esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Setup and Results Evaluation
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ML-Based Algorithm** & \
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Failure Prediction** \\ **Rate (\%)** \\ \end{tabular} \\ \hline Kernel Naive Bayes & 92.8 & 7.2 \\ \hline Linear SVM & 87.7 & 12.3 \\ \hline Quadratic SVM & 96 & 4 \\ \hline Medium Gaussian SVM & 95.9 & 4.1 \\ \hline Fine KNN & 90 & 10 \\ \hline Medium KNN & 85.1 & 14.9 \\ \hline Weighted KNN & 89.6 & 10.4 \\ \hline Developed ANN Model & 98.4 & 1.6 \\ \hline Proposed Encoding-based Model & 98.8 & 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
Figure 10: Performance validation of the developed ANN model. Note that the green circle indicates the best performance intersection of validation and testing. probabilistic approaches, the prediction for the encoding-based method is performed based on a more mature analysis than the process of conventional ML approaches. Figure 11: Confusion matrix: (**a**) number of observations of Naive Bayes, (**b**) evaluation of True Positive Rate (TPR) and False Negative Rate (FNR) of Naive Bayes, (**c**) evaluation of PPV and FDR of Naive Bayes, (**d**) number of observations of Quadratic SVM, (**e**) evaluation of TPR and FNR of Quadratic SVM, (**f**) evaluation of PPV and FDR of Quadratic SVM, (**g**) number of observations of the proposed encoding-based model, (**h**) evaluation of TPR and FNR of proposed encoding-based model, (**i**) evaluation of PPV and FDR of the encoding-based model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Sensitivity** & **F1-Score** \\  & **(\%)** & **(\%)** & **(\%)** \\ \hline Gaussian Naive Bayes & 96.6 & 91 & 93.7 \\ \hline Quadratic SVM & 96.8 & 95.2 & 95.8 \\ \hline Developed ANN & 98.2 & 96 & 97 \\ \hline Proposed Encoding-based Model & 98.2 & 98.2 & 98.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The classification performance of proposed and the employed competitive models. As can be observed in Table 9, the presented model achieves excellent accuracy in each iteration, realizing 100% accuracy in six groups. The overall accuracy after K iterations of cross-validation for the testing data is 98.18%. K-fold cross-validation has also been performed on all 750 records of the dataset, and the proposed encoding-based model delivers slightly higher accuracy (98.8%) since, in this case, each fold contains more data samples so it can learn better. The minimum and maximum accuracy for the proposed model are 97.3% and 100%, respectively. This demonstrates that the proposed model can perform very well even when the dataset contains hundreds of samples (less than 1k, as in this scholarly research study) or when there are some underrepresented data samples. Furthermore, the two-stage encoding technique for breaking down the correlation among a patient's data, which involves calculating RS and VS, allows code refactoring through coding the extended comparisons with fewer lines of instructions.
###### Abstract
It is also worth noting that the studies were carried out using different concentrations of emulsified oil, and the adsorption capacity was highly dependent on the initial oil concentration, as it increased with the initial oil concentration. This article reports the synthesis of an rGO-iron (III) oxide nanocomposite (rGO-Fe\({}_{2}\)O\({}_{3}\)-NC) to remove emulsified oil from high-salinity water simulating PW streams. The prepared nanohybrid was characterized, and its performance for removing emulsified oil from high-salinity water, as simulated PW, was measured in batch and breakthrough/column setups. Moreover, the adsorption isotherms and kinetics were investigated, and the regeneration and recycling of the nanohybrid were also analyzed. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**adsorbent**} & \multirow{2}{*}{**Oil Type**} & **Oil Conc,** & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & **mag/g** \\ \hline Chitosan–sodium oleate-grafted Fe\({}_{3}\)O\({}_{4}\) & Diesel & 10,000 & 88\% removal & [29] \\ expanded perlite-grafted APTES-Fe\({}_{3}\)O\({}_{4}\) & Crude oil & 3000 & 90\% removal & [30] \\ Multi-wall carbon nanotubes & m-xylene & 80 & 40 & [31] \\ GO core–shell Fe\({}_{3}\)O\({}_{4}\)@oliec acid & Crude oil & 30,000 & 47 & [32] \\ Multi-wall carbon nanotubes & Toluene & 110 & 80 & [31] \\ Multi-wall carbon nanotubes & Ethlybenzene & 80 & 80 & [31] \\ Bentonite granules & Motor oil & 15,000 & 176 & [33] \\ Sepolite granules & Motor oil & 15,000 & 184 & [33] \\ Zeolite fine powder & Motor oil & 15,000 & 192 & [33] \\ Activated carbon from cork & Sunflower oil & 150 & 233 & [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The adsorption capacity of the adsorbent used for removing emulsified oil from wastewater.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## Abbreviations  
