esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adsorption Experiments of Polymers Towards Methylene Blue
#### 3.3.5 Reutilization Studies  
The ability to reprocess the adsorbent is an essential criterion for industrial applications, from an economical and green point of view. Therefore, we evaluated the reversibility of the adsorption/desorption process. The trapping of MB by the \(\beta\)-CD polymers could be explained by the formation of the inclusion complexes as well as by its incorporation in the polymer network through additional interactions that supplement the inclusion phenomena [78]. In these cases, solvents are good candidates for the desorption of the entrapped compounds. Desorption studies were conducted to validate the recyclability of polymers using PEGCD1 as a model. After adsorption of MB (50 mL at 5 mg/L) during 4 h at pH 6, PEGCD1 was desorbed by ethanol using Soxhlet extraction. Then  
\begin{table}
\begin{tabular}{c c c c} \hline  & **EGCD** & **PEGCD1** & **PEGCD2** \\ \hline \(K_{L}\) (L/mg) & 2.77 & 8.82 & 1.87 \\ \(Q_{\text{max}}\) (mg/g) & 10.68 & 6.37 & 15 \\ \(R_{L}\) & 0.003–0.014 & 0.002–0.010 & 0.005–0.020 \\ \(R^{2}\) & 0.93 & 0.96 & 0.99 \\ \hline \end{tabular}
\end{table}
Table 2: Adsorption isotherm parameters for MB adsorption.  
\begin{table}
\begin{tabular}{l c c} \hline
**Adsorbents** & **Q\({}_{\text{max}}\) (mg/g)** & **References** \\ \hline \(\beta\)-CD polymers containing carboxylic group & 17.7 & [40] \\ \(\beta\)-CD polymers crosslinked by citric acid & 105.0 & [74] \\ \(\beta\)-CD hydrogels & & 23.6 & [75] \\ Hydrogels from poly-CD and polyadamantane & 20.2 & [76] \\ CD grafted thiacalix(4)arene polymers & 5.4 & [77] \\ Polymers of CD based on PEDGE (our study) & 15.0 & \\ \hline \end{tabular}
\end{table}
Table 3: Maximum adsorption capacities of MB for various \(\beta\)-CD-based polymers based on the Langmuir results.  
the polymer was dried before reuse. The adsorption capacities remained constant during three adsorption/desorption cycles (Figure 10)  
These results suggested that CD polymers based on PEDGE exhibited efficient recyclability and could be considered as a promising adsorbent for dye removal.
## 2 Materials and Methods
### Methodology for Molecular Docking  
The binding mechanism of the three silver(I) complexes was investigated at molecular level using molecular docking. To validate and specify the target for anti-bacterial and anti-fungal potential ofthese compounds, seven different protein targets i.e., \(N\)-myristoyl transferase (PDB ID 11 YL) [45], dihydrofolate reductase (DHFR) (PDB ID 4 HOF) [46] from _C. albicans_ were selected as fungal targets, while dihydrofolate reductase (PDB ID 3 FVV) [47], gyrase B (PDB ID 4 URM) [48] from _S. aureus_, DNA (PDB ID 1 BSA) [49] thymidyllate kinase of _P. Aeruginosa_ (PDB ID 3 UWK) [50], and undecaprenyl diphosphate synthase (PDB ID 4H2M) [51] from _E. coli_ were selected based on their direct role in the drug resistance. Crystal structures of all these proteins in complex with their cognate ligand were retrieved from the Protein Data Bank. All proteins were prepared, protonated, charged, and minimized by structure preparation module in MOE [52]. Solvent molecules were removed as required. The compounds were built using the builder module in MOE. Complexes were further charged and minimized by MMFF94x forcefield [53]. For docking, an induced fit docking protocol was used with default MOE docking parameters i.e., Triangle Matcher Algorithm with two rescoring functions London dG and GBVI/WSA dG. For each complex a total thirty conformations were generated and saved in mdb format.
## 2 Theoretical Framework  
The theoretical framework covers radio wave propagation through free space, rain, cloud, gases, and the total attenuation propagation loss.
## 1 Introduction
Advancements in eddy covariance technology have made accurate measurements of carbon exchange in wetland ecosystems possible. This study is based on four years of data from eddy correlation flux towers and automated meteorological stations within the study area. By investigating the CO\({}_{2}\) fluxes in the Momoge salt marsh ecosystem, this study analyzes the daily, seasonal, and interannual variation in the net ecosystem exchange (NEE). Additionally, by exploring the correlations between net CO\({}_{2}\) exchanges and major environmental factors at both daily and monthly scales, the results provide new insights into the local climate characteristics and land-atmosphere interactions in the Momoge wetlands. These findings can provide localized and accurate data for climate change modeling, global ecosystem modeling, and developing strategies for reducing greenhouse gas emissions.
