esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China
## 3 Parametric Study of GSER Application Status
### Average burial Depth of Coal Seams  
Early GSER technology in China was mainly applied to shallow and simple coal seam mining conditions and gradually applied to medium-depth and deep, complex coal seam conditions with the continuous improvement of its technical system and changes in coal resource mining conditions. Among 587 application cases of GSER technology, a clear indication of the average burial depth of coal seams was found in 360 cases. The project with the shallowest average burial depth of coal seams is in the Jingu Coal Mine in Shanxi Province, with an average burial depth of only 65 m. The GSER along the fully mechanized mining face 14-31010 in No.12 Mine, Pingdingshan Coal Group Co. Ltd., located in Henan Province in China, demonstrates the deepest burial depth of coal seams among all engineering cases, with an average depth of 1112 m. Figure 5 shows the application of GSER technology under the average burial depth of different coal seams at a statistical interval of 100 m.  
As shown in Figure 5, there are 34 application cases of GSER engineering projects with an average burial depth of less than 200 m in coal seams, 266 cases of GSER engineering projects in coal seams within a burial depth of 200-700 m (accounting for a total of 73.89%), and 60 cases of GSER engineering projects in coal seams buried with an average burial depth exceeding 700 m. As shown in Figure 6, when the average burial depth of 700 m is taken as the boundary for the medium burial depth of coal seams, the current GSER technology is mainly applied to the conditions of medium burial coal seams in China. When the burial depth exceeds 400 m, the number of engineering applications gradually decreases with the increasing burial depth. The main reason is explained as follows. With the increase in mining depth, the range of mining impact and mining stress intensity on the working face gradually increases, leading to increased difficulty in controlling the surrounding rock of the retained entry. Additionally, the mechanical performance requirements for the entry-side structures also increase accordingly, especially under the condition of burial depth above 700 m. The existing retained entry surrounding rock support technology cannot fully achieve efficient, safe, and stable control of the retained entry surrounding rock.  
Figure 5: Statistical chart of the GSER engineering projects at different buried depths.
# Mapping the Most Susceptible Regions to Fire in Portugal
## 4 Discussion
Moreover, the south border of the fire scar, where the fire event is likely to have started [54], was extremely susceptible to burn, which constitutes an important factor to promote the amplification of the fire's intensity and severity. In the case of A2, despite low to moderate values of susceptibility, the presence of some points in the south and north-eastern part of the fire scar which were highly susceptible to fire, in conjunction with potential vegetation stress caused by hot and dry summer conditions [54], could be the triggering factor for this event. This analysis also enabled the finding of high PC\({}_{\text{REC}}\) signals in other minor fires, indicating that, despite the favourable conditions of vegetation for burning, the events were possibly mitigated earlier due to external factors, e.g., firefighters, efficient combat, unfavourable fire weather conditions. Studies related to the identification of areas with wildfire susceptibility in Portugal have been performed during the last years, mainly since the harsh fire season of 2017. The centre of the country was strongly disturbed by the wildfires of this year [10; 19] and these events left deep scars in vegetation, which remained for a long period. Relying on variables related to elevation and slope angle, which have a strong influence on vegetation distribution, flammibility and local climate [64], and land cover and fire weather indices, these studies mapped the areas with high exposure and vulnerability to fire in the country [23; 29; 31]. In our work, besides those variables, the applied method also addresses variables related to vegetation activity and fuel accumulation. The results not only agree with the findings of these studies, but also provide new insights regarding the role of vegetation activity, which was revealed to be a major driver and contributor in exacerbating the ecosystems' susceptibility to burn. The study also contributed to fire prevention in Portugal, allowing national authorities to apply efficient policies over the areas that are more susceptible to burn. The approach used here allows the assessment of the areas that need special attention, especially regarding biomass control and landscape management. Furthermore, although we do not address the meteorological variables that have an important role in mediating fire intensity (such as air temperature, wind speed, soil moisture or relative humidity), the considered variables can be updated periodically (e.g., yearly or bi-yearly), revisiting the identification of fire susceptible regions before the start of each fire season. In the context of climate change, with hotter and drier summers, the ecosystems' susceptibility, as well as the exposure and human vulnerability to fire events, will increase [30]. Nevertheless, fuel management may constitute a key factor in partially mitigating the occurrence of increasingly large and intense wildfires in Portugal and may avoid the return of catastrophic fire seasons as the ones experienced in Portugal in 2003, 2005, and especially 2017.
