esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Hybrid Community Detection Methodology for Social Networks
## 2 Background  
Due to community detection's significant importance and its widespread application in various scientific sectors, affluent graph clustering algorithms and network community extraction methods have been introduced to tackle this computationally demanding and conceptually challenging problem. The profuse amount of research can roughly be distinguished to either classic or hybrid approaches regarding the type of information employed to disclose the graph's subjacent community structure.  
Principally, the classic approaches are iterative processes that traditionally rely on either the elementary network topology properties [1; 2; 3; 4; 5; 19; 20; 21] or the user content information [4; 11; 12; 13]. The major drawbacks of those approaches are on one hand, the heavy computational demands that require the repetitive recalculation of a global similarity measure, which can implicitly be interpreted as the repetitive traversing over the entire network for each vertex calculation, and on the other hand the inaccurate generated hierarchy structure due to the lack of social information context consideration, in the case of network topology oriented approaches.  
On the contrary, the more sophisticated hybrid methods [7; 13; 14; 15; 16; 17; 18; 22; 23; 24; 25; 26; 27; 28] try to combine all the intrinsic information modalities by defining either global probabilistic inference-based models or global heuristic measure-based models to unveil the underlying communities. Even if their generated outcome is considered more accurate and further qualitative comparing to the respective of classic algorithms, since all the different information aspects are taken into account, their application is limited to particularly small networks due to the exhaustive statistical analysis required.
## 3 Test Data Statistics Analysis
### Determination of PLS Correction Power Index of Irregular Lump Samples  
According to the \(I_{\mathrm{s(50)}}\) that was obtained (Table 2), the correction power index \(m\) can be calculated by Equation (4). The fitting relationships between \(\ln(I_{\mathrm{s(50)}}/I_{\mathrm{s}})\) and \(\ln(D_{\mathrm{e}}/50)\) of typical location samples are shown in Figure 6.  
\[m=\frac{\ln(I_{\mathrm{s(50)}}/I_{\mathrm{s}})}{\ln(D_{\mathrm{e}}/50)} \tag{4}\]  
The determination coefficient of \(\ln(F)\)-\(\ln(D_{\mathrm{e}}/50)\) is quite low, but these results are similar to the experimental results of various rocks that had been explored by relevant scholars [7; 28; 29]. Therefore, this study's fitting relationships are reliable.  
The results show that the \(m\) range is from 0.42 to 0.51. Among these, the \(m\) average for lump-structure carbonaceous slate is 0.44, for the layer-structure carbonaceous slate loaded perpendicular to the lamina plane it is 0.46, and for the layer-structure carbonaceous slate loaded parallel to the lamina plane it is 0.49. It can be seen that the \(m\) of layer-structure carbonaceous slate is different because of the different rock structure and different loading direction, but it is close to 0.45, which is suggested by the standard of the ASTM. Based on this study, the equation \(I_{\mathrm{s(50)}}=(D_{\mathrm{e}}/50)^{0.44}I_{\mathrm{s}}\) can be used for calculating the corrected PLS of lump-structure carbonaceous slate, and the equation \(I_{\mathrm{s(50)}}=(D_{\mathrm{e}}/50)^{0.46}I_{\mathrm{s}}\) can be used for calculating the corrected PLS of layer-structure carbonaceous slate in which the loading direction is perpendicular to the lamina plane. The equation \(I_{\mathrm{s(50)}}=(D_{\mathrm{e}}/50)^{0.49}I_{\mathrm{s}}\) can be used for calculating the corrected PLS of layer-structure carbonaceous slate in which the loading direction is parallel to the lamina plane.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Location** & & & & \(P\)-\(D\)\(c^{2}\) **Determination** & **Correction Power** & **In(F)-In(D/50)** \\
**Number** & & & & **\(I_{\mathrm{s(50)}}\)(MPa)** & **Coefficient** & **Index** & **Determination Coefficient** \\  & & & & **R\({}^{2}\)** & **_m_** & **_R\({}^{2}\)_** \\ \hline
1 & & & 0.0635 & 0.8288 & 0.4427 & 0.4144 \\
2 & & & 0.0751 & 0.7329 & 0.4222 & 0.3012 \\
3 & & & 0.1942 & 0.8385 & 0.4548 & 0.4704 \\
4 & & & 0.3052 & 0.8223 & 0.4569 & 0.3667 \\
5-V & & & 0.9328 & 0.8075 & 0.4553 & 0.3713 \\
6-V & & & 0.8911 & 0.8376 & 0.4516 & 0.5719 \\
7-V & & & 1.1196 & 0.8251 & 0.4598 & 0.4058 \\
8-V & & & lamina plane (\(\perp\)) & 1.2503 & 0.8203 & 0.4639 & 0.4505 \\
9-V & & & 1.0424 & 0.8485 & 0.4696 & 0.4769 \\
10-V & & & 0.6416 & 0.7926 & 0.4763 & 0.4126 \\
5-P & & & 0.5877 & 0.7994 & 0.4851 & 0.3468 \\
6-P & & & 0.5101 & 0.6915 & 0.4704 & 0.4036 \\
7-P & & & 0.7100 & 0.7379 & 0.5144 & 0.3288 \\
8-P & & plane & 0.6152 & 0.7943 & 0.4835 & 0.3526 \\
9-P & & & 0.4875 & 0.7486 & 0.4941 & 0.3319 \\
10-P & & & 0.5632 & 0.7784 & 0.4939 & 0.3571 \\ \hline \end{tabular}
\end{table}
Table 2: Results of point load strength testing.
