esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Monitoring and Detection Technology of SF\({}_{6}\) and Its Decomposition Components  
The insulation state of the equipment can be monitored and diagnosed by analyzing the leakage and decomposition characteristics of SF\({}_{6}\) in power equipment. At present, a variety of gas analysis technologies have been reported, including detection equipment, testing materials, and so on. Monitoring technology can obtain continuous data on various variables in the environment in real time and can be directly applied to environmental management work such as environmental change trend analysis, pollutant emission estimation, environmental quality evaluation, etc., to improve work efficiency and data quality. At the same time, it is a non-interference real-time observation and detection of the environment in its natural state and will not affect the environment itself, thus obtaining more accurate and reliable data. The accuracy of monitoring equipment is generally high, which can more accurately reflect the real situation of environmental variables. In addition, unattended automatic monitoring can be achieved, greatly reducing the input of human and material resources ([PERSON] et al., 2018).  
At present, the mainstream greenhouse gas monitoring technology is based on the interaction of light and gas components as the physical mechanism, according to the characteristic spectrum of the target component, with the help of spectral analysis algorithm, combined with optomechanical-electrical engineering technology, to achieve the non-contact quantitative inversion of greenhouse gas concentration in response to differences in time, space, and distance. The ideal optical method is characterized by a short response time, high accuracy, lack of requirement for calibration, high sensitivity, moderate cost, and the ability to detect a gas in real time. Common greenhouse gas spectroscopy monitoring technologies mainly include non-dispersive infrared spectroscopy (NDIR), Fourier transform spectroscopy (FTIR), differential optical absorption spectroscopy (DOAS), differential absorption lidar (DIAL), tunable diode laser absorption spectroscopy (TDLAS), off-axis integrating cavity output spectroscopy (OA -ICOS), optical cavity ring-down spectroscopy (CRDS), laser heterodyne spectroscopy (LHS), space heterodyne spectroscopy (SHS), photoacoustic spectroscopy (PAS), Raman spectroscopy (RS), silicon photomultiplier method (SiMP), etc. The typical techniques for detecting SF\({}_{6}\) gas and its decomposition gases are described in detail below.
## 2 Experimental Program
### Quasi-Static Test Setup  
The quasi-static tests were conducted using an MTS machine. The testing was performed by displacement control, with a rate determined following [12]. Figure 3 shows the test setup used. Three-point bending was applied to the beams, and to facilitate even loading, a fibreboard was used. The displacement was halted every 5 kN to study the progression of damage.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Beam Type** & \(\rho\) **[\%]** & \(\rho_{w}\) **[\%]** & \(P_{m}\) **[kN]** & \(P_{\mathrm{\tiny{\circ}}}\) **[kN]** & \(\theta\) **[\({}^{\circ}\)]** & \(\gamma\) **[-]** \\ \hline A & \(1.91\) & \(0\) & \(137.4\) & \(64.8\) & - & \(0.49\) \\ B (\& E) & \(1.91\) & \(0.42\) & \(137.4\) & \(139.2\) & \(24.05\) & \(1.01\) \\ C (\& F) & \(1.91\) & \(0.74\) & \(137.4\) & \(170.4\) & \(32.92\) & \(1.24\) \\ D & \(1.91\) & \(1.49\) & \(137.4\) & \(187.0\) & \(45\) & \(1.36\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reinforcement amount and capacities determined for each beam type.  
Figure 2: Schematic of the RC beams tested (all dimensions in mm).  
The vertical mid-point displacement read from the MTS machine had displacement components from not only the beam but also the deformation of the fibreboard and the development of contact in the system. This system responds as a series of springs as presented in Figure 4a, where \(k\) is the stiffness of the springs and \(F_{y}\) the yield strength in case the spring develops plastic deformation. To determine the mid-point displacement of the beam only, the later tests (A1-D1) for the static residual capacity after impact measured the mid-point displacement using digital image correlation (DIC). The setup for this is presented in Figure 4b. The open-source Python software Py2 Dic (see [13]) was used to determine the vertical displacement at the mid-height of the mid-point of the beam. The results using DIC were validated by applying the procedure on the piston of the MTS machine in the pictures taken. The results using DIC show to converge well with the displacement measured by the MTS machine.
