esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This work investigates the MA-ABE technique that helps attribute revocation and computation outsourcing for resource-constraint devices in fog computing. We proposed a cloud-fog-terminal network system and an attribute-revocation technique based on multiauthority ciphertext policy-attribute-based encryption (MA-CP-ABE). The secret keys are generated dynamically by introducing the attribute-group key. The proposed method reduces the operation time for the encryption and decryption process. We outsource cryptography operations to the fog node, reducing the end user's computational cost. Eventually, simulated outcomes are used to assess the algorithm's computational cost.  
Energy consumption is the energy consumed when transmitting data from end devices to fog nodes. It is a matter of fact that with the increase in the data size, the energy consumption of the encryption and decryption algorithms will increase. Hence, energy consumption is a critical matter that can be evaluated in the future. We also plan to design a CP-MA-ABE scheme with conditional privacy that supports the revocation mechanism. We also intend to design a handover scheme that reduces the overhead caused by re-encryption and decryption. The aforementioned futuristic directions will have a lot of study value and impact in this area.  
Figure 6: Computing overhead of the decryption algorithm.  
Figure 7: Time of computation in seconds versus the number of revoked attributes.  
**Author Contributions:** Conceptualization, X.Y.; methodology, X.Y.; software, X.Y. and S.T.; validation, X.Y. and H.A.; formal analysis, F.H.; investigation, X.Y. and H.A.; resources, X.Y.; data curation, H.A.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y. and S.T.; visualization, X.Y.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (No. 6206019), and the China Postdoctoral Science Foundation (No. 2021M690305). The authors also extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number RGP.2/312/44.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data
## 3 Results
### Synthetic Model Data Tests
Notably, high-order OC-seislet stacking (Figure 10d) reduces the impact of velocity errors, effectively compresses dipping reflection waves and breakpoint diffraction waves, and provides a more accurate amplitude relationship for the wavelets, which results in the better fidelity of the stacking results. A further comparison of the entire stacking sections, shown in Figure 11, demonstrate that high-order OC-seislet stacking significantly improves amplitude fidelity (Figure 11b), with the SNR increasing to 17.17 dB, compared to 9.173 dB for conventional DMO stacking. Additionally, false events caused by inaccurate velocities in moveout correction are visible in the conventional DMO stacking (Figure 11a). Figure 6: Transform coefficients sorted from large to small, normalized, and plotted on a decibel scale. Solid line: coefficients based on Haar basis. Dashed line: coefficients based on CDF 9/7 basis. Figure 7: Comparison of stacking results using different methods. (**a**) NMO stacking; (**b**) conventional DMO stacking; (**c**) high-order OC-seislet stacking. Figure 8: Comparison of zero-offset trace and stacked traces using different methods at dipping layer position (CMP location 1.32 km). (**a**) Zero-offset trace; (**b**) NMO stacked trace; (**c**) conventional DMO stacked trace; (**d**) high-order OC-seislet stacked trace. Figure 10: Comparison of zero-offset trace with noise and stacking results using different methods at dipping layer position (CMP location 1.32 km). (**a**) Zero-offset trace; (**b**) NMO stacked trace; (**c**) conventional DMO stacked trace; (**d**) stacked trace using high-order OC-seislet with thresholding. Figure 3.0 Synthesized French model data with strong random noise added.
## 3 Results and Discussion  
### Phytochemical Analysis of the Pure C. Operculatus Leaf Powder  
#### 3.1.1 Phytochemical Screening  
Qualitative phytochemicalical screening of _C. operculatus_ was performed to detect the presence of essential phytoconstituents, such as alkaloids, saponins, flavonoids, terpenoids, and steroids (Table 2). As shown in Table 2, _C. operculatus_ leaves contain various phytochemicals or polyphenols (such as flavonoids, saponin, phenols, terpenoids, and alkaloids), which are known to have potent antioxidant and/or free radical scavenging activities. Terpenoids are the main antioxidant compounds of essential oils. The mode of action underlying the plant's antioxidant properties may thus be mainly related to that family of compounds in essential oils (EOs); it may also be due to other chemicals (flavonoids, saponins, phenols, and alkaloids) that may contribute alone or with synergic effects to the antioxidant activities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Methanol** & **Ethanol** & **Acetone** & **Chloroform** \\ \hline Flavonoids & + & + & + & – \\ \hline Terpenoids & + & + & + & + \\ \hline Alkaloids & \(-\) & \(-\) & + & \(-\) \\ \hline Phenols & + & + & + & + \\ \hline Saponins & \(-\) & + & \(-\) & \(-\) \\ \hline \hline \end{tabular} Note: + = present and \(-=\) absent. Each data point corresponds to triplicates.  
