esa-sceva/satcom-chunk-collection huggingface dataset:
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 1 Introduction
Lastly, to accurately handle nodes that may be misclassified as noise but actually have specific connection patterns, we propose a distance-based structural similarity method. This method calculates the structural similarity between noise points and the existing clusters, reassigning the noise points to the cluster with the highest structural similarity. Our main contributions can be summarized as follows:  
1. ADPSCAN: We propose a structural graph clustering algorithm that utilizes adaptive density peak selection to significantly reduce the dependency on parameters \(\epsilon\) and \(\mu\), characteristic of traditional SCAN algorithms. Our method utilizes a distance-based structural similarity metric to accurately reclassify noise nodes. Our method not only enhances the flexibility of the clustering process but also boosts the algorithm's adaptability to diverse datasets. 2. Adaptive Density Peaks Selection: We propose a method that utilizes the generalized extreme value distribution to automatically select density peaks from decision graphs, overcoming the limitations of existing density peak-based structural graph clustering algorithms that require manual selection of cluster centers. 3. Noise Re-clustering: To address the issue of misclassifying noise nodes, we propose a distance-based structural similarity method that effectively reassigns noise nodes to the correct clusters. 4. Experimental Validation: We conduct extensive experiments on various real and synthetic network datasets. The results demonstrate that our approach significantly enhances clustering accuracy.
## 3 Results and Discussion  
### Bio-Mixture Effect on Soil Physical Properties  
Soil supplementation with bio-mixture had non-negative effects on most of the physical characteristics of the soil in both depths (0-20 cm, 20-40 cm) (Table 3). More specifically, bio-mixture amendments substantially reduced the bulk density of the soil, as a result of the lower bulk density of the bio-mixture and the differences between the soil and the treatments being significant (\(p<0.05\)), without existing differences between the bio-mixtures treatments. The reductions compared with the non-amended soil (C) at the first depth (0-20 cm) were 16%, 20% and 15% for 1% biomix, 2% biomix and 3% biomix, respectively. The reductions compared with the non-amended soil (C) at the second depth (20-40 cm) were 17%, 13% and 12% for 1% biomix, 2% biomix and 3% biomix, respectively (Table 3). Additionally, there was an expected increase of 13% in saturated water content of the amended soils in the first depth (0-20 cm), due to the larger porosity of the bio-mixture, and a slightly smaller increase of 10% in the second depth (20-40 cm), as soil samples did not contain bio-mixture. In both depths, the increase of saturated water content in bio-mixtures treatments in relation to control soil was also significant (\(p<0.05\)). It was also observed that the available water content (difference between water content at field capacity and permanent wilting point) decreased at depth 0-20 cm from 8.8% to 8.1%, 5.9% and 5.8% for 1% biomix, 2% biomix and 3% biomix, respectively. A similar trend was pointed out at depth 20-40 cm, as a decline in the available water content was observed from 8.9% to 8.3%, 6.3% and 6.2% for 1% biomix, 2% biomix and 3% biomix, respectively. Significant differences (\(p<0.05\)), wherever they exist, between the treatments for field capacity and permanent wilting point, are presented in Table 3. Results indicated that greater effects were found in the upper layer (0-20 cm depth), while at the 20-40 cm depth the same trend was observed, nominating that soil particles' movement took place downwards.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Depth (cm)**} & \multirow{2}{*}{**Treatment**} & **Saturated Water** & **Field** & **Permanent** & **Bulk** \\  & & **Content (\%)** & _v/v_ & **Capacity (\%)** & **Wilting** & **Density** \\  & & & _v/v_ & **Point (\%)** & **(g/cm\({}^{3}\))** \\ \hline \multirow{4}{*}{0–20} & C & 45.6 b & 33.8 a & 25.0 a & 1.63 a \\  & Biomix 1\% & 51.6 a & 32.0 ab & 23.9 a & 1.37 b \\  & Biomix 2\% & 50.3 a & 29.7 b & 23.8 a & 1.30 b \\  & Biomix 3\% & 51.6 a & 29.9 b & 24.1 a & 1.38 b \\ \hline \multirow{4}{*}{20–40} & C & 47.0 b & 33.6 a & 24.7 ab & 1.58 a \\  & Biomix 1\% & 52.3 a & 30.7 ab & 22.4 b & 1.31 b \\  & Biomix 2\% & 51.1 ab & 29.2 b & 29.9 ab & 1.37 b \\  & Biomix 3\% & 52.1 a & 32.4 ab & 26.2 a & 1.39 b \\ \hline \hline \end{tabular}
\end{table}
Table 3: Saturated water content, field capacity, permanent wilting point and bulk density changes in the samples of the four treatments \({}^{2}\).