## 3 Results and Discussion
### In silico Analysis
The binding is also reinforced by the development of pi-pi stacking with Trp20, an amino acid that is part of the AR active site. Quercetin interacts through the formation of a hydrogen bond and a pi-pi stacking with the crucial amino acids Thr113 and Trp111, respectively. In addition, the binding is enhanced by the creation of a hydrogen bond and a pi-pi stacking with Cys298 and Trp79 amino acids, which are involved in the active site of AR. In the case of quercetin the presented interaction pattern is in accordance with published studies [46]. Finally, the carboxyl acid group of rosmarinic acid creates hydrogen bonds with the crucial amino acids Tyr48 and His110. Moreover, the hydroxyl group of its phenolic rings form hydrogen bonds with the amino acids Val47 and Thr113, which are critical for binding. An additional pi-pi stacking with the crucial amino acid Trp111 and a hydrogen bond with Cys298 stabilize the binding into AR. The representative docking poses of the abovementioned compounds are illustrated in Figure 3.  
Figure 3: Representative binding poses and the crucial interactions of (**a**) caftaric acid, (**b**) naringenin, (**c**) quercetin, and (**d**) rosmarinic acid derived from molecular docking studies in the active site of AR. Hydrogen bonds are depicted with dashed yellow lines and pi–pi interactions with dashed blue lines. Figure 2: Chemical structures of caftaric acid, naringenin, quercetin, and rosmarinic acid.
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
###### Abstract  
Prosumers, users that consume and produce energy, increase the diversity of energy system operations as distributed sources. However, they can reduce energy system reliability by increasing uncertainty. This study presents a novel transaction mechanism based on dynamic pricing for enhancing energy system reliability. The proposed dynamic energy-reliability pricing-based transaction mechanism (ERT) increases the controllability of prosumer uncertainties by a two-dimensional pricing mechanism based on time and reliability status, as compared to conventional time-based one-dimensional energy prices. Under the proposed ERT, utilities and prosumers exchange information about the utility price and the prosumer's intent in order to ensure that demand is met. A two-way information infrastructure built for prosumer energy trading is used for this task. The utility enhances system reliability using this information, and the prosumer increases revenue through pricing selection. The practical implementation of the proposed ERT is described for both utilities and prosumers. A case study using practical renewable generation data revealed that the proposed ERT improves not only the reliability factor of the utility but also prosumer revenue as compared to a conventional energy-based dynamic pricing case. It is also shown that an economical optimum point that maximizes prosumer net revenue exists when electrical energy storage (EES) is applied to enhance performance. Increasing to the EES capacity provided room for uncertainty management, net revenue is improved, but the economic burden by the EES cost is increased. Under the proposed ERT, the optimal point results in greater EES capacity and higher net revenue enhancement than the conventional case.  
dynamic pricing; electricity pricing; prosumer; reliability; renewable energy; uncertainty
## 2 Results and Discussion
### Identification and Quantification of Individual Phenolic Compounds
[29] in acerola pulp: 4-hydroxybenzoic acid (1.19 mg/100 g), salicylic (9.50 mg/100 g), trans-cinnamic (1.19 mg/100 g) and p-coumaric (21.37 mg/100 g), which was the only one identified in common with this study. The mixed pulp of jambolan and acerola evaluated by the authors showed two more acids than acerola: 3,4-dihydroxybenzoic acid (23.14 mg/100 g) and vanillic acid (26.61 mg/100 g). In the seven varieties of acerolas studied by [PERSON] et al. [60], three phenolic acids were observed (caffeic -0.03 to 0.06 mg/100 g; trans-cafaric -0.342 to 0.452 mg/100 g; chlorogenic 0.168 to 0.384 mg/100 g), with acid chlorogenic being