esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Preliminaries
### Effects of Dielectric Fluids
#### 2.1.1 Interfacial Tension  
Molecular attraction at the solid-liquid interfaces, particularly between the DUT and the dielectric fluid's surface, causes interfacial tension (IFT). Surface tension influences several factors, including the dielectric fluid's characteristics, environmental conditions, viscosity-temperature coefficient (VTC), and the DUT's contact area.  
Surface tension induces fluid movement, generating additional reflected forces acting both normally and tangentially on the device's surface. The excess reflected forces introduce surface energy, which gradually causes the DUT to deviate from its initial position [17].  
Two main factors, the drag coefficient (or resistance) and the drag force the device exerts on the fluid, influence the flow around a SOIC package. The friction factor represents the ratio between the fluid's kinetic energy per unit volume of the SOIC and the drag force exerted per unit area:  
\[f\cdot K_{e}=\frac{F_{f}}{A}, \tag{1}\]  
where \(F_{f}\) represents the force resulting from the fluid's motion, \(A\) is the characteristic area of the wet surface projected on a plane perpendicular to the fluid's approach velocity, and \(K_{e}\) denotes the characteristic kinetic energy per unit volume [18]. \(K_{e}\) is obtained as  
\[K_{e}=0.5\cdot\rho\cdot\vec{v}^{2}, \tag{2}\]  
where \(\vec{v}\) represents the fluid's approximate external velocity and \(\rho\) represents its density.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 5 ETC-Based Urban Traffic Management: Applications and Practices
### Transformation of Highway Technology to Urban Applications
Targeting the coexistence of electronic toll collection lanes and compound toll collection lanes at Chinese highway toll plazas, they employed real toll data-driven methods to analyze the capacity of toll plaza entrance and exit lanes under mixed traffic conditions. This method can be directly applied to capacity analysis for urban toll road segments and parking facilities [50]. [PERSON] et al. investigated traffic flows on toll highways with electronic and traditional toll plazas, discovering that electronic and manual toll vehicles enter their respective lanes at low densities while mixing at each toll plaza at high densities. They derived fundamental diagrams for electronic and manual toll vehicles, with these fundamental diagram analysis methods equally applicable to urban mixed traffic flow research [51]. [PERSON] et al. investigated traffic congestion dynamics using highway electronic toll collection system data, demonstrating how real Taiwan highway system data can monitor traffic congestion evolution on fundamental diagrams. They discovered that congestion characteristics form cyclic trajectories, with areas enclosed by loops increasing as congestion severity increases. The area enclosed by loops in fundamental diagrams serves as a measure of economic losses due to congestion. This congestion dynamics analysis method provides important theoretical foundations for congestion management on urban expressways and arterials [52]. It is noteworthy that although these studies are primarily based on highway environments, their core technologies and methodologies can be fully transferred to urban traffic environments. Scenarios such as urban expressways, arterial toll road segments, and large parking facilities share similar traffic characteristics with highways, enabling ETC systems to achieve comparable effectiveness in these scenarios. Particularly, in the current context of urban traffic digital transformation, these mature highway ETC technologies provide solutions and technical pathways for urban traffic management, facilitating the rapid enhancement of intelligent levels in urban traffic management.
## 4 Experimental Investigations  
### Objective Experiments  
To evaluate the performance of the presented HRTF individualization method, the leave-one-out cross validation approach was used [24]. Each subject in the database was taken out successively as the testing subject with the remaining 55 ones being used for training.  
The spectral distortion was used as the objective evaluation metric. The results of the presented HRTF individualization method was compared with other two existed HRTF individualization methods, which had been verified to be effective previously. One was database-matching approach proposed in [7], the other was general regression neural network based method presented in [25].
