esa-sceva/satcom-chunk-collection huggingface dataset:
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 4 Discussion  
This paper investigated the reinforcement and stabilization effects of micropile on slopes through carrying out static and dynamic model tests. However, some literature [12] has only studied the performance of micropiles under horizontal loads from static tests alone, and other literature [18] has only studied the dynamic characteristics of micropile reinforced landslides through a shaking table. As such, through systematic research and comparison, the reinforcing mechanism, seismic response mechanism, and failure modes of the micropiles were discussed, which is very important for the application of micropile in landslide prevention and slope stability design.  
Applying the results of these tests to the reinforcement of soil landslides, we can consider increasing the number of longitudinal steel bars nearly three times the pile diameter above and below the sliding surface of the micropiles in the design process, which can avoid damage caused by an excessive bending moment. In addition, the results show that the damage degree and landslide thrust of the rear row piles are significantly greater than the piles in the first two rows, which can be taken into account to enlarge the rear row pile diameter to improve bending resistance as well as to enhance the overall stability of the micropiles through the reduction of pile spacing, etc. Therefore, the reinforcement capacity of the rear row of piles can be strengthened during the design process, especially in earthquake areas. Of course, other scholars can learn about the reinforcement and stability effect of micropile on a slope as well as the reinforcement mechanism, seismic response mechanism, and failure mode of micropile from this article. The results will also help other researches understand the proportion of landslide thrust that each row of piles bears when multi-row piles are used to reinforce a landslide. The proportions are not equal, but they are specific. Meanwhile, the distribution of earth pressure, bending moment, and axial force behind each row of piles can also be understood through this research.  
Figure 17: Dynamic axial force distribution for each row of piles (shaking table test).  
On the other hand, in the future, more in-depth research can be conducted in the following two directions: (1) different types of seismic waves can be input for the study of soil-pile dynamic interaction, and (2) different types of micropile groups can be considered for the study of the supporting effect of piles on landslide treatment.
# Binary Starling Murmutation Optimizer Algorithm to Select Effective Features from Medical Data
## Appendix A
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{\(k\) = 3, \(N\) = 30} & \multicolumn{6}{c}{\(k\) = 5, \(N\) = 20} \\ \cline{3-10}  & & **Diabetes** & **Heart** & **Hepatics** & **COVID-19** & **Diabetes** & **Heart** & **Hepatics** & **COVID-19** \\ \hline S1-BSMO & Avg & 83.3024 & 89.7759 & 81.8452 & 97.6496 & 82.9358 & 88.9528 & 77.9683 & 97.6013 \\  & Max & 84.5112 & 91.1126 & 92.5556 & 98.0808 & 84.0629 & 91.3534 & 92.5714 & 97.9844 \\ \hline S2-BSMO & Avg & 83.1656 & 89.6198 & 83.3028 & 97.5525 & 82.995 & 88.9851 & 77.8085 & 97.5018 \\  & Max & 83.9979 & 91.6603 & 96.8889 & 97.9414 & 84.226 & 92.1922 & 96.6667 & 98.2263 \\ \hline S3-BSMO & Avg & 82.8792 & 89.3916 & 80.6788 & 97.6031 & 82.7702 & 89.4225 & 78.8535 & 97.4893 \\  & Max & 84.0439 & 92.1316 & 94 & 97.9846 & 84.4498 & 92.9411 & 96.6667 & 98.1287 \\ \hline S4-BSMO & Avg & 82.9226 & 90.0263 & 82.091 & 97.5329 & 82.8662 & 89.0141 & 78.5044 & 97.5208 \\  & Max & 83.5297 & 93.4569 & 98.3333 & 98.0334 & 84.1876 & 91.7804 & 92.4643 & 98.099 \\ \hline V1-BSMO & Avg & 83.3034 & 89.275 & 82.3873 & 97.3381 & 82.916 & 89.014 & 82.316 & 97.303 \\  & Max & 84.5946 & 91.9784 & 89.9596 & 97.6875 & 83.93 & 90.58 & 88.611 & 97.645 \\ \hline V2-BSMO & Avg & 82.8698 & 89.5967 & 85.3515 & 97.3886 & 82.777 & 90.147 & 78.368 & 97.291 \\  & Max & 83.2788 & 91.9132 & 100 & 97.8924 & 83.495 & 92.77 & 92.051 & 97.604 \\ \hline V3-BSMO & Avg & 83.0581 & 89.6372 & 84.2314 & 97.4291 & 82.752 & 89.063 & 80.449 & 97.369 \\  & Max & 84.4839 & 92.3453 & 94.6429 & 98.2173 & 84.24 & 92.867 & 96.349 & 97.735 \\ \hline V4-BSMO & Avg & 83.0346 & 90.0785 & 84.4764 & 97.4154 & 83.013 & 89.097 & 81.099 & 97.38 \\  & Max & 83.6545 & 92.453 & 100 & 98.0199 & 83.788 & 91.567 & 93.099 & 97.932 \\ \hline Threshold- & Avg & 83.7292 & 91.6759 & 85.2687 & 97.7006 & 83.564 & 91.301 & 81.078 & 97.67 \\ BSMO & Max & 85.4714 & 93.7512 & 97.5 & 98.3656 & 84.936 & 92.869 & 95.325 & 98.032 \\ \hline \multicolumn{10}{c}{\(k\) = 5, \(N\) =30} & \multicolumn{6}{c}{\(k\) = 7, \(N\) =30} \\ \cline{3-10}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{3-10}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline S1-BSMO & Avg & 83.2288 & 89.2841 & 81.3914 & 97.7266 & 83.487 & 89.175 & 84.47 & 97.634 \\  & Max & 84.242 & 91.4817 & 95.0256 & 98.2616 & 84.571 & 89.756 & 96.5 & 98.064 \\ \hline S2-BSMO & Avg & 83.1426 & 89.403 & 78.9332 & 97.5923 & 83.224 & 89.761 & 81.5
# Numerical Analysis of the Impact of Thermal Spray Insulation Solutions on Floor Loading
###### Abstract
A more accurate model is an elastic layer presented in an approximate manner. The coefficients included in calculations are dependent on the material constants specific to this layer [10]. Another option is to assume that the substrate is liquid whose specific gravity is later included in calculations. Then, the buoyancy is exerted by the weight of the plate that lies on a given floating substrate [11]. Figure 1 schematically presents the Winkler elastic substrate, while Formula (1) indicates the above-described correlation [11]. \[\mathrm{p}=K_{z}\cdot w \tag{1}\]  
where:  
\(\mathrm{p}\)--load [kN/m\({}^{2}\)]  
\(w\)--deflection [m]  
\(K_{z}\)--subgrade stiffness [kN/m\({}^{3}\)]  
Two-parameter substrate models such as Vlasov, Pasternak or Gorbunov-Posadov systems of equations are much less frequently used, since it is more difficult to make calculations using them [12; 13; 14; 15]. Floors in heated buildings are thermally insulated to minimize heat loss. The literature provides insufficient information regarding the effect of a thermal insulation layer on the total load capacity of the floor slab above it. There are many thermal insulation materials, i.e., polystyrene, mineral wool, glass wool, resole foam, foam glass, or cellulose insulation material. Recently, the popularity of polyurethane foam as a thermal insulator has been noticeably increasing [16; 17; 18]. The production of polyurethanes constitutes approximately 7% of the total plastics production in Europe [19; 20]. Certainly, polyurethane can be used as floor insulation, e.g., sandwich panels with a polyurethane foam core in aluminum cladding. The calculations that include the cooperation of a reinforced concrete slab and sandwich panels demonstrate an increase in the value of the subgrade stiffness reaction in combination with a decrease in the value of bending moments [7]. However, floor insulation is very rarely made in the form of spraying, as insulation boards are much easier to lay. The work contributes to further considerations on floor design as sandwich elements, with different stiffness moduli of individual layers. In the literature available to engineers, there are no algorithms for designing such floors. The issue is particularly important from the point of view of contractors and investors. Floors in building structures are a cost-generating element; hence, their correct design is critical. The paper includes an insulation layer made of spray-applied polyurethane  
Figure 1: Winkler substrate model [11]. foam. Although such a solution is rarely used in floor making, its undoubted advantage, apart from thermal insulation properties, is the tightness of the layer.
# Thermal Comfort and Sustainability in University Classrooms: A Study in Mediterranean Climate Zones
## 2 Materials and Methods
### Adaptive Model and Comfort Parameters  
In this study, comfort parameters for building thermal conditioning (temperature, relative humidity and air velocity) were determined, and an adaptive thermal comfort model was proposed for AC and NV modes of operation. The comfort zone and neutral temperature (\(T_{n}\)) were determined based on TSV and \(T_{op}\), and the temperatures were calculated using the [PERSON] method [22; 43; 47]. For TSV, 90% and 80% of the thermal acceptability are between \(-\)0.5 and 0.5 and \(-\)1 to 1, respectively, on the ASHRAE seven-point scale [2; 43].  
