esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
## 3 Results
### Point Forecasting  
Given that the proposed comprehensive GPR model also provides point forecasts, its performance is compared to four baselines models: (1) Regression tree (RT), (2) Support vector regression (SVR), (3) Back-propagation NN (BPNN), and (4) QR.  
The purpose of this evaluation is to demonstrate that the proposed method can improve forecasting models with limited data better than the baseline models. The test data set is used to fine-tune the hyper-parameters of each approach, and the best-performing model is then selected. The comparison of the forecasting results of various methods on test data is summarized, and Table 5 outlines the best possible results for each method. It is apparent from Table 5 that the GPR-based approach outperforms the four baselines based on the results in three different scenarios (the year 2020-2022).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Scenario 1 (2020)**} & \multicolumn{3}{c}{**Scenario 2 (2021)**} & \multicolumn{3}{c}{**Scenario 3 (2022)**} \\  & **MAE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline GPR (Exponential) & 35.01 & 48.11 & 0.99 & 48.02 & 60.47 & 0.98 & 38.40 & 48.01 & 0.99 \\ \hline GPR (Squared exp.) & 54.80 & 70.34 & 0.98 & 48.27 & 61.32 & 0.98 & 86.57 & 106.98 & 0.95 \\ \hline GPR (Matern 3/2) & 38.43 & 53.01 & 0.98 & 41.44 & 50.10 & 0.98 & 77.09 & 93.98 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training set errors based on three scenarios.  
QR and the proposed model have the highest R\({}^{2}\) values of 0.98 and 0.99, respectively. This displays a significant correlation between actual and predicted values. As compared to other approaches, RT's performance was subpar. Hence, regression tree methods overfit and have large variances due to poor performance on unseen test data [64]. Regarding BPNN, this parametric algorithm has several parameters to optimize. Hence, samples from small training data cannot reveal all hidden patterns. For QR, parameter estimation is difficult with limited data [38]. After QR, Kernel function-based SVR has performed well compared to RT and BPNN with an average R\({}^{2}\) of 0.94. As a result, GPR, QR, and SVR are preferred models for estimating the impact of uncertainty.  
Compared to QR, BPNN, RT, and SVR, the RMSE index of the proposed model has been averagely improved in the case of electricity demand by 36%, 58%, 79%, and 49%, respectively. Similarly, the performance error MAE shows an improvement of 38%, 60%, 80%, and 54%, respectively. The findings indicate that for point forecasting, the proposed GPR model has better performance with an acceptably smaller error than the other four baseline models despite trend change phenomena triggered by unprecedented events such as pandemics and extreme weather conditions.
## 3 Results
### Emission Characteristics
#### 3.2.5 Smoke Opacity  
Figure 9 displays the smoke opacity for _Moringa oleifera_ biodiesel and its blends with diesel tested at different engine speeds. For diesel, the average smoke opacity is higher than MOB10 and MOB20 by 33.49% and 22.73%, respectively. The MOB10 blend has the lowest average smoke opacity (32.2%) compared to MOB20 and neat diesel. At higher engine speeds, the smoke opacity of _MOB_ blends increased significantly. Several studies have shown that the smoke opacity was lower due to more oxygen contents in biodiesel. A lower ratio of carbon-hydrogen and non-availability of aromatic compounds in the biodiesel reduced the smoke emissions [72]. According to [PERSON] and [PERSON] [73], more oxygen in biodiesel blends can reduce smoke exposure in exhaust gasses. [PERSON] et al. [74] found that combustion of biodiesel blends occurs earlier than diesel. Smoke emissions are reduced due to advanced injection timing, which results from the combustion process's quick start. In contrast, diesel has higher sulfur content than biodiesel blends, which is the main reason for high smoke opacity [75].  
Figure 8: Nitrogen oxides emissions of _Moringa oleifera_ biodiesel blends and diesel at various engine speeds.  
