esa-sceva/satcom-chunk-collection huggingface dataset:
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Methodology
#### 2.4.1 Multiple Linear Regression (MLR)  
The foundation of machine learning regression is linear regression [69]. Ordinary least squares regression (OLS) is generally used for the estimation of coefficients of the relationship between several independent variables and the dependent variable. The relationship between a set of independent variables and dependent variable is assumed to be linear. If the regression process involves one independent variable, it is termed simple linear regression; if multiple independent variables are involved, it is termed multiple linear regression (MLR). The effect of each independent variable on the dependent variable is denoted in form of the coefficients in Equation (3):  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{n}x_{n}+\varepsilon, \tag{3}\]  
where \(y\) represents the dependent variable, \(\beta_{n}\) represents the coefficient of the independent variable \(x_{n}\), and \(\varepsilon\) is the error term associated with the model.  
Linear regression is a widely used machine learning model due to its simplicity. However, there are some potential problems associated with it. First, linear models assume a linear relationship between the dependent variable and independent variables. This assumption might not be valid in every case. If the relationship between dependent variable and independent variable(s) is non-linear, linear regression models will have poor performance. Second, it is assumed that the error terms in linear models are always uncorrelated. If any correlation exists in the error terms, it can reduce the performance of linear models. It is assumed that linear models have a constant variance in the error terms (homoscedasticity), which is always not true [70]. Another major concern about using linear models is the presence of outliers and high leveraged points in the dataset which clearly affect the performance of linear models. Finally, it is assumed that the independent variables in the linear models are not correlated with each other (i.e., absence of multicollinearity in the datasets). The presence of multicollinearity in independent variables violates this assumption and reduces the performance of linear models.
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 5 Simulation Model  
### Setup  
The simulation model is built according to the system architecture described in Section 3. The model is implemented by CSIM19 [47]. Unless stated otherwise, the simulation is conducted under the simulator's default setting. The major explicit parameters are shown in Table 3.  
A client only submits a new request when the previous submitted request is satisfied or misses its deadline, which exhibits a closed system model [11]. The request generation interval of each client is shaped by \(THINKTIME\), which follows the Exponential distribution. The clients' data access pattern is shaped by the Zipf distribution [48], where the skewness is controlled by the parameter \(\theta\). Specifically, when \(\theta\) equals 0, it means the random distribution (all the data items have the equal access probability). With an increased value of \(\theta\), the data access pattern becomes more skewed.  
For real-time algorithms, the relative deadline of a request \(q_{i}\) (\(RD_{i}\)) is computed by:  
\[RD_{i}=(1+uniform(\mu^{-},\mu^{+}))*T^{serv}_{i}\]  
The deadline of \(q_{i}\) (\(Dl_{i}\)) is computed by:  
\[Dl_{i}=AT_{i}+RD_{i}\]  
where \(AT_{i}\) is the arrival time of \(q_{i}\). \(\mu^{-}\)_and_\(\mu^{+}\) represent the minimum and the maximum laxity, respectively. \(T^{serv}_{i}\) represents the service time of \(q_{i}\). A request is feasible for serving as long as its slack time remains greater than its service time. The system routinely removes the infeasible requests from the service queue.  
The channel bandwidth is 1 unit/tick, i.e., the server disseminates one unit-sized data item in each broadcast tick. For the real-time and the non-real-time scheduling, the item size is 1 unit. For the stretch optimal scheduling, the sizes of data items are generated using random distribution [49]:  
\[DataItemSize[i]=SIZMIN+\lfloor random(0.0,1.0)\times(SIZEMAX-SIZEMIN+1)\rfloor\]  
\begin{table}
\begin{tabular}{l c c l} \hline \hline
**Parameter** & **Default** & **Range** & **Description** \\ \hline \(ClientNum\) & 400 & 100\(\sim\)600 & Number of clients \\ \(THINKTIME\) & 0.01 & — & Request generation interval \\ \(m\) & 1000 & — & Size of the database \\ \(SIZEMIN\), \(SIZEMAX\) & 1, 30 & —, 10\(\sim\)60 & Min. and Max. data item size \\ _CacheSize_ & 60 & 30\(\sim\)180 & Client cache size \\ \(\theta\) & 0.4 & 0.0\(\sim\)1.0 & Zipf distribution parameter \\ \(\mu^{-},\mu^{+}\) & 120, 200 & 80\(\sim\)160, 160\(\sim\)240 & Min. and Max. laxity \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
where \(SIZEMIN\) and \(SIZEMAX\) are the minimum and the maximum data item size in the database, respectively, and \(i=1,2,\cdots,m\).
