esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Machine Learning Applications in Big Data
### Retail and E-Commerce  
In the retail and e-commerce industries, ML plays a pivotal role in enhancing customer experience, optimizing operations, and driving sales. One of the most visible applications is in personalized recommendations, where ML algorithms analyze customer data, including browsing history, purchase patterns, and demographic information to recommend products tailored to individual preferences. Big companies have successfully implemented recommendation systems that significantly boost customer engagement and sales [62; 63; 64; 65].  
Predictive analytics is another critical application in retail, where ML models forecast demand, optimize pricing strategies, and manage inventory. By analyzing historical sales data, market trends, and external factors such as seasonality and economic conditions, these models can predict which products are likely to be in demand and adjust pricing accordingly to maximize revenue. ML also enables dynamic pricing, where prices are adjusted in real time based on factors such as competitor pricing, customer behavior, and inventory levels [66; 67; 68; 69].  
Besides personalization and predictive analytics, ML is used in supply chain management to optimize operations from production to delivery. By analyzing data from various sources, including suppliers, logistics providers, and point-of-sale systems, ML models can optimize inventory levels, reduce lead times, and minimize stockouts. This leads to more efficient operations, lower costs, and better customer service [70; 71; 72; 73].
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 4 Results
### Experiment Results Analysis

Table 2: Comparison of Longitude Prediction Errors Among Different Models. \begin{table}
\begin{tabular}{c c c c} \hline
**Model** & _MAE_ & _MSE_ & _RMSE_ \\ \hline PSO-BP & 0.03319885 & 0.00192588 & 0.04388492 \\ PSO-LSTM & 0.04516268 & 0.00475778 & 0.06897666 \\ PSO-GRU & 0.03420336 & 0.00276886 & 0.05261996 \\ PSO-BiLSTM & 0.01900496 & 0.00070055 & 0.02646798 \\ PSO-BiGRU & 0.03123733 & 0.00200800 & 0.04481072 \\ PSO-CNN-LSTM & 0.02013612 & 0.00087166 & 0.02952389 \\ PSO-CNN-GRU & 0.02916388 & 0.00147378 & 0.03838990 \\ PSO-GRU-LSTM & 0.01653731 & 0.00060200 & 0.02453578 \\ PSO-CNN-LSTM-Attention & 0.01806733 & 0.00075004 & 0.02738693 \\ ABC-CNN-RGRU-Attention & 0.02459865 & 0.00118986 & 0.03449439 \\ SSA-CNN-RGRU-Attention & 0.01623029 & 0.00096784 & 0.03111023 \\ WOA-CNN-RGRU-Attention & 0.02685349 & 0.00185097 & 0.04302294 \\ PSO-CNN-RGRU-Attention & 0.01508747 & 0.00054552 & 0.02335655 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of latitude errors predicted by different models. According to the visual comparison of Figures 11-22, the PSO-CNN-RGRU-Attention model demonstrated the best fit in the longitude and latitude prediction tasks. Furthermore, by analyzing the detailed evaluation metrics in Tables 2 and 3, it is clear that the PSO-CNN-RGRU-Attention model significantly outperformed other models, including PSO-BP, PSO-LSTM, PSO-GRU, PSO-BiLSTM, PSO-BiGRU, PSO-CNN-LSTM, PSO-CNN-GRU, PSO-GRU-LSTM, PSO-CNN-LSTM-Attention, ABC-CNN-RGRU-Attention, SSA-CNN-RGRU-Attention, and WOA-CNN-RGRU-Attention, in terms of both longitude and latitude prediction accuracy.
## 4 Results and Discussion
### Experimental Settings  
To simulate the edge device training, we established an experimental setup consisting of ten edge devices and a server equipped with an NVIDIA GeForce 3070 with 8 GB of memory. We conducted experiments using YOLOv8-N [46] with 3.2 MB and ResNet18 as the backbone networks and employed the following four datasets to evaluate the performance:1. CIFAR-10 dataset: A classic benchmark dataset primarily used for image classification. It comprises 60,000 32 \(\times\) 32 color images divided into 10 classes, with 6000 images per class.
2. Fashion-MNIST dataset: A popular benchmark dataset used for image classification tasks involving articles of clothing. It comprises 60,000 training samples and 10,000 testing samples, with each image being a 28 \(\times\) 28 grayscale picture.
3. COCO dataset: A widely used image recognition and annotation dataset which contains over 330,000 images covering 80 different object categories. Each image has detailed annotations, including bounding boxes, segmentation masks, and class labels.
4. TT100K dataset [47]: A large-scale traffic sign benchmark dataset comprising 100,000 high-resolution 2048 \(\times\) 2048 images that encompass 30,000 instances. The dataset exhibits diverse scenes, such as sunny, cloudy, and rainy conditions. Each instance with annotations has its labels, bounding box, and pixel-level masks.  
