esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset  
A total of 13 specimens were used in this research, where every specimen had different compression, tension, and shear reinforcement amounts depending on the model arrangement constructed using the Box-Behnken experimental design requirements. The reinforced concrete beam cross section was 200 \(\times\) 150 mm with a 1020-mm length. We used  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Symbol** & \begin{tabular}{c} **Minimum** \\ **Value** \\ \end{tabular} & \
\begin{tabular}{c} **Middle** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Value** \\ \end{tabular} \\ \hline Compression Area of Steel (mm\({}^{2}\)) & \(\mathbf{X}_{1}\) & 157.00 & 235.50 & 314.00 \\ Tension Area of Steel (mm\({}^{2}\)) & \(\mathbf{X}_{2}\) & 157.00 & 235.50 & 314.00 \\ Shear Area of Steel (mm\({}^{2}\)) & \(\mathbf{X}_{3}\) & 169.56 & 197.82 & 226.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ranges of input parameters.  
Figure 1: Box-Behnken experimental method.  
10 mm diameter size steel bars for both compression and tension reinforcement and 6 mm diameter size steel bars for the shear reinforcement (see Figure 2). The length between the supports was 890 mm and both supports were rollers, giving freedom in the longitudinal direction of the specimens with a loading rate of 0.1 MPa/s. The labels of the specimens were M1, M2, M3, M4, M5, M6, M7, M8, M9, M10, M11, M12, M13.  
The arrangement of the specimens is shown in Table 2. A total of 15 models were created using the Box-Behnken design method with the support of MATLAB code and models 13, 14, and 15 were the same arrangement; therefore only 13 models would be utilized in both the experimental tests and numerical simulations. Three input parameters were considered (compression reinforcement area of steel bars, tension reinforcement area of steel bars, and shear reinforcement area of steel bars). The minimum, middle, and maximum values of each input parameter were identified depending on the existing range for each input parameter (see Table 2).  
It is worth mentioning that the outputs are the flexural strength and the stiffness of the reinforced concrete beam, which were being predicted, and would then be utilized to undergo optimization of the design. All specimens were prepared using a concrete mixture with mechanical properties shown in Table 3, determined from the compression cylinder test and split cylinder tension test of a total of six specimens of concrete cylinders with dimensions of 20 \(\times\) 10 cm. The specimens were kept in water for curing for 28 days.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Area of Compression Steel** & **Area of Tension** & **Area of Shear** \\  & **Bars (mm\({}^{2}\))** & **Steel Bars (mm\({}^{2}\))** & **Steel Bars (mm\({}^{2}\))** \\ \hline
1 & 157.00 & 157.00 & 197.82 \\
2 & 157.00 & 314.00 & 197.82 \\
3 & 314.00 & 157.00 & 197.82 \\
4 & 314
## 2 Materials and Methods
### SPME-GC-MS Analysis  
Accurately weigh the sample 3 \(\pm\) 0.1 g in a 20 mL headspace vials, add a certain amount of saturated NaCl solution (10 mL), put it in a constant temperature magnetic stirrer and balance it [32]. After 10 min equilibration at 50 \({}^{\circ}\)C, SPME fiber (50/30 \(\upmu\)m DVB/CAR/PDMS by Supelco) was exposed for 30 min in the vial headspace. Then, the fiber was immediately desorbed for 5 min in a gas chromatograph injection port operating in split mode with split ratio of 5:1 at 250 \({}^{\circ}\)C.  