## 2 System Reliability Evaluation Method of a Single Failure Criterion
### System Reliability Estimation Based on Multivariate Copulas
#### 2.2.3 Failure Criterion III: Failure of Any Three Adjacent Components  
Failure criterion IIIrepresents a series-parallel system, which assumes that failure of any three components will cause the whole structure to fail, as shown in Figure 4.  
The system reliability evaluation method of failure criterion IIIis basically the same as the system reliability evaluation method of failure criterion II. The nonlinear correlation among three components  
Figure 4: Series-parallel system composed of multiple sub-parallel systems (sub-parallel system with three components).  
Figure 3: Series-parallel system composed of multiple sub-parallel systems (sub-parallel system with two components).  
in a subparallel system is considered. The relationship between each subparallel system is also considered to be independent of each other. With Equations (1) and (7), the system reliability of failure criterion III(failure of any three components) can be obtained based on the ternary copula function, as follows:  
\[\begin{array}{ll}P_{f_{\text{III}}}&=1-\prod\limits_{i=1}^{n-2}\Bigl{(}1-P \Bigl{\{}Z_{ii}(\mathbf{X})\leq 0,Z_{ij}(\mathbf{X})\leq 0,Z_{ik}(\mathbf{X}) \leq 0\Bigr{\}}\Bigr{)}\\ &=1-\prod\limits_{i=1}^{n-2}\Bigl{(}1-C\Bigl{(}P_{f_{Z_{ii}}},P_{f_{Z_{ij}}},P_ {f_{Z_{ik}}}\Bigr{)}\Bigr{)},\ j=i+1,\,k=i+2.\end{array} \tag{11}\]  
where \(C(\cdot)\) represents the ternary copula function and \(P_{f_{\text{III}}}\) represents the system failure probability of failure criterion III.
# Application Assessment of Electrical Cables during Smoldering and Flaming Combustion
## 3 Results and Discussion
### Toxic Gas Emission during Flaming and Smoldering Combustion  
Diagrams of emission of CO\({}_{2}\), CO, SO\({}_{2}\), NO\({}_{2}\), NO, HCN, HCl, HBr and HF in the chamber during smoldering combustion initiated by the heating field or by the flow of electric current through wires and flaming combustion for each type of cable are shown in Figure 8a-c.  
Based on the above results, it can be concluded that the highest measured concentration of gas was observed for carbon dioxide and that the greatest concentration of carbon dioxide was emitted during the smoldering combustion initiated by the flow of electric current through cable wires in an amount of 950 ppm. In addition, the largest amounts of other toxic gases were released during the smoldering combustion initiated by the flow of electric current through wires. Toxic gases, such as hydrogen fluoride (HF), hydrogen cyanide (HCN) and hydrogen chloride (HCL), were emitted in concentrations of up to 10 ppm. In addition, very toxic gases, such as NO2 and NO, were released in concentrations of up to 8 ppm. Emission of hydrogen bromide (HBr) was noted only for cable B during smoldering combustion initiated by the flow of electric current through wires in an amount of 8 ppm. The observed phenomenon undoubtedly depends on the type of material used for the insulation of copper conductors, cable coatings and cable construction. Smoldering combustion initiated by the flow of electric current through the wires of cables with insulation and with coatings of plasticized PVC causes much higher emissions of CO and HCl compared to cables made of other materials, such as halogen-free, non-plasticized PVC and silicone rubber.  
