esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Regression Models  
The regression models used in this work include linear regression, random forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), extra trees, Adaboost, ElasticNet, ridge regression, and lasso regression. Each model offers unique advantages and is suited for different types of data and modeling scenarios.  
Linear regression, a conventional method in statistical modeling, assumes a linear relationship between the GHI and the predicted values, making it both interpretable and simple to implement [41]. In contrast, ensemble methods like random forests [19] and extra trees combine predictions from multiple decision trees, helping to mitigate overfitting and capture complex interactions within the GHI data through randomized tree construction processes [19; 42; 43].  
XGBoost, an optimized gradient-boosting algorithm, sequentially builds decision trees to improve predictive accuracy by focusing on previously underperforming predictions, refining the model with each iteration [13]. SVM, on the other hand, constructs hyperplanes in a high-dimensional space to maximize the margin between the predicted and actual GHI values, making it suitable for datasets with distinct separations [21].  
KNN, a non-parametric method, predicts GHI values by averaging those of its nearest neighbors, making it an intuitive and effective tool for recognizing local patterns within the data. Adaboost, another ensemble method, sequentially adjusts the weights of weak learners based on their performance, improving overall prediction accuracy by focusing on GHI values that were previously mispredicted [23].  
Ridge regression mitigates the effects of multicollinearity by shrinking coefficients toward zero, particularly in datasets with highly correlated predictors. In contrast, lasso regression promotes sparsity by eliminating less-significant predictors, enhancing model interpretability and reducing complexity [26]. ElasticNet combines the penalties of lasso and ridge regression to balance feature selection and coefficient regularization, which is particularly useful when dealing with multicollinearity in the GHI data [11].  
Each regression model was iteratively optimized during training using techniques such as cross-validation and grid search to identify the optimal hyperparameters, ensuring robust performance. The 2018 dataset served as a test set to evaluate and compare the model's predictive accuracy using metrics like RMSE % and r. This comprehensive approach  
Figure 3: Basic architecture for ENAS.  
allowed for the selection of the most suitable regression model for accurate GHI forecasting in Zacatecas under various sky conditions.
## 1 Introduction
### Determination of the Operating Parameters of a Two-Stage Injector Unit  
The operating parameters of a two-stage injector unit were determined in accordance with the algorithm shown in Figure 3.  
For the multivariate calculations, the measured operation parameters of the 6.6 barA steam bleed as well as the parameters of the 3.0 and 10 barA bleeds determined based on the expansion curve were taken.  
The results of calculations of the two-stage injector unit driven by the steam bleeds of 3.0 and 6.6 barA are shown in Figure 4. In this system, using an ejection ratio for the first stage \(\sigma_{1s}=(0.143-0.5)\), and \(\sigma_{2s}=(0.143-0.667)\) for the second stage, it is possible to obtain a steam pressure of 2.41 barA and a temperature of 223.1 \({}^{\circ}\)C on the outlet from the injector.  
Using the bleed steam with a pressure of 3.0 barA for the first stage and 10 barA for the second stage, with the ejection ratios \(\sigma_{1s}=(0.143-0.5)\) and \(\sigma_{2s}=(0.143-0.667)\), respectively, it is possible to obtain a steam with a pressure of 2.97 barA and a temperature of 259.5 \({}^{\circ}\)C. Figure 5 shows the relation between the steam pressure on the outlet from two-stage injector unit and the injection ratio of individual injectors.  
In Figures 6 and 7, the relations of the possible-to-obtain steam pressures on the outlet from a two-stage injector unit is presented for both injectors driven by 6.6 barA and 6.6 and 10 barA, respectively. For the first variant, using the ejection ratio \(\sigma_{1s},\sigma_{2s}\) from the range of 0.143-0.667, it is possible to obtain steam with a pressure of 2.89 barA and a temperature of 231.6 \({}^{\circ}\)C. When driven by bleed steam pressure of 6.6 and 10 barA, the outlet steam pressure of 3.57 barA and a temperature of 268 \({}^{\circ}\)C can be obtained.  