## 4 Experimental Analysis
### Experiment Setup  
#### 4.1.1 Datasets  
To evaluate the effectiveness of TCReC in addressing transmission instability caused by channel interference in edge computing environments, we selected two widely recognized and representative network traffic datasets: ISCX-VPN-2016 and CIC-IDS-2017. These datasets contain rich and diverse traffic patterns that could be observed in real-world edge computing and IoT scenarios. Specifically, ISCX-VPN-2016 includes seven different types of Internet application communication data, while CIC-IDS-2017 covers a broad range of normal and malicious traffic, including 12 kinds of attack types such as DoS, PortScan, and Infiltration. The characteristics of these datasets make them particularly suitable for evaluating TCReC's ability to reconstruct meaningful traffic features under packet loss conditions induced by wireless channel interference and environmental dynamics.
# Review of Blockchain Tokens Creation and Valuation
## 3 Blockchain Tokens Development
### Creation
#### 3.3.2 ERC-721 Non-Fungible Token  
ERC721 is a standard for representing NFTs [101]. Unlike ERC20 tokens, which are fungible, thus identical, and can be exchanged on a one-for-one basis, each ERC721 token is unique, with distinct characteristics that have the purpose of preventing them from being interchangeable. It enables the representation of unique digital assets on the blockchain, representing anything from digital art to ownership records. This standard has paved the way for a new digital economy with one-of-a-kind assets that are verifiable and tradeable on the Ethereum network.  
Algorithm 3 shows a token implemented using the ERC721 standard. The first step is to import the standard ERC721 token interface and inherit it in the contract. Then, the constructor (line 10) will specify the token name and symbol. A function must be defined to mint tokens by calling the _safeMint_ method from the ERC721 standard (line 12), and various conditions can be added, such as having a fixed total supply, to increase demand.  
The interface that every ERC721 token must use is presented in Algorithm 4. It defines functions to check the balance of an owner or who owns a specific _tokenId_.  
```
1: pragma solidity '0.8.20;
2:Interface IERC721{
3:function balanceOf(address owner) external view returns (uint256 balance);
4:function ownerOf(uint256 tokenId) external view returns (address owner);
5:function safeTransferFrom(address from, address to, uint256 tokenId) external;
6:function approve(address to, uint256 tokenId) external;
7:function setApprovalForAll(address operator, bool approved) external;
8:function transferFrom(address from, address to, uint256 tokenId) external view returns (uint256);
9:function getApproved(uint256 tokenId) external view returns (address operator);
10:Function isApprovedForAll(address owner, address operator) external view returns (bool);
11: }
```  
**Algorithm 3**Smart Contract 3--DemoTokenERC721.  
The _transfer_ function checks that the contract recipients are aware of the ERC721 protocol, thus preventing the undefined locking of the tokens. The _approval_ function permits the sending of tokens to other accounts, while _setApprovalForAll_ approves an operator to transfer all tokens owned by the caller.  
Finally, in this case, there are variations of this standard, each adding new specific functionality such as ERC721 Enumerable, ERC721 Metadata, or ERC721 Burnable [102].
## 3 Correction of Mathematical Model  
### Correction Method of Empirical Formula  
In order to ensure the accuracy of the transient temperature field calculations, the mathematical model of bearing temperature analysis, including the heat generation mathematical model and convective heat transfer coefficient calculation formula, was corrected based on the performance evaluation experiment on rolling bearings before the thermal performance analysis. At present, the rolling bearing performance experiments with the bearing lubrication experiment bench for a high-speed traction motor shown in Figure 2 include a temperature rise experiment, durability experiment and rapid acceleration experiment. Considering that the corrected mathematical model must cater to rolling bearing temperature analysis under all working conditions, the temperature rise experiment is selected as the correction basis. Under the condition of fixed axial and radial load, the bearing speed is increased gradually, to compare the temperature rise of the experiment and the simulation at a certain speed. The transient evaluation index and equivalent coefficient are introduced to determine the mathematical model suitable for the temperature calculation of a rolling bearing in a traction motor. The correction process is shown in Figure 4, and the specific correction process is as follows:  
Step 1: Determine the basic parameters required for rolling bearing temperature analysis based on the relevant empirical formula for rolling bearing temperature analysis.  
Step 2: Extract the experimental temperature curve of the rolling bearing temperature rise experiment and select the transient evaluation index according to curve change.  
Step 3: With reference to the basic parameters of the rolling bearing, calculate the heat generation and lubricant convective heat transfer coefficient under the corresponding working conditions of the temperature rise experiment according to empirical formulas.  
