esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Network Model and Analytical Methods
### A Bank Correlation Network Model  
Based on a bank-asset bipartite network, we construct connected edges among banks based on the correlation of all banks' investment portfolios and establish a connected network model among banks [23]. For any 2 bank nodes \(i\) and \(j\), we calculate the correlation coefficient \(\rho_{ij}\) of their investment portfolio vector and obtain the correlation coefficient matrix:  
\[C=(\rho_{ij}), \tag{1}\]  
where \(\rho_{ij}\in[-1,1]\). The value of \(\rho_{ij}\) is close to 1, indicating a strong positive correlation between two bank nodes' investment portfolios; conversely, a value close to \(-1\) indicates a strong negative correlation. In order to avoid the negative weight of the connected edges in the network, we refer to the work of [PERSON] (1999) [10] and convert the correlation coefficient \(\rho_{ij}\) to the distance value \(d_{ij}\), whose calculation formula is as follows:  
\[d_{ij}=\sqrt{2(1-\rho_{ij})}, \tag{2}\]  
where \(d_{ij}\in[0,2]\). At this point, the relationship between \(\rho_{ij}\) and \(d_{ij}\) is monotonically decreasing. In order to ensure that the correlation coefficient and the weight of the edge change in the same direction, we introduce another formula as follows:  
\[w_{ij}=e^{-d_{ij}},w_{ij}\in[\frac{1}{e^{2}},\frac{1}{e}] \tag{3}\]  
Figure 1: A bilateral network of banks and assets.  
According to this formula, we obtain the final weight matrix \(W=(w_{ij})\). By combining this with the set of nodes, the bank correlation network can be constructed. The correlation network is a weighted and undirected network. Furthermore, since there is a correlation-based connection between any two banks, the network is a complete network model.
# Clinical Trial Classification of SNS24 Calls with Neural Networks
## 2 Literature Review
The RCNN model showed better accuracy than when compared to CNN or RNN models, achieving an accuracy score of 76.51%. In more recent research, transformers were compared against CNN models and hierarchical self-attention networks (HiSAN) [12]. The experimental results showed that generally, the CNN and HiSAN models achieved better performance than the BERT (Bidirectional Encoder Representations from Transformers) models. [PERSON] et al. [13] compared five deep learning based classification models, including DNN, RNN, CNN, RCNN, and RMDL (Random Multimodal Deep Learning), in automatic classification of four benchmark biomedical datasets. Among all deep learning models, the RMDL model provides the best classification performance on three datasets, and the RCNN classifier performs best on one dataset. Moreover, the work compared the performance between deep learning and shallow learning algorithms and showed that all the deep learning algorithms provided better classification performances. [PERSON] et al. [14] explored using text classification approaches for the automatic detection of non-medical prescription medication usage. Their work compared transformers-based language models, fusion-based approaches, several traditional machine learning and deep learning approaches. The results showed that the BERT and fusion-based models outperformed the others using machine learning and other deep learning techniques. [PERSON] et al. [15] explored using several word representations and classification approaches for clinical text classification. They experimented and analyzed the impact on MIMIC-III and CLEF ShARe datasets. The results showed that the tailored traditional approaches of Word2 Vec, FastText or GloVe were able to obtain or exceed the BERT contextual embeddings. [PERSON] et al. [16] compared a group of approaches, including an active learning approach, SVMs, Naive Bayes, and a BERT classifier, on three datasets for biomedical text classification. The active learning approach obtained an AUC (areas under the curve) greater than 0.85 in all cases, being able to more efficiently reduce the number of training examples for equal performance than the other classifiers. These related works above present an idea of applying neural network methodology in the area of clinical triage. As we can see, deep learning approaches such as CNN, RNN, and other models have been widely applied in the area of clinical text classification, and have shown powerful feature learning capability and play an important role in text classification. Exploring deep learning approaches for SNS24 clinical triage classification is a meaningful contribution, and having better-performed models will have an important impact on the SNS24 clinical triage service.
