esa-sceva/satcom-chunk-collection huggingface dataset:
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 5 Conclusions  
This manuscript presents a model for the prediction of the attenuation induced by rain on FSO links. The model retains the analytical formulation of the methodology developed by [PERSON] to predict rain attenuation on millimeter-/micro-wave terrestrial links, but employs different rain rate-to-specific attenuation conversion coefficients that were derived for the optical range and for different DSD types using basic single scattering theory. Besides being simple and of quick applicability, the proposed model has been shown to provide accurate predictions, regardless of the path length 1 km \(\leq L\leq\) 5 km. The model also takes into account the multiple scattering effects induced by rain, included in terms of a gain that contributes to reducing the overall predicted attenuation. Although additional data are required to corroborate the model's prediction performance, the results obtained in this work are encouraging and suggest that the proposed model represents a useful tool to dimension FSO links in areas where fog is negligible, i.e., where rain is the main impairment to the link performance and availability. Future work includes assessing the prediction accuracy of the proposed model using long-term statistically meaningful experimental data (CCDF of rain attenuation) that are being collected by an FSO link recently installed at Politecnico di Milano for research purposes. Indeed, to the best of the authors' knowledge, no data like these are currently available in the literature to further test the model's prediction accuracy.  
[PERSON] and R.N. contributed to conceptualization. All the authors contributed to develop the methodology as well as the software tools and to carry out simulations. [PERSON] and U.A.K. prepared the original draft. All the authors were in charge of writing the revised version of the manuscript. Finally, U.A.K. applied for funding. This project was funded by Erasmus Mundus INTACT program, [[http://www.em-intact.eu/](http://www.em-intact.eu/)]([http://www.em-intact.eu/](http://www.em-intact.eu/)).  
**Acknowledgments:** [PERSON] would like to thank the Erasmus Mundus INTACT program, who awarded the staff mobility scholarships (in 2015) for a two-month visit at Politecnico di Milano, Italy.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Responses of Vegetation Dynamics to Climatic Drivers
#### 3.3.4. Abrupt Change in NDVI and Its Relationship to Climatic Drivers
It should be noted that the sequence of abrupt change points in series does not necessarily reflect the causal and effect relationship, _i.e._, series with earlier change point does not have to be the causal factor. Similar change points in NDVI and climatic drivers provide potential explanations for the accelerated or slowed down NDVI trends. The abrupt change points in NDVI and climatic drivers did not fully match with each other, either due to the fluctuation in the data or the change of NDVI trends cannot be fully attributed to the change in climatic drivers. For example, the accelerated growing season NDVI trends in Loess Plateau cannot be explained by jumps in either temperature or precipitation. The changes of NDVI trends in Loess Plateau could mainly be a result of non-climatic factors, which will be discussed further in Section 3.3.5. Nevertheless, the comparison between the abrupt change of climatic drivers and NDVI provides explanations for the nonlinear NDVI behaviors in most regions.
# Online Batch Selection for Enhanced Generalization in Imbalanced Datasets
## 4 Experimental Framework  
In this section, the experimental framework will be presented. _BSBS_, _BSBS-R_, _NBBS_, and the combination of the last two will be compared with each other. We will apply and test the above algorithms along with 11 data transformation techniques, which were mentioned in Section 2, in order to create a better environment for testing imbalanced datasets and to ensure the robustness of the overall procedure. The techniques consist of four undersampling methods (RUS, ENN, ClusterCentroids, and Tomek links), five oversampling methods (ROS, SMOTE, Adasyn, borderline-SMOTE, and KMeans-SMOTE), and two hybrid ones (SMOTEENN and SMOTETomek). The combination of the data transformation methods and _BSBS_ (and its variants) will also be examined to see if the combination of the two leads to better generalization performance of the models. All the data transformation algorithms were run with their default hyperparameters (for more details, see the code repository at the end of the paper).  
