esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Response Analysis by Utilizing the Interval Quantitative Results
### Correlation Representation Achieved by Novel Optimization-Based Strategy
The above optimization procedure can be carried out by following steps:  
(i) By utilizing the orthogonal test design under available static and dynamic uncertainty data, take all the sample combinations into deterministic differential equation as shown in Equation (19), and then acquire the set of response curves \(u_{i}^{(S)}(t)=\{u_{i}(t,1),\ u_{i}(t,2),\ldots,\ u_{i}(t,S)\}\). (ii) Assume a sufficiently small increment \(\Delta t\), and conduct the time-discretization operation to obtain  
\[u_{i}^{(S)}(t_{1})=\{u_{i}(t_{1},1),\ u_{i}(t_{1},2),\ldots,\ u_{i}(t_{1},S)\} \tag{42}\]  
and  
\[u_{i}^{(S)}(t_{2})=\{u_{i}(t_{2},1),\ u_{i}(t_{2},2),\ldots,\ u_{i}(t_{2},S)\} \tag{43}\]  
where \(t_{1}=j_{1}\Delta t\) and \(t_{2}=j_{2}\Delta t\) (\(j_{1},j_{2}=1,2,\ldots\)). (iii) Construct a two-dimensional rectangle space (\(u_{i}(t_{1})-u_{i}(t_{2})\)) generated from Equations (42) and (43), and obtain the smallest rotary rectangular domain (as shown in Figure 1) according to Equation (41), with rotation angel \(\theta_{i}^{*}(t_{1},t_{2})\) and the two semi-axes \(\beta_{i}^{r}(t_{1})\) and \(\beta_{i}^{r}(t_{2})\). (iv) According to the smallest interval set, the auto-covariance function \(Cov_{u_{i}}(t_{1},t_{2})\) can be given by  
\[Cov_{u_{i}}(t_{1},t_{2})=\frac{\left(\beta_{i}^{r}(t_{1})\right)^{2}-\left(\beta_ {i}^{r}(t_{2})\right)^{2}}{3}\cdot\sin\theta_{i}^{*}(t_{1},t_{2})\cdot\cos \theta_{i}^{*}(t_{1},t_{2}) \tag{44}\]  
Similarly, referred by Equation (9), the auto-correlation coefficient function \(\rho_{u_{i}}(t_{1},t_{2})\) reads  
\[\rho_{u_{i}}(t_{1},t_{2})=\frac{Cov_{u_{i}}(t_{1},t_{2})}{|\delta u_{i}(t_{1}) |\cdot|\delta u_{i}(t_{2})|} \tag{45}\]  
(v) By traversing all the response components \(u_{i}(t)\), the auto-correlation characteristics of the interval process vector \(\mathbf{u}(t)\) are eventually gained. In summary, this section presents an integral process of uncertainty propagation analysis via limited time-invariant and time-variant sample data. It is noted that the reliability estimation will be expounded on the basis of the dynamic response results. Therefore, the more precisely we can obtain the bounds properties as well as the auto-correlation descriptions of \(u_{i}(t)\), the more reliable safety evaluation will be achieved. In fact, the work in Sections 2 and 3 provides the necessary data entry condition for the following structural time-dependent reliability analysis under circumstances of missing information.