The adaptive thermal comfort model was established through linear regression analysis, considering two different approaches. In the first, \(T_{op}\) is used as the independent  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equipment** & **Parameter** & **Probe** & **Range** & **Accuracy** \\ \hline Thermal & T\({}_{a}\) (\({}^{\circ}\)C) & HP3217R & \(-\)40 to 100 \({}^{\circ}\)C & \(\pm\)1/3 DIN \\ microclimate & T\({}_{g}\) (\({}^{\circ}\)C) & TP3275 & \(-\)30 to 120 \({}^{\circ}\)C & \(\pm\)2 \({}^{\circ}\)C \\ HD32.1 & RH (\%) & HP3217R & 0 to 100\% RH & \(\pm\)1.5\% RH (0 to 90\% RH) \\ instrument & \(\mathrm{(DeltaOhm)}\) & \(\mathrm{v_{a}}\) (m/s) & AP3203 & 0.02 to 5 m/s & \(\pm\)(0.05 + 5\% measure) m/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specification of the equipment and probes used [46].  
variable and TSV as the dependent variable. In the second, \(T_{m}\) acts as the independent variable, while \(T_{c}\), calculated using Griffiths' method according to Equation (4), is defined as the dependent variable.  
\[T_{c}=T_{op}+\frac{(0-TSV)}{\alpha} \tag{4}\]  
The value \"0\" indicates a neutral condition, while \(\alpha\) corresponds to the constant that defines the rate of change of wind chill as a function of indoor air temperature, also known as Griffiths' coefficient [52]. For this study, \(\alpha\) is considered with values of 0.25, 0.33 and 0.50 [22; 43; 53; 54; 55].
## 3 Proposed STPAP Beamforming Algorithm
### Novel MVDR-Based Criterion
\begin{array}{ll}\underset{\mathbf{W}_{l}(n)}{\text{Minimize}}&\mathbf{W}_{l}^{ \text{H}}(n)\widetilde{\mathbf{R}}(n)\mathbf{W}_{l}(n)\\ \text{s.t.}&\mathbf{W}_{l}^{\text{H}}(n)\widetilde{\mathbf{C}}_{l}=1&n=1,2,\cdots N\end{array}  \tag{16}\]  
with  
\[\widehat{\mathbf{C}}_{l}=\left[ \begin{array}{cccccc}c_{\text{h},1,l}&c_{\text{ v},1,l}&\cdots&c_{\text{h},M,l}&c_{\text{v},M,l}&0&\cdots&0\end{array} \right]^{ \text{H}}, \tag{17}\]  
where \(\mathbf{W}_{l}(n)\in\mathbb{C}^{2 MK\times 1}\) represents the weight vector for the \(l\)-th GNSS signal and its expression can be written as \(\mathbf{W}_{l}(n)=\widetilde{\mathbf{R}}^{-1}(n)\widetilde{\mathbf{C}}_{l}[\widetilde{ \mathbf{C}}_{l}^{\text{H}}\widetilde{\mathbf{R}}^{-1}(n)\widetilde{\mathbf{C}}_{l}]\). Besides, \(\widehat{\mathbf{C}}_{l}\in\mathbb{C}^{2 MK\times 1}\) denotes the constraint vector for the \(l\)-th GNSS signal, which indicates that \(L\) adaptive filters are required if there are \(L\) GNSS satellites in the FOV and thus it will definitely increase the burden of computation. Thus, it is easily found that the performance of the proposed MVDR-CMS criterion is better than that of the MVDR-CSS criterion in terms of the computational complexity. It is noticed that the theoretical analysis is based on the joint space-time-polarization architecture, while it can also be applied to the space architecture and space-time architecture when the polarization discriminator is removed.
###### Abstract  
Worldwide, the shortage of fresh water has increased exponentially due to population growth and contamination of available water, especially in water tables that provide water for general consumption. One of the main pollutants of water is arsenic (As), present in the environment and in most mining/metallurgical processes, which is a major health risk, especially as a carcinogen. In the region of Matehuala, San Luis Potosi (SLP), Mexico, a highly productive mining area, arsenic concentrations of 138.1 mg/kg have been found in soils--6.2 times higher than what is allowed in domestic soils, while in water it is reported up to 158 mg/L, exceeding permissible limits for human consumption. In addition to As pollution, the region suffers from water shortage both in the city and in rural communities. Therefore, it is necessary to explore new technologies to provide the population with fresh water. This paper presents a feasibility study on the use of an atmospheric water generator (AWG) to capture fresh water in the region of Matehuala, SLP. The region was found to have the necessary environmental conditions to use AWGs, with an annual average relative humidity (RH) of approximately 60%. Using a mathematical model of a dehumidifier, water harvesting can be evaluated under the region's prevailing climatic conditions. The month with lowest harvest was found to be January, with 0.89 to 3.6 L/day, while the month with largest harvest was August at 3.9 to 18 L/day and water production costs of 0.0093 and $ 0.038 USD/L, respectively. The study concludes that the use of AWGs would help alleviate water shortages, thus benefiting marginalized people or communities, preserving ecosystems and the environment.  
