esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Interfacial Tension Measurements (IFT)
#### 3.3.2 Effect of Temperature on IFT  
The effect of temperature on IFT reduction in the surfactant solution was determined. As shown in Figure 4 as the test temperature rose, the time required for the system to the lowest interfacial tension (\(\mathrm{IFT_{min}}\)) became shorter. According to Brownian motion, with an increase in experimental temperature, the movement speed of the surfactant molecules increases, and the diffusion speed from the solution body to the interface becomes faster, resulting in the IFT reaching the lowest value faster. Furthermore, with an increase in test temperature, \(\mathrm{IFT_{min}}\) shows a trend of decreasing first and then increasing. On the one hand, an increase in temperature decreases the viscosity of crude oil, which can effectively reduce \(\mathrm{IFT_{min}}\)[29]. On the other hand, with an increase in the test temperature, the adsorption and desorption rate of molecules on the interface increased, making the interface film more unstable, and the \(\mathrm{IFT_{min}}\) increased (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **40 \({}^{\circ}\)C** & **50 \({}^{\circ}\)C** & **60 \({}^{\circ}\)C** & **70 \({}^{\circ}\)C** \\ \hline t/min (\(\mathrm{IFT_{min}}\)) & 20 & 15 & 10 & 10 \\ DCB (\(\mathrm{IFT_{min}}\)/mN\(\cdot\)m\({}^{-1}\)) & \(4.0\times 10^{-3}\) & \(2.5\times 10^{-3}\) & \(4.5\times 10^{-3}\) & \(4.3\times 10^{-2}\) \\ DCB (\(\mathrm{IFT_{min}}\)/mN\(\cdot\)m\({}^{-1}\)) & 0.1 & 0.13 & 0.4 & 1.3 \\ t/min (\(\mathrm{IFT_{min}}\)) & 35 & 30 & 20 & 15 \\ DSB (\(\mathrm{IFT_{min}}\)/mN\(\cdot\)m\({}^{-1}\)) & \(3.5\times 10^{-3}\) & \(2.8\times 10^{-3}\) & \(2.7\times 10^{-3}\) & \(1.7\times 10^{-2}\) \\ DSB (\(\mathrm{IFT_{equ}}\)/mN\(\cdot\)m\({}^{-1}\)) & 0.075 & 0.19 & 0.3 & 0.51 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters on the lowest interfacial tension (\(\mathrm{IFT_{min}}\)) and the equilibrium interfacial tension value (\(\mathrm{IFT_{equ}}\)) of the required time vary with temperature.  
Figure 3: Variation of the interfacial tension with the surfactant concentration for DCB (**a**) and DSB (**b**) against Xinjiang crude oil at 40 \({}^{\circ}\)C.  
In addition, the long carbon chain of the surfactant molecule was aggregated with the increase in temperature, and the force with crude oil decreased, resulting in an increase in the equilibrium interfacial tension value (IFT\({}_{\text{equ}}\)) [20].
## 4 Methods That Use Large Language Models
### LLM-Based Fake News Detection
#### 4.3.2 Fact-Checking and Verification  
Automated fact-checking and verification using large language models (LLMs) has shown considerable promise in improving the accuracy and efficiency of identifying misinformation. Various innovative approaches have been developed to leverage the capabilities of LLMs in this domain.  
One approach, named Fine-grained Feedback with Reinforcement Retrieval (FFRR), aims to enable more informed and accurate evidence retrieval for LLM-based fact-checking on real-world news claims [73]. This method involves generating intermediate questions from various perspectives of a claim through prompting to retrieve relevant documents. FFRR then collects fine-grained feedback from the LLM on the retrieved documents at both document-level and question-level, using this feedback as rewards to refine the list of retrieved documents and optimize the retrieval policy for intermediate questions. The FFRR approach, evaluated on two public datasets, significantly outperforms state-of-the-art LLM-enabled and non-LLM baselines.  
Another method, known as Hierarchical Step-by-Step (HiSS) prompting, directs LLMs to decompose a claim into several subclaims and verify each of them step-by-step through multiple question-answering sessions [68]. This technique addresses two main challenges in news claim verification: omission of necessary details and fact hallucination. The process includes claim decomposition, subclaim verification, and final prediction. Initially, the LLM decomposes the claim into subclaims to ensure every explicit point is addressed. It then verifies each subclaim by generating and answering a series of insightful questions,consulting external sources like Google Search when necessary. After verifying the subclaims, the LLM makes a final prediction and classifies the original claim. Experiments on two public misinformation datasets show that HiSS prompting outperforms state-of-the-art fully-supervised approaches and strong few-shot ICL-enabled baselines.  
