esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validation
### Energy Recovery Rate with Different Pressure of the Accumulator  
Similar with the simulation process with the different initial pressure of the accumulator, the initial pressure of the accumulator ranged from 19 MPa to 22 MPa at the interval of 1 MPa when the braking intensity was kept at 0.4 and the initial braking speed was set as 300 rpm. Evolutions of speed of the flywheel and pressure of the accumulator in this section were shown in Figure 14a,b, respectively, and the actual braking time, change of the kinetic energy, recycled energy amount, and the energy recovery rate with the different initial pressure of the accumulator were calculated and summarized in Table 7. It could be observed that the braking time in the experimental process was close to that in the simulation process, but there were obvious differences between the calculated energy recovery rate in the experimental process and that in the simulation process. Especially when the initial pressure of the accumulator was 22 MPa, the actual energy recovery rate was 69.13%, which was much less than the simulation result of 87.32%. We hypothesized that the pressure loss in the hydraulic system and frictional resistance for the inertial flywheel would be larger with the increase of initial pressure of the accumulator, and these energy losses were ignored in the simulation process.  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline Parameters & \
\begin{tabular}{c} Braking intensity \\ Initial pressure of the accumulator (MPa) \\ Initial braking speed (rpm) \\ \end{tabular} & 0.30 & 0.35 & 0.40 & 0.45 & 0.50 \\ \hline \multirow{3}{*}{Results} &
\begin{tabular}{c} Braking time (s) \\ Kinetic energy change (J) \\ The recycled energy by the accumulator (J) \\ Energy recovery rate (\%) \\ \end{tabular} & 19 & 19 & 19 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The experimental results in the operating mode with different braking intensity.  
Figure 14: Experimental results with the different initial pressure of the accumulator: (**a**) Evolution of speed of the flywheel, (**b**) Evolution of pressure of the accumulator.
## 1 Introduction  
Kiwiberry (_Actinidia arguta_) is a plant which can be grown in different parts of the world and originates from eastern China [1]. Kiwiberry fruits are ten-fold smaller than Kiwi (_Actinidia chinesis_) and have smooth, hairless skin. Clean and thin skin allows fruits to be eaten whole but affects their shelf life. They can be stored up to one week after reaching consumption maturity [2]. Kiwiberry fruits are a good source of vitamin C, carotenoids chlorophylls, anthocyanins, and phenolic acids [1; 2]. Such composition results in strong antioxidant properties, which was confirmed by displaying protective activity against induced hypercholesterolemia on rats [3].  
Convective drying provides relatively inexpensive products [4]. Vacuum or sublimation drying (freeze-drying) offer a higher quality product but at significantly higher costs [5]. Previously reported results indicate that, among different drying methods of kiwiberry such as convective drying, infrared-assisted air drying, microwave-assisted air drying, and the two methods vacuum drying are the most promising and worth further investigation [6]. Therefore, based on these results in this experiment, the convective method was used because of its simplicity and popularity in the industry whereas the vacuum drying was chosen as a method for obtaining products with potentially improved retention of bioactive compounds and better rehydration properties [6].  
The most common dietary antioxidants are carotenoids, flavonoids, and vitamins C and E. During the last decade, researchers analyzed the impact of antioxidants on people's health [7; 8]. These experiments increased interest in investigating foods that possess strong antioxidant properties. Epidemiological studies suggest that consumption of such products reduces the risk of chronic diseases such as previously mentioned cardiovascular diseases and cancer [9].  
This work aimed (i) to analyze the impact of drying methods performed at different temperatures on selected chemical parameters of dried kiwiberry fruits and (ii) to characterize drying kinetics using a mathematical modeling approach. Within the secondary aim of the study, obtained by means of mathematical modelling, the effective water diffusion coefficient that describes water removal efficiency was compared with analyzed properties of dried kiwiberry.
## 3 Geo-AI Application in Hydropower Functional Zoning  
This Special Issue also includes a paper on applying geospatial artificial intelligence in hydropower functional zoning. Hydropower development is key to achieving energy conservation and emission reduction strategies, so studying ecological function zoning from the perspective of hydropower functions has relatively high value. In 'Hydropower Functional Zoning with Crowdsourced Geospatial Data: A Case Study in Sichuan Province', [PERSON] et al. [5] proposed a new framework for hydropower functional zoning based on geographic spatial data crowdsourcing, focused on data collection and utilized crowdsourcing geospatial data and spatial clustering methods. The authors combined the Open Street Map (OSM) with paper reports and geological database data, applied the theoretical framework of water ecological zoning, and combined the spatial clustering results of the hydropowerdevelopment evaluation to carry out hydropower functional zoning. This paper selected Sichuan Province, which is rich in hydropower resources, as the research area and provided new ideas for developing regional hydropower projects and sustainable management of river basins.