Step 4: Establish a 3D temperature field solution model of the rolling bearing and bearing chamber, apply load and boundary conditions, and use the finite element method to analyze the transient temperature field of the rolling bearing.  
Figure 4: Correction process flow chart of the empirical formula.  
Step 5: Extract the transient evaluation index of the simulation temperature curve obtained by the finite element method, compare the transient evaluation index of temperature rise experiment, and calculate the difference value of each index. If the difference value meets the given accuracy requirements, continue to the next calculation step; otherwise, change the equivalent coefficient, multiply the equivalent coefficient with the empirical formula as a new empirical formula, and calculate a heat generation and lubrication convective heat transfer coefficient to calculate iteratively; return to step 4 to re-analyze the transient temperature distribution.  
Step 6: Check the durability experiment results, determine the final equivalent coefficient, and multiply the final equivalent coefficient with the relevant empirical formula as the corrected empirical formula.
## 2 Results
### Changes in Kegg Pathways during Acute HCV Infection
\begin{table}
\begin{tabular}{|l|c|c|c|c|c|c|c|} \hline  & \multirow{2}{*}{**Genes in**} & \multicolumn{2}{c|}{**6 h**} & \multicolumn{2}{c|}{**48 h**} & \multicolumn{2}{c|}{**72 h**} \\ \cline{3-8} \cline{7-8}
**KEGG Pathway name** & \multirow{2}{*}{**Genes in**} & \multirow{2}{*}{**DEGs**} & \multirow{2}{*}{**_p value**} & \multirow{2}{*}{**DEGs**} & \multirow{2}{*}{**_p value**} & \multirow{2}{*}{**DEGs**} & \multirow{2}{*}{**_p value**} \\  & & **DEGs** & & & & & **DEGs** \\  & & **(EGs)** & & & & **(EGs)** & **_p value_ \\ \hline
**MAPK signaling** & 268 & 5 (168) & 0.003 & 13 (173) & 0.01 & 33 (177) & 0.0005 \\ \hline
**Cytokine-cytokine receptor interaction** & 265 & & & 9 (90) & 0.005 & 19 (92) & 0.002 \\ \hline
**Antigen processing and presentation** & 78 & 2 (36) & 0.02 & & & & \\ \hline
**RIG-I-like receptor signaling** & 71 & & & & & 12 (44) & 0.001 \\ \hline
**Toll-like receptor signaling** & 102 & & & & & 11 (58) & 0.03 \\ \hline
**NOD-like receptor signaling** & 59 & & & & & 9 (40) & 0.02 \\ \hline
**Cytosolic DNA-sensing pathway** & 56 & & & & 7 (28) & 0.02 \\ \hline
**Epithelial cell signaling in _H. pylori_** & 68 & & & & 11 (53) & 0.01 \\ \hline
**Adipocytoking signaling** & 68 & & & 7 (50) & 0.002 & 13 (48) & 0.0008 \\ \hline
**TGF-beta signaling** & 85 & & 6 (68) & 0.03 & 13 (55) & 0.02 \\ \hline
**Regulation of autophagy** & 34 & & & & 5 (18) & 0.03 \\ \hline
**Apoptosis** & 89 & & & & 12 (68) & 0.04 \\ \hline
**ECM-receptor interaction** & 85 & & 4 (37) & 0.04 & & \\ \hline
**Jak-STAT signaling** & 155 & & & & 15 (77) & 0.01 \\ \hline
**Hepatitis C** & 135 & & & & & 20 (98) & 0.002 \\ \hline
**mTOR signaling** & 52 & 2 (41) & 0.02 & & & \\ \hline
**Hedgehog signaling** & 56 & & & 5 (30) & 0.004 & & \\ \hline
**Notch signaling** & 47 & & & & 9 (37) & 0.01 \\ \hline
**Renal cell carcinoma** & 70 & 2 (58) & 0.04 & & & \\ \hline
**Basal cell carcinoma** & 55 & & & 4 (31) & 0.02 & & \\ \hline
**Hematopoietic cell lineage** & 88 & & 4 (29) & 0.02 & & \\ \hline
**Osteoclast differentiation** & 128 & & & 7 (76) & 0.02 & 19 (76) & 0.0001 \\ \hline
**Insulin signaling** & 138 & & & & & 19 (110) & 0.01 \\ \hline
**Type II diabetes mellitus** & 48 & & & 4 (27) & 0.01 & & \\ \hline
**Aminoacyl tRNA biosynthesis** & 63 & & & & 13 (41) & 0.0001 \\ \hline
**Ribosome biogenesis in eukaryotes** & 81 & & & & 12 (44) & 0.001 \\ \hline
**Sphingolipid metabolism** & 40 & & & & 9 (33) & 0.005 \\ \hline
**Glutathione metabolism** & 50 & 2 (39) & 0.03 & & & \\ \hline
**Cyanoamino acid metabolism** & 7 & 1 (4) & 0.02 & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Enriched Kegg pathways during acute HCV infection. Differentially expressed genes (DEGs) at each time point were analyzed with software that utilizes a [PERSON]’s exact test (_p_ < 0.05 considered significant) to determine enriched pathways. The background gene list included only genes that were expressed (EGs), while genes that were not expressed were excluded from statistical analysis.
