esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Try-On Systems in Fashion Consumption: A Systematic Review
## 1 Introduction
### Technology and Applications of Virtual Try-On Systems  
The application of IIT in the fashion industry is primarily manifested through VTO systems which use websites, VR, AR, and avatars to allow consumers to try on clothes virtually [4]. VR immerses users in digital simulations of the real-world settings, creating detailed computer-generated environments [16; 17; 18]. Through VR, retailers can vividly demonstrate intricate product details using sophisticated visual and auditory storytelling, giving customers an experience that closely mimics reality [19]. AR is defined as the technology that combines virtual objects with the real environment, aligns them interactively in real-time [20]. It enhances, extends, edits, or modifies the experience of the real world using digital information [21]. A defining attribute of AR is its interactive, synchronous, real-time, and computer-generated essence [22; 23; 24; 20; 25]. In VTO systems, avatars or virtual models represent users in virtual environments, granting shoppers the chance to assess the fit of selected virtual garments in a realistic scenario. Considerable progress in digital fashion technologies has led many retailers to develop their own VTO systems.  
As shown in Figure 1, current VTO implementations can be categorized into three types based on imaging techniques and avatar types used: website-based VTO, VR-based VTO, and AR-based VTO. Website-based VTO can be further divided into single fitting and mix-and-match systems according to the amount of items allowed for fitting. Single fitting systems, such as Amazon Outfit-VITON, My Virtual Model, and Fits.me, allow users to try on one item at a time [26]. Mix-and-match sites like Guess.com, Taobao AI Fitting Room, and Uniqlo 4D Fitting Room, enable users to try on multiple items at once [27].  
VTO systems that employ VR or AR are considered advanced forms of IIT [7]. In these systems, users often appear as 3D avatars, including real-time projections of themselves. VR-based VTO, such as Meta Avatars store and NIKELAND, creates computer-generated virtual shopping environments that require VR headsets. Conversely, AR-based VTO employs scanners or cameras to project users in real-time, showing the clothes or products they are virtually wearing, as seen in popular AR VTO applications like Zeekit and POIZON [6].
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
## 3 Results and Discussion
### Central Composite Rotatable Design (CCRD)
Regardless of the statistical definition of experimental conditions, the removal/immobilization of sodium in zeolite was also studied and evaluated in the subsequent stages to identify the \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Variation Source** & **SS** & **d.f** & **MS** & **F Cal** & **F Tab a** & \(p\) \\ \hline \multicolumn{6}{c}{Na removal} \\ \hline Regression & 1.53 & 5 & 1.53 & 1.97 & 1.79 & \textless{}0.25 \\ \hline Sediments & 0.93 & 6 & 0.15 & & & \\ \hline Total & 2.43 & 11 & & & & \\ \hline \multicolumn{6}{c}{B removal} \\ \hline Regression & 0.227 & 5 & 0.227 & 3.89 & 3.11 & \textless{}0.1 \\ \hline Sediments & 0.07 & 6 & 0.012 & & & \\ \hline Total & 0.29 & 11 & & & & \\ \hline \hline \end{tabular}  
* SS—sum of squares; d.f—degrees of freedom; MS—mean square; F—Fisher’s ratio; \(p\)—probability. a Values are tabulated according to [PERSON] et al. [62].  
\end{table} 
Table 7: Analysis of variance for sodium and boron removal variables resulting from the \(2^{2}\) factorial design. overall behavior of the \"complex\" water and its potential impact on the concentration in irrigation water as an additional positive aspect.
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 2 Materials and Methods
### Antioxidant Assay  
A 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay was conducted to assess the efficacy of the biogenic IONPs in scavenging the free radicals. Different concentrations (50, 100, 150, 200, and 250 mg/mL) of the biogenic IONPs were prepared using methanol as the solvent. A 1 mM solution of DPPH was prepared by dissolving it in 100 mL of methanol. A 2 mL aliquot of DPPH solution was mixed with the biogenic iron oxide nanoparticles of varying concentrations. The solution was subjected to incubation at ambient temperature for 30 min under dark conditions. Ascorbic acid was used as a positive control, whereas an equal amount of methanol and DPPH was used as a blank. The absorbance of the reaction mixtures at a wavelength of 517 nm was measured using a UV spectrophotometer, and the percentage of inhibition was calculated using the following equation:  
\[\%\ DPPH scavenging=[(A-B)/A]\times 100,\]  
whereas A is the absorbance of the control and B is the sample absorbance [41].
# Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
## 3 Computed Tomography Imaging Speeding Up  
Optimisation of Computed Tomography based on finding a balance between exposure times and image quality has considerably developed and provides for faster and more accurate algorithms. This issue is getting more attention due to its being important in increasing the risk of death caused by cancer [17; 18; 19]. The most effective way to protect from radiation is to omit redundant examinations steps. Nowadays, numerous researchers attempt to improve CT image quality by applying various techniques, such as Iterative Reconstruction and Model Based Iterative Reconstruction. Unfortunately, the issue of radiation doses still goes largely unaddressed. The author in [7] presents an adaptive acquisition model, which turns out to be proficient in reducing dosage levels. This procedure does not need any hardware modifications because it employs so-called two-step adaptive acquisition techniques [20; 21]. The idea of this algorithm is given below. It starts with projecting the object using the lowest possible dosage levels. In this way, the CT image reconstruction algorithm initial guess is calculated. The next step is to determine image features' delineation. In this step, the further line projections are incorporated sequentially. Finally, the method refines key image features location until the convergence is reached. The convergence criterion is to obtain sufficient image quality in the fewest possible projections. This algorithm takes advantage of using Ridgelets based model, which is useful in classifying image edges in the multiscale modelling analysis [22]. The adapted CT image reconstruction algorithm objective is to derive the solution from the adaptive Ridgelet approximation. In this work, the projection lines number are minimised while the angles are kept untouched. The Adaptive Tomography Acquisition (ATA) [23] algorithm tends to adaptably take the samples of the line projections associated with the object edges. At the start the image is not reconstructed and the algorithm tries to reconstruct it. The Ridgelet transform (RT) has constrained directional features. It only works for image lines singularities. The essence of RT is to convert line singularities into point singularities using the projection slice theorem performed on the Radon transform. The finite ridgelet transform (FRIT) consists of the two following operations: 1. Deriving the Discrete Radon Transform, 2. Calculating a wavelet transform. The Finite Radon transform (FRAT) is done in two stages: 1. Deriving the 2D Fast Fourier transform (FFT) of the image and (applying 1D inverse Fourier Transform (iFFT) on Radon projection 2. Applying 1D wavelet transform to its output. The ATA exploits the Ridgelet model. This method updating the Ridgelet coefficients tries to obtain the only line projections that are associated with edge singularities and determined by the level of multiscale image decomposition, see Figure 1.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
###### Abstract  
Water use efficiency (WUE), given by the ratio between organic matter production and water consumption, could be considered as a very important ecological indicator for assessing vegetation system growth conditions by combining organic matter production and water consumption. It is especially important for regional vegetation sustainable management by creating enough organic matter with restricted water supply. Furthermore, proper analysis of WUE is vital for the evaluation and future plans of ecological restoration projects in ecologically fragile regions such as the Three-North region of China. In this study, ecosystem WUE across the Three-North region of China from 2001 to 2017 was obtained, and the variation trends and major influencing factors were also analyzed. The results demonstrated that (1) the average WUE across the Three-North region of China is 0.7376 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)mm\({}^{-1}\cdot\)y\({}^{-1}\); (2) the spatiotemporal variation trends of WUE are similar to those of gross primary production (GPP); and (3) in the southeastern parts of the Three-North region, the vegetation conditions are better with sustainable improvements, while in Xinjiang Province, the sustainable degradation areas are widely spread. The results of this research reveal large spatial heterogeneity of WUE, with high WUE mainly in the southeastern region with sufficient precipitation and afforestation programs. For those areas far away from this region, WUE is not satisfactory, suggesting that, for a sustainable vegetation growth, it is important to consider the water supply to maintain suitable vegetation cover. Furthermore, the results of this research are important for future ecological restoration and sustainable management of environment.  
