esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effect of Food Processing and Culinary Preparations on PAs, TAs and OAs
### Infusion
Regarding TAs, the transfer rate of TAs has scarcely been studied (Figure 3). In this sense, the EFSA evaluated the transfer rate of TAs from teas and herbal teas into infusion drinks, showing a transfer rate of about 50% [35]. However, great variability was found in the results achieved. Due to this fact, the EFSA recommended a more exhaustive study of the transfer rate of TAs in infusions and specifically, in children's infusions [35]. In this line, recent work of our research group evaluated the transfer rate of TAs in fennel samples finding a transfer rate between 64 and 88% for atropine and 47 and 57% for scopolamine [101]. Similarly, [PERSON] et al. (2019) confirmed the transfer rate of TAs to hot water in tea preparations. The results obtained showed a transfer rate between 20 and 92% from the plant to the tea water in all the assayed analytes [81]. Despite these works, more information is necessary to determine the transfer rate of TAs during the preparation of teas and herbal tea infusions. Likewise, it is important to evaluate other parameters that may influence the transfer rate of these alkaloids, such as the matrix, crushing of the sample, whether the product is fresh or dry, infusion temperature, infusion time, type of sample, etc. All of them should be carefully studied and, the determination of degradation products and their exposure through these types of products. In the case of OAs, the consumption of poppy seed tea can become potentially dangerous, because consumers can add as many poppy seeds into the tea as they wish. Accordingly, some authors have evaluated the occurrence of OAs in some tea infusions prepared with poppy seeds [53; 102; 103; 104], finding very high levels of OAs in some cases. For instance, [PERSON] et al. (2018) stated that alkaloid yields varied among extractions, but regardless of the extraction conditions, lethal amounts of morphine were extracted from the poppy seed coats by homemade methods [53]. Therefore, the study of this type of sample is essential to highlight the dangers of this consumption practice and to warn the authorities of the need to control this type of product that may pose a potential risk to consumers' health. In addition, recent work by our research group evaluated the transfer rates of OAs from poppy seeds to teas during the brewing process. The transfer rates were less than 100% for some analytes, and confirmed that for some parameters, such as the infusion temperature and time, had a significant impact on the transfer rate [104]. Considering the results obtained, it would be interesting to confirm with further studies whether the lack of OA transfer from poppy seeds to the infusion is due to the thermal degradation of the OAs at high temperatures. Likewise, it is also important to consider the investigation of the presence of degradation compounds from OAs and their risk to the health of consumers.
## 4 Artificial Neural Networks
### Simulations
#### 6.2.1 Packet Delivery Ratio  
The packet delivery ratio (PDR) is defined as the ratio between the packets that are received and the number of packets sent. This is one of the most used metrics for protocol comparison. In our simulations the proposed NN model exhibits the best performance, then the OSPF, as depicted in Figure 17.
## 3 Results and Discussion
### Effect of NaSt on the Hydration Products and Pore Structure of AASRSS
#### 3.3.4 Sem  
To further clarify the effect of NaSt on the pore structure of AASRSS, the microstructure was observed using SEM, and the results are shown in Figure 9. It can be clearly observed that, on one hand, with the incorporation of NaSt, the microcracking of AASRSS was reduced significantly; on the other hand, the matrix became denser and a large amount of micro-closed pores formed inside the specimen (darker part). EDS was used to determine the elements on the surface of the AASRSS pore wall. Since light elements such as C and O are sometimes difficult to detect due to their low energy, using EDS for light elements increases the acquisition time and reduces the acceleration voltage. To avoid the interference of C elements in air, we minimized the contact between the sample and air during the sample preparation. The EDS results show that the AASRSS pore wall contains a large amount of C elements. The trace carbonization that occurs during the sample preparation process should not produce so many C elements. Thus, the EDS results indicate that the pore wall is covered with a wax-like layer of a NaSt compound, which has hydrophobic groups [33]. All these together result in a decrease in the water sorptivity of AASRSS with the incorporation of NaSt.
