esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Comparative Analysis
#### 5.2.3 Impact of Average Energy Consumption  
The average energy consumption refers to the average amount of energy consumed by the nodes in the network. The greater the energy consumption, the faster the nodes get exhausted. In Figure 8, the average energy consumption of the proposed work is compared with the existing works with respect to simulation time. From the figure it is clear that the simulation time and average energy consumption are directly proportional to each other. The average energy consumption of the proposed MiA-CODER mechanism is low  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **\# of Sensor Nodes** & **Simulation Time (s)** \\ \hline MiA-CODER & 94.95 \(\pm\) 0.5 & 94.26 \(\pm\) 0.5 \\ SRC & 83 \(\pm\) 0.5 & 86.2 \(\pm\) 0.5 \\ H3 & 77.35 \(\pm\) 0.5 & 81.4 \(\pm\) 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative analysis of coverage rate.  
Figure 6: Simulation time vs. coverage rate.  
compared to other existing works due to the implementation of Sierpinski triangles-based unequal clustering and sleep scheduling of nodes in the network. This reduces the energy consumption of the nodes in the network thereby increasing the lifetime of the nodes. The existing approaches do not provide an efficient energy consumption mechanism which increases the energy consumption of the nodes and reduces the lifetime of the network.
## 4 Implementation
### Baseline Algorithms  
To fully explore and evaluate the contribution of EFTransformer, we utilize four baseline methods mentioned below for comparison. The C4.5 decision tree directly classifies the data after channel aggregation and serves as a traditional baseline. CIC-RF, following [35], extracts flow-level statistical features, merges channels, and uses a random forest for malicious traffic detection. FCM-SVM, based on [36], combines fuzzy C-means clustering with a Gaussian kernel SVM to first cluster and then classify the data. Enhanced-MLP, from [37], uses a multi-layer perceptron with a clustering algorithm and introduces Dropout to improve generalization. These methods were compared with the proposed EFTransformer.  
C4.5 decision tree: For the data after channel aggregation, we use the C4.5 decision tree as the classifier for analysis.  
CIC-Random Forest (RF) [35]: CIC-Flow extracts statistical flow features, including the time interval, packet count, and duration; performs channel merging based on this; and uses a random forest with 200 Bootstrap aggregate decision trees for malware detection.  
FCM-SVM [36]: For the data after channel merging, a hybrid detection method combining fuzzy C-means (FCM) clustering and a radial basis function support vector machine (RBF-SVM) is adopted. FCM is responsible for clustering the input data, and the SVM with a Gaussian kernel shows strong nonlinear separation ability that is suitable for the detection of encrypted malicious traffic.  
Enhanced-Multi-layer Perceptron (MLP) [37]: The enhanced version combines an MLP with a clustering algorithm and uses its deep fully connected architecture to effectively model nonlinear relationships in network traffic data. The MLP is usually implemented with a two-hidden-layer configuration, and the ReLU activation function is used to solve the common gradient vanishing problem in deep networks. In addition, a Dropout layer is introduced during the training process to randomly deactivate some neurons, thereby preventing overfitting and improving the generalization ability of the model.
## 5 Discussion
### Effect of Attention Mechanism on the Accuracy of the Model  
Landslide deformation is influenced by numerous factors such as topography, geomorphology, stratigraphic structure, rainfall, and earthquakes. Their impacts on the landslide are reflected in the monitored displacement data. This leads to the prediction of displacement at a specific time in the future. Displacements at different times in the early stage will have varying effects on the prediction. The T-GCN model comprises GCN and GRU neural networks. The GRU can capture the temporal correlation of landslide displacements, but it is not trained to focus on the important parts of historical time series data. After integrating the attention mechanism into the GRU, the model can automatically learn the important information hidden in the data, thereby enhancing the prediction performance of the model.  
As depicted in Figure 9, by comparing the prediction results of two spatiotemporal prediction models, T-GCN and A3T-GCN, at 10 monitoring points on the Tangjiao 1# landslide, it can be clearly observed that regardless of whether MAE, MAPE, or RMSE is used as an evaluation index, on the whole, the A3T-GCN model has higher prediction accuracy at most monitoring points (a smaller value of the evaluation metric indicates higher accuracy). This demonstrates that integrating the attention mechanism into the T-GCN can improve the prediction accuracy.