\end{table}
Table 2: Detection of phytochemical constituents in extracts from _C. operculatus_ leaves, depending on the type of solvent used for the extraction procedure.
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 4 Conclusions  
In this work, we suggested an effective spectral-spatial classification method for HSI at superpixel level. The introduced similarity between two pixels effectively integrates the advantages of the correlation coefficient being sensitive to the spectra shape and the Euclidean distance being sensitive to spectra difference. In addition, the improved nonparametric SLIC algorithm can directly partition HSI with arbitrary dimensionality into superpixels. This facilitates its further application in the field of agricultural remote sensing. Furthermore, to better measure the similarity of a pair of superpixels defined, smaller weights are assigned to the noise pixels located in these two superpixels to weaken the influence of them on it. More important, the proposed superpixel-level classification framework has made a successful attempt to reduce the hyperspectral data volume. Experimental and comparative results on three real hyperspectral datasets confirm the validity of the proposed HSI classification method. Compared with three existing superpixel-level HSI classification approaches, the advantage of this method is simple to calculate and easy to understand. It means that the proposal is more likely to be applied in other remote sensing issues. In this work, the choice of the optimal segmentation scale is based on the experimental result. In the absence of a reference ground truth, it is worth to further solve this problem better. Moreover, we will also focus on the development of new spectral-spatial HSI classification method at superpixel level in the case of a limited number of training samples.  
Conceptualization, F.X. and C.L.; Methodology, F.X., C.L. and C.J.; Software, C.L.; Formal Analysis, C.J. and N.A.; Data Curation, C.L., C.J. and N.A.; Writing--Original Draft Preparation, F.X.; Writing--Review and Editing, F.X. and C.J. Visualization, C.L.; Funding Acquisition, C.J. and F.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grants number 41801340); Natural Science Research Project of Liaoning Education Department (Grants number Lj2019013), and the APC was funded by the National Natural Science Foundation of China (Grants number 41801340).  
We would like to thank the authors of EPF, IFRF, and SuperPCA for sharing the source code.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Data Analysis  
The extraction results for analytes from artificial urine samples were compared with those obtained from a standard solution containing each analyte at a concentration of 10 \(\upmu\)g/mL. After the extraction process, two key parameters--extraction efficiency (R) and enrichment factor (EF)--were calculated for each analyte. Since the extraction methods were still in the optimization phase and had not yet been validated, the analytical instrument's response, measured as peak height, was used to assess these parameters.  
Extraction efficiency (R) was determined by comparing the signal intensity (peak height, H) of each analyte after extraction using the MEKC method to the signal intensity (peak height, H\({}_{0}\)) of the analyte in an untreated sample of the same concentration (10 \(\upmu\)g/mL). This ratio provided a measure of the effectiveness of each extraction method. Equation (1). Extraction efficiency (R):  
\[\text{R}=\text{H}/\text{H}_{0}\times 100\% \tag{1}\]  
where: R--extraction efficiency; H--signal height of the analyte after extraction, sample evaporation and dissolution in 2 mM borax and MEKC analysis; H\({}_{0}\)--signal height of an analyte at the same concentration level after MEKC analysis without an extraction procedure.  
While analyte recovery is commonly used to evaluate the efficiency of extraction techniques, it cannot be calculated when the method has not been validated and the post-extraction analyte concentration is unknown [36; 38; 39]. In such cases, the apparatus response--expressed as peak height or area--is used for signal analysis. In this study, peak heights, calculated as the average of three measurements for each analyte across each extraction procedures, were used in the calculations (Equation (1)).  
