esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Selective Deep-Learning-Based Clustering Ensemble
## 3 Improved Selective Deep Clustering Ensemble
### Selective Clustering Ensemble
#### 3.2.2 Consensus Ensemble  
In the consensus ensemble of ISDCE, we considered cluster-wise diversity inside the same base clusterings to enhance the consensus performance [23]. We adopted the local weighting idea to evaluate the reliability of clusters. Given the ensemble \(\Pi=\{\pi^{1},\ldots,\pi^{m}\}\), where \(\pi^{m}=\{C_{1}^{m},\ldots,C_{n^{m}}^{m}\}\) denotes the m-th base clustering in \(\Pi\) and \(C_{i}\) indicates the \(i\)-th cluster, the uncertainty of cluster \(C_{i}\) for \(\pi^{m}\) is computed as follows:  
\[H^{m}(C_{i})=-\sum_{j=1}^{n^{m}}p\Big{(}C_{i},C_{j}^{m}\Big{)}\log_{2}p\Big{(} C_{i},C_{j}^{m}\Big{)} \tag{11}\]  
where  
\[p\Big{(}C_{i},C_{j}^{m}\Big{)}=\frac{\Big{|}C_{i}\bigcap C_{j}^{m}\Big{|}}{|C _{i}|} \tag{12}\]  
where \(n^{m}\) is the number of clusters in \(\pi^{m}\) and \(C_{j}^{m}\) is the \(j\)-th cluster in \(\pi^{m}\). \(|C_{i}|\) indicates the number of objects in \(C_{i}\).  
Therefore, the uncertainty (or entropy) of \(C_{i}\) for ensemble \(\Pi\) can be calculated as follows:  
\[H^{\Pi}(C_{i})=\sum_{m=1}^{M}H^{m}(C_{i}) \tag{13}\]  
where M is the number of base clusterings in \(\Pi\).  
Then, we constructed a ensemble cluster index (ECI) to compute the reliability of clusters, which is defined as follows:  
\[ECI(C_{i})=e^{-\frac{H^{\Pi}(C_{i})}{\beta M}} \tag{14}\]It is easy to see from the above equation that \(ECI(C_{i})\in\)(0,1] and, the smaller the uncertainty of the cluster, the larger the ECI value. \(\theta>0\) is a coefficient that adjusts for the effects of cluster uncertainty.  
Then, based on ECI, we exploited the weighted graph partitioning consensus function. We defined the weighted graph as \(G=(V,L)\) and computed the link weight between two nodes \(v_{i}\) and \(v_{j}\) as follows:  
\[l_{ij}=\begin{cases}ECI(v_{j}),&\text{if }v_{i}\in X,v_{j}\in\mathcal{C},\text{ and }v_{i}\in v_{j}\\ ECI(v_{i}),&\text{if }v_{j}\in X,v_{i}\in\mathcal{C},\text{ and }v_{j}\in v_{i}\\ 0,&\text{otherwise}.\end{cases} \tag{15}\]  
where \(X\) is the original data and \(\mathcal{C}=\{C_{1},C_{2},\cdots,C_{N_{c}}\}\) is all of the clusters in the ensemble \(\Pi\). \(N_{c}=n^{1}+\ldots+n^{M}\) denotes the sum of clusters in \(\Pi\).  
The time complexity of ISDCE can be roughly divided into two parts: deep clustering and clustering ensemble. The time complexity of deep clustering section is \(O(nd_{h}k+nD^{2})\), where \(d_{h}\), \(k\), and \(D\) are, respectively, the dimension of the embedding layer, number of clusters, and maximum number of neurons in hidden layers. The time complexity of clustering ensemble section is \(O(Mn^{m}+kln+k(n^{m})^{2})\), where \(n^{m}\) is the number of clusters in \(\pi^{m}\) and \(l\) is the average number of links connecting to a node in the graph.
