esa-sceva/satcom-chunk-collection huggingface dataset:
# A Finite Element Method for Nonlinear and Fully Coupled Transient Hydrothermal Transfer
## 1 Introduction
[16] constructed a generic theoretical formulation and finite element implementation to model moisture and heat transport in a layered structure consisting of distinct materials. Analytical results were then compared with experimental data to validate the model. Their model considers the effect of temperature on the moisture but ignores the influence of the moisture on the temperature. Therefore, it can be regarded as a semi-coupling. Recently, [PERSON] and [PERSON] [17] proposed a finite element analysis for transient coupled heat and moisture transfer in the cylinders and cylindrical panels of the porous medium. The material properties were considered as constants, and the time-dependent responses were solved numerically by using the Runge-Kutta method. Recently, [PERSON] et al. [18] considered a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection was proposed based on the discrete differentiation of the original data. The research is critical to understanding the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Very recently, [PERSON] et al. [19] proposed a temperature and humidity regulation wall (THRW) integrating bio-based HM and PCM model for hygrothermal and energy performance improvement. The temperature, vapor pressure, and heat load on the internal surface of the walls were evaluated. The results show that the optimal wall design varies depending on whether prioritizing hygrothermal or energy performance. [PERSON] et al. [20] reviewed the historic evolution of building greenery with integrated photovoltaic (BGIPV) system, tracing its journey from initial conceptualization and technological research to practical application. As can be seen from the existing works, the heat and moisture transfer have been studied for (1) with coupling between temperature and moisture but without moisture and temperature-dependent material properties and (2) without coupling between temperature and moisture but with temperature and moisture-dependent material properties. In this paper, a fully coupled finite element model of heat and moisture transfer is proposed, taking into consideration the moisture- and temperature-dependent material properties. The finite element space discretization is used to obtain a system of first-order differential equations about time. The differential equations are solved by the finite difference scheme to obtain the time-dependent response. Some case studies are carried out to show the capability of the proposed finite element model.
## 2 Study Area and Data  
### Study Area  
The study area consisted of six regions located in the USA and China (Figure 1 shows the six regions labeled A-E). Region A is located in the Mississippi Alluvial Plain, spanning Arkansas, Missouri, Tennessee, and Mississippi, and is the most important rice-planting area in the USA. The sowing date of rice in Region A started in early April, concentrated in May, and continued until mid-late June, while its harvest period began in middle and late August, concentrated in September, and continued to middle and late October [35]. Region B was located in the Sacramento Valley, California, which is one of the most important rice-producing areas in the US; its rice planting area accounts for 20.4% of the total rice planting area in the United States. The rice growth period in Region B lasted from April to October. Regions D, E, and F were located in the Songnen, Sanjiang, and Liaohe plain, respectively, the three most important rice-producing regions in Northeast China. Region C was the Shuangcheng District. The study area in Northeast China spans more than 1000 km from both east to west and north to south. The growth period of rice in Northeast China generally lasts from mid to late April to the end of September. The main phenological stages of rice in different regions of Northeast China, such as the transplanting and maturity stages, can differ by 20-30 days in 1 year [36]. For region C (Shuangcheng District), the phenological stages of rice may differ by more than 10 days between different years [36].
## 3 Results
### SpO\({}_{2}\) approximation error from model (8)  
The blood oxygenation parameter can be approximated from the model (8). Two equations, (17) and (19) were derived to approximate the SpO\({}_{2}\) value. Equation (17) only depends on estimated non-HbA1c and HbA1c molar concentrations, whereas (19) depends on light-wavelength-dependent properties. So, approximating SpO\({}_{2}\) with (19) can render different SpO\({}_{2}\) approximations for different wavelengths of light. Figure 4 shows the SpO\({}_{2}\) approximation error for (17).  
The molar concentration based SpO\({}_{2}\) approximation results give an error range of \(-4\%\) to \(-1.9\%\) in the range of 70 to 100% reference SpO\({}_{2}\) values. Figure 5 depicts the SpO\({}_{2}\) approximation error of (19) for 465 nm, 525 nm, and 615 nm.  
The 3-dimensional plots of Figures 4 and 5a, c illustrate the approximation error of SpO\({}_{2}\) using model (8). The X, Y, and Z axes of the plots are reference HbA1c, reference SpO\({}_{2}\), and SpO\({}_{2}\) approximation error, respectively. The 2-dimensional plot beside the 3-dimensional figure depicts the Y-Z plane, which is the SpO\({}_{2}\) approximation error vs. reference SpO\({}_{2}\) values.  
