esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Main Theoretical Results
### Monotone-Iterative Technique
)\), use the continuity of the functions \(\vartheta,\Delta\) and we obtain the fractional integral equation \[\chi(\sigma)=\lim_{m\to\infty}\varsigma^{(m)}(\sigma)\] \[=\lim_{m\to\infty}\Big{\{}\Big{(}\varsigma^{(m-1)}(0)-\frac{1}{\mu} \Delta(\varsigma^{(m-1)}(0),\varsigma^{(m-1)}(a))\Big{)}e^{(\xi-1)\xi}E_{\gamma} (-\lambda(\frac{\sigma}{\xi})^{\gamma})\] \[\quad+\frac{1}{\xi^{\gamma}\Gamma(\gamma)}\int_{0}^{\sigma}( \sigma-\tau)^{\gamma-1}e^{(\xi-1)(\frac{\sigma-\tau}{\xi})}E_{\gamma,\gamma}( -\lambda\big{(}\frac{\sigma-\tau}{\xi}\big{)}^{\gamma})\] \[\qquad\times\Big{(}\theta(\tau,\varsigma^{(m-1)}(\tau))+\lambda \varsigma^{(m-1)}(\tau)\Big{)}\,d\tau\Big{\}} \tag{13}\] \[=\Big{(}\chi(0)-\frac{1}{\mu}\Delta(\chi(0),\chi(T))\Big{)}e^{( \xi-1)\frac{\sigma}{\xi}}E_{\gamma}(-\lambda\big{(}\frac{\sigma}{\xi})^{ \gamma})\] \[\quad+\frac{1}{\xi^{\gamma}\Gamma(\gamma)}\int_{0}^{\sigma}( \sigma-\tau)^{\gamma-1}e^{(\xi-1)(\frac{\sigma-\tau}{\xi})}E_{\gamma,\gamma}( -\lambda\big{(}\frac{\sigma-\tau}{\xi}\big{)}^{\gamma})\Big{(}\theta(\tau, \chi(\tau))+\lambda\chi(\tau)\Big{)}d\tau\] \[\qquad\text{for}\;\;\sigma\in(0,a].\]  
Thus, the function \(\chi\in C([0,a])\) is a mild solution of the GPDE (4) on \([0,a]\). Similarly, the sequence \(\{\alpha^{(k)}(\cdot)\}_{k=0}^{\infty}\) is monotone, bounded and equicontinuous on \([0,a]\). Thefore, it is convergent uniformly to \(\mathcal{W}(\sigma)=\lim_{n\to\infty}\varpi^{(n)}(\sigma)\) and \(\mathcal{W}(\sigma)\) is a mild solution of the GPDE (4) on \([0,a]\) and \(\chi(\sigma)\leq\mathcal{W}(\sigma),\;\sigma\in[0,a]\). In Theorem 2 the existence of mild solutions of GPDE (4) is indirectly proved. Let the conditions 1 and 2 of Theorem 2 be fulfilled and the function \(\Delta\in C(\mathbb{R}\times\mathbb{R},\mathbb{R})\), be non-increasing with respect to both of its arguments. Then the claims of Theorem 2 are satisfied with \(\mu=1\) in Equation (7) of the operator \(\Omega\). If additionally in Theorem 2 we assume that any mild solution of GPDE (4) \(y\in C^{\gamma,\xi}[0,a]\) then according to Theorem 1 any mild solution of GPDE (4) is a solution of the same problem and the sequences of mild lower solutions and mild upper solution in Theorem 2 will converge to a solution of GPDE (4). If GPDE (4) has a unique solution, then operator \(\Omega\) has a unique fixed point and GPDE (4) has a unique mild solution. Let the conditions of Theorem 2 be fulfilled and GPDE (4) have unique solution \(y\in C^{\gamma,\xi}[0,a]\). Then additionally to the claims of Theorem 2 both sequences \(\{\varsigma^{(k)}(\sigma)\}_{k=0}^{\infty}\) and \(\{\varpi^{(k)}(\sigma)\}_{k=0}^{\infty}\) approach this solution of GPDE (4).
# Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching
## 4 Discussion  
Since this work was not to newly train a deep learning network, but to employ a suitable pre-trained and pre-validated deep learning model for cost-effective and automated background removal, the suitability of the employed Monodepth2 was additionally tested. To show the effectiveness of depth estimation results, the relative mean depth values between the ROI and background regions within FOV images were calculated as shown in Figure 12. The higher depth values mean the closer region from the digital camera mounted on the UAV. It can be easily observed that the ROIs' relative mean depth values were consistently higher than the background ones without any overlap between them. This means that the relative depth values were successfully estimated using Monodepth2, and the corresponding ROIs were properly extracted. Here, Monodepth2 works well because this work is under assumption that the structural ROI is much closer than the background objects from the UAV. However, the depth estimation errors may increase when the working distance between the ROI and background objects is similar.  
In addition, since one of the critical obstacles to construct the large-scale structural exterior map is the computational cost, the computational time was compared between the raw image and optimal image cases in Table 1. The structural exterior map establishment took 9 h 13 min 47 s using 118 raw images, while it just conducted within 1 h 18 min 57 s using 35 optimal images. Note that the computational times were estimated when it comes to CPU of Intel(r) Xeon E5-2630 v4 with 64 gigabytes of memory. The optimal image case includes the optimal image selection time of 40 min 49 s, which is about 51.69% out of total 1 h 18 min 57 s. These results indicate that the proposed technique is critical for structural exterior map construction performance, and also can extremely reduce the computational cost of almost 85.7%.  
Figure 11: Comparison of structural exterior map construction results using (**a**) raw images and (**b**) optimal images.
# Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
## 3 Proposed Wind Power Curve Model
### Experiment Setting  
In order to make a comparative study comprehensively, five parametric models (e.g., 3-PLF, 4-PLF, 5-PLF, 6-PLF and MHTan) and five nonparametric models, back-propagation neural network (BPNN), ANFIS, ELM, WELM, ORELM, were utilized. For all models, the only input was wind speed, and the output was wind power. Moreover, all models were trained with the same processed data generated by the FCM-based model for a fair comparison.  
In the paper, similar to [10], the backtracking search algorithm (BSA) was employed to optimize the model parameters in all parametric models with the least squares loss function. Grid search was employed to tune the parameters in SVR. For BPNN, the number of neurons in hidden layers are determined by the Hecht-Nelson method [9]. For ELM-based regression models, the optimal number of hidden layers can be obtained when the modeling error of the observed model on the validation set reaches the minimum. The candidate number of hidden layers can be selected from the set \([20,40,60,\cdots,200]\).  
In order to test the performances of different models, four error indicators, mean absolute error (MAE), root mean square error (RMSE), normalized mean absolute percentage error (NMAPE) and the coefficient of determination (\(R^{2}\)), were employed. For all models, the values of different error  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **All Samples (\#)** & **Training Samples (\#)** & **Validation Samples (\#)** & **Test Samples (\#)** \\ \hline A & 5500 & 4500 & 400 & 600 \\ B & 6500 & 5500 & 400 & 600 \\ C & 7500 & 6000 & 500 & 1000 \\ D & 6000 & 5000 & 400 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The numbers of different types of samples in four datasets.  
indicators were computed based on the same test set. Assuming that \(y_{l}\) denotes the lth measured value in the test set, and \(\hat{y}_{l}\) is the corresponding forecast (\(l=1,\cdots,L\)), \(\overline{y_{l}}\) is the mean of all measured values, \(L\) is the number of samples in the test set, the calculations for the four error indicators are presented in Table 3. For MAE, NMAPE and RMSE, a smaller value indicates a better estimator. However, the closer the value of \(R^{2}\) is to 1, the better the model.
