esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Important Elements for Improving the Safety of Motorways
## 2 Literature Review
### Traffic Accident Prediction Model Using AI  
Explaining the frequency of traffic accidents with simple equations or statistical models is challenging due to their diverse and complex causes. Consequently, AI-based traffic accident prediction models have emerged, leveraging deep learning's complex and robust model structures to estimate variable relationships and enhance predictive accuracy significantly. This has spurred the active development of prediction models in road traffic safety.  
[PERSON] (2019) highlighted the severity of traffic accident issues and emphasized the need to improve road safety to mitigate social and economic losses. They developed a prediction model using a Deep Neural Network (DNN) to forecast both the frequency and severity of traffic accidents, drawing upon various data categories such as human factors, environmental factors, road characteristics, and vehicle characteristics. Evaluating the model against the widely used Support Vector Machine (SVM) for traffic accident severity prediction revealed the DNN's superior performance, achieving a remarkable prediction accuracy of 95% and an F1-score of 93%.  
[PERSON] et al. [20] tackled the inverse problem of reconstructing traffic accidents using data-driven machine learning techniques. Their approach involved building a DNN capable of reconstructing initial accident conditions based on damage and structural changes (accident debris) resulting from accidents. This methodology successfully estimated the initial collision conditions of accidents, providing a valuable tool for forensic analysis and offering engineering solutions to address failure and vehicle safety issues.  
[PERSON] and [PERSON] [21] cautioned against overly complex algorithms or deeply layered architectures when developing deep learning prediction models for traffic accident severity. They conducted a comparative study of three models: simple Long Short-Term Memory (LSTM), a deeper LSTM architecture, and a stacked model combining LSTM and a DNN. Surprisingly, the results favored the simple LSTM model, demonstrating superior performance over the other models evaluated.  
[PERSON] et al. [22] developed a safety performance function utilizing a Deep Belief Network (DBN) for highways in Ontario, Colorado, and Washington, USA. They compared the performance of the DBN model with a traditional statistical model, the negative binomial regression model, using actual traffic accident data from these regions. The results demonstrated that the DBN model could estimate traffic accident frequency with accuracy similar to that of the negative binomial regression model. Moreover, the DBN-based model exhibited higher efficiency compared to the negative binomial regression model.
## 3 Results
### Environmental Impact and Accident Analysis  
#### 3.3.1. Accident Analysis  
We conducted a theoretical analysis to evaluate the performance of the R-barricade in the event of a traffic accident. The specific aspects of the analysis include the following:  
* Impact Protection: We analyzed the design parameters and material strength to assess the barricade's ability to protect construction zones during a collision. Through calculations and material property analysis, we determined that the R-barricade can effectively absorb collision energy, reducing potential harm to drivers and pedestrians. Specifically, when a vehicle collides with the barricade at a speed of 50 km/h, the material of the barricade is designed to deform and absorb energy, thereby reducing the impact force on passengers inside the vehicle while simultaneously protecting workers within the construction zone;  
Figure 8: This diagram demonstrates the deployment scenario of the R-barricade in urban road construction (the picture is drawn by the author).  
* Material Strength: Material analysis was conducted to study the durability and impact resistance of the materials used, ensuring that they provide sufficient strength and stability during real-world accidents. The selected materials exhibit high toughness and impact resistance, maintaining structural integrity during vehicle collisions and preventing secondary injuries;
* Comparison with Safety Standards: We compared the performance of the R-barricade under different collision scenarios with relevant international and national traffic safety standards. This includes an evaluation of the deformation, stability, and secondary injury risks to vehicles after a collision. For example, theoretical calculations based on the European Standard EN 1317 indicate that after impact [56], the deformation and stability of the R-barricade remain within safe limits, ensuring that it does not pose further risks to subsequent vehicles.
## 2 Related Work
### Feature Fusion and Attention Mechanisms  
To enhance a model's ability to represent multi-scale information and respond to key regions, researchers have extensively adopted feature fusion strategies and attention mechanisms. Feature pyramid structures, such as FPN, strengthen feature representations across different semantic layers. Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP) further improve the integration and perception of contextual information. On this basis, various attention mechanisms have been employed to enhancethe model's ability to focus on discriminative features. Self-attention, channel attention mechanisms, such as SE-Net [23], and spatial attention modules have shown efficacy in improving feature representation across different dimensions. Axial attention [24], which decomposes attention computation along horizontal and vertical directions, significantly reduces computational complexity while preserving strong spatial modeling capacity, and it has achieved promising results in segmentation tasks.  
