Estel Color Off Buy Online

Estel Color Off Buy Online

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Paint for eyebrows and eyelashes ENIGMA Estel (Estel)Estel Enigma paint will give a rich color to eyelashes and eyebrows and make the look very beautiful and expressive. The paint causes allergic reactions, so its use is absolutely safe even for people with sensitive eyelid skin.

The kit contains all the tools necessary for coloring, so this professional paint can be used both by eyebrow masters, for working with clients, and for home coloring of eyebrows and eyelashes. The plastic consistency of the coloring mixture makes it convenient to apply it, it does not spread at all and is as comfortable to use as possible.

The palette includes 9 fashionable shades and allows you to perfectly match the color of the eyebrows and eyelashes to the color of the hair.Components are easy to mix and doseThe coloring mixture is plastic, easy to apply, contains a shimmering pigmentThe paint is easy and safe to useEconomical to useConvenient coloring kit contains everything you need

Remove make-up from eyes and eyebrows before coloring.For coloring eyelashes, place protective eyelid sheets under the eyelashes, first applying a protective compound or use silicone patches for eyelashesSqueeze 2 cm of cream paint into a bowl and add 5-6 drops of developing emulsion (3: 1) to the contents. Using a stick or spatula, mix thoroughly until a homogeneous mass is obtained.Gently apply the prepared mixture on the eyebrows and eyelashes. The duration of action is not more than 15 minutes. For eyebrows with gray hair, it is recommended to increase the exposure time to 20-25 minutes.After the exposure time has elapsed, carefully remove the paint with a cotton swab, and then rinse thoroughly with water.Attention! For coloring eyebrows and eyelashes, use only a freshly prepared mixture.

To date, several algorithms have been developed for acquiring gap-free LST data. These methods can be roughly divided into three categories [13,14,15]. The first category includes spatial information-based methods. Because of the spatial continuity and autocorrelation traits possessed by the LST, these methods are commonly used, especially in the research field of gap-filling data retrieved from Landsat 7 satellites [16,17], to reconstruct target pixels using the information of adjacent cloud-free pixels, such as through spline curve interpolations [18,19], geostatistical interpolation methods [20], and inverse distance weighting [21]. However, the performance of these spatial information-based methods is weakened due to their reliance on only spatial information while neglecting temporal information when the missing data extend over a relatively large range [22,23]. The second category comprises multitemporal information-based methods, which can be used to reconstruct gap-free pixels representing the same region at different times. Xu et al. [24] developed the harmonic analysis of the time series algorithm to reconstruct eight-day MODIS LST data based solely on time series LST data retrieved from Landsat satellite images. Although multitemporal information-based methods work well in regions with large quantities of missing data, the performance of these methods is weakened when large gaps exist in the acquisition time of multitemporal data due to land cover type variations [23,25]. The last category refers to hybrid methods derived based on spatiotemporal information; these methods are also regarded as the most promising methods [14] because they incorporate abundant information. Data fusion is a classical method belonging to this category. Proposed by Feng et al. [26], the spatial and temporal adaptive reflectance fusion model algorithm has been widely popular for generating high spatiotemporal resolution LST by integrating Landsat and MODIS LST data [27,28]. The method uses all the data at time t1 and coarse resolution data at time t2 to predict the fine resolution data at time t2. To decrease the systematic error caused by the heterogeneous surface, three weight functions, including spectrum, time, and space, are introduced. However, it is sensitive in heterogeneous areas, and the accuracy of its predictions remains to be fully assessed [29]. Traditional methods are widely used to obtain gap-free LST data, but the performances of these methods are affected by multiple factors, such as limited effective original data, land cover changes, sensitivity to high spatial heterogeneity, and applied accuracy verification [15]. Recently, machine learning models have been used to better describe nonlinear relationships due to the complicated formation of LST data [30]. Zhao et al. [31] applied a random forest (RF) regression model to reconstruct gap-free LST data in 2015 in southwestern Europe using Terra/MODIS daytime observations by building nonlinear links between the LST and related parameters. Their method achieved a correlation coefficient (R2) of 0.94 with a good visual performance. Xiao et al. [32] constructed an RF regression model to reconstruct a time series gap-free LST in Chongqing City, and the results showed that R2 value between the reconstructed LST and in situ LST measurements reached 0.89. Although machine learning models have good effects with respect to evaluation metrics and visual performance, their results are limited by the quality of the training dataset. Moreover, it is also hard to find appropriate auxiliary spatial parameters with fine resolution given the complex formation of the dependent variables.

Second, the SFRFR model establishes more precise links between LST and the related spatial parameters. The LST is an essential surface parameter, and its formation is inseparable from multiple spatial factors, such as the LUCC, ST, and solar radiation. Traditional linear function-based methods, including SI, have weak performances in characterizing spatial LST fluctuations when the study area possesses high spatial heterogeneity; thus, adopting nonlinear relationships between the LST and the related elements to simulate LST variations increases the accuracy of the results. This phenomenon is consistent with the findings of previous studies, which showed that the nonlinear relationships between LST and related parameters are more stable than linear relationships [54]. The unique adaptive splitting mechanism of DTs enables the RF model to better construct nonlinear linkages between the LST and the corresponding spatial parameters. Although both the SFRFR and RF regression models use nonlinear relationships to simulate the LST, the better performance of the SFRFR model may be due to its ability to incorporate the spatial feature of the LST in the model, leading to more spatial information being considered in the model. 59ce067264

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