esa-sceva/satcom-chunk-collection huggingface dataset:
# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 7 Short-Term Scheduling
### Comparison of Short-Term Scheduling Methods  
This section presents a brief comparison of the short-term scheduling methods discussed above. The following scenario includes four maintenance interventions and a fleet of 50 units, where only two units can be received per day for any of these maintenance interventions. In addition, four units are allowed to be out of service per day and maintenance is carried out from Monday to Thursday only.  
The two short-term scheduling methods used to produce short-term schedules and both have used the long-term schedule depicted in Scheme 4.  
Schemes 5 and 6 depict a schedule generated with optimisation (using the optimisation-based technique) and a schedule generated without optimisation (using the computer-based technique), respectively. With this simple scenario, the optimisation model showed a lower cost of maintenance, i.e., 32 against 57 without optimisation. To make the computer-based technique without optimisation perform better, more instructions should be hard coded while such complexity is left for the solver in the case of the optimisation-based tech  
Figure 10: Rolling stock management tool.  
nique. As shown in the tables, the two schedules are relatively similar; the main change is that the computer-based method has scheduled maintenance intervention '1' for unit '11119' 11 days before the due date, whilst the optimisation-based technique has moved things around to allow this task to be done much closer to the due date, only 4 days early. The optimisation-based technique also has reduced the earliness of maintenance intervention '1' for unit '11101' from 4 days to 1 day. Overall, these comparatively small differences in the schedules have delivered significant benefits in cost, based on the cost scale used in this study.  
As the optimisation scheduling method outperforms the computer-based one, it was used to evaluate the virtual depot approach when linked to the scheduling process, which is discussed in Section 6. Further details about the comparison between these two scheduling methods as well as the evaluation of the short-term optimisation method can be found in [33].
## 2 Overview and Outline of Machine Learning Analysis
### Supervised Machine Learning  
There are several supervised ML methods [24] used to carry out regression analysis on dimensionally reduced data to perform spectral analysis, such as the \(K\)-nearest neighbor (KNN) algorithm, extra tree regression (ETR), and support vector regression (SVR). In this study, we adopted the KNN algorithm, which requires a lower cost of computation. KNN is a supervised ML method that can be used for both classification and regression problems. Supervised ML basically consists of two parts: the training part, which is used to calculate the optimal parameters from the training data, and the prediction part, which makes predictions using the calculated training parameters. However, KNN does not have the part to calculate the optimum parameters from the training data. Since the training data are learned and predictions are made directly, the computational load is small.  
KNN is a simple algorithm that learns the training data they are when training. The procedure used for solving a regression problem for input data is as follows.  
1. Calculate the distance between the input data and the training data.
2. Chose \(K\) training data from their data points closest to the input data.
3. Obtain the average value of the indications of the training data and use it as the result of the regression problem.  
In KNN, when predicting a regression problem, the distance between the input data and the training data is generally calculated using Euclidean distance. When the two points \(P\) and \(Q\)--defined by variables \(p_{i}\) and \(q_{i}\), respectively--for which we want to find the distance are represented by \(P(p_{1},p_{2},...,p_{n})\) and \(Q(q_{1},q_{2},...,q_{n})\), the multidimensional Euclidean distance \(d(P,Q)\) is given by the following equation:  
\[d(P,Q)=\sqrt{\left(p_{1}-q_{1}\right)^{2}+\left(p_{2}-q_{2}\right)^{2}+\ldots +\left(p_{n}-q_{n}\right)^{2}}. \tag{2}\]  
When KNN handles a large amount of data, the prediction of unknown data takes a long time because KNN performs a neighborhood search on a large amount of training data. In addition, since \(d(P,Q)\) is used to measure the distance between data, there appears to be only a small distance difference between data when the number of data dimensions is large, and it is often impossible to train with a high accuracy. Therefore, KNN is effective when the amount of data (spectra) is small or when the number of dimensions (channel number of time-of-flight spectrum) is small. The number of neighborhood training data points, \(K\), to be acquired when making predictions with KNN is not determined within the inference or prediction, and \(K\) is called hyperparameter. A smaller \(K\) leads to a more complicated decision boundary, therefore overfitting is more likely to occur. Therefore, to improve the generalization performance, it is necessary to adjust an appropriate \(K\) using training data.
# Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
###### Abstract  
This paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI) algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models.  
Keywords:signal processing; parameter estimation; moving data window; gradient search; Newton search +
Footnote †: journal:
## 3 Methodology
### Temperature-Sharpening Methods Based on Statistical Relationships
#### 3.2.2. GWR-based LST Downscaling Algorithm  
GWR is an extension of traditional standard regression techniques, such as ordinary least squares, that permits local (rather than global) parameter estimation [56]. It can be used to investigate nonstationary relationships between the dependent variable and the independent variables using geographically varying regression coefficients. The main objective of the GWR-based downscaling algorithm is to address the spatially heterogeneous relationships between LST and the other land surface parameters. According to the surface characteristics of the HRB and previous studies of this area [19; 23], _NDVI_, albedo and the digital elevation model (DEM) data were selected as auxiliary variables for use in LST downscaling. DEM is recognized as an important factor in characterizing variations in LST [23]. The surface albedo was selected because it influences the surface radiation balance and is mostly negatively related to soil moisture over barren agricultural areas [19]. Because longitude and latitude were the input parameters in the GWR model, these two parameters were not explicitly selected as auxiliary variables in the LST downscaling process. The specific steps followed in the GWR-based LST downscaling algorithm are shown in Figure 3. They are summarized as follows:  
1. Perform spatial aggregation of the _NDVI_, _DEM_ and albedo values through pixel averaging. _NDVI\({}_{30}\)_, _DEM\({}_{30}\)_ and _albedo\({}_{30}\)_ denote these variables at the pixel resolution of HJ-1B, whereas _NDVI\({}_{990}\)_, _DEM\({}_{990}\)_ and _albedo\({}_{990}\)_ represent the aggregated variables at the pixel resolution of MODIS.  
Figure 2: Flow chart describing the DisTrad downscaling method.  
Use GWR to establish a nonstationary relationship between \(T_{990}\) and \(NDVI_{990}\), \(albedo_{990}\), and \(DEM_{990}\), which can be expressed as: \[LST_{i}^{990}=\beta_{0}^{990}(\mu_{i},v_{i})+\beta_{1}^{990}(\mu_{i},v_{i})NDVI_{ i}^{990}+\beta_{2}^{990}(\mu_{i},v_{i})albedo_{i}^{990}+\beta_{3}^{990}(\mu_{i},v_{i}) DEM_{i}^{990}+\epsilon_{i}^{990}\] (8) where \(\beta_{0}^{990}(\mu_{i},v_{i})\), \(\beta_{1}^{990}(\mu_{i},v_{i})\) and \(\beta_{2}^{990}(\mu_{i},v_{i})\) represent the regression coefficients, and \(\epsilon_{i}^{990}\) represents the residual.
3. Use weighted least squares estimation to obtain the regression coefficients and the residuals at a resolution of 990 m. The ordinary kriging interpolation method is used to interpolate the regression coefficients and residuals to a resolution of 30 m. Ordinary kriging has been shown to be reasonably accurate in LST downscaling [(23)].
4. The final downscaled LST values at a resolution of 30 m can be calculated using the auxiliary variables at a resolution of 30 m, i.e., \[LST_{i}^{30}=\beta_{0}^{30}(\mu_{i},v_{i})+\beta_{1}^{30}(\mu_{i},v_{i})NDVI_{ i}^{30}+\beta_{2}^{30}(\mu_{i},v_{i})albedo_{i}^{30}+\beta_{3}^{30}(\mu_{i},v_{i}) DEM_{i}^{30}+\epsilon_{i}^{30}\] (9)  
Figure 3: Flowchart describing the GWR-based LST downscaling algorithm.
## 6 Experimental Results and Simulations
### Experimental Setup  
For both tested datasets, we used a K-fold cross-validation method for training and evaluating the proposed method with k = 5. Compared to the simple hold-out validation, the K-fold cross-validation has the advantage of approximating the generalization error. It allows the users to test all the data by using different folds of training and testing sets. Thus each sample has the chance of being appeared in the training and testing set [21; 94].  