We adopted the mean average precision (mAP) and accuracy to assess the performance of the detection while also conducting a quantitative analysis of the communication parameters. The model's performance is evaluated under non-IID data with the Dirichlet(\(\alpha\)). The degree of non-IID is adjusted through parameter \(\alpha\) (\(\alpha>0\)), where a larger \(\alpha\) indicates datasets that are closer to being IID. The learning rate \(\eta\) is 0.01 and local epochs \(L\) is 3.
# Improved CycleGAN for Mixed Noise Removal in Infrared Images
## 2 Methods  
### Mixed Noise in Infrared Image  
Generally, the mixed noise in the infrared image mainly includes the following three components: one is the thermal noise generated by the electrical characteristics of the imaging device itself, the second is the channel noise introduced by the influence of interference signals during image transmission and demodulation and the third is the shot noise generated at the optical elements and optoelectronic devices in the infrared image acquisition system due to light variations in the imaging area [21]. Among them, the probability distribution of thermal noise and channel noise can be modeled as follows and is called Gaussian noise.  
\[p(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{1}{2}(\frac{x-p}{\sigma})^{2}} \tag{1}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of the Gaussian noise, respectively. A larger value of \(\sigma\) represents a stronger intensity of Gaussian noise [22].  
The probability distribution of the shot noise satisfies Equation (2) and is called Poisson noise.  
\[p(x)=\frac{e^{-\lambda}\lambda^{k}}{k!} \tag{2}\]  
where \(k\) is the obtained observed pixel value containing Poisson noise, \(\lambda\) denotes the average intensity of the noise at each pixel value and a larger value of \(\lambda\) represents a stronger intensity of Poisson noise.  
Although the above noises have multiple sources and different distribution forms, they all cause interference by being superimposed on the original image signal. Assuming that the pixel value at a certain place in a clean infrared image is \(X_{i}\), and the noise values of Gaussian noise and Poisson noise at the corresponding position are \(G_{i}\) and \(P_{i}\), respectively, the pixel value \(Y_{i}\) of the infrared image containing the mixed noise at that place can be expressed as follows:  
\[Y_{i}=X_{i}+G_{i}+P_{i} \tag{3}\]  
While the hybrid noise can be modeled as a superposition of Gaussian and Poisson distributions [23], the interference generated by thermal and channel noise is usually significantly larger than that of the shot noise in the imaging process of infrared images.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 5 Conclusions  
In this paper, we introduced ISTGCN, a novel spatio-temporal graph convolution network designed for traffic prediction, which consistently outperforms several state-of-the-art models. Our experimental results across multiple real-world traffic datasets demonstrate that ISTGCN achieves significantly lower error values across key metrics--RMSE, MAE, and MAPE--compared to models such as USTGCN, LSGCN, Graph WaveNet, ASTGCN, and DCRNN. Specifically, our model demonstrates an overall reduction of 37.80% in RMSE, 46.75% in MAE, and 28.49% in MAPE compared to the USTGCN model. These reductions highlight the improved accuracy and reliability of our proposed model in both short- and long-term traffic forecasting. ISTGCN leverages a more effective integration of spatio-temporal features, enabling it to better capture complex traffic patterns, especially over longer prediction intervals (15, 30, and 45 min). This results in enhanced predictive performance and reduced errors, particularly in long-term forecasts. These findings demonstrate the robustness of ISTGCN in modeling real-time traffic data, making it a more accurate and efficient solution for intelligent transportation systems.  
This research received no external funding.  
Not applicable.  
The datasets analyzed during this research are available from the PeMS dataset repository ([[[http://pems.dot.ca.gov/](http://pems.dot.ca.gov/)]([http://pems.dot.ca.gov/](http://pems.dot.ca.gov/))]) from May to June 2012. The pre-processed dataset used in this study can be accessed via the GitHub repository at [[[https://github.com/[PERSON]/USTGCN](https://github.com/[PERSON]/USTGCN)]([https://github.com/[PERSON]/USTGCN](https://github.com/AmitRoy7781/USTGCN))]. The authors declare that there are no conflicts of interest related to this publication. There are no financial, personal, or professional relationships that could have influenced the research.
## 3 Materials and Methods
### SMOTE for Data Balancing  
SMOTE [65] is a widely used technique for handling class imbalance when building a classifier. An imbalanced dataset is one in which the underlying output classes are not evenly distributed. When dealing with the classification challenge of an imbalanced dataset, SMOTE is often employed [66]. SMOTE generates synthetic samples for the underrepresented class through interpolation, enhancing data diversity and improving model generalization. Therefore, SMOTE was applied to address the class imbalance issue by increasing the minority class instances. While SMOTE introduces synthetic examples during training, this can sometimes lead to less interpretable decision boundaries as these synthetic data points do not represent real observations. Moreover, SMOTE may increase model variance if the generated samples are overly simplistic or do not capture true underlying data patterns. Therefore, the model's interpretability via SHAP and LIME was conducted on original test samples to preserve real-world interpretability. Furthermore, model performance was evaluated using 10-fold cross-validation.