climatological analysis; freshwater harvesting; fresh water shortage; mining pollution; photovoltaic energy +
Footnote †: journal: Applied Sciences
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
###### Abstract  
Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance.  
additive quantile regression averaging; forecasts combination; machine learning; point and interval forecasting; renewable energy; wind energy +
Footnote †: journal:  
_Article_
# Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?
## 4 Experiment
### Performance Measures  
We implemented all performance measures described in [4] (sensitivity (recall), specificity, precision, F-measure, G-mean, Mathew's correlation coefficient, balanced classification rate, average precision, and weighted average precision), finally settling on a specific family of measures with their accompanying charts:  
**average precision**[65], defined as:  
\[AP=\frac{\sum_{k=1}^{n}(P(k)*rel(k))}{\text{number of fraud transactions}} \tag{1}\]  
A similar measure is defined in [66], which yields very similar results but is somewhat stricter when punishing erroneous predictions. The rationale behind both of these measures is the same and can be summed up as \"algorithm A is superior to algorithm B only if it detects the frauds before algorithm B\" [66]. We also computed a **weighted** version of the average precision where transactions were weighted according to the corresponding amount in \(\xi\). Weighted measures were related to the non-trivial questions like \"is it better to predict/prevent a single fraudulent transaction of 100\(\xi\) or multiple transactions of 5\(\xi\) each?\". AP was chosen over all the other measures because it reflects (in one number) the ranking nature of this problem.  
As we considered this problem a detection problem (and not \"only\" classification), we also used ranked precision and recall charts, both weighted and non-weighted, and AP aligned well with these charts in our experiments. Typical precision and recall charts are shown in Figure 5.  
The abscissa shows the rank, which is the number of transactions ordered by fraud probability, and the ordinate shows the actual fraud percentage.  
This kind of visualization maps well to the business case: a fraud detection system delivers transactions in the descending order of fraud probability. Human experts can examine only a portion of them--a number that certainly depends on the number of experts. At a certain point, the cost of (additional) experts exceeds the fraud losses. In any case, it is essential to achieve high precision at small ranks, and a sudden drop in precision could indicate a cut-off point in expert team size.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Meaning** \\ \hline \multirow{2}{*}{trans} & Only basic (transactional) set of 66 features. If omitted, full feature set, \\  & which includes aggregated features, is used. \\ \hline
5 & 5\% fraud rate \\ \hline
50 & 50\% fraud rate \\ \hline \multirow{2}{*}{[small | balance]} & small is obtained through undersampling of majority class and \\  & balanced with a combination of undersampling and oversampling. If \\  & none of these appears, then the integral dataset has been used. \\ \hline A, B, C & Model position, see Figure 4 \\ \hline RF, LR, or NN & random forest, logistic regression, or neural network, respectively \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviations used to present experiment results.
## 2 Materials and Methods
### Analytical Methods  
Chemical analyses were carried out to confirm proximate composition of dietary treatments, and main ingredients (Tables 1 and 3) [24]. Samples were dried at 105 \({}^{\circ}\)C for 12 h to determine gravimetrically dry matter and followed by ashing at 550 \({}^{\circ}\)C for 12 h. Total nitrogen was measured by combustion (CHNS auto-analyzer, Leco Corp., St. Joseph, MI, USA) and crude protein was calculated by nitrogen conversion (%N \(\times\) 6.25). Total lipid was gravimetrically determined via Folch extraction [25]. Finally, gross energy was measured via an adiabatic bomb calorimeter (Parr 6200, Par Instrument Company, Moline, IL, USA).  
