esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Real-Time Data Process Analysis Based on NoSQL for IoT Pavement Quality Management Platform
## 4 Experiment and Result
### Analysis of Experiment Results  
According to the experimental results, the write performance of MongoDB was 0.405 ms per data point on average, whereas that of MySQL was 21.146 ms per data point on average. Thus, MongoDB showed excellent performance with a significant difference from that of the comparison system. It can clearly be observed that for write operations, MongoDB was approximately 52 times faster than MySQL. On the other hand, the read performance of MongoDB was 0.299 ms per data point on average, whereas that of MySQL was 0.306 ms per data point on average. Because the two DBs exhibited only a slight difference in this regard, the levels of their search performance can be considered to be similar. However, MongoDB is flexible in data search because it is composed of a document format, and thus it is easier to improve and expand the performance because the database can easily interoperate with document search engines such as ElasticSearch. Meanwhile, the delete performance of MongoDB was 15.909 ms per data point on average, whereas that of MySQL was 74.763 ms per data point on average. Thus, MongoDB demonstrated excellent performance with a large difference in speed and, in delete operations, was approximately 4.7 times faster than MySQL.  
The overall performance test results showed that the create, read, update, and delete (CRUD) performance of MongoDB, which is a NoSQL DB, is higher than that of MySQL, which is an RDBMS. This performance difference can be considered to be due to the differences in data structure and query structure of NoSQL and RDBMS, even though the DB models themselves may have a performance difference.  
In this study, each DB was tested for very simple data query performance, and techniques such as indexing and distributed processing were not applied. However, it can be observed that the NoSQL DB demonstrated a better general performance. Therefore, it can be determined that a NoSQL DB will be more suitable for the big data platform for data collection, processing, and analysis.
## 3 Results
### Theil-Sen and Mann-Kendall Trend Analysis  
Theil-Sen and Mann-Kendall trend analysis demonstrated variations in the Chl-a concentrations in the coastal sea surfaces of the Guangxi Beibu Gulf (Figure 9), and the statistical results of each trend are shown in Table 6.  
The area of this study area was 4141.8 km\({}^{2}\), of which the area with an obvious decrease in its Chl-a concentration was 193.55 km\({}^{2}\), the area with a less obvious decrease was 356.72 km\({}^{2}\), the area with no obvious change was 383.69 km\({}^{2}\), the area with a less obvious increase was 724.45 km\({}^{2}\), and the area with an obvious increase was 761.49 km\({}^{2}\). The remaining 1721.900 km\({}^{2}\) in the study area showed no significant change. The concentration of Chl-a in the coastal sea surface has exhibited an increase in recent years.  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Trend of Chl-a Concentration** & **Area (km\({}^{2}\))** \\ \hline Obvious decrease & 193.550 \\ Less obvious decrease & 356.720 \\ No obvious change & 383.690 \\ Less obvious increase & 724.450 \\ Obvious increase & 761.490 \\ Failed the significance test & 1721.900 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical table of the area of each trend.  
Figure 9: Trend analysis diagram of chlorophyll concentration.
# Pigment Dyeing of Atmospheric Pressure Plasma-Treated Cotton Fabric
## 2 Experimental
### Colour Yield  
Reflectance values of pigment-dyed cotton fabric were measured at 590 nm (\(\lambda_{\text{maximum}}\)) by a spectrophotometer (Macbeth Color-Eye 7000A, Datacolor, Hong Kong, China). The reflectance measuring condition was illuminant D65 with a 10\({}^{\circ}\) standard observer. In order to ensure complete opacity for colour yield measurements, the pigment-dyed cotton fabric was folded four times. Eight reflectance measurements were conducted for each sample so as to collect more accurate results. The reflectance value was then converted into \(K/S\) values according to the Kubelka-Munk function as shown in Equation (1) in order to evaluate the uptake (colour yield) of the pigment dye.  
\[\frac{K}{S}=\frac{(1\ -\ R)^{2}}{2R} \tag{1}\]  
where  
\(K\) is the absorption coefficient of the colourant;  
\(S\) is the scattering coefficient of the dyed substrate; and  
\(R\) (\(R\) = %\(R\)/100) is the reflectance of the coloured sample.  