## Materials and Methods
### Satellite Imagery
#### 2.2.3 Sentinel-2  
Sentinel-2 is owned by the European Space Agency (ESA); it was launched to support activities such as land monitoring, emergency management, security, and climate change issues. In order to fulfill such objectives, the mission was designed in such a way that it has high revisit frequency (five days at the equator), high spatial resolution (up to 10 m in some bands), and having wider swath (290 km). It has been providing unprecedented freely available data to the public since June 2015. The Sentinel-2 product level 2A images (Table 2) used for this study were also downloaded from the Planet Explorer Beta. Only images with a cloud cover \(<\)50% and images from December 2016 to May 2017 were used. The average number of Sentinel-2 images available for interpretation at each location was 5.6 with a range from two to 10 images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellite** & **Spatial Resolution (m)** & **Product Level** \\ \hline PlanetScope & 3 & 3B \\ RapidEye & 5 & 3B \\ Sentinel-2 & 10 & 2A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Satellite images used in this study.
## 3 Results and Discussion
### Durability Properties
#### 3.2.3 Acid Attack Resistance (H\({}_{2}\)SO\({}_{4}\) Attack)  
Acids have a strong tendency to react with the CHs of concrete in order to produce a calcium salt and water. There are many aggressive acids, such as acetic acids, nitric acids, hydrochloric acids, and sulfuric acids (H\({}_{2}\)SO\({}_{4}\)). Concrete structures handling the acidic wastewaters of different industries are extremely vulnerable to deterioration. Therefore, it is very necessary to decrease the overall CH content of concrete. In this research, H\({}_{2}\)SO\({}_{4}\) was used for acid attacks on concrete mixes with various levels of MAB and TMAB. H\({}_{2}\)SO\({}_{4}\) reacts with CH and forms calcium sulfate which causes deterioration of the microstructure (due to increased internal pressure) and leads to loss in mass and mechanical strength of concrete. The results of acid attack are presented in terms of loss in mass of the specimens after 7, 28, 56, and 90 days for each mix, see Figure 12.  
The general trend in results indicates that the incorporation of either MAB or TMAB results in significant improvement in the H\({}_{2}\)SO\({}_{4}\) attack resistance of concrete. Increasing the incorporation level of bentonite not only consumes the CH in a pozzolanic reaction, but results in an overall reduction of the net CH content of binder [21; 22]. OPC contains a very high content of CaO, whereas bentonite is rich in SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), therefore, increasing MAB/TMAB level results in the reduction of CH (a primary hydration product of cement). It was also noticed that for a given age and incorporation level, TMAB-added mixes were more resistant to acid attack than MAB-added mixes. Not only does bentonite reduce the CH content of concrete, but it reduces the pore volume. Reduction in pore volume can also control the penetration of SO\({}_{4}\)\({}^{-2}\) ions from the solution into the microstructure that may cause the deterioration of concrete.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 4 Discussion  
SMARTerra is a near real-time tool built on a WebGIS platform that may be easily extended to any agricultural area with access to hourly data from a meteorological station network. It automatically computes risk indices for various pests and diseases, generates daily bulletins, and sends them to registered users to support crop management planning.  
Any risk model based on agrometeorological inputs can be easily accommodated within SMARTerra; we provided two examples concerning rice blast and locust egg hatching. Beyond the service and implementation features, our system is innovative in a key aspect: data visualization and risk index predictions are made at the farm scale, with resolutions up to 10 m.  
This approach shifts DSSs for agriculture from the district to the farm level, potentially enabling measured data and risk predictions tailored to specific crops.  
Testing the accuracy of emergencies forecast at such high resolution is challenging. For rice blast, the main difficulty lies in the lack of precise experimental data. Monitoring is typically conducted at only a few points per crop and across a limited number of crops per district, leading to a subjective assessment of the presence of _Pyricularia oryzae_. In this frame, only qualitative estimates are feasible, showing significant pattern similarity.  
The case study of locust egg hatching highlights a significant issue that is likely to become more pronounced with the shift towards high-resolution DSSs: the need for a well-defined approach that incorporates regularization techniques to reduce sensitivity to minor parameter changes.  