Figure 9: Smoke opacity of _Moringa oleifera_ biodiesel blends and diesel at various engine speeds.
# A Practical Method for Controlling the Asymmetric Mode of Atmospheric Dielectric Barrier Discharges
## 3 Examples and Mechanisms of the Discharge Mode Control
### Numerical Regulating Example and the Underlying Mechanism
Such a high initial value of electron density can only ignite an unmatured discharge, forming an AP1 mode. Analogously, if the seed electron density reaches a high level at the beginning of discharge (\(>\)8 \(\times\) 10\({}^{15}\) m\({}^{-3}\)), then the subsequent discharge cannot develop to a mature one and will form several weak current pulses with the maximum density being less than 0.2 mA/cm\({}^{2}\), as shown in Figures 6a and 7b. Figure 5: Evolution of the seed electron level as a function of the breakdown number, i.e., the average electron density right at the beginning moment of each breakdown. The \(f_{\mathrm{c}}\) in (**a**–**c**) are 8 kHz, 20 kHz, 30 kHz, respectively. The red circles indicate the transition points of the driving frequency. Based on the above analyses, the seed electron density for pursuing SP1 mode should be approximately limited in the range from \(2\times 10^{13}\) to \(8\times 10^{15}\) m\({}^{-3}\) under our simulation conditions. Ignited by an appropriate seed electron level, the intensity of the subsequent discharge should be moderate so that the residual electrons can be dissipated completely, as shown in Figure 7c, leading to the SP1 mode. It should be pointed out that, being a control method of seed electron density, the higher driving frequency is able to limit the dissipative process of discharge. Therefore, the cooperation between seed electron level and dissipative time will lead to a less intense AP1 mode when\(f_{\rm c}\) is beyond 30 kHz, as can be seen from the current waveform and seed electron evolution illustrated by Figures 4 and 5. We have carried out a parameterized sweep in terms of \(f_{\rm c}\) from 30 to 100 kHz, and the results show that, within this range of frequency, such a less intense AP1 discharge can impose a restriction on the drastic variation of the seed electron density and finally, the initial density of electrons before the breakdown varies within the abovementioned range (from \(2\times 10^{13}\) to \(8\times 10^{15}\) m\({}^{-3}\)). That is to say,  
Figure 6: The relationship between the average electron density and discharge current density. (**a**) average electron density at the starting moment of the discharge vs. current density at the instant of current peak, (**b**) current density at the instant of current peak vs. maximum average electron density. Figure 7: Spatiotemporal distribution of \(\log_{10}\) (\(n_{\rm e}\)) during the \(33,34,35,36\) and \(37\) applied voltage cycle when\(f_{\rm c}\) is set to be (**a**) 8 kHz, (**b**) 20 kHz, (**c**)30 kHz. The black dashed lines divide off different applied voltage cycles. increasing the driving frequency would be a practical strategy to manipulate the discharge symmetry and some more in-depth investigations would be carried out in our future study.
