esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Evaluation Metrics  
The evaluation metrics used in this study are of two types. The first type evaluates the capability of the OD algorithm to detect the poisoned samples in the poisoned model and it is expressed as follows:  
\[P_{d}=\frac{N_{OD}(Th_{P})}{N_{P}(Th_{P})} \tag{1}\]  
where \(N_{OD}(Th_{P})\) is the number of samples with the highest outlier score from the OD algorithm for a specific value of the poisoned percentage \(Th_{P}\). \(N_{P}(Th_{P})\) is the number of poisoned samples in the data set for a specific value of the poisoned percentage \(Th_{P}\). As described before, \(N_{P}(Th_{P})\) is simply \(S_{Train}\times Th_{P}\), where \(S_{Train}\) is the size of the training set (i.e., 2250 samples).  
The second type is the performance of the classification to distinguish LOS from NLOS samples. It is estimated using three metrics: accuracy, precision, and recall. Accuracy is expressed as follows:  
\[Accuracy=\frac{TP+TN}{FP+FN+TP+TN} \tag{2}\]  
Precision is expressed as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]Recall is expressed as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
where TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives, and FN is the number of false negatives.  
In this specific study, the improvement or difference in performance between the sanitized and the original poisoned model is even more relevant because it measures the improvement in accuracy/precision/recall of the OD algorithm due to sanitation, which is in turn based on how well the OD algorithm is able to select the legitimate samples and discard the poisoned ones. These metrics expressed in accuracy/precision/recall are simply the following:  
\[Accuracy_{I}=Accuracy_{S}-Accuracy_{P} \tag{5}\]  
\[Precision_{I}=Precision_{S}-Precision_{P} \tag{6}\]  
\[Recall_{I}=Recall_{S}-Recall_{P} \tag{7}\]  
where \(Metric_{S}\) is the metric of the ML algorithm on the sanitized training portion of the data set and \(Metric_{P}\) is the metric of the ML algorithm using the poisoned training portion of the data set. The metric can be accuracy, precision, and recall.
## Materials and Methods
### HPLC-MS Analysis of Phenolic Compounds  
Phenolic compound analysis was performed using the HPLC-DAD-MS method and the Agilent 1200 HPLC system with a DAD detector linked with an Agilent 6110 quadrupole single MS detector. The separation of phenolic compounds was performed on a chromatographic column: Eclipse XDB C18, dimensions 4.6 mm \(\times\) 150 mm, 5 \(\upmu\)m, Agilent, at the temperature 25 \({}^{\circ}\)C. The mobile phase consisted of water + 0.1% acetic acid/acetonitrile (99/1) \(v/v\) as eluent A and 100% acetonitrile + 0.1% acetic acid as eluent B. The gradient used over the first 2 min mobile was 95% phase A, 5% B followed by a linear increase of solvent B to 40% until min 18, a linear increase of phase B to 90% over the next 12 min, maintenance for 2 min and then a return to 95% A and 5% B in the next minute, with maintenance for column balancing for 5 min. The total analysis time was 30 min with a mobile phase flow of 0.5 mL/min. The monitored wavelengths were between 240 nm and 370 nm. For MS analysis, positive ionization ESI mode was used. The ionization parameters were: capillary voltage: 3000 V; temperature: 35 \({}^{\circ}\)C, nitrogen flow: 8 L/min. The data acquisition was done in full scan mode in the mass range m/z: 100-1000. The identification of phenolic compounds in the samples was obtained by comparing retention times, UV and mass spectra with those of the injected standards. Quantification of the different phytochemicals were based on peak areas of six different concentrations in six replicate assays, being expressed by the linear regression coefficient R\({}^{2}\), and calculated as equivalents of representative standard compounds. All contents were expressed as micrograms per g dry weight.
## 2 Materials and Methods
### Data Sets  
#### 2.3.1 MODIS Datasets  
Due to the long time series and accessibility on a large scale of the MODIS dataset, the MODIS land and atmosphere products from 2003 to 2015 were collected to estimate GPP and ET, including the MYD09A1 surface reflectance, MYD11A1 land surface temperature and emissivity, MCD12Q1 land cover classification, MYD15A2 LAI, MCD43B3 albedo and atmosphere products (MYD04L2 aerosol and MYD07L2 atmosphere profiles). NDVI and EVI data were obtained from Terra and Aqua MODIS (MOD13A2 and MYD13A2) products to calculate the vegetation cover fraction.
# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 5 Conclusions and Future Work  
Through this study, we effectively utilized a GNN for end-to-end network performance prediction, focusing specifically on delay. We evaluated four distinct models to identify the most efficient one. This included the original RouteNet model and its enhanced versions, notably models with an added attention layer. Our comparative study of the original RouteNet and its modified forms provided insightful findings regarding the impact of our enhancements. Among these, the Modified RouteNet model enhanced with an attention layer showed superior performance in terms of predictive accuracy, as indicated by higher R-squared values and lower loss metrics, indicating a stronger fit and better generalization abilities. These results highlight the potential of incorporating attention mechanisms in graph-based neural network models to improve their precision in predicting complex networked systems.  
The Modified RouteNet model with attention has shown enhanced predictive accuracy, but its complex nature leads to increased convergence times. Future research should concentrate on refining the model's architecture and training methods to lower computational demands while maintaining accuracy. Investigating more efficient training algorithms might help strike a balance between the model's complexity and computational efficiency.  
Conceptualization, B.R.D., B.K.D. and P.M.; methodology, B.K.D. and B.K.A.; software, B.K.D.; validation, B.R.D., P.M. and B.K.A.; formal analysis, B.K.D.; investigation, B.R.D. and P.M.; resources, B.R.D. and P.M.; data curation, B.K.D.; writing--original draft preparation, B.K.D.; writing--review and editing, B.R.D., P.M. and B.K.A.; visualization, B.K.D.; supervision, B.R.D. and P.M.; project administration, B.R.D.; funding acquisition, B.R.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the University Grants Commission, Nepal, under research grant ID: CRG-078/79/Engg-01 (Context Issues and Solutions towards 5G Network Migration of Nepal - CISNetMiN) principally investigated by Dr.  
and take full responsibility for the content of the publication. We are thankful to reviewers for their constructive comments.
## 4 Case Study
This index shows the percentage of generated hydropower compared to the producible maximum hydropower at each hydropower plant. A high percentage of this index shows that the algorithm could generate the required hydropower for downstream demands [6]: \[VR=\frac{N_{t=1}^{T}\left((N_{m,t}\big{|}N_{m,t}<PPC_{m})\vee(PPC_{m}\big{|}N_ {m,t+1}\geq PPC_{m})\right)}{T.PPC}\times 100,\] (16) where \(VR\) is the volumetric reliability index, \(N_{t=1}^{T}\left((N_{m,t}\big{|}N_{m,t}<PPC_{m})\vee(PPC_{m}\big{|}N_{m,t+1}< PPC_{m})\right)\) is the total generated hydropower during the operational periods, and \(T.PPC\) is the maximum generated hydropower;
3. Vulnerability index. The average amount of failure occurrences in the system is measured by the vulnerability index. The average hydropower deficits are measured by the vulnerability index: \[V=\frac{\sum\limits_{t=1}^{T}\left[(PPC_{m}-N_{m,t}\big{|}N_{m,t}<PPC_{m}) \vee(0\big{|}N_{m,t}\geq PPC)\right]}{T},\] (17) where \(\sum\limits_{t=1}^{T}\left[(PPC_{m}-N_{m,t}\big{|}N_{m,t}<PPC_{m})\vee(0 \big{|}N_{m,t}\geq PPC)\right]\) is the total hydropower deficit;_RMSE_ (root mean square error). The root mean square error between the generated hydropower and hydropower point capacity, as shown by Equation (18). A low value for this index represents better performance of the algorithm based on more generated hydropower: \[RMSE=\sqrt{\sum_{t=1}^{T}\frac{\left(PPC_{m}-N_{m,t}\right)^{2}}{T}};\] (18)
5. _MAE_ (mean absolute error). The mean absolute error between the generated hydropower and hydropower plant capacity, which is measured based on the following equations. A low value for this index is suitable and shows a reduced deficit: \[MAE=\sum_{t=1}^{T}\frac{\left|PPC_{m}-N_{m,t}\right|}{T}.\] (19) The mathematical model for multireservoir operation is considered based on the following criteria:
1. The water level for the multireservoir system is considered to be the decision variable, and the initial position of the crows is considered based on the water level;
2. The continuity equation is used to compute the outflow based on Equation (9);
3. Different constraints are checked: If these constraints are not satisfied, the penalty functions are used;
4. The objective function is computed for each member based on Equation (13);
5. The number of operational periods (\(m\)) is compared to the total number of periods (\(M\)). If this is satisfied, go to the next level. Otherwise, return to the second step;
6. The decision variables or crow positions are updated based on Equations (1) and (2);
7. The memories of crows are updated based on Equation (5);
8. The convergence criterion is checked. If it is satisfied, the algorithm finishes. Otherwise, the algorithm returns to the second step.
