esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Higher-Order Equivalent Single Layer Approach
\begin{array}{c}A^{(\tau\eta)}_{nm\;(pq)}=\sum\limits_{k=1}^{l}\int\limits_{ \xi_{k}}^{\xi_{k+1}}\overline{B}^{(k)}_{nm}F_{\eta}F_{\tau}\frac{H_{1}H_{2}}{H_ {1}^{2}H_{2}^{2}}d\zeta\\ A^{(\bar{\tau}\eta)}_{nm\;(pq)}=\sum\limits_{k=1}^{l}\int\limits_{\xi_{k}}^{\xi_{ k+1}}\overline{B}^{(k)}_{nm}F_{\eta}\frac{\partial F_{\tau}}{\partial\xi}\frac{H_{1}H_{2}}{H_ {1}^{2}H_{2}^{2}}d\zeta\\ A^{(\tau\bar{\eta})}_{nm\;(pq)}=\sum\limits_{k=1}^{l}\int\limits_{\xi_{k}}^{\xi_{ k+1}}\overline{B}^{(k)}_{nm}\overline{\partial\xi}_{\tau}\frac{F_{\tau}}{\partial \xi}\frac{H_{1}H_{2}}{H_{1}^{2}H_{2}^{2}}d\zeta\\ A^{(\tau\bar{\eta})}_{nm\;(pq)}=\sum\limits_{k=1}^{l}\int\limits_{\xi_{k}}^{\xi_{ k+1}}\overline{B}^{(k)}_{nm}\frac{\partial F_{\tau}}{\partial\xi}\frac{F_{ \tau}}{\partial\xi}\frac{H_{1}H_{2}}{H_{1}^{2}H_{2}^{2}}d\zeta_{\tau}^{2}\end{array}  \tag{38}\]  
for \(\tau,\eta=0,1,2,...,N,N+1\), \(n,m=1,2,3,4,5,6\) and \(p,q=0,1,2\). For the sake of conciseness, the symbols \(\tau,\eta\) denote the shear function order, whereas \(\tilde{\tau},\tilde{\eta}\) stand for the corresponding derivatives with respect to \(\xi\). On the other hand, \(\overline{B}^{(k)}_{nm}\) specifies the elastic constant of the material. It assumes the following definition for \(n,m=1,2,3,6\)  
\[\overline{B}^{(k)}_{nm}=\overline{E}^{(k)}_{nm} \tag{39}\]  
whereas for \(n,m=4,5\) one gets  
\[\overline{B}^{(k)}_{nm}=\kappa\overline{E}^{(k)}_{nm} \tag{40}\]  
where \(\kappa=1/\chi\) is the shear correction factor. If the structural model requires this correction, the value of \(\chi\) is set equal to \(\chi=1.2\), otherwise the unitary value is employed (\(\chi=1\)). Equations (39) and (40) are general and allows to introduce also the plane stress hypothesis. In fact, \(\overline{E}^{(k)}_{nm}\) denotes the elastic constant of the material of the \(k\)-th py. If the plane stress hypothesis is required, the reduced elastic coefficients must be used (\(\overline{E}^{(k)}_{nm}=\overline{Q}^{(k)}_{nm}\)). On the other hand, the non-reduced coefficients are employed (\(\overline{E}^{(k)}_{nm}=\overline{C}^{(k)}_{nm}\)). These aspects will be specified properly by adding specific subscripts and superscripts to the notation used to denote the structural model. For instance, the Reissner-Mindlin theory needs both the shear correction factor and the plane stress-reduced elastic coefficients. As a consequence, it will be indicated as FSDT\({}^{\chi=1.2}_{RS}\), where \(RS\) stands for \"reduced stiffness\". Finally, it should be specified that the quantities \(\overline{E}^{(k)}_{nm}\) depend on the third coordinate \(\zeta\) if the layer is made of FGM. In other words, one gets \(\overline{E}^{(k)}_{nm}(\xi)\). A set of three motion equations for each order of kinematic expansion \(\tau=0,1,2,...,N,N+1\) is obtained. In compact matrix form, they assume the following aspect  
\[\mathbf{D}^{*}_{\Omega}\mathbf{S}^{(\tau)}=\sum\limits_{\eta=0}^{N+1}\mathbf{ M}^{(\tau\eta)}\tilde{\mathbf{u}}^{(\eta)} \tag{41}\]  
The equilibrium operator \(\mathbf{D}^{*}_{\Omega}\) is defined as follows:  
\[\mathbf{D}^{*}_{\Omega}=\left[
## 4 Theoretical Investigation of Bond Behaviour
### Bond Strength Prediction Using an Artificial Neural Network (ANN)
#### 4.1.1 ANN Model Development  
In this study, a backpropagation feed-forward neural network was used to develop the neural network fitting model. Since the model was developed after the experimental inves  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen No.**} & \multicolumn{2}{c}{**Experimental Value of Bond Stress, MPa**} & \multicolumn{2}{c}{**Predicted Values of Bond Stress, MPa**} \\ \cline{2-5}  & **Stress, MPa** & **ACI 440.1R-06 [3]** & **ACI440.1R-03 [4]** & **CSA S806-02 [5]** \\ \hline
6-1-1 & 3.30 & 13.12 & 21.32 & 25.01 \\ \hline
6-2-1 & 3.49 & 7.87 & 21.32 & 25.01 \\ \hline
6-3-1 & 7.57 & 6.12 & 21.32 & 25.01 \\ \hline
10-1-1 & 9.08 & 12.91 & 12.79 & 14.59 \\ \hline
10-2-1 & 7.83 & 7.66 & 12.79 & 14.59 \\ \hline
10-3-1 & 5.36 & 5.91 & 12.79 & 14.59 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of experimental and predicted values of bond strength.  
