esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 3 Results
### Maize_1  
Figure 1 displays the results obtained through the evaluation of the entire test set using the metrics MAAPE and NRMSE. Based on the observed prediction performance, it is evident that model C (conventional) outperformed algorithm A (augmented) by 55.11% in terms of MAAPE and 51.11% in terms of NRMSE. However, when restricting the analysis to the top 20% of the test dataset (MAAPE_20 and NRMS_20), it was observed that algorithm A (augmented) exhibited a greater performance compared to model C, showing a reduction of 45.16% in MAAPE_20 compared with MAAPE and 43.21% in NRMSE_20 versus NRMS. For more detailed information, refer to the results presented in Table 2.  
Figure 1: Prediction accuracy performance results for the Maize_1 dataset using conventional (C) and augmented (A) methods, in terms of mean arctangent absolute percentage error (MAAPE), mean arctangent absolute percentage error for the best 20% cultivars (MAAPE_20), normalizing the mean square error (NRMSE), and normalizing the mean square error for the best 20% cultivars (NRMSE_20).
# The Additive Manufacturing of Glass: A Critical Review
### General AM Overview and Examples
#### 1.3.2 Relevance to Architecture  
The emergence of AM in general has already received and made considerable attention and impact in the domain of architecture and construction. There are numerous reports regarding the AM of cementitious materials and concrete [18], and even a 3D-printed bridge in Amsterdam fabricated from stainless steel [19]. The application of AM in architecture and construction has predominately focused on the following: (i) demonstrating the technology, (ii) showcasing flexibility in remote construction, and (iii) demonstrating the speed of automated construction. However, the emergence of large-scale glass AM can open new architectural possibilities and catalyse innovation areas that may include sustainable facade design. For example, the potential of using recycled glass in the construction of buildings can be explored through additive manufacturing. Despite the development of large-scale AM processes for construction with the aforementioned materials [20,21], the development of large-scale 3D-printed glass has not been widely explored to date. The potential to apply recycled glass at a large scale to building facades therefore provides possibilities for the circular economy and sustainable building design.  
Figure 4: Example of a complex glass structure prepared by extrusion-based additive manufacturing. (**a**) A 3D representation of the G-code file and (**b**) the design realised in glass by material extrusion. The glass component reveals the ability to vary colour, and also includes unique, asymmetrical surface detail (design by [PERSON] (New York, NY, USA)).  
Figure 5: Example of glass structures prepared by extrusion-based additive manufacturing, called ‘brain coral’ (designed by [PERSON] and [PERSON] (Seattle, WA, USA)). The images indicate internal structures that are asymmetrical, and component complexity that would be tortuous to reproduce using conventional glass production methods.  
Architectural tectonics (the integration of skin, structure, jointing, and material) has evolved in response to the available processes of construction. With an innovative new process for fabricating glass, there is significant potential to explore new architectural and structural design possibilities, with the early stages of this exploration shown in Figure 6. This will reduce the number of parts and trades, improve building performance, and generate new design potential. Three-dimensional printing has the capacity to create complex forms, which enable efficiencies of material through structural and design optimisation and levels of detail which are not feasible in standardised construction.
# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
## 3 Proposed Method  
### Data Preparations  
We first introduce a method that generates a 360-degree, multi-view RGB image-depth map pair data set using the Z-depth rendering function provided by 3D graphic software, Autodesk Maya 2018 [25]. Second, we present a neural network architecture that estimates depth maps. Finally, the experimental results are discussed. In addition, we also show the results of synthesizing CGHs, numerical reconstruction, and optical reconstruction using RGB image-depth map pairs in the next section. In this study, a data set of multi-view RGB image-depth map pairs was generated using Z-depth rendering provided by Maya software. To extract the RGB image-depth map pairs in Maya software, we devised two identical 3D objects located near the origin and a virtual camera with a light source that shoots these two solid objects, so that the virtual camera could acquire depth difference information between two solid figures during camera rotation around the origin. Depth measurements along the Z-direction from the camera were made using a luminance depth preset supplied by Maya software.  
