithm (GA) and its enhanced variants, incorporating Adaptive Mutation (GA-AM) and Adaptive Mutation with Tabu Search (GAAM-TS). The comparison is based on multiple evaluation metrics under a controlled computational environment.
## 3 Analysis--Governing Equations
### Solution of the Governing Equations  
It was stipulated for the computations that at any hour of the year, the storage system must contain enough hydrogen to meet the entire energy demand of the cluster of buildings for seven days (168 h). This stipulation was adopted so that in the event of any equipment malfunction or damage, e.g., from strong storms or tornadoes, the managers of the microgrid would have enough time to repair the system and, if necessary, purchase hydrogen. This becomes a constraint in the solution of the equations that determine the operation of the microgrid.  
The solution of the governing equations was obtained by iteration using the following procedure:  
1. The year starts with an assumed quantity of stored energy, \(S_{0}\).
2. A number of wind turbines (one to four) is specified for the generation of the wind energy. The hourly wind generated energy is determined.
3. An area, \(A_{i}\), for the PV systems is assumed. The hourly solar-generated energy is determined.
4. The hourly energy surplus or deficit is determined. The total energy in the storage system is calculated at every hour of the year.
5. The stored energy at the end of the year, \(S_{8760}\), is calculated and compared to \(S_{0}\). If \(S_{0}<S_{8760}\), the solar system's area, \(A_{i}\), is increased. If \(S_{0}>S_{8760}\), the solar system's area, \(A_{i}\), is decreased. The calculations starting at step 3 are repeated until \(S_{0}=S_{8760}\).
6. The correct value for \(S_{0}\) for step 1 is obtained by a second iteration. This iteration makes use of the constraint that on the hour of minimum storage, the system must have enough energy to satisfy the entire demand of the microgrid for the next 168 h.