esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### The Importance Ranking of Contributing Factors  
Table 5 summarizes feature importance for each model under the two sampling methods, quantified by mean absolute SHAP values. These feature importance patterns are further illustrated by the bi-modal violin plots of SHAP value distributions for each model under both sampling methods (Figure 13).  
Under the GLHM sampling, STI exhibited the highest importance in SVM (0.294) and NB (0.065), while NDVI was the most influential feature for SVM-RF (0.320) and XGBoost (0.172). Slope was notably important in NB (0.137). Other features such as TWI and profile curvature showed moderate influence (e.g., TWI: 0.203 for SVM, 0.165 for SVM-RF).  
In the Random sampling group, STI remained highly important for SVM (0.392) and SVM-RF (0.345). NDVI was also among the top features for these models (SVM: 0.227; SVM-RF: 0.254). Lithology and elevation gained relatively more importance compared to GLHM, with lithology at 0.090 in NB and elevation up to 0.126 in SVM. Generally, feature  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Contributing Factors**} & \multicolumn{3}{c}{**Models**} & \multirow{2}{*}{\begin{tabular}{c} **SVM-RF** \\ **(GLHM)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **XGBoost** \\ **(GLHM)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **NB** \\ **(GLHM)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **SVM-RF** \\ **(GLHM)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **XGBoost** \\ **(GLHM)** \\ \end{tabular} } \\  & & & & & & & \\ \hline NDVI & 0.0544 & **0.2358** & **0.3202** & **0.1715** & 0.0366 & **0.2268** & **0.2542** & 0.0822 \\ STI & 0.0649 & **0.2936** & **0.2947** & **0.1003** & 0.0573 & **0.3924** & **0.344  
importance values were lower and exhibited greater variability under Random sampling, which likely reflects the noisier nature of the data acquired through this method.  
The consistent prominence of STI, TWI, and NDVI across models and sampling strategies highlights their critical roles in landslide susceptibility. These findings underscore the critical role of hydro-dynamic processes in landslide initiation within the Tianshan region. Specifically, the combined effects of spring snowmelt and intense summer rainfall create dynamic hydrological conditions that are pivotal in triggering landslide events.  
This analysis revealed nuances in feature reliance based on model complexity and data quality. For instance, slope consistently emerged as a key factor in simpler models like Naive Bayes, especially under GLHM sampling, affirming the significance of terrain steepness. The increased importance of lithology and elevation under Random sampling suggests that models may depend more heavily on fundamental geological features when confronted with higher levels of label noise. This observed variability in feature importance between the two sampling methods powerfully highlights the direct impact of data quality and noise levels on model interpretability and the features models prioritize for prediction.
# Effect of Audio-Visual Factors in the Evaluation of Crowd Noise
## 3 Results
### Effect of Human Voice Volume and Other Sounds on Acoustic Evaluation

Table 2: Significance of the effects of sound type and volume level on acoustic evaluation. even felt slightly quiet for the low- and medium-volume human voice superimposed with birdsong and music (\(p<0.01\)). With respect to volume level, sound loudness increased significantly with increasing volume level; that is, the higher the volume, the louder the participants felt (\(p<0.01\)). There is an interaction between acoustic comfort and sound preference in terms of sound types and volume levels. With respect to sound types, acoustic comfort and sound preference were rated significantly higher than the superimposed traffic sounds for the low- and medium-volume conversational sounds overlaid with birdsong and music sounds, but the high-volume conversational sounds overlaid with birdsong were rated lower than the superimposed music and traffic sounds. After superimposing birdsong and music sounds with human voices at low- and medium-volume levels, participants' acoustic comfort ranged between 3.0 and 4.0. Sound preference ranged between 3.0 and 4.5, and participants felt comfortable and liked it (\(p<0.01\)). In contrast, after superimposing birdsong and music over high-volume vocals, the acoustic comfort was between 1.0 and 2.5 and the sound preference was between 1.0 and 2.5. Participants felt uncomfortable and disliked it (\(p<0.01\)). When traffic sounds were superimposed over low- and medium-volume human voices, the participants felt uncomfortable and disliked it. The participants felt very uncomfortable and disliked it when traffic noise was superimposed over high-volume human voices. Their evaluation scores ranged from 2.0 to 2.5 for acoustic comfort (\(p<0.01\)) and 1.5 to 2.0 (\(p<0.01\)) for sound preference; the participants felt very uncomfortable and disliked it very much. Concerning volume, when the sound type was birdsong or music, acoustic comfort and preference decreased significantly as the volume of the human voice increased (\(p<0.05\)). Moreover, when the sound type was traffic, acoustic comfort and preference decreased significantly only when the human voice volume was high. Acoustic comfort and preference were higher at low volume than at high volume (\(p<0.01\)). It was observed that sound type and volume, as well as their interactions, affected participants'  
Figure 4: Mean ratings of evaluation indicators under the effect of acoustic factors. acoustic evaluations different degrees. Regardless of voice volume, birdsong and music were rated higher than traffic, and regardless of the type of sound, acoustic evaluations were significantly higher for low-volume voices than for high.
