esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Profile Monopole Meander Line Antenna for WLAN Applications
###### Abstract  
An antenna assumes a significant role in expanding the levels of communication to meet the demands of contemporary technologically based industry and private data services. In this paper, a printed compact meander line patch antenna array for wireless local-area network (WLAN) applications in the frequency span of 2.3685-2.4643 GHz is presented. The impedance matching of the antenna is generated by applying a partial rectangular-shaped ground plane backside of the meander line antenna. The proposed antenna evolved on the Rogers RT5880 substrate with a dielectric permittivity of 2.2, and the height of the substrate was 1.575 mm to accomplish the lowest possible return loss. The proposed antenna was developed to achieve particular outcomes, for example, voltage standing wave ratio (VSWR) 1.32, reflection coefficient 20 dB with a bandwidth of 94.2 MHz, a gain of 2.8 dBi, and an efficacy measurement of 97%. This antenna is appropriate for WLAN applications that utilize a 2.4 GHz resonance frequency. The overall dimensions of the antenna are 15 mm \(\times\) 90.86 mm.  
monopole antenna; meander line; WLAN; reflection coefficient 20 June 2022  
8 July 2022  
Published: 18 August 2022
# Behavior of Two-Chord Steel-Concrete Composite Columns under Axial Compression
## 1 Introduction
The usage of high-strength concrete indicated that the performance of a column could be improved by increasing the diameter-to-thickness ratio of a steel tube and by lowering the axial compression ratio. The instability behavior of prismatic multi-tube latticed steel columns using the proposed elastic buckling loads and the modification of slenderness ratios was presented by [PERSON] [18]. An experimental investigation of composite columns made from CFST connected by steel linking plates was analyzed by [PERSON] [19]. The experimental program studied the slenderness ratio, the thickness of the steel linking plates, and load eccentricities. It was demonstrated that members with a greater slenderness had lesser load-bearing capabilities; additionally, the steel connector plates had a significant influence on containing the specimens' side deflections. The connector plate was proven to be an important element of the shear force transferring process by [PERSON] [20]. The presented research investigates the behavior of two-chord hollow steel tube (TCHST) columns and two-chord concrete-filled steel tube (TCCFST) columns. The objectives of this study are to present the results of a series of experimental tests, to investigate the influence of concrete compressive strength on the performance of TCCFST members, and to define the axial load-carrying capacity of TCCFST members using current design codes or standards. The novelty of the presented research is in the application of high-strength materials in the formation of a built-up two-chord steel-concrete composite column. In this study, a two-chord column is made with only horizontal battems instead of the typical lattice shape with diagonal and diagonal + horizontal bracings. Moreover, the column plastic capacities obtained from the codes AISC-360 [21], DBJ/T-13 [22], and Eurocode 4 [23] in determining the plastic resistance of the TCCFST columns are validated. The strengths of the materials used in this research are higher than the limits defined in the codes AISC-360 and Eurocode 4. High-strength materials are widely available for structural application, but the use of those materials in composite columns is still restricted due to the lack of design guidelines and insufficient field experience. Thus, an additional aim of this study is to provide recommendations on the application of TCCFST made from high-strength materials.
# Analysis of Hydration-Mechanical-Durability Properties of Metakaolin Blended Concrete
###### Abstract  
Metakaolin (MK) is a highly active pozzolanic material and MK is increasingly used in producing high performance concrete. This study presents an integrated hydration-mechanical-durability model for evaluating various properties of metakaolin blended concrete. First, a blended hydration model is proposed for simulating the hydration of composite binders containing MK that considers the dilution effect and pozzolanic reaction due to metakaolin addition. The interactions between the metakaolin reaction and cement hydration were taken into account by means of the contents of calcium hydroxide and capillary water in hydrating the composite binder. The reaction degrees of cement and metakaolin were calculated using the hydration model. Second, the gel-space ratio of the MK blended concrete was determined based on the reaction degrees of the composite binders and proportions of concrete mix. Moreover, concrete compressive strength was calculated using the gel-space ratio. Third, the volumetric phase fractions were calculated based on the reaction degrees of the binders. Chloride penetrability of metakaolin blended concrete was analyzed through the capillary porosity of concrete. The proposed integrated hydration-mechanical-durability model is valuable for the material design of metakaolin blended concrete.  
