esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of Microcapsules
#### 3.2.6 Antimicrobial Activity  
The antimicrobial activity of all emulsions and dried powders was tested on two foodborne bacteria, _Escherichia coli_, a Gram-negative bacteria, and _Listeria innocua_, a Gram-positive bacteria, as a surrogate of the pathogenic _Listeria monocytogenes_. The emulsions were added to wells created in media inoculated with one of the test bacteria. After 24 h, the inhibition zones were measured. Emulsions containing sunflower oil instead of TC (control) showed no inhibition zone. The diameters of the inhibition zone for the different emulsions are shown in Figure 4. All emulsions and dried powders exhibited antimicrobial activity on both tested bacteria. In addition, all samples showed a smaller inhibition zone against _E. coli_ than against _L. innocua_. This result can be explained by the fact that Gram-negative bacteria have an additional hydrophilic membrane embedded with lipopolysaccharideacharide molecules that prevent the penetration of hydrophobic compounds such as essential oils. Among the emulsions, L3 and L7 showed larger inhibition zones than those emulsified using pea protein (P3 and P7). This result is consistent with that of particle size. Indeed, the emulsions prepared with lecithin exhibited smaller sizes than those emulsified with pea protein. This smaller size could provide a higher active surface area and efficiency of EOs to destroy the cell wall of microorganisms [1]. The antimicrobial activity of emulsions prepared with lecithin was reduced after drying. In contrast, powders containing pea protein showed relatively the same antimicrobial activity as their emulsion before drying. Among the spray-dried powders, L3S and L7S exhibited larger inhibition zones than P3S and P7S. This could also be attributed to the smaller particle size of the emulsion (0.09 \(\upmu\)m) at pH 7 prepared using lecithin rather than the particle size of the emulsion prepared at pH 3 (0.11 \(\upmu\)m).  
Different results were obtained for freeze-dried powders. P3L and P7L, containing pea protein, exhibited a larger inhibition zone compared to freeze-dried powders containing lecithin (L3L and L7L). This can be explained by the effect of encapsulation efficiency. Because lecithin-emulsified emulsions had lower encapsulation efficiency, the antimicrobial activity of the freeze-dried lecithin-containing powders was changed. In addition, the powders were prepared at different pH values. This parameter did not have a significant effect on the diameters of the inhibition zone. One can conclude that the pH of emulsions before drying had no significant effect on the antimicrobial activity of powders after drying. In addition, the drying methods applied in this study successfully preserved the antimicrobial activity of _trans_-cinnamaldehyde. Spray-drying and freeze-drying processes could provide the possibility of fabricating antimicrobial powders that can be applied for food preservation applications.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Dead Sensor Nodes  
The first constraint in WBANs is reducing energy use. In this section, we evaluate the proposed method's performance using the number of dead nodes as a metric. By selecting CHs to send biological data to specific gateway nodes, the proposed cluster routing protocol plays a vital role in balancing power consumption among biomedical sensors. CHs are chosen in each round based on two significant parameters: distance and energy. Figure 8 compares the proposed algorithm and existing algorithms for dead sensor nodes.  
Figure 6: Throughput analysis.  
Figure 7: Residual energy analysis.  
This functionality considers network statistics such as the node's average bit rate, the distance to the base station, and the remaining battery life. However, the fitness function must consider the dependability of transfer among nodes. As a result, the likelihood of biological data transfer and retransmit failure is high. This results in increased power consumption, which reduces the network's lifetime. This study defines the most beneficial function and considers various metrics to enhance the effectiveness and grid lifespan.
## 1 Introduction
### This Paper's Significance and Contribution  
As technology becomes increasingly intertwined with our daily lives, it is imperative to protect the integrity of networks. This study highlights the importance of addressing Sybil attacks and their potential consequences. The proposed algorithm, powered by robust machine learning techniques, contributes to global efforts to enhance network security in the face of ever-evolving cyber threats. Regarding the remainder of this article, Section 2 provides a comprehensive review of the literature on cybersecurity, network security, Sybil attacks, machine learning, social network security, and algorithmic and machine-learning-based detection. Our research aimed to investigate how the SybilSocNet algorithm can effectively identify and mitigate Sybil attacks in online social networks (OSNs) by leveraging a combination of machine learning techniques, including nearest neighbors (KNN), support vector machine (SVM), and random forest, via the extraction of patterns from raw data preprocessed as matrices.  