# Noise Analysis and Structural Optimization of Automobile Scroll Compressor Air Valve
## 1 Introduction
Notably, the high frequency resonance effect of the system is noticeable from 1800 Hz to 6400 Hz, making it an effective noise reduction solution [11]. [PERSON] avoided vibration and noise caused by reversing reciprocating compressor pistons during shutdown by modifying the motor algorithm [12]. [PERSON] et al. successfully reduced the noise of an air-conditioning compressor by correctly setting the appropriate centrifuge clearance, motor air clearance, and coil length [13]. The study conducted by [PERSON] et al. shed light on the noise control technology of screw refrigeration compressors, highlighting the problems associated with gas pulsation and the serious vibrations and noise it generates. Through their extensive research, they tested various techniques such as acoustic wave interference, gas-solid resonance, and porous mufflers. Their efforts culminated in the optimization of a single two-stage screw refrigerant compressor for noise mitigation purposes. Experiments have shown that noise is effectively mitigated by these strategic optimization interventions in a variety of operational scenarios. This research provides crucial insights for rectifying the noise problem of screw refrigeration compressors [14]. [PERSON] et al. conducted an in-depth study of muffler baffle configurations, dimensions, chamber volume, and inlet-outlet positions. Through his comprehensive review, he determined that a circular baffle offers a wide bandwidth range with a transmission loss of 58 dBA. In addition, he found that about 75% of rectangular plate baffles exhibit a 67 dBA transmission loss. This led to the conclusion that a dual chamber muffler has a wider bandwidth range and a higher transmission loss than a single chamber muffler [15]. An expansion muffler is a specific type of resistance muffler that relies on the principle of sudden cross-section expansion or contraction. This causes a significant change in the acoustic impedance of the muffler pipeline, thus impeding certain frequency sound waves from passing through the pipeline. These sound waves are then reflected back to the noise source, while other sound waves traveling in different directions of the same frequency are offset to each other, allowing the muffling function to be achieved [16]. This research focuses on improving the internal architecture of the automobile scroll compressor without changing the external dimensions of the valve. Simulation analysis is carried out under real-world operating conditions to ensure the effectiveness of the project'sultimate goal--reducing the sound pressure level and sound power of the automobile scroll compressor.
## 4 Example Application  
### Overview of an Enterprise  
A road transport enterprise for hazardous chemicals in Hunan Province was selected as the object of accident risk assessment. The company is a professional logistics enterprise engaged in dangerous goods warehousing, transportation, distribution, logistics, and financial integration services. The company owns more than 60 sets of controllable tank vehicles, including 15 new 45 m stainless steel tank cars purchased in 2011. The company has a professional hazardous chemical transportation management team. All the vehicles use information management methods and are equipped with a GPS/GIS positioning system and video surveillance system to realize the whole process of visual safety supervision, ensuring the safe and efficient driving of the vehicles.  
The combined weighting-cloud model was used to evaluate the risk of road transport accidents involving hazardous chemicals in this enterprise.
## 4 Discussion
### Short Implants and Implant Stability
For the same reasons, a high degree of primary stability is more strictly required for single implant [PERSON] et al. (2018); [PERSON] et al. (2018). Absolute optimal insertion torque or ISQ value was not identified in previous publications. Most publications proposed 30-35 Ncm to be the minimum insertion torque [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018) and 60-65 as minimum ISQ value for successful early or immediate loading [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018). This study designed on clinical aspects following lower single molar replacement using short implants with immediate functional loading concept. In our experiment, type IV bone quality were excluded under strict inclusion criteria and the target values at surgery were achieved for the peak insertion torque and ISQ were 35-50 Ncm and greater than 65, respectively. The long and short implant groups showed the mean insertion torque of 40.53 \(\pm\) 5.35 Ncm and 38.89 \(\pm\) 4.85 Ncm and the mean ISQ value of 81.53 \(\pm\) 6.26 and 78.69 \(\pm\) 5.08, respectively, at surgery with no significant difference between two groups in neither insertion torque nor ISQ value (\(p\)-value \(>\) 0.05).
## 3 Results  
### Requirements Analysis  
The requirements analysis resulted in functional as well as non-functional (e.g., technical) requirements for the annotation tool. Requirements were formulated and documented as user stories. The requirements analysis also established a data annotation workflow that has to be supported by the annotation tool (Figure 1).  
