esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### In Silico Prediction of the Mechanism by Molecular Docking Analysis
Another study investigating the antioxidant activity of thyme oils found that the EO's lipoxygenase inhibition was primarily due to thymol, para-cymene, andlinalo [57]. Similarly, gamma-terpinene was previously reported as an in vitro lipoxygenase inhibitor [58]. Given their highly similar structures, except for para-cymene having an aromatic ring and gamma-terpinene having a cyclohexadiene ring, both compounds exhibit a similar binding pattern within the lipoxygenase binding domain near the iron site, where both compounds interact through multiple hydrophobic interactions with the surrounding amino acids (Figure 3). No relevant literature is available regarding the direct inhibition activity of para-cymene against xanthine oxidase. However, in the case of gamma-terpinene, a study investigating the antioxidant and xanthine oxidase inhibitory activity of various sunflower essential oils discovered the monoterpene, as mentioned above, as one of the EO's major constituents. According to a Pearson correlation analysis, gamma-terpinene was one of the constituents associated with antioxidant and anti-xanthine oxidase activity [59]. Similar to the previous case, both compounds exhibit nearly the same conformation in the active domain of xanthine oxidase, interacting with the same amino acids via hydrophobic interactions. The binding patterns shown by both compounds are depicted in Figure 4. Given that six compounds scored higher than the co-crystallized ligand (the endogenous hypoxanthine) in the case of xanthine oxidase, five of which are major components of OEO, accounting for approximately half of the OEO weight content, we can easily predict that the oil could have a significant inhibitory activity towards xanthine oxidase. Figure 2: Graphical representation of docking scores related to antioxidant protein targets, corresponding to the components above 1% of the OEO content (**A**) and OEO constituents above 5% of the OEO content (**B**) (representing above 65% of the volatile oil); lines are generated for each compound based on the docking scores calculated for each target protein as a percentage of the NL’s docking score used as the positive control (100%); the lines and are plotted as series in a radar chart, where the proteins represent the corners of the chart.
# Configuration Design and Gait Planning of a Six-Bar Tensegrity Robot
## 2 Configuration Design of the Six-Bar Tensegrity Robot
### Balance Matrix  
For tensegrity structures in three-dimensional space, it is assumed that there are 4 nodes, as shown in Figure 2, and Node \(i\) is connected to the other three nodes: \(j\), \(h\), and \(k\).  
When the structure is in equilibrium, any node in the structure should be force-balanced; that is, the resultant force on Node \(i\) is 0. Thus,  
\[\overset{\rightarrow}{t}_{ji}+\overset{\rightarrow}{t}_{hi}+\overset{ \rightarrow}{j}_{ki}+\overset{\rightarrow}{f}=\overset{\rightarrow}{0} \tag{1}\]  
where \(\overset{\rightarrow}{f}\) represents the external force vector, and \(\overset{\rightarrow}{t}_{ji}\) represents the direction from Node \(j\) to Node \(i\). Similarly, \(\overset{\rightarrow}{t}_{hi}\) and \(\overset{\rightarrow}{t}_{ki}\) are not described.  
In the Cartesian coordinate system, the external force vector can be divided into three axial components: \(f_{ix}\), \(f_{iy}\), and \(f_{iz}\), and the internal forces can be expressed by the product of the elongation and the internal force scalar. Therefore, the equilibria of Node \(l\) in the \(x\), \(y\), and \(z\) directions are expressed as  
\[\begin{array}{l}\frac{x_{i}-x_{j}}  
If the tensegrity structure is considered in the d-dimensional space and has \(n\) nodes and \(b\) bars, a matrix of \(dn\times b\) can be obtained, as shown in Equation (4).  
\[\begin{bmatrix}\cdots&0&\cdots\\ \cdots&\vdots&\cdots\\ \cdots&\frac{x_{i}-x_{j}}{l_{ij}}&\cdots\\ \cdots&\frac{y_{i}-y_{j}}{l_{ij}}&\cdots\\ \cdots&\frac{z_{i}-z_{j}}{l_{ij}}&\cdots\\ \cdots&0&\cdots\\ \cdots&\vdots&\cdots\\ \cdots&-\frac{x_{i}-x_{j}}{l_{ij}}&\cdots\\ \cdots&-\frac{y_{i}-y_{j}}{l_{ij}}&\cdots\\ \cdots&\frac{z_{i}-z_{j}}{l_{ij}}&\cdots\\ \cdots&0&\cdots\\ \cdots&\vdots&\cdots\end{bmatrix}\begin{split}\vdots\\ t_{ij}\\ \vdots\\ \vdots\\ \vdots\\ \vdots\\ \vdots\\ \end{split}=\begin{split}\vdots\\ f_{ix}\\ f_{iy}\\ f_{iz}\\ \vdots\\ \end{split} \tag{4}\]  
The above formula can be unified as:  
\[[\mathbf{A}]\{\mathbf{t}\}=\{\mathbf{f}\} \tag{5}\]  
where **[A**]** is the equilibrium matrix, \(\{\mathbf{t}\}\) is the internal force of each element, and \(\{\mathbf{f}\}\) is the external load of the nodes.  