Section 3 outlines the details of our algorithm. We begin by preprocessing the raw data to create matrices containing relevant information that can be used by the machine learning algorithms to detect patterns. To manage the large dataset efficiently, we divide the raw data into seventy-one smaller matrices rather than working with a single, massive matrix, which would require significant computational resources. This approach allows us to process the data using workstations instead of a supercomputer. Interestingly, the combined size of these seventy-one smaller matrices is still substantially smaller than the size of a single, large matrix, without compromising the valuable information needed for the machine learning methods.  
Section 3 explains the research methodology applied in this study, followed by Section 4, presenting an analysis of the collected secondary open-source data. Section 5 discusses the findings, as well as reports on implications and limitations, and concludes with some open questions for future research.
## 2 Islanding Detection Methods  
There are a number of essential aspects that can be used to assess how well islanding detection approaches work. The non-detected zone (NDZ) is the most prevalent feature, while the Q factor is the second. Due to the disparity in power supply and load, NDZ is regarded as the region where islanding cannot be identified. The islanding detection approach fails to detect the islanding during the required time interval if the mismatch of active and reactive power between the DG-generating power and load-consuming power (\(\Delta\)P, \(\Delta\)Q) is too small [2]. The ratio of the maximum stored energy to the energy lost every cycle at a specific frequency, multiplied by two (\(\pi\)), is known as the Q factor and mathematically calculated as:  
\[\text{Q}=\text{R}\,\sqrt{(\text{C}/\text{L})}, \tag{1}\]  
where, R, C, and L are the resistance, capacitance, and inductance of load respectively.  
The Q factor can be affected by the local load and when its value is high, the time of islanding detection can be delayed. According to the load type, non-linear loads cannot make islanding identification harder since they emit continuous power and current harmonics. The relationship between the Q factor and NDZ is proportional as presented below:  
\[(\text{V}/\text{V}_{\text{max}})^{2}-1\leq(\Delta\text{P}/\text{P})\leq( \text{V}/\text{V}_{\text{min}})^{2}-1, \tag{2}\]  
\[\text{Q}\,(1-(\text{f}/\text{f}_{\text{min}})2)\leq(\Delta\text{Q}/\text{P}) \leq\text{Q}\,(1-(\text{f}/\text{f}_{\text{max}})2), \tag{3}\]  
where, fmin, fmax, Vmin, and Vmax are the minimum and maximum frequency/voltage respectively. P and V are the nominal active power and voltage, while Q is the Q factor.  
Hence, if the Q factor is high, then NDZ can be larger. Therefore, for efficient and accurate islanding detection, NDZ and Q factor value must be reduced [1].  
The classification of islanding detection methods has several categories, local (passive, active, and hybrid methods) and remote methods, signal processing based methods, and computational intelligent based methods, as shown in Figure 2. The description of each method is presented below:  
Figure 2: Islanding Detection Methods Categories.
## 4 Discussion  
This study simulated meteorological field at different heights over the Yellow Sea and its surrounding areas in October 2021 using the WRF model at 50 m, 70 m, 90 m, 150 m, 220 m, and 300 m. By comparing the simulated results with observations from nearby meteorological stations, the rationality of the chosen WRF parameterization schemes and the applicability of the WRF model in the study area were validated. Furthermore, the usability of these two retrieval products was demonstrated by comparing the WRF simulation data with two ocean satellite-derived wind speed products. Figure 11 represents the flowchart of this study. This study analyzed the vertical wind profiles of the entire atmospheric column during the study period, as well as the average wind fields at different heights over the near-sea surface, and calculated the distribution of wind power density over the near-sea surface. These results provided the necessary theoretical support for selecting offshore wind farm locations. Additionally, this study analyzed the performance of the HY-2C and CFOSAT ocean satellites over the Yellow Sea in October 2021 and presented a qualitative relationship between satellite-retrieved surface wind speeds and high-altitude wind power density. In the following sections, a comparative analysis will be conducted between the ocean's ten-meter wind speeds derived from the satellite inversions and ERA5 data. Additionally, this study will present the outlook for future research.