## 3 Results
### Organic Compounds
& 97.8 \(\pm\) 0.6 & 98.1 \(\pm\) 0.4 & \multirow{2}{*}{MW1} & g/dm\({}^{3}\) & 0.24 \(\pm\) 0.13 & 0.05 \(\pm\) 0.02 \\ \hline \multirow{2}{*}{C2} & g/dm\({}^{3}\) & 0.38 \(\pm\) 0.1 & 0.30 \(\pm\) 0.07 & \multirow{2}{*}{C2} & g/dm\({}^{3}\) & 0.5 \(\pm\) 4.1 & 98.1 \(\pm\) 0.3 \\ \cline{2-7}  & \% rem. & 92.4 \(\pm\) 0.5 & 95.9 \(\pm\) 0.3 & & \% rem. & 93.0 \(\pm\) 0.16 & 0.2 \(\pm\) 0.06 \\ \hline \multirow{2}{*}{MW2} & g/dm\({}^{3}\) & 0.8 \(\pm\) 0.06 & 0.62 \(\pm\) 0.03 & \multirow{2}{*}{MW2} & g/dm\({}^{3}\) & 0.1 \(\pm\) 0.07 & 0.06 \(\pm\) 0.01 \\ \cline{2-7}  & \% rem. & 95.7 \(\pm\) 0.5 & 97.7 \(\pm\) 0.3 & & \% rem. & 92.2 \(\pm\) 1.2 & 98.2 \(\pm\) 0.4 \\ \hline \multirow{2}{*}{MW1} & g/dm\({}^{3}\) & 0.38 \(\pm\) 0.1 & 0.30 \(\pm\) 0.07 & \multirow{2}{*}{MW1} & g/dm\({}^{3}\) & 0.22 \(\pm\) 0.13 & 0.05 \(\pm\) 0.02 \\ \cline{2-7}  & \% rem. & 97.8 \(\pm\) 0.6 & 98.1 \(\pm\) 0.4 & \multirow{2}{*}{MW1} & g/dm\({}^{3}\) & 0.22 \(\pm\) 0.13 & 0.05 \(\pm\) 0.03 \\ \hline \multirow{2}{*}{C3} & g/dm\({}^{3}\) & 0.38 \(\pm\) 1.8 & 0.30 \(\pm\) 0.07 & \multirow{2}{*}{C3} & g/dm\({}^{3}\) & 0.38 \(\pm\) 0.12 & 0.25 \(\pm\) 0.03 \\ \cline{2-7}  & \% rem. & 97.8 \(\pm\) 1.8 & 0.30 \(\pm\) 0.07 & \multirow{2}{*}{C3} & g/dm\({}^{3}\) & 0.38 \(\pm\) 0.12 & 0.25 \(\pm\) 0.03 \\ \hline \multirow{2}{*}{MW3} & g/dm\({}^{3}\) & 1.99 \(\pm\) 1.01 & 1.26 \(\pm\) 0.27 & \multirow{2}{*}{MW3} & g/dm\({}^{3}\) & 0.45 \(\pm\) 0.19 & 0.27 \(\pm\) 0.05 \\ \cline{2-7}  & \% rem. & 88.1 \(\pm\) 1.9 & 92.1 \(\pm\) 1.7 & \multirow{2}{*}{MW3} & g/dm\({}^{3}\) & 0.46 \(\pm\) 3.5 & 90.8 \(\pm\) 0.8 \\ \hline \multirow{2}{*}{C4} & g/dm\({}^{3}\) & 3.19 \(\pm\) 2.19 & 2.02 \(\pm\) 0.24 & \multirow{2}{*}{C4} & g/dm\({}^{3}\) & 0.68 \(\pm\) 0.28 & 0.47 \(\pm\) 0.03 \\ \cline{2-7}  & \% rem. & 81.1 \(\pm\) 5.8 & 86.7 \(\pm\) 1.1 & \multirow{2}{*}{} & \% rem. & 76.4 \(\pm\) 3.6 & 83.7 \(\pm\) 1.8 \\ \hline \multirow{2}{*}{MW4} & g/dm\({}^{3}\) & 2.82 \(\pm\) 1.68 & 2.04 \(\pm\) 0.29 & \multirow{2}{*}{MW4} & g/dm\({}^{3}\) & 0.64 \(\pm\) 0.22 & 0.46 \(\pm\) 0.06 \\ \cline{2-7}  & \% rem. & 87.6 \(\pm\) 4.4 & 87.2 \(\pm\) 1.8 & \multirow{2}{*}{MW4} & g/dm\({}^{3}\) & 0.74 \(\pm\) 5.1 & 84.2 \(\pm\) 0.9 \\ \hline \multirow{2}{*}{C5} & g/dm\({}^{3}\) & 11.19 \(\pm\) 0.75 & 9.63 \(\pm\) 0.65 & \multirow{2}{*}{C5} & g/dm\({}^{3}\) & 2.43 \(\pm\) 0.26 & 2.15 \(\pm\) 0.01 \\ \cline{2-7}  & \% rem. & 32.1 \(\pm\) 4.1 & 39.8 \(\pm\) 4.1 & \multirow{2}{*}{Cwere obtained in SER2 (55 \({}^{\circ}\)C), with removal rates exceeding 90% (Table 4, Figure 3b). Incremental increases in effluent TOC were noted for variant V5 (5.0 gCOD/dm\({}^{3}\)). Final TOCs for S1 SER2 (55 \({}^{\circ}\)C) and S2 SER2 were 2.15 \(\pm\) 0.10 g/dm\({}^{3}\) and 1.98 \(\pm\) 0.26 g/dm\({}^{3}\), respectively (Table 4, Figure 3a). Effluent TOC in the V6 groups ranged from 2.49 \(\pm\) 0.06 g/dm\({}^{3}\) (S2 SER2) to 2.60 \(\pm\) 0.25 g/dm\({}^{3}\) (S1 SER1) (Table 4, Figure 3a). Peak efficiency, reaching 13.8 \(\pm\) 6.5%, was demonstrated for S2 SER2 (Table 4, Figure 3b).
## 2 Criticality Indexes
### Hydraulic Index  
During a failure event, the network pressure is likely to decrease, since isolating a pipe may block the regular flow path and detouring other longer paths will increase the flowrate and overall head losses. The reduction of pressure at the nodes is measured using a pressure decline (\(PD\)) index, which compares the normal pressure and declined pressure during a failure event while consideringthe nodal demand as a weighting factor; this is so that a node with a higher demand has more influence on the index. Equation (4) represents the calculation of the \(PD\) index.  