The identification of volatile compounds was performed by gas chromatograph (Thermo Fisher Scientific) coupled to ISQ mass spectrometry using a Trace GC-MS Thermo Fisher Scientific, equipped with a TG-5 MS chromatographic column (30 m \(\times\) 0.25 mm with 0.25 \(\upmu\)m film thickness). Initial column temperature was held at 40 \({}^{\circ}\)C for 5 min, and then increased to 120 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, with hold time of 1 min, and finally to 250 \({}^{\circ}\)C at 13 \({}^{\circ}\)C/min, with hold time of 5 min. Helium was used as the carrier gas at 1 mL/min constant flow. The temperature of ion source and transfer line was 230 \({}^{\circ}\)C, and 280 \({}^{\circ}\)C, respectively. The mass detector was operated in full scanning mode in the range between _m_/2 50-500, with IE energy of 70 eV. Chromatograms and spectra were recorded and processed using Xcalibur software (Thermo Fisher Scientific).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Numbers** & **Sensor Types** & **Sensitive Substances** & **Detection Ranges** \\ \hline S1 & TCS822 & Alcohol, solvent vapors & 50–5000 ppm \\ S2 & TCS2602 & General air contaminants & 1–30 ppm \\ S3 & TCS813 & Carbon monoxide, ethanol, methane, hydrogen, isobutane & 50–10,000 ppm \\ S4 & TCS2620 & Solvent vapours & 50–5000 ppm \\ S5 & MQ136 & H\({}_{2}\)S & 1–200 ppm \\ S6 & TCS2600 & Carbon monoxide, hydrogen & 1–30 ppm \\ S7 & MQ139 & R11, R22, R113, R134A, halogen & 10–1000 ppm \\ S8 & TCS2610 & Ethanol, hydrogen, methane, isobutane/propane & 500–10,000 ppm \\ S9 & MQ137 & Ammonia and amine compounds & 5–500 ppm \\ S10 & TCS2611 & Ethanol, hydrogen, isobutane, methane & 500–10,000 ppm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Gas sensor array and its properties.  
Figure 1: Block diagram of detection device system.  
Compounds were identified by comparison of their mass spectra and retention times with those of injected authentic standards (C7-C40) [33].  
RI (Retention index) was calculated as follows under single linear programming warming conditions:  
\[RI=100n+100\times\frac{T_{Rx}-T_{Rn}}{T_{Rn+1}-T_{Rn}} \tag{1}\]  
In the formula: \(T_{Rx}\) is the retention time of the components to be measured; \(n\) and \(n+1\) represent the number of carbon atoms of n-alkanes, respectively; \(T_{Rn}<T_{Rx}<T_{Rn+1}\).
## 5 Findings and Analysis
### Complexity Examination
This combination of efficiency and performance makes it particularly relevant in contexts where optimizing both space and energy usage is paramount. The merits of the suggested multiplier design are compellingly demonstrated through the implementation results outlined in Table 3. These results provide strong evidence for the assertions regarding decreased area complexity, as well as significant improvements in the ADP and PDP. By successfully minimizing resource consumption without compromising performance, the suggested design offers noteworthy practical advantages. This is especially crucial for IoT applications designed for individuals with disabilities, where essential factors such as energy efficiency, effective use of space, and overall operational performance are critically important. These enhancements underscore the configuration's capability to meet the challenges of resource-limited settings. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Design** & \(r\) & \begin{tabular}{c} **A** \\ **[Kgates]** \\ \end{tabular} & \begin{tabular}{c} **D** \\ **[\(ns\)]** \\ \end{tabular} & \begin{tabular}{c} **P** \\ **[\(mW\)]** \\ \end{tabular} & \begin{tabular}{c} **ADP** \\ **PDP** \\ \end{tabular} & \begin{tabular}{c} **A** **Saving** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **P** **Saving** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **ADP** **Saving** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **PDP** \\ **(\%)** \\ \end{tabular} \\ \hline [PERSON] [19] & 283 & 6083 & 15.6 & 202 & 95,117 & 3163 & 99.8 & 96.9 & 99.9 & 98.8 \\ \hline [PERSON] [20] & 283 & 4276 & 9.8 & 170 & 41,720 & 1655 & 99.7 & 96.2 & 99.8 & 97.7 \\ \hline [PERSON] [43] & 283 & 2631 & 4.8 & 109.5 & 12,562 & 523 & 99.6 & 94.2 & 99.5 & 92.8 \\ \hline [PERSON] [44] & 283 & 3771.2 & 4.8 & 140 & 18,005 & 670 & 99.7 & 95.5 & 99.6 & 94.3 \\ \hline [PERSON] [50] & 283 & 3274 & 12.8 & 131 & 41,904 & 1671 & 99.7 & 95.1 & 99.8 & 97.7 \\ \hline Proposed & 283 & 4.5 & 11.2 & 3 & 51 & 34 & - & - & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of Efficiency in Various Multiplier Designs for \(r=283\).
# Characterization of Heat-Polymerized Monomer Formulations for Dental Infiltrated Ceramic Networks
## 3 Results and Discussion  
### Viscosity  
Viscosity is the material property describing the resistance to flow, which is affected by intermolecular interactions. Figure 2 shows the average viscosity of each monomer formulation from a rotational shear sweep test. As the shear stress rate had no effect on viscosity, all formulations showed Newtonian rheological behavior. BisGMA showed the greatest viscosity at 520 Pa\(\cdot\)s at 25 \({}^{\circ}\)C, attributed to its high molecular weight and the strong hydrogen bonding from its two pendant hydroxyl groups [42; 43]. The viscosity of TEGDMA, attributed to its low molecular weight [44], is relatively low (0.0084 Pa\(\cdot\)s),which naturally decreases the viscosity of the monomer formulations as percentage weight increases in accordance with other studies [45; 46].  
