esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Review of Similar Works  
The detection of events and situations using deep learning techniques has been the subject of increasing interest in recent years. Various researchers and teams have addressed this problem in different contexts, giving rise to innovative approaches and methodologies. Recent research has explored the use of deep neural networks to identify crying babies in homes. These systems are based on analyzing unique acoustic patterns associated with calls, allowing algorithms to distinguish between different types of infant sounds. The results have shown high levels of accuracy in detecting crying episodes, which is helpful for parents and caregivers [10].  
Another field of research focuses on identifying emergency sirens in urban areas, using convolutional neural networks to analyze audio recordings in real time. These systemshave the potential to quickly alert authorities in case of critical situations [11]. Detecting specific acoustic events, such as glass breaking in commercial establishments, has also been addressed with deep learning, training neural networks to recognize sound patterns of breaking glass to prevent theft and vandalism.  
The proposed work differs from the previous ones due to its comprehensive approach and adaptability in various scenarios. Although there is research in the field with similar objectives, the methodology of this work is unique for several reasons, among which adaptability stands out. Unlike many approaches focusing on specific situations, this approach can detect various events and conditions, from a crying baby to emergency sirens or broken glass. This enhances its versatility and usefulness in different contexts.  
Since the algorithm has benefited from rigorous training and validation that prioritizes generalization, generalization allows it to recognize acoustic patterns in new and unknown situations [12]. One of the key features is its ability to operate in real time, which is essential for monitoring security and quickly responding to emergencies. The synergy between efficient signal processing techniques and optimized neural networks ensures accurate and timely detection [13].  
To make an effective comparison between similar works and the current proposal, several parameters have been considered that highlight the essential characteristics of each approach, which are presented in Table 1. These parameters reflect the algorithm's specialization, generalization, real-time operation, versatile applicability, and efficiency [14]. These parameters have been used to categorize the efficiency of each work in the comparative table. The categorization is based on how each work addresses these key aspects and to what extent it meets the detection needs of specific events or situations [15]. This assessment provides an overview of the strengths and limitations of each approach based on its scope, real-time capability, and applicability in various situations.
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 3 Experimental Data and Analysis Indicators
### Analysis Indicators  
#### 3.3.1 Root Mean Square Error (RMSE)  
The root mean square error is used to measure the positioning precision of the positioning system in the three directions of east, north, zenith, and three-dimensional space. That is, the difference between the calculated estimated value and the reference value is calculated as:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(x_{i}-x_{0}\right)^{2}}{n}} \tag{21}\]  
\[3\text{DRMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(x_{i}-x_{0}\right)^{2}+\left(y_ {i}-y_{0}\right)^{2}+\left(z_{i}-z_{0}\right)^{2}}{n}} \tag{22}\]  
where i is the epoch time; n is the total epoch; \(x_{i},~{}y_{i},~{}z_{i}\) are the estimated value scalculated in the three directions of the north, east, and zenith, respectively, at the i-th epoch time; and \(x_{0},~{}y_{0},~{}z_{0}\) are the reference values of the observation station.
###### Abstract  
The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.  
land cover; Qilian Mountains; Sentinel-2; GEE cloud platform; machine learning +
Footnote †: journal: Computer Science
# Two-Stage Metaheuristic Algorithms for Order-Batching and Routing Problems
## 5 Computational Experiment
### Initial Batch Solution  
As described in Section 4.1, five initial solutions were provided; according to the four order types and eight scenarios, 32 cases were generated in Table 2. Each type of problem was matched with random seeds set at 10, 20, and 30.  
These five initial solutions were tested in 32 cases (Figure 4). According to the mean value, the SO and RB methods yielded fewer effective solutions compared with the other methods. By contrast, the FFD, BF, and BFD methods yielded effective solutions. Figure 4 indicates that the objective value tended to increase from order numbers 10, 20, and 40 to 80 and that the objective values associated with the same order number differed according to various scenarios, thus explaining the scattered points illustrated in this figure. In general, the solutions derived using the BF method were less effective than those obtained using the BFD method, and when the order was small, the FFD method yielded more effective solutions than did the BFD method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Number of Items** & **Capacity of the Orders** & **Vehicle Capacity** \\ \hline
1 & 1–3 & 1–3 & 10 \\
2 & 1–5 & 1–3 & 10 \\
3 & 1–3 & 1–3 & 20 \\
4 & 1–5 & 1–3 & 20 \\
5 & 1–3 & 1–5 & 10 \\
6 & 1–5 & 1–5 & 10 \\
7 & 1–3 & 1–5 & 20 \\
8 & 1–5 & 1–5 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample problems.  
Figure 4: Comparison of five initial solutions.  