tigation, a backpropagation feed-forward neural network is best suited for developing the ANN model's desired output, which was already obtained from the experimental results. The multilayered backpropagation network was created consisting of three layers--the input layer, the hidden layer with nodes, and the output layer--as shown in Figure 23 below:
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 2 GNSS and IMU Integrated Filter Design
### Error State Kalman filter
#### 2.2.2 Discrete Time Kinetic Model  
In order to apply the derived state and measurement equations to the sensor fusion filter, they must be discretized based on the sampling time. The original continuous time equations are transformed into their discrete time form.  
The recursive expression for the motion model of the nominal state is  
\[\begin{array}{l}\mathbf{p}_{(k+1)}=\mathbf{p}_{k}+\mathbf{v}_{k}\Delta t+\frac{1}{2}[\bm {R}_{k}(\mathbf{a}_{mk}-\mathbf{a}_{bk})+\mathbf{g}]\Delta t^{2}\\ \mathbf{v}_{(k+1)}=\mathbf{v}_{k}+[\mathbf{R}_{k}(\mathbf{a}_{mk}-\mathbf{a}_{bk})+\mathbf{g}_{k}] \Delta t\\ \mathbf{q}_{(k+1)}=\mathbf{q}_{k}\otimes\mathbf{q}_{k}((\mathbf{\omega}_{mk}-\mathbf{\omega}_{bk}) \Delta t)\\ \mathbf{a}_{b}(k+1)=\mathbf{a}_{bk}\\ \mathbf{\omega}_{b}(k+1)=\mathbf{\omega}_{bk}\end{array} \tag{12}\]  
The kinematic model of the error state is expressed recursively as a function of the non\(-\)negative integer \(k\) representing the kth time step:  
\[\begin{array}{l}\textit{ffi}\mathbf{p}_{k+1}=\textit{ffi}\mathbf{p}_{k}+\textit{ffi} \mathbf{v}_{k}\Delta t\\ \textit{ffi}\mathbf{v}_{k+1}=[-\mathbf{R}_{k}(\mathbf{a}_{mk}-\mathbf{a}_{bk})\textit{ffi}\mathbf{ \theta}_{k}-\mathbf{R}_{k}\textit{ffi}\mathbf{a}_{bk}]\Delta t+\textit{ffi}\mathbf{v}_{k}- \mathbf{w}_{vk}\\ \textit{ffi}\mathbf{\theta}_{k+1}=\mathbf{R}_{k}^{T}((\mathbf{\omega}_{mk}-\mathbf{\omega}_{ bk})\Delta t)\textit{ffi}\mathbf{\theta}_{k}-\textit{ffi}\mathbf{\omega}_{bk}\Delta t+ \mathbf{w}_{k}\\ \textit{ffi}\mathbf{a}_{b(k+1)}=\textit{ffi}\mathbf{a}_{bk}+\mathbf{w}_{ak}\\ \textit{ffi}\mathbf{\omega}_{b(k+1)}=\textit{ffi}\mathbf{w}_{bk}+\mathbf{w}_{wk}\end{array} \tag{13}\]  
The Gauss random noise for velocity, attitude, acceleration bias, and angular velocity bias are denoted as \(\mathbf{w}_{bk},\mathbf{w}_{k},\mathbf{w}_{ak},\mathbf{w}_{bk}\), their mean is zero, and their covariance matrices are obtained by integrating the covariances of \(\sigma_{\mathbf{a}_{\alpha}}\), \(\sigma_{\mathbf{a}_{\omega}}\), \(\sigma_{\mathbf{a}_{\omega}}\) and \(\sigma_{\mathbf{a}_{\omega}}\) over the step time \(\Delta t\).  