_applied sciences_  
_Article_  
**Data Privacy Security Mechanism of Industrial Internet of Things Based on Block Chain**  
**[PERSON] 1,2, [PERSON] 1,* and [PERSON] 3**  
1 Key Laboratory of Information and Communication Systems, Ministry of Information Industry,  
Beijing Information Science and Technology University, Beijing 100101, China; [EMAIL_ADDRESS]  
2 Key Laboratory of the Ministry of Education for Optoelectronics Measurement, Technology and Instrument,  
Beijing Information Science and Technology University, Beijing 100101, China  
3 Intelligent Research Center, National Pipeline Network Group Research Institute, Beijing 100101, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-13691117939  
**Abstract** In order to solve the problem that data of the industrial Internet of Things (IIoT) is easily tampered with and therefore, the authenticity of data may be questioned, a data-privacy security mechanism of the IIoT based on blockchain is proposed. At the same time, to solve the problem of master node selection and low efficiency in the practical Byzantine fault tolerance (PBFT) algorithm, a reward mechanism based on node behavior is introduced and an improved PBFT algorithm is proposed. The improved PBFT algorithm is more efficient, in line with the application scenarios of the IIoT. Comparative analysis results showed that the proposed blockchain-based data-privacy security mechanism of the IIoT is superior to other models in terms of consensus efficiency, throughput, and block generation speed.  
**Keywords:** IIoT; data privacy; blockchain; PBFT; security mechanism
## 3 Results and Discussion
### Sealing Performance
The variation in the opening force in values was relatively small, and was within 120 N. Meanwhile, the leakage of the sealing medium displayed an upward trend with the increase of the sealing temperature, which was consistent with the extent of the thermal deformation. The leakage value at the sealing temperature of 350 K was more than twice as much as that at 100 K, illustrating that the ambient-temperature gradient improved the sealing performance again. The relationship between the sealing performance and the rotational speed is presented in Figure 11. From Figure 11a, both the pressure loss at the inlet of the seal and the pressure increase at the outlet of the seal were affected significantly by the rotational speed, while the amount of variation was relatively small. On the basis of these effects, the opening force of the sealing medium showed an upward trend with the increase in the rotational  
Figure 10: Relationship between the sealing performance and the sealing temperature. (**a**) Pressure boundaries between the seal rings; (**b**) Opening force and leakage. (\(P_{\text{in}}\) = 5.0 MPa, \(P_{\text{out}}\) = 0.1 MPa, \(T_{\text{out}}\) = 350 K, \(n\) = 50,000 rpm, \(h_{0}\) = 3 \(\upmu\)m). speed, while the leakage increased initially before decreasing, reaching a maximum value of 1.444 g\(\cdot\)min\({}^{-1}\) at the rotational speed of 20,000 rpm. However, such a trend in the leakage was opposite to that in Ref. [35], in which the leakage decreased initially before increasing. Without the influence of the ambient-temperature gradient, the temperature increase increased and thermal deformation was enhanced with the increase of the rotational speed, leading to the decrease in the opening force and the increase in the leakage. The values of the leakage were lower, and changed little under the ambient temperature-gradient condition, owing to its contributions in decreasing thermal deformation. Therefore, the ambient temperature gradient contributed to improving the sealing performance. Finally, the relationship between the sealing performance and the basic film thickness is presented in Figure 12. As demonstrated in Figure 12a, the pressure at both the inlet and outlet of the seal changed significantly as the basic film thickness increased from 2 \(\upmu\)m to 10 \(\upmu\)m. The pressure loss at the inlet of the seal decreased from 4.9 MPa to 2.96 MPa, and the pressure rise at the outlet of the seal increased from 0.3 MPa to 0.87 MPa. Meanwhile,  
Figure 11: Relationship between the sealing performance and the rotational speed. (**a**) Pressure boundaries between the seal rings; (**b**) Opening force and leakage. (\(P_{\text{in}}\) = 5.0 MPa, \(P_{\text{out}}\) = 0.1 MPa, \(T_{\text{in}}\) = 200 K, \(T_{\text{out}}\) = 350 K, \(h_{0}\) = 3 \(\upmu\)m). with the increase of the clearance gap, the opening force of the sealing medium decreased by more than 50%, and the leakage value increased sevenfold. Therefore, the increased clearance played a dominant role in controlling the sealing performance.