Figure 4: Relation of the steam pressure on the outlet from the two-stage injector unit driven by steam bleads 3.0 and 6.6 barA in the function of the ejection ratio \(P_{22}=f(\sigma_{1s},\ \sigma_{2s})\).  
Figure 5: Relation of the steam pressure on the outlet from the two-stage injector unit driven by steam bleads 3.0 and 10 barA in the function of ejection ratio \(P_{\rm 12}=f(\sigma_{\rm 1s},\,\sigma_{\rm 2s})\).  
Figure 6: Relation of the steam pressure on the outlet from the two-stage injector unit driven by steam bleads 6.6 barA for both injectors in the function of the ejection ratio \(P_{\rm 2}=f(\sigma_{\rm 1s},\,\sigma_{\rm 2s})\).  
The determined parameters of the outlet steam of the injectors were used as input data for the heat balance calculation of systems with the use of overpressure heat exchangers.
# The Feasibility of Shadowed Image Restoration Using the Synthetic Aperture Focusing Technique
## 3 Results
### Experimental Results
The outputs of echo strength analysis are normalized using the maximum echo strength of the raw data and SAFT-processed events at the specific scan positions, i.e., SP\({}_{\text{OBS}}\). In Figure 6, the normalized echo strengths of observed echoes (raw data; in dark blue) and restored images (in pink) along the axial direction for the B-mode scan and pitch/catch mode scan are displayed. For the B-mode scan (Figure 6a), it can be seen that the observed events are refocused at the positions where the targets are imaged. For the pitch/catch scan (Figure 6b), the performance of the SAFT process in image restoration becomes more obvious. The images of the detected targets are not just refocused but also repositioned. Although the background noise comes with the restoration  
Figure 4: (**a**) Original B-mode scan data. Except for T\({}_{2}\) and T\({}_{3}\), the other targets are all echo imaged. The intersection of the dark blue dashed lines indicates the image of T\({}_{\text{OBS}}\). A phantom echo which is question marked at the lower center position of SP\({}_{\text{OBS}}\) is identified as a multiple of T\({}_{\text{OBS}}\). (**b**) Restored image of (**a**) which is processed by SAFT. All of the target images, except for T\({}_{2}\) and T\({}_{3}\), are well restored. The intersections of the dark blue dashed lines and pink dashed lines show the position of T\({}_{\text{OBS}}\) before and after the SAFT process. process, the image resolution is enhanced. Detailed information of observed (raw) data and SAFT-processed data are tabularized in Tables 1 and 2, respectively. Figure 5: (**a**) Original pitch/catch scan data. Except for T\({}_{1}\), which is positioned too close to T\({}_{\text{OBS}}\) and falls within the anechoic zone, echo signals from the other targets are all detected. (**b**) Restored image of (**a**) which is processed by SAFT. The performance of SAFT in image restoration can be shown by the upward shift of the dark blue dashed line to the pink dashed line. Figure 6: Echo strength analyses of (**a**) B-mode scan and (**b**) pitch/catch scan at SP\({}_{\text{OBS}}\). Through the processed noise with restoration, the resolutions of imaged targets are enhanced.
## 3 Method and Process
### Reservoir Facies Identification Method
#### 3.1.2 Fusion Method Based on PCA Dimensionality Reduction and Noise Reduction  
Principal Components Analysis (PCA) is the most widely used data dimensionality reduction algorithm, which preserves the most important features of high-dimensional data. PCA can eliminate useless information, suppress noise, compress data, and achieve the goal of improving data processing speed by removing noise and unimportant features, making data simplified and easy to use. In the process of predicting the reservoir facies of Fractured-Vuggy dolomite gas reservoirs, multiple complex logging parameters such as GR, AC, DEN, CNL, RT, etc. are faced, which will increase the difficulty of distinguishing geological, geophysical, and other characteristics. Based on PCA, effective information in high-dimensional space can be extracted and replaced by the number of principal components when the cumulative contribution rate of information is greater than 90%, providing fused characterization data for reservoir facies recognition.
