esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Experiments
### Experimental Descriptions  
In this paper, NSL-KDD, UNSW-NB15 and CICIDS2017 datasets are used to evaluate the algorithm proposed.  
* KDD99 is the most famous and widely used dataset in the intrusion-detection field. However, it has been more than 20 years and does not cover various known attacks. More importantly, it has a large number of duplicate records, which will lead to algorithm deviation. NSL-KDD [43], a revised version of KDD99 that overcomes some native shortcomings of KDD99, is proposed. Like KDD99, each TCP connection record is represented by 42 items, with 41 attribute characteristics and 1 type identifier in NSL-KDD.
* The UNSW-NB15 dataset [44] is generated by the Australian Security Laboratory using the DIA PerfectStrom tool. The real normal network traffic and artificial attack traffic in modern networks are combined in the dataset. The dataset contains 9 types of attacks and 43 features in total. The first feature represents the sample number and can be deleted, so there are 42 available features.
* The CICIDS2017 dataset [45] is provided by the Canadian Institute of Network Security. It contains the real normal and attack traffic and is more suitable for simulating the existing network environment. In the CICIDS2017, the CICFlowMeter is used to extract more than 80 network flow features, including 6 basic features and more than 70 functional features.  
It is worth noting that the attack classes for the KDD99 and CICIDS2017 datasets include the main attack class and the sub-attack class. According to the literature, the main attack class is used in this paper. The number of features and the attack classes contained in the three datasets are shown in Table 1.  
The described experiments were carried out on an Intel(r) Core(tm) i5 CPU @ 1.70 GHz computer with 4.00 GB RAM running Windows10 Enterprise Edition (64 bit). The proposed soft-voting ensemble scheme was implemented using Python. The machine-learning classifiers used in this paper were implemented by calling the scikit-learn class library [46]. The PyCharm community edition 4.0.6 (Windows version) was used as the programming tool, and the program compiler environment was Python 3.7.0 (Windows version).  
Data preprocessing, including data cleaning, data normalization, feature selection, and data segmentation, was performed. The train_test_split () method in the scikit-learn was used to perform the dataset segmentation. The randomly selected data was divided into training set and testing set according to the ratio of 1:1. The SelectKBest function in the scikit-learn library was used to select features. The importance of features was evaluated by the chi-square value.
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
## 5 Conclusions  
In this paper, we investigated a QoS-based D2D NOMA system in a multi-cell interference-aware cooperative environment. The topology of D2D transmitters was modeled by GPP. As the users are located in the close proximity to each other, the power allocation coefficients were modeled on user data rate instead of channel conditions. A closed-form expression for the outage probability of Q-CNOMA was derived for the end receiver when the power allocation is based on users' desired rates. Simulation results further verify the correctness of the analytical results with Monte-Carlo simulations. Sim  
Figure 4: Impact of \(M_{2}\) and \(\alpha\) on the outage probability of the proposed Q-CNOMA system.  
Figure 3: Impact of \(M_{1}\) and \(R_{e}\) on outage probability of proposed Q-CNOMA system.  
ulation results demonstrate that the proposed Q-CNOMA system achieves better outage probability than a Q-CNOMA system with fixed power allocation coefficients, Q-NOMA system with enlarged coverage area, NOMA-based system, and paired D2D OMA system. The simulation results further elaborate that increasing the number of users in Phase I increases the outage. However, this effect is reduced by introducing relay nodes in the same cell. As compared to the number of users in Phase I, the outage probability is more affected by increasing the users in Phase II, as verified by simulations. The proposed scheme can have prominent applications in future sensor networks where nodes have a limited power source. Future works can be carried out on relay selection in the proposed Q-CNOMA system, to further enhance the system's performance. Another interesting dimension is to investigate the current system underlying the cellular system with multiple antenna transceivers.  
Conceptualization, M.A.H. and A.A.; Methodology, M.A.H. and A.A.; Software, T.A., Formal analysis, A.A., T.A., S.S. and A.M.; Investigation, T.A., A.A., W.N. and M.A.H.; Resources, A.A., A.M. and I.R.; Data curation, T.A.; Project administration, T.A., S.S. and A.A.; Funding acquisition, T.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data is available upon request from the corresponding author. The authors declare no conflict of interest.