## 2 Numerical Analysis Method
### Turbulence Models  
The type of turbulence model has an important influence on the numerical solution time, the reliability of the results, and the flow and viscous properties of the flowing fluid. In this study, the realizable k-\(\epsilon\) model, which calculates the Reynolds stress similarly to the actual turbulence, was applied to simulate the flow characteristics and compressible turbulence characteristics of hydrogen under high pressure in a check valve [21; 22; 23; 24].  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial p}{ \partial x_{j}}\bigg{[}\mu\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}-\frac{2}{3}\delta_{ij}\frac{\partial u_{t}}{ \partial x_{i}}\right)\bigg{]}+\frac{\partial}{\partial x_{j}}(-\rho u_{i}^{ \prime}u_{j}^{\prime}) \tag{5}\]  
\[-\rho u_{i}^{\prime}u_{j}^{\prime}=\mu_{t}\left(\frac{\partial u_{i}}{\partial x _{j}}+\frac{\partial u_{i}}{\partial x_{i}}\right)-\frac{2}{3}\bigg{(}\rho k+ \mu_{t}\frac{\partial u_{k}}{\partial x_{k}}\bigg{)}\delta_{ij} \tag{6}\]  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\epsilon} \tag{7}\]  
The realizable k-\(\epsilon\) model derived from the Reynolds Averaged Navier-Stokes (RANS) equation, which is expressed as Equation (5), is solved for the Reynolds stress term and the average velocity gradient term by applying the Boussinesq hypothesis, which is shown in Equation (6). At this time, the \(\mu_{t}\) (turbulent viscosity) of the realizable k-\(\epsilon\) model is expressed as in Equation (7).  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{j}}\big{(} \rho ku_{j}\big{)}=\frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(}\mu+\frac{ \mu_{t}}{\sigma_{k}}\bigg{)}\frac{\partial k}{\partial x_{j}}\bigg{]}+G_{k}+G_ {b}-\rho e-Y_{M}+K_{k} \tag{8}\]  
\[\frac{\partial}{\partial t}(\rho e)+\frac{\partial}{\partial x_{j}}\big{(} \rho eu_{j}\big{)}=\frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(}\mu+\frac{ \mu_{t}}{\sigma_{e}}\bigg{)}\frac{\partial e}{\partial x_{j}}\bigg{]}+\rho C_ {1}S_{e}-\frac{\rho C_{2}\epsilon^{2}}{k+\sqrt{\
u\epsilon}}+C_{1\epsilon} \frac{\epsilon}{\bar{k}}C_{3\epsilon}G_{b}+S_{e} \tag{9}\]  
The realizable k-\(\epsilon\) model is governed by the transport equation, where the turbulent kinetic energy (k) term in Equation (8) and the turbulent dissipation rate (\(\epsilon\)) term in Equation (9) are summarized by the transport equation, and \(Y_{M}\) represents the effect of the fluctuating expansion of the dissipation rate in the turbulent flow. \(\sigma_{k}\) and \(\sigma_{e}\) represent the turbulent Prandtl numbers for the turbulent kinetic energy and the turbulent dissipation rate, and \(G_{k}\) and \(G_{b}\) are the turbulent kinetic energy summarized by the buoyancy force and the averaged velocity gradient [25].  
Figure 2: Grid dependency result and mesh information of the check valve. (**a**) Grid dependency result of average velocity at y = 6.8 mm; (**b**) Generated mesh of check valve.
## 3 Results and Discussion
### Phonon Dispersion Curves and Lattice Thermodynamic Properties
The internal energy \(E\), Helmholtz free energy \(F\) and entropy \(S\) of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\), calculated in the temperature range of 0\(\ldots\)1000 K using a 2 \(\times\) 2 \(\times\) 1 supercell (\(Z=8\)) and expressed per mole of Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) molecules at the molecular volume of 130.268 A\({}^{3}\), are shown in Figure 7a-c, respectively. Due to the vibrational contributions, \(E\) and \(S\) increase gradually with increasing the temperature, while \(F\) exhibits a non-linear decrease. Besides, the calculated zero-point energy of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) phase is \(F_{0}=E_{0}=89.04\) kJ/mol. (Note that the values of thermodynamic properties plotted in Figure 7 are expressed per mole of Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) formula units). A critical parameter to understand the thermal behavior of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) is the heat capacity at a constant volume, which can be expressed as  
\[C_{V}=\frac{\partial F}{\partial T}=k_{\rm B}\int_{0}d\omega g( \omega)(\frac{\hbar\omega}{k_{\rm B}T})^{2}\frac{\exp(\frac{\hbar\omega}{k_{ \rm B}T})}{[\exp(\frac{\hbar\omega}{k_{\rm B}T})-1]^{2}}. \tag{11}\]  
The calculated temperature-dependent heat capacity of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) is shown in Figure 7d. One can see that \(C_{V}\) increases sharply at low temperatures (0-300 K). Then \(C_{V}\) increases smoothly up to 270 J/K per mole of Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) formula units.
