esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 2 Materials and Methods
### Swelling Index Prediction through AI Models  
The controlling parameters of the _ELM_, _DNN_, _SVR_, _RF_, _LASSO_, _PLS_, _Ridge_, _KRidge_, _Stepwise_, and _GP_ algorithms used in this study are listed in Table 3. The performance of each models for selected optimal inputs (_PSO_, _GT_ and _FS_) during training and validation phases is presented in Table 10. Six statistical performance indicators have been used to compare between proposed models in order to select the best one. We mention the Mean Absolute Error (_MAE_), Root Mean Square Error (_RMSE_), Index of Scattering (_IOS_), Nash-Sutcliffe Efficiency (_NSE_), Pearson correlation coefficient (_R_), and Index of Agreement (_IOA_). The data were divided into two parts, i.e., 80% for training and 20% for validation (700 samples for training and 175 for validation). It was indicated from Table 10 that the \(C_{s}\) modeled with several machine learning methods produced _MAE_ (5.6 \(\times\) 10\({}^{-3}\) to 12.4 \(\times\) 10\({}^{-3}\)), _RMSE_ (0.007 to 0.0154), _IOS_ (0.165 to 0.355), _NSE_ (\(-\)0.88 to 0.75), \(R\) (0.59 to 0.94), and _IOA_ (0.69 to 0.95) in the training phase. Similarly, in the validation phase, we obtain _MAE_ (10.6 \(\times\) 10\({}^{-3}\) to 13.6 \(\times\) 10\({}^{-3}\)), _RMSE_ (0.013 to 0.017), _IOS_ (0.298 to 0.363), _NSE_ (\(-\)1.68 to 0.13), \(R\) (0.53 to 0.71), and _IOA_ (0.69 to 0.82). Furthermore, the finding clearly indicates that the _FS-RF_ (the optimal inputs of FS method trained by RF method) presents the most appropriate model that gives the highest accuracy in terms of _MAE_ (5.6 \(\times\) 10\({}^{-3}\)/10.6 \(\times\) 10\({}^{-3}\)), _RMSE_ (0.007/0.013), _IOS_ (0.165/0.298), _NSE_ (0.75/0.13), \(R\) (0.94/0.71), and _IOA_ (0.95/0.82) during the training/validation phase. In addition, the most appropriate _FS-RF_ model clearly follows the criteria of minimum values of error metrics (_MEA_, _RMSE_ and _IOS_) and higher values of _NSE_, \(R\), and _IOA_ for the phase of training and validation. Furthermore, this model is closely followed by the _FS-DNN_ model, which gives an acceptable accuracy and ranked second. Moreover, the results reveal the poor performance of the _PSO-Step_ model in predicting the swelling index. With respect to the performance of machine learning models, during the training phase, the hierarchy follows the order of _FS-RF_, _GT-RF_, _PRO-RF_, _FS-DNN_, _GT-DNN_, _PSO-DNN_, _PSO-SVR_, _FS-GP_, _GT-GP_, _PSO-GP_, _GT-SVR_, _GT-Step_, _FS-SVR_, _GT-Lasso_, _GT-PLS_, _FS-PLS_, _GT-Kridge_, _FS-Kridge_, _PSO-LS_, _GT-LS_, _PSO-ELM_, _FS-ELM_, _PSO-Lasso_, _PSO-Kridge_, _PSO-Ridge_, _GT-Ridge_, _FS-Ridge_, _FS-LS_, _FS-Step_, _FS-Lasso_, _PSO-PLS_, _GT-ELM_, and _PSO-Step_. Finally, the scatter plots between target and output swelling index values of each model are presented in Appendix A.
