esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Cp-Abe  
In CP-ABE [14], the access policy is represented as a tree structure defined over a set of attributes. The CP-ABE scheme comprises the following four algorithms:  
* \(Setup\big{(}1^{\lambda}\big{)}\rightarrow(PK,~{}MSK)\): The setup phase takes the security parameter \(\lambda\) as input to generate public keys and a master secret key pair \((PK,~{}MSK)\).
* \(KeyGen(MK,~{}\mathbb{U})\to SK\): The key generation phase takes the master secret key \(MSK\) and the universe of user's attributes \(\mathbb{U}\) as input to obtain the secret attribute key \(SK\) as output.
* \(Encrypt(PK,~{}M,~{}\mathcal{T})\to CT\): The encryption phase takes the public keys \(PK\), a plaintext \(M\), and an access policy \(\mathcal{T}\) as input. It outputs the ciphertext \(CT\), where the access policy \(\mathcal{T}\) is implicitly embedded within \(CT\).
* \(Decrypt(CT,~{}SK)\to M\) or \(\bot\): The decryption phase takes the ciphertext \(CT\) and the secret attribute key \(SK\) as input. If \(SK\) satisfies the access policy embedded in \(CT\), the algorithm successfully recovers the plaintext \(M\). Otherwise, it outputs an error, denoted as \(\bot\).
## 2 Literature Review
### Smart Mobility
#### 2.2.1 Cyber Threats or Attacks  
As the above research indicates, selecting IoT technology is critical for creating smart mobility systems [40,41]. However, the security provided by IoT devices is not guaranteed. The devices have the propensity to have little computing power, and their hardware restrictions prevent them from having built-in security mechanisms. This makes the devices susceptible to vulnerabilities [42, 43, 44]. The IoT devices in a smart city's ecosystem control critical transportation infrastructures, so they need strict secure guarantees. In this point of view, a \"Cybersecurity via Determinism\" paradigm for the next-generation \"Industrial and Tactile Deterministic IoT\" is presented in this study [45]. Specifically, in layer 3, there is a new addition of a forwarding sub-layer called deterministic packet switches (D-switches), which are straightforward and safe. This sub-layer supports many deterministic software-defined wide area networks (SD-WANs) in addition to three new tools that may be used to improve online safety: access control, rate control, and isolation control. A software-defined networking (SDN) control plane will set up each D-switch with several deterministic schedules to support D-flows. The control plane of a SDN can insert millions of deterministic virtual private networks (Dvpns) into layer 3. This paradigm has a number of advantages, including the following:  
1. All congestion, interference, and distributed denial-of-service (DDOS) assaults are eliminated.
2. The size of the buffers in D-switches is cut in half.
3. Delays in end-to-end IoT communications are brought down significantly.
4. The D-switches do not require gigabytes of memory to store large IP routing tables.
5. Hardware support is provided in layer 3 for the US NIST Zero Trust Architecture.
6. Packets within a DVPN can be entirely encrypted using quantum-safe encryption, which is resistant to attacks by quantum computers using existing quantum algorithms.
7. The likelihood of an undiscovered cyberattack against a DVPN may be arbitrarily tiny using lengthy quantum-safe encryption keys.
8. Savings can approach thousands of dollars annually via decreased capital, energy, and operating expenses.  
Because of this, the attack surface is substantially smaller, meaning there are fewer packets to assault; thus, the danger posed by this attack is significantly decreased. In addition, using encrypted packets for signals eliminates any possibility that a rogue control-plane packet may get past the authorization check [45].