\[PD_{i}=\frac{\sum_{j=1}^{N}\left[\left(1-\frac{P_{e,j}}{P_{n,j}}\right)\times Q_ {n,j}\right]}{\sum_{j=1}^{N}Q_{n,j}}, \tag{4}\]  
Here, \(PD_{i}\) is the pressure decline index with the isolation of pipe \(i\); \(P_{e,j}\) is the pressure at node \(j\) after the event; and \(P_{n,j}\) is the normal pressure at node \(j\).
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 1 Introduction
Given the high accuracy of short-term forecasts, the differential integrated moving average autoregressive model (ARIMA) is utilized to forecast IMFs [9]. A back-propagation neural network (BPNN), as a traditional neural network model, has better non-linear fitting ability and performs well in terrestrial network traffic forecasting [10]. BPNNs are utilized to forecast the residual from the ARIMA. However, numerous hyperparameters in BPNNs result in several problems, i.e., slow convergence and local optima [11]. The grey wolf optimization algorithm (GWO) is a group intelligence algorithm derived from the social hierarchy and hunting mechanisms of grey wolves [12]. It has been proved that GWO has a more reasonable global optimum search mechanism, which makes it more suitable for parameter optimization problems. Therefore, GWO is utilized to optimize the hyperparameters  
Figure 1: Long\(-\)range baseline and short\(-\)range fluctuations of mega\(-\)constellation network traffic. of the BPNN and prevent the BPNN from local optimality. Thus, the self-similar satellite network traffic hybrid forecasting model based on EMD-ARIMA-BPNN is proposed. In the proposed EMD-ARIMA-BPNN model, constellation network traffic is first decomposed into multi-order IMFs with EMD. Secondly, IMFs are reconstructed to obtain network traffic residuals, which are utilized as the input for a BPNN optimized by GWO. The LRD properties in residuals can be adequately captured by the GWO-BPNN. Finally, the reconstructed IMFs and forecasted residuals are combined as satellite network traffic forecasting results. The main contributions of this paper are described as follows:  
* Firstly, the LRD properties of satellite network traffic can result in deteriorated forecasting accuracy and increased computational complexity. Empirical mode decomposition (EMD) is utilized to decompose LRD traffic into multi-order IMFs with single frequencies and SRD to reduce forecasting complexity;
* Secondly, to address the problems of the uneven initial population distribution and slow convergence rate of the standard GWO, an opposition-based learning strategy (OBL) and cosine-based non-linear convergence factor are utilized to improve the GWO, i.e., IGWO. The forecasting accuracy of the BPNN is improved by utilizing IGWO to optimize the hyperparameters;
* Finally, we design the total flow of the LEO mega-constellation traffic forecasting process based on OMNET++. The effectiveness of the proposed satellite network traffic forecasting model is verified through comparative experiments. The rest of the paper is organized as follows: Section 2 introduces the current research status of satellite network traffic forecasting. Section 3 constructs the satellite network traffic generation model and proposes the EMD-based network traffic decomposition. Section 4 proposes the ARIMA-based IMFs forecasting model and BPNN-based residual forecasting model. The LEO mega-constellation network traffic hybrid forecasting model is constructed. Section 5 summarizes this paper and proposes future work.