The virtual camera acquires 1024 pairs (for each shape of both RGB images and depth maps) while rotating 360-degrees around the axis of rotation. Shapes of 3D objects that we used for the study were a torus, cube, cone, sphere, dodecahedron, and icosahedron. We used a total of 9832 images for the experiments in this study. Among them, 4916 are RGB images and the remaining 4916 are depth map images. Both RGB images and depth map images are classified into six shapes (torus, cube, cone, sphere, dodecahedron, and icosahedron). The first four shapes (torus, cube, cone, sphere) are contained in data set 1 and were used in the first experiment where the depth map estimations were used during the training phase. Of the total data, 60% were used for training and 40% were used for the tests where each instance consists 1024 views. Four objects in data set 1 were learned together without being separated.  
The remaining shapes (dodecahedron and icosahedron) were used in the second experiment where the estimations for their objects were only used for testing purposes. We conducted this experiment to demonstrate our proposed method's generalizability in unseen shapes. Table 1 shows the camera settings used to create 3D objects, and Table 2 shows the data specification for the entire experiment.  
\begin{table}
\begin{tabular}{c c} Distance from virtual camera to 255 depth & 11 cm \\ Margin from depth boundary to object & 2 cm \\ Distance from virtual camera to 0 depth & 28.7 cm \\ Distance between two objects (Center to center) & 8.3 cm \\ Distance from 0 depth to 255 depth & 14.2 cm \\ Radius of camera rotation path (R) & 20 cm \\ \end{tabular}
\end{table}
Table 1: Camera settings for 360-degree 3D content.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 1 Introduction
Due to the high model complexity of LSTM, the hyperparameters need to be optimized to ensure their effectiveness in different datasets and scenarios. In this regard, [PERSON] et al. [20] proposed an LSTM hyperparameter optimization method based on particle swarm optimization (PSO). By automatically adjusting the key parameters of LSTM, it not only improves the prediction accuracy of the model but also reduces the need for manual intervention. [PERSON] et al. [21] proposed a PSO-based BP neural network regression prediction method and developed a PSO-BP joint prediction model using wind farm SCADA data to evaluate the prediction error by monitoring whether the residuals exceeded the threshold. In addition, [PERSON] et al. [22] improved the wavelet core limit-learning machine (WKELM) based on the enhanced ant lion optimization algorithm (CALO) and enhanced the global optimization capability by introducing the Cauchy mutation operator. [PERSON] et al. [23] used machine-learning technology to build a state-monitoring system based on SCADA data and successfully predicted the failure of the turbine gearbox by using the bagged regression method, giving a warning 68 days in advance. In the recurrent neural network (RNN) model processing temporal data, a GRU as an improved RNN has less of a gating mechanism and a higher computational efficiency than LSTM. GRU is able to effectively handle the dependence of long time series, but in practice, the choice of its hyperparameters has an important impact on the prediction accuracy of the model. In order to ensure that the model achieves the best prediction effect, it is crucial to optimize the reasonable hyperparameters. Therefore, current research is focused on the improvement of fault prediction accuracy by optimizing the GRU hyperparameters  
Based on this, this paper utilizes historical normal and fault data collected from the wind turbine SCADA system, combined with the GRU network model, to implement fault early warning for wind turbines. To avoid the issue of poor fitting results caused by manual parameter selection, the ant lion optimizer (ALO) algorithm is used to optimize the GRU model parameters. Compared to similar optimization algorithms, such as particle swarm optimization (PSO), the ALO demonstrates higher computational efficiency in solving complex problems involving global optimization, multimodality, and high dimensionality. To overcome the limitations of the optimization algorithm itself, the paper not only improves the ALO algorithm but also employs the PCC method for selecting input features, further enhancing the model's computational efficiency and accuracy.
## 3 Results and Discussion
### Photocatalytic Efficiency  
#### 3.5.1 Comparison of Catalytic Activity  
In general, dye molecules are gradually adsorbed on the surface of a catalyst, before the photocatalytic reactions occur, and ultimately reach saturation. According to Equation (1), the percentages of MB absorbed on the surfaces of Sample A and Sample B during its adsorption-desorption process (30 min), were 2.3% and 4.2%, respectively. This clearly shows that a large number of oxygen vacancies on the surface of ZnO NPs contribute to the adsorption of dye molecules onto the photocatalytic surface. Figure 7a shows the decomposition of MB (4 mg/L), using different ZnO nano-catalysts exposed to UV light. Remarkably, MB is unstable under UV irradiation, and over 13% of the dye was photo-decomposed, even in the absence of a catalyst.  