metakaolin; hydration; strength; chloride penetrability; model Article
## 2 Materials and Methods
### Machine Learning Methods
#### 2.2.3 Decision Trees Regression  
Decision trees are a supervised learning algorithm that can be used for both classification and regression. Decision trees are fruitful in data science as they are easy to understand and interpret and can handle continuous and categorical data. A decision tree is a graphical representation of a set of decisions and the possible consequences of those decisions. It is constructed starting at the root node and progressing through the tree, making decisions based on the values of the properties at each node. The decision tree to be used is selected according to the type of dependent variable. The decision tree model to be used is the classification tree when the dependent variable is categorical, and the model to be used is the regression tree when the dependent variable is continuous [29].  
Decision trees are a statistical method. In this method, a tree structure is first created, and the data in the data set is processed into this tree. The structure of decision trees consists of root nodes, nodes, and branches. The root node, the first stage of the tree structure, is created. The root node represents the dependent variable. As you go down the tree structure that starts with the root node, the data sets are divided into small pieces and branches, and new nodes are created. Algorithm selection varies according to the type of target variable. The most frequently used algorithms in decision trees are entropy for categorical variables and Gini for continuous variables; For continuous variables, it is the Least Squares method [30].  
In decision trees, the data set is divided into training and testing. Training data is used to create a model. Test data is used to calculate the success and error of the model. If the accuracy is acceptable, the rules are used to predict new data. There are many algorithms used in decision trees. CART, C4.5, C5.0, ID3, and CHAID are the most commonly used [31].  
Entropy is a measure of uncertainty. If a dataset has a single label, it has a lower entropy. Therefore, the data should be divided in a way that minimizes entropy. If the splitting process is good, the predictions are successful [32].  
\[\mathrm{H}=-\sum\mathrm{P(x)}\log\mathrm{p(x)} \tag{11}\]  
The information gained is used to determine the best split. Information gain:  
\[\mathrm{Gain(S,D)}=\mathrm{H(S)}-\sum_{\mathrm{V\in D}}\frac{|\mathrm{V}|}{| \mathrm{S}|}\mathrm{H(V)} \tag{12}\]  
S is the original data set, and D is a partition of the set. Each V is a subset of S. The information gain is defined as the difference between the entropy of the original dataset before the split and the entropy value of each attribute.
## 3 ATON Framework
### Presentation Layer
#### 3.6.4 Query System  
CH-oriented Web3D applications should provide interactive methods to query the virtual environment during exploration or inspection tasks. This is vital for semantic annotations (described in the next section), measuring tools or generic inspection: interactive routines are needed to perform intersections with complex or basic shapes, while for basic 3D models this is not particularly challenging (most Web3D libraries provide intersection methods with the underlying scene-graphs), several issues arise when the virtual environment becomes more complex or device resources are limited. For desktop-based web browsers, performance can be slightly impacted (small framerate drops) while for mobile devices and Web3R this can be devastating. The latter strictly require low-latency response times to deliver a consistent, smooth and acceptable experience for the final user while querying the space. A basic solution for Web3D/WebXR applications in general is to perform queries only on geometrically-simple shapes, although certain tasks (e.g., measuring) require intersection with more complex geometries.  
In order to overcome these issues meshes need to be spatially indexed [66]. \(BVH\) (Bounding Volume Hierarchy) trees are employed in ATON (see Figure 10) to query complex geometries very efficiently, while maintaining high frame rates on client devices [67]. This solution allows web-applications to query 3D surfaces very efficiently, for different reasons, including navigation purposes (orbit mode re-targeting, first-person locomotion), semantic annotations, measuring and spatial user interface elements. Furthermore, BVH trees can be also adopted for Cesium' 3D Tiles (see Figure 10, bottom row) to improve performances of interactive queries on large multi-resolution datasets. Current BVH implementation in the framework is based on an open-source library ([[https://github.com/gkjohnson/three-mesh-bvh](https://github.com/gkjohnson/three-mesh-bvh)]([https://github.com/gkjohnson/three-mesh-bvh](https://github.com/gkjohnson/three-mesh-bvh)), accessed on 22 November 2021) to accelerate ray-casting routines in Three.js. ATON offers an interactive 3D selector (visually represented as a sphere) with a location and radius that can be used to perform different tasks on queried surfaces.