# Generic Digital Twin Architecture for Industrial Energy Systems
## 4 Proof-of-Concept: Digital Twin Instantiation
### Knowledge Representation Inside the Shared Knowledge Base
#### 4.1.1 Base Service Ontology  
The Base Service Ontology holds the necessary information for a general service description, which helps to discover and access the service. The implementation of the Base Service Ontology consists of a service description based on OWL-S profile classes and properties, additionally defined Quality of Service (QoS) metrics for the services, and a specification of a service endpoint. OWL-S provides a set of vocabulary and semantic rules for formal description of Web Services. The information about a service is described using the OWL-S Profile, a subclass of ServiceProfile. More Information about OWL-S can be found in [26]. The main concepts and relations of the ontology are depicted in Figure 4. The grey concepts are part of OWL-S, whereas the white concepts are extensions and explained in more detail.  
BaseService--The service is a subclass of the owl-s:Service class and described by the owl-s:Profile. The BaseService concept aggregates the essential information about the service, like the service endpoint, the current status, and quality metrics. A service is always related to a PhysicalEntity inside the ontology (not depicted in Figure 4). Thus, the services of a component or a whole asset can be retrieved from the Shared Knowledge base for the purpose of service discovery.  
ServiceCategory--In order to classify a BaseService and define it as a certain functional service, the abstract class ServiceCategory is used. The categories correspond to the functional service types defined in the EDTA and also used for the grouping of the domain ontologies in Figure 3.  
Quality Metrics--OWL-S has no capabilities for describing QoS for a certain service. To counteract, eleven additional properties are added to describe certain QoS metrics for a service, like Accessibility, ResponseTime, Availability, etc. This information can be used during service discovery to choose the best service if more than one is available.  
Figure 3: Top Level and Domain Ontology structure.  
Status--To gain information about the current service status, the last time of invocation, the invoked method, that invoked the service, and a service status message (\"OK\", \"In Use\",\"Warning\",\"Error\") is related with the service entity.  
ServiceEndpoint--Each service has at least one service endpoint which enables service invocation or subscription in order to exchange data with a service. The endpoint categories can be flexible in nature and comprise of different protocols. In the prototypical implementation, a REST endpoint is specified in the ontology. However, endpoints are not restricted to that type, e.g., SOAP could also be used.
## 3 Proposed System
### Channel Model of the Indoor MIMO-VLC System  
The line of sight (LOS) VLC channel is generally considered in this proposed system. The channel matrix \(\mathbf{H}\) coefficients are given in Equation (8).  
\[h_{jk}=\left\{\begin{array}{cc}\frac{A_{j}}{d_{jk}^{2}}r_{0}\Big{(}\varphi_{ jk}\Big{)}t_{s}\Big{(}\phi_{jk}\Big{)}ga_{a}\Big{(}\phi_{jk}\Big{)}cos\Big{(}\phi_{jk} \Big{)}&0\leq\phi_{jk}\leq\phi_{c}\\ 0&\phi_{jk}>\phi_{c}\end{array}\right. \tag{8}\]  
where, \(j=k=1,2,3,\dots,\ M\); the distance among the transmitting LED \(k\) and PD receiver \(j\) is denoted as \(d_{jk}\); the emergence angle related to the transmitter axis is \(\varphi_{jk}\); the incidence angle related to the receiver axis is \(\phi_{jk}\); the PD's Field Of View (FOV) is \(\phi_{c}\); the optical filter's gain is \(t_{s}\Big{(}\phi_{jk}\Big{)}\) and the optical concentrator's gain is \(g_{a}\Big{(}\phi_{jk}\Big{)}\). The optical concentrator's gain is expressed in Equation (9).  
\[g_{a}\Big{(}\phi_{jk}\Big{)}=\left\{\begin{array}{cc}\frac{m^{2}}{\sin^{2} \left(\phi_{c}\right)}&0\leq\phi_{jk}\leq\phi_{c}\\ 0&\phi_{jk}>\phi_{c}\end{array}\right. \tag{9}\]  
where, the related refractive index is \(m\). The \(r_{0}\Big{(}\varphi_{jk}\Big{)}\) specifies transmitting LED's Lambertian radiant intensity that is expressed in Equation (10).  
\[r_{0}\Big{(}\varphi_{jk}\Big{)}=\frac{n+1}{2\pi}\cos^{n}\Big{(}\phi_{jk}\Big{)} \tag{10}\]  
where, the \(n\) of Equation (10) is specified in Equation (11).  