Figure 5: EPR spectrum of different ZnO NPs at room temperature (_f_ = 9.774 GHz).  
Therefore, the decomposition of MB is related to photolysis of itself and photocatalytic oxidation of the ZnO catalyst, in which absorption of the dye molecules and photocatalysis both occur on the surface of the catalyst. Without considering adsorption, photodegradation was complete (over 99% of MB was degraded) after 50 min for Sample B, whereas Sample A, under the same testing conditions, took nearly 80 min. Photodegradation of various dyes fits a pseudo first-order kinetic model [44]:  
\[\text{Ln }(C_{0}/C_{t})=k_{app}\ t \tag{2}\]  
where \(C_{0}\) is the concentration of dyes before illumination and \(C_{t}\) is the concentration of dyes at time, \(t\), and \(k_{app}\) is the apparent rate constant.  
Figure 7b shows that the photodegradation of MB, in both Sample A and Sample B, conforms to the pseudo first-order kinetics approximation. The rate constants are calculated to be 0.091 \(\pm\) 0.005 min\({}^{-1}\) (\(R\) = 0.989) and 0.110 \(\pm\) 0.005 min\({}^{-1}\) (\(R\) = 0.996), respectively. The above results indicate that photocatalytic efficiency in photodecomposing MB is higher for vacuum-treated ZnO NPs than for pristine ZnO NPs. The improvement in photocatalytic efficiency is due to the decrease in particle size, caused by the reduction in particle aggregation and the increase in surface oxygen vacancies.  
Figure 6: Room temperature photoluminescence (PL) spectrum of different ZnO NPs, at 325 nm excitation.  
Figure 7: (**a**) Extent of decomposition of MB, over time, in the presence of different ZnO NPs and (**b**) the pseudo first-order kinetics curve.
## 3 Results
### The Standard Deviation Analysis of Each Parameter Type  
As shown in Tables 15 and 16, the standard deviation of RE removal thresholds are 10.38 ms, 4.37 ms and 6.01 ms corresponding to 0.31 s, 0.91 s and 1.51 s reverberation time. The standard deviation of FER removal thresholds are 9.35 ms, 11.14 ms and 13.72 ms, respectively. The standard deviation of ITDG extension thresholds are 12.62 ms, 13.89 ms and 19.39 ms, respectively, and the standard deviation of LR removal thresholds are 27.59 ms, 50.64 ms and 44.10 ms, respectively. Through the above data comparison, RER removal, FER removal and ITDG extension are less discrete compared with LR removal. The results mean that with the exception of LR removal, the distribution of the other three parameters is more concentrated, and for LR removal, some people can distinguish the difference of perceptual reverberation clearly while other people hardly distinguish the difference of perceptual reverberation.  
Figure 12: The error bars of the average thresholds distribution with standard errors of each parameter for different reverberation times. (**a**) The error bar of RER removal. (**b**) The error bar of FER removal. (**c**) The error bar of ITDG extension. (**d**) The error bar of LR removal.
###### Abstract  
This study presents an experimental and computational investigation into the machinability of additively manufactured (AM) fiber-reinforced PETG during external CNC turning. A series of machining trials were conducted under dry conditions, with cutting speed (\(Vc\)), feed (\(f\)), and depth-of-cut (\(ap\)) as the primary input parameters. The corresponding surface roughness (\(Ra\)) and tool-tip temperature (\(T\)) were recorded as key output responses. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to model the process behavior, utilizing a 3-3-3 architecture with triangular membership functions. The resulting models demonstrated high predictive accuracy across training, testing, and validation datasets. Experimental results revealed that elevated feed rates and depth-of-cut significantly increase surface roughness, while combinations of high cutting speed and feed contribute to elevated tool temperatures. Multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) algorithm was employed to minimize both \(Ra\) and \(T\) simultaneously. The Pareto-optimal front indicated that optimal performance could be achieved within the range of 100-200 m/min for \(Vc\), 0.054-0.059 mm/rev for \(f\), and 0.512-0.516 mm for \(ap\). The outcomes of this research provide valuable insights into the machinability of reinforced polymer-based AM components and establish a robust framework for predictive modeling and process optimization.  