## 6 Comparative Analysis  
In addition to the literature review of past works presented in Section 2, we also reviewed some other significant works in the specific field of CC and BD integration, with the aim of security. This section presents a comparative analysis of the related works that we have distinguished in the following paragraphs. Initially, we analyzed what each of them deals with, presented from the oldest to the most recent.  
[PERSON] et al. [28] proposed D2 ES, which is a new data encryption approach, focused on selective data encryption regarding privacy classification schemes under time limitations. [PERSON] et al. [29] proposed an algorithm with adaptive task allocation concerns that is used in heterogeneous Cloud infrastructures to decrease the makespan of Cloud infrastructure and achieve an energy-efficient environment. [PERSON] et al. [30] proposed an SDN-based BD management scenario in regard to optimized network resource consumption, such as network bandwidth and data storage units. Finally, [PERSON] et al. [31] proposed MOPA in regard to the problem of scheduling workflow under security constraints, along with simultaneously reducing the execution time and the cost for BD applications in a Cloud infrastructure.  
Table 7 shows that most of the related works involved and tried to solve the challenges related to _Computation (Processing) and Analysis_. Furthermore, most of the former related works dealt with _Privacy_, _Security_, and _Management_. The challenges that were not a main research interest were _Energy Efficiency_ and _Access Control_. Consequently, we can conclude that there are many open issues in the field of CC and BD integration that need to be solved. To sum up, there is a need for more research in the area of Security and Management of BD in a CC environment, and thus, we propose a new encryption method of the data in a sustainable Cloud environment that will deal with and solve challenges such as the Management and Security of data, which, in many cases, are IoT-based Big Data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Big Data and Cloud Computing** & & & & & \\
**Integration Challenges** & & & & & \\ \hline [PERSON] et al. [28] & \(\surd\) & \(\surd\) & & & \\ \hline [PERSON] et al. [29] & & & & \(\surd\) & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Related work comparison.  
Figure 7: Performance of data processed (megabytes) comparison of the AES, RC5, RSA, and proposed model.
# Seismic Response Analysis of Buried Nuclear Power Plant Structures in Non-Bedrock Sites
## 1 Introduction
### Embedded Nuclear Power Plant Structure  
The embedded nuclear power plant structure is composed of four integrated components: a steel containment structure, auxiliary building, shared mat foundation, and a spatial steel truss system. As depicted in Figures 1 and 2, the facility measures78 m (length) \(\times\) 58.85 m (width) \(\times\) 61.6 m (height) with a designed embedment depth of 37.2 m, positioning the containment top at ground level while maintaining the main structure underground. Finite element modeling was implemented using S4/S3 shell elements (1.1 m/0.93 m mesh) for containment/auxiliary structures, C3D10 tetrahedral elements (1.11 m/2.2 m mesh) for foundation/internal components, and T3D2 truss elements for the steel framework. Tie constraints were systematically applied at structural interfaces including truss-roof connections, foundation-structure junctions, and containment-internal component bonds.  
The concrete grade is C45 (Poisson's ratio of 0.17), and the steel used is Q235 steel (Poisson's ratio of 0.3).
# Characterization and Clinical Trial of an Innovative High-Speed Lancing Device
## 3 Results and Discussion  
### Characterization of Innovative High-Speed Lancing Device  
Figure 4a presents the 3D structurally expanded view of the new lancing device. Figure 4b presents a simulation of its 3D assembly. The actual dimensions of the finished product are a length of 105 mm, an outer diameter of 16 mm, and a mass of 15.5 g.  