# A Project Scheduling Game Equilibrium Problem Based on Dynamic Resource Supply
###### Abstract  
In a resource-constrained project scheduling problem, most studies ignore that resource supply is a separate optimization problem, which is not in line with the actual situation. In this study, the project scheduling problem and the resource supply problem are regarded as a dynamic game system, with interactive influences and constraints. This study proposes a Stackelberg dynamic game model based on the engineering supply chain perspective. In this model, the inherent conflicts and complex interactions between the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) and the Multi-Period Supply Chain Problem (MPSCP) are studied to determine the optimal equilibrium strategy. A two-level multi-objective programming method is used to solve the problem. The MRCPSP is the upper-level planning used to optimize project scheduling and activity mode selection to minimize project cost and duration; MPSCP is a lower-level planning method that seeks to make resource transportation decisions at a lower cost. A two-layer hybrid algorithm, consisting of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), is proposed to determine the optimal equilibrium strategy. Finally, the applicability and effectiveness of the proposed optimization method are evaluated through a case study of a large hydropower construction project, and management suggestions for related departments are provided.  
multi-cycle supply chain; project scheduling; Stackelberg dynamic game; two-level multi-objective programming; GA with double strings; particle swarm optimization  
2020 August 2020  
Accepted: 6 September 2022  
Published: 9 September 2022  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 5 Inversion Test of an Ideal Model
### Two-Dimensional Model Inversion Test  
To assess the effectiveness of the ISSA inversion method in comparison to other algorithms, a theoretical model with an electrical structure was designed, as depicted in Figure 10a. The model background consists of a three-layer H-shaped geoelectric structure and includes two low-resistivity thin plate anomalies, one in the horizontal direction and the other at an oblique angle. There are 11 measurement points spaced at intervals of 10 M along the conductor source. The data obtained from the 1D inversion of these 11 measurement points using various algorithms were interpolated and smoothed to generate the cross-sectional images shown in Figure 10b-d.  
Figure 10b presents the results obtained using the traditional OCCAM smooth inversion method. In this image, the resolution for depicting low-resistivity layers at depths of 150-190 M is inadequate. Additionally, the accurate resistivity information between the shallow low-resistivity thin plate and the underlying low-resistivity layer is obscured. This method is not suitable for geological regions with continuous resistivity changes.  
In contrast, the results obtained through nonlinear methods, as shown in Figure 10c,d, effectively overcome the issue of low accuracy and exhibit greater sensitivity to changes in the resistivity of geological layers with fewer boundary ambiguities; however, the stability of the inversion using the original SSA algorithm is insufficient, as shown in Figure 10c. The inversion result for the low-resistivity thin layer deviates from the theoretical model. In comparison, the result obtained using the ISSA algorithm, as shown in Figure 10d, is  
Figure 10: Two-dimensional geoelectric model and inversion results of different algorithms. (**a**) Real model, (**b**) OCCAM inversion, (**c**) SSA inversion, (**d**) ISSA inversion.  
closer to the actual geological structure as a result of more explicit boundaries and more precise anomaly location determination.