# Integrated Earthquake Catalog of the Ossetian Sector of the Greater Caucasus
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Agency Abbreviation** & **Agency** & **With Magnitude** \\ \hline AFAD & Disaster and Emergency Management Presidency, Turkey & 59 \\ \hline ATA & The Earthquake Research Center Ataturk University, Turkey & 1 \\ \hline AZER & Republican Seismic Survey Center of Azerbaijan National Academy of Sciences, Azerbaijan & 22 \\ \hline BCIS & Bureau Central International de Sismologie, France & 1 \\ \hline CSEM & Centre Sismologique Euro-Mediterranean (CSEM/EMSC), France & 190 \\ \hline DDA & General Directorate of Disaster Affairs, Turkey & 78 \\ \hline DRS & Dagestan Branch, Geophysical Survey, Russian Academy of Sciences, Russia & 130 \\ \hline EIDC & Experimental (GSETT3) International Data Center, U.S.A. & 1 \\ \hline IDC & International Data Centre, CTBTO, Austria & 9 \\ \hline ISC & International Seismological Centre, United Kingdom & 975 \\ \hline ISK & Kandilli Observatory and Earthquake Research Institute, Turkey & 13 \\ \hline MOS & Geophysical Survey of Russian Academy of Sciences, Russia & 1193 \\ \hline NEIC & National Earthquake Information Center, U.S.A. & 3 \\ \hline NNC & National Nuclear Center, Kazakhstan & 1 \\ \hline NORS & North Ossetia (Alania) Branch, Geophysical Survey, Russian Academy of Sciences, Russia & 340 \\ \hline SPITAK & Armenia & 6 \\ \hline TIF & Institute of Earth Sciences/National Seismic Monitoring Center, Georgia & 896 \\ \hline TOTAL: & 3918 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the ISC catalog. (see Supplementary Materials). The application of this method to create the most complete earthquake catalog with a unified magnitude scale for the Arctic zone of the Russian Federation is given in [4; 5; 6]. For this reason, a description of the method is not provided in this article. The only thing that should be noted is that it is based on the construction of a three-parameter metric \(Ro\):  
\[Ro=\sqrt{\frac{DT^{2}}{\sigma_{T}{}^{2}}+\frac{DX^{2}}{\sigma_{X}{}^{2}}+\frac{ DY^{2}}{\sigma_{Y}{}^{2}}}, \tag{1}\]  
where \(\sigma_{T}\), \(\sigma_{X}\), \(\sigma_{Y}\) are the standard deviations of time, longitude, and latitude differences between the nearest events from the two input catalogs. Prior to merging, each of the input catalogs (Table 1) was examined for internal duplicates. Statistical analysis did not reveal any anomalous groups of close events (see Supplementary Figure S2).
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 3 Related Work
### Time Series Data Augmentation  
The remarkable success of deep learning in fields such as computer vision (CV), natural language processing (NLP), and speech processing heavily relies on the availability of large-scale training data to avoid overfitting. However, many time series tasks, such as classification, prediction, and anomaly detection, face the challenge of limited labeled data. Data augmentation, which generates synthetic data covering unexplored regions of the input space while maintaining label consistency, has emerged as a crucial tool for improving model generalization. Although data augmentation techniques in the domains of images and speech have matured, time series data, with their unique temporal dependencies, multivariate dynamic associations, and task-specific nature (such as the differences between classification and anomaly detection goals), require the design of specialized augmentation methods.  
Early research primarily borrowed simple transformations from the image domain, such as cropping, flipping, and jittering, and applied them to time series data [10]. Subsequently, researchers began exploring the transformation of time series into the frequency domain (e.g., Fourier Transform) or time-frequency domain (e.g., wavelet transform), where modifications to spectral components generated new samples [11]. Another approach involved the use of signal decomposition techniques, such as STL and EMD, to separate the trend, seasonality, and residual components, with independent enhancement of these sub-components followed by reconstruction [12].  
With the rise of deep learning, generative models such as GANs and VAEs have been extended to time series generation, notably with models like TimeGAN and AnoGAN. Another line of work focused on embedding-space augmentation, where perturbations were applied to intermediate representations in models, such as LSTM hidden states, to enhance feature robustness [13]. Additionally, automatic augmentation techniques leveraging reinforcement learning or optimization algorithms have been explored to dynamically search for optimal augmentation strategies [14; 15].
