esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Docking Studies  
Considering the several pieces of evidence establishing that RSV is able to inhibit the invasion and migration events in different tumors, which involve dramatic reorganization of the cytoskeleton components [16], first, we performed docking studies of RSV derivative **2** on two cytoskeleton major targets, namely tubulin and actin. We initially analyzed the binding of derivative **2** with tubulin by performing docking simulations using the 3D structure of tubulin as the protein target. Binding energies of the complex formed by the compound and the protein were calculated by Autodock, and their affinity was obtained using the expression Ki = exp (deltaG/(R*T)). As already reported by us [33; 34; 35], the outcomes from our simulation runs were clusterized and the analysis of the clusters and the visual inspection of the ligand:protein binding site were considered a marker for the quality of the interaction.  
In this specific case, the derivative **2** binds, with good energy (\(-9.02\) kCal/mole), the interface formed between the subunits \(\alpha\) and \(\beta\) of the tubulin tetrameric assembly, in correspondence with the binding site of Vinblastine (Figure 2). In particular, this compound forms hydrogen bonds with residues Glu \(\alpha\)254, Asp \(\beta\)179, Tyr \(\beta\)224, and hydrophobic interactions with amino acids Leu \(\alpha\)248, Pro \(\alpha\)25, Val \(\alpha\)328, Val \(\alpha\)353, and Tyr \(\beta\)210, Pro \(\beta\)222, Tyr \(\beta\)224. Our compound is fully superposed to the binding site of Vinblastine, thus we can assess that the derivative **2** can inhibit the regular polymerization process of tubulin.  
Then, we analyzed the binding of the derivative **2** to actin using the three-dimensional structure of actin as the protein target [19]. The derivative **2** also binds with a good energy (\(-7.36\) kcal/Mole) to a cleft in the proximity of the Profilin binding area (Figure 3). This compound, therefore, could favor and stabilize the formation of Actin:profilin complex, hampering the correct mechanism of cellular remodeling and plasticity [36]. In deeper detail, the derivative **2** creates hydrogen bonds with actin residues Arg 116 and His 371 and with Profilin residues Arg 74, Asn124, and Lys 125. This binding is further stabilized by hydrophobic interactions with residues Pro 109, Leu 110, Pro 112, Ile 136, Tyr 169, Pro 172,and a \(\pi\)-\(\pi\) stacking with Phe 375 belonging to actin and with Ile 73 and Tyr 128 belonging to Profilin.  
Figure 2: (**A**) Tetrameric alpha-beta-alpha-beta (alpha represented as salmon ribbons, beta as purple ribbons) tubulin assembly. The binding site of derivative **2** (cyan sticks) is located at the interface between beta and alpha subunits, in correspondence to the Vinblastine binding site (highlighted as green sticks). (**B**) Reports the superposition of the Vinblastine (green) binding site (as determined by X-ray Crystallography) to that of the derivative **2** (cyan), as determined by our simulations. (**C**) Reports the interactions between the protein assembly and the derivative **2**.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 4 Experimental Results and Analysis
### Cluster Building Time  
This is the time taken by an algorithm to perform clustering. A clustering algorithm takes nodes and their corresponding fitness value as input, and elects CH and their associate members as output. The time elapsed between taking input and producing output is termed the cluster building time. This time denotes the computational complexity of clustering algorithms.  
UAV nodes have very low computational power and memory. High cluster building time adversely affects task-oriented performance (sensing and aggregating environmental data). It also consumes more energy and reduces UAVs' lifetime. Figures 7 and 8 shows that increase in the number of nodes will also increase the cluster building time but EALC is much better than ACO and GWO. The reason behind is that ACO and GWO start with multiple random solutions and iteratively converge towards the optimal solution, whereas, EALC generates only one solution and only adds those nodes that have the highest fitness into a solution. This short cluster building time reduces the delay to find a route. It also saves the nodes energy that is spent during complex computations.  
Figure 8: Cluster building time at a 3000 \(\times\) 3000 m\({}^{2}\) grid size.  
Figure 7: Cluster building time at a 2000 \(\times\) 2000 m\({}^{2}\) grid size.