Although the aforementioned methods effectively improve the model's understanding of semantics and structure, face parsing remains uniquely challenging due to the complex structural dependencies and semantic interactions among pixels. Therefore, how to simultaneously enhance global context modeling and preserve boundary details and local texture remains an urgent problem that needs to be addressed.
# CircleVis: A Visualization Tool for Circular Labeling Arrangements and Overlap Removal
## 3 Proposed Approach
### Overlap Removal
#### 3.2.2 Interactions  
For this work, three user interactions to reduce overlaps are discussed: semantic zooming; changing the label font size; and merging or splitting arcs.  
Figure 8: Example of 1D algorithm overlap reduction: (**a**) before use; (**b**) after use.  
**Semantic Zoom:**  
In semantic zoom, the radius of the circle becomes larger in such way that the area of each arc is expanded, allowing for extra label-drawing space. This action is useful for reducing label overlap. Figure 9 illustrates an example of this interaction. In Figure 9a, an overlap between the \"_Hungary_\" and \"_Switzerland_\" labels is observed. However, the overlap between the previous labels disappears in Figure 9b when using semantic zoom, or, specifically, by increasing the circle radius and re-drawing the circle labels.  
**Changing label font size:**  
For this interaction two font size options are presented: a global option, for which font size is changed for all labels, and a local option, where the font size is changed only for specific labels or overlapped ones. For example, Figure 10a shows an overlap between \"_Filand_\" and \"_North Macedonia_\". As seen in Figure 10b, when the global font size of the labels is decreased, the overlap between these labels is eliminated. Moreover, Figure 11a shows overlap between \"Cameroon\" and \"Cote Divoire\". As seen in Figure 11b, reducing the font sizes of these labels eliminates label overlapping.  
**Figure 10**: Example of removing overlap using _global font size change_: (**a**) before use; (**b**) after use.  
Figure 9: Interaction: _semantic zoom_, (**a**) before use; (**b**) after use.
## 2 Literature Review
### Ship Routing, Fleet Deployment, and Demand Acceptance  
Several studies investigate the optimization of ship routing and fleet deployment under complex operational constraints. [PERSON] et al. provide a comprehensive overview, addressing network design, ship routing, and speed optimization, emphasizing the necessity of integrated decision making to enhance operational efficiency [1]. [PERSON] and [PERSON] review network optimization, highlighting that routing decisions must carefully account for port capacity constraints, vessel types, and market demand characteristics, typically employing mathematical programming to minimize total operational costs [24].  
To minimize costs or maximize profits, [PERSON] et al. develop a model for fleet deployment and speed optimization, explicitly considering fuel consumption heterogeneity, through integrated decisions on node selection, ship assignment, and speed setting [25]. [PERSON] et al. construct a MILP model to optimize route and schedule design within port time window constraints [26]. [PERSON] et al. propose a MILP model combined with global optimization algorithms to handle heterogeneous fleets and reduce total operational costs [18]. Multi-objective approaches are also proposed in response to environmental and regulatory pressures. [PERSON] et al. introduce a tactical-level joint optimization model for heterogeneous fleets, balancing CO\({}_{2}\) emissions reduction and profit maximization [27]. [PERSON] et al. incorporate low-carbon operations and fuel bunkering into a cost-minimization model for greener shipping [28]. [PERSON] and [PERSON] survey various multi-objective strategies addressing multiple performance indicators including customer satisfaction and carbon emissions [24]. To solve large-scale container liner shipping optimization problems, researchers formulate MILP or MINLP models and employ exact algorithms, heuristics, and matheuristics [25; 27; 28; 29].  
Demand acceptance optimization is also receiving increasing attention. [PERSON] et al. model time-sensitive demand using integer nonlinear programming and heuristics to maximize profit [30]. [PERSON] et al. propose a holistic MILP model that integrates demand elasticity with fleet deployment and speed optimization [10]. [PERSON] et al. consider joint planning of cargo allocation, speed, and fleet deployment to minimize fuel and operational costs [31]. [PERSON] et al. adopt a two-stage robust optimization framework under demand uncertainty [32], while [PERSON] et al. summarize multi-objective planning approaches for demand acceptance, focusing on tonnage limits and route capacity [33].
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## 4 Experiments and Results
### Implementation Details  
All the models are performed in a supervised learning manner and implemented on the Jupyter notebook platform using PyTorch. These models are trained by backpropagation using gradient descent, with the adaptive learning-rate method Adam [66] as the optimizer (learning rate is set to \(8\times 10^{-3}\) for convolutional SCT/SC attention model and \(2\times 10^{-3}\) for other models; weight decay is set to \(2\times 10^{-4}\) for convolutional SCT/SC attention model and \(2\times 10^{-5}\) for other models). We use the binary cross-entropy loss function for our classification task. The statistical models are obtained by minimizing the loss function on the training dataset. All these models are trained on one machine with Tesla P100-PCIE-16 GB GPU.  