# A Cross-Layer Optimization QoS Scheme in Wireless Multimedia Sensor Networks
## 2 Network Model and Design of Queues
### Virtual Queue at The Transport Layer  
The number of packets generated by session \(m\) in time slot \(t\) is denoted by \(A_{m}(t)\). We assume that \(A_{m}(t)\) is limited by \(A_{max}^{(m)}\), which is the maximum allowable number of packets generated by session \(m\) in a time slot. \(r_{m}(t)\in[0,A_{m}(t)]\) is the number of packets of session \(m\) injected into the network layer in time slot \(t\). \(\eta_{m}(t)\in[0,A_{max}^{(m)}]\) is the auxiliary variable which is used to limit the lower bound of \(r_{m}(t)\). The source node of each session maintains a virtual queue. The virtual queue at the source node of session \(m\) is denoted by \(Y_{m}\), which is updated as:  
\[Y_{m}(t+1)=\max\left\{Y_{m}(t)-r_{m}(t),0\right\}+\eta_{m}(t)\;. \tag{7}\]  
If \(Y_{m}\) is guaranteed to be stable, \(\overline{\eta_{m}}\leq\overline{r_{m}}\) can also be guaranteed [45]. Therefore, when \(Y_{m}\) is stable, \(\overline{\eta_{m}}\) can be used to limit the lower bound of \(\overline{r_{m}}\).
# Advanced Default Risk Prediction in Small and Medium-Sized Enterprises Using Large Language Models
## 2 Related Work
[19] demonstrated that employing the lasso-logistic model to identify and predict critical credit risk factors effectively promotes sustainable supply chain finance. [PERSON]. [20] employed BP neural networks and K-means clustering to study the financial risks of listed mining companies, achieving significant predictive outcomes. [PERSON]. [21] proposed a graph-based supply chain mining method, addressing the issue of data scarcity in financial risk analysis of micro and small enterprises, providing new insights for risk prediction using machine learning methods. Additionally, refs. [22; 23; 24] also applied machine learning methods to risk prediction for micro and small enterprises. Concerning factors influencing risk prediction, refs. [12; 25] verified the importance of core financial indicators such as debt ratio, current ratio, and income growth rate for default prediction from a financial perspective, while research on bill default prediction remains relatively limited. Recently, large language models (LLMs) based on the transformer architecture [26] have achieved significant breakthroughs, demonstrating strong reasoning and generalization capabilities across diverse domains, including law, education, programming, and healthcare [27; 28; 29; 30]. LLMs are also increasingly being applied in financial research. Researchers explored the application potential of ChatGPT in portfolio selection, particularly in stock picking. Compared to quantitative investment models, ChatGPT excels in stock selection, though the weight allocation still requires further optimization [31]. [PERSON] and colleagues trained a large-scale language model, BloombergGPT, with 363 billion tokens of financial data and 345 billion tokens of public data, amounting to 500 billion parameters. This model performs exceptionally well on financial tasks and remains competitive in general NLP tasks [32]. [PERSON] and [PERSON] assessed the performance of GPT as a financial intelligence advisor in financial literacy tests. The results showed that GPT-4 achieved a high score of 99% in the financial literacy test, while GPT-3.5 (Davinci) and GPT-3.5 (ChatGPT) scored 66% and 65%, respectively, highlighting financial literacy as a new strength of the latest model [33]. To provide a comprehensive overview of the progress and limitations of existing research, we present a detailed comparison in Table 1. This table highlights the key aspects of related work, including research objects, data types, methods/models, innovations, and flexibility. By analyzing these aspects, it is clear that most existing research focuses mainly on financial data from large or listed companies, with limited adaptability to the unique characteristics of SMEs. In contrast, the proposed method emphasizes the integration of non-financial data and uses the flexibility of large language model prompt learning. This innovation not only addresses the issue of data scarcity but also provides a more dynamic and real-time solution for predicting default risks in SMEs.
## 5 Discussion and Future Research Directions
### Advanced Leak Detection in Water Networks  
The leak detection topic presented encompasses various approaches and techniques for detecting and locating leaks in water distribution networks. These methods involve machine learning algorithms, wireless sensor networks, and transient modeling. Machine learning techniques include classifiers such as KNN, Decision Trees, neural networks, support vector machines, and Random Forests. Generally, these methods have demonstrated effectiveness in identifying and locating leaks across diverse scenarios, with varying levels of accuracy depending on the technique and the water distribution network's characteristics. Employing these approaches can help tackle the issue of leaks in water distribution networks, consequently reducing economic losses and environmental impact.  