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
###### Abstract  
Double- and triple-walled carbon nanotubes (DWNTs and TWNTs) consist of coaxially-nested two and three single-walled carbon nanotubes (SWNTs). They act as the geometrical bridge between SWNTs and multi-walled carbon nanotubes (MWNTs), providing an ideal model for studying the coupling interactions between different shells in MWNTs. Within this context, this article comprehensively reviews various synthetic routes of DWNTs' and TWNTs' production, such as arc discharge, catalytic chemical vapor deposition and thermal annealing of pea pods (_i.e._, SWNTs encapsulating fullerenes). Their structural features, as well as promising applications and future perspectives are also discussed.  
carbon nanotubes; double-walled carbon nanotubes; triple-walled carbon nanotubes; synthesis; catalytic chemical vapor deposition; arc discharge; fullerenes; pea pods  
[PERSON] 1,2, [PERSON] 3, [PERSON] 3, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 4, [PERSON] 5, [PERSON] 3,* and [PERSON] 6,7,*
## 3 Discussion  
### Metabolome Analysis of D. mobile  
Metabolomics is a new subject that has developed rapidly after genomics and proteomics and is an important part of systems biology [33]. Metabolomics is widely used for the (qualitative and quantitative) analysis of small molecular metabolites (with a relative molecular mass of less than 1000) in organisms or cells. The main chemical constituents isolated from \(D\). _nobile_ were alkaloids, phenanthrene, biphenyl, sesquiterpenoids, and other types [16]. The _Dendrobium nobile_ alkaloid is one of the main active ingredients in \(D\). _nobile_ and has anti-cataract, anti-tumor, nerve protective, hypoglycemic, hypolipemic, and anti-oxidation pharmacological effects [34; 35; 36; 37]. Moreover, sesquiterpenes have been reported to be present, which are represented by the special structure of the picrotoxane-type. At the same time, it was found that the sesquiterpenes of \(D\). _nobile_ have neuroprotective, immunomodulatory, and anti-tumor physiological activities [5]. As a relatively new research field in the post-genomic era, metabolomics has been effectively applied to assess the changes in metabolites between different tissues, species, or developmental stages [33; 38]. In this paper, the metabolites of \(D\). _nobile_ seedlings treated with MeJA were identified and analyzed comprehensively and systematically using UPLC-MS/MS metabolomics. A total of 615 metabolites were analyzed and identified in this article, including forty-one alkaloids, nine terpenoids, and fifteen polysaccharides. These results indicate that \(D\). _nobile_ has strong alkaloid metabolic and polysaccharide activities, findings that are consistent with the transcriptomic analysis results. However, the lack of a structural analysis of the compounds in the potential pathways suggests that UPLC-MS/MS cannot easily, quickly, and systematically identify the structures of the compounds in unknown pathways without standards. As one of the four basic substances of life, polysaccharides widely exist in higher plants, animals, microorganisms, lichens, and algae [39]. Polysaccharides play anti-tumor, anti-inflammatory, anti-viral, hypoglycemic, anti-aging, anti-coagulant, immune stimulatory, and other bioactive roles [40]. Natural polysaccharides isolated from fungi and plants are used in the food and pharmaceutical industries because of their non-toxic and negligible side effects, as well as their medicinal value. The derivatives of immunologically active polysaccharides often have other activities as well [41]. Therefore, the research and development of polysaccharides has become a hot spot in recent years. The results of this paper provide reference values for the development of \(D\). _nobile_ as a raw material.  
Figure 11: GC–MS was used to detect the catalytic product. (**A**) Product extracted from yeast engineering bacteria (WAT11). (**B**) Product extracted from yeast engineering bacteria (WAT11/pESC-TRP). (**C**) Product extracted from yeast engineering bacteria (WAT11/pESC-TRP::DnSQS-2). (**D**) Mass spectrum of the 33.622-min peak in (**C**).