The solutions obtained using the FFD, BF, and BFD methods according to the order size are presented in Table 3. As shown in the table, all the FFD, BF, and BFD methods returned valuable solutions, with the differences among the solutions being minor. The BFD method tended to generate the minimum objective value and minimum mean objective value, followed by the FFD, and then, the BF methods, which yielded the least effective solutions. The blue shadowed cells show the best results among these three methods.  
Table 4 shows the mean value and standard deviation of the initial solution approaches. Because the BFD method yielded the minimum mean value, it was used to test hypotheses involving other initial solutions. Table 5 presents the test statistics. The mean objective values obtained using the SO and RB methods differed significantly from those obtained using the BFD method, and the Z value exceeded 1.645. However, the difference between the mean values obtained using the FFD and BF methods and those derived using the BFD method was not significant; because 0.08 and 0.1 are less than 1.645, these values do not belong to the reject region, and \(H_{0}\) was thus not rejected.
# Agricultural Waste-Based Biochar for Agronomic Applications
## 4 Effect of Biochar on Greenhouse Gas Emissions
The results presented so far are inconsistent, and sometimes biochar is reported as negatively affecting greenhouse gas emissions, depending on the type of biochar and soil properties (199,207). In contrast, several studies have reported positive results, providing sufficient evidence that biochar can effectively reduce greenhouse gas emissions and mitigate climate change (154). In a field experiment in Australia performed by [PERSON] et al. (2008) with biochar derived from cattle waste, the authors observed no significant changes in greenhouse gas emissions in biochar-amended red Ferrosol soil as compared to the control. In an earlier investigation, [PERSON] et al. (2009) showed that biochar addition to paddy fields in China resulted in positive effects on soil properties and significant reduction in N\({}_{2}\)O emissions, while CO\({}_{2}\) remained unchanged and CH\({}_{4}\) was only reduced in the second paddy cultivation cycle.
# Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
## 3 Background
### Graph Convolutional Network  
The traditional GCN consists of \(L\) graph convolutional layers, where each layer constructs embeddings for each node by aggregating the embeddings of its neighboring nodes in the previous layer:  
\[X^{(l+1)}=\sigma(Z^{(l+1)}) \tag{1}\]  
Figure 4: Hybrid botnet.  
Figure 3: Decentralized botnet.  
\[Z^{(l+1)}=A^{\prime}X^{(l)}W^{(l)} \tag{2}\]  
where \(X^{(l)}\in\mathbb{R}^{N\times F_{1}}\) represents the l-th layer embeddings of all n nodes, \(X^{(0)}=X\), \(A^{\prime}\) is the normalized and regularized adjacency matrix, \(W^{(l)}\in\mathbb{R}^{F_{1}\times F_{(l+1)}}\) is the feature transformation matrix learned for downstream tasks, and \(\sigma(\cdot)\) is typically the ReLU activation function.  
Semi-supervised node classification is a widely used application of GCN. In this application, the objective is to learn the weight matrices in Equation (1) by minimizing the loss function:  
\[L=\frac{1}{|Y_{L}|}\sum_{i\in Y_{L}}loss\Big{(}y_{i},z_{i}^{(L)}\Big{)} \tag{3}\]  
where \(Y_{L}\) contains the labels of all labeled nodes, \(y_{i}\) represents the true label of node \(i\), and \(z_{i}^{(L)}\) denotes the final predicted result for node \(i\), which is the \(i\)-th row of matrix \(Z^{(l)}\). In practice, a cross-entropy loss is commonly used for node classification in multi-class or multi-label problems.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
###### Abstract  
The number of publications on acoustic scene classification (ASC) in environmental audio recordings has constantly increased over the last few years. This was mainly stimulated by the annual Detection and Classification of Acoustic Scenes and Events (DCASE) competition with its first edition in 2013. All competitions so far involved one or multiple ASC tasks. With a focus on deep learning based ASC algorithms, this article summarizes and groups existing approaches for data preparation, i.e., feature representations, feature pre-processing, and data augmentation, and for data modeling, i.e., neural network architectures and learning paradigms. Finally, the paper discusses current algorithmic limitations and open challenges in order to preview possible future developments towards the real-life application of ASC systems.  
acoustic scene classification; machine listening; deep neural networks +
Footnote †: journal: Medical  
As a complementary read to this article, [PERSON] et al. published an in-depth overview of ASC methods using \"traditional\" feature extraction and classification techniques prior to the general transition to deep learning based methods in [3]. Other related survey articles focus on deep learning methods for AED [4; 5] or summarize algorithms submitted for various machine listening tasks including ASC for a particular year of the DCASE challenge such as [6]. Methodologies and common datasets for evaluating ASC algorithms are not further addressed in this article.