Generally speaking, the higher the \(K/S\) value of the measured sample, the better the uptake (colour yield) of the pigment-dyed cotton fabric.
## Appendix B Relationship between Sun-Earth and Sun-Barycenter Frames  
The Earth-Moon barycenter is shifted from the Earth's center of mass by the distance of \(\mu\cdot\mathrm{DU}\approx 4670.7\) km. Applying the law of sines to the triangle formed by this vector and the geocentric and barycentric spacecraft position vectors, we have  
\[\frac{\sin\Delta\theta}{\Delta x}=\frac{\sin\theta_{B}}{R_{\oplus}+h_{p}}, \tag{116}\]  
where \(\Delta x=\mu\cdot\mathrm{DU}\approx 4670.7\) km, \(\Delta\theta=\theta_{B}-\theta_{G}\) is the difference between the barycentric and geocentric spacecraft phases, and \(h_{p}\) is the parking orbit altitude. The solar phase \(\theta_{S}\) does not depend on the choice of the frame's origin: the difference is on the order of arcseconds. From the relations  
\[\begin{split}\alpha_{B}&=\theta_{B}-\theta_{S}+\pi,\\ \alpha_{G}&=\theta_{G}-\theta_{S}+\pi,\end{split} \tag{117}\]  
we conclude that the difference \(\Delta\alpha=\alpha_{B}-\alpha_{G}\) is the same:  
\[\Delta\alpha=\Delta\theta=\arcsin\left(\frac{\Delta x\sin\theta_{B}}{R_{\oplus }+h_{p}}\right). \tag{118}\]The maximum difference for \(h_{p}=200\) km equals to arcsin (\(4670.7/6571\)) \(\approx 45.3\) deg, which, obviously, cannot be neglected. Hence, the frame's origin should be specified so that the TLI burn phase and polar angle can be understood correctly.
## 2 Mathematical Modeling of PV Cell
### Ideal Solar PV Cell Modeling  
The ideal solar PV cells with photocurrent (\(I_{ph}\)) vary from the optimal outcome because of optical and electrical losses. Figure 1 represents a typical solar PV cell, which is the simplest PV model as the effect of parallel and series resistances is not reckoned.  
The cell output is expressed by I-V characteristics and expressed in mathematical terms as follows:  
\[I=I_{ph}-I_{d} \tag{1}\]  
where \(I_{d}\) signifies the diode current, which represents the recombination and diffusion current in the quasi steady-state emitter region and PN junction region with excess concentration. This current in the diode is represented by the Shockley Equation as follows:  
\[I_{d}=I_{0}\bigg{(}e^{\frac{V_{d}}{\hbar V_{d}}}-1\bigg{)} \tag{2}\]  
where \(I_{0}\) represents the saturation diode current, \(V_{d}\) is known as the diode voltage, \(V_{t}\) is the equivalent thermal voltage and \(N\) is the number of cells in series [35]. An ideal solar photovoltaic cell does not take into account the effects of internal resistance, and thus does not establish a stable relationship between the cell current and voltage.  
Figure 1: Equivalent circuit of an ideal solar PV cell.
# Enhancing Resilience in Digital Twins: ASCON-Based Security Solutions for Industry 4.0
## 2 Literature Review
### Research Gap  
From our analysis of the literature, it became clear that many studies gave limited attention to ensuring the authenticity and integrity of sensor data integrated into Digital Twins. While a few papers addressed securing data transmission channels, they predominantly relied on conventional encryption and authentication methods like AES, SHA-256, and RSA. This gap in the research and proposed solutions is worrisome because field sensors are often constrained by power limitations, making traditional encryption impractical for securing them. Future research should therefore prioritize lightweight algorithms to safeguard data confidentiality, integrity, and authenticity in Digital Twin applications.  
Despite significant contributions to its development, the use of Digital Twins for security in Industry 4.0 remains in its infancy, with several research gaps awaiting exploration and enhancement. This section identifies and explores three potential research areas.  
