esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Specific Findings
#### 3.2.2 Driver Emotion Modulation Based on Unisensory and Multisensory Inputs  
With the proliferation of intelligent sensors, there is an increasing number of studies related to the use of sensors for emotion recognition. However, to promote driving safety, integrating artificial intelligence with human intervention is still essential [51]. We reviewed 34 articles on driver emotion modulation based on unisensory and multisensory inputs, as summarized in Table 2. The analysis was performed from various viewpoints, including sensory aspects, emotional factors, driving performance, the effects of driver emotion modulation, cultural factors, and human factors. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Sensory** & **Driver** & **Driver** & **Driving** & **Effectiveness of Driver Emotion** & **Cultural** & **Human** \\
**Input(s)** & **Emotion(s)** & **Performance** & **Measurement** & **Modulation** & **Factors** & **Factors** \\ \hline \multirow{3}{*}{[52]} & haptic, vocal & calmness & DP \(\downarrow\) & BR, HR & reduced drivers’ breathing rate and level of arousal; safety and driving performance were not impaired; drivers preferred haptic stimulation over voice & — & cognition \\ \hline \multirow{3}{*}{[53]} & haptic & stress & PR \(\downarrow\) & BR, HR, PR & haptic breathing guidance could increase both the rate and intensity of breathing as well as heart rate; all participants (except two) stated that haptic breathing guidance intervention was safe, provided that it was not delivered in inappropriate scenarios & — & \\ \hline \multirow{3}{*}{[54]} & haptic & stress & DP \(\downarrow\) & SCR, HR, BT & haptic guidance could effectively regulate breathing rate, increasing comfort; however, haptic guidance was not useful in complex maneuvers & — & behavior \\ \hline \multirow{3}{*}{[55]} & visual & negative, positive, calmness & LE \(\uparrow\) & speed & pleasant images degraded longitudinal control to the greatest extent & — & individual differences \\ \hline \multirow{3}{*}{[56]} & visual & negative, positive & DP \(\downarrow\) & speed & intersection type and position influenced drivers’ emotional states & — & behavior \\ \hline \multirow{3}{*}{[57]} & visual & negative & DP \(\downarrow\) & PR & both blue and orange lighting enhanced lane maintenance for drivers & — & behavior \\ \hline \multirow{3}{*}{[58]} & visual & negative & DP \(\downarrow\) & EEG & cool hues had better regulation quality than warm hues for color attributes; positive expression had better outcomes than negative expression for expression attributes & — & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of studies related to driver emotion modulation based on unisensory and multisensory inputs.
## 5 Reliability-Based Sensitivity Analysis  
Based on the reliability evaluation method, reliability-based sensitivity is always used to investigate the relationship between random variables and system reliability. The changing rule of system parameters can be analyzed via the indices of reliability-based sensitivity. Based on Equation (16), the derivative of reliability index \(\beta\), \(\beta=\mu_{z1}(\mu_{z2})^{-1/2}\) is investigated to represent the reliability-based sensitivity as follows:  
\[\begin{array}{ll}\frac{\partial R(\beta)}{\partial\beta}=&\phi(-\beta) \Bigg{\{}1-\beta\bigg{[}\frac{1}{8}\frac{\mathrm{E}\alpha_{H}}{\mu_{Z2}^{2}}H_ {2}(-\beta)+\frac{1}{24}\bigg{(}\frac{\mu_{Z4}}{\mu_{Z2}^{2}}-3\bigg{)}H_{3}(- \beta)+\frac{1}{22}\bigg{(}\frac{\mu_{Z2}}{\mu_{Z2}^{2}}\bigg{)}^{2}H_{5}(- \beta)+\cdots\bigg{]}\\ &-\bigg{[}\frac{1}{8}\frac{\mathrm{E}\alpha_{H}}{\mu_{Z2}^{2}}H_{1}(-\beta)+ \frac{1}{8}\bigg{(}\frac{\mu_{Z4}}{\mu_{Z2}^{2}}-3\bigg{)}H_{2}(-\beta)+ \frac{5}{22}\bigg{(}\frac{\mu_{Z2}}{\mu_{Z2}^{2}}\bigg{)}^{2}H_{4}(-\beta) \bigg{]}\Bigg{\}}\end{array} \tag{18}\]According to the formula of \(\beta\), the derivative can be expressed as \(\partial\beta/\partial\mu_{z1}=1/(\mu_{z2})^{1/2}\). Also, the relationship between reliability index \(R\) and mean value \(\mu_{x1}\) can be expressed as  