# Machine Learning Algorithms for the Prediction of the Seismic Response of Rigid Rocking Blocks
## 3 Machine Learning Algorithms
### Regression  
For the regression sub-problem, the following algorithms were implemented and compared:* Regression tree: Regression trees are decision trees in which the target variables can take continuous values instead of class labels in leaves. A decision tree (DT) comprises the following three parts: The nodes (representing the attributes), the branches, and the leaves. The last part is a leaf; the top part is known as the root. Between the root and the leaf, we find the branches. The DT collects and considers the answers to the questions related to the data in order to develop decision rules. This process keeps running and stops when leaves or nodes without branches are found. Testing data are used to determine the generalization potential of the tree. It is important to mention that there is only a single root/a single decision rule from the root towards each leaf [74]. The implementation of this algorithm was carried out in MATLAB.
* Gradient boosting regressor: The algorithm works similarly to the gradient boosting classifier. however, instead of using decision trees, it uses Regression Trees as its base models. Given that it has the same computational limitations as its classification variant, we again opted to use the histogram-based optimized algorithm. The implementation was also carried out using scikit-learn.
* ANN regressor: The algorithm is almost identical to the ANN classifier. The difference is that the activation function of the output layer is usually linear, instead of sigmoid or \(tanh\).
* Random forest regressor: This is an ensemble of Regression Trees. Randomness is introduced to each individual tree by training it on a bootstrapped sample of the original dataset. The final model output is the average of the ensemble.  
Finally, we note that, by construction, tree-based algorithms are not susceptible to features with different scales. However, ANN was also employed, which can be susceptible to such scaling. To alleviate this, different scaling techniques can be applied, such as normalization, MinMax scaling or quantile transformer, among others [75]. In the present study, the datasets were preprocessed using a standard MinMax scaler, thus normalizing all the features in the range [0, 1].
## 4 Evaluation Experiment
### Experiment 1: Evaluation of Sound Image Localization Accuracy
Hence, the improvement of sound localization is more significant at positions (a), (c), (e), and (g) than that at positions (b), (d), (f), and (h). Focusing on the direction of the sound image, the error \(D_{\text{Err}}\) of the proposed method is about 0.90 m at positions (a)-(d), although the error \(D_{\text{Err}}\) of the conventional method is about 1.13 m. In other words, the proposed method accurately reconstructs the 3-D sound image in front of the listening position. However, the error \(D_{\text{Err}}\) of the proposed method is about 1.27 m at positions (e)-(h), although the error \(D_{\text{Err}}\) of the conventional method is about 1.39 m. In other words, the accuracy of the reconstruction of the 3-D sound image behind the listening position degrades for both the conventional and proposed methods. This is because the dummy head microphone represents a human auditory system, that is, the accuracy of the sound localization behind the listening point is lower than that in front of the listening point. However, the error \(D_{\text{Err}}\) of the proposed method is lower than that of the conventional method in all conditions except for position (h), where it is inferred that the reverberation of the room affects the sound localization. From these results, it can be said that the proposed method can reproduce the 3-D sound image well in  
Figure 10: Estimation of the position of the reconstructed 3-D sound image \((\hat{r},\hat{\theta},\hat{\phi})\). terms of distance and direction compared with the conventional method. Additionally, the error of the distance is still large because the proposed method does not consider the room reverberation [22]. Hence, we will improve the proposed method to overcome this problem. Figure 11: Estimated position of 3-D sound image \((\hat{r},\hat{\theta},\hat{\phi})\).
## 3 Theoretical Framework  
Solar loading lock-in thermography does not use artificial heat sources, such as heat lamps; its only heat processes are naturally occurring. These consist of the following: heat transfer within the structure (conduction); heat transfer with the surrounding air, which is free to move (convection); and heat transfer via radiation (solar irradiation, radiative heat loss). The fourth heat process, evaporative cooling, was not included in this study.  
To estimate thermal material properties, the value of the heat flux input is needed. Therefore, a direct measurement of the insolation was conducted using a pyranometer.  
As described in our previous work [1], the infrared camera was set up in a stationary position to capture a set of thermal images regularly spaced in time. These images represented a 2D temperature field that was assumed to capture the surface temperature of a structure after radiance calibration with a blackbody source. Effects such as emissivity/reflectivity can be corrected to improve absolute measuring accuracy for a given surface. These kinds of corrections were not performed in the scope of this work but are hypothesized to further increase measurement accuracy in future research [1].  