Water use efficiency; Three-North region; human activities 2021  
13 July 2021  
16 July 2021
## 3 Reliability Sensitivity Calculation  
The expression of the \(i\)-th hyperplane through the projection point \(S_{i}^{*}\) and parallel to the tangent plane of the limit state surface at the MPFP can be written as follows [28]:  
\[\text{g}_{i}(\mathbf{v})=\mathbf{a}\cdot(S_{i}^{*}-\mathbf{v})=\sum_{j=1}^{n}a_{j} \bigg{(}S_{ij}^{*}-\mathbf{v}_{j}\bigg{)}=0 \tag{10}\]  
The reliability index can be easily evaluated as [28]:  
\[b_{i}=\frac{\mu_{g_{i}(\mathbf{v})}}{\sigma_{g_{i}(\mathbf{v})}}=\frac{\sum_{j=1}^{n}a _{j}\bigg{(}S_{ij}^{*}-\mu_{\mathbf{v}_{j}}\bigg{)}}{\left(\sum_{j=1}^{n}a_{j}^{2} \sigma_{g_{j}}^{2}\right)^{1/2}} \tag{11}\]  
where \(\mu_{\mathbf{v}_{j}}\) and \(\sigma_{\mathbf{v}_{j}}\) are the mean and standard deviation of the standard normal variable \(\mathbf{v}_{j}\), respectively. In consideration of \(\sum_{j=1}^{n}a_{j}^{2}\sigma_{\mathbf{v}_{j}}^{2}=1\), \(\partial\Phi_{(0,1)}(-b_{i})/\partial b_{i}=-e^{-b_{i}^{2}/2}/\sqrt{2\pi}\), the reliability sensitivity of \(\hat{p}_{f}\) with respect to \(\theta_{\mathbf{v}_{j}}\) can be calculated as [28]:  
\[\frac{\partial\hat{p}_{f}}{\partial\mu_{\mathbf{v}_{j}}}=\frac{1}{N\sqrt{2\pi}} \sum_{i=1}^{N}e^{-b_{i}^{2}/2}a_{j} \tag{12}\]  
Figure 1: Axis orthogonal importance Latin hypercube sampling method. The standard deviation is [28]  
\[ \begin{split}\sigma\bigg{(}\frac{\partial\hat{p}_{f}}{\partial\mu_{v_ {j}}}\bigg{)}&=\sqrt{\frac{1}{N-1}\left[E\bigg{(}\frac{\partial \hat{p}_{f}}{\partial\mu_{v_{j}}}\bigg{)}^{2}-\bigg{(}E\bigg{(}\frac{\partial \hat{p}_{f}}{\partial\mu_{v_{j}}}\bigg{)}\bigg{)}^{2}\right]}\\ &=\sqrt{\frac{1}{N-1}\left\{\frac{1}{N}\sum\limits_{i=1}^{N} \left[\frac{a_{j}}{\sqrt{2\pi}}e^{-b_{i}^{2}/2}\right]^{2}-\bigg{(}\frac{ \partial\hat{p}_{f}}{\partial\mu_{v_{j}}}\bigg{)}^{2}\right\}}\\ \sigma\bigg{(}\frac{\partial\hat{p}_{f}}{\partial\sigma_{v_{j}}} \bigg{)}&=\sqrt{\frac{1}{N-1}\left[E\bigg{(}\frac{\partial\hat{p}_ {f}}{\partial\sigma_{v_{j}}}\bigg{)}^{2}-\bigg{(}E\bigg{(}\frac{\partial\hat{ p}_{f}}{\partial\sigma_{v_{j}}}\bigg{)}\bigg{)}^{2}\right]}\\ &=\sqrt{\frac{1}{N-1}\left\{\frac{1}{N}\sum\limits_{i=1}^{N} \left[\frac{a_{j}}{\sqrt{2\pi}}b_{i}e^{-b_{i}^{2}/2}\right]^{2}-\bigg{(}\frac{ \partial\hat{p}_{f}}{\partial\sigma_{v_{j}}}\bigg{)}^{2}\right\}}\end{split}  \tag{13}\]  
respectively. If random variables are all the normal variable, they can be transformed into the standard normal space by [28]:  
\[ \begin{split}\mathbf{v}_{j}=\frac{X_{j}-\mu_{X_{j}}}{\sigma_{X_{j}}} \hat{p}_{f}=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}\frac{l[\mathsf{g}_{ X}(\mathbf{v}_{i})]f_{X}(\mathbf{v}_{i})}{p_{V}(\mathbf{v}_{i})}\end{split}
## 4 Results and Discussion
### PID Controller  
From the previous section, the four algorithms with the best fitness during tuning of the PD controls were GWO, HGS, MPA and SMA; these are taken for the PID control. Eighteen parameters were identified (Equation (17)), so it was necessary to increase the number of individuals. Here, two simulations are reported, one with 80 and one with 100 individuals. The number of evolutions and repetitions is 40 and 30, respectively. Table 7 shows the RMSE statistics for the simulation with 80 individuals, 40 evolutions and 30 repetitions of the four algorithms under test. The HGS algorithm obtained the best value (\(RMSE=0.032594329329481\)). We also show the best values for all algorithms for the mean, standard deviation, and worst. Figure 15a shows the RMSE during the 40 iterations. The three best values are HGS, MPA and GWO. In the box plot, the lowest variability is obtained by GWO, HGS, and SMA. The highest variability of the RMSE value is in MPA algorithm. The 18 parameters identified by each algorithm are shown in Table 8, where the HGS parameters are the best for obtaining a lower RMSE  
