esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Force Analysis  
The forces acting on the moving particles in a quiescent fluid have been widely studied [5; 7; 36]. When the moving particles reach the terminal velocity, the particles are in a force equilibrium. During sedimentation, the particles are mainly subject to gravity \(G\), and buoyancy \(F_{B}\), and the total drag force \(F_{D}\) (Figure 8).  
The forces acting on the particle in Figure 8 can be expressed as  
\[G=F_{B}+F_{D} \tag{3}\]  
The gravity on the particles can be expressed as  
\[G=mg=\frac{1}{6}\pi d^{3}\rho_{p}g \tag{4}\]  
And the buoyancy of the particles during settling:  
\[F_{B}=V\rho_{l}g=\frac{1}{6}\pi d^{3}\rho_{l}g \tag{5}\]  
where \(d\) is the particle diameter, \(\rho_{p}\) is the particle density, and \(\rho_{l}\) is the fluid density.  
In non-Newtonian fluids, the power law model, which has been used widely in previous work to describe the fluids' rheology, has the following form [37]:  
\[\tau=k\gamma^{n} \tag{6}\]  
where \(k\) is the consistency index and \(n\) is the power law index.  
Meanwhile, \(n\) and \(k\) are the power law constants in a power law fluid. For a Rheogram of 0.5 wt% PAAm solution (Figure 3), we can determine that the value of \(n\) is 0.484 and the value of \(k\) is 0.572 (Goodness of fit = 0.997) by the curve fitting equation (Equation (6)).  
A modified particle Reynolds number was defined for the settling particle in the shear thinning fluid [38]:  
\[Re^{*}=\frac{d^{m}{u_{p}}^{2-n}\rho_{l}}{k} \tag{7}\]  
where \(u_{p}\) is the sedimentation velocity of particles.  
The total drag force of the particle settling in a power law fluid was given by [39]  
\[F_{D}=3\pi kd^{2}\Big{(}\frac{u_{p}}{d}\Big{)}^{n}f(n) \tag{8}\]  
where _f(n)_ is a function of the power law constant.  
Figure 8: Schematic of the forces acting on the particle.  
From the above equation, we can obtain the following:  
\[f(n)=\frac{d^{n+1}g(\rho_{p}-\rho_{l})}{18{\it{k}}{u_{p}}^{n}} \tag{9}\]  
In our experiments, the terminal velocities of single particle settling in the PAAm solution are 1.44 m/s (for stainless steel), 1.24 m/s (for ZrO\({}_{2}\)), and 0.77 m/s (for Si\({}_{3}\)N\({}_{4}\)), respectively.  
Therefore, the values of \(f(n)\) are 5.539 (for stainless steel), 4.295 (for ZrO\({}_{2}\)), and 2.377 (for Si\({}_{3}\)N\({}_{4}\)), respectively.  
Apart from this, the values of \(Re^{*}\) are 321 (for stainless steel), 256 (for ZrO\({}_{2}\)), and 124 (for Si\({}_{3}\)N\({}_{4}\)), respectively.
# Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
## 3 Background and Approach  
We assess the performance of specific BiLSTM variants in classifying and labelling information sensitivity in a particular context. Token relationships in this problem definition are sequential; therefore, we focus on sequential models such as BiLSTM and CRF, with supervised training. We chose a bidirectional LSTM, over simple LSTM, for better modelling of temporal nuances in a corpus, as BiLSTMs perform forward and backwards passes on sequential data and model data dependencies in both directions. Finally, we introduce auxiliary features, namely, POS tags, Information Content (IC) and WordShape (WS).
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 5 Discussion
### Security Level
#### 5.1.2 Meaningful Words  
The results of applying various adversarial perturbations to meaningful words and testing recognition were analyzed. The key findings were as follows:  
* CTC achieved complete recognition for 10% of the images. This technique was not as successful at obscuring meaningful words, as it had a higher rate when compared to using meaningless words. Machine vision systems may have exploited semantic understanding.