All the optimizers were investigated using the same common settings (swarm size = 20, maximum iterations = 70, \(\alpha\) = 0.99, \(\beta\) = 0.01, Number of runs = 10). The internal parameters of the applied algorithms were selected according to trials and errors on small simulations and recommended settings in the literature [82]. For instance, [PERSON] and [PERSON] [34] recommended the \(a\) parameter to be from 2 to 0, while [PERSON] et al. [83] recommended the value 10 for the parameter \(G_{0}\) in BGSA. The parameter values for the BBA algorithm were obtained from [PERSON] et al. [95]. Table 4 shows the detailed parameters settings that are used in this paper for each algorithm.  
Due to the stochastic nature of meta-heuristic algorithms, each experiment is repeated 10 times, and the results are recorded in terms of average (Avg) and standard deviation (Std). In addition, the non-parametric Wilcoxon statistical test with a 5% degree of significance is also performed to detect the significant difference between the obtained results of different algorithms. The interest in non-parametric statistical analysis has grown recently in the field of computational intelligence [96].
## 3 Acoustic Simulation
### Fabrication of Ultrasonic Cleaner  
Model b from Figure 4 was selected as optimum from the results of the FEM acoustic simulation. The model has been replicated, and an outer body of the ultrasonic cleaner has been manufactured with the help of stainless-steel material (308). The body of the cleaner contains a timer, LED indicators, and hot air vents. A labelled figure of ultrasonic cleaner is shown in Figure 5.  
Internal Structure and Transducers Placement  
Transducer placement was determined using FEM acoustic simulation. From Section 3.2.1, the location of the transducer (PZT disc) was identified. The results show that the transducer gave the best performance when attached to the bottom of the tank. For simulations, two stacked piezoelectric discs were used; the reason behind this configuration is to produce maximum ultrasonic waves which can create more pressure-tension change in the simulation. For experimental design. a single disc that was 2.5 mm thick with a diameter of 50 mm was used. The attached PZT with the tank is shown in Figure 6. The size of the tank used for this research work is 140 mm \(\times\) 140 \(\times\) 152 mm (L \(\times\) W \(\times\) D). The piezo disc details are listed in Table 1. The PZT disc was attached to the container using general epoxy (available as steel epoxy in the market). Epoxy was applied only at the corner of the PZT disc. The FR4 material was placed between the PZT disc and the steel tank.  
Figure 5: Fabricated Ultrasonic Cleaner.  
Figure 6: Ultrasonic Tank with PZT Disk (Ultrasonic Transducers).
## 3 Extended State Observer
^{T}\,\tilde{f}_{2}=f_{2}(\mathbf{X}_{2})-f_{2}(\hat{\mathbf{X}}_{2}), \tilde{f}_{5}=f_{5}(\mathbf{X}_{5})-f_{5}(\hat{\mathbf{X}}_{5}).\]  
Before moving on, it is necessary to introduce the following assumption that is widely used in the proof of ESO's stability. **Assumption 1** ([34; 53]). : The functions \(\mathbf{F},f_{2},\) and \(f_{5}\) are globally Lipschitz in their auguments, i.e., there exist positive numbers \(\gamma_{1},\gamma_{2},\gamma_{5}\) such that  
\[ \begin{split}&\parallel\mathbf{F}(\mathbf{X}_{4})-\mathbf{F}(\hat{\mathbf{X}}_{4}) \parallel\leq\gamma_{1}\parallel\mathbf{\vartheta}_{4}\parallel\leq\gamma_{1} \parallel\mathbf{\vartheta}\parallel\\ &\parallel f_{2}(\mathbf{X}_{2})-f_{2}(\hat{\mathbf{X}}_{2})\parallel\leq\gamma_{2} \parallel\mathbf{\vartheta}_{2}\parallel\leq\gamma_{2}\parallel\mathbf{\vartheta} \parallel\\ &\parallel f_{5}(\mathbf{X}_{5})-f_{5}(\hat{\mathbf{X}}_{5})\parallel\leq\gamma_{5} \parallel\mathbf{\vartheta}_{5}\parallel\leq\gamma_{5}\parallel\mathbf{\vartheta} \parallel\end{split}  \tag{11}\]  