## 2 Geographical and Geological Setting  
La Palma Island (Figure 1), located at 28\({}^{\circ}\)39\({}^{\prime}\)59\({}^{\prime\prime}\) N-17\({}^{\circ}\)51\({}^{\prime}\)59\({}^{\prime\prime}\) W, is one of the main eight emerged volcanic islands that comprises the Canary archipelago. The Canary Islands are an intraplate archipelago that was formed from the Miocene to the Holocene in a passive margin [7] on an oceanic lithosphere [8; 9; 10] of Jurassic age [11; 12]. The geological history of each island has been different over time, with activity beginning on each of them at different times and each one evolving differently, which makes it difficult to establish a general evolutionary model for all of them [13; 14; 15]. In the Canary Islands, the plate-scale stress field is conditioned by the Mid-Atlantic Ridge, the kinematics between Eurasia and Africa and other tectonic structures, such as regional fault systems [16; 17].  
The island has a complex geology formed essentially by three volcanic units bound by unconformities: the basal complex, the older volcanic series, and the recent volcanic series [18; 19; 20]. Debris avalanche deposits appear at the top of the basal complex as do old volcanic sequences related to large flank collapses. The recent volcanic series has taken place in the Cumbre Vieja volcanic ridge (CV) located in the south part of the island. CV is a large rift-type volcano trending in a N-S direction, its main eruptive vents being aligned on the rift axis with more than 125 Ka, and it is predominantly composed of basaltic lavas and pyroclasts [20; 21]. CV had at least seven of the fourteen historical eruptions that occurred in the Canary Islands. On 19 September 2021, a new eruption opened on the west of the island after 50 years of dormancy with the eruption of Teneguia in 1971 in the southern part of the island [18; 19; 20; 21].  
The Tajogaire 2021 volcanic eruption had a violent strobalian style (Volcanic Explosivity Index 3 (VEI)) with high effusive activity. During the first stages of lava emission, the La Palma volcano issued basanite-tephrine lavas, developing mostly a'a flows that eventually increased in basanite composition, increasing fluidity [22]. The eruption lasted more than 85 days and the lava field covered an area over 1200 ha, reaching a maximum thickness of up to 70 m and affecting almost 3000 buildings (Cabildo de La Palma [[https://volcan.lapalma.es/](https://volcan.lapalma.es/)]([https://volcan.lapalma.es/](https://volcan.lapalma.es/)) (accessed on 15 January 2024), PEVOLCA, Copernicus Emergency, ref. [23]).
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 5 Conclusions  
In this article, we presented the recording and reproduction techniques for spatial audio. The techniques that have been explored include binaural recording and rendering, soundfield recording and reproduction, and multi-zone reproduction. Binaural audio that works with a pair of headphones or a few loudspeakers has the advantage of being easily incorporated into the personal audio products, while soundfield reproduction techniques that rely on many loudspeakers to control sounds within a region are mainly used in commercial and professional audio applications. Both techniques strive for the best immersive experience; however, in soundfield reproduction, the spatial audio effect is not restricted to a single user or some spatial points. Therefore, a wide range of emerging research directions has appeared in this area, such as multi-zone reproduction and active noise control over space.  
In binaural techniques, generating individualized dynamic auditory scenes are still challenging problems. Future research directions include adaptation to an individualized HRTF based on the available datasets, real-time room acoustic simulations for a natural AR/VR listening experience, and sound source separation for augmented audio reality. In soundfield reproduction, interference mitigation and room compensation robust to acoustic environment changes remain as the major challenges. Further opportunities exist in higher-order surround sound using an array of directional sources and wave-domain active room compensation to perform sound field reproduction in reverberant rooms.  
The authors acknowledge National Natural Science Foundation of China (NSFC) No. 61671380 and Australian Research Council Discovery Scheme DE 150100363.  
The authors declare no conflict of interest.
