esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methods
### Flash-Flood Potential Assessment
#### 3.3.5 FFPI Differences  
The next stage of the study was the calculation of FFPI differences between 2019 and 1989. In order to correlate the variation of FFPI between 1989 and 2019 with land-use/land-cover change for the same period, the mean values of FFPI for both years were calculated at the same grid-cell level of 1 km\({}^{2}\), which was also used for TRD\({}_{\text{SDLUI}}\). This procedure was performed by using the Zonal Statistics form of the Spatial Analyst extension of ArcGIS for Desktop 10.5. Subsequently, FFPI values calculated for 1989 and 2019 were integrated into a relative evolution, according to Equation (5):  
\[\text{RD}_{\text{FFPI}(1989-2019)}=\frac{\left|\text{FFPI}_{2}-\text{FFPI}_{1 }\right|}{\text{FFPI}_{1}}\times 100\ (\%) \tag{5}\]  
where RD\({}_{\text{FFPI}}\)--relative difference for the flash flood potential index  
FFPI\({}_{1}\)--flash flood potential index at the starting date (1989)  
FFPI\({}_{2}\)--flash flood potential index at the end date (2019)
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 2 Methodology
### Regression Tree  
RT is a type of decision tree specifically designed for regression tasks, where the goal is to predict a continuous output variable [31]. It can model non-linear relationships and interactions between features without requiring data normalization or assumptions about linearity. RT operates by recursively partitioning the feature space into regions and fitting a simple model (such as a constant value) within each region. Figure 1 shows an RT. It consists of nodes, branches, and leaves. Each internal node represents a decision based on a feature, each branch represents the outcome of that decision, and each leaf (terminal) node represents a predicted value. The primary goal at each internal node is to choose a feature and a threshold that best split the data to minimize the variance of the target variable in the resulting subsets. The best split results in the greatest reduction in impurity, often measured by mean squared error (MSE).  
The model begins with the entire dataset at the root node, followed by finding the best split. It is achieved by considering all possible thresholds for each feature and calculating  
Figure 1: Illustration of a regression tree.  
the variance reduction for each possible split. The outcome is the feature and threshold that lead to the maximum variance reduction retained. Subsequently, the data are divided into subsets based on the chosen feature and threshold. The splitting process is repeated for each subset, creating new internal nodes and branches until a stopping criterion is met. Lastly, once the stopping criterion is reached, a constant value (usually the mean of the target values in that subset) is assigned to each leaf node.
# Estimating Interference with a Two-Dimensional Viterbi Algorithm for Bit-Patterned Media Recording
## 5 Conclusions  
We proposed an interference estimator that uses 2D VA to improve detection performance. In our proposed model, we grouped the sums of products between the signal and the interference into the symbols to apply the 2D VA. In the 2D VA, because each symbol has six levels, we used a trellis with 36 states and 6 input branches. The detected symbols were chosen to estimate the ITI or ISI (depending on the directions and the parameters of the channel). The simulation results show that our proposed model can improve the performance of previous studies. Especially with the optimal structure, the proposed model can achieve gains of 0.5 and 2.5 dB at a BER of \(10^{-4}\) compared to the models in [18, 24], respectively. In addition, the simulation shows that the proposed model can achieve a better BER performance of serial detection with a TMR effect lower than 20% and a position fluctuation less than 18%.  
In the current research, we exploited the signal from a given direction (horizontal or vertical directions) to estimate interference (ITI or ISI). Thus, in the near future, we will develop a model to exploit and combine the information from both directions (horizontal and vertical).  
Conceptualization, T.A.N. and J.L.; methodology, T.A.N. and J.L.; software, T.A.N.; validation, T.A.N. and J.L.; formal analysis, T.A.N.; investigation, T.A.N. and J.L.; writing--original draft preparation, T.A.N.; writing--review and editing, T.A.N. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A2C1011154).  
Not applicable.  
Figure 8: BER performance of the proposed model according to position fluctuation at SNR = 15 dB [18, 24].