\[\begin{array}{l}\mathbf{W}_{\
u}=\sigma_{\mathbf{a}_{\
u}}^{2}\Delta t^{2}\mathbf{I}\\ \mathbf{W}_{\
u}=\sigma_{\mathbf{a}_{\
u}}^{2}\Delta t^{2}\mathbf{I}\\ \mathbf{W}_{\Lambda}=\sigma_{\mathbf{a}_{\
u}}^{2}\Delta t\mathbf{I}\\ \mathbf{W}_{\Omega}=\sigma_{\mathbf{a}_{\
u}\lambda}^{2}\mathbf{I}\end{array} \tag{14}\]  
The standard deviation of Gaussian white noise for acceleration and angular velocity are denoted by \(\sigma_{\mathbf{a}_{n}}\) and \(\sigma_{\mathbf{a}_{n}}\), respectively. Similarly, \(\sigma_{\mathbf{a}_{\omega}}\) and \(\sigma_{\mathbf{a}_{\omega}}\) are used to represent the standard deviation of Gaussian white noise for acceleration and angular velocity bias, respectively.
## 2 System Architecture, Spectrum Sharing Technique, and Interference Management
### Licensed Assisted Access  
Since small cells can operate at multiple bands, unlike LSA, a licensed spectrum band can also be operated simultaneously along with an unlicensed or license-exempt spectrum band by using the well-known carrier aggregation (CA) techniques to increase the system bandwidth and hence the spectral efficiency. Such spectrum access methods are termed as LAA. Typically, license-exempt bands include 60 GHz, 5 GHz, and 2.4 GHz industrial, scientific, and medical (ISM) bands. Due to no licensing fee and no need for considerable modifications on the existing mobile infrastructure because of using the existing CA techniques to aggregate both the licensed and unlicensed bands, LAA is considered as one of the cost-effective solutions to increase the system spectrum bandwidth. The only requirement to enable LAA is to enable SBSs of an MNO with multiple bands. Though operating at ISM bands has a common advantage of no licensing fee, 60 GHz band benefits from a number of aspects as compared to 2.4 GHz and 5 GHz bands, particularly in short range indoor communications, as follows:  
* Availability of a huge amount of unused spectrum (57 to 66 GHz) can address high network capacity,
* High attenuation (specifically, additional free-space losses of 27.96 and 21.58 dB, respectively, on top of what at 2.4 GHz and 5 GHz for the same distance [22]) and large in-building material absorption results in low co-channel interference from neighboring cells,
* Small wavelength resulting in low aperture areas and hence enabling an array of antennas to deploy in small spaces to support high antenna directivity, and
* Low level of multipath effect and hence high possibility of the existence of line-of-sight (LOS) components at 60 GHz band than that at 2.4 GHz and 5 GHz band  
Due to the aforementioned benefits, we consider 60 GHz as an unlicensed spectrum to aggregate with a licensed spectrum for LAA. Figure 8 shows a multiband enabled small cell operating at the licensed spectrum of MNO 1 and unlicensed spectrum of 60 GHz.