## 2 Materials and Methods
### Experiments
#### 2.1.5 Data Acquisition  
For each phantom, prepared phantom liquid media (with viscosity nearly that of water) was added to the container, stirred gently and allowed to come to rest (3-5 min). DCS measurements were obtained by moving the optical fiber probe vertically toward the liquid surface. The probe was situated at the center of the phantom container and oriented such that the line connecting the center of the source and detector fiber was perpendicular to the flow channel and was positioned using custom probe holder (mounted on a linear optical stage that allowed the probe head to be moved away or toward the liquid surface). Once the liquid phantom was prepared, the probe was moved till it just broke the surface tension of the liquid medium. Five scans were recorded for each phantom with changing flow condition. Each DCS scan was acquired for 1s and had count rates ranging between 80-100 kHz. The probe was then removed, new media added, and the process repeated for depths \(D_{1}\)-\(D_{3}\).
## 3 6G State-of-the-Art  
For this vision to become a reality, 6G will be the critical enabler; it will connect everything, provide full-dimensional wireless coverage, and integrate all functions, including sensing and communication, as well as computation, caching, and control [375]. It will also enable full-vertical applications by integrating all functions, including as sensing, communication, computation, caching, and control, as well as location, radar, navigation, and imaging, into a single platform. It is anticipated that 6G would be an autonomous ecosystem with intelligence and consciousness on par with humans. In the coming years, 6G networks will be a significant enabler for the intelligent information society, as they are expected to provide performance superior to 5G networks while also catering to the needs of developing services and applications. Our vision of 6G, as well as IoT and machine learning applications for autonomous networks and revolutionary energy efficiency, are discussed in detail throughout this paper.  
Despite the fact that 5G supports URLLC, Zong et al. [376] argue that it has some limitations, such as the weakness of short packet, sensing-based URLLC. This may limit the dependability of low-latency services with high data rates, which are required for AR, MR, and VR. Furthermore, smart devices are expected to exponentially increase data traffic and necessitate high-speed data transfer, both of which are unaccounted for in 5G  
Figure 4: Published articles in between 2014 and 2021.  
Figure 3: Articles categorized based on their contribution.  
standards [377]. Similarly, 5G does not support advanced IoT technologies that necessitate the convergence of communication, detection, control, and computing functions. As a result, the need for 6G emerges to support these IoT technologies. In comparison to 5G, 6G will be more reliable, have lower latency, and will be fully integrated with ML, XR, IoT, and blockchain technologies [378]. In addition, 6G wireless networks will: (1) With super-high throughput demands, support SHD and EHD videos. (2) Provide extremely low-latency communications for the industrial Internet (about 10 s). (3) Support the Internet of Nano-Things and the Internet of Bodies by using smart wearable devices and intraday communications enabled by implantable nanodevices and nanosensors that consume very little power. (4) Support underwater and space communications to considerably expand human activity's bounds, such as deep-sea touring and space exploration. (5) In new scenarios, such as HSR, provide consistent service experiences. (6) Boost 5G vertical applications like Massive IoT and completely autonomous vehicles.
## 2 Related Work
### Tarifit-Specific Research and Digital Linguistic Landscape  
Computational research specifically targeting Tarifit is notably sparse. Recent pioneering work includes Awarai [3], which introduced the first automatic speech recognition system designed specifically for Tarifit. Most relevant to the current study, [PERSON] [29] conducted an in-depth analysis of word boundaries in Standard Amazigh writing through the lens of Tarifit Facebook users. Her findings highlighted significant orthographic variation, inconsistent digital writing practices, mixed script usage (Latin, Tifinagh, Arabic), and irregular tokenization patterns. These observations are directly pertinent to translation evaluation, as they underscore the complexity of real-world Tarifit text and the challenges it presents to automated systems.  
The broader sociolinguistic context of Tarifit is also crucial for understanding its computational challenges. [PERSON] et al. [4] examined Amazigh language policy in Morocco, revealing the complex multilingual environment where Tarifit speakers frequently engage in code-switching between Tarifit, Arabic, and French. [PERSON] and [PERSON] [5] further documented similar multilingual practices in social media contexts, illustrating dynamic linguistic mixing that poses considerable challenges for automated translation systems.  
These issues are further exacerbated by the broader challenge of linguistic underrepresentation in NLP. [PERSON] et al. [1] highlighted the critical underrepresentation of languages like Tarifit in mainstream NLP systems. [PERSON] and [PERSON] [30] surveyed emerging trends in less-resourced language processing, identifying key challenges specific to various Amazigh varieties.  