Table 9 contains the RMSE statistics for the simulation with 100 individuals, 40 iterations and 30 repetitions. The algorithm that obtains the best performance is the HGS with a \(RMSE=0.032594309723623\). The GWO algorithm obtains the best average and standard deviation for this case. The MPA obtains the worst values. Figure 15b show the RMSE during the 40 iterations. The three best values are HGS, GWO and MPA. GWO, HGS, and SMA obtain the lowest variability in the box plot. This graph shows a smaller dispersion of the optimization solutions compared to the simulation with 80 individuals. The 18 parameters identified by each algorithm are shown in Table 10. The HGS parameters are the best for obtaining the lowest RMSE. The 18 parameters identified by each algorithm are shown in Table 8, where the HGS parameters are the best for obtaining a lower RMSE. We evaluated the performance of optimized parameters obtained (Table 10) for all six dynamics, (x, y, z, \(\phi,\theta,\psi\)) and algorithms (Figures 16-21) at 40 iterations with 100 individuals. The desired trajectories are the same as for the PD controller experiment. The best trajectory is determined by the minimal value of cost function achieved by HGS as shown in Table 9. The similarity of the performance of the six dynamics with the PD controller implementation can be noted, even when the number of parameters to optimize is larger and the number of individuals is bigger. Figure 14: Tracking performance in three–dimensional space changing desired trajectory.
## 6 Conclusions  
Aiming at the problem of large-scale constellation phase control in low orbit, the strategy of constellation station holding was investigated in this study. First, the relationship between the satellite phase angle drift rate and the semi-major axis attenuation was analyzed mathematically. A phase-holding loop control method combining semi-major axis overkill control and passive control was proposed. In addition, the calculation process of the phase-holding control parameters (maintenance control period and semi-major axis control amount) was derived, which provides theoretical support for constellation station holding. Second, the absolute station position control of large-scale constellations was simulated. The simulation results showed that to ensure the phase angle deviations were within the range of \(\pm\)0.1\({}^{\circ}\) for all the satellites in the constellation in low-solar-activity years, about 13 days were required on average to adjust the satellite's semi-major axis by about 71 m. The relative position control of small constellations was simulated. The simulation results showed that only four orbital maneuvers were needed to achieve the \(\pm 5^{\circ}\) phase angle threshold maintenance task over 300 days for all satellites in the constellation. Finally, the comparison of the control strategy simulation results provides data support for the selection of maintenance control strategies for different-scale LEO constellations.  
Conceptualization, M.H. and F.L.; methodology, M.H.; simulation, F.L., W.X., C.L., W.G. and Y.R.; writing--original draft preparation, M.H. and F.L.; writing--review and editing, M.H.; visualization, F.L., W.X. and C.L.; supervision, W.G. and Y.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c} \hline
**Control Strategy** & **Frequency of Orbit Control** & **Occupied Resources** \\ \hline Absolute control & A single star maintained a control period of 13.29 days and consumed more fuel. & Each satellite in the constellation was controlled independently, requiring fewer measurement and control resources. This method is suitable for large-scale constellation phase maintenance. \\ \hline Relative control & Only four orbit-control tasks were needed for satellites \(1-3\), \(2-2\), \(2-4\), and \(3-6\), consuming less fuel. & The calculations were complex, and the resource consumption was large. This method is suitable for small-scale constellation phase maintenance. \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of simulation results with control strategies.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are available from the corresponding author upon reasonable request. Data access is restricted to research and educational applications.  