From all these figures (Figures 4 and 5), we can see that the SpO\({}_{2}\) values do not depend on the HbA1c values. Furthermore, for the approximation with (19), the error range changes depend upon the wavelength selection. A reduction in the oxygenation of the blood sample increases the approximation error of SpO\({}_{2}\). As a result, the minimum approximation error can be found for 465 nm (\(-9\) at SpO\({}_{2}\) 70% and about 1 at SpO\({}_{2}\) 100%).
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
###### Abstract  
Navigation Signal based on Compound Carrier (NSCC), is proposed as the potential future global navigation satellite system (GNSS) signal modulation scheme. NSCC, a kind of multi-carrier (MC) signal, is generated by superposition and multi-parameter adjustment of sub-carriers. Therefore, a judious choice of parameter configation is needed. The main objective of this paper is to investigate the performance of the NSCC which is influenced by these parameters and to demonstrate its structure characteristics and superiority, employing a comprehensive evaluation system. The results show that the proposed NSCC signal processes full spectral efficiency and limited out of band (OOB) emissions, satisfying the demands of crowed frequency resources. It also presents better performance in terms of spectral separation coefficients (SSCs), tracking accuracy, multipath mitigation capability and anti-jamming reduction compared with the legacy navigation signals. NSCC modulation represents a serious candidate for navigation satellite augmentation systems, especially for signals applied in challenging environments.  
GNSS; NSCC; tracking performance; multipath effect; modulation scheme +
Footnote †: journal: Sensors
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 1 Introduction
The emergence of software-defined networking (SDN) has effectively addressed the shortcomings of traditional network architectures, providing a solid platform for research into the deployment of MTD strategies. In current research, the generation of path hopping space mostly uses the SMT (satisfiability modulo theories) constraint solving method. This method has a relatively high time complexity when dealing with large-scale and high-complexity networks, and it rises exponentially with the increase in nodes, so this method cannot be used in large-scale networks. Secondly, there is a problem in current research that the constraints for generating path hopping space are too strict or insufficient. Being too strict will shrink the path hopping space, and some paths will be reused multiple times in a short time. Insufficient constraints may lead to the unavailability of hopping paths or the repeated use of key nodes in the short term. Finally, the current research on the triggering method of path hopping is relatively single, lacking mechanisms to adaptively adjust hopping strategies and hopping cycles for different attacks. These will reduce the diversity, unpredictability, and effectiveness of path hopping. This paper proposes an SDN-based multi-constraint and multi-policy path hopping active defense method (SDADM), with the following main contributions:  
* SDADM utilizes depth-first search (DFS) traversal to compute all paths between communication host nodes, expanding the path hopping space while reducing the complexity of path generation. Furthermore, constraints are applied to residual band width, selection periods, similar paths, and critical nodes to decrease the likelihood of repetitive use of similar paths and critical nodes. * SDADM designs two path hopping strategies and calculates path hopping periods based on the weights of residual bandwidth and critical nodes. It achieves adaptive switching of path hopping and hopping periods under different attack scenarios using the residual bandwidth of paths in the network as a threshold. * Simulation experiments using Mininet demonstrate that SDADM can expand the path hopping space while ensuring normal communication performance. It effectively defends against eavesdropping and link-flooding attacks, enhances the diversity and unpredictability of path hopping, and strengthens the network's ability to defend against malicious attacks. The content of this paper is arranged as follows. Section 2 introduces the related work of this study. Section 3 introduces the overall architecture of SDADM. Section 4 introduces the design concept and execution process of the path hopping strategy algorithm. Section 5 shows the experimental verification results and analysis of SDADM. Section 6 concludes this paper.