for all \(\mathbf{X}_{i},\hat{\mathbf{X}}_{i}\)\((i=2,4,5)\). **Theorem 1**. : _Consider the ESO's estimation error dynamics in Equation (10), let \(\mathbf{P}\) be the positive definite symmetric matrix satisfying \(\mathbf{A}^{T}\mathbf{P}+\mathbf{P}\mathbf{A}=-\mathbf{I}_{18}\) for the Hurwitz matrix \(\mathbf{A}\) defined, if the observer bandwidths \(\omega_{1}\) and \(\omega_{2}\) are designated such that \(\alpha=\omega_{m}-2\omega_{1}\gamma_{1}\mu_{2}-2\omega_{1}\gamma_{2}\mu_{2}-2 \omega_{2}\gamma_{5}\mu_{5}>0\) with \(\omega_{m}=\min\{\omega_{1},\omega_{2}\}\) and \(\mu_{i}=\parallel\mathbf{P}\delta_{i}\parallel(i=2,3,5,6)\), then the ESO is stable and the estimation error is ultimately bounded and asymptotically approach to the origin._  
Proof. : Choose a Lyapunov candidate function as  
\[V_{1}(\mathbf{\vartheta})=\mathbf{\vartheta}^{T}\mathbf{P}\mathbf{\vartheta} \tag{12}\]  
Thus, we have  
\[\lambda_{min}(\mathbf{P})\parallel\mathbf{\vartheta}\parallel^{2}\leq V_{1}\leq \lambda_{max}(\mathbf{P})\parallel\mathbf{\vartheta}\parallel^{2} \tag{13}\]  
where \(\lambda_{min}(\mathbf{P})\) and \(\lambda_{max}(\mathbf{P})\) are minimum and maximum eigenvalues of \(\mathbf{P}\), respectively. The time derivative of \(V_{1}\) is  
\[
# Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells
## 3 Results and Discussion
### Classification of Variables  
As already mentioned, the detection of the most important variables for the training of the models is crucial. To do this, it was necessary to run the previous process in Python for 40 h to obtain 1800 iterations, of which 250 iterations correspond to those cases where _KNN_ and _SVM_ were calculated at the same time.  
Next, Table 1 shows the best results obtained according to different criteria, such as the success in the classification with _KNN_ and _SVM_ (columns 1 and 2), and proportion of  
Figure 6: Result when applying _SOM_ to the available data [23].  
bad classifications caused between groups 1 and 2 with respect to group 3, using _KNN_ and _SVM_ respectively (columns 4 and 5). Therefore, the value 0.9331 in row 5 and column 5 was interpreted by associating 93.331% to the percentage of bad classifications (applying _SVM_ with the indicated variables) that were not related to group 3, to which only 6.669% corresponded. Therefore, a high proportion represented a smaller error in the classification of elements related to group 3. The third column is the sum of the fourth and fifth columns.  
In addition, the frequency of appearance of each of the 28 variables in all the iterations (1800 in total) was studied based on two criteria:  
* Success with _KNN_ greater than 68.5% (seventy-fifth percentile);
* The proportion of bad classifications not related to group 3 higher than 79.4% (eighty-fifth percentile).  
From the first criteria, it was deduced that good candidate variables were 2, 4, 5, and 7 whereas bad candidate variables were 1, 3, 6, 26, 27, and 28. From the second criteria, it was concluded that good candidate variables were 2, 4, 6, 23, and 28 whereas bad candidate variables were 1, 7, 25, and 27. Comparing the results obtained, it was possible to conclude that there was a relationship between the importance of a variable and its frequency of appearance following the two criteria mentioned.  
The same type of criteria could be considered for _SVM_. However, it was possible that given the low number of iterations (250), the results were not entirely reliable.