## 2 Bus Impedance Power Flow Method
### Iterative Current Injection Method  
The iterative bus impedance power-flow calculation method was originally presented in [40]. The \(Z_{bus}\) matrix power-flow calculation method uses the following matrix form of Ohm's law for current that flows in each node and voltage induced to each node:  
\[\mathbf{V^{(k)}=Z_{bus}I^{(k)}}. \tag{1}\]  
In Figure 2, the currents that flow to the constant power load are calculated by  
\[\mathbf{I^{(k)}_{load}=\left(\frac{S_{load}}{V^{(k)}}\right)^{*}}. \tag{2}\]  
The current that flow to the constant current load are calculated by  
\[\mathbf{I^{(k)}_{load,I}=\left|\left(\frac{S_{load}}{V_{nom}}\right)^{*} \right|\angle\left(\delta_{V^{(k)}}-\delta_{s}\right)}. \tag{3}\]  
The currents that flow to the constant impedance load are calculated by  
\[\mathbf{I^{(k)}_{load,z}=\frac{V^{(k)}}{\left(\frac{V^{(k)}_{nom}}{S_{load}} \right)}}. \tag{4}\]  
The currents that flow to the ground through parallel elements are also calculated by  
\[\mathbf{I^{(k)}_{parallel}=Y_{parallel}V^{(k)}}. \tag{5}\]  
The currents flowing to loads and the ground are added by  
\[\mathbf{I^{(k)}=I^{(k)}_{load}+I^{(k)}_{parallel}+I^{(k)}_{line}}. \tag{6}\]  
Figure 1: Power system network.  
The currents are also iteratively used in Equation (1). However, the currents in Equation (6) are estimated from the initial nominal voltage. That is, they do not take the voltage variation caused by loads, generators, shunt capacitors, and transformers into account. Therefore, Equations (1) and (6) are repeated until the following convergence:  
\[\Delta\mathbf{V^{(k)}}=\mathbf{V^{(k)}}-\mathbf{V^{(k-1)}}. \tag{7}\]
## 2 Results and Discussion
### VOC Metabolomes of the Chassis and Mutant Strains
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{
\begin{tabular}{c} **Retention** \\ **Time (min)** \\ \end{tabular} } & \multirow{2}{*}{**Formula**} & \multirow{2}{*}{**Weight**} & \multicolumn{2}{c}{**Absolute Amount**} \\ \cline{6-9}  & & & & & & \(\Delta\)_Talig4:neo_ & \(\Delta\)_Talig4:neo_ & \(\Delta\)_Talig4:neo_ \\ \hline
1 & 1-Pentanol & 3.146 & C5H12O & 88.15 & \(1.07\times 10^{8}\) & \(2.68\times 10^{7}\) & \(2.68\times 10^{7}\) \\ \hline
2 & 2-Ethyl-1-hexanol & 10.781 & C8H18O & 130.23 & \(3.35\times 10^{7}\) & \(2.07\times 10^{6}\) & \(2.59\times 10^{6}\) \\ \hline
3 & Phenylethyl alcohol & 12.723 & C8H10O & 122.16 & \(3.47\times 10^{7}\) & \(8.68\times 10^{6}\) & \(1.09\times 10^{7}\) \\ \hline
4 & 3-Octanol, acetate & 12.950 & C10H20O2 & 172.26 & \(2.01\times 10^{5}\) & * & \(1.01\times 10^{5}\) \\ \hline
5 &
\begin{tabular}{l} 2-Heptenoic acid, \\ 4-nitrophenyl ester \\ \end{tabular} & 13.159 & C13H15 NO4 & 249.26 & \(7.80\times 10^{5}\) & \(5.04\times 10^{4}\) & \(2.08\times 10^{5}\) \\ \hline
6 & \(\beta\)-Cubenene & 18.511 & C15H24 & 204.35 & \(6.58\times 10^{6}\) & \(1.95\times 10^{5}\) & * \\ \hline
7 & \(\beta\)-Cubenene & 18.620 & C15H24 & 204.35 & \(7.54\times 10^{6}\) & \(1.64\times 10^{6}\) & * \\ \hline
8 & cis-\(\alpha\)-Bergamotene & 18.741 & C15H24 & 204.35 & \(3.70\times 10^{5}\) & * & * \\ \hline
9 &
\begin{tabular}{l} Bicylo[5.2.0]nonane, \\ 4-methylene-2,8,8-trimethyl-2-vinyl- \\ \end{tabular} & 18.849 & C15H24 & 204.35 & \(7.57\times 10^{5}\) & * & * \\ \hline
10 &
\begin{tabular}{l} Cyclohexane, 1,2-diethenyl-4- \\ (1-methylethylene)-, cis- \\ \end{tabular} & 18.958 & C13H20 & 176.33 & * & \(1.88\times 10^{6}\) & \(1.69\times 10^{6}\) \\ \hline
11 & \
\begin{tabular}{l} 1,3a-Ethano-3 aH-indene, \\ 1,2,3,6,7,7-hexahydro- \\ 11,2,2,4,7a-tetramethyl-, \\ [1R- \\ (1.alpha,3a.alpha,7a.alpha)]- \\ \end{tabular} & 18.969 & C15H24 & 204.35 & \(1.13\times 10^{6}\) & * & \(2.30\times 10^{6}\) \\ \hline \multirow{4}{*}{12} &
