esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Collapse Risk Assessment Based on Site Inspection Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Evaluating Indicator**} & \multicolumn{5}{c}{**Factor Level**} \\ \cline{3-6}  & **I** & **II** & **III** & **IV** \\ \hline Design factors & Excavation span (m) (x\({}_{1}\)) & \textless{}7 & 7–10 & 10–14 & \textgreater{}14 \\  & Depth-to-height ratio (x\({}_{2}\)) & \textgreater{}20 & 15–20 & 10–15 & \textless{}10 \\ \hline Geological factors & Surrounding rock grade (x\({}_{3}\)) & 81–100 & 61–81 & 41–60 & \textless{}40 \\  & Bias (x\({}_{4}\)) & \textless{}10\({}^{\circ}\) & 10–25\({}^{\circ}\) & 25–40\({}^{\circ}\) & \textgreater{}40\({}^{\circ}\) \\ \hline  & Excavation methods (x\({}_{5}\)) & CRD & CD & Bench cut method & Full face method \\  & Stratum reinforcement & Full section curtain & Pipe shed support & Curtain grouting & Anchor bolts and \\  & measures (x\({}_{6}\)) & grouting and & and advance support & support & advance support \\  & factors & Waterproof and drainage & 76–100 & 51–75 & 26–50 & 0–25 \\  & measures (x\({}_{7}\)) & & & & \\  & main stiffness of initial & & & & \\  & support (x\({}_{8}\)) & \textgreater{}2 & 1–2 & 0.5–1 & \textless{}0.5 \\ \hline  & monitoring and & & & & \\  & measurement (x\({}_{9}\)) & \textgreater{}4 times/day & 3 times/day & 2 times/day & 1 times/day \\  & Construction quality & & & & \\  & qualification (x\({}_{10}\)) & 90–100\% & 80–90\% & 70–80\% & 60–70\% \\  & Accuracy of geological & & & & \\  & survey (x\({}_{11}\)) & \textgreater{}90\% & 75–90\% & 60–75\% & \textless{}60\% \\  & Timeliness of main & & & & \\  & support (x\({}_{12}\)) & \textless{}30 min & 30–60 min & 60–120 min & \textgreater{}120 min \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation index and collapse risk factor grade. Figure 4: Receiver Operating Characteristic Curve for different n_estimators and learning rates. (**a**) The receive operating characteristic curve of model when learning_rate is the variable. (**b**) The receive operating characteristic curve of model when n_estimators is the variable. optimal model is obtained when learning_rate is equal to 1 and n_estimators is equal to 100. AUC and ACC are both at maximum, which are 0.96, and 95.5%. The reason is that the model will overfit if n_estimators and learning_rate are too large, while the model will underfit if n_estimators and learning_rate are too small. Through the trained model, the above 60 tunnel sections are used as samples for risk assessment, and the results are shown in Table 5. The classification accuracy reaches 56%, which is too low to be used for construction site guidance. The results of on-site inspection come from expert experience, which are greatly influenced by subjectivity. In addition, due to the small data set and uneven distribution of the number during model training, the prediction accuracy of some risk levels is too low, and the overall accuracy is reduced.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 4 Experimental Setup  
This section describes the experimental setup used to evaluate the performance of the implemented voltage equalizer topologies. It provides detailed information on the laboratory equipment, battery pack specifications, and developed PCB prototypes. All the test scenarios established to validate each balancing strategy are also introduced, enabling equitable comparison between the proposed implementations.
# Electronic Cigarettes' Toxicity: From Periodontal Disease to Oral Cancer
## 4 Chemical Composition of e-Liquids and Vapors and the Implications in Oral Carcinogenesis
### Heavy Metals  
The International Agency for Research on Cancer (IARC) has classified cadmium (Cd), nickel (Ni), and chromium (in its hexavalent-oxidation state) as carcinogens to humans (Group 1), while inorganic lead (Pb) is classified as a probable carcinogen (Group 2A). Electronic cigarettes can represent a source of toxic and carcinogenic metals. [PERSON] et al. used inductively coupled plasma mass spectrometry to assess toxic metals (cadmium, chromium, lead, manganese, and nickel) concentrations in e-liquid samples [78].  