* Efficient lightweight encryption algorithms are anticipated to be crucial for the advancement of Digital Twin technology, enabling precise replication of physical objects and processes. Achieving this precision requires deploying numerous small, resource-constrained IoT sensors on a large scale to monitor various aspects of the physical status being replicated. This scenario highlights the necessity for future research to develop and deploy encryption algorithms optimized for resource-constrained devices.  
* Remote access control for DT: Secure remote access control to the virtual counterpart of an ICS component for troubleshooting and testing by vendors is identified as a gap in the literature. In traditional industrial setups, vendors of ICS components have remote access control to physical objects for maintenance and operational tasks. However, the mechanisms for managing such access in the context of Digital Twins are not well-defined. Future research should explore methods to securely enable remote access to one or more components of the DT, addressing potential security and operational challenges inherent in virtualized industrial environments.
* Human-computer interaction: Future research could consider exploring the human-computer interaction (HCI) aspect of DT technology. This includes investigating how DT models are interacted with by users and exploring novel methods to enhance user experience. Enhancing the HCI aspect of DT technology may potentially improve model accuracy and reliability by reducing human errors.
# Caroenoid Biosynthesis in _Fusarium_
## 5 Down-Regulation of Caroenoid Production: The _CarS_ Gene
In brief, the samples were run in a reverse-phase C18 column with a binary-gradient elution (acetone : deionized water) at a flow rate of 1 mL/min. Detection was performed at 450 nm and online UV/Visible absorption spectra were acquired in the wavelength range 350–600 nm. The UV/Visible spectra of the identified carotenoids are shown above. The peak X corresponds to an unknown carotenoid with maximal absorbance at 480 nm, whose identity is under investigation. The peak 1′ is assigned as a neurosporaxanthin isomer. Phytofluene cannot be detected at 450 nm, but traces of this caroten were detected at 368 nm (data not shown). The inner picture shows the pigmentation of 7-day old colonies of the wild strain IMI58289 and the _carS_ mutant SG39 grown on minimal DG medium in the dark. covers the most relevant CrgA domains. These include two amino-terminal RF domains, one of them initially disregarded in CarS because of a wrong intron assignation in the _F. fuijkuroi_ genome annotation [71], and a LON protease domain [79]. However, CrgA has two glutamine-rich regions and a carboxy-terminal isoprenylation site, the first one underrepresented and the second absent in CarS. In other proteins, the RF domains interact with E3 ligase-type enzymes that mediate ubiquitylation of target proteins, frequently as a label for their degradation. The function of CrgA of _M. circinelloides_ has received considerable attention, and the available information may provide some clues on the hypothetical way of action of CarS in _Fusarium_. At least one of the RF domains of CrgA is essential for its regulatory function in carotenogenesis, suggesting that this protein might function as an E3 ubiquitin ligase [78]. However, CrgA interacts with one of the three WC proteins of _M. circinelloides_, MCWC-1b, to trigger its degradation through an ubiquitylation-independent mechanism [80]. There are other similarities between CarS and CrgA functions. First, as found for the _carS_ mutants in _Fusarium_, carotenoid biosynthesis maintains a detectable photoinduction in the _crgA_ mutants of _M. circinelloides_[77]. Second, both CrgA and CarS are involved in other processes in addition to carotenogenesis, pointing to wider regulatory functions. This is supported by the alterations in growth and sporulation by the _crgA_ mutants of _M. circinelloides_[81; 82], and in gibberell and bikaverin productions by the _carS_ mutants of _F. fuijkuroi_[83; 84]. Interestingly, _carS_ mutants have not been described in other _Fusarium_ species and they could not be found in mutagenesis screenings in _F. verticilloides_ ([PERSON] and [PERSON], unpublished observations). However, the _carS_ gene of _F. fuijkuroi_ is able to restore a basal carotenoid production upon introduction in a _carS_ mutant of _F. oxysporum_, although not totally at the level of expression of the structural genes, indicating a functional conservation in both species [68].
## 2 Materials and Methods
### Statistic Evaluation of Classification Results
#### 2.3.2 Area under the Receiver Operating Characteristics  
The AUROC is calculated to determine the classifier's capability to distinguish between different classes. It is based on the receiver operating characteristics (ROC) curve that plots the true positive rate (TPR) against the false positive rate (FPR) in pairs for a certain threshold, in our case the predictive probability [42].  