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 5 Mechanisms of Persister Cell Formation
### Persistence as \"Stuff That Happens\"  
Type II persisters may also be explained as cells that arise as an inevitable consequence of \"errors\" during cell cycle and division, which introduce phenotypic heterogeneity. This formation is referred to as \"persistence as stuff that happens\" (PaSH) [9], asserting that persistence may not be an adaptive trait but, rather, a result of stochastic processes. It is also possible that the different persister classes described above have distinct evolutionary origins. In this scenario, antibiotics do not directly induce the production of persister cells but, rather, \"unmask\" an already-existing subpopulation in a power-law decay curve (Figure 3) [8]. According to this model, persister cells are not a response to antimicrobial stress but are continuously present within the bacterial population. To test this hypothesis, researchers studied the persistence of _S. aureus_ (Newman strain) when exposed to various antibiotics, including ciprofloxacin, gentamicin, vancomycin, and oxacillin, at concentrations ranging from the minimum inhibitory concentration (MIC) to 10 \(\times\) MIC. These  
Figure 2: Production pathways of ppGpp and its activation of the type I TA system of HokB–SokB. An amino acid starvation triggers a stringent response, leading to (p)ppGpp accumulation and the subsequent activation of the HokB–SokB type I TA system. The HokB toxin disrupts membrane potential, contributing to bacterial persistence and survival under stress conditions. The full arrows indicate that it is direct effect. The dotted ones indicate a more indirect effect. In the case of membrane depolarization, the HokB membrane-associated toxin must be activated by the anti-toxin SokB in order to create the pores on the membrane creating the depolarization, which in turn will trigger the increase in persistence.  
experiments led to the development of a mathematical model to predict persister cell generation and selection. Interestingly, cultures pre-treated with gentamicin or ciprofloxacin exhibited an increased persistence rate when later exposed to other antibiotics. While the researchers acknowledge that antimicrobials can stress populations and lead to cell division arrest (type I persisters), they also suggest that persisters may arise from stochastic errors in cell replication, similar to mutations [8; 9; 21].
## 3 Batch Authentication in the Vehicle-to-Grid (V2G) Network
### Existing Authentication Protocols in the Vehicle-to-Grid (V2G)
#### 3.2.2 Hybrid Public Key Infrastructure (PKI)-Based Protocols  
In 2010, [PERSON] _et al._[82] had introduced a hybrid authentication protocol for the V2G system in order to authenticate the messages one-by-one. Further, [PERSON] _et al._[83] proposed a multi-domainnetwork-based protocol that incorporates a comprehensive hybrid PKI model (hierarchical and peer-to-peer certifications). However, it does not discuss authentication. Afterwards, [PERSON] _et al._[84] proposed a batch authentication protocol (UBAPV2G) that is able to achieve low authentication delay, computational cost, and communication traffic compared with the one-by-one authentication scheme.
## 3 Method  
The nature of the research question and the availability of a suitable cohort of students make a case study approach appropriate to explore the research question in two parts; (a) does computer-mediated feedback, suitably informed by best practice principles, offer an improved educational experience for students, and (b) does this method have the potential to allow large-scale courses to be delivered in a cost-effective way to people in developing and developed areas alike. The first part will be answered based on the empirical evidence gathered by a series of student surveys, while the second part will be deduced, based on the results of the first.  
In general, case studies allow the underlying factors of phenomena to be explored, including those occurring in an educational context. The case study described here involved the participation of 312 university level students doing a first year, first semester Communications course. The course proceeded over a standard 13-week semester and across three campuses at an Australian university. One campus is in a low Socio-Economic Strata (SES) (15 students), one in a low to middle SES (166 students), and one in middle to high SES (141). 85% of students are male, 65% of participants are domestic students, while 35% are international, mainly from East Asia and India.  
