esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Comparative Experiments
#### 3.2.1 Experimental Results on the MNIST Dataset  
According to the experimental results shown in Figure 9, the proposed method demonstrates significant performance advantages on the standard public MNIST dataset. In the comparative experiments, models such as GANomaly, f-AnoGAN, MemAE, and SQUID exhibit varying degrees of performance in anomaly detection tasks. Specifically, GANomaly, f-AnoGAN, and MemAE fail to effectively reconstruct anomalous regions, resulting in noticeable residual defects or blurred areas, leading to missed detections. Although the SQUID model in Figure 9e can reconstruct the general shape of anomalous digits, it lacks  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Training Set** & **Validation Set** & **Testing Set** \\ \hline Normal samples & 1200 & 600 & 200 \\ Broken spring strip & 0 & 15 & 22 \\ Missing bolt & 0 & 12 & 18 \\ Foreign object occlusion & 0 & 13 & 20 \\ \hline \end{tabular}
\end{table}
Table 4: Sample quantity distribution by category in training, validation and testing sets.  
precision in detailing anomalous regions and exhibits residual background interference. In contrast, the proposed method in Figure 9 exhibits superior performance in anomaly detection tasks by precisely localizing and effectively reconstructing anomalous regions, significantly reducing both false positives and missed detections, thus demonstrating strong robustness and accuracy.  
According to the experimental results presented in Table 5, the performance of different models on the MNIST dataset is compared. The proposed method demonstrates superior performance across all evaluation metrics, achieving an AUC of 94.0%, an ACC of 92.5%, and an F1 score of 91.6%, significantly outperforming the other models. SQUID also exhibits commendable results with an AUC of 92.5%, ACC of 91.3%, and F1 score of 90.2%. MemAE and f-AnoGAN show similar performance, obtaining an AUC and ACC of around 91.2% and 90.1%, respectively, although their results are somewhat lower and do not reach the performance level of other models. In contrast, GANomaly performs relatively poorly across all three metrics, notably achieving the lowest F1 score of 86.2%. Overall, the experimental results on the MNIST dataset clearly illustrate the effectiveness and advantages of the proposed method in anomaly detection tasks, highlighting significant improvements across multiple key metrics and further confirming its superiority.
# Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study
## 2 Methods
### Stereo Matching  
Stereo-matching technology [32] is the most critical research topic in binocular ranging systems, and the accuracy of image matching directly affects the accuracy of subsequent binocular ranging. However, the stereo-matching process has some limitations and sources of error, such as disparity range limitation, illumination variation, and mismatching factors. These factors can lead to errors or inaccuracies in binocular stereo matching. To reduce these errors, this study first adopts object detection. Then, it associates the feature information obtained from object detection with binocular stereo matching, enriching the features and coordinate information. This enhances the accuracy of binocular ranging and reduces errors. Nevertheless, there are numerous existing stereo-matching algorithms. To meet the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \multicolumn{3}{c}{**Left Camera**} & \multicolumn{3}{c}{**Right Camera**} \\ \hline \multirow{2}{*}{Intrinsic Matrix} & \(\begin{bmatrix}2186.8&0&655.5445\\ 0&2188.3&512.99\\ 0&0&1\end{bmatrix}\) & \(\begin{bmatrix}2114.0&0&647.918\\ 0&2115.6&528.0488\\ 0&0&1\end{bmatrix}\) \\ \hline \multirow{2}{*}{Distortion} & \(\begin{bmatrix}-0.3846&0.2401&0&0&0.003&0\end{bmatrix}\) & \(\begin{bmatrix}-0.4261&0.1959&0&0.002&0\end{bmatrix}\) \\ \hline \multirow{2}{*}{Translation} & \multicolumn{3}{c}{\(\begin{bmatrix}-119.6552&-0.0624&0.6339\end{bmatrix}\)} \\ \hline \multirow{2}{*}{Rotating} & & & \(\begin{bmatrix}0.9999&-0.012&-0.0116\\ 0.0119&0.9999&-0.0047\\ 0.0117&0.0045&0.9999\end{bmatrix}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Binocular camera calibration of internal and external parameters.  