* EOT reached the highest complete recognition at 23.33%. Its distortions were the least effective at obscuring meaningful words, since it achieved the weakest obfuscation, as measured by recognition success. It also had 13.33% incomplete words and 33.33% unrecognized words, indicating mixed effectiveness.
* IAN achieved a partial recognition rate of 33.33%, with 13.33% completely recognized, indicating that it obscured some of the meaningful words.
* JSMA obtained the lowest complete and partial recognition rates of 6.66%.  
JSMA creates a saliency map that identifies the pixels/features that have the greatest influence on the model's prediction of the meaningful word.
* To cause a misclassification, JSMA needs to introduce larger perturbations to those influential pixels/features, as the model has higher confidence in recognizing the meaningful word.
* This results in a higher non-recognition rate (60%) for meaningful words, as the adversary needs to make more substantial changes to bypass the CAPTCHA.  
This provided evidence that JSMA's distortions most successfully disrupted semantic analysis, maximally confounding machine solvers as intended. This suggests that JSMA most strongly perturbed the meaningful words.
* Finally, SGTCS resulted in an intermediate complete recognition of 6.66% and a partial rate of 13.33%, with 63.33% unrecognized.  
In general, the techniques were less effective at obscuring meaningful compared to meaningless words. JSMA appeared most promising, as it most strongly distorted meaningful text images. This has implications for generating more robust adversarial CAPTCHAs that use both types of text.
## Results
### Test Liquid  
A suitable fluid had to be found that had a refractive index to match that of the silicone material of the replication (Elastosil(r) RT 601). Aqueous glycerol solutions were prepared and tested. The temperature dependence of the refractive index of silicone and the aqueous glycerol solution were measured using a refracometer. The results are depicted in Figure 6.  
The refractive index decreases with increasing temperature for both the silicone and the aqueous glycerol solutions. However, the gradients of decrease for the silicone and the solutions are different. The spread of the refractive index for the tested solutions is nearly constant over the whole temperature range. Refractive index matching solutions can be found for the considered temperature range. Table 2 lists the appropriate concentrations of the aqueous glycerol solution for various temperature subranges.  
If other materials such as PMMA are used [2] for the replicate, the fluid has to be adjusted or another fluid has to be chosen accordingly.  
\begin{table}
\begin{tabular}{c c} \hline
**Temperature Subrange (\({}^{\circ}\)C)** & **Concentration of the Aqueous Glycerol Solution (wt \%)** \\ \hline
14–16 & 59.25 \\
17–19 & 59.00 \\
19–21 & 58.75 \\
22–24 & 58.50 \\
24–26 & 58.25 \\ \hline \end{tabular}
\end{table}
Table 2: Temperature ranges for a refractive index matching of an aqueous glycerol solution and Elastosil® RT 601.  
Figure 6: Refractive indices of aqueous glycerol solution and Elastosil® RT 601 as function of temperature.  
This method can also be used to investigate the flow of a particular fluid applying the concept of similitude. The concept of similitude is especially useful when the chosen refractive index matching liquid features a density or viscosity different to the original fluid. Similar flow conditions in channel flow can be established by keeping the Reynolds number constant. Therefore, the velocity in this setup could be adjusted to match the relevant Reynolds number. A second possibility is to adjust the temperature of the setup. Matching refractive indices of silicone and aqueous glycerol solution can be found by varying the glycerol concentration. This also leads to a change in fluid density and viscosity. A third method for adjusting the density and viscosity of the test liquid to the original fluid is to use additives such as thickening agents when preparing the refractive index matching liquid.
