esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Sample Results of Reconstructed \(Ndtu\) Temporal Profiles and Identified Phenological Cycles  
To assess how well our proposed approach performs in generating accurate \(NDVI\) time series and reliably identifying crop phenological cycles, Figure 4a illustrates both the original and reconstructed \(NDVI\) time series derived from our suggested methodology for three randomly sampled fields. The three samples were selected to be representative of the crop class studied within the 662 DWD station (cf. Figure 1), namely sugar beet, corn and winter wheat.  
Visual inspection of the three crops studied showed that the proposed UE-WS approach, which initially focuses on approximating the upper envelope of the \(NDVI\) time series and smoothing remaining noise, performed well in faithfully capturing distinct phenological seasonality from growth to dormancy for each crop and reconstructing high-quality \(NDVI\) time series that matched uncontaminated observations and adjusted con  
Figure 4: (**a**) Example temporal profiles of \(NDVI\) demonstrating the main stages of \(NDVI\) time series reconstruction using UE-WS for three crop samples at station 662. (**b**) Additionally, an example of identifying the phenological cycle for all crop parcels in DWD station 662 is illustrated in Figure 1.  
Once the resulting high-quality \(NDVI\) time series from the compound approach UE-WS was obtained, we used the experimental setup described above in Section 3.2.1 to identify the phenological cycles of all parcels within DWD station 662 (Figure 4b). In general, the implemented approach for identifying phenological cycles proved effective in selecting appropriate start and end dates for candidate cycles for all crops and for almost all samples. The phenological cycles of the three crops, although different in time and date, have some similarities. For example, they are generally characterised by the beginning of the growing season, in which the \(NDVI\) generally rises sharply as the chlorophyll content increases in the tillering stage. In the following, period it no longer increases and in the ripening period, \(NDVI\) decreases as the plants dry out.
## 3 Results
### Vegetation Dynamics Under Different Drought Intensities  
To illustrate the temporal variations in vegetation in Central Asia during the growing seasons of 2012 and 2019, we selected severe drought (0.8 \(<\) TVDI \(<\) 1), moderate drought (0.6 \(<\) TVDI \(<\) 0.8), mild drought (0.4 \(<\) TVDI \(<\) 0.6), normal (0.2 \(<\) TVDI \(<\) 0.4), and moist (0 \(<\) TVDI \(<\) 0.2) conditions (Figure 6a-j). The dynamics of the RSVI, EVI, FVC, GPP, and LAI for the drought year and the years preceding and following the drought (2012, 2011, and 2013) indicate that in 2011, the year prior to the drought, all five indicators reached their peak values during the growing season in July, specifically 0.28, 0.56, 0.32, 0.15, and 0.50, respectively. Under drought conditions, most indices peaked in July, including EVI (0.28), FVC (0.56), LAI (0.15), and RSVI (0.50). Only GPP reached its peak after the drought, in August 2013, with values of 0.30, 0.35, and 0.22. Under normal conditions, all indices peaked in May, while under wet conditions, they peaked in April. This pattern was similar during drought years around 2019, indicating that the timing of vegetation peaks fluctuates due to drought conditions.  
Figure 5: **(a)** Average TVDI from 2000 to 2020, **(b)** Central Asian vegetation drought distribution based on the TVDI in 2012, and **(c)** Central Asian vegetation drought distribution based on the TVDI in 2019. Drought severity in the different climate regions: **(d)** 2012 and **(e)** 2019.  
Based on the attribution analysis formulas presented in Table 4, it was evident that the extent of vegetation loss caused by drought in 2012 was significantly greater than that caused by drought in 2019. In 2012, drought resulted in losses of 0.019 for the EVI, 0.014 for the FVC, 0.016 for the GPP, 0.192 for the LAI, and 0.504 for the constructed RSVI. Conversely, in 2019, the corresponding losses were \(-\)0.003 for the EVI, 0.009 for the FVC, \(-\)0.007 for the GPP, 0.000 for the LAI, and \(-\)0.009 for the constructed RSVI. This also highlighted that when assessing vegetation losses using indices with different ecological meanings, the results may vary due to differences in the chosen indices. Compared to other indices, the RSVI constructed by us exhibited greater loss in the drought year of 2012, indicating a stronger response between the RSVI and drought.