TPR and FPR are defined as:  
\[TPR =\frac{TP}{TP+FN} \tag{6}\] \[FPR =\frac{FP}{FP+TN}=1-Specificity \tag{7}\]  
where \(TP\) is the number of true positive results, \(FN\) is the number of false negative results, \(FP\) is the number of false positive results and \(TN\) is the number of true negative results.  
We got a probability value for each class for each single output from the classifier. Taking this probability value as a threshold, we then computed the above variables for the output from the complete data set. After we successfully performed these calculations on all the outputs from the data sets, we could plot the resulting sets of TPR and FPR as an ROC curve. The AUROC represents the area under the ROC curve. An AUROC of 1 represents completely separable classes and an AUROC of 0.5 describes a classifier that is just as good as chance. For calculations, we adapted [PERSON]'s code from [43].
## 3 Results
### PSO-RVFLNS Parameter Setting and Classification Effect  
The number of hidden layer neurons in RVFLNS has a significant influence on the recognition accuracy. The results of analyzing the impact of the number of neurons under different activation functions on recognition accuracy are shown in Figure 14.  
The recognition accuracies of different activation functions and number of neurons are shown in Table 1:  
Sigmoid was selected as the activation function, and the recognition accuracy was high when the number of neurons in the hidden layer was 400. To test the optimization performance of particle swarm optimization algorithm on RVFLNS, 50 groups of data were randomly selected from the training sets for testing. The learning factor \(c1=c2=1.49445\), a number of iterations \(K=300\), and a population size \(POP=30\) was set for testing. The test output of RVFLNS and PSO-RVFLNS is shown in Figures 15 and 16.  
From the results in Figures 15 and 16, it can be seen that the PSO-optimized RVFLNS algorithm has higher classification accuracy and better classification effect than the ordinary RVFLNS algorithm. The model was tested using 6 \(\times\) 200 sets of data from the test set, and features processed by LBP-TOP and NSST-CNN were extracted, respectively. The features extracted by the two methods in series were combined and then input into PSO-RVFLNS for classification. The confusion matrix of the final classification is shown in Figure 17. In Figure 17, (a) is the recognition result of NSST-CNN and LBP-TOP features, and (b) and (c) are the recognition result of NSST-CNN and LBP-TOP, respectively. The NSST-CNN using LBP-TOP has the highest recognition accuracy and improves the resolution of adjacent conditions. Therefore
## 4 Basic Data Striping Schemes in Cloud-Based Systems
### Sharing Methods
#### 4.1.5 [PERSON]'s and [PERSON]'s Publicly Verifiable Secret Sharing (PVSS)  
Some applications of a secret sharing scheme, such as e-auction, e-voting and multiparty computation, require public verifiability. Namely, it must be publicly verifiable without revealing the secret or any of its shares that all the \(n\) shares are consistently generated from a unique share-generating polynomial such that any \(t\) of them can reconstruct the same secret. This capability is crucial to overcome the problem of dishonest share dealers.  
[PERSON] and [PERSON] [19] devised a publicly verifiable secret sharing system that supports dishonest detection for the dealer, or for one-third of the players, even if those players are coordinated by an adaptive attacker who can change strategies in real-time depending on what information has been revealed. The PVSS is essentially a combination of secret sharing and publicly verifiable encryption and works as follows:  
1. The dealer creates shares S1, S2,..., S_n_ for each participant P1, P2,..., P_n_ respectively;
2. The dealer encrypts the shares for each participant using their public keys and publishes the ciphertexts Ei(Si);
3. The dealer also publishes a string ProofD to show that each Ei encrypts Si and the reconstruction protocol will result in the same secret S;