## 5 Implementation of the Methods and Algorithms Used
### Technical Parameters of the Emitter  
Each emitter taken into account in the model under consideration should be described with parameters:  
* \(c\) height of the emitter calculated from ground level--\(h\) [m];
* Internal diameter of the emitter outlet--\(d\) [m];
* Speed of the substance at the emitter outlet--\(\varphi\) [m/s];
* Temperature of the substance at the emitter outlet--\(T\) [K];
* Maximum substance emission--\(E\) [mg/s].  
It is also possible to convert the parameters into emitters with rectangular outlets. The above parameters constitute the set of input data necessary to implement the Pasquill formula. In addition, calculation constants depending on the atmosphere equilibrium factor are also used. The applied algorithm is used to simulate the real process of dispersion of pollutants in the atmosphere. The output data are values of one-hourly concentrations of particulate matter at user-specified distances from the emitter (x, y, z).  
For the purpose of applying the optimisation process, it was indispensable to develop maps of the studied area. With the use of QGIS desktop 2.10.18 (multi-platform, user  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Type of Land Cover** & **Length \(Z_{0}\)** \\ \hline
1. & Water & 0.00008 \\ \hline
2. & Meadows, pastures & 0.02 \\ \hline
3. & Cropland & 0.035 \\ \hline
4. & Orchards, bushes, spinneys & 0.4 \\ \hline
5. & Forests & 2.0 \\ \hline
6. & Compact rural development & 0.5 \\ \hline
7. & City of up to 10 thousand inhabitants & 1.0 \\ \hline
8. & City of 10 to 100 thousand inhabitants & \\ \hline
8.1. & -low buildings & 0.5 \\ \hline
8.2. & -average buildings & 2.0 \\ \hline
9. & City of 100 to 500 thousand inhabitants & \\ \hline
9.1. & -low buildings & 0.5 \\ \hline
9.2. & -average buildings & 2.0 \\ \hline
9.3. & -high buildings & 3.0 \\ \hline
10. & City of more than 500 thousand inhabitants & \\ \hline
10.1. & - low buildings & 0.5 \\ \hline
10.2. & -average buildings & 2.0 \\ \hline
10.3. & -high buildings & 5.0 \\ \hline \end{tabular}
\end{table}
Table 2: Values of the aerodynamic roughness length \(Z_{0}\)[40].  
friendly, free geoinformation program), a model of maps was created according to the assumptions of proper representation of distances in the field. The next step was to move the maps to AutoCAD, where the coordinate grids were superimposed, and individual points with specific \(Z_{0}\)-grid values were extracted. As a result of the transformations made, the values of the existing pollution background and \(Z_{0}\) values for individual coordinates were transferred to the text file. The data set, which contained the area of the Opolskie Province, necessary to create the map, was made available on the basis of license number DRP-IV.7522.1.132.2016.JST_16_N by the Marshal of the Opolskie Province.
## 3 Application to a Case Study
### Damage Identification of the Case Based on Strain Modal  
Based on the finite element model, analyzing the position corresponding to the mutation of the strain modal difference before and after the case damage in Table 5 to locate the damaged bar, and the magnitude of the amplitude can reflect the degree of damage. The first-order strain modal shapes of all bars before and after damage are plotted. Because of limitations of space, only the first-order strain difference diagram of 50% damage of the member were shown in Figure 10. We can see that first, compared with other layers of members, all members in the damage layer have more prominent and obvious changes in strain modal. Second, in the damage layer, the strain modal of the damaged member changes more sharply than that of other members, so the strain modal can not only locate the damage layer, but also accurately locate the damaged member.