## 2 Literature Review
The field of EEG signal processing has witnessed significant progress in recent years, particularly in feature extraction and classification techniques, driven by the demand for more accurate and real-time applications [26; 27; 28; 29; 30; 31; 32; 33] in areas such as brain-computer interfaces (BCIs), healthcare [34; 35; 36; 37; 38], and neuroscience research [39; 40; 41; 42]. One of the recent trends is the application of deep learning methods in EEG signal processing [43]. Convolutional Neural Networks (CNNs) have gained popularity due to their ability to automatically extract features from raw EEG data without the need for manual feature engineering [44; 45; 46; 47]. For instance, CNNs have been successfully employed to decode motor imagery tasks in BCIs, leading to improved classification accuracies. Additionally, hybrid models combining CNNs with other deep learning architectures, such as Long Short-Term Memory (LSTM) networks, have been explored to capture both spatial and temporal features of EEG signals, further enhancing performance. Another promising approach involves the use of transfer learning [48; 49], which leverages pretrained models on large datasets to improve the generalization of EEG classification models on smaller, domain-specific datasets. This is particularly useful in the context of EEG, where obtaining large, labeled datasets is challenging. Transfer learning has shown potential in tasks such as emotion recognition and mental workload estimation, where it helps in reducing training time and improving model accuracy. In feature extraction, the fusion of traditional methods such as Wavelet Transform (WT) with advanced machine learning techniques has been explored to enhance the representation of EEG signals. For example, continuous wavelet transform (CWT) has been used to capture both time and frequency domain features, which are then classified using machine learning models such as Support Vector Machines (SVMs) or Random Forests. Additionally, methods such as Empirical Mode Decomposition (EMD) have been combined with deep learning models to extract intrinsic mode functions that are more representative of the underlying brain activity. Finally, recent studies have also focused on improving the interpretability of EEG classification models. Explainable AI (XAI) techniques are being integrated into EEG processing pipelines to provide insights into how models make decisions, which is crucial for applications in clinical settings. This not only enhances the trustworthiness of the models but also aids in the discovery of novel neurophysiological patterns.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 3 Background
### Unsupervised Learning and K-Means Clustering  
Unsupervised learning aims to uncover patterns in unlabeled data by discovering hidden structures without predefined labels. Unlike supervised learning, which uses labeled datasets, unsupervised learning focuses on clustering, identifying correlations, and reducing data dimensionality. The clustering process relies on similarity metrics, with the Euclidean distance being the most common [34]. Other metrics, such as the correlation coefficient, may be used as well. Cluster analysis identifies high-density groups with similar observations within clusters. The number of clusters and similarity metric both have significant impacts on the results [35]. The K-means algorithm is a widely used unsupervised technique for clustering data into predefined clusters [36]. It partitions a dataset of \(N\) observations into \(k\) clusters by iteratively assigning observations to the nearest centroid, calculated as the mean of the points in a cluster. Starting with random centroids, the algorithm alternates between assigning observations based on the Euclidean distance and recalculating the centroids until reaching either convergence or a set number of iterations [37]. Using the Euclidean distance, K-means clustering seeks to minimize the within-cluster sum of squares (WCSS). Challenges include handling non-spherical clusters, outliers, and empty clusters, which are often resolved by reassignment or adjusting the number of clusters \(k\). The elbow method determines the optimal \(k\) by plotting the WCSS against various values of \(k\) to find the 'elbow' point. Silhouette analysis assesses cluster compactness and separation, aiding in the selection of an appropriate \(k\) value.  
Silhouette Score and Elbow Method  
The Silhouette Score measures how well an object fits within its cluster compared to other clusters, ranging from \(-1\) to \(1\). Positive values indicate that objects are more similar to their cluster members than those in other clusters, while negative values suggest the opposite [38]. For an object \(i\), the cluster \(B\) with the minimum distance to \(i\) (i.e., \(d(i,B)=b(i)\)) is considered the closest alternative if \(i\) cannot be assigned to cluster \(A\). Determining \(b(i)\) requires considering clusters other than \(A\); hence, \(k\) must be greater than 1. The Silhouette Score \(s(i)\) combines \(a(i)\) and \(b(i)\) to evaluate cluster quality [38].  
