esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Digitalization of MGs
### Big Data Analytics
By analyzing data on energy generation, consumption, and storage, microgrid operators can identify areas where additional capacity may be required and take proactive measures to address the issue. Overall, the benefits of big data analytics in MGs are numerous and significant. As the adoption of MGs continues to grow, the importance of big data analytics in this context will only continue to increase. However, there are several challenges regarding big data analytics in MGs. One of the significant challenges of big data analytics in MGs is the collection and integration of data from various sources [150]. MGs use a combination of DERs such as solar panels, wind turbines, and batteries, making data collection and integration complex [146]. The data collected from these sources may be in different formats and may require preprocessing before analysis. In addition, data quality is crucial in big data analytics, as it can affect the accuracy and reliability of the analysis. In MGs, data quality can be affected by data errors, missing data, and data incompleteness. Poor data quality can lead to inaccurate predictions and inefficient energy management. Another significant challenge of big data analytics in MGs is data security and privacy [145]. MGs collect sensitive information related to energy usage patterns, which must be kept secure to prevent data breaches and unauthorized access. As MGs grow in size and complexity, the scalability of big data analytics becomes a significant challenge. Big data analytics requires a considerable amount of computing power and storage, which may be difficult to scale up as the microgrid expands [144]. On the other hand, potential solutions for these challenges can be sorted out. Standardization of data collection and integration processes can help overcome the challenge of data collection and integration. The use of standardized data formats and protocols can simplify the integration of data from various sources, making data analysis more efficient. To address the challenge of data quality, microgrid operators can implement data quality assurance techniques such as data cleansing, data validation, and data enrichment. These techniques can help ensure that data are accurate, complete, and consistent, improving the quality of data analysis. To address the challenge of data security and privacy, microgrid operators can implement data security measures such as encryption, access controls, and intrusion detection systems. These measures can help protect sensitive data and prevent unauthorized access. Cloud computing can provide a scalable and cost-effective solution to the scalability challenge of big data analytics. Cloud-based big data analytics platforms can provide on-demand computing resources, enabling microgrid operators to scale up their analytics capabilities as the microgrid expands. By addressing these challenges, microgrid operators can effectively implement big data analytics and achieve the full potential of MGs in enhancing energy sustainability and resilience.
## 6 Robust Analysis
### The Influence of Ambient Noises  
The Gaussian white noise was added to the obtained modal shapes as in Equation (12):  
\[\varphi^{\prime}_{ij}\ =\ \varphi_{ij}\big{(}1+rand()\cdot\epsilon\cdot\varphi_{ \max,j}\big{)} \tag{12}\]  
where \(\varphi^{\prime}_{ij}\) is the modal displacement of the \(j\)th point of the \(i\)th modal shape with noise, \(\varphi_{ij}\) is the corresponding modal displacement without noise; \(\epsilon\) is the noise level; and \(rand()\) represents the random standard normal distribution.  
To weaken the influence of noise, each \(\varphi^{\prime}_{ij}\) was decomposed and reconstructed by wavelet transform and averaged \(n\) times. Figures 16 and 17 show the influence of ambient  
Figure 14: Quantitative evaluation by modal flexibility difference curvature in Case 1: (**a**) quantitative damage evaluation; and (**b**) linear fitting of damage indicators.  
Figure 15: Quantitative evaluation by modal flexibility difference curvature in Case 2: (**a**) quantitative damage evaluation; and (**b**) linear fitting of damage indicators.  
noise on the modal curvature difference and the modal flexibility difference curvature; the shear stud was damaged at the mid-span of the SCCB (1250 mm). Figures 16a and 17a indicate that the two calculated indicators located the damaged studs successfully when the noise level was 1% and 0.6%, respectively. With the increase in the average times \(n\), the robustness to ambient noises also increased. Figures 16b and 17b show that when the average times \(n=1000\), the corresponding noise level can be up to 8% and 5% for the modal curvature difference and the modal flexibility difference curvature, respectively. By comparison, the proposed modal curvature difference showed a better robustness to ambient noises than the modal flexibility difference curvature.