In another study, [PERSON] et al. analyzed the potential of heavy metals transfers from the metallic compartments of e-cigarettes into the e-liquids during use. They found that heavy metal contents usually increased after using electronic devices [79].  
There are multiple mechanisms by which metals can induce carcinogenesis: genotoxicity, mutagenesis, oxidative stress, epigenetic modifications, competition with essential metal ions and cancer-related signaling pathways. For example, Cd induces DNA damage by generating ROS and inhibiting DNA repair. Cd also promotes epigenetic changes,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compound** & **Mean Concentration (\(\mu\)g/L) \(\pm\) SD** \\ \hline NNN & 4.06 \(\pm\) 9.34 \\ NNK & 1.71 \(\pm\) 1.69 \\ NAT & 6.36 \(\pm\) 12.52 \\ NAB & 0.90 \(\pm\) 1.72 \\ Total TSNAs & 12.99 \(\pm\) 18.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The levels of TSNAs in 105 replacement liquids of E-cigarettes using LC–MS method ([PERSON] and [PERSON], 2013).  
Figure 6: Formation of tobacco-specific nitrosamines.  
altering the DNA methylation status. Cr (VI) also induces genotoxicity and DNA damage. Ni facilitates carcinogenesis through epigenetic mechanisms, oxidative stress, and DNA damage [80; 81].
# Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management
## 3 The Internet of Things and Its Application in Supply Chains
### IoT in Supply Chains: Weaknesses and Threats
For example, cloud-based solutions for monitoring IoT data may be subject to manipulation and privacy legislation issues that arise when exporting substantial amounts of confidential and highly sensitive information to external services in other jurisdictions [31; 60]. Additionally, these solutions may cause opacity and increase information asymmetry between supply chain exchange partners. A compounding factor is that centralized systems act as a black box, and the participating nodes do not know how their data is stored, managed, utilized, and secured [67]. Blockchain technology can help to alleviate several of these problems.
# Stomatal Response of Maize (_Zea mays_ L.) to Crude Oil Contamination in Soils
## 4 Discussion
### PHE Uptake and Stomata  
Accumulation of PAHs in aerial plant tissues may be from root through transpiration stream and from diffusion via leaf stomata [14]. In the present work, transpiration rates in all plants were similar (Figure 3B). Besides, PHE concentrations of maize leaf in contaminated treatments were lower than that of the control. PHE could volatilize from contaminated soil. These results indicated PHE accumulation in maize aerial tissues might be from foliar uptake pathway which was controlled by stomata. This suggestion was also confirmed by the fact that PHE concentration in maize leaf was significantly positively correlated with stomatal length (r = 0.98, \(p<0.01\)). Besides, previous studies also confirmed that foliar uptake was the dominant pathway of PHE accumulation by plant [44; 45; 46].  
Since stomata play an important role in uptaking pollutants by plants grown in contaminated soils, measurements influencing stomatal movement can be applied in phytoremediation of contaminated soil for different purposes. For example, ABA can be used on maize husk for inhibition of PAH accumulation by grain due to being able to induce stomatal closure and inhibit a light-induced stomatal opening [47], when maize plant is considered for phytoremediation of PAHs-contaminated soil. Thus, safe food will be produced. In contrast, since fusicoccin can prevent dark-induced stomatal closure [48], it can be used on hyperaccumulators for extracting more pollutants in remediation of soil contaminated with heavy metals, which are taken up by plant root and transferred to aboveground tissues with a transpiration stream that closely relates to a stomatal opening. Therefore, phytoremediation of soils contaminated with organic pollutants or heavy metals can benefit from the controlling of stomatal movement. However, the data for the effect of phytohormone on maize plant was not provided in this study, which needs to be investigated in future work.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 4 Implementation and Results
### Step 4 Vulnerability Exploitation
This efficiency stems from LLMs' ability to automatically analyze potential vulnerabilities in ports and autogenerate executable attack commands, which is a capability that conventional tools typically lack. Figure 5: Successful session establishment and acquisition of authority. Figure 6: Successful password stealing.
# Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric Evaluated** & \multicolumn{2}{c}{**Single-Cell**} & \multicolumn{2}{c}{**Multicell**} \\ \hline DL PRB utilization vs. number of users & \(\bullet\) & 50\% PRB use with 58 UE devices & \(\bullet\) & 50\% PRB use with 45 UE devices \\  & \(\bullet\) & 100\% PRB use with 140 UE devices & \(\bullet\) & 100\% PRB use with 100 UE devices \\ \hline DL data volume vs. PRB utilization & \(\bullet\) & Stable until 80–90\% PRB use & \(\bullet\) & Stable until 90\% PRB use \\ \hline DL cell throughput vs. PRB utilization & \(\bullet\) & Maximum TP speed obtained was & \(\bullet\) & Maximum TP speed obtained was \\  & 1.2 Gbps & & 800 Mbps \\ \hline  & \(\bullet\) & With 100 UE devices, 800 Mbps & \(\bullet\) & With 100 UE devices, 750 Mbps \\  & TP speed & TP speed & TP speed \\ DL cell throughput vs. number of users & \(\bullet\) & With 200 UE devices, there was an increase of 50\% in the TP speed & \(\bullet\) & With 200 UE devices, there was an increase of 7\% TP in the TP speed \\  & (1.2 Gbps) & & (800 Mbps) \\ \hline DL cell edge user throughput vs. & \(\bullet\) & Average of 20 Mbps with 40\% & \(\bullet\) & Average of 20 Mbps with \\  & PRB utilization & PRB utilization & & 38\% PRB utilization \\ \hline DL cell edge user throughput vs. number of users & \(\bullet\) & Average of 20 Mbps with & \(\bullet\) & Average of 20 Mbps with \\  & of users & 20 UE devices & & 42 UE devices \\ \hline DL user throughput vs. PRB utilization & \(\bullet\) & With 50\% PRB use, 500 Mbps & \(\bullet\) & With 50\% PRB use, 480 Mbps \\ \hline DL user throughput vs. number of users & \(\bullet\) & With 100 UE devices, 375 Mbps & \(\bullet\) & With 100 UE devices, 190 Mbps \\ \hline DL delay vs. PRB utilization & \(\bullet\) & Maximum delay of 45 ms with 100\% PRB use & \(\bullet\) & Maximum delay of 55 ms with 100\% PRB use \\ \hline DL delay vs. number of users & \(\bullet\) & Delay increased rapidly with & \(\bullet\) & Delay increased rapidly with \\  & \(>\)140 UE devices & & \(>\)140 UE devices \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the results obtained. Figure 23: DL delay vs. number of users. in a multicell scenario. An average throughput of 20 Mbps for the cell edge users was observed and the maximum delay was 45 ms for a single-cell scenario and 55 ms for multicell scenario.
## 2 Geographic Ontologies
### Ontological Categories and Languages  
The objective of the Towntology project [8, 13] was to design ontologies for urban planning. A COST group was created with people coming from different countries. Since concepts were different in different languages, the preliminary question was \"do we design either separate mono-lingual ontologies or a unique ontology in English, including everything, and deriving other ontologies in various languages?\" There was no clear-cut answer, so the output was two-fold: some subgroups constructed urban ontologies in their own vernacular languages, whereas others in English.  
Figure 4: Example of administrative subdivisions with spatial relations.  
Figure 3: Example of ontology-based on spatial relations.  
The well-known English term \"bank\" represents both a riverside and a financial institution. In other words, the first meaning will be translated in French and Spanish, respectively, as \"_rive_\", \"_ribera_\" and the second by \"_banque_\" and \"_banco_\".  
Let us examine a special case: In the French language, the word \"_quasi_\" defines a wharf, an embankment, a train platform or a street along a river. In Spanish, especially in Barcelona, \"_rambla_\" is a ravine or a special kind of broad avenue. In Venice, \"_riotera_\" is a special type of pedestrian lane, whereas other denominations are used, such as _salizada_, _sottoportego_, _ramo_, _fondamenta_, _campiello_, _corte_, _calle_, _riva_, _etc_. As far as I know, those terms have no equivalent in English.  