## 3 Experimentation and Results  
In this section, experiments are carried out to verify the validity of the investigated NIALM, by a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset [32]. The UK-DALE dataset contains records of power consumption measured and collected from five different households in the UK. In each house, the authors in [32] recorded both whole-house power consumption (power demand) from the mains every 6 s and power consumption by concerned individual electrical appliances every 6 s. Figure 6 shows the considered historical power demand on two typical days (Sunday 7 December 2014 (Figure 6a,b) and Thursday 4 December 2014 (Figure 6c,d)) in House 1 from the UK-DALE dataset, which is considered, parsed, and used to experimentally validate the performance of the investigated methodology in load recognition. The summarization of the UK-DALE dataset can be found in [32]. In Figure 6a,c, we show the total power demand in the mains. Also, we show the power demand by the concerned individual electrical appliances and all other submeters [32] considered together and treated as a single individual in power absorptions for load disaggregation. As shown in Figure 6b,d, the thin white gap between the power demand in the mains and the summed-up power demand illustrates the amount of power demand, base load, which is not concerned/metered. The concerned electrical appliances, including the considered submeters as a single individual in power absorptions to \(P_{i}\) in Equation (2), are listed in Table 1; their power demand is shown in Figure 6 and the base load, \(P_{base}\) in Equation (2), is assumed to have a constant value of 150.0 watts for simplicity's sake. Figure 7 shows the power demand of the several individual electrical appliances targeted in this paper and listed in Table 1. The behavior of the power demand by the electrical appliances can be found in [32]. For example, Figure 7a shows the power demand by the fridge running and doing its respective job of compressor on (state 1: its mean power consumption is ~90.0 watts with a standard deviation of ~44.0) or defrost (state 2: its mean power consumption is ~245.0 watts with a standard deviation of ~16.0). In this paper, the load classes considered from the targeted electrical appliances in Table 1 and recognized are shown in Table 2.  
Figure 6: _Cont._
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 7 Experiments
### Larger Monitored Area  
The larger monitored area considered here is a 2D square with 400 m of side. The graphs presented here show \(T_{total}\) of the four heuristic methods, in three different scenarios.  
In Scenario 2, whose results are illustrated in Figure 14, we consider 150 uniformly randomly deployed sensor nodes and the drone's speed of 2 m/s. We increased the amount of data stored in each sensor node (X-axis) and analyzed \(T_{total}\) (Y-axis). Incremental-Move and Decremental-Move presented the best performance in practically all experiments. The strategy to gather data during the drone movement reduced the overall time up to 25% when the nodes had more data stored than the original heuristics. In this scenario, the data routes tend to be longer than in the previous scenario. The tour created by the original heuristics considers several nodes to send data to the drone by data routes with many hops. Since some of these nodes send data to the drone in movement, the reduction of \(T_{total}\) is bigger. Here, we also verify that Decremental-Move outperformed all heuristics. This occurs because the Decremental heuristic tends to create a longer tour, increasing the number of nodes able to send data during the drone movement.  
Figure 15 presents the results of the experiments in Scenario 3. In this figure, the X-axis represents the number of sensor nodes varying from 100 to 250, and the Y-axis represents \(T_{total}\). We consider 60 Kb of data stored in each sensor node, and the drone speed is 2 m/s. The two heuristics with the proposed Algorithm 3 outperformed the original heuristics in practically all experiments. It is important to note that the growth of all heuristics is linear. Hence, the heuristics analyzed here can be used in scenarios with more sensor nodes.  
In Scenario 4, we analyze the influence of the drone's speed on the overall data gathering time. Figure 16 presents the results of the experiments in this scenario. We consider 60 Kbits of data in each sensor node, and the number of nodes in the monitored area is 150. In this scenario, all heuristics take advantage of the higher drone speed. Even Incremental-Move and Decremental-Move, which consider data gathering during the drone movement, reduced \(T_{total}\) with the speed growth.  
Figure 13: Drone hovering time for data gathering (\(T_{collecting}\)) in a small monitored area.  
Figure 14: Overall data gathering time (\(T_{total}\)) in a large monitored area.  
Figure 15: Varying the number of nodes in the WSN—Large Monitored Area.
## 3 Results  
### The Aurora Event during 18-23 November 2003  
A global severe geomagnetic storm (Dst-index \(\leq-\)200 nT) occurred during 18-23 November 2003, producing intense auroral events. The temporal variations in key space weather parameters such as V\({}_{\rm sm}\), IMF B\({}_{\rm Z}\), Dst index, and AE index during this period are shown in Figure 1.  
Figure 1 shows that the solar wind speed began to decrease continuously from 18 November, and reached its lowest value of 441 km/s around 02:00 UT on 20 November.  