When the external load of the nodes is in equilibrium:  
\[[\mathbf{A}]\{\mathbf{t}\}=0 \tag{6}\]  
Solving Equation (6), the internal force density vector \(\{\mathbf{t}\}\) of each element can be obtained.
## 3 Multi-Objective OPP Model
### The Second Objective Function Considering ANOBC
#### 3.2.3 Impact of ZIB and SCADA  
According to the observability rules proposed in Section 2.3, Equation (11) can be updated to:  
\[\mathbf{M}_{\text{ok}}=\mathbf{A}\mathbf{X}_{\text{ok}}+\mathbf{O}_{\text{o}}, \tag{13}\]  
where \(\mathbf{O}_{\text{o}}\!=\left[\text{o}_{\text{oi}}\right]_{N\times 1}\) is the increased measurement redundancy of ZIB, which is a binary column vector. \(o_{\text{oi}}\) can be calculated as follows according to the observability rules:  
\[o_{\text{oi}}=\sum\limits_{z=1}^{N_{Z}}u_{z,i},\ u_{z,i}=\{0,1\},\ z\in Z, \tag{14}\]  
\[\sum\limits_{i=1}^{N}u_{z,i}=\left\{\begin{array}{l}1\sum\limits_{i=1}^{N} A_{i,z}m_{\text{oi}}\geq\sum\limits_{i=1}^{N}A_{i,z}-1\\ 0\sum\limits_{i=1}^{N}A_{i,z}m_{\text{oi}}<\sum\limits_{i=1}^{N}A_{i,z}-1 \end{array},\ z\in Z,\right. \tag{15}\]  
where \(N_{Z}\) is the number of ZIBs; \(u_{z,i}\) is a 0-1 auxiliary variable, indicating whether bus \(i\) can be observed by the ZIB characteristic of bus \(z\); and \(Z\) is the ZIB number set.  
When the power flow of branch \(p\)-\(q\) is measured by SCADA, we can calculate the voltage phasor at one end according to the voltage phasor at the other end. Thus, the measurement redundancy corresponding to the buses at the ends of branch \(p\)-\(q\) can be updated to:  
\[\begin{array}{l}m_{\text{op}}=\mathbf{A}_{p}\mathbf{X}_{\text{ok}}+o_{\text{op}}+ \left(1-g_{\text{op}}\right)g_{\text{oq}}\\ m_{\text{oq}}=\mathbf{A}_{q}\mathbf{X}_{\text{ok}}+o_{\text{oq}}+\left(1-g_{\text{oq}} \right)g_{\text{op}}\end{array}, \tag{16}\]  
where \(\mathbf{A}_{p}\) and \(\mathbf{A}_{q}\) are row \(p\) and row \(q\) of \(\mathbf{A}\), respectively.  
Buses whose injections can be measured by SCADA have the same characteristics, meaning that these buses can be treated as the ZIBs in the OPP model. As is expected, the realization of network observability combined with SCADA can induce a synchronization error. Therefore, the accuracy of state estimation should be verified after planning. Ifthe accuracy fails to meet the requirements, the considered number of SCADA should be appropriately reduced.
## 6 Showcasing the Implementation of Parametric Event Transformation in a Smart Farming Domain
### An IoT-Enabled Greenhouse: A Brief Description  
We assume that there is a greenhouse with a roof vent through which the air temperature is controlled. When the door is open, warm air escapes from the greenhouse, reducing the temperature of the air in the greenhouse. When the door is closed, the temperature is maintained and gradually increases according to the weather conditions.  
Farmers wish to control greenhouse conditions, in this case, air temperature, by opening or closing the roof vent for a specific or extended period of time. Their decisions are based on their knowledge, experience, and intuition. The goal of this setup is decision  
Figure 5: Event processor topology of Time Windowed Moving Average Calculation Composite Transformation.  
making based on information from the primary data produced by the sensors. Furthermore, automation of the opening or closing of the roof vent is an additional goal that is currently outside the scope of this research.  
