esa-sceva/satcom-chunk-collection huggingface dataset:
# IWF-TextRank Keyword Extraction Algorithm Modelling
## 2 Materials and Methods
### Overview of the IWF-TextRank Algorithm Model  
The classic TextRank algorithm is processed using the text as a unit and does not require prior dataset training. The principle is simple and easy to operate, but there are obvious shortcomings:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Semantic Integration** & **Weighting Method** & **Performance** \\ \hline NE-Rank & no & \begin{tabular}{c} Simple weighting \\ based on word \\ frequency \\ \end{tabular} & \begin{tabular}{c} Unable to capture \\ deep semantic \\ relationships \\ \end{tabular} \\ \hline SemanticRank & yes & \begin{tabular}{c} Context-based \\ semantic weighting \\ \end{tabular} & \begin{tabular}{c} Applicable to \\ general-context \\ semantics \\ \end{tabular} \\ \hline IWF-TextRank & yes & \
\begin{tabular}{c} Multi-factor weights \\ and BP neural \\ network \\ \end{tabular} &
\begin{tabular}{c} Excellent extraction \\ effect, especially \\ suitable for data in \\ specific fields \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Keyword extraction algorithm performance comparison table.  
* It is influenced by high-frequency words, and therefore these need to be screened in combination with other features in order to achieve a better result;
* The initial weight of each word node is set to 1 by default, and, according to the actual situation of each word node, the weights are not the same [24];
* Word weight evaluation based on co-occurrence relations may not fully capture the deep semantic associations between words [25].  
In order to overcome the shortcomings of the classical TextRank algorithm, this paper proposes an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank), which integrally considers the multiple features of words to improve the accuracy of keyword extraction. The algorithm flow is shown in Figure 1. The IWF-TextRank algorithm mainly consists of four steps:  
1. Text preprocessing. The raw traffic accident data undergo segmentation, duplication removal, lexical tagging, and filtering to form the initial candidate keyword set. Jieba, a popular Chinese text segmentation tool, is used with a custom dictionary to ensure the accurate recognition of domain-specific vocabulary.
2. Multivariate feature extraction. Statistical computational work is performed on each feature to determine the level of feature importance.
3. Comprehensive weight calculation. On the basis of determining the importance of features, the comprehensive weight of words is calculated by combining the BP neural network with the ordinal relationship analysis method.
4. Construction of word map and extraction of keywords. The calculated word weights are used as inputs to the TextRank algorithm and, based on this, the word map model is constructed using the word co-occurrence relationship, and then the keywords are extracted.
# COVID-19 Lung Injury: Unique and Familiar Aspects of Pathophysiology
## 5 Therapeutic and Supportive Strategies for COVID-19
### Pharmacotherapy Targeting Inflammation  
Many anti-inflammatory and immunosuppressive therapies have targeted a proposed \"hyper-active\" immune response in COVID-19. Few have been successful, perhaps due to the complex immunopathology of the disease. Among these, dexamethasone, IL-6 antagonists, and baricitinib have shown notable results.  
Dexamethasone is a corticosteroid which enacts anti-inflammatory and/or immunosuppressive effects via the glucocorticoid receptor [187]. The Randomized Evaluation of COVID-19 Therapy (RECOVERY) group assessed the effects of 6 mg of daily oral or intravenous (i.v.) dexamethasone for \(\leq\)10 days vs. standard care in 6425 hospitalized patients. Results showed a significant reduction in mortality in mechanically ventilated patients, while patients not receiving oxygen exhibited a non-significant increase in mortality [188]. These findings have since been recapitulated in other studies and appear to be consistent with knowledge of COVID-19 immunopathology [178]. Namely, suppression of innate responses in the early stage (i.e., patients not receiving oxygen) could facilitate viral proliferation and contribute to poor outcomes.  
Tocilizumab, sarilumab, and silukimab are recombinant monoclonal antibodies that function as IL-6 antagonists by binding membrane-bound and soluble forms of IL-6 receptor (e.g., tocilizumab, sarilumab) or by binding IL-6 directly (e.g., silukimab) [189]. The World Health Organization (WHO) Rapid Evidence Appraisal for COVID-19 Therapies (REACT) group examined the efficacy of IL-6 antagonists compared to standard care or placebo and observed a slight reduction in all-cause 28-day mortality risk (22% vs. 25%). Of note, a higher percentage of secondary infections occurred among IL-6 antagonist-treated patients (21.9% vs. 17.6%) although the difference was not statistically significant [190].  