## Materials and Methods  
### Experimental Materials and Sample Preparation  
Feed enterprises often conduct the test of raw protein materials before feed production. Thus, the actual test sample mainly contains one or more different protein raw material, rather than the finished feed product. In this study, the test samples were compounded, which consists of WPC and SDPP at different concentrations to investigate the feasibility of detecting SDPP by both EN and NIRS. WPC was provided by Guangzhou Wheysubime Biotechnology Company Limited (No. 3, Guquan Road, Guangzhou High-Tech Industrial Development Zone, Guangzhou, China) and had a protein content of more than 80%, a fat content under 8%, and water content below 5%. SDPP was provided by Wuhan Yuancheng Gongchuang Technology Company Limited and had a protein content greater than 76% and water content below 10%. All materials were sealed and stored at 4 \(\pm\) 1 \({}^{\circ}\)C and 65% \(\pm\) 3% RH.  
The test samples were compound raw materials used for creep feed, consisting of WPC and SDPP at different concentrations. All test samples were ground 3 times (10 seconds each) by a grinding miller (MFJ-W300) and sieved through a 0.5 mm standard screen hole. An electronic analytical balance (BSA224S-CW) was adopted to ensure that each sample contained 8 g of the compound sample. To construct the qualitative discrimination model, 90 pure samples and 90 impure samples were selected to detect whether the samples contained SDPP. Twenty concentrations from 1%-20% were selected to build the quantitative prediction model to predict the concentrations of SDPP in the samples (Table 1). The calibration samples and prediction samples were prepared separately. In total, 360 test samples were prepared for the electronic nose and NIRS tests.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Analysis Method**} & \multirow{2}{*}{\
\begin{tabular}{c} **SDPP** \\ **Concentration** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Classes** \\ \end{tabular} } & \multicolumn{2}{c}{**Number of Samples**} & \multirow{2}{*}{**Total**} \\ \cline{3-3} \cline{5-6}  & & & **Calibration Set** & & **Prediction Set** \\ \hline \multirow{2}{*}{Qualitative Analysis} & Pure & 1 & 60 & 30 & 90 \\ \cline{2-6}  & Impure & 1 & 60 & 30 & 90 \\ \hline Quantitative Analysis & 1–20\% & 20 & 6 & 3 & 180 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on the test samples prepared for both electronic nose (EN) and near-infrared spectroscopy (NIRS).
## 3 Results and Discussion
### Freezing Completion Effect in Aquatic Products at Different Stacking Modes
For NSMs and UPT-PSMs, the widths of gaps are almost equal, but when using UPF-PSMs, the middle gap can be much wider than the gaps on both two sides. Wider gaps can ease the forced convection in the refrigerated room, thereby improving the heat transfer rate [16]. For a residence time of 24 h, the freezing completion effects at PSMs are better than those at their corresponding NSMs, especially at UPF-PSMs. The application of UPF-PSMs can ensure the fastest freezing completion process. The highest temperatures in all horizontal planes are summarized in Figure 8. The temperature gradually drops from the bottom to the top of the refrigerated room, because closer positions to the refrigerating units afford more intense forced convection, which enhances the freezing completion effect. All curves are fitted as linear functions by means of Equation (9). The absolute values of \(a_{1}\) can be seen in Table 3. The \(a_{1}\) values for NSMs and UPT-PSMs are of the same order, while those for UPF-PSMs are much smaller. For a residence time of 24 h, aquatic products at UPF-PSMs almost reach a steady state, while the other modes are still in the course of intense heat exchange. Along with the temperature uniformity in products, the uniformity of flow distribution and the efficiency of air circulation in a refrigerated room need to be analysed as well. The objects of further studies were UPF-PSMs with two-piles, UPT-PSMs with six-piles, NSMs with two-piles and NSMs with six-piles. Figure 5.0  
Figure 5.1 Highest temperature in chosen planes (y = 0.35, 1.05 and 1.75 m) in a horizontal direction. Numerical experiments with longer time steps were conducted as well. It was found that NSMs require over twice-longer residence time, as compared with UPF-PSMs, to achieve the same freezing completion effect. Simultaneously, the number of packages at NSMs was no more than twice as large as that of packages at UPF-PSMs. For two-piles, the number of studied packages at UPF-PSM was 54; while 28 packages were accommodated in an NSM arrangement. If the number of packages is the same at both stacking modes, UPF-PSMs completes freezing quicker. The more packages there are, the more expressed this advantage is.