## 2 [PERSON]'s TMD as a Reference Mass Damper
### Equations of Motion and Frequency Response to Harmonic Excitation  
The equations of motion for the primary structure equipped with the [PERSON]'s TMD under harmonic force excitation can be written as follows:  
\[\begin{split} m_{1}\ddot{x}_{1}+k_{1}x_{1}+c_{2 dh}\big{(}\dot{x}_ {1}-\dot{x}_{2}\big{)}+k_{2 dh}(x_{1}-x_{2})=f_{exc},\\ m_{2}\ddot{x}_{2}-c_{2 dh}\big{(}\dot{x}_{1}-\dot{x}_{2}\big{)}-k_{2 dh }(x_{1}-x_{2})=0,\end{split} \tag{5}\]  
where \(\ddot{x}_{1}\), \(\dot{x}_{1}\), and \(x_{1}\) are the absolute acceleration, velocity, and displacement of the primary structure with its mass \(m_{1}\) and stiffness \(k_{1}\), while \(\ddot{x}_{2}\), \(\dot{x}_{2}\), and \(x_{2}\) are the absolute acceleration, velocity, and displacement of the TMD with mass \(m_{2}\), passive spring stiffness \(k_{2 dh}\) (1), and passive viscous damper \(c_{2 dh}\) (3), and \(f_{exc}\) is a harmonic force excitation of amplitude \(F_{exc}\) and varying angular frequency \(\omega\):  
\[f_{exc}=F_{exc}sin(\omega t). \tag{6}\]  
The relative displacement of the mass \(m_{2}\) with respect to the structural mass \(m_{1}\) is:  
\[x_{d}=x_{1}-x_{2}. \tag{7}\]  
The frequency response to harmonic excitation can be obtained by representing the response as harmonic components, \(x_{1}=X_{1}e^{i\omega t}\), \(x_{d}=X_{d}e^{i\omega t}\). Based on the equations of motion (5), the amplitude \(X_{1}\) of forced response of the primary structure displacement, and the amplitude \(X_{d}\) of relative displacement, can be expressed as follows [44]:  
\[\frac{X_{1}(\omega)}{F_{exc}}=\Bigg{|}\frac{k_{2 dh}-\omega^{2}m_{2}+i\omega c _{2 dh}}{\left(k_{1}-\omega^{2}(m_{1}+m_{2})\right)\left(k_{2 dh}-\omega^{2}m_{ 2}+i\omega c_{2 dh}\right)-\omega^{4}m_{2}^{2}}\Bigg{|}, \tag{8}\]  
\[\frac{X_{d}(\omega)}{F_{exc}}=\Bigg{|}\frac{\omega^{2}m_{2}}{\left(k_{1}- \omega^{2}(m_{1}+m_{2})\right)\left(k_{2 dh}-\omega^{2}m_{2}+i\omega c_{2 dh} \right)-\omega^{4}m_{2}^{2}}\Bigg{|}. \tag{9}\]  
The above equations will be later used in numerical examples.