## 4 Experiments and Results
### Distance Prediction
Figure 7: Graphic of the \(RMSE\) (initial distance on the left and final distance on the right) of the proposed methods with \(N_{0}\) between \(-114\) dBm\(/\)Hz and \(-174\) dBm\(/\)Hz. extra trees showed similar performance. However, transformer exhibited slightly better performance than the others, achieving 31.25. In Table 7, the general metrics for all the proposed regression models are presented for the initial distance prediction. It is worth noting that, in all the metrics, random forest, XGB, LGBM, transformer, and extra trees exhibited the highest and similar performance. Transformer performed better in correlation coefficient, \(MAPE\), and \(R^{2}\), demonstrating a high level of correlation between the predictions and the actual values of distances. In terms of \(MSE\) and \(RMSE\), LGBM achieved the lowest values, indicating that the differences between the predictions and actual values are small. The lowest \(MAE\) values were exhibited by XGB. In Table 8, general metrics for all the proposed regression models are presented for final distance prediction. Extra trees exhibited the best performance in all the metrics except for \(MAPE\), \(R^{2}\), and correlation coefficient, where transformer exhibited the best performance.
# Fault Classification and Localization Scheme for Power Distribution Network
###### Abstract  
In this paper, a fault protection diagnostic scheme for a power distribution system is proposed. The scheme comprises a wavelet packet decomposition (WPD) for signal processing and analysis and a support vector machine (SMV) for fault classification and location. The scheme is tested on a reduced Eskom 132 kV power line. The WPD is used to extract fault signatures of interest and the SVM is subsequently used for fault classification and locating various fault conditions. Furthermore, we investigate the effectiveness of the SVM scheme using different samples of the cycles for fault classification and location. The results show that the fault classification and location on a distribution line can be determined rapidly and efficiently irrespective of the fault impedance and incipient angle with minimum estimation error. Lastly, the proposed scheme is tested on a grid-integrated system with renewable energy sources.  
fault classification; fault location; power distribution system; support vector machines; wavelet packet decomposition +
Footnote †: journal: Applied Sciences  
10 November 2022  
Accepted: 20 November 2022  
Published: 22 November 2022  
MPDI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Responses of Terrestrial Evapotranspiration to Extreme Drought: A Review
## 4 Mechanisms of Terrestrial ET Variation
### Water Supply  
Water supply, specifically, the soil moisture content (SMC), plays an important role in terrestrial ET by influencing both soil evaporation and plant transpiration during droughts. An important framework based on the separation of the soil moisture content describes the determination of ET under various climate regimes. Under wet conditions, corresponding to the SMC values above the critical value \(\theta_{CRIT}\), ET is independent of the water supply and is limited by energy. When the SMC id below \(\theta_{CRIT}\), the SMC is the dominant control of ET. Under water-stressed situations, when SMC decreases, the soil moisture potential becomes more negative, and ET decreases through limiting the diffusion of water molecules from roots. Under extremely dry situations, the SMC falls below the wilting point \(\theta_{WIIT}\) (\(\theta_{WIIT}\leq\theta\leq\theta_{CRIT}\)), and ET drops to near zero and no longer changes. According to this useful framework, three climate regimes can be defined based on the ET changes associated with the soil moisture content: dry (\(\theta<\theta_{WIIT}\)) and wet (\(\theta>\theta_{WIIT}<\theta_{CRIT}\)) climate regimes, in which the soil moisture content is independent of ET, and a transitional climate regime (\(\theta_{WIIT}\leq\theta\leq\theta_{CRIT}\)), in which the soil moisture content strongly constrains ET variability.  
This concept has been widely used in LSMs as a \"bucket model\", in which the general expression is as follows:  
\[ET=\beta ET_{POT} \tag{1}\]  
\[\beta=\frac{\theta-\theta_{WILT}}{\theta_{CRIT}-\theta_{WILT}}\;\;\text{for} \;\;\theta_{WILT}\leq\theta\leq\theta_{CRIT} \tag{2}\]  
where \(\theta\) is the soil moisture content, \(\beta\) is the evaporation factor or stress factor, \(\theta_{CRIT}\) is the critical value of the SMC which determines whether the SMC is the first-order control for ET, and \(ET_{POT}\) is the potential evaporation.  