\[n=\frac{-\ln(2)}{\ln(\cos(\varphi_{1/2}))} \tag{11}\]  
The \(\varphi_{1/2}\) represents the corresponding transmitter semiangle at half power.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Positioning Techniques  
There are various positioning algorithms proposed in the literature. A popular and practical solution is through triangulation, which is to determine the coordinates of a user (point) by knowing the distances (lateration) and/or angles (angulation) to/from the transmitters. The problem can thus be transformed to a traditional geometric problem. There are three kinds of algorithms based on triangulation: RSS, TDOA, and AOA methods. RSS and TDOA are lateration techniques, whereas AOA is an angulation technique. Please note that, apart from RSS-, TDOA-, and AOA-based methods, there exist alternative approaches based on proximity, fingerprinting, and image sensors. Additionally, hybrid systems have been developed to improve the overall precision. Further exploration and examination of various variations and alternative methodologies will be undertaken in subsequent discussions.
## 5 Conclusions and Future Work
These factors influenced not only the quality of the annotations by the specialists but may have also led to data-drift, significantly impacting the performance of the trained models and revealing their poor ability to generalize among different data. In future work, it could be interesting to use f-AnoGAN to provide region proposals about the location of a defect, as proposed in the original paper [14]. These coarse segmentations could then be further assessed through a subsequent classification approach, consisting of an alternative method to the defect detection step presented in our work. Moreover, although we have used the anomaly scores metric to establish the defective/normal threshold for the reasons previously stated in Section 4.2, this threshold could also be defined with the z-distance metric in mind, since it provided promising results in terms of the difference between the normal and defective classes. Conceptualization, R.C. ([PERSON]), A.C.M., J.G. and F.S. ; methodology, R.C. ([PERSON]), A.C.M., J.G. and F.S. ; implementation, R.C. ([PERSON]), A.C.M. and J.G. ; writing--original draft preparation, all; writing--review and editing, F.S. ; supervision, F.S. ; funding acquisition, A.K., A.G.e.S.R., R.C. ([PERSON]) and F.S. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the project Visual and Acoustics Inspection of Ceramics (VALCeramics), co-funded by Portugal 2020 framed under the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), by Agencia Nacional de Inovacao and European Regional Development Fund under Grant POCI-01-0247-FEDER-069987. Not applicable. In light of involved manufacturing plant privacy concerns, the image datasets acquired with pieces of Materamica used in the aforementioned study will remain private. However, the research team is currently undertaking efforts to create a new dataset that is based on similar data, with the aim of making it publicly available. The authors thank [PERSON] and [PERSON] from Centro Tecnologico da Ceramica e do Vidro (CTCV) and [PERSON], [PERSON], [PERSON] from Materamica factory for their expertise as inspectors which was critical for the work. The authors declare no conflict of interest.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 3 Stealth Communication
### Challenges and Threats to the Existing System  
The goal of stealth communication is to hide private signals in noise, so the eavesdropper cannot detect the existence of the signal [2, 3, 4, 81, 82, 83, 84]. An effective hiding method maximizes the capability of authorized users to recover the stealth signal with private keys and minimize the detectability of the stealth signal for eavesdroppers. If the stealth system is not properly designed, the BSS method discussed in Section 2 can be utilized by eavesdroppers to separate the stealth signal from noise. The following subsubsection studies the threat of BSS attack to the stealth communication system and the corresponding defending mechanism.  
Methods to Address the Threats  
The design of the asymmetric access to the stealth channel is based on the fact that the separation process is not completely blind for authorized users with private keys. Therefore, extreme cases out of the capability of the separation system discussed in Section 2 were tested to defend the BSS attacks from eavesdroppers. Such cases include:  
* Gaussianity and kurtosis of the signal: Most BSS methods, including the one discussed in Section 2, cannot separate the mixed signals when all the original signals are Gaussian signals. This is because the last step (ICA) is to rotate the mixed signal based on the change of kurtosis at different independent component directions. If both the stealth signal and noise have Gaussian distribution, or Gaussian-like signals (Equations (8) and (9)), the kurtosis is equal to 3 at all the directions, and the mixed signals cannot be rotated to separate the mixed signal. Therefore, the system is immune to BSS attacks when both the stealth signal and noise are Gaussian signals.