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 3 The Mechanical Properties of Diaphrags
### Mechanical Properties of the SCD
#### 3.2.1 Without initial stress  
In order to facilitate the analysis, we first discuss the case in which the CD is not provided with a flat center zone, and the deflection equation of CD without initial stress can be given by [42]  
\[P=a_{p}E\frac{h_{d}^{4}}{R_{d}^{4}}\frac{\omega_{0}}{h_{d}}+b_{p}\frac{E}{(1-v^ {2})}\frac{h_{d}^{4}}{R_{d}^{4}}\frac{\omega_{0}^{3}}{h_{d}^{3}}, \tag{5}\]  
where  
\[a_{p}=\frac{2(q+1)(q+3)}{3\Big{(}1-\frac{v^{2}}{q^{2}}\Big{)}}, \tag{6}\]  
\[b_{p}=32\frac{1-v^{2}}{q^{2}-9}\bigg{[}\frac{1}{6}-\frac{3-v}{(q-v)(q+3)}\bigg{]}, \tag{7}\]  
and for a sinusoidal corrugation profile [42]  
\[q^{2}=\frac{s}{l}\bigg{[}1+1.5\frac{H^{2}}{h_{d}^{2}}\bigg{]}. \tag{8}\]  
Figure 5: The schematic view of the shallow corrugated diaphragm (CD).  
where \(q\) is the corrugation profile factor, which is larger than 1 for CDs. For FDs (\(H=0\)), \(q\) is equal to 1. If the CD has a flat center zone, it can be compensated by the factor \(s\!/l\). For a rectangular corrugation profile [42]  
\[\frac{s}{l}=\frac{R_{d}+2 NH}{R_{d}}, \tag{9}\]  
where \(N\) is the number of corrugations. From Equations (6) and (7), it is obviously that with the increase of \(q\), \(a_{p}\) increases rapidly and \(b_{p}\) decreases rapidly, and the decrease of \(b_{p}\) indicates that the cube of the central deflection (the nonlinear component) of the diaphragm decreases. Therefore, the linear deformation range of the diaphragm can effectively increase by controlling the corrugation structure parameters [41].
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Underwater Acoustic** & **Underwater Radio** & **Underwater Optical Wireless** \\  & **Communication** & **Frequency Communication** & **Communication** \\ \hline Information transmission rate & \(\sim\)kbps & \(\sim\)Mbps & \(\sim\)Gbps \\ Transmission distance & \(<\)20 km & \(<\)100 m & 100–200 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different underwater wireless communication methods. based on ofdm after channel coding. Through numerical simulation, the researchers found that Hamming code and LDPC code could both improve the system performance [16]. In this paper, the polarization coding technology is applied to the underwater OAM optical communication system. In order to weaken the influence of ocean turbulence on signal transmission, the bit error rate of the communication system is reduced and the signal transmission distance is increased. Based on the Monte Carlo method, this paper polarizes the ocean turbulence channel and constructs the polarization code word. The bit error rate performance of the OAM optical communication system with Polar code and without Polar code is compared by simulation. Finally, the bit error rate performance of decoding algorithms with different polarization codes is explored. The first chapter presents the introduction, which introduces the research status of channel coding in the field of wireless optical communication. The second chapter introduces the principle and construction process of the ocean turbulence channel model. The third chapter introduces the basic theory of channel coding and the steps for the channel polarization of the ocean turbulent channel based on the Monte Carlo method. The fourth chapter presents the analysis of the simulation results, and chapter five provides the conclusion.
## 6 Perspectives: Greenhouse Artificial Neural Networks Application
### Agriculture 4.0 and the ANNs
#### 6.1.2 Smart Agriculture  
SA and PA are booming, but they could take advantage of technology in the agricultural world. SA is an agricultural management concept that uses modern technology to increase the quantity and quality of products, access to GPS, soil scanning, data management, and IoT technologies. In the case of smart greenhouses, evaluation of production, energy loss and increased labor costs is essential as a result of manual intervention against environmental impact. In addition, to control the climate, monitoring must be intelligent so that there is no need for manual intervention. The parameters necessary for efficient product production are determined by various sensors and the data are transferred to a cloud-based environment for evaluation [192].  
One of the main disadvantages of the current agricultural greenhouses is the efficient and intelligent information management. That is, what is needed for the efficient implementation of technologies such as IoT is the design and implementation of the general system as shown in Figure 6. The design of these systems where internet or local area network (LAN) technology is used will allow sensors, controllers and computers to be combined to connect people and \"things\", thus obtaining data, and remote control and intelligent network management [193].  
Figure 6: Agriculture 4.0 applied in a greenhouse.  
