esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
One of the objectives of spillway design is to dissipate energy at high dams in order to prevent downstream scouring. Therefore, it has a significant effect on the safety of dams. Scouring is one of the most serious problems that may happen downstream of the spillway. In this study, soft computing models are developed for the prediction of scouring depth downstream of the ski-jump spillway. The novelty of the study lies in the use of a recently developed HHO algorithm for the optimization of ANN parameters for the development of scouring depth prediction models using the experimental data collected from the previous literature. The performance of the hybrid ANN-HHO model was compared with ANN-PSO, ANN-GA, traditional ANN, and WM to display the efficacy of the newly proposed hybrid model, i.e., ANN-HHO. The performance of the ANN-HHO model was found to better in terms of all statistical metrics and graphical comparisons, where results indicate that the ANN-HHO model can predict scouring depth with the lowest value of RMSE, MAPE, and MAE and the highest CC and WI. The performance of ANN and WM was found to be poor in SD prediction. These results undoubtedly establish the efficacy of the new algorithm in the optimization of ANN model parameters and improve its prediction efficiency significantly. In the future, a study can be conducted to optimize the parameters of other AI models using the newly developed HHO algorithm for prediction of scour depth for other hydraulic structures.  
Conceptualization, S.S.S. and M.A.G.; methodology, Y.T. and M.A.; software, Y.T.; validation, Y.T. and M.A.; formal analysis, Y.T., A.M. and S.S.S.; investigation, S.S., M.A.G., A.M., N.A.-A., and K.-W.C.; writing--original draft preparation, S.S.S., M.A.G., A.M., Y.T., M.A., N.A.-A., and K.-W.C.; writing--review and editing, S.S.S., M.A.G., A.M., N.A.-A., and K.-W.C.; visualization, N.A.-A., and K.-W.C.; supervision, N.A.-A., and K.-W.C.; project administration, N.A.-A.; funding acquisition, N.A.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **MAE** & **RMSE** & **CC** & **WI** & **MAPE** \\ \hline \multicolumn{5}{c}{**Training**} \\ \hline ANN-HHO & 0.1791 & 0.2626 & 0.9557 & 0.9769 & 43.0994 \\ \hline WM & 0.2104 & 0.3558 & 0.9480 & 0.9463 & 35.2936 \\ \hline \multicolumn{5}{c}{**Testing**} \\ \hline ANN-HHO & 0.1760 & 0.2538 & 0.7765 & 0.8030 & 30.5081 \\ \hline WM & 0.1868 & 0.2701 & 0.7821 & 0.7793 & 27.3691 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of performance metrics of hybrid ANN-HHO model with Wu Model (WM).
## 2 Materials and Methods
### Experimental Procedure
#### 2.2.2 Pilot-Scale Experiment  
Pilot-scale experiments were conducted in a water treatment plant (WTP) intake station during late spring (April and May) and summer (July), which takes raw water from Han-river in Korea.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Lab-Scale Test** & **First Pilot-Scale Test** & **Second Pilot-Scale Test** \\ \hline Turbidity (NTU) & 4.6 & 1.1 & 15.6 \\ DOC (mg/L) & 3.4 & 0.9 & 0.8 \\ UV\({}_{254}\) (cm\({}^{-1}\)) & 0.034 & 0.015 & 0.027 \\ pH & 7.8 & 6.9 & 6.7 \\ chlorophyll-a (mg/m\({}^{3}\)) & 97.1 & 0.5 & 0.7 \\  & 134.6 ng/L geosmin & 100.0 ng/L geosmin & 100.0 ng/L geosmin \\ Target chemicals & 159.0 ng/L 2-methylisobenz (2-MIB) & 80.0 ng/L 2-MIB & 80.0 ng/L 2-MIB \\ \hline \end{tabular}
\end{table}
Table 1: Water quality parameters of raw water used in the lab-scale and pilot-scale tests.  
