esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 5 Experimental Results and Analysis
### Impact of the Number of Packets  
For data sampling, we can choose a different number of packets. Below, we evaluate its impact on device identification. We use two indicators in the following experiment: accuracy (acc) and \(F_{1}\). The accuracy acc is computed as:  
\[acc=\frac{TP+TN}{TP+FN+FP+TN} \tag{4}\]  
In addition, the recall is calculated as follows:  
\[recall=\frac{TP}{TP+FN} \tag{5}\]  
The precision is computed as follows:  
\[precision=\frac{TP}{TP+FP} \tag{6}\]  
The \(F_{1}\) value is the harmonic mean of precision and recall, which is a comprehensive indicator of the two:  
\[F_{1}=2\times\frac{precision\times recall}{precision+recall} \tag{7}\]  
Since the model is a multi-classifier, to obtain \(F_{1}\), we take each of the 15 categories as positive samples in turn and the other 14 categories as negative samples. The \(F_{1}\) value is then calculated as an average.  
The experimental results with the number of packets as the variable are shown in Figure 8. When the number of packets reaches 20, the prediction accuracy can reach 99%. The results show that the prediction accuracy will first increase and then decrease as the number of packets increases. We believe this is because the features in a single sample become complete as the number of packets increases, and when a sample con  
Figure 7: The confusion matrix of 15 devices.  
tains a packet sequence that is too long, the model cannot obtain features in it well. Overall, the evaluation shows that the transformer model achieves a high prediction accuracy for device identification.
# Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage
## 4 Conclusions
This study also demonstrates that Sentinel-1 dual polarimetric SAR data can be a good alternative to other commercial data which enables users to access freely available of a constant long-term data archive for applications requiring long-range time series. Although the use of single orbit direction (descending) provides possibility of monitoring and investigating agricultural growth stages, future studies should use both the ascending and descending orbits to have independent results from different angles of view. In addition, the relationship between backscatter values of multi-temporal Sentinel-1 data and vegetation variables will be incorporated to improve the crops mapping and classification accuracy as a future work. Conceptualization, R.N. and F.B.S. ; Formal analysis, R.N. ; Investigation, R.N., F.B.S., S.A, Z.C, A.S. and M.U: Methodology, R.N., F.B.S., S.A and Z.C; Software, R.N. and Z.C; Supervision, R.N., F.B.S. and Z.C; Validation, R.N., F.B.S. and S.A; Visualization, R.N., Writing--original draft, R.N. ; Writing--review and editing, R.N., F.B.S., S.A, Z.C, A.S and M.U. This research received no external funding. The authors would like to thank Yildiz Technical University Remote Sensing and Istanbul Technical University National Innovation and Research Center for Geographical Information Technologies labs for providing the entire hardware and software theme required for this research. This study is part of first author's Ph.D. thesis. The authors declare no conflict of interest.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 4 Discussion
However, exceeding the temperature threshold can negatively impact pollination and reduce fruit set, leading to lower almond yield. Therefore, monitoring and considering the long-term mean maximum April-June temperature is essential for accurately predicting almond yield. Other studies, such as the study by [PERSON] et al. [55], have also considered that warm springs accelerate fruit development. The intricate relationships unveiled throughout this analysis underscore the necessity of employing sophisticated ML models for understanding the dynamics influencing almond production. The interdependence of variables like irrigation, climate indicators, and vegetation indices highlights the need for advanced analytical tools, and the application of sophisticated machine learning (ML) models becomes imperative. Unlike simpler models, such as linear regression, which assume linear relationships between the variables, the complexities of almond production necessitate more sophisticated approaches. The utilization of advanced ML models, like the XGB model applied in this study, allows for the exploration of intricate, non-linear relationships among various contributing factors. In the realm of almond production, where variables often exhibit non-linear dependencies, these models excel in discerning patterns that may elude simpler methodologies. Some limitations must be acknowledged. Optimal results were achieved through the utilization of a proprietary/paid platform, potentially limiting accessibility for certain users. Furthermore, the lower resolution of data provided by open platforms may impede the identification of smaller orchard areas. Nevertheless, our study underscores the viability of utilizing freely available remote sensing data. While the data were sourced from multiple farmers, expanding the dataset could enhance the robustness of our findings. Despite the abovementioned limitations, the current study methodology holds promise for adaptation and implementation in various agricultural settings worldwide. Another important point is the careful analysis of features to increase the overall performance of the ML regression models. In this way, this study not only allows for the prediction of almond yields, but also enables the identification of the key factors that significantly influence these predictions. Furthermore, the models developed allow the implementation of early prediction of seasonal almond yields, with the potential integration of climate data and extreme weather events. The comparison between open and proprietary RS data shows that these models can be implemented using these two types of datasets. As such, these results provide valuable insights for farmers and other sector stakeholders, in the decision-making process, which can enhance the sustainability of the almond sector in Portugal.