For the end-to-end binned FCNN with the automatically capturing weighting factors model proposed in the 2D space segmentation problem setting, the training dataset consists of 4 original representative spectra samples and 60 simulated ones (by adding some random noise, 15 simulated spectral curves are generated based on each original spectrum), for a total of 64. The reason why we use only a small amount of training data samples in this model is that in this case, the data is only two-dimensional, so the amount of training examples is much larger than the data dimension, which is well-acceptable. The spectra drawn in a dot-dash line and marked in red or blue are used as the basis of the training set, as shown in Figure 1. In this model, we set the initial training epochs to 550. As the number of bins increases, the training examples in each bin decreases linearly, which may cause the model to easily overfit. To deal with this problem, two early training stop criteria are applied, that is, when the training accuracy is greater than 99.0% or the training loss is less than 0.15. The epochs required during training is shown in Figure 4.  
For the applied models in the 1D time series data analysis setting, there are 31 original spectra samples with 1550 simulated ones as mentioned above (by adding some random noise, 50 simulated spectral curves are generated based on each original spectrum), a total of 1581. The small random noise in the simulated spectral data is generated using the Mersenne Twister [67] as the core generator. The 31 original spectra samples used as the basis for training are also shown in Figure 1. Their training information, such as training epochs, training time, memory consumption, etc., are listed in Table 1. The analysis  
Figure 4: Training information and classification result of the end-to-end binned FCNN with automatically capturing weighting factors model.
## 3 Datasets
### Reference Greenhouses  
The reference objects or ground truth for this work consisted of 694 individual greenhouses located in the study area and managed by Vicasol, an agricultural cooperative of Almeria. Vicasol provided valuable information related to those crops growing under plastic coverings corresponding to these reference greenhouses in autumn 2013, such as type of crop, date of planting and date of removal (Table 1). We can see that these data fit well with the typical transplant pattern in the study area (Table 2). This information consisted of an Excel file, where the cadastral reference of each greenhouse was provided. It is important to note that the Vicasol data only vaguely reflect the distribution of the main crops for the whole of Almeria. This fact should be taken into account in further works.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Crop**} & **No.** & **Total Area** & **Average Date** & **SD Planting** & **Average Date** & **SD Removal** \\  & **Greenhouses** & **(ha)** & **of Planting** & **(Days)** & **of Removal** & **(Days)** \\ \hline Aubergine & 80 & 58 & 12 August 2013 & 11.8 & 3 June 2014 & 21.5 \\ Cucumber & 62 & 54 & 3 September 2013 & 22.5 & 14 January 2014 & 50 \\ Pepper & 192 & 152 & 27 July 2013 & 12.9 & 1 May 2014 & 44.8 \\ Tomato & 360 & 307 & 20 August 2013 & 11.2 & 5 May 2014 & 42.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the 694 reference greenhouses. SD: Standard deviation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Crop**} & **June** & **July** & **August** & **September** & **October** & **November** \\ \hline Aubergine & & & Transplant & Transplant & & \\ Cucumber & & & Transplant & Transplant & & Transplant \\ Pepper & & Transplant & Transplant & & \\ Tomato & & & Transplant & Transplant & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Schematic summary of the typical transplant pattern for the four studied crops.
# Normalizing Flows for Out-of-Distribution Detection: Application to Coronary Artery Segmentation
###### Abstract  
Coronary computed tomography angiography (CCTA) is an effective imaging modality, increasingly accepted as a first-line test to diagnose coronary artery disease (CAD). The accurate segmentation of the coronary artery lumen on CCTA is important for the anatomical, morphological, and non-invasive functional assessment of stenoses. Hence, semi-automated approaches are currently still being employed. The processing time for a semi-automated lumen segmentation can be reduced by pre-selecting vessel locations likely to require manual inspection and by submitting only those for review to the radiologist. Detection of faulty lumen segmentation masks can be formulated as an Out-of-Distribution (OoD) detection problem. Two Normalizing Flows architectures are investigated and benchmarked herein: a Glow-like baseline, and a proposed one employing a novel coupling layer. Synthetic mask perturbations are used for evaluating and fine-tuning the learnt probability densities. Expert annotations on a separate test-set are employed to measure detection performance relative to inter-user variability. Regular coupling-layers tend to focus more on local pixel correlations and to disregard semantic content. Experiments and analyses show that, in contrast, the proposed architecture is capable of capturing semantic content and is therefore better suited for OoD detection of faulty lumen segmentations. When compared against expert consensus, the proposed model achieves an accuracy of 78.6% and a sensitivity of 76%, close to the inter-user mean of 80.9% and 79%, respectively, while the baseline model achieves an accuracy of 64.3% and a sensitivity of 48%.  