\[\frac{\partial R}{\partial\mu_{x1}}=\frac{\partial R(\beta)}{\partial\beta} \frac{\partial\beta}{\partial\mu_{Z1}}\frac{\partial\mu_{Z1}}{\partial\mu_{x1}} \tag{19}\]  
Introducing Equation (18) into Equation (19), the mean value reliability-based sensitivity can be achieved, which can evaluate the effects of the parameters in the transposition system of the power tool servo. Similarly, the derivative of \(\beta\) can be derived by \(\partial\beta/\partial\mu_{z2}=-\mu_{z1}/(2\mu_{z2}^{3/2})\). Considering the relationship in Equation (16), the derivative of \(R(\beta)\) can be calculated by  
\[\frac{\partial R(\beta)}{\partial\mu_{Z2}}=\varphi(-\beta)\left[\frac{1}{2} \frac{\mu_{Z3}}{\mu_{Z2}^{4}}H_{2}(-\beta)+\frac{1}{6}\frac{\mu_{Z4}}{\mu_{Z2 }^{5}}H_{3}(-\beta)+\frac{1}{12}\frac{\mu_{Z3}^{2}}{\mu_{Z2}^{7}}H_{5}(-\beta)\right] \tag{20}\]  
Based on the calculation law, the reliability-based sensitivity of covariance is expressed as  
\[\frac{\partial R}{\partial\mu_{x2}}=\frac{\partial R(\beta)}{\partial\beta} \frac{\partial\beta}{\partial\mu_{Z2}}\frac{\partial\mu_{Z2}}{\partial\mu_{x2}} \tag{21}\]  
Based on Equation (13), Equation (21) can be rewritten as  
\[\frac{\partial R}{\partial\mu_{x2}}=\frac{\partial R(\beta)}{\partial\beta} \frac{\partial\beta}{\partial\mu_{Z2}}\left\{\sum_{i=1}^{n}\left[\left.\frac{ \partial g(\mathbf{X})}{\partial X_{i}}\right|_{X=\mu_{x1}}\right]^{2}\mu_{X,2} \right\} \tag{22}\]  
Hence, the reliability-based sensitivity of covariance can be used to reflect the influence of the parameters' dispersion degrees. Both the reliability-based sensitivity of the mean value and covariance can represent the relationship between system reliability and the parameters in the transposition system, which is beneficial for controlling and optimizing.
## 4 System modeling
### Unsupervised learning methods
#### 4.2.1 Self-organizing map (SOM)  
SOM is an unsupervised learning neural network. The most significant advantage of SOM is its capability to project a high-dimensional space onto a two-dimensional space while preserving the topology property of high-dimensional data. In terms of structure, SOM is a one-layer neural network. The learning process of SOM is based on a competitive algorithm which can be separated into three major steps: (1) locating the winner neuron whose weight vector is the closest to an input training vector, (2) adjusting the winner weight vector towards the input vector, and (3) updating the weight vectors of the neurons within a neighboring area of the winner neuron. These three steps reflect the mechanisms of the electric signal transfer among the brain neurons.  
Figure 6: Self-organizing map of sensor responses on a 5\(\times\)5 rectangular grid with clusters indicating the sample subgroups labeled using the following scheme: SP-D0, SP-D1, SP-D2 and SP-D3 are packaged vegetable kept at 10\({}^{\circ}\)C for 0–3 days, respectively; EC-D0, EC-D1, EC-D2 and EC-D3 are packaged vegetable inoculated with _E. coli_ on the first day of inoculation and after stored at 10\({}^{\circ}\)C for 1–3 days, respectively. (Reprinted with the permission from Elsevier).  
SOM was recently applied to detect the pathogen contaminated packaged fresh vegetables [78]. Figure 6 shows a SOM based grouping result for the fresh vegetables with and without _E. coli_ contamination. SOM has also been used for E-nose based _S. aureus_ infection identification in hospital environment [79].