Figure 2: Viscosity of monomer formulations. (**a**) Shear sweep of monomer formulations at 25 °C; (**b**) temperature sweep of monomer formulations.  
According to [PERSON]'s law, the viscosity affects the fluid flow behavior into a porous substrate. Lower viscosity is needed for ease of flow, while a higher viscosity affects infiltration rate and pressure. One group [9] used pure UDMA for their PICN material, which at 10 Pa-s, is several magnitudes greater than the 5:5 wt% BisGMA:TEGDMA (0.181 Pa-s), and UDMA:TEGDMA (0.090 Pa-s) commonly used by others. Monomer formulations greater than 10 Pa-s would be difficult for infiltration; therefore, with regard to viscosity, the formulations of BisGMA:TEGDMA 9:1 and 8:2 would fall out of this range. Viscosity is significantly influenced by changes in temperature; Figure 2b shows complex viscosity with increasing temperature. Monomer formulations with viscosity less than 0.1 Pa-s were excluded from the temperature sweep. Temperature has such a pronounced effect on viscosity it is able to reduce viscosity by orders of magnitude. Therefore, most monomer formulations can reach low viscosities suitable for infiltration.  
BisGMA in general shows higher viscosities compared to UDMA formulations, due to the differences in chain length of the used monomers. As consequence, the effect diminishes at higher TEGDMA ratios. A target viscosity for infiltration purposes should meet the criteria of sufficient flowability/pentetrability into the network by retaining a minimum polymerization shrinkage and shrinkage stress at network interfaces. For BisGMA:TEGDMA, the optimum ratio is thus found between 7:3 and 5:5, while for UDMA:TEGDMA formulations, the window of applicability might be found between 9:1 and 6:4.
_Applied_  
_sciences_  
_Article_  
**Frequency and State-of-Charge Restoration Method**  
**in a Secondary Control of an Islanded Microgrid**  
**without Communication**  
[PERSON] 1 and [PERSON] 2,*()  
Received: 2 January 2020; Accepted: 21 February 2020; Published: 25 February 2020  
**Featured Application: This work is applicable to a secondary control of distributed energy resources in a microgrid without using communication devices.**  
**Abstract: This paper presents a control method for inverter-interfaced distributed generation (DG) and energy storage systems (ESSs) in an islanded microgrid. The proposed method is focused on secondary control, particularly frequency restoration and maintaining the ESSs' state of charge (SOC). To recover frequency deviation due to load change, an ESS is used as a droop-controlled grid-forming source. However, the grid-forming ESS cannot manage its own SOC, since it cannot control its own output power; hence, grid-feeding DGs are used to maintain the SOC within a desired range. Management of the SOC, as well as frequency restoration, is conducted by using local controllers without any communication devices, since dependency on communication may deteriorate system reliability in the case of failure. The proposed method for maintaining SOC can be realized by adjusting the system frequency, which is the only value that can be measured locally with almost the same value at every node in a steady state. Frequency restoration can be achieved by a simple ON/OFF scheme of the integral controller with a hysteresis loop to solve problems caused by differences between frequency measurements or set points among DGs.**  
**Keywords: frequency control; islanded microgrid; secondary control; state of charge; without communication**
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 3 Results  
### Yield Data and Statistical Analyses  
The descriptive statistics of the original and filtered yield data are presented in Table 4. Original data ranged from 0.86 to 501.23 Mg ha\({}^{-1}\) for the 2018/2019 crop season, and from 10.39 to 498.21 Mg ha\({}^{-1}\) for the 2019/2020 crop season. The coefficient of variation (CV) of the filtered data was 11% for the 2018/2019 crop season, and 13% for the subsequent season. The results highlighted that there is a requirement to exclude discrepant values from the original data to better understand the actual spatial variability of the fields. For that, the data filtering was carried out to decrease values of standard deviation (SD) and average yield. As a result of the filtering process, the minimum and the maximum values of sugarcane yield for the 2018/2019 crop season were 36 and 86.4 Mg ha\({}^{-1}\), which are more realistic. The mean value of yield for the 2019/2020 crop season was about 71 Mg ha\({}^{-1}\). The observed SD for the filtered yield data from the 2018/2019 and 2019/2020 crop seasons were 7.06 and 9.55 Mg ha\({}^{-1}\).  
