esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Prediction of Manned Aircraft Trajectory in Terminal Areas
### Model Training and Testing  
Before training, the parameters for the neural network need to be determined. Different parameters for the input layer and output layer of the GRU trajectory prediction model are compared first. After comparison, it is finally determined that the number of training samples in a single batch is five; the timestep of the input network is twenty; the timestep of the input network in the landing phase is twenty-five. In the take-off phase, the input data dimension of each sample is set as [20; 4]. In the landing stage, the input data dimension of each sample is [25; 4]. The output data dimension is [1; 3]. The initial parameters are randomly selected in the [0, 1] interval.  
The other parameters for the GRU trajectory prediction model are also compared to determine an appropriate value. The maximum number of iterations of the network in the take-off stage and the landing stage are both set to be 200. The learning rate is 0.0001. The number of hidden layers in the take-off and landing stage is set to three. The number of neurons in a single hidden layer is 90 for both the take-off and landing stages. The initial parameters are randomly selected in the [0, 1] interval, and Sigmoid is selected as the activation function.  
For the LSTM model, the network adopts three hidden layers with 100 neurons in each layer. The timestep of the input network is 35. The learning rate is 0.0001, and the number of training samples in a single batch is five. The activation function is ReLU. The model takes five main features as input, including latitude, longitude, altitude, horizontal velocity, and vertical velocity, to generate time series data for each variable.  
The computer environment for this study is configured as Win 10 (64-bit) operating system, Intel(R) Core(TM) i5-8265U 1.60 GHz processor, 16 GB memory, and 4 GB video memory. The Python 3.8 programming language, Pycharm-based development environment, Tensorflow, and Keras model framework are utilized for model training and testing.
## 1 Introduction
Lately, Ensemble Particle Swarm Optimizer EPSO [19] has been proposed to combine all features of PSO, CLPSO, HPSO-TVAC, and LIPS variants in only one variant to offer robust performance in various optimization problems with different features. It is worth mentioning that the EPSO variant has an effective performance in nonlinear complicated systems. While the free lunch theorem (NFL) states that no optimizer is perfect to be employed as the best method for any optimization problem at hand [20]. Hence, any algorithm may fail to converge and provide unexpected performance as a result of the impact of some control parameters on the exploration as well as the exploitation phases. Accordingly, the behavior of the algorithm may be changed according to the variations of these parameters. Therefore, several attempts are still performed aiming to achieve a compromise between the diversification and intensification during the search process of the algorithm to provide an appropriate behavior especially in the case of non-linear optimization problem [21; 22; 23; 24; 25]. Lately, merging the dynamic behavior of the chaos maps and the met-heuristic algorithms has affirmed its efficiency in improving the consistency and accuracy of the standard algorithms [26; 27]. In that approach, the uniform or Gaussian distributions are replaced by chaos maps in the original algorithm to use their statistical and dynamical properties in adjusting control parameters of the basic versions of the algorithms. The Chaotic Grasshopper Optimizer [12; 28], Chaotic Flower pollination algorithm (CFPA) in [29], Chaotic Differential Evolutionary algorithm [30], Chaotic Whale Optimization Algorithm (CWOA) [31] and Chaotic Salp Optimizer [26] are some examples for that approach. The proposed chaotic variants of the GOA, FPA, GWO, and SSA algorithms have shown their efficiency in various optimization problems. Therefore, In this manuscript, ten chaos maps are merged with EPSO to propose novel variants named chaotic Ensemble Particle Swarm optimizer (C.EPSO) to boost the EPSO performance while solving the non-linear optimization problem of PMSM modeling. Firstly, to validate the results of C.EPSO variants, they are compared with the results of the original EPSO intensively using different statistical analysis. Furthermore, an excessive comparison is established between the results of the novel variants and that of the previous techniques listed in literature. The comparison clarifies that C.EPSO proves its superiority in accuracy, consistency, higher convergence rate and lower execution time. The final recommendation is C.EPSO with Gauss/mouse map is the proper variant to estimate the parameters of equal and variable order fractional models. The manuscript is arranged as follows: Section 2 shows the fractional models of the PMSM motor. The optimization problem is mathematically modeled in Section 3. Section 4 presents the main equations of basic EPSO algorithm, the chaos maps functions and the proposed chaotic variants of EPSO. Simulation and results are emerged in Section 5. Section 6 summaries the conclusion.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 6 Experimental Evaluation Setup
### Evaluation Metrics
#### 6.2.3 Location Traceability  
Location traceability measures the tracking probability of a vehicle being traced at various locations. Traceability is inversely proportional to the level of location privacy. Let \(T_{v}\) is the location traceability strength of an adversary to find a vehicle location in a vicinity. Here we measure location traceability in terms of the vehicle anonymity set. The higher the location anonymization, the lower will be the location tracking percentage. \(T_{v}\) can be computed as given below [51].  