# Deformation Characteristics of Mechanical Seal Face under Alternating Loads
## 3 Results
### Alternating Working Conditions
#### 3.2.1 Alternating Medium Pressure  
The thermal-mechanical coupling deformation trend of the seal ring under the alternating medium pressure is similar to the results in the stable state shown in Figure 2. The contact wear occurs at the inner diameter of the seal face and there is a convergent gap at the outer diameter of the seal face. The thermal-mechanical coupling deformation of the whole rotating ring is also larger than that of the stationary ring under the alternating medium  
Figure 5: Radial distribution of axial displacement of the end face of the rotating ring under different rotational speed conditions: (**a**) Thermal deformation; (**b**) Thermal–mechanical coupling deformation.  
pressure due to the smaller elastic modulus of the rotating ring. The axial displacement of the end face of the rotating ring along the radial direction under alternating medium pressure with different amplitudes and periods is described in Figure 6. From this figure, it can be known that the deformation trends of the end face of the rotating ring based on different amplitudes are close to that of the results with different periods. They are all in a convex shape, and the convex position is closer to the inner diameter of the end face, which may cause this position to be severely worn. This coincides with the results in the stable state shown in Figure 4.  
In Figure 6, the amplitude and period of the alternating medium pressure have little influence on the coupling deformation of the inner diameter region of the end face of the rotating ring. However, the axial displacement near the outer diameter of the end face increases slightly as the amplitude of the alternating medium pressure increases, as shown in Figure 6a. This is due to the increased temperature difference between the inner and outer diameters and increased sealing pressure as the increasing amplitude. When the amplitude \(M\) is 0.5, the maximum axial displacement of the end face of the rotating ring \(\mid\Delta l\mid\) can reach up to 0.789 \(\upmu\)m, larger than the stable result of about 0.57 \(\upmu\)m. The taper angle on the outer diameter of the end face is also increased under the alternating conditions compared with the stable results.  
In Figure 6b, although the coupling deformation trend of the end face of the rotating ring under different periods is in a similar convex shape to that of results with different amplitudes, there is no obvious difference in the coupling deformation displacement of the end face as the period increases. The maximum coupling deformation displacement of the end face is maintained at the value of about 0.73 \(\upmu\)m. The periods of the alternating loads have little effect on the coupling deformation of the end face of the rotating ring.
## 3 Results and Discussion
### Two-Stage Anaerobic Co-Digestion of CSL
The ability of _Limosilactobacillus_ strains to enhance hydrogen production in AD systems suggests their potential utility in co-digestion processes involving substrates like corn steep liquor and agricultural wastes. By improving the hydrolysis stage and facilitating hydrogen accumulation, these bacteria could contribute to more efficient bioenergy production. Another new study [38] showed that, in a semi-continuous process, lactic acid fermentation (LA) in the acidogenic reactor significantly enhanced the two-stage anaerobic digestion of food waste, increasing biomethane production by 17.02% and organic matter removal by 4.84%. LA fermentation improved substrate utilization and shifted microbial communities in the methanogenic reactor, notably increasing the _Methanosarcina_ abundance while decreasing the _Methanosaeta_ abundance. This microbial shift lowered the hydrogen pressure and favored the conversion of LA to acetic acid, enhancing the methanogenesis-related enzyme activity and overall biogas quality. Figure 4: Relative abundance of the bacterial communities in hydrogen bioreactor and methane bioreactor classified to phylum level. _Clostridium_ sp. (provided with 2.13% in bioreactor 1) are the key bacteria producing hydrogen as a byproduct of their metabolism. It is likely that the interaction between the bacterial-biofilm-forming community (_Hallella_) and the hydrogen-producing community is of paramount importance for the development of a stable microbial consortium for sustainable hydrogen production. In bioreactor 2, the dominant species were _Hallella multisaccharivorax_ (11%), _Levilinea saccharolytica_ (7.05%), _Fontisphaera persica_ (2.39%), and _Amiwichip vyrvatiphilus_ (2.23%). Bioreactor 2 contained a more diverse microbial community with a higher proportion of Chloroflexota and Synergistota, which are associated with methanogenesis and the secondary degradation of organic matter. Bacilotta play a key role in carbohydrate fermentation to hydrogen, while Bacteriotta and Synergistota participate in syntrophic