esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selection Results of Characteristic Wavelengths
The extracted characteristic wavelengths account for 21.31% of the full wavelengths. Figure 8 shows the results of CARS after 100 times of operation. The curve (a) shows an exponential decay, which means that with Monte Carlo (MC) sampling, the number of selected variables decreases, and the algorithm forcibly removes some wavelength points. The curve (b) is the trend diagram of RMSECV, from which the trend of slow decline, gradual rise, and then rapid rise can be seen. When the number of ARS is 73, the RMSECV value reaches the smallest, indicating that the redundant information in the spectrum is eliminated. The curve (c) represents the regression coefficient path, where \"\" represents the sampling times when the lowest point of residual corresponds to the lowest RMSECV value, by means of which the optimal subset is determined. Finally, seven characteristic wavelengths were selected by CARS for subsequent modeling analysis. Table 3 shows the characteristic wavelengths and the corresponding molecular vibration extracted by CARS [29]. The extracted characteristic wavelengths account for 2.87% of the full wavelengths. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **NO.** & **Wavelengths (nm)** & **Molecular Vibration** \\ \hline \multirow{6}{*}{UVE} & & 900.9, 907.8, 928.4, 931.8, 935.3, 938.7, 949.0, 952.4 & Third overtone of C-H \\  & & 955.8, 959.3, 993.4, 1000.3, 1007.1 & Second overtone of O-H \\  & & 1013.9, 1017.3 & Second overtone of C-H \\  & 52 & 1064.8, 1068.2 & Second overtone of N-H \\  & & 1128.9, 1132.3, 1135.6, 1139.0, 1142.3, 1145.7, 1149.0, 1152.4, 1155.7, 1159.1, 1165.8, 1175.8, 1209.2, 1212.5, 1222.5, 1222.5, 1222.5, 1225.8, 1229.2, 1232.5, 1235.8, 1239.1, 1242.5 & Second overtone of C-H \\  & 1249.1, 1255.7, 1259.0, 1269.0, 1275.6, 1298.7, 1351.3, 1387.3, 1390.5 & Second overtone of C-H \\  & & 1539.2 & First overtone of O-H \\  & & 1609.4, 1634.8, 1644.3, 1679.0 & First overtone of C-H \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The characteristic wavelengths and the corresponding molecular vibration extracted by uninformative variables elimination (UVE). Figure 7: Variable stability analysis based on uninformative variables elimination (UVE). In summary, three characteristic wavelength extraction algorithms extracted different quantities of wavelengths, among which CARS extracted the least number of characteristic wavelengths and UVE extracted the largest number of characteristic wavelengths. During the degradation of proteins and fats, white shrimp with different days of refrigeration produce significant amounts of C-H, N-H, and H\({}_{2}\)O. The characteristic wavelengths extracted by RFA, UVE, and CARS are highly correlated with freshness.
## 3 Results and Discussion
### Microstructural Analysis  
#### 3.4.1 XRD Analyses  
Figure 9 shows the XRD patterns of mixtures containing 100% OPC and 15 vol% CS, collected after 28 days of water curing (initial condition) and after 180 days of exposure to a MgSO\({}_{4}\) solution. In Figure 9a, distinct diffraction peaks corresponding to portlandite (CH), ettringite (E), albite (A), belite (B), and mayenite (N) are observed before the sulfate exposure. A and B are unhydrated clinker phases, while E (AFt) and CH are typical hydration products. At 180 days of exposure, a decrease in the intensity of the CH peaks was observed, suggesting the consumption of this phase for the generation of AFt and gypsum. This behavior was also observed by [PERSON] et al. [78], who reported a decrease in CH peaks and an increase in peaks corresponding to ettringite, gypsum and brucite. This phenomenon was attributed to the consumption of Ca\({}^{2+}\) induced by the presence of SO\({}_{4}\)\({}^{2-}\), which favors the formation of gypsum when the mixtures are exposed for prolonged periods in this solution. On the other hand, it should be also emphasized that C-S-H, due to its low crystallinity, is not detected by XRD [73] either before or after exposure.  
Figure 8: Voids (%) of concrete mixtures at different ages.  
Figure 9: XRD patterns of cementitious matrices with (**a**) 100% OPC and (**b**) 15% CS after 28 days of curing (initial) and 180 days of exposure to MgSO\({}_{4}\) solution.  
