esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Building Energy Consumption Forecasting
### Forecasting Methods  
Forecasting methods can successfully anticipate energy utilization by creating a relationship between energy and the system that consumes it by reducing the waste created by oversupply and undersupply [25]. In recent years, different techniques have been proposed to capture this knowledge in the form of predictions [26]. In the building segment, smart meters that measure and transmit energy usage are being widely installed. These open the door to new applications and results in the energy sector that allow for better forecasting accuracy [27]. Energy utilization forecasting for buildings has a huge incentive in energy proficiency and manageability research. Precise energy forecasting methods have various ramifications in arranging and energy optimization [28]. A great deal of building energy forecasting methods have been created in the previous two decades from different points of view [29]. There are three fundamental methodologies in building energy forecasting and modeling: physical, data-driven and hybrid methods [30,31]. Physical methods estimate energy use using thermodynamic rules to solve the heat and mass balance inside a building, which is also called a \"white-box\" as the internal logic is known [32]. Data-driven depends on time-series statistical assessments and machine learning s of rules to evaluate and estimate the building energy utilization, and is also called a black-box since it predicts energy utilization ignoring the physical characteristics of the building [33]. Figure 1: Building energy consumption forecasting objectives. The hybrid method known as a grey-box, which was first presented in the 1990s to improve HVAC control frameworks' effectiveness [34], and combines physical and data-driven methods [35; 36]. Physical methods permit monitoring and modeling and investigate the cycle bit by bit where all the conditions are known [37]. However, they cannot deal with the complex energy resulting from the occupants' behavior in this manner, bringing about huge forecast mistakes. Therefore, these models are not commonly used for exact load expectations [38]. To build up an accurate physical method, many input variables are required, which makes it complex to acquire all the necessary information. More significantly, the unsure and unreliable data suggest a difference between the model outcomes and reality [39]. There have been studies on physical methods that have focused on-the demand forecast model for several energy sources [40], namely energy consumption for a building elevator [41], electrical consumption for cooling [42; 43], energy demand [44; 45; 46], load forecasting [47], electric and heat load [48] and heating consumption [49]. Based on the aforementioned studies, a summary of their contributions and limitations is presented in Table 1.
## 5 Discussion
### Difference Deduced by Upscaling Methods
#### 5.2.1 Simple Average Method  
The TM value was first averaged then compared directly with the MODIS data. Figure 5 shows a comparison between the MODIS daily albedo data and the TM data. The results indicate that these datasets agreed well with RMSE less than 0.03 and \(R^{2}\) greater than 0.92. Although the recommended aggregation scales were scale 2 and 3 (21 \(\times\) 21 and 23 \(\times\) 23 TM pixels), the difference in accuracy between the scales was not significant (\(R^{2}\) ranged from 0.9341 to 0.9442; RMSE ranged from 0.0239 to 0.0249; the biases were nearly identical).
# Towards a New Digital Era: Observing Local E-Government Services Adoption in a Chinese Municipality
## 3 E-Government in China
### Local E-Government Services Via SNS
As for Government official accounts (Figure 1d), citizens can experience mini one-stop-service as with a traditional website portal, but in a simpler and streamlined way if they subscribe to the official accounts. Figure 1: Interfaces of Weibo and WeChat governmental e-services (mobile version): (**a**) interface of Weibo account “Chongqing weifabu” created by Chongqing government press office; (**b**) Public city- service platform embedded in “wallet” function of WeChat application; (**c**) Interface of public city-service platform on WeChat; (**d**) Interface of government official account on WeChat. Though there were already existing government portals and official mobile apps in China, WeChat and Weibo public services have received rapid and sustained attention [46]. The reason that governments decided to collaborate with established internet giants may be that using commercial platforms could save repetitive hardware/software investment and, furthermore, governments may utilize their enormous user-bases without promoting extra services simultaneously. Consequently, the Chinese model of government services making use of social networking applications is taking the lead in achieving usage while other nations may have worried about releasing personal data and outsourcing government services to private corporations. Without judging the pros and cons of this movement, this paper is intended to explore how citizens choose and adopt these e-government services. As a research objective, it tries to observe the adoption of traditional government e-services and new Weibo and WeChat e-services. Do the determinants of traditional e-government services adoption apply to new social networking applications as well? By probing into the reality of citizen adoption, hopefully governments can get closer to e-government services implementation in this new digital era.