Figure 5: Stereo calibration chart.  
requirements of accurate and fast stereo matching in complex scenes, this study selects the Semi-Global Block Matching algorithm (SGBM) [33] as the matching algorithm for bar-shaped obstacle features. At the same time, the SGBM stereo matching algorithm not only has high real-time performance and measurement accuracy but also possesses an efficient execution capability.
## 5 Visual MIMO with Color-Space-Based Modulation  
In a past paper [17], it was shown that higher data rates are possible in long-range transmissions for mobile optical communications by a camera using the concept \"visual-MIMO\". According to the concept, the optical transmission of a light-emitting array is received by the photodetector elements (i.e., pixels) of a camera. The image sensor of a camera consists of these pixels, defined as an array of receiver which is inherently highly directional. This system provides a degree of freedom in selecting and combining a subset of receiver elements that receive a strong signal from the transmitter and have a large SNR. Conceptually, this visual MIMO system may be quite similar to RF-MIMO antenna selection, but visual-MIMO has less overhead and less complexity to process at the camera receiver due to the use of image processing and computer vision algorithms [17].  
Figure 9 presents the color-independent visual-MIMO communication system [23; 24]. GCM-based visual-MIMO helps to achieve better performance in terms of SER than conventional light-emitting diode (LED) communication. Using this scheme, it is possible to transmit different colors (symbols) at the same time through LED array. Most importantly, the proposed visual -MIMO system can easily adapt to changes in the target color via image processing [23; 24].  
In general, the capacity of a color-independent visual MIMO system depends on the number of bits per symbol, the array size (M \(\times\) M), and the frame rate of the camera. The capacity of the proposed system is defined as,  
\[\text{Capacity }[\text{bps}]=\text{n }[\text{bps}/\text{symbol}]\times\text{N }[\text{symbols}/\text{frame}]\times\text{F }[\text{frames}/\text{sec}], \tag{8}\]  
where N = M \(\times\) M.
## 3 Materials and Methods  
### Dataset  
Model training and validation were performed in 84 T1-weighted contrast-enhanced (T1 CE) MRI volumes with colorectal metastasis in the liver from the ethically approved Oslo-CoMet Study (COMET) [22]. The data were collected from seven different MR machines (Philips Medical System: Achieva, Intera, Ingenia, and SIEMENS: Aera, Avanto, Skyra, SonataVision). All images were T1 CE MRI, with variations in the protocol, timings, and machine-specific image parameters. Based on domain expert ground truth (GT) annotations, there was an average of 2.8 lesions per case, with a median size of 1.574 \(\pm\) 18.117 mL (range 0.021-236.23 mL). The smallest and largest lesion volumes corresponded to 0.001% and 15.61% of the total liver volume, respectively. The liver occupies, on average, 6.7 \(\pm\) 2.1%, with the largest tumor of 1.15% of the total MRI volume (Figure 1).  
GT segmentations were performed by two medical image processing experts with at least three years of experience in liver MRI diagnostics. Annotations from T1 CE MRI were done using 3D Slicer (www.slicer.org, accessed on 15 May 2022) and ITK snap (www.itssnap.org, accessed on 15 May 2022) software tools. The number of tumors and their approximate spatial locations were confirmed by radiology reports and 2D lesion annotations (Figure 2d). The annotations were performed semi-automatically and applied to 2D slices from each volume [42], which the domain expert manually corrected as needed using a brush tool. Volumetric segmentation masks for liver and lesions were then generated from the annotations yielding non-overlapping mask values, with a background as class zero, liver parenchyma as class one, and tumors as class two.  
