esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Throughput Performance Limits of Multimode Underwater Acoustic Networks
### Modeling of Throughput Performance Limits in Multimode Networks
#### 3.1.2 Action Modeling  
Action modeling is similar to state modeling in that the number of states per node remains constant at \(N\). Since state \(\hat{S}\) includes the time slot from \(t-G\) to \(t-1\), the signal transmitted during this period will be received in time slots \(t\) to \(t-1+G\), and action \(A\) will directly impact each node's ability to successfully receive. To evaluate the return function, network action \(A\) must include the actions of all nodes in slots \(t\) to \(t-1+G\). Therefore, action \(A\) can be represented as a matrix \(N\times G\), where \(a_{i,k}\) indicates the action of node \(i\) in slot \(t-1+k\). Each node has \(2N-1\) actions, including \(N-1\) sending actions and idle actions.
## 4 Discussion
On the other hand, areas near bodies of water and areas covered with vegetation are concentrated cool areas. Industrial areas are one of the concentrated hot areas and record the highest temperatures compared to those of the surrounding urban environment, as expected due to high heat emissions [38,39]. The industrial areas in the white-shaded areas in Figure 7 include zones H, I, J, and K. Additionally, urban areas become concentrated hot areas due to the presence of buildings and human-made structures, resulting in surface temperature increases [40]. Anthropogenic heating significantly contributes to the urban heat island effect, especially due to the use of air conditioning in buildings [41] in urban central areas. Urban areas represented by the black-shaded areas consist of zones B, C, D, E, F, and G [39]. Furthermore, [PERSON] and [PERSON] [20] state that the emission of carbon dioxide (CO\({}_{2}\)) and carbon monoxide (CO) in industrial areas contributes to temperature increases in the surrounding areas. This study found that bodies of water and vegetated areas are concentrated cool areas, characterized by relatively lower temperatures. Previous studies such as those by [PERSON] et al. [40], [PERSON] et al. [41], and [PERSON] et al. [42] have also found that vegetation and bodies of water can reduce surface temperatures in urban areas. This explains why concentrated cool areas consist of bodies of water and vegetation. Examples of concentrated cool areas include water bodies such as the Sarawak River, as well as vegetation areas like wetland areas, botanical gardens, and Kuching Cultural City Forest. Furthermore, the effect of bodies of water in mitigating temperature due to the urban heat island effect has been considered by a small number of researchers. The Normalized Difference Water Index (NDWI) has been studied by [PERSON] and [PERSON] [38,39] using the Land Water Surface Index (LWSI) to investigate the relationship between bodies of water and the surface temperature in urban areas. Previous studies have found that the selected water indices have a positive relationship with the temperature, meaning that temperatures decrease as the index value increases. Additionally, the distance between bodies of water or coastlines and green areas affects the surface temperature, as proximity to water sources or green vegetation leads to lower surface temperatures [38; 39; 41; 42].
## 3 Preliminaries and Definition
### Cryptographic Background
#### 3.1.4. Identity-based Sequential Aggregate Signature (IBSAS) Problem  
We introduce the CDH-type computational problem IBSAS by recapitulating the definition in [29], on which the security of the identity-based sequential aggregation scheme is based.  