## 3 Results and Discussions
### Fitting of Curves  
It is well known that the permeability information of sand samples is very important for groundwater exploration, and the induced polarization method is sensitive to hydraulic parameters. The time required for ions to reach equilibrium after the current is cut off in the electric double layer theory is relaxation time, and this diffusion seems to have a  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Grain Size (mm)**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Water**} & \multicolumn{2}{c}{**Piezometer Water**} & \multicolumn{2}{c}{**Water Level**} & \multirow{2}{*}{**Hydraulic**} & \multirow{2}{*}{**Permeability**} & \multirow{2}{*}{**Permeability**} \\  & & & **Flow** & & **Level (cm)** & & & **Gradient** & **Coefficient** & **Permeability** \\  & & & **\(\mathbf{w}\) (m\({}^{3}\))** & & & **\(\mathbf{H_{1}}\)** & \(\mathbf{H_{2}}\)** & **(cm)** & **\(\mathbf{I}\)** & **K (m/s)** & **(m\({}^{2}\))** \\ \hline
0.1-0.2 &  
relationship with permeability. Therefore, grain size is used as a link to analyze permeability and relaxation time in this paper.  
According to the experimental data, the power function equation can be used to fit the relaxation time and permeability of the same grain size sand sample; thus, the empirical equations for the relationship between the permeability and relaxation time can be obtained, and the relationship between relaxation time and permeability can be established. The fitting equation is given below.  
\[k=0.003\tau^{1.4315}. \tag{10}\]  
The relationship between relaxation time and permeability within the same grain size is shown in Figure 8 below. The dotted line in Figure 8 represents the fitted curve.
# Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey
## 3 Adaptive Detrending, Denoising, Multiscale Decomposition, and Fractal Analysis  
Observational data may manifest both ordered and disordered behavior. To fully characterize a complex signal, it is desirable to synergistically use chaos and random fractal theory [38]. However, this goal is not easy to achieve, since a measured data set often contains noise and may also be nonstationary. This makes detecting chaos very difficult. On the other hand, many phenomena contain a rhythmic activity, such as diurnal cycle. This makes fractal analysis difficult since the essence of a fractal is scale-free. To tackle these problems, frequency-domain filtering and wavelet analysis have been widely used to filter away the undesired features in the data. With the rapid accumulation of complex data in all branches of science and engineering, it is important to have better approaches to solve these problems. In this section, we discuss an adaptive algorithm, which has a number of interesting properties: (1) it can accurately determine a trend in the signal; depending on the purpose of applications, one may treat the trend and associated nonstationarities as noise, and remove them, or retain them, as the signals one wishes to further study (such as the global warming trend); (2) it is more superior in reducing noise in the signals than linear filters, wavelet methods, and chaos-based methods; (3) it can conveniently decompose a complex signal into many functions of different frequency; (4) it is excellent in obtaining fractal properties from the data, especially when the data contain a strong and nonlinear trend. The method has been successfully applied to study traffic flow [146; 174], various kinds of geophysical data including soil temperature, soil moisture, air temperature, and wind speed [175; 176; 177], tree rings [147], variation of electricity consumption with time [179], single neuron firing [145], clinical scalp EEG [180], ngram usage [149], quantum modeling of exciton diffusion in light harvesting systems [181], sentiments in novels [182; 183], newspaper advertisements [184], textual cultural heritage [185], and global terrorism [148]. The method will be very useful for analyzing various kinds of geophysical time series that have been rapidly accumulating in recent years. Here, we will only present the key elements of the method; a concrete example of combining this method with a machine-learning method (random forest) for distinguishing epileptiform discharges from normal electroencephalograms can be found in [PERSON] et al. [180].