This formula indicates that the soil moisture content is a key constraint for ET changes in transitional regimes, for instance, grasslands and shrublands, where the soil water content is always limited. Both observations and satellites confirm the control of ET by the soil moisture content in the dry-wet transition regime. For example, [PERSON] et al. [101] found that water-limited sites show a decrease in the soil moisture content and subsequent evapotranspiration and an almost linear dependency of ET on the soil moisture content. Due to the lack of soil moisture observations at all depths across large areas, precipitation is frequently used instead of the soil moisture content. Notably, underground water and irrigation also have a nonnegligible impact on the available water stress, especially in semiarid and arid areas [29].
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 4 Numerical Results
In addition to output power, the output voltage is an important value. Figure 9 displays the output voltage of the rectifier versus the input power for an output load of 2 k\(\Omega\). We observe that we have a greater output voltage for 18 dBm input power, but for low input voltage (such as 0 dBm), we have remarkable output voltages. When we have maximum power conversion efficiency, the output voltage is equal to 3.8 V, while it is equal to 7.5 V when the output load is 2 k\(\Omega\) and the input power is 18 dBm (Figure 9). As can be expected, as the input power increases, so does the output voltage. Figure 8: Power conversion efficiency versus input power \(p_{i}n\). Figure 7: Power conversion efficiency versus the output load \(R_{L}\). Figure 10 below illustrates the layout of our dual-band rectifier circuit. Table 2 compares the proposed rectifier to previous works that are full- and half-wave rectifiers operating at frequencies of 3.5 and 5 GHz, respectively. It is remarkable that among the designs, the proposed circuit design is the only one that works at sub-6 GHz 5G bands and only at 3.5 and 5 GHz simultaneously. Figure 10: Layout of the proposed rectifier. Figure 9: Output voltage versus input power.
# Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model
## 3 Related Work
### Formal Verification  
Formal verification is one of the most accurate methods for verifying system accuracy and also one of the earliest methods to check the behavior of smart contracts. It adopts logical language to formally model smart contracts, and proves the correctness of smart contract functions and runtime security through rigorous mathematical reasoning. In 2016, [PERSON] et al. [43] proposed a formal verification framework for smart contracts, which adopted the Promela modeling language to model the smart shopping contract and used the SPIN tool to perform model testing. However, this method was limited to a single contract, excluding the contract interaction process and complex contracts. [PERSON] et al. proposed the EVM formal model of smart contracts and leveraged the Isabelle/HOL proof tool [44] to define the EVM formal model in detail and verified the security of the model. [PERSON] et al. [45] presented the formal semantic framework KEVM, which provides an executable and readable formal model of complete EVM semantics, including all EVM instructions. [PERSON] et al. [46] outlined a framework for analyzing and verifying the security of smart contracts using the F* functional programming language. The source code of the contract and the EVM bytecode are compiled into F* language, and the security and functional correctness of the real-time runtime are verified respectively. VerX [47] is the first automated verifier able to automatically prove functional properties of Ethereum smart contracts. Additionally, the delayed predicate approach is the key to achieving automatic verification of smart contracts, which is a new symbolic engine that can soundly handle Ethereum features. Although formal verification technology can understand the code at a higher semantic level, the automation of this vulnerability detection method is relatively low, requiring experienced researchers to participate in the verification work, which increases the detection cost.
## 3 Method: Unbalance Prevention Control in Dehydration Based on BO
### Application of the Surrogate Model  
Figure 2 shows the overall flow of the BO process, explaining how the surrogate model predicts performance based on each UBT and the experimental data and how the acquisition function gradually searches for the optimal unbalance prevention control specification UBT based on the performance scores.  
The surrogate model predicting the distribution of the control specifications uses a non-parametric method called the GP to estimate the unknown objective function (performance of the unbalance prevention control specification). The GP predicts both the mean and uncertainty (variance) of the DS through the given experimental data, enabling a performance estimation of the UBT regions not yet explored in the experimental space. As more observation data is accumulated, the uncertainty of the estimation decreases, and the likelihood of finding the optimal UBT increases.  