## 3 Materials and Methods
### Local Estnet
\begin{split} F_{t_{P+k}}&=\sigma_{g}\big{(}W_{F}^{ \prime}HST+U_{F}^{\prime}HY_{t_{P+k-1}}+b_{F}^{\prime}\big{)},\\ I_{t_{P+k}}&=\sigma_{g}\big{(}W_{I}^{\prime}HST+U_{I}^ {\prime}HY_{t_{P+k-1}}+b_{I}^{\prime}\big{)},\\ O_{t_{P+k}}&=\sigma_{g}\big{(}W_{O}^{\prime}HST+U_{O}^ {\prime}HY_{t_{P+k-1}}+b_{O}^{\prime}\big{)},\\ C_{t_{P+k}}&=F_{t_{P+k}}\odot C_{t_{P+k-1}}+I_{t_{P+k }}\odot\tanh\big{(}W_{C}^{\prime}HST+U_{C}^{\prime}HY_{t_{P+k-1}}+b_{C}^{\prime }\big{)},\\ HY_{t_{P+k}}&=O_{t_{P+k}}\odot\tanh\big{(}C_{t_{P+k }}\big{)},\end{split}  \tag{10}\]  
where \(W_{F}^{\prime}\), \(W_{I}^{\prime}\), \(W_{O}^{\prime}\), and \(W_{C}^{\prime}\) are weight matrices for the input of the spatio-temporal representations \(HST\). \(U_{F}^{\prime}\), \(U_{I}^{\prime}\), \(U_{O}^{\prime}\), and \(U_{C}^{\prime}\) denote the weight matrices allocated to the state of memory cell \(HY_{t_{P+k-1}}\) from the previous LSTM unit. \(b_{F}^{\prime}\), \(b_{I}^{\prime}\), \(b_{O}^{\prime}\), and \(b_{C}^{\prime}\) stand for bias. Subsequently, FCs are utilized to transform \(HY_{t_{P+k}}\in\mathbb{R}^{n_{i}\times d_{\text{model}}}\) into the desired output \(Y_{t_{P+k}}\in\mathbb{R}^{n_{i}\times F}\). We implement the same operation on the decoder of all predicted timesteps and gain all the predicted sequences \(Y\in\mathbb{R}^{Q\times n_{i}\times F}\). Finally, the parameters of the local ESTNet deployed on ECS \(i\) are optimized through a minimizing mean squared error (MSE) loss function, represented as  
\[MSE=\frac{\sum_{s_{n}=1}^{n_{i}}\sum_{t_{q=1}}^{Q}\Big{(}y_{s_{n},t_{q}}-y_{s_ {n},t_{q}}\Big{)}^{2}}{n_{i}\times Q}+\frac{\lambda}{2}\|W\|^{2}, \tag{11}\]  
here, \(n_{i}\) is the total number of sites in the urban region \(i\), and \(Q\) denotes the length of the prediction sequence. The predicted and observed values at the timestep \(t_{q}\) on the site \(s_{n}\) are \(y_{t_{q},s_{n}}\) and \(\hat{y}_{t_{q},s_{n}}\), respectively. \(\lambda\) represents the regularization. \(W\) is the trainable parameter.
## 5 Conclusions  
In this study, we made breakthroughs in optimizing and evaluating schemes of green wave control. Representative evaluation indicators and influencing factors were selected, and a sensitivity analysis and grey relational analysis were combined to conduct innovative research into methods to identify the factors influencing the operational effect of the green wave, for which we collected data on traffic on Eshan Road in Wuhu City. Following this, we proposed different methods of optimization and verified their effects through simulations in Vissim software. The results showed a significant positive correlation among pedestrian crossings, heavy vehicle traffic, and the value of influence. These are important factors influencing the operational effect of the green wave; however, there was no prominent linear correlation among design speed, signal timing, and value of influence. Through this study, it was found that measures related to traffic management may be more effective in improving the effect of the green wave than relying solely on optimizing the scheme for green wave control. Additionally, optimizing schemes for green wave control under the premise of implementing measures related to traffic management significantly improved the operational effect.  
The significance of this paper is that it provides an effective method to identify factors influencing the operational effect of the green wave. It also provides feasible measures for improving its operational effect, which is important for improving traffic efficiency on arterial roads by guiding urban traffic management. Due to limitations imposed by the collected data, we selected representative evaluation indicators and influencing factors based on conventional analysis. In addition, only data on the traffic on a specific green wave road were used, which hindered generalizing the results of this study to green wave control at large. Especially in the analysis of the influencing factors, signal timing is a complex factor. How to carry out correlation analysis of influencing factors through traffic data of multiple green wave roads to determine their degree of influence under different conditions, is the next research work to be carried out.  
Conceptualization, Z.L. and X.Z.; methodology, Z.L.; software, R.W.; validation, Z.L., X.Z. and R.W.; formal analysis, X.Z.; investigation, Z.L.; resources, Z.L.; data curation, Y.L.; writing--original draft preparation, X.Z.; writing--review and editing, Z.L.; visualization, Y.X.; supervision, Z.L.; project administration, Y.X.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the General Project of Anhui Natural Science Foundation in 2022 (grant no. 2208085 ME147), 2022 Provincial Quality Engineering Project for Universities (grant no. 2022 kjzls035) and Hefei University Postgraduate Cooperative Education Base Project (grant no. 2021 Yjyxm07).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
The approach enhances real-time data analysis and decision-making at the edge of the network, enabling more proactive and personalized patient care. * **Implementation of Raspberry Pi in Fog Layer:** The research showcases the practical implementation of Raspberry Pi microcomputers within the fog layer. By employing the KNN algorithm on these devices, the system effectively analyzes patient data, providing timely alerts and insights for healthcare providers. * **Experimental Validation:** The proposed system was experimentally tested and evaluated using an IBM SPSS dataset real-world data. The results validated the system's efficiency, demonstrating its potential to improve healthcare outcomes through prompt abnormality detection and notification. The upcoming sections of this paper unfold as follows: The following section offers an in-depth exploration of the existing literature on healthcare and data mining, providing a comprehensive overview. Subsequently, we outline the proposed methodology and the necessary systems for implementation. The succeeding section is dedicated to conducting simulations and executing the proposed approach. Finally, the concluding section encapsulates our findings and conclusions and provides recommendations for future research.