## 3 Our Approach
### Applying Tensor Decomposition with Different Rank Methods
#### 3.3.2 Tucker Decomposition  
Tucker tensors are composed of core tensors multiplied by each matrix along the mode [43]. Tucker decomposition for the Nth tensor decomposes the outer product matrices as follows:  
\[\mathcal{X}=\sum_{r_{1}=1}^{R_{1}}\sum_{r_{2}=1}^{R_{2}}\cdots\sum_{r_{R}=d}^{ R_{d}}g_{r_{1}r_{2}\cdots r_{d}}\mathbf{u}_{\mathbf{1}_{r_{1}}}\circ\mathbf{u}_{ \mathbf{2}_{r_{2}}}\circ\ldots\mathbf{u}_{\mathbf{d}_{r_{d}}} \tag{3}\]where r is the rank and g is the core tensor.  
The factor matrices (\(\mathbf{U_{1}}\),\(\mathbf{U_{2}}\), and... \(\mathbf{U_{d}}\)) can be considered principal components for every mode. The core tensor (\(\mathcal{G}\in\mathbb{R}^{R_{1}\times R_{2}...\times R_{d}}\)) shows the different interactions between the components [43].  
There are several available algorithms for determining the Tucker decomposition for a given tensor, including High-Order Singular Value Decomposition (HOSVD) and High-Order Orthogonal Iteration (HOOI). HOSVD can be considered the basic definition of PCA, in which the best component that captures the variations in mode n is found. In this paper, we use HOOI Algorithm 3, an alternative least squares (ALS) algorithm that uses the HOSVD outcome for initialization of the factor matrices [44].  
Tucker decomposition is used on convolutional and fully connected layers. Finding the best Tucker approximation is an NP-hard problem. We use the same approaches we used for CP decomposition to select the Tucker rank.  
```
0: Data tensor \(\mathcal{X}\in\mathbb{R}^{n_{1}\times n_{2}\times...\times n_{d}}\), ranks \(\mathbf{R}\) for each mode.
0: Core tensor \(\mathcal{G}\) and factor matrices \(\boldsymbol{U}_{1}\in\mathbb{R}^{n_{1}\times R},\boldsymbol{U}_{2}\in\mathbb{ R}^{n_{2}\times R},\ldots,\boldsymbol{U}_{d}\in\mathbb{R}^{n_{d} \times R}\).
1:procedureHOOI-Tucker(\(\mathcal{X},\mathbf{R}\))
2: Initialize \(\boldsymbol{U}_{1},\boldsymbol{U}_{2},\ldots,\boldsymbol{U}_{d}\) using HOSVD
3:while criteria not satisfied do
4:for\(n=1,\ldots,N\)do
5:\(\boldsymbol{Y}_{n}\longleftarrow\mathcal{X}\times\boldsymbol{U}_{1}^{T} \times\cdots\times\boldsymbol{U}_{n-1}^{T}\)\(\times\boldsymbol{U}_{n+1}^{T}\times\cdots\times\boldsymbol{U}_{N}^{T}\)
6:\(\boldsymbol{U}_{n}\longleftarrow\boldsymbol{R}_{n}\)\(\triangleright\) Leading singular vector of \(\boldsymbol{Y}_{n}\)
7:endfor
8:endwhile
9:return\(\mathcal{G},\boldsymbol{U}_{1},\boldsymbol{U}_{2},\ldots,\boldsymbol{U}_{d}\)
10:endprocedure
```  
**Algorithm 3** HOOI for Tucker Decomposition [44]
## 3 Case Analysis and Discussion
### Cluster Results Verification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & \multicolumn{4}{c}{\(R_{3}\)} & \multicolumn{2}{c}{\(R_{4}\)} \\ \cline{2-7}  & **Mean** & **Range** & **Percent Over Threshold** & **Mean** & **Range** & **Percent Over Threshold** \\ \hline Dangerous & 0.159 & [0.003, 2.859] & 10.98\% & 0.647 & [0.005, 3.584] & 4.02\% \\ \hline Aggressive & 0.136 & [0.001, 2.771] & 7.76\% & 0.605 & [0.001, 1.563] & 3.24\% \\ \hline Safe & 0.117 & [0.001, 2.421] & 5.68\% & 0.428 & [0.002, 2.036] & 0.51\% \\ \hline Conservative & 0.111 & [0.002, 1.659] & 4.95\% & 0.294 & [0.001, 1.564] & 0.57\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cluster results of the car-following risk and lane-changing risk indicators. Table 10 presents a comparison between the driving behavior risk measurements obtained from different models and the expert evaluation results. The average error was calculated as the ratio of the total number of cluster results measured by the model to the expert evaluation results. The error range was determined by counting the error size for each experimental vehicle. Firstly, when only considering the lateral stability indicator and the longitudinal stability indicator, the average error of the model risk measurement was the largest, with a significant discrepancy between the results identifying the dangerous and conservative types and the expert evaluation results. Subsequently, the car-following risk indicator and the lane-changing risk indicator were added to the model, resulting in a 4.36% and 5.67% improvement in the model