esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology and System Design
### VoteChain: Voting Process  
Figures 3 and 4 illustrate the flow of a secure e-voting session offered by the VoteChain. The initial step involves user login to the VoteChain website or the Decentralized Application (DApp). Employing the voter credentials stored in the CEC database, user authentication is enhanced through the use of a One-Time Password (OTP) delivered via SMS.  
Crucially, the outcome of successful voter registration is safeguarded within the CEC database (off-chain), distinct from Ethereum's main chain (on-chain). This meticulous process serves to uphold the reliability and authenticity of voters throughout the voting procedure.  
It is imperative to note that each voter's private account is established on the Ethereum framework and a service node akin to a bitcoin wallet (MetaMask). Ganache, a personal Ethereum blockchain, facilitates testing and provides a view of on-chain transactions and blocks. Meanwhile, MetaMask functions as a service node and cryptocurrency wallet, enabling the payment of the requisite Ether (ETH) gas fee to cast a vote.  
Upon the formation of Ganache and MetaMask accounts, voters receive a public key, private key, and MetaMask username. Importantly, the voter's Ganache account credentials (public and private keys) are imported and linked with the MetaMask account, serving as the foundation for ballot casting and future elections.  
To cast a vote using DApp, the voter logs into the CEC website and selects \"Vote\" from the DApp menu. Following the confirmation and verification of voter credentials, the voter submits their private key to cast a vote. Concurrently, MetaMask credentials are  
Figure 3: Secure e-voting Session.  
provided by either the voter or CEC authority to log transactions and blocks on Ganache. Subsequently, this block is mined and seamlessly integrated into Ethereum's immutable main chain, marking the successful execution of transactions and block mining on the main chain.  
Voter credentials undergo validation, ensuring the casting of an immutable vote in accordance with smart contract protocols. This meticulous process is designed to safeguard the privacy and security of both the ballot and the cast vote.  
During the voting phase, the involvement of a third party is entirely unnecessary, distinguishing this process from traditional client- server systems. Moreover, voters have the flexibility to submit their votes by logging into the dedicated voters' link on the CEC website, providing an additional layer of accessibility to the voting process.
## 3 Research Findings
### Evaluation of NSGA II and MOPSO Algorithms  
To assess and validate the implemented code in MATLAB, a sample problem has been designed on a smaller scale for the proposed algorithms. Output variables obtained from the first successful run of each algorithm are shown; therefore, the size of the problem set for initial validation is determined based on randomly generated parameters following a uniform distribution. After designing the problem and generating random data, the designed problem is solved using metaheuristic algorithms in 100 iterations, and performance metrics for comparing multi-objective metaheuristic algorithms are determined for each algorithm. Table 7 shows the average and performance metrics obtained from the execution of NSGA II and MOPSO algorithms.  
Table 7 summarizes the performance metrics of NSGA-II and MOPSO for small-sized problems. Key insights include NSGA-II's superior solution diversity and MOPSO's faster computational time.  
According to the results in Table 7, the computational time obtained from solving the sample problem with the MOPSO algorithm is less than that with the NSGA II algorithm. However, the NSGA II algorithm has performed better in finding a greater number of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Optimal Level of Factors**} & \multicolumn{4}{c}{**Levels of Factors**} \\ \cline{2-5}  & **3** & **2** & **1** & **Parameters** \\ \hline
100 & 100 & 75 & 50 & nPop \\
70 & 150 & 100 & 70 & nRep \\
0.7 & 0.7 & 0.6 & 0.5 & W \\
1 & 1.5 & 1.25 & 1 & C1 \\
1 & 1.5 & 1.25 & 1 & C2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The optimal Levels of factors used for MOPSO algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MOPSO Algorithm** & **NSGA-II Algorithm** & **Index** \\ \hline
6.64 & 18.88 & Computational time \\
569,563.94 & 573,954.21 & Average of the first objective function \\
49,371.86 & 49,622.11 & Average of the second objective \\  & & function \\
9 & 10 & NPF \\
35,751.92 & 36,643.30 & MSI \\
0.381 & 0.476 & SM \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison metrics of metaheuristic algorithms in solving the sample problem.  
viable solutions. Therefore, the Pareto front obtained from solving the sample problem with the NSGA II and MOPSO algorithms is shown in Figure 6.  