In traditional agriculture, pesticides, fertilization and irrigation depend on the experiencing of farmers; however, it does not guarantee the accuracy of parameters such as temperature, humidity, lighting and other indicators that are difficult to determine and adjust only by experience. In a smart greenhouse, by having a large number of sensors, the collected data can be communicated via the Internet and, therefore, to an operator. The operator might also have an Internet interface to control fertilization, irrigation, heating, lighting and other parameters [194].  
The amount of information generated can be used to develop more robust models that predict the behavior of greenhouse parameters, and thus speak of an adaptable AI with more complex learning capacity. The challenge is that these models coexist with technologies such as CC and IoT. The ANNs, specifically deep NNs, which are powerful tools for prediction and optimization are an option for various applications in agriculture [137] and greenhouse agriculture. CNNs, RNNs and long short-term memory neural network can consider various types of information and handle a large amount of data, the potential of having information stored in the cloud and access to it from anywhere would allow training and use of networks to be more efficient, since any unit with computing capacity could use this information simultaneously and not only depend on each neural network, NNs could operate in parallel.
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 4 Experimental Results  
In this section, to evaluate the effectiveness of our method in enhancing privacy, we conduct comprehensive experiments by comparing our with other VCSs.
## 4 Discussion
Potential future approaches may look for complementarity between satellite indexes with a significantly reduced number of sensors. Reducing the number of sensors may decrease the initial investment cost for farmers, but at the same time, farmers may rely on satellites for scheduling precision irrigation interventions.
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Application** & **Main Input Datasets or Causative Factors** & **Algorithms Used** & **Algorithm with the Best Accuracy** \\ \hline
[82] & Estimating wheat yields & MODIS NDVI and climate data time series & RF, Cubist, XGBoost, MLP, SVR, GP, kNN, MARS, average ensemble, bayesian data fusion & SVR \\ \hline
[83] & Downscaling soil moisture & Sentinel-1 radar, monthly NDVI, land cover, topography, and surface soil properties. & RF & — \\ \hline
[84] & Winter wheat yield prediction & Spectral indices calculated from UAV-based hyperspectral data & SVR, GP, RR, RF, stacking & Stacking \\ \hline
[43] & Forest biomass estimation & Landsat spectral variables, vegetation indexes, texture measures, and terrain factors & RF, XGBoost, CatBoost & CatBoost \\ \hline
[47] & Landside susceptibility mapping & Lithology, bedding structure, distance to fault, slope, aspect, plan curvature, profile curvature, elevation, distance to river, and NDVI & DBN, CNN, ResNet, stacking, simple averaging ensemble, weighted averaging ensemble, boosting & Stacking \\ \hline
[85] & Spatial prediction of landslide & Slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall & LSSVM, MADT & MADT \\ \hline
[86] & Predicting flood probabilities & Elevation, slope angle, aspect, plan curvature, SPI, TWI, sediment transport index, drainage density, mean annual rainfall, proximity to rivers, proximity to roads, proximity to the coastline, soil texture, geology, land cover, wind speed, and mean sea level & LWLR, random subspace, REPTree, RF, M5P model tree, stacking & \\ \hline
[87] & Flood susceptibility mapping & Elevation, slope, aspect, NDVI, mean monosonal rainfall, plan curvature, drainage density, population density, land cover, proximity to rivers, proximity to roads, geology, and soil texture & Bayesian regularization back propagation neural network, CART, EBF, weighted average ensemble algorithm & Weighted average ensemble algorithm \\ \hline
[88] & Forecasting agricultural commodity prices & Prices of energy commodities, exchange rate, interaction between commodities prices in domestic, and foreign markets & RF, GBM, XGBoost, stacking, MLP, SVR, kNN & XGBoost \\ \hline
[89] & Wheat yield prediction & Normalized difference red edge index, temperature, precipitation, relative humidity, sunshine duration, solar radiation, growing degree days, Shannon diversity index of precipitation evenness, abundant and well-distributed rainfall, and days after planting & LR, RR, Lasso, ENR, SVR, kNN, DT, RF, GBDT, MLP, XGBoost \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._