Despite the exclusion of more than 50% of the original data, which was expected due to the local and global filtering [44], the number of points per hect  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Season** & **Dataset** & \(\mathbf{n}\) & **Minimum** & **Median** & **Mean** & **Maximum** & **SD** & **CV (\%)** \\ \hline \multicolumn{1}{c}{} & & & \multicolumn{6}{c}{**Mg ha\({}^{-1}\)**} & \\ \hline
2018/2019 & Original & 53,759 & 0.86 & 70.85 & 71.20 & 501.23 & 23.53 & 33.04 \\
2018/2019 & Filtered & 16,202 & 36.03 & 64.43 & 64.31 & 86.35 & 7.06 & 10.98 \\
2019/2020 & Original & 67,716 & 10.39 & 72.52 & 112.90 & 498.21 & 81.96 & 72.60 \\
2019/2020 & Filtered & 28,247 & 42.74 & 65.82 & 70.92 & 107.90 & 9.55 & 13.47 \\ \hline \end{tabular} \(n\): number of samples. SD: standard deviation. CV: coefficient of variation.  
\end{table}
Table 4: Descriptive statistics of the sugarcane yield data considering all fields.  
Figure 2: Flow chart of the methodology for data acquisition and processing.  
the requirement of at least 23 samples ha\({}^{-1}\) according to the calculated grid (Table 5). Table 5 summarizes the best-adjusted variogram model used for interpolating yield data with an RMSE of 0.53 and 1.41 Mg ha\({}^{-1}\) for 2018/2019 and 2019/2020 sugarcane growing seasons.  
An exploratory analysis of the dataset from both crop seasons indicated that the linear correlation between VIs and sugarcane yield did not present values greater than 0.50. Such results will negatively influence the MLR prediction accuracy since there is no strong linear relationship between dependent and independent variables. That is the main reason for combining multiple imagery dates (time-series) and RF regression as an alternative to perform yield prediction.
# Effect of Stiff Hangers on the Longitudinal Structural Behavior of Tied-Arch Bridges
## 5 Conclusions  
The main conclusions of this paper can be listed as follows:  
1. In this paper, the effect of stiffening hangers on the structural longitudinal in-plane behavior of arch bridges has been studied. The effect of the stiffness of the hangers, the relevance of the stiffness of arch and deck, and the consequences of hinges at the top or end or the hangers has been shown.
2. Generally, stiff hangers are an efficient way to reduce the bending moments both at the arch and the deck, and to reduce deflection under live loads.
3. The stiffness of the hanger is the most relevant factor governing its efficiency.
4. Pinned hangers at one of their ends offer an intermediate efficiency compared to hangers fixed at both ends. In some cases, they can be an advantageous alternative, since the arch-hanger or deck-hanger connection may be simpler when no bending moment has to be transferred.
5. In order to maximize its efficiency, the stiffness of the hangers must be carefully studied according to the relative stiffness of the arch and the deck, and according to the global flexural stiffness of the bridge.
6. The most adverse load combination usually happens in arch bridges when half the deck is loaded. However, when the hangers are fixed and for symmetrical load cases, the values of the bending moments near the ends of the deck might be more adverse than asymmetrical load distributions. To solve this problem, this paper proposes a combination of fixed and pinned hangers, carefully studied for a given bridge, which could be almost as efficient as a configuration wherein all the hangers are fixed.  
Figure 15: Effect of mixed fixed-pinned hangers on the reduction of bending moments under \(q_{3}\).  
**Acknowledgments:** The authors wish to thank the Universidad Politecnica de Cartagena (UPCT, Spain) for the funding provided, through the research project 2007_2420, directed by the second author. In addition, the authors wish to thank the Seneca Foundation (Murcia Region, Spain), for the funding of the first author's research scholarship (FPI).  