interactions supporting methanogenesis. According to [PERSON] et al. (2018), [39], a high content of _Levilinea saccharolytica_ is associated with increased methane production in anaerobic environments. Similar results were reported by [PERSON] et al. (2019) [40], where Bacteroidota dominated methanogenic bioreactors. In our previous study [41], where only corn steep liquor was used as a substrate, the dominant phylum in both bioreactors was Firmicutes (now renamed Bacillota), comprising 58.61% in bioreactor 1 and 36.49% in Bioreactor 2. In the current study, this phylum is represented with 46.78% in bioreactor 1 and relatively less in Bioreactor 2. Another significant similarity was the higher content of the genus Bifidobacterium in the first bioreactor and its significantly lower values in the second one--22.91% with only corn steep liquor as a substrate compared to 7.84% in the current study. Figure 5: Relative abundance of the bacterial communities in hydrogen bioreactor and methane bioreactor classified to genus level.
## 1 Introduction
Although CWs show several advantages in treating polluted water, they still have some limitations; for example, CWs require a large area of operation and long hydraulic retention time (HRT) as well. Therefore, to get the high water quality after treatment, it is necessary to investigate some methods to improve CWs' treatment efficiency, for example, incorporating with other treatment processes for complete removal of contaminants [17; 18]. Recently, advanced oxidation processes (AOPs) using chemical oxidants, such as ozone and hydrogen peroxide (with or without photoassistance), have been under consideration to remove humic acid. Photocatalytic processes, or photocatalysis (PC) using TiO\({}_{2}\) as photocatalysts, is one of the most attractive processes because of its abundance, low cost and complete destruction of humic acid molecules by the direct oxidation process [19]. As mentioned above, the undesirable effects of NOM (represented by HA) and TC on human and animal life are serious, thus requiring efforts to limit the concentration of these compounds in water. There are various methods which can be used to deal with such a problem. To a certain extent, each technique shows good performance in removing HA and TC; however, they are recognized as either expensive or have low removal efficiency. For that reason, it is very important to seek effective methods, which not only improve the contaminants' removal efficiency but also cut down the cost of installation. Both CW and PC are being considered as promising methods that can bring benefits in terms of technical, economic and environmental aspects. In fact, these methods of purifying water can be optimized to be more viable, at a lower cost and with less energy, while at the same time minimizingthe use of chemicals and impact on the environment. However, there have been few reported studies that demonstrate the capability of CW to reduce HA and TC in polluted water as well as the potential to combine both CW and PC in removing HA from raw water. To fill in this knowledge gap, the main objectives of this study were:  
1. To investigate the feasibility of a CW for treating water with varying initial dissolved organic carbon (DOC) concentrations and with different HRTs. 2. To evaluate the removal efficiency of TC by using only a CW system for treatment. This experiment was conducted under the condition of 1 day-HRT and with different initial TC concentrations. 3. To examine the feasibility of combining CW and PC in treating water with influent DOC concentrations ranging from 10 mg/L to 12 mg/L. The integrated system, biological degradation in a CW model and chemical degradation with a TiO\({}_{2}\)-photocatalytic process, was operated in sequence. CW was considered here as a pre-treatment system, while PC played the role of a post-treatment process.
## 3 Results and Discussion
### Localization of AE Events
In summary, localization technology of AE events can be used for damage identification, and it is of great significance to obtain the structural state information and develop corresponding maintenance measures using AE technology to locate the damage region of the in-service RC structures. Furthermore, although locating the AE events in a three-dimensional way can determine the location of the cracks and cracks' extension states more accurately, the number of required sensors, the difficulty of the localization algorithm and computation will greatly increase. Furthermore, the density value of the AE event location points near or away from cracks, and the distance between the cracks and the AE event location points are interesting issues, which can be quantitively analyzed by the regression or correlation analysis method. With regard to wondering whether the distance between the AE events location points and the cracks at different load stages is affected by the steel corrosion ratio, this also has become the topic upon which the authors will further deliberate on.