## Appendix B Java Code Snippets
```
publicclassMeasurement{ privatelongid; privatelongsensorId; privatedoublevalue; privateintunit; privateinttype; privateDatetime; }
```
Listing A4 Measurement class with JPA annotations. ```
@Entity @Table(name=\"measurements\") publicclassMeasurement{ @Id @Column(name=\"measurement_id\") privatelongid; @Column(name=\"sensor_id\") privatelongsensorId; @Column(name=\"value\") privatedoublevalue; @Column(name=\"unit\") privateintunit; @Column(name=\"measurement_type\") privateinttype; @Column(name=\"measurement_time\") privateDatetime; }
```
Listing A5 Spring Boot Measurement Service. ```
@Service publicclassMeasurementService{ privatefinalMeasurementRepositorymeasurementRepo; @Autowired publicMeasurementService(MeasurementRepositorymeasurementRepo){ this.measurementRepo=measurementRepo;
```
publicclassMeasurementDTO{ privatelongid; privatelongsensorId; privatedoublevalue; privateintunit; privateintype; privateDatteme; publiclonggetId(){ returnid; } publicvoidsetId(longid){ this.id=id; }...
```
Listing A6: Data transfer object for measurement instances. ```
@Service publicclassMeasurementService{ privatefinalMeasurementRepositorymeasurementRepo; @Autowired publicMeasurementService(MeasurementRepositorymeasurementRepo){ this.measurementRepo=measurementRepo; } publicList<MeasurementDTO>getMeasurementsForDevice(longdeviceId){ List<Measurement>measurements=this.measurementRepo.findBySensorId(deviceId); List<MeasurementDTO>measurementDTOList=measurements.stream().map(Measurement::toDTO).tolist(); returnmeasurementDTOList; }
```
Listing A7: Measurement service method for getting the device measurements. ```
@Controller @RequestMapping(value='/measurements') publicclassMeasurementWebController{ @RequestMapping(method=RequestMethod.GET) publicStringgetMeasurements(){ return~measurements'; } }
```
Listing A8: Controller class with mapping to method. ```
@Controller @RequestMapping(value='/measurements') publicclassMeasurementWebController{ privatefinalMeasurementServicemeasurementService; @Autowired publicMeasurementWebController(MeasurementServicemeasurementService){ this.measurementService=measurementService; } } @GetMapping publicStringgetMeasurementsForDevice(Modelmodel){ List<MeasurementDTO>measurements=this.measurementService.getMeasurementsForDevice(100); model.addAttribute(~measurements',measurements); return~measurements'; } }
```
```
@RestController publicclassMeasurementRestController{@Autowired privateMeasurementRepositoryrepoMeasurements; @RequestMapping(value=\"/getDeviceMeasurements\",method=RequestMethod.GET) List<Measurement>findDeviceMeasurements( @RequestParam(name=\"deviceId\",required=false)LongdeviceId ){ longsensorId=Optional.ofNullable(deviceId).orElse(100L); List<Measurement>measurements=this.repoMeasurements.findBySensorId(sensorId); System.out.println(\"measurements:_\"+measurements); returnmeasurements; } }
```

Listing 10: Endpoint for microservice access. ## Appendix C Thymeleaf Sample Code

```
<html> <head> <metacharset=\"ISO-8859-1\"> <title>MyMeasurementService-Measurements</title> </head> <body> Measurements-template </body> </html>
```

Listing 11: Initial static HTML5 page.