Figure 9b shows the XRD patterns of the mixture containing 15 vol% CS. Characteristic phases of CS, such as fayelite (Fe\({}_{2}\)SiO\({}_{4}\)) and magnetite (Fe\({}_{3}\)O\({}_{4}\)), as well as cement hydration products, such as portlandite (CH) and ettrinigite, are identified in the diffractometric pattern. In addition, a decrease in the intensity of the peaks corresponding to CH is observed, which is attributed to the (i) dilution effect generated by the lower amount of alite and belite phases due to the partial replacement of OPC and (ii) the low initial pozzolanic reactivity of the copper slag [4, 41]. At 180 days of exposure, peaks corresponding to gypsum were identified, suggesting that the SO\({}_{4}\)\({}^{2-}\) ions initially reacted with the CH to form gypsum [87], partially consuming this compound, evidenced by the decrease in the intensity of the peaks of this phase when compared to the pre-exposure (initial) diffractogram.
## 1 Introduction
Henceforth, while operating on the principles of cloud computing, an SGMS shall be overseen, operated, and managed by telecommunications operators upon its completion. Leveraging internet transmission, system administrators can remotely access client information and exert control over smart meters [4]. This platform, cultivated through mobile telecommunication networks, enables a gas operator to forge a \"gas cloud\" exclusive to the natural gas operator, founded upon a cooperative and mutually advantageous model [5; 6; 7]. The smart meter communicates data encompassing parameters such as temperature, pressure, and issues alerts through a wireless apparatus [8; 9; 10]. Subsequently, it conveys this information from a controller to an intelligent meter cloud server management platform within the innovative information and communication technology (ICT) framework of the Telecom operator employing LTE-Advanced (LTE-A) and fifth-generation (5G) cellular systems [11; 12; 13]. Telecom operators use software-defined wide area network (SD-WAN) technology to transform the traditional distributed network control architecture into a centralized control architecture through a centralized controller (SDN controller), which can effectively improve network usage efficiency. The core spirit of flexible management and control is to provide a virtual abstraction of the entire network and programmability of application services, and to provide more network services and novel smart meter cloud management through applications [14]. Currently, the access network equipment mainly deployed (e.g., GPON, LTE, NBIoT, xDSL & FTTX, Satellite) is still based on the traditional distributed management architecture. Therefore, how to introduce the current access network equipment into SDN-WAN management to obtain the advantages of programmable management and control of application services and network resources is shown in Figure 1. Users can also access the usage status of smart meters through communication terminal equipment [15; 16; 17; 18]. This architectural design is predicated upon the noble principle of enhancing the critical infrastructure of the nation while seamlessly amalgamating the distinctive attributes of multi-layer heterogeneous networks. The symbiosis of a mobile network operator (MNO) and multimedia service provider (MSO) exemplifies a commendable instance of heterogeneous integration, poised to fortify the burgeoning wave of multi-vendor network amalgamation and enhance the network resilience of smart cities. Figure 1: A novel AMI in a Zigbee satellite network. The structure of this paper is based on the hierarchy of global M2M connectivity (G-M2 MC) technology, as depicted in Figure 2. It unfolds as follows: Section 2 elucidates the design principles governing the AMI for heterogeneous wireless sensor networks (HWSNs). Subsequently, Sections 3-5 delve into investigations concerning key determinants and resolutions pertaining to the SD-WAN network within the context of the SGMS system's AMI architecture, aligned with IEEE802.15.4-TG4g. Finally, Section 6 culminates in a conclusion.
# Analysis of the Working Performance of a Back-to-Back Geosynthetic-Reinforced Soil Wall
## 4 Conclusions  
A series of FEM analyses were conducted on the basis of a well-performed BBGRSW in an actual railway system. The design and parameters of the GRSW were investigated and optimized. The main conclusions are as follows:  
1. Reinforcement length was an important parameter for the performance of a BBGRSW. The deformation and settlement showed a downward trend with the increase in the geogrid length. The post-construction lateral deformation with a 5.0 m reinforcement was approximately 58.06 mm. This indicated that the BBGRSW could be considered to be performing poorly or potentially unstable with reinforcements shorter than 0.7 \(H\). The post-construction settlement was larger than that required by the Chinese railway department. The walls had a superior performance with longer reinforcements as the deformation with a fully covered reinforcement was only 5.2% of that with a 5.0 m long reinforcement and 24.8% of the settlement.