# Anthraquinone Rhein Exhibits Antibacterialativity against _Staphylococcus aureus_
## 3 Results
### Effect of Rhein on S. aureus Biofilm Biomass  
Due to the antibiotic-resistant nature of biofilms, conventional antibiotics are inefficient for treating biofilm-associated infections. Therefore, new compounds capable of disrupting the biofilm are needed to render the bacteria non-tolerant to the treatment. The results show that Rhein significantly reduced the biomass of mature biofilms in the concentration range of 50 to 3.13 \(\upmu\)g/mL. In detail, treatment with Rhein at 50 \(\upmu\)g/mL disrupted _S. aureus_ ATCC 1167, multisensitive, quinolone, macrolides and methicillin-resistant strains and beta-lactamase producers biofilm biomass by 63.8, 52.9, 50, 47.1, 47.4 and 46.9%, respectively. A matrix reduction of 42.7, 32.7, 26.4, 27.3, 26.4 and 33.1 % occurred for the same strains after exposure to 25 \(\upmu\)g/mL of Rhein. The compound at MIC concentration caused the degradation of the biofilm produced by the standard multisensitive _S. aureus_ ATCC 1167, by the multisensitive, quinolone, macrolides and methicillin-resistant and producing beta-lactamase strains of 34.7, 26.7, 17.8, 27.3, 18.9 and 20.5%, respectively. Residual biomass greater than 78.8 % remained after exposure with the compound at the dose of 6.25 \(\upmu\)g/mL. Biofilm disintegration of less than 12.3% occurred after treatment with Rhein at 3.13 \(\upmu\)g/mL (Figure 9).  
Figure 5: LIVE/DEAD BacLight staining after treatment of _S. aureus_ with Rhein analyzed under a fluorescence microscope. (**A–C**) treatment with 25 \(\upmu\)g/mL; (**D–F**) treatment with 12.5 \(\upmu\)g/mL; (**G–I**) treatment with 6.25 \(\upmu\)g/mL; (**J–L**) bacteria not treated with Rhein; (**M–O**) bacteria treated with Vancomycin (CTR+).  
Figure 8: Rhein effect on _S. aureus_ catalase production. (**A**) untreated bacteria; (**B**) treatment with 6.25 \(\mu\)g/mL; (**C**) treatment with 3.13 \(\mu\)g/mL.  
Figure 6: Killing kinetics of Rhein on _S. aureus_ ATCC 6538 (**A**), multisensitive (**B**), resistant to beta-lactams (**C**), resistant to quinolones (**D**), constitutively resistant to macrolides (**E**) and resistant to methicillin (**F**). ***: \(p\)-value = 0.0001.  
Figure 7: Efficacy of Rhein in inhibiting the hemolytic activity of _S. aureus_. (**A**) Quantitative analysis, ***: \(p\)-value \(<\) 0.0001, ns: non-significant; (**B**) qualitative analysis.
Application of Response Surface Methodology for Optimisation of Simultaneous UHPLC-PDA Determination of Oleanolic and Ursolic Acids and Standardisation of Ericaceae Medicinal Plants  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 2 and [PERSON] 1**  
Department of Pharmacognosy, Faculty of Pharmacy, Medical University of Lodz, 1 Muszynskiego St., Lodz 90-151, Poland; [EMAIL_ADDRESS]  
Department of Biology and Pharmaceutical Botany, Faculty of Pharmacy, Medical University of Lodz, 1 Muszynskiego St., Lodz 90-151, Poland; [EMAIL_ADDRESS] (L.K.); [PERSON]H.W.)  
Correspondence: [EMAIL_ADDRESS]; Tel.: +48-42-677-9168  
Academic Editor: [PERSON]  
Received: 5 July 2016; Accepted: 24 August 2016; Published: 29 August 2016  
A fast and sensitive ultra-high performance liquid chromatography-photodiode array (UHPLC-PDA) method for simultaneous quantification of oleanolic acid (OA) and ursolic acid (UA) in plant materials was developed. A central composite design combined with a response surface methodology was utilized to establish optimal separation conditions. The final separation was accomplished on a Zorbax Eclipse XDB-C18 column (1.8 \(\upmu\)m, 100 mm \(\times\) 3 mm I.D., Agilent, Santa Clara, CA, USA) using a mixture 90:10 (\(v/v\)) of methanol and 1% (\(w/v\)) aqueous orthophosphoric acid as a mobile phase at a flow rate of 0.44 mL/min and temperature of 18 \({}^{\circ}\)C. The analysis was completed in 6.2 min with satisfactory resolution of 1.5 between the target analytes. The developed method proved to be precise (relative standard deviations below 3.2%), accurate (recoveries in the range of 95.27%-98.60%), and sensitive (limits of detection (LODs) in the range of 0.047-0.051 mg/mL). The method was then successfully applied to evaluate OA and UA content in real samples of selected Ericaceae plant materials (leaves of _Arctostaphylos uva tursi, Vaccinium myrtillus, Vaccinium vitis ideaa, Gaultheria procumbens_). The content of OA and UA in investigated samples varied in the range of 0.74-4.47 mg/g dry weight (dw) and 1.30-18.61 mg/g dw, respectively.  
