esa-sceva/satcom-chunk-collection huggingface dataset:
# A Five-Step Approach to Planning Data-Driven Digital Twins for Discrete Manufacturing Systems
## 4 Results and Discussion
### Step 4
For the assembly of Product 12, 19 blue tiles are needed. With the help of the digital agent, the digital twin receives information from the real system that it is not possible to assemble Product 12, but it can assemble the other two products from Scenario 4. For the remaining two products, the FI algorithm computes the best order sequence and sends the information to the real system. Figure 16b,c show the state in the WBPFP and buffers after all scenarios are completed. The WBPFP with base parts is empty, and there are 11 finished products in the second row. There are still 21 red tiles (Figure 16d), 16 green (Figure 16e) and 7 blue tiles (parts) (Figure 16f) in the buffers. Table 3 shows the order sequence proposed by the digital twin using the FI algorithm. Other results refer to the machine vision building block, where each product is inspected. The times required to inspect the product in a simulation environment (digital twin) and the times required to inspect the product in a real system are also shown. From the small difference between the simulation and real times, it can be concluded that the digital twin is functioning properly and is properly validated. Machine vision sends information about the suitability of the product to the digital twin, and the digital twin informs the real system whether the product should go into the WBPFP or into the box. In the case study, all products were correct, only product 12 could not be manufactured due to missing blue tiles. The Figure 17 shows how the digital twin works for the case study. In the fourth scenario, the digital twin lists which products it can assemble (\"CAN MAKE\" in the green box in Figure 17) and which products it cannot assemble (\"CANNOT MAKE\" in the red box in Figure 17). The digital twin displays a table with \"TRUE\" values for products that can be assembled and \"FALSE\" values for products that cannot be assembled. In the case of Product 12, due to the missing blue tiles, the value \"FALSE\" and the reason why it cannot be assembled are displayed in the table (lower left side of Figure 17). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order Sequence** & **Product** & \multicolumn{2}{c}{**Simulation**} & \multicolumn{2}{c}{**Machine Vision**} \\  & & **Time [s]** & & **Real Time [s]** & **Information** \\ \hline
1 & 3 & 16 & 16 & Product is OK \\
2 & 2 & 17 & 16 & Product is OK \\
3 & 1 & 16 & 16 & Product is OK \\ \hline
5 & 4 & 17 & 16 & Product is OK \\ \hline
6 & 8 & 16 & 16 & Product is OK \\
7 & 9 & 16 & 16 & Product is OK \\
8 & 6 & 16 & 16 & Product is OK \\
9 & 7 & 16 & 16 & Product is OK \\ \hline
11 & 10 & 17 & 17 & Product is OK \\
12 & 12 & / & / & NO product \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Collected data from the digital twin showing the order sequence, the duration of the inspection process in the digital and real environment, and information about the correctness of the finished product.
# Direct Measurement of Aerosol Liquid Water Content: A Case Study in Summer in Nanjing, China
## 3 Results and Discussion
### Changes in ALWC in Different Air Parcels  
We explored the dependence of the ALWC on different air parcels, as PM\({}_{2.5}\) with different origins differs in chemical compositions, thus affecting the ALWC [66; 67; 68]. The 36 h backward trajectories (initialized at a height of 500 m) were calculated by the MeteoInfo software (version 3.6) driven by the Global Data Assimilation System (GDAS) 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) reanalysis product [69]. The trajectories were grouped into three clusters: Cluster 1 (C1, 38.17% of the total) originated from the eastern sea; cluster 2 (C2, 35.08% of the total) began from the Shandong peninsula but intercepted sea air; and cluster 3 (C3, 26.75% of total) was the shortest, starting from Northern Zhejiang province. The mean values of the PM\({}_{2.5}\), ALWC, AQI, and visibility of the three clusters are shown in Figure 9b. C2 had the highest ALWC (5.34 \(\mu\)g m\({}^{-3}\)), yet its visibility, PM\({}_{2.5}\), and AQI were the lowest, mainly because C2 had the highest RH (64.36%) among the three clusters. On the other hand, the ALWC of C3 (4.42 \(\mu\)g m\({}^{-3}\)) was higher than that of C1 (3.66 \(\mu\)g m\({}^{-3}\)), mainly owing to the higher PM\({}_{2.5}\) level of C3 (21.62 \(\mu\)g m\({}^{-3}\)) than C1 (19.48 \(\mu\)g m\({}^{-3}\)), as their RH values were very close (62.68% vs. 62.77%). Both the PM\({}_{2.5}\) and AQI were the highest in C3, as it originated from an inland area and did not include clean sea air.
