esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Application Layer
#### 3.1.7 WebSocket  
Established by the Internet Engineering Task Force (IETF) in 2011, WebSocket facilitates continued full-duplex communication over a single TCP connection. Initially designed for web servers and web browsers, WebSocket has gained increasing popularity in IoT applications [64, 65]. Contrasting HTTP's request-response model, WebSocket supports both the request-respond and publish-subscribe model using real-time data transfer, thus enabling servers to provide continuous updates to clients via an ongoing WebSocket connection. This dynamic capability is particularly important in IoT applications and is integral for V2I applications requiring sustained data exchange for real-time analytics and decisions. The real-time data transfer capability of WebSocket is particularly valuable in V2I use cases requiring bi-directional communications and interaction between vehicles and infrastructural elements like roadside units or traffic control centres [66].  
For instance, WebSocket is suitable for facilitating instantaneous relay of traffic light status to oncoming vehicles, enabling them to adjust their speed or plan alternate routes, thereby improving traffic efficiency and safety. Refer to Figure 8(a) for publish and subscribe architecture, Figure 8(b) for request and respond architecture, and Table 1 for the characteristics of application layer protocols.
# Visualization of Distributed Plasticity in Concrete Piles Using OpenSeesPy
## 1 Introduction and Background
### State of the Art in Fiber Modeling and Visualization
Fiber-based models are thus often reserved for specialized analyses, where detailed representation of axial-flexural interaction, post-yield behavior, and damage evolution are necessary. The visualization of fiber model results has also advanced. Tools such as VFO (Visualization for OpenSees) [39] and custom Python-based scripts now allow engineers to extract and display strain distributions, monitor the formation of plastic zones, and assess compliance with performance criteria in design standards. Traditional commercial FEM platforms typically emphasize force and displacement outputs, which may suffice for building and bridge design, but fall short for wharf structures that require tracking material-level strain states. In models relying on lumped hinges, strain-based limit states must be inferred, while solid-element approaches are computationally expensive and often require manual extraction of strain data from critical sections. The integration of Python with structural analysis platforms has substantially enhanced the flexibility and efficiency of numerical modeling workflows. OpenSeesPy [40], the Python interface to the OpenSees framework [41], leverages Python's extensive scientific ecosystem to streamline model development, execution, and post-processing. In addition to its built-in constitutive models, OpenSeesPy supports the implementation of user-defined materials, allowing researchers to incorporate advanced material formulations. This integration has led to the development of open-source toolkits that simplify numerical model creation, meshing, load application, and both pre- and post-processing tasks [42; 43]. In parallel, more advanced frameworks have emerged, incorporating machine learning techniques and structural optimization capabilities to further expand analytical possibilities [44; 45]. This synergy enables the creation of tailored 2D and 3D representations using packages such as matplotlib and pyvista, facilitating intuitive interpretation of fiber response. Moreover, both OpenSeesPy and Python are open-source, promoting accessibility and customization. These capabilities offer a compelling solution for advanced yet practical performance-based assessment of RC wharf structures, bridging the gap between academic rigor and engineering practice.
## 6 Actor-Critic Methods
### Soft Actor-Critic (SAC)  
Soft Actor-Critic (SAC), introduced by [PERSON] et al. [2], is an off-policy reinforcement learning algorithm that combines maximum entropy reinforcement learning with actor-critic methods. The primary goal of SAC is to optimize a stochastic policy by maximizing both the expected cumulative reward and the entropy of the policy, encouraging exploration and robustness to model uncertainty. The objective of SAC can be expressed as follows:  