2. The performance of a BBGRSW may be superior with same-layered reinforcements, especially with a fully covered reinforcement in limited spacing. For a lateral deformation with an 8.0 m long reinforcement, it was 84.2% of that with a cross-arranged reinforcement with 94.5% tension. The same-layered reinforcement was 72.7% of the cross-arranged reinforcement on the settlement.
3. The FOS of the BBGRSW increased with a longer reinforcement and peaked at 4.27 for an 8.0 m long same-layered reinforcement. The cross-arranged reinforcement was 3.03, which was weaker in stability compared with the same-layered reinforcement.  
Figure 15: Factor of safety with different reinforcement lengths and arrangements.  
The FOS for the cross-arranged reinforcement was smaller than the same-layered reinforcement with longer reinforcements.  
On the basis of the site monitoring content, this study discussed the performance of a BBGRSW in the limited space of the reinforcement length and layout. Additional research is needed for BBGRSWs with different top widths and wall slopes. The conclusions presented in this paper depend on the conditions from the field, and several other non-evaluated factors such as external loading, soil compaction, the type and stiffness of the soil, facing, and reinforcement were not considered. Hence, more research is needed.  
Conceptualization, G.Y. and Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, H.W. and Z.W.; formal analysis, Y.Z.; investigation, Y.Z.; resources, H.W. and Z.W.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, G.Y. and H.W. 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 numbers 52079078 and 51709175, and the National Science Foundation of Hebei Province, grant numbers E2018210097, and E2019208159.  
Not applicable.  
**Data Availability Statement:** All data have been included in the article.  
The authors declare no conflict of interest.
# Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation
###### Abstract  
In this study, a method is presented for estimating the number density of fine dust particles (the number of particles per unit area) through numerical simulations of multiply scattered ultrasonic wavefields. The theoretical background of the multiple scattering of ultrasonic waves under different regimes is introduced. A series of numerical simulations were performed to generate multiply scattered ultrasonic wavefield data. The generated datasets are subsequently processed using an ultrasound data processing approach to estimate the number density of fine dust particles in the air based on the independent scattering approximation theory. The data processing results demonstrate that the proposed approach can estimate the number density of fine dust particles with an average error of 43.4% in the frequency band 1-10 MHz (wavenumber \(\times\) particle radius \(\leq\) 1) at a particle volume fraction of 1%. Several other factors that affect the accuracy of the number density estimation are also presented.  
ultrasound; multiple scattering; attenuation; independent scattering approximation
## 2 Materials and Methods
### Device Fabrication  
The device was fabricated using standard PDMS soft lithography. A master mold was prepared by patterning SU-8 photoresist (SU-8 3025, MicroChem, Westborough, MA, USA) onto a silicon wafer via photolithography. PDMS prepolymer (Sylgard 184, Dow Corning, Midland, MI, USA) was mixed at a 10:1 base-to-curing-agent ratio, poured over the mold, and cured at 65 \({}^{\circ}\)C for 2 h in a dry oven. After curing, the PDMS replica was peeled off and inlet/outlet holes were punched. The PDMS channel and a glass slide were treated with air plasma using a plasma system (Femto Science, Hwaseong-si, Republic of Korea) at 45 W for 30 s and immediately brought into conformal contact. Before bonding the PDMS body to the glass substrate, the Nafion membranes were fabricated on a glass slide. The Nafion membrane was microflow-patterned [38] on the glass substrates utilizing a Nafion resin solution and then baked at 70 \({}^{\circ}\)C. The Nafion-patterned glass and PDMS microfluidic bodies were bonded using a plasma bonding process.  
Pipette tips (10 \(\mu\)L, Neptune Scientific, San Diego, CA, USA) were inserted into the inlet and outlet reservoirs and used as solution inlets and outlets. To insert the pipette tips, holes in the PDMS body were created using a biopsy punch (Integra Life Sciences, Princeton, NJ, USA). The blood sample solution was injected into the pipette tip of the inlet reservoir, while the pipette tip of the outlet reservoir was connected to a syringe pump via flexible tubing (Tygon LMT-55, Ismatec, Glattbrugg, Switzerland).
# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 4 Experimental Evaluation
### Evaluation Setup  
#### 4.1.1 Dataset  
LiveLab ([[http://livelab.recg.rice.edu/traces.html](http://livelab.recg.rice.edu/traces.html)]([http://livelab.recg.rice.edu/traces.html](http://livelab.recg.rice.edu/traces.html))) dataset is generated by a number of iPhone 3 GS users including 24 Rice University students from February 2010 to February 2011 and 10 Houston Community College students from September 2010 to February 2011. The dataset consists of several SQL files, which describe applications run by users, periodic output from the modem regarding the cellular base station the phone is connected to, phone calls made or received by users, charging state of the phone so on.  