the only one present in all varieties. Two of these acids (caffeic and chlorogenic) were also identified in the acerola pulp of our study, with values close to those of some varieties. Flavonoids formed the largest group among those determined, both in quantity and diversity, with acerola also standing out in diversity and quantity, contributing to the blend and also surpassing guava and pitanga pulps. Only two flavonoids (catechin and rutin) were identified by [PERSON] et al. [29] in acerola pulp and in the mixed pulp of jambolan and acerola, with catechin standing out in both samples with 21.96 mg/100 g in acerola and 35.27 mg/100 g in the mixed pulp. Among the flavanols, epigallocatechin gallate and epicatechin gallate were not detected in guava. The latter was also absent in pitanga. Catechin presented higher levels in all samples, with acerola surpassing the level of other samples. Four flavonoids were found in acerola and three in guava and pitanga, where isorham-netin was not detected. Kaempfer 3-glucose was the main flavonol, with the highest levels in pitanga and little expressive value in guava. In the flavone group, hesperidin was only detected in acerola and absent in guava and pitanga, while among anthocyanins, only malvidin 3-glucose was identified and only in pitanga. In stilbenes, trans-resveratrol was found only in acerola, which also contributed to the blend. [PERSON] et al. [60], studying the phenolic profile in acerola genotypes, also reported concentrations of trans-resveratrol ranging from 0.234 to 0.385 mg/100 g in addition to four flavanols in common with the acerola pulp in the present work, among which the authors did not identify epigallocatechin gallate and reported the presence of procyanidin A2. The authors found the presence of the same flavonoids and flavones plus naringenin. Much higher values were identified by [PERSON] et al. [56] in ripe acerola with 871 mg/100 g of catechin, 704 mg/100 g of epicatechin, 1657 mg/100 g of rutin, 213 mg/100 g of quercetin, 1178 mg/100 g of quercetin and 162 mg/ 100 g of kaempfer, which may be mainly related to the variety of acerola and also growing and processing conditions.
# Combining Parallel Stochastic Methods and Mixed Termination Rules in Optimization
## 2 Adopted Algorithms
### The Method Multistart
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Problem** & **Best** & **Mean** & **DoubleBox** & **All** \\ \hline CAMEL & 2801 & 2675 & 2547 & 2547 \\ CIGAR10 & 3987 & 3674 & 3638 & 3638 \\ CM4 & 3863 & 3299 & 3144 & 3144 \\ DISCUS10 & 2611 & 2409 & 2405 & 2405 \\ EASOM & 2420 & 2232 & 2232 & 2232 \\ ELP10 & 1705 & 1741 & 1586 & 1586 \\ EXP4 & 2751 & 2558 & 2558 & 2558 \\ EXP16 & 2898 & 2676 & 2676 & 2676 \\ GKLS250 & 2796 & 2553 & 2422 & 2422 \\ GKLS350 & 2947 & 2645 & 3833 & 2558 \\ GRIEWANK2 & 4405 & 2839 & 4282 & 2820 \\ GRIEWANK10 & 5327 & 4561 & 4797 & 4248 \\ POTENTIAL3 & 3289 & 3231 & 3222 & 3170 \\ POTENTIAL5 & 4926 & 4881 & 7282 & 4730 \\ POTENTIAL6 & 6699 & 5537 & 7534 & 5077 \\ POTENTIAL10 & 10,582 & 6737 & 29,865 & 6539 \\ HANSEN & 3788 & 2687 & 3062 & 2587 \\ HARTMAN3 & 2807 & 2743 & 2550 & 2550 \\ HARTMAN6 & 3081 & 2915 & 2809 & 2809 \\ RASTRIGIN & 3558 & 2990 & 2829 & 2829 \\ ROSENBROCK8 & 4279 & 4263 & 4036 & 3969 \\ ROSENBROCK16 & 5761 & 5432 & 5170 & 5170 \\ SHEKEL5 & 3132 & 2823 & 12,476 & 2816 \\ SHEKEL7 & 3064 & 2855 & 11,520 & 2856 \\ SHEKEL10 & 3128 & 2942 & 14,786 & 2866 \\ SINU4 & 3047 & 2740 & 2673 & 2657 \\ SINU8 & 3177 & 2824 & 4113 & 2828 \\ TEST2N4 & 3037 & 2839 & 2681 & 2681 \\ TEST2N9 & 4188 & 3106 & 3105 & 3067 \\ TEST30N3 & 2943 & 3130 & 2762 & 2762 \\ TEST30N4 & 3202 & 3185 & 2915 & 2915 \\
**Total** & **125,483** & **110,393** & **169,982** & **106,023** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._

Figure 3: Statistical comparison of function calls with different termination rule of PSO method. Once more, the proposed termination scheme outperforms the other stopping rules in almost every test function. Likewise, the same experiment was carried out for the Multistart method and the results are depicted in Table 6 and the statistical comparison is shown in Figure 4.

