esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of BGLSP on Physical Characteristics of CSB  
Figure 4 illustrates the effects of varying BGLSP substitutions on the moisture content, spread ratio, and specific volume of CSB. The moisture content of CSB is a critical factor influencing its shelf life and susceptibility to deterioration. As depicted in Figure 4a, the moisture content in CSBs exhibited variability. Notably, the moisture levels in CSBs increased significantly as the BGLSP concentration rose from 5% to 7%, likely attributable to the enhanced fiber content (\(p<0.05\)) [12]. The spread ratio and specific volume are crucial quality indicators for CSB. An increase in specific volume and a decrease in the height-to-diameter ratio enhance the esthetic appeal of CSB, thereby increasing its consumer preference. Research indicates that the augmented specific volume and improved softness of CSB can be ascribed to the gluten's capacity to retain air within the dough [25]. Figure 4b,c shows a significant decrease (\(p<0.05\)) in these indicators with increased BGLSP levels. At 10% BGLSP, the spread ratio and specific volume decreased by 18.96% and 29.65%, respectively, compared to CSB made from wheat flour. This reduction could be attributed to the enhanced dilution of gluten proteins by BGLSP, coupled with the limited utilization of dietary fiber in BGLSP by yeast [12]. BGLSP could compete with gluten for water absorption, weakening gluten's air-holding capacity and thus reducing these quality metrics [18].  
Figure 4: Effects of BGLSP on (**a**) moisture content, (**b**) spread ratio, and (**c**) specific volume of CSB. Abbreviations: BGLSP, broken \(G\). _lucidum_ spore powder; CSB, Chinese steamed bread.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
###### Abstract  
Community detection in complex networks remains a significant challenge due to noise, outliers, and the dependency on predefined clustering parameters. This study introduces GraphDBSCAN, an adaptive community detection framework that integrates an optimized density-based clustering method with an enhanced graph partitioning approach. The proposed method refines clustering accuracy through three key innovations: (1) a K-nearest neighbor (KNN)-based strategy for automatic parameter tuning in density-based clustering, eliminating the need for manual selection; (2) a proximity-based feature extraction technique that enhances node representations while preserving network topology; and (3) an improved edge removal strategy in graph partitioning, incorporating additional centrality measures to refine community structures. GraphDBSCAN is evaluated on real-world and synthetic datasets, demonstrating improvements in modularity, noise reduction, and clustering robustness. Compared to existing methods, GraphDBSCAN consistently enhances structural coherence, reduces sensitivity to outliers, and improves community separation without requiring fixed parameter assumptions. The proposed method offers a scalable, data-driven approach to community detection, making it suitable for large-scale and heterogeneous networks.  
community detection; GraphDBSCAN; DBSCAN; outlier removal; K-nearest neighbor; autoencoder; Newman-Girvan clustering; graph analysis +
Footnote †: journal:  
Article
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 4 Results
### Effectiveness of Interactive Quality Control  
The five steps of knowledge-based interactive quality control were not conducted simultaneously. The steps based on geographical rules and the thematic maps were applied firstly in the automatic classification stage, and then the spatial consistency checking was conducted after forming a whole land cover map. The steps based on the verification points and statistics information were used in the last stage. Although all five steps were conducted to revise the classification errors contained in the CLC-SW2010 product, this section only shows two representative examples due to space limitations.  
Figure 9 shows a typical example for revising residential land at high altitude by using a township map. The residential land in mountainous areas was often omitted by automatic classification approaches, but this omitted residential land was artificially added with the help of the township map and the high-resolution Google Earth images.  
Figure 8: The decision tree for woodlands divisions.  
Figure 9: Interactive quality control based on the existing thematic map (township map). (**a**) and (**b**) are the results of unrevised and revised residential lands, respectively; (**c**) is the Landsat TM images and the locations of township; (**d**) is the additional residential lands with the help of the township map and high-resolution satellite images; (**e**) is the high-resolution Google Earth images.  
A typical spatial discontinuous phenomenon is shown in Figure 10c,g, which mainly comes from confusing the evergreen shrubs with the deciduous shrubs. In this case, the classification trees used for distinguishing the evergreen shrubs and the deciduous shrubs needed to be reconstructed by adjusting training sample sets and feature sets for each mapping unit until a satisfactory result was obtained. After revision, the spatial discontinuous phenomenon was removed in the post-checking classification results (Figure 10d,h).