Figure 8: _Cont._  
Figure 8: (**a**–**c**) Toxic gas emissions for all cable samples as a result of smoldering and flaming combustion. (**a**) Smoldering combustion initiated by the heating field. (**b**) Smoldering combustion initiated by the flow of electric current through wires. (**c**) Flaming combustion.
# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 3 Foundation Model-Enhanced Coastal Land Cover Recognition  
### Category System of Coastal Land Use/Land Cover  
A benchmark dataset concerning coastal land use needs a specific taxonomy that enables describing the characteristics of coastal land covers. Thus, this paper developed a new land use recognition system for coastal land uses by unifying domain taxonomies and benchmark remote sensing datasets. The ontologies include the USGS Land Use and Land Cover Classification System and NOAA Coastal Change Analysis Program (C-CAP) as well as geospatial taxonomies and gazetteers including USTopographic, USGS Geographical Name Information System (GNIS), and OpenStreetMap. Moreover, the benchmark remote sensing datasets include UC Merced (2017), SAT-4/SAT-6 (19), SIRI-WHU (20), RSSCN7 dataset (2020), RSC11 dataset (2020), RSC11 dataset (2030), RSI-CB (2040), WHU-RS19 (25), AID (17), PatternNet (18), and NWPU-RESISC45 (16).  
Above all, the land cover forms selected in our benchmark dataset for coastal land use include airport, beach, circular farmland, cloud, commercial area, dense residential area, desert, forest, freeway, golf course, ground track field, harbor, industrial area, intersection, island, meadow, mountain, overpass, palace, parking lot, pier, railway, rectangular farmland, river, runway, sea ice, ship, iceberg, sparse residential area, stadium, storage tank, tennis court, terrace, thermal power station, wharf, waters, and wetland. Accordingly, we collected 700 images for each land use category from the remote sensing benchmark datasets.  
Figure 2: Architecture of the proposed methodology.
## 5 Massive MIMO
### Classification of Applications  
Massive MIMO is considered a key technology for the 5G and beyond networks because it can significantly enhance spectrum efficiency, reduce frequency spectrum constraints, and completely utilize existing space resources. The mMIMO can be classified into two applications categories smartphones and base stations as shown in Figure 6.
## 3 Experimental Results
### Characteristics of Fracture Propagation
#### 3.1.1 Characteristics of Main Fractures  
The characteristics of main fractures is one of the significant aspects to study the fracture propagation. According to the observation, the main fractures can be divided into three types (Figure 6): (1) type-A is a longitudinal fracture which is parallel to the axial direction of perforation; (2) type-B is a transverse fracture developing in the transition zone of hard-soft and soft-hard rocks which is perpendicular to the axial direction of perforation, and cutting through the whole specimen; (3) type-C is an oblique fracture developing in the upper layer of the specimen which is oblique to the axial direction of perforation and only cuts through part of the specimen.  
Figure 4: A schematic diagram of the SC-CO2 jet fracturing kettle.  
Figure 5: _Cont_.  
Figure 5: The unfolded images of fractures on the surfaces of specimens after experiments. (**a**) Experimental results of group A; (**b**) experimental results of group B; (**c**) experimental results of group C; (**d**) experimental results of group D.  
Figure 6: _Cont._  
In hard rocks (group A), both the main fractures of specimen A1 and A3 were longitudinal fractures, and the longitudinal fractures propagated completely through the specimens and cut the specimens to two pieces (Figure 5a), and the figures of the fractures in group A were approximately symmetrical.  
In soft rocks (group B), longitudinal and oblique fractures were well-developed in three specimens (Figure 5b). The longitudinal fractures terminated when they extended to the oblique fractures. The oblique fractures only developed in the upper layers of specimens and propagated through part of upper layers at the direction oblique to the axial direction.  
In hard-soft rocks (group C), the specimen C1-1, C1-2, and C2-2 had no fractures due to the low injection pressure that had not reached the breakdown pressure of specimens. The specimens with fractures had the common characteristic that the fractures were transverse at the middle of specimens (Figure 5c). The transverse fractures initiated from the perforation tip and extended to the surface along a radial direction.  
In soft-hard rocks (group D), all three types of fractures were created (Figure 5d). All specimens in group D had longitudinal and transverse fractures, and longitudinal fractures terminated when they extended to the transverse fractures, and the transverse fractures propagated through the whole specimens. The oblique fractures were found in the upper layers of specimen D1-1 and D3-2 which propagated through part of the upper layers.  
