esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
### Conclusions  
This paper proposes a method for identifying the science-technology topic association evolutionary pathway, which breaks through the limitations of the unidirectional perspective in traditional topic evolution research and effectively addresses Question 1 in Section 2.2. Specifically, this paper takes the scientific papers and patent research in the field of cybersecurity from 2005 to 2024 as research samples. Combining the LDA topic model and the Word2 vec model, we conducted topic recognition and word vector transformation operations on these two types of research samples, respectively, and explored the association evolution between science and technology topics through similarity calculation methods. This method successfully reveals the dynamic and collaborative evolution law of science-technology topics in the field of cybersecurity. It provides a new theoretical perspective for understanding the interaction mechanism between scientific research and technological innovation in this field. The specific research conclusions are as follows: First, by constructing the science-technology topic similarity matrix and the association evolutionary graph, this study answers Question 2: \"What evolutionary types will be presented by science-technology topic association evolution in the field of cybersecurity?\" It was found that the association of science-technology topics in this field presents five types of evolution, namely, division, merging, inheritance, co-occurrence, and independent development. Second, among the 5382 science-technology topics, 3040 (about 56.48%) topic pairs have a similarity greater than the threshold of 0.6686. This indicates a high degree of collaboration between science and technology topics. Meanwhile, based on the topic recognition results and the association evolution, this paper finds that the science-technology topics show a symmetrical effect of \"initial accumulation-fluctuating differentiation-deep collaboration\". Third, according to the method of classifying the strength of association based on topic similarity, this paper divides the science-technology topics with collaboration into three categories: weak correlation, moderate correlation, and strong correlation. Among them, the association evolution of strongly correlated science-technology topics shows a symmetrical effect of \"technology-science-technology\" and \"science-technology/technology-science\". By integrating conclusions two and three, this study provides a comprehensive answer to Question 3, revealing the presence of collaborative and symmetrical effects in the associative evolution process of science-technology topics within the field of cybersecurity.
## 3 Results
### The Effect of the Foot Positions (SCFP and SFP) on CVs  
Table 3 shows the distribution of CVs across all parameters and the effect of each foot placement condition. No significant differences in CVs of stabilometric parameters were found between the two foot positions, only a trend of their increase in SFP (Table 3). CVs of all plantar pressure parameters were significantly larger in SFP with respect to SCFP; the only exception was for the CV of right Pmean at Ff and Pmax at Tf and Rf bilaterally, showing no significant differences. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Coefficient of**} & \multirow{2}{*}{**LS**} & \multicolumn{2}{c}{**SCFP (A)**} & **SFP (B)** & **SCFP vs. SFP (A vs. B)** \\ \cline{3-5}  & & \multicolumn{2}{c}{**Median (25 th–75 th Percentiles)**} & \multicolumn{2}{c}{_p_**-Value**} \\ \hline \multirow{2}{*}{Load Tf [\%]} & 1 & 1.4 (1.1; 1.9) & 2.1 (1.0; 3.6) & **0.035 (B)** \\ \cline{2-5}  & r & 1.0 (0.9; 1.7) & 1.8 (1.0; 2.6) & **0.041 (B)** \\ \hline \multirow{2}{*}{Load Rf [\%]} & 1 & 3.0 (2.3; 4.3) & 6.8 (2.5; 10.9) & **0.004 (B)** \\ \cline{2-5}  & r & 2.70 (2.2; 3.6) & 4.8 (3.8; 7.8) & **0.006 (B)** \\ \hline \multirow{2}{*}{Load Mf [\%]} & 1 & 7.8 (4.2; 15.0) & 14.4 (7.9; 28.2) & **0.032 (B)** \\ \cline{2-5}  & r & 4.5 (2.2; 17.0) & 16.5 (6.6; 28.7) & **0.001 (B)** \\ \hline \multirow{2}{*}{Load Ff [\%]} & 1 & 3.0 (1.8; 4.94) & 6.7 (4.2; 10.6) & **<0.001 (B)** \\ \cline{2-5}  & r & 1.9 (1.5; 3.4) & 4.7 (2.9; 5.8) & **0.002 (B)** \\ \hline \multirow{2}{*}{Pmean Tf [KPa]} & 1 & 4.1 (2.5; 5.0) & 5.8 (3.0; 11.3) & **0.041 (B)** \\ \cline{2-5}  & r & 3.7 (2.8; 5.2) & 5.4 (3.2; 11.1) & **0.010 (B)** \\ \hline \multirow{2}{*}{Pmean Rf [KPa]} & 1 & 4.9 (2.4; 8.2) & 8.1 (3.1; 15.2) & **0.027 (B)** \\ \cline{2-5}  & r & 4.8 (3.0; 6.5) & 7.8 (4.3; 12.9) & **0.006 (B)** \\ \hline \multirow{2}{*}{Pmean Mf [KPa]} & 1 & 4.7 (3.7; 7.9) & 10.9 (6.9; 16.7) & **0.002 (B)** \\ \cline{2-5}  & r & 5.1 (3.8; 8.4) & 12.2 (8.1; 18.1) & **0.002 (B)** \\ \hline \multirow{2}{*}{Pmean Ff [KPa]} & 1 & 3.2 (2.2; 3.9) & 6.9 (4.0; 8.9) & **<0.001 (B)** \\ \cline{2-5}  & r & 2.8 (2.0; 5.3) & 4.3 (3.1; 10.0) & 0.052 \\ \hline \multirow{2}{*}{Pmax Tf [KPa]} & 1 & 5.5 (3.5; 7.8) & 5.9 (4.2; 10.1) & 0.089 \\ \cline{2-5}  & r & 5.7 (3.1; 7.6) & 5.8 (3.1; 12.9) & 0.160 \\ \hline \multirow{2}{*}{Pmax Rf [KPa]} & 1 & 5.5 (3.5; 7.8) & 5.9 (4.2; 10.1) & 0.089 \\ \cline{2-5}  & r & 5.7 (3.1; 7.6) & 5.8 (3.1; 12.9) & 0.160 \\ \hline \multirow{2}{*}{Pmax Mf [KPa]} & 1 & 6.5 (5.7; 9.5) & 11.8 (6.1; 17.4) & **0.016 (B)** \\ \cline{2-5}  & r & 5.8 (4.6; 14.2) & 15.1 (7.5; 19.8) & **0.035 (B)** \\ \hline \end{tabular}
\end{table} 
Table 3: Significant differences (level of significant \(\alpha\) = 0.05) of plantar pressure and stabilometric parameters between each condition of foot position via the [PERSON] signed rank test. The Effect of Different Foot Positions (SCFP and SFP) on Stabilometric and Plantar Pressure Parameters  
Table 4 shows the median values (25 th and 75 th percentiles) of stabilometric and plantar pressure parameters in two foot positions and their significant differences.
## 4 Results and Discussion
### Optimization Results and Verification Test  
#### 4.2.1 Primary Experiment Optimization Results  
Derivation of Optimal Conditions  
In this study, experiments were conducted to optimize the performance of the dust-spraying cleaning system. The resulting optimized conditions are presented in Table 6.  
When the above conditions were applied, the predicted value for the reduced point cloud was derived as 2940. This value represents an optimized result based on the experimental data. Each parameter was analyzed independently, and the combination of all variables in their optimized state demonstrated high reliability.  
As shown in the graphs in Figure 6, each variable changes linearly within its appropriate range. In particular, it was confirmed that for spray distance, the optimization effect can be maximized through fine adjustment between its maximum value and the optimized value.  
These results serve as crucial data for establishing design and operational standards for enhancing cleaning performance. Furthermore, they also suggest the possibility of achieving a higher level of optimization through modeling and additional experimental designs based on the interactions of spray pressure, speed, distance, and number of sprays.
