esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Solution Architecture Overview  
The proposed solution is based on a multi-blockchain architecture, where two distinct blockchain technologies are combined. The DID Management Blockchain (DID-MB) is dedicated to the management of DIDs and is implemented on top of the Cheqd [25] blockchain platform which is designed to provide decentralized identity solutions. The DPP Data Blockchain (DPP-DB) focuses on handling data models associated with DPPs and is implemented on top of the Hyperledger Fabric blockchain platform [26] designed from the start for enterprise use cases. This dual-blockchain approach leverages the unique strengths of each system to create a more comprehensive and efficient solution for product lifecycle management. The service gateway acts as a middleware (an intermediary layer) that connects applications and services to the underlying blockchain networks. The middleware can provide additional services such as data validation, synchronization, and conflict resolution. The middleware acts as security manager and ensures that the interaction with the two blockchains is efficient and secure.  
The process of creating a DPP involves using decentralized identifiers to create a unique ID for the product stored in a verifiable data registry, which then is linked to the product passport. This is achieved using a DID resolver, in this case, provided by cheqd.io, offering similar functionalities to a standard Global Trade Item Number (GTIN) but with additional advantages like decentralization and cryptographic verification. Each DID ID is accompanied by a DID document that includes metadata.  
Figure 1 presents the block diagram of the proposed solution.  
The User Application integrates the required components (controllers) to interact with multi-blockchain infrastructure. In a simplified scenario, the following use cases are covered by this application: (1) create a DPP for a new product which includes a DID document and an associated DPP document and (2) lookup and interact with the DPP document based on the product DID identifier. The interaction for creating a new DPP entry is presented in Figure 2a, while interactions for accessing an existing DPP entry based on a unique ID is presented in Figure 2b.  
Figure 1: Solution proposal block diagram.  
By segregating DIDs and DPPs across different blockchains, the system enhances overall security. Each blockchain can be optimized for its specific purpose, reducing the risk of compromising both identifiers and product data in the event of a security breach. This approach allows each blockchain to scale according to its unique requirements. The DID blockchain can focus on efficiently managing identity verifications, while the DPP blockchain can be optimized for handling complex product lifecycle data. This flexibility allows for different consensus mechanisms, data structures, and privacy settings suitable for DIDs and DPPs.
## 5 Simulation and Industrial Application
### Application  
In order to test practical effect of the proposed adaptive denoising method for the machinery sound signal based on WTD-IFOA, an industrial application was operated in a fully-mechanized coal mining working face. The shearer is an important machine in automatic coal mining, and working condition monitoring for the shearer is of great necessity. Traditional monitoring methods are mainly based on the vibration signal [35], even though the working life of the vibration sensors are very short due to the bad working condition and contact-measurement. Coal output is seriously restricted by the frequent maintenance. In the August 2015, an online monitoring system through the shearer cutting sound signal was built in the 71,507 coal mining face in the NO.2 Mine of Yangquan Coal Industry Group Corporation. However, there existed a large number of noise signal among the initial signal because of the harsh working environment. To eliminate the background noise from the sound, an industrial microphone was installed and WTD-IFOA was applied. The three-dimensional model of the coal mining shearer and field sound collection is presented in Figure 10.  
To illustrate the effectiveness of the proposed method, a piece of field sound signal with the length of 0.5 s was extracted and denoised. The original sound and the denoised one are shown in Figure 11. Then, 4096 points FFT was conducted to analyze the frequency components of the two signals, as presented in Figure 12. It can be seen in Figure 12 that the pro  
Figure 10: (**a**) Three-dimensional model of the shearer; and (**b**) collection of field cutting sound.  
Figure 9: Comprehensive comparison of the five methods: (**a**) Mean square error of the five methods; (**b**) Peak value error of the five methods; and (**c**) Computation time of the five methods.  
decreased in amplitude compared with the original field signal. The reason lies in that the scope saltation caused by the noise component was removed. Moreover, frequency components of original signal shown in Figure 12 had a disordered distribution, and it was difficult to identify the working state of the shearer. On the contrary, it was regular for the denoised signal. Some wave peaks appeared in the spectrogram, and other areas were stable. Different spectra of the collected signal reflected different working conditions of the shearer. Thus, the working state could be identified according to the wave peaks.