## 2 Materials and Methods
### Life-Cycle Assessment of the Material Alternatives  
#### 2.4.1 Life-Cycle Assessment Approach  
Life-cycle assessment (LCA) is the most widely used holistic methodology--a multi-stage process whose detailed definition is given in the international standards of the series ISO 14040 [66] and ISO 14044 [67]. According to [66], LCA is defined as the \"compilation and evaluation of the inputs, outputs and the potential environmental impacts of a product system throughout its life cycle\". LCA serves the purpose of expressing the potential environmental impacts and damages associated with a product or service system along the whole life cycle, in a way that supports comparisons between alternatives, both at the level of the individual substance emission and at the level of the entire studied system [68]. The LCA methodology comprises two steps: life-cycle inventory analysis, and life-cycle impact assessment. LCA is especially effective in comparisons of products, e.g., building materials that differ in their raw material composition but have the same functionality [69]. In such cases, LCA can serve as a basis for decision-making to improve sustainability in the construction industry [70]. The LCA methodology is also normed in Vietnam through TVCN ISO 14040: 2009 [71], TVCN ISO 14041: 2011 [72], TVCN ISO/TR 14047: 2018 [73], and TVCN ISO/TR 14048: 2012 [74].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Method** & **Unit** & **Benchmark IC 2** \\ \hline Polycyclic aromatic hydrocarbons (PAHs) & DIN ISO 18287: 2006 [56] & mg/kg & 30 \\ Hydrocarbons (C10-C40) & DIN ISO 16703: 2005 [57] & mg/kg & 1000 \\ Extractable organically bound halogens (EOX) & DIN 38414-5 17: 2014 [58] & mg/kg & 10 \\ Zinc & DIN EN ISO 11885 (E22): 2009 [59] & mg/kg DM & 1500 \\ Mercury & DIN EN 1483 (E 12): 2007 [60] & mg/kg DM & 5 \\ Nickel & DIN EN ISO 11885 (E22): 2009 & mg/kg DM & 500 \\ Copper & DIN EN ISO 11885 (E22): 2009 & mg/kg DM & 400 \\ Chromium & DIN EN ISO 11885 (E22): 2009 & mg/kg DM & 600 \\ Cadmium & DIN EN ISO 11885 (E22): 2009 & mg/kg DM & 10 \\ Lead & DIN EN ISO 11885 (E22): 2009 & mg/kg DM & 700 \\ Arsenic & DIN EN ISO 11885 (E22): 2009 & mg/kg DM & 150 \\ pH value & DIN 38404-5: 2009 [61] & & 5.5–12 \\ Electric conductivity & DIN EN 27,888 (C 8): 1993 [62] & \(\upmu\)S/cm & 2000 \\ Chloride & DIN EN ISO 10304-1 (D 20): 2009 [63] & mg/L & 100 \\ Sulfate & DIN EN ISO 10304-1 (D 20): 2009 & mg/L & 200 \\ Arsenic & DIN EN ISO 11885: 2009 & \(\upmu\)g/L & 60 \\ Lead & DIN EN ISO 11885: 2009 & \(\upmu\)g/L & 200 \\ Cadmium & DIN EN ISO 11885: 2009 & \(\upmu\)g/L & 6 \\ Chromium & DIN EN ISO 11885: 2009 & \(\upmu\)g/L & 60 \\ Copper & DIN EN ISO 11885: 2009 & \(\upmu\)g/L & 100 \\ Nickel & DIN EN ISO 11885: 2009 & \(\upmu\)g/L & 70 \\ Mercury & DIN EN ISO 12,846 (E 12): 2012 [64] & \(\upmu\)g/L & 2 \\ Zinc & DIN EN ISO 11885: 2009 & \(\upmu\)g/L & 600 \\ Phenol index & DIN EN ISO 14402: 1999-12 [65] & mg/L & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical investigation program and analysis methods.