**Author Contributions:** Both authors have contributed equally to this work.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Phytohormonal Regulation in Salinity and Water Deficit Stresses
### Ethylene  
Ethylene is a gaseous molecule and a well-studied plant phytohormone that carries out important molecular modulation responsible for enhancing the tolerance of the plant to abiotic stress and regulating several metabolic pathways for secondary metabolite biosynthesis. Under salinity and drought stresses, the accumulation of ethylene was observed in many plants. A considerable body of evidence suggests that ethylene functions by monitoring gene expression as a part of its signal transduction pathway. In the absence of ethylene, very important genes that contribute to the plant's response to stress will not be expressed; on the contrary, they will be degraded by E3 ubiquitin ligase [53; 54]. Those genes are known as _ethylene insensitive 2_ (_EIN2_) and _ethylene insensitive 3_ (_EIN3_); they are two key intracellular ethylene-mediated gene regulators in the plant's defense response. With ethylene, _EIN2_ accumulates and thus facilitates the accumulation of the EIN3 proteins. The EIN3 proteins have a very important role in the genetic activation of a crucial stress-inducible transcription factor known as ethylene response factor 1 (_ERF1_) [53; 55]. The _ERF1_ then assists as a transcription factor and recognizes a GCC _cis_-acting element in the promoter regions of ethylene-responsive genes and triggers transcription. This finding shows only one example of how ethylene as a plant hormone contributes to regulating gene expression in the plant's response to abiotic stresses such as drought and salinity.
## 2 Governing Equations for Incompressible Fluid  
Fluid flows are governed by the classical Navier-Stokes equations including the continuity equation and momentum conservative equation:  
\[\frac{1}{\rho}\frac{d\rho}{dt}+\
abla\cdot\mathbf{v}=0 \tag{1}\]  
\[\frac{d\mathbf{v}}{dt}=-\frac{1}{\rho}\
abla P+\mathbf{g}+\frac{1}{\rho} \
abla\cdot\mathbf{\tau} \tag{2}\]  
where \(\rho\) is the density, \(\mathbf{v}\) is the velocity, \(P\) is the pressure, \(\mathbf{g}\) is the body force (gravitational force in this case), and \(\mathbf{\tau}\) is the viscous stress tensor. For incompressible fluids, the mass density takes a constant.  
The viscosity stress tensor \(\mathbf{\tau}\) in the momentum conservative equation is related to the velocity by the following equation:  
\[\tau_{ij}=\tau_{ji}=\mu\left(\frac{\partial v_{i}}{\partial x_{j}}+\frac{ \partial v_{j}}{\partial x_{i}}\right) \tag{3}\]  
where \(\mu\) is the dynamic viscosity coefficient. The two RHS gradients can be approximated in particle form [8]:  
\[\left(\frac{\partial v_{i}}{\partial x_{j}}\right)_{a}=\left(\frac{v_{a}^{i}- v_{b}^{i}}{r_{ab}}\right)\left(\frac{x_{a}^{j}-x_{b}^{j}}{r_{ab}}\right) \tag{4}\]  
The suffix \(i\), \(j\) indicates the tensor index of value 1, 2, or 3; \(a\), \(b\) indicates different particles; and \(b\) must be a neighboring particle of \(a\).  
For incompressible fluids, the mass density takes a constant, so that Equations (1) and (2) reduce to  
\[\
abla\cdot\mathbf{v}=0 \tag{5}\]  
\[\frac{d\mathbf{v}}{dt}=-\frac{1}{\rho}\
abla P+\mathbf{g}+\frac{\mu_{eff}}{ \rho}\
abla^{2}\mathbf{v} \tag{6}\]  
where \(\mu_{eff}\) is the effective viscosity of the fluid.  
_Free surface_  
The pressure on the free surface is assigned to be zero as reference atmospheric pressure.  
_Boundary conditions_  
On the velocity boundaries, the velocities are assigned to be a prescribed velocity.  
\[\mathbf{v}=\overset{\mathbf{\wedge}}{\mathbf{v}} \tag{7}\]  
If this prescribed velocity is zero, this equation denotes a fixed fluid boundary condition or an infinite fluid boundary condition, where the fluid is not disturbed in the far field away from the interested finite domain.
## 2 Methodology  
The schematic flowchart of the proposed system is presented in Figure 1. The system consists of three stages: UAV data acquisition, real-time 3D reconstruction using RTAB-Map, and results display. RTAB-Map is responsible for both camera position estimation and dense point cloud 3D reconstruction. The drones used in this system are equipped with obstacle avoidance capabilities, allowing them to navigate close to structures and capture high-resolution images or videos. These images/videos provide clear visualization of pipeline routing, pipe deformation, water penetration, sewage sludge, and other defects at elevations and angles that are inaccessible or difficult for humans to reach.  
Figure 1: Flowchart of UAV acquisition system based on RTAB-Map for urban pipeline.