## 3 Results and Discussion
### Phenolic Compounds Analysis by HPLC-DAD  
Phenolic substances type and concentration is responsible for the biological activities and rely on the flower source of the honey [43]. Due to the botanical and regional source, phenolic compounds differ between the various honey samples. Results of phenolic compound analysis for honey samples are reported in Table 2. Quercetin is present in all honey samples, whereas gallic acid is found in only three samples. The concentration of gallic acid in Talh (_Acacia gerrardii_ Benth) is 1.08 mg/100 g, whereas in Athel (_Tamaria aphylla_) and Sidr (_Ziziphus spina-christi_ L.) is 0.81 mg/100 g, 0.97 mg/100 g respectively. The range of quercetin is 0.28-2.68 mg/100 g, gallic acid 0.81-1.08 mg/100 g. Catechin 1.14 mg/100 g found in Talh (_Acacia gerrardii_ Benth) honey sample only. HPLC analysis revealed high total phenolic compounds 6.75 mg/100 g is found in Spring flower honey, followed by Sidr (_Ziziphus spina-christi_ L.) honey 5.91 mg/100 g, whereas the low total phenolic compounds 1.09 mg/100 g was found in Athel (_Tamaria aphylla_) honey. Similar results are reported for phenolic compounds in Australian honey samples 1.58 mg/100 g [36]. The content of gallic acid in present study 0.81-1.08 mg/100 g are lower than previously described by [44] in laverned honey samples up to 237.20 mg/100 g. Salicylic acid 1.44-4.19 mg/100 g is greater as compared to the reported values 4 mg/kg in chestnut honey samples [45]. Quercetin content 0.28-0.46 mg/100 g in all honey samples except Spring flower honey 2.68 mg/100 g. The outcome of this study is comparable to the study of [46], they revealed quercetin 0.33 mg/100 g in Australian eucalyptus honey. Chlorogenic acid is one of the common phenolic acids in honey samples but it was not found in honey samples of the current study and comparable to the results of [47] for Langnese honey sample.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Phenolic**} & & & & & \\
**Compounds/Samples** & **Talh** & **Athel** & **Spring Flower** & **Sidr** & **Langnese** \\ \hline Gallic acid & 1.08 \(\pm\) 0.67 \({}^{\text{a}}\) & 0.81 \(\pm\) 0.65 \({}^{\text{a}}\) & 0.97 \(\pm\) 0.61 \({}^{\text{a}}\) & ND & ND \\ Catechin & 1.14 \(\pm\) 0.93 & ND & ND & ND & ND \\ Chlorogenic acid & ND & ND & ND & ND & ND \\ Tamic acid & 1.09 \(\pm\) 0.53 \({}^{\text{b}}\) & ND & ND & 1.26 \(\pm\) 0.35 \({}^{\text{b}}\) & ND \\ Salicylic acid & ND & ND & 3.1 \(\pm\) 0.91 \({}^{\text{c}}\) & 4.19 \(\pm\) 0.17 \({}^{\text{c}}\) & 1.44 \(\pm\) 0.13 \({}^{\text{c}}\) \\ Quercetin & 0.34 \(\pm\) 0.14 \({}^{\text{d}}\) & 0.28 \(\pm\) 0.05 \({}^{\text{d}}\) & 2.68 \(\pm\) 1.17 \({}^{\text{d}}\) & 0.46 \(\pm\) 0.11 \({}^{\text{d}}\) & 0.31 \(\pm\) 0.02 \({}^{\text{d}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Phenolic compounds (mg/100 g) in honey samples analyzed by HPLC.  
Figure 2: Total phenolic contents (TPC) assay of honey samples.