## 3 Results
### Sample Generation and Data Series Analysis with Traditional Three Features  
The process of generating the simulated data samples is as follows. The load level is randomly set to 0.9, 1.0 or 1.1 times of the base load (the generator outputs are adjusted proportionally). Three-phase short-circuit faults are applied on the selected 33 transmission lines (excluding transformers and islands), at 10% to 80% positions, with an interval of 10%. The circuit breakers might trip the faulted line at 0.05 s, 0.1 s, 0.15 s or 0.20 s after the fault occurrence. A total of 3168 samples are obtained, of which two-thirds are randomly selected as training samples, and the rest are test samples.  
The sampling time step is 0.01 s. The sampled data of all three-time stages include two consecutive points before the fault, two consecutive points immediately after the fault occurrence, and 6 consecutive points after the fault is cleared. The system variables original selected as input features are rotor angles of 10 generators, rotor speeds of 10 generators and voltage amplitudes of 39 buses, all in units of per unit (p.u.). To verify the effectiveness of the corresponding four sampling series in Section 2.1.1, the basic prediction results of these three types of input features are compared in Figure 2.  
In this paper, the SVM model used is libsvm2.0 [38], and the optimal penalty coefficients C and RBF kernel parameters of the SVM classifiers are obtained by grid search [36] and 5-fold cross-validation using the training samples. Then, the entire training samples and the optimal SVM parameters are used to retrain the SVM classifiers. Finally, the prediction accuracy of SVM classifiers is obtained by testing the test samples. The results for these three types of input features from all four sampling series are shown in Figure 9.  
It can be seen from Figure 9 that Series 4 has the highest prediction accuracy among all 4 series, which is greater than 98.00% for all three types of input features; while the accuracies are lower than 97.35%, 97.35% and 97.60% for Series 1, 2 and 3, respectively. This means that the feature set that contains all of the pre-fault, during-fault and post-fault stages can better characterize the transient behavior of the system. Therefore, in the following subsections, the input features will include the 10 data points (sampled like Series 4) of each trajectory variable from all three-time stages.
## 3 Model of Underwater Wireless Sensor Network
### Energy Consumption for Receiving Data  
The energy required to receive data is related to the packet size and energy consumed when receiving 1 bit data. In general, the constant \(E_{elec}\) is used to represent energy consumed by the node when it receives 1 bit data. Therefore, energy consumption when receiving \(n\) bit data is:  
\[E_{r}=n\times E_{elec} \tag{7}\]
## 3 Model and Assumptions
### Deployment Distribution  
During the actual deployment, sensors naturally land at random locations in their territory. We use a two-dimensional Gaussian distribution used extensively in the literature [18, 21, 26, 27] to model the likelihood of a node being a certain distance from its intended group deployment point. While we use the Gaussian distribution model in this paper for exposition purposes, our detection algorithm **Disccard** does not preclude use of other probability distribution functions. Let \((x_{G},y_{G})\) be the intended group deployment point for nodes in a group \(G\). A sensor node \(n\in G\), during deployment, lands at the coordinates \((x,y)\) as given by the Gaussian distribution:  
\[f(x,y) = \frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(x-x_{G})^{2}}{2\sigma^{2}} }\cdot\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(y-y_{G})^{2}}{2\sigma^{2}}} \tag{1}\] \[= \frac{1}{\sigma^{2}2\pi}e^{-\frac{(x-x_{G})^{2}+(y-y_{G})^{2}}{2 \sigma^{2}}}\]  
Here, \((x_{G},y_{G})\) is the group deployment point for group \(G\) and \(\sigma\) is the standard deviation of the Gaussian distribution. We note that Equation (1) above denotes the probability distribution of having a node at a given point \((x,y)\).