The enrichment factor (EF) (Equation (2)) was calculated based on the understanding that the analytes were not directly measured during the extraction phase but were insteaddissolved in only 50 \(\upmu\)L of sample buffer after evaporation (offline preconcentration of analytes). Equation (2). Enrichment factor (EF):  
\[\text{EF}=\text{H}/\text{H}_{0} \tag{2}\]  
where: EF--Enrichment factor; H and H\({}_{0}\)--as described above.
## 4 Discussion
### Implications  
Protecting the receptor is the terminal goal, and, therefore, the screened optimal technology mix does not necessarily include the site's ultimate remediation technologies; these technologies can be selected after reviewing them in the order of their scores during subsequent effectiveness modeling [50; 51]. For instance, in this case study, the preferred in situ contamination plume remediation technology consists of natural attenuation monitoring; if it is found during modeling that natural attenuation monitoring will not yield the desired remediation effects during the period of validity, enhanced bioremediation can be assessed. If funding permits, different types of technology can be employed jointly; the technologies employed jointly can be selected in accordance with their ranking order. This principle can be also used to adjust the technologies during the course of a remediation project, especially when the initially selected technologies may not yield the desired results.  
During the technology screening process, attention must be paid to the spatial distribution characteristics of contamination and contamination migration processes in order to clarify the spatial locations and relationships of the contamination source, contamination plume, and potential receiving bodies. In order to enhance the specificity of technology screening, each remediation technology must be assessed on the basis of different standards when used in different contaminated areas or for different remediation links.  
Compatibility assessment must be performed for technologies in the preferred technology mix, and a dynamic score adjustment and re-screening plan must be made when technologies are incompatible. The goal is to enhance the compatibility of the remediation mix as a whole and realize an integrated remediation solution.  
With regard to the establishment of an easy-to-implement technological and environmental indicators system, the technology score matrix can be used directly in the case of most petrochemical-contaminated sites; in the case of environmental indicators, the safety, environmental friendliness, resource/energy consumption, and sustainability of remediation technologies during the four stages of design, construction, operation, and maintenance can be assessed.  
By providing ideas and principles concerning technology screening via classified assessment, the technology screening mix method presented in this paper offers powerful expandability. For example, during the classified assessment of remediation technologies in Step 3, various types of decision-making support systems can be employed, and other screening techniques can be used as well. These methods can be used in a flexible manner in light of the actual situation. The proposed procedure is not only suitable for hydrocarbon-contaminated unconsolidated sediment sites but also suitable for other contaminants and lithologies. When the contaminated sites or conditions vary, we simply need to follow the procedure and construct a new mix.
## 2 Related Work  
### CNN-Based HSI Classification  
Recently, the 1D, 2D, and 3D CNNs have been utilized to extract various kinds of HSI features via multiple non-linear transformation architectures [17]. [PERSON] [18] used two 2-D CNN networks to extract spatial, local spectral, and global spectral information at the same time. In addition, the SE structure [11] was applied to enhance the feature extraction capability of the two streams. [PERSON] proposed an architecture with two FCNs for HSI classification based on 3-D and 1-D FCNs [19]. The 3-D branch extracted the spatial spectral information, and the 1-D branch extracted the spectral information as a supplement. However, we only extracted local spectral features and ignored the non-adjacent spectral features.  
A complete convolutional neural network generally consists of multiple convolutional layers superimposed on each other. For each convolutional layer, the calculation process can be formulated as follows:  
\[\hat{\mathbf{f}}_{\mathrm{ij}}(\mathrm{x})=\sigma\bigg{(}\Big{(}\mathrm{w}^{ \mathrm{l}}\mathrm{*x}\Big{)}_{\mathrm{ij}}+\mathrm{b}_{\mathrm{l}}\bigg{)}, \tag{1}\]  
where \(\mathrm{w}^{\mathrm{l}}\) is the lth kernel function with the learned weights, \(\mathrm{b}_{\mathrm{l}}\) is the bias, and \(\sigma\) is the activation function.  