* Bandwidth of the signal: The requirement of a Gaussian signal is a strict restriction to the stealth signal since most digital signals are not Gaussian signals. Another means of defending against the BSS attack is to expend the bandwidth of the noise signal. The sampling time must be short enough, so the sampled signal is not a time average of the mixed signal. By using the mode-locked laser to improve the sampling time, mixed signals with a bandwidth of up to 50 GHz can be properly sampled. Bandwidth of 50 GHz is an ultra-wideband for interference management, while for stealth communication with noise applied intentionally, bandwidth beyond 50 GHz can be deployed. Experimental results have demonstrated using noise bandwidth of 150 GHz-5000 GHz to hide signals.
* Linear and nonlinear operation: For interference management, the signals are mixed with linear functions. To hide signals in noise, both linear and nonlinear operations can be applied to mix the signals and noise. Experimental results show that with nonlinear operations, the SOI cannot be identified by the statistical properties of the mixed signals, which means the stealth system with a nonlinear mixing function can effectively defend the BSS attack.
## 4 Conclusions  
In this work, a novel strain of biohydrogen-producing KBH 6958 was locally isolated from UKM's lake soil sample. Based on morphological, phenotype, BIOLOG and 16s rRNA analyses, isolate KBH 6958 was identified as _Enterobacter_ sp. KBH 6958 (GenBank no. KM657475), which was capable of producing biohydrogen from a variety of carbon sources. Locally isolated _Enterobacter_ sp. KBH 6958 was able to consume sugars in both AFEX OPEFB hydrolysate and DAA OPEFB molasses and convert them to biohydrogen gas through butyric-acetic acid pathway. As anticipated, the cumulative biohydrogen production from AFEX OPEFB hydrolysate was nearly 45% higher than that observed in DAA OPEFB molasses fermentation. After fermenting AFEX OPEFB hydrolysate with _Enterobacter_ sp. KBH 6958, the biohydrogen yield was found to be 1.55 mol /mol, whereas the biohydrogen yield from DAA OPEFB molasses was 1.19 mol /mol. Acetic acid was found to be the dominant metabolite in AFEX OPEFB hydrolysate, whereas butyric acid was found to be the dominant metabolite in DAA OPEFB molasses. The ability of _Enterobacter_ sp. KBH 6958 to survive and produce biohydrogen from DAA OPEFB molasses was equally important aspect to be considered, as this is an important indicator of the isolate's ability to grow in medium contaminated with sugar-degraded compounds. As a result, a furfural-tolerant isolate _Enterobacter_ sp. KBH 6958 is highly advantageous in the production of biohydrogen from OPEFB and other pretreated lignocellulosic materials.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13010656/s1](https://www.mdpi.com/article/10.3390/app13010656/s1)]([https://www.mdpi.com/article/10.3390/app13010656/s1](https://www.mdpi.com/article/10.3390/app13010656/s1)), Figure S1: Negative gram-stained isolate KBH 6958 under 10\(\times\) magnification using light microscope.  
Conceptualization, Investigation, Methodology, Writing. P.M.A.: Resources, Data curation, Writing, Supervision. J.M.J.: Writing--review and editing, Supervision, Funding acquisition. N.S.E.: Investigation, Visualization. N.S.J.: Investigation, Writing--review and editing. J.P.T.: Writing--Review and editing. S.F.A.M.: Writing--Review and editing. M.S.S.: Investigation, Visualization. N.A.B.: Investigation, Visualization. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UKM-YSD Chair for Sustainability, UKM-YSD-2021-005 on a project entitled \"Native enriched inoculum for consolidated bioprocessing of biohydrogen production from AFEX pre-treated OPEFB\".  
Not applicable.  
The authors declare no conflict of interest.
# Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks
## 2 Related Work
### Detection of Phishing Scams on Ethereum  
Phishing scams are among the most severe cybercrimes aimed at Ethereum users, and many efforts have been made to detect phishing [3]. [PERSON] et al. [8] proposed trans2 vec, which used a weighted random walk to generate the embeddings of nodes, and then employed a one-class SVM model to classify the embeddings to detect phishing nodes. [PERSON] et al. [10] extracted graph-based cascade features from transaction records and developed a lightGBM-based dual-sampling ensemble algorithm to identify phishing accounts. [PERSON] et al. [9] obtained statistics on the transaction information as features of nodes and then used a graph convolutional network (GCN) and autoencoder technology to extract the structural features of the subgraph. The output of the GCN and handcrafted features are concatenated to obtain the final result for classification. To detect potential phishing scammers, [PERSON] et al. [14] proposed a multi-channel graph classification model (MCGC) with multiple feature extraction channels for GNN to extract richer information from the input graph.  
Although the approaches mentioned above have been able to complete the detection of Ethereum phishing, their methods of processing graph data are designed for simple subgraphs, thus ignoring the global structural information of the Ethereum transaction network. In addition, they do not work in multiedge graphs. To make full use of the transaction information and structural information, we propose a novel method that transforms the transaction network into some inter-node graphs for feature extraction.
# Green Production and Biotechnological Applications of Cell Wall Lytic Enzymes
## 4 Production of CWLEs in Microbial and Plant Expression Systems
### Heterologous Expression of CWLEs in Fungi and Bacteria
Amongst the many advantages of this expression system, the low secretion level of native CWLEs [66] allowed pure enzymes to be obtained from the culture filtrates without requiring further expensive purification procedures [67]. Recombinant cellulases [68; 69; 70; 71] and pectinases [72; 73; 74] were expressed in high yields by _P. pastoris_. Differently from _S. cerevisiae_, _P. pastoris_ does not heavily glycoslate the secreted proteins, thus avoiding hyperglyosylation and, therefore, protein instability. Different tools have been developed in order to improve the expression levels of CWLEs in yeast. These include the use of synthetic [75] and constitutive promoters, such as the \(\mathrm{P}_{\mathrm{TEFI}}\) and \(\mathrm{P}_{\mathrm{PGK1}}\) promoters from _S. cerevisiae_[61; 76]. Codon-usage optimization is another approach that has succeeded in increasing the expression yields of cellulases and hemicellulases from _Pichia pastoris_[68; 77]. Lastly, selection of multicopy transformants (i.e., mutants carrying multiple construct copies integrated in their genome) has allowed high expression yields to be reached for several cellulolytic enzymes [76; 77]. Considering that plant biomass is efficiently degraded by the synergistic action of cellulases, hemicellulases, pectinases, and ligninases, an interesting perspective from the heterologous expression of CWLEs is the possibility of expressing multiple enzymes in the same recombinant strain. Co-expression of multiple genes can be obtained by cloning multiple genes in a single plasmid [78] or by combining different strains expressing different enzymes [79; 80; 81]. Optimization of these systems may generate microbial cell factories capable of expressing complete sets of glycoside hydrolases, thus reducing the production cost of recombinant CWLEs.
## 2 Materials and Methods
### Analytical Experiments
#### 2.3.2 Degree of Conversion  
The degree of DC of UTBCs due to photo-irradiation was determined using the Fourier transform infrared-attenuated total reflection (FTIR-ATR) method. Samples were pressed in stainless-steel disks (5 mm diameter and 2 mm thickness, \(n\) = 4), and their FTIR spectra were recorded (denoted as uncured) using a Nicolet iS10 FTIR spectrometer (Thermo Scientific, Madison, WI, USA). Subsequently, the specimens were covered with plastic strips and glass slides, irradiated for 40 s using a light-curing unit (3M ESPE Elipar S10, LED curing light; wavelength of 430-480 nm; the intensity of approximately 1200 mW\(\cdot\)cm\({}^{-2}\)), and then their FTIR spectra were analyzed (cured). By comparison of the peak areas of vinylic C=C at 1638 cm\({}^{-1}\) before and after curing with respect to the unaffected aromatic C=C band at 1608 cm\({}^{-1}\) as an internal reference, the DC can be quantified, as in Equation (1). Generally, the FTIR peak integration (the area, A) is proportional to its mole fraction in the spectra and, when bonds are of the same properties, their mole ratios can be adequately compared.  
\[DC(\%)=\left[1-\frac{\left(\frac{A_{1638}}{A_{1608}}\right)_{cured}}{\left( \frac{A_{1638}}{A_{1608}}\right)_{uncured}}\right]\times 100 \tag{1}\]