# Evaluation of the Adhesive Potential of Bacteria Isolated from Meat-Related Sources
## 3 Results and Discussion
This would suggest a positive correlation between biofilm formation on polystyrene and the existence of attractive van der Waals interactions at the interface of hydrophobic cells and the inert material. Many studies focusing on the fundamental mechanisms governing microbial adhesion have proved the importance of Lewis acid-base, i.e., electron-donor/electron-acceptor, interactions [28; 29]. In the present study, apart from xylene, bacterial affinities to another two solvents, namely ethyl acetate and chloroform, were investigated to determine Lewis acid-base properties (Table 1). Among the fourteen stains, only _L. curvatus_ showed a slightly stronger affinity for ethyl acetate than for chloroform, implying an electron-accepting characteristic. For _L. sake_, equal affinity was observed to both solvents. _L. plantarum_ was predominantly an electron donor but a weak electron acceptor referring to its better affinity for chloroform. The three strains of _Lactobacillus_ exhibited completely different behaviors in this test. However, their affinity values were maintained at a consistently low level. This is in line with all passive performances in regard to this genus through the previously mentioned assays. All other strains displayed a better affinity for chloroform than for ethyl acetate, indicating their possession of a superior ability to donate electrons and a basic character. Among them, _Pseudomonas_ and _Aeromonas_ spp. had an affinity value for chloroform calculated higher than 70% (surpassing 90% for _P. fragi_ and _P. putida_ II), which made them the strongest electron donors, and were followed by _S. liquefaciens_ and _R. aquutilis_ with a value of around 60%, concerning the same indicator. Different from the outstanding performances in the swarming test (Figure 2), both _C. braakii_ and _E. coli_ showed low potential for electron-related affinities. Taking all the 14 isolates into consideration, the electron donor/acceptor properties were correlated with the biofilm-forming process of engaged microorganisms (Table 2). On the whole, isolates with a better capacity to donate electrons (basic character) attached relatively easier to the polystyrene surface. It was demonstrated that Lewis acid-base interactions played a major role compared to electrostatic and van der Waals forces in the process of biofilm development [30]. However, these are not in line with a finding when investigating cell surface properties of marine bacteria, which suggested that the contribution of an electron-donating character in bacterial adhesion was at a low level [31]. ([PERSON] et al., 2003). The disagreement might result from the difference in the source of bacteria.
## 4 Evaluation of One-Hop Neighbor Wireless Link Status Based on Fuzzy Logic Model
### Q-Learning Routing Decisions
#### 4.4.2 Updates of the Q-Values  
Each node maintains a wireless link status fuzzy logic evaluation [Fuzzy(s, \(C_{i}\)) in (14)] for candidate nodes. These evaluation values are used as an input for the Q-learning to select the next-hop forwarder online. Accordingly, the fuzzy evaluation value must be fresh enough at each node to reflect the up-to-date topology information in its vicinity. Thus, it frequently updates upon the reception of Hello messages and eradicates stale neighbor information in the absence of new advertisements of neighbors [26; 42]. In an Asymmetric fading environment, a node may receive Hello packet from a node that is not in its one-hop neighbor list. Since each node is only required to maintain the information about its vicinity and traffic source nodes, the proposed protocol is scalable. Each entry of Q-values is initialized to 0. When a forwarder node has a data packet to send, it selects the best next-hop forwarder candidate by applying the Q-learning algorithm as follow:  
Figure 6: Fuzzy membership function plots for neighbor fuzzy weight.  
\[Q_{c_{i}}^{c_{x},d} \leftarrow(1-\alpha)\times Q_{s}^{C-i,d}\alpha\times Fuzzy_{c_{i}}\] \[\times\{R_{t+1}+\gamma\times\max_{a\in A}Q(C_{i},d)\} \tag{14}\]  
where \(Fuzzy_{C_{i}}\) is the Fuzzy logic evaluation value of the best candidate wireless link between the forwarder and the next-hop forwarder. We set the learning rate \(\alpha\) to 0.8, and the discount-rate parameter \(\gamma\) to 0.9. The possible reward values \(R_{nt}\) is calculated as follows:  
\[R=\begin{cases}1&d=C_{i}\\ 0&\textit{otherwise}\end{cases} \tag{15}\]
# Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model
## 2 Background  
### Ethereum and Smart Contract  
As a representative of blockchain technology, Bitcoin can execute simple decentralized transaction scripts, but cannot run Turing-complete codes, so it is not considered a Turing-complete smart contract platform [29]. Ethereum [30], regarded as blockchain 2.0, supports the operation of Turing-complete smart contracts. It is the first and most popular smart contract platform to support smart contract operation. With the emergence of Ethereum, more and more blockchain platforms have begun to support the execution of Turing complete smart contracts, such EOS [31], NEO [32], and Fabric [33].  