## 2 Study Area and Data
### Land Cover Data  
The land cover data are derived from the European Space Agency (ESA) WorldCover 10 m 2020 product for free ([[https://esa-worldcover.org/en](https://esa-worldcover.org/en)]([https://esa-worldcover.org/en](https://esa-worldcover.org/en)), accessed on 20 May 2024) [37]. The WorldCover product provides a global land cover map for 2020 at 10-m resolution based on Sentinel-1 and Sentinel-2 data. It comprises 11 land cover types, which is consistent with the classification of land cover by the Food and Agriculture Organization (FAO) ([[https://www.fao.org/4/x0596e/X0596e00.htm#P-1_0](https://www.fao.org/4/x0596e/X0596e00.htm#P-1_0)]([https://www.fao.org/4/x0596e/X0596e00.htm#P-1_0](https://www.fao.org/4/x0596e/X0596e00.htm#P-1_0)), accessed on 20 May 2024). Based on this, we determine the land cover types corresponding to the stations and categorize both \"Moss and lichen\" and \"Bare/sparse vegetation\" types as bare land. Therefore, the land cover types corresponding to the stations include tree cover, shrubland, grassland, and bare land, which are also the focus of this study.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 1
#### 3.1.2 Case 1: Analysis of 1000-10,000 Frequency Range (Hz)  
According to Table 8, deviations in this range suggest bulk winding movement, clamping pressure loss, or mechanical displacement. The clamping system secures the windings in position, and any shifts in this range suggest early-stage mechanical instability.  
Figure 6a reveals a progressive increase in deviation beyond 6000 Hz, approaching the control limit at 9500 Hz. The \(C_{p}\) and \(C_{pk}\) indices decline toward 1.2, nearing the marginal stability threshold set in Table 4.  
Figure 6b further validates instability, with range values converging toward the \(R\)-limit, confirming progressive movement of windings relative to the original fingerprint.  
Remarks: The transformer is exhibiting early mechanical displacement, necessitating continuous monitoring. If \(C_{p}\) drops below 1.0 in future tests, intervention will be required.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 6 Conclusions  
Signal detection methods based on MMSE filtering in massive MIMO systems are widely used, but matrix inversion with higher complexity makes it more difficult to implement them in practical applications. Some methods of approximate inversion, such as Neumann series expansion, has reduced the detection complexity, but due to a large degree of detection performance loss; others avoid the complex matrix inversion and directly estimate the signal vector. Although, computational complexity is reduced by orders of magnitude, detection performance needs to be improved. Based on the MMSE criterion, this paper proposes a low-complexity, hybrid, iterative SDGS joint detection algorithm, which directly estimates the user's transmitted symbol vector and can quickly converge to obtain an ideal estimation value with a few simple iterations. The matrix inversion operation is avoided, and  
Figure 4: Hard decision bit error rate (BER) performance comparison with high fading level at 128 \(\times\) 16 antenna configuration.  
Figure 5: Soft decision bit error rate (BER) performance comparison with slow fading level at 128 \(\times\) 16 antenna configuration.  
algorithm complexity is kept at \(O\big{(}K^{2}\big{)}\). In addition, in order to make full use of soft information, the algorithm is applied to the soft decision, and an approximate calculation method of the LLR for channel decoding is given, which further improves the signal detection performance. Theoretical derivation and simulation results show that the SDGS algorithm can be used as one of the most effective solutions for signal detection in massive MIMO systems.  