Following the accelerometer experiment, the measured accelerations of the lancet after the lancing devices are used to prick fingers are compared, as presented in Figure 5a. Figure 5a reveals that the acceleration of the conventional lancing device is 0.236 m/s\({}^{2}\), and the lancet clearly vibrates four times as the spring vibrates. The high-speed lancing device has an acceleration of 2.23 m/s\({}^{2}\), which is 9.4 times that of the conventional lancing device, and its lancet reaches a stable state very rapidly after the first piercing. Integrating the acceleration signals with respect to time in Matlab yields velocity in the time domain. As presented in Figure 5b, the maximum velocity with which the conventional lancing device pierces the skin is 1.468 m/s, and the maximum velocity at which the high-speed lancing device does so is 2.62 m/s, which is 1.78 times the former. From here, the developed high-speed lancing device has an iron cube of greater mass to strike the lancet holder, so the lancet pierces at a higher speed. Integrating velocity with respect to time yields the relationship between the displacement of lancet and time, which is plotted in Figure 5c. In Figure 5c, the displacement of 5.5 mm corresponds to the position where the lancing device, attached to a rotational cap, comes in contact with the skin. As revealed by the figure, the conventional lancing device pierces the skin three times, and the lancet  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables (Age)** & **Number (\(n\))** & **\%** \\ \hline Below 20 & 10 & 10\% \\
21–30 & 30 & 30\% \\
31–40 & 23 & 23\% \\
41–50 & 24 & 24\% \\
51–60 & 10 & 10\% \\ Above 61 & 3 & 3\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Age distribution of the participants taking clinical trials of pain (\(N\) = 100).  
Figure 4: Innovative high-speed lancing device: (**a**) structurally expanded view; (**b**) schematic 3D assembly.  
remains in the skin for a total duration of 0.018 s. The lancet of the high-speed lancing device pierces the skin only once, and remains in the skin for only 0.004 s, which is 0.014 s less than the former value. Since the lancet of the new high-speed lancing device pierces the skin only once and stays in the skin for a much shorter period than does that of the conventional device, the pain is expected to be much less and the healing time of the wound shorter. The commonly marketed lancing device is sold at USD 13-50. The prepared high-speed lancing device, due to its simple structure, has its cost lower than conventional lancing device, but its functions surpass all other lancing devices produced by internationally famous pharmaceutical company.
## 2 Results and Discussion
### Understanding of the Reaction Mechanism  
To have better understanding of the reaction pathway, several control experiments were performed. Initially, the reaction of styrene was performed without using oxidant and \(o\)-aminobenzamide, no oxidation product was detected in GC showing the starting material only. Whereas, an oxidation reaction of styrene under optimized reaction conditions (without \(o\)-aminobenzamide) afforded the expected benzaldehyde in 78% GC yield (Figure 5). Based on the above experiments a plausible reaction mechanism for the synthesis of quinazolinones from styrene and \(o\)-aminobenzamide is proposed (Figure 5). It is established that benzaldehyde is an oxidation product of styrene which on condensation with \(o\)-aminobenzamide afforded imine intermediate that eventually, through cyclization-oxidation afforded the quinazolinone.  
Figure 4: Synthesis of benzothiadizine-1,1-dioxides: substrate scope \({}^{a}\).
# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 4 Employment Scam Detection & Relevant Problems
### Wikipedia Vandalism  
Crowdsourced online encyclopedias like Wikipedia are susceptible to vandalism; in other words, blatantly unproductive false edits that undermine entries credibility and integrity, thereby forcing administrators to manually amend the content. Reported incidents vary from easily spotted vulgar language to inconspicuous alterations in articles such as placing arbitrary names in historical narratives and tampering with dates.  
The Wikipedia platform provides access to full revision history where spiteful alterations of the context may be located easily, and if necessary reverted. Metadata such as whether an article has been edited anonymously may be indicative of cases of vandalism, while the contributions of eponymous users (or users with a static IP address) are stored and can be analyzed to discover systematic attempts to undermine platform's objectiveness. Thus, the reputation of a user inside the platform as well as the extent of alteration of an article across time may serve as additional strong signs of ill-motivated content.  