## 5 Experimental Results  
The procedure of experiments consists of four main steps: production of test samples with various surface quality, subjective perceptual experiments and data analysis, calculation of individual IQA metrics using the assumed division into regions, and combination of metrics and optimization of weights, as illustrated in Figure 3.  
The first experiments have been conducted assuming the use of individual elementary metrics for all 107 images converted to grayscale and divided into 4, 9, and 16 regions. For each of them, three correlation coefficients have been calculated to identify potentially the most useful metrics for further combinations. The obtained results are presented in Table 1. As may be noticed, in most cases similar values of PLCC and SROCC have been obtained for individual metrics, hence in further experiments, only Pearson's correlation has been used as the optimization criterion. For many metrics the choice of the number of regions does not change the correlation values significantly, hence further experiments have been conducted assuming the division into 16 regions. The best PLCC results obtained for various combinations of two metrics are presented in Table 2 (to save space only the values higher than 0.67 have been presented). As may be seen, in most combination the FSIM has been used, hence it has been used as the \"fixed\" metric for the combinations of three metrics (only the two others have been changed during the optimization of weights, although the FSIM exponent has been optimized as well). The best PLCC results achieved for such combinations of three metrics are presented in Table 3.  
Figure 3: Illustration of the experimental procedure. In view of results presented in Table 3, the best results may be obtained using the combination of FSIM and CW-SSIM with a third metric (preferably MCSD), however good results may also be achieved using FSIM and MCSD as two basic metrics used in the combinations. Considering these results, the application of more metrics has led to an even slightly better correlation with MOS values as presented in Table 4, where the obtained rank order correlations are additionally presented as well for comparison with Table 1. As may be seen, the best PLCC values are not always equivalent to the
## 1 Introduction  
The requirement elicitation process represents the first phase of every software and IS project development. The importance of this phase has been widely discussed through countless research and survey reports. Its susceptibility impact can be viewed in the report by the [PERSON] group [1], depicting a 31% failure ratio for software development projects. Moreover, 51% of these undergo serious challenges that extend the duration of the project, further increasing budget costs. Successful software delivery demands correct software specifications and requirements through applying appropriate elicitation techniques. Therefore, selecting a proper elicitation technique requires extracting more accurate and complete requirements that reflect stakeholders' actual desires. Hence, each technique has its strengths and weaknesses depending on the case scenario of the requirement. According to the authors of [2], most software engineers select an elicitation technique for several reasons, whereas they typically select one specific strategy (i.e., favoring one method for all possible scenarios). Otherwise, they tend to presume the technique's effectiveness during the application process. The literature for the requirement elicitation phase is rich with numerous contributions that vary from primitive to advanced applications of technology and methodologies. However, studies show that most of the research published in this field is focused on the level of requirement identification and classification [3]. Unquestionably, successful implementation of the elicitation technique selection process using machine learning is tangible in the requirement elicitation field. Nevertheless, studies have uncovered the limitations practical machine learning applications in the field [4; 5; 6]. We need to enhance the requirement elicitation process with more than just automated operation using machine learning. Instead, we need to create a model that opts to learn and think like humans. Hence, this paper aims to create a requirement technique selection model using deep learning technology that reduces the software engineers' intervention in the technique selection process with a more robust and effective alternative that can generate more accurate decisions. Eventually this could lead to more precise requirement reports reflecting the actual needs of stakeholders, thus increasing the success ratio of the ongoing software development project. Thus, the main contributions of our model are as follows:  
* Automating the technique selection process to reduce human error;
* Building a robust decision-making model;
* Producing proper requirements and increasing the success ratio of IS projects.  
The remaining part of the paper is organized as follows. Section 2 presents the related study. Section 3 illustrates the strategy used to implement the proposed methodology in order to develop a deep learning model for elicitation technique selection. Section 4 presents the deep learning model implementation results and model validation. Section 5 discusses the model results. Section 6 presents the conclusions of our research in this paper.