From 18 November, the solar wind speed continuously decreased and reached its lowest value of 441 km/s around 02:00 UT on 20 November, as shown in Figure 1. During this period, the IMF B\({}_{\rm Z}\) and Dst-index remained relatively stable, while the AE index showed small fluctuations, but also remained relatively stable. Thus, the solar wind speed significantly increased and reached its first peak of 699 km/s around 09:00 UT on 20 November, carrying the IMF B\({}_{\rm Z}\) to the Earth's magnetosphere and reconnecting with the geomagnetic field. The high-energy particles carried by the solar wind injected into  
Figure 1: Temporal variations in solar wind speed V\({}_{\rm sm}\), the interplanetary magnetic field IMF B\({}_{\rm Z}\), disturbance storm time index Dst, and the auroral electrojet index AE (from top to bottom) during 18–23 November 2003.  
the atmosphere along the magnetic field lines, colliding with the atoms and molecules of the upper atmosphere. This collision caused the ionization and excitation of neutral components, triggering reactions (1)-(3).  
Subsequently, the Dst index rapidly decreased, marking the entry of the magnetic storm into the main phase. Within a short period, a significant change in the AE index values was observed, with an overall upward trend after fluctuating greatly from its minimum value of 40 nT around 02:00 UT on 20 November, and reaching a maximum value of 1698 nT around 16:00 UT on the same day. Within 14 h, it increased by a factor of 42, indicating the occurrence of a strong aurora [51] during this period. The IMF B\({}_{Z}\) reached its lowest value of \(-\)45.6 nT around 16:00 UT on 20 November, and the Dst index reached its lowest value of \(-\)422 nT between 20:00 UT and 21:00 UT on the same day, indicating that this was an extreme magnetic storm event [36]. Subsequently, the solar wind speed gradually decreased, signifying the entry of the magnetic storm into the recovery phase. On 23 November, the IMF B\({}_{Z}\), Dst index, and AE index gradually returned to a quiet level. It is worth noting that at around 12:00 UT on 22 November, the AE index displayed a gradually increasing trend, reaching 1296 nT, presumably due to a small peak in the solar wind speed (580 km/s) around 11:00 UT on the same day.
## 2 Theory  
### BB-DMAS Beamforming  
Compared to the original version of DMAS beamforming which relies on signal coherence among radio-frequency (RF) channel waveform [2], baseband DMAS (BB-DMAS) beamforming does not require oversampling and an additional band-pass filter to extract the RF-DMAS signal spectrally shifted to harmonic frequency [6]. Specifically, BB-DMAS beamforming first demodulates the received channel data down to the baseband and scales the channel magnitude using \(p\)-th root. After channel summation, the \(p\)-th power is performed to restore the signal dimensionality. Given the time-compensated baseband signal of the \(n\)-th RxCh as \(s_{n}=a_{n}e^{j\phi_{nk}}\), the image output of BB-DMAS beamforming can be expressed as:  
\[y_{\text{BB-DMAS}}=\left(\frac{1}{N}\sum_{n=1}^{N}\sqrt[]{a_{n}}e^{j\phi_{nk}} \right)^{p}, \tag{1}\]  
where the degree of signal coherence among channels is determined by the \(p\) value (\(p>1\)). When the \(p\) value increases, DMAS beamforming introduces more signal coherence into its image output and thus will have better clutter suppression. Nonetheless, when the \(p\) value is too high, DMAS image would suffer from high speckle variation, noticeable dark-region artifacts and highlighting effect of the focal zone. Note that BB-DMAS beamforming degenerates to DAS beamforming when \(p\) equals unity.
# Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia
## 4 Indoor and Outdoor Probability Models
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Probability**} & \multicolumn{2}{c}{**Vehicle**} & \multicolumn{2}{c}{\(P(R)=min(1,max(0,aR^{2}+bR+c))\)} \\  & & **Density** & **a** & **b** & **c** \\ \hline \multirow{4}{*}{Highway} & \multirow{4}{*}{\(P^{H}_{LOS}(R)\)} & Low & \(1.5*10^{-6}\) & \(-0.0015\) & \(1\) \\  & & Medium & \(2.7*10^{-6}\) & \(-0.0025\) & \(1\) \\  & & High & \(3.2*10^{-6}\) & \(-0.003\) & \(1\) \\ \cline{2-6}  & \multirow{4}{*}{\(P^{H}_{NLOS}(R)\)} & Low & \(-2.9*10^{-7}\) & \(0.00059\) & \(0.0017\) \\  & & Medium & \(-3.7*10^{-7}\) & \(0.00061\) & \(0.0150\) \\  & & High & \(-4.1*10^{-7}\) & \(0.00067\) & \(0\) \\ \hline \multirow{4}{*}{Urban} & \multirow{4}{*}{\(P^{U}_{LOS}(R)\)} & Low & \(P_{l}(R)=min(1,max(0,0.8548*e^{-0.0064k}))\) \\  & & Medium & \(P_{m}(R)=min(1,max(0,0.8372*e^{-0.0114k}))\) \\ \cline{2-6}  & & High & \(P_{h}(R)=min(1,max(0,0.8962*e^{-0.017k}))\) \\ \cline{2-6}  & \multirow{4}{*}{\(P^{U}_{NLOS}(R)\)} & & \(1\)-\(P^{U}_{LOS}(R)\) \\ \cline{1-1} \cline{3-6}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: LOS and NLOS probability formulas for urban and highway scenarios under different vehicle densities. In this paper, outdoor LOS and NLOS probability models are utilized to estimate path loss in urban and highway scenarios. In these scenarios, population density, vehicle traffic density, and call rates are significantly higher than in semi-urban and rural areas. Path loss and signal power are influenced by frequent and heavy rainfall. Therefore, in this paper we focus on the urban and highway scenarios in order to estimate the optimal path difference to enhance path loss.
# Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains
## 4 Experimental Simulation  
### A Particular Class of Gaussian PMC Model  
In order to verify the effectiveness of the proposed PF-PMC-PHD filter and compare the tracking performance with the particle PHD filter based on the HMC model (PF-HMC-PHD), the experimental simulation uses a special Gaussian PMC model, with \(p(\boldsymbol{x}_{k}|\boldsymbol{x}_{k-1})\) and \(p(\boldsymbol{y}_{k}|\boldsymbol{x}_{k})\) that are the same as \(f_{k|k-1}(\boldsymbol{x}_{k}|\boldsymbol{x}_{k-1})\) and \(g_{k}(\boldsymbol{y}_{k}|\boldsymbol{x}_{k})\) of the HMC model while relaxing the independence assumption. The two models have the same local physical properties, but remember that in general \(p(\boldsymbol{x}_{k}|\boldsymbol{x}_{k-1})\) will not be equal to \(p(\boldsymbol{x}_{k}|\boldsymbol{x}_{k-1},\boldsymbol{y}_{k-1})\) and \(p(\boldsymbol{y}_{k}|\boldsymbol{x}_{k})\) will not be equal to \(p(\boldsymbol{y}_{k}|\boldsymbol{x}_{k},\boldsymbol{x}_{k-1},\boldsymbol{y}_ {k-1})\) in the PMC model, while the HMC model satisfies Equation (2). Suppose that for all \(k\), the HMC model satisfies  
\[p(\boldsymbol{x}_{0})=N(\boldsymbol{x}_{0};\boldsymbol{m}_{0},P_{0}) \tag{22}\]  
\[f_{k|k-1}(\boldsymbol{x}_{k}\Big{|}\boldsymbol{x}_{k-1})=N(\boldsymbol{x}_{k };F_{k}\boldsymbol{x}_{k-1},Q_{k}) \tag{23}\]  
\[g_{k}(\boldsymbol{y}_{k}|\boldsymbol{x}_{k})=N(\boldsymbol{y}_{k};H_{k} \boldsymbol{x}_{k},R_{k}) \tag{24}\]  
The corresponding Gaussian PMC model can be expressed by the following formula [11]:  
\[p(\boldsymbol{\xi}_{0})=N\Bigg{(}\boldsymbol{\xi}_{0};\left[\begin{array}{ c}\boldsymbol{m}_{0}\\ H_{0}\boldsymbol{m}_{0}\end{array}\right]\!,\left[\begin{array}{cc}P_{0}&(H_{ 0}P_{0})^{\mathsf{T}}\\ H_{0}P_{0}&R_{0}+H_{0}P_{0}H_{0}^{\mathsf{T}}\end{array}\right]\Bigg{)} \tag{25}\]  
\[p_{k|k-1}(\boldsymbol{\xi}_{k}|\boldsymbol{\xi}_{k-1})=N(\boldsymbol{\xi}_{k}; B_{k}\boldsymbol{\xi}_{k-1},\boldsymbol{\Sigma}_{k}) \tag{26}\]  