Figure 6 illustrates the Pareto front obtained from NSGA-II and MOPSO for a small-sized problem. NSGA-II demonstrates a broader and more diverse Pareto front, indicating its superior ability to balance conflicting objectives such as cost minimization and demand coverage maximization. MOPSO, while computationally faster, shows limited diversity in its solutions.
## 4 Case Study
### IAQ Sensors  
The assessment of indoor air quality (IAQ) and thermal comfort is carried out by integrating the data available from a number of sensors. In particular, IAQ is ensured by the presence of sensors capable of simultaneously analyzing the concentration level of tVOC, PM2.5, PM10 and CO\({}_{2}\) in terms of ppm. For this reason, one air quality sensor per room is planned. Considering the recirculation of air in the different rooms due to thermal loads, it is not necessary to provide for a higher number, since small variations of values within the rooms are not relevant for monitoring IAQ and thermal comfort (the minimum measurable value and the error of these sensors are normally lower than the concentration variations naturally present in the rooms). The sensors will also be able to provide air quality evaluations through aggregated metrics (IAQ indices), and SW-side algorithms will be implemented to check compliance with the relevant ISO standard.
## 4 Results and Discussion
### Parametric Study on Breakthrough Curves of AC/Zeolite 13X Layered Bed
#### 4.1.3 Effect of AC/Zeolite 13X Ratio on Breakthrough Curves  
This section investigates the effect of different AC/zeolite 13X ratios on the breakthrough curves of layered beds and compares the results with those of the AC single-layer bed. The specific results are presented in Figure 5. The addition of zeolite 13X to the AC bed improves the CO roll-up shape typically observed in the AC single-layer bed. Meanwhile, the roll-up height increases. As the proportion of zeolite 13X increases, the roll-up height further rises, suggesting that the adsorption amount has been significantly enhanced. The breakthrough of the impurity gases CO and CO\({}_{2}\) is notably delayed, whereas N\({}_{2}\) shows minimal change. The breakthrough curves on the single-layer bed indicate that the breakthrough times of CO and CO\({}_{2}\) on zeolite 13X are longer than those on AC, highlighting that the incorporation of zeolite 13X has somewhat optimized the dynamic separation of CO and CO\({}_{2}\) in the layered bed. Notably, the N\({}_{2}\) breakthrough time on layered beds with AC:13X ratios of 8:2 and 6:4 is nearly identical, though slightly earlier than in the AC single-layer bed. This may be due to AC's stronger adsorption capacity for N\({}_{2}\) compared  
Figure 4: Effect of feed flow rate on the breakthrough curve of AC/zeolite 13X = 6:4 layered bed.  
Figure 3: Effect of feed pressure on the breakthrough curve of AC/zeolite 13X = 6:4 layered bed.  
to zeolite 13X. The addition of zeolite appears to reduce the bed's dynamic separation efficiency for N\({}_{2}\), though variations in the zeolite 13X ratio have minimal impact on this effect within the range studied.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 5 Analysis of Resilience in Existing Works
### Monitoring  
Continuous monitoring in real-time is one of the most important requirements to secure the IoMT networks. A global view of the entire IoMT network is required for the monitoring. Tracking of all types of network events, as well as tracking of performance, such as packet loss, latency, etc., come under the part of monitoring. [PERSON] et al. [85] presented a methodology with the Identity-Based Network Coding Scheme to protect from different message attacks in the IoMT network. Although this model provides security and monitoring in data transmission in the IoMT environment, potential attacks, such as DDoS attacks, may also lead to data leakage. [PERSON] et al. [54] also worked on monitoring and security issues in the IoMT environment. They integrated blockchain technology with hyperledger fabric to enable secure data transmission in IoMT networks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Primary Technology** &
\begin{tabular}{c} **Combination of** \\ **Other Technology** \\ \end{tabular} & **Monitoring** & **Diagnosis** & **Recovery** & **Control** \\ \hline