The pyranometer was fixed horizontally, effectively measuring global horizontal irradiation (GHI). As different captured structures faced different angles, a correction from GHI to the respective vertical angle surfaces was conducted. This correction was performed via a two-step process:  
1. Calculate the theoretical values for direct horizontal irradiation (DHI) and direct vertical irradiation (DVI) at the angle of interest.
2. Correct the pyranometer GHI data using the factor DVI/DHI.  
The correction can be found in Figure 1.  
Both DHI and DVI can be determined using the air mass coefficient method [8], where DVI must be calculated for each angle of interest (e.g., 180\({}^{\circ}\) = facing south, 225\({}^{\circ}\) = facing south west).  
For horizontally oriented surfaces, no correction to the pyranometer data was necessary, as they were recorded horizontally.  
After correction of the irradiation value, multiple approaches from active thermography to determine thermal material properties were evaluated:  
1. Determining thermal effusivity \(b\) via step-heating thermography approach [6];
2. Determining thermal effusivity \(b\) via linear effusivity fit approach [7];
3. Determining surface heat capacity \(c_{S}\)[9].  
The results of these three methods are discussed in the next section.
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 3 Gaussian Filtering  
The Gaussian kernel, hereinafter denoted by \(g_{\sigma}\), is a continuous function defined by the following equation:  
\[g_{\sigma}[m,n]=\frac{1}{2\pi\sigma^{2}}e^{-\frac{1}{2}\left(\frac{m^{2}+n^{2} }{\sigma^{2}}\right)}, \tag{1}\]  
where \(\sigma\) is the standard deviation. It is important to note that the Gaussian kernel is always normalized.  
The procedure of Gaussian filtering is based on applying the kernel defined in (1) to an image. It works by convolving the image with a Gaussian kernel, determining the weights with which pixels affect the filtering result.
## 1 Introduction
### Recovery Mechanisms of Polymer Flooding
#### 1.2.1 Mobility Control  
Polymer increases aqueous phase viscosity and hence improves the sweep efficiency of the reservoir [35; 36]. The main recovery mechanism of polymer flooding is improved mobility control, as defined in Equation (1), in the porous media, to produce a uniform displacement front and hence improve oil bank recovery. However, mobility control of the polymer solution is challenged by the salinity of formation brine. This limitation is encountered in multiple approaches, as outlined in the following:  
* Polymer solutions are prepared in high concentrations, to overcome the effects of formation brine salinity. There is a decrease in polymer in situ viscosity, but high concentrations of polymer solutions can sweep the reservoir to contribute to additional oil recovery. On a commercial scale, high-concentration polymer flooding was performed in the Daqing Field and produced 20% original oil in place (OOIP) [37]. Field tests concluded that high-concentration polymer injection is safe and commercially economical.
* A couple of pilot tests and many laboratory experiments have concluded that low-salinity pre-flush can be performed to decrease the salinity and hardness of formation brine [38; 39; 40]. Polymer flood after the pre-flush is expected to have suitable in situ polymer viscosity, hence contributing to higher oil recovery.
* Recently, the concept of the hybrid EOR method, using combined low-salinity water flooding with polymer flooding, has also shown promising results for oil recovery [41; 42]. Low salinity also decreases the salinity of formation brine in a similar manner as pre-flush and polymer flooding and afterward retains good mobility control.
* Mixing polymer solutions in low-salinity water is also an effective approach to handling harsh conditions for reservoir brine [43; 44; 45]. This method produces high-viscosity polymer solutions, utilizing the lower concentrations of the polymer product.  
The presented information is good evidence of the dependence of polymer mobility control on formation/mixing brine and will thus be advantageous for investigating the role semi-harsh conditions for polymer viscosity in situ response.
# RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations
Among all experiments, the Radar and IMERG dataset results of the examples in Figure 8 showed the most similar patterns with radar observations for heavy rainfall regions. Figure 8: The examples on 01 UTC 27 July 2019 of prediction results with RAIN-F+ dataset, radar and IMERG fusion dataset, radar only dataset, and reference radar observations depending on the resolution of \(32\times 32\) (**upper row**), \(64\times 64\) (**middle row**), and \(256\times 256\) (**bottom row**). Figure 9: The reference rain maps on 01 UTC 27 July 2019 of radar, IMERG, and surface observations from RAIN-F+ dataset. For all input resolution types, the radar-only dataset shows underestimated prediction results, while the results from the multi-modal dataset show heavier rainfall over the comparative coverage of the area. This trend is also shown in the scatter plots for all validation dataset represented in Figure 10. The scatter plots from the radar-only dataset showed the limitation in predicting rain rate over 10 mm/h for all input resolution types. The scatter plots from the RAIN-F+ dataset are not much different for the various input resolution, while others are considerably varies depending on the resolution.