\[J(\pi)=\mathbb{E}_{\pi}\left[\sum_{t=0}^{\infty}\gamma^{t}(r(s_{t},a_{t})+ \alpha\mathcal{H}(\pi(\cdot|s_{t})))\right], \tag{208}\]  
where \(\mathcal{H}(\pi(\cdot|s_{t}))=-\mathbb{E}_{a\sim\pi}[\log\pi(a|s_{t})]\) is the entropy of the policy, \(\alpha\) is the temperature parameter that balances exploration (via entropy maximization) and exploitation (via reward maximization), and \(\gamma\) is the discount factor. SAC employs two key networks: a stochastic policy \(\pi(a|s;\theta)\) parameterized by \(\theta\), and two Q-value functions \(Q_{\phi_{1}}(s,a)\) and \(Q_{\phi_{2}}(s,a)\) parameterized by \(\phi_{1}\) and \(\phi_{2}\), respectively. The use of two Q-value functions addresses the overestimation bias typically observed in value-based reinforcement learning methods. The target Q-value is computed as follows:  
\[y_{t}=r(s_{t},a_{t})+\gamma\mathbb{E}_{s_{t+1}\sim\mathcal{P}}\big{[}\min(Q_{ \phi_{1}}(s_{t+1},a_{t+1}),Q_{\phi_{2}}(s_{t+1},a_{t+1}))-\alpha\log\pi(a_{t+1} |s_{t+1})\big{]}. \tag{209}\]  
The Q-value networks are updated by minimizing the Bellman residual:  
\[L_{Q}(\phi_{i})=\mathbb{E}_{(s_{t},a_{t},r_{t},s_{t+1})\sim\mathcal{D}}\Big{[} \big{(}Q_{\phi_{i}}(s_{t},a_{t})-y_{t}\big{)}^{2}\Big{]}, \tag{210}\]  
where \(\mathcal{D}\) is a replay buffer storing transitions. The policy \(\pi(a|s;\theta)\) is optimized to maximize the expected entropy-augmented reward:  
\[J_{\pi}(\theta)=\mathbb{E}_{s_{t}\sim\mathcal{D}_{\mathcal{H}}\sim\pi_{ \theta}}\big{[}\alpha\log\pi(a_{t}|s_{t})-Q_{\phi}(s_{t},a_{t})\big{]}. \tag{211}\]  
The temperature parameter \(\alpha\) can be adjusted dynamically to control the trade-off between exploration and exploitation. The temperature is updated by minimizing the objective:  
\[L_{\alpha}(\alpha)=\mathbb{E}_{a_{t}\sim\pi_{\theta}}\big{[}-\alpha\log\pi(a _{t}|s_{t})-\mathcal{H}_{\text{target}}\big{]}, \tag{212}\]  
where \(\mathcal{H}_{\text{target}}\) is the desired minimum entropy. The step-by-step pseudocode for the SAC algorithm is presented in Algorithm 11. SAC has been shown to outperform traditional reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) in tasks involving high-dimensional continuous action spaces. Its ability to encourage exploration through entropy maximization makes it particularly effective in environments with sparse rewards and high uncertainty.
## 4 Proposed Model
### Bidirectional Gated Recurrent Unit for Classification  
In traditional neural network and CNN models, weights are updated during backpropagation, due to which the problem of vanishing gradient and exploding gradient occurs. To resolve these issues, LSTM and GRU are used as advanced versions of the Recurrent Neural Network (RNN). However, LSTM has some limitations over GRU. LSTM has more parameters, besides being time-consuming and less efficient. It needs more data for generalization. Therefore, GRU is considered a better classification model as compared to the traditional neural networks, CNN, and LSTM models. Figure 7 shows the overall architecture of GRU.  
Figure 6: Smote Over Sampling Tomik Link (SOSTLink) sampling.  
GRU is proposed by [PERSON] et al., which is the advanced version of RNN [38]. It easily learns long-term dependencies and resolves the problem of vanishing gradient [39]. The structure of GRU is slightly different from LSTM. LSTM consists of three gates, whereas GRU consists of two gates. Update and reset are the two gates of GRU. The update gate is the combination of input and forget gate of LSTM, and the reset gate is applied directly to the previous hidden state. Thus, GRU has fewer parameters, faster training process, and requires less data for generalization. For short-term dependencies, the reset gate is activated, and for long-term dependencies, the update gate is used. GRU uses a combination of both gates, so input sequences are passed through the deep network and all the gradients are kept. The relationship between the input and output gates is described in Equations (4)-(7).  
\[u_{(t)}=\sigma(W_{u}x_{t}+W_{u}h_{t-1}+b_{u}) \tag{4}\]  
\[h_{(t)}=tanh(W_{\cdot}[r_{t}]*h_{t-1}+Wx_{t}) \tag{5}\]  
\[h_{(t)}=(1-u_{(t)})*h_{t-1}+u_{t}*h_{t} \tag{6}\]  
\[r_{(t)}=\sigma(W_{r}x_{t}+W_{r}h_{t-1}+b_{r}) \tag{7}\]  
where \(\mathrm{r}(t)\) is the reset gate, \(\mathrm{u}(t)\) is update gate, and \(\mathrm{W}\) is parameter. \(\sigma\) is the sigmoid function and \(\tanh\) is hyperbolic tangent function.  