## 3 Mathematical Model  
### Dimensional Equations of the System  
If we take all the components of the considered system to be ideal, the system can be described by a set of second order coupled ODEs, given by:  
\[m_{1}\ddot{x}_{1}+F_{\text{RI}}(\dot{x}_{1})+F_{\text{SI}}(x_{1})+K_{\text{C}}( t)(x_{1}-x_{2})=0 \tag{1}\]  
and,  
\[m_{2}\ddot{x}_{2}+F_{\text{R2}}(\dot{x}_{2})+F_{\text{S2}}(x_{2})+K_{\text{C}} (t)(x_{2}-x_{1})=0 \tag{2}\]  
where,  
1. \(F_{\text{Ri}}(\dot{x}_{i})=C_{i}\dot{x}_{i}+\frac{T_{i}\dot{x}_{i}}{\left( \dot{x}_{i}^{2}+\varepsilon^{2}\right)^{\frac{1}{2}}}\); \(i=1,2\). \(F_{\text{Ri}}(\dot{x}_{i})\) is the resistance force of the bearings and the term \(\frac{1}{\left(\dot{x}_{i}^{2}+\varepsilon^{2}\right)^{\frac{1}{2}}}\) is the smooth approximation of the function \(\text{sign}(\dot{x}_{i})\).
2. \(F_{\text{Si}}(x_{i})\) is the force due to the magnetic spring. \(F_{\text{Si}}(x_{i})=F_{M}(\delta-x_{i})-F_{M}(\delta+x_{i})=F_{\text{MO}} \Big{[}\frac{1}{\left\{1+d_{1}(\delta-x_{i})\right\}^{4}}-\frac{1}{\left\{1+d _{1}(\delta+x_{i})\right\}^{4}}\Big{]}\); \(i=1,2\). The idea of the above formula is considered by the two assumptions: (i) the repulsive force between two magnets is defined by the simplest expression of the inverse square law, where the dipole expression has been considered. (ii) when \(x_{i}=\delta\), the first term of the \(F_{\text{Si}}(\dot{x}_{i})\) expression becomes the unity, when \(x_{i}=-\delta\), the second term becomes unity, and the expression \(F_{\text{Si}}(\dot{x}_{i})\) becomes negative value.
3. The stiffness coupling of the considered system, \(K_{\text{C}}(t)=\Big{(}\frac{K_{1}+K_{2}}{2}\Big{)}+\Big{(}\frac{K_{1}-K_{2}} {2}\Big{)}\cos(2\omega_{\text{e}}t)\). It varies periodically having the linear frequency, \(f=2\cdot\frac{\omega_{\text{e}}}{2\pi}\), where \(\omega_{\text{e}}\) is the angular frequency of oscillation.  
The Equations (1) and (2) are the expressions in the dimensional form. Those equations will be useful to explain the system's dynamics experimentally. For the numerical works, we have transformed those equations into non-dimensional forms. The development of the equations is given below.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability  
In this section, the simplified matrix formulation for sensitivity of the filtering probability to the variation of the initial, transition and observation parameters is proposed. Let us consider the sensitivity functions \(p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{\mathbf{i}:t})(\theta)\) for a filtering probability and the variation of one of the model parameters, viz., \(\theta\). From Equation (2), the forward recursive filtering probability expression, it follows that  
\[p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{\mathbf{i}:t})(\theta)=\begin{cases}o_{v, \mathbf{e}^{\mathbf{i}}}(\theta)\cdot\gamma_{v}(\theta)&\text{if $t=1$}\\ o_{v,\mathbf{e}^{\mathbf{i}}}(\theta)\cdot\sum_{z=1}^{n}a_{z,v}(\theta)\cdot p (x_{v}^{t-1},\mathbf{y}_{\mathbf{e}}^{\mathbf{i}:t-1})(\theta)&\text{if $t>1$}\end{cases} \tag{6}\]where  
\[\gamma_{v}(\theta) =\begin{cases}\gamma_{v}(\theta_{\gamma})&\text{for }initial\text{ } parameter\text{ }variation\theta=\theta_{\gamma}\\ \gamma_{v}&\text{otherwise}\end{cases}\] \[a_{z,v}(\theta) =\begin{cases}a_{z,v}(\theta_{a})&\text{for }transition\text{ } parameter\text{ }variation\theta=\theta_{a}\\ a_{z,v}&\text{otherwise}\end{cases}\]  
and  
\[o_{v,\mathbf{e}^{t}}(\theta)=\begin{cases}o_{v,\mathbf{e}^{t}}(\theta_{o})& \text{for }observation\text{ }parameter\text{ }variation\theta=\theta_{o}\\ o_{v,\mathbf{e}^{t}}&\text{otherwise}\end{cases}\]  
The sensitivity function \(p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{1:t})(\theta)\) coefficients are computed by the proposed method using a series of Forward matrices \(F^{k},k=1,...,t\). The sensitivity of the filtering probability for transition, initial and observation parameter variations is formulated in the next subsections.
###### Abstract  
Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model's predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.  
traffic CO; traffic CO prediction; neural networks; GIS; land use/land cover (LULC) 2019