oleanolic acid; ursolic acid; response surface methodology; Ericaceae; _Vaccinium vitis ideaa; Vaccinium myrtillus; Arctostaphylos uva tursi; Gaultheria procumbens_; triterpene acids
## 3 Experimental Study on Vibrated Grout-Enriched CSGR Material of Tangba Protection Dam
### Raw Materials
#### 3.2.4 Natural Aggregate  
The riverbed sand, gravel and rock aggregate near the upstream of the Qianwei Navigation and Hydropower Hub Project was selected for the test. A total of 22 groups of particle screening tests were conducted based on gradations ranging from 150-80 mm, 80-40 mm, 40-20 mm, 20-5 mm, and below 5 mm. The gradation envelope of the gravel aggregate is depicted in Figure 4 (51 to 522 represent the gradation of the material, \(M\)1, \(M\)2, and \(M\)3 represent the finest, average and coarsest gradation, respectively). Among these gradations, the proportion of extra-large stones (80-150 mm) ranges from 16% to 32%, averaging 22.3%. The proportion of boulders or large stones (40-80 mm) ranges  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Product**}} & \multicolumn{5}{c}{**Chemical**} & \multicolumn{5}{c}{**Physical**} \\ \cline{2-11}  & \multirow{2}{*}{**PI**} & \multirow{2}{*}{**CI\({}^{-}\)**} & \multirow{2}{*}{**Na\({}_{2}\)SO\({}_{4}\)**} & \multirow{2}{*}{**Alkali**} & \multirow{2}{*}{**Density**} & \multirow{2}{*}{**Rate Ratio**} & \multirow{2}{*}{**Reduction**} & \multirow{2}{*}{**Water**} & \multicolumn{2}{c}{**Difference in**} & \multicolumn{2}{c}{**Ratio of Compressive**} \\  & & & & **(\%)** & & & **(\%)** & & **Ratio** & **Setting Time (min)** & & **Strength (\%)** \\ \hline GK-4A & 7.0–8.0 & 0.02 & 10 & 1.5 & 0.6 & 43.0 & 25.4 & 110 & 90 & 180 & 160 & 155 \\ GYQ & 7.0–8.0 & 0.03 & 13 & 1.3 & 0.8 & 25 & 6.2 & 90 & 115 & 100 & 98 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical and physical parameters of the admixtures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Chemical**}} & \multicolumn{5}{c}{**Physical**} \\ \hline \hline
**SO\({}_{3}\)** & **f-CaO** & **Density** & **Fineness** & **Water Demand** & **Moisture Content** & **Strength Activity** \\
**(\%)** & **(\%)** & **(\%)** & **(g/cm\({}^{3}\))** & **(\%)** & **Ratio (\%)** & **Ratio (\%)** & **(\%)** & **Index (\%)** \\ \hline
2.42 & 2.6 & 2.47 & 20.0 & 98 & 0.5 & 70.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical and physical parameters of the fly ash.  
from 23% to 33%, with an average of 28.9%. Medium stones (20-40 mm) constitute 17% to 19%, averaging 18.9%. Small stones (5-20 mm) represent 7% to 14%, with an average of 11.1%. The proportion of sand (less than 5 mm) varies from 10% to 29%, averaging 18.8%. The coarsest gradation's sand ratio within the envelope line is 10.3%, while the finest gradation's sand ratio is 28.1%. The average fineness modulus of the sand is 1.44, indicating it is super fine sand, with an average mud content of 2.3%.