In our experiments, we choose two traces: App usage trace which records applications run by users and corresponding launching time, cellular base station trace which records the cell tower id that the phone is connected and time the event occurred. For each user, we extract three types of nodes, time, location and application, and links between application and time or location or the previous App, respectively, from this information.  
In the pre-processing, we first remove all the cellphone's basic Apps including _SpringBoard_ (Desktop), _com.apple.mobileSMS_ (Short Messaging Service) and _com.apple.mobilephone_ (Phone Call).  
These three basic applications take up more than 900,000 of the total 1.3 million usage records. Then, we selected five users with the maximum usage records: A07, A04, A12, B04, B02, each of which has more than 15,000 records. Next, the attributed heterogeneous networks were constructed for each user at two temporal scales as time nodes: _hour of day_ and _interval of day_. Each day was divided into four intervals, denoted by 1 (from 12:00 a.m. to 5:59 a.m.), 2 (from 6:00 a.m. to 11:59 a.m.), 3 (from 12:00 p.m. to 5:59 p.m.), and 4 (from 6:00 p.m. to 11:59 p.m.). As attributes of time nodes, _day of month_, _day of week_, and _interval of day_ all participate in training. We will perform a comparison experiment for the two scales. Then, for each user, we sort the application usage records by ascending order of time respectively for the sake of finding the previous App. Finally, we extract cellular base station information as the location nodes. Because the cellular base station is recorded separately from application usage information, we select the cellular information during five minutes before the application start and five minutes after the end of the application for every App usage record and decide to select the result that is closest to the middle time of the application. At present, the one-hot value of each cellular base station is the attribute corresponding to each location node.  
In this paper, we do not highlight how to select attributes as nodes' features and how to set parameters in our experiment, which is the future work. We mainly compare AHNEAP with baselines.
## 3 The End-to-End Instrument Performance Simulation System (EIPS) Framework
### EIPS Components  
#### 3.2.1 Sensor Parameter Model  
A sensor parameter model has been developed for modelling of sensor characteristics. The sensor model simulates the instrument and mission profile, taking into account key instrument design factors such as observation geometry, scan strategy, detector type, instrument configuration, channel characteristics, and calculates the radiometric sensitivity of the instrument in terms of noise equivalent temperature difference (NETD) per spectral band and the SRF (and the SRF files essentially contains the list of sounding spectral channels, with their associated bandwidths). Here, we define NETD as the temperature difference that would produce a detected signal-to-noise ratio of 1 in a 0.5 second integration time, or a post-detection bandwidth of 1 Hz.
# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 4 Convolution Neural Network Assisting Diagnose
### Training and Testing Process
#### 4.3.1 Single Modality Image Training Model  
T2-MRI and PET image of the same parts of brain from 101 patients with Alzheimer's disease and 80 normal cognitive people as control groups in total 5430 images of each modality were collected from the ADNI database and stored separately in two three-dimensional matrices. Each matrix was read into the network with its corresponding binary classification label. Because of the few training and test data, it was necessary to use the set aside method to verify the generalization ability of CNN.  
Set aside was the method that divided data set \(D\) into two mutually exclusive sets, one of which was the training set \(S\), and the other one is the test set \(T\). After the model training completed, \(S\) and \(T\) was used to evaluate the test error [27].  
Figure 15: Constitution of the implemented datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**K-Fold** & **CNN** & **DeU-Net** & **GoogLeNet** & **InceptionV3** & **MobileNetV2** & **VGG16** \\ \hline
1 & 92.35\% & 92.78\% & 91.02\% & 93.04\% & 92.84\% & 92.99\% \\
2 & 95.32\% & 95.32\% & 95.66\% & 95.46\% & 95.10\% & 93.56\% \\
3 & 96.52\% & 96.52\% & 96.78\% & 96.43\% & 96.32\% & 96.73\% \\
4 & 96.97\% & 96.97\% & 97.01\% & 96.99\% & 96.57\% & 96.81\% \\
5 & 97.45\% & 97.45\% & 97.62\% & 97.41\% & 97.28\% & 97.39\% \\
6 & 97.52\% & 97.74\% & 97.81\% & 97.76\% & 97.86\% & 97.68\% \\
7 & 97.76\% & 97.88\% & 97.92\% & 97.81\% & 97.95\% & 97.89\% \\
8 & 97.83\% & 97.92\% & 97.92\% & 97.92\% & 97.98\% & 98.00\% \\
9 & 97.96\% & 98.01\% & 98.01\% & 97.99\% & 98.04\% & 98.03\% \\
10 & 98.03\% & 98.05\% & 98.02\% & 98.01\% & 98.06\% & 98.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy table of 10-fold cross validation.