Our current study addresses critical gaps within this research landscape by providing the first systematic evaluation of in-context learning for Tarifit translation, establishing baseline performance metrics and optimal ICL configurations for this underrepresented but culturally significant Berber language.
# Post-Occupancy Evaluation in High-Rise Apartent Buildings in Vietnam
## 2 Materials and Methods
### Data Collection
#### 2.2.2 Long-Term Indoor and Outdoor Environmental Data  
Indoor temperature, humidity, atmospheric pressure, and CO\({}_{2}\) concentration were collected in 98 rooms of the 49 investigated apartments between June 2020 and June 2021 using Testo 160 IAQ WiFi data loggers, as shown in Table 1. These data loggers were installed at mid-height (approximately 1.5 m) on interior walls that were not directly exposed to sunlight, ensuring representative environmental measurements. Each apartment was equipped with two data loggers, one in the main bedroom and one in the living room, to capture the differences in environmental conditions between these frequently occupied spaces. The measurement interval was set to 15 min, and data were transmitted in real-time to a cloud-based server for remote access and analysis.  
AC operating states (on/off) and room occupancy states (occupied/unoccupied) were not measured directly but were derived from indoor environmental data using developed recognition algorithms [28]. The AC operation recognition based on room temperature and absolute humidity demonstrated high accuracy. However, occupancy recognition using CO\({}_{2}\) concentration was less reliable in large, well-ventilated spaces with low occupant CO\({}_{2}\) release rates, such as living rooms during the day. The dataset is publicly available in [29].  
The weather data were collected through a Davis Vantage Pro2 weather station installed on the campus of Hanoi University of Civil Engineering.
## 3 Results
### Validity  
The relationship between GRF AI, knee extensor strength, hip extensor strength, hand grip strength, and walking speed is presented in Table 4.  
Pearson correlation coefficients indicated a low correlation between GRF AI Rt and right hand grip strength (r = 0.268) and between GRF AI Lt and left hand grip strength (r = 0.316). No significant correlations were observed between GRF AI and the other outcome measures (Table 4). HHD and hand grip strength showed moderate-to-high correlation (r = 0.896-0.913) in both the right and left sides (Table 5). A low correlation was found between right and left hand grip strength and walking speed, with Pearson correlation coefficients of 0.375 and 0.302, respectively (Table 5).  
For additional validity analysis, we attempted to analyze outliers using the 1.5 (IQR) rule [27]. Therefore, the statistics of GRF AI Rt and GRF AI Lt were analyzed, but the outlier part was not confirmed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Rt Hip Knee Extensor** & **Rt Hand Grip** & **Walking Speed** \\ \hline GRF AI right neutral STS & 0.034 (0.072) & 0.191 (0.251) & 0.165 (0.488) \\ GRF AI Rt & 0.045 (0.359) & 0.268 * (0.042) & 0.187 (0.115) \\ \hline \multicolumn{3}{c}{**Lt Hip Knee Extensor**} & **Lt Hand Grip** & **Walking Speed** \\ \hline GRF AI left neutral STS & 0.094 (0.591) & 0.217 (0.238) & 0.044 (0.278) \\ GRF AI Lt & 0.064 (0.654) & 0.316 * (0.009) & 0.117 (0.437) \\ \hline \hline \end{tabular}  
* Correlation is significant at the 0.05 level; (), \(p\) value. GRF AI, ground reaction force asymmetry index; Rt, right; Lt, Left; GRF AI Rt, ground reaction force asymmetry index at maximal weight shift to the right at seat-off; GRF AI Lt, ground reaction force asymmetry index at maximal weight shift to the left at seat-off.  