## 2 Collision Avoidance Method
### Assessment Methods on the Collision Risk
#### 2.1.3 Type-1 Fuzzy Inference System  
The linguistic representation and subjective concept like the collision risk originating in the imprecise and vague meaning of words can be dealt from the fuzzy logic [30]. Hence, in order to prevent the collision accidents at sea, the \(CRI\) was computed by \(D_{CPA}\) and \(T_{CPA}\) using fuzzy reasoning [15; 16; 17; 18; 19]. In particular, the Type-1 Fuzzy Inference System based on Near-Collision (T1 FIS-NC) consisted of the Sugeno fuzzy logic based near collision data via Adaptive Neuro Fuzzy Inference System (ANFIS) [19]. At this time, non-dimensionalized input parameters \(D_{CPA}\)/\(L\), \(T_{CPA}\)/(\(L\)/\(V\)) via ship length (\(L\)) and ship velocity (\(V\)) were used. Figure 5 shows membership functions (Collision, Danger, Threat, and Attention) with linguistic variables.  
Figure 4: Illustration of the closest point of approach.  
\(D_{CPA}\)/\(L\) and \(T_{CPA}\)/(\(L\)/\(V\)) were used for antecedent parameter \(x_{1}\), \(x_{2}\), and \(R_{c}^{i}\) was used for the consequent parameter. Fuzzy inference rules of T1 FIS-NC were composed as  
\[Rule_{i}\ :if\ x_{1}\ \textit{is}\ \mu_{(D_{CPA}/L)i}\ \textit{and}\ x_{2}\ \textit{is}\ \mu_{(T_{CPA}/\oint_{i})i}\ \textit{then}\ R_{c}^{i}=k_{0}^{i}+k_{1}^{i}x_{1}+k_{2}^{i}x_{2} \tag{6}\]  
where \(k_{0}^{i}\), \(k_{1}^{i}\), \(k_{2}^{i}\) are fuzzy sets of the consequent part assigned from the T1 FIS-NC. Accordingly, Table 1 presents a total of fuzzy inference rules, consisting of a combination of linguistic variables for each membership function.  
Figure 6 shows surface based on a total 16 of fuzzy inference rules, and the \(CRI\) (i.e., \(R_{c}\)) can be computed from 0.0 to 1.0 via the fuzzy inference table as presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(D_{CPA}\)/\(L\)} & \multirow{2}{*}{Collision} & \multirow{2}{*}{Danger} & \multirow{2}{*}{Threat} & \multirow{2}{*}{Attention} \\ \cline{3-3} \cline{5-8}  & & & & & & \\ \hline
**Collision** & 0.994 & 0.773 & 0.477 & 0.021 \\
**Danger** & 0.777 & 0.662 & 0.401 & 0.017 \\
**Threat** & 0.395 & 0.423 & 0.335 & 0.015 \\
**Attention** & 0.062 & 0.246 & 0.152 & 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy inference rule of the T1 FIS-NC.  
Figure 5: Membership function of the Type-1 Fuzzy Inference System based on Near-Collision (T1 FIS-NC): (**a**) \(D_{CPA}\)/\(L\) and (**b**) \(T_{CPA}\) /(\(L\)/\(V\)).  
Figure 6: Surface of the T1 FIS-NC.
# There Is No Cross Effect of Unstable Resistance Training on Power Produced during Stable Conditions
## 3 Results
### Pre-Post Training Changes in Upper and Lower Body Muscle Power
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Load** & **Pre-Training** & \
\begin{tabular}{c} **After 4** \\ **Weeks a** \\ **Mean (SD)** \\ \end{tabular} &
\begin{tabular}{c} **After 8** \\ **Weeks b** \\ **Mean (SD)** \\ \end{tabular} & \(p\)**-Values a** & **Effect Sizes a** & \(p\)**-Values b** & **Effect Sizes b** \\ \hline
20 & 312.1 (33.7) & 327.4 (34.5) & 350.0 (43.2) & 0.341 & 0.45 & 0.004 & 0.58 \\
30 & 365.2 (35.4) & 380.6 (33.6) & 400.9 (39.5) & 0.333 & 0.45 & 0.311 & 0.55 \\
40 & 396.7 (41.8) & 421.9 (43.2) & 443.4 (42.8) & 0.254 & 0.59 & 0.276 & 0.50 \\
50 & 430.1 (37.6) & 450.9 (40.5) & 471.8 (41.6) & 0.309 & 0.53 & 0.298 & 0.51 \\
60 & 448.3 (43.5) & 470.1 (42.4) & 487.4 (45.2) & 0.263 & 0.51 & 0.293 & 0.39 \\
65 & 421.8 (36.5) & 435.3 (40.4) & 449.0 (46.3) & 0.360 & 0.35 & 0.408 & 0.32 \\
70 & 335.5 (36.0) & 348.2 (33.5) & 356.6 (32.7) & 0.414 & 0.53 & 0.570 & 0.25 \\
75 & 246.9 (34.3) & 263.1 (35.3) & 270.1 (31.9) & 0.288 & 0.47 & 0.581 & 0.21 \\ \hline \hline \end{tabular}  
*–Changes in Mean Power before and after 4-Week Training, b–Changes in Mean Power from 4-Week to 8-Week Training.  