###### Abstract
As mentioned above, the application of amphoteric substances inspired the preparation of amphoteric chitosan to replace conventional emulsifiers in the emulsification process. Therefore, the goal of this study was to modify chitosan as an amphoteric substance to replace sodium acrylates copolymer as an emulsifier in the preparation of emulsion, and to discuss the advantages of amphoteric chitosan as an emulsifier by examining the amount of emulsifier required, the antibacterial effect, the moisture retention, and the emulsion stability. Furthermore, carbon dioxide (CO\({}_{2}\))-enhanced oil recovery (EOR), and CO\({}_{2}\)-enhanced gas recovery (EGR) in shale reservoirs have recently attracted attention; however, both nuclear magnetic resonance (NMR) and compositional simulations of CO\({}_{2}\) injection in shales confirmed that adsorption out-weighed molecular diffusion in determining CO\({}_{2}\) injection rate [31]. Whether the emulsifier, such as amphoteric chitosan, will play a role in solving the problem, is the question behind one of our future studies. \begin{table}
\begin{tabular}{c c c c} \hline
**Improved** & **Modified Method** & **Test** & **Reference** \\
**Properties** & & **Modified Method** & **Test** & **Reference** \\ \hline stability stability & SSL (sodium stearoyl 2-lactamase) /chitosan & steady shear rheometry & [10] \\ stability & anionic surfactant-chitosan complexes & adsorption kinetics & [11] \\ stability & chitosan/lithium dodecyl & turbidity & [12] \\ sulfate complexes & measurements & [13] \\  & chitosan-graft-oligosynolerylamide- & solubility, cloud point and & \\ stability & graft-oligosvine & interfacial tensions & [13] \\  & (CSNLYS) copolymers & & \\ stability & different molecular weight and degree & & \\ stability & of acetylation & & \\ Stability & cationic type surfactant & storage stability, settlement, sieve test & [21] \\ stability & chitosan previously dispersed in lactic & rheological assays & [22] \\ stability & acid solutions & & \\ stability & pH value control & dynamic interfacial pressure & [23] \\ stability & pH value control & electroprobretic mobility, & [24] \\ \hline emulsification efficiency & addition of _Escherichia coli_ & emulsification index, droplet size & [25] \\ emulsification efficiency & addition of tapioca starch & elongation at break, water & [26] \\ \hline antimicrobial activity & molecular weight of chitosan, degree & bacteria growth & [27] \\  & of deacetylation & moisture-retention ability and & \\ water-binding sites & carboxymethyl-hexanoyl chitosan hydrogel & drug encapsulation & [17] \\ nanostructural evolution & carboxymethyl-hexanoyl chitosan & critical aggregation concentration (cac) and & \\  & emulsion breakdown & increased degree of deacetylation & chitosan–oil aggregation & [28] \\ \hline \end{tabular}
\end{table} 
Table 1: Some studies associated with chitosan molecule used in emulsification and characterization studies.
# An Investigation on the Synthesis of Alkali Activated Materials from Thermally Modified Clays
## 3 Results and Discussion
### Precursor Characterization
In fact, some air bubbles and minor voids were visible on the surface of the SL1.10 specimens, along with increases in the open porosity (OP) and water absorption (WA) values (Table 5). Increases in porosity can compromise SL1.10 stability and partially explain the reduced CS values observed. In addition, alkaline hydrolysis occurs at the interface between the alkaline media and solid particles (kaolinite minerals), and high S/L ratios can reduce diffusion, limit the degree of reaction, and ultimately reduce the binder formation. Reductions in CS were not, however, accompanied by reductions in AD, which were increased by the higher solid load existing in the SL1.10 systems. Conversely, lowering the S/L ratio to 0.90 yielded more workable pastes but resulted in less dense and mechanically resistant binders. In addition to the higher amount of free water in these systems, increased dosages of activating solution and Na\({}^{+}\) content that assist in dissolution may also hinder the polycondensation rate as the solid load available for reaction is limited and dispersed in a very fluid medium. The SL0.90 binders displayed limited CS increments from 7 to 28 days, not being able to outperform the SL1.0 samples. Therefore, the CS results suggest that SL1.00 binders allow for higher dissolution rates of the starting precursors while not hindering the polymerization reactions. Overall, all binders displayed sufficient CS for civil engineering applications and low AD values at 28 days that ranged from 1.28 \(\pm\) 0.03 g/cm\({}^{3}\) to 1.36 \(\pm\) 0.01 g/cm\({}^{3}\), whereas OPC pastes typically had AD greater than 1.80 g/cm\({}^{3}\). Considering these results, 1.00 was selected as the S/L ratio for further examination of the MK and CRC binders. Figure 3: The apparent density and compressive strength of the alkali activated MK-binders at 7 and 28 days of curing as a function of the solid-to-liquid mass ratio.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 7 Simulation Results and Discussion  
### Simulation Results  
The response of the three network architectures to data traffic patterns were expressed as the percentage of tasks that exceeded a processing time of 10 ms during the 24-h period. Figures 9 and 10 show the simulation results for the three architectures for Scenarios 1 and 2. The response of each architecture is shown with appropriate labels.  