## 1 Introduction
In addition to the extracted user text, user-generated data is another additional direction for retrieving data. These data can reduce the users' uncertainty towards business or E-commerce products, which helps analyze user opinions and polarity sentiments for applying decision-making strategies [16]. Whenever a machine learning (ML) technique is applied to analyze polarity sentiments, there must be a set baseline and accuracy parameters to follow. The first step in analyzing sentiments is to remove stop words, elongation terms, symbols, and irony terms that can affect the accuracy and performance of the analysis process [17]. The training phase performs a feature extraction applied to the ML technique. In contrast, the testing or prediction phase applies the features to the classifier models to determine the term polarity. The contribution of the paper is presented as follows:  
1. Presenting a current perspective of the primary strategies and algorithms for Arabic sentiment analysis with a thorough examination of applicable dialects, binary and multi-classification, and annotation algorithms. 2. Providing a multi-stage methodology for feature generation and selection of Arabic terms using TF-IDF. 3. Proposing a model using the FFF-SA algorithm that adopts a forward filter for the feature selection method for scoring and registering the accuracy of each \(k\)-chunk feature of Arabic terms and the subsequent accumulative features. 4. Measuring and scoring the accuracy for each conducted result, proving the high performance of the NB model with the RF method. The paper sections are organized as follows. Section 2 explains the main sentiment analysis mechanisms that are conducted to predict user opinions. Section 3 highlightsthe comparative analysis of recent annotation algorithms with their applied dialects and performance. Section 4 explores the proposed methodology for cross-validation, feature generation, and feature selection of data. Section 5 provides the proposed FFF-SA algorithm for sorting, selecting, and filtering polarity term features. The experimental results are explained in Section 6, and the conclusion and future works conclude the paper in Section 7.
###### Abstract  
Stabilising the natural colour of wood species exposed to light in indoor conditions is a challenge that could be better addressed based on a deeper understanding of the occurring phenomena and influencing factors. This paper investigates comparatively the light-induced colour changes for three hardwood species, namely, European maple (_Acer pseudoplatanus_ L.), European ash (_Frazimus excelsior_ L.) and European walnut (_Juglans regia_ L.), as well as the influence of finishing with three types of clear, colourless waterborne lacquers: acrylic-polyurethane (F1), acrylic (F2) and polyurethane (F3) on their colour stability. Colour measurements in the CIELab system in conjunction with an artificial accelerated ageing test under the action of UV-VIS radiation, simulating natural light passing through window glass, and two types of test samples were employed to highlight the influence and contributions of the wood substrate and of the coating films to the global colour modifications. Coating films applied on 1 mm clear glass slides were employed as a sort of \"detachable\" finish for this purpose. Direct exposure to UV-VIS light caused visible colour changes for both uncoated and coated wood surfaces, the values of the calculated colour differences (\(\Delta\)E) after a 72 h exposure being dependant on both the wood species and the coating material. Excepting two situations for walnut, statistically significantly higher colour differences were obtained for the uncoated samples: maple (9.36 units), ash (8.39 units), walnut (6.20 units), compared to the coated ones: maple (4.92-5.71 units), ash (2.25-3.94 units), walnut (4.74-7.70 units). The wood substrates underlying the coating films were found to bring the maximum contribution to the overall colour changes in the clear coated surfaces, while the coating films employed in this research demonstrated a fairly good colour stability to UV-VIS light exposure, with maximum colour changes (\(\Delta\)E) up to only 1.30 units. Overall, the wood species and the type of coating were found as influencing factors in interaction with the light-induced colour changes in wood surfaces in indoor conditions.  