## 5 Simulation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**} \\ \hline
**Compared Index/Method** & \begin{tabular}{c} **IFCME** \\ **(\(m=2\))** \\ \end{tabular} & \begin{tabular}{c} **IIFPFCME** \\ **(\(\eta=0.01\))** \\ \end{tabular} & \begin{tabular}{c} **IIFPFCME** \\ **(\(\eta=0.1\))** \\ \end{tabular} & \
\begin{tabular}{c} **IIFPFCME** \\ **(\(\eta=1\))** \\ \end{tabular} \\ \hline RMSE & 0.1668 &
\begin{tabular}{c} 2.7094 \(\times\) \\ \(10^{-4}\) \\ \end{tabular} & 0.0027 & 0.0233 \\ \hline Average times on ten (sec.) & 0.005133 & 0.00203 & 0.001978 & 0.002283 \\ \hline Average number of ten iterations (\(l\)) & 28.7 & 8.9 & 9.1 & 10.9 \\ \hline \hline \multicolumn{5}{c}{(b)} \\ \hline
**Compared Index/Method** & \begin{tabular}{c} **IFCMC** \\ **(\(m=2\))** \\ \end{tabular} & \begin{tabular}{c} **IIFPFCMC** \\ **(\(\eta=0.01\))** \\ \end{tabular} & \
\begin{tabular}{c} **IIFPFCMC** \\ **(\(\eta=0.1\))** \\ \end{tabular} &
\begin{tabular}{c} **IIFPFCMC** \\ **(\(\eta=1\))** \\ \end{tabular} \\ \hline Average number of ten iterations (\(l\)) & 13.3 & 8.5 & 10 & 9.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of the comparison for IDS4 with different approaches: (**a**) using IFCME and IIFPFCME under different \(\eta\); and (**b**) using IFCMC and IIFPFCMC under different \(\eta\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**} \\ \hline
**Compared Index/Method** & \begin{tabular}{c} **IFCME** \\ **(\(m=2\))** \\ \end{tabular} & \begin{tabular}{c} **IIFPFCME** \\ **(\(\eta=0.01\))** \\ \end{tabular} & \begin{tabular}{c} **IIFPFCME** \\ **(\(\eta=0.1\))** \\ \end{tabular} & \
\begin{tabular}{c} **IIFPFCME** \\ **(\(\eta=1\))** \\ \end{tabular} \\ \hline RMSE &
\begin{tabular}{c} \(\longrightarrow\) \\ \end{tabular} & 0.0437 & 0.4428 & 0.4616 \\ \hline Average times on ten (sec.) & 0.007077 & 0.003865 & 0.006962 & 0.00694 \\ \hline Average number of ten iterations (\(l\)) & 39.6 & 18.9 & 35.2 & 34.7 \\ \hline \hline \multicolumn{5}{c}{(b)} \\ \hline
**Compared Index/Method** & \begin{tabular}{c} **IFCMC** \\ **(\(m=2\))** \\ \end{tabular} & \begin{tabular}{c} **IIFPFCMC** \\ **(\(\eta=0.01\))** \\ \end{tabular} & \begin{tabular}{c} **IIFPFCMC** \\ **(\(\eta=0.1\))** \\ \end{tabular} & \
\begin{tabular}{c} **IIFPFCMC** \\ **(\(\eta=1\))** \\ \end{tabular} &
\begin{tabular}{c} **IIFPFCMC** \\ **(\(\eta=1\))** \\ \end{tabular} \\ \hline RMSE & 0.6437 & 0.0522 & 0.0522 & 0.0522 \\ \hline Average times on ten (sec.) & 0.008791 & 0.006091 & 0.005886 & 0.006728 \\ \hline Average number of ten iterations (\(l\)) & 12.6 & 8.1 & 7.8 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the comparison for IDS3 with different approaches: (**a**) using IFCME and IIFPFCME under different \(\eta\); and (**b**) using IFCMC and IIFPFCMC under different \(\eta\).
## 1 Introduction  
The continuous interest in the efficient use and reuse of resources in the wood and agricultural sector for upcycled applications [1] is of great interest nowadays [2, 3] in the context of the circular economy [4].  
Steered by the paucity of non-renewable resources, the interest for wood could exceed its sustainable supply within the next few decades [5]. The wood demand has increased steadily, not only in the industry or for energy production (more than 50%), while the supply of wood is limited in specific regions of the world [6]. This leads to the need for substitutes for wood in engineered wood products (e.g., particleboards, PB). A promising alternative for wood in composites is provided by agricultural residues. A lot of research on agricultural waste has been carried out for PB based on wheat straw [7, 8], rice straw [9, 10], rapeseed [11], hemp shives [12, 13], cotton dust [14], or sunflower stalks [15] and topinambour [16]. Brewer's spent grain is also a raw material for PB [17] together with tree bark [18; 19; 20]. The advantages of these agricultural and forestry by-products include reduced costs, ample availability, biodegradability, and renewability, followed by an enlarged flexibility and sound insulation [21; 22]. Some drawbacks in using agricultural residues in lignocellulosic composites include unequal availability over the year, the manufacture of products with these raw materials cannot run year-round, no industrialized processing yet, big storage facilities, and different necessary pre-treatments [23; 24; 25]. Walnut and hazelnut shells are agricultural residues available in high quantities, but despite their thermal utilization [26], with no industrial use yet. Nut shells can exhibit high hardness and toughness [27]. Worldwide walnut production in 2019/2020 was roughly 965,400 tons and 528,070 tons for hazelnut [28]. Taking in account that roughly 67 % of the total fruit weight is comprised of the shell leads to 646,818 tons of walnut shells and roughly 353,807 tons of hazelnut shells each year [29]. Especially in Iran and Turkey research on the solely use of nutshells and nutshells in combination with wood has been done. The authors in [30] studied the properties of particleboard from hazelnut husks and combined with European black pine (_Pinus nigra_ Arnold) [31; 32]. Walnut shells were studied by [33; 34]. Other research refers to particleboards manufactured with peanut hulls mixed with European black pine [35], peanut shell flour [36], and almond shells [37; 38; 39].  