## 3 Methodologies
### Assessment Unit and Sample Selection  
This study involved multiple factor categories, with the data presented in a raster format that was regular in shape and convenient for computation. Using slope units as assessment units requires the extraction of statistical metrics for each factor, thereby significantly increasing workload and potentially introducing errors. By selecting appropriately sized raster units, the uncertainty is reduced while meeting accuracy requirements. In this study, raster units with a resolution of 30 m \(\times\) 30 m were selected as assessment and mapping units. Statistical analysis revealed that the study area contained 3,721,512 raster cells.  
The landslide catalog data comprise point data, which may have spatial resolution inconsistencies, posing challenges in accurately representing the areal characteristics of landslides. To address this, a buffering method was implemented to align landslide morphology more closely. Buffers were created around the landslide points and extended outward, based on the landslide area. Buffered areas within slope units containing landslides were retained, whereas those within slope units without landslides were excluded. Non-landslide samples were selected using five methods. The samples were selected from the slope units without landslides to form the \"Slope\" dataset (Figure 5a). An 800 m-radius buffer was established around landslide points, and the samples from outside this buffer formed the \"Buffer\" dataset (Figure 5b). The buffer distance was determined based on the largest recorded landslide in the study area, which was 1600 m in length and 1300 m in width. Additionally, the non-landslide samples were selected from very-low-susceptibility zones identified by the LR, SVM, and GBDT models, resulting in the \"Pre-LR\", \"Pre-SVM\", and \"Pre-GBDT\" datasets, respectively.  
Figure 4: Correlation coefficients between influencing factors.
## 3 Results and Discussion
### Environmental Performance  
The current Spanish technical standard envisages the use of some sustainable technologies, but it does not clearly establish practical recommendations or limits because, unfortunately, there is not an extended use of such technologies to date. Thus, in this case, in order to define the limits, research investigations and industry recommendations were used.  
Tire-pavement noise measures determine the tire-pavement noise emission in dB (A) at a traffic speed of 80 km/h. In this case, the Acoustics Laboratory of the University of Castilla La Mancha already proposed a performance classification based on the reached noise reduction; therefore, the label boundaries were established according to their previous work (see Figure 23a) [25].  
Asphal-concrete mixtures may obtain the lowest label classification, while open graded friction courses should present a better performance in terms of noise reduction due to their higher air void content. It should be kept in mind that this label scale is based on initial values (a few months after construction), and, at the end of road surface lifespan, the noise reduction may be lower.  
In the case of the production temperature of the mixtures, the limits were established according to the technological developments achieved by the industry. In terms of energy consumption per ton of  
Figure 23: Tire-pavement noise emissions (**a**), production temperature (**b**), and percentage of recycled asphalt pavement (RAP) labels (**c**).  
Figure 21: Macrotexture and skid resistance global label.  
Figure 22: “Splash and spray” effect label.  
asphalt, four different technologies are distinguished: hot mix asphalt, warm mix asphalt, half warm asphalt, and cold asphalt, and the temperature limits between them are already well defined by the industry (see Figure 23b).  
Finally, the Spanish technical standard recently included the use of recycled asphalt pavement (RAP) to replace virgin aggregates at intermediate and base layers, but it does not consider their use for wearing courses even though they were successfully tested on various pilot sections [20]. Thus, according to the technical standard recommendations and the results of some investigations, the boundaries were set (see Figure 23c) [35].
# Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds
## 4 Experiments
### Methods for Comparison  
In our experiments, four representative image matting methods are selected for comparison, and we can choose them based on the following principles: (1) the method is related to the proposed BRMFGMatting method or can form a better comparative study, (2) the method significantly impacts the field with many citations, (3) the source code of the method can be searched on the GitHub, and (4) it can also be applied to various image video matting.  
