esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework to Build a Big Data Ecosystem Oriented to the Collaborative Networked Organization
## 3 Proposed Framework
### Big Data Assurance Dimension  
A CNO that tries to capture the value of big data needs guidelines based on varied techniques. Without clear guidelines, companies will face challenges in using data as a business driver and will be exposed to other risks and costs [48]. Therefore, a framework may be obsolete for a CNO if: (1) it does not enable the six Vs and/or (2) it only supports one big data technique. This dimension allows the building of an ecosystem aware of big data. The sub-dimension six Vs validates the objective of big data, that is, the acquisition, processing and the analysis of datasets that comply with the six vs. Each V is marked in a checklist when the ecosystem fulfills its purpose. Additionally, the CNO-BD framework extends this dimension through the sub-dimension Techniques guide, which allows the big data ecosystem to be extended. This sub-dimension provides guidelines of techniques to analyze big data in the CNO; see Table 4. Although the way each technique can be used in each type of CNO is indicated, this does not mean that the technique is limited to such manifestations. Additionally, it is not an exhaustive list. Therefore, there are more applications of each technique in CNOs. These techniques are used in the programmable module dimension. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Description and Models/Algorithms** & **Example CNO Applications** \\ \hline \multirow{4}{*}{Classification} & This classifies data by comparing a set of data that was previously categorized. It is based on supervised learning. Models/algorithms: Logistic regression algorithms. Support vector machine. C4.5 & * Determination of the probability of success or failure to form a VE or a VO in a VBE. * Measurement of the behavior of a participant in a VVC. \\ \hline \multirow{4}{*}{Association rule learning} & This is known as market basket analysts. It is based on association rules to identify the relevance of frequent relationships. Models/algorithms: Association rule mining algorithm. Eclat algorithm. & * Determination of the type of product and which channel it is returned by in a closed-loop SC. * Analysis of biological data to obtain new associations through a VL. \\ \hline \multirow{4}{*}{Statistical modeling} & Although this is useful in a variety of problems, it is typically used to determine forecasts and trends. Models/algorithms: Linear regression algorithms. Markov models. Time series. & * Determination of the effect of a new policy on a VG established by external regulatory frameworks. * Estimation of status or condition of the equipment that uses IoT in Industry 4.0 (VE). \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Big data analysis techniques oriented to the CNO.
## 4 Numerical Example
### FI Method  
This method proposes the communication between the NUMPRESS reliability analysis program and the KRATA external FEM program. The NUMPRESS program was developed at the Institute of Fundamental Technological Research of the Polish Academy of Sciences [35]. The KRATA program was created by Rador [36]. In this work, the connection between reliability software and external FEM program _FI method_ was addressed. The most important part was identified: Basic random variables implicitly influence the limit state function. This was checked through analyzing which of variables are involved in modifying input data files used by programs that generate values of the considered external variables. The diagram (Figure 4) shows the details of the interaction between a reliability program and a finite element method module.  
At the beginning, using the method of constant arc length and the method of the current stiffness parameter, the equilibrium path was determined. The most important issue was appointment of the limit point: \(q\) = 7.20 cm, and \(\mu\) = 6.65. On the basis of these, the limit function as the condition of the non-exceeding of the admissible vertical load multiplier of node 1 was formulated:  
\[g(x)\ =\ 1\ -\ \mu(\mathbf{X})/6.65, \tag{8}\]  
where \(\mu(\mathbf{X})\) is the function of random variables describing by explicit or implicit form for the load multiplier nearby the limit point, and \(\mathbf{X}\) = {\(P\), \(EA\), \(Z\)} is the vector of random variables.  
Figure 3: The geometry of spatial structure.  
In the reliability analysis of the truss structure, the following variables are used: \(P=10\mu\) (load of node 1); axial stiffness, \(EA\); and coordinate \(Z\) of node 1. Random variables are not correlated. A description of the random variables is shown in Table 1.  