Observing the tables with the corresponding statistical and quantitative comparisons, it is evident that the calls to the objective function for the proposed termination rule are fewer than any other rule in any method. Also, the proposed termination rule was applied to a recent global optimization method named Grey Wolf Optimizer [87] and the results are shown in Table 7 and the corresponding statistical comparison in Figure 5.

Once again the combinatorial termination technique achieves the lowest number of function calls of all available techniques. Each optimization method and termination criterion function differently and yield varying results. This variation is further influenced by the differing nature of the cost functions involved. When integrated into the overall algorithm, each method, in combination with the termination criterion that best suits it, can converge to optimal solutions more quickly, thereby enhancing the algorithm's overall performance.
###### Abstract  
Human-shark conflict has been managed through catch-and-kill policies in most parts of the world. More recently, there has been a greater demand for shark bite mitigation measures to improve protection for water users whilst minimizing harm to non-target and target species, particularly White Sharks (_Carcharodon cardarias_), given their status as a Threatened, Endangered, or Protected (TEP) species. A new non-lethal shark bite mitigation method, known as the Shark-Management-Alert-in-Real-Time (SMART) drumline, alerts responders when an animal takes the bait and thereby provides an opportunity for rapid response to the catch and potentially to relocate, tag, and release sharks. Thirty-six White Sharks were caught on SMART drumlines in New South Wales, Australia, and tagged with dorsal fin-mounted satellite-linked radio transmitters (SLRTs) and acoustic tags before release. Thirty-one sharks were located within 10 days, 22 of which provided high-quality locations (classes 1 to 3) suitable for analysis. Twenty-seven percent and 59% of these sharks were first detected within 10 and 50 h of release, respectively. For the first three days post-release, sharks moved and mostly remained offshore (>3.5 km from the coast), irrespective of shark sex and length. Thereafter, tagged sharks progressively moved insbore; however, 77% remained more than 1.9 km off the coast and an average of 5 km away from the tagging location, 10 days post-release. Sharks were acoustically detected for an average of 591 days post-release (ranging from 45 to 1075 days). Although five of the 36 sharks were not detected on acoustic receivers, SLRT detections for these five sharks ranged between 43 and 639 days post-release, indicating zero mortality associated with capture. These results highlight the suitability of SMART drumlines as a potential non-lethal shark bite mitigation tool for TEP species such as White Sharks, as they initially move away from the capture site, and thereby this bather protection tool diminishes the immediate risk of shark interactions at that site.  
bather protection; post-release movement; shark movement; SMART drumline +
Footnote †: journal: Journal of the American Statistical Association
###### Abstract  
Process discovery aims to discover process models from event logs to describe actual business processes. The quality of event logs has an impact on the quality of process models, so preprocessing methods can be used to improve the quality of event logs. Chaotic activities may exist in real business scenarios, and the occurrence of chaotic activities is independent of other activities in the process and can occur at any location in the event log at any frequency. Therefore, chaotic activities seriously affect the model quality of process discovery. Filtering chaotic activities in event logs can effectively improve the quality of event logs and thus improve the quality of process models. The traditional chaotic activity filtering algorithm makes it difficult to balance accuracy and time performance. Therefore, a direct method for filtering chaotic activities is proposed in this paper. By analyzing the relationship between activities, chaotic activities are identified in the log according to the characteristics of chaotic activities and the direct following relationship of activities as the judgment condition, and the filtering of chaotic activities in the event log is realized. In addition, this paper proposes an indirect chaotic activity filtering method, which identifies and filters chaotic activities in the log by analyzing the influence of the existence of different activities on the overall chaos degree of the log. The proposed method is compared with the traditional chaotic activity filtering method on several simulation/ real data sets, and the accuracy and running time between the multi-group event logs and the process models generated before and after chaotic activity filtering are analyzed, further verifying the effectiveness and feasibility of the proposed method. By summarizing the experimental results, it is found that the accuracy of the proposed chaotic activity filtering methods is greater than that of the frequency-based filtering method and is close to that of the entropy-based chaotic activity filtering methods. Moreover, compared with other filtering methods used in the experiment, the chaotic activity filtering method proposed in this paper can improve the efficiency by 23.4% on average for simulation logs, and by 84.25% on average for real event logs. It is concluded that compared with other filtering methods, the proposed chaotic activity filtering methods have higher accuracy and can effectively improve the time performance of chaotic activity filtering. Therefore, the chaotic activity filtering method proposed in this paper can balance the accuracy and time performance, and can ensure the integrity of the filtered event log to a certain extent.  