The experiments revolve around four datasets, three of which are naturally imbalanced, and one is artificially imbalanced. The naturally imbalanced datasets are the ozone level detection dataset [42], the adult dataset [42], and the default of credit card clients [42]. The artificially imbalanced dataset is MNIST [43], which will be modified in order to become imbalanced. Each dataset has a different imbalance ratio in order to assess the performance of the methods in different situations. Imbalance ratio is defined as the fraction of the number of samples of the majority classes over the number of samples of the minority classes. More details about each dataset are given in the following section. The architectures of the networks, which were trained for each dataset, can be found in the Appendix. For each setup, the weights of the networks are initialized to the same values. All networks are being trained with the Adam optimizer [44] with 0.001 learning rate using the categorical cross-entropy loss function. For the first three datasets, the networks were trained for 10 epochs, while for MNIST, they were trained for 15.  
The experimental process can be summarized in the following steps:The dataset is processed by a data-transformation method (of the ones mentioned above). For example, we apply ROS on the ozone dataset and the samples of the minority class are duplicated in order to reach the number of samples of the majority class
2. The transformed dataset, then, is fed to the neural network to start the training process and apply _NBSBS-R_.
3. At the start of each epoch a noise mask is computed and applied to the dataset. The only exception is the first epoch, where we do not have any loss values yet.
4. Then, the usual training of a neural network ensues, where stochastic gradient descent (or some variant) is applied to every batch at that epoch.
5. At the end of each epoch, the new losses are stored, and the samples are evaluated either as easy or hard. Finally, the easy samples are swapped out and a new epoch starts.
## 3 Results (Table 2)
Immediately after the APS injection, there were cases of minor fever or swelling due to pain, which is common in the case of APS only. As expected, the fever and swelling were reduced after a few days. \begin{table}
\begin{tabular}{l l l l l l l} \hline  & \multicolumn{2}{c}{**KOOS Avg. **} & \multicolumn{2}{c}{**KOOS Pain**} & \multicolumn{2}{c}{**KOOS Symp**} & \multicolumn{2}{c}{**KOOS ADL**} & \multicolumn{2}{c}{**KOOS Sport**} & \multicolumn{2}{c}{**KOOS QOL**} \\  & \multicolumn{2}{c}{**(Figure 1)**} & \multicolumn{2}{c}{**(Figure 2A)**} & \multicolumn{2}{c}{**(Figure 2B)**} & \multicolumn{2}{c}{**(Figure 2C)**} & \multicolumn{2}{c}{**(Figure 2D)**} & \multicolumn{2}{c}{**(Figure 2E)**} \\ \hline Pre & 39.5 & 46.0 & 47.6 & 57.7 & 24.3 & 40.4 \\ A-alone & \(\pm\)11.5 & \(\pm\)13.6 & \(\pm\)15.0 & \(\pm\)16.2 & \(\pm\)15.4 & \(\pm\)11.5 \\ \hline Pre & 45.0 & 53.6 & 51.6 & 67.9 & 26.8 & 44.1 \\ A + E & \(\pm\)12.7 & \(\pm\)16.3 & \(\pm\)13.1 & \(\pm\)15.1 & \(\pm\)14.3 & \(\pm\)12.7 \\ \hlineFigure 3: Mean improvement in the KOOSs between APS and APS + ESWT groups. The mean improvement value (IV) at Pre-3M showed no significant difference between the two groups; however, the mean IV at 3-6M showed a significant difference between the two groups * \(p\) < 0.05). KOOS, Knee Injury and Osteoarthritis Outcome Score; APS, autologous protein solution; ESWT, extracorporeal shock wave therapy; Pre-3M, before 3 months; 3-6M, from 3 to 6 months. Figure 2: The average KOOS subscores in the APS and APS + ESWT groups: (**A**) pain, (**B**) other symptoms, (**C**) activities of daily living (ADL), (**D**) sports and recreation, and (**E**) quality of life (QOL). APS, autologous protein solution; AVG, average; ESWT, extracorporeal shock wave therapy. Most KOOS subscores decreased from 3 months to 6 months in the A-alone group but increased in the A + E group.
## Appendix A Parsing and Resolving Hard Limits and Linked Equations
### Resolving Hard Limits  
Hard limits are user-defined logical conditions that determine the eligibility of specific patient characteristics, ensuring biological and logical plausibility within the synthetic cohort. Each parameter may have an associated hard limit expressed as a logical formula, provided as a string by the user. These formulas incorporate short parameter names and standard logical functions, including the following:  
* Logical operators: AND(0, OR), IF()
* Comparison operators: \(>\), \(<\), \(=\), \(<\)
* Example Scenario Menopause: \"and (Female = 1, Age > 40)\" ensures menopause only applies to females over 40.  