The aim of this study was to compare and evaluate the influence of the nutshell type (hazelnut and walnut) and resin (bonded with melamine urea formaldehyde (MUF) and polyurethane (PUR) 10% each) on the mechanical and physical properties and on the free formaldehyde content of particleboards produced solely from the above-mentioned nutshells. Other studies have dealt with PB bonded only with UF, so this study brings a novel process of gluing the nut shell panels with MUF and PUR as the properties of these boards including the Brinell hardness and formaldehyde content have not been reported yet.
## 1 Introduction
The alteration of flavor metabolism in grapes in response to enhancement of UV radiation has attracted great interest [15,16]. Excessive UV radiation has been reported to promote the accumulation of phenolics and carotenoids in grapes [15,16,17]. Our previous results also suggested that supplemental UV-A, UV-B, and UV-C radiation differentially improved the biosynthesis of anthocyanins and flavan-3-ols in grapes _in vitro_[18,19]. Some studies also reported the modifications of volatile profile, such as terpenes [20,21, 22], C13-norisoprenoids [20,23], and isobutyl methoxypyrazine (IBMP) [24] of grape berries and wines after applying different intensities of UV-radiation to grapes. In general, enhancing UV radiation increased the level of terpenes [20,25,26,27]. However, reduced UV radiation treatment showed inconsistent results. For example, some investigations indicated that UV-B radiation reduction down-regulated the accumulation of terpenes in Grindelia chiloensis (Asteraceae) and Malbec grapes [22,28]. Similarly, the concentration of terpenes was lower in Sauvignon Blanc wine made of the grapes covered by UV radiation-reducing sheets in field [21]. However, [PERSON] _et al._, (2015) reported that blocked UV radiation treated Pinot Noir grape wine showed the similar level of terpenes compared to wine made of normal grapes [20]. The level of isobutyl methoxypyrazine in Sauvignon Blanc grapes under attenuated UV radiation was as similar as that in normally grown grapes [24]. \(\beta\)-Damascenone level was higher in Riesing wine made of UV exclusion-treated grapevines [23], whereas the concentration of the other C13-norisoprenoids was not significantly altered [20]. These results suggested that reduced UV-radiation effect on volatile metabolisms might be dependent on variety and cultivation differences. It should be also noted that these previous studies only focused on some specific volatile compounds in grapes and wines. Most of these supplemental UV radiation effect studies were conducted in growth chambers or greenhouses [25; 29] using isolated fruits [26; 27]. Only a few field experiments have been studied using lessened UV radiation with polyester films [20; 21; 30]. In this study, a field study was carried out with two types of filter films that removed much of the radiation at shorter wavelength (\(\leq\)380 nm). The objectives of the study were to examine the response of grape flavor metabolism to attenuated UV radiation and the stage-dependence of this response. The effect of attenuated UV radiation on the formation of volatiles during the grape development stages was assessed in terms of their biosynthetic pathways. The results of this study could provide the guidance to viticulturists and winemakers. From their perspectives, it is essential to identify the viticulture practices that can improve grape and wine aroma quality since some cultivation measurements, such as plastic greenhouse, rain shelter cultivation and fruit bagging, could result in the reduction of solar UV radiation.