STL Seasonal and trend decomposition using Loess  
AR Autoregressive  
SARIMA Seasonal Autoregressive Integrated Moving Average Model  
LSTM Long short-term memory  
RNNs Recurrent neural networks  
1D-CNN One-dimensional convolutional neural network  
BiLSTM Bidirectional long short-term memory  
PSD Power spectral density  
FFT Fast Fourier Transform  
MAE Mean absolute error  
RMSE Root mean square error  
MAPE Mean absolute percentage error
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 3 Magneto-Optical Transparent Nano-Ceramics  
### Brief Introduction  
Magneto-optical material is a new type of optical functional material that has a magneto-optical effect in the ultraviolet to infrared band. Optical devices, such as magneto-optical switches, magnetometers, and magneto-optical sensors, with various functions, are made by using the magneto-optical properties of these materials and the interaction and conversion of light, electricity, and magnetism. According to the type of materials, they can be divided into magneto-optical glass, magneto-optical crystal, magneto-optical transparent ceramics, etc. The thermal conductivity of magneto-optical ceramics is equivalent to that of magneto-optical crystals, and the thermal diffusion performance is good, which can effectively prevent thermal damage in the process of using lasers. Compared with crystals, magneto-optical ceramic materials can more easily obtain larger sizes and can be made into large-diameter magneto-optical components, with high fracture toughness and good thermal shock resistance [37]. This means that magneto-optical transparent ceramics is a new type of magneto-optical dielectric material in recent years with the advantages of a large size, Verdet constant, high laser damage threshold, and high thermal conductivity [38; 39; 40]. At present, the reported magneto-optical transparent ceramic materials mainly include TGG transparent nano-ceramics and TAG transparent nano- ceramics.
## 1 Introduction  
Biosurfactants are biological surface-active compounds largely produced by a wide variety of microorganisms. They have environmentally friendly properties, such as low toxicity and high biodegradability. Biosurfactants are capable of lowering surface and interfacial tensions effectively and thus are potential substitutes for widely used chemically synthesized surfactants. Thus, biosurfactants show promise for use in agricultural, food, pharmaceutical, cosmetic, specialty chemical, and oil production industries as well as in bioremediation technology. The hydrophobic portion of these molecules is commonly made up of fatty acids (saturated, unsaturated, or hydroxylated), whereas the hydrophilic portion is usually composed of peptides or mono-, di-, or polysaccharides [1]. Due to the presence of hydrophobic and hydrophilic moieties within a single molecule, (bio)surfactants tend to migrate toward an interface with different degrees of polarity and hydrogen bonding, such as an air/water or oil/water interface [2].  
Production of effective lipopeptide biosurfactants (LPBSs) was first reported from Gram-positive _Bacillus subtilis_ IAM1213 [3]. Since then, various types of LPBSs with significant surface activity and/or anti-microbial activity have been isolated from other _Bacillus_ strains [4; 5; 6; 7]. Gram-negative _Pseudomonas_ spp. also produce a variety of LPBSs [8; 9; 10; 11; 12]. Gene clusters encoding multi-modular nonribosomal peptide synthetases (NRPSs) for LPBS production have been cloned and characterized from these two genera, and demonstrate their different evolutionary lineages. The systematic modular organization of NRPSs allows structural alteration of lipopeptides by swapping domains or modules of NRPSs to create novel lipopeptides [13; 14]. This article aims to summarize current knowledge of the structural diversity of LPBSs and their biosynthetic systems in the genera _Bacillus_ and _Pseudomonas_. Current possibilities and limits in the modification of their biosynthetic genes for the synthesis of novel compounds are also discussed.