# Exploring the Determinants of Service Quality of Cloud E-Learning System for Active System Usage
## 3 Research Methodology  
This section deals with the various aspects of the research design used in the present study and how they are documented. It helps the researcher to understand the research study, ways and means of data gathering and subsequent analysis to accomplish the research objectives [38]. The research began by exploring the requisite knowledge, objectives, and a hypothesis from the framework [39]. The present study uses a quantitative method for construct testing in the conceptual model. The approach uses top-down deductive, imbibing framework formation and hypothesis development.  
The primary data is collected through a questionnaire survey administered among the identified respondents as given in Appendix A. The questionnaire survey has both multiple-choice answers in the demographics section and a closed-ended question which has the 5-point Likert scale. The questionnaire survey is developed in 3 different sections. In Section 1, the researcher introduces himself and the objective of the study. Section 2 consists of a respondent's profile demographic items and a survey based on constructs related to the service quality of E-Learning is discussed in Section 3. The validity of this instrument was tested by published research studies and backed by six E-Learning expert peers. The expert group has E-Learning experience of more than five years and the experts belong to different universities. The expert group was selected voluntarily from engineering, medicine, computer science, science and humanities and social science disciplines.  
The pilot study is used for finding errors and ambiguity in the questionnaire [40]. It measures the instrument by conducting content reliability. The reliability is carried out based on the pilot study data obtained from 30 randomly selected respondents from theengineering, science, and computer science faculty. Later, these respondents were excluded from the actual data survey. An instrument consisting of several items must be subjected to a consistency check, which is usually accomplished using the Cronbach coefficient (\(\alpha\)) [41]. The questionnaire items or statements have a coefficient measure varying from 0 to 1 unit. The use of the Cronbach coefficient as a tool to measure the reliability of an instrument is undisputable.  
Upon establishing an acceptable level of reliability in the pilot study, the researchers collected data confidently using the instrument developed for this study. Data were gathered from Saudi Arabian universities. In total, 497 responses were received from the respondents. However, 27 responses were rejected due to incomplete information; later, these 27 responses were removed from the analyses. Thus, in total, 474 valid responses were identified for subsequent data analysis. For the present study, a non-probability and convenience sampling design was used.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Key Metrics  
Video description merges computer vision (CV) with natural language processing (NLP). BLEU [8], METEOR [9], CIDEr [10], ROUGE-L [11], SODA [12], SPICE [13], and WMD [14] serve as crucial metrics for evaluating auto-generated captions for videos and images. Derived from NLP, these metrics assess various aspects like \(n\)-gram overlap, semantic similarity, and uniqueness, providing rapid and cost-effective evaluations that complement human assessments of adequacy, fidelity, and fluency. Each metric has unique strengths and aims to align with professional translation quality. Table 2 summarizes the metric names, purposes, methodologies, and limitations, with detailed descriptions following below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NO** & **Metric Name** & **Designed for** & **Methodology** & **Limitation** \\ \hline
1 & BLEU [8] & Machine Translation & \(n\)-gram precision & Only consideration of precision and lack of recall measures. \\
2 & METEOR [9] & Machine Translation & \(n\)-gram with synonym matching & METEOR’s synonymy is limited by WordNet and is complex to implement. \\
3 & CIDEr [10] & Image Description Generation & Term frequency-inverse document summary weighted \(n\)-gram similarity & Fails to fully mimic human judgment. \\
4 & ROUGE-L [11] & Document Summarization & \(n\)-gram recall & Fails to account for semantic similarity. \\
5 & SODA [12] & Dense Video Captioning & _F-measure_ using precision and recall & Ignores caption diversity. \\
6 & SPICE [13] & Image Description Generation & Scene-graph synonym matching & Fails to capture the syntactic structure of a sentence. \\
7 & WMD [14] & Document Similarity & Earth Mover’s Distance on word2 vec & Fails to handle long documents. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Metrics Used for Video Description Evaluation.  
Figure 15: An instance from the MSVD dataset is provided along with its corresponding ground truth captions. Observe the varied descriptions of the same video clip. Each caption presents the activity either wholly or partially in a distinct manner.