# Antibacterial Effect of Topically Administered Transcamic Acid in Large Joint Arthroplasty
## 2 Materials and Methods
### Microdilution Technique for the Determination of MIC and MBC of Tranexamic Acid  
For bacterial strains listed in Table 1, minimal inhibitory concentrations (MIC) of TXA were determined by the microdilution technique [30] in 96-well microtiter plates (sterile, F-Bottom, \(\mu\)Clear, Black) from Greiner bio-one (Frickenhausen, Germany). Tranexamic acid powder was purchased from Sigma-Aldrich (St. Louis, MO, USA) and dissolved in brain heart infusion (BHI) broth to obtain a stock concentration of 133.3 mg/mL. Final tested TXA concentrations to determine MICs ranged from 100 mg/mL to 0.1 mg/mL. Two-times dilutions of TXA were prepared in BHI broth. Bacterial suspensions were prepared by suspending 5 individual colonies from fresh overnight BHI agar plates in 5 mL BHI and incubated at 37 \({}^{\circ}\)C with shaking for 24 h. The culture was then adjusted to match the turbidity of a McFarland 0.5 Standard (1-2 \(\times\) 108 cfu/mL) and further diluted to obtain a final bacterial concentration of 5 \(\times\) 105 cfu/mL in each well. Positive growth controls (bacterial suspensions without TXA) were assessed along with each tested strain. BHI alone and BHI with all tested TXA concentrations (without bacteria) were also assessed as negative growth controls. Microtiter plates were incubated at 37 \({}^{\circ}\)C for 24 h. To determine the proliferation of bacterial cells at different TXA concentrations, PrestoBlue(tm) Cell Viability Reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) was added to wells according to the manufacturer's instructions. Plates were then incubated in the dark for 2 h at 37 \({}^{\circ}\)C. The assay was performed in triplicate for each strain. Fluorescence was measured with a Tecan Infinite 200 Pro MPlex plate reader (Tecan, Mannedorf, Switzerland) with excitation and emission wavelengths set at 560 nm and 590 nm, respectively. Fluorescence values (mean values from three replications \(\pm\) standard deviation) were plotted vs. TXA concentration. MIC values were determined as the lowest TXA concentrations that prevented the visible growth of bacteria. To determine the possible bactericidal activity of TXA (minimal
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 5 Main Strategies for Nanoencapsulation of Essential Oils
### Lipid-Based Nanoencapsulation
#### 5.2.3 Solid Lipid Nanoparticles  
Solid lipid nanoparticles (SLNs), also known as nanostructured lipid carriers, are suspensions of nano-sized solid lipid particles dispersed in an aqueous medium. SLNs (Figure 2C) are mainly prepared from solid lipids, such as fatty acids (e.g., palmitic acid), triglycerides (e.g., trilaurin), steroids (e.g., cholesterol), and partial glycerides (e.g., glyceryl monosterate) [114]. From a formulation point of view, SLNs are very similar to emulsions, but they are obtained using a lipid that is solid at room temperature to form a lipid phase, which results in solid dispersed particles rather than oil droplets. Due to the solid nature of SLNs, they have higher stability and longer storage time than aqueous liposomes [121]. The volatility and instability of EOs could be significantly reduced by SLNs systems [122; 123]. Nevertheless, there are only two basic production techniques for large-scale production of SLNs in food processing: thermal homogenization and cold homogenization [124]. [PERSON] et al. [123] prepared _Z. multifora_ essential oil-loaded SLNs as delivery agents of eugenol using glyceryl monosterate (lipid phase), Tween 80, and Poloxamer (surfactant). Their results showed that solid lipid nanoparticles exhibited stronger antifungal activity at low doses than free oil. [PERSON] et al. [125] prepared cholesterol-loaded curcumin SLNs using the high-pressure homogenization method. The prepared SLNs showed a particle size range of 112-163 nm, and the drug embedding rate was up to 71%. The results have shown that cholesterol lipid structure leads to enhanced permeability of bacteria, and this can improve the antibacterial ability of curcumin.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 6 Application of the Proposed Model: TDI Calculation
### Overall Description  
The main challenge when designing the TDI was to properly capture the differences between demand response strategies in order to fairly select water heaters to interrupt while minimizing the discomfort of users. Therefore, evaluating the TDI calculation is important to have real scenarios that capture the utilization profile of the users of electric water heaters. Evaluating the proposed methodology over a real scenario is not an easy task. The scenario must include a set of real water heaters large enough to carry out experiments on real water uses, and real interventions must be set up. To overcome these difficulties, a common approach in the related literature [22; 26] consists of using simulations of thermal appliances.  
A simulator was implemented to perform the experimental evaluation in those scenarios where real data is not available. The consists of two modules: the _individual water heater module_ simulates the energy dynamics of a water heater, based on the work by [PERSON] [26] and the _household utilization_ module generates scenarios of water utilization for a group of households, using a vector of hourly probabilities of water usage as input.  
The proposed TDI is evaluated in three different scenarios accounting for different number of water heaters, households, and priorities. Scenario #1 considers two water heaters and real data for both temperature and the electrical state (ON/OFF) of the water heaters. Scenarios #2 and #3 considers a large number of water heaters, for which real data about the electrical state are available. In turn, the developed temperature and water utilization models, implemented in the simulator, were applied to validate the proposed TDI. The main details of the evaluation are reported in the following subsections.