The research method focuses on three successive assessment items (due in semester weeks four, six and nine). Each item is submitted via the Learning Management System (LMS) assignment submission function. The process is paperless, with students not needing to print out their work, however it was necessary for the documents to be developed in a word processor and lodged either as a PDF (Portable Document Format) or MS Word compatible document. Students are not obliged to buy MS Word, they can use a free word processor if they wish, and save it in either of the previously mentioned formats. The requirement for particular formats comes from the third party plagiarism checker. If such checking is not necessary, almost any submission format is acceptable by the LMS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data collection** & **Procedure** \\ \hline First Survey & In Week 0, students surveyed as to their expectations for assessment feedback. \\ \hline Second Survey & In week 10, after the third and final assessment task was concluded, the students completed an anonymous web-survey. \\ \hline Third Survey & In week 10 of the following semester (5 months later), the students completed a follow-up \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data Collection Instruments.  
The assessor/marker displays the submissions online, and the feedback is given by entering mark-appropriate, pre-written text into the feedback text-box or, alternatively, a file containing the feedback can be attached.  
A series of three data collection surveys were performed, as shown in Table 1.
## 4 Experiments and Analysis of Results
### Case Study 2: JNU Dataset
#### 4.5.2 Fault Classification Model Evaluation  
To evaluate the impact of generated data from different generative models on classification performance, this study compares GAN, ACGAN, WGAN-GP, DCGAN, and the proposed MDFT-GAN. The experimental results are presented in Table 9. Although ACGAN and DCGAN are the better performers among the compared models, regarding MDFT-GAN, it outperforms all the other models with an accuracy of 99.91 and an F1 score of 99.90. Therefore, this suggests that superior data quality facilitates subsequent fault diagnosis.  
To further validate the effectiveness of the classification model proposed in this paper, it was compared with several mainstream classification models. The results are detailed in Table 10.  
In machine learning models, due to the limitations of traditional feature extraction methods in capturing high-dimensional representations, the ability of random forests and support vector machines to classify complex fault signals remains limited. The performance of deep learning models varies, with 2D-ResNet performing relatively well, but with an accuracy of 98.12.  
Figure 18: Comparison of t-SNE for different generated models. (**a**) GAN. (**b**) ACGAN. (**c**) WCGAN-GP. (**d**) DCGAN. (**e**) Proposed 50 Samples. (**f**) Proposed 30 Samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison** & **Sample Size** & \multicolumn{2}{c}{**Evaluation Metrics (\%)**} \\ \cline{2-4}
**Models** & **per Category** & **Accuracy** & **F1** \\ \hline
[18] & 50 & 98.12 & 98.14 \\
[28] & 50 & 98.10 & 98.11 \\
[20] & 50 & 90.62 & 91.45 \\
[38] & 50 & 98.13 & 98.30 \\ Proposed & 50 & 99.91 & 99.90 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification performance of data generated by different models.  
However, the proposed method performs the best on all evaluation metrics, achieving 99.25 on all metrics. These results highlight the superiority of MDFT-GAN in generating high-quality synthetic data and efficiently capturing the features of complex fault signals.