To examine the coagulation efficiency of PMC-based coagulant at pilot-scale (109 m\({}^{3}\)/h), varying doses of the PMC-based coagulant (0, 10, 20, 30, and 40 mg/L) were evaluated for chlorophyll-a, turbidity, and UV\({}_{254}\) removal. Online chlorophyll-a concentration measurement was carried out using a PLC automatic control system (Namyangju, Korea). The concentrations of chlorophyll-a in the raw water ranged from 2.3 to 156 mg/m\({}^{3}\).  
After the PMC-based coagulation process, the treated water was branched to conduct the UV AOPs experiments. The flow rate in UV system was around 1.8 m\({}^{3}\)/hr, and geosmin, 2-MIB were spiked into the influent of the UV reactor. The UV reactor was a LPA15 system with one low-pressure amalgam lamp rated at 871.8 mJ/cm\({}^{2}\). The contaminant and oxidant (i.e., hydrogen peroxide and chlorine) stock solutions were injected into the UV influent pipe through separate injection ports. The effluent sampling port was sufficiently far downstream from the injection ports to ensure that the stream was well mixed before reaching the sampling port and that representative samples were collected. The schematic design of the combined treatment is shown in Figure 1.
## 2 Methodology
### GPR Image Preprocessing
#### 2.3.1 Removal of Direct Wave  
In the echo signals GPR receives, the reflected waves of the ground and the directly coupled waves between the transmitting and receiving antenna are called direct waves. They are prominent in B-Scan images and significantly impact the recognition and extraction of target reflection information. If the direct wave is not processed effectively, the accuracy of the subsequent 3D reconstruction will be seriously reduced.  
Since the direct wave data are isochronal and stable, the direct wave data of different measurement lines at the same depth are almost identical. Therefore, the direct wave data can be removed by subtracting the average value of each row of data to enhance the effective reflection data. The expression is shown in Equation (3):  
\[\hat{u}_{k}(t)=u_{k}(t)-\frac{1}{N}\sum_{k=1}^{N}u_{k}(t) \tag{3}\]  
where \(\hat{u}_{k}(t)\) is the data of measurement line \(k\) after removing the direct wave, \(u_{k}(t)\) is the raw data of measurement line \(k\), and \(N\) is the number of measurement lines.
# A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
## 4 Design and Implementation
### Initialization  
The network is composed of randomly deployed sensor nodes and a \(BS\). The solution we propose begins with clustering. The \(BS\) divides the network into \(N\) clusters, i.e., \(N\) groups of source nodes, headed by a \(CH\). Obviously here, \(N\) must be greater than \(p\) since \(p\) is a sub-group of \(CH\) nodes. \(p\) is predefined by the user before deploying the network, taking into consideration the network size.  
Next, the \(BS\) chooses, for each source node, a set of the nearest \(p\)\(CH\)s and sends their identities to it. As a result, the paths to be used for routing data elements from each source node are established and shared with the \(BS\).  
Data are transmitted from source nodes to \(CH\) nodes and then from \(CH\) nodes to the \(BS\).
## 2 Materials and Methods  
### Synthesis of Bis(2-hydroxyethyl)ammonium Erucate Protic Ionic Liquid  
Erucic acid (CAS No. 112-86-7) was obtained from Alfa Aesar (Kandel, Germany). Bis(2-hydroxyethyl)amine (CAS No. 111-42-2) was obtained from Acros Organics (Darmstadt, Germany). All the reagents were analytical grade. Therefore, they were used as such without any preparation.  
Generally, during the alkylation of the amine, a reaction between the ammonium cation (secondary amine) and the carboxylic acid anion occurs [31], as shown in Figure 1.  
Figure 1: Scheme of amine alkylation.  