Another innovative approach uses LLMs to produce weak labels for 18 credibility signals through prompting [74]. Initially, an open-ended prompt generates answers to questions regarding the news article text, which are then mapped to predefined classes using a task-agnostic restrictive prompt. For credibility signals, an instruction prompt is used alongside the article text, followed by a credibility signal prompt. This process is repeated for each credibility signal. The zero-shot prompt is used to detect misinformation as a binary classification task. If simple string matching rules fail to map the model's answer to a label, a task-agnostic category mapping prompt is employed. This method, which combines zero-shot LLM credibility signal labeling and weak supervision, outperforms state-of-the-art classifiers on two misinformation datasets without using any ground-truth labels for training.
## 4 Discussion
The results of this study show that near-infrared spectroscopy technology provides measurements of protein content and other indicators with comparable accuracy to laboratory physical-chemical methods and even outperforms them in some aspects. This offers a new option for quality monitoring in practical production settings [23]. Scholars like [PERSON] have analyzed the chemical properties of wheat grains stored under specific conditions using FT-NIR and NIR, demonstrating the effectiveness of both models in the rapid assessment of wheat grain storage quality [24]. Similarly, [PERSON] applied FT-IR to identify wheat genotypes based on spectral peaks [25]. However, current researchin this area has primarily focused on the application of spectroscopic techniques, with limited studies on spectroscopy accuracy. This experiment, based on established wheat quality measurement standards, conducted an in-depth comparative analysis of accuracy in measuring wheat quality across different genotypes and locations. In this experiment, the accuracy of FT-NIR in determining wheat quality of different genotypes in different plots was analyzed in an in-depth comparative manner from the standard basis of wheat quality measurement. To reduce model dependence as well as to improve the broad applicability of the study, several wheat genotypes were purposely selected for measurement in this study. This random sampling helps to better reflect the diversity in actual agricultural production, which improves the reliability and generalizability of the results and enriches the database of the FT-NIR model. However, the main limitation of this study is the limited number of samples and the use of data from a single season. In future research endeavors, it is advisable to consider expanding the sample size to further enhance the applicability of Fourier transform near-infrared spectroscopy (FT-NIR) technology across a broader range of wheat genotypes. Additionally, in-depth exploration of optimizing spectroscopic instrument parameters, refining preprocessing methods, and optimizing model algorithms can be undertaken to further enhance the accuracy and stability of measurements. Furthermore, there is potential to explore the practical application of FT-NIR technology in actual production settings, enabling rapid assessment of wheat quality and thereby improving agricultural production efficiency. By doing so, this technology could potentially contribute to advancing the efficiency and effectiveness of wheat quality evaluation.
## 5 Challenges
### Data Quality and Management  
The healthcare sector faces significant challenges in managing data quality [242] due to the inherent complexity and diversity of data from wearables, electronic health records (EHRs) and patient monitoring systems. Poor data quality can result in serious consequences, including misdiagnoses, erroneous analyses, and inappropriate treatment plans. Data collected from IoT devices and wearable sensors often contain noise, which obstructs meaningful insights. Furthermore, inconsistencies in data collection processes complicate analysis, while missing data introduce critical gaps in patient records, ultimately compromising the quality of care. The overwhelming volume of data also contributes to clinician fatigue, which can lead to suboptimal decision making. Adding to these challenges is the lack of standardized datasets, evaluation protocols, and interoperability among IoMT devices, which creates a significant barrier to scaling IoMT solutions. Variations in data formats, sensor specifications, and communication protocols result in ecosystem fragmentation, making it difficult to achieve seamless integration. Addressing these issues requires the development of globally recognized standards for datasets and evaluation metrics.  
Standardization efforts will not only improve interoperability but also facilitate the fair benchmarking of DL models, driving more reliable and scalable IoMT applications. For instance, Apple Inc. proposed platforms called ResearchKit and CareKit as data brokers to standardize the format of data that are collected and processed for healthcare records through Apple's devices [243].  
AI has been proposed to standardize the data format as well. For instance, in healthcare, 10 MT data aggregation refers to collecting and analyzing data from 10 MT devices to review a patient's health [244]. Such 10 MT data from 10 MT devices include data such as biometric information, clinical information, and data from medical devices themselves [244]. ML plays a key role in the 10 MT data aggregation, such as predicting the results based on the data collected. However, ML faces challenges in obtaining various data formats collected from multiple sources. An ML score system can be added to evaluate the data collected before processing the data. In this, any data with a low score will be highlighted and evaluated before moving to the next step [244].  