As the depth of the network increases, the training process suffers from the problem of vanishing gradients. Therefore, the activation function is always conducted between the CNN layers to solve this problem. The Rectified Linear Unit (ReLU) is the most commonly used activation function, which is defined as  
\[\sigma(\mathrm{x})=\mathrm{max}(0,\mathrm{x}). \tag{2}\]  
Another essential layer is batch normalization (BN) which is implemented to increase the convergence speed of the network and can also replace the Dropout to alleviate overfitting. The BN operation is defined as  
\[\widetilde{\mathrm{X}}=\frac{\mathrm{x}-\mu_{\mathrm{B}}}{\sqrt{\sigma_{ \mathrm{B}}^{2}+\epsilon}} \tag{3}\]  
where \(\mu_{\mathrm{B}}\) and \(\sigma_{\mathrm{B}}\) stand for the mean and variance of \(\mathrm{x}\).  
At the end of the classification network, a classifier is often used to convert the feature map into the corresponding probability distribution. The classifier in this paper is the Softmax function, as follows:  
\[\mathrm{Softmax}(x_{\mathrm{i}})=\frac{\mathrm{e}^{x_{\mathrm{i}}}}{\sum_{0}^ {\mathrm{j}}\mathrm{e}^{\mathrm{j}}}\,\ \mathrm{i}=0,1,2,\ldots\mathrm{j}-1. \tag{4}\]  
Our basic spectral full-channel interactive (SFCI) feature extraction branch comprises a convolution layer, batch normalization (BN), and a ReLU layer. The convolution layer in SFCI is conducted to extract spectral information at a long distance to make full use of the spectral information. The ReLU and BN layers are utilized to avoid overfitting.
## 2 Scenario Definition
These parameters already include the effects of the receiver antenna's gain for each signal component. In Equation (4), the vector has been defined as:  
\[\mathbf{d}_{m}\triangleq\left[d_{m}\left(t_{0}\right),\ldots,d_{m}\left(t_{N -1}\right)\right]^{T}\in\mathbb{C}^{N\times 1}, \tag{5}\]  
with each of its component being equal to:  
\[d_{m}\left(t_{n}\right)=s\left(\alpha_{m}t_{n}-\tau_{m}\right)e^{\mathrm{i} \omega_{c}\left(\alpha_{m}-1\right)t_{n}}. \tag{6}\]  
\(s\left(t\right)\in\mathbb{C}\) represents the received filtered baseband navigation GNSS signal spread by the pseudorandom code of the satellite considered. In addition, for each component, we have that: \(\tau_{m}\) is the time-delay at the instant \(t_{0}\); \(\alpha_{m}=\left(1+v_{m}/c\right)\) is the coefficient capturing the effect of the Doppler shift, where \(c\) is the signal propagation speed and \(v_{m}\) the equivalent relative velocity between the transmitter and the receiver; and \(\omega_{c}=2\pi f_{c}\), where \(f_{c}\) is the signal carrier frequency. \(\tau_{m}\) and \(\alpha_{m}\) will also be assumed constant over time intervals of up to 1 s. This assumption for \(\alpha_{m}\) is justified by the very low rate of change of the Doppler shift observed by a static receiver [67]. Given the maximum rate of change of the relative speed possible in this case, i.e., \(dv_{m}/dt|_{Max}\approx 0.178\) m/s\({}^{2}\)[67], \(\alpha_{m}\) can vary as little as \(\approx\)5.94\(\times 10^{-10}\) within a 1-s interval. In this paper, the front-end's low-pass filter is implemented as a fifth-order Butterworth filter. The distortion introduced by this filter is already taken into account in the signal model \(s(t)\). The sampling frequency of the front-end is considered equal to the Nyquist rate with complex sampling, i.e., \(f_{s}=2B_{fe}\), where \(B_{fe}\) is the one-sided bandwidth (3 dB) of the low-pass filter. Finally, no quantization loss is considered in our analysis.