[44; 45; 46] & Bayes Network & Blockchain & ✓ & \(\times\) & \(\times\) & ✓ \\ \hline
[47; 48; 49; 50] & Blockchain & Fog Computing & \(\times\) & \(\times\) & \(\times\) & ✓ \\ \hline
[15; 51; 52; 53] & Blockchain & Software-Defined Network & ✓ & \(\times\) & \(\times\) & ✓ \\ \hline
[54; 55; 56] & Hyperledger Fabric & Blockchain & \(\times\) & \(\times\) & \(\times\) & ✓ \\ \hline
[57; 58; 59; 60] & Deep Learning & Cryptography & ✓ & \(\times\) & \(\times\) & ✓ \\ \hline
[61; 62; 63] & K-Means (Unsupervised Learning) & Software-Defined Network & ✓ & \(\times\) & \(\times\) \\ \hline
[64; 65; 66; 67] & Machine Learning & Artificial Intelligence & ✓ & \(\times\) & ✓ & \(\times\) \\ \hline
[68; 69; 70; 71; 72] & Machine Learning & Microservices Architecture & \(\times\) & ✓ & \(\times\) & ✓ \\ \hline
[73; 74; 75; 76] & Software-Defined Network &
\begin{tabular}{c} Network Function \\ Virtualization \\ \end{tabular} & ✓ & \(\times\) & \(\times\) & ✓ \\ \hline
[77; 78; 79] & Software-Defined Network & Edge Computing & ✓ & \(\times\) & ✓ & \(\times\) \\ \hline
[80; 81; 82; 83; 84] & Software-Defined Network & Microservices Architecture & ✓ & ✓ & \(\times\) & ✓ \\ \hline
[18; 81] & Microservices Architecture & Fog Computing & \(\times\) & ✓ & \(\times\) & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of resilience in existing works.  
The above-mentioned works are designed for the scenario of cyber-attacks in the network. In this scenario, a network is partially infected with some malicious intruders. A large portion of the network can still be in operation during the cyber-attack. The existing works in Table 2 indicate that capturing real-time data for monitoring can be achieved primarily through SDN technology.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 2 Related Work
### Cosmos Interoperability
#### 2.1.2 Inter-Blockchain Communication
Anyone can run a relay, and depending on the blockchain, they may be rewarded with transaction fees. The Relayer additionally arranges and broadcasts an IBC Acknowledgement transaction to ensure the token transfer has been completed on the receiver's blockchain. The IBC Acknowledgement transaction is included in the sender's block, Blockchain-A's block, as proof that the token transfer was successful. The Cosmos' inter-blockchain communication protocol is represented in Figure 2. In contrast, Figure 3 shows a Sequence diagram of the above-described mechanism from steps 1 through 12-1 and 12-2 (as shown in Figure 2). Inter-blockchain communication protocols must manage instances when transactions are not sent on time or to their destination because they operate across a distributed network and rely on possibly untrustworthy Relayers to relay messages. Suppose an IBC RecvPacket fails to be included in a block unexpectedly, then in that case, the Relayer generates and forwards an IBC Timeout transaction. As soon as the transmitting blockchain receives the IBC Transfer transaction, it begins the Token Unlock procedure. Table 1 lists the types of transactions within the Cosmos IBC protocol. Figure 2: Cosmos Fungible Token Transfer Flowchart.
## 3 Results
### Devices with Different Areal Densities of Nanostructures  
The illuminated current-voltage curves for different areal density of larger nanostructures are shown in Figure 2. Note that devices at the same distance from the left edge of the substrate have  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multicolumn{2}{c}{**Estimated Number of Large**} & **Estimated Areal Density** & \(I_{\mathbf{sc}}\) & \(V_{\mathbf{sc}}\) & \multirow{2}{*}{**FF**} & **Power** \\  & **Nanostructures in 850 μm2** & \multicolumn{2}{c}{**(μm\({}^{-2}\))**} & **(μm\({}^{-2}\))** & **(μm\({}^{-1}\))** & **(V)** & **(μm\({}^{-1}\))** \\ \hline
3 & 2 & 0.002 & 110.00 & 0.80 & 0.49 & 42.60 \\
9 & 6 & 0.007 & 98.00 & 0.70 & 0.52 & 35.46 \\
15 & 61 & 0.071 & 85.50 & 0.60 & 0.53 & 26.88 \\
19 & 67 & 0.079 & 80.50 & 0.57 & 0.53 & 24.10 \\
25 & 71 & 0.084 & 71.09 & 0.54 & 0.53 & 20.37 \\
30 & 94 & 0.11 & 61.47 & 0.45 & 0.50 & 13.86 \\ \hline \hline \end{tabular} Notes: \(I_{\mathbf{sc}}\) = short circuit current; \(V_{\mathbf{sc}}\) = open circuit voltage; \(FF\) = fill factor.  