# Design and Performance Evaluation of eLoran Monitoring System
## 2 Principle and Structure
### Near-Field Monitoring Station  
According to the wave propagation characteristics of the eLoran, the BPL monitoring system is constructed to match the near-field and far-field monitoring, so as to continuously monitor the integrity and accuracy of the BPL timing signal in the monitoring coverage area, and to ensure that the BPL timing signal can be used, usable, and good.  
The structural components of the eLoran monitoring system are mainly composed of three parts: the near-field monitoring station, far-field monitoring station, and data processing center.  
The near-field monitoring station is located at the BPL time service station, which is mainly used to monitor the accuracy of the broadcasting control of the time service station, collect the BPL signal from the root of the transmitting antenna through the current transformer, and demodulate the time code information and 1 PPS (plus per second) signal contained in the BPL signal through the BPL monitoring receiver. The structure of the near-field monitoring station is shown in Figure 2, which is mainly composed of four parts: the data acquisition equipment, time interval counter, time-frequency keeping equipment, and enhanced Roland timing terminal.  
Figure 1: Working principle of eLoran time service system.  
The time-frequency keeping unit consists of a cesium atomic clock, 5071A (accuracy \(\pm\)1.0 \(\times\) 10\({}^{-12}\), stability \(\leq\) 1.2 \(\times\) 10\({}^{-11}\) for 1 s averaging time, long-term stability \(\leq\) 5.0 \(\times\) 10\({}^{-14}\) for 5-day averaging time), and a fiber optic traceability system, which provides standard 10 MHz and 1 PPS signals, and traces back to the Chinese standard time UTC (NTSC) kept by the National Timing Center through optical fiber. The 1 PPS signal output from the BPL monitoring receiver is compared with the 1 PPS output from the time-frequency keeping unit through the time interval counter (model number SR620, 25 ps single-shot time resolution), and the control accuracy of the BPL signals can be analyzed through the analysis of the comparison data [14; 15; 16; 17; 18].
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 4 Results and Discussion
### Data Fusion Method Evaluation
#### 4.2.3 Multi-Source Data Fusion for High-Dynamic Analysis
This effect is attributed to the image fusion module's ability to learn and highlight the splash features and melt pool morphology in visible-light images and accurately learn the temperature distribution and gradients of the melt pool. It retains the temperature gradients of the normal areas while emphasizing the morphology of the melt pool in temperature anomaly areas and reducing the intensity of the temperature distribution, thereby balancing the temperature anomalies. The results indicate that the proposed method can provide strong support for melt pool status monitoring and defect monitoring. In summary, the proposed data fusion method effectively mitigates the abnormal melt pool temperatures caused by droplet splashing and emissivity errors. The fused data generated by this method incorporate accurate temperature distribution and high-dynamic motion characteristics of the melt pool, which facilitate precise analysis of the melt pool's thermal history and enable the monitoring and localization of melt pool defects resulting from changes in temperature distribution. Additionally, the extraction results of the melt pool contour demonstrate the superiority of the proposed method over single-data-source approaches in melt pool condition monitoring, thereby providing robust data support for enhancing the accuracy of melt pool condition monitoring.
## 4 Evaluation of Automatic Idling Control of CAVs
### Case Study with Haeundae-Gu in Busan, Korea  
Figure 5 shows the study area, Haeundae-gu, in Busan, Korea. The basic input data required to build the VISSIM network such as the geometry, traffic flow (i.e., traffic volume and turning ratio), and traffic signal information (i.e., cycle length, phase, phase sequence, etc.) has been collected from an up-to-date aerial photo, traffic impact assessment reports, and the regional Road Traffic Authority with field survey complementation. After one hour of warm-up time, during which the network was populated with a sufficient number of vehicles, the vehicles entering the network for the next one hour were used for the analysis.  