## 5 Complexity Analysis  
To analyze the time-complexity of the proposed NN-based detector, it should be firstly noted that in the following simulations and sea trials, the networks were all trained once in advance and were not retrained online. All parameters of the networks was fixed when  
Figure 6: Block diagram of training strategy. All networks are only trained once offline and tested with fixed hyperparameters online in the simulations and sea trials.  
conducting simulations and experiments. Thus the large batch size of the dataset was not included in the computation. Hence, only \(N_{\text{L}}\) and \(N\) were considered. The computations of both the LS estimator and minimum distance detector were included for the calculation of complexity of other equalizers. Consider the following LS channel estimator  
\[\hat{h}(t)=\frac{r(t)}{s(t)}. \tag{20}\]  
The number of multiply-accumulate operations (MACC) of the above process is the number of subcarriers \(N\). For the minimum distance detector, each symbol experiences \(M\) times of complex operations, which contains \(3 MN+5N\) MACC.  
As a similar implementation of the NN-based equalizer, the ELM-based detector in [34] was considered for performance comparisons. According to [34], the number of MACC of the ELM-based detector was derived and listed in Table 2. Ignoring the addition with constant terms, the time complexities of different detectors are compared in Table 2. It can be seen that the computation of the proposed NN-based detectors is less than that of MMSE for 1 order of magnitude. The complexity of the ELM-based detector is the highest, which is 2 orders of magnitude larger than the proposed NN-based detector. Although the ZF equalizer shows the lowest complexity, it has been proved to be a suboptimal algorithm in noisy channels, which could be substituted by more advanced methods.
## 2 Materials and Methods
### Modeling and Harmonization of Environmental Criteria  
The modeling and harmonization of environmental abiotic criteria across agricultural areas within the study site was performed according to the methodological framework proposed by [PERSON] and [PERSON] [29]. This process was executed in four segments utilizing global open data sources: (1) climate criteria, which were modeled using CHELSA datasets [30], encompassing factors such as air temperature, precipitation, relative humidity, insolation, and bioclimatic variables; (2) soil criteria represented by SoilGrids data [31,32], which included metrics on soil nitrogen, organic carbon, pH, and texture; (3) topographical criteria, which were derived from the Copernicus digital elevation model (C-DEM) data [33], incorporating elevation, slope, aspect, and the topographic wetness index; and (4) spectral criteria, which were sourced from Sentinel-2 imagery, providing independent remote sensing data for monthly median spectral bands. To ensure compatibility among these data layers, all environmental criteria were harmonized to a unified coordinate grid system aligning with the input LAI and FAPAR data. This harmonization involved reprojection to the HTRS96/TM system and resampling to a spatial resolution of 300 m. Environmental criteria with native resolutions exceeding 300 m were upscaled using a mean cell area weighted method, while those with lower native resolutions, such as CHELSA data, underwent downscaling using bilinear interpolation. Finally, a correlation analysis among the harmonized environmental criteria was conducted by employing Pearson's correlation coefficient to identify and exclude redundant criteria, defined by a correlation coefficient exceeding 99%.
## 3 Results
### Analysis of the GDL Surface  
The GDL/channel interface is at the location where the through-plane flow leaves the GDL. This interface was analyzed by evaluating the mass-related quantiles defined by Equation (4). For two realizations, the 70%, 50%, and 20% quantiles are shown in Figure 4. With lower quantile levels, the sizes of the areas illustrated by the contour lines in the post-processing step decreased, and some characteristic situations could be identified. Areas that are concentric for the three quantile levels with similar shapes are marked with labels, e.g., \"a\" in Figure 4. In a similar manner, the areas can still be concentric, but with a modified shape of the inner area (for \(q=20\%\)), labeled with \"b\". Another situation is marked with \"c\". The inner area was split into two, identifying two small pores in the GDL that were hidden, when higher quantile levels were shown. Positions where only one or two areas were nested--labeled as \"d\"--showed pores, where less gas left the GDL. Furthermore, positions where \"b\" and \"c\" are combined are labeled accordingly.  