## 3 Materials and Methods
### Chromatographic Analysis  
All analyses were carried out on a Varian Saturn 2000 (Varian, Inc., Walnut Creek, CA, USA) GC-MS system composed by a Star GC 3400 CX (Varian, Inc.) gas chromatograph connected to an ion-trap mass detector. The GC apparatus was equipped with a 1078 split/splitless injector with a SPME liner inside. All injections were performed in a split mode with a 50:1 split ratio. A Varian FactorFourTM VF5-ms (Varian, Inc.) capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness) was used and the carrier gas was Helium II, supplied at a flow rate of 1.0 mL/min. The column oven temperature program was the following: initial temperature 35 \({}^{\circ}\)C for 5 min, then raised at 5 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C and held for 1 min, and finally increased to 280 \({}^{\circ}\)C with at a rate of 10 \({}^{\circ}\)/min and held for 5 min. Retention indices of the extracted compounds were determined on the basis of the observed retention times of aliphatic hydrocarbons (C\({}_{7}\)-C\({}_{24}\)). These data were collected by HS-SPME/GC-MS analysis of the Retention Index Standard after dilution with a water-ethanol mixture, under application of the same temperature program used in the analysis of wine volatiles.
# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 2 Related Research
### Acoustic Feature Vectors for Symptom Detection  
In this study, we define acoustic symptoms as sound patterns that reflect specific physiological or environmental conditions. For example, respiratory illnesses may alter vocalization characteristics, producing distinguishable acoustic symptoms such as wheezing or coughing. To detect these symptoms using machine learning techniques, feature vectors must be extracted from the raw acoustic data.  
Commonly used acoustic features include time-domain features such as pitch, intensity, jitter, and RMS and frequency-domain features such as formant frequencies and MFCCs [5; 6; 7]. Spectrograms are capable of capturing both temporal and frequency information and are therefore widely used as input representations for AI-based classification tasks [14]. A spectrogram is generated by applying the short-time Fourier transform (STFT) to audio data, creating a two-dimensional time-frequency representation that can be used directly in neural network training or visualized as an image for image-based learning models. Figure 1 shows an example image of a generated spectrogram [15; 16].  
The Mel-spectrogram is a variant of the spectrogram that reflects human auditory perception. Since human sensitivity to sound decreases as frequency increases, the Mel-scale adjusts the frequency axis using a Mel-filter bank, as shown in Figure 2. Applying this filter bank to an STFT-based spectrogram yields Mel-spectrum frames for each frequency band. These frames are stacked to form a Mel-spectrogram as shown in Figure 3, which retains the essential acoustic characteristics while reducing data dimensionality.  
Figure 1: Acoustic spectrogram image.  
Figure 3: Mel-spectrogram image.  
Figure 2: Mel-filter bank (number of Mel bands: 20). Each colored line indicates a filter designed according to the mel scale.  
Mel-frequency cepstral coefficients (MFCCs) are derived from a Mel-spectrogram by applying a discrete cosine transform (DCT). This transformation removes frequency correlations and compresses the data by concentrating most of the information into lower-order coefficients [17,18]. The resulting MFCCs, as shown in Figure 4, provide a compact yet informative representation of the original acoustic signal.
# A Robust Fixed-Time Sliding Mode Control for Quadrotor UAV
## 3 Robust Stabilization
### Desired Modes for the \(x\)-Axis Dynamics of the Horizontal Displacement  
From Equation (5a):  
\[\ddot{x}=\frac{\tan\theta(g+r)}{\cos\phi}+\Gamma_{x} \tag{29}\]  
Assuming that \(\cos\phi\) must have a value close to 0 because the \(y\)-axis is stabilized in a fixed-time, the Equation (29) is:  
\[\ddot{x}=\tan\theta(g+r)+\Gamma_{x} \tag{30}\]  
Then, from Equation (5a) it follows that:  
\[\ddot{x}=\tan\theta^{d}(g+r)+e^{\prime}_{x}(g+r)+\Gamma_{x} \tag{31}\]  
where we obtain:  
\[e^{\prime}_{x}=\tan\theta-\tan\theta^{d} \tag{32}\]and follows the same procedure presented in the previous subsection. The following variable is defined and it is added that \(k_{x}>0\).  