## 5 Application Domains
De-identification of identity while still recognizing client emotions can preserve client privacy. **Advertisement:** One reason why many social media companies mine their users' data is to identify customer interests and gain insights that can drive sales [118]. Naturally,this raises concerns with regards to user data ownership and privacy. De-identifying the corresponding sensitive data while still understanding user's preferences towards certain products can supplement data mining. **Entertainment:** Another possible usage of social behavioral information is adaptive entertainment experiences [119]. For instance, movies and/or video games that change the narrative based on the user's emotional responses can be created. However, such applications require the storage and analysis of user information. Users might be more willing to participate when user data are protected and anonymized. **Psychology:** Personality traits can be revealed from the digital footprints of the users [19]. A personality trait de-identification system can be used to protect sensitive user information and implement privacy-preserving user identification systems. Furthermore, this concept can be applied in user behavior modeling problems such as predicting the likeliness to take a particular action, for example, clicking on a particular ad. Moreover, personality traits-based de-identification can be used in conjunction with other privacy-preserving measures such as data anonymization to further ensure user privacy protection within OSNs. **Consumer Services:** Replacement of traditional identification cards by biometrics is the future of many establishments, such as driver license offices or financial services. De-identification of some real-time information obtained by security cameras for identity verification would ensure additional protection relative to sensitive user data [120].
## 1 Introduction
Additionally, review studies on thermal hydrolysis [30], nitrogen, and phosphorus removal [31] of sludges are well presented. Despite the large variety of approaches that have been established on the disposal of excess sludges, most of the technologies are still in the theoretical exploration stage with very high cost for industrial application. Therefore, the sludge disposal method must be rationally selected [32]. The excess sludge in the petrochemical industry is produced by the sewage treatment of petroleum refining and chemical production processes. It has typical industrial characteristics. Therefore, the choice of the sludge disposal method in the petrochemical industry cannot be completely based on the disposal method of municipal sludge. Physical and chemical properties of the excess sludge should be studied in depth so as to select effective disposal methods, and to establish reasonable sludge disposal strategy, for providing reference on disposal of excess sludge in the petrochemical industry. However, the research on the pollution components and dangers of excess sludge in the petrochemical industry has not been reported publicly. In addition, the basic data and reference basis for the research on the targeted disposal of excess sludge in the petrochemical industry are lacking. Furthermore, due to the absence of comprehensive disposal strategies on petrochemical excess sludge, various disposal methods are improperly selected and adopted, giving rise to a series of environmental issues. In this study, a novel classification and disposal strategy for petrochemical excess sludge is proposed. Based on the strategy, hazard analysis of petrochemical excess sludge samples obtained from three different Chinese regions are performed. In addition, environmental impacts caused by different disposal approaches of the Cq sludge sample are analyzed. Specifically, as natural radioactive elements that are widespread in the Earth's crust, uranium and thorium in the Cq sludge sample are investigated. To the best of our knowledge, no previous published work was found in which the analysis of radioactive elements in petrochemical excess sludge have been involved. The proposed strategy provides a basis for the selection of reasonable petrochemical excess sludge disposal methods.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
### Plan Executor Module Technical Implementation
#### 3.1.3 Autonomous Operation Capabilities  
The Plan Executor operates autonomously with configurable parameters for rescheduling criteria, planning horizons, and optimization weights. It manages the timing of operational updates to prevent system flooding with control messages while ensuring responsiveness to critical changes. The module supports both periodic and event-driven rescheduling, enabling flexible adaptation to different operational requirements and constellation characteristics.