To make good and correct decisions, the farmers introduce smart management of the greenhouse by installing six sensors that, among other things, measure the air temperature and an actuator to open and close the roof vent automatically, mechanically, and remotely.  
The data produced by the sensors may be helpful, but more are needed to make a decision. This is because the produced values are raw data that have yet to be transformed into meaningful information with domain-specific semantics. The CEP Model addresses this issue by providing a methodology, along with the functionality, for transforming IoT Physical events, i.e., data from the physical world, into meaningful business domain events, i.e., information and insights with semantics.  
Next, based on the exact setup as shown above, we present some scenarios based on which the user, i.e., the farmer, wants to obtain the greenhouse's temperature. Although for each scenario, the main objective is for the farmer to know the temperature of the greenhouse, the way to calculate the temperature differs. For example, the temperature may refer to the whole greenhouse or a part of it; it may have been calculated differently (e.g., average, last temperature, maximum or minimum temperature, and more), and with a different number from sensors, it can be for a specific time window, and more. The basis for the scenario descriptions are the concepts presented in Section 4.  
It is clarified that each scenario corresponds to a user requirement. Each scenario demonstrates the transformation pipeline in which events generated by sensors installed in the greenhouse are ultimately transformed into meaningful events. Moreover, each transformation is parametric, thus allowing farmers to test different ways of calculating temperature.
## 3 Datasets
### Senti-Cro-CoV-Tweets Dataset  
From the retrieved tweets, we first selected a representative sample of 10,000 tweets and constructed an annotated sentiment dataset, Senti-Cro-CoV-Tweets, with negative, neutral, positive and sarcasm categories. The sentiment annotation was performed in two phases: the goal of the first phase was to obtain the annotation instructions, and the goal of the second was to obtain the annotation of the sentiment in the dataset.  
The first phase was necessary in order to be able to discuss and define a list of common annotation instructions and to instruct the principal annotator. Initially, we selected a random subset of 100 tweets and performed two rounds of annotations. In the first round, six human annotators, including one expert in linguistics, labeled each tweet without being instructed in advance. Prior to annotation, only four labels were determined: Negative, neutral, positive, and sarcasm. Note that later in the experiments, we relabeled all sarcastic tweets as negative, but initially, sarcasm was regarded as a separate category. The consistency of initial annotations provided by six annotators is assessed by Inter-Annotator Agreement (IAA) in terms of Fleiss' kappa coefficient. Fleiss' kappa measures how well multiple annotators can make the same annotation decision in an annotation category. In the first round, the IAA was 0.33, which is a fair agreement (please note: the Fleiss' kappa ranges from 0 to 1: values lower than 0.4. imply fair agreement, between 0.41 and 0.60--moderate, above between 0.61 and 0.8--substantial, above 0.81--almost perfect agreement [58].) After the first round of independent annotations, six annotators discussed at the level of individual instances the argumentation for the annotation category and identified vague points of annotation, and subsequently resolved possible issues and doubts. This resulted in a final list of detailed instructions for sentiment labelling which enabled training the principal annotator according to the instructions. Next, in the second round of annotations, all six annotators and the new annotator annotated 100 tweets according to the set of instructions and achieved an IAA of 0.62. At this stage, we had agreement upon the variations and nuances in the annotation.  
Figure 1: Frequency of COVID-19-related tweets during the three pandemic waves.  
In the second phase, the rest of the 10,000 tweets were annotated by the principal annotator in accordance with the agreed instructions with the support of the linguistic expert. After the annotation procedure, the distribution of sentiment was as follows: 4914 neutral tweets; 3730 with negative sentiment, 475 with positive sentiment and 841 tweets annotated as sarcasm (which we treat as a negative sentiment). In the initial dataset, there were also 40 tweets in English language which are not included in the final version of the Senti-Cro-CoV-Tweets dataset, so the size of the Senti-Cro-CoV-Tweets dataset is 9960 tweets.
## 4 Experiments and Analysis
### Model Evaluation Indicators  
For this study, an indicator test of the text classification model was carried out. The model evaluation indicators of the classification algorithm are often measured using the confusion matrix, as shown in Table 4. The data obtained from the confusion matrix are extended by calculation to obtain four secondary metrics--accuracy, precision, recall, and F1-score--which are the core metrics for evaluating classification models [49].  