Baricitinib is an oral disease-modifying antirheumatic drug (DMARD) that reduces transcription of pro-inflammatory cytokines by inhibiting Janus kinase (JAK) 1 and 2 [191]. Originally indicated in rheumatoid arthritis, baricitinib was trialed in patients receiving standard of care (systemic corticosteroids plus antivirals) at 4 mg daily for \(\leq\)14 days. While baricitinib did not significantly reduce progression to ventilatory support (primary endpoint), it was associated with reduced 28-day and 60-day mortality compared to standard care (8% vs. 13%, and 10% vs. 15%, respectively). Notably, incidence of thrombotic events was similar between the groups and could suggest baricitinib did not impact coagulation either mechanistically or by virtue of timing in disease stage [192].
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
## 3 Measurement Campaigns
### Equipment Setup  
The experimental setup contains two rover sensor collections: (1) the reference sensors and (2) the smartphones, as well as an RTK base station as illustrated in Figure 2.  
The hardware used are a collection of geodetic and automotive-grade GNSS and IMU sensors to produce a reference positioning solution and raw measurement comparisons. The sensors are mounted on and in a box on top of the vehicle as shown in Figure 3. A NovAtel SPAN geodetic receiver + IMU combination and geodetic antenna is utilized, and a NovAtel base station is located on an open rooftop within a 5 km baseline to generate a post-processed, smoothed RTK/IMU tightly-coupled reference solution using the Inertial Explorer software. An Xsens MTI-7 automotive-grade IMU is also used to provide a low-cost inertial sensor alternative to the smartphone IMUs.  
Two phone models are used in this study, the Xiaomi Mi 8 and Samsung Galaxy S21+. Both support multi-constellation (GPS+GLONASS+Galileo+BeiDou) tracking, as well as dual-frequency signals (L1 and L5). The GnssLogger (Google) and Geo++ Rinex Logger  
Figure 3: Photos of strapped reference sensors used in measurement campaigns.  
Figure 2: Schematic of equipment setup for measurement Campaigns.  
(Geo++) apps are used concurrently to log raw IMU measurements and GNSS observations, respectively. To mimic real-world driving, the phones are attached on the dashboard instead of placing on the vehicle roof. This study assumes the IMU frame to remain stationary relative to the vehicle frame throughout the experiment. The smartphones are equipped with consumer-grade inertial sensors. Their factory specifications are detailed in Table 2 in comparison with the automotive/industrial-grade Xsens MTi-7 example used alongside. The former class of inertial sensors usually cost less than 10 USD, whereas the latter class can cost up to 1000 USD. The specifications show a general weaker performance of smartphone IMUs. Traditionally, it is expected that GNSS-denied navigation outages using an automotive/industrial grade IMU will be less than one minute. By extension, smartphone IMUs should fill shorter GNSS-denied navigation outage periods. For the IMU model in Galaxy S21+, in-run bias is not found in factory datasheet or in the literature, and future work can include static IMU testing to compute Allan variance.
# Study on Frost Heave and Thaw Settlement Characteristics of Sanya Estuary Channel Soil Layer
## 4 Test Results and Analysis
### Analysis of Frost Heaving and Thawing Properties of Silty Clay Under Different Moisture Contents
At moisture contents of 24%, 27%, 30%, 33%, and 36%, the displacement rate changes for silty clay during the freezing phase are 0.102 mm/min, 0.112 mm/min, 0.128 mm/min, 0.163 mm/min, and 0.171 mm/min, respectively. During the thawing phase, the rates are \(-\)0.080 mm/min, \(-\)0.123 mm/min, \(-\)0.158 mm/min, \(-\)0.201 mm/min, and \(-\)0.329 mm/min, respectively. Based on the data, it can be concluded that higher  
Figure 8: Samples of Silty Clay after Freezing and Thawing under Different Moisture Contents. Figure 9: Frost Heave Rate and Thaw Settlement Coefficient of Silty Clay as a Function of Moisture Content. frost heave ratios and thaw settlement coefficients correlate with greater displacement rate changes. Silty clay exhibits the fastest rate of change, while gravel sand shows the slowest. Furthermore, higher moisture content accelerates the rate of displacement change due to frost heave and thaw settlement.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### Application Prospects Based on Case Study Insights  
The successful deployment of the urban sound monitoring system at the University of Auckland's city campus offers valuable insights into its potential for broader urban applications. This case study envisions several critical application scenarios, particularly in urban noise pollution monitoring, smart city infrastructure, and public safety.