For all \(a_{1},b_{1},a_{2},b_{2}\in\mathbb{Z}_{p}\) and generator \(g\in\mathbb{G}\), the associated oracle \(\mathcal{O}_{g,a_{1},b_{1},a_{2},b_{2}}^{IBSAS}(\cdot)\) taking as input \(m\in\mathbb{Z}_{p}\) is defined as  
\[\text{Oracle }\mathcal{O}_{g,a_{1},b_{1},a_{2},b_{2}}^{IBSAS}(m)\] \[r,x\stackrel{{\$}}{{\leftarrow}}\mathbb{Z}_{p}\] \[\text{Return }(g^{rx}g^{a_{1}b_{1}}g^{ma_{2}b_{2}},g^{r},g^{x}).\]  
We then define the \(IBSAS\)-advantage of an algorithm \(\mathcal{A}\) as  
\[\text{Adv}^{IBSAS}(\mathcal{A})\stackrel{{ def}}{{= }}\text{Pr}[C=(m^{\prime},g^{r^{\prime}x^{\prime}}g^{a_{1}b_{1}}g^{ma_{2}b_{2}},g^{r^{\prime}},g^{x^{\prime}}):\] \[a_{1},b_{1},a_{2},b_{2}\stackrel{{\$}}{{\leftarrow }}\mathbb{Z}_{p};\] \[C\stackrel{{\$}}{{\leftarrow}}\mathcal{A}^{O_{g,a_{1 },b_{1},a_{2},b_{2}}^{IBSAS}(\cdot)}(g,g^{a_{1}},g^{b_{1}},g^{a_{2}},g^{b_{2}})],\]  
where we require that the oracle was not queried about \(m^{\prime}\in\mathbb{Z}_{p}\) by \(\mathcal{A}\). [29] showed that the IBSAS problem is hard in the generic bilinear group model.
###### Abstract  
This paper presents the results of experimental, analytical, and numerical studies on determining the driving torque and power of a vertical-axis wind turbine (VAWT) with planetary blade motion forced by a carousel rotor. First, experimental studies in the wind tunnel laboratory were conducted to determine the tip speed ratio \(\lambda\) for the real-scale wind turbine model under self-starting conditions. Then, an analytical kinematic model of the turbine was developed. Finally, computational fluid dynamics (CFD) analysis was conducted to verify the analytical approach and examine aerodynamic interferences between particular turbine blades. The main objective of the study was to verify the accuracy of the simplified analytical approach to calculating the driving torque and turbine power compared to the numerical results based on 2D analysis using computational fluid dynamics. The obtained results showed good agreement considering the modeling of the motion of the three dual-coherent blades of the wind turbine. Comparing the analytical and CFD approaches, the error in determining the average driving torque and the average turbine power was about 1%. An additional objective of the study was to use the developed analytical method to calculate the starting torque and demonstrate the main advantage of the carousel wind rotor, which is its higher starting torque compared to the H-type Darrieus rotor.  
wind turbine; VAWT; carousel wind rotor; mechanical properties; driving torque; wind tunnel test; CFD analysis; computational fluid dynamics Article
# Enhancement of CO2 Removal Efficacy of Fluidized Bed Using Particle Mixing
## 3 Results and Discussion
### Experimental Strategy and Data Acquisition  
The experimental setup was first tested for any leakage by ensuring the input and output gas flows were equal. Next, a three-step adsorption experiment was carried out. The first step was to start a controlled flow of nitrogen to test column containing the adsorbent for two hours (7200 s) to remove any traces of carbon dioxide and oxygen. During this purging step, the concentrations of both gases were monitored and recorded using the O\({}_{2}\)/CO\({}_{2}\) analyzer. Next, the controlled flow of carbon dioxide was started for another two hours to allow its adsorption by the adsorbent present in the test section of the column. Finally, the CO\({}_{2}\) was stopped to let the desorption step proceed for another two hours before discontinuing the flow of nitrogen, as shown in Figure 4. The flow and duration of both nitrogen and CO\({}_{2}\) gas flows were always controlled using electronic mass flow controllers that were connected to the data acquisition system. By monitoring the gas concentration data, the duration of two hours (7200 s) was found to be sufficiently long to ensure completion of each of the three steps of the adsorption studies, i.e., purging, adsorption, and desorption. Thus, a total of six hours (21,600 s) were required for each complete adsorption experiment.  
Figure 3: [PERSON]’s classification-based particle-mixing strategy used for the proposed assisted fluidization technique.  
The data sampling frequency was kept at 10 Hz, thus recording 10 data samples every second. Each data sample involved recording the following: (1) time, (2) pressure drop, (3) N2 flow rate, (4) CO2 flow rate, (5) CO2 concentration, and (6) oxygen concentration. The O2/CO2 analyzer provided both CO2 and O2 concentrations. Note that all outputs were in volts and were converted into appropriate units with the help of calibration data. The responses of O2/CO2 analyzer are presented in Figure 5a,b, while the response of the CO2 and N2 mass flow controllers are shown in Figure 4. Note that the sharp decline in the oxygen concentration occurred (Figure 5b) due to purging of the experimental setup using nitrogen.