\[\
u_{x}=\dot{x}+k_{x}(x-x_{d}) \tag{33}\]  
and also by defining the variable \(s_{x}\).  
\[\dot{v}_{x}=\ddot{x}+k_{4}\dot{x} \tag{34}\]  
Subsequently, the same \(y\)-axis methodology is performed to obtain \(e_{x}\), \(\dot{e}_{x}\) and \(\ddot{e}_{x}\) on the \(x\)-axis.  
\[e_{x}=\tan\theta(g+r)+\
u_{x}+k_{x}\dot{x}+\Gamma_{x} \tag{35}\]  
Differentiating the above:  
\[\dot{e}_{x}=\frac{\dot{\theta}}{\cos^{2}\theta}(g+r)+\tan\theta r+\dot{v}_{x}+k _{x}\ddot{x}+\dot{\Gamma}_{x} \tag{36}\]  
Differentiating again and introducing Equation (5a), it follows that:  
\[\ddot{e}_{x}=2\frac{\tan\theta}{\cos^{2}\theta}\theta^{2}(g+r)+2\frac{\dot{ \theta}}{\cos^{2}\theta}\dot{r}+\tan\theta\ddot{r}+\dot{v}_{x}+k_{x}x^{(3)}+(g +r)\frac{\tau_{\theta}+\Gamma_{\theta}}{\cos^{2}\theta}+\Gamma_{x} \tag{37}\]  
where \(\dot{\Gamma}_{x}\) is the double derivative of the perturbation in the translational movement of \(x\) and \(\Gamma_{\theta}\) is the perturbation in the rotational movement of \(\theta\). In view of Equation (37), we propose a sliding surface \(s_{cy}\):  
\[s_{ex}\triangleq\dot{e}_{x}+\beta_{x}e_{x} \tag{38}\]  
Then, a Lyapunov function is proposed:  
\[V=\frac{1}{2}s_{ex}^{2} \tag{39}\]  
Differentiating the above:  
\[\dot{V}=\dot{s}_{ex}=s_{ex}(\ddot{e}_{x}+\beta_{x}\dot{e}_{x}),\ \ \beta_{x}>0 \tag{40}\]  
The following sliding mode controller is proposed:  
\[\ddot{e}_{x}+\beta_{x}\dot{e}_{x}=-k_{1x}\operatorname{sgn}(s_{ex})-k_{2x}|s_ {ex}|^{\kappa_{x}}\operatorname{sgn}(s_{ex})-k_{3x}|s_{ex}|^{\gamma_{x}} \operatorname{sgn}(s_{ex})-k_{4x}x \tag{41}\]  
Finally, we have that:  
\[\tau_{\theta}=\frac{1}{g+r}\cos^{2}\phi[-k_{1x}\operatorname{sgn}(s_{ex})-k_{ 2x}|s_{ex}|^{\kappa_{x}}\operatorname{sgn}(s_{ex})-k_{3x}|s_{ex}|^{\gamma_{x} }\operatorname{sgn}(s_{ex})-k_{4x}x-\dot{e}_{x} \tag{42}\]  
\[-2\frac{\tan\theta}{\cos^{2}\theta}\theta^{2}(g+r)-2\frac{\dot{\theta}}{\cos^{ 2}\theta}\dot{r}-\tan\theta\ddot{r}-\dot{v}_{x}-k_{x}x^{(3)}-\Gamma_{\theta}- \ddot{\Gamma}_{x}]\]  
where \(\delta_{x}\) is an external perturbation such that \(|\Gamma_{\theta}+\dot{\Gamma}_{x}|<\delta_{x}\) for a given \(\delta_{x}>0\).