BGRU is used in [40] for natural language processing.We get our motivation from work in [40] and used BGRU in our proposed model as shown in Figure 8. Bidirectional GRU is the latest version of bidirectional RNN. To make a prediction of a current observation, it uses information from the previous time step and forward time step. In GRU, information flows from left to right by computing each value. The output of GRU is passed to BGRU as input. In the final prediction, information flows from right to left starting from final time step and moving to the initial time step. In our proposed model, we use five layers: GRU, BGRU, flatten, dropout, and dense. We use 100 neurons in GRU layer and 50 neurons in the bidirectional layer. Moreover, the flatten layer is used to convert multidimensional data into one-dimensional data.
## 1 Introduction
### Objective  
To overcome the problems of the algorithms discussed above, it is necessary to develop a new algorithm that is efficient, operational, and relatively simple. We use variational tempering to meet these requirements. In this work, we consider the channel estimation problem of the pilot signal and channel coefficients, assuming there is orthogonal access between the different sensors and the data fusion center. To reduce the complexity of the direct joint posterior density maximization, we propose a simpler VI algorithm that solves the channel estimation problem, adopting the concept of tempering. By formulating the channel estimation problem into a probabilistic graphical model, CEVTI can estimate the channel coefficiency and the transmitted signal in a low-complexity manner while simultaneously guaranteeing convergence at the same time. Simulations show that CEVTI has higher accuracy than state-of-the art algorithms.  
The main objective of this paper is to obtain channel coefficiency by making the best use of trained pilot signals. To achieve this objective, we have performed the following tasks:  
1. Modeling of the channel estimation problem into a variational message passing algorithm;
2. Evaluation of the performance error bound of our novel CEVTI algorithm;
3. Use of a Monte Carlo simulation to verify the bit error rate (BER), convergence rate, and mutual information of the CEVTI approach; and
4. Established the efficiency and superiority of our new algorithm, CEVTI.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 4 Applications
### Tai-Tung Fire Bureau Building
#### 4.2.2 Results  
Table 7 presents the accuracy values for the three tasks performed showing higher values for Test set-1 than Test set-2. The models performed well for damage detection with respective 90% and 80% accuracy values. Both OLR models had labeled event #3 as a damaged case. This is interesting because even though there was no reported visual damage for event #3, [PERSON] and [PERSON] [48] stated the possibility of nonlinearity during this event. For the damage assessment task, the accuracy values for Test set-1 and Test set-2 were 80% and 70%, respectively. The \(\text{OLR}_{\text{X}}\) model accurately detected the severe damage state (major damage as described in Section 2.1.3) of event #5 for sensor 19. It labeled the damage state of sensor 16 as moderate (moderate damage as described in Section 2.1.3) and indicated that damage was present for event #3. \(\text{OLR}_{\text{Y}}\) assessed the damage state of event #5 for sensor 20 as moderate and detected minor damage for event #3.  
Figure 9 presents the confusion matrix for the two test sets to better understand the performance of \(\text{OLR}_{\text{X}}\) and \(\text{OLR}_{\text{Y}}\) models. It shows \(\text{OLR}_{\text{X}}\) correctly detected all the damaged responses as damaging. However, it misclassified one response from event #3 as damaged. On the other hand, \(\text{OLR}_{\text{Y}}\) misclassified both damaged and undamaged responses falsely labeling one of the responses of event #5 as undamaged. Hence, the accuracy for this model was lower compared to \(\text{OLR}_{\text{X}}\) for both the damage detection and damage assessment tasks.  
For damage location, the first story had been identified as the location of the damage for event #5 which was consistent with the observed damages [48]. Per Equation (21), the location was determined for any sample indicating damage. Hence, the model determined damage on the first story for event #3. This reduced the location detection accuracy values for both \(\text{OLR}_{\text{X}}\) and \(\text{OLR}_{\text{Y}}\) models to 67% and 50%, respectively (Table 7). As stated before, no visible damage had been identified for event #3 but the present study provided  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Damage Detection** & **Damage Assessment** & **Damage Location** \\ \hline
**Test set-1** & 90.0 & 80.0 & 66.7 \\
**Test set-2** & 80.0 & 70.0 & 50.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Damage detection, assessment, and location identification tasks accuracy (%) for TFB Building.  
Figure 9: Confusion matrices for two test sets of TFB building to evaluate (**a**) \(\text{OLR}_{\text{X}}\) and (**b**) \(\text{OLR}_{\text{Y}}\).  
further evidence that the structure was most probably weakened by event #3 and became vulnerable to damage for subsequent earthquake events, especially #5 listed in Table 6.