The fracturing results have been summarized in Table 4. Generally, the fractures developing in soft rocks and soft-hard rocks are complicated, and the fractures in hard rocks and hard-soft rocks are simple.  
Figure 6: The different types of main fractures. (**a**) Type-A, longitudinal fracture; (**b**) type-B, transverse fracture; (**c**) type-C, oblique fracture.
## 4 Results
### Image Feature and Matching Algorithms  
The first test consisted of testing the algorithms with all combinations of images for both test setups and image data set with images larger in size; test images are shown in Figure 14. Tested parameters were the average number of features on images, the number of matched features between images with and without outlier removal, processing time and normalized time for two images and matching between them, and maximum RAM usage of the algorithm. The results provided a good starting reference for algorithm selection and showed the potential benefits and limits of the algorithms, as shown in Table 3. KAZE and ORB algorithms were unable to complete the task on larger image datasets, with default settings, due to the lack of memory (the test computer had 16 GB of RAM) and had to be limited manually to a max of 55,000 features per image. The number of detected features and matched features varied greatly between algorithms. Normalized times for each test image dataset were used to compare the times objectively because the time of processing is dependent on the test computer configuration. Similar tests of the same algorithms done by other researchers [35] showed different results, further stating that the algorithm selection is mostly test scene dependent.  
The second test consisted of testing the matching points from between epoch that are located on the top of the beam, within the ROI. Points were selected manually to match the area of the wooden beam that is modeled as a reference. The test compared the number of used points in ROI and statistical parameters of the calculated residuals, as shown in Table 4. Normalized time was calculated for each test image by dividing all the duration times with the longest duration. Normalized time provides a simpler and more intuitive way of comparing duration than comparing times in seconds.  
Figure 13: Numerically calculated displacement for the beam (Source: Own compilation).  
Calculated statistical parameters of displacement showed no statistically significant difference. For significance testing, a two-sample \(t\)-test was used with a 5% significance level.  
A comparison of the test results from the first and second test showed that the number of features does not correlate directly with the number of usable features for RGB+D. Based on the test results shown in Tables 3 and 4, the ORB algorithm was shown to be the optimal algorithm for this test case, with good computing performance, the lowest processing times and the highest number of usable features detected in the ROI, and was used for image matching and sequential displacement calculation for the test scenes.
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### Pharmaceuticals
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Compounds** & **Matrix** & \begin{tabular}{c} **Extraction** \\ **Technique** \\ \end{tabular} & **Optimal Times** & **Handling** & \begin{tabular}{c} **Recovery** \\ **Accuracy (\%)** \\ \end{tabular} & **LOD** \\ \hline UV filters (2,4-dihydroxy-4-methoxybenzophenone, benzophenone-3, octocycle, and octyldimethyl- & River water and wastewater & SBSE & \begin{tabular}{c} Extraction: 180 min \\ Desorption: 15 min \\ \end{tabular} & Practical & 25-89 & 5-10 & [64] \\ p\_aminobenzenoic acid) & & & & & & \\ \hline Benzotriazole UV stabilizes (UV V P, UV 329, UV 326, UV 327, UV 571, UV 360) & Seawater and wastewater & SBSE & \begin{tabular}{c} Extraction: 120 min \\ Desorption: 20 min \\ \end{tabular} & Practical & 68.4-92.2 & 18.4-55.1 & [71] \\ \hline Personal care products & & & & & \\ (benzotriazole, 2,4-dihydroxybenzophenone, benzopyran|pbarn, 2,4-dihydroxy-4-methoxybenzophenone, benzophenone-3) & Wastewater & SBSE & \begin{tabular}{c} Extraction: 240 min \\ Desorption: 15 min \\ (60 min for PA) \\ \end{tabular} & \begin{tabular}{c} Optimal times \\ depend on coatings \\ \end{tabular} & \textless{}1–80 & 5.0–10.0 & [72] \\ \hline BPA, APs & Seawater & DLIME & \begin{tabular}{c} Extraction: 5 min \\ Centrifugation: 3 min \\ \end{tabular} & \begin{tabular}{c} Without any \\ dispersant agent \\ simplifies the process \\ \end{tabular} & 84–104 & \begin{tabular}{c} 5-30 \\ (LOO) \\ \end{tabular} & [74] \\ \hline APs & Wastewater & HF-LPME & Extraction: 30 min & 97–109 & \begin{tabular}{c} 100 \\ (LOO) \\ \end{tabular} & [75] \\ \hline PFOS and PFOA & Surface water and wastewater & IT-SPME & 25 min & \begin{tabular}{c} Lower handling \\ 40 samples/day \\ \end{tabular} & 81.1–85.4 & 1.5–3.2 & [80] \\ \hline PFOS and PFOA & River water & SPME & \begin{tabular}{c} Extraction: 60 min \\ Desorption: 15 min \\ \end{tabular} & 88–120 & 2.5–7.5 & [81] \\ \hline PFOS & Tap, river and well water & VALIME & \
\begin{tabular}{c} Extraction: 2 min \\ Centrifugation: 2 min \\ \end{tabular} &
\begin{tabular}{c} Not require the use of \\ certain sample \\ preparation apparatus \\ \end{tabular} & 90.8–105.1 & 1.6 & [82] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Microextraction techniques to determine UV filters, alkyphenols, bisphenol A and PFCs in environmental water samples by liquid chromatography-tandem mass spectrometry.