Old, standard approaches relying on district-scale data analysis often employ averaging and pooling strategies, which can act as simple yet effective regularization methods that may obscure very localized dynamics of diseases and pests, as well as their dependence on model parameters. Fortunately, the egg hatching case considered in this study benefits from a substantial number of observations, thanks to the efforts of regional agencies. This allows for the implementation of systematic analysis methods, with a portion of the annotated data reserved for calibration and testing. Such an approach enables fine-tuning the model to accurately reflect the dynamics of locust spread in Sardinia and, more broadly, the spread of crop pests where effective risk models are available. It also supports the training of data-driven algorithms using machine learning or deep learning.
## 1 Introduction
Manipulating magnetic particles by a magnetic field has a wide variety of applications including magnetohydrodynamic pumping in microchannels, fluid mixing, stabilizing or agitating magnetic particles containing fluid, and supporting bioreactions in microchannels [41]. When a magnetic field is applied on LaNi\({}_{5}\), nickel particles form in the compound and diffuse to the surface. Nickel is a strong paramagnetic material at room temperature and becomes ferromagnetic at 4.2 K [42; 43; 44]. Although experimental results state LaNi\({}_{5}\) as a paramagnetic material, calculations of [PERSON] et al. [45] showed that, theoretically, it can exhibit properties of a very weak ferromagnetic material. [PERSON] et al. [46] and [PERSON] et al. [47] stated a significant magnetic entropy change in a nickel alloy (Ni\({}_{2+x}\)Mn\({}_{1-x}\)Ga) because of an increase in its nickel content. [PERSON] et al. [48] calculated and measured magnetic characteristics and the electron structure of three metal hydrides which are known as RNi\({}_{5}\) compounds. Their study included LaNi\({}_{5}\), YNi\({}_{5}\), and CeNi\({}_{5}\). According to their results, these hydrides do not demonstrate similar magnetic characteristics and each one has its individual magnetic properties. Thus, they cannot be categorized and characterized in the same series of compounds based on their magnetic characteristics. In the present work, the heat transfer inside a metal hydride bed with LaNi\({}_{5}\) as the hydride material is studied. The difference in heat transfer with and without an external magnetic field is tested and the results are presented. The following sections describe the details.
## 2 System Model and Problem Formulation
### Latency Model  
The processing delay of tasks includes the uplink transmission delay, the computation delay, and the downlink transmission delay. Therefore, the total processing delay of tasks for user \(m\) can be expressed as,  
\[T_{i}=\Sigma_{i\in M}\Bigl{(}t^{up}+t^{com}+t^{down}\Bigr{)}, \tag{4}\]where \(t^{com}\) is the computation delay. Generally, the latency of tasks would be mainly affected by the task traffic from the user devices. As there are a lot of packets that are sent intermittently in marine communication, task traffic is emergent and autocorrelation. In this paper, we use the \"on/off\" model to formulate the tasks' traffic. Based on the \"on/off\" model, the task state of each user device would vary between the on state and the off state. We also have the following assumptions for the tasks of user devices,  
\[\lambda=\left\{\begin{array}{l}\lambda_{on}=\frac{1}{\frac{N_{on}}{N_{off}} \sum_{i=1}^{N_{off}}t_{on}^{i}},\\ \lambda_{off}=\frac{1}{\frac{N_{off}}{N_{off}}\sum_{i=1}^{N_{off}}t_{off}^{i}},\end{array}\right. \tag{5}\]  
where \(\lambda_{on}\) and \(\lambda_{off}\) are the exponential distribution parameters, which can affect the channel available time, and then the latency. \(N_{on}\) is the number of tasks occurring in the user devices, and the \(N_{off}\) is the number of tasks not occurring in the user devices. \(t_{on}\) and \(t_{off}\) are the state duration.
## 2 Related Studies
### Methods of Controlling the Traffic of Both Self-Driven and Human-Driven Vehicles
#### 2.1.4 Control of Entry into an Intersection  
Reference [3] extends a previous method [9] that avoids conflict in entering an intersection (hereafter \"conflict situation\") by making each vehicle reserve its intersection passing time. It proposes to prevent interferences between a self-driven vehicle and a human-driven vehicle by restricting the direction that a human-driven vehicle can take on each of the three types of lane: Through lane, right-turn lane, and left-turn lane. This method can be effective only at a large intersection where there are several lanes in each direction.  