Historical data are always used to train ML models. However, lots of historical data might include incomplete information without a standard data structure. In order to solve this issue, ML or AI models can be added to deal with these incomplete data [245]. Also, AI tools to convert data into a consist format not only improve overall system performance but also simplify integration within healthcare systems like electrical health records (EHRs). This also ensures regulatory compliance and enables local response for prompt decision making.
###### Abstract  
Tropospheric delay is one main factor affecting the accuracy of precise point positioning (PPP) ambiguity-float and fixed solutions. Investigations mainly focused on evaluating the contributions of tropospheric corrections to the accuracy and reliability of PPP solutions. The tropospheric corrections generally contained the zenith tropospheric delay (ZTD) and the horizontal gradients estimated from relative positioning or PPP. However, the estimated tropospheric delays can be partly absorbed by the carrier phase residuals if the stochastic model is not well-defined. Therefore, along with the ZTD and horizontal gradients, the carrier phase residuals from PPP backward filter are considered to reconstruct slant tropospheric delay (STD). Based on the proposed STD model, its marginal effects on GPS PPP were investigated. Results indicated that the consideration of carrier phase residuals for STD modeling can improve the three-dimensional accuracy to 0.5 cm/1 cm/1.2 cm in the South/North/Up (N/E/U) components. Then, the effects of internal and external STD corrections on PPP float and fixed solutions were analyzed. Compared to the ZTD + gradients augmentation, STD corrections from the same station could improve the PPP accuracy by 51%/51%/60%; the large improvements were because the multipath error and observation noise were eliminated. In comparison, the improvement was 14%/28%/31% using external STD corrections, which indicated the effects of unmodeled tropospheric errors in the phase residuals. The ambiguity-fixing results indicated that the fixing rate of PPP ambiguity was increased by 30% with STD augmentation. As the BeiDou System (BDS) suffered longer convergence than that of GPS, the benefits of STD modeling to the BDS observations were also validated. Overall, the results validated the performance of STD-augmented PPP, which demonstrated the potential application of high-accuracy troposphere products.  
precise point positioning; zenith tropospheric delay; slant tropospheric delay; carrier phase residuals 2019 9 4884 10.3390/apr9224884
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 3 Methodology
### Design of the Conv-for-Origin Module
#### 3.1.2 Positional Embedding  
After the above convolutional neural network, the original CSI sequence is completely disrupted, and the subsequent self-attention dot product operation does not consider the position information between features. To obtain the position information of different antenna sub-features in the CSI matrix, we add position embedding to the module, and use the trigonometric function to output a position-encoding matrix with the same size as the CSI matrix. We add the obtained position code matrix with the CSI matrix, so that the CSI can obtain rich positional information. There are many options for positional encoding sequences, some are learned and some are fixed [36]. The formula of the position encoding we used is as follows:  
\[PE_{(pos,2i)}=\sin(pos/10,000^{2i/d_{model}}) \tag{1}\]  
\[PE_{(pos,2i+1)}=\cos(pos/10,000^{2i/d_{model}}) \tag{2}\]  
where \(d_{model}\) is the dimension of the CSI matrix, \(pos\) is the position of the sub-feature, and \(i\) represents the dimension. The output of the Conv-for-Origin module consists of two parts. We compute the output matrix as:  
\[\texttt{Conv-for-Origin}(x)=conv(x)+pos(conv(x)) \tag{3}\]  
where \(x\) is the origin CSI, \(pos\) is the position embedding function, and \(conv\) is the proposed convolutional neural network. As shown in Figures 3 and 4, we visualize CSI. Compared with the original CSI (in Figure 3), the processed CSI (in Figure 4) has more obvious features between different antennas in the vertical direction, especially between adjacent antenna differences.