# Minimization of _n_th Order Rate Matching in Satellite Networks with One to Many Pairings
## 2 Dynamic Capacity Allocation  
### System Model  
A practical and realistic scenario where fewer M GWs serve more N UEs (\(\mathrm{M}\leq\mathrm{N}\)) under O2M pairings in an HTS system, including a non-generative GEO satellite, is studied. Especially, in each time-slot of time multiplexing SGD concept, similar to [9; 10], each GWi, with \(\mathrm{i}\in\mathbf{M}\) where \(\mathbf{M}=\{1,\ 2,\ \cdots,\ \mathrm{M}\}\), serves \(k_{i}>0\) UEs and \(\sum\limits_{\mathrm{i}=1}^{\mathrm{M}}\mathrm{k}_{\mathrm{i}}=\mathrm{N}\). Moreover, each UEj, with \(\mathrm{j}\in\mathbf{N}\) where \(\mathbf{N}=\{1,\ 2,\ \cdots,\ \mathrm{N}\}\), has requested capacity RCj and the offered capacity, \(\mathrm{OC}_{\mathrm{ij}}\), between GWi and UEj, is expressed by the Shannon formula \(\mathrm{OC}_{\mathrm{ij}}=\mathrm{B}_{\mathrm{C}}\mathrm{log}_{2}(1+\gamma_{ \mathrm{ij}})\) where \(B_{\mathrm{C}}\) is the bandwidth and \(\gamma_{\mathrm{ij}}^{-1}=\mathrm{CNIR}_{\mathrm{up,j}}^{-1}+\mathrm{CNIR}_{ \mathrm{dnj}}^{-1}\). The RCs and OCs are in bps, and the total carrier to interference plus noise ratio CNIRij, named as \(\gamma_{\mathrm{ij}}\), includes the CNIRs of feeder link i and downlink j. Afterwards, the \(\mathrm{CNIR}_{\mathrm{up,j}}=\mathrm{CNIR}_{\mathrm{CS,i}}10^{-\mathrm{Att}_{ \mathrm{i}}/10}\), where \(CNIR_{\mathrm{CS,i}}\) is the clear sky CNIR for the feeder link i and \(Att_{\mathrm{i}}\), expressing the total atmospheric attenuation originated by the rain, clouds, scintillation and atmospheric gases, is the same as that in [9; 10]. Finally, each GWi offers \(\mathrm{OC}_{\mathrm{ij}}^{{}^{\prime\prime}}=\mathrm{OC}_{\mathrm{ij}}\Big{/} \mathrm{k}_{\mathrm{i}}\), because its bandwidth is shared among its \(k_{i}\) simultaneously served UEs under the O2M scenario.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Evaluating Security and Privacy  
Public blockchains aim to provide security and privacy for user transactions. However, incidents of thefts and data leaks frequently occur [70]. Figure 6 graph helps evaluate such vulnerabilities and how critical nodes, articulation points, and bridges can be identified in a network. In a real-world scenario, these elements could represent vulnerable points in a blockchain network, where targeted attacks could significantly disrupt the network's topology and functionality.
# The YouGovern Secure Blockchain-Based Self-Sovereign Identity (SSI) Management and Access Control
## 3 The YouGovern SSI Solution
### Use Case Elaborated  
Use cases are elaborated by adding details to the contact between the user and the software system, as shown in Figure 6. The user first signs in or signs up with his MetaMask account, and his address is recorded in the blockchain; he can also change his account address. Then he needs to enter all the detailed information about himself. There is also an option to upload a file to IPFS, in which the hash of the file is internally stored in a smart contract. Now, other users can send a request to see that data by sending a request transaction from a function. Then the owner of the data will see the request, and he can accept or reject that request. If the user who requested the data gets his request approved, then he can download the file containing all the data for a particular user only.  
In the context of YouGovern, an \"authority\" refers to a designated smart contract role or a verified blockchain address with the capability to issue or validate credentials. Verification occurs when the authority queries the DID registry and the content hash of the user's credentials, checking integrity against IPFS-stored data. Only addresses registered in the trusted issuer list within the smart contract can perform verification or approval actions. This trust model is enforced cryptographically via signature validation and enforced access  
\begin{table}
\begin{tabular}{c c} \hline
**User Intention** & **System Responsibility** \\ \hline Sign-up with details & Stores user’s data on the blockchain as well as on IPFS \\ \hline Govt. verification & Govt. Authority will verify the user’s data \\ \hline Users can request to & Will fetch other user’s details and show it to a user who \\ see another user data & requested it \\ \hline Download user data & After the user’s account address has granted access to see data, \\  & he can directly download files or see data in fields from the \\  & blockchain smart contract \\ \hline Grant access role & Data owners can grant access to other accounts by \\  & their addresses \\ \hline \end{tabular}
\end{table}
Table 3: Essential Use Cases.  