os 2022  
Accepted: 6 April 2022  
Submitted: 11 April 2022  
Out-of-distribution; normalizing flows; coronary computed tomography angiography; human segmentation
# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
###### Abstract  
(1) Background: Bone metastasis is one of the most frequent diseases in breast, lung and prostate cancer; bone scintigraphy is the primary imaging method of screening that offers the highest sensitivity (95%) regarding metastases. To address the considerable problem of bone metastasis diagnosis, focused on breast cancer patients, artificial intelligence methods devoted to deep-learning algorithms for medical image analysis are investigated in this research work; (2) Methods: Deep learning is a powerful algorithm for automatic classification and diagnosis of medical images whereas its implementation is achieved by the use of convolutional neural networks (CNNs). The purpose of this study is to build a robust CNN model that will be able to classify images of whole-body scans in patients suffering from breast cancer, depending on whether or not they are infected by metastasis of breast cancer; (3) Results: A robust CNN architecture is selected based on CNN exploration performance for bone metastasis diagnosis using whole-body scan images, achieving a high classification accuracy of 92.50%. The best-performing CNN method is compared with other popular and well-known CNN architectures for medical imaging like ResNet50, VGG16, MobileNet, and DenseNet, reported in the literature, providing superior classification accuracy; and (4) Conclusions: Prediction results show the efficacy of the proposed deep learning approach in bone metastasis diagnosis for breast cancer patients in nuclear medicine.  
Keywords:bone metastasis; breast cancer; whole body; scintigraphy; deep learning; image classification; convolutional neural networks +
Footnote †: journal: Medical Image Analysis
## 5 Discussion
### Broader Industrial Applicability and Future Dataset Expansion  
While the DRFAN demonstrates strong performance on standard benchmarks and the rice grain dataset, we acknowledge that the current industrial validation is limited to a relatively small and domain-specific dataset. To ensure broader applicability, especially in other manufacturing or quality inspection scenarios, it is necessary to evaluate the DRFAN on additional real-world datasets with higher visual complexity, such as printed circuit board (PCB) defects, fabric flaw detection, or pharmaceutical packaging.  
These tasks often involve subtle, high-frequency anomalies embedded within repetitive backgrounds--conditions where super-resolution models must balance texture fidelity with robustness to noise. We plan to expand our evaluations to such datasets in future work. Additionally, to mitigate domain-specific overfitting, we will explore unsupervised domain adaptation and multi-domain training strategies, enabling the model to generalize across varying types of industrial image degradations.  
By extending the DRFAN to diverse industrial tasks, we aim to enhance its value as a universal lightweight SR backbone for embedded inspection systems across agriculture, electronics, and manufacturing sectors.
## 3 Results and Discussion
### Fir Honeydew Honey Sample  
As many studies have shown that the biological activity of honey depends on its botanical and geographical origin, as well as on the weather conditions during its production, it is always necessary to characterise the honey in question [34]. Microscopic analyses of pollen grain and honeydew honey elements of tested samples confirmed that the tested honeydew honey sample was FHH [26].  
The electrical conductivity of the tested FHH was 1.23 mS/cm; the ash and water content were 0.62% and 17.9%, respectively, which is comparable to the results of different Croatian honeydew honey samples tested by [PERSON] et al. [35]. Honeydew honey are generally characterised by higher values of electrical conductivity, ash and water content compared to blossom honeys [8; 36; 37]. The concentrations of sugars were: glucose 24.46 g/100 g, fructose 30.34 g/100 g, and both fructose and glucose 54.80 g/100 g, which complies with EU Directive 110/2001 [2]. Sucrose was not present. These data are comparable to the concentrations of glucose and fructose found in fir honey produced in France and Spain [38; 39].  
The total content of phenolic compounds in the analysed FHH was 220 mg GAE/100 g of honey, and the total content of flavonoids was 2.7 mg QE/100 g of honey, which met the criteria for honeydew honeys [40; 41]. Our data showed that FHH collected in the mountainous region of Gorski Kotar in Croatia contained higher amounts of total polyphenols (\(\sim\)50%) than the FHH collected in Transylvania in Romania; Slovenia; Poland; and Italy [42; 43; 44; 45]. Also, the amount of flavonoids in FHH from Gorski Kotar, Croatia, was higher (\(\sim\)45%) than in the FHH from Transylvania [42]. Polyphenols and flavonoids in honey are very important for biological activity, including antitumor, antibacterial and antioxidant properties [46; 47].