## 4 Advanced Strategies for eNO\({}_{3}\)R Toward Ammonia and Nitrogen
### Advanced Strategies Toward eNO\({}_{3}\)R to Nitrogen
#### 4.2.1 Nanoscale Zero-Valent Iron (nZVI)  
nZVI for nitrate reduction to nitrogen, leveraging various strategies to enhance their catalytic performance. Nanoscale zero-valent iron supported on ordered mesoporous carbon (nZVI/OMC) has demonstrated excellent performance, achieving a maximum nitrate removal capacity of 315 mg N g\({}^{-1}\) Fe with a high selectivity towards N\({}_{2}\) of up to 74%. The uniform dispersion of iron nanoparticles within the carbon matrix and the large surface area of the electrode contributed to its superior electrocatalytic activity. Additionally, the presence of hydrogen generated during the reduction process not only acted as a reductant but also protected the nZVI from oxidation, thereby enhancing its stability and performance over extended operation periods [143]. Moreover, efforts have been made to explore the mechanism of nitrate reduction mediated by iron-based electrodes, such as polymeric beads supported nZVI (nZVI/D201), which further elucidated the role of in situ formed Fe\({}^{0}\)@FexOy-Fe(II) structures in facilitating the electrochemical reduction process. These advancements underscore the potential of iron-based materials in developing efficient and stable electrodes for nitrate reduction, although challenges related to complex preparation processes remain a consideration for practical applications [144]. Bimetallic nanoparticles, such as iron-nickel (Fe/Ni) and iron-palladium (Fe/Pd), as well as trimetallic nanoparticles like palladium-copper-iron (Pd-Cu-nZVI), represent advanced strategies to boost nitrate reduction efficacy and facilitate the production of non-toxic nitrogen gas. These nanoparticles function synergistically, where the iron component of nZVI acts as an electron donor, initiating nitrate reduction. In bimetallic systems, metals like palladium enhance the conversion of nitrite to nitrogen gas, thereby promoting a more complete detoxification of the water [180]. Furthermore, adjusting the composition ratios, such as maintaining a 2:1 ratio of Pd to Cu in Pd-Cu-NZVI, optimizes the selectivity toward nitrogen gas formation [180]. Durability remains a challenge for nZVI catalysts due to leaching and oxidation. [PERSON] et al. [181] addressed this by protecting Fe nanoparticles with ultrathin graphene nanosheet layers (Fe@Gnc), maintaining over 96% NO\({}_{3}^{-}\) conversion rate and N\({}_{2}\) selectivity after 960 h of cycling. This graphene nano chainmail prevents active site agglomeration and accelerates electron/ion transport, suggesting a potential strategy for enhancing the durability and efficiency of Fe-based catalysts for nitrogen conversion processes [181].
## 6 Results and Discussion
### Binary Classification  
The metrics for testing and training accuracy are essential for assessing how well a classification model generalizes. While teaching accuracy gauges the prototype's performance on the training data, testing accuracy assesses the model's precision in class label prediction on an independent dataset. A lower training loss denotes better performance, while a higher testing accuracy denotes better generalization ability. A confusion matrix illustrates the model's effectiveness by showing the number of true positives, true negatives, false positives, and false negatives for every class. The true positive level (sensitivity) and false positive level (specificity) are traded off in the ROC-AUC curve at various classifications onsets: a higher AUC value typically denotes better model performance. Figure 12 shows model accuracy with no augmentation.  
Figures 13-16 show the confusion matrix, ROC_AUC curve, and Accuracy_Loss plot with no augmentation, ADAYSN, Random_Oversampler, and SMOTE, and for a binary class. ANN, 1D CNN, and 2D CNN are the three neural network models that are compared using confusion matrices. The models' performances in terms of identifying two categories, MSA and GLF, are visualized. The counts of false positives, false negatives, and true positives for each model are shown in the matrices. With the most accurate classifications (true positives) and the fewest misclassifications (false positives and false negatives) for both MSA and GLF, the 2D CNN model seems to be the most visually accurate. Although all models show relatively high performances, the 2D CNN surpasses the ANN and 1D  
Figure 12: Model accuracy with no augmentation.  
CNN in terms of recognizing the occurrences of both categories, indicating that it is the most effective architecture for this specific classification task.  
Figure 14: _Cont._  
Figure 13: Confusion matrix, ROC_Curve, and Accuracy_Loss plot with no augmentation and for a binary class.  
Figure 14: Confusion matrix, ROC_Curve, and Accuracy_Loss plot with ADASYN and for a binary class.  
Figure 15: Confusion matrix, ROC_Curve, and Accuracy_Loss plot with Random_Oversampler and for a binary class.