## 3 Results and Discussion
### NH\({}_{3}\) Volatilization  
Ammonia volatilization was higher in the early period in composting and all three vermicomposting treatments (Figure 5a). The mixing of compost piles also enhanced the NH\({}_{3}\) emissions. The peaks were observed after mixing the first interval of 20 days, and earthworm activities also showed high emissions due to increased temperature. VPM showed higher emissions in initial days as compared to other treatments. The cumulative ammonia flux in COM, VCM, VPM, and VBC were evaluated as 3.36, 2.70, 3.33, and 3.06 kg N ha\({}^{-1}\), respectively (Figure 5b). The COM showed a significant difference from VCM (\(p<0.05\)). VCM emitted about 3.3 kg N ha\({}^{-1}\), which is significantly lower than VPM among vermicomposting treatments while showing significantly lower than COM. Composting with biochar amendment reduced nitrogen loss [70], and VBC clearly showed that vermicomposting with biochar also reduced NH\({}_{3}\) volatilization. Differences of NH\({}_{3}\) volatilization could be explained by the physical  
Figure 4: Mineral forms of nitrogen: NH\({}_{4}\)-N content (**a**), NO\({}_{3}\)-N content (**b**), and dissolved organic carbon (**c**) in all treatments as a mean of three replicates; error bars represent standard errors (\(n=3\)).  
and chemical properties of different substrates exhibiting dissimilar temperature and moisture [71] (Table 2). During pre-composting, the emissions may have occurred due to ammonium nitrogen contents in raw composted materials, then decreased gradually during the observation period.  
[PERSON] et al. also reported the mixing of compost piles for better aeration caused an increase in NH\({}_{3}\) volatilization in different time intervals and the same trend [72]. The decreasing trend of NH\({}_{3}\) loss in composting resulted from increased temperature, which made the material easily degradable, and this phenomenon is described well by [PERSON] et al. [25]. NH\({}_{3}\) emission results emphasized that vermicomposting of all treatments with cow dung (VCM) have lower loading compared to traditional composting.
# Biosurfactants' Potential Role in Combating COVID-19 and Similar Future Microbial Threats
### Biosurfactants  
Biosurfactants are amphiphilic compounds synthesized by bacteria, fungi, or plants, capable of lowering the surface tension of liquids. Their hydrophilic moieties can be made of acid, cationic peptide, anion, sugar (monosaccharide, disaccharide, or polysaccharide) and hydrophobic moieties made of hydrocarbon or fatty acid chains. They are environmentally friendly and have been shown to have many industrial applications sometimes performing better than synthetic surfactants [3; 4; 5; 6; 7]. Their physico-chemical properties include; high stability in a wide range of environmental conditions such as extreme pH, temperature and also salt concentration [8; 9], high biodegradability with a high rate of mineralization by soil microcosmos [10], low toxicity, surface tension reduction, foaming capacity and antimicrobial activity against pathogens [11; 12; 13; 14].  
Well known classifications of biosurfactants are based on their charges and molecular structures. Biosurfactants obtained from microbes are generally anionic or neutral, while a few are cationic [7]. Long-chain fatty acids generally characterize the hydrophobic moiety, while organic acid, alcohol, amino acid or carbohydrate functional groups characterize the hydrophilic moiety [7]. They are also broadly grouped based on their chemical structures as high molecular weight and low molecular weight molecules. Low molecular weight molecules are the glycolipids, phospholipids and lipoproteins, while the high molecular weight groups are the polymeric and particulate biosurfactants [15] (Figure 1). In addition to their environmental friendliness and wide industrial applications, biosurfactants are more sustainable because they can be produced from cheap feedstock and industrial and urban waste, and can be recycled [16; 17]. However, there are still challenges such as the pathogenicity of the main biosurfactant producer strains _Pseudomonas_ and _Bacillus_ and the relatively higher cost of large-scale production that make biosurfactants less commercially competitive than petroleum-derived synthetic surfactants [18]. Nevertheless, there is a growing scientific research interest in the improvement of the commercial competitiveness of biosurfactants. Bacterial strains are being engineered, new feedstocks are tested, and fermentation designs investigated to develop innovative ways to improve production efficiency. Amidst these challenges, it is expected that biosurfactants will occupy a significant market share, which is projected to be about USD 5.52 billion by 2022, growing at a CAGR (compound annual growth rate) of 5.6% [19].