To synthesize bis(2-hydroxyethyl)ammonium erucate, the bis(2-hydroxyethyl)amine was alkylated using erucic acid, with a 1:1 molar ratio. An acid-base neutralization reaction occurs in this process, forming a protolytic ionic liquid (PIL). The synthesis was carried out in a thermostated three-necked flask connected to a reflux condenser, a thermometer, and a dropping funnel. First, the thermostated flask was placed in a glycerol bath to equilibrate the mixture's temperature. Then, erucic acid was added to the flask and heated to 100 \({}^{\circ}\)C while constantly stirring (400 rpm). When the required temperature was reached, the bis(2-hydroxyethyl)amine was added dropwise (6 drops/min). Furthermore, the reaction continued at a temperature of 100 \({}^{\circ}\)C for 24 h. The scheme of bis(2-hydroxyethyl)ammonium erucate formation is shown in Figure 2.  
The formation of the IL-DEE was inspected using Fourier transform infrared (FTIR) spectroscopy. The FTIR spectra were taken using a JASCO FT/IR-4X Spectrometer (Tokyo, Japan). The scan range was set to 4000-500 cm\({}^{-1}\), with an accumulation of 50 and a resolution of 4 cm\({}^{-1}\).  
Figure 3 presents FTIR spectra of bis(2-hydroxyethyl)ammonium erucate. The absorption band appearing in the range of 1800-1400 cm\({}^{-1}\) is of low intensity and is characteristic of carbonyl, (C = O) stretching, and (N-H) plane bending vibrations, while 3000-2800 cm\({}^{-1}\) confirms the distinct ammonium peak, (N-H), and (O-H) stretching vibrations. In addition, a peak at 722 cm\({}^{-1}\) is visible in the spectrum, indicating the presence of alkyl chains with at least four consecutive methylenes (-CH2-).  
Figure 3: FTIR spectra of bis(2-hydroxyethyl)ammonium erucate.  
Figure 2: Scheme of bis(2-hydroxyethyl)ammonium erucate formation.
## 3 Results and Discussion
### Effects on Photoelectrochemical Performance
#### 3.4.2 Mott-Schottky Plots
This question could be solved by comparing the I-V plots of the doped samples and the undoped sample in dark. Furthermore, the higher photocurrent of the Co-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) compared with the Ni-doped sample may be attributed to a larger carrier density (see Figure 7 and Table 2). However, this observation could not justify that the Co doping outperformed the Ni doping since the dopant concentrations were not known. In fact, the PEC activity of a doped photoelectrode depends not only on the identity of the dopant but also on the dopant concentration, distribution and the morphology and microstructure of the doped photoelectrode [46]. In order to compare the effects of different dopants, Co-doped and Ni-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorod arrays with the same dopant concentration and distribution should be synthesized in future. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Slope** & **Charge Carrier Density (m\({}^{-3}\))** & **Flat-Band Potential E\({}_{\text{fb}}\) (V)** \\ \hline undoped & \(6.04\times 10^{3}\) & \(1.65\times 10^{25}\) & \(-0.226\) \\ Co-doped & \(2.66\times 10^{3}\) & \(3.74\times 10^{25}\) & \(-0.220\) \\ Ni-doped & \(3.98\times 10^{3}\) & \(2.50\times 10^{25}\) & \(-0.218\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Charge carrier density and flat potential of the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) samples determined by Mott-Schottky plots. Figure 8: Mott–Schottky plots of the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorod arrays.