\[T_{v}=[1-Pr(|ASS|)]. \tag{59}\]  
The probability of vehicle anonymization is denoted by \(Pr\) in Equation (59). When \(T_{v}\) is equal to 1, the adversary or attacker successfully tracks a vehicle. The vehicle location anonymization procedure reduces the traceability of a vehicle in the concerned region.
# Metal-Organic Frameworks as Promising Textile Flame Retardants: Importance and Application Methods
## 2 Textile Finishes  
Textile processing is one of the oldest and most technologically complex industries. It is an extensive process aimed at transforming fibers into yarns and fabrics. As the last stage of the textile process, finishing is considered essential for the development of textile originality, aesthetic appreciation, and profitable recognition [8].  
Generally, finishing can be divided into physical, chemical, or biological [9; 10]. The choice of technique depends mainly on the structure, the type of fiber used (natural or chemical), and the objective to be achieved [11].  
In particular, chemical finishes can be applied at different stages of the textile chain in the production of a substrate; i.e., they can be incorporated into the spinning process (polymer solution of synthetic and human-made fibers), and they can also be applied during the tertiary processing stage, after the dyeing stage, and before the garment is made [8]. In this way, this class of finishes is able to impart new functionalities to textile materials and make them suitable for special applications [12]. Chemical substances are added to the substrates to achieve the desired function [11].  
Functional finishes provide textiles with improved comfort and functional properties through chemical treatment. The study of textile substrates with nanoscale chemical finishes is extensive and is one of the areas to be explored to ensure the functionalization of textiles, both to improve existing methods and to create new techniques [13]. Nanomaterials applied to textiles are an innovative option for producing multifunctional materials [14].  
Antimicrobial, anti-UV clothing, catalytic substrates with air purification function, anti-static capacity, flame retardant, moisturizing, insect repellent, antiperspirant capacity, water or oil repellency, and the release of drugs and fragrances are some of the functionalities achieved through nanotechnology.  
Metal-organic frameworks, which belong to the class of chemical finishes and nanomaterials, are nanoparticles that can be applied to textiles with the aim of superficially modifying the material, allowing the textile to have various functionalities. In addition, their wide applicability has led to the development of research in catalysis [15], electrodes for water splitting [16], gas or liquid separation processes [17], or even drug carriers [18].  
Regardless of the technique used in textile processing, especially in chemical finishing, they are responsible for discharging wastewater containing various excessive chemical products, which leads to the accumulation of innumerable contaminants in the natural environment, causing harmful effects and instability in the ecosystem [19; 20]. Thus, they pose serious threats to human health and the ecosystem due to their toxic, mutagenic, carcinogenic, or teratogenic effects [21; 22; 23; 24].