The results obtained for Scenario 1 demonstrated each architecture's response to normal daily traffic. For the CCC architecture, the percentage of tasks that required more than 10 ms of processing time was always greater than the 5% specification threshold. For the HCC architecture, the percentage of tasks that required more than 10 ms was less than the threshold limit at all times. The MEC architecture performed the best, with the lowest percentage of tasks (-1%) that required more than 10 ms.  
Figure 9: Individual network architecture’s response to data traffic shown in Scenario 1.  
Scenario 2, which simulated crowd gathering at two regions, demonstrated each network architecture's behavior when the data spike occurred. The CCC architecture demonstrated robust response as expected. This was because, as long as the total amount of data traffic at a given time was the same, the processing time for data traffic remained largely unchanged. However, the percentage of delayed tasks was above the threshold, which is similar to the results of Scenario 1. Conversely, the MEC architecture was significantly sensitive to the data traffic spike. As shown in the Scenario 2 results in Figure 10, the percentage of delayed tasks during the data traffic spike rose to nearly 50%, demonstrating that the architecture is vulnerable to such disturbances. Finally, the HCC architecture's response also demonstrated its sensitivity to the data traffic spike, but in a considerably more inhibited manner than the MEC architecture. The peak percentage of delayed tasks was approximately 15%, which was significantly lower than that of MEC architecture.  
The results of the simulation-based analysis demonstrated the advantages and disadvantages for three network architectures. The CCC architecture was robust to data traffic spikes. However, due to the physical distance between the central cloud and base stations, the number of delayed tasks was always above the permitted threshold. The MEC architecture performed reasonably well under normal condition. However, it was considerably vulnerable if a data traffic spike occurred. The HCC architecture offers a compromise between the CCC and MEC architectures, as it meets the specification threshold for delayed task, but still impedes the detrimental effect of data traffic spikes.
# Influence of Oak Chips and Oak Barrel Ageing on Volatile Profile in Chardonnay Wine of Romania
## 3 Results
### Volatile Profile of White Wines Aged with Non-Toasted and Light-Toasted Oak Chips
#### 3.2.1 Alcohols  
In our study, the concentration of volatile alcohols varied both depending on the time and type of ageing. Three major alcohols (iso-butanol, isopentyl alcohol and 2-phenylethanol) were quantified in wine samples. Those alcohols showed different trends during ageing. As can be observed in Table 3, the iso-butanol significantly decreased during 3 months of ageing, regardless of the methods, while isopentyl alcohol and 2-phenylethanol have increased concentration. After 3 months of ageing, the major alcohol concentrations were higher, regardless of the method (N3M-142 mg/L; L3M-144 mg/L and BAR3M-143 mg/L). By using LLE-GC-MS analysis were detected and quantified 9 minor alcohols (hexanol, 2,3-butanediol, 3-methylthio-1-propanol, benzyl-alcohol). In the minor group, hexanol was the highest alcohol, and its concentration was dependent on ageing time and significantly dependent on ageing method. As can be observed from Table 3, the non-toasted barrel ageing method favored the concentration of hexanol, which supplied a leafy and grassy note to the wine samples [31]. Also, the minor alcohols had different trends during ageing. Most of them (4 methyl-1-pentanol, 2-nonalol, 1-heptanol, 3 methylthio-1-propanol) decreased after 3 months of ageing. The highest increase was detected for 2,3-butanediol in BAR1M-BAR3M variants (1686-4973 \(\mathrm{\SIUnitSymbolMicro g}\)/L).