Matching two ontologies is a very complex task [14] in which semantic and structural aspects are involved together with some indicators to qualify the degree of quality or the confidence of the matching.  
However, matching ontologies in different languages is a more complex task [15]. Indeed, the authors proposed using translation techniques as an intermediate step to translate the conceptual labels within an ontology. This approach essentially removes the natural language barrier in the mapping environment and enables the application of monolingual ontology mapping tools. They show that the key to this translation-based approach to cross-lingual ontology mapping is selecting an appropriate ontology label translation in a given mapping context with some confidence structure.  
However, in the case of matching geographic ontology, the geometric characteristics of geographic features must be taken into account. For instance, a river, a city, a road, an island or any kind of feature have very different geometric structures that can help matching.
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 3 Results
### Simulation Environment  
Referring to the rural scenario in 3 GPP [24], \(D\) was set to 1732 m. We supposed there were three TN cells in the area; thus, \(N=4\). The width of the area \(L\) was set to 3500 m. To compare the performance of the proposed scheme with different user densities, 5 to 30 users per TN cell were deployed, which means \(M\) was set from 15 to 90. The TN cells worked at 4 GHz with 30 MHz bandwidth. The GEO satellite worked in the Ka band (20 GHz) with 400 MHz bandwidth. The threshold of TN was set to 85%. Considering that the simulation area only accounts for a small part of the satellite coverage area, the threshold of NTN was set to 30%. There was no signal interference between the cells of TN and NTN. Other parameters mentioned in the previous sections were configured as in Table 1.  
Membership functions of the three inputs trained by ANFN are shown in Figure 4. The performances of the proposed scheme based on FEM are described in the following subsections.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
###### Abstract
differentiated seven foreign material types, six dockage types, and two exceta types from Canada Western Red Spring while using near-infrared (NIR) hyperspectral imaging and testified the classification practicable [14]. [PERSON] et al. discriminated five ochratoxin A (OTA) concentration levels in stored wheat using NIR hyperspectral imaging and developed linear, quadratic and Mahalanobis statistical discriminant models to fulfill the classification [15]. However, as we all know, rarely is the research of differentiating wheat varieties themselves using hyperspectral imaging encountered. Previous studies have shown that HSI is a powerful tool for detecting the quality of grains, but it commonly contains much noise due to the influence of system, environment, and other factors [16; 17]. These noises will seriously affect the accuracy and robustness of modeling, so it is necessary to choose appropriate methods to reduce and even eliminate its effects. In addition, hyperspectral data is a hypercube consisting of hundreds of wavebands and it contains lots of redundant information and collinear variables. Extracting part of wavelengths with fingerprint feature is a common method to reduce redundant information. This also contributes to simplify discrimination model and promote the detection performance. Therefore, the leading goal of this paper is to explore and accomplish varieties classification of wheat seeds while using HSI technology. The specific objective is: (1) to evaluate the distinguishability of wheat varieties qualitatively; (2) to develop discriminant models based on full wavelengths and feature wavelengths; (3) to analyze and compare the effects of different preprocessing and feature extraction methods on discriminant models; and, (4) to visualize the classification results combined with image processing technology.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 6 Analysis of in-Orbit Performance and Modeling Modification  
The Luojia 1-01 satellite was launched into orbit on 2 June 2018 from the Jiuquan launch site, in China. Since it was deployed successfully, all systems have been operating smoothly. The temperatures of instruments inside and outside the satellite cabin are at a rational level. By analyzing the telemetry data related to temperature, and comparisons to the ground thermal balance test data, the thermal model is further modified and updated.
## 5 Discussion  
This paper focuses on developing a trustworthy method for identifying the modal identification and comprehensive analysis on three types of acceleration responses, and was conducted to verify the validness and practicability of the combined methods.  