The proposed solutions combine the aforementioned characteristics with Natural Language Processing (NLP) and machine learning classification. [PERSON] et al's. preliminary study [5] and PANWikipedia vandalism corpus using Amazon's Mechanical Turk [6] set a common ground for researchers working in the field. [PERSON] and [PERSON] [46] proposed a shallow syntactic semantic modelling based on topic specific n-tags and syntactic n-grams models trained on web search results about the topic in question. Other researchers such as [PERSON] et al. [47] trained an active learning statistical language model to address existing incomplete datasets and suggested a three type taxonomy of vandalism instances: (a) \"Blanking\" or \"Large-scale Editing\", defined as a 90% difference in context length between two consecutive revisions, (b) \"Graffitti\", namely the insertion of unproductive, irrelevant or unintelligible text and (c) \"Misinformation\" that involves changes in existing entities such as names, brands or locations. [PERSON] et al. [48] boosted vandalism detection by using stylometric and sentiment analysis. In particular, their study was based on the fact that Wikipedia authors strive to maintain a neutral and objective voice in contrast to vandals who aim at polarization and provocation. Meanwhile, latest researches [49] based on spatial (e.g., edit timestamp, revision length, user registration timestamp) and temporal features (e.g., geographical location, country reputation, content categories) resulted in lightweight and robust solutions yet to be thoroughly field-tested and evaluated.
## 3 Results
### Main Topics in Laboratory-Scale Experimentation of UHS
#### 3.2.2 Main Fluid Types
Assuming that H\({}_{2}\) can penetrate the rock mass, it could displace the interstitial brine acting as the non-wetting phase in a two-phase flow. The entry pressure, necessary for the gas to invade the medium and replace the pore liquid, is defined by the gas/liquid interfacial surface difference, which is a function of the surface tension and the pore size. The flow pattern of hydrogen distribution in the partially desaturated pore space will mostly depend on the so-called mobility number (i.e., the viscosity ratio of the displaced fluid to the displacing gas) [72], as the capillary number is quite insignificant due to the small viscous forces of hydrogen. Due to its low density, hydrogen's viscosity is one or two orders of magnitude lower than brine viscosity, which increases the probability of viscous fingering between these two phases during underground storage and transport [30]. Viscous fingering results in an increased mixing of the H\({}_{2}\) and brine phases, which decreases the effectivity of H\({}_{2}\) recovery. [PERSON] [58] determined capillary pressure and relative permeability parameters under two sets of underground hydrogen storage conditions (a \"shallower\" porous media reservoir at 55 bar and 0 \({}^{\circ}\)C and a \"deeper\" one at 100 bar and 45 \({}^{\circ}\)C) using a lower Triassic sandstone as a reservoir and hydrogen-water fluid pair, while [PERSON] et al. [73] used numerical modelling to provide these parameters for the hydrogen/brine/sandstone system under different reservoir conditions. [PERSON] et al. [74] measured parameters (i.e., wettability and rock-fluid interfacial tension) influencing fluid flow in caprocks. The wettability of the rock determines the affinity of hydrogen or water to adhere to the pore surfaces of the rock. A rock with high wettability for water can inhibit hydrogen movement, favoring pore water saturation [24]. Laboratory measurements show very low H\({}_{2}\)-wettability values [75], which means minimal residual trapping of this gas. The wettability of a given rock to H\({}_{2}\) depends on the temperature, pressure, salinity, and organic surface concentration, and the contradictory conclusions published in the literature call for further research on this parameter [69]. [PERSON] et al. [76] provide a comprehensive review of the wettability parameter in the context of UHS. To investigate these processes in a controlled manner, laboratory tests in autoclaves simulate pressure and temperature conditions similar to those found in underground reservoirs (reservoir and seal). These tests allow studying how hydrogen interacts with the rock matrix, considering wettability, permeability, density, viscosity, and friction angles, among others. In addition, these experiments allow observation of gas migration in relation to capillary interactions and possible gas losses (e.g., low viscosity) across geological barriers, providing critical data for assessing leak risk and long-term storage capacity.