In [4], a low-priority vehicle coming to an intersection sends the roadside unit a request message to enter the intersection. A priority vehicle that has received this message decides whether to enter the intersection by examining the distance from the intersection to see if it can safely reduce its speed. This method does not consider other factors. Another problem is that the communication between vehicles and the roadside unit is one way. The vehicles involved cannot directly communicate with each other. The method does not work if the requesting vehicle cannot identify the responding vehicle and needs to brake hard to avoid an accident. The present paper classifies yielding situations into four yielding patterns and defines how the vehicles involved behave in each pattern. The vehicles' decisions are based on a number of conditions: What is the priority level of the road the given vehicle is running, whether there is enough space to move into, whether there is a traffic light, whether there is a conflict situation, and whether there are pedestrians wanting to cross the road. The low-priority vehicle and the priority vehicle identify each other and communicate the former's yielding request and the latter's response reliably using a handshake protocol.  
In [5], vehicles that approach an intersection directly exchange vehicle information before they arrive at the intersection. If the times they pass through the intersection overlap, one of them is selected to play a traffic light role. It broadcasts traffic signal information so that the vehicles that have received this information behave as if the intersection had a traffic light. This virtual traffic light (VTL) method [6] is designed in detail for practical application. Reference [7] presents an improved VTL, which considers each vehicle's moving direction in detecting a conflict situation, and allows the vehicles to go into the intersection if there is no conflict, thereby reducing the time needed for the vehicles to pass through the intersection. These methods assume only human-driven vehicles but have the potential for controlling traffic that involves both human-driven and self-driven vehicles. These are aimed at improving traffic efficiency by eliminating stops. They do not consider pedestrians.
## 3 Materials and Methods
### Alignment of Research Questions with Analytical Approaches  
**RQ1:**: _How consistently do five state-of-the-art LLMs (openai/01-miti, x-ai/grok-2-1212, Llama-3.3-70B-Instruct, Gemini-Flash-1.5-8B, DeepSee-R1-70B-Distill) extract key methodological fields from aging-related tDCS trial protocols?_Analysis Method and Justification: Fleiss' Kappa (\(\kappa\)), for categorical data, and intraclass correlation coefficient (ICC), for numeric data, were selected.  
Justification: Fleiss' Kappa is ideal for assessing agreement among multiple raters (LLMs) when classifying categorical variables. ICC is appropriate for measuring reliability in extracting numeric data, as it assesses the consistency or conformity of quantitative measurements made by different observers (LLMs).  
**RQ2:**: _Does a simple ensemble consensus (majority vote for categorical fields; mean/median for numeric fields) outperform the best individual model in accuracy and inter-rater reliability?_  
Analysis Method and Justification: Ensemble consensus metrics (majority vote for categorical and averaging methods for numeric variables) were compared directly to individual LLM performance using \(\kappa\) and ICC.  
Justification: Ensemble methods typically outperform individual classifiers due to a reduction in individual biases and errors. Majority voting for categorical fields and averaging numeric fields are robust consensus-building methods, and comparing these to the best-performing individual model helps clarify the practical advantage of multi-model ensemble approaches.  
**RQ3:**: _Which attribute classes (binary, categorical, and continuous numeric) pose the most significant challenge to automated extraction?_  
Analysis Method and Justification: Performance metrics (Fleiss' Kappa and ICC) were calculated separately for binary, categorical, and numeric fields, facilitating direct comparison across attribute classes.  
Justification: Analyzing attribute-specific extraction difficulties provides targeted insights for future methodological refinements. By differentiating attribute classes, the analysis explicitly identifies where LLM-driven pipelines might require additional human oversight or prompt engineering improvements.  
The selected analytical framework systematically evaluates model performance, captures inter-model variability, and clearly distinguishes individual versus ensemble outcomes. This structured approach ensures transparency and reproducibility, while explicitly aligning each research question with an appropriate, well-established statistical measure.  