## 2 Materials and Methods
### Experimental Equipment and Experimental Scheme
#### 2.2.4 Data Monitoring  
During the experiment, the data acquisition system was used to monitor the temperature at different heights inside and at the top of the saline soil column in real time, the conductivity at different heights inside the soil column, changes in external water replenishment, and height changes during the migration of fluorescein-labeled liquid water. Because the volumetric moisture content and conductivity are measured, it is necessary to calibrate and convert the data collected by the CS655 sensor to obtain the moisture and salt contents in the soil at different heights.
# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 3 Results and Discussion
### Downscaling Results
#### 3.2.1 Comparison Before and After Downscaling
In Wudaoliang, the R values are about 0.4, and the central root mean square error is about 0.1 (cm\({}^{3}\)/cm\({}^{3}\)). Because soil moisture has strong spatial heterogeneity, coarse resolution (10 km or 25 km resolution) soil moisture is difficult to express the spatial distribution of soil moisture, which limits the application of soil moisture products in drought prevention, farmland moisture conditions, and other fields. The high-resolution soil moisture dataset obtained by downscaling the coarse resolution soil moisture product can better reflect the spatial distribution characteristics and heterogeneity of soil moisture. The correlation between coarse resolution and high-resolution data before and after downscaling is reduced, indicating that the two have certain differences [12; 21; 22; 37; 38]. The downscaled soil moisture can be more relevant to the actual ground measurement data by adjusting the spatial resolution and processing details, showing characteristics and patterns that are closer to the actual surface conditions, which is also the purpose of this study to obtain a high-resolution dataset by downscaling. Figure 7: Soil moisture distributions before and after downscaling in the Wudaoliang area. SMAP_10 km is the original soil moisture; SMAP_NOVV_1 km is the downscaled soil moisture without SAR backscattering coefficient data; SMAP_1 km is the downscaled soil moisture with added SAR backscattering coefficient data. Figure 8: Scatter plot of before and after downscaling in the Shandian River. The red dotted line in the figure indicates the 1:1 line.
# Review on Corrosion Inhibitors for Oil and Gas Corrosion Issues
## 1 Introduction  
In oil and gas industries, the corrosion issue has always been of great importance, with consequences similar result to those of natural disaster. Corrosion normally occurs in oil and gas pipelines. Since the pipelines play the role of transporting oil and gas from the wellheads to the processing facilities, they are exposed to the continuous threat of corrosion, from the date of commissioning up to decommissioning or abandonment. According to [1], the rough estimation of the aggregate yearly cost of corrosion is $1.372 billion, which is the total of surface pipeline and facility costs ($589 million), down-hole tubing costs ($463 million), as well as capital expenses ($320 million).  
Corrosion inhibitors are one of the mediums applied to minimize corrosion in petroleum industries. For an optimum inhibition to be achieved, the inhibitors must be added above a certain minimum concentration. There are plenty of techniques, e.g., cathodic protection [2; 3], organic coatings [4; 5; 6], and application of first-rate corrosion-resistant alloys [7], that can be implemented to fight against corrosion, yet film-forming inhibitors are still known to be the unrivalled method of defense for mild steel in an acidic environment [8; 9]. The film-forming inhibitors are used in industries to create a molecular layer right on the surface of the steel and aliphatic tail as a second layer in hydrocarbon to prevent the water from contacting the steel surface and causing corrosion [10].  
Recently, the rise of the \"green\" chemistry concept in the fields of science, technology and engineering [11; 12] is restraining the application of commercial corrosion inhibitors by implementing certain theories or ideas to reduce the contamination [13] from being discharged into the environment as well as coming up with the eco-friendly chemicals [14; 15; 16]. As a movement to support this concept, the use of green-based corrosion inhibitors like plant extracts [17], chemical drugs [18], and ionic liquids [19; 20] are being practiced. These green inhibitors are organic compounds that function through the adsorption on the surface of the metal to prevent the occurrence of corrosion. Moreover, fruit-based corrosion inhibitors are also one of the natural elements utilized due to their richness in vitamins,minerals, and phenolic compounds. Nevertheless, the corrosion inhibitors adhere to certain factors like concentration, rate of dispersion, velocity, temperature, film persistency, pH, flow regime, and fluid composition, as well as the presence of instabilities able to perturbate the flow in minimizing corrosion.  