## 4 Results and Discussion
### Time-Domain Characteristics
#### 4.1.1 Aerodynamic Lift
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Maximum** & **Minimum** & **Mean** & **Standard** & **Standard** & **Mean** \\  & **(N)** & **(N)** & **(N)** & **(N)** & **Difference** & **Difference** \\ \hline Contact strip & & & & & & \\ +support-280 & 44.2242 & \(-6.24535\) & 21.39542 & 6.60428 & - & - \\ Bracket-280 & \(-21.8436\) & \(-61.0492\) & \(-41.34639\) & 8.59548 & - & - \\ Panhead-280 & 7.87 & \(-60.6928\) & \(-19.95097\) & 12.08294 & - & - \\ Contact strip & 60.4527 & \(-26.5484\) & 33.04816 & 7.99576 & 21.07\% & 54.46\% \\ Bracket-320 & \(-23.7578\) & \(-100.617\) & \(-68.01941\) & 13.63352 & 58.61\% & 64.51\% \\ Panhead-320 & \(-10.0657\) & \(-93.9362\) & \(-34.97125\) & 14.18627 & 17.41\% & 75.29\% \\ Contact strip & 67.7074 & \(-25.5528\) & 33.75717 & 9.68588 & 46.66\% & 57.78\% \\ +support-360 & & & & & & \\ Bracket-360 & \(-43.6264\) & \(-111.306\) & \(-81.01173\) & 12.59806 & 46.57\% & 95.93\% \\ \hline Panhead-360 & 2.8301 & \(-110.0323\) & \(-47.25456\) & 13.16243 & 8.93\% & 136.85\% \\ Contact strip & 92.6164 & 13.3197 & 52.56508 & 10.52163 & 37.23\% & 145.68\% \\ Bracket-400 & \(-49.2168\) & \(-116.733\) & \(-81.84115\) & 12.75881 & 48.44\% & 97.79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results of panhead aerodynamic lift passing through tunnel at different speeds. Figure 11: Comparison curves of panhead aerodynamic lift passing through tunnel at different speeds.
## 4 Improvement to CAD
### Results of Incorporation into CAD  
The analysis was performed with the same cases that were used for the analysis in Section 3. The training data set was used to determine the parameter A(Pre-screen) for the optimal approach. The other approaches were tested against the same test set in order to be unbiased. An AFUM-based CAD system [44] was used as the CAD basis.  
The results were good at low numbers of false positives in all three techniques, and it is at high and medium numbers of false positives where techniques distinguish themselves. Using the probabilistic approach to incorporate pre-screening into CAD is shown to work well at low numbers of false positives per image and can improve the performance by over 70%, but at high levels of false positives per image, this technique has minimal effect. This is expected since using Bayes Theorem merely reduces the probability of the false positives and does not eliminate them.  
The results of the strict approach are identical to the results of the probabilistic approach at low levels of false positives, but diverge at higher levels of false positives. Since this approach eliminates the false positives instead of just diminishing them, the results at high levels of false positives per image are worse than the probabilistic approach because true positives are eliminated. However, in medium levels of false positives, the performance is significantly better than the probabilistic approach.  
The optimal approach was tuned to determine the best performance at both low levels of false positives and the overall area under the ROC curve. The performance under both converged to the strict approach; however, this may be due to the pre-screening technique that was chosen.  
The overall performance is still strongly dependent on the effectiveness of the CAD system. The accuracy of the pre-screening is essential in order to prevent true positives from having their probabilities diminished, and the specificity is important for improving the effectiveness of the CAD system.  
The incorporation of the classification results back into the original CAD system does significantly improve the original CAD system, as shown in Figure 12. The results of incorporating our classification into CAD were good, increasing the accuracy by up to 71% at a set level of false positives per image. The improvement is most apparent at low levels of false positives. Incorporating asymmetry into CAD can improve the effectiveness at low levels of false positives per image. We incorporated it as an afterthought, while it would be more effective as a feature used at the beginning of the CAD prompt calculation process. However, we did determine that asymmetry is a powerful technique by itself or incorporated into CAD. This indicates that further research into techniques that can compare images and thus measure asymmetry in mammograms may significantly improve the effectiveness of CAD algorithms.