The graph of the dependence between the values of the current stiffness parameter (CSP) on the vertical displacement of node 1, for nonlinear geometrical solution, is shown in Figure 5. Moreover, the equilibrium path is presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Random Variable**} & \multicolumn{1}{c}{**Probability**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Standard**} & \multicolumn{1}{c}{**Coefficient of**} \\  & **Density Function** & **Value** & **Deviation: \(\sigma\)** & **Variation** \\ \hline Load “\(P\)” & Normal & 10 kN & 1 kN & 10\% \\ Axial stiffness “\(EA\)” & Normal & 428,610 kN & 42,861 kN & 10\% \\ Coordinate “\(Z\)” node 1 & Normal & 0.524 m & 0.05 m & 9.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the random parameters.  
Figure 4: Diagram of cooperation between NUMPRESS program and external FEM module.  
Figure 5: Dependence of the current stiffness parameter (CSP) on the vertical displacement of limit point.  
Figure 6: Equilibrium path of spatial structure.
# Blockchain-Based Decentralized Architecture for Software Version Control
## 1 Introduction
We adopted the Hyperledger Fabric  
Figure 1: Software configuration management process. network to develop the decentralized blockchain to maintain data integrity and proposed an efficient blockchain-based version control system. One of the areas where blockchain technology can help is document sharing and version control. To improve security and scalability, a decentralized version control solution without the need for a single authority and central repository is required. The document version approval process typically involves a large number of approvers, each of whom has a log that must be updated depending on the signatures received from all approvers across the distributed network. As a result, all parties involved in the registration process must keep a consistent record, which is not supported by current document version control systems [24]. In this paper, we propose BDA-SCV, a blockchain-based decentralized architecture for software version control, on top of which all SCM processes can be implemented. We have used smart contracts to create a blockchain-based system for version control for code files, which diminishes the need for a trusted third-party authentication between the developers and the approvers. The four major components of our proposed approach are a decentralized repository, blockchain, smart contract, and consensus algorithm. For a distributed repository, we have considered the IPFS distributed file system. We have used a proof of authority consensus algorithm, where the authority is responsible for adding development to the specific project. The Hyperledger toolset is used for implementation and the Hyperledger Fabric Client is deployed on a cloud environment, to which the developers will be connected, and the Hyperledger network is deployed where the ledger and IPFS system are deployed. This paper's main contributions can be summarized as follows:  
1. Review of work done previously on the version control system and blockchain;
2. Proposal of a decentralized blockchain-based architecture for software version control;
3. Implementation of the proposed approach using Hyperledger Fabric on a cloud platform. The rest of the paper is structured as follows. First, we discuss the background study for the research, followed by related work in which we explain the different related papers. Then, that we propose our approach to BDA-SCV and present implementation, ending the article with future work and conclusions. Figure 2: Tradition version control.
## 2 Materials and Methods
### Hyperspectral Acquisition  
A visible/near-infrared (VIS/NIR) hyperspectral imaging system (Figure 1) in the spectral range of 400-1100 nm was used in this study. A line illumination source with fiber bundle structure was equipped for better imaging. The hyperspectral imaging system was mainly composed of a high-performance back-illuminated 12-bit charge coupled device (CCD, charge coupled device) camera (Sensicam QE, PCO AG, Kelheim, Germany), an imaging spectrograph (ImSpector V10E, Spectral Imaging Ltd., Oulu, Finland), an illumination unit (Oriel Instruments, Stratford, CT, USA) equipped with a quartz tungsten halogen lamp and optical fiber, a laser displacement sensor (CD33-120N-422, OPTEX, Rancho Dominguez, CA, USA), a computer supported with a data acquisition and control software (Camera Control Kit V2.19, PCO AG, Kelheim, Germany), Microsoft Visual Studio 2010 (Microsoft Corporation, Redmond, WA, USA) and OpenCV 2.4.10 (Willow Garage, Inc., Palo Alto, CA, USA), and a translation stage (horizontal and vertical) operated by stepper motor (AH-STA03300, NEWLABS CO, LTD, Beijing, China). The optical fiber was used to transform the light and form a line of light on the surface of the sample. The whole system was maintained in a dark sealed container to avoid the effects of external light. The resolution of the system was 2.8 nm spectrally and 150 um spatially. The image generated by this system was of 1376 \(\times\) 1040 (spatial \(\times\) spectral) pixels originally, and 2 \(\times\) 2 binning was performed on the final images of 688 \(\times\) 520 to improve the signal-to-noise ratio of the images.  