In contrast to the Bitcoin system, the Ethereum platform not only leverages blockchain technology to maintain decentralized payments, but also enables users to develop smart contracts according to their needs. A smart contract is a piece of program code that runs on the blockchain. Any user can write a smart contract and deploy it to the blockchain. This process is irreversible. Users can call functions in the contract through external accounts to realize the interaction with Ethereum. Furthermore, smart contracts can complete transactions by sending messages to other contracts or calling functions. After the transaction verification of the entire network node is passed, the state changes are recorded in the block.  
Ethereum supports smart contract programming languages such as Solidity [34], LLL, Serpent, and Vyper, but the vast majority of contracts are written in Solidity. The syntax of Solidity is similar to Javascript, which supports sophisticated features such as inheritance, library functions, user-defined types, and so on. Compared with traditional contracts, smart contracts have obvious advantages. First, smart contracts can automatically execute when certain preconditions are met without the need for intermediaries, which greatly saves costs; second, smart contracts enable direct transactions between users, thus speeding up the process of contract verification and execution; Third, smart contract has a copy on each device connected to the Internet, so that the execution process will not break the circuit.
## 3 Materials and Methods
### Validation Methods
#### 3.3.5 Auc  
For a binary classification problem, the evaluation of the performance is typically illustrated with the receiver operating characteristic (ROC) curve, which plots the true positives versus the false positive rate at various threshold settings. It is convenient to reduce it to a single scalar value representing expected performance. A common method is to calculate the area under the ROC curve (AUC). An ideal classifier achieves an AUC equal to 1, while the classifier that makes a random decision achieves an AUC equal to 0.5 [38; 39].
## Appendix A Description on Datasets and Extracted Features
### Problem 6  
EEG is a physiological signal that is related to brain activity, and it is possible to find out the intention of a person to move one's body by analyzing the EEG. Brain-computer interface competition IV-2b database contains EEG with label information indicating whether a subject imagined movement of the right or left hand when the signal was measured. Problem 6 is based on the database, and a common spatial pattern algorithm was used to extract features from EEG [41]. The algorithm divides EEG into several frequency bands  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Features** \\ \hline Time domain & Mean and median of RR intervals, root mean square of the successive RR interval differences (RMSSD), standard deviation of RR interval differences (SDSD), previous four features for relative RR intervals, standard deviation, skewness, and kurtosis of RR intervals, ratio of SDRR over RMSSD, percentage of RR interval differences more than 25 and 50 ms \\ \hline Spectral feature & Spectral power of RR intervals for very low (\(\leq\)0.04 Hz), low (0.04–0.15 Hz), and high (0.15–0.4 Hz) frequency bands, ratio of the low to the high-frequency spectral power for RR intervals \\ \hline Nonlinear measure & Two Poincaré plot descriptors \\ \hline \hline \end{tabular}
\end{table}
Table 10: HRV-related features for problem 2.  
including 4-8, 8-12,..., and 36-40 Hz and uses spatial filters to increase the discriminability of each class.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 6 Numerical Results
### Realistic Deployments  
We now proceed to assess realistic deployments for light and heavy industries with machines introduced in Section 3. To this aim, Table 1 summarizes the machines' dimensions (width \(1/\lambda_{x}\) and length \(1/\lambda_{y}\)) and transparencies, \(\kappa\). One critical observation is that the transparency of the chosen machines is quite high. The rationale is that their operational range is rather large, especially for RG2 and Iwa 2 machines, while the structure is slim. Note that other machines may have significantly different transparency values.  
Figure 8: Comparison of random and constant machine heights.  
Figure 9: Average blockage probability.  
The blockage probability as a function of site occupancy probability is shown in Figure 10 for UE location of \((10,10)\) m, BS height of \(h_{A}=4\) m, UE height of \(1\) m. Analyzing the presented results, one may observe that for such high transparency probabilities, the blockage probability is mainly determined by machine size as this parameter affects denseness of machine deployment. Nevertheless, for all types of machines, the blockage probability is very low, implying that communications are feasible at almost all times.