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 3 Pectin Extraction Methods
### Green methods for Pectin Extraction
#### 3.2.2 Enzymatic Extraction  
Enzymes are used to improve the extraction process by hydrolyzing the matrix of the plant cell wall. These reactions disintegrate the cell wall and increase cell permeability. Enzymatic extraction depends on the concentration of the enzyme, reaction temperature, time, the particle size of the plant material, and type of enzyme [78]. The enzymatic extraction of pectin is carried out by pectinases which are enzymes mainly extracted from fungi. Pectinase is a generic term designating several enzymes species, including esterases (pectin methyl esterases), hydrolases (endo- and exopolygalacturonases), and lyase (pectin lyses). The enzymatic treatment interferes with the glycosidic bonds of the pectin and ensures their breakage. This action decreases the viscosity of the solution, facilitating filtration and centrifugation. This extraction method has the advantage of being less polluting than the previous ones. Additionally, pectinases have specific reactivity to pectin. However, enzymatic production remains expensive and the reaction is difficult to control. Finally, this method can lead to a degradation of the pectin and a loss of its properties [79]. In this method, cellulase, protease, alcalase, hemicellulose, pectin lyase, xylanase, \(\alpha\)-amylase, protease (such as Neutrase from Novozymes), \(\beta\)-glucosidase, endo- and exopolygalacturonase,and pectinesterase are most commonly used [1]. Table 2 summarizes the physico-chemical properties of pectins extracted by green methods, including enzymatic extraction.
# Changes in Meteorological Elements and Its Impacts on Yunnan Plateau Lakes
## 2 Methodology
### Innovation Trend Analysis (ITA)  
In this study, innovative trend analysis (ITA) [24] was used to analyze the trends in climate factors over the past 40 years for the Yunnan Plateau region. The ITA method divides time series into two sub-series of equal length, arranges these series in ascending order and generates a corresponding scatter diagram. The first and second sections of the time series are placed on the X and Y axes, respectively. If the data points are distributed near the line of equality (1:1), the series show no distinct trend, if the points are plotted above the 1:1 line, the series are characterized by an upward trend, and if below the line, the trend is downward, as in Figure 2. The major advantage of the ITA method is that it provides a visual interpretation of time series trends that does not have restrictive assumptions and is not significantly influenced by a few missing data points in the time series.  
The slope \(S\) of the linear trend plotted by ITA can be calculated as follows:  
\[S=\frac{2(\overline{y_{2}}-\overline{y_{1}})}{n} \tag{1}\]  
where \(y_{1}\) and \(y_{2}\) are the average of the two subseries and \(n\) is the total number of data points in the entire time series. The standard deviation of the slope (\(\sigma_{s}\)) is given by the following equation:  
\[\sigma_{s}=\frac{2\sqrt{n}}{n\sqrt{n}}\sigma\sqrt{1-\rho_{y_{1}y_{2}}} \tag{2}\]  
where \(\sigma\) is the standard deviation of the entire time series, and \(\rho_{y_{1}y_{2}}\) is the correlation between the subseries, calculated as follows:  
\[\rho_{y_{1}y_{2}}=\frac{\frac{n}{2}(\Sigma y_{1}y_{2}-\Sigma y_{1}\Sigma y_{2 })}{\sqrt{\big{[}\frac{n}{2}(\Sigma y_{1}{}^{2})-(\Sigma y_{1})^{2}\big{]} \big{[}\frac{n}{2}(\Sigma y_{2}{}^{2})-(\Sigma y_{2})^{2}\big{]}}} \tag{3}\]  
The significance of the trend was also assessed using the ITA technique. For a given significance level, the critical slope (\(S_{crit}\)) is determined and if \(|S|>|S_{crit}|\), then the trend is  
Figure 2: Example of an ITA application over a time series.  