Code Availability  
The Python code that generated and processed these data--including API calls, data cleaning routines, and multi-LLM evaluation scripts--is available in the Appendix B and online via GitHub [[https://github.com/ricyoung/LLM-Pipeline-for-Clinical-Trial-Data-Extraction](https://github.com/ricyoung/LLM-Pipeline-for-Clinical-Trial-Data-Extraction)]([https://github.com/ricyoung/LLM-Pipeline-for-Clinical-Trial-Data-Extraction](https://github.com/ricyoung/LLM-Pipeline-for-Clinical-Trial-Data-Extraction)); accessed on 2 March 2025.
## 2 ML Methodologies
### Supervised Learning
#### 2.1.2 Regression
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Ref.** & **Description** \\ \hline Decision tree regression & [92] & Builds a regression model by recursively splitting the data based on the values of the input variables. Each internal node represents a tested value. Decision trees can capture complex nonlinear relationships and are easily interpretable. \\ \hline Random forest regression & [93] & An ensemble learning method that combines multiple decision trees to perform regression. It constructs a multitude of decision trees and generates predictions by averaging the predictions of individual trees. Random forest regression is capable of handling nonlinear relationships and reducing overfitting. \\ \hline Gradient boosting regression & [60] & Builds a regression model by iteratively adding weak learners, typically decision trees, to minimize the loss function. It constructs an ensemble of models where each subsequent model focuses on reducing the errors made by the previous models. Gradient boosting regression can effectively capture nonlinear relationships and handle complex datasets. \\ \hline Neural network regression & [94] & Utilizes artificial neural networks (ANNs) to perform regression. ANNs consist of interconnected nodes (neurons) organized into layers. By learning the weights and biases of these connections, neural networks can model complex nonlinear relationships between input variables and the target variable. \\ \hline K-nearest neighbor (KNN) regression & [95] & Predicts the target variable based on the average of the values of its k-nearest neighbors in the feature space. KNN regression can capture nonlinear relationships by considering local patterns in the data. \\ \hline Gaussian process regression & [96] & Uses a Gaussian process to model the relationship between the input variables and the target variable. It can capture complex nonlinear relationships and provides uncertainty estimates for predictions. \\ \hline Support vector machines (SVMs) & [97] & Originally designed for classification, SVM can be extended to regression tasks. It aims to find a hyperplane that best separates the data points while maximizing the margin. SVM regression can handle nonlinear relationships by using kernel functions. \\ \hline Bayesian regression & [98] & Combines prior knowledge with observed data to estimate the posterior distribution of the model parameters. Bayesian regression can capture nonlinear relationships by using flexible probabilistic models. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Nonlinear regression algorithms for water resources management.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
### Motivation  
The major motivation of this paper is to highlight the superiority of CCA in comparison with PCA. As discussed above, many researchers have addressed high dimensionality of HSI and subsequently classified it. To the best of our knowledge, this is the first study to employ CCA while treating HSI as multi-view and obtaining reduced spectral features. The reduced features have been projected to SVM for semantic segmentation (dense per pixel prediction), owning to its aforementioned advantages and good performances.  
Pairwise CCA (PCCA) and Multi-Set CCA (MCCA) have been employed to extract features in the multi-view fashion. While both techniques share the common objective of un covering correlations across multiple views, they differ significantly in their approach and applicability. PCCA is a variant of CCA that operates by computing pairwise correlations between each pair of views and extracts features between two views at a time.  
On the other hand, MCCA is generalized CCA, which extends the concept of CCA to more than two views simultaneously. Unlike PCCA, which focuses on pairwise comparisons, MCCA considers the joint correlation structure across multiple views. It aims to uncover common latent factors that are shared across all views, capturing the global relationships among all datasets simultaneously.  
The suitability and effectiveness of the CCA-SVM approach in semantic segmentation of landcover HSI, can further enhance the opportunities among the research community in this domain.