## 4 Solution Derivation
The average degree of consolidation \(U_{p}(t)\), which is defined by excess pore-water pressure, can be calculated from the following formula:  
\[U_{p}(t)=\frac{\int_{0}^{H}[\sigma(z,t)-u(z,t)]dz}{\max\left[\int_{0}^{H}\sigma (z,t)dz\right]} \tag{35}\]  
Substituting Equations (8) and (33) into Equation (35) yields  
\[U_{p}(t)=\phi(t)-\frac{1}{1+\zeta/2}\sum_{m=1}^{\infty}\frac{C_{m}}{M}\left[ \phi(T_{\rm v})-\int_{0}^{T_{\rm v}}\phi(T_{\rm r})\sum_{j=1}^{2}A_{j}e^{x_{j }(T_{\rm v}-T_{\rm r})}dT_{\rm r}\right] \tag{36}\]  
For instantaneous loading, \(\phi(t)\) can be expressed as  
\[\phi(t)=\phi(0)=1 \tag{37}\]  
By substituting Equation (37) into Equations (33) and (36), the solutions for excess pore-water pressures and average degree of consolidation can be obtained as follows  
\[u(z,t)=q_{0}\sum_{m=1}^{\infty}\left[1-\sum_{j=1}^{2}\frac{A_{j}}{x_{j}}\left( e^{x_{j}T_{\rm v}}-1\right)\right]C_{m}\sin\left(\frac{M}{H}z\right) \tag{38}\]  
\[U_{p}(t)=1-\frac{1}{1+\zeta/2}\sum_{m=1}^{\infty}\frac{C_{m}}{M}\left[1-\sum_ {j=1}^{2}\frac{A_{j}}{x_{j}}\left(e^{x_{j}T_{\rm v}}-1\right)\right] \tag{39}\]  
For ramp loading, \(\phi(t)\) can be expressed as  
\[\phi(t)=\left\{ \begin{array}{cc}t/t_{0},&0\leq t\leq t_{0}\\ 1,&t>t_{0}\end{array} \right. \tag{40}\]  
where \(t_{0}\) is the construction time of ramp loading. By substituting Equation (40) into Equations (33) and (36), the solution for excess pore-water pressure and average degree of consolidation can be obtained as the following expressions:  
\[u(z,t)=\left\{ \begin{array}{cc}q_{0}\sum_{m=1}^{\infty}\left[\frac{T_{\rm v} }{T_{\rm v0}}\left(1+\sum_{j=1}^{2}\frac{A_{j}}{x_{j}}\right)+\frac{1}{T_{\rm v 0}}\sum_{j=1}^{2}\frac{A_{j}}{x_{j}^{2}}\left(1-e^{x_{j}T_{\rm v}}\right) \right]C_{m}\sin\left(\frac{M}{H}z\right),&0\leq t\leq t_{0}\\ q_{0}\sum_{m=1}^{\infty}\left[1+\sum_{j=1}^{2}\frac{A_{j}}{x_{j}}+\frac{1}{T_{ \rm v0}}\sum_{j=1}^{2}\frac{A_{j}}{x_{j}^{2}}e^{x_{j}T_{\rm v}}\left(e^{-x_{j }T_{\rm v0}}-1\right)\right]C_{m}\sin\left(\frac{M}{H}z\right),&t>t_{0}\end{array} \right. \tag{41}\]\[U_{p}(t)=\left\{ \begin{array}{ll}\frac{T_{\rm v}}{T_{\rm v0}}-\frac{1}{1+\zeta/2 }\sum\limits_{m=1}^{\infty}\frac{C_{\rm w}}{M}\left[\frac{T_{\rm v}}{T_{\rm v0}} \left(1+\sum\limits_{j=1}^{2}\frac{A_{j}}{x_{j}}\right)+\frac{1}{T_{\rm v0}} \sum\limits_{j=1}^{2}\frac{A_{j}}{x_{j}^{2}}\left(1-e^{x_{j}T_{\rm v}}\right) \right],&0\leq t\leq t_{0}\\ 1-\frac{1}{1+\zeta/2}\sum\limits_{m=1}^{\infty}\frac{C_{\rm w}}{M}\left[1+ \sum\limits_{j=1}^{2}\frac{A_{j}}{x_{j}}+\frac{1}{T_{\rm v0}}\sum\limits_{j=1} ^{2}\frac{A_{j}}{x_{j}^{2}}e^{x_{j}T_{\rm v}}\left(e^{-x_{j}T_{\rm v0}}-1 \right)\right],&t>t_{0}\end{array} \right. \tag{42}\]  
where \(T_{\rm v0}=c_{\rm v}t_{0}/H^{2}\). For sinusoidal loading, \(\phi(t)\) shown in Figure 2 can be expressed as  
\[\phi(t)=\frac{1}{2}\left[1+\sin\left(\frac{2\pi t}{T}\right)\right] \tag{43}\]  
where \(T\) is the period of sinusoidal loading.