## 2 Background Theory
In order to solve this equation, we use a variational calculation based on a good choice of wave function and a minimization of the Hamiltonian mean value \(<\mathcal{H}>\). \[E_{(D^{+},X)}=\text{min}\frac{<\Psi|\mathcal{H}|\Psi>}{<\Psi|\Psi>}, \tag{12}\]  
In our last studies [25; 40], we have demonstrated that the lowest states (n,l,m) of neutral donor (\(D^{0}\)) in spherical QDs correspond to 1s (1,0,0) and 1\(p\) (1,1,0) states. The same concept is used in order to built the variational wave functions of the \((D^{+},X)\) complex. In these conditions, the trial wave function of the \((D^{+},X)\) for the fundamental state (1s) is taken as:  
\[\Psi^{(D^{+},X)}_{1s}=A_{0}\,j_{0}(r_{e})\,j_{0}(r_{h})Y_{0}^{0}(\theta_{e}, \varphi_{e})Y_{0}^{0}(\theta_{h},\varphi_{h})\,e^{-\beta_{1s}\,r_{e}-\eta_{1s} \,r_{eh}} \tag{13}\]  
while the first excited state trial wave function 1\(p\) is taken as:  
\[\Psi^{(D^{+},X)}_{1p}=A_{1}\,j_{1}(r_{e})\,j_{1}(r_{h})r_{e}r_{h}Y_{1}^{0}( \theta_{e},\varphi_{e})Y_{1}^{0}(\theta_{h},\varphi_{h})\,e^{-\beta_{1p}\,r_{ e}-\eta_{1p}\,r_{eh}} \tag{14}\]  
\(A_{0}\) and \(A_{1}\) are the normalization constants. \(j_{0}(r_{i})=\frac{\sin(\pi r_{i}/R)}{r_{i}}\) and \(j_{1}(r_{i})=\frac{\sin(\pi r_{i}/R)}{(\pi r_{i})^{2}}-\frac{\cos(\pi r_{i}/R )}{\pi r_{i}}\) (\(i=e,h\)) are the first and second order of the the first kind of Bessel function respectively [41]. They represent the two first states obtained analytically by solving the Schrodinger equation of one particle in spherical QD. \(Y_{0}^{0}(\theta_{i},\varphi_{i})=1/\sqrt{\frac{1}{4\pi}}\) and \(Y_{1}^{0}(\theta_{i},\varphi_{i})=\sqrt{\frac{3}{4\pi}}\cos(\theta_{i})\) are the spherical harmonics for the fundamental and the first excited states respectively. The exponential factors \(e^{-\beta_{i}r_{e}}\) and \(e^{-\eta_{i}r_{eh}}\) (\(i=1s,1p\)) traduce the Coulomb interactions of the electron with the ionized donor and the hole respectively. The \(\beta_{i}\) and \(\eta_{i}\) are variational parameters. We underline that in the 1\(p\) state, \(j_{1}(r_{i})\) represents the radial polynomial resulting from the resolution of the Schrodinger equation of Hydrogen-like atom. In these conditions, the energies are obtained by minimizing the mean value (12) by respect to the parameters \(\beta_{i},\eta_{i}\). Furthermore, to compare the optical properties behaviour of \((D^{+},X)\) with the exciton \(X\) and the neutral donor \(D^{0}\) (donor is fixed on the sphere center), it is necessary to follow the same method to determine the ground and first excited states energies of \(X\) and \(D^{0}\) with the same accuracy as that of the complex \((D^{+},X)\).
## 3 Material and Methods
### System Design
#### 3.1.2 Dynamic Beaconing  
Beaconing technologies are common methods that find the user's location [35] and send the diverse information [36]. However, traditional beaconing only sends the data and their location is fixed. It leads to a decrease in the system's feasibility because the beacon must be set and deployed before the user uses it.  
To increase feasibility, the system uses dynamic BLE beaconing with the mobile device. Dynamic BLE beaconing indicates that the mobile device acts as a beacon that not only transmits data but also receives it. It allows users to receive the matching person's ID as well as to send their IDs. So, not only can one user authenticate the matching person, but all users can authenticate each other. This makes it difficult for a malicious user to interfere with the authentication because the malicious user would have to interfere with the authentication process for all users, not just one user. Additionally, because BLE beacons are implemented on mobile devices, the location of the beacon is not fixed. It allows users to have and use BLE beacons easily and conveniently. Data received using dynamic BLE beaconing are passed to the user's digital twin through the mobile device.