## 5 Conclusions  
This study presents a comprehensive process simulation, exergy analysis, and indicator-based sustainability evaluation to compare pyrolysis and gasification processes, to produce second-generation biofuels. This approach lets energetic and efficient-based hotspots and improvement breaks, from a thermodynamic and sustainability perspective, to be identified. The framework combined feedstock selection, process simulation, exergy analysis, and sustainability assessment to support decision making, research, and development progress, to advance the TRLs of these technologies. This approach was proposed as a straightforward tool for researchers to first screen technologies at early design stages, allowing optimization at low maturity phases. The results revealed that the gasification process is thermodynamically more inefficient than pyrolysis, with a resource exergy efficiency of 3%. Energy consumption and utility requirements seem to be significant drawbacks of the gasification process to reach high energetic efficiencies. The pyrolysis process delivers a product with a higher energy content than is consumed during its processing, with an efficiency of 110.6%. The sustainability evaluation confirms the results given by the exergy assessment, indicating pyrolysis as a more attractive alternative. However, there is still potential in the gasification process, which can reach higher efficiency levels with some improvements. The ways forward to improve this process might involve using process optimization strategies or developing technological improvements. Regarding the pyrolysis process, even though the simulated setting is more refined than gasification, some improvements are still needed to make this process energy friendly. Future works might include intensification strategies and integration methods, such as heat, mass, or property integration, to achieve the most suitable topologies, in terms of energy and global sustainability.  
Conceptualization, S.M. and P.P.; methodology, S.M. and P.P.; software, J.R.; validation, J.R. and P.P.; resources, J.R.; programming; S.M.; writing, S.M.; review and editing, S.M., P.P. and J.R.; supervision, P.P. and J.R.; project administration, J.R.; funding acquisition, J.R. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded through the strengthening and sustainability plan for research groups of the Universidad de Cartagena.  
Not applicable.  
The authors want to thank the Universidad de Cartagena and the Novo Nordisk Foundation Center for Biosustainability for their support in developing this research.  
The authors declare no conflict of interest.
## 4 Implementation of Policy Hiding in CP-ABE Using Logical Connective
### Setup (\(\mathbf{1^{\lambda}}\)) \(\rightarrow\) Pk, Msk  
The Attribute Authority (AA) ran the setup algorithm by taking \(\mathbf{1^{\lambda}}\) as a security parameter in the setup module. The setup algorithm produced a tuple N = (\(p1p2p3p4\); \(\mathbb{G}\); \(\mathbb{G}_{T}\); \(e\)) which contained four prime numbers, \(p1,p2,p3,p4\). Meanwhile, four distinct ordered subgroups given as \(\mathbb{G}p1\), \(\mathbb{G}p2\), \(\mathbb{G}p3\), \(\mathbb{G}p4\) are structured based on the prime numbers. Let \(\mathbb{G}\) and \(\mathbb{G}_{T}\); be a cyclic group with order N. Therefore, the attribute authority uniformly chooses \(a\), \(\alpha,\alpha 1,\beta\in_{\mathbb{R}}\mathbb{Z}_{N}\) and \(g,g1\in\mathbb{G}p\). \(H,\ H_{1}\) were set as public hash functions, with \(H\) mapped the attribute value, \(AV_{x}\) to an element in \(\mathbb{Z}_{N}\), and \(H_{1}\) was a pseudo-random function that mapped elements in \(\mathbb{G}\) and \(\mathcal{M}\) to elements in \(\mathcal{M}\). \(e\) is a bilinear map, and it will be computed value \(Y\), Public Parameters \(PK\), and Master Key \(MSK\) as:  
\[Y=e(g,\ g_{1})^{\alpha 1},\]  
\[PK=\Big{\{}N,\ g,\ g^{a},g^{a1},g^{\beta},Y\Big{\}},\]  
Figure 3: Process sequence chart of the proposed CP-ABE scheme.  
\[MSK=\{a,\alpha,\alpha_{1},\beta,g_{1}\}\]
# A Comparative Study on the Laser Welding of Ti6 Al4V Alloy Sheets in Flat and Horizontal Positions
## 4 Discussion
### Effects of Gravity on Porosity  
Porosity content results indicated that using the flat welding position will result in less porosity when compared with the horizontal welding position. This can be attributed to the different degree of ease with which the pores can escape from the solidifying weld pool under different welding positions. As shown in Figure 10a, in flat welding, the pores formed in the weld pool will float upwards under the action of melt flow and buoyancy, which has been observed using an X-ray transmission imaging system by [PERSON] et al. [13; 14]. Some pores can escape through the top surface of the weld pool before it solidifies, while others that are not able to escape will remain in the welds and form porosity. For horizontal welding, shown in Figure 10b, the pores formed also float upwards and move away from the weld centerline. However, the uppermost surface of the weld pool is now in contact with the unmelted base rather than free space. This restricts those pores from escaping; consequently, almost all end up entrapped within the weld bead. Therefore, a high porosity content, located above the weld centerline, exists within the horizontal welds, as revealed in Figure 6, which is also visible from the cross section of the horizontal weld shown in Figure 4b.  