\end{table} 
Table 3: Mean power (W) during squats on a BOSU ball prior to and after 4 and 8 weeks of instability resistance training.
## 2 Materials and Methods
### Measuring Spatial Accessibility of the MOH Healthcare Centers Using 2 SFCA Method
Calculating the provider-to-population ratio (\(R\)) for each healthcare provider; thus, for each provider (\(j\)), search all population locations (\(k\)) that are within a threshold travel time (\(d_{0}\)) from location (\(j\)) (that is, catchment area \(j\)), and calculate the provider-to-population ratio, (\(R_{j}\)), within the catchment area:  
\[R_{j}=\frac{S_{j}}{\sum_{k\in\{d_{ij}\leq d_{a}\}}P_{k}}\, \tag{1}\]  
where (\(P_{k}\)) is the population of district (\(k\)) whose centroid falls within the catchment (\(d_{ij}<d_{0}\)), (\(S_{j}\)) is the number of providers at location (\(j\)); and (\(d_{ij}\)) is the travel time between (\(k\)) and (\(j\)). Shortly, this defined the provider-to-population ratio (\(R_{j}\)) within a catchment area. This represents a potential demand for the healthcare provider. Step 2. Calculating the accessibility score (\(A\)) for each population district; thus, for each population location (\(i\)), search all provider locations (\(j\)) that are within the threshold travel time (\(d_{0}\)) from location (\(i\)) (that is, catchment area \(i\)), and sum up the provider-to-population ratios, (\(R_{j}\)), at these locations:  
\[A_{i}^{F}=\sum_{j\in\{d_{ij}\leq d_{a}\}}\ R_{j}=\sum_{j\in\{d_{ij}\leq d_{a} \}}\ \frac{S_{j}}{\sum_{k\in\{d_{ij}\leq d_{a}\}}\ P_{k}}\,\]  
where (\(A_{i}^{F}\)) represents the accessibility at population location (\(i\)) based on the (2 SFCA) method, (\(R_{i}\)) is the provider-to-population ratio at the provider location (\(j\)) whose centroid falls within the catchment centered at (\(i\)) (i.e., \(d_{ij}<d_{0}\)), and (\(d_{ij}\)) is the travel time between (\(i\)) and (\(j\)). A greater value of (\(A_{i}^{F}\)) means a better accessibility at a location. Shortly, the second step allocates provider-to-population ratios to the population. To perform this process, the ID fields were added to attribute tables of healthcare centers and population districts to create the origin-destination (OD) cost matrix. These reference fields actually represent the OriginID and DestinationID in the matrix table. After creating the matrix table, functions such as \"Join\" and \"Sum\" were used to assign the provider-to-population ratios and calculate the accessibility score for each population district. Use of these functions created a series of tables that actually represent the procedures of execution of the 2 SFCA method. However, the results of 2 SFCA will show the level of spatial accessibility to healthcare centers within a catchment threshold. In other words, the provider-to-population ratios will be calculated and summed up to identify and analyze spatial access disparities to healthcare services within the catchment threshold.