# Optical Design and Stray Light Analysis of Underwater Spectral Radiometer
## 2 Theoretical Foundation
As shown in Figure 1, when the instrument is absent, the upward spectral radiance at point \(z_{0}\) is related to the upward spectral radiance at point \(0\) on the water surface as follows:  
\[L_{u}^{true}(z_{0})=L_{u}^{true}(0)exp(-K_{u}z_{0}) \tag{2}\]  
where \(K_{u}\) is the attenuation coefficient of the water body, which mainly depends on the absorption and scattering of the water body. Since the scattering coefficient of the water body is much smaller than the absorption coefficient of the water body, only the influence of the absorption coefficient \(a\) on the attenuation of the water body is considered. When the instrument exists, there is no ascending spectral radiance at the water surface due to the occlusion of the instrument, and what the instrument observes is the value of the ascending radiance underwater \(z_{0}\) propagating to the water surface \(0\):  
\[ \begin{split} L_{u}^{measure}(0)&=L_{u}^{true}(0) exp(-K_{u}z_{0})exp(-az_{0})\\ &\approx L_{u}^{true}(0)exp(-2 az_{0})\end{split}  \tag{3}\]  
The relationship between \(z_{0}\) and the underwater refraction Angle \(\theta_{\omega\omega}\) of the zenith Angle of the sun and the instrument radius \(R\) is as follows:  
\[z_{0}=\frac{R}{\tan(\theta_{\omega\omega})} \tag{4}\]  
The measurement error percentage caused by the self-shadowing effect of the instrument is denoted by \(\varepsilon\), which represents the difference between the actual value and the true value divided by the true value. \[\varepsilon=\frac{L_{u}^{true}-L_{u}^{measure}}{L_{u}^{true}} \tag{5}\]  
The expression of self-shading error is obtained by simultaneous Equations (2)-(5):  
\[\varepsilon=1-exp(-\frac{2 aR}{\tan\theta_{\omega\omega}}) \tag{6}\]  
According to Formula (6), the measurement error caused by the self-shadow effect of the instrument is related to the water absorption coefficient \(a\), the outer radius of the sensor \(R\), and the refraction angle \(\theta_{\omega\omega}\) of the solar zenith angle [15]. In order to ensure the final measurement accuracy of the instrument, efforts should be made to minimize the measurement error caused by the instrument's self-shadowing effects. Strictly controlling the overall dimensions of the instrument is crucial for meeting the design requirements of miniaturization and broad spectral coverage.
# Investigations on the Complex Band Diagram of Flexural Wave through the Fluid-Loaded Phononic Plate
## 2 Descriptions and Model Formulations  
The strongly coupled structural-acoustic wave guide is designed as an infinite phononic plate with heavy external fluid in this study. The representative unit cell that has a two-dimensional periodicity in the _xoy_ plane is sketched in Figure 1. The fluid domain is assumed to have a finite height and a radiation boundary condition is imposed on its exterior surface to approximate the exact wave-absorbing phenomenon.  
The finite element method is employed to obtain the dynamic property of the system. The discrete formulations for a structure-fluid interaction problem without incident waves can be derived as [38]:  
\[\left[\begin{array}{cc}\mathbf{M}_{s}&\mathbf{0}\\ \rho_{f}\mathbf{R}^{\mathrm{T}}&\mathbf{M}_{f}\end{array}\right]\left[\begin{array} []{c}\ddot{\mathbf{u}}\\ \ddot{\mathbf{p}}\end{array}\right]+\left[\begin{array}{cc}\mathbf{C}_{s}& \mathbf{0}\\ \mathbf{0}&\mathbf{C}_{f}\end{array}\right]\left[\begin{array}{c}\dot{ \mathbf{u}}\\ \dot{\mathbf{p}}\end{array}\right]+\left[\begin{array}{cc}\mathbf{K}_{s}&- \mathbf{R}\\ \mathbf{0}&\mathbf{K}_{f}\end{array}\right]\left[\begin{array}{c}\mathbf{u} \\ \mathbf{p}\end{array}\right]=\left[\begin{array}{c}\mathbf{F}_{m}\\ \mathbf{0}\end{array}\right] \tag{1}\]  
where, \(\mathbf{K}_{s}\), \(\mathbf{M}_{s}\), and \(\mathbf{C}_{s}\) represent the global stiffness, mass, and damping matrix of the plate; \(\mathbf{K}_{f}\) and \(\mathbf{M}_{f}\) are the global matrices for the fluid domain; \(\rho_{f}\) is the density of the fluid medium; \(\mathbf{C}_{f}\) represents the \"damping\" matrix arising from the radiation boundary; matrix  
Figure 1: Schematic diagram of a two-dimensional periodic unit cell model of the fluid-loaded plate.  
\(\mathbf{R}\) denotes the coupling effects between the plate and the fluid; \(\mathbf{u}\) and \(\mathbf{p}\) denote the vectors of displacement and pressure, respectively. \(\mathbf{F}_{m}\) represents the applied force to the plate.