## 4 The Data Filtering Based Iterative Least Squares Algorithm
Form \(\hat{\mathbf{\phi}}_{j,k}(t)\) and \(\hat{\mathbf{\phi}}_{j\ell,k}(t)\) by Equations (56) and (57), update the parameter estimate \(\hat{\mathbf{\vartheta}}_{k}\) by Equation (55). 7. Read \(\hat{\mathbf{\vartheta}}_{j,k}\) by Equation (68), compute \(\hat{x}_{j\ell,k}(t)\) and \(\hat{x}_{j,k}(t)\) by Equations (60) and (65). 8. Give a small positive \(\varepsilon\), compare \(\hat{\mathbf{\theta}}_{k}=\left[ \begin{array}{c}\hat{\mathbf{\vartheta}}_{k}\\ \hat{\mathbf{c}}_{k}\end{array} \right]\) with \(\hat{\mathbf{\theta}}_{k-1}\), if \(\|\hat{\mathbf{\theta}}_{k}-\hat{\mathbf{\theta}}_{k-1}\|\leq\varepsilon\), obtain the iterative time \(k\) and the parameter estimate \(\hat{\mathbf{\theta}}_{k}\), increase \(k\) by \(1\) and go to Step 2; otherwise, increase \(k\) by \(1\) and go to Step 3. **Remark**: The computational complexity implies the computational amount of multiplications and adds in the algorithm, depending on the sizes and lengths.
## 3 Results and Discussion
### Cone Calorimetric Analysis
#### 3.1.1 Heat Release Rate (HRR)  
HRR is the most important performance parameter that characterizes fire intensity. The maximum HRR is the peak heat release rate (pHRR). The greater the HRR or pHRR is, the greater the risk of the polymer material becomes in the fire [10; 25].  
Figure 1 shows the heat release rate curves for different samples. Figure 1a-c) are the contrast analysis curves of the three-phase material, the two-phase material, and the pure EP; Figure 1d compares the heat release rate of the three-phase composites with different content reinforcement. From Figure 1a, the peaks of F5 and F5N5 are smaller than that of EP, and F5N5 is lower than F5. From Figure 1b, the peaks of F7 and F7N7 are also smaller than that of EP. From Table 2, the pHRR of F7N7 is reduced by 12.12% compared to F7. From Figure 1c, the peaks of F10 and F10N5 are also smaller than those of EP. From Table 2, the pHRR of F10N5 is 16.49% less than F5. The above shows that certain CNTs and CF can synergistically inhibit the thermal cracking rate of EP composites and slow the flame propagation, thus enhancing its flame retardant properties. From Figure 1d, the pHRR of F7N7 is the lowest, and the peak value is the smallest, which is reduced by 34.65% than the EP, indicating that the CNTs and CF under the ratio have the best synergistic flame retardant effect.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Samples** & **1** **TTI (s)** & **b** **pHRR (kW-m\({}^{-2}\))** & **c** **THR (MJ)-m\({}^{-2}\))** & **d** **TSP (m\({}^{2}\))** & **c** **mCOY (kg\(\cdot\)kg\({}^{-1}\))** \\ \hline EP & 52 & 971.7 & 98.8 & 30.1 & 0.073 \\ F5 & 69 & 792.7 & 92.5 & 27.3 & 0.069 \\ F7 & 80 & 722.6 & 88.2 & 27.4 & 0.076 \\ F10 & 62 & 840.2 & 88.9 & 22.2 & 0.068 \\ F15 & 98 & 793.3 & 101.7 & 30.0 & 0.075 \\ F5N5 & 73 & 648.1 & 75.0 & 16.8 & 0.074 \\ F5N10 & 92 & 937.0 & 122.0 & 27.8 & 0.064 \\ F7N7 & 76 & 635.0 & 80.3 & 21.3 & 0.071 \\ F10N5 & 80 & 701.7 & 99.3 & 23.4 & 0.067 \\ \hline \end{tabular}  
* TTI means the time to ignition. \({}^{\mathrm{b}}\) pHRR means the peak heat release rate. \({}^{\mathrm{c}}\) THR means the total heat release.
* TSP means the total smoke product. \({}^{\mathrm{e}}\) mCOY means the mean yield of CO.  
\end{table}
Table 2: Cone calorimeter data of carbon fiber (CF)/EP and carbon nanotubes (CNTs)/CF/EP composites [26].  
Figure 1: Heat release rate (HRR) of different samples. (**a**) HRR of epoxy resin (EP), F5, F5N5, and F5N10; (**b**) EP, F7, and F7N7; (**c**) EP, F10, and F10N5; and (**d**) EP, F5N5, F5N10, F7N7, and F10N10.