A numerical 5-storey frame structure simulated by SAP2000 was first analyzed. The frequencies estimation results from the noise-added response by several methods are consistent with each other and with the small relative error of less than 1%. In terms of the damping ratio, more deviations can be found, where the associated accuracy of the HPBW and HHT methods strongly depend on the limited length and nonstationarity of the acceleration response. It can be also noted that by employing the MEMD, the mean absolute relative deviation of damping ratios identified by the FBFFT method falls to 3.92% from 6.42% and by the SSI method, it reduces to 1.50% from 6.17%, indicating that it significantly reduces the errors of damping ratio caused by sinusoidal noise effect in the original signal. On the other hand, with the help of MEMD, the system order which was hard to be determined in SSI can be simply set as two or four (due to some residue noise or mode mixing) in both two cases. Meanwhile, the analysis of the shaking table model test in seismic excitations demonstrates that the accuracy of the damping identification is affected by the adopted method and there exists a trend that the modal damping ratio decreases with the increase in modes.  
The MEMD-based modal identification approach was applied to the Canton Tower under nonstationary earthquake excitation. The modal parameters of the first five vibration modes are identified and compared with other traditional methods (i.e., HPBW and HHT) and previous study for the same structure by [PERSON] [44]. The frequencies identified by various methods are quite consistent while damping ratios display more uncertainties: for instance, [PERSON] [44] obtained a small fourth-order damping ration of 0.37%, while a large value, 1.13%, may be seen in Table 8 by the HPBW.  
Figure 17: Comparison of the identified mode shapes of the Canton Tower.
# Measurement Model of Full-Width Roughness Considering Longitudinal Profile Weighting
## 4 Calculation Process of Full-Width Roughness  
Figure 4 illustrates the calculation process of full-width roughness involving the following key operational steps: (1) acquiring the raw elevation point cloud data of the road surface; (2) extracting effective elevation point cloud data within the lane, including road boundary recognition, lane outlier removal, and other point cloud data noise processing; (3) obtaining the weighting model of longitudinal profile, including determining lane categories, calculating the lateral distribution model of wheel trajectories, etc.; (4) computing the equivalent longitudinal profile elevations; and (5) calculating roughness indicators such as IRIF based on the equivalent profile elevations. The primary focus of this study corresponds to steps (3) and (4).  
Figure 4: Calculation process of full-width roughness.  
Figure 3: The ratio of cumulative sum \(S\)_off\({}_{norm}(x)\)_ to the value of \(F\)s.  
In operational step (1), the raw elevation point cloud data of the road surface can be acquired through vehicle-mounted, airborne 3D laser scanning systems, or other data acquisition devices. It is important to note that the point cloud should cover the entire roadway width to facilitate the subsequent extraction of road edge lines from the data.  
During operational step (2), various data processing techniques [14; 19; 20; 21; 22; 26; 27] can be employed to identify and determine the boundaries of the roadway, thereby defining the scope of the point cloud data. Following this, preprocessing of these raw data is carried out, which includes identifying and eliminating outliers within the lanes. These outliers may arise from equipment errors or foreign objects on the road surface. Ultimately, only the elevation point cloud data within the lanes are retained, serving as the foundation for further analysis of Full-width roughness.  
Operational steps (3) and (4) constitute the focal points of this study and have been described in detail in Sections 2 and 3, respectively.  
In operational step (5), the IRI\({}_{\text{F}}\) is computed using standard roughness calculation methods, such as commercial software [28] or programs written according to publicly available methods. This indicator serves as a key metric for evaluating both road surface roughness and the measurement performance of the instrumentation.  
As operational steps (1), (2), and (5) are not the primary focus of this study and have been extensively explored in previous research works [17; 28; 29], further elaboration on these steps is omitted.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 5 Experiment Analysis
### Localization Accuracy
As shown in Figure 13, when there is an impaired AP in the localization environment, the traditional fingerprint localization algorithm has a poor localization effect, and only 30% of the localization error is within 1 m. The fingerprinting algorithm based on the exclusion method is better than the traditional fingerprinting algorithm, with the localization error greater than 70% within 1 m. The algorithm proposed in this paper generally can achieve a better localization effect, and the localization error can be controlled within 1 m. Therefore, the algorithm proposed in this paper has better robustness to the harsh localization environment.