# Correlation Study of Auroral Currents with External Parameters During 10-12 October 2024 Superstorm
## 1 Introduction
(2024) [10] first conducted a quantitative study on the correlation between auroral currents and external parameters during geomagnetic storms, and this paper extends their research. The novelty behind this study is as follows: FACs and PEJs during storms may not follow a uniform pattern but instead vary depending on the specific geomagnetic storm event. Moreover, during the geomagnetic storms in May and October, the variation patterns of solar wind external parameters over time were different. For example, during the May event, Pd increased throughout the main and recovery phases of the storm, whereas during the October event, Pd only increased during the main phase. Therefore, it remains unclear whether the dominant external factors that correlate best with the observed phenomena are the same or different, and this study aims to clarify this issue. Although the Swarm satellites were located in similar local time sectors as during the May 2024 storm, the October storm occurred when the two hemispheres were in opposite seasons. This unique timing provides an opportunity to explore whether hemispheric differences in FAC and PEJ behavior are linked to seasonal variations in ionospheric conductivity or to hemispheric differences in geomagnetic field configuration. Finally, the differences in the density and location of the dawn and dusk auroral currents also require further verification through additional super geomagnetic storm events. Thus, this study presents a valuable opportunity to further investigate the local time and hemispheric asymmetries of FACs and PEJs in response to severe geomagnetic storms. The remainder of this paper is organized as follows: Section 2 outlines the instrumentation and data processing methods used in this study. The analysis of the observations for the event is presented in Section 3. Section 4 discusses the results, and Section 5 provides the conclusions.
## 2 Literature Review
### Model-Driven Methods  
The classical CTM is derived from the Lighthill-Whitham-Richards (LWR) model, which pioneers the application of the continuity equation from fluid mechanics to model traffic flow [10,11]. Through the iterative solution to the LWR model, the trends in traffic volume, speed, and density changes can be acquired and used for predicting the future traffic state. To simplify the equation for the solution, the classical CTM discretizes the LWR model in both time and space [6]. In this way, the continuous motion of vehicles is transformed into successive transmissions between adjacent cells, reducing the calculating pressure of model solving. Because of its simplicity and efficiency, the application of the classical CTM under various traffic scenarios is widely studied, leading to a series of extended CTMs [12].  
By describing the traffic flow at signalized intersections, some traffic flow model-based CTMs improve their predictive capabilities. For example, [PERSON] et al. [13] modify the right half of the demand function as a linear decreasing function to reflect various headways in different signal phases. [PERSON] et al. [14] add compensatory terms to the function for startup loss time and dissipation waves to describe the capacity drop of cells at signalized intersections. [PERSON] and [PERSON] [15] define the blocking matrix to reflect spatial queuing interactions between cells, allowing for an accurate description of the physical queue evolution at signalized intersections. These CTMs depict the operational characteristics of traffic flow at signalized intersections, which enhance their predictive performances. However, the traffic flow models on which model-driven CTM relies are often too idealistic to be met in real traffic, such as uniform traffic flow or equal headways [12].  
Further, some model-driven CTMs discuss the impact of cell size on predictive performances. For instance, [PERSON] et al. [16] model complex urban roads through cells with variable lengths and densities. These variable cells are connected by different types of cell connections, including simple connection, merge connection, and diverge connection. [PERSON] and [PERSON] [17] utilize variable-length cells to model signalized intersections and reflected traffic states through the density of downstream congested cells, the density of upstream free cells, and the position of the congestion waves. These model-driven CTMs indicate that variable-sized cells are advantageous in discretizing road space at signalized intersections.