3D printing; additive manufacturing; ANFIS; CFRP; machining; PETG; surface roughness; tool-tip temperature Article
## 4 Findings
### Descriptive Statistics and Moran's I of Variables
The analysis, underpinned by a distance-decay spatial weighted function revealed significant positive spatial autocorrelation in all observed years (2005-2021), with [PERSON]'s I values ranging from 0.306 to 0.451 and \(p\)-values consistently below 0.001. These results indicate that provinces with similar income levels tend to be geographically clustered. The variation in [PERSON]'s I values across different years suggests fluctuating degrees of this clustering effect. For instance, the highest value in 2005 (0.451) points to a stronger geographic clustering of provinces with similar income levels, whereas the value in 2011 (0.306), though lower, still indicates a significant but less pronounced clustering pattern. Consequently, due to the observed autocorrelation as indicated by [PERSON]'s I, spatial econometric modelling is deemed more appropriate than conventional econometric methods. Figure 1: Time Series Chart of the Average GPP by Sector (2005–2021). The map of Thailand displaying the average household income by province in 2021 vividly illustrates the income disparity across different regions (Figure 2). The colour gradient represents various income levels, with lighter shades indicating higher income. Bangok metropolitan appears as a significant area of high income, as expected due to its status as the capital and economic hub. Additionally, the Eastern region and certain parts of the Southern region also display elevated income figures. Conversely, the provinces in the Northern and Northeastern regions are broadly marked with darker shades, signifying lower average household incomes. The LISA cluster map reinforces these observations by presenting clusters of high-income provinces (H-H) in darker shades, particularly in the Bangok metropolitan area, some Central region provinces, and the Eastern Economic Corridor. Conversely, clusters in the Northern region, as well as a large area in the Northeastern region, exhibit low-income clusters (L-L). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Moran’s I** & \(p\)**-Value** \\ \hline
2005 & 0.451 & \textless{}0.001 \\
2007 & 0.401 & \textless{}0.001 \\
2009 & 0.355 & \textless{}0.001 \\
2011 & 0.306 & \textless{}0.001 \\
2013 & 0.378 & \textless{}0.001 \\
2015 & 0.311 & \textless{}0.001 \\
2017 & 0.400 & \textless{}0.001 \\
2019 & 0.321 & \textless{}0.001 \\
2021 & 0.376 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Moran’s I Test for Spatial Autocorrelation of Average Household Income. Figure 2: Average Monthly Household Income in Baht by Province and LISA Cluster Map.
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
## 2 Modeling of Three Body Dynamics of Maglev Satellite
### Definition of Coordinate System and Position Vector  
The coordinate systems used in the dynamic modeling process include the inertial coordinate system \(O_{i}X_{i}Y_{i}Z_{i}\), orbital coordinate system \(O_{o}X_{o}Y_{o}Z_{o}\), satellite platform system \(O_{p}X_{o}Y_{p}Z_{p}\), MM system \(O_{r}X_{r}Y_{r}Z_{r}\), and payload subsystem system \(O_{p}X_{p}Y_{p}Z_{p}\). Figure 3 shows the definition and relative relationships of the platform subsystem centroid, payload subsystem centroid, satellite platform, and MM system.  
Figure 1: Structure diagram of maglev satellite.  
Figure 2: A multi-body dynamic modeling scheme for LMFPs.  
The generalized coordinates for modeling are as follows: the projection \(\mathbf{X}_{pf}\) of the center of mass position of the platform subsystem in the inertial frame, the integral value \(\mathbf{\vartheta}_{b}\) of the platform angular velocity vector projection in the platform system, the relative angle of the MM to the platform \(\theta_{r}\), the projection \(\mathbf{X}_{pl}\) of the center of mass position of the payload subsystem in the inertial frame, and the integral value \(\mathbf{\vartheta}_{p}\) of the angular velocity of the payload subsystem.  