process mining: chaotic activities; pre-processing; process model; direct following relationship; event log +
Footnote †: journal: Applied Sciences
## 3 Methods and Materials
### Methodology
#### 3.2.4 Training and Validation Model 1, Model 2, and Model 3  
After customizing the VGG16 model to fit our task, the training and validation steps were applied. We gave specific values to specific parameters needed for the training phase such as:  
* Epochs = 40: The number of times the model will iterate over the entire training dataset.
* Batch_size = 32: The number of samples that will be propagated through the network at one time. After processing this batch, the model's weights will be updated.
* Callbacks: These are special functions that can be called during training at certain points.  
Figure 3: VGG16’s pre-trained model architecture.  
* They are used for various purposes, such as saving the model after each epoch or stopping training early if the validation loss stops improving.
* Verbosity mode = 1: This controls the verbosity of the output, and \"1\" means progress updates will be shown during training.
* Optimizer = Adam: Adam is a popular optimization algorithm used for training machine learning models, especially deep learning models. \"Adam\" represents a versatile second assessment. It is an augmentation of the Stochastic Slope Plunge (SGD) calculation that determines versatile learning rates for every boundary.  
After defining all the needed parameters and their values, training and validation will begin, where the training phase is the period during which the model is exposed to the training dataset. During this phase, the model learns to map input features to output labels. This is achieved by adjusting its weight based on the loss from predictions and true labels. The validation phase involves evaluating the model's execution on a different approval dataset that the model has not seen during preparation. This helps to monitor how well the model generalizes to new, unseen data.
# In Vitro Antioxidant Effects of Coenzyme Q10 on Cellular Metabolism in Aged Mesenchymal Stem Cells
## 2 Materials and Methods  
### Cell Culture and CoQ10 Treatment  
In these experiments, mesenchymal stem cells derived from human exfoliated deciduous teeth of children aged between 7 and 12 years were utilized and prepared according to protocols described in previous studies [24, 25]. Parental consent was obtained following strict ethical standards, adhering to the principales outlined in the 1964 Declaration of Helsinki. The dental pulp tissue was carefully extracted, washed with phosphate-buffered saline (PBS) (Gibco, Life Technologies, Bleiswijk, Netherlands), and cut into 0.5-1 mm\({}^{3}\) fragments to facilitate MSC isolation from tissue explants. SHEDs were cultured in alpha minimum essential medium (Gibco, Thermofisher, Paisley, UK). The medium was supplemented with 10% penicillin/streptomycin (Life Technologies, Grand Island, NY, USA) and refreshed twice a week. Cells were passaged when they reached 80-90% confluence to ensure optimal growth conditions. Cells from passages 5 (P5) and 16 (P16) were used in the experiments. To minimize passage-dependent effects and avoid confounding influences from culture conditions or donor variability, all MSCs from the dental pulp were grown under identical conditions, including the same basal medium, supplements, and passage protocols to ensure consistency. To minimize donor-specific variations, young and aged cells from the same donor were consistently compared, while replicating the experiment with at least two additional donors. Cells were always plated at the same density, using consistent culture conditions before treatment, to reduce variability in cell cycle distribution among experimental groups.  
Coenzyme Q10 (Sigma-Aldrich, St. Louis, MO, USA) was prepared as a 10 mM stock solution in ethanol (Sigma-Aldrich, Ayrshire, UK). The stock solution was diluted in cell culture medium to final concentrations of 10 uM, 25 uM, 50 uM, and 70 uM. MSCs were incubated with these concentrations for 24 h to assess the effects of the chosen dosages. The selection of CoQ10 doses (10 and 50 uM) for the further assessment of metabolic and gene expression changes was based on their distinct effect on cell viability and their frequent use in the literature as effective low-to-medium concentrations for modulating stem cell metabolism, providing a foundation for future studies investigating long-term effects and minimizing the potential toxic effects of higher concentrations [4,26]. Control cells were grown under similar conditions as the test cells, adding the corresponding ethanol concentration to the culture medium, without CoQ10.