## 2 Materials and Methods
### Experimental Flow Tests
To calculate \(d_{h}\) and the corresponding \(A_{\it eff}\), the general Bernoulli energy equation was resolved in terms of overall pressure or head loss from the experiments. The _mstth_ obtained from CT is substituted for the mean roughness height (\(\varepsilon\)) in the Colebrook-White equation [5; 19]. Equation (5) represents the re-arranged energy equation, including the summation of minor losses. \[P_{1}-P_{2}=f_{d}\,^{*}\,\frac{l\cdot\rho\cdot V^{2}}{d_{h}\cdot 2\cdot g}+ \Sigma\xi\frac{V^{2}}{2\cdot g} \tag{5}\]  
where:  
\(P_{1}\)--Pressure at the inlet of the channel (Pa);  
\(P_{2}\)--Pressure at the outlet of the channel (ambient pressure) (Pa);  
\(f_{d}\)'--Assumed Darcy--Weisbach friction factor (no unit);  
\(\xi\)--Head loss coefficient (no unit);  
\(V\)--Velocity of the fluid flow (m/s);  
\(\rho\)--Density of the fluid (kg/m\({}^{3}\));  
\(d_{h}\)--Hydraulic diameter of the channel (m). Due to the transition from the funnel-shaped geometry to the inlet of the channel, a minor loss coefficient of 0.8 was applied, and an additional minor loss coefficient of 0.35 was added to consider the losses due to the curvature in the channel geometry [11; 12]. To resolve Equation (5), all the velocity parts were converted in terms of \(d_{h}\) to obtain a non-linear equation that can be resolved to determine \(d_{h}\) in an iterative manner by assuming a Darcy-Weisbach friction factor\(f_{d}\)'. Equations (6)-(8) represent the formulation for fluid flow velocity, the non-linear energy equation in terms of \(d_{h}\) and the formulation for Reynold's number. \[V=\frac{Q}{A_{\it eff}}=\frac{4\cdot Q}{d_{h}\,^{2}\cdot\pi} \tag{6}\]  
\[\frac{H\cdot g\cdot\pi^{2}}{8\cdot Q^{2}}\cdot d_{h}\,^{5}-\Sigma\xi\cdot d_{ h}=f_{d}\,^{*}\cdot l \tag{7}\]  
\[R_{\it ed}=\frac{\rho\cdot V\cdot d_{h}}{\mu} \tag{8}\]  
Using the Newton-Raphson iterative method, Equation (7) is resolved to obtain \(d_{h}\) based on the convergence criteria when \(f_{d}\,^{*}=f_{d}\), which is correlated with the aid of the Colebrook-White equation (Equation (4)). This is followed by the determination of Reynold's number (\(R_{ad}\)) for the particular mass flow rate to verify whether the flow is in the turbulent regime. The entire set of equations is resolved for \(d_{h}\) and \(A_{\textit{eff}}\) as a mathematical model with an in-house-developed MATLAB code. Finally, the experimental \(d_{h}\) is compared to the diameters obtained from the CT analysis using the Sphere method.