## 4 Experiment Study
### Evaluation Metrics  
A series of evaluation metrics were chosen, including root mean squared error (RMSE), mean absolute error (MAE), symmetric mean absolute percentage error (SMAPE), mean absolute percentage error (MAPE), and correlation coefficient (R), to test the suggested model and better assess its effectiveness. The related mathematical expressions are shown below:  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}}{\left({Y_{i}-X_{i}}\right)^{2}}} \tag{15}\]  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}}{\left|{Y_{i}-X_{i}}\right|} \tag{16}\]  
\[SMAPE=\frac{1}{N}{\sum_{i=1}^{N}}\frac{\left|{Y_{i}-X_{i}}\right|}{\left({ \left|{Y_{i}}\right|+\left|{X_{i}}\right|}\right)/2} \tag{17}\]  
\[MAPE=\frac{1}{N}{\sum_{i=1}^{N}}{\left[{\frac{{Y_{i}-X_{i}}}{{X_{i}}}}\right]} \tag{18}\]  
\[R=\frac{{\sum_{i=1}^{N}}\left({Y_{i}-\overline{Y}}\right)\left({X_{i}- \overline{X}}\right)}{\sqrt{\sum_{i=1}^{N}}{\left({Y_{i}-\overline{Y}}\right) ^{2}{\sum_{i=1}^{N}}{\left({X_{i}-\overline{X}}\right)^{2}}}} \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Max (Mw)** & **Min (Mw)** & **Med (Mw)** & **Mean** & **Std (Mw)** & **CV (\%)** & **Skew** & **Kurt** \\ \hline Dataset & 12591.59 & 6823.94 & 9391.1 & 9335.93 & 1068.91 & 0.1145 & \(-\)0.0571 & \(-\)0.858 \\ \hline Training & 12591.59 & 6883.69 & 9551.95 & 6568.26 & 1053.28 & 0.1114 & \(-\)0.0725 & \(-\)0.782 \\ \hline Testing & 11390.69 & 6823.94 & 8995.8 & 9065.98 & 1056.53 & 0.1165 & \(-\)0.0114 & \(-\)1.131 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical properties of the dataset.
## 2 Materials and Methods
### Data
#### 2.2.6 FLUXCOM Gross Primary Productivity  
The upscaled GPP based on FLUXNET2015 [65], denoted as FLUXCOM, was compared with the optimized GPP from CCDAS. The FLUXCOM GPP was developed with a representativeness-based upscaling approach using flux sites that sampled in similar environmental conditions. Strong reflections of the spatiotemporal variability in the distribution of flux observations may be seen in the spatiotemporal variability and accuracy of the upscaled GPP dataset. Because the accessible flux sites provide a good sample of the ecoregions, the dataset has higher accuracy and lower uncertainty in those areas. This product has the spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) and was therefore re-gridded to the CCDAS resolution. We only used the available data from 1982 to 2011 for comparison.
# A Simulation of Contact Graph Routing for Mars-Earth Data Communication
## 2 Model of CGR
### Arrangement of Data  
Data size \(D(t)\) is generated randomly between \(10\times 2^{6}\) KB up to \(20\times 2^{8}\) KBytes per minute. Due to the link capacity of the rovers, the data should be distributed into \(n_{d}(t)\) packets whose maximum size is 256 KBytes.  
\[D(t)=\Sigma_{w=1}^{n_{d}(t)}dt_{w}(t), \tag{9}\]  
where \(dt_{w}\leq 256\) KBytes for all \(w\).  
A starting node for delivery is set for each packet. The vector _Starts_ is an \(n_{d}(t)\)-sized vector containing the initial node of each data packet. For \(i=1,2,\ldots n_{d}(t)\), \(s_{i}\in\)_Starts_\((t)\)  
\[s_{i}=\left\{\begin{array}{ll}1,&\mbox{if starting from PR,}\\ 2,&\mbox{if starting from CR,}\end{array}\right. \tag{10}\]  
where PR is Perseverance Rover and CR is Curiosity Rover.  
Sending data can be started at the same time, but the route taken needs to be different due to the limited capabilities of the links that are used. The delivery of several data packets can be performed at different times if the link to be taken is already fully occupied, so that all data cannot be passed at the same time.
# A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method
## 3 Adaptive Model Predictive Current Control for Inverters
### Parameters Modification
The parameters can be modified according to following expressions. \[\left\{ \begin{array}{lcl}L_{P}(k)=L_{P}(k-1)+\Delta L&if&L-L_{P}>\varepsilon_ {L}\\ L_{P}(k)=L_{P}(k-1)-\Delta L&if&L-L_{P}<-\varepsilon_{L}\\ L_{P}(k)=L_{P}(k-1)&if&-\varepsilon_{L}<L-L_{P}<\varepsilon_{L}\\ \end{array} \right. \tag{22}\]  
\[\left\{ \begin{array}{lcl}R_{P}(k)=R_{P}(k-1)+\Delta R&if&R-R_{P}>\varepsilon_ {R}\\ R_{P}(k)=R_{P}(k-1)-\Delta R&if&R-R_{P}<-\varepsilon_{R}\\ R_{P}(k)=R_{P}(k-1)&if&-\varepsilon_{R}<R-R_{P}<\varepsilon_{R}\\ \end{array} \right. \tag{23}\]  
where \(\Delta L\) and \(\Delta R\) are the unit changes of inductance and resistance respectively. \(\varepsilon_{L}\) and \(\varepsilon_{R}\) are the maximum acceptable errors of inductance and resistance respectively. The complete scheme of the proposed A-MPC current control process is given in Figure 5.  