\end{table}
Table 1: Representative Device Data.  
Figure 2: Illuminated current-voltage curves for different devices shown in Figure 1. Note the similarity in device performance between devices with the same areal density of high aspect ratio nanostructures, i.e., same distance from the left edge of the substrate shown in Figure 1 (e.g., device 2 and device 3). The illuminated I-V curve for a reference device deposited on a flat substrate is shown for comparison.  
comparable performance. The corresponding illuminated I-V curve parameters for representative devices of different areal densities of larger nanostructures are listed in Table 1. In Figure 3, the increased areal density of larger nanostructures for these devices is indicated by a corresponding scanning electron microscope (SEM) top-view image of each of the representative devices listed in Table 1. In each SEM image, the larger dome-like features correspond to larger structures, allowing for estimation of the areal density based on the number of larger features for each device over an area of -850 \(\upmu\)m\({}^{2}\) (see Table 1).
## 3 Results and Discussion  
### Distribution of RA and Fiber Recovery  
The tested parent concrete was crushed, sieved, and floated to obtain RA and RF, which were counted and reported in Table 3. For the RA, the distribution of fractions was found to be similar between N-3 and N-9. For the RF, differences were observed between the two parent concretes. Firstly, N-9 had a higher fiber recovery rate than N-3, of more than 50% of the virgin fiber used in the parent concrete. These results are in line with those obtained by [PERSON] et al. (2014) on fiber recovery from parent concrete with 6 kg/m\({}^{3}\) of polypropylene fibers, where recovery rates of 44% to 64% were found.  
Additionally, differences in the distribution of RF shapes were also observed: the distribution of N-9 was more uniform when compared with N-3, whereas the distribution of N-3 was more concentrated in long and bent RF. Therefore, the effect of fiber content on the distribution of RA fractions is not significant but influences the recovery rate and shape of RF.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 6 Experiment Analysis
### Time Consumption of the Act_Audit Phase  
The comparison results of the Act_Audit phase is shown in Figure 8. In this phase, the RAAC scheme used zero-knowledge proof, on the basis of the chameleon hash algorithm to verify the user's identity. To make the comparison fair, the time consumption of the trace phase in RAAC scheme was not counted. The Act_Audit phase in the PCAC was less burdensome than that in the RAAC. In the PCAC, the Act_Audit phase took about 30 ms, in average, while it cost more than 60 ms in the RAAC scheme.  
Figure 6: Time Consumption in the Data Obtainment Stage.  
Figure 7: Time Consumption in the Encrypt and Decrypt Phase.
# Bioengineered Skin Substitutes: Advances and Future Trends
## 3 Cell-Based Skin Substitutes  
Chronic wounds influence a patient's life quality, leading to impaired mobility, sleep disturbance, psychological stress, and chronic pain [24]. A central problem causing poor skin wound healing in chronic wounds is impaired vascularization and cell activity [25]. Therefore, progress in the tissue engineering field aims for the development of cell-based wound substitutes or wound coverages that improve wound healing by promoting cell migration, differentiation, and vascularization [24]. The majority of cell-based skin substitutes are comprised of a scaffold upon which cells are seeded/cultured. Scaffolds are designed to integrate into the host tissue and provide an appropriate environment for cell growth, infiltration, and differentiation [26]. Thus, those scaffolds need to exhibit some specific characteristics such as appropriate porosity distribution to deliver oxygen and nutrients to cells and drive out carbon dioxide and waste products to allow cell proliferation, and expansion and biocompatibility. Moreover, the scaffold should mimic the mechanical properties of native human skin, which has an elastic modulus in the range of 88-300 kPa [27]. Accordingly, different skin templates have been engineered worldwide and there are various commercial cell-based templates for wound healing applications. Figure 1 demonstrates a classification and summary of commercial skin substitutes with examples discussed in this review.