considered significant. For significance levels of 5% versus 1%, the critical slope is given by the following equation:  
\[\begin{split} S_{crit}&=1.96\times\sigma_{s}\\ S_{crit}&=2.58\times\sigma_{s}\end{split} \tag{4}\]
## 3 Methodology
### Grid-Based Xianjiang Hydrological Model  
The GXAJ model is a spatially distributed conceptual hydrological model [66] developed from the lumped Xinanjiang (XAJ) model [67]. On a grid cell basis, the GXAJ model uses the saturation excess runoff scheme of the lumped XAJ model for runoff calculation [67]. At the permeable part of a grid cell, runoff generation occurs when soil tension water storage reaches its capacity value. In the impervious region of a grid cell, direct runoff is produced when precipitation exceeds open-water evaporation. The evapotranspiration (ET) parameterization scheme of GXAJ is presented as a three-soil-layer model. In the upper soil layer, actual ET occurs at the potential ET rate (PE). After the exhaustion of soil tension water in the upper layer, ET proceeds to the lower layer at a decreased rate that is proportional to the tension water storage in that layer. When the total ET in the upper and lower layers is less than a pre-set threshold, denoted as a fraction of PE, ET further proceeds to the deep layer to maintain this pre-set minimum value. GXAJ employs a gravitational water reservoir with bottom and side outlets to divide the computed total runoff into surface, interflow, and groundwater runoffs and adopts three linear reservoirs to simulate the hillslope concentration processes of the three runoff components in each grid cell. The Muskingum routing method is used to represent the routing effect of the river channel system that connects each grid cell. The GXAJ model uses gridded precipitation and PE as its forcing inputs. The daily minimum and maximum air temperature data are used to calculate the daily PE through the air-temperature-based Hargreaves method [68], and a sinusoidal function is adopted to disaggregate the daily PE into PE values at sub-daily temporal scales. The GXAJ model has a total of 15 parameters, including 8 parameters for ET and runoff calculations and 7 for hillslope runoff concentration and streamflow routing. Several model parameters are very sensitive and need to be calibrated. For instance, the coefficient of PE (K) predominately controls the simulated total runoff. The product of K and PE calculated using the air-temperature-based Hargreaves method [68] represents the corrected PE input for the hydrological model. A large K value gives a high PE value, tends to generate high actual ET, and hence produces a low total runoff and vice versa. The areal mean free water storage capacity (SM) represents the depth of the gravitational water reservoir and largely regulates the magnitude of high flow. A low SM usually partitions a large proportion of overland flow from the computed total runoff and generates high flood peak flows. The recession constants of surface, interflow and groundwater runoffs (CS, CI, and CG) regulate the recession rate of each runoff component. Low values of CS, CI, and CG may accelerate runoff recession and augment high flows to a certain extent. For the detailed description of the GXAJ model parameters and their suggested physical values, please refer to [PERSON] [67].
## 3 Technology Overview
### Semantic Web Services (SWS)
#### Sawsdl  
SAWSDL is a specification for semantically annotating relevant elements in the WSDL file [9]. With WSDL, one is concerned with service signature, service location and the protocol to use when invoking the Web service. SAWSDL, on the other hand, is concerned with mapping elements from WSDL to a higher conceptual level _i.e._, the ontology level. To achieve this SAWSDL defines the following extension attributes [9]:  
* _modelReference_-associates, via a URI, a WSDL component with a semantic concept. This attribute is typically used on element declarations, type definitions, interfaces (or port types) and operations.
* _liftingSchemaMapping_-added to element declarations and type definitions for specifying the mapping from XML to the semantic layer.
* _loweringSchemaMapping_-added to element declarations and type definitions for specifying the mapping from the semantic layer to XML.  
The _liftingSchemaMapping_ and _loweringSchemaMapping_ attributes support data mediation between Web services. Take for example, two Web services that are trying to communicate where the output from the first does not match the input of the second. The output from the first Web service can be lifted to the ontology layer by its _liftingSchemaMapping_. The second Web service, which is pointing at the _same_ ontology concept, provides a _loweringSchemaMapping_ that lowers from the ontology to the input format it requires [11].
## 2 Materials and Methods
### Fairness Adjustments  
We build our fairness adjustment method upon previous post-processing algorithmic fairness work. [PERSON] et al. [18] propose a method that helps to adjust the model's outputs to ensure fairness when there are only two possible outcomes. [PERSON] et al. [19] suggest a way to adapt this method for situations with more than two outcomes, such as the breast cancer stage classification task that we study here. To mitigate the issue of sparse samples for some groups, as is the case with our dataset, we introduce a minor adjustment, an epsilon term, to the TPR and FPR calculations to avoid division errors. By analyzing predicted and true labels alongside sensitive attributes such as race, we engineer 'adjusted' predictions that meet predefined fairness criteria. The resulting predictors aim to balance false positive and true positive rates (for equalized odds) or synchronize true positive rates (for equal opportunity) to ensure fairness across different demographics.  