Future research endeavors should prioritize the development of efficient machine learning methods for accurate and effective leak detection and localization in water networks. This task involves network zoning and the utilization of hybrid approaches, blending various techniques for optimal outcomes. A key part of this process is the innovation of sensor technology and data acquisition methods, leading to enhanced datasets and, consequently, more robust models. Considerations such as the materials used in pipes and the overall design of the network significantly influence these developments. Moreover, translating current research to real-world networks, integrating analytics with intelligent water management systems, and quantifying the socio-economic and environmental impacts of improved leak detection are vital steps forward.  
Simultaneously, there is a notable gap in the integration of smart water technologies in leakage management, referring to the limited use of advanced technologies, such as sensors, data analytics, and machine learning for detecting and controlling leakages in water distribution systems. Despite their potential to enhance the efficiency and accuracy of leakage management, the adoption of these technologies is still in its nascent stages. Thus, further research is required to explore these smart water technologies' potential in leakage management and develop effective strategies for their integration.  
In addition, current leakage detection methods exhibit inadequate accuracy and reliability, suggesting limitations in the techniques employed in identifying leakages in water distribution systems. Current methods, such as acoustic, flow, and pressure sensors, suffer from constraints related to accuracy, sensitivity, and reliability. For example, acoustic sensors are susceptible to background noise, and flow sensors may fail to detect minor leaks. Such limitations can trigger false positives or negatives, leading to inefficient leakage management. Therefore, future research must focus on devising more accurate and reliable leakage detection methods, potentially involving the use of machine learning algorithms and data fusion techniques.
## 3 A General Framework for Grid Partitioning Techniques and Grid Renumbering Methods  
### Parmetis-Based Grid Partitioning Strategy  
**Metis** and **ParMetis** are tools that work on physical model partitioning. Usually, using the **Metis** library for mesh slicing of physical models using a serial strategy is an ideal solution. However, it is slow under large-scale mesh partitioning. **ParMetis** is a parallel version of **Metis**, an **MPI** parallel library. It has many built-in algorithms for mesh partitioning and repartioning of unstructured graphs.  
**ParMetis** started out as a tool for large-scale graph partitioning, but mesh models in CFD can also be abstracted to graphs. Therefore, **ParMetis** is particularly well suited to deal with unstructured meshing problems in large scale numerical simulations. Simulations of fluid problems often require thousands of cluster nodes to be computed simultaneously. Before the computation, a regional decomposition of the computational grid has to be performed.Communication is required between different regions in the computation. Therefore, grid region decomposition has a greater impact on load balancing and inter-process communication. **PartMetis** enables the partitioned area grid to be approximately the same and the number of intersections to be minimized. Therefore, the amount of data exchanged is minimized, which in turn significantly reduces the communication time between processes.  
**ParMetis** provides the **ParMETIS_V3_PartGeomKway** routine for computing partitions of graphs derived from finite element meshes where vertices have coordinates associated with them. Given a graph distributed between processor and vertex coordinates, **ParMETIS_V3_PartGeomKway** uses the space-filling curve method to quickly compute an initial partition. The graph is redistributed according to this partition and **ParMETIS_V3_PartKway** is then called to compute the final high quality partition. Information about the partitioning process of **Parmetis** is available in [25].
## 1 Introduction
Recently, a combined TDR-penetrometer, which has two electrodes on the shaft in the same direction, was developed to ensure that the electrodes were fixed to the shaft [30]. These TDR-penetrometers are light and portable, and are thus useful for the shallow subsurface characterization of a natural hill slope. However, for these TDR-penetrometers, there are several issues to be modified, such as the number of electrodes and the air gap between the probe and soil. Furthermore, to date, a TDR-penetrometer has not been used for the estimation of the volumetric water content in the active layer. Compared to the approach of applying TDR-penetrometers at a limited number of test locations, the electrical resistivity survey has the advantage of providing a continuous resistivity profile of a greater volume of soil. Nevertheless, few trials have been performed where the results obtained using a TDR penetrometer have been integrated with those obtained from an electrical resistivity survey. This study demonstrates the development of a penetration-type TDR (PTDR) and its application with an electrical resistivity survey for the evaluation of the water content in the active layer. First, the basic principle of the TDR technique and design of the PTDR developed in this study are introduced. The engineering properties of the soils sampled at Ny-Alesund, Svalbard are provided. The PTDR is then used to establish the relationship between the volumetric water content and the dielectric constant. The temperature effect is then investigated. Finally, the field test results of the PTDR and the electrical resistivity survey obtained at the same site are discussed.