In K-means clustering, the elbow method identifies the optimal number of clusters \(k\) by plotting the distortion (inertia value) against \(k\). The distortion measures the total distance between points and their cluster center. As \(k\) increases, points become closer to their centers, reducing the distortion. The rate of decrease slows, forming an elbow in the plot. The \(k\) at this elbow is optimal, as further increases in \(k\) yield minimal improvements in the explained variance [39].
# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 1 Introduction
Similarly to our work, they tried to perform dependence estimation for specific tasks, but used mutual information as estimation target and they focus only on high dimensional variables such as images. To measure statistical dependence, correlations have been widely used in the fields of statistics and machine learning. However, due to a large amount of types of nonlinear dependence, it is very challenging to distinguish the types with a correlation coefficient. When using CNNs for dependence learning, we should consider one of its important disadvantages; CNNs have a huge number of parameters and with small dataset, would run into an overfitting problem, and thus they require much more training data than other existing learning algorithms. In this paper, we present a dependence representation learning algorithm in which CNNs extract task-specific dependence representations from visualizations of feature pairs. To the best of our knowledge, this is the first method that directly captures dependencies from visualizations of random variables. As the inputs of CNNs, we use 2D histograms, which are compressed scatter plots. In order to show that CNNs  
Figure 1: Various dependence types with the same distance correlation of 0.5.  
can learn dependence representations from 2D histograms, we have conducted three sorts of experiments: In the first experiment, we used a CNN to perfectly classify eight dependence types of a synthetic dataset. Next, we trained the CNN to simultaneously predict Pearson and distance correlations. Our results demonstrated that the CNN can predict the correlations well. Finally, we applied the dependence learning method and found that it outperformed the AutoLearn feature generation algorithm in terms of average classification accuracy and generated half as many features.
# Using Canola Oil Biodiesel as an Alternative Fuel in Diesel Engines: A Review
## 3 Properties of Canola Oil Biodiesel  
COB is a good alternative fuel, and has great advantages compared with other biodiesel fuels. It contains approximately 40-45% oil (the same as sunflower oil), which is higher than other oilseeds, including soybeans (about 18-20%) [55]. The high-performance liquid chromatography (HPLC) chromatogram of COB is shown in Figure 5. Based on the location of the peaks in Figure 5, COB mainly contains triglycerides (16.174 min) and trace amounts of diglycerides (16.943 min) [56]. Some important physicochemical properties of COB will directly affect the application of COB to diesel engines, such as density and viscosity, oxygen content, cetane number, heating value, and oxidation stability. Table 1 shows the properties of COB and its blends with diesel oil [57].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Properties (Units)** & **Pure Diesel** & **BD 100 1** & **BD 10 2** & **BD 20 3** & **BD 30 4** & **Test Method** \\ \hline Density (kg/m\({}^{3}\) at 15 \({}^{\circ}\)C) & 836.8 & 880 & 842 & 846 & 850 & ASTM D941 \\ Viscosity (mm\({}^{2}\)/s at 40 \({}^{\circ}\)C) & 2.719 & 4.290 & 2.818 & 2.991 & 3.172 & ASTM D445 \\ Calorific value (MJ/kg) & 43.96 & 39.49 & 43.29 & 42.71 & 42.12 & ASTM D4809 \\ Cetane index & 55.8 & 61.5 & - & - & - & ASTM D4737 \\ Flash point (\({}^{\circ}\)C) & 55 & 182 & - & - & - & ASTM D93 \\ Pour point (\({}^{\circ}\)C) & \(-21\) & \(-8\) & - & - & - & ASTM D97 \\ Oxidation stability (h/110 \({}^{\circ}\)C) & 25 & 15 & - & - & - & EN 14112 \\ Ester content (\%) & - & 98.9 & - & - & - & EN 141103 \\ Oxygen (\%) & 0 & 10.8 & - & - & - & - \\ \hline \end{tabular}  
* Pure canola oil biodiesel; 2 10% vol. of canola oil biodiesel blend with 90% Vol. of pure diesel; 3 20% vol. of canola oil biodiesel blend with 80% vol. of pure diesel; 4 30% vol. of canola oil biodiesel blend with 70% vol. of pure diesel.  
\end{table}
Table 1: Properties of canola oil and its blends with diesel oil [57]. BD: Canola oil biodiesel blended with diesel fuel.  