Then, pork meat samples were tiled on the black board at room temperature for 30 min. With the system described above, the samples were placed on the translation stage, and the vertical height of the camera from the sample was adjusted according to the preset height. Then, each sample was scanned line by line with 80 ms exposure time according the total length of the pork meat sample to build a hypercube. The scan speed of the system was about 50 mm/s, considering the exposure time and data processing. A hypercube with three dimensions (\(x,y,\lambda\)) was achieved for every sample, which the \(x\)-dimension of the cube stands for the length of one line (688 pixels in this study), the \(y\)-dimension of the cube stands for the line number which reflects the scanning length of the sample, and the \(\lambda\)-dimension stands for the 520 bands in the range of 400-1100 nm. In this way, each pixel of the hyperspectral image represents a spectrum.
## 2 Materials and Methods
### Analysis of Prediction Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Source** & **DF** & **Sum of Squares** & **Mean Square** & **F Ratio** & **Prob \textgreater{} F** & **Max RSq** \\ \hline Lack Of Fit & 30 & 135.09267 & 4.50309 & 1.4629 & 0.1312 & 0.8829 \\ Pure Error & 39 & 120.04749 & 3.07814 & - & - & - \\ Total Error & 69 & 255.14016 & - & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 6: Lack-of-fit error. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Term** & **Estimate** & **Std Error** & **t Ratio** & **Prob \textgreater{} l t l & **Lower 95\%** & **Upper 95\%** \\ \hline Intercept & 15.24063 & 0.480734 & 31.7 & \textless{}0.0001 & 14.28159 & 16.19966 \\ \(\text{x}_{1}\) & \(-2.08862\) & 0.225926 & \(-9.24\) & \textless{}0.0001 & \(-2.53933\) & \(-1.63791\) \\ \(\text{x}_{5}\) & 0.903898 & 0.225926 & 4 & 0.0002 & 0.453188 & 1.354608 \\ \(\text{x}_{6}\) & 0.805741 & 0.225926 & 3.57 & 0.0007 & 0.355031 & 1.256451 \\ \(\text{x}_{3}\) & \(-0.58184\) & 0.225926 & \(-2.58\) & 0.0122 & \(-1.03255\) & \(-0.13113\) \\ \(\text{x}_{1}\text{x}_{1}\text{x}_{1}\) & \(3.453701\) & 0.680272 & 5.08 & \textless{}0.0001 & 2.096597 & 4.810806 \\ \(\text{x}_{1}\text{x}_{5}\text{x}_{5}\) & \(-0.51656\) & 0.240367 & \(-2.15\) & 0.0351 & \(-0.99608\) & \(-0.03704\) \\ \(\text{x}_{1}\text{x}_{2}\) & \(-0.68031\) & 0.240367 & \(-2.83\) & 0.0061 & \(-1.15983\) & \(-0.20079\) \\ \(\text{x}_{3}\text{x}_{2}\) & 0.71 & 0.240367 & 2.95 & 0.0043 & 0.230481 & 1.189519 \\ \(\text{x}_{1}\text{x}_{5}\text{x}_{3}\) & 0.51625 & 0.240367 & 2.15 & 0.0352 & 0.036731 & 0.995769 \\ \(\text{x}_{1}\text{x}_{1}\text{x}_{1}\text{x}_{1}\text{x}_{1}\text{x}_{1}\text{x}_{1} & \(-0.47839\) & 0.168911 & \(-2.83\) & 0.0061 & \(-0.81536\) & \(-0.14142\) \\ \(\text{x}_{1}\text{x}_{5}\text{x}_{6}\text{x}_{2}\) & \(-0.68906\) & 0.240367 & \(-2.87\) & 0.0055 & \(-1.16858\) & \(-0.20954\) \\ \(\text{x}_{1}\text{x}_{5}\text{x}_{3}\text{x}_{2}\) & 0.94625 & 0.240367 & 3.94 & 0.0002 & 0.466 where \(DEGR_{con}\)--degreasing concentration, \(DEGR_{tim}\)--de-greasing time, \(DEGR_{temp}\)--de-greasing temperature, \(KTL_{tim}\)--time of cataphoresis, and \(KTL_{tim}\)--cataphoresis voltage. To ensure the complexity of the conducted analysis and to confirm the accuracy and suitability of the chosen model, it is necessary to examine the residuals. The difference between the observed values and the predicted values calculated using the predictive model must be assessed. The achieved significance level of the Shapiro-Wilk test [29], illustrated in Figure 3, indicates a Gaussian distribution of the residuals, meaning that the value is greater than 0.05. Therefore, it can be concluded that the predictive model is statistically and numerically valid.