4. Anybody knowing the public keys for the encryption methods Ei, can verify the shares;
5. If one or more verifications failed, the dealer fails and the protocol is aborted;
6. Each participant Pi decrypts their shares Si using Ei(Si);
7. The participant releases Si plus a string ProofPi to show that the released share is correct;
8. Dishonest participants who failed to decrypt Si or to generate ProofPi will be excluded;
9. The secret S can be securely reconstructed from the shares of any qualified set of participants.  
[PERSON] [20] carried out a critical survey of five existing PVSS algorithms which claimed strong security and high efficiency. Surprisingly, in practice none of the algorithms can achieve efficiency as high as O(tn) exponentiations, which is widely believed to be feasible.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Proposed Algorithms  
TinyCleanEDF, as presented in Algorithm 1, is an advanced solution tailored for data cleaning and anomaly detection in IoT systems using federated learning. This algorithm partitions the data stream into subsets, each processed on separate edge devices. Each device operates a federated learning model, trained locally with its data subset. These models synchronize with a central server, which aggregates their parameters to refine the global model. This distributed framework enables efficient anomaly detection and data cleaning directly at the edge, with anomalies being pinpointed when data deviate from established patterns. Moreover, TinyCleanEDF employs an autoencoder at each node for feature extraction. These autoencoders are trained to reconstruct inputs from their compressed representations, effectively distilling significant data characteristics from complex datasets. This process is crucial for simplifying data, making them more manageable and highlighting vital information. The algorithm is inherently dynamic, consistently updating both federated models and autoencoders to integrate new data observations. Such continuous adaptation ensures the system's ongoing relevance and efficacy. Through its integration of local data processing, anomaly detection, and feature extraction, TinyCleanEDF stands out as a robust and comprehensive solution for upholding data integrity and quality in intricate IoT environments. EdgeClusterML, outlined in Algorithm 2, is an innovative algorithm designed for dynamic and self-optimizing clustering in IoT networks. This algorithm combines reinforcement learning (RL) with a self-organizing map (SOM) to adaptively cluster data at the edge. In the initial step, EdgeClusterML initializes a dynamic clustering model using RL, where the quality of actions in different states is evaluated using a Q-function, \(Q(s,a)\) and optimized through a defined reward function, \(R(s,a)\). The learning rate \(\alpha\) and discount factor \(\gamma\) are set to guide the learning process. Subsequently, the algorithm deploys an SOM for efficient data clustering. The SOM is initialized with random weights and fine-tuned using a neighborhood function and learning rate. As data flow through the system, EdgeClusterML dynamically clusters them using the SOM, constantly updating the model based on the data's characteristics. RL is then employed to optimize clustering decisions, adjusting parameters through an \(e\)-greedy policy and updating the Q-function based on observed rewards. This process leads to intelligent and responsive clustering, tailoring the data organization to the changing patterns and needs of the IoT environment. Finally, the clustered data are stored, ensuring organized and efficient data management at the edge.
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 3 Methodology
### Pre-Defined Affinity Graph Representation  
In the macro-level graph section, we show different types of correlations among regions with multiple graphs, including: (1) the neighborhood graph \(\mathcal{G}_{N}=(V,\ A_{N})\), which encodes the spatial proximity; (2) the SimRank graph \(\mathcal{G}_{Sim}=(V,\ A_{Sim})\), which encodes the similarity in distant regions; (3) the Functional similarity graph \(\mathcal{G}_{poi}=(V,\ A_{poi})\), which enhances the representation of spatial similarity in neighborhood graphs; (4) the Cosine similarity graph \(\mathcal{G}_{cosine}=(V,\ A_{cosine})\), which considers the similarity of node volume at a macro level. Note that it is straightforward to extend our approach to model new types of relevance by constructing correlation graphs.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Extended Finite Element Method (XFEM)
#### 3.1.3 Comsol Multiphysics  
COMSOL Multiphysics is a versatile software package that supports XFEM for crack growth simulation. It offers a highly flexible and customizable environment, allowing engineers to define their own crack propagation criteria and algorithms. COMSOL's XFEM module includes features such as Adaptive Mesh Refinement, crack branching, and coalescence, making it suitable for a wide range of crack growth problems. The software'sintegration with other physics modules enables multidisciplinary simulations considering coupled phenomena [111, 112]. COMSOL offers a free trial version, but it is limited in duration and functionality. Educational institutions may provide access to a full version for students.