We leverage ROC curves to discern optimal fairness thresholds. Aligning ROC curves across groups leads to predictors that fulfill equalized odds, whereas mismatches may necessitate varying thresholds or probabilistic adjustments to achieve fair treatment. We identify optimal predictors by analyzing the intersections of group-specific convex hulls formed from these ROC curves. We manipulate conditional probabilities within the protected attribute conditional probability matrices through linear programming, optimizing against a fairness-oriented loss function. This process also incorporates an element of flexibility, allowing the loss function to penalize inaccuracies differently based on protected group membership.  
Our fair predictors ensure a balanced representation of demographic groups by equalizing various fairness metrics. We explore two different multi-class fairness criteria, although the method could generalize to other fairness metrics as well.  
We aim to minimize the same expected loss function for multiple classification that was used by [PERSON] et al. [19]:  
\[E[l(\hat{y}^{adj},y)]=\sum_{\alpha\in A}\sum_{i=1}^{|C|}\sum_{j\
eq i}W_{ij}^{ \alpha}Pr(A=\alpha,Y=j)l(i,j,\alpha) \tag{5}\]where \(W^{a}_{ij}=Pr(Y_{adj}=i|\hat{Y}=j,A=\alpha)\) are the protected attribute conditional confusion matrices.  
To preserve fairness at the individual prediction level, we adopt a stochastic approach. Instead of simply selecting the most probable class, we construct predictions by sampling from the adjusted probabilities. Due to insufficient sample sizes within each demographic group, we encountered instances of zero values for FPs, TPs, FNs, and TNs. To implement our method, we used existing software for calculating fairness metrics, which was originally developed based on binary classification [13; 20]. We add an epsilon term (0.001) to the denominator of each of the four measurements (FPs, TPs, FNs, and TNs) to prevent division errors when calculating the confusion matrix and the fairness metrics (equalized odds and equal opportunity).
## 5 Experimental Signal Analysis
### Time Domain--Experimental Signal Analysis  
In addition to the frequency domain, the time domain signal also verifies the validity of the proposed fault diagnosis vibration model. It is used to calculate the RMS and peak values for the time domain signals for the nine operating conditions. The nine operating conditions' ranges are 1200 rpm from 32 N-m to 80 N-m, 2100 rpm from 32 N-m to 80 N-m and 3000 rpm from 32 N-m to 80 N-m. Figure 29a,b show that the RMS and peak for the sun and ring fault vibration signals are greater than the RMS and peak for the health signals. For the planet gear fault, the RMS and peak values at 1200 rpm are not significantly greater than the health signal, and the peak value at 3000 rpm for loads of 32 N-m and 56 N-m are not greater than the health signal, possibly because there are three planet gears in the gearbox that only has artificially seeded wear, so the impact on the time domain signal is small. In all the experiments, an increase in the input speed causes a stronger vibration, rather than an increase in the output load.  
Using the time domain quantitative metrics, time domain waveforms and the analysis results in the frequency domain sidebands, gear failures can be detected.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 5 Classifiers Used for Identification
### Decision Trees with a Decision-Making Module  
The classifier using decision trees had a structure analogous to that of the neural classifier; it consisted of two modules. The first is a set of decision trees, each adapted to recognize a single device. The second is the deciding module, which provides the final answer according to the following rules:  
* If only one tree has identified a device, its response is that of the following classifier: \[\exists i:[Y_{i}=1]\wedge\forall j\
eq i:\left[Y_{j}=0\right]\ \Rightarrow\ Decision=Y_{i};\] (5a)
* If no tree has given a positive answer, the classifier answers 'do not know', as follows: \[\forall i:[Y_{i}=0]\ \Rightarrow\ Decision=0;\] (5b)
* If several trees have given a positive answer, the classifier also answers 'do not know', as follows: \[\exists i:[Y_{i}=1]\wedge\exists j\
eq i:\left[Y_{j}=1\right]\Rightarrow Decision=0.\] (5c)  
The CART (classification and regression trees) algorithm, which uses a binary division of the input elements for its operation, was used to construct decision trees. At each node of the tree, the dataset is divided into two subsets, and the division of the data is implemented via the Gini criterion, which divides the input set into possibly homogeneous result sets. The remaining tree parameters were chosen experimentally.