## 3 Methodology
### Data Preprocessing  
Data preprocessing played a crucial role in guaranteeing the validity and dependability of the outcomes in our investigation. The OULA dataset was subjected to a rigorous cleaning process, wherein missing data were carefully handled to eliminate any null values. In order to address the potential bias caused by anomalies, noise reduction techniques, such as the Interquartile Range (IQR), were implemented. It is imperative to acknowledge that the handling of missing data is not only crucial for maintaining the integrity of the model but also for ensuring the generalizability of this study across other institutions. The absence of a standardized approach to data treatment poses a potential challenge when making comparisons between institutions and colleges, as it may lead to distorted or less dependable results. The approach employed places a strong emphasis on maintaining data integrity, hence ensuring the production of robust results. This characteristic allows for the adaptability of the methodology in many educational environments.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 1 Introduction  
Energy efficiency is a very important indicator of the modernity of an economy and the level of public awareness. The basic element influencing energy efficiency is the energy intensity of individual sectors of the economy, but no less important is consumer awareness of their energy consumption profile. It is extremely important to provide consumers with real-time information on the operating costs of individual electrical appliances. This task can be achieved by monitoring the operation of electrical appliances via NILM methods, a diagram of which is shown in Figure 0(a). An alternative solution is to use the intrusive ILM (intrusive load monitoring) method, a diagram of which is shown in Figure 0(b). Both methods are being developed in parallel, as evidenced by the large number of publications on both non-intrusive and intrusive monitoring methods. However, they are characterized by different methodologies and somewhat different requirements for data acquisition and algorithms for device identification. The ILM method involves measuring power consumption at every point where electrical devices are connected, which makes it a very accurate method and, if in addition smart network outlets connected to a central device are used, a convenient load monitoring system using the Internet of Things can be achieved. The disadvantage of this solution is the need for a large number of components, of whichcertain costs are associated. The alternative method (NILM) is based on measurements at a single, central point of the facility's power supply [1], making it much cheaper but, unfortunately, much more difficult to implement due to the need to distribute the collective energy to individual devices currently in operation. This paper addresses the issue of non-intrusive monitoring.  
The key point of this paper is the use of binary classifiers for multiclass classification and dedicated data selection for this task, which is based on the mRMR method. To improve the reliability of the classification, a further innovation was applied by extending the number of classes of the identified devices. It is also important to highlight the global approach to load identification for the NILM. For this purpose, classes (types) of appliances were identified rather than individual appliances, as is performed in most works. For example, the term 'hoover' refers to all vacuum cleaner-type electricity consumers. In this work, each appliance type is represented by several different units, operating in different modes and under different power conditions.
## 4 Experiments and Results
As can be seen from Figure 15, when parameter drop remains unchanged and parameter g keeps increasing, the running time of the model presents a stable linear growth. (4) Conclusions:  
In this part, we give the running time of GAFM under the influence of the two parameters drop and g. It can be seen that, similar to the accuracy, the impact of the two parameters on the running time is positively correlated, that is, negatively correlated with the speed. The impact of drop is also volatile, and the impact on the time is less than that of the parameter g. It can be seen that the influence of parameter g is more stable and significant. Figure 5:0 Running time of GAFM under the influence of drop. Figure 5:1 Running time of GAFM under the influence of g.  