Pregnancy: \"and (Female = 1, Age > 15, Age < 50)\" restricts pregnancy to females aged 15-50.  
Smoking Status: \"Age \(\geq\) 12\" ensures no patient under 12 years old is assigned smoking status. Steps to resolve hard limits for each parameter and each patient are as follows:  
1. Substitution: Replace parameter names within the formula with actual patient-specific values. Example: Patient 1: Age = 35, Female = 1 Formula: and (Female = 1, Age > 40) Substituted: and (1 = 1, 35 > 40)
2. Evaluation: Assess the substituted formula logically. Result: and(TRUE,FALSE) \(\rightarrow\) FALSE
3. Assignment: If the formula evaluates to FALSE, set the parameter value to zero (Xi = 0).  
This procedure ensures that no biologically impossible or inconsistent patient characteristic is assigned.
# A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
## 1 Introduction
Consequently, designing effective pre-training tasks to complete model pre-training is an effective approach for realizing general feature extraction methods. In reviewing the current research on vector shape features, particularly those focused on graph neural networks (GNNs) [16; 21] and graph convolutional neural networks (GCNs) [18; 47], there is an emerging trend of capturing information directly from vector data in an attempt to solve end-to-end problems. However, these approaches often require specifying abstract graph rules or defining the attributes of graph nodes and edges, which can hinder the application of the models to more general tasks owing to manual interventions and data transformations. The purpose of this work is to develop a general, automated, and data-driven method for extracting and representing shape features from vector data, including both lines and polygons. This model is designed to minimize manual intervention and adapt to various vector shape types, operating directly on raw vector data without the need for extensive preprocessing. The ultimate goal is to generate robust and informative shape feature representations that can be effectively applied to a range of downstream geospatial tasks. To achieve this, this study proposes a transformer-based pre-trained shape feature re-expression model, namely, PSBT. By designing three pre-training tasks and utilizing self-supervised learning, the model was pre-trained on large-scale vector data from the existing OpenStreetMap (OSM) datasets and tested on downstream tasks. The results indicate that, for the same tasks, the PSBT model can enhance the training speed by more than five times and improve the performance by 5-10%. The contributions of this study are as follows:  
1. A transformer-based PSBT feature extraction network was designed by implementing an end-to-end method that directly processed the latitude and longitude coordinates to extract the features, thereby eliminating the need for manual feature engineering. 2. Three self-supervised pre-training tasks were proposed, enabling the use of general vector data for model pre-training without the need for labeled samples. 3. Two downstream task networks were designed for single-sample and sample pair classification. Experiments on polygon shape classification show that with the application of PSBT, the network training efficiency can be improved by 500%, and the accuracy can be increased by 5-10%. 4. The contributions of the three pre-training tasks to the re-expressed features are discussed. The experimental results show that integrating multiple pre-training tasks effectively enhances feature representation capabilities. 5. The applicability of PSBT to line element and sample pair matching tasks is discussed and experimentally validated, confirming that the features extracted by PSBT possess a degree of generality.
# ALPINE: Active Link Prediction Using Network Embedding
## 4 Query Strategies for ALPINE
### Heuristics  
The heuristic strategies includes the degree- and probability-based approaches for evaluating the utility of the candidate node pairs. Intuitively, one might expect the connections between high-degree nodes to be important in shaping the network structure; thus, we proposed two degree-related strategies: **page-rank.** and **max-deg.** Meanwhile, as networks are often sparse, queries that result in the discovery of new links--rather than the discovery of non-links--are considered more informative, and this idea leads to the **max-prob.** and **min-dis.** strategies.  
With strategy **page-rank.**, each candidate node pair is evaluated as the sum of both nodes' PageRank scores [31]: \(u_{A}(i,j)=\mathrm{PR}_{i}+\mathrm{PR}_{j}\), while for **max-deg.**, the utility is defined as the sum of the degrees: \(u_{A}(i,j)=\sum_{k:\{i,j\}\in E}a_{ik}+\sum_{l:\{j,l\}\in E}a_{jl}\). The probability-based strategies both tend to query node pairs that are highly likely to be linked. This is true by definition for **max-prob.**: \(u_{A,X^{*}}(i,j)=P(a_{ij}=1|X^{*})\) and approximately true for **min-dis.**: \(u_{A,X^{*}}(i,j)=-||x_{i}^{*}-x_{j}^{*}||_{2}\), as nearby nodes in the embedding space are typically linked with a higher probability.