We selected four methods to compare with ours in the comparative experiment. BGMv2 [7] assists in recovering the alpha and foreground by inputting an additional background and works better for high-resolution images. RVM [12] uses a looping architecture to utilize the video's temporal information, significantly improving temporal consistency and matting quality. No auxiliary inputs, such as trimap or precaptured background images, are required. MODNet [13] uses a lightweight matting objective decomposition network (MODNet) without additional inputs to simultaneously optimize a series of subobjectives with explicit constraints, which uses an efficient spatial pyramid pooling (e-ASPP) module to fuse multiscale features for semantic estimation, improving model efficiency and robustness. PP-Matting [15] is a trimap-free architecture that enables high-precision natural image matting. This method uses high-resolution detail branching (HRDB) and semantic context branching (SCB) for semantic segmentation subtasks to achieve the detail matting task well.  
In this section, we report the evaluation results of our proposed model on three data sets: alphamatting.com, the Composition-1k test set, and the PPM-100 data sets. Both quantitative and qualitative results are shown in this section. We evaluate the quantitative results presented based on the sum of absolute differences (\(SAD\)), mean square error (\(MSE\)), gradient error (\(Grad\)) and connectivity error (\(Conn\)) [13] as follows:  
\[SAD=\sum_{i}\lvert a_{i}-a_{i}^{*}\rvert \tag{6}\]  
\[MSE=\frac{1}{n}{\sum_{i}}(a_{i}-a_{i}^{*})^{2} \tag{7}\]  
\[Grad=\sum(\
abla a_{i}-\
abla a_{i}^{*})^{q} \tag{8}\]  
\[Conn=\sum_{i}(\varphi(a_{i},W)-\varphi(a_{i}^{*},W)) \tag{9}\]  
For all these metrics, lower values indicate better performance.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 4 Measurement Scenarios and Data Preparation
### Data Collection Process  
The required data for experimental evaluations presented in Section 7 were collected in three scenarios (two small rooms, a hall, and four corridors) at seven different places in indoor environments (Figure 3). The two rooms were the (6 m \(\times\) 6 m) laboratory environment and approximately the (8 m \(\times\) 6 m) communication room in which different items of furniture were placed. The collected data in narrow corridors were intended for MP conditions, where the direct LOS could  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of Hardware** & **Properties** & **Values** \\ \hline UWB module & Module name & DWM1000 \\  & Data rate & 6.8 Mbps \\  & Center frequency & 3993.6 MHz \\  & Bandwidth & 499.2 MHz \\  & Channel & 2 \\  & Pulse-repetition frequency (PRF) & 16 MHz \\  & Reported precision & 10 cm \\  & manufacturer & Decawave \\ \hline Microcontroller (MCU) & Module type & STM32L476 RG \\  & Development board & NUCLEO-L476 RG \\  & Manufacturer & STMicroelectronics \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of the primary hardware used in the experimental evaluation.  