accuracy. This demonstrated that the introduction of two indicators, the car-following risk indicator and the lane-changing risk indicator, could effectively enhance the evaluation of driving behavior risk. Finally, the measurement results of the risk measurement model composed of four indicators that we established were consistent with the expert evaluation results, with an accuracy rate of 95.54%. Through experimentation, it could be confirmed that the indicator setting of the driving behavior risk measurement model is reasonable and effective. In contrast to previous research [5; 33], which often focused on individual indicators (e.g., following distance and speed) or specific behaviors (e.g., serpentine driving), our model accounts for lateral and longitudinal stability, car-following risk, and lane-changing risk to provide a more comprehensive quantification of the driving behavior risk. The trajectory feature variables used in our model can be easily and cost-effectively extracted from surveillance and UAV videos. This will enables a real-time risk assessment of the driving behavior.
## 4 Experiment and Results
### Data Augmentation and Data Equalization  
#### 4.2.1 Data Augmentation  
If the number of training samples is too small, the model is prone to over-fitting, which has a bad classification performance. Therefore, existing samples need to be augmented.  
We mainly perform data augmentation operations, such as flipping transformation, rotation, adding noise, and image brightness contrast adjustment on the square remote sensing image obtained by cutting. These methods can increase the diversity of the data set, which can obtain a model with strong generalization ability. Due to the diversity of the orientation of the objects of the remote sensing image, the direction of the rotary operation can be 90 degrees, 180 degrees and 270 degrees clockwise. For a square image, the shape of the rotated image does not change. The process of acquiring remote sensing images is greatly affected by the environment. Here, we increase sample diversity by adjusting the illumination and contrast of the image.  
The introduction of Gaussian noise can effectively suppress the over-fitting of the model. This allows the network to learn low frequency features in the image relatively easily. Because high frequency features can affect the learning of low frequency features, is the network prone to overfitting when learning high frequency features. Adding Gaussian noise with a mean of zero to the image can distort its high-frequency features and reduce its impact on the network learning process. In addition, the characteristics of the low frequency can be used to cancel the influence of Gaussian noise.  
Figure 5: Partial training set and labels.  
Figure 6: Part of the cutting images in the training set.  
An example of a data set augmented is shown in Figure 7.
## 4 Results and Discussion
### Comparison between Improved YOLOv4 and Other Target Detection Algorithms  
Three target detection algorithms that are often used in crop identification were selected to further verify the effectiveness and superiority of the improved algorithm. The self-collected data sets of stem nodes in the wild field environment were used to test and evaluate their performance, as shown in Table 3. In order to ensure robustness, we have conducted several experiments, but we found that there was almost no difference in the results of each performance evaluation experiment. The following, main reasons might explain why this is the case. First of all, the training set and test set was the same dataset to verify the performance of each algorithm, which can greatly reduce the randomness of the results. Secondly, the above-mentioned deep learning algorithm ensures a fair degree of robustness through the large amount of data, and the error can be corrected within the neural network. Finally, because the recognition object of this paper is only a single target, which greatly reduces the difficulty of recognition, the difference between each experiment is very small. Therefore, under the condition of the same experimental hyperparameter settings, the differences between the repeated experiments were inconsiderable.  