Figure 6: Elaborated Use Cases.  
rules. For example, a university acting as an authority can verify a graduate's credentials by calling the verification function, which cross-checks the DID, credential hash, and issuer identity.
## Materials and Methods
### Model Performance Metrics  
#### 2.4.1 Statistical Analysis  
Three accuracy validation metrics were selected to evaluate the precision of the model simulation outcomes: R\({}^{2}\), RMSE, and MAE.
## 3 Materials and Methods
### Factors Weighting  
For this purpose, the professional viewpoints of experts are used to prioritize factors. Each expert assigns a rank to each factor according to its importance and influence. A weighting is assigned to each of them after different experts have determined their rank. This step is divided into four phases [79]:  
* Determining the comparative importance of the average value \(\left(S_{j}\right)\): \[S_{j}=\frac{\sum_{i}^{n}A_{i}}{n}\] (1) where, \(n\) stands for the number of experts, \(A_{i}\) shows the offered ranks by the experts for each factor, and \(j\) represents the number of the factor.
* Determining the coefficient \(K_{j}\) for each factor, as given in Equation (2): \[K_{j}=\left\{\begin{array}{ll}1&\qquad\qquad j=1\\ S_{j}+1&\qquad\qquad j>1\end{array}\right.\] (2)
* Calculating the recalculated weight \(Q_{j}\) as follows: \[Q_{j}=\frac{K_{j-1}}{K_{j}}\] (3)
* Finally, the relative weight of the factor is obtained using the following equation: \[W_{j}=\frac{Q_{j}}{\sum_{i=1}^{m}Q_{i}}\] (4) where, \(W_{j}\) denotes the relative weight of the \(j\)-th factor, and \(m\) represents the total number of factors. A detailed description of SWARA technique can be found in References [81; 82; 83; 84].
## 4 Material and Methods
### Study Design, Data Source, and Participants
Of these, 48,053 (13.7%) were excluded because no triage data were recorded or because they were immediately transferred to an ICU. Thus, 302,966 patients were included in the study (Table 2). The final medical decision corresponded to 99,340 (32.8%) admissions and 203,626 (67.2%) discharges. The main characteristics are summarized in Table 2, and the full table (including all features, missing values, and imputation strategies) is given in Supplementary Material S1.3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Overall** \\ \hline
**Demographic characteristics** & \\ Number of patients & 302,966 (100\%) \\ Age & 51 (22) \\ Sex & \\ Male & 156,621 (51.7\%) \\ Female & 146,345 (48.3\%) \\
**Clinical triage characteristics** & \\ Heart rate (/min) & 85 (17.8) \\ Systolic blood pressure (mmHg) & 136 (24) \\ Diastolic blood pressure (mmHg) & 77 (24) \\ Blood oxygen saturation (\%) & 99 (2) \\ Body temperature (\({}^{\circ}\)C) & 36.6 (0.8) \\ Capillary blood glucose level (mmol/L) & 7.72 (4.78) \\ Capillary blood ketone level (mmol/L) & 1.13 (3.40) \\ Oxygen flow (L/min) & 0.65 (4.5) \\ Capillary blood hemoglobin level (dg/dL) & 11.4 (3.1) \\ Expired breath alcohol level (g/L) & 1.87 (0.83) \\ Bladder volume (mL) & 366 (320) \\ Pain intensity & 3 (3) \\ FRENCH triage scale grade & \\
1 & 930 (0.1\%) \\
2 & 15,174 (5.1\%) \\
3 & 136,839 (45.9\%) \\
4 & 85,235 (28.7\%) \\
5 & 60,280 (20.2\%) \\
**Outcome** & \\ Admission & 99,340 (32.8\%) \\ Discharge & 203,626 (67.2\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Principal characteristics of the study population. The French Emergency Nurses Classification in Hospital (FRENCH) triage scale is detailed in Supplementary Material S1.5 and in [PERSON] et al. [55]. Data are expressed as the mean (SD) or \(n\) (%).