D. Horizontal comparison  
In this section, we compare the effects of GAFM with other models, and show the growth of one model against the benchmark. (1) Experimental effects on dataset 1:  
As can be seen from Figure 16, the performance of GAFM model on dataset 1 is significantly better than that of existing models. As can be seen from Figure 17, the running time of GAFM model on dataset 1 is significantly less than that of Wide & Deep model and AFM model, which is similar to that of PNN model. Figure 5:2 The accuracy of each model is compared. Figure 5:3 The running time of each model is compared. (2) Dataset 2 is the experimental effect on the 1 M Movielens dataset:  
As can be seen from Figure 18, similarly, the GAFM model performs well in accuracy. It is obvious from Figure 19 that compared with dataset 1, the running time of GAFM on dataset 2 is significantly less than that of other models, which has obvious advantage. Figure 5:4 The running time of each model is compared. Figure 5:5 The accuracy of each model is compared. (3) The experimental effect on dataset 3, namely the Movielens dataset with the size of 10 M:  
We can draw a basic conclusion from Figures 20 and 21 that GAFM has more and more obvious advantages in accuracy and time with the increase of dataset size. (4) Performance growth:  
Annotation. The improvments part is the growth of accuracy and the growth of speed. It can be concluded from Table 2 that GAFM's time optimization is particularly obvious in the two factors of accuracy and time, and GAFM has incomparable advantages compared with AFM which is obviously backward in running speed. Figure 5:6 The running time of each model is compared. Figure 5:7 The accuracy of each model is compared.
# Water on Actinide Dioxide Surfaces: A Review of Recent Progress
## 2 Oxygen Vacancies on AnO\({}_{2}\) Surfaces
### Experimental Work  
A thorough experimental study of oxygen vacancies on AnO\({}_{2}\) surfaces has only been performed on UO\({}_{2}\) (111) surface. Artificial oxygen vacancies on UO\({}_{2}\) (111) surfaces were created using Ar\({}^{+}\) sputtering, and the oxidation states of surface actinide atoms were studied using X-ray photoelectron spectroscopy (XPS) [45]. A comprehensive review of XPS spectra of uranium oxide powder, thin films, and single crystals can be found in the work of [49]. Figure 4 shows the XPS spectra of stoichiometric UO\({}_{2}\) (111) surface (A), the surface after Ar\({}^{+}\) sputtering at 300 K (B), and after Ar\({}^{+}\) sputtering at 90 K (C) [45]. The stoichiometric surface has the U4_f_\({}^{7/2}\) and U4_f_\({}^{5/2}\) peaks at 380.0 and 390.8 eV, and two characteristic satellite peaks S\({}^{1}\) and S\({}^{2}\) at 386.7 and 397.3 eV [45]. Similar peak positions have been reported in various studies for the U\({}^{4+}\), suggesting that uranium atoms on the stoichiometric surface remain in U\({}^{4+}\) as in the bulk UO\({}_{2}\)[50; 51]. After sputtering with Ar\({}^{+}\) at 300 K, a shoulder on the lower binding energy side of the 4\(f\) peaks appears. The broad shoulder has been attributed to uranium atoms in different oxidation states from +4 to 0 (uranium metal) and the slight shift of the U4_f_\({}^{7/2}\) and U4_f_\({}^{5/2}\) is owing to the formation of an _n_-type semiconductor upon the creation of oxygen vacancies [45]. The formation of different oxidation states of uranium atoms on the surfaces with oxygen vacancies is evidenced in the fit of XPS spectra, as shown in Figure 3[49]. An improved fit of the shoulder of U4_f_\({}^{7/2}\) peak can be obtained via the inclusion of peaks corresponding to the different oxidation states between +4 and 0, indicating the presence of uranium atoms in the reduced states [49]. However, deriving the structure of an oxygen vacancy at the atomic layer of UO\({}_{2}\) surface and its electronic properties from these XPS data is still not possible without the complementation of DFT simulations. This is because most of the surface analysis techniques such as XPS and Auger electron spectroscopy (AES) have a penetration depth of a few nanometers [52], thus the signal from XPS and AES typically represents an average over a few nanometers of the materials.
# Innovative Ultrasonic Spray Methods for Indoor Disinfection
## 3 Methods
### Experimental Setup  
To determine the droplet size characteristics produced using the developed spraying methods, an experimental setup was assembled, with its structural diagram shown in Figure 12.  