This review paper assesses the trilateral view of corrosion: mechanism of corrosion, sources of corrosion, commercial and green-based corrosion inhibitors. Hence, this paper is anticipated to serve as the foundation for future research on green corrosion inhibitors. Note that comprehensive discussions or analysis regarding the current state and the future of green-based inhibitors will not be covered.
## 4 Experimental Verification
### Damage Cases  
Twenty-four damage cases were simulated by the laboratory test model. All damage conditions can be divided into two categories. EDC1-EDC16 belongs to the first category, which simulates one single hanger damaged at a time, and the hangers S2-S5 on the south side of the model bridge were preset as the damaged hanger separately. The degree of damage was set to 12.5%, 25%, 37.5% and 50%, respectively. EDC17-EDC24 belongs to the second category, in which damage occurred in two hangers simultaneously. The degree of damage was also set to 12.5%, 25%, 37.5%, and 50%, respectively. Table 5 lists all the damage conditions investigated in the test model.
## 2 3D Localization Models for L-Shaped Antenna Arrays  
Figure 1a,b present a brief comparison between the traditional 3D localization model using an L-shaped array [20;21] and the model applicable to our H-ISL [22].  
The reference 3D localization model in [20], set in aspherical coordinate system, consists of two satellites. One satellite is positioned at Cartesian coordinates \(C=(x_{C},y_{C},z_{C})\)--a first spacecraft that can be the servicer in the IOD scenario--and the other at \(T=(x_{T},y_{T},z_{T})\)--a second spacecraft that can be the serviced space device. The Cartesian coordinates of \(C\) and \(T\) can be retrieved from polar coordinate system as follows:  
\[\begin{bmatrix}x_{T}\\ y_{T}\\ z_{T}\end{bmatrix}=\begin{bmatrix}cos\Phi cos\theta\\ cos\Phi sin\theta\\ -sin\Phi\end{bmatrix}R+\begin{bmatrix}x_{C}\\ y_{C}\\ z_{C}\end{bmatrix}, \tag{1}\]  
where \(R\) is the distance between the \(C\) and \(T\) satellites, \(\theta\) is the azimuth of the incident signal, and \(\varphi\) is the angle of pitch. In general, the location of satellite C--provided by a Global Navigation Satellite System (GNSS)--can be considered as a known parameter. Still there are six unknowns to be found for 3D localization (1), which are \(x_{T},y_{T},z_{T},R\), \(\varphi\), and \(\theta\), respectively.  
In our proposed 3D localization model, in Figure 1b, the origin of the Cartesian coordinate system is spacecraft \(C\); thus the location of this satellite should be determined with other means, such as GNSS localization. The \(T\) spacecraft with the x-axis forms a plane; in this plane, one can measure the angle between the x-axis and the line-of-sight (here defined as the segment between point \(C\) and \(T\)). Let us denote this angle \(\alpha\), referred to as the LoS angle. Similarly, the y axis forms an angle with the LoS, this will be denoted by \(\beta\). The resulting 3D localization model is as follows:  
\[\begin{bmatrix}x_{T}\\ y_{T}\\ z_{T}\end{bmatrix}=\begin{bmatrix}\cos\alpha\\ \cos\beta\\ \sqrt{\sin^{2}\alpha+\cos^{2}\beta}\end{bmatrix}R. \tag{2}\]  
The derivation of the proposed 3D localization model in Equation (2) is described in Appendix A. In this model, to determine the position of spacecraft \(T\), 3 measurements are necessary: the range measurement, \(R\), and two LoS angles, \(\alpha\) and \(\beta\). In the next chapter, we present how these are measured by the H-ISL with an L-shaped antenna array.