## 5 System Implementation and Field Testing
### Implementation Details
#### 5.1.3 Detecting/Counting Subsystem  
The detecting/counting subsystem consists of a programmable Arduino UNO microcontroller board, an infrared sensor and a notebook laptop. We used the Arduino UNO board and the infrared sensor to emulate the Visitor Detecting Module. Moreover, we used the notebook laptop to run processes emulating the Queue Length Computing Module and the Visitor Count Cumulating Module. To provide highly real-time values of the queue length and the visitor count, we had the notebook laptop send these values to the central subsystem every time the values changed.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
###### Abstract  
This paper proposes an effective global path planning technique for cellular-connected UAVs to enhance the reliability of unmanned aerial vehicles' (UAVs) flights operating beyond the visual line of sight (BVLOS). Cellular networks are considered one of the leading enabler technologies to provide a ubiquitous and reliable communication link for UAVs. First, this paper investigates a reliable aerial zone based on an extensive aerial drive test in a 4G network within a suburban environment. Then, the path planning problem for the cellular-connected UAVs is formulated under communication link reliability and power consumption constraints. To provide a realistic optimization solution, all constraints of the optimization problem are defined based on real-world scenarios; in addition, the presence of static obstacles and no-fly zones is considered in the path planning problem. Two powerful intelligent optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used to solve the defined optimization problem. Moreover, a combination of both algorithms, referred to as PSO-GA, is used to overcome the inherent shortcomings of the algorithms. The performances of the algorithms are compared under different scenarios in simulation environments. According to the statistical analysis of the aerial drive test, existing 4G base stations are able to provide reliable aerial coverage up to a radius of 500 m and a height of 85 m. The statistical analysis of the optimization results shows that PSO-GA is a more stable and effective algorithm to rapidly converge to a feasible solution for UAV path planning problems, with a far faster execution time compared with PSO and GA, about two times. To validate the performance of the proposed solution, the simulation results are compared with the real-world aerial drive test results. The results comparison proves the effectiveness of the proposed path planning method in suburban environments with 4G coverage. The proposed method can be extended by identifying the aerial link reliability of 5G networks to solve the UAV global path planning problem in the current 5G deployment.  
Keywords:UAV; drone; path planning; trajectory; cellular connected; cellular network; 4G; link reliability; intelligent optimization; PSO; GA +
Footnote †: journal: Sensors
## 3 Experiments and Analysis of Results
### Analysis of Prediction Results  
#### 3.2.1 Quantitative Analysis of Prediction Results  
The prediction results at different indoor temperatures are shown in Figure 8. The predicted value of the thermal dissatisfaction rate expressed by thermal discomfort body language was noted as \(BPD\) and the actual thermal dissatisfaction rate was noted as \(APD\). The Bayesian thermal dissatisfaction rate predicted values show a changing trend of low in the middle and high on both sides, which was consistent with the changing trend of \(PMV\)-\(PPD\) model predicted values and actual thermal dissatisfaction rate, and the lowest Bayesian thermal dissatisfaction rate predicted value is 16.28% near the suitable temperature. When the indoor temperature was low, the thermal dissatisfaction rate was higher, and as the indoor temperature rises to the appropriate temperature, the thermal dissatisfaction rate gradually decreases; at this time, when the indoor temperature is raised again, the thermal dissatisfaction rate gradually increases.  
Comparing the model strengths and weaknesses, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used for quantitative analysis of the prediction results of the multi-algorithm thermal dissatisfaction rate. MAE and RMSE reflect the degree of difference between the predicted and actual value, and their definitions are detailed in Equations (17) and (18).  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}\left|\hat{\theta}_{i}-\widetilde{\theta}_{i} \right|, \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Actor-network & \(1\times 10^{-4}\) & \begin{tabular}{c} Soft update parameters \\ of target network \\ \end{tabular} & \(1\times 10^{-2}\) \\ \begin{tabular}{c} Critic-network \\ learning rate \\ discount factor \(\tau\) \\ \end{tabular} & \(1\times 10^{-3}\) & \
\begin{tabular}{c} Maximize reply \\ buffer capacity \\ \end{tabular} & 50,000 \\
\begin{tabular}{c} 50,000 \\ activation function \\ \end{tabular} & tanh \\ batch\_size & 128 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings in the DDPG algorithm.  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{m}{(\hat{\theta}_{i}-\widetilde{\theta}_{i})^{2 }}}{m}}, \tag{18}\]  
where, \(\theta_{i}\) is the Bayesian-based predictive value of the thermal dissatisfaction rate of the \(i_{th}\) set of experimental data, \(\theta_{i}\) is the Actual thermal dissatisfaction rate of the \(i_{th}\) set of experimental data, i = 1, 2,..., \(m\).  
The method proposed in this paper exhibits higher accuracy and better prediction performance than the _PMV-PPD_ model in actual building thermal environments, as evidenced by a reduction of 0.208 in MAE and 0.232 in RMSE. Table 5 presents the evaluation results of the thermal dissatisfaction rate prediction model.