## 4 Discussion
This result shows that KoBERT, an LLM model based only on Korean, is more appropriate than BERT for recognizing intent and NER. Also, EDA is more suitable as an augmentation considering the relationship between words in a sentence. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Augmentation** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Augmented** \\ **Ratio** \\ \end{tabular} } & \multicolumn{4}{c}{\
\begin{tabular}{c} **INTENT (F1-Score)** \\ **ALL** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy** \\ **ID** \\ \end{tabular} } \\ \cline{5-8}  & & Original & & & 95.5 & 94.4 & 79.2 & 89.7 & 91.9 \\ \cline{2-8}  & & & 10 & 96.0 & 95.5 & 82.7 & 91.4 & 93.5 \\ BERT & & 20 & 94.2 & 95.5 & 82.8 & 90.8 & 93.1 \\  & & 30 & 92.0 & 95.4 & 81.2 & 89.5 & 92.4 \\ \cline{2-8}  & & 10 & 95.5 & 95.9 & 83.9 & 91.8 & 93.8 \\ FastText & & 20 & 93.8 & 95.1 & 80.6 & 89.8 & 92.4 \\  & & 30 & 94.1 & 96.1 & 84.8 & 91.7 & 93.8 \\ \hline \multirow{2}{*}{KoBERT} & \multicolumn{2}{c}{Original} & \multicolumn{2}{c}{95.2} & \multicolumn{1}{c}{96.9} & \multicolumn{1}{c}{85.1} & \multicolumn{1}{c}{92.4} & \multicolumn{1}{c}{94.5} \\ \cline{2-8}  & & 10 & 96.0 & 97.3 & 91.6 & 94.9 & 96.0 \\  & & 20 & 97.3 & 97.7 & 91.5 & 95.5 & **96.6** \\  & & 30 & 94.5 & 96.0 & 84.4 & 91.6 & 93.6 \\ \cline{2-8}  & & 10 & 96.4 & 96.9 & 87.6 & 93.6 & 95.2 \\ FastText & & 20 & 92.9 & 97.1 & 87.6 & 93.0 & 95.0 \\  & & 30 & 96.5 & 96.6 & 86.6 & 93.2 & 94.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the intent classification. Batch size of 16 and learning rate of \(3\times 10^{-5}\) were used as training parameters. The bold font has the best accuracy in this comparison. Figure 5: A The screen capture is a translation of line numbers 1 through 5 from Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Augmentation** & \multirow{2}{*}{**Augmented**} & \multirow{2}{*}{**Ratio**} & \multicolumn{1}{c}{**EUTITY (F1-Score)**} & \multirow{2}{*}{**Accuracy**} \\ \cline{3-14}  & &
## 2 Materials and Methods
### Statistical Analysis  
The normality of continuous variables across pain groups was tested using the Kolmogorov-Smirnov test. Descriptive analyses included medians (Mdns) and interquartile ranges (IQRs) for continuous variables and absolute and relative frequencies for categorical ones. Means and standard deviations (SDs) were also reported to complement the interpretation of continuous measures. Thus, in order to examine the relationship between pain groups (no pain, LP, WP) and ADL dependence (more than one limitation in BADLs or IADLs), Chi-square tests (\(\chi^{2}\)) were applied. When significant, pairwise comparisons of independent proportions were carried out with Bonferroni adjustment, and Cramer's V was used to quantify the strength of the association between variables. Kruskal-Wallis tests (H) were used to assess differences in the number of limitations across pain groups (no pain, LP, WP). Kruskal-Wallis tests (H) were used to assess differences in the number of limitations across pain groups. If the results were significant, Mann-Whitney U tests (U) with Bonferroni correction were performed for pairwise comparisons. Rank biserial correlation coefficients (rrb) were calculated to quantify effect size. Finally, to explore factors associated with the number of BADL and IADL limitations, Poisson regression models were fitted. Predictors included pain distribution, physical inactivity, HGS ratio, mobility, and upper limb/fine motor limitations, sociodemographic variables (age, sex, BMI, and education), mental health (depression and perceived loneliness), cognitive functioning, and dispositional optimism. Model selection was based on the values of the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Assumptions of independence, residual normality, and absence of influential observations were also verified. Results were reported as adjusted prevalence ratios (aPR) with 95% confidence intervals. Analyses were conducted using IBM SPSS Statistics v.27 (IBM Corp, Armonk, NY, USA), with statistical significance set at \(p<0.05\).