wood; colour; clear waterborne coatings; UV-VIS radiation; accelerated artificial test; colour changes +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 3 Simulation Results and Interpretation
### Scenario 2: Effect on the Number of Targets
#### 3.2.3 Simulation with Clutter Model 3
Table 9 shows the methods used and their advantages as well as their limits, to be able to track the difficulties, improvements, and progress made in terms of impact in the research on target detection methods. Our proposed detection method, compared to others (as presented in Table 9), has limitations due to the variation of certain parameters used to simulate the different types of clutter as well as the extreme scenarios that we have defined; other methods also have limitations. In some cases, if the configuration of the proposed method is changed, the separation of targets is limited. Therefore, prior knowledge of the parameters of the search and targets is essential for the efficient performance of our proposed method.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 2 Materials and Methods
### Adsorption Thermodynamics  
Thermodynamic considerations of an adsorption process are necessary to investigate whether the process is spontaneous. Gibb's free energy change (\(\Delta G^{0}\)) is an indication of spontaneity of the chemical reaction. Gibb's free energy of the process can be determined by both standard enthalpy (\(\Delta H^{0}\)) and standard entropy (\(\Delta S^{0}\)) [39; 40; 41; 42]. The free energy of an adsorption process is related to the equilibrium constant by the Van't Hoff equation:  
\[\Delta G^{0}=-RT\ln K_{d} \tag{6}\]  
where \(R\) is the universal gas constant (\(8.314/mol^{-1}\)\(K^{-1}\)) and \(T\) is the temperature (\(K\)). The value of \(\ln\)\(K_{d}\) can be obtained by plotting \(\ln\) (\(q_{e}\)/\(C_{e}\)) vs. \(q_{e}\) for the adsorption of metallic ions on SDS-chitosan and extrapolating \(q_{e}\) to zero [43; 44]. The thermodynamic parameters of the adsorption for the equations were also calculated by using the Langmuir constant (\(K_{\rm L}\)) or Freundlich constants (\(K_{\rm F}\)) instead of \(K_{\rm d}\).  
\[\ln K_{d}=\frac{\Delta S^{0}}{R}-\frac{\Delta H^{0}}{RT} \tag{7}\]  
The slope and intercept of the Van't Hoff plot of ln\(K_{\rm d}\) vs. \(1/T\) were used to determine the values of \(\Delta H^{0}\) and \(\Delta S^{0}\) based on equation (6). The plot of \(\Delta G^{0}\) vs. \(T\) can also give \(\Delta H^{0}\) and \(\Delta S^{0}\) by the following equation [43; 44; 45]:  
\[\Delta G^{0}=\Delta H^{0}-T\Delta S^{0} \tag{8}\]  
Effect of Competitive Anions (Cl\({}^{-}\), \(\mbox{NO}_{2}\)\({}^{-}\), \(\mbox{NO}_{3}\)\({}^{-}\), and \(\mbox{PO}_{4}\)\({}^{3-}\)) on the Sorption of Cr(VI)  
The effect of competitive anions on the sorption of Cr(VI) was studied in the following experiment. In this experiment, the initial concentration of Cr(VI) was taken as 1 mg: L\({}^{-1}\). Each SDS-modified chitosan (1.0 g) bead was placed into a 200 mL conical flask contacting 50 mL of Cr(VI) solution in the presence of chloride (Cl\({}^{-}\)), nitrite acid (\(\mbox{NO}_{2}\)\({}^{-}\)), nitric acid (\(\mbox{NO}_{3}\)\({}^{-}\)) and phosphate acid (\(\mbox{PO}_{4}\)\({}^{3-}\)) ions at different concentrations of 50, 100, and 200 mg\(\cdot\) L\({}^{-1}\). Other experimental conditions (pH 4, contact time 24 h, and sorbent dosage 1.0 g\(\cdot\)L\({}^{-1}\)) and methods were essentially the same as those mentioned above in Section 2.3.
## 4 Discussion  
### Superiority of IMERG Product  
The GPM satellite is equipped with a dual-frequency precipitation radar and integrates active radar observation technology to enhance its ability to detect light rain and solid precipitation. It can provide multi-angle information on cloud precipitation particles, enabling monitoring at a high spatiotemporal resolution and the analysis of precipitation at the global scale. Among various GPM products for the analysis of precipitation, IMERG-F is corrected by ground station observations, and usually the quality of its data is superior to that of other products [33]. Nonetheless, satellite data are somewhat inaccurate, since they estimate ground precipitation via an indirect method [1; 3; 68]. Therefore, numerous studies have attempted to evaluate the spatiotemporal patterns and variability of IMERG data by comparing them with observations derived from meteorological stations [17; 19; 26; 58; 69]. Although tropical ocean regions account for the majority of global precipitation, there are almost no meteorological stations over the ocean, meaning that the satellite inversion can only be validated using data obtained from land-based stations [8; 70]. The contribution of this study is compressively evaluating GPM IMERG products using a wider range of gauge observation data at the hourly scale and over the latest two decades. The new findings emphasized the changes of precipitation at hourly scale and the improvement of detectability, under the comparisons of IMERG product performances over different regions.  