# A Fast Simulation Model of Pantograph-Stitched-Catenary Interaction in Long-Distance Travel
## 3 Validation and Discussion
### Validation
#### 3.1.2 Validation Based on the EN50318-2018 Standard and FEM
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Speed [km/h]** & \multicolumn{2}{c}{**275**} & \multicolumn{2}{c}{**320**} \\ \hline
**Pantograph** & **Leading** & **Tailing** & **Leading** & **Tailing** \\ \hline \(F_{m}\) [N] & 143.1(143–144) & 143.7(142–144) & 169(169) & 169(169) \\ \hline \(\sigma\) [N] & 24.1(20.2–24.7) & 28.9(24.4–36.2) & 23.2(20.5–24.7) & 36.9(30.4–38.3) \\ \hline \(\sigma\) (0–5 Hz) [N] & 14.8(11.7–15.2) & 17.9(17.0–18.2) & 13.0(11.8–13.3) & 22.9(20.4–24.2) \\ \hline \(\sigma\) (5–20 Hz) [N] & 18.5(16.5–19) & 25.5(16.4–27.4) & 19.1(15.2–20.9) & 28.8(21.5–29.8) \\ \hline Actual maximum of contact force [N] & 198.5(185–199) & 225.2(203–252) & 229.2(210–232) & 242.3(239–255) \\ \hline Actual minimum of contact force [N] & 95.6(92–102) & 86.2(56–88) & 121.4(105–128) & 74.4(43–78) \\ \hline Range of vertical position of the point of contact [mm] & 22.4(18–25) & 32.5(26–36) & 21.6(13–23) & 59.8(38–63) \\ \hline Maximum uplift at support [mm] & 68.2(55–79) & 71.3(51–79) & 88.6(74–95) & 91.1(69–95) \\ \hline Percentage of loss of contact [\%] & 0(0) & 0(0) & 0(0) & 0(0) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results of the pantograph–catenary interaction with respect to different pantographs and speeds. Figure 5: Time histories of the pantograph–catenary contact forces at different velocities and pantographs: (**a**) \(V\) = 275 km/h and (**b**) \(V\) = 320 km/h.
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 5 QKR Mechanism and Strategy  
Figure 7 depicts the QKR algorithm and the state changes of the QKPs. A, C, and D are QKPs deployed on nodes A, C, and D respectively. Path A\(\rightarrow\)F\(\rightarrow\)C\(\rightarrow\)D may be selected to serve the A-D node pair to share a key sequence. When the C\(\rightarrow\)D relay process fails, the initial state of the key pool is as follows: QKP A and D, respectively, have two key sequences belonging to the A-C node pair and the C-D node pair. And there are four key sequences in QKP C, two of which belong to the A-C node pair and another two key sequences belonging to the C-D node pair. There are three types of recycling methods: partial recycling, all recycling, and mixed recycling.  
The partial-recycling algorithm selects a pair of key sequences between the A-C key pair and the C-D key pair to recycle according to some decision factors, including the value of the key, the degree of the node pair, and so on. And the sequence of key pair that was not selected will be destroyed.  
The all-recycling algorithm is to recycle both parts of the key, and then perform some post-processing operations on the recycled key such as mixing and transformation. However, this algorithm will bring additional consumption of the original key.  
The mixed-recycling algorithm can achieve the balance between the security of the recovery key and the normal encryption of the original service. The mixed-recycling algorithm analyzes the historical data of the service and selects one from the partial recycling and the all-recycling algorithms, according to the characteristics of the historical service.