## 4 Non-Thermal Plasma  
Non-thermal plasma (NTP) is a novel and emerging antimicrobial tool that demonstrates the possibility of improved biofilm decontamination and sterilization [30; 31; 32; 33]. Plasma is the fourth fundamental states of matter after solid, liquid, and gas. The various ingredients present in plasma such as free radicals, reactive oxygen, and nitrogen species [34], and positive and negative ions [35; 36] are believed to play a significant role as antimicrobial agents. Thermal (equilibrium) and non-thermal (non-equilibrium) plasma are the two plasma types. They are differentiated based on the relative energy levels of electrons and heavy particles of the plasma [37]. A thermal plasma is generated at high pressure and power and contains electrons and heavy particles at the same temperature. In contrast, NTP is generated at low pressure and power, and contains electrons at higher temperature and heavy particles at room temperature [37; 38]. Previously, thermal plasma was used for tissue removal, sterilization, and cauterization [39]. The problem with this type of plasma treatment is the high heat production and damage to tissues and other surfaces. Conversely, NTP can carry out the same functions without causing any such harm or side effects [40]. NTPs such as Dielectric Barrier Discharge (DBD) and jet plasmas are gaining much interest because of their non-thermal nature. It enables new applications in biological and medical fields where substratum to be treated are mostly living tissue, cells, and biomaterials [41]. As listed in Table 2, recent investigations with NTP show promising results for the sterilization and decontamination of biofilms of different bacterial species with this novel technology. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Bacterial Strain**} & **Plasma Type and** & & **Substrate for Biofilm** & \\  & **Parameters (Voltage, Frequency, Power, Working Gas, and Flow Rate)** & **Inactivation Yield** & **Formation with Time** & **Ref.** \\ \hline _Neisseria gonorrhoeae_ & Jet: 10 kV and 10 kHz, He at 2 L/min & & 7 log reduction & Coverslips (20 min) & [42] \\ \hline _Enterococcus faecalis_ & Jet: 18 kV and 10 kHz, Ar/O\({}_{2}\) (2\%) at 5 L/min & No CFU detected & Root canal (10 min) & [43] \\ \hline _Streptococcus mutants_ & DBD: 580 kHz and 2 W/cm\({}^{3}\) power density, He at 2 L/min & & 98\% killed & Tooth slices (30 s) & [44] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Uses of non-thermal plasma (NTP) for biofilm decontamination or sterilization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Plasma Type and**} & \multicolumn{2}{c}{**Substrate for Biofilm**} & \multicolumn{1}{c}{**Ref. **} \\
**Bacterial Strain** & \ \begin{tabular}{c} **Parameters (Voltage,** \\ **Frequency, Power,** \\ **Working Gas, and Flow** \\ **Rate)** \\ \end{tabular}  & **Inactivation Yield** &
\begin{tabular}{c} **In***** **V  
Non-thermal jet plasma devices employing atmospheric pressure plasma are commercially available [63]. kINPen(r) is a pen-like device developed for biomedical applications that allows precise
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 2 Experimental Procedure
### Pulsed and Step Heating Thermography  
These techniques were used only on the defect plate mounted 1.5 m from the IR camera. Despite being mounted near windows, the defect plate was always in a shadow during the experiments. During pulsed thermography, the defect plate was heated by a 2400 W flash lamp and thermal images were recorded at a frequency of 15 Hz immediately after flashing the sample. To heat the surface uniformly, the flash lamp was around 0.3 m from the object with the angle of around 15\({}^{\circ}\) with respect to the normal of the defect plate. The pulsed thermography experiment is depicted in Figure 3. The spatial resolution of thermal images obtained by active thermography was almost 0.5 mm per pixel.  
The step heating thermography experiment, shown in Figure 4, used two 500 W halogen lamps to continuously heat the surface. The sample was heated between 10 and 75 s and the thermal evolution on the surface of the specimen was recorded at a frequency of 15 Hz. Once heating was finished,  
Figure 3: Pulsed thermography experiment.  
Figure 2: Passive thermography experiment.  
thermal decay was recorded with the same frequency. The room temperature was 23 \(\pm\) 2 \({}^{\circ}\)C during the experiment. The thermal contrast associated with the defects could be observed after a few seconds of heating.
## 4 Experimental Results
### Classification Performance  
In order to validate the classification performance, we used a 10-fold cross-validation. With this scheme the set of examples is divided into 10 folds of equal size: nine folds are used to train the learning algorithm, and the remaining fold is used to test it. The entire procedure is repeated 10 times, until every fold is used as test set once. Note that the splitting within each iteration was _stratified_ by diagnosis so as to have approximately the same number of examples from each diagnostic group in each fold. The entire procedure was repeated ten times, with different permutations of the training and test examples, in order to obtain a better generalization of the performance.  
The results obtained are expressed in terms of traditional performance metrics: accuracy, area under the ROC curve (AUC), sensitivity and specificity. In the following, the mean values are reported, averaged over all the cross-validation iterations. Also the standard errors are reported.  