The accuracy of a classification model represents the ratio of samples correctly predicted by the model for all samples. In general, the higher the accuracy, the better the classifier. The accuracy is calculated as follows:  
\[\text{Accuracy}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{ FN}} \tag{8}\]  
The precision of a classification model is defined as the percentage of samples with true positive class among all samples predicted to be positive class, calculated as:  
\[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
The recall of a classification model is defined as the percentage of samples with true positive classes that are correctly predicted, and the formula is as follows:  
\[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
The F1-score is the summed mean of precision and recall (see Equation (8)), which combines the precision and recall results and is closer to the smaller of the two; therefore,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predict True** & **0** & **1** \\ \hline
0 & TP & FN \\
1 & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{X}\backslash\mathbf{Y}\) & **1** & **2** & **3** & **4** \\ \hline
1 & **0.9039** & **0.9039** & **0.9039** & **0.9039** \\
2 & 0.8981 & **0.9039** & **0.9039** & **0.9039** \\
3 & 0.8981 & 0.8995 & **0.9039** & **0.9039** \\
4 & 0.8872 & 0.8981 & 0.8995 & **0.9039** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results with the weighted parameters, with the bold represents the best experimental results.  
when the precision and recall are close, the F1-score is large. A higher value of F1 indicates a better model prediction effect.  
\[\text{F1}= \frac{2\text{TP}}{2\text{TP}+\text{FP}+\text{FN}} \tag{11}\]
# Decision-Time Learning and Planning Integrated Control for the Mild Hyperbaric Chamber
## 3 Model Learning  
In sequential decision making, environment dynamics are typically formulated as a Markov Decision Process (MDP) [20]. We define MDP in MHBC control as a tuple \(\langle S,A,P,R,\gamma\rangle\), where \(S\) represents the state space; A represents the action space; and \(P\) is the state transition function, describing the probability of transitioning to state \(s^{\prime}\) after taking action \(a\) in state \(s\). \(R\) is the reward function, which depends on the current state \(s\) and action \(a\), and \(\gamma\) is the discount factor, determining the importance of future rewards. Given a predefined state space, action space, and discount factor, the core components of the environment model are the state transition function \(P\) and the reward function \(R\). In MHBC control, the reward function \(R\) is explicitly defined. Therefore, the model learning process focuses on learning the state transition function \(P\).  
Previous system identification approaches for MHBC control have primarily relied on physics-based mathematical models. However, in real-world scenarios, fluid dynamics within the chamber are highly complex and influenced by unknown factors. These factors make it challenging to achieve accurate system identification using purely physics-based formulas. With advancements in deep learning, parameterized deep neural networks have demonstrated the ability to approximate complex system dynamics more effectively. In this chapter, we propose a deep neural network-based method that leverages state context representation to enhance model learning. Additionally, we introduce optimizations for model outputs, improving the accuracy and robustness of the learned environment model.
## 4 Methodology  
This study involved five distinct stages. The first of these was calculating the satellite positions over Maynooth University, the second involved extracting 2D polygons of the campus, followed by creation of a surface model of the campus. In the fourth stage, the 3D Analyst viewshed functions were applied to ascertain visibility and, in the final stage, the accuracy of the prediction results were assessed.
## 3 Damage-Healing Formulations
### Fracture Mechanics Based Healing Model  
A crack closing model applied on linear and isotropic viscoelastic materials was developed by [PERSON] [93]. Time-dependent constitutive equations based on continuum mechanics were proposed in which the crack length and contact size are predicted, and the whole healing process is considered. The crack healing model was based on crack area reduction \(d_{b}\), which is related to the Poisson's ratio, fracture process zone, effective bond energy and the tensile bond force. \(\dot{a}_{b}\) is expressed as follows:  
\[\dot{a}_{b}=\pi\left[4\Gamma_{b}^{{}^{\prime}}\right]^{\left(2+1\right)/m} \left[\left(1-v^{2}D_{1}^{+}\gamma_{m}/C_{m}\right)\right]^{1/m}\left[E_{R}^{ +}/\left(1-v^{2}\right)K_{I}^{R}\right]^{2\left(1+1/m\right)}/8\sigma_{b}^{2} C_{m}^{2}, \tag{17}\]  
where \(\Gamma_{b}^{{}^{\prime}}\), \(E_{R}^{+}\), \(\sigma_{b}\), \(v\) and \(K_{I}^{R}\) are the effective bond energy, elastic modulus, tensile bond force, Poisson's ratio and mode I stress intensity factor. \(\gamma_{m}\), \(C_{m}\), \(D_{1}^{+}\) and m are material constants. The crack closing model is known by the fact that there is no difference between the crack closing based on wetting and on diffusion. Moreover, the model is formulated based on different materials that make it difficult to be realized in the case of anisotropic material.  