## 2 Methods Used for Determining the Compressive Strength of Concrete
### SonReb Method  
SonReb is one of the most common and accurate combined methods for determining the compressive strength of concrete, in which the most reliable results are obtained [8; 26; 36; 37; 38; 39]. In the SonReb method, a correlation is sought between the results of tests with UPVM, the elastic rebound method, which are independent variables, and the obtained compressive strength in a destructive test (dependent variable) of test specimens or cores taken from a reinforced concrete structure in service [4; 40]. Many factors, as in the elastic rebound method and UPVM, can influence this relationship. In this combination, they sometimes have opposite effects on the measurement results of the different methods. This leads to a reduction or compensation in measurement errors and an improvement in the reliability of concrete compressive strength assessments. For example, an increase in moisture in concrete leads to an increase in the velocity of the ultrasonic pulse and a decrease in the rebound number when using a Schmidt hammer [1]. The models proposed by different authors vary (linear, power, exponential, polynomial and other) and are strongly influenced by the data used for the specific reinforced concrete structure; to date there is no universal model solution [1]. When there are no data available regarding the concrete used in the reinforced concrete structure, the following equation is applied [8; 41; 42]:  
\[\mathrm{f_{c,SonReb}}=\mathrm{a.R^{b}.V_{UPV}}^{c}, \tag{8}\]  
where \(\mathrm{a,\,b}\) and \(\mathrm{c}\) are constants.  
The factors \(\mathrm{a,\,b}\) and \(\mathrm{c}\) can be obtained using the regression analysis function of Microsoft Excel and determining the correlation between the compressive strength determined via the destructive method and the results of the tests conducted with the elastic rebound method and UPVM. With the obtained equations, isocurves can be plotted, from which concrete compressive strength can be determined based on the non-destructive measurements alone [1; 40; 43]. The formula for \(\mathrm{f_{c,SonReb}}\) can be used in determining the compressive strength of concrete by means of the SonReb method for the locations of the reinforced concrete structure under study from which cores cannot be sampled.  
In [1; 8; 26; 37; 38; 40; 41; 43], polynomial, power, linear and other models are cited that relate concrete compressive strength, the rebound number and the ultrasonic pulse velocity.
## 5 Discussion--Comparison of Different Electrode Station Configurations
* From the cases of typical design, the optimal values for the maximum absolute potential \(V_{\mathit{rel\_max}}\) and the electrode-station-to-remote-earth resistance \(R_{\mathit{el}}\) result from the case of placing straight frames in a row with respect to the dam, parallel to its axis (first configuration). * From the cases of typical designs, only when placing straight frames on the inner perimeter of the dam that are adapted to the outline of the pond, as well as adapting them to a T-shaped dam (seventh and eighth configurations, respectively), is the maximum electric field strength (within the area of the frame that is out of operation) \(E_{\mathit{off}}\) less than 2.5 V/m. * The variations are not significant among the typical design cases and no arrangement appears to prevail over the others. * The placement of straight frames radially or perimetrically to a central base (ninth and tenth configurations, respectively) improves the field results (and especially the values of the absolute potential and ground resistance of the electrode station), while slightly limiting the zone of influence. Nevertheless, apart from field results, for the selection of the final form of construction of the electrode station, the way of their construction, the respective cost, as well as the cost of their operation, with technical-economic criteria, must be taken into account. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**No.** & **Configuration** & \(d_{\alpha}\)**(m)** & \(\begin{array}{c}1d_{\alpha}\,\mathrm{l}\\ \mathbf{(m)}\end{array}\) & \(d_{\alpha}\)**(m)** & \(\begin{array}{c}1d_{\alpha}\,\mathrm{l}\\ \mathbf{(m)}\end{array}\) & \(\mathbf{S}_{\sigma}\)**(m\({}^{2}\))** & \(\begin{array}{c}E_{\mathit{m}}\end{array}\) & \(\begin{array}{c}E_{\mathit{m}}\end{array}\) & \(\begin{array}{c}V_{\mathit{rel\_max}}\\ \mathbf{(V)}\end{array}\) & \(\mathbf{R}_{\mathit{el}}\) (\(\Omega\)) & \(\begin{array}{c}\mathbf{No. of}\\ \mathbf{Table}\end{array}\) \\ \hline
1 & \
\begin{tabular}{c} Straight frames in a row, placed \\ parallel to a 50 m dam \\ \end{tabular} & 36.34 & 36.34 & 52.30 & 52.30 & 7602 & 2.95 & 19.73 & **17,300** & **14.82** &
\begin{tabular}{c} 11 and 15 in \\ \end{tabular} \\ \hline
2 &
\begin{tabular}{c} Straight frames in a row, placed \\ vertical to a 50 m dam \\ \end{tabular} & 35.72 & 41.72 & 48.92 & 48.92 & 7577 & 2.87 & **19.08** & 18,397 & 15.76 & 1 \\ \hline
3 &
\begin{tabular}{c} Straight frames in two \\ to the axis of a 50 m dam, aligned \\ with each other \\ \end{tabular} & 36.78 & 44.78 & 45.70 & 45.70 & 45.70 & 7455 & 3.05 & 20.03 & 21,476 & 18.40 & 2 \\ \hline
4 &
\begin{tabular}{c} Straight frames in two \\ overlapping rows, placed parallel \\ to the axis of a 50 m dam, aligned \\ with each other \\ \end{tabular} & 36.95 & 44.95 & 46.27 & 46.27 & 7579 & 3.35 & 20.14 & 21,581 & 18.49 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of the simulation of field results for an electrode station with 6 frames for the area of Stachtoroi, where “*” denotes the calculation of the corresponding circular disk area.