Furthermore, the final amount of porosity in a weld, especially when horizontal welding, will depend on the amount of pores formed during welding, which is closely related to the stability of the keyhole and the fluid flow characteristics in the weld pool. Results (Figure 6) show that higher power (2.5 kW) at higher welding speed (20 mm/s) result in less porosity compared with lower power (2.2 kW) at lower speed (8 mm/s). This agrees with results reported previously [15], which indicated through computational fluid dynamic (CFD) modeling that the fluid flow behind the keyhole can be quite unstable, and vortices and pores can form, particularly more so when using lower power and lower welding speed conditions. In contrast, when using higher power and welding speed conditions, CFD modelling predicted that the fluid flow would be less unstable, and fewer pores would result.  
Figure 9: Schematic representation of the molten metal flow and movement of the weld pool surface due to the action of gravity in (**a**) the flat position and (**b**) the horizontal position.
## 5 Discussion
### Difference Deduced by Upscaling Methods
#### 5.2.2 Aggregation with PSF  
MODIS gridded products are the outputs of a sampled image system that combines an imaging system with a sampling procedure. The imaging system used was characterized by the sensor PSF, which is considered as the convolution of line spread functions in the along-scan and the along-track directions (K
# Systematic Mapping Study on Remote Sensing in Agriculture
## 3 Results of the Systematic Mapping Study
### MQ8. What Are the Different Types of Spectral Information Used?  
This last mapping question refers to the type of spectral information of the images used in the research. As described, a wide range of the electromagnetic spectrum has proved to be useful in RSA. Each class can be suitable for some specific problem, or it can be applied to different tasks. The classification of the papers is presented in Table 5. As the labels of multispectral and hyperspectral data are not incompatible with the rest of categories, some papers are classified into more than one class. In addition, many works use different types of images, so they can also be classified in different classes. For example, in [204] three types of images (RGB, multispectral, and thermal images) are compared for the problem of high-throughput plant phenotyping, using UAV and in-field cameras.  
According to these results, standard RGB images (i.e., the visible spectrum) are clearly the most frequent type of images employed (46/106). Hyperspectral images are also found very frequently (28/106), in many cases mounted on UAVs or acquired from satellites. Apart from the visible spectrum, the following bands most used in the research are thermal and near infrared. In the opposite side, LiDAR and short wavelength infrared are to less commonly used.
# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
###### Abstract  
Exchanging messages with stable connections in missions composed of multiple unmanned aerial vehicles (UAV) remains a challenge. The variations in UAV distances from each other, considering their individual trajectories, and the medium dynamic factors are important points to be addressed.In this context, to increase the stability of UAV-to-UAV (U2U) communication with link quality, this paper presents an interface manager (IM) that is capable of improving communication in multi-UAV networks.Given a predefined set of available individual wireless interfaces, the proposed IM dynamically defines the best interface for sending messages based on on-flight conditions sensed and calculated dynamically from the wireless medium. Different simulation scenarios are generated using a complex and realistic experimental setup composed of traditional simulators such as NS-3, Gazebo, and GzUAV. IEEE 802.11n 2.4 GHz and 802.11p 5 GHz interfaces are used for the IM selection. The IM performance is evaluated in terms of metrics from the medium-access-control (MAC) and physical layers, which aim to improve and maintain the connectivity between the UAVs during the mission, and from the application layer, which targets the reliability in the delivery of messages. The obtained results show that compared with the cases where a single interface is used, the proposed IM is able to increase the network throughput and presents the best proportion of transmitted and received packets, reception power (\(-\)60 dBm to \(-\)75 dBm), and loss (\(-\)80 dB to \(-\)85 dB), resulting in a more efficient and stable network connections.  
wireless networks; UAV; mobility; IEEE standards; message reliability; connection stability 2021214255[[https://doi.org/10.3390/s21134255](https://doi.org/10.3390/s21134255)]([https://doi.org/10.3390/s21134255](https://doi.org/10.3390/s21134255))