Figure 13: Distribution diagram of \(\gamma\) coefficient of BN layer. (**a**) Normal training epochs. (**b**) Sparse training epochs.  
From Table 3, the improved YOLOv4 algorithm had the strongest comprehensive performance among the four algorithms. In terms of average precision (_AP_), the improved YOLOv4 model was 1.3% and 0.3% higher than SSD and YOLOv3, and only 0.1% lower than before the improvement. However, in terms of the _Params_, _FLOPs_ and model size, the improved YOLOv4 algorithm was only about 1/3 of SSD and 1/10 of YOLOv3 and YOLOv4. The comparison of results adequately illustrated the superiority of the improved YOLOv4 algorithm model.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 6 Learning Paradigms
### Transfer Learning  
Many ASC algorithms rely on well-proven neural network architectures from the computer vision domain such as AlexNet [73; 79], VGG16 [12], Xception [56], DenseNet [44], GoogLeNet [79], and Resnet [38; 69]. Transfer learning allows the fine-tuning of models that are pretrained on related audio classification tasks. For instance, [PERSON] et al. used the AudioSet dataset to pretrain four different neural network architectures and fine-tune them using a task-specific development set [26]. Similarly, [PERSON] et al. took a pretrained SoundNet [80] network as basis for their experiments [27; 81]. [PERSON] et al. used the VGG16 model as the seed model, which was pre-trained for object recognition in images [12]. [PERSON] et al. pre-trained a CNN in a supervised fashion using weak label annotation of the AudioSet dataset. The authors compared three transfer learning strategies to adapt the model to novel AED and ASC target tasks [82]. In the ASC tasks of the DCASE challenge, the use of publicly-available external ASC datasets is explicitly allowed. However, most recently winning algorithms did not use transfer learning from these datasets, but focused instead on the provided training datasets combined with data augmentation techniques (see Section 4).
## 3 The Proposed Co-Training Scheme
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Algorithms**} \\ \cline{2-6}  & \(Co(Vote^{SEC}_{soft})\) & \(Co(Vote(all))\) & \(Co(Vote(DT,LR))\) & \(Co(Vote(all))\) & \(Co(LR)\) \\ \hline bands & **0.646 \(\pm\) 0.096** & 0.632 \(\pm\) 0.068 & 0.643 \(\pm\) 0.082 & 0.603 \(\pm\) 0.049 & 0.594 \(\pm\) 0.028 \\ breast & 0.652 \(\pm\) 0.07 & 0.664 \(\pm\) 0.076 & **0.67 \(\pm\) 0.118** & 0.669 \(\pm\) 0.032 & 0.649 \(\pm\) 0.072 \\ bupa & 0.554 \(\pm\) 0.049 & **0.588 \(\pm\) 0.085** & 0.586 \(\pm\) 0.059 & 0.502 \(\pm\) 0.042 & 0.512 \(\pm\) 0.032 \\ chess & **0.966 \(\pm\) 0.013** & 0.947 \(\pm\) 0.007 & 0.962 \(\pm\) 0.01 & 0.955 \(\pm\) 0.008 & 0.947 \(\pm\) 0.011 \\ colic.ORIG & 0.745 \(\pm\) 0.056 & 0.761 \(\pm\) 0.043 & **0.762 \(\pm\) 0.039** & 0.708 \(\pm\) 0.083 & 0.735 \(\pm\) 0.049 \\ diabetes & 0.765 \(\pm\) 0.05 & **0.769 \(\pm\) 0.034** & 0.68 \(\pm\) 0.054 & 0.723 \(\pm\) 0.055 & 0.75 \(\pm\) 0.042 \\ h-statlog & 0.717 \(\pm\) 0.036 & 0.7 \(\pm\) 0.027 & 0.653 \(\pm\) 0.082 & 0.706 \(\pm\) 0.052 & **0.77 \(\pm\) 0.034** \\ kr-vs-kp & **0.966 \(\pm\) 0.019** & 0.947 \(\pm\) 0.008 & 0.963 \(\pm\) 0.012 & 0.953 \(\pm\) 0.008 & 0.946 \(\pm\) 0.012 \\ mammographic & 0.786 \(\pm\) 0.019 & **0.795 \(\pm\) 0.022** & 0.779 \(\pm\) 0.039 & 0.791 \(\pm\) 0.025 & 0.785 \(\pm\) 0.026 \\ monk-2 & **1 \(\pm\) 0** & 0.918 \(\pm\) 0.019 & **1 \(\pm\) 0** & 0.879 \(\pm\) 0.047 & 0.78 \(\pm\) 0.03 \\ pima & 0.673 \(\pm\) 0.018 & 0.664 \(\pm\) 0.027 & 0.652 \(\pm\) 0.034 & 0.664 \(\pm\) 0.033 & **0.685 \(\pm\) 0.03** \\ saheart & 0.65 \(\pm\) 0.034 & 0.7 \(\pm\) 0.073 & 0.675 \(\pm\) 0.038 & 0.688 \(\pm\) 0.039 & **0.716 \(\pm\) 0.019** \\ sick & 0.974 \(\pm\) 0.003 & 0.962 \(\pm\) 0.007 & **0.976 \(\pm\) 0.004** & 0.959 \(\pm\) 0.008 & 0.939 \(\pm\) 0.006 \\ tic-tac-toe & 0.807 \(\pm\) 0.069 & 0.805 \(\pm\) 0.037 & 0.799 \(\pm\) 0.056 & 0.733 \(\pm\) 0.038 & **0.822 \(\pm\) 0.036** \\ vote & 0.914 \(\pm\) 0.026 & 0.891 \(\pm\) 0.026 & 0.888 \(\pm\) 0.033 & **0.916 \(\pm\) 0.024** & 0.888 \(\pm\) 0.02 \\ wdbc & 0.958 \(\pm\) 0.021 & 0.963 \(\pm\) 0.01 & 0.943 \(\pm\) 0.031 & **0.972 \(\pm\) 0.012** & 0.956 \(\pm\) 0.02 \\ wisconsin & 0.974 \(\pm\) 0.006 & **0.98 \(\pm\) 0.01** & 0.935 \(\pm\) 0.04 & 0.972 \(\pm\) 0.011 & 0.969 \(\pm\) 0.016 \\ \hline \hline \end{tabular} Bold highlighted means the best value per dataset.

\end{table} 
Table 4: F\({}_{1}\)-score (\(\pm\)stdev) values for the best five variants (labeled ratio 10%).
## 3 Results and Discussions
### Iron-Reducing Power Analysis  
The iron-reducing power was measured as the EC\({}_{50}\). A lower value of EC\({}_{50}\) indicates a higher reduction power as it requires less mg of raw material to achieve a value of reducing power equal to 0.5. The ability of _M. nodifornum_ extracts to chelate ferrous ion was shown in Figure 3.  
The showed results are in accord with the previous (Figure 2) and confirm that the extracts of _M. nodifornum_, obtained with ethanol (EC\({}_{50}\) 17.10 \(\pm\) 0.20), N-hexane (EC\({}_{50}\) 15.11\(\pm\)3.27) and SFE (EC\({}_{50}\) 15.44 \(\pm\) 0.45), are stronger as antioxidants, in respect to the extract in water and acetone (\(p\) \(<\) 0.05).  
It was difficult to compare these data with the literature because, as observed by [PERSON] et al. [10], the reducing power of Fe\({}^{3+}\) extracts differs considerably, depending on the species of _Mesembryanthemun_ genera [10].  
Our results showed a comparable reducing activity among the SFE, ethanol and hexane extracts, respect tp water and acetone (\(p\) \(<\) 0.05) (Figure 3), suggesting to use the SFE technique to obtain ecofriendly _M. nodifornum_ extracts [7].  
Figure 3: Reducing power (EC\({}_{50}\), mg/mL) in different extracts of dried leaves of _M. nodiforum_. Lowercase letters (a–b) indicate significant differences between different solvents (\(p\) \(<\) 0.05).