## 3 Applications of Smooth Particle Hydrodynamics in Tunnel and Underground Engineering
### The Cutterhead Cuts Soil during Tunnel Construction  
In relation to the process of tunnel construction, [PERSON] et al. [22] simulated the cutting process and the interaction between the tool and both non-cohesive and cohesive soils using a shear failure model combined with a contact algorithm. The simulation results for non-cohesive and cohesive soils under various operating conditions were analyzed separately and then compared.  
This paper provides a comparison between the SPH simulations and the experimental results for non-cohesive soils. As shown in Figure 9b, the simulation outcomes closely match the experimental results in Figure 9a. The numerical model, as depicted in Figure 9c, successfully replicates the process observed in the experiment. The number and shape of the small blocks chipped off by the blade align well with the experimental findings. Additionally, the model accurately represents the zigzag surface of the cracked cohesive soil mass and the detailed features at the tips of the chipped soil blocks. These results indicate that the model is effective in predicting the cutting behavior of cohesive soils. The model suggests that shear failures are most likely to occur in regions where plastic strains have accumulated over time. To mitigate tensile instability in all cohesive soil simulations, an artificial stress method was applied. One of the key advantages of numerical simulation is its ability to analyze microphysical quantities, such as stress and strain, which are challenging to measure experimentally.  
Figure 8: Deformation of the retaining wall system: (**a**) the evolution of the horizontal displacement at the top of the retaining wall, and (**b**) the displacement profile at 3.0 s.
## 2 Materials and Methods
### Data Processing  
The motor task was divided into two phases: the \"Pickup\" phase, which starts from ball grabbing to reaching the throw position, and the \"Pass\" phase, which covers the motion from the hand far back to the completion of the throw. XSENS Analyze software was used to identify valid trials (highest hand velocity [25; 26]) and specific events defining the different stances of interest.  
Primarily, the data from the different exercises were separated in a custom MATLAB script (v R2020a, The MathWorks, Natick, MA, USA) [7; 17]. The phases were extracted from the data obtained during the data collection and normalized using 3 points of interest. The initial point was defined as the beginning of the stance, set at the 0% frame, while the endpoint of the stance was established as the 100% frame. An additional point was used to define the 50% frame of the stance. The different points used are referenced in Table 1 and Figure 1.  
For the purpose of this study, kinematics for shoulder, elbow, and trunk were kept and analyzed. The joint coordinate system used in this study adhered to the recommendations set forth by International Society of Biomechanics (ISB), wherein positive rotations are defined as shoulder abduction, internal rotation, flexion, elbow flexion, and internal rotation (pronation) [27].  
For each phase, the peak values defined as the maximum rotation value, the minimum rotation value, and the range of motion (ROM) were computed. The whole data processing was carried out in MATLAB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase of the Movement** & **\% of the Overall** & **Event** & **Implementation** \\ \hline  & 0 & Player starts to move & 40\% of the mean CoM speed between frame 1 \\ Pickup & 50 & Ball grab & Minimum CoM position along the z axis \\  & 100 & Player getting in & Mean CoM position between the maximum \\  & & throw position & and the minimum CoM positions \\ \hline  & 0 & End of the pickup & End pickup \\ Pass & 50 & Hand far back (Start of throw) & Elbow further back \\  & 100 & Ball thrown & Maximum hand position along the \\  & & x axis + 50 ms \\ \hline  & & & \\ \end{tabular} Note: center of mass = CoM.  
\end{table}
Table 1: Description of the different phases of the movement and their implementation in the MATLAB scripts.