## 3 Case Study
### Results  
The IT, ME, and TE of the normal group were significantly smaller than those of the patient groups, as shown in Figure 3 (IT: F(2, 34) = 35.78, \(p\) < 0.001; ME: F(2, 34) = 7.67, \(p\) = 0.002; TE: F(2, 34) = 13.32, \(p\) < 0.001). Tukey tests on IT classified the normal group (mean \(\pm\) SE; 0.49 \(\pm\) 0.02) as the fastest group, svMCI (0.64 \(\pm\) 0.04) as the middle group, and SVaD (0.84 \(\pm\) 0.05) as the slowest group. Similarly, Tukey tests on ME grouped the normal group (0.78 \(\pm\) 0.03) as the best group, svMCI (1.38 \(\pm\) 0.09) as the middle group, and SVaD (1.72 \(\pm\) 0.09) as the worst group. TE slightly differed from IT and ME, although the trend remained; Tukey tests on TE classified the normal group (3.07 \(\pm\) 0.13) as the best group and svMCI (3.80 \(\pm\) 0.22) and SVaD (4.33 \(\pm\) 0.21) as the worst groups.  
TT was significantly different according to participant group (F(2, 34) = 23.01, \(p\) < 0.001). Tukey tests on TT classified the normal group (1.18 \(\pm\) 0.05) and svMCI (1.38 \(\pm\) 0.09) as the fastest groups and SVaD (1.88 \(\pm\) 0.10) as the slowest group. Lastly, DT was not significantly different between groups (F(2, 34) = 2.58, \(p\) = 0.089), although it increased from the normal group (0.61 \(\pm\) 0.04) to the svMCI (0.78 \(\pm\) 0.07) and SVaD (0.89 \(\pm\) 0.07) groups in ascending order.  
Figure 3: \(Cont.\)Figure 3: _Cont._  
Large percentages of svMCI cases in ME and TE and SVaD cases in IT, ME, and TE exceeded the 99% confidence intervals of the normal group, as shown in Figure 4. Subjects with svMCI showed larger deficits in ME (50%) and TE (50%) than IT (25%), DT (25%), and TT (17%). Those with SVaD revealed larger deficits in IT (62%), ME (54%), and TE (54%) than DT (31%) and TT (31%).
## 2 Materials and Methods  
### Preparation of Titania on Stainless Steel  
For the deposition of TiO\({}_{2}\), titanium-di-isopropoxide bis(acetylacetonate) ((CH\({}_{3}\))\({}_{2}\)CHO)\({}_{2}\) Ti(C\({}_{5}\)H\({}_{7}\)O\({}_{2}\))\({}_{2}\)) (Sigma-Aldrich, St. Louis, MO, USA) was procured from Sigma Aldrich. The precursor solution consisted of 6.83 mM concentration of (C\({}_{16}\)H\({}_{28}\)O\({}_{6}\)Ti) dissolved in ethanol p.a. using an ultrasonic device. TiO\({}_{2}\) was deposited at a substrate temperature of 460 \({}^{\circ}\)C by using the Ion Layer Gas Reaction, which was carried out in Helmholtz-Zentrum Berlin fur Materialien und Energie (HZB), Germany, with a carrier gas (N\({}_{2}\)) flow rate of 5 L/min. The Spray-ILGAR technique was developed and patented by HZB [9]. The solution was sprayed by an ultrasonic nebulizer, which are transported by the carrier gas (nitrogen) and subsequently deposited on the heated substrate, for 12 min. The substrate was inclined at an angle of 45\({}^{\circ}\) in order to get a homogeneous layer, in order to dramatically minimize turbulence of the aerosol. Experiments conducted using different concentrations of TiO\({}_{2}\) precursor (3.42 and 13.66 mM), different heating temperature (500 and 550 \({}^{\circ}\)C) and different spraying durations (6 and 24 min) were also carried out in order to study their effects on the photocatalytic activity.