The experimental setup allows for the measurement of droplet size characteristics based on the parameters of ultrasound. The setup includes the following components: the tested sprayer (1), an ultrasonic generator (2), a liquid supply system based on a peristaltic pump (3), a receiver to eliminate pressure pulsations (4), an air compressor for creating excess pressure (5), a liquid flow regulator (flow meter) (6), and a nozzle (optional) (7). Additionally, the setup includes a Malvern SprayTec (SprayTec 2000, Malvern Panalytical Ltd, Malvern, Worcestershire, UK) optical aerosol analyzer (8), a vacuum aspirator (optional) (9), and a sound pressure level meter (optional) (10).  
Figure 11: Ultrasonic atomizer for cavitation combined method: (a) sketch of ultrasonic atomizer; (b) atomizer assembled with electronic generator. 1—Langevin piezoelectric transducer; 2—radiating pad-concentrator; 3—reflecting pad; 4—piezoceramic elements; 5—working tool; 6—technological volume; 7—nozzle; 8—fitting; A–cavitation area.  
All droplet size measurements were conducted using a Malvern SprayTec particle analyzer [26], which operates on the principle of laser diffraction. The analyzer measures particle dispersion at frequencies up to 10 kHz, in a size range from 0.1 to 2000 \(\upmu\)m. During measurements, the analyzer's laser beam was positioned 50 mm from the sprayer's nozzle. Settled tap water at a temperature of 23 \({}^{\circ}\)C was used as the sprayed liquid.  
Ultrasonic vibrations in the developed sprayers were excited using custom-designed electronic generators. These generators were constructed using a half-bridge circuit with independent excitation, phase-locked frequency control, amplitude stabilization of the sprayer's oscillations, and output power adjustable in the range of 0-200 W. The generator is controlled by an STM32 family microcontroller.
###### Abstract  
Accurate and prior identification of local severe storm systems in pre-convection environments using geostationary satellite imagery measurements is a challenging task. Methodologies for \"convective initiation\" identification have already been developed and explored for operational nowcasting applications; however, warning of such convective systems using the new generation of geostationary satellite imagery measurements in pre-convection environments is still not well studied. In this investigation, the Random Forest (RF) machine learning algorithm is used to develop a predictive statistical model for tracking and identifying three different types of convective storm systems (weak, medium, and severe) over East Asia by combining spatially-temporally collocated Himawari-8 (H08) measurements and Numerical Weather Prediction (NWP) forecast data. The Global Precipitation Measurement (GPM) gridded product is used as a benchmark to train the predictive models based on a sample-balance technique which can adjust or balance the samples of three different convection types to avoid over-fitting any type of dataset. Variables such as brightness temperatures (BTs) from H08 water vapor absorption bands (6.2 \(\upmu\)m, 6.9 \(\upmu\)m and 7.3 \(\upmu\)m) and Total Precipitable Water (TPW) from NWP show relatively high ranks in the predictive model training. These sensitive variables are closely associated with convectively dominated precipitation areas, indicating the importance of predictors from both H08 and NWP data. The final optimal RF model is achieved with an accuracy of 0.79 for classification of all convective storm systems, while the Probability of Detection (POD) of this model for severe and medium convections can reach 0.66 and 0.70, respectively. Two typical sudden convective storm cases in the warm season of 2018 tracked by this algorithm are described, and results indicate that the H08 and NWP based statistical model using the RF algorithm is capable of capturing local burst convective storm systems about 1-2 h earlier than the outbreak of heavy rainfall.  
Keywords:convective storm; geostationary satellite; numerical weather prediction; nowcasting; random forests +
Footnote †: journal: Remote Sensing, 2019, 11, 383; doi:10.3390/rs11040383
# A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
## 4 Research Status of the Numerical Simulation of Fissure Grouting in Rock Masses
### Future Research Priorities  
Numerical simulation of grouting in rock fissures is a fundamental technology for important engineering projects such as mines and tunnels. It can provide important references and guidance for grouting scheme design and actual construction. In addition, numerical simulation technology can effectively improve the efficiency and quality of grouting, reduce construction difficulties, and provide important guarantees for environmental protection and personnel safety. In the future, research on numerical simulation of grouting needs to continue to expand into new fields, increase research depth to meet the needs of practical applications, and promote the continuous upgrading and development of grouting technology and methods. In the authors' opinion, in future research on numerical simulation of grouting in rock fissures, further exploration is needed in the following areas:  
1. An optimization method for grouting based on numerical simulation technology should be developed while continuing to improve the accuracy and computational efficiency of simulation technology. Interdisciplinary collaboration should be strengthened by combining numerical simulation with traditional construction processes and physical experiments to optimize grouting schemes and improve engineering quality. In-depth research on grouting processes and technologies should continue to comprehensively improve construction efficiency and engineering benefits.