where \(\boldsymbol{\xi}_{k}=(\boldsymbol{x}_{k},\boldsymbol{y}_{k})\) and  
\[B_{k}=\left[\begin{array}{cc}F_{k}-F_{k}^{2}H_{k-1}&F_{k}^{2}\\ H_{k}F_{k}-H_{k}^{2}H_{k-1}&H_{k}^{2}\end{array}\right]\!,\boldsymbol{\Sigma}_ {k}=\left[\begin{array}{cc}\Sigma_{k}^{11}&{\Sigma_{k}^{21}}^{\mathsf{T}}\\ \Sigma_{k}^{21}&\Sigma_{k}^{22}\end{array}\right] \tag{27}\]  
\[\Sigma_{k}^{11}=\mathsf{Q}_{k}-F_{k}^{2}R_{k-1}(F_{k}^{2})^{\mathsf{T}} \tag{28}\]  
\[\Sigma_{k}^{21}=H_{k}\mathsf{Q}_{k}-H_{k}^{2}R_{k-1}\Big{(}F_{k}^{2}\Big{)}^{ \mathsf{T}} \tag{29}\]  
\[\Sigma_{k}^{22}=R_{k}-H_{k}^{2}R_{k-1}(H_{k}^{2})^{\mathsf{T}}+H_{k}\mathsf{Q}_ {k}(H_{k})^{\mathsf{T}} \tag{30}\]
## 3 Results and Discussion
### Effect of Coating Technology on Optical Performance  
It can be seen from Figure 4 that the color aberration of No. 1-8 experimental samples was less than 1.2 compared with No. 0 sample without microcapsules, which indicated that the coating technology had no effect on the chromatic aberration of the coating film.  
It can be seen from Figure 5 that among No. 1-8, the glossiness of coating films No. 5-8 was lower, which was because microcapsules were added to the topcoat, and the particles of microcapsules reduce the glossiness of the coating films. The glossiness of No. 1 and No. 2 coating film was relatively higher, because microcapsules were added in primer, the primer coating times were less, and the microcapsule particles were also reduced, so the coating film surface was smoother, diffuse reflection was reduced [34], and glossiness was higher.  
Figure 3: FT-IR of Nippon water-based emulsion microcapsules.  
Figure 2: SEM of Nippon water-based emulsion microcapsules.
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 4 Experimental Study  
This section provides empirical evidence regarding how a multi-class segmentation problem in which buildings and roads are extracted from high-resolution satellite imagery should be adequately tackled. For this purpose, the following key questions are answered through individual experiments:  
* Can binary decomposition strategies be beneficial to address remote sensing multi-class semantic segmentation problems?
* Can a multi-task learning scheme be used to further improve the performance in remote sensing semantic segmentation problems?
## 2 Results and Discussion
### Artemia salina Leach Lethality Bioassay  
_Artemia salina_ Leach (brine shrimp) is a small crustacean widely utilized as a model organism in the toxicity assessment of plant extracts [24]. The brine shrimp lethality bioassay has been applied as an alternative method for the preliminary estimation of toxicity because it shows several advantages, such as rapidity, cost-effectiveness, ease of handling and maintenance under laboratory conditions, and adaptability to various testing conditions [25].  
The results of the bioassay showed the absence of toxicity against brine shrimp larvae for both extracts of the _M. frticulosa_ subspecies. Indeed, the median lethal concentration values were found to be above 1000 \(\upmu\)g/mL, thus indicating their potential safety based on [PERSON]'s toxicity criterion [26]. The obtained results agree with those reported for the hydroalcoholic extracts of the aerial parts from the _M. incana_ intraspecific taxa previously investigated, which were tested under the same experimental conditions [2; 18].  
Figure 3: Free radical scavenging activity (DPPH assay) (**A**), reducing power (**B**), and ferrous ion chelating activity (**C**) of the hydroalcoholic extracts obtained from the aerial parts of _M. frticulosa_ subsp. _frticulosa_ and subsp. _coronopifolia_. Reference standard: BHT (**A**,**B**), EDTA (**C**). Values are expressed as the mean \(\pm\) SD (\(n=3\)).