## 2 Experimental Section
### Methods Validation (Quality Assurance)  
To ensure the validity of the method and quality of the results, a variety of parameters, such as linearity, accuracy, recoveries, limit of detection (LOD), limit of quantification (LOQ), sensitivity and selectivity/specificity were accounted for. Linearity was determined by analysing calibration standards and obtaining the correlation coefficient (r\({}^{2}\)) value of \(\geq\)0.995. Accuracy was determined using the recovery test through spiking the sample with the standard of interest with a known concentration of 10 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, and the analysis was carried out in five replicates. Sensitivity is the capability of the method to discriminate between small differences of concentration of analytes [21]. The LOD is the smallest concentration of an analyte in the test sample that can be reliably distinguished from zero concentration, and it is calculated at concentrations given by 3 times the standard deviation of signal-to-noise ratio. LOQ is defined as the lowest concentration of an analyte that can be determined with acceptable precision and accuracy, and it is given by concentrations 10 times the standard deviation of signal-to-noise ratio. Specificity/selectivity is the ability of a method to respond to a particular analyte of interest in the presence of possible interferences, such as impurities, degradants and matrix effects. This was ensured by using the mass spectrometer as a detector where the detector was set to selectively detect only ions of the targeted PAHs [22]. Specificity of the method was ensured by comparing spiked and non-spiked samples under SIM mode to ensure that the electron ionization detector source on the GC-MS instrument was set to target and identify only the PAHs of interest by using specific \(m/z\) values.
## 3 Methodology
### Proposed Integration of PSO and FCM Algorithms
#### 3.2.4 PSO-FCM  
To overcome FCM's local minima trapping and achieve better clustering results, we harness the strengths of both FCM and PSO. In particular, the PSO-FCM algorithm uses FCM's objective function (6) as the fitness function and PSO's global search method to find cluster centers.  
The PSO-FCM algorithm utilizes particles in PSO to represent potential solutions for FCM's cluster centers. The positions of particles in a swarm of \(Q\) particles encode the cluster centers \(\mathbf{z}\). Each particle \(q\) is defined as \(x_{q}=(d_{q1},d_{q2},\cdots,d_{qC})\), representing candidates in the swarm (\(Q\)) for clustering the data. The updated dimension values representing cluster centers generate \(\mathbf{z}\) by decoding \(g_{best}\) when PSO recognizes an accepted cluster center or reaches a predefined number of iterations. The convergence of both algorithms toward the same objective function for optimal clustering drives the minimization of fitness functions in PSO-FCM. This fusion of FCM and PSO, through PSO-FCM, capitalizes on the advantageous features of both. Below, Algorithm 2 summarizes the PSO-FCM algorithm.  
```
Input: \(t_{max}\), \(m\), \(c_{1}\), \(c_{2}\), \(\omega_{0}\), \(\omega_{1}\). Initialization \(x_{q}^{(0)}=(d_{q1},d_{q2},\cdots,d_{qC})\), \(v_{q}^{(0)}=(v_{q1},v_{q2},\cdots,v_{qC})\) \(\mathit{pbest}_{q}=x_{q}{}^{(0)}\) For each particle  Calculate the \(fitness\) of particle. \(\mathit{temp}=\min\{fitness(\mathit{pbest}_{1}),fitness(\mathit{pbest}_{2}), \cdots,fitness(\mathit{pbest}_{Q})\}\); \(\mathit{gbest}=\mathit{temp}\);  Repeat until \(t_{max}\) iterations are reached  Update \(v_{q}\) and \(x_{q}\).  Calculate the membership \(u_{qj}\).  Update the \(fitness\) of the particle;  If \(fitness(x_{q}^{(t)})<fitness(\mathit{pbest}_{q})\) \(\mathit{pbest}_{q}=x_{q}^{(t)}\);  Update \(p_{best}\);  if \(fitness(\mathit{pbest}_{q})<fitness(\mathit{gbest})\), \(\mathit{gbest}=\mathit{pbest}_{q}\).  Decode \(g_{best}\) to obtain cluster centers \(\mathbf{z}\).