Figure 5: Complete scheme of the proposed control method.
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 2 Related Works
### Prior-Based Methods  
This type of dehazing method utilizes prior-information based on observations about characteristics of haze-free images or haze-degraded images in order to solve the inverse problem of the atmospheric scattering model. [PERSON] et al. [9] proposed the dark channel prior (DCP) for single-image haze removal. The DCP is based on the observation that the pixels of a haze-free image tend to have very low intensity in at least one color (red, green, or blue) channel. [PERSON] [10] developed a cost function in the framework of Markov random fields, which was based on two basic observations: haze-free images have more contrast, and the airlight tends to be smooth. [PERSON] et al. [11] presented the color attenuation prior which states that the relationship among the scene depth, the brightness, and the saturation is linear. They estimated the depth map of the scene using this prior and a supervised linear model. [PERSON] et al. [21] observed that the pixels of a haze-free image in a given cluster are often non-local, which was dubbed as the non-local prior. [PERSON] et al. [22] constructed color ellipsoids that were statistically fitted to haze pixel clusters in the RGB space and then calculated the transmission values through color ellipsoid geometry. [PERSON] et al. [23] proposed a confidence prior to accurately estimate scene transmissions for image dehazing. However, these hand-crafted priors do not always hold true for diverse real-world scenes with different haze statistics. For example, the DCP is unreliable for sky regions or white objects.
## 3 Proposed Method
### Second Stage: MYO and LVC Segmentation
#### 3.2.1 Coarse Segmentation  
The coarse segmentation substage starts with preprocessing the ROIs passed as input. After zero padding is applied, the size of ROIs is set to \(160\times 160\), which was chosen due to being the largest among the ROIs extracted in the previous stage. So, they are submitted to the EAIS-Net, a network proposed in this work, depicted in Figure 3.  
EAIS-Net uses EfficientNet B3 [12] for feature extraction in the encoder path. The main idea is to benefit from the main mechanisms used by this network, which are the MBConv blocks composed of two structures: inverted bottlenecks (IBs) and squeeze-and-excitation (SAE) blocks. IB blocks have fewer parameters to alter than conventional convolutions because they employ depth-wise convolutions [46]. Additionally, the SAE Blocks aim to calibrate feature maps and prioritize the most significant ones during the learning process by giving them larger weights [14]. The choice for the B3 model was made based on tests and the promising outcomes in the ImageNet and ImageNet-V2 challenges reported for the EfficientNet family [47].  
EAIS-Net uses proposed structures called AIS blocks in the decoder path, followed by upsampling \(2\times 2\), always halving the filters generated (from 512 to 32). The acronym AIS refers to the combination of Attention, Inception, and SAE blocks, as seen in Figure 4.  
Consider g as the output of a preceding block and x as the skip connection originating from the encoder path. The Attention block receives both x and g as inputs. With a scale of 2, the Upsampling operator takes g as input. The two outputs are concatenated and subsequently fed into the Inception block, followed by the SAE block, which produces the final output of the AIS block.  
Figure 4: Architecture of the AIS Blocks used by the EAIS-Net.  
Figure 3: EAIS-Net: the proposed network for the coarse segmentation of MYO and LVC.  
The incorporation of Attention blocks in this work draws inspiration from the study conducted by [PERSON] et al. (2015). These blocks assign weights to specific regions within feature maps transmitted through skip connections. As a result, the most relevant regions of the map receive more weight than the others. Furthermore, the use of Inception blocks ([PERSON] et al., 2016) aims to learn features at different scales as the size of the structures of interest changes throughout the CMRI volume.  