## 2 Materials and Methods
### Statistical Analysis and Mathematical Validation Model  
A fourth-order polynomial multiple regression method [37] was used to obtain a mathematical model capable of characterising the electrical bioimpedance measurements of adulterated milk samples:  
\[|Z_{L}|\ =\ Y\ =\ \beta_{00}+\beta_{10}X_{1}+\beta_{01}X_{2}+\beta_{20}X_{1}^{ 2}+\beta_{02}X_{2}^{2}+\beta_{03}X_{2}^{3}+\beta_{04}X_{2}^{4}+\beta_{14}X_{1} X_{2}^{4} \tag{2}\]  
where the dependent variable \(Y\) corresponds to the magnitude of the electrical bioimpedance (\(|Z_{L}|\)) and the independent variables \(X_{1}\) and \(X_{2}\) represent the temperature (\({}^{\circ}\)C) and dilution (percentage of water) of the sample, respectively. The regression coefficient \(\beta_{00}\) represents the constant portion of the model, \(\beta_{10}\) and \(\beta_{01}\) the linear effect of temperature and dilution, respectively, and \(\beta_{02}\) and \(\beta_{20}\) their quadratic effects. The coefficients \(\beta_{03}\) and \(\beta_{04}\) represent the cubic and fourth-order dilution effects, and finally, \(\beta_{14}\) the interaction effect. The regression polynomial of Equation (2), the value of its coefficients, and the statistical analysis of the experimental data were determined from the Minitab software (version 16; State College, PA, USA), at low (10\({}^{1}\) Hz), medium (10\({}^{3}\) Hz), and high frequency (10\({}^{6}\) Hz). The statistically non-significant terms of the complete polynomial were gradually excluded from the model to improve the quality of the data fit, as well as the terms limited by the degrees of freedom of the variables. For the validation of the model, varied samples with different water percentages were analysed at random temperatures (between 16 and 37 \({}^{\circ}\)C) and frequencies (10\({}^{1}\), 10\({}^{3}\), and 10\({}^{6}\) Hz).
# Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools
## 3 Survey Methodology
### Collating Research-Informed Ethical Hacking Tools  
The inclusion of 100 EH tools was driven by the aim of achieving a balance between depth and breadth in our analysis. The selection process was systematic and rigorous, inspired by the PRISMA methodology [37], with identification, screening and inclusion phases.  
Initially, we initiated a collaborative research project involving cohorts of students from the MSc Cybersecurity program at Teesside University (UK). Despite the absence of a dedicated module on EH in their curriculum, the students showed considerable interest in working within this domain. This project allowed them to integrate EH professionalism with their research interests.  
The students' initial submissions yielded over 200 academic references. However, after a careful review process conducted by the authors, approximately 30 tools aligned with the research scope and were included in this paper. Many tools found by the students were excluded for these reasons:  
* Difficulty of the students in distinguishing between research-informed and practitioner tools.
* Confusion between papers describing the design and implementation of a tool and those describing its application.
* The approach of identifying tools first and then searching for papers to support the findings leads to the above misconceptions.  
Following the criteria outlined in Section 3.1, the authors expanded the total count of EH tools to 185. These tools were then resampled, and 100 tools were finally selected. The final selection was based not only on adherence to the criteria in Section 3.1 but also on the fact that these tools were not merely applications of existing methodologies, frameworks, or aggregations of practitioner tools.  
In fact, among the 85 candidates excluded in the final round, 28 focused on mitigation tools and techniques, 18 on methodologies and frameworks, 14 on the application of practitioner tools, 13 were surveys, 5 addressed socio-technical aspects, 4 were simulation tools, and 3 focused on education.  
For details of the 100 tools surveyed in this paper, see Table 1.  
Moreover, as discussed in Section 6, to include a significant number of tools that could reflect the current state-of-the-art, we had to relax on the criteria of availability of the source code. Therefore, in this survey, we considered 41 tools that satisfy all other criteria, but no source code has been published.