Figure 2a: Make Regression Model visualizes the surrogate model, showing the performance prediction modeling of unbalance using the GP. In this figure, the predicted distribution of the DS for each UBT value and the uncertainty range for that score can be visually confirmed. The predicted mean values are represented by a red curve, indicating the expected performance of the corresponding UBT values. The training data is shown as blue dots, representing the score distribution obtained from actual experiments applying the UBT values. The predicted uncertainty range is shown as a gray-shaded area of \(\pm 2\sigma\), indicating the confidence interval of the predicted values. Regions where the predicted DS  
Figure 2: Step-by-step components of the BO: surrogate model, sampling, and acquisition function.  
mean values (red curve) are high indicate control specifications with excellent performance. In contrast, regions with larger gray shading represent areas where the confidence in performance prediction is low and high uncertainty.  
When estimating the predicted DS function value \(f_{DS}\), for a given Input Parameters UBT x, the GP-based predictive modeling is expressed as follows: [18]  
\[f_{DS}(x)\ \sim\ GP\big{(}\mu_{DS}(x),k_{DS}\big{(}x,x^{\prime}\big{)}\big{)}, \tag{5}\]  
Here, \(\mu_{DS}(x)\) is the mean function, and \(k_{DS}(x,x^{\prime})\) is the covariance function (kernel function).  
Given observed data \(D=\big{\{}(x_{i},f(x_{i}))\big{\}}_{i=1}^{N}\) the predictive distribution of the predicted function value \(f_{DS}^{i+1}\) for a new input \(x^{*}\) is calculated as follows: [18]  
\[p\Big{(}f_{DS}^{i+1}\Big{|}x_{i+1},D\Big{)}=N\Big{(}\mu_{DS}(x_{i+1}),\sigma_{ DS}^{2}(x_{i+1})\Big{)}, \tag{6}\]  
Here, D is the set of observed data; \(x_{i+1}\) is the new input value to be predicted, and \(f_{DS}^{i+1}\) represents the DS for the new input value. \(\sigma_{DS}^{2}(x_{i+1})\) indicates the variance representing the uncertainty of the prediction.
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
###### Abstract  
At the present stage, China's energy development has the following characteristics: continuous development of new energy technology, continuous expansion of comprehensive energy system scale, and wide application of multi-energy coupling technology. Under the new situation, the accurate prediction of power load is the key to alleviate the problem that the planning and dispatching of the current power system is more complex and more demanding than the traditional power system. Therefore, firstly, this paper designs the calculation method of the power load demand of the grid under the multi-energy coupling mode, aiming at the important role of the grid in the power dispatching in the comprehensive energy system. This load calculation method for regional power grid operating load forecasting is proposed for the first time, which takes the total regional load demand and multi-energy coupling into consideration. Then, according to the participants and typical models in the multi-energy coupling mode, the key factors affecting the load in the multi-energy coupling mode are analyzed. At this stage, we fully consider the supply side resources and the demand side resources, innovatively extract the energy system structure characteristics under the condition of multi-energy coupling technology, and design a key factor index system for this mode. Finally, a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is proposed, to carry out load forecasting for multi-energy coupling scenarios. Aiming at the complexity energy system analysis and prediction accuracy improvement of multi-energy coupling scenarios, this method applies minimal redundancy maximal relevance model to the selection of key factors in scenario analysis. It is also the first time that adaptive fireworks algorithm is applied to the optimization of adaptive fireworks algorithm, and the results show that the model optimization effect is good. In the case of A region quarterly load forecasting in southwest China, the average absolute percentage error of a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is 2.08%, which means that this model has a high forecasting accuracy.  