Lastly, the output block uses pointwise convolutions with the Softmax activation function. This process culminates in generating the initial segmentation for the MYO and LVC. However, this result may have some errors. Based on that, a hypothesis emerged that the results could be improved by including a second segmentation substage applied under a new ROI. This process will be described in the following section.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**BE of Mn 2p3/2 (eV)**} & \multicolumn{2}{c}{**BE of O 1s (eV)**} & \multicolumn{1}{c}{**Au 4f (eV)**} \\ \cline{3-5}  & & **OL** & **OS** & **Au\({}^{5+}\)** \\ \hline Au/MnO\({}_{2}\)-NW & 641.7 & 530.1 (46) \({}^{*}\) & 532.0 (39) \({}^{*}\) & 85.4 (20) \({}^{*}\) \\ MnO\({}_{2}\)-NW & 642.6 & 529.5 (52) \({}^{*}\) & 531.5 (34) \({}^{*}\) & no signal \\ Au/MnO\({}_{2}\)-comm & 642.0 & 530.1 (56) \({}^{*}\) & 532.0 (30) \({}^{*}\) & 86.5 (18) \({}^{*}\) \\ MnO\({}_{2}\)-comm & 642.7 & 530.0 (56) \({}^{*}\) & 532.2 (29) \({}^{*}\) & no signal \\ Au/MnO\({}_{2}\)-NF & 641.7 & 529.8 (41) \({}^{*}\) & 531.6 (47) \({}^{*}\) & 85.5 (26) \({}^{*}\) \\ MnO\({}_{2}\)-NF & 642.6 & 530.0 (61) \({}^{*}\) & 532.0 (28) \({}^{*}\) & no signal \\ \hline \hline \end{tabular} \({}^{*}\) % of O or Au species.  
\end{table} 
Table 4: Binding energies measured by XPS before and after the deposition of Au NPs at the surface of the different MnO\({}_{2}\) supports (OL—lattice oxygen species, OS—oxygen ions). Figure 5: XPS spectra of Mn 2p (**A**), O 1s (**B**) and Au 4f (**C**) regions for the prepared catalysts. As observed in Table 4, the deposition of Au NPs on MnO\({}_{2}\) supports led to the formation of a significant amount of Au\({}^{S+}\) species. Specifically, the Au\({}^{S+}\) species ratio was \(\sim\)26 % in Au/MnO\({}_{2}\)-NF, which is a higher proportion in comparison with the other supports. This could be responsible for the enhanced catalytic activity of this catalyst. Indeed, previous works on supported Au nanoparticles has indicated surface Au\({}^{S+}\) species as being the most catalytically active sites in oxidation reactions [25; 32].
# Effective Adsorption of Phenoxyacetic Herbicides by Tomato Stem-Derived Activated Carbons
## 4 Conclusions  
Tomato stem-derived activated carbon (TS-AC) was produced through chemical activation using either potassium hydroxide (KOH) or sodium hydroxide (NaOH) as activating agents, with activation temperatures ranging from 550 to 750 \({}^{\circ}\)C. It was observed that an increase in the activation temperature resulted in an enhancement of the BET surface area of the resultant product; furthermore, NaOH was identified as a marginally superior activator.  
Figure 9: The tomato stem-derived activated carbon (TS-AC) regeneration efficiency using methanol.  
The produced activated carbons were assessed for their efficacy as adsorbents in the remediation of phenoxyacetic herbicides, including 2,4-dichlorophenoxyoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyoxyacetic acid (MCPA) from aqueous solutions. Adsorption kinetics were analyzed using pseudo-first-order (PFO) and pseudo-second-order (PSO) models, with the PSO model best fitting the experimental data. A strong correlation was observed between the adsorption rate of the herbicides and the mesopore volume of the TS-AC.  
Additionally, the adsorption mechanism was investigated using the Weber-Morris (intraparticle diffusion) model, indicating that film diffusion constituted the rate-limiting step in the adsorption process. Subsequently, equilibrium adsorption data were analyzed using the Freundlich and Langmuir isotherms, revealing a superior fit with the Langmuir model. The maximum adsorption capacities of the herbicides exhibited a strong dependence on the porous structure of TS-AC (the micropore volume and the BET surface area). The adsorption of both herbicides was pH-dependent, while ionic strength exhibited no significant effect. Furthermore, regeneration experiments demonstrated that the AC could be reused up to three times, maintaining over 85% herbicide removal with methanol elution. These results demonstrate that agricultural wastes, such as tomato stems, can be successfully valorized through simple activation techniques, with the resultant activated carbons serving as proficient adsorbents for extracting phenoxy herbicides from aqueous environments.  
Conceptualization, K.K., B.D., and A.S.; methodology, K.K., B.D., and A.S.; formal analysis, M.S.; investigation, K.K., B.D., and A.S.; resources, K.K. and B.D.; data curation, B.D.; writing--original draft preparation, K.K., B.D., A.S., and M.S.; writing--review and editing, M.S.; visualization, K.K.; supervision, A.S.; validation, M.S. All authors have read and agreed to the published version of the manuscript.  
This study received no external funding.  
Not applicable.  
Data availability StatementData are available on request from the authors.  
The authors declare no conflicts of interest.