# The Extended Information Systems Success Measurement Model: e-Learning Perspective
## 4 Methods and Materials
### Sample Demographics  
The sample consisted of respondents across different categories of gender, age, and academic titles. The 381 respondents from the UNS encompassed staff with other academic titles, including 26 (6.8%) teaching associates, 107 (28.1%) teaching assistants, 98 (25.7%) assistant professors, 86 (22.6%) associated professors, 44 (11.5%) full professors, and 20 (5.2%) self-declared others, while 149 respondents from the PICB held the following titles: 12 (8.1%) professor coordinators, 84 (56.4%) professors, 4 (2.7%) assistants, 29 (19.5%) invited assistants, 17 (11.4%) guest lecturers, and 3 (2%) declared as others.  
The largest share of UNS respondents was in the second category--respondents between 31 and 40. The smallest percentage of respondents fell into the last, fifth category--over 60 years old. At the PICB, most respondents were in the third category--between 41 and 50--and the fourth category--between 51 and 60 years old. The smallest percentage of respondents fell into the last, fifth category--over 60 years old. To obtain more reliable information about e-learning IS success, it was necessary to determine the respondents' previous experience using this or similar ISs, and the average e-learning IS daily usage. The distribution of respondents according to their expertise and everyday usage is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **No.** & **\%** & **No.** & **\%** \\ \hline
**Experience in using e-learning IS** & & **UNS** & & **PICB** \\ \hline Without prior experience & 140 & 36.7 & 10 & 6.7 \\ \hline Less than 1 year & 64 & 16.8 & 38 & 25.5 \\ \hline Between 1 and 3 years & 62 & 16.3 & 14 & 9.4 \\ \hline More than 3 years & 115 & 30.2 & 87 & 58.4 \\ \hline Total & 381 & 100.0 & 149 & 100.0 \\ \hline
**Average e-learning IS daily usage** & & & & \\ \hline Less than 1 h & 223 & 58.5 & 29 & 19.5 \\ \hline Between 1 h to 3 h & 140 & 36.7 & 74 & 49.7 \\ \hline Between 3 h to 5 h & 15 & 3.9 & 34 & 22.8 \\ \hline Between 5 h to 7 h & 3 & 0.8 & 11 & 7.4 \\ \hline More than 7 h & 0 & 0 & 1 & 0.7 \\ \hline
**Total** & 381 & 100.0 & 149 & 100.0 \\ \hline \end{tabular}
\end{table}
Table 3: Sample structure according to experience in using IS and average daily usage.
# Highly Reliable Fuzzy-Logic-Assisted AODV Routing Algorithm for Mobile Ad Hoc Networks
## 3 Proposed Fuzzy-Logic-Assisted AODV Routing Protocol
### Fuzzy Rules  
Fuzzy logic systems can use human experience and preferences to solve any complex problems. Fuzzy rules are a set of independent rules based on experience and simulation experiments to achieve certain effects [15]. This paper chooses the \"if-then\" reasoning relationship. To realize high reliability, based on communication experience, the fuzzy rules set in this paper are shown in Table 1. It can be seen that the value of REL (reliability) increases with the decrease of ND and the increase of NS and NRE. Nodes with high REL value are most likely chosen as relay nodes. For example, according to QoS requirements and inference system, if the input delay is smaller, i.e., ND less than 0.1, the stability is higher, i.e., NS greater than 0.9, and the residual energy is more, i.e., NRE more than 0.9, the output reliability is higher, as shown in the yellow area of input and output inference surface graph in Figure 5. It is effective to avoid choosing nodes with low REL value as a relay node during the route discovery process.  
Figure 4: Membership function of NRE.  
Figure 5: Input and output surface graph of fuzzy inference. (**a**) Inference surface between ND and NS and REL; (**b**) inference surface between ND and NRE and REL; (**c**) inference surface between NS and NRE and REL.