```  
**Algorithm 2** PSO-FCM algorithm  
In computing the fitness of each particle \(q\), we first derive the cluster center \(z_{q}\) from the particle's position representation. Subsequently, we calculate the membership degree \(u_{qj}\) for each data point \(x_{q}\) using Equation (8). By utilizing these cluster centers and membership degrees, we evaluate the \(fitness\) of each particle \(q\) using Equation (6). Notably, the minimization of the fitness function in PSO-FCM aligns with minimizing the objection function \(J\) in FCM. Both algorithms define the same objective function, thereby aiming for optimal clustering. PSO-FCM leverages the strengths of both FCM and PSO, utilizing their favorable features to minimize the fitness function in pursuit of achieving optimal clustering. Equations (9) and (10) update the velocity \(v_{q}\) and position \(x_{q}\) of each particle \(q\)
## 4 Discussion
That is to say, under the premise that the initial communication network is undirected and connected, the minimal communication radius causes the largest convergent speed. The minimal communication radius means the minimal edge set of the connected interaction network of the UAV system, at the same time, requires minimal computation capability for each UAV during the whole evolutionary process. This conclusion can be easily used to extend the distributed control algorithm from small groups to large-scale groups of UAVs and to skillfully avoid the awkwardness of exponential growth caused by the increase in group size. Scalability performance is examined by increasing the robot group size. In this case, communication radius \(D\) is fixed to 4 m. The energy cost \(EE(t)\) is drawn versus time when the number of UAVs is double increased from 40 to 160. Figure 5 shows the good scalability performance of the flocking system (1) under control protocol (11), as the converge time almost the same following with the increase of group size. I have labeled the coordination value of three points on the three curves. There are \(N(N-1)/2\) edges between any two UAVs for a system consisting of \(N\) UAVs. When \(N=40\), then we have \(N(N-1)/2=780\) edges, and the average distance between any two  
Figure 4: Time cost of flocking system (1). Figure 3: Energy costs of 80 UAVs versus time under different connected communication radii and relative specific functions. (**a**) energy costs; (**b**) specific functions. UAVs is \(1493.6695/780=1.915\approx 2\). Similarly, when \(N=80\), then we have \(N(N-1)/2=3160\) edges, and the average distance between any two UAVs is \(6107.8093/3160=1.93\approx 2\); when \(N=160\), then we have \(N(N-1)/2=12,720\) edges, and the average distance between any two UAVs is \(24,788.5314/12,720=1.95\approx 2\). They all equal to the sqrt of communication radius D. Thus, Figure 5 have proved the conclusion that the stable individual distance \(d^{*}=\sqrt{D}\). Furthermore, Figure 5 reinforces this conclusion that the converge times for different group sizes are almost the same when the value of the connected communication radius is small enough.
# HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network
## 4 Optical Experiments  
### Conditions for Optical Experiments  
For experimental testing of the proposed HoloForkNet network, digital holograms of 3D scene phase objects were recorded. The scheme of the experimental setup is shown in Figure 10.  
Figure 9: Correlation between the reconstruction quality and the number of objects in the planes. The holograms contained 3D scenes consisting of eight object planes.  
A 200 mW Nd:YAG crystal laser with a wavelength of 532 nm was used. The beam passed through a filtration system (lens L1 and pinhole) and was collimated using lens L2. Beam splitter BS1 divided the light into reference and object beams. Beam splitter BS2 divided the object beam into two directions: towards spatial light modulators SLM1 and SLM2. SLM1 is a phase-only LCoS SLM HoloEye PLUTO-2 (1920 \(\times\) 1080 pixels; pixel size is 8 \(\times\) 8 \(\upmu\)m). SLM2 is a phase-only LCoS SLM HoloEye GAEA-2 (4160 \(\times\) 2464 pixels; pixel size is 3.74 \(\times\) 3.74 \(\upmu\)m). The images of the objects were displayed on the SLMs. The beams are reflected from the SLMs, and every pixel obtains its own phase shift. After that, the beams were combined through BS2 and passed through the beam splitter, BS3. The reference beam was reflected from mirror M and combined with the object beam using BS3. The resulting interference pattern was recorded by a Flare 48 MP monochrome camera (7920 \(\times\) 6004 pixels; pixel size is 4.6 \(\times\) 4.6 \(\upmu\)m). The digital camera and SLMs were controlled by a computer.  
This setup allows for the recording of digital holograms of 2D and 3D scenes. The minimum distance used from the SLM to the camera was 67 cm. If the distances from BS2 to SLM1 and SLM2 are almost identical, then quasi-2D scene holograms are recorded. If one of the modulators is off, a hologram of one 2D phase object is recorded. If the distances from BS2 to SLM1 and SLM2 are unequal, then 3D scene holograms are recorded. The 3D scene consists of two different object planes, which were displayed on SLM1 and SLM2. Holograms of phase objects were experimentally recorded in this mode. The depth of the recorded 3D scene was modulated by moving SLM2.