As for MOVES, the \(VSP\) operates in numerous operating modes at various speeds and accelerations collected from the microscopic simulation model, VISSIM. This study considers the passenger cars and gasoline as the vehicle type and fuel type in Table 1. Also, Table 2 indicates the default values corresponding to individual variables in Equation (1), excluding geographical factor (i.e., network slope) [53].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & **Unit: Kg/h** \\ \hline  & & & **Passenger Car** & & **Transit Bus** & & **Passenger** & **Long-Haul** \\
**Op. Mode** & **Op. Mode Description** & **Gasoline** & **Diesel** & **Diesel** & **CNG** & **Diesel** & **Diesel** \\ \hline
33 & \(VSP<6\) & 8.55 & 8.55 & 33.32 & 40.12 & 10.54 & 34.03 \\
35 & \(6\leq VSP<12\) & 13.71 & 13.71 & 101.90 & 82.54 & 16.13 & 104.05 \\
37 & \(12\leq VSP<18\) & 17.86 & 17.86 & 159.49 & 137.56 & 21.32 & 162.85 \\
38 & \(18\leq VSP<24\) & 23.29 & 23.29 & 223.29 & 192.59 & 27.70 & 227.99 \\
39 & \(24\leq VSP<30\) & 31.02 & 31.02 & 287.09 & 247.61 & 36.30 & 293.13 \\
40 & \(30\leq VSP\) & 39.55 & 39.55 & 350.89 & 302.64 & 48.04 & 358.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: VISSIM network (10 km \(\times\) 10 km) of Haeundae-gu in Busan, Korea.  
This study has calibrated the Haeundae-gu network with the three most significant queue length-related parameters, such as emergency stopping distance, lane change distance, and average standstill distance, out of 50 internal parameters based on the afternoon non-peak traffic demand using a Monte Carlo experiment. The simulation network was calibrated with three parameters adjusted to 7.2 m, 30.3 m, and 2.0 m, respectively. For validation purposes, the morning non-peak traffic demand has been input to the calibrated model, and it estimated the queue length within a reasonably acceptable range on the main artery. Therefore, the simulation outputs derived from this network are reliable enough to evaluate the environmental effect of the automatic vehicle idling control function [49].
is valuable because the situations faced by visually impaired users is not so varied and scenes with similar backgrounds and situations appear repeatedly in video content such as movies, dramas, CCTV, and vehicle black boxes.  
In this paper, a method to generate multiple captions for a single image using the variational autoencoder (VAE) [9] structure and image attention information is proposed. Previous studies on generating captions using VAE deal with sentence information. They transformed the last hidden state of the RNN, the sentence embedding, to latent representation [10; 11; 12]. When generating captions, variation is given to the latent variable to create sentences of various styles. However, the methods only focused on the sentence generation problem and they did not use image information. In the image captioning task, information about images plays an important role. We use image related features in the VAE-based captioning model and have verified that the network generates more diverse captions than those in previous studies. Image-caption pairs in the dataset give information about the image regions to which the captions are related and this was applied to our model. There are not yet many studies about making multiple captions from a single image, so there are no popular or established methods for diversity evaluation at this time. Therefore, we propose an evaluation procedure that contains sampling methods and metrics that refer to previous papers [11; 13]. The main contributions of our paper are as follows:  
* A method for extracting caption attention maps (CAMs) from an image-sentence pair is proposed.
* A captioning model is proposed that generates multiple captions per image by adding randomness to the image region-related information using VAE.
* A methodology is introduced for evaluation of multiple image captioning tasks, which has not been done in previous studies, and it is used to evaluate our base and proposed model.