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 2 Materials and Methods
### Estimation of Time-Dependent Parameters in Non-Linear ODE Models via the Augmented Kalman Smoother
#### 2.2.3 Expectation-Maximization Algorithm for AKS Initial Parameters  
To ensure the effective operation of a Kalman Smoother algorithm, appropriate initial values for \(x_{0}\), \(P_{0}\), \(Q\) and \(M\) need to be provided. To achieve optimal starting conditions, an Expectation-Maximization (EM) algorithm can be employed. Its expectation step (E-step) consists of the AKS algorithms. Using the smoothed estimates \(x_{k|N}\) and \(P_{k|N}\), a maximum-likelihood estimation is carried out for the covariance matrices \(Q\) and \(M\). This involves determining update equations for \(Q\) and \(M\) by maximizing the following target function [64] with respect to these matrices:  
\[L= -\frac{1}{2}\ln\lvert P_{0}\rvert-\frac{1}{2}\mathrm{Tr}\Big{[}P_ {0}^{-1}P_{0|N}\Big{]}-\frac{N}{2}\ln\lvert Q\rvert-\frac{1}{2}\mathrm{Tr} \Bigg{[}Q^{-1}\sum_{k=1}^{N}\Sigma_{k}\Bigg{]} \tag{15}\] \[\mathrm{where}\qquad\Sigma_{k}= P_{k|N}+(x_{k|N}-b(x_{k-1|N}))\cdot(x_{k|N}-b(x_{k-1|N}))^{ \mathrm{T}}\] \[+F_{k}\cdot P_{k-1|N}\cdot F_{k}^{\mathrm{T}}-P_{k-1,k|N}\cdot F_ {k}^{\mathrm{T}}-F_{k}\cdot P_{k-1,k|N}^{\mathrm{T}}\] \[\Omega_{k}= (y_{k}-G\cdot x_{k|N})\cdot(y_{k}-G\cdot x_{k|N})^{\mathrm{T}}+ G\cdot P_{k|N}\cdot G^{\mathrm{T}}\]  
Analytically, it can be shown that the maximum is reached for  
\[Q_{\mathrm{new}}=\frac{1}{N}\sum_{k=1}^{N}\Sigma_{k}\qquad\mathrm{and}\qquad M _{\mathrm{new}}=\frac{1}{N}\sum_{k=1}^{N}\Omega_{k}\,, \tag{16}\]  
which then serve as the input for the next EM iteration [64]. Furthermore, the values for \(x_{0|N}\) and \(P_{0|N}\) obtained via the AKS from the previous iteration are used as the initial values \(x_{0}\) and \(P_{0}\) for the next iteration, which corresponds to the maximization step (M-step) of the EM algorithm. These iterative steps are repeated until a chosen convergence criterion reaches a certain level \(\alpha\):  
\[\frac{\left|\left(\sum\limits_{i}Q_{i}+\sum\limits_{i}M_{i}+\sum\limits_{i}x_{0,i}+\sum\limits_{i}P_{0,i}\right)-\left(\sum\limits_{i}Q_{\text{new},i}+\sum \limits_{i}M_{\text{new},i}+\sum\limits_{i}x_{0|N,i}+\sum\limits_{i}P_{0|N,i} \right)\right|}{\left|\left(\sum\limits_{i}Q_{i}+\sum\limits_{i}M_{i}+\sum \limits_{i}x_{0,i}+\sum\limits_{i}P_{0,i}\right)\right|}<\alpha \tag{17}\]  
Here, \(\alpha\) denotes the relative tolerance for the difference between the consecutive hyper-parameter estimates \(Q\), \(M\), \(x_{0}\) and \(P_{0}\). The iteration process continues until the relative difference between the consecutive hyperparameter estimates falls below the tolerance level \(\alpha\). In the combined application of the AKS, it is necessary to provide an initial guess for these hyperparameters to start the EM algorithm. However, the presented analyses indicate that the choice of these initial values is not crucial for achieving high-quality final results. A visualization of the method in the form of pseudo code is shown in Algorithm 1.
# Stability Study of Erythrhitol as Phase Change Material for Medium Temperature Thermal Applications
###### Abstract  
Sugar alcohols belong to a promising category of organic phase change materials (PCM) because of their high latent heat and density compared to other PCM. However, some sugar alcohols have shown latent heat degradation when heated above their melting temperature. Most of the available studies report the structural changes of erythroid during cycling rather than its thermal stability at constant temperature. This study aimed to assess the effect of thermal treatment on erythroid thermal, chemical and physical properties, as well as to find means to enhance its thermal stability. Erythrhitol and its mixtures with antioxidant were heated and maintained at different temperatures above its melting point. Erythrhitol was analyzed before and after thermal treatment via Fourier-transform infrared spectroscopy and differential scanning calorimetry. It was suggested that the degradation of latent heat follows a first order reaction. Mixtures of erythroid with antioxidant had a lower degradation rate compared to pure erythroid under air. Sample browning was observed along the heating treatment of mainly pure erythroid. Antioxidant was found to help to reduce erythroid degradation. No chemical composition changes were detected in samples under argon atmosphere and overall good thermal stability was found throughout the testing period.  