Total amino acid (TAA) quantification was performed by mass detection following high performance reverse-phase liquid chromatography with pre-column derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl (AQC). Analyses were undertaken on a Shimadzu Nexera X2 series UHPLC (Shimadzu Corporation, Kyoto, Japan), coupled with a Shimadzu 8030 Mass Spectrometer using a modification of the Waters AccQ-tag system (Waters Corporation, Milford, MA). Bovine serum albumin (BSA) (ICN), milk powder (NIST SRM 1549a) and a well characterized aquafeed were used as reference materials. Samples or reference materials were hydrolyzed using phenolic 6N HCl at 112 \({}^{\circ}\)C according to the protocol for complex feed samples outlined by Waters Corp. (1996).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Fishman** & **STD SBM** &
\begin{tabular}{c} **TLP** \\ **SBM** \\ \end{tabular} & **11 sA4 null SBM** \\ \hline \multicolumn{5}{c}{Proximate composition (g kg\({}^{-1}\))} \\ \hline Dry matter & 911 & 949 & 918 & 926 \\ Protein & 719 & 495 & 521 & 462 \\ Lipid & 117 & 119 & 116 & 118 \\ Ash & 126 & 54 & 52 & 58 \\ \hline Amino acid (g kg\({}^{-1}\)) & & & & \\ \hline ASP & 67.7 & 46.0 & 48.5 & 48.7 \\ SER & 29.8 & 19.6 & 20.8 & 21.6 \\ GLU & 97.4 & 69.5 & 73.4 & 69.1 \\ GLY & 45.2 & 16.0 & 16.3 & 16.7 \\ HIS & 16.2 & 9.1 & 10.2 & 9.3 \\ ARG & 39.7 & 24.6 & 26.0 & 25.0 \\ THR & 30.8 & 14.3 & 15.2 & 16.3 \\ ALA & 40.7 & 16.3 & 17.5 & 17.6 \\ PRO & 31.6 & 19.3 & 20.5 & 19.7 \\ CYS & 6.5 & 5.5 & 5.2 & 5.3 \\ TYR & 24.4 & 11.7 & 13.9 & 13.2 \\ VAL & 34.4 & 16.7 & 17.5 & 16.7 \\ MET & 22.8 & 2.8 & 3.0 & 3.1 \\ LYS & 58.9 & 21.1 & 23.7 & 23.4 \\ ILE & 29.0 & 15.8 & 16.7 & 17.5 \\ LEU & 52.6 & 27.6 & 30.4 & 29.8 \\ PHE & 30.1 & 18.2 & 18.8 & 20.4 \\ TAU & 7.8 & ND & ND & ND \\ \hline \multicolumn{5}{c}{ND = not detected.} \\ \end{tabular}
\end{table}
Table 3: Key ingredients proximate and amino acid composition (g kg\({}^{-1}\)).
## 3 Smart Irrigation System Considering Optimal Energy Management with Predictive Control
### Expert System for the Generation of Optimal Irrigation--(First Stage)
According to the irrigation time, the necessary electrical demand for the day is obtained by multiplying the irrigation time \(t_{r}\) with the pump power, thus sending the forecasting or necessary demand for the whole day to the next stage, which is in charge of supplying electric power with the optimal and efficient management of the pump start-up throughout the whole day.
## 4 Conclusions  
In this study, we investigated the effects of the conventional flux (ZnCl\({}_{2}\):NH\({}_{4}\)Cl) for hot-dip galvanization and developed a new flux that improved the coating properties of Zn-Mg-Al ternary alloy coatings on steel sheets. The coating quality depends on the surface preparation techniques and utilization of a suitable flux for the alloy composition. The conclusions drawn from this study are: The conventional flux usage for the Zn-Mg-Al alloy coating deteriorated the coating quality by forming coating defects owing to the formation of AlCl\({}_{3}\) compounds. The addition of approximately 30 wt.% SnCl\({}_{2}\) fostered the best coating surface quality owing to the complete decomposition of the flux during hot dipping. SnCl\({}_{2}\) in the flux eliminated the AlCl\({}_{3}\) residues in the coating by a spontaneous substitution reaction at the bath temperature, as confirmed by the thermodynamic calculations. Therefore, the complete flux decomposition enhanced the wettability of the molten metal on the steel substrate, which promoted the formation of a stable inhibition layer with superior coating adhesion.  
Conceptualization, M.-S.O.; methodology, K.-y.K.; software, M.-S.O.; validation, S.G.; investigation, K.-y.K.; data curation, M.-S.O.; writing--original draft preparation, S.G.; writing--review and editing, M.-S.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the [National Research Foundation of Korea (NRF)] grant funded by the Korea Government (Ministry of Science and ICT) [No. 2022R1A2C1008972]. This work was also supported by the Technology Innovation Program (20012941, 20016850) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). This work was also supported in part by the Technology Development Program (S3160560) funded by the Ministry of SMEs and Startups (MSS, Korea).  
Not applicable.  
Figure 9: Red rust formation in the GI and Zn–1 Mg–1 Al coatings with different flux compositions.