## 3 Results and Analysis
### Pearson Correlation Analysis  
The quality of soil nutrient status is a comprehensive reflection of various nutrient factors, and there is a certain correlation between different soil nutrient factors [8,35]. Pearson correlation analysis of soil nutrient factors of the nine tobacco planting areas can lead to a better understanding of the relationship between nutrient factors. The results (Table 4) show that there is a significant positive correlation between pH value and organic matter, total N, exchangeable Ca and exchangeable Mg and a significant negative correlation with available B. Among them, the correlation coefficient between pH value and exchangeable Ca reached 0.613. It can be seen that the acidity and alkalinity of soil significantly affected the concentration of calcium ions. Where the pH value is high, the exchangeable calcium content is high.  
In addition, the positive correlation coefficient between soil organic matter and total N is 0.588, and there is a significant positive correlation between the two. The main reason is that the N content in the soil mainly exists in the form of organic N, and the organic N mainly comes from the inorganic degradation of organic matter.  
Figure 3: Distribution of exchangeable Ca, exchangeable Mg, available B and available Zn in tobacco growing soil.
In contrast, GBT utilizes a minimally invasive, patented technology that combines the staining and removal of plaque, tartar, and deposits to effectively eliminate biofilm without damaging hard and soft tissues. This innovative approach is based on scientific findings that soft biofilm (Figure 3)--dental plaque--is the primary etiological factor in caries, gum and periodontal diseases [15; 16], and also influences the progression of systemic diseases such as atherosclerosis, respiratory diseases, diabetes, and heart diseases [17]. Figure 3: Bacterial biofilm disclosed in the Guided Biofilm Therapy protocol. Figure 2: The 8 steps of GBT (source: [[https://www.ems-dental.com/en](https://www.ems-dental.com/en)]([https://www.ems-dental.com/en](https://www.ems-dental.com/en)) (accessed on 15 August 2024)). In contrast to available studies that focused on anxiety management methods related to conventional preventive procedures, this study introduces an innovative approach through the use of Guided Biofilm Therapy. The knowledge gained could be particularly valuable, as GBT is increasingly being offered in dental practices. In addition to assessing patient anxiety, accurate diagnosis of dental health status is crucial in understanding the clinical factors contributing to anxiety. For this purpose, we utilized the Diagnocat AI program, an advanced diagnostic tool that enhances the precision of radiographic analysis. The Diagnocat AI program is an advanced diagnostic tool based on artificial intelligence, used for analyzing radiographic images in dentistry. Diagnocat employs machine learning algorithms to automatically detect pathologies such as caries, periodontal issues, and abnormalities in bone structures, significantly enhancing the accuracy and efficiency of diagnoses. With integration into radiological systems, Diagnocat supports dentists by enabling rapid and precise diagnostic conclusions and monitoring treatment progress. The literature suggests that Diagnocat AI is an effective tool in clinical practices, enhancing diagnostic accuracy and aiding treatment planning. For instance, studies demonstrate that Diagnocat achieves high sensitivity (up to 92.3%) and specificity (up to 97.87%) in detecting periapical changes in radiographs [18,19]. The diagnostic accuracy for other qualitative features remains under-researched and underanalyzed, and is continually refined through ongoing machine learning. Undoubtedly, dentists using Diagnocat can benefit from the ability to standardize measurements, reduce subjectivity, and ensure consistent diagnostic results [20]. However, it is emphasized that final diagnostic decisions should be made by the clinician, taking into account the full clinical context of the results, as was the case in the conducted study.