not be distinguished because of multiple signal reflections from the narrow walls. Figure 2c illustrates a concrete example of this MP condition in terms of the FP and RX powers. In all cases, the data were collected for both static and dynamic conditions. In the dynamic case, the device attached to the PC stayed static, while another device was held by a human during random walks. In the static scenario, the two transceivers were at a vertical position of 90\({}^{\circ}\) pointing to the antenna of the DWM1000 module as an upward position without any rotation. However, the antenna of the device held by a human during the dynamic condition was randomly rotated between 0\({}^{\circ}\) and 180\({}^{\circ}\) in some cases of the data collection process. Moreover, the NLOS conditions by blocking the communication between two transceivers using a human as an obstacle were conducted in all cases depicted in Figure 3. Besides, a thick concrete wall, pieces of concrete block, and a mixture of wood and metal were also applied as parts of the obstacles for NLOS conditions in the two small rooms and their environments.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 1 Introduction
Specifically, we propose two variational recurrent auto-encoder extensions that can process the noisy Kepler time series by using the time information of each observation. We extend the idea of using all the information on the time series by including the standard re-scaling pre-processing task into the learning model as an end-to-end architecture. The motivation to extract the information on the original scale of the light curve, based on Flux standard deviation (_F-std_), emerges from the fact that the relative sizes of exoplanets and noise are correlated with the _F-std_ of the signal. The concept of _quality_ used and proposed in this paper is based on obtaining a compact yet robust representation (i.e., parsimonious), with the capability of smooth time series and with less correlated features (disentangled). In addition, good quality should imply an informative representation of the behavior on the time series, for example by having a better performance on classification tasks (such as time series categories). The need to learn quality representation in astronomy has potential uses in cases such as denoising, large-scale data processing, clustering, manifold learning, characterization, and other unsupervised applications. Our experiments show that the proposed variational models are robust in learning patterns with less noise (denoising effect), having a balanced behavior between reproducing the original time series and smoothing. Indeed, the resulting light curve could be compared to the isolated simulation of a transit object. The proposed quality factor of the representation was also achieved, representing an improvement over the deterministic counterparts proposed thus far. This paper is organized as follows. In Section 2, the main methods for light curve representation are jointly described with our proposed methods and their main differences. Then, the experiment setting and results were introduced in Section 3. A brief discussion is presented with regard to the advantage of the proposed methods in Section 4. Finally, Section 5 summarizes the conclusions of this work.
## 1 Introduction  
The rapidly increasing elderly population has led to a challenge in the management of a healthy aging environment and functional ability [1]. Physical activity is known to be an efficient method for improving the walking, standing, muscle strength, and balance skills of older people, thereby decreasing the risk of falling and enhancing their quality of life [2, 3, 4]. Exergames, a blend of \"exercise\" and \"games,\" are digital or video games created to encourage physical activity by incorporating exercise into the gaming experience. These games use interactive technology, such as motion sensors, cameras, or wearable devices, to involve players in physical movements, promoting exercise as part of the gameplay. Recently, immersive technology-based exergames (IEG), such as virtual reality (VR) and augmented reality (AR) technologies, to enhance older adults' physical health and quality of life have been widely considered as effective interventions. The fact that VR exergames can effectively enhance balance and physical mobility in the elderly has been validated by several studies [5, 6, 7].  
Positive acceptance and attitudes of older people have been reported in many exergames studies [8, 9, 10, 11]. Performance expectancy (PE) and effort expectancy (EE) as the constructs of the unified theory of acceptance and use of technology (UTAUT) have been proven to have an impact on the intention to use [12]. However, little information is available on the factors that would influence older people's PE and EE in IEG. This study focused on the understanding of physical postures (PP), perceived physical safety (PPS), and extended PE and EE constructs of UTAUT of immersive exergames among older people.
# Power Control Method for Energy Efficient Buffer-Aided Relay Systems
## 7 Conclusions  
In this study, a relay system has been considered, in which a ground source node (SN) sends information to a destination node (DN) through an unmanned aerial vehicle relay node (RN) with a buffer. When the transmit power of SN is fixed, we have proposed a two-dimensional surface fitting method to simply obtain the near-optimal transmit power of RN to maximize the energy efficiency (EE) of the RN. To this end, the RN needs its power consumption model and the distance from a DN. From the rigorous numerical results, it has been verified that the proposed power control method can significantly improve the EE of RN compared to a fixed power control method based on spectral efficiency, and achieve near-optimal performance.  
Conceptualization, J.J. and H.L.L.; methodology, J.J.; software, J.Z.; validation, J.Z and X.K.; investigation, J.Z.; writing, original draft preparation, J.J.; review and editing, H.L.L, J.Z. and X.K.; visualization, J.J. and H.L.L; supervision, J.J.; project administration, J.J.; funding acquisition, J.J.  
This research was supported by the National Research Foundation of Korea (NRF) grant (2018R1A4A1023826) funded by the Korean government (MSIT).  
The authors declare no conflicts of interest.