# Effects of Flexural Stiffness on Deformation Behaviour of Steel and FRP Stress-Ribbon Bridges
## 2 Design Procedure of the Bridge with Flexural-Stiff Ribbons
The minimum required cross-section area can be obtained from condition (4), accounting for expressions (5) and (6), as follows:  
\[A\geq\frac{H}{f_{d}}\bigg{(}1~{}+~{}\frac{24\cdot u_{q,lim}\cdot E}{5\cdot a_{c }^{2}\cdot h_{c}\cdot k^{2}\cdot l_{q}}\bigg{)} \tag{7}\]  
where \(k\) is the slenderness coefficient from Equation (2). The following equation determines the required cross-section height:  
\[h_{c}~{}=~{}\left\{\left(\frac{12\cdot u_{q,lim}}{5\cdot l_{q}^{2}\cdot k^{2} \cdot a_{c}^{2}}\right)^{2}~{}+~{}\frac{f_{d}}{k^{2}\cdot E\cdot a_{c}^{2}} \right\}^{1/2}~{}-~{}\frac{12\cdot u_{q,lim}}{5\cdot l_{q}^{2}\cdot k^{2}\cdot a _{c}^{2}} \tag{8}\]  
Remarkably, the compressive stresses in ribbons increase with increasing the flexural stiffness (\(El_{y}\)) because of the bending effect. That can cause stability problems of the flexural-stiff ribbons. Besides, the magnitude of compressive stresses must be limited in structural elements made from FRP [15; 20; 27]. For simplicity, this study assumes the following stress limitation in the ribbons:  
\[\sigma\geq 0 \tag{9}\]  
The above condition limits the height of the cross-section, accounting for expressions (4) and (5), as follows:  
\[u_{q}(x)\ =\ \Phi(\gamma,\ x/l,(kl)\ ^{-\ 1},\ \Omega[kl,\ kx])\leq u_{q,lim} \tag{2}\]0  
where \(M\) is the bending moment in the ribbon. Equations (5), (7), and (8) include the shape coefficient that is calculated, as follows:  
\[u_{q}(x)\ =\ \Phi(\gamma,\ x/l,(kl)\ ^{-\ 1},\ \Omega[kl,\ kx])\leq u_{q,lim} \tag{2}\]1  
The recurrent relationship between Equations (7), (8), and (11) forms the iteration process. At the first iteration, an approximate value of the coefficient \(\alpha_{c}\) is assumed. For this purpose, Equation (11) was applied to the profiles listed in the European Norm for Structural Sections in Steel (EN 10365). Table 1 summarises the estimated values of the coefficient \(\alpha_{c}\) for the first iteration. The iterative process is repeated, beginning Equations (8) and (7) and using the coefficient \(\alpha_{c}\) calculated by the Equation (11) at the previous iteration, as long as the difference between the assumed (beginning the iteration) and newly obtained \(\alpha_{c}\) value remains significant.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 5 Simulations and Parameter Effects
### Simulation Results
#### 5.2.2 Rebroadcast Nodes  
The percentage of rebroadcast nodes refers to the percentage of the nodes that have received and rebroadcast the message. To calculate this measure, the total number of nodes that have rebroadcast message is recorded; then, the percentage of rebroadcast nodes is computed relative to the total number of nodes in the network. The percentages were plotted as the number of nodes in the network was increased from 50 to 250 nodes. The effect of parameter activation in the proposed protocol for each of the considered measures is shown in Figure 3.  
For the majority of the controlled parameters, the percentage of rebroadcast nodes appears to increase as the number of nodes in the network increases. This is to be expected, since the more nodes there are in a network, the lower the percentage of utilisation given similar parameters for evaluation. However, the influences of the different factors on the rebroadcast node percentage are evident in Figure 3, with values ranging from as little as 1% up to 18%. Of the five parameters, the sink distance (_f_3) parameter results in the lowest percentages of utilisation across the different numbers of nodes in the network. The impact of the next-node distance (_f_2) shows the most variability, with the percentage of rebroadcast nodes increasing with the total number of nodes up to a 150-node network and then decreasing as more nodes are introduced into the network. As previously seen in Figure 2, the impacts of the residual energy (_f_1) and message priority (_f_5) on the percentage of rebroadcast nodes are similar. The effect of the node density (_f_4) on this evaluation metric is the second worst, with the percentage only slightly rising as the number of nodes increases from 50 to 100 but dropping by almost 6% as the number of nodes further increases to 200 and 250.