multi-energy coupling; load forecasting; adaptive fireworks algorithm; least squares support vector machine; integrated energy system +
Footnote †: journal: Applied Sciences
## 7 Numerical Results
### Comparison with the Literature  
The FGM annular plate defined in the work by [PERSON] and [PERSON] [90] is considered in the current section to validate the proposed approach. The plate middle surface is described by the following position vector  
\[\mathbf{r}(x,\theta)=(R_{i}+x)\cos\theta\,\mathbf{e}_{1}-(R_{i}+x)\sin\theta\, \mathbf{e}_{2} \tag{91}\]  
where \(x,\theta\) are the principal coordinate of the surface, assuming \(x\in[0,L]\) and \(\theta\in[0,2\pi]\). The inner radius is denoted by \(R_{i}\), whereas the outer one can be computed as \(R_{out}=R_{i}+L\). A linear variation is applied along the radial direction to define the thickness profile  
\[h(x)=H\Big{(}1.2-0.4\frac{x}{L}\Big{)} \tag{92}\]  
in which \(H\) is a constant parameter. The following relations must be taken into account to describe completely the plate geometry  
\[H/R_{out} =0.1 \tag{93}\] \[R_{i}/R_{out} =0.2 \tag{94}\]  
For the sake of completeness, both the structure and its radial cross-section are depicted in Figure 4a. The upper surface of the plate is made of Silicon nitride and a linear variation of the mechanical properties is assumed to describe the functionally graded composite layer. On the other hand, the lower surface is completely made of Stainless steel. The first 20 natural frequencies (apart from free body motions) are shown in Table 2 in their dimensionless form  
\[\Omega=2\pi f\,R_{out}\sqrt{\frac{\rho_{M}}{E_{M}}} \tag{95}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\Omega\) & **Ref. [90]** & **FSDT\({}_{RS}^{\chi=1.2}\)** & **ED2\({}^{\chi=1.2}\)** & **ED3** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: First 20 frequency parameters \(\Omega\) for a completely free (FF) annular plate with variable thickness [90]. The LGDQ solution is obtained for several Higher-order Shear Deformation Theories (HSDTs) by setting \(I_{N}=I_{M}=41\) and \(I_{PN}=I_{PM}=14\).  
As far as the boundary conditions are concerned, the structure is taken completely free (FF). The LGDQ solutions are obtained by setting \(I_{N}=I_{M}=41\) and \(I_{PN}=I_{PM}=14\). Three different structural models are considered: FSDT\({}_{RS}^{\chi=1.2}\), ED2\({}^{\chi=1.2}\), and ED3. It should be noted that both the first and second order theories require the shear correction factor \(\chi=1.2\), whereas the hypothesis of plane stress is take into account only in the FSDT. From the results shown in Table 2, it can be noted that the numerical values are in good agreement with the reference solutions for each model.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 2 Related Works
### Current Connectivity Verification Method: Alt-Svc  
The current method, namely Alt-Svc [4], was originally developed to perform the transition from HTTP/1.1 to HTTP/2. The specific connection confirmation procedure is as follows:  
1. The client makes an HTTP request using the old protocol.
2. The server sets the corresponding protocol version, address, etc. in the \"Alt-Svc\" field (The \"Alt-Svc\" field may contain cache information, in which case the client  
Figure 1: Overview of new and old HTTP protocols.  
uses it based on the given expiration date, unless it detects a change in the network configuration) of the response header and sends the request using the old protocol.
3. The client sends the HTTP request using the new protocol from the next request based on the information obtained from the \"Alt-Svc\" field.  
Furthermore, in the context of HTTP/3, the specification stipulates that clients fall back on the old HTTP if they cannot connect to HTTP/3 [3; 9]. Three primary problems with the Alt-Svc procedure exist. First, the fact that the response header that is sent with the old protocol contains connection information for the new protocol guarantees software support of the server itself; however, this does not guarantee that the intervening network path accepts the new protocol. That is, if the server supports the new protocol but the network path does not, the communication after the protocol switchover waits for a timeout. This phenomenon cannot be ignored in the context of HTTP/3, which is incompatible with the transport layer. Second, whereas QUIC eliminates duplicate handshakes to save time in initiating communication, this procedure requires communication in the old protocol first (unless there is a valid cache), which indicates that this benefit is not available. Finally, both the server and client are required to support the old protocol. If a new server is built using only the new protocol, it will be inaccessible to clients using Alt-Svc (even if the client implements the new protocol). Given the above, the first of these problems is likely to become more pronounced at the onset of protocol transition, and the second and third problems are likely to become enhanced after the transitional period. Hence, Alt-Svc is not a general-purpose method that can be effective in the long term.