[PERSON] conceived and designed the presented idea and developed the theory, performed the simulations, and wrote the paper. [PERSON] analyzed the research and performed experimentations, and [PERSON] provided extensive technical support throughout the research. S.K. provided extensive support in the theoretical analysis and provided funding support.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2016R1D1A1B03934653).  
The authors declare no conflicts of interest.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 4 Discussion
According to this study, ZnO-NPs have anti-proliferative and anti-tumor effects on many cancer types. According to ref. [61], ZnO-NPs were able to treat bladder cancer patients with Dox chemoresistance. However, in this investigation, we evaluated PEITC's ability to effectively trigger apoptosis. Additionally, we examined for the first time how ZnO-NPs and Dox together could decrease tumor growth, both in vivo and in vitro. The EAT model was employed to accomplish this. When compared to the healthy control group, our findings showed that oral treatment of ZnO-NPs alone considerably reduced the burden of solid tumors without modifying the degree of oxidative stress, as seen by a non-significant rise in MDA and a non-significant decline in TAC. The anti-tumor impacts of the ZnO-NP therapy were comparable to those due to the Dox therapy. Interestingly, we discovered that when both treatments were used together, the tumor burden was dramatically reduced compared to when each treatment was used alone. These results were further supported by PEITC's capacity to considerably reduce ILS and MST when used alone or in conjunction with Dox. Additionally, the tumor tissues examined based on their histopathology revealed that all the groups that received PEITC, either alone or in conjunction with other treatments, saw a drop in the number of mitosis divisions and an increase in the number of apoptotic bodies [62; 63]. Remarkably, the maximum dose of ZnO-NPs and Dox showed a significant reduction in cellular viability, which was higher than the effects of both drugs alone. Overall, our results showed that the combination therapy was significantly more effective than ZnO-NPs or Dox alone at inhibiting cellular survival, both in vivo and in vitro. A multi-step process, carcinogenesis involves both mutation and enhanced cell proliferation [57]. Overproduction of reactive oxygen species (ROS), weak antioxidant defenses, and/or defective DNA repair processes can all lead to oxidative damage to cellular macromolecules [31]. ROS easily oxidize cellular fatty acids to create lipid peroxyl radicals and lipid hydroperoxides, which grow into MDA [64]. ZnO-NPs' purported antioxidant action is one of the putative methods through which it exerts its anti-tumor effects. Ref. [65] showed that Dox produced oxidative stress in every part of the body. The ZnO-NP/Dox combination treatment significantly reduced the elevated MDA level caused by Dox treatment alone and greatly enhanced the lowered TAC. Consequently, the combination treatment provided the best tumor growth inhibition with the least amount of oxidative stress. This is an amazing finding. This can be explained by the fact that catalase, glutathione peroxidase, and superoxide dismutase are just a few of the many antioxidant enzymes that are thought to be strongly induced by ZnO-NPs [66]. Therefore, these enzymes may eliminate the ROS that causes oxidative damage, which preserves lipid molecules and lowers the production of MDA [67].