## 2 Materials and Methods
### Test for Oil Repainting Removal
#### 2.4.3 Gas Chromatography Coupled with Mass Spectrometry (GC-MS)  
Separation and identification of organic compounds was performed using a gas chromatograph (7820A GC-MS, Agilent Technologies, Palo Alto, CA, USA) equipped with a Split/Splitless injection port and a mass spectrometer (GC/MSD 5977B, Agilent Technologies). An SLB\({}^{\mathrm{\otimes}}\)-5 MS capillary GC column (L \(\times\) I.D. 30 m \(\times\) 0.25 mm, df 0.25 \(\upmu\)m; Sigma-Aldrich, Supelco, Darmstadt, Germany) was used for chromatographic separation. The carrier gas (He, purity 99.995%) was used in constant flow mode at 20 mL/min. The mass spectrometer was operated in the EI-positive mode (70 eV). The analytical procedure based on the GC-MS used for the analysis of lipids and proteinaceous fractions has already been described in the literature and is a widely regarded technique nowadays across the heritage science community for the detection and identification of organic components of paintings [31; 32]. For the analysis of monosaccharides, the procedure used is described in the work of [PERSON] et al. [33]. The study for the determination of the linseed oil present in all repainted samples was possible thanks to the identification of the main components of polyunsaturated fatty acids, Linolenic C\({}_{18}\)H\({}_{30}\)O\({}_{2}\) (16-20%) and Oleic C\({}_{18}\)H\({}_{34}\)O\({}_{2}\) (22-42%), and saturated fatty acids, Palmitic C\({}_{16}\)H\({}_{32}\)O\({}_{2}\) (19-25%) and Stearic C\({}_{16}\)H\({}_{36}\)O\({}_{2}\). The characterization of the animal glue binders by GC-MS was performed, identifying the main amino acids present in the samples P1 aCa(A) and P2 vCa(A), such as glycine (32-35%), proline (15-21%), hydroxyproline (19-24%), glutamine (5-7%), and alanine (10-13%). For the Arabic gum study, the study was aimed at characterizing the monosaccharides that make up this type of gum in the analyzed samples P1 aGa(A) and P2 vGa(A). This analytical technique has been used for the characterization of the original polychrome layers and repainting layers before and after the application of the artificial ageing protocol (AAP) and to determine the effectiveness of this applied ageing methodology. This analytical technique has been used for the effectiveness of the application of cleaning treatments for the removal of oil repainting as well.
# Shear Lag Effect on Box Steel Beams with Wide Curved Flanges
## 7 Conclusions  
The phenomenon of shear lag leads to the uneven distribution of longitudinal stresses within the cross-section of the beam, occurring due to the shear flexibility of the flanges. The problem of quantifying the impact of shear lag has been addressed for decades through analytical, experimental, and numerical methods, and a divergence in results can be observed depending on numerous assumptions that need to be adopted when simplifying this complex problem. Initial analyses conducted by analytical methods had to introduce a series of simplifications to the problem to derive expressions suitable for practical engineering use. With the later introduction of numerical methods, extensive parametric analyses were conducted, quantifying the impacts of individual parameters.  
In the case of a wide-flange box beam with curved flanges, observing flange fibers more distant from the junction with the web and thus also from the neutral axis, there is a problem in quantifying the superposition of the effects of [PERSON]'s hypothesis of plane sections and the effect of shear lag. Various parameters and their ratios were considered in setting up the parametric analysis and curves were obtained showing this interaction, represented as a reduction in the impact of shear lag and the activation of a larger part of the flange of a wide-flange beam in the static system of a continuous beam. The analysis concluded that this reduction is smaller at the support of the continuous beam than in the middle of the span, and for mild curvatures of the flange, the curve largely coincides with the one given in the current Eurocode standard.  
The results show that steel beams with low flange curvatures behave like those with flat flanges, while the gradual rounding of flanges leads to an increase in effective width factor up to about double its value. The presented analysis, thus, confirms the hypothesis of the activation of a larger ratio of flange due to the interaction of shear lag and plane section hypothesis for box beams with wide curved flanges.  
Conceptualization, H.V., A.V. and M.S.; methodology, A.V.; software, H.V.; validation, A.V. and H.V.; formal analysis, A.V. and H.V.; investigation, A.V., H.V. and M.S.; resources, A.V., H.V. and M.S.; data curation, A.V., H.V. and M.S.; writing--original draft preparation, H.V.; writing--review and editing, A.V. and M.S.; visualization, H.V.; supervision, A.V.; project administration, A.V.; funding acquisition, A.V. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy restrictions.  
Author [PERSON] was employed by the company IVICOM Consulting Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.