## 2 Materials and Methods
### Bio-Optical Assessment through Chlorophyll a Pulse Amplitude Modulated (PAM) Fluorometry  
Pulse amplitude modulated (PAM) chlorophyll fluorescence measurements were performed using a FluorPen FP100 (Photo System Instruments, Drasov, Czech Republic) on 15 min dark-adapted samples using a 1 mL cuvette. Culture cell density was assessed daily, using a non-actinic light to measure minimum chlorophyll fluorescence (F\({}_{0}\)). Analysis of chlorophyll transient light curves (Kautsky plot) was carried out using the OJIP test according to [41]. Fluorometric analysis parameters and their description can be accessed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**OJIP Test** & \\ \hline Area & Corresponds to the oxidized quinone pool size available for reduction and is a function of the area above the Kautsky plot \\ \hline N & Reaction center turnover rate \\ \hline S\({}_{\text{M}}\) & Corresponds to the energy needed to close all reaction centers \\ \hline M\({}_{0}\) & Net rate of PS II RC closure \\ \hline \(\gamma_{\text{RC}}\) & Probability that a PS II chlorophyll molecule will function as an RC \\ \hline P\({}_{\text{G}}\) & Grouping probability between the two PS II units \\ \hline ABS/CS & Absorbed energy flux per cross-section \\ \hline TR/CS & Trapped energy flux per cross-section \\ \hline ET/CS & Electron transport energy flux per cross-section \\ \hline DI/CS & Dissipated energy flux per cross-section \\ \hline RC/CS & Number of available reaction centers per cross-section \\ \hline TR\({}_{\text{0}}\)/DI\({}_{\text{0}}\) & Contribution or partial performance due to the light reactions for primary photochemistry \\ \hline \(\delta_{\text{RO}}/(1-\delta_{\text{RO}})\) & Contribution of PS I, reducing its end acceptors \\ \hline \(\psi_{\text{0}}/(1-\psi_{\text{0}})\) & Contribution of the dark reactions from Q\({}_{\text{A}}\)\({}^{-}\) to PC \\ \hline \(\psi_{\text{EO}}/(1-\psi_{\text{EO}})\) & Equilibrium constant for the redox reactions between PS II and PS I \\ \hline RE\({}_{\text{0}}\)/RC & Electron transport from PQH\({}_{\text{2}}\) to the reduction of PS I end electron acceptors \\ \hline RC/ABS & Reaction center II density within the antenna chlorophyll bed of PS II \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fluorometric analysis parameters and their description.
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
## 4 Stability Analysis
### Force Control Stability
#### 4.2.3 Force Along \(t\)-Axis  
The forces along the \(t\)-axis are controlled by the rotational pose command around \(\mathbf{o}\), that is, \(\eta_{o}=\psi\) considering the frame convention. From (14) one can write:  
\[f_{t}=k_{o}\psi+[k_{tt}\,k_{m}][t\,\eta_{n}]^{\top}\, \tag{35}\]where \(k_{o}=\frac{d_{w}k_{m}}{\|d\|^{2}}\) and \(k_{rn}=\frac{d_{w}k_{r}}{\|d\|^{2}}\). If we substitute the orientation controller in the dynamic model, considering the results of Theorem 1, we obtain:  
\[m_{33}\ddot{\ddot{y}}+(c_{33}+k_{D})\dot{\psi}=-k_{p}\ddot{\psi}-(c_{31}\dot{ \phi}+c_{32}\dot{\theta})\, \tag{36}\]  
where \(m_{ij},c_{ij}\) are extracted from \(M\) and \(C\). It is worth noting that \(m_{33}\) and \(c_{33}\) are constant with respect to \(\psi\) and its derivatives. Substituting \(\psi\) and from (35) in (36) we get:  
\[\frac{m_{33}}{k_{o}}f_{t}+\frac{k_{D}+c_{33}}{k_{o}}f_{t}+\frac{k_{P}}{k_{o}}f _{t}=-k_{1}\int_{0}^{t}\dot{f}_{t}-k_{2}\ddot{f}_{t}+h_{t}\, \tag{37}\]  
with  
\[\begin{array}{rl}h_{t}=&-(c_{31}\dot{\phi}+c_{32}\dot{\theta})+(\frac{m_{33 }k_{t}}{k_{o}}\dot{t}+\frac{k_{t}(k_{D}+c_{33})}{k_{o}}\dot{t}+\frac{k_{t}k_{P }}{k_{o}}t)+\\ &&+(\frac{m_{33}k_{s}}{k_{o}}\dot{\eta}_{n}+\frac{k_{n}(k_{D}+c_{33})}{k_{o}} \dot{\eta}_{n}+\frac{k_{n}k_{P}}{k_{o}}\eta_{n})\.\end{array} \tag{38}\]  
Following the same procedure of force along the \(n\)-axis, the Routh-Hurwitz criterion enforces the controller coefficients to be chosen as:  
\[k_{pf}+\frac{1}{k_{o}}>\frac{m_{33}}{k_{D}+c_{33}}k_{lf}\, \tag{39}\]  
which is fulfilled by setting appropriately high proportional and derivative gains and sufficiently low integral gain, and for slowly-varying force commands \(f_{t}\to f_{d}\).  