The gas within the mass-related quantiles was flowing through a certain fraction of the total quantile area of the GDL, which is shown in Figure 3b. The sizes of the largest area show large variation in Figure 3c.  
Through-plane transport was simulated in microstructures as specified in Sections 2.1 and 2.3. They were applied to 25 realizations of the geometry model. Table 2 summarizes the detailed results of the analysis of the mass-related quantiles obtained from the velocity field. The total quantile area \(s_{\text{tot}}\) is presented for \(q=70\%\), 50%, and 20% and, also, the number of areas (number of regions, #reg.) belonging to the quantiles and the average \(s_{\varnothing}\) and maximum size \(s_{\text{max}}\) of the areas. The maximum size is also presented in Figure 3c. The largest area can be of interest for fuel cell and stack modeling when the location of the pores emphasized in the GDL surface is required. The large spread of the values in Figure 3c is in line with the variation coefficient of \(s_{\text{max}}\) in Table 2.  
Figure 4: Velocity at the GDL/channel interface for realization Nos. 5 and 15. Mass-related quantiles for \(q=70\%\), 50%, and 20%. Labels “a”–“d” are described in Section 3.1.
## 8 Validation with Measurements
The result from FT is compared to a reference value from simulation (vs. sim.) and to reference values obtained through a measurement (vs. meas.) at the respective distance. Measurements of four different antennas were used as indicated in the legend. Figure 13: Radiated power density at \(z=5\) mm obtained through simulation abd measurement and the proposed forward transformation algorithm for (**a**) the cavity-fed dipole array antenna and (**b**) the horn antenna loaded with slot arrays. Note that the measurements are performed over a \(3\lambda\times 3\lambda\) area, whereas in the simulations, a larger area is considered for calculations.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 2 Related Work
### Mixing Methods  
Data augmentation is a crucial component for successful deep learning. It helps mitigate sample size issues and prevents overfitting, effectively reducing the epistemic uncertainty of the model [14]. Mixup [15] is a simple yet powerful data augmentation technique that improves the performance and robustness of many state-of-the-art models. Given its effectiveness, Mixup has been extended into various derivatives. Manifold Mixup [16] combines input features within the network's hidden layers. Additionally, Cutmix [17] proposes mixing patches to create more visually meaningful augmentations. Patchup [18] extends this idea by mixing patches in the hidden space. Progress has also been made in applying these techniques to natural language processing. In their research, [PERSON] et al. [19] investigated the application of Mixup for sequence classification tasks. Their findings suggest that blending words and sentences can produce advantageous outcomes. Along similar lines, SSmix [20] was proposed, featuring a method where less critical words are replaced based on saliency, thereby enhancing the overall performance. MixText [21] applied Mixup for semi-supervised text classification, whereas MixSpeech [9] adapted it for the speech domain. Moreover, MixLoss [22] generalized MixSpeech to sequence generation tasks.
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
###### Abstract  
No reference image quality assessment (NR IQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce two low-level feature distributions (TLFLD) based method for NR IQA. Different from the deep learning method, the proposed method characterizes image quality with the distributions of low-level features, thus it has few parameters, simple model, high efficiency, and strong robustness. First, the texture change of distorted image is extracted by the weighted histogram of generalized local binary pattern. Second, the Weibull distribution of gradient is extracted to represent the structural change of the distorted image. Furthermore, support vector regression is adopted to model the complex nonlinear relationship between feature space and quality measure. Finally, numerical tests are performed on LIVE, CISQ, MICT, and TID2008 standard databases for five different distortion categories JPEG2000 (P2K), JPEG, White Noise (WN), Gaussian Blur (GB), and Fast Fading (FF). The experimental results indicate that TLFLD method achieves superior performance and strong generalization for image quality prediction as compared to state-of-the-art full-reference, no reference, and even deep learning IQA methods.  