\end{table}
Table 4: Concurrent validity analysis of force plate (r, Pearson correlation) (_n_ = 47).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a) Intrarater Reliability of Mean GRF Ratio**} \\ \hline  & \multicolumn{2}{c}{Intrarater ICC (CI)\_day 1} & \multicolumn{2}{c}{Intrarater ICC (CI)\_day 2} & \multicolumn{2}{c}{Intrarater ICC (CI)} \\ \cline{2-7}  & Right side & Left side & Right side & Left side & Right side & Left side \\ \hline GRF AI & 0.947 & 0.974 & 0.931 & 0.970 & 0.965 & 0.979 \\ (0.914–0.969) & (0.959–0.985) & (0.889–0.959) & (0.952–0.982) & (0.948–0.979) & (0.969–0.987) \\ \hline \multicolumn{7}{c}{**(b) Single-Time Test–Retest Reliability**} \\ \hline  & \multicolumn{4}{c}{Intrarater ICC (CI)} \\ \hline  & \multicolumn{4}{c}{Right side} & Left side \\ \hline GRF AI & 0.911 (0.815–0.947) & 0.930 (0.874–0.961) \\ \hline \hline \end{tabular}  
* GRF AI, ground reaction force asymmetry index; ICC, intraclass correlation coefficient; CI, confidence interval  
\end{table}
Table 3: Reliability of sit-to-stand test performance conducted two times 1 month apart (_n_ = 47).
# Integrated Plug High-Strength Geocell Reinforcement in Foundation Design for Square Footing
## 3 Results and Discussion  
The data from model tests [12], as depicted in Table 1, are utilized to compare the results obtained from the developed theoretical model. An example of the calculation process can be found in Appendix A. The minimum and maximum errors observed are 9% and 18%, respectively. The data from the model tests conducted by [PERSON] et al. (2018) [25] are also provided in Table 1 for comparative analysis. The errors observed are 15%. Therefore, the developed theoretical model reasonably accurately calculates the bearing capacity of traditionally IPGC-reinforced foundations.  
Figure 10 further illustrates the effect of the diameter of the maximum cell size (\(d_{0}\)) on the ultimate bearing capacity (\(p_{u}\)) of an IPGC-reinforced foundation under a square footing  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Literature**} & \(B\) & \(\gamma\) & \(\varphi\) & \(h\) & \(d_{0}\) & \(D_{u}\) & \(F\) & \(\alpha\) & \(P_{u}\) & \(P_{\text{exp}}\) & **Error** \\  & /m & /kN/m\({}^{3}\) & /\({}^{\circ}\) & /m & /cm & /cm & /kN & \(\alpha\) & /kPa & /kPa & /\% \\ \hline \multirow{3}{*}{[PERSON] et al. (2014) [12]} & \multirow{3}{*}{0.3} & \multirow{3}{*}{18.1} & \multirow{3}{*}{35.4} & \multirow{3}{*}{0.05} & 11.1 & & \multirow{3}{*}{9.9} & 0.83 & 888.0 & 749.7 & 18 \\ \cline{4-10}  & & & & & 20.1 & & & 0.72 & 607.9 & 670.6 & 9 \\ \cline{4-10}  & & & & 28.2 & & & 0.51 & 610.9 & 559.8 & 9 \\ \hline [PERSON] et al. (2018) [25] & 0.5 & 18.6 & 41.0 & 0.15 & 22.0 & 5.0 & 0.27 & 0.99 & 596.8 & 705.1 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the model tests and the theoretical equation.  
Figure 9: Relationship between the influence coefficient of the top space and the top space of IPGC from the bottom of the footing in the experiments.  
using the proposed analytical model. It can be observed that the ultimate bearing capacity of the IPGC-reinforced foundation decreases with the increase in cell diameter. When \(d_{0}\) is more significant than \(1.8B\), the confinement of IPGC can be neglected. The ultimate bearing capacity is similar to those of the unreinforced foundation (295 kPa in [PERSON] et al., 2014 [12]), indicating that the shear plane occurred inside a cell of the IPGC, resulting in a local failure of the foundation.
# Comprehensive Analysis of Maritime Cybersecurity Landscape Based on the NIST CSF v2.0
## 5 NIST CSF-Based Analysis of Maritime Cybersecurity
### Protect
#### 5.3.5 Technology Infrastructure Resilience
[57] examined the cybersecurity measures of a company in real time, outlining the company's procedures and protective technology. They concluded that the company had the capability to withstand a large-scale cyberattack. Although their contexts differ, both of these studies highlight the crucial role of protective technologies in ensuring the integrity and security of data exchange, demonstrating the pivotal measures that should be implemented for successful threat mitigation in the digital era. Lastly, in the context of protecting communication systems, [PERSON] et al. [110] introduced MARMAC, a cost-effective method for retrofitting and protecting nautical communication systems. On the other hand, [PERSON] et al. [111] combined blockchain with electronic seals (e-seals) to enhance data security and traceability during port terminal operations. Not to be left out, [PERSON] et al. [91] proposed an IoT-powered system focusing on maritime transport communication systems to promote network security, reliability, and efficiency.