## 3 Results and Discussions
### Effect of Divalent Cations on Microbial Community Compositions  
The microbial community composition of the anammox granules was relatively unchanged at either the phylum or species levels, regardless of the experimental conditions (Figure 7). _Plantcomyectes_-harboring anammox bacteria accounted for the largest distribution (51.1-56.0%) in the anammox granules, followed by _Chloroflexi_ (23.7-29.3%) and _Ignavibacteriae_ (7.2\(\sim\)9.2%). The remaining phyla were present at less than 3%. In general, the phyla _Chloroflexi_ and _Ignavibacteriae_ were frequently observed as major members in the anammox bioreactors [38]. _Chloroflexi_ and _Ignavibacteriae_ might reduce nitrate to nitrite, which can be used by the anammox bacteria [38]. In addition, _Chloroflexi_, typical filamentous bacterium, might provide a stabilizing backbone, cores, or carriers for the microbial aggregates, and use the soluble microbial products and the EPS released from the decaying anammox bacteria as a carbon source [39; 40; 41]. In this study, because _Chloroflexi_ was the second largest member in our anammox granules, they would play an important role in maintaining the stability of the anammox granules.  
No significant differences were observed in the community composition of the anammox bacteria in the anammox granules (Figure 7b). More than 97% of the phylum _Plantcomyectes_ were found to be anammox bacteria. Uncultured anaerobic ammonium-oxidizing bacterium (accession no. LC192412) related to Ca. _Kuenenia stuttgartiensis_ showed the largest component (68.5-71.6%), followed by Ca. _Procedia_ (16.1-19.1%), Ca. _Jettenia_ (3.0-6.7%), and Ca. _Kuenenia_ (4.6-6.9%). It is known that Ca. _Kuenenia_ can grow well at 15-20 g NaCl/L and can be acclimated at 30 g NaCl/L [42; 43]. Ca. _Jettenia_ is sensitive even at 4 g NaCl/L [44]. Ca. _Brocadia_ has a relatively low salinity stress tolerance [8].  
As the salt concentration was increased from 0 g NaCl/L to 30 g/NaCl, Ca. _Kuenenia_ increased from 0.02% to 36.9% while Ca. _Jettenia_ only increased to 0.27% and Ca. _Brocadia_ decreased from 1.92% to 0.04% [45]. In this study, however, there was no significant change in the community composition of the anammox bacteria in our experimental conditions. The experimental time was too short to observe the variations of the microbial (anammox bacteria) community composition. These results suggest that the alleviation of salinity stress by the augmentation of divalent cations might be not caused by microbial community changes.  
Figure 6: Na\({}^{+}\) concentration in bound EPS and soluble EPS at C1 (no salt and no cation), C2 (5 g NaCl/L and no cation), C3 (5 g NaCl/L and 200 mg/L Ca\({}^{2+}\)), and C4 (5 g NaCl/L and 70 mg/L Mg\({}^{2+}\)).
## 4 Bromelain Use as an Exfoliant Agent in Cosmetic Applications
### Bromelain Stabilizers and Inhibitors in Cosmetic Formulations  
In cosmetic formulations, maintaining the activity of bromelain, a thiol endopeptidase, requires a careful balance of stabilizers and the avoidance of inhibitors. Stabilizers play a crucial role in enhancing bromelain's longevity and efficacy and include mostly non-ionic, but otherwise soft, surfactants, which are preferred over ionic ones for causing less activity loss [44; 53]; however, recent studies claim that the binding of ionic surfactants is greater than that of non-ionic ones. Plus, among ionic and anionic surfactants, anionic ones, like sodium dodecyl sulfate (SDS), may vastly alter protein conformation [44; 53; 54]. Polyol stabilizers such as PEG, and particularly a mix of PEG (8% \(w/w\)) and ammonium sulfate (15% \(w/w\)), enhanced both the activity (stem and fruit had 4.0 and 3.6 units/mg, respectively) and stability. Meanwhile, an aqueous mixture of 15% \(w/w\) PEG-3000 and 20% magnesium sulfate (20%) results in an enzyme activity of 5.23 units/mg in AIPS [44; 55]. Additionally, stabilizers, like soluble polymers (i.e., _Shizophyllum_ commune-derived (SC) glucan) [56] and chitosan-based nanoparticles [57], enhance enzyme activity and improve stability via encapsulation. In general, certain salts (i.e., sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)) [58] and calcium chloride (CaCl\({}_{2}\)) [59]) facilitate enzymatic activity, while lyoprotectors, like maltose, maintain enzyme activity in freeze-dried formulations [57; 60]. Finally, cream-gel formulations have shown notable promise in providing a suitable base for bromelain, especially when stored at 4 \({}^{\circ}\)C [61]. Chemical additives, like EDTA and non-ionic surfactants, may enhance bromelain's stability and activity, while, in order to prevent its degradation, incorporating antioxidants (i.e., Vitamin C, Vitamin E) and maintaining appropriate storage conditions are also recommended [20; 42; 54; 62; 63; 64; 65].  