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 5 Discussion
### Future Directions  
The advent of technological advancements has created room for significant improvements in green hydrogen production and is shaping the future of integrated solar, wind, and green hydrogen systems. The adoption of hydrogen-based technology presents substantial opportunities for economic growth, improvement in human well-being, and addressing pressing environmental concerns. Recently, there has been a growing interest in the method of water electrolysis for hydrogen generation, driven by technological advancements and the accessibility of cost-effective power sources. Emerging trends such as floating offshore renewable energy platforms, advanced AI-driven control systems, and solid-state electrolysis technology are opening up new possibilities for more efficient and cost-effective hydrogen production. The expansion of hydrogen applications beyond energy storage to include industrial processes, synthetic fuel production, and transportation is creating new markets and improving the economic case for green hydrogen. Also, through hydrogen-based energy storage for grid stabilization, large-scale hydrogen storage systems can provide long-duration energy storage to stabilize grids with a high penetration of renewable energy. The incorporation of hydrogen in steel production as a reducing agent could significantly reduce carbon emissions and the overall energy consumption.  
The hydrogen market's growth depends on future hydrogen prices, technology advancements, potential greenhouse gas restrictions, and alternative energy costs. Hydrogen shows promise as a future fuel due to various social, economic, and environmental factors, potentially reducing oil imports and vehicle emissions. The current research focuses on hydrogen synthesis from diverse sources and improving storage and transportation methods. While the experimental results are promising, their large-scale feasibility needs careful evaluation. The urgent need for clean energy is clear, but transitioning from fossil fuels faces significant political and economic challenges. The shift to renewable energy, including hydrogen, will be gradual due to the extensive restructuring required in global energy infrastructure, which underpins the world economy and power dynamics.
## Nomenclature  
Parameters  
\(T\)Total number of time slots in a scheduling day  
\(\Delta t\)Time interval  
\(\lambda_{I}^{TR}\)Time-of-day tariffs at time \(t\)  
\(\lambda_{Loss}\)Cost coefficient for network losses  
\(\lambda_{I}^{\text{VSC,DC}}\)Interaction tariff at time \(t\)  
\(c_{gp}\)Operational cost coefficient of ESS  
\(r_{ij}^{\text{AC}}\), \(x_{ij}^{\text{AC}}\)Resistance and reactance of AC branch \(ij\)  
\(r_{ij}^{\text{DC}}\)Resistance of DC branch \(ij\)  
\(r_{j}^{\text{VSC}}\), \(x_{j}^{\text{VSC}}\)VSC resistance and reactance at node \(j\)  
\(\mu\)DC voltage utilization rate  
\(S_{j}^{\text{VSC}}\)VSC capacity at node \(j\)  
\(P_{ijR}^{load}\), \(Q_{ijR}^{load}\)Active and reactive load at node \(j\) at time \(t\)  
\(\text{P}^{TR,min}\), \(P^{TR,max}\)Lower and upper limits of active power purchased  
\(Q^{TR,min}\), \(Q^{TR,max}\)Lower and upper limits of reactive power purchased  
\(P_{ij}^{\text{AC,min}}\), \(P_{ij}^{\text{AC,max}}\)Lower and upper limits of active power on branch \(ij\)  
\(Q_{ij}^{\text{AC,min}}\), \(Q_{ijR}^{\text{AC,max}}\)Lower and upper limits of reactive power on branch \(ij\)  
\(U_{I}^{\text{AC,min}}\), \(U_{I}^{\text{AC,max}}\)Lower and upper limits of voltage magnitude at node \(j\)  
\(I_{ij}^{\text{AC,min}}\), \(I_{ij}^{\text{AC,max}}\)Lower and upper limits of current on branch \(ij\)  
\(P_{cb}\),\(max\),\(\text{CSES}\), \(p_{dcb}\),\(max\),\(\text{CSES}\)Maximum charging and discharging power of the entire CSES  
\(P_{e}^{ch,max}\),\(\text{CSES}\), \(P_{e}^{ch,max}\),\(\text{CSES}\)Maximum charging and discharging power limit for subnetwork  
\(P_{j}^{ch,max}\),\(\text{ESS}\), \(P_{j}^{ch,max}\),\(\text{ESS}\)Maximum charging and discharging power limit for the ESS at node \(j\)  
\(E^{min}\),\(\text{CSES}\), \(E^{max}\),\(\text{CSES}\)Minimum and maximum CSES SOC  
\(E^{min,\text{ESS}}\), \(E^{max}\),\(\text{ESS}\)Minimum and maximum ESS SOC  
\(\eta_{dcb}\), \(\eta_{dcb}\)Charging and discharging efficiency  
\(\rho_{k}\)\(K\)th penalty parameter  
\(\varepsilon\)ADMMConvergence threshold