no reference image quality assessment; low-level feature; generalized local binary pattern; gradient; deep learning +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Open vs. Closed Configurations  
Brayton batteries can be operated in open or closed systems. Open systems intake air from the environment and release it, reducing investment costs due to unnecessary heat dissipation components. Closed systems require heat dissipation components and enable operation under pressure, increasing the power density of the compression and expansion machinery. The choice of working fluid in a closed loop depends on the configuration, with fluids with high specific heat capacity (cp) and low isentropic exponent (\(\kappa\)) being preferred when direct thermal storage is used. Closed loops offer flexibility in temperatures during different loop phases and allow for a higher temperature ratio, reducing the size of thermal storage. Partial load operation in closed loops can be regulated by adjusting the fluid mass flow rate. This allows for flexible operation over a wide range of charging and discharging powers without significant impact on overall efficiency. To compensate for temperature differences between charging and discharging cycles, heaters and coolers can be used. This dissipates the heat generated by irreversibilities to the environment, allowing the storage to operate in a cyclic manner.
###### Abstract  
The aim of this study was to evaluate the effect of surface treatment and storage time on immediate repair bond durability of methacrylate- and ormocer-based bulk fill composites. In total, 265 discs were divided into 32 groups (n = 8/group) according to: (1) Material: X-tra fil and Admira Fusion _X_-tra; (2) Surface treatment: oxygen inhibition; matrix; Futurabond M+; Silane/Futurabond M+; Admira Bond; Silane/Admira Bond; ceramic repair system; and Silane/Cimara bond; and (3) Storage time: 24 h and 6 months. Each disc received three micro-cylinders from the same material. Specimens were subjected to micro-shear bond strength testing either at 24 h or 6 months. Data were analyzed using ANOVA/Tukey's test/Student _t_-test (_p_ = 0.05). All experimental factors had significant effect on bond strength (_p_ < 0.0001). Drop in bond strength was noticed in both materials after six months (_p_ < 0.05), except for Admira Fusion _X_-tra treated with silane/cimara adhesive (_p_ = 0.860). Both materials showed insignificant values with Admira bond either at 24 h or 6 months (_p_ = 0.275 and \(p\) = 0.060, respectively). For other treatments, _X_-tra fil showed significantly higher values at 24 h and 6 months (_p_ < 0.05). Ceramic repair system can be used to immediately repair both methacrylate- and ormocer-based composites.  
bulk fill; immediate repair; bond strength +
Footnote †: journal: Applied Sciences
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results
### Comparison of Accuracy of Remote Sensing Inversion Models for Forest Stand Age  
In this study, 30% (203 pairs of data) of data were selected as reference data to verify the dataset of forest stand age retrieved by remote sensing methods. The relationship between reference forest stand age data and remote sensing simulated forest stand age is shown in Figure 8. Figure 8a shows that the relationship between reference data and forest stand age simulated by the MLR model is very significant; the \(R^{2}\) is 0.61, the slope of the fitting line is 1.12 and the root-mean-square error (\(RMSE\)) is 10.98. We found that the model underestimated the forest stand age when the forest stand age was greater than 50. The main reason is that the vegetation index has different saturation effects with the increase in age, but the linear regression model cannot better eliminate the saturation effect of the VI when estimating forest stand age. Also, Figure 8b shows that the relationship between the reference age and forest stand age simulated by the RF model is very significant; the \(R^{2}\) is 0.63, the slope of the fitting line is 1.09 and the \(RMSE\) is 10.77. Although the model also underestimated the forest stand age when the forest stand age was greater than 50, compared with the MLR model, the error of the RF model was weakened, and the slope decreased from 1.12 to 1.09. In short, although the machine learning model is a \"black box\" model, it solves fitting problems such as data collinearity and improves the regression accuracy of the model.