Referring to Figure 4, the vector definitions used in the dynamic modeling analysis of the platform subsystem are shown in Table 1.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 3 Performance Prediction Model
### Step 4: Model Validation  
The acceptability of the hypothesized relationships between variables was evaluated based on the residual errors. The model validation process split the dataset randomly into two sets--the first set consisted of 80% of instances that were used in training the hypothesis, and the second set had the remaining 20% of instances which were used for validating the hypothesis. The generalization ability of the trained regression models is measured using the percentage of error margin and the coefficient of determination, i.e., R\({}^{2}\) values. To ensure the stability of the trained model, k-fold cross validation was implemented, where the model was evaluated \(k\) times, with a unique subset for validation. In this work, it was considered to be k = 5. Table 4 provides the five-fold validation evaluation results for both models. The consistency in the evaluation results ensures stability in the models.
## 3 Materials and Methods
### Methodology
#### 3.2.3 Accuracy Assessment and Validation  
The results of the XGBoost classifier were evaluated using the test dataset. For the assessments, the precision, recall and F1-score were utilized as performance metrics. In addition, receiver operating characteristics (ROC) curve, area under the curve (AUC),  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Description** & **Value** \\ \hline subsample & Subsample ratio of the observations for each tree & 0.3 \\ reg\_lambda & L2 regularization term & 3.0 \\ reg\_alpha & L1 regularization term & 0.01 \\ n\_estimators & Number of gradient boosted trees & 100 \\ min\_child\_weight & Minimum weight to create a new node & 5 \\ max\_depth & Maximum depth of a tree & 10 \\ learning\_rate & Shrinkage factor & 0.3 \\ gamma & Regularization term & 3 \\ colsample\_bytree & Subsample ratio of features for each tree & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimized hyperparameters for the XGBoost method in the study.  
confusion matrix and the precision-recall curve were produced for the assessment. Furthermore, a visual inspection by the expert (last author) was carried out in the landslide inventory (the five polygons), which were not used in the modeling. The ROC curve is often used for evaluating classifier's performance visually [53]. In some situations, such as class imbalance problem, the ROC curve can falsely yield to high performance. The precision-recall curve can give additional information related to the model performance and be a powerful assessment tool when it is evaluated together with the ROC curve [54]. The confusion matrix is a summary of classification performance. Precision, recall, accuracy and F1-score are calculated from the confusion matrix. The AUC is a measure of how well the classifier can distinguish between the classes [53]. The equations used for the calculation of the performance metrics are given in Table 4.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods
### Dynamic Time Warping
#### 2.3.3 Implementation of the Object-based DTW for SITS  
The object-based DTW workflow is implemented into eCognition Developer software, version 9.4 (Trimble Geospatial), a commercial software package for OBIA applications. The object-based customized algorithm works on a stack of layers (e.g., vegetation indices), a complete segmentation of the scene into objects and reference samples for each class of interest. An object is classified into the class to which it had the minimum DTW dissimilarity value. Hence, the time complexity of the algorithm is \(O(S\times T)\), where \(S\) and \(T\) are the sizes of the sequences compared.
## 2 Materials and Methods  
### Materials  
All basic materials were supplied commercially and were utilized as received without any extra purification. Conductive Soda Lime glass fluorine-doped tin oxide (FTO, 12-15 \(\Omega\)/sq) was sourced from MSE pro\({}^{\text{TM}}\) supplies. Titanium dioxide (TiO\({}_{2}\)) paste (30 NR-D), formami-dinium iodide (FAI), and methylammonium bromide (MABr) were procured from Greatcell solar. Lead (II) iodide (PbI\({}_{2}\)) 99.9%, and lead (II) bromide (PbBr\({}_{2}\)) 99.9% were purchased from Luminescence technology corp. (Lumtec). Bis(trifluoromethane)sulfonimide lithium salt (Li-TFSI 99.9%), acetonitrile anhydrous, (99.8%), spiro-MeOTAD 99% (HPLC), chlorobenzene (95%), tris(2-(1H-pyrazol-1-yl)-4-tert-butylpyridine) cobalt(III) tri[bis(trifluoromethane)sulfonimide] FK209 Co(III) TFSI salt and 4-tert-Butylpyridine (TBP 98%) were bought from Sigma-Aldrich. Cesium iodide (CsI 99.998%) was purchased from Alfa Aesar. N,N-dimethylformamide (DMF, 99.5%) and dimethyl sulfoxide (DMSO, 99.9%) were acquired from Fisher Scientific, Hampton, NH, USA.