## 4 Discussion  
The present study documented that a 3-month nutrition-oriented CDSS intervention during the COVID-19 period reduced body weight, body fat, glucose and lipid biomarkers, with borderline reductions in lean body mass. In multivariate linear regression models, serum triglycerides (total sample) were inversely associated to % weight loss, while a pattern characterized by high BMI/lean tissue (women) was positively related to % weight loss. The importance of our findings lies in the fact that a hospital-based weight loss intervention during the COVID-19 epidemic was possible with the use of new technologies and evidence-based procedures, such as those ensured by the use of CDSS. Indeed, several programs based on tele-health and remote consultations were developed to assist patients with obesity during weight loss, especially during COVID-19 [15; 16; 17]. However, the present study firstly reports the use of a CDSS in a hospital setting for distance counseling in obesity. In parallel, electronic medical records have been useful so far to screen individuals for obesity [27] and CDSS-created alerts have been used to further refer patients to professionals for obesity management [28]. Several CDSS programs have been also created for childhood obesity prevention and treatment [29; 30; 31]. In parallel, through the use of CDSS, cost and time are reduced, and thus, more time can be dedicated to each patient [32]. However, as recently reviewed, many contemporary CDSS are still focused on alerts and reminders [33], and only \(\sim\)24% of them include patient education features, dietary advice and other lifestyle recommendations [33]. It has been suggested that future CDSS should target screening, diagnosis and management of multiple chronic diseases and that user-friendly interfaces on top of alerts should be developed [33]. Hopefully, the Nutrinet(r) CDSS used in the present study fulfills the aforementioned aspirations. Indeed, it ensures the implementation of all the steps of the NCP including nutritional assessment, diagnosis, intervention, monitoring and evaluation [4; 5]. It facilitates dietary assessment, physical activity assessment, formulation of personalized tailored dietary plans, provision of guidelines, definition of nutritional and physical activity goals and monitoring of nutritional and physical activity goals [4; 5]. It thus aims at assisting nutritional care provision at all stages and is not only centered on alerts. Moreover, the Nutrinet(r) CDSS can be used by both the dietitian and the patient
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
## 1 Introduction
Using continuum damage theory and introducing a Weibull distribution to describe the evolution of damage variables [27], the rock stress-strain curve can be divided into four stages: undamaged loading, stable damage propagation, intensified damage propagation, and stable damage propagation to saturation. Based on the analysis of the elastic modulus, a thermo-mechanical coupled damage constitutive model for rocks can be developed [28]. Researchers have also established constitutive models for different types of rocks based on this foundation [29; 30], which hold significant implications for practical engineering and rock mechanics research. The aforementioned research has made significant contributions to the analysis of the mechanical properties of rocks under the coupled effects of temperature and confining pressure. However, there are still some limitations. The current studies involve a relatively limited range of rock types, and research on rocks under the coupled effects of temperature and confining pressure often employs high-temperature pretreatment followed by conventional triaxial tests in the laboratory. This approach makes it difficult to accurately reflect the real-time stress-strain response of rocks under high-temperature and high-pressure conditions. Moreover, the failure structure models of rocks under the coupled effects of temperature and confining pressure remain underexplored, and the selected probabilistic microelement strength models and strength criteria are relatively simplistic. In addition, in the existing structural models, the elastic modulus is often fitted using polynomial functions, which hinders a detailed description of the elastic modulus development trend for specific rock masses. Furthermore, the comprehensive effects of temperature and confining pressure on the elastic modulus have not been fully considered. Therefore, this study employs a combined approach of laboratory experiments and theoretical calculations to investigate the room temperature uniaxial compression tests and conventional triaxial compression tests of four typical rocks under the coupled effects of temperature and confining pressure: granite, red sandstone, gray sandstone, and shale. The research aims to explore the deformation evolution and failure characteristics of these four types of rocks under the combined influence of temperature and confining pressure. A fitting model for the elastic modulus of rocks under these coupled conditions is proposed, and the parameters of the Drucker-Prager criterion are revised to reflect the effects of temperature and confining pressure. Additionally, a constitutive model is constructed to accurately capture the stress-strain state of rocks under these combined effects, providing insights into rock deformation characteristics that align with practical engineering conditions.
# Unveiling the Core Patterns of High-Latitude Electron Density Distribution at Swarm Altitude
## 2 Data
The orientation of the IMF, specifically the \(B_{y}\) and \(B_{z}\) components, has a strong influence on the spatial distribution of the TOI within the polar cap region. In the Northern Hemisphere, the TOI moves towards the dawn sector with positive IMF \(B_{y}\) and towards the dusk sector with negative IMF \(B_{y}\). Furthermore, when negative IMF \(B_{z}\) values are involved the TOI appears narrower and more intense over the polar cap with respect to periods characterized by positive IMF \(B_{z}\) values. This behavior is consistent with the influence of the \(B_{z}\) and \(B_{y}\) IMF components on the polar ion convection pattern; \(B_{z}\) governs the cross polar cap potential and ion convection pattern, while \(B_{y}\) determines the dawn-dusk asymmetry in this convection. While we do not specifically analyze the average distribution of electron density with respect to IMF orientation in this study, it is clear that this description aligns well with the average distribution obtained during periods of high geomagnetic activity, in which the IMF \(B_{z}\) component is negative, as we discuss later.