## 2 Materials and Methods
### Data Statistical Processing  
The toluene and ferrous iron concentration data were presented as mean \(\pm\) S.D. [PERSON]'s multiple range test, conducted using SPSS Statistics 22.0, was employed to assess the differences among the three systems, with a significance level set at \(p\) = 0.05.  
To investigate the degradation kinetics of toluene in various systems with different iron oxides as electron acceptors, we assumed first-order kinetics for the reactions. To determine the degradation rate constants, we conducted a series of experiments where the concentration of toluene was monitored over time. The concentration data were then processed through calculating the natural logarithm of the ratio of the remaining concentration at time \(t\) (C\({}_{t}\)) to the initial concentration (C\({}_{0}\)). This relationship is represented as ln(C\({}_{t}\)/C\({}_{0}\)).  
Subsequently, we plotted ln(C\({}_{t}\)/C\({}_{0}\)) against time (\(t\)) for each system. According to the first-order reaction kinetics equation, ln(C\({}_{t}\)/C\({}_{0}\)) = \(-kt\)., where \(k\) is the degradation rate constant. The slope of each line corresponds to the negative of the first-order degradation rate constant (\(k\)). Through determining these slopes, we were able to calculate the degradation rate constants for toluene in each system with different iron oxides.  
The mean absolute percentage error (MAPE) is a metric used for assessing the accuracy of a predictive model. The calculation process is as follows:  
For each observed value \(y_{i}\) and its corresponding predicted value \(\hat{y_{i}}\), calculate the absolute percentage error (APE):  
\[\text{APE}_{I}=\left|\frac{y_{i}-\hat{y_{i}}}{y_{i}}\right|\times 100 \tag{6}\]  
Compute the average of all APEs, resulting in the mean absolute percentage error (MAPE):  
\[\text{MAPE}=\frac{1}{n}\text{$\sum_{i=1}^{n}$APE}_{I} \tag{7}\]  
Here, \(n=3\), represents the number of observed values.
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 3 Strategies Enhancing Remanufacturing
### Design for Remanufacturing (DfRem)  
Design for remanufacturing is a concept that has developed into a research hotspot due to the problems that are encountered during the remanufacturing stage. These problems arise due to poor product design which makes it difficult for the various stages of remanufacturing to be carried out, especially with regard to disassembling. According to [104], design for remanufacturing (DfRem) is a means of supporting sustainable manufacturing goals within a firm. Remanufacturing is preferable to recycling from an environmental perspective as it returns a product to working condition rather than reducing it to raw materials. In a review on DfRem, [PERSON] et al. [105] recognized several key problems with design for remanufacturing in practice; firstly, there is a lack of knowledge and understanding amongst designers. Secondly, even if the idea is understood, there are few products being remanufactured and even fewer designed for remanufacturing. Finally, there are few tools for DfRem.  
One of the main reasons why many products are not designed to be remanufactured is because the original equipment manufacturers (OEMs) are not heavily involved in  
Figure 7: Lifecycle data flow.  
remanufacturing. Due to this limitation, the product encounters disassembly issues during remanufacturing. OEMs getting involved in remanufacturing would further improve the design of products since they would want to reduce the problems encountered during the remanufacturing stage. Disassembly is the first stage of remanufacturing and it is definitely the most important stage because if the product is not well-disassembled, then the cores can be further damaged. The reasons cited for remanufacturing are mostly environmentally focused, such as recovery of components at the end of a product's useful life based on take-back and end-of-life legislation in countries like Germany [106] and increased environmental awareness among businesses. [PERSON] et al. [49] studied three OEMs from the UK mechanical industrial sector to determine what internal and external factors affect the integration of DfRem into the design process. It was discovered that many of the factors are similar to those that influence design for environment decisions, but DfRem was not primarily identified as one of the factors which influence environmental degradation. The objective is to design a product that can be disassembled to facilitate salvage of recyclable material and to safely dispose of unrecyclable material. This is because only a few components are remanufacturable for some products, while the rest of the components are recycled, so the manufacturer has to take all of this into consideration. Remanufacturing is an environmentally focused driver for disassembly, although economic factors, such as service and maintenance, are also heavily dependent on disassembly [50].