Conversely, inhibitors may compromise bromelain's activity. Heavy metals (Ag\({}^{+}\), Hg\({}^{2+}\), and Cu\({}^{2+}\)), for example, can directly inhibit its proteolytic function [66; 67], iodoacetate may obstruct its active site [46], and anhydrous gels may create an incompatible environment for this enzyme [42; 43]. High temperatures and oxidation are also detrimental as potent inhibitors, leading to enzymatic degradation and activity loss [44]. Exposure to light and the presence of various oils and surfactants in complex cosmetic products can also affect bromelain's stability and structure [45]. To summarize, the main challenge in incorporating bromelain into cosmetic formulations is to maintain its enzymatic activity over a longer period of time. Factors such as pH fluctuations, temperature variations, and interactions with other ingredients can lead to the degradation of the enzyme. Strategies such as enzyme encapsulation, particularly for nanoemulsions, pH buffering systems, and stabilizers are currently being explored to improve the long-term efficacy of bromelain in skin care products [45; 62].
## 5 Performance Evaluation Results and Discussion
### Simulation Model  
#### 5.2.1 Communication Model  
D2D communication was modeled using a unit disc model without packet loss by interference or congestion, with a 50-m propagation range for the pedestrian case and a 100-m propagation range for the vehicle case (Figure 15). The transmission rate of D2D communication was set at 1 Mbps. The area in which mobile hosts moved was 500 m by 500 m for the pedestrian case and 1000 m by 1000 m for the vehicle case. A data request was generated every 5 s, and the POI was selected by a uniform random number. Both the propagation range of D2D communication and the length of a side of the area for the vehicle case is twice as large as that for the pedestrian case. Thus, the average number of mobile hosts in the D2D communication range of a mobile host for the vehicle case is expected to be equal to that for the pedestrian case when the total number of mobile hosts for the vehicle case is equal to the total number of mobile hosts for the pedestrian case.
## 2 Materials and Methods
### Single-Line Formation Test  
To determine the optimal laser power and scan rate, a single-line formation test was performed, as shown in Figure 1. The Co-Cr alloy powders were homogeneously applied onto the substrate using the powder application system; subsequently, each length (7.0 mm) of powder was selectively irradiated with the laser. The laser powers used were 100, 150, and 200 W, and the scanning rate ranged from 3.66 to 300 mm/s. The energy density was calculated using the following equation [9]:  
\[\text{Energy density (J/mm)}=P/v \tag{1}\]  
where \(P\) is the laser power (W), and \(v\) is the scanning rate (mm/s).  
To establish the optimal process parameters, an objective function (\(F\)) was determined [12; 13]. This considers the relationship between the melting zone area (\(A_{1}\)) and the penetration zone (\(A_{2}\)), as follows:  
\[F=A_{1}/A_{2} \tag{2}\]  
where  
\[A_{1}=(W_{1}+W_{3}/2)\times H_{1} \tag{3}\]  
in which \(H_{1}\) and \(W_{1}\) are the height and width, respectively, of the upper part of the line. \(W_{3}\) represents the length of the lower part of the trapezium that is circumscribed to the line. \(A_{2}\) can be determined by  
\[A_{2}=H_{2}\times W_{2}/2 \tag{4}\]  
where \(H_{2}\) and \(W_{2}\) are the penetration zone areas, where remelting occurs. Finally, \(F\) can be calculated using the following equation:  
\[F=[(W_{1}+W_{3})\times H_{1}]/H_{2}\times W_{2} \tag{5}\]