###### Abstract  
Accurate prediction of coal spontaneous combustion levels is crucial for preventing and controlling spontaneous combustion in goat areas. To address the ambiguity in classification standards of coal spontaneous combustion warning levels, 21 groups of coal samples from different mining areas were subjected to experiments with programmed temperatures, generating a database of 336 sets of temperatures and data on indicator gas concentrations. An unsupervised learning approach combining t-distributed Stochastic Neighbor Embedding (t-SNE) and k-means clustering was proposed to perform dimensionality reduction and clustering of high-dimensional data features. The clustering results of the original data were compared with Principal Component Analysis (PCA) and Stochastic Neighbor Embedding (SNE) methods to determine coal spontaneous combustion warning levels. The indicator gases and warning levels were input into a trained Support Vector Classifier (SVC) to establish a classification model for coal spontaneous combustion warning levels in goat areas. The results showed that the maximum Maximal Information Coefficients (MICs) between temperature and CO and O\({}_{2}\) concentrations were 0.95 and 0.81, respectively, indicating strong nonlinear relationships between indicator gases and warning levels. The t-SNE method effectively extracted nonlinear mapping relationships between the indicator gas features, while the k-means clustering categorized coal spontaneous combustion data using distance as a similarity measure. By combining the t-SNE and k-means methods for accurate dimensionality reduction and clustering of goat spontaneous combustion data, the warning levels were classified into six categories: safe, low risk, moderate risk, high risk, severe risk, and extremely severe risk. The application in the Longgu mine demonstrated that the SVC method could accurately classify spontaneous combustion warning levels in field goat areas and implement corresponding response measures based on different warning levels, providing a valuable reference for spontaneous combustion prevention in goat areas.  
Keywords:coal spontaneous combustion; warning level; unsupervised learning; support vector classifier; classification model +
Footnote †: journal: Applied sciences
## 3 Methodology
### First Stage Zero-Shot Classifiers (Sentence Transformers)  
The proposed method has two stages (see its schematic representation in Figure 3). In the first stage, the emotion detection problem is tackled (see Figure 4). The core of this stage is the zero-shot classifier that does not require any training. The idea is to transfer the knowledge it already has to a new task. Zero-shot learning involves training a classifier on a set of labels and then testing it in new data having different labels that the classifier has not been trained on. Classical zero-shot learning needs the provision of a descriptor for an unknown class for a model in order to predict that class without being trained on known representatives of it [51]. This machine learning method is based on a pre-trained model that can observe classes that were not observed during training and has a predictor of which class the input text belongs to. The zero-shot model returns probabilities for the given emotions and thus determines their relations to the input text.  
In our experiments, we have tested four zero-shot transformer models as follows:  
* The _bart-large-mnli_ model [52] is a zero-shot sequence classifier proposed in [53]. The model was trained on tweets, emotional occurrences, fairy tales, and artificial sentences. It has nine emotions (anger, disgust, fear, guilt, joy, love, sadness, shame, surprise), as well as the \"none\" class (if no emotion applies). The approach offers the sequence to be categorized as the multi-genre natural language inference (MNLI) and creates a hypothesis from each possible label. Then, label probabilities are created from the entailment and contradiction probabilities.
* The _Fb-improved-zeroshot_ model [54] is a zero-shot model for German and English academic searchlog classification created by ETH Zurich students and based on [53]. The _bart-large-mnli_ model was used to train and then fine-tune this model.
* The COVID-Twitter-BERT (CT-BERT), a transformer-based model, is the foundation of the _covid-twitter-bert-v2-mnli_ model [55], which was pre-trained on a corpus of Twitter conversations about COVID-19 [56]. CT-BERT was designed to work with the COVID-19 content, particularly from social media. The emotion toward vaccines is captured by the model. The dataset comprises three classes: positive (towards vaccinations), negative, and neutral/others.
* The _bart-large-mnli-yuhoo-answers_ model [57] refined the _bart-large-mnli_ model on Yahoo Answers subject categorization. The model may be used to forecast whether the topic label can be assigned to a certain sequence.