###### Abstract  
Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate-value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m\({}^{3}\), and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.  
remote sensing; GOCI; chlorophyll-a; data reconstruction; DINEOF; CS-DINEOF +
Footnote †: journal: Applied Sciences
# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 4 Experiments and Discussion
### Method Comparison
#### 4.4.2 Qualitative Analysis  
Multi-Scene Comparison  
Solid waste piles are widely distributed in various urban land use types, including roads, buildings, grasslands, forests, rivers, etc. Figure 6 shows the segmentation results in the building and road scenes. PSPNet and DAnet models have false detections (marked in red), mistakenly recognizing building and road edges as solid waste. FCHarDNet, ConvNeXt, UNet, SFNet, and SwinTransformer models have a large number of missed detections (marked in yellow), while the SWM proposed in this study has the fewest missed detections, consistent with the quantitative analysis results. Figure 7 shows the segmentation results in the river-forest scene. All models are able to accurately detect it, with a small amount of gray garbage on the bank in the upper left corner. Among them, PSPNet, DAnet, and SegFormer have missed detections on the boundaries. Figure 8 shows the segmentation results in the road-grassland scene. There is a single yellow pattern on the road, which is a pedestrian's parasol. Deeplabv3 plus and SwinTransformer mistakenly identify it as solid waste, while other models generally recognize the garbage on the grassland as background. However, the segmentation result of the SWM in this study is basically consistent with the label boundary. Overall, the diverse scales, textures, shapes, and distribution environments of solid waste make it difficult to accurately segment the targets from the background. The main advantage of our proposed model lies in its higher recall rate, which enables the detection of a larger number of missed instances. As seen from Figures 6-8 (showing three demonstration areas), our proposed model tends to extract the solid-waste piles in their complete form, i  
Extensive Examples  
In order to further demonstrate the reliable performance of the SWM in large-area solid waste segmentation tasks, Figure 9 presents segmentation results in diverse scenes. The segmentation results show that three types of garbage can be segmented, indicating the generalization ability of the model proposed in this paper to accurately segment complex boundaries of solid waste in different distribution scenarios. The model is capable of achieving complete segmentation of solid waste occurrences at different scales, with very few missed and false detections, making it suitable for large-area solid waste monitoring.  
Figure 8: Segmentation results in road and grassland scenes.  
Figure 7: Segmentation results in river and forest scenes.
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 3 Proposed Algorithm  
After considering a decomposable Volterra model, a low-complexity adaptive Volterra algorithm based on a filtered-error structure is proposed in this section for both nonlinear active noise control (NANC) with a linear secondary path (LSP) and that with a nonlinear secondary path (NSP). By using the Kronecker product decomposition, the system's computational complexity can be reduced dramatically, which is promising for active noise control systems.
# An Automatic Transformer from Sequential to Parallel Java Code
## 7 Discussion
### Validity Treats  
Our approach aims at ensuring the correctness of transformation and performance improvements by minimizing the potential overhead of parallel threads. As the approach is based on the static analysis of code, there are some scenarios that cannot be evaluated.  
Firstly, when subsequent instructions, or blocks of instructions, are found to have some data dependence, and their needed estimated computational efforts are low, then we do not transform the code into a parallel one. On the one hand, this prevents cluttering the code with instructions that start parallel execution and perform synchronization when performance gain is uncertain. However, on the other hand, the analysis only estimates the computational efforts of instructions, and inaccuracies of estimates could occur, e.g., when there are cycles, because the executed repetition numbers are unknown, when there are calls to methods provided by external libraries, etc. For this, our tool might not gather all potential performance gains. The estimation of execution time could be improved, and some indications given by the developer could be considered.  
Secondly, the static analysis alone cannot guess the method that will be executed at runtime when the analyzed code uses polymorphism. In the case of polymorphism, we assume the worst case, and consider the less favorable data dependence among methods and the less favorable performance gains. Therefore, in some cases, we could miss opportunities for parallel execution.  
Thirdly, when a fragment of code uses several references to objects belonging to the same type, by statically analyzing the code, it is very difficult to distinguish the different instances, e.g., when a variable is conditionally assigned one among several references. We miss the opportunity to have parallel execution for distinct objects and further opportunities for performance gains.  
Finally, another possible area for performance gain that could be overlooked by our approach is when instruction reordering is possible, i.e., the subsequent dependent statements could be moved far away from each other while preserving correctness.  
The above limitations have been embedded in our analysis and tool to ensure that when executing the transformed code, the behavior is correct, although at the expense of performance in a few cases.