## 2 Materials and Methods
### Data Acquisition  
The Landsat imagery with the least cloud cover was acquired from July to August for the years 1980, 1985, 1990, 2000, 2005, 2010, 2015, and 2020. Table 1 illustrates the details of remote sensing data used to estimate forest cover for the years 1980 to 2020 with a 5-year temporal interval. In addition to Landsat MMS having 60 m spatial resolution with four bands, Landsat ETM+ and Landsat OLI with 30 m spatial resolution were used, respectively. Visible and near infrared (NIR) bands were mainly used for the vegetation index's computations, classification, and analysis. The satellite data were obtained from the USGS web portal. For topographic normalization, the Digital Elevation Model with a spatial resolution of 30 m (SRTM) was used for this study.  
Figure 2 represents the stages of research. Pre- and post-processing techniques were applied to the Landsat images with ENVI and ArcGIS 10.5 software. The change in vegetation cover for each time was then estimated from each pixel. The categorization accuracy was assessed using statistical measures such as the kappa coefficient, user and producer error, and the statistical significance of overall accuracy. Forest cover and woodland cover changes for each of the time periods 1980, 1985, 1990, 2000, 2005, 2010, 2015, and 2020 were ascertained and evaluated.  
\begin{table}
\begin{tabular}{l c c} \hline
**Satellite Instrument** & **Spatial Resolution** & **Temporal Availability** \\ \hline Landsat MMS & 60 m & 1980 \\ \hline Landsat TM & 30 m & 1985, 1990,1995 \\ \hline Landsat ETM+ & 30 m & 2000, 2010 \\ \hline Landsat OLI & 30 m & 2015, 2020 \\ \hline Digital elevation model (SRTM) & 30 m & \\ \hline \end{tabular}
\end{table}
Table 1: Datasets used with description.  
Figure 1: Map of the study area (Malam Jabba region).
## 4 Experimental Simulation Analysis  
Firstly, the effects of parameters such as the number of IFFT points, different chaotic maps, and semi\(-\)spread spectrum factors on the performance of the MC\(-\)ODBR\(-\)DCSK  
Figure 7: Comparison of DBR of data subcarriers in different systems.  
system are verified. Secondly, the BER performance of the new system is compared with SR\(-\)ODBR\(-\)DCSK, ODBR\(-\)DCSK and DCSK under the same experimental conditions. The experimental procedure uses the controlled variable method. The values of the parameters involved in the experiments are taken as in Part 2. The initial iterative value of the chaotic map involved in the experiment is 0.04.
# Flue Gas Composition and Treatment Potential of a Waste Incineration Plant
## 2 Methods and Materials
### Post-Combustion Flue Gas Cleaning Technology  
Dry sorbent injection is an approved technology that can be applied to the controlling system. A reactive sorbent is injected into the upper part of the furnace (high temperature) or in the duct downstream from the boiler (low temperature) to react directly with the products of combustion. This technology has economic advantages including lower equipment costs (first cost) as well as decreases in the ongoing operation and maintenance costs. It also has a lower life-cycle cost than other technologies. Many gas pollutants can be removed depending on the sorbent use. Commonly used sorbents in dry sorbent injection systems include [29]:  
* Calcium hydroxide to mitigate HCl, HF, SO\({}_{2}\), SO\({}_{3}\), and Se.
* Sodium bicarbonate to capture HCl, SO\({}_{2}\), and SO\({}_{3}\), and to some extent, HF.
* Powdered activated carbon to adsorb heavy metals and organics compounds.  
Reaction products are gathered in a downstream particulate control device together with the fly ashes and unreacted sorbent, which can be used as feedstock to cement plants or for stabilization of earthen structures, and can achieve 90% removal efficiency [29].  
The development of emission restrictions for acidic gases from 2010 until the updated regulations in 2018 is shown in Table 9. The regulations have become more restrictive with requirements for increased concentrations of cleaning and reducing pollutants.  