## 3 Background of the Methods Used
### Classification and Regression Tree (CART)  
As one of the most popular statistical methods, the CART (classification and regression tree) has been widely employed to handle classification and regression problems [64]. Inspired by the growth process of trees, the construction of CART trees generally consists of roots, leaves, branches, and nodes. By means of binary recursive classification techniques, CART algorithms divide each sample set into two sub-sample sets, and thus, there are two branches in each none-leaf node [65; 66].  
The CART was first proposed and developed by [PERSON] et al. (1984). Different from traditional statistical methods, the CART is mainly established by many binary decision trees and is easy to interpret and understand. Especially for the complex data and significant variables, the CART tends to show better prediction accuracy compared with previous prediction methods. The most prominent advantages of the CART are the keen ability to distinguish the importance of variables and eliminating outliers.
## 3 Proposed Framework
### System Architecture  
The system architecture follows a three-layer structure comprising IoT devices, edge nodes, and a central server. In this architecture, IoT devices collect data and perform local model training, edge nodes serve as intermediate aggregators, and the central server is responsible for global model aggregation. By leveraging edge nodes for local aggregation, the hierarchical design helps reduce communication costs and enhances scalability. This localized aggregation effectively reduces the communication burden on the central server, making the architecture particularly suitable for IoT environments with constrained resources.  
Let \(N\) denote the number of IoT devices, indexed by \(i\in\{1,2,\ldots,N\}\), and let \(E\) denote the number of edge nodes, indexed by \(e\in\{1,2,\ldots,E\}\). Each IoT device \(i\) collects local data \(D_{i}\) and trains a local model \(w_{i}\). The edge node \(e\) aggregates the local models received from a subset of IoT devices \(S_{e}\subset\{1,2,\ldots,N\}\) connected to it, and the central server aggregates the updates from all edge nodes. This approach not only reduces network congestion but also effectively utilizes the computational capabilities of edge nodes.
###### Abstract
The exponential growth of social media use [9], coupled with research that links depression with the use of language [10; 11] has made this proposal possible. Several workshops have proposed shared tasks [12; 13] in which new solutions arise. Nevertheless, in the literature, the proposals are mainly based on representations that contain, in their majority, objective features, that is, features based on the structure of the text, or quantitative aspects of it, such as n-grams [14], parts-of-speech, and number of words [15; 16]. Even though in the literature there are proposals that use sentiment analysis, these proposals do not use only subjective information of the posts. These proposals combine them with other objective features and have limited information on sentiment, emotion, and other subjective features [17; 18; 19; 20]. Moreover, several problems arise in the use of AI for decision making when such decisions are not interpretable. The psychological field requires an AI model that provides explanations so that experts understand the reason behind a decision made by it because they need to assess its validity [21]. Since the psychological field is a medical field, the decisions taken by the specialist affect human lives. If they use a model to help with diagnosis, they need supplementary information to support and validate their diagnosis. Although some algorithms that can provide explanation have been used in the literature, the explainability of the algorithm has not been exploited or taken into consideration using only information linked with emotions and sentiment [16; 22]. Hence, in this paper, we propose a contrast pattern-based classifier using only features based on emotion and sentiment analysis, which are easier to understand by human experts. The patterns extracted from this representation are used as an explanation of the final decision and are associated with human language, making them understandable to experts. The contributions of this paper are as follows:  
* A feature representation based on sentiments and emotions, allowing an accurate classification for detecting depressive posts. An explainable model based on contrast patterns achieving better area under the curve (AUC) and F1 regarding other state-of-the-art classifiers designed for detecting depression. * A set of extracted contrast patterns describing depressive posts in a language close to experts in the application area. The paper is organized as follows. Section 2 presents the previous works in the topic. Section 3 presents the databases, representations, and classifiers used to construct the models used for evaluation. Section 4 presents the results obtained in AUC and F1 measures and the statistical comparison between our model and other state-of-the-art models. Section 5 presents a demonstration on how the contrast patterns extracted can be interpreted. Finally, Section 6 presents our conclusions and future work.