Keywords:phase change material; sugar alcohol; erythroid; latent heat storage; thermal stability; degradation kinetics +
Footnote †: journal: Applied
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 3 Results and Analysis
### Results and Analysis for Two Layers

Table 5: Summary of the data obtained for 100 training epochs with two layers and the tests of the different \(R\) packages. 2, 4, and 5 of Table 6). As for the test accuracy, the best model achieves 79%, which is keras_cnn_100_150 implemented with convolutional layers. For this overall accuracy value in tests or any other reported in Table 6, it is feasible to apply an analysis similar to the one conducted in Section 3.1. This analysis demonstrates the limited informative capacity of this global metric to reflect class-specific sample predictions. For instance, based on the expectation generated by a 79% accuracy in tests, one would anticipate 294, 93, and 115 samples, respectively, for each assessed class. However, as detailed class accuracy in Row 4, Columns 15, 16, and 17 of Table 6 indicates, the actual predictions reach 96%, 52%, and 58%, corresponding to 358, 61, and 84 predicted samples, respectively, for the involved classes. This discrepancy from the estimates provided by the global accuracy underscores, once again, that global metrics do not provide a faithful overview of class-specific predictions of a trained model. The analysis of overall accuracy versus class-specific accuracy in models such as keras_250_300 and keras_cnn_100_150 reveals a significant contrast, with high success rates in training that do not directly translate into effective generalization during testing, where accuracy notably decreases. This phenomenon is accentuated when examining class-specific accuracy, where despite high percentages being achieved in class N, classes V and B display considerably lower accuracies, highlighting inequalities in the model's predictive capability. Furthermore, the comparison between models trained with balanced and unbalanced data indicates that although data balancing may not optimize overall accuracy, it promotes a fairer distribution in the detection of all classes, improving equity in classification. Sensitivity and specificity by class complement this picture, showing that a high ability to identify true negatives does not necessarily ensure equitable detection of all positive classes, underscoring the need for strategies that promote balanced and effective performance in class-specific classification of the developed models.
# A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks
## SEIRS-D Agent-Based Model
### Agents  
Six main agents compose the SEIRS-D agent-based model--sensor nodes, malware, network topology, the phenomenon of interest, human action and devices. These agents have been selected after analysing the different environments and characteristics that may be present while a wireless sensor network is working. The main features considered for each agent are the following: (1) importance of participation level in the network, and (2) contribution to the malware infection process. In Table 1, the \(j\)-th type agents have labelled by \(1\leq j\leq 6\), the characteristics associated to these agents have indexed by \(1\leq k\leq 15\), the values that these characteristic can assume are denoted by \(q\), and finally, the probabilities associated to these values have been expressed by \(0\leq P_{k,q}\leq 1\) such that \(\sum_{q}P_{k,q}=1\) for each \(k\). For example, the probability that a certain sensor has low (\(q=1\)) computational capacity (\(k=2\)) is \(P_{k,q}=P_{2,1}\) such that \(P_{2,1}+P_{2,2}=1\). Figure 1: Scheme with SEIRS-Dmodel. Therefore, the agents with their specific characteristics, their corresponding values and probabilities are describes as follows:  
* Sensor nodes: are responsible for collecting data directly from the environment, being the principal element within the WSN. As a consequence, we will consider the following seven characteristics of sensors nodes: 1. Type of sensor: sensor, sink and cluster-head nodes are the types of sensors that can be part of the network, as well as the technical specifications that allow each node to perform different functions. 2. Computational capacity: it has been classified as high and low. A node with high capacity can have a general-purpose processor, static memory, batteries as a power source, a sensor and an internal wireless antenna. A node with low capacity can have a processor with
## 2 Conceptual Model and Hypothesis
### Facilitated Appropriation  
In many cases, technology serves another purpose than the one originally intended. Likewise, in the IoT spectrum, products and applications can be appropriated in many ways. Appropriation is the way in which technologies are adopted, adapted, and incorporated into working practice. The next element integrated in the proposed model is the concept of facilitated appropriation, acting as factor of influence for PU.  
Facilitated appropriation (FA) can be seen as a more comprehensive and systematic appropriation. While engaging in facilitated appropriation, individuals are inspired to see and engage with additional features on a device, exclusively dealing with their problems or other personal purposes [27; 28]. In this research, it is proposed that if individuals feel they are facilitated to appropriate IoT products and applications, they would be more likely to view the technology as useful.  
**Long-term orientation and flexibility.** Facilitated appropriation will be examined by means of long-term orientation and flexibility, which are the exogenous variables affecting facilitated appropriation. Long-term orientation (LTO) is a salient aspect of national culture values and as such influences consumers' decision-making processes [32]. [PERSON] and [PERSON] [27; 28] found that among cultural values, only LTO predicts user's tendency to engage in facilitated appropriation. Based on these arguments, the following hypothesis is recommended.  