## 2 Materials and Methods
### Characterization of Material Structure and Glass Transition  
Dynamic Mechanical Analysis (DMA) was used to characterize the structure of 3D-printed polyurethane-based aligners. The tests were conducted on a TTDMA (Triton Technology/Mettler Toledo, Columbus, OH, USA). DMA is the most sensitive known method for tracking changes in material structure [32]. The mentioned TTDMA device can conduct measurements with a frequency from 0.001 to approximately 500 Hz, over a temperature range of \(-190\)\({}^{\circ}\)C to 600 \({}^{\circ}\)C. For project purposes, testing was conducted at a frequency of 1 Hz to facilitate comparison with literature-published data, and the temperature interval was determined according to the needed material characteristics, 20 to 100 \({}^{\circ}\)C. This method measures the damping of mechanical vibrations due to a phase shift \(\delta\) between stress and strain (loss factor, tan \(\delta\)) in the material over a specified temperature range during constant heating and/or cooling rate, and the corresponding complex modulus of elasticity E* = E\({}^{\prime}\) + iE\({}^{\prime\prime}\). The complex modulus is time or frequency dependent and consists of a real component E\({}^{\prime}\), which is the storage modulus, and the imaginary component E\({}^{\prime\prime}\), which is the loss modulus. The former represents the energy stored in the material during the dynamic loading, and the latter represents the dissipated energy. The loss factor tan \(\delta\) is equal to the ratio of E\({}^{\prime\prime}\)/E\({}^{\prime}\). In the case of pure elastic behavior, the storage modulus equals the elastic modulus and the \(\delta\) is equal to zero. Although the E\({}^{\prime}\) is time dependent, it is comparable to the elastic modulus measured at the tensile test machine and is a useful parameter for the characterization of the materials. During dynamic loading, deformation amplitudes are very small to assure anelastic behavior, i.e., there is not any plastic deformation of materials, and the cycle can be repeated several times if justified [33]. Any change in structure is reflected in the mechanical spectrum. At low temperatures, changes in the part of the structure with high eigenfrequencies, such as rotations of side groups, are monitored, while at higher temperatures, changes in whole chains or segments of polymer chains are tracked, allowing a precise monitoring of the glass transition. Testing was conducted on printed specimens in the form of a cuboid, with a maximum width of 3 mm, a thickness of 1 mm, and a length of 12 mm. The distance between the clamps was 7.5 mm. Due to the complexity of measurements and the detailed analysis of results, the justification for repeating cycles was determined based on the results of the first cycle for individual samples. Although the DMA method is sensitive to changes in the material, it requires measurements on samples of defined dimensions and sufficient rigidity, thus it is limited to temperatures below the melting point.
## 4 Experiments and Analysis of Results
### Experimental Settings  
#### 4.3.1 Experimental Environment and Model Parameters  
The experimental environment was as follows: the Windows 10 operating system, Python 3.8, PyTorch 1.8.1, and CUDA 11.1, with an NVIDIA Tesla V100 16 GB GPU. The model parameters were set as follows: the pre-trained language model used was RoBERTa-wwm-ext, which included 12 transformer layers and a 768-dimensional hidden layer. The Adam optimizer was employed, with three heads included in the multi-head self-attention mechanism. The batch size was 4, the learning rate (lr) was defined as \(1\times 10^{-5}\), Dropout was 0.2, and the maximum sequence length was 200.
# Cloud Seeding Evidenced by Coherent Doppler Wind Lidar
## 3 Verification Experiment
In detail, Figure 3d-h exhibit Doppler spectra with distinct double-peak distribution, which are easy to separate into aerosol and raindrop components with double-peak fitting. Figure 3i,j presents biased unimodal structures with spectrum broadening, in which overfitting may occur and cause misleading results for too many freedom degrees in double-peak fitting. To address it, it is necessary to reduce the scope of the fitting solutions by restrictions. Union double-peak fittings are applied with fixed \(f_{a}-f_{r}\) to separate aerosol and rain components for these overlapped spectra at the same altitude, because the main difference of Doppler velocity is caused by vertical velocity. Figure 3k,l shows biased single-peak Doppler spectra with small spectrum broadening, indicating that the signal intensity of one component is significantly weaker than that of the dominating component. The hidden peak is searched for using the residual spectrum between the observed spectrum and a Gaussian spectrum [46]. Compared with spectra shown in Figure 3d-j, the spectrum broadening is not obvious in Figure 3g,h,k,l, while the normalized skewness significantly deviates from 0. The verification experiment indicates that the CDWL shows a good performance in power spectra measurement. Different types of components can be accurately identified and separated by multiple parameters derived from lidar spectra, which are useful for studying aerosol, precipitation, and clouds.