# Optimal Mobility-Aware Wireless Edge Cloud Support for the Metaverse
## 1 Introduction
In this paper, by explicitly considering the terminals, user mobility, service decomposition and models of metaverse regions with embedded AROs, a joint optimization framework (OptimT) is constructed for the metaverse MAR application in the edge-supported network. The proposed optimization framework seeks a balance between energy consumption and service delay under a given level of user perception quality. To reveal the influence of capacity and cost of terminals on the metaverse MAR application, this OptimT scheme is further compared with another optimized framework (OptimNT) proposed in our previous work [16], which only focuses on ECs and neglects terminals. Figure 2: Illustrative toy examples of caching by a metaverse MAR application. (**a**) Traditional caching without user mobility and service decomposition. (**b**) Proactive caching with user mobility and service decomposition.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 4 Discussion
These scanners present an interesting option for surveying indoor spaces due to their low weight, ease of manipulation, and, in particular, the short time needed for scanning. Of all tested SLAM scanners, Emesent Hovermap ST-X performed best, closely followed by FARO Orbis, which is characterized by a lower level of noise compared to Emesent Hovermap ST-X. This scanner, in addition, offers an important additional feature-surveying of reference points during scanning and their direct consideration in the measurement trajectory, which prevents the need for subsequent identification of reference points during post-processing and minimizes the introduction of further errors and improving accuracy. Of all tested scanners, the GeoSLAM ZEB Horizon RT performed worst; it is, however, necessary to point out that GeoSLAM ZEB Horizon RT is a representative of an older generation of scanners, both from the perspective of the software and scanning head. Limitations of this study lie in the character of the testing area, which limits the generalizability of our results to the interior spaces with the character of corridors and should not be used to evaluate the performance of SLAM scanners in natural or outdoor environments. The total length of the corridor of 80 m can also be perceived as a limitation. On the other hand, this disposition is typical of residential as well as office buildings, for which the SLAM scanners are primarily intended.
## 6 Evaluation
### Development of the Prototype  
For the evaluation of the proposed concept, a prototype was developed according to the system architecture defined in Figure 6. The modular system architecture leads to a micro-service implementation of the single components in the business logic layer. These services can be consumed by multiple clients that form the presentation layer, e.g., the proposed mobile and web apps.  
The business logic layer employs current technologies, such as Spring Boot, Hibernate, the Java Persistence API, and a Postgres database, to provide functionalities, data persistence, and the API routes for the connectivity to client components. The micro-services are deployed and orchestrated using Docker. The business logic services are implemented with regard to the proposed concept. The backend is secured with the OAuth2 authorization flow as an established authorization method provided through Keycloak. The file-based data persistence is implemented according to the openCDE-conform web service as specified in DIN SPEC 91391-2:2019 [58]. This technical specification defines the container-based exchange of documents and construction-specific metadata via web services. The implementation of an openCDE-conform web service enables the system to store the BCC persistently and access it for information retrieval.  
The Blockchain Communication Backend is implemented using a NodeJS server, which offers a REST API via the Express package and is written in TypeScript. Blockchain identities, i.e., private keys for signing transactions, are currently stored in a mongoDB database and accessed via mongoose from the NodeJS server. The underlying blockchain is the consortium variant GoQuorum, which runs on a virtual machine. The NodeJS server accesses the blockchain and creates transactions using the EthersJS package. Smart contracts are developed with the Solidity programming language, and the Hardhat tool suite is used for compiling contracts, running test cases, creating TypeScript interfaces, and deploying to the blockchain.  
The frontend of the web app (see Figures 10 and 11) was developed using Angular. To provide convenient access to IFC-based information within the system, a viewer for IFC files was implemented in the frontend. Therefore, the XbimWebUI components were used, which are part of the Xbim Toolkit [59]. XbimWebUI offers a WebGL-based viewer as a typescript library. To present IFC files on a website, the models must first be converted into a binary format provided by the Xbim Toolkit, which is also deployed in a separate micro-service. The prototype is still under development and will be validated shortly based on the first two application scenarios. In particular, the interaction of the individual implemented components still needs to be extensively tested and evaluated.