# Modeling and Control of an Articulated Multibody Aircraft
## 2 Development of the Multibody Equations of Motion
### Forces and Moments  
The resultant forces and moments acting on the aircraft, expressed as contributions from the central body and the abdomen, are represented by the RHS of Equations (28) and (43) respectively. The forces and moments acting on the aircraft are due to aerodynamics \((F_{A},M_{A})\), gravity \((F_{G},M_{G})\), and propulsion \((F_{P},M_{P})\), which ultimately must be represented in the body frame coordinate system as follows  
\[\sum_{k}[F_{k}]^{B}=[F_{A}]^{B}+[F_{G}]^{B}+[F_{P}]^{B} \tag{46}\] \[\sum_{k}[M_{k}]^{B}+\left[[s_{n}]^{B}\times[F_{T}]^{B}\right]=[M_{ A}]^{B}+[M_{G}]^{B} \tag{47}\]  
The propulsive force is assumed to act parallel to the aircraft \(x_{B}\) axis and produces no moments, which yields  
\[[F_{P}]^{B}=\begin{bmatrix}T_{n}\\ 0\\ 0\end{bmatrix} \tag{48}\]  
The resultant gravitational forces are estimated by summing the contributions from the central body and the abdomen  
\[[F_{G}]^{B}=[R]^{B}\begin{bmatrix}0\\ 0\\ m\ g\end{bmatrix} \tag{49}\]  
where \(g\) is the acceleration due to gravity.  
As the gravitational force acting on the central body produces no moment, the resultant gravitational moment is produced by the abdomen  
\[[M_{G}]^{B}=([R]^{BT}[s_{ij}]^{T}+s_{jk})\times[R]^{BI}\begin{bmatrix}0\\ 0\\ m^{T}g\end{bmatrix} \tag{50}\]  
The resultant aerodynamic forces and moments produced for fixed-wing aircraft are well established [96; 127] and are represented as  
\[[F_{A}]^{B}=\frac{1}{2}\rho V^{2}S_{ref}\begin{bmatrix}C_{x}\\ C_{y}\\ C_{z}\end{bmatrix}\quad\text{and}\quad[M_{A}]^{B}=\frac{1}{2}\rho V^{2}S_{ref} \begin{bmatrix}C_{l}\ b_{ref}\\ C_{m}\ c_{ref}\\ C_{m}\ b_{ref}\end{bmatrix} \tag{51}\]  
where \(\rho\) is the density of air, \(c_{ref}\), \(b_{ref}\) and \(S_{ref}\) are the respective reference chord, span and area of the wing. \(C_{(x,y,z,l,m,n)}\) are the dimensionless aerodynamic coefficients.
## 3 Results and Discussion
### Source Assessment
#### 3.5.1 Correlation Analysis  
Table 3 represents the correlation matrix of polycyclic aromatic hydrocarbons (PAHs), highlighting the Pearson correlation coefficients between various compounds found in the different agricultural soils. The values indicate the strength and direction of the relationships between pairs of PAHs, ranging from \(-1\) to \(1\). A coefficient of \(1\) reflects a perfect positive correlation, where the increase in one PAH is proportional to the increase in another, while \(-1\) indicates a perfect negative correlation, where an increase in one corresponds to a decrease in the other. The results revealed that a significant association of some PAHs was found in different agricultural soils. The following PAHs showed a statistically significant relation among PAHs: Acy/Ace/BaA/BbF/BkF/BaP, Ace/Flu/BaA/Chry/BbF/BkF/BaP, Flu/BaA/Chry/BbF/BaP, Phe/Pry, BaA/Chry/BbF/BaP, Chry/BbF/BaP, BkF/BaP, BkF/BaP in research areas; Acy/BaA/Chry/BaP, Ace/BaP, Phe/Fin, BaA/Chry/BaP, Chry/BaP, BkF/BkF, and DBahA/BghiP in industrial areas; Acy/Ant/BaA/Chry, Ace/Flu, Phe/Fin, Ant/BaA, BaA/Chry, BbF/BkF/BaP/Ind, BkF/BaP/Ind, and BaP/Ind in local market areas; Acy/BkF, Ace/Pry, BaA/Chry/BbF/BaP, Chry/BbF/BaP, and Ind/BghiP in coastal areas; and Phe/BbF, Ant/BaP, BaA/Chry/BghiP, and Chry/BghiP in rural areas. The relationships provide insight into how these compounds may co-occur or behave oppositely, which is critical for understanding their environmental transport, fate, and potential health risks. High correlations between certain PAHs could also suggest shared sources or similar environmental behaviors. [PERSON] et al. [27] also reported a similar pattern of correlation among the PAHs.
# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
## 3 Experiments
### Facial Expression Recognition Using Facial Geometry Displacement-Based SVM for CK+ Database  
Using the SVM classifier based on facial landmark displacement, we achieved 94.80% facial expression recognition performance for seven facial expression categories. Table 5  
Figure 6: Examples of dense motion flow of different facial expressions. Optical flow is color-coded by its direction and intensity. (**a**) Anger, (**b**) Contempt, (**c**) Disgust, (**d**) Fear, (**e**) Happiness, (**f**) Sadness, (**g**) Surprise.  
shows the confusion matrix of the facial expression recognition performance from the landmark displacement in the sequence. The facial-geometry-based SVM classifier shows superior facial expression recognition performance to the CK+ baseline facial expression recognition by the multi-class SVM using the combination of similarity-normalized shape and canonical appearance features in [43] or the SVM classifier using facial feature point displacement [21].  
We further investigate the facial expression recognition performance of the SVM with different representations of the facial geometry and its displacement. The landmark point itself, point with normalization by centers of eye location, intensity and angle of the landmark flow, and landmark flow in coordinate (\(\delta x\), \(\delta y\)) are tested. Table 6 shows the performance of facial expression recognition for the CK+ database according to different input data types of the SVM. In this additional experiment, the SVM classifier using the direct landmark displacement in Equation (5) can achieve 96.64% recognition performance, as shown in Table 7, which is higher than that when using the displacement intensity and angle by 1.83%.
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 2 Sewage Treatment
### With Nanotechnologies
#### 2.2.7 Bentonite  
Bentonite (BTE) is an aluminosilicate formed by one octahedric aluminum oxide layer and two tetrahedric silicon oxide layers, which results in a 1:2-type structure with the chemical formula Al\({}_{2}\)H\({}_{2}\)Na\({}_{2}\)O\({}_{13}\)Si\({}_{4}\)[241; 242]. This clay is frequently employed worldwide as an adsorbent because of its excellent cost-effectiveness ratio. It is also widely available anywhere in the world. Some authors explain bentonite's high adsorption capacity with the NH\({}_{4}\)\({}^{+}\) group, which is formed thanks to its high CEC, with a value generally around 40-130 cmol\(\cdot\)kg\({}^{-1}\). Some studies conducted in 2017 by scientists revealed that raw bentonite has a NH\({}^{4+}\) adsorption capacity of 19.01 mg\(\cdot\)g\({}^{-1}\), which implies 53% adsorption efficacy, which can rise to 81% when the quantity of nanoclay is increased up to a concentration of 40 g\(\cdot\)L\({}^{-1}\)[243; 244; 245; 246; 247].  
As with many other nanocavalys, modifications can be made to bentonite to change its properties. After analyzing the SSA (45-68 m\({}^{2}\cdot\)g\({}^{-1}\)) [248], diameter, and pore volume of BTE modified with benzylhexadecyldimethylammonium _chloride_ and a natural unmodified bentonite, [PERSON] and [PERSON] concluded that, without modifications, the values of these parameters were more specific and better than those recorded after modification [249]. Moreover, cationic surfactants increased the cationic affinity of clay and their anionic adsorption. The modification made by the action of these elements only occurred on surface layers [250].  
An example of these cationic surfactants appears in another study, which modified bentonite with cetrimonium bromide to give pores and a larger surface area after modifications. This led to greater adsorption capacity and, therefore, to amaranth dye removal [251]. Different parameters, such as pH [252; 253; 254; 255], quantity of adsorbent [256; 257], temperature, and contact time [258; 259; 260] among other elements, influenced bentonite's adsorption capacity.  
Figure 7: Sepiolite SEM.  
One study employed Zr-bentonite to remove the phosphates that abounded in textile water to adsorb them under acidic pH conditions. Removal efficiency came close to 100%, but removal efficiency lowered as pH rose. Another important factor was the quantity of NaCl in the medium because removal efficiency increased at lower NaCl concentrations. The final phosphate removal capacity in that study was 95% [261, 262].