Although many research institutions run precipitation monitoring projects based on data obtained from meteorological stations, few of these are available at the hourly scale. Hourly precipitation data can provide information to elucidate the mechanism underlying the development of extreme precipitation events and their occurrence, whereas such insights may be lost by averaging values over entire days [71; 72; 73]. In this study, hourly precipitation data from the ISD, CMA, and U.S. USCRN datasets were used as a test bed to compressively validate the GPM IMERG product over the globe. Specifically, precipitation amount, frequency, and intensity were evaluated as they are crucial parameters for describing precipitation and play key roles in climate research, water resource management, and disaster prevention. The accuracy and reliability of the IMERG estimates of these parameters across different regions were compared with hourly precipitation observations obtained from ground stations between 2001 and 2020. Due to the estimate obtained from infrared band calibrated by microwaves, IMERG was found to facilitate the detection of light and solid precipitation [17; 26].
## 2 Results and Discussion
### Physicochemical Parameters in Juice  
The selected parameters recorded for the juice obtained from the edible part of the fruit (seed) are reported in Table 4, along with their statistical analysis. The titratable acidity (TA) was significantly different (\(p\leq 0.05\)) among the accessions. The lowest average value was obtained in the Dente di Cavallo cultivar and AP2 accession (TA, 0.37% and 0.50%, respectively), while LI1 showed significantly higher titratable acidity (3.96%) compared to the other samples. The TA and pH of a juice can be correlated with its acidity; in particular, the TA, which measures the total concentration of organic acids (expressed as % citric acid), can contribute to the sensorial profile of pomegranate juice and is often used to classify the pomegranate cultivars. Sweet cultivars have a TA value below 0.9%, while cultivars with values between 1 and 2% are classified as sweet-sour [29]. According to this classification, the majority of studied accessions can be classified as sweet-sour, except AP2 and DC (sweet), and are therefore preferably intended for processing. Regarding pH, the accessions AP2 and BA1 showed the highest pH values, 4.53 and 4.24, respectively. The other accessions had pH values comparable to those of the Dente di Cavallo cultivar (Table 4). The range of pH values determined in this study was similar to those obtained by other authors for both commercial cultivars [29; 30] and local germplasm [31; 32; 33]; pH values have been reported from 2.56 to 4.3 and from 2.8 to 3.8 for cultivars grown in Spain [29] and in Turkey [30], respectively. Regarding the local pomegranate germplasm, [PERSON] et al. [31] have detected values from 3.08 to 4.0 in Sicilian genotypes, as well as by [PERSON] et al. [32] in local Campanian accessions. However, all the accessions showed average values higher than the range reported by the US Food and Drug Administration (pH range 2.93-3.20) [29]. The taste and quality of pomegranate seeds and the juice also depend on the total soluble solids (TSS) parameter, which can indicate the level of sweetness. The TSS values ranged from 12.9 \({}^{\circ}\)Brix for BA1 to 17.6 for DC. Although all the local accessions had lower values compared to the commercial cultivar Dente di Cavallo, the recorded TSS value was higher than 12%, a threshold considered as acceptable for commercial use [34]. The TSS content determined in the accessions of this study was very similar to commercial pomegranate cultivars grown in Italy (15-18 \({}^{\circ}\)Brix) [22] and Turkey (15-17 \({}^{\circ}\)Brix) [30] and/or for local Italian genotypes analyzed by other authors, such as [PERSON] et al. [17] in Apulian genotypes (15-18 \({}^{\circ}\)Brix), [PERSON] et al. [31] in Sicilian cultivars (12.9-16.8 \({}^{\circ}\)Brix), and [PERSON] et al. [25] in five pomegranate accessions collected in Viterbo (Lazio) (12.9-17.8 \({}^{\circ}\)Brix).