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 3 System Architecture
### Pedestrian-Crossing Detection  
YOLO (You Only Look Once) employs a deep neural network to detect and classify objects in a single forward pass, offering both high speed and accuracy. Unlike traditional two-stage object detection methods that separately generate candidate regions and classify objects, YOLO directly predicts bounding boxes and class probabilities in a unified framework. This end-to-end architecture significantly improves detection efficiency and has become a widely adopted approach in real-time object detection tasks. Numerous researchers have leveraged and optimized the YOLO architecture for various applications. For instance, [PERSON] et al. [35] proposed a real-time vehicle detection system based on the YOLO architecture for deployment on embedded devices. By quantizing the network parameters, their model achieved a mean Average Precision (mAP) of 69.79% and a detection speed of 28 frames per second (FPSs), making it suitable for resource-constrained environments. [PERSON] et al. [36] integrated YOLO with a Support Vector Machine (SVM) to develop a high-resolution automotive radar system capable of classifying pedestrians and vehicles. Their hybrid model demonstrated classification accuracy exceeding 90% in experimental evaluations.  
[PERSON] et al. [37] explored the feasibility of replacing LIDAR with a stereo-vision system for distance estimation. By capturing two slightly offset images from a pair of cameras and applying a YOLO-based deep neural network, their system estimated the distance to detected objects using principles of stereo imaging. Furthermore, [PERSON] et al. [38] conducted a comparative study on real-time vehicle detection performance across several object detection models and obtained the result that YOLO outperformed the other models in terms of F1-score, precision, recall, and mAP while achieving near-optimal processing speed--second only to SSD. Given its superior balance between accuracy and inference speed, YOLO is particularly well-suited for real-time applications such as advanced driver-assistance systems (ADASs) and Internet of Vehicles (IoV) platforms. Therefore, in this research, YOLO is adopted as the core object detection module for identifying pedestrian crossings under diverse environmental conditions.
## 4 Conclusions  
Based on the results obtained, we can conclude that the validated analytical method is suitable for quantifying 16 mycotoxins in oat, almond, soy and rice beverages. Analytical data showed that out of total beverage samples, 95% of them presented levels of mycotoxins, and the most detected is ENB (22%). ENB showed a high registered level with 108.9 ng/mL. The co-occurrence of mycotoxins (up to two) was also observed in some positive samples. Risk assessment shows that the intake of mycotoxins through the consumption of these beverages in Valencia does not represent a risk for the population, except for AFs that are classified as carcinogenic compounds by international authorities. More investigations and monitoring studies are recommended to assess the multi-mycotoxin occurrence and the bioaccessibility of these compounds in these matrices to assess the potential risk for the consumers.  
Conceptualization, [PERSON] and A.J.-G.; methodology, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and A.J.-G.; project administration, [PERSON]; funding acquisition, [PERSON], [PERSON], [PERSON]-G. and C.J. All authors have read and agreed to the published version of the manuscript. This research was funded by Spanish Ministry of Economy and Competitiveness AGL2016-77610-R.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{3}{c}{**Combinations of Mycotoxins Present in the Same Sample (Incidence)**} \\ \hline
**2 Mycotoxins** & **3 Mycotoxins** & **4 Mycotoxins** & **\textgreater{}5 Mycotoxins** \\ \hline TENT + ZEA & ENA1 + B + B1 (3) & BEA + ENA + A1 + B + B1 (3) & BEA + ENA + A1 + B + B1 + HT-2 + TENT + ZEA \\ ENB + HT2 & ENB + B1 + ZEA & BEA + AOH + ENB + B1 & BEA+ ENA + B + B2 + HT-2 + TENT \\ ENB + B1 & BEA + ENB + ZEA & BEA + ENA1 + B + B1 (2) & BEA + AOH + ENA + A1 + B + B1 + HT-2 + TENT \\ ENB + ZEA & BEA + ENB + B1 & & BEA + ENA + B + B1 + HT-2 + TENT + ZEA \\ ENB1 + ZEA & BEA + ENB + ZEA & & BEA + ENA + A1 + B + B1 + HT-2 \\  & BEA + ENB + B1 (3) & & \\  & BEA + ENA1 + B & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Combinations of the co-present mycotoxins. the number of samples containing these combinations is indicated in parentheses.