# Native Plant Capacity for Gentle Remediation in Heavily Polluted Mines
## 3 Results
### Nutrient Pool in Leaf and Root of Dittrichia Viscosa
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{**Trace-Element Concentration in Roots (ppm)**} \\ \hline  & **Zn** & **Pb** & **Cu** & **Mn** & **Fe** \\ \hline
**CC** & 188.9 \(\pm\) 64.5a & 259.4 \(\pm\) 47.9a & 1.9 \(\pm\) 0.9a & 106.0 \(\pm\) 28.5a & 421.2 \(\pm\) 52.6a \\  & (71.6-294) & (196.6-353.5) & (0.5-3.6) & (72.1-162.6) & (321.8-500.8) \\  & 145.3 \(\pm\) 16.4a & 200.6 \(\pm\) 8.5a & 2.4 \(\pm\) 1.0a & 136.6 \(\pm\) 7.1a & 490.4 \(\pm\) 83.9a \\
**CN** & (124.7-177.7) & (186.6-215.9) & (0.32-3.7) & (127.9-150.8) & (387.6-656.8) \\ \hline
**p-value** & 0.512 & 0.275 & 0.827 & 0.512 & 0.827 \\ \hline
**DC** & 301.2 \(\pm\) 114.7a & 421.2 \(\pm\) 51.6a & 0.7 \(\pm\) 0.7 & 41.5 \(\pm\) 8.8a & 442.6 \(\pm\) 83.2a \\  & (83.5-472.8) & (349.2-521.3) & (0.0-2.3) & (24.1-53.1) & (278.1-547.1) \\
**DN** & 18.6 \(\pm\) 1.1b & 317.1 \(\pm\) 10.9b & 33.7 \(\pm\) 2.2a & 376.2 \(\pm\) 85.6a \\  & (17.2-20.8) & (296.6-334.1) & 0 & (29.3-36.7) & (269.2-545.4) \\ \hline
**p-value** & **0.049** & **0.049** & 0.317 & 0.512 & 0.512 \\ \hline
**EC** & 215.4 \(\pm\) 15.1a & 174.2 \(\pm\) 15.4a & 12.2 \(\pm\) 0.6a & 18.1 \(\pm\) 3.5a & 149.9 \(\pm\) 45.4a \\  & (186.1-236.4) & (145.4-198.2) & (11.1-13.4) & (12.2-24.5) & (76.7-233.1) \\
**EN** & 14.2 \(\pm\) 9.0b & 137.2 \(\pm\) 5.3a & 12.5 \(\pm\) 2.1a & 22.5 \(\pm\) 8.5a & 103.5 \(\pm\) 25.4a \\  & (5.1-32.3) & (128.3-146.7) & (10.1-16.6) & (10.2-38.9) & (58.8-146.9) \\ \hline
**p-value** & **0.049** & 0.126 & 0.512 & 0.827 & 0.512 \\ \hline \end{tabular}
\end{table} 
Table 3: Trace-element concentrations in roots. Mean \(\pm\) SE value is indicated, and minimum and maximum values are shown in brackets. The non-parametric Kruskal–Wallis test was applied. Different letters indicate significant differences at a significance level \(<\) 0.0505 within the same element and plant species. CC = contaminated soil under _C. salviofilius_, CN = uncontaminated soil under _C. salviofilius_, DC = contaminated soil under _D. viscosa_, DN = uncontaminated soil under _D. viscosa_, EC = contaminated soil under _E. pithyusa_, EN = uncontaminated soil under _E. pithyusa_. were higher in plants under non-contaminated soils than in contaminated soils but only with statistically significant differences for Fe in roots. The average biomass of leaves was 238.8 g \(\pm\) 32.3 (SE) and 254.0 g \(\pm\) 38.7 (SE) of roots for _D. viscosa_ individuals in uncontaminated soil, while in contaminated soil the average biomass of leaves was 204.7 g \(\pm\) 30.4 (SE) and 117.0 g \(\pm\) 9.5 (SE) in roots. The species studied accumulated an average of up to 369 g of Zn per kg of aerial biomass and 35 g in the root biomass.