The selected sorbent to reduce acidic gases can be directly injected in the boiler at a temperature range of between 850-1000 \({}^{\circ}\)C. Many operating plants (in operation mode) have been developed to adapt and meet the requirements of the new regulations for acidic gases by using the method of directly injecting sorbent using silo, a screw for injection needs, and a dosing control system, after scaling-up the concentration of gases at the stack effluent for a precise dosing process.
## 4 Algorithm Implementation  
To address the constrained multi-objective optimization in the PPOP, this study proposes an integrated model combining deep-network-based risk predictions with a modified PPS-MOEA/D algorithm. The framework primarily consists of two key components: risk prediction and path optimization. Initially, a deep spatio-temporal graph attention network (DSTGAT) is employed to generate hourly risk predictions for all street networks within the study area, providing essential intelligence guidance for subsequent path planning. The emphasis of this paper lies in the implementation of the optimization algorithm; thus, the architectural details and implementation specifics of the deep learning prediction algorithm can be seen in another work [50].  
Owing to the numerous realistic constraints inherent in our research task, the PPS-MOEA/D framework was selected to address the constraint-handling mechanism, which is specifically divided into push and pull phases. During the push phase, the population's search and convergence processes disregard constraint limitations, allowing traversal through infeasible regions while simultaneously mapping the constraint landscape. In the subsequent pull phase, the algorithm leverages the information gathered during the push phase to guide parameter settings, thereby enhancing adaptability. The core philosophy of MOEA/D lies in decomposing the multi-objective optimization problem into multiple single-objective subproblems, utilizing a neighborhood search mechanism to maintain population diversity and optimize efficiency. Through the implementation of uniformly distributed weight vectors and localized searches within neighborhoods, MOEA/D efficiently approximates the Pareto front while maintaining a balance between global and local search capabilities. Consequently, these methods are integrated to tackle the constrained multi-objective optimization problem, with empirical evidence demonstrating their efficacy. However, the raw codes cannot be directly applied to the practical problem under investigation in this study, necessitating substantial modifications to achieve the predefined objectives. The corresponding flowchart is presented in Figure 2.
## 2 Survey of Commercial Small Platforms
### Passive Microwave
#### 3.1.3 Signals of Opportunity (SoOp): GNSS-R, and Receiver of SoOp  
The utmost sensors used for oceanography (SARs and radar altimeters) have features that make them difficult to board on nano-satellites, most notably the power requirements, and the antenna size. An attractive option to explore the sea surface topography is the use of reflected Global Navigation Satellite Systems (GNSS) signals [34; 35]. GNSS reflectometry is a favourable technique to perform some ocean measurements with small satellites [36]. The advantage of this technique is the capability to operate in all-weather conditions with a spatial resolution of \(\sim\)25 km. In the last two decades, a big effort has been made to develop models that prove the feasibility of using GNSS signals, proving to be successful for sea surface, altimetry measurements [37; 38], wind speed [39; 40], soil moisture [41; 42; 43; 44; 45], ice thickness [46], ice cover [47], and others. A few characteristics of GNSS-R missions have been identified and summarized in Table 14.  
The current and planned missions using GNSS-R technology are presented in Table 14, such as TechDemosat-1 (TDS-1) [48], the Cyclone Global Navigation Satellite System (CYGNSS) [36], and FSSCAT [49; 50].  
TDS-1 was launched in June 2014 and it includes a GNSS-R payload with a mass of around 1.5 kg and approximately 10 W power consumption. It demonstrated the capabilities of GNSS-R for low power, low cost, and low mass. This payload measures complete delay-Doppler Maps (DDM) providing scientific-quality data [51]. The CYGNSS mission takes advantage of a constellation of eight microsatellites (weighting 17.6 kg) that provide nearly gap-free Earth coverage over Equatorial regions, with an average revisit time of seven hours and a median revisit time of three hours. CYGNSS was launched on December 2016. FFSCAT is a tandem mission of two 6U Cubesats (\({}^{3}\)Cat-5/A and \({}^{3}\)Cat-5/B) featuring a hybrid microwave radiometer/GNSS- Reflectometer and a hyperspectral imager. FSSCAT will be the first nanosatellite mission to complement the Copernicus program [49]. Its main focus is over Polar Regions, and it will be launched in 2019.  