## 3 Materials and Methods
### Methodology
#### 3.2.2 Stage II: Classification
In this study, for every iteration, around 10% of the total samples were hidden from all the training data, which were considered unseen testing samples, and others were used for training. This process was repeated 10 times to avoid any possibilities of bias in the study. In this study, we used three metrics for the purpose of evaluating the efficacy of our proposed approach. The three evaluation metrics used are accuracy, receiver operating characteristics (ROC) curve, and area under curve (AUC). 1. Accuracy: One parameter for assessing classification models is accuracy. The percentage of predictions that our model correctly predicted is known as accuracy. The following is the actual definition of accuracy (as shown in Equation (6)): \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}\] (6)where \(TP\) = True Positives, \(TN\) = True Negatives, \(FP\) = False Positives, and \(FN\) = False Negatives. 2. Receiver Operating Characteristics (ROC): The receiver operating characteristic curve is a chart that displays how well a classification model performs across all categorization levels. Two parameters are shown on this curve: True Positive Rate (TPR) = \(\dfrac{TP}{TP+FN}\) (7) False Positive Rate (FPR) = \(\dfrac{FP}{FP+TN}\) (8) TPR (Equation (7)) vs. FPR (Equation (8)) are shown on a ROC curve at various classification levels. More items are classified as positive when the classification threshold is lowered, which raises the number of both False Positives and True Positives. 3. Area Under Curve (AUC): Integral calculus is used to calculate the AUC; it measures the full two-dimensional region beneath the entire ROC curve from (0,0) to (1,1). An overall assessment of performance across all potential classification criteria is provided by the AUC. The AUC may be seen as the likelihood that the model values a randomly chosen positive example higher than a randomly chosen negative example.
# Secure Trust-Based Blockchain Architecture to Prevent Attacks in VANET
## 4 Proposed Blockchain in VANET
### Design of Proposed Blockchain Architecture
#### 4.1.2 System Authentication  
The second phase is crucial for authenticating the vehicles in the network prior to communicating and exchanging data with each other. Assuming there is now another vehicle, Vehicle B, in the same network, known as \(\mathrm{V_{B}}\), with which \(\mathrm{V_{A}}\) wishes to communicate. This phase begins with \(\mathrm{V_{A}}\) sending its \(\mathrm{C_{A}}\) to \(\mathrm{V_{B}}\) to authenticate the identity of \(\mathrm{V_{A}}\). When \(\mathrm{V_{B}}\) receives the certificate from \(\mathrm{V_{A}}\), \(\mathrm{V_{B}}\) checks whether the \(\mathrm{C_{A}}\) delivered is valid as of the delivery date and time. The certificate would contain the public key and signature of \(\mathrm{V_{A}}\) as well as the expiration date of \(\mathrm{C_{A}}\). In order to authenticate \(\mathrm{V_{A}}\), \(\mathrm{V_{B}}\) checks the expiration of \(\mathrm{C_{A}}\). If \(\mathrm{C_{A}}\) has not expired, \(\mathrm{C_{A}}\) is still valid. Then, \(\mathrm{V_{B}}\) checks \(\mathrm{CertBC}\) to see if \(\mathrm{C_{A}}\) is present in the blockchain. If \(\mathrm{C_{A}}\) is present in \(\mathrm{CertBC}\), then \(\mathrm{V_{B}}\) proceeds to check \(\mathrm{RevBC}\) to see if the \(\mathrm{PU_{A}}\) is absent in the blockchain. If all these three conditions are fulfilled, only then will the communication between \(\mathrm{V_{A}}\) and \(\mathrm{V_{B}}\) take place. Failure of any of the three conditions mentioned would indicate that either the \(\mathrm{C_{A}}\) has expired and is invalid or the \(\mathrm{PU_{A}}\) is invalid. Hence, the authentication process will be halted immediately. When the identity of \(\mathrm{V_{A}}\) is authenticated, \(\mathrm{V_{B}}\) then sends a query to the nearest \(\mathrm{RSU}\) to obtain the current trust value of \(\mathrm{V_{A}}\). Initially, \(\mathrm{RSU}\) checks the identity of \(\mathrm{V_{B}}\) using the ID of \(\mathrm{V_{B}}\), \(\mathrm{ID_{B}}\). If the \(\mathrm{ID_{B}}\) is valid, \(\mathrm{RSU}\) then obtains the trust value of \(\mathrm{V_{A}}\) from TrustBC and sends the trust value to \(\mathrm{V_{B}}\). However, \(\mathrm{V_{B}}\) is rejected if the \(\mathrm{ID_{B}}\) is found to be not true. Once the trust value is calculated, \(\mathrm{V_{B}}\) will receive the details of \(\mathrm{V_{A}}\) and proceeds to the third phase, as shown Figure 7.  