is analyzed by several researchers [32; 33; 34; 35]. Production of FGMs using centrifugal casting involves two types of procedures: Ex-situ FGM and in-situ FGM. Ex-situ processing involves the previous synthesis of the metal matrix composite (MMC) by the stirring casting method [36] followed by the casting of the composite slurry in the centrifugal casting unit. During the in-situ processing, the reinforcement is added to the base metal mass directly in the shell of the centrifugal casting system in which the liquid alloy of the metal matrix is already present. The in-situ method was also used in this paper. The samples were analyzed by means of metallographic analysis (MA), X-ray diffraction analysis (XRD), scanning electron microscopy (SEM), dilatometric analysis (DIL) and differential thermal analysis (DTA). Considering the literature background, the present research shows how the coating of SiC particles with a thin layer of metallic Cu essentially influences their incorporation into the molten aluminum alloy metal mass. By now, the most frequently reported method applied by other researchers was stirring casting, while this paper is focused on the use of centrifugal casting in the in-situ configuration and on the emerging advantages in obtaining FGMs. Furthermore, by means of thermal analysis, the authors aimed to demonstrate that the coefficient of thermal expansion of these Al-SiC composites can be controlled by varying the proportion of SiC.
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Machine Learning Models
#### 3.1.6 Random Forest (RF) Classifier  
The random forest (RF) classifier is an ensemble machine learning technique that builds numerous decision trees during training and then pools their predictions to boost accuracy and mitigate overfitting. These decision trees are generated using a method called bagging (Bootstrap Aggregating). A bootstrap sample is generated by randomly drawing samples from the training data and replacing them with new ones for each tree in the forest. Random forest can be represented as the aggregation of the predicted output (\(\hat{g}_{i}\)) from individual decision trees:  
\[\hat{y}_{i}=\frac{1}{N}\sum_{k=1}^{N}f_{k}(\mathbf{x_{i}}) \tag{6}\]  
In Equation (6), \(f_{k}(\mathbf{x_{i}})\) is the prediction of the \(k\)-th decision tree for the \(i\)-th sample and \(N\) represents the number of decision trees in the forest.  
To further increase the randomization and variety of the trees, we evaluated a random subset of features for splitting at each decision tree node. The risk of overfitting is mitigated, and tree decorrelation is improved by combining bootstrapped samples and feature randomization. Each tree in the forest makes its own classification or regression during prediction, and the final output is decided by majority vote (classification) or the average (regression) [49]. Due to its effectiveness in many machine learning applications, random forest is frequently employed as an ensemble technique to improve predicted accuracy, resilience, and generalization.
## Abbreviations  
AT (along-track); DInSAR (differential interferometric synthetic aperture radar); 3D (three-dimensional); MAI (multiple aperture InSAR); USGS (United States Geological Survey); GCMT (Global Centroid Moment Tensor); CENC (China Earthquake Networks Center).
# Robust Backstepping Control of Wing Rock Using Disturbance Observer
## 1 Introduction
In [31], an adaptive neural tracking control was considered for a class of stochastic nonlinear systems with input saturation, and a smooth nonaffine function of the control input signal was designed to approximate the input saturation function. In [32], a modified fault tolerant control law was designed to handle the input saturation problem. Constrained adaptive backstepping control was proposed based on the command filters in [33]. Inspired by [34], the auxiliary system is designed to weaken the influence of the input saturation based on the backstepping technique in this paper. The backstepping design method has been widely employed in the control of nonlinear systems. In recent years, many robust control methods have been introduced to combine with backstepping technique for the uncertain MIMO nonlinear systems [35; 36; 37; 38; 39]. However, the ESO-based robust backstepping technique should be further developed for the wing-rock motion due to the relatively easy realization. Inspired by the above discussions, a robust attitude tracking control law is proposed for the wing-rock motion in the presence of unsteady aerodynamic disturbances, external disturbances, system uncertainties and input saturation. In this paper, considering the characters of different disturbances, efficient processing methods are adopted. The system uncertainty is estimated online by adaptive RBFNN. By taking full advantage of the output of the RBFNN and the known model information of the unsteady aerodynamic disturbance, the ESO is employed to estimate the compounded disturbance, which consists of the unsteady aerodynamic disturbance, external disturbance, and the unknown neural network approximation error. By making full use of the known information of the system, the disturbance estimation performance will be greatly improved. In addition, it is meaningful in the wing rock control at high AOA. The paper is organized as follows. Problem formulation and preliminaries are described in Section 2. Section 3 presents the detailed design process of the disturbance observers. In Section 4, the robust backstepping wing rock control law is developed considering the input saturation. In Section 5, simulation results are given to demonstrate the effectiveness of the proposed robust tracking control scheme, followed by concluding remarks in Section 6.