**Acknowledgments:** We thank all authors for their contributions to this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Analysis of Application Parameters of Hydraulic Slotting Technology in Jointed Coal Reservoirs
## 1 Introduction
The field test results revealed that the cross-slotting arrangement doubled the coal roadway tunneling speed. [PERSON] et al. [27] simulated hydraulic slotting using different lateral pressure coefficient models developed by the Particle Flow Code, and investigated the crack propagation patterns and kinetic energy evolution laws with different lateral pressure coefficients. [PERSON] et al. [28] obtained the laws governing the influence of the slotting radius, gas pressure, and coal seam permeability on the effective influence radius by carrying out a numerical simulation. They reported that the effective radius was positively correlated with the slotting radius and permeability, and negatively correlated with the gas pressure. Additionally, the stress distribution near the borehole was consistent with the gas pressure distribution. However, the aforementioned studies did not consider the cleats and joints in coal seams, and only considered the basic theory and technical principle of hydraulic slotting technology to increase the coal seam permeability. Moreover, previous studies have not provided suggestions and guidelines for the application of hydraulic slotting technology to jointed coal reservoirs. The typical hydraulic slotting application parameters are the borehole angles, water jet pressure, and rotation speed. In this study, we investigated the law governing the influence of the application parameters on the hydraulic slotting effect by carrying out numerical simulation and experimental testing. Various suggestions for practical applications are provided in this paper.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis
### Error Analysis  
The above test results show that the designed area measuring instrument can achieve ideal measurement accuracy, but there are still small errors. The causes of area measurement error are mainly divided into hardware error and software error. The hardware errors of satellite and receiver are mainly related to the signal frequency, signal modulation mode, satellite, and receiver types. The type and positioning method of the receiver have a significant impact on the estimation of the region. For example, in the measurement area, the accuracy of differential GPS measurements is higher than that of autonomous positioning systems [32]. The positioning error of satellite navigation is mainly divided into satellite errors, propagation errors, and receiving errors, which are generated by different hardware modules during propagation, measurement or reception [33,34]. Software errors mainly come from the discretization of the area by the numerical method, which leads to a reduction in accuracy. For this kind of error, the accuracy error caused by noise interference and the multipath effect of the satellite signal can be reduced by replacing the satellite receiving module with higher accuracy or improving the filtering algorithm [35].  
In summary, through hardware upgrades and algorithm optimization, as well as the unique advantages of the BeiDou system, we can further reduce the error range and improve the overall measurement accuracy and reliability of the area measuring instrument.
## 2 Literature Review and Related Work  
### Multi-Stage, Multi-Level Assembly Job Shop Scheduling Problem  
In recent years, with the increase in customized product orders, the production model of multi-variety and small-batch manufacturing has gradually become mainstream [11]. Products under this model typically have complex multi-level structures, which significantly increases the complexity of scheduling. Additionally, processed jobs need to be transported in batches to the assembly stage, and the transportation time cannot be overlooked [12]. As a result, the MsMIAJSP has become a key focus in recent research.  
[PERSON] et al. [13] addressed the distributed MsMIAJSP considering maintenance, aiming to minimize the maximum tardiness. [PERSON] et al. [14] proposed batch transfer and mixed-model assembly for the MsMIAJSP with the optimization objectives of minimizing manufacturing and transportation costs. Building on this, [PERSON] et al. [15] further considered lot streaming with the objective of minimizing total completion time and production cost. All of the above studies already conducted relevant work on the MsMIAJSP, primarily focusing on improving production efficiency and economic benefits. However, with the increasing awareness of energy consumption and sustainability in modern manufacturing systems [16], there is a growing need to address energy efficiency in the EEMsMIAJSP, where the optimization objectives are not only centered on production efficiency but also on minimizing energy consumption throughout the entire scheduling process.  
In the EEMsMIAJSP, energy consumption becomes a critical factor due to the involvement of multiple production stages and levels, where idle times, machine setups, and transportation processes can lead to significant energy wastage. Therefore, optimizing both production schedules and energy usage concurrently presents a complex, multi-objective problem that requires advanced strategies and algorithms to balance production efficiency and energy conservation.