Figure 6: System initialization of proposed solution.
# A Novel QoS Provisioning Algorithm for Optimal Multicast Routing in WMNs
## 3 The Proposed NMHA Algorithm  
### Description of NMHA Algorithm  
According to the algorithm analysis above, it is concluded that [PERSON]'s deterministic algorithm takes the lead in finding the optimal path for the unicast routing, and the MPH algorithm is fit for multicast routing problem subjected to one QoS constraint. Therefore, combining [PERSON]'s deterministic algorithm with the MPH algorithm, the NMHA algorithm is presented in this paper to deal with MCOMPR problem. The following are its steps:  
**Step 1:** Convert the graph \(\mathbf{G}(\mathbf{V},\mathbf{E},\stackrel{{\rightarrow}}{{ \mathbf{W}}},\stackrel{{\rightarrow}}{{\mathbf{L}}})\) to the new graph \(\mathbf{G}^{\mathrm{N}}(\mathbf{V},\mathbf{E},\mathbf{W}^{\mathrm{N}},\tau)\) based on the technique of the scaling and rounding;  
**Step 2:** Convert the graph \(\mathbf{G}^{\mathrm{N}}(\mathbf{V},\mathbf{E},\mathbf{W}^{\mathrm{N}},\tau)\) to the new graph \(\mathbf{G}^{\mathrm{N}}_{\mathrm{K}}(\mathbf{V}^{\mathrm{N}}_{\mathrm{K}}, \mathbf{E}^{\mathrm{N}}_{\mathrm{K}},\mathbf{W}^{\mathrm{N}}_{\mathrm{K}},\tau)\) based on the technique of the auxiliary graph construction;  
**Step 3:** Choose any node \(d_{1}\) from the set \(\mathbf{D}\) of multicast destination nodes, let \(j=1\), and initialize the generation of tree \(\mathbf{T}_{0}{}^{\mathrm{opt}}=\{s\}\), \(\mathbf{V}_{\mathbf{T}_{0}}=\{s\}\);  
**Step 4:** Calculate the cost \(w_{1}\left(d_{k},\mathbf{T}^{\mathrm{opt}}_{j-1}\right)\) from \(d_{k}(k\leq q)\) in \(\overline{\mathbf{D}\cap\mathbf{T}^{\mathrm{opt}}_{j-1}}\) to \(\mathbf{T}^{\mathrm{opt}}_{j-1}\) for all nodes \(d_{k}(k\leq q)\) by [PERSON]'s deterministic algorithm;  
**Step 5:** It can reach the minimum value by comparing formula (2):  
\[w_{1}(d_{j},\mathbf{T}^{\mathrm{opt}}_{j-1})=\min\{w_{1}(d_{k},\mathbf{T}^{ \mathrm{opt}}_{j-1})|d_{k}\in\overline{\mathbf{D}\cap\mathbf{T}^{\mathrm{opt} }_{j-1}}\} \tag{2}\]  
By connecting the node \(d_{j}\) to \(\mathbf{T}^{\mathrm{opt}}_{j-1}\) through the shortest \(\mathrm{PATH}\left(d_{j},\mathbf{T}^{\mathrm{opt}}_{j-1}\right)\), the updated tree can get the result of \(\mathbf{T}^{\mathrm{opt}}_{j}=\mathrm{PATH}\left(d_{j},\mathbf{T}^{\mathrm{ opt}}_{j-1}\right)\cup\mathbf{T}^{\mathrm{opt}}_{j-1}\); until \(i>q\), go to **Step 4**;  
**Step 6:** Until \(i>q\), go to **Step 7**; otherwise, let \(j=j+1\), then repeat **Step 5**;  
**Step 7:** If \(w_{1}^{\mathrm{N}}(\mathbf{T}^{\mathrm{opt}}_{j-1})<\tau\), find the Steiner generating tree; Else No feasible multicast routing tree.