From the segmented dataset, 60 volumes (75%) were used for training, 10 (12.5%) for validation, and 10 (12.5%) for the testing (test set #1). The data split was manual with respect to the patient and representation of all scanner variations through the subsets. The test set represents all machines that were presented in the dataset (Figure 3). Some patients had several MRI sessions that were taken into account during the split to avoid the presence of the same patient in different subsets. Four additional MRI volumes were left unannotated and used as test set #2.  
Figure 1: Distribution of whole liver (**a**)—and tumor volume (**b**) as a percent of total MRI volume in the expert annotated dataset.  
Figure 2: The process of dataset GT creation by the medical expert: (**a**) an example of a slice of MRI volume; (**b**) liver segmentation outline; (**c**) liver segmented and shown in 3D on top of MRI volumes; (**d**) MRI volume annotated with an arrow; (**e**) lesion segmentation outline; (**f**) lesion segmented and shown in 3D on top of MRI volumes.
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 2 Evaporites and Bedded Evaporite Deposits
### Bedded Enaporite Deposits and Caverns in the U.S. and Canada
#### 2.4.2. Caverns in the Bedded Evaporites  
According to [131], there are 286 cavern fields in bedded salt deposits globally. The characteristics of these deposits, such as depth, thickness, and mineralogy, play a vital role in the suitability of cavern development for storage purposes. Salt caverns are artificial cavities created through a process called solution mining, which involves injecting water into the salt deposit to dissolve it, producing brine, and leaving a cavern suitable for storage. The location and storage uses of existing caverns in the U.S. and Canada are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **Location** & **Existing Caverns** & **Usage** \\ \hline \multirow{8}{*}{U.S.} & & 11 & LPG storage \\  & Arizona & \textgreater{}4 & LPG storage \\  & & \textgreater{}4 & Brine production \\  & Utah & 4 & LPG storage \\  & West Virginia & 10 s & Brine production \\  & Virginia & NA & Brine production \\  & & 4 & Gas storage \\  & & 21 & LPG storage \\  & & 14 & LPG storage \\  & & 85 & LPG storage \\  & & 43 & LPG storage \\  & & 100 & Natural gas storage \\  & Kansas & 71 & LPG storage \\  & & 72 & LPG storage \\  & & 53 & LPG storage \\ U.S. & & 24 & LPG storage \\  & & NA & Brine production \\  & & NA & Brine production \\  & Ohio & 1 & LPG storage \\  & & NA & Brine production \\  & Michigan & 8 & LPG storage \\  & & 8 & LPG storage \\  & & 2 & LPG storage \\  & New York & 15 & LPG and natural gas storage \\  & & 16 & LPG storage \\  & & 18 & LPG storage \\  & West Texas & 8 & LPG storage \\  & & 10 & Natural gas storage \\  & & 3 & Natural gas storage \\  & & 6 & Natural gas storage \\  & & 3 & Liquids storage \\  & & 4 & Liquids storage \\  & & 2 & Liquids storage \\  & Alberta & 2 & Liquids storage \\  & & 4 & Liquids storage \\  & & 11 & Liquids storage \\  & & 17 & Liquids storage \\  & & 13 & Liquids storage \\  & & 5 & Liquids storage \\ Canada & & 8 & Liquids Storage \\  & Ontario & 6 & Liquids storage \\  & & 4 & Liquids storage \\  & & 9 & Liquids storage \\  & & 4 & Liquids Storage \\  & Saskatchewan & 6 & Liquids storage \\  & & 3 & Liquids storage \\  & & 21 & Natural gas storage \\  & Nova Scotia & 3 & Natural gas storage \\ \hline \hline \end{tabular}
\end{table}
Table 5: Existing caverns in bedded salts (U.S. and Canada) [111].  
Salt caverns for storage purposes are primarily found in halite-rich bedded salt deposits. The reported caverns in bedded rock salts (Table 5) have been mainly used for natural gas storage and liquid storage. However, no caverns have been reported for storage in potash- and trona-rich beds.