\begin{tabular}{l} (15,4a,7R)-1,4a-Dimethyl-7- \\ (prop-1-en-2-yl)- \\ 1,2,3,4,4a,5,6,7- \\ octahydronaphthalene \\ \end{tabular} & 19.041 & C15H24 & 204.35 & \(7.92\times 10^{5}\) & \(9.25\times 10^{4}\) & * \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identities and contents of the VOCs detected by SPEM-GC-MS a. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.
## 3 Results and Discussion  
### Weather Conditions  
Monthly averages of accumulated rainfall collected in the 2014 to 2018 years were found to be significantly higher than climatological (_Pclim_) values (Figure 3). Accumulated annual precipitation levels registered for 2014, 2015, and 2017 are 2033 mm, 1938 mm, and 1821 mm, respectively, approximately 32%, 26%, and 18% higher than climatology values. On the other hand, the years 2016 and 2018 presented annual accumulated very close to climatology (differences of \(-6\%\) and \(-7\%\)). Monthly, the winter months presented greater variability in precipitation and air temperature. Average monthly air temperatures observed for the first six months of both years (2014 and 2015) were consistently lower than those recorded climatologically. On the other hand, for the months of July, September, and October 2014, the average monthly temperature was slightly higher than the climatology value. The second half of 2015 also presented temperatures falling below climatological values except for the month of August, for which values surpassed the climatological average at approximately 4 \({}^{\circ}\)C. Average annual temperatures for 2014 (19.7 \({}^{\circ}\)C), 2015 (19.6 \({}^{\circ}\)C), 2016 (19 \({}^{\circ}\)C), 2018 (19.6 \({}^{\circ}\)C), and the climatological average (20.2 \({}^{\circ}\)C) were found to be very similar. The 2017 year was the only year with a positive difference from \(\mathit{Tair}_{clim}\), recording an average annual temperature of 20.3 \({}^{\circ}\)C.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.3 Forward Secrecy  
Even if all secret values of smart meters and data concentrators are obtained by the adversary, all previous session keys must remain secure. If \(\{PID_{i},M_{1},M_{3},M_{4}\}\) and \(\{M_{5},M_{7},M_{8}\}\) are obtained by means of eavesdropping, the adversary \(A\) can calculate \(M_{2}=T_{s}(M_{1})\) and \(\{ID^{\prime}_{i}||E^{\prime}_{i}\}=C_{3}\bigoplus H_{0}(M_{2})\), but \(A\) must obtain \(M_{6}=T_{u}(M_{5})=T_{v}(M_{1})\) to calculate the previous key \(SK=H(ID_{i}||PID_{i}||E_{i}||E_{i}^{new}||M_{2}||M_{6})\). Due to the computational difficulties of CMDL and CMDH problems, it is not feasible for \(A\) to obtain random numbers \(u\) and \(v\) from \(M_{1}\) and \(M_{5}\). So, \(A\) cannot successfully calculate \(SK\), and the scheme achieves forward secrecy.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 4 Technical Discussion
### 4.1. Note Specifications and Applications
#### 4.1.2 Power Consumption
\begin{tabular}{c} 64-kB \\ non-volatile \\ FRAM \\ \end{tabular}  & Not mentioned & Not mentioned & \ \begin{tabular}{c} LoRa module \\ (RMR59N) \\ \end{tabular}  & 8681.MHz & \begin{tabular}{c} 2-km in LOS or \\ 201 with \\ microsecond band \\ \end{tabular}  & +5 to +20 dBm & Smart cities \\ \hline  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Flatroom**} & \multicolumn{4}{c}{**CPU**} & \multicolumn{4}{c}{**Radio Transceiver**} & \multirow{2}{*}{**Tr Power**} & \multirow{2}{*}{**Applications**} \\ \cline{2-2} \cline{6-9}  & **Type** & **RAM** & **FLASH** & **EEPROM** & **Type** & **Freq** & **Range** & \\ \hline \multirow{3}{*}{[22]} & MSP430F8969 & \begin{tabular}{c} 64-kB \\ non-volatile \\ FRAM \\ \end{tabular}  & Not mentioned & Not mentioned & \ \begin{tabular}{c} LoRa module \\ (RMR59N) \\ \end{tabular}  & 8681.MHz & \begin{tabular}{c} 2-km in LOS or \\ 201 with \\ microsecond band \\ \end{tabular}  & +5 to +20 dBm & Smart cities \\ \hline  