Figure 4: Log of healing based on fracture load recovery [92]. (Copyright, 1981, Journal of Applied Physics).  
Figure 3: Five stages of healing of two random-coil chains on opposite crack surfaces [92]. (Copyright, 1981, Journal of Applied Physics)  
Based on the healing variable proposed in [92] and the crack area reduction in Equation (17) [93], a new formulation was proposed in [94; 95] in which they used the variable \(R\) to simulate the healing effect in bituminous materials as follows:  
\[\frac{d\phi\left(t,X\right)}{dt}=\dot{a}_{b}=\beta\left[\frac{1}{D_{1}k_{m}} \left\{\frac{\pi W_{c}}{4\left(1-v^{2}\right)\sigma_{b}^{2}\beta}\right\} \right]^{-1/m}, \tag{18}\]  
where \(W_{c}\), \(\beta\) and \(k_{m}\) are the work of cohesion, the healing process zone and material constant, respectively. The rest of the parameters are defined previously in Equation (17). It should be noted that the parameters related to the proposed Equation (18) are difficult to be identified due to the lack of enough experimental data.
## 3 Overview of Routing Protocols  
Over the years, several routing protocols have been proposed for ad hoc networks. The existing routing protocols can be mainly classified into three categories as shown in Figure 1: proactive routing protocols, on-demand routing protocols and hybrid routing protocols [10]. In this work, we will go through more evaluation of the classical routing protocols and the proactive and the reactive types while more performance metrics and network parameters are involved.
## 3 Results
### Integration of Various Remote Sensing Metrics  
Based on the characterization of various metrics and AIC values, DHI-min, DHI-cum, DHI-sea, GPP, CV, ET, and LST were selected as the major metrics for the development of an integrated biodiversity index. For the AIC values, the RF model gave the lowest AIC for species richness of the three animal classes when compared to SVM and NN (Figure 6). The species richness data of each animal class predicted by the same machine learning model were quite similar, and RF exhibited a higher r-square value than SVM and NN in species richness of all three animal classes (Figure 7). The r-square values of mammals, birds, and amphibians were 0.76, 0.76, 0.77, respectively, according to the RF model; 0.7, 0.71, and 0.66, respectively, according to the SVM model; and 0.67, 0.67, and 0.67, respectively, according to the NN model. In terms of the root mean squared error (RMSE), the prediction of the RF model exhibited a lower RMSE than the other two models for all three animal classes. Overall, the RF model outperformed the other two models for species richness prediction. We used the RF model to integrate the seven metrics and develop a multivariate integration index.  
The multivariate integration index exhibited a higher explained variance than the univariate metrics, and exhibited more than 76% explained variance for the species richness of the three animal classes on a global scale. For a single animal class, the gap between the multivariate integration  
Figure 5: Geographic distribution of DHI-cum, DHI-min, mammal richness. (**a**) North America, (**b**) Argentina and Chile, (**c**) Amazon.  
index and the univariate metrics was more than 30% (Figures 3 and 7). On the whole, the simulated species richness showed a consistent spatial pattern with in situ richness (Figure 8). At regional scales, the simulated species richness was in good agreement with in situ species richness in most regions. However, the differences between simulated species richness and in situ species richness were observed. For mammals, simulated species richness was higher than in situ species richness in 40\({}^{\circ}\)-60\({}^{\circ}\)S zones and 120\({}^{\circ}\)-140\({}^{\circ}\)E, and simulated species richness was lower than in situ species richness in 0\({}^{\circ}\)-10\({}^{\circ}\)N and 30\({}^{\circ}\)-60\({}^{\circ}\)W. For amphibians, simulated species richness was lower than in situ species richness in 30\({}^{\circ}\)-60\({}^{\circ}\)W. Although disagreements between simulated species richness and in situ species richness were found in some regions, the differences were relatively small in magnitude.  
Figure 6: The Akaike information criterion (AIC) values of the three models.  
Figure 7: The correlation between predicted species richness and in situ species richness. (**a**) training dataset, (**b**) prediction dataset. The measured values were obtained for 10,000 0.1-degree pixels randomly selected from terrestrial areas of the globe, and 5000 pixels were randomly selected for training and prediction separately.