Figure 2 shows the classification performance of the three classifiers. Although quite comparable performance can be observed for all of them, it can be noticed that ANN slightly outperformed SVM  
and RF both in terms of accuracy (i.e., \(0.75\pm 0.01\)) and AUC (i.e., \(0.83\pm 0.01\)). Interestingly, SVM and RF provided a specificity higher than sensitivity; instead, with ANN this trend is reversed as a sensitivity higher than specificity was obtained (i.e., \(0.80\pm 0.02\) and \(0.70\pm 0.01\), respectively). The mean value of sensitivity for ANN was found to be statistically significant different from that obtained by the other two classifiers (\(p\)-value \(<0.0001\) and \(p\)-value \(=0.0003\) in the comparison with SVM and RF, respectively, with the Mann-Whitney U test). Concerning the other performance metrics, no statistically significant difference, at the significance level of 0.01, was found between the three models.  
Another important question arising concerns the agreement between the three classification models on the labels to be assigned to the test examples. In fact, in principle, two distinct models could perform similarly but misclassifying different examples. In order to evaluate the inter-annotator agreement, we calculated the well-known [PERSON]'s \(\kappa\) between the pair-wise predictions. This value ranges from \(-1\) to \(+1\): high values indicate good agreement; zero o lower values indicate chance agreement. We observed a \(\kappa\) of \(\sim\)0.67 between ANN and the other two models and a \(\kappa\) of \(\sim\)0.85 between RF and SVM.
## 1 Introduction
### Significant Approaches towards Acquiring Energy Efficiency
It emphasizes the way the cluster head nodes build up communication among the cooperative nodes in the network to efficiently utilize energy resources in the network. **Energy Efficient Cognitive Radio:** A cognitive radio (CR) is described as a radio that adapts its transmitter parameters to the environment in which it operates [13]. A CR is a smart radio that can dynamically select a wireless communication channel and adapt its parameters of transmission and reception accordingly [11]. The underlying software-defined radio (SDR) technology is anticipated to produce fully reconfigurable wireless transceivers that automatically adapt their communication parameters to network requirements, enhancing context awareness [14]. However, CR needs considerable energy consumption compared to standard appliances due to the enhanced complexity of fresh and complex features [15]. Constructing an energy-efficient cognitive radio WSN is the main challenge in the smart use of battery energy in this context. Recent cognitive radio studies focus on transmitter energy control, residual energy-based channel allocation and network coding and CR combination. Open research problems include the creation of MAC, routing or clustering protocols that take advantage of cognitive radio possibilities.
## 5 Conclusions  
In this study, an enhanced elastoplastic Discretized Virtual Internal Bond (DVIB) model based on a modified Stillinger-Weber (SW) potential was used to analyze tunnel deformation in brittle and quasi-brittle geomaterials under excavation-induced stress conditions. The results confirm that the model effectively captures the coupled processes of plastic deformation and fracture evolution, enabling the independent control of plastic flow and Poisson's ratio through a bilinear force-displacement relationship in the two-body potential, while maintaining angular rigidity in the three-body component. Additionally, fracture-energy calibration in the failure bond length determination ensures mesh-insensitive fracture propagation, enhancing numerical consistency across discretization resolutions.  
These findings establish the SW-DVIB model as a significant advancement in the numerical geomechanics. Unlike previous DVIB-based models, which either impose fixed Poisson's ratios or rely on ideal plastic formulations, this approach provides a unified framework capable of simulating anisotropic deformation and progressive failure mechanisms. Tunnel simulations demonstrate that the Poisson's ratio influences the deformation magnitude and damage distribution, further validating the physical basis of the angular interaction component and reinforcing its importance in tunnel stability assessments.  
Beyond its theoretical contributions, the proposed model offers practical advantages for numerical analysis and underground excavation designs. Conventional continuum-based approaches often struggle to capture discontinuous failure patterns and stress-induced anisotropy, and they require remeshing or predefined fracture paths to simulate fracture evolution. In contrast, this model enables direct calibration using physically meaningful parameters, such as Young's modulus, fracture energy, and Poisson's ratio, making it particularly effective for applications in heterogeneous geological conditions. By offering a computationally efficient and physically consistent framework, this study strengthens the predictive modeling of tunnel stability in brittle rock masses, thereby addressing key challenges in underground engineering.  
Conceptualization, D.K.; Methodology, D.K.; Software, D.K. and A.K.; Validation, S.J.; Writing--original draft, D.K.; Writing--review and editing, D.K., S.J., A.K., and D.M. All authors have read and agreed to the published version of the manuscript.  
This study did not receive external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article. The data are presented in this study are available in insert article.  
The authors declare no conflicts of interest.