## 4 Design
### Dataset
#### 4.1.1 Projects  
To study the quantitative prediction of program performance based on the move method, we established two criteria for the selected projects. Firstly, the projects must have experienced at least one instance of MMR. Secondly, the execution time of these projects had to be tested. Only those projects that fulfilled both of these conditions could be included in the dataset.  
To obtain accurate execution time measurements, we collected Java microbenchmark projects from GitHub that were specifically designed by developers for performance evaluation. Then, RefactoringMiner [24] was used to detect whether they involved MMR. The tool can identify a total of 55 refactoring types, including MMR, by analyzing the differences between two code versions (typically pre- and post-commit Git versions) and matching them against predefined refactoring patterns. This process identifies specific code changes and outputs the results in a structured data format. RefactoringMiner's detection report contains multiple dimensions of information: refactoring types, affected code elements, locations, contextual information, and relevant commit details. We first identified projects to apply MMR based on the type of refactoring reported. Second, we learned the details of the refactoring operation based on the method name and its location information in the code element. This information can help us to restore the process of developers performing refactoring operations as much as possible, so as to effectively eliminate the interference of other code changes. Thus, we were able to focus on the impact of mobile function refactoring on program performance.  
Using the RefactoringMiner tool, we identified 15 projects encompassing 36 movable methods. The details of these projects, including submissions, URLs, and the methods involved in MMR operations, are publicly available at [[https://github.com/Foule145](https://github.com/Foule145)]([https://github.com/Foule145](https://github.com/Foule145)) /research/blob/main/details.html (accessed on 1 March 2025). It is worth noting that these projects may encompass other refactoring-type operations or changes to the code logic, in addition to MMR. To exclude the influence of these extraneous code changes on program performance, we performed a manual review to determine the baseline version of each  
Figure 2: Dataset construction.  
project that was not refactored. Subsequently, we performed the same MMR operations as the original project on this baseline version, ensuring that the sole change within the project was MMR. This step is crucial in precisely identifying the metrics affected by MMR, as it effectively minimizes interference, thereby ensuring the accuracy and reliability of the selected features.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
In image deblurring tasks, regularization methods are especially useful for reducing errors caused by blur or noise in the image. For example, [PERSON] et al. [17] proposed a blind image deconvolution model based on adaptive weighted total variation regularization, and used the alternating direction method of multipliers (ADMM) of the multiplier to effectively solve the non-smooth mode. Regularization techniques are also widely applied in other fields. For instance, [PERSON] et al. [18] proposed a deep learning network that combines various regularization strategies for cancer detection. However, such models still face many challenges, such as how to avoid image information loss under extreme blur conditions, how to ensure the training stability of the generated network, and how to maintain the universality of regularization methods when processing different types of images--these remain key issues in current research. To leverage the strengths of both variational and deep learning models, we propose an efficient and interpretable variational neural network specifically designed for image blind motion deblurring. Our main contributions can be summarized as follows:* We propose a variational neural network that incorporates an algorithm unfolding for image blind motion deblurring. Our model achieves high efficiency and interpretability, requiring fewer data and parameters for effective training, while maintaining robustness against noise. * To accurately estimate the blur kernel, we applied an \(L_{0}\) regularizer to the image gradient information and solved the corresponding variational model using the fast fourier transform. This approach enhances model stability during training and improves convergence speed. * In the image restoration stage, we employ the Gabor filter approximate convolution kernel to effectively capture features of varying scales and orientations in the image. Additionally, the use of the Gaussian radial basis function approximate activation function mitigates the gradient disappearance problem and enhances the capture of local image features, significantly improving the model's learning and generalization capabilities. * To avoid the limitations of algorithm unfolding and enhance image detail restoration, we introduce spatial and channel attention mechanisms. The spatial attention mechanism enables the model to concentrate on the contextual information surrounding each pixel, while the channel attention mechanism effectively adjusts the importance of different feature channels. This dual approach improves the model's ability to learn and extract key image features, thereby enhancing the deblurring effect.
# Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components
## 2 Materials and Methods  
As shown in Figure 1, the general workflow of this work can be divided into four main parts. The data acquisition, the data fusion, the use of 3D data to simplify and structure the data basis and the final classification based on obtained HSI and 3D features. The individual parts are described in detail in the following subsections.  
Figure 1: Workflow consisting of fusion, simplifying and classification steps.
# Design and Applications of GLANCE: GLanceable Alarm Notification for a User Centered Experience
### Daling with Notifications in Network Management
In this direction, this paper describes research that originated within the framework of cooperation between the University of L'Aquila and Technolabs S.p.a, a former Italian ICT company located in L'Aquila, and aimed at designing a proof-of-concept for a glanceable alarm notification component for Telecommunication (TLC) network management systems, based on a peripheral display approach. The adopted methodological approach allowed us to maintain focus on the company's problem-solving interest and research interest, favoring the generalization of achieved results. The study was conducted with the following main research questions in mind: _\"Can the fault notification component of a network monitoring tool be effectively defined according to a peripheral display paradigm?\"_, and more specifically _\"To which extent is it possible to offer a glanceable overview along with information on individual faults?\"_ and _\"Can (and to which extent) results achieved for a TLC network be generalizable to a more general context of information coming from heterogenous sources?\"_ This first design macro-phase led to the simple yet effective GLANCE (GLanceable Alarm Notification for a user Centered Experience) model, based on a visual coding technique oriented for comprehension and reaction, and a transition strategy oriented to interruptions and reaction. A second design macro-phase studied the applicability of GLANCE results to a personal customizable multichannel notification tool and to a service-oriented fault monitor for digital terrestrial television broadcasting networks, also leading to an enrichment of the model. The remainder of the paper is organized as follows. Section 2 provides an overview of the two design macro-phases underlying overall and intermediate objectives. Then Section 3 discusses results from the first design macro-phase and Section 4 surveys the results of the application studies of the second design macro-phase. Section 5 discusses the overall design experience and its results, and, finally, Section 6 draws conclusions.
###### Abstract  
Vehicle trajectory prediction is an important research basis for the decision making and path planning of the intelligent and connected vehicle. In the connected vehicle environment, vehicles share information and drive cooperatively, and the intelligent and connected vehicles are able to obtain more accurate and rich perception information, which provides a data basis for accurate prediction of vehicle trajectories. However, attaining accurate and effective vehicle trajectory predictions poses technical challenges due to insufficient extraction of vehicular spatial-temporal interaction features. In this paper, we propose a vehicle trajectory prediction model based on graph convolutional neural network (GCN) in a connected vehicle environment. Specifically, using the driving scene information obtained by the intelligent and connected vehicle, the spatial graph and temporal graph are constructed based on the spatial interaction coefficient (SIC) and self-attention mechanism, respectively. Then, the graph data are entered into the interaction extraction module, and the spatial interaction features and temporal interaction features are extracted separately using the graph convolutional networks, which are fused to obtain the spatial-temporal interaction information. Finally, the interaction features are learned based on the convolutional neural networks to output the future trajectory information of all vehicles in the scene by one forward operation rather than a step-by-step process. The ablation experiment results show that the method proposed in this study to model the spatiotemporal interaction among vehicles based on SIC and self-attention mechanism reduces the prediction error by 5% and 12%, respectively. The results from the model comparison experiment show that the proposed method engenders an 8% improvement in prediction accuracy over the state-of-the-art solution, providing technical and theoretical support for trajectory prediction research of intelligent and connected vehicles.  
intelligent and connected vehicle; trajectory prediction; graph convolutional network; spatial interaction coefficient; self-attention mechanism; spatial-temporal interaction