2. Polymers are complex diffusers with phase-transition characteristics. When establishing numerical models, the phase transition characteristics of high polymers from liquid to solid during diffusion should be considered. The internal driving force of the expansion of polymer slurry due to chemical reactions and the influence of water on the chemical reaction of the slurry should be considered to establish a more accurate numerical model of polymer diffusion. In future research on grouting in rock fissures with high polymers, the influence of various factors such as fissure dip angle, fissure networks, and fissure seepage should be comprehensively considered. Further analysis should be carried out to establish a prediction model with lower error in  
Figure 2: Fissure grouting calculation model [72].  
order to obtain results that are closer to the real grouting environment in fissured rock masses.
3. Due to the uneven distribution of fissures in rock masses, the grouting volume has high uncertainty. Therefore, accurately determining the properties, occurrence, roughness, density, and scale of grouting holes and nearby rock fissures in rock masses is the basis for predicting the diffusion distance of slurry correctly. Currently, research on strengthening the detection methods for rock fissure parameters is not sufficient. While further exploring the fissures themselves, more superior and intelligent numerical simulation methods need to be developed to better simulate real fissures.
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 3 Contamination Test in Wind Tunnel  
### Samples  
The sample was big-small sheds composite insulators named Type-A. The technical parameters and structure of the sample are shown in Table 2 and Figure 4, in which H is structure height, D is big-shed diameter, and d is small-shed diameter.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**NSDD (mg/cm\({}^{2}\))** & **NSDD\({}_{\rm T}\)** & **NSDD\({}_{\rm B}\)** & **NSDD\({}_{\rm W}\)** & **NSDD\({}_{\rm L}\)** & **NSDD\({}_{\rm H}\)** & **NSDD\({}_{\rm M}\)** \\ \hline Average value & 0.342 & 0.167 & 0.208 & 0.301 & 0.316 & 0.193 \\ \(\sigma\) & 13.5\% & 11.1\% & 8.6\% & 9.6\% & 9.3\% & 8.2\% \\ \hline
**ESDD (mg/cm\({}^{2}\))** & **ESDD\({}_{\rm T}\)** & **ESDD\({}_{\rm B}\)** & **ESDD\({}_{\rm W}\)** & **ESDD\({}_{\rm L}\)** & **ESDD\({}_{\rm H}\)** & **ESDD\({}_{\rm M}\)** \\ \hline Average value & 0.061 & 0.031 & 0.037 & 0.054 & 0.056 & 0.035 \\ \(\sigma\) & 13.6\% & 9.1\% & 8.8\% & 10.5\% & 10.2\% & 8.7\% \\ \hline K & K\({}_{\rm T/B}\) & K\({}^{\star}\)\({}_{\rm T/B}\) & K\({}_{\rm W/L}\) & K\({}^{\star}\)\({}_{\rm W/L}\) & K\({}_{\rm H/M}\) & K\({}^{\star}\)\({}_{\rm H/M}\) \\ Average value & 1/0.49 & 1/0.51 & 1/1.45 & 1/1.46 & 1/0.61 & 1/0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contamination degree and non-uniformity results for field operating insulators.  
Figure 4: Structure of the samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Parameters (mm)**} \\ \cline{3-4}  & & **D** & **d** & **H** \\ \hline
**Type-A** & Silicon & 135 & 105 & 660 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structure parameters of the tested insulators.  
The contaminants were simulated by SiO\({}_{2}\) powder. After screening, 250 mesh, 400 mesh, 800 mesh and 2000 mesh SiO\({}_{2}\) powder samples were obtained for wind tunnel contamination test. The optical microscope magnification of SiO\({}_{2}\) powder sample is shown in Figure 5 and diameter situations of tested SiO\({}_{2}\) powder are shown in Table 3, where d is the particle diameter.