Choosing coefficients according to (34) and (39) makes the system over damped, that is, without overshoot, which is that \(f\) is not getting higher than \(f_{d}\). Thus, if \(f_{d}\) is the output of (10), contact maintenance is ensured.
# Modeling and Parametric Simulation of Microplastic Transport in Groundwater Environments
## 2 Materials and Methods
### Microplastics Balance Equations in the Aqueous Phase and for the Solid Phase  
The balance equations for MPs are as follows:  
\[\frac{\partial C_{t}}{\partial t}=-\
abla\bullet f \tag{1}\]  
\[C_{t}=\theta C+\rho S \tag{2}\]  
\[f=-\theta D\
abla C+v\theta C \tag{3}\]  
Here, \(Ct=\) total microplastics (N/m\({}^{3}\))  
\(f=\) total microplastics flux (N/m\({}^{2}\)s)  
\(C=\) microplastics in the aqueous phase (N/m\({}^{3}\))  
\(S=\) microplastics of the solid phase (N/kg)  
\(\theta=\) volumetric water content (-)  
\(\rho=\) soil bulk density (kg/m\({}^{3}\)),  
\(D=\) diffusivity (m\({}^{2}\)/s)  
\(\
u=\) average pore water velocity (m/s)  
By substituting Equations (2) and (3) into Equation (1) for one-dimensional flow, we obtain:  
\[\theta\frac{\partial C}{\partial t}+\rho\frac{\partial S}{\partial t}=\theta D \frac{\partial^{2}C}{\partial x^{2}}-v\theta\frac{\partial C}{\partial x} \tag{4}\]  
Figure 1: Transport of MPs in a saturated porous medium.  
MPs in the solid phase can be expressed using the following attachment-detachment model for colloids [38]:  
\[\rho\frac{\partial S}{\partial t}=k_{a}\theta C-k_{d}\rho S \tag{5}\]  
Here,  
\(k_{a}\) = the first-order attachment coefficient (1/s)  
\[k_{a}=\frac{3(1-\theta)}{2d_{50}}\eta\alpha\upsilon\]  
\(\eta\) = collector efficiency (-)  
\(\alpha\) = microplastic sticking efficiency (-)  
\(d_{50}\) = the median porous medium grain diameter (m)  
\(k_{d}\) = the first-order detachment coefficient (1/s)  
The \(k_{d}\) term is usually ignored because it is very small compared to \(k_{a}\)[10], and Equation (5) can be simplified as follows:  
\[\rho\frac{\partial S}{\partial t}=k_{a}\theta C \tag{6}\]  
In this study, the transport of MPs was modeled by writing and executing finite element method (FEM) codes to solve the governing equations shown in Equations (4) and (6). Finite element analysis procedures are widely used to determine the solutions of balance equations. [PERSON]'s residual method is employed to determine the governing partial differential equations of Equations (4) and (6). The detailed development of this procedure is described in [44].