# Determination of Optimum Frequency for Electromagnetic-Assisted Nanofluid Core Flooding
## 1 Introduction
Thus, the phasor is represented as \(\overset{\rightarrow}{E}(r)=\overset{\rightarrow}{E}_{0}e^{-\gamma r}= \overset{\rightarrow}{E}_{0}e^{-\alpha r}e^{-j\beta r}\), where \(r\) is the propagation distance, \(\gamma\) (m\({}^{-1}\)) is the propagation constant, which is a complex quantity of the form \(\gamma=\alpha+j\beta\), where the real part \(\alpha\) (Np/m) is the attenuation factor and the imaginary part \(\beta\) (rad/m) is the phase constant. Generally, oil reservoirs are a low-loss medium, that is \(\omega\varepsilon\gg\sigma\). Hence, the irradiated EM waves are absorbed and attenuated as they propagate and spread within the medium [6; 32]. Therefore, the attenuation factor \(\alpha\) and phase constant \(\beta\) becomes:  
\[\alpha =\frac{\sigma}{2}\sqrt{\frac{\mu}{\varepsilon}} \tag{2}\] \[\beta =\omega\sqrt{\mu\varepsilon} \tag{3}\]  
The skin depth/penetration depth \(\delta\) in low-loss media is \(\delta=\frac{2}{\sigma}\sqrt{\frac{\varepsilon}{\mu}}\), which is the inverse of the attenuation constant indicating the distance travelled by the wave in the medium before its amplitude reduces to 1/e of its initial value on the surface of the transmitter [5]. The half-wave dipole antenna was used in this study due to its extensive application in the EM enhanced oil recovery method, because of its radiation pattern [17; 28; 29; 33; 34]. This type of transmitter focuses the EM energy in the center of the dipole and exhibits an omnidirectional radiation pattern [6; 32]. The relationship between the length of a half-wave dipole and its operating wavelength has been established by [6]. The conventional method is to design an antenna with a calculated length based on a predetermined frequency of propagation. However, considering the constraint and practical feasibility of transmitter placement in a reservoir well, it is reasonable to fix the antenna dimension based on reservoir geometry. Thereafter calculating its operating frequency over a range of possible values of reservoir EM properties (\(\varepsilon\), \(\mu\) and \(\sigma\)) for optimal EM radiation. Hence, this research investigates the impact of reservoir effective EM properties at different fluid saturations on the operating efficiency of a half-wave dipole antenna. The ensuing EM propagation pattern of the antenna in a cylindrical oil reservoir model and its operating frequencies were analyzed.
# Can the WiMAX IEEE 802.16 Standard Be Used to Resolve Last-Mile Connectivity Issues in Botswana?
## 6 Conclusions and Summary  
The literature review indicates that WiMAXIEEE 802.16e has a data transfer rate or speed of 70 Mbps, which is required for online transactions and standard quality of experience [28]. The simulation results indicate that settlements within 5 to 15 km can access Non-line of Sight, and the same infrastructure can relay signal to the next base station, with fewer infrastructure installations.  
On the other hand, when using Wi-Fi as a hotspot, the current government initiative, it extends up to 90 m from the base station and cannot solve the last-mile connectivity and access challenges. However, adopting the WIMAX IEEE 802.16e technology to address the last-mile access challenge requires institutional and regulatory frameworks.  
This study proposes a primary public network to meet universal and affordable connectivity and access. The changes could also force MNOs to diversify away from the dominant voice services, thereby achieving the national goals for an information-based economy.  
Figure 7: Traffic Dropped.  
**Author Contributions:** Conceptualization, B.M.E. and M.M.; methodology, B.M.E.; software, M.M.; validation, B.M.E. and M.M.M.; formal analysis, B.M.E.; investigation, M.M.; resources, M.M.; data curation, B.M.E.; writing--original draft preparation, M.M.; writing--review and editing, B.M.E.; visualization, B.M.E. and M.M.M.; supervision, B.M.E.; project administration, B.M.E.; funding acquisition, B.M.E. and M.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in the simulation are contained in this article.  
**Acknowledgments:** We would like to thank the Sol Plaatje University for assistance, as well as the Botswana Ministry of Transport and Communications (MTC), Botswana Fabre Network (BoFiNET), and Botswana Communication Regulatory Authority (BOCRA) for assisting with information on communication infrastructure about Botswana. We are also thankful to MaSIM and FNAS of the North-West University.  
**Conflicts of Interest:** The authors of this article declare no conflict of interest.