**Hypothesis 4 (H4).**_A consumers' facilitated appropriation can predict higher perceived usefulness towards IoT products and applications._
# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 1 Introduction
Therefore, in this paper, we study a novel unmanned sweeper vehicle sweeping method based on target distribution posture analysis, which firstly uses the inverse perspective transformation (IPM) together with You Only Look Once version 5 (YOLOv5) to identify obstacles and monolithic garbage in the roadway, followed by the use of LIDAR local elevation and Gaussian mixture model (GMM) to tolerate similar obstacles as well as garbage targets, and for the complex garbage piles by identifying the PE (permutation entropy) entropy for their complementary detection. Finally, all the garbage regions are subjected to convex packet ensemble generation by [PERSON]'s scanning method, and the optimal sweeping path problem for unmanned sweepers is solved using a simulated annealing (SA) algorithm. In summary, our contributions are as follows:  
1. The accurate identification of monolithic trash and obstacles was accomplished using YOLOv5 and combined with GMM to reduce redundant parts. 2. For complex garbage piles, a novel roadway garbage area discrimination method based on PE value analysis is proposed to obtain accurate garbage area information in the roadway plane. 3. The traditional unmanned sweeper vehicle sweeping path planning problem is transformed into a Traveling Salesman Problem-Coverage Path Planning(TSP-CPP) problem based on Contributions 1 and 2, and a simulated-annealing-algorithm-based unmanned sweeper vehicle path planning method is proposed. This paper is organized as follows. Section 2 provides a literature review on target detection, situation analysis, and a literature review on path planning for unmanned sweepers. In Section 3, a detailed framework of a sweeping method based on target distribution posture analysis is described. Experiments and results are presented in Section 4. Finally, we conclude the discussion of this study in Section 5.
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
## 6 Experimental Verification  
### Description of Experimental Data  
This experiment adopted the data of condition monitoring and the fault diagnosis comprehensive test bench produced by Houde Company in Jiangsu, China for experimental verification, and the experimental equipment is shown in Figure 4. Four prevalent fault states were selected, namely, normal gear state, tooth root fracture, tooth surface wear, tooth surface pitting, and tooth surface cracking. Multiple sets of vibration signals under different states were acquired using acceleration sensors. The gearbox features oil-immersed lubrication, with the motor speed set to 1800 r/min, a sampling frequency of 12 kHz, a sampling duration of 10 min, and a sample length set to 1024. This resulted in 50 sample sets for each state, amounting to a total of 250 sample sets, with 200 sets designated for training and 50 sets for testing.
## 3 Overview of 31P qNMR for Phosphorus Compounds
### External Standard Method for qNMR  
The external standard method involves preparing a series of standard solutions with different concentrations by using a known quantity of standard samples [28]. Under the same experimental conditions, a standard curve is generated, with the characteristic peak area on the vertical axis and the concentration on the horizontal axis, to measure the nuclear magnetic resonance spectrum of an unknown sample solution. The corresponding peak area is calculated, and the concentration of the unknown sample is determined from the standard curve. [PERSON] et al. [29] systematically evaluated the data acquisition workflow using certified reference materials (caffeine as the analyte and dimethyl sulfoxide as the calibrator) to identify key factors for accuracy and precision in external standard qNMR. Their study highlighted that the external standard method, wherein the standard material is measured separately from the samples, offers significant advantages. Notably, it minimizes artifacts resulting from internal mixing and prevents contamination of analyte samples by the reference compound.  
Quantitative nuclear magnetic resonance (qNMR) spectroscopy presents numerous benefits compared to traditional chromatographic methods, such as superior specificity, rapid processing, high accuracy, excellent precision, and versatility. Its core principle relies on the direct proportionality between the integral of each NMR signal and the number of nuclei represented by that signal in the sample, ensuring a linear response without requiring calibration curves. For reliable quantification through qNMR, it is critical that the target compound is fully soluble; does not interact negatively with solvents, matrices, or internal standards; and exhibits well-separated quantitative peaks for both the analyte and internal standards. Moreover, qNMR facilitates quantification without necessitating the physical separation of sample components and allows the analysis of multiple compounds using a single internal standard [30]. The internal standard method is particularly popular for its simplicity, as it eliminates the need for calibration curves. Meanwhile, the externalstandard method is better suited for natural products when appropriate reference materials are available.