Users are assigned the roles of administrator, annotator, or verifier according to their responsibilities for different parts of the annotation workflow. Each user, along with basic identifying data used for authentication purposes, is assigned a location identifying the site in multicentric collaborative annotation settings like those typical of the MII, where many university hospitals collaborate in and contribute to the national data sharing infrastructure. Administrators start the process by importing medical metadata, typically consisting of local item identifiers uniquely identifying the source system and data item, along with any descriptive information available from the source context, into the annotation tool using a CSV file. Imported metadata is tagged with the location of the uploading administrator to enable the display of provenance information during the annotation process and support the annotator in assessing the applicability and validity of previous annotation choices for the annotation task at hand; in addition, location assignment controls annotator and verifier work allocations--by default, annotators and verifiers are presented only with annotation tasks originating from their location. Annotators can then search terminologies for fitting codes and use collaborative features provided by the tool to annotate data uploaded to their location. Verifiers accept or deny annotations and finally, administrators can then export data with its annotations as a CSV file.
## 3 Materials and Methods
### Discrete Fracture Network (DFN) Modelling  
This study mapped fracture patterns based on the DEMs and exposed outcrops. The geologically mapped fracture networks were then used to investigate the fracture formation process and determine fracture distribution statistics and scaling. Two-dimensional (2D) discrete fracture models were generated from the Niah's regional lineaments and limestone outcrops in the northern area of the Subis build-up. The traced DFNs were then utilised to quantitatively model the fracture distribution using FracPaQ, an open-source MATLAB toolbox [62].  
The topology of lineament and outcrop-scale fracture networks was investigated thoroughly. Topology is a tool that allows for the proper characterisation of the connectivity and relationships between fractures [14; 63]. In this part, the I-X-Y model is applied to characterise the dominant control for the fracture network. [PERSON] [63] devised the I-X-Y model, which is a ternary plot for the interconnectivity of a fracture network based on scanlines. It depicts the relative proportions of isolated (I), splay or abutment (Y), and intersecting (X) nodes in the fracture network (Figure 4). A better-connected fracture pattern plots closer to the triangle's base, where there is a higher fraction of X + Y nodes (Figure 5).  
Figure 3: Surface drainage system bounded by the Niah River Basin (Official Website of Department of Irrigation and Drainage Sarawak). The density of the drainage decreases when closer to the Niah-Subis Limestone (light blue).
## 1 Introduction
The application of UV-C radiation to plant products brings significant benefits, such as reducing pesticide use and extending shelf life, which in turn reduce losses. Furthermore, modern LED lamps have very low electricity consumption and can be disposed of without harming the environment. There are four categories based on wavelength range: UV-A (315-400 nm), UV-B (280-315 nm), UV-C (200-280 nm), and vacuum UV (100-200 nm) [22]. The UV-C range has a high antimicrobial efficacy. UV-C can cause, according to some authors [23; 24], biological stress in plants and trigger the defense mechanisms of plant tissues, with consequent increases in antioxidant compounds. The main documented effects of UV-C as a postharvest treatment are to reduce respiration rates [25], deterioration in fruit tissues [26], and delaying senescence and ripening in several fruit and vegetable species, such as apples, citrus fruits, tomatoes, peaches, grapes, and guavas [27]. As a result of the high economic value of green asparagus and its high perishability, there has been increase in the demand for new and milder technologies to enhance its shelf life. The short shelf life of asparagus is due to a high respiration rate, which continues after harvesting. Moreover, the domestic storage period is very short and not longer than 3-5 days [12]. To our knowledge, there is little information about the effect of different doses of UV-C on the maintenance of freshness attributes and antioxidant compounds of green asparagus during storage. Therefore, the present study aims to investigate the effects of different doses of UV-C on green asparagus, to identify the best dose to reduce the effect of lignification on hardness and color, and, in the meantime, to save the antioxidant compounds for up to 3 weeks.
## 3 Application to Immersed Tunnel
### Boundary Conditions and Input Load  
#### 3.3.1 Boundary Conditions  
This model adopted VSAB for dynamic calculation. The seismic response of the immersed tunnel is a static-dynamic coupling problem; the static calculation (i.e., in situ stress balance and hydrostatic pressure) should be carried out before calculating the dynamics such as seismic load and hydrodynamic pressure load. In the static analysis, the static boundary is imposed on the bottom and four sides of the model in Figure 8. On the other hand, the conversion of static boundary to dynamic boundary should be considered before the calculation of dynamics. For this purpose, the static constraints of the five boundary surfaces are removed, and the corresponding restraining force is applied [41]. Secondly, ground springs and dampers are added to the boundary surface nodes to set VSAB. The spring stiffness and damping coefficient can be calculated using Equations (8) and (9).