Figure 4: Schematic diagram of transesterification mechanism; where R\({}_{1}\), R\({}_{2}\), and R\({}_{3}\) are the long hydrocarbon chains (also called the fatty acid chains).  
Figure 5: High-performance liquid chromatography (HPLC) chromatogram of canola oil biodiesel (reproduced with permission from [56], Elsevier, 2017).
# Traffic Sign Detection Based on the Improved YOLOv5
## 1 Introduction
The input network uses the image for data enhancement, adaptive anchor frame calculation, and adaptive scaling; the backbone network mainly uses the CSPDarknet53 network, which is employed to extract the rich features information of the image; the neck layer is used for the feature fusion, which is responsible for fusing image information from different scales to obtain more accurate recognition results; the output network is responsible for outputting the detected target information. [PERSON] et al. proposed an improved YOLOv5 network model for detecting small targets such as traffic signs; after improving the attention mechanism in the network, small targets can be detected effectively, but there is still some loss in detection accuracy [17]. Therefore, for the problem that detection accuracy and speed cannot be well balanced, this research illustrates a new YOLOv5 algorithm that improves traffic sign recognition. The main contributions of this paper are as follows:  
* The CBAM is fused with the CSP1_3 model in YOLOv5 to form a new CSP1_3 CBAM model, which not only highlights key features but also suppresses invalid features to achieve effective detection of small target regions. * The SiLU activation function in the YOLOv5 network is improved, and the ACONC activation function is used, which renders the activation function network more capable of better convergence and improves the robustness of YOLOv5. * The loss function in the YOLOv5 network is improved, and the SIoU loss function is used, which optimizes the training model to improve its accuracy for small targets. The remaining portions of this work are broken down into the following sections: The improved YOLOv5 algorithm is introduced in Section 2. The experimental validation and results analysis are introduced in Section 3. The conclusion concludes this article in Section 4.
# Characterization and Selection of _Lactobacillus_ Strains with Potential Robotic Applications
## 3 Results and Discussion  
### The Effect of Temperature on the Growth of Lactobacillus Strains  
_Lactobacillus_ spp. growth can be influenced by various external factors, such as pH, temperature, oxygen concentration, water activity and the composition of the culture medium. The optimal temperature for lactobacilli varies between 30 and 40 \({}^{\circ}\)C, whereas the optimal pH values for their growth vary between 5.5 and 6.2 [20]. The determination of optimal growth conditions of _Lactobacillus_ strains is an important step from a technological point of view in the development of probiotic products.  
In the first stage of a series of experiments, the growth characteristics of _Lactobacillus_ strains under two different regimes of cultivation (30 \({}^{\circ}\)C and 37 \({}^{\circ}\)C) were determined. The two parameters (number of viable cells and titratable acidity) were determined at different time intervals (0 h, 4 h, 8 h, 12 h, 16 h, 20 h, 24 h, 36 h and 48 h). The temperature impact on the growth and lactic acid production of the four studied strains is illustrated in Figure 1A,B and Figure 2A,B.  
Figure 1: Viable cells productivity of _Lactobacillus_ strains during cultivation at 30 \({}^{\circ}\)C (**A**) and 37 \({}^{\circ}\)C (**B**) for 48 h. Values are expressed as means \(\pm\) SD. Letters a–d indicate significant differences (\(p\) < 0.05) between the samples, \(n\) = 3.  
As seen from the results, the _Lactobacillus_ strains demonstrated the ability to adapt, grow and produce metabolites in both temperature regimes of cultivation (30 \({}^{\circ}\)C and 37 \({}^{\circ}\)C). However, better proliferation expressed by higher viable cell counts and higher lactic acid production were observed when the four strains were cultivated at 37 \({}^{\circ}\)C, reaching the highest values at the end of the cultivation process (Figure 1A,B and Figure 2A,B). At the 48 th hour, the strain _L. casei_ ATCC 393 exhibited the lowest viable cell counts, 10 log CFU/mL and 11.2 log CFU/mL, but the highest lactic acid production, 2.5% and 3% (at 30 \({}^{\circ}\)C and 37 \({}^{\circ}\)C, respectively), compared to the other strains. In contrast, at the same hour of cultivation, the strain _L. rhamnus_ RC-10 reached the highest viable cell count (11.8 log CFU/mL) at 30 \({}^{\circ}\)C (but lower than that at 37 \({}^{\circ}\)C--11.98 log CFU/mL), and the lowest lactic acid production--1.5% and 1.65% at 30 \({}^{\circ}\)C and 37 \({}^{\circ}\)C, respectively.