## 3 Non-Thermal Technologies in Food Processing
### Ozonation  
The use of ozone (O\({}_{3}\)) in the food industry has become widespread as it acts primarily as a disinfect against a variety of microorganisms but also as a means of shelf-life extension of many foods. Meat [134; 135], poultry [136], eggs [137], seafood [138], fruits [139; 140], vegetables [141; 142; 143; 144; 145], juices [146], spices and herbs [147; 148], dairy and dairy products [149], and dry foods have been treated with ozone for different purposes, among them sanitization and disinfection purposes [150; 151]. Ozone as a non-thermal treatment has great antioxidant activity against bacteria, fungi, viruses, protozoa, and vegetative cells [152; 153]. Ozone destroys microorganisms by gradually oxidizing vital cellular components, starting first with the sulfhydryl groups and amino acids, contained in proteins and enzymes, and continuing with the oxidation of polyunsaturated fatty acids [154]. Ozone also has significant antioxidant activity against mycotoxins, which are highly toxic substances formed in a variety of agricultural products [155; 156; 157], at all stages of their distribution and marketing [158], with the processed products showing little or no chemical residues and pollutants [159]. Ozone has also been shown to modify certain functional properties of foods. Increased shelf life, improved texture, as well as reduced viscosity are some of the effects of ozonation on starch modification in various starch sources such as rice, corn, potato, cassava, and wheat sources [151]. Figure 5 presents the main uses of ozone technology in food processing. Figure 5: Use of ozone technology in food processing.
## 4 Expansion of previous work
### Dataset derived metadata - feature metadata
Of particular interest is its specific treatment for feature attributes as well as the general ability to integrate any ISO 19109 application schema with other ISO standard schemas. Here, any feature attribute (GF_AttributeType) can have atomic metadata items associated with it by sub-classing entities beneath the GF_QualityAttributeType specialisation of the GF_MetadataAttributeType entity (Figure 2). Attribute types accordingly defined (specifically, to carry feature metadata information such as quality) obtain their value type definitions and value domains from the ISO 19115 MD_Metadata entity. A six-field element set similar to what can be expected within formal production environments for the purposes of feature-level data tracking was consequently elaborated and mapped using the ISO framework (Table 3). Aliases serve as shorthand field labels, while full element definitions are included within dataset metadata instances by default to permit interpretation. Field domains may be enforced in applications where stringent compliance is required, however these were not currently applied. For new data holdings, the schema may be incorporated at the point of survey through the inclusion of a data dictionary within the surveying equipment, thereby facilitating pre-population prior to registering the dataset with a GIS. Existing datasets may have equivalent entries mapped to the schema if such entries exist; alternatively the schema may be appended to an incumbent attribute table for subsequent completion. Once implemented, fields may be analysed and extracted in accordance to the dataset metadata element for which they may be applied. The _origin_ field is used to populate the GEMINI element _Originator_ in the current prototype. Multiple entries are generalised to indicate the single dominant originator reflecting instances where only one entry is allowed; multiple summary values may also be used where permitted. _Spatial resolution_ is calculated from _precision_ using the lowest common denominating value of feature precision, although this may also be computed on the basis of average value if preferred. The date range _Date_, used to indicate the data capture period, is calculated with functions identifying the minimum recorded _capture_date_ and maximum _edit_date_ of features within the dataset. Dataset provenance, as treated by the _Lineage_ element to store 'information about the events or source data used in the construction of the dataset' [37] is similarly populated. Metadata fields used to track feature history (_i.e._, _capture_process_, _capture_date_, _editor_, _edit_date_) contribute here, used to append details of each process step to the _Lineage_ element. Figure 2: Attributes of ISO 19109 feature types, with an emphasis on the route to metadata-relevant subclasses. Feature metadata instances are catered for by GF_QualityAttributeType’s dependency on the ISO 19115 entity MD_Metadata. (Subset taken from ISO 19109:2005).
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
###### Abstract  
Subjected to continuous wave loading, the responses of pile foundations and seabed develop gradually, severely affecting the serviceability of piled structures. This paper presents the results of a series of flume experiments on pile foundations in fine sandy and silty seabed under regular wave loading. Pile-head displacement and pore water pressure were measured and the effects of pile diameter, cross-section, pile stiffness and wave height were investigated. The experimental results indicate that the pore pressure in fine sandy seabed varied only slightly even under 640 s of wave loading but showed an increase of 15.7-25.9% around a pile. In silty seabed with much lower permeability, pore pressure accumulated quickly due to piles and oscillated impressively at the depth of soil liquefaction. Based on the comparison between the calculated and measured pile-head displacement, we found that the response of smaller-diameter piles in lower-permeability seabed was much more easily magnified by the induced pore pressure. Increasing the pile diameter and attaching fins could lead to a smaller response of piles. Wave height was a major factor in the experiments that affected the development of response.  
pile foundation; wave-induced response; pore pressure; fine sandy seabed; silty seabed; flume experiment +
Footnote †: journal: Applied Sciences