## 4 Conclusions  
To summarize, magneto-Raman spectroscopy has been successfully performed on monolayer WS\({}_{2}\) transferred on SiO\({}_{2}\)/Si substrate and Au film, respectively. Dramatic variations of Raman intensity were observed as a function of magnetic field, which can be associated with the symmetry breaking as a result of the electron motion induced by the magnetic field. Strikingly, prominent differences were observed in the magnetic field evolutions for WS\({}_{2}\) on SiO\({}_{2}\)/Si substrate and Au film. The magneto-optical Raman intensity for WS\({}_{2}\) on Au film was much lower than that for WS\({}_{2}\) on SiO\({}_{2}\)/Si. It is demonstrated  
Figure 5: Magneto–Raman intensity for (**a**,**b**) WS\({}_{2}\)/SiO\({}_{2}\) and (**c**,**d**) WS\({}_{2}\)/Au as a function of magnetic field. Green, orange and purple symbols represent 2 LA(M), \(E^{\prime}\) and \(A^{\prime}_{1}\) modes, respectively.  
that the plasmonic effect-induced charge transfer plays an important role in the magneto-optical Raman effects of WS\({}_{2}\). Our results provide useful information for understanding the magneto-optical properties of TMDs and further fabricating TMD-based magneto-optical devices.  
Investigation, W.L. and Z.L.; materials, S.T., Y.H. and H.X.; formal analysis, K.Z.; writing--original draft, Y.Y.; project administration, Y.Y.; funding acquisition, C.G. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (grant nos. 2018 YFB0703500, 2016 YFA0200800 and 2016 YFA0200400), the National Natural Science Foundation of China (grant nos. 11704401, 11674387, 12074420, 11574369, 61905274, and 91323304), and the Scientific Equipment Development Project of Chinese Academy of Sciences (Grant No. YJKYYQ20170027).  
Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fabrication of an Anti-Icing Aluminum Alloy Surface by Combining Wet Etching and Laser Machining
## 4 Conclusions  
This research work proposes a hybrid laser processing technology and wet etching technology to prepare a hierarchical structure on a 2524 aluminum alloy. In addition, an ic delay experiment, ice adhesion experiment, and dynamic water repellency experiment were carried out to verify the excellent anti-icing performance of the sample to improve aircraft safety. Based on the experimental results and theoretical analysis, the key conclusions are summarized as follows:  
1. Specific laser processing parameters and wet etching parameters can effectively prepare a uniform hierarchical structure on the surface of the aluminum alloy. The structure can meet the standard of super-hydrophobicity.
2. The freezing time of droplets on the treated sample surface was approximately 10 times longer than that of the untreated sample. The nanostructure samples can maintain good hydrophobicity after the droplet is frozen and melted, which means that it has a certain degree of durability against freezing.  
3. The ice adhesion experiment on the surface of the sample shows that the adhesion strength of the treated sample decreased by approximately 75% compared to the untreated samples. This indicates that the ice can easily be removed even when the surface is frozen.
4. The dynamic water repellency experiment revealed that the contact time between the impinging droplet and sample surface mainly depends on the dynamic wettability of the sample surface, that is, the hysteresis of contact angle. The smaller the hysteresis of the contact angle, the shorter the contact time between the impinging droplet and the sample surface. The contact time between the droplet and sample obtained by the experiment is consistent with the theoretical calculation.  
[PERSON] and [PERSON] were responsible for the overall planning of the paper, [PERSON] was responsible for the establishment of the icing adhesion test device and icing delay test device, [PERSON] carried out the icing delay experiment, [PERSON] and [PERSON] carried out the ice adhesion experiment and dynamic water repellency experiment, [PERSON] and [PERSON] guided the completion of data processing and paper writing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [the National Natural Science Foundation of China] grant number [No. 51875285], [the Natural Science Foundation of Jiangsu Province] grant number [No. BK20190066], [College Young Teachers Fund of the Fok Ying Tung Education Foundation] grant number [No. 20193218210002], and [the Fundamental Research Funds for the Central Universities] grant number [NO. NE2020005].  
Not applicable.  
The authors sincerely thank [PERSON] and [PERSON] of Nanjing University of Aeronautics and Astronautics (NUAA) for helping, editing, and polishing the language of this manuscript.  
The authors declare no conflict of interest.