# A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control
###### Abstract
As a result, the suggested solutions are more practical and feasible. The significant contributions of this research are as follows:  
* SIMULINK simulation of nonlinear X3D quadrotor model to validate control approaches. * Two linear control systems are implemented: the conventional PID and the LQR control system. * Two nonlinear control systems are implemented: fuzzy control and model reference adaptive PID controller (MRAPC) using MIT rules. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Controllers** & **Strengths** & **Limitation** \\ \hline
**PID** & Gain selection is simple; steady-state error can be avoided. &
\begin{tabular}{c} **Cannot deal with disturbance or noise, and** \\ cannot handle multiple configurations** \\ simultaneously. \\ \end{tabular} \\ \hline
**LQR** & It can handle many inputs and outputs. & Not able to overcome steady-state errors. \\ \hline
**Backstepping** & The model must be systematic and recursive; a precise model is not essential. It can control system nonlinearities, overcome inadequate disturbances, and guarantee & Over-parameterization; selecting appropriate parameters is difficult. \\ \hline
**Fuzzy Logic** & It provides a viable solution to a complex and uncertain model and does not demand a precise model. &
\begin{tabular}{c} Control rules and system analysis are difficult \\ to develop. It takes a long time to adjust the parameters. \\ \end{tabular} \\ \hline
**Hoo** & When the system is multivariable and the channels are cross-coupled, it performs well. & A well-designed model is required. \\ \hline
**Sliding Mode Controller** & The performance of high nonlinearity is excellent. Less sensitivity to perturbations and uncertainty in the model. & The chattering problem can lead to system instability. \\ \hline
**Model Predictive Control (MPC)** & Predicts future state behaviors; works with multiple input and output & Tracking is slow. \\ \hline
**Adaptive Controller** & When parameters are uncertain, the dynamic and disturbance model are always changing; engineering & It takes time to adapt to the new parameters. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Strengths and Limitations of Quadrotor Control Techniques. * Performance comparison of all controllers for quadrotor trajectory tracking based on transient response. The proposed controllers' performance is anticipated to be better in the presence of parameter uncertainty and external disturbances. The following is the format of this paper: The mathematical model of an X3D quadrotor is presented in Section 2. The quadrotor's control methods are discussed in Section 3. The study and comparison of the aforementioned control strategies for an X3D quadrotor are presented in Section 4. The conclusion is found in Section 5.
###### Abstract  
Third-order nonlinear susceptibilities \(\chi^{(3)}(-2\omega;\omega,\omega,0)\) and \(\chi^{(3)}(-\omega;\omega,-\omega,\omega)\) responsible for electric-field-induced second harmonic generation and the optical Kerr effect were measured and directly compared for tellurite and chalcogenide glasses. The nonlinear coefficients were found by measuring the second harmonic radiation from samples under the action of an external field of terahertz pulses and by the classical z-scan technique, respectively. The influence of ambient air and helium gas on second harmonic generation was analyzed. It was demonstrated that both susceptibilities \(\chi^{(3)}(-2\omega;\omega,\omega,0)\) and \(\chi^{(3)}(-\omega;\omega,-\omega,\omega)\) have close absolute values, which shows the dominant role of nonresonant electronic nonlinearity in the studied glasses.  
tellurite glasses; chalcogenide glasses; third-order nonlinearity; terahertz pulses; second harmonic generation; optical Kerr effect; z-scan +
Footnote †: journal:  
Article  
Cubic Nonlinearity of Tellurite and Chalcogenide Glasses: Terahertz-Field-Induced Second Harmonic Generation vs.
## 3 Analysis and Discussion of Physical Modeling Results
### Fractured Reservoirs with Various Dip Angles
When the dipping angle was larger, the superposition effect decreased, with a relatively smaller amplitude and larger frequency. At high dipping angles, as the angle increased, the velocity and density anomalies were more concentrated in the vertical direction; therefore, the amplitude increased and the frequency decreased.
## 2 Results and Discussion
### Unsupervised Analyses of Cinnamon by SPME/GC-MS
#### 2.2.3 Analysis of the Commercial Cinnamon Products VOCs Dataset  
Additionally, the dataset of commercial products was analyzed separately, Supplementary Figure S2C,D. The results showed that the PCA score plot produced a stronger model with a PC1 responsible for 61.9% of the total variance (82.1%). The PCA score plot could segregate four commercial products into three clusters. The model confirmed the previous findings that CP-2 and CP-4 were highly related and could be observed to be different from CP-1 and CP-3 along PC1. Additionally, CP-1 and CP-3 could be segregated along PC2, Supplementary Figure S2C.  
PCA loading plot indicated that coumarin, hexenol and hexenol isomer, and (_E_)-cinnamaldehyde were four potential markers responsible for commercial products' segregation Supplementary Figure S2D, being more related to CP-1, CP-3, and CP-2 and CP-4, respectively. These findings were in agreement with the previous results shown in the analysis of the whole dataset (Figure 3C). Prominently, CP-1 and CP-3 used low grades of cinnamon drugs, likely _C. cassia_, in their production.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 3 Numerical Analysis  
The hybrid FSO-PON using the NOMA-VLC system is numerically measured considering fiber CD, ISI, FWM, noise, VLC losses and VLC beam divergence.