The European Space Agency (ESA) conducted the studies of a space-borne demonstrator called Passive Reflectometry and Interferometry System In-Orbit Demonstrator (PARIS IoD) [52; 53; 54]. PARIS IoD was later reincarnated into the GEROS experiment on board the International Space Station [55], but it was never implemented.  
Novel techniques using signals of opportunity, such as from Direct Broadcast Satellite (DBS) television at Ku- or X-bands, can be used to measure precipitation and winds over the sea surface [56], and these signals are sensitive to detect fluctuations of the sea surface roughness.  
In this regard, the SGR-ReSI [57] payload onboard TDS-1 is selected as a possible candidate to cover the measurements with gaps such as wind speed over the sea surface (horizontal), sea ice cover, sea ice thickness, and soil moisture [6].
# Endowed Polyphenols in Advanced Delivery Systems for Vaginal Infections
## 6 Polyphenols as Antimicrobial Agents
### Tannic Acid  
Tannic acid, first identified in 1795, is a hydrolyzable polyphenic molecule found in almost every aerial plant tissue, both herbaceous and wood types [134]. The molecular structure is not yet fully clarified; it is described as consisting of a central glucose molecule with ten gallic acid molecules attached to it [135]. It is worth noting that the natural source of the compound has a big impact on its phenolic composition, i.e., tannic acid from oriental nutgalls has less penta- and hexagalloy esters, leading to higher molecular weight, while tannic acid from Turkish nutgall has a predominance of penta- and hexa-esters, and thus a lower molecular weight [136].  
For the last century, this phenolic acid has been used for a variety of applications, such as the manufacture of ink, printing fabrics, photography, or as a coagulant in rubber production [136]. Due to its various unique properties, this compound has been subjected to extensive research, including its antiviral and antimicrobial activities. It can inhibit the adhesion of viruses and bacteria to surfaces, as well as interfere with the metabolism of both Gram-negative and Gram-positive bacteria and viruses [137]. Some of the microorganisms for which tannic acid has antimicrobial activity include _E. coli_[96; 138], _S. aureus_ (MSSA and MRSA) [139; 140], and _C. albicans_[141]. Furthermore, it also possesses antioxidant properties, as demonstrated by the formation of complexes with Fe(II), inhibiting the participation in Fenton reactions [142].
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 3 Methodology
### Data Preprocessing
#### 3.3.1 Data Cleaning  
The data cleaning step included the elimination of unwanted and redundant data to reduce the complexity of the text and to prepare it for subsequent steps. English and Arabic numbers, emojis, URLs, symbols, and punctuation marks were considered unwanted data, so we removed them. Additionally, we removed repeated characters and elongation, and we used a single occurrence instead of duplication. Generally, a dataset that comprises tweets contains other unwanted information, such as hashtags and username mention, so these were also ignored and eliminated from the data. Additionally, we removed the stop words that are often distributed within texts and that were also considered a form of unwanted data.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 3 Proposed Scheme
### Server Registration Process  
This phase covers the process of enrolling the servers, with the \(TA\) as depicted in Table 3:  
1. Server (\(S_{s}\)) picks an identity \(S_{ID_{s}}\), and sends it through a protected link to the \((TA)\).
2. \(TA\) obtains the registration request from \(S_{s}\), opts for a random number \(RND_{1}\in\mathcal{Z}_{p}^{*}\), a temporary identity \(PR_{ID_{s}}\in\mathcal{Z}_{p}^{*}\), and computes \(X_{s}=H(S_{ID_{s}}||RND_{1})\), \(SKEY_{TA_{s}}=H(X_{s}||MSK)\). \(TA\) sends the message that contains \(\{PR_{ID_{s}},SKEY_{TA_{s}}\}\) to \(S_{s}\) over the trusted channel. \(TA\) further save the parameter \(\{PR_{ID_{s}},ENC_{MSK}[S_{ID_{s}},X_{s}]\}\) in the database.
3. Upon receiving the response from \(TA\), \(S_{s}\) saves the parameters \(\{PR_{ID_{s}},S_{ID_{s}},SKEY_{TA_{s}}\}\).