# Protection of Digital Elevation Model--One Approach
## 4 Discussion
### Discussion of the Difference between the \"Commercial\" and the Non-Repudiated and Protected DEM  
Commercial DEM data are open for use on the market and can be downloaded on the Internet. Are the published DEM data to locations real? Do the data show the real geographic area? By using a protected and non-repudiation DEM, the user is sure that the data comes from a source he trusts and that they did not change during delivery or after the digital signing (if it is performed at the time of the data creation).  
The DEM data source may deny that the data originates from it. However, digital signatures technology and PKI firmly prove who is the data source. Non-repudiation is realized by the inability of the sender to subsequently revoke the sending message if his digital signature is successfully verified.  
In military applications, it is important to protect the transfer of DEM data. If the enemy reveals the contents of the operation, which was planned using DEM data, they will be able to take appropriate measures to make the operation unsuccessful.  
The model allows the preservation of confidentiality of DEM data using message encryption by a symmetric cryptographic algorithm, while secrecy of the key is realized by encryption with an asymmetric cryptographic algorithm. In this model, trust is built by the certification authority trusted by all users, as it issues a digital certificate to all.
## 3 Materials and Methods
### GeoSpark System Overview
#### 3.6.1 GeoSpark Architecture  
GeoSpark [32] is an extension of the Apache Spark core and provides additional tools to manage geospatial data, e.g., geospatial datatypes, indexes and operations. The architecture of GeoSpark, as introduced in Figure 3, consists of the following three layers:  
1. The Apache Spark Layer consists of all the components present in Spark and performs data loading and querying.
2. The Geospatial RDD Layer extends Spark and supports three types of RDD, i.e., Point, Rectangle and Polygon RDD. In addition, it contains a geometrical operations library for every RDD.
3. The Geospatial Query Processing Layer is used to perform different types of geospatial queries.  
Figure 3: An Overview of GeoSpark Layers.  
GeoSpark provides Spatial RDD that allows efficient loading, transformation, partitioning, in-memory storing as well as indexing of complex spatial data for different input data sources such as CSV, WKT, GeoJSON, Shapefile, etc., which, through the GeoSpark Spatial SQL interface, are compatible with Spark. It also provides eight types of spatial objects, namely Point, Multi-Point, Polygon, Multi-Polygon, Line String, Multi-Line String, GeometryCollection, and Circle. Hence, spatial objects in a Spatial RDD can consist of different geometry types. Furthermore, it supports three types of spatial objects, which are Point, Rectangle and Polygon, for which the corresponding spatial RDDs can be defined. These structures can be used as input to several spatial queries, such as spatial \(k\)-NN, spatial range and spatial join.  
In following, taking advantage of the GeoSpark architecture, the main parts of our approach will be presented.
## 4 Numerical Results
### Vortex Core Identification and Vortex Modelling
Typically, the wing-tip vortex cavitation begins to form in the location slightly downstream from the wing-tip and gradually approaches the tip from the back in the early stage of cavitation. This phenomenon is related to the distribution of lower pressure region formed downstream of the wing-tip along the vortex core trajectory. Given that the wing-tip vortex is dissipated by the numerical damping, as it is convected downstream, the tip-vortex is reproduced by applying the Scully vortex model on the plane perpendicular to the vortex core trajectory. To regenerate the vortex structure, the initial vortex core-radius and tangential velocity of the Scully vortex model should be determined. The initial vortex core-radius is defined as half the distance between the points of maximum and minimum velocities on the plane perpendicular to the vortex core trajectory. The distance can be easily estimated using the tangential velocity distribution, as shown in Figures 11 and 13. The initial tangential velocity can also be calculated using the same approach. Along the downstream direction, the physical vortex dissipation due to  
Figure 16: Variation of pressure coefficient along the vortex core line according to (**a**) grid types and (**b**) turbulence models. Grids types are varied with the Reynolds stress model and the turbulence models are varied with the coarse grids. Figure 17: Variation of pressure and turbulent kinetic energy along vortex core line in a Reynolds-averaged Navier-Stokes (RANS) flow field using Reynolds Stress turbulence model. flow viscosity is predicted using Equation (7), which provides an estimation for the increase in the vortex core-radius. Figure 18 compares the variation of pressure coefficient along the vortex core trajectory and the distribution of z-directional velocity in the tip-vortex flow between the RANS and vortex-regenerated flow fields. The strength of the vortex becomes weaker as it is convected downstream in the RANS results. Conversely, the vortex structure is maintained as almost constant in the flow field regenerated using the vortex model, which is more similar to the experimental observation [32].