# On the Efficacy of Turbulence Modelling for Sloshing
###### Abstract
The results were not particularly conclusive in that the models which performed well for plunging breakers were often inaccurate when capturing the dynamics of spilling breakers. While no-model simulations produced acceptable results, they found that, in general, using turbulent models can improve the accuracy and efficiency relative to the no-model cases. Therefore, it is believed that the predictability of turbulence closure models for multi-phase flow is case dependent, depending largely on the surface-breaking characteristics. In this work, we investigate the accuracy of turbulence models in predicting the forces and associated dissipated energy of fluid sloshing in a vertically oscillating tank. This is performed by comparing with validated and unique experimental data obtained within the SLOWD project [59]. As shown in the coming sections, relying on a single case, as was done in [PERSON] et al. [55], [PERSON] and [PERSON] [56], [PERSON] et al. [57], to compare between simulation setups can be misleading. Whereby a setup that works well in one case can fail for another apparently similar setup. Therefore, we aim to improve understanding of how to characterise when different models are the most appropriate by providing a comparison between different turbulence modelling using the VoF approach for a number of modes of sloshing dynamics defined according to their Froude number (Fr). The range of Fr numbers considered here corresponds to different sloshing dynamics ranging from a weak sloshing motion through to a very turbulent and violent sloshing. This allows for a more reliable and conclusive comparison between the models and provides a basis to make a decision as when to apply each within a complex sloshing simulation involving multiple regimes simultaneously. This paper is organised as follows: a problem description and definition of the formulations to compute the dissipation is presented in Section 2, followed by the numerical setup in Section 3. Then, results and discussion of the accuracy of the numerical simulations are presented in Section 4. A summary of conclusions and future works are presented in Section 5.
# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 4 Discussion
A practical and efficient application would rely on the integration of such low-cost hyperspectral sensors with ground vehicles that can collect data while performing other agricultural practices (i.e., spraying, trimming, etc.). Such application will be extremely useful to vine growers to scout for infected vines over a larger acreage in a relatively small time-frame.
## 3 Experiment
### Data Analysis  
To ensure the data acquisition accuracy of the instruments, the sampling frequencies of the dynamic multi-parameter physiological detector and GPS data were set to 1 Hz. As shown in Figure 5, during the experiment, the data were collected for the standard freeway section (A), advance warning area (B), upstream transition area (C), 40 m median opening (D), 70 m median opening (E), 100 m median opening (F), 130 m median opening (G), activity area (H) and downstream median opening (I). The data were corrected according to the recorder's records. After removing the abnormal data, the experiment yielded a total of 109,056 HRV physiological data and 106,531 vehicle GPS data for 48 participants under 4 median opening lengths.  
Figure 5: Details of each part of the experimental road.
# Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
## 3 Our Proposed DL Detection Models
In the down-sampling process, we used convolutional layers of 32 filters, each of size \(3\times 1\), and then we used four convolutional layers with filter sizes 64, 128, 256, and 512, each filter of size \(3\times 1\), between each two layers. We down-sampled the size of the data by pooling. After this process, we used an inverse process of four deconvolutional layers with sizes 512, 256, 128, and 64 filters. Each filter is of size \(1\times 3\). We up-sampled a \(1\times 2\) layer between each two deconvolutional layers, as shown in Figure 4. After each internal layer, we used a relu activation layer, as given above in Equation (12), with batch normalization. After these two processes, we used a pooling layer followed by a convolutional layer of two filters, each one of size \(1\times 1\). We apply softmax activation as in (13) and use a cross-entropy loss process to obtain the minimum loss between the original data and the output of the DL system as in (14). At the outputs of our systems, we get two values of accuracy and loss. The model trains over a number of iterations until we obtain results with maximum accuracy and minimum loss. The BER is calculated as BER = 1 - ACCURACY, and ACCURACY is the ratio of number of correct predictions to the total number of input samples. Performances of the DL decoder and the regular ACO-OFDM decoder that used the MMSE estimator are compared. In the next section, we present the results of our proposed DL models and compare them with the results of the ACO-OFDM decoder that uses the MMSE estimator.