# Method to Control the Deformation of Anti-Slide Piles in Zhenzilin Landslide
## 2 Engineering Background, Site Investigation, and Laboratory Test
### Laboratory Test of Sandstone and Mudstone  
A site investigation area (H) located at the rear edge of the Zhenzilin landslide with a steep cliff in the Silurian strata was studied. To further study the physical and mechanical properties of bedrock, some rock samples were obtained and tested in the laboratory (see Figure 4). The experimental results pertaining to geotechnical parameters are listed in Table 1. Rocks at the investigation site in this study show similar characteristics to those of the Zhenzilin landslide bedrock.  
Laboratory compressive strength tests were conducted to determine the modulus of subgrade reaction of the bedrock. The compressive strength test device system was developed based on the rock test code of highway engineering [34; 35]. The results of the laboratory compressive test show that the compressive strengths of the moderately weathered mudstone and weathered limestone were 12.4 MPa and 38.59 MPa, respectively. The corresponding modulus of subgrade reaction were \(1\times 10^{5}\) kN/m\({}^{3}\) and \(3.4\times 10^{5}\) kN/m\({}^{3}\), according to a study on the design of an anti-slide pile [36].  
Figure 3: Investigation. (**a**) Ground cracking behind anti-slide piles. (**b**) Displacement of the anti-slide pile head in site investigation.
# Urban Free-Space Optical Network Optimization
## 3 A Genetic Algorithm for FSO Communication Networks
### Fitness (Grade) Function  
After changes are applied to solution graphs (through _crossover_ and _mutation_), the GA needs to re-evaluate their grades to pass the \"best\" solutions to the next generation (see Algorithm 4, lines 5-9). The GA literature commonly terms this calculation as the \(fitness\) function.  
In this paper, the \(fitness\) function corresponds to the cost of the graph according to the specific problem at hand (as defined in Section 2). This makes the presented GA rather general because it can be adapted to solve any problem in Section 2.2 through Section 2.5. For instance, in case the combined vertex and edge cost is of interest (see Section 2.5), the fitness (grade) of a graph \(G=(V,E)\) would be  
\[fitness\left(G\right)=\sum_{v\in V}c\left(v\right)+\sum_{e\in E}c\left(e\right) \tag{1}\]
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
###### Abstract  
Spray dust removal is currently the primary method of dust control technology, while it exhibits low efficacy in dust removal capability. A Phase Doppler Particle Analyzer (PDPA) experimental system was constructed to study the influence of dust suppressants on the coupling behavior of dust-mist particles using comparative methods. According to the experimental results of the atomization effect of the spray, the Sauter Mean Diameter (D32) of the mist size of the dust suppressants showed an increasing trend compared to water. This trend became less obvious with an increase in spray pressure, and a reduction in the surface tension of the dust suppressants promoted an increase in the particle size distribution of water mist. According to the test results of the dust-mist coupling behavior experiment, compared with water, the coupling efficiency of Dodecyl Alcohol (DA), Alkylphenol Polyoxyethylene (OP-10), and Sodium Dodecyl Sulfate (SDS) increased by 27.0%, 20.3%, and 15.0%, respectively. This indicates a proportional relationship between the wetting performance of the dust suppressants and the dust-mist coupling rate and an inverse relationship between the surface tension of the dust suppressant solutions and the dust removal efficiency. The research findings hold major possibilities for enhancing the dust removal efficiency.  
mist dust collection; dust suppressants; coupling behavior; dust removal efficiency; PDPA Article