[36] & \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \
\begin{tabular}{c}
\begin{tabular}{c} \end{  
It is also worth noting that the ICARUS note that utilizes the STM32L476 RG MCU can consume as little as 22 nA during the sleep state. The STM32L476 RG MCU and ARM Cortex-M4 MCU-CC3200 in the ICARUS note have the advantage of operating at up to 80 MHz clock frequency. However, utilizing high-speed and powerful processors leads to increased energy consumption and cost. Therefore, it is necessary to optimize power consumption versus processing speed. The ATmega1281 MCU is appropriate for applications that do not require high clock frequency operation. In order to minimize the power consumption in WSNs, dynamic power management techniques including dynamic scaling, duty cycling, data aggregation, data prediction and data compression should be implemented and improved.
## 4 Experimental Results
### Exploration and Exploitation Analysis  
Keeping exploration and exploitation in balance can be achieved by ensuring sufficient diversity among individuals. In this way, an algorithm can avoid being trapped in a local solution and ultimately produce a better solution to a particular optimization problem. However, exploration-exploitation and diversity measurements alone cannot prove that  
Figure 12: Convergence curves of comparative algorithms with \(n=40\) for three models: (**a**) SDM, (**b**) DDM, and (**c**) PVMM.  
one algorithm is better than another for solving optimization problems. Some experiments are presented in this section to evaluate the exploration-exploitation and diversity of solutions in the comparative algorithms for the SDM problem. Figure 13 shows variations in exploration, exploitation, and population diversity among the individuals of competitive algorithms during the iterations. Calculations were made according to the procedure detailed in [42].  
Figure 13: Exploration-exploration and diversity of comparative algorithms with SDM; (**a**) exploration, (**b**) exploitation, and (**c**) diversity measurement.  
Figure 13a,b shows that, in contrast to the other algorithms, SSA and GWO exhibited a greater percentage of exploration during the iterations than exploitation. It is shown in Figure 14 that these two algorithms had average exploration-exploitation ratios of 80%:20% and 76%:24%, respectively. This was due to the high diversity in the population in these two algorithms, as shown in Figure 13c. Comparatively, the GAs and WSO provided the greatest level of exploitation capabilities, with an average value of 99%. Further evidence of this can be found in Figure 13c, demonstrating that these two algorithms were unable to provide sufficient diversity throughout the iteration process. Thus, premature convergence is one of the main weaknesses of these algorithms.  
Figure 13a,b also shows that the ICO, CO, DE, PSO, GA, TLBO, SEDE, JAYA, PGJAYA, and WSO were all explorative at first, but after a few iterations, they were deemed exploitative algorithms. Similar results can be observed for the diversity measure in these algorithms which, after a few iterations, dropped (see Figure 13c). It must be noted, however, that spikes in the population diversity characteristic were observed in the CO due to the leave-the-prey-and-go-back-home strategy.