esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Furthermore, the reduction fraction of Cd; oxidizable fractions of Pb, Cu, and Zn; and all the residual fractions were the main nonbioavailable fractions (Table 2, significant correlations of each fraction with the available metals). The total concentration of metals has been used as an indicator to evaluate soil contamination. However, more and more researchers believe that the available fraction of metals is better when evaluating the metal uptake effect by plants [43]. Tobacco has different responses regarding the accumulation and mobility of Cd, Cu, Pb, and Zn [44; 45], and tobacco preferentially accumulates Cd rather than Pb [46]. The distribution of Cd in the tobacco leaves followed the order lower \(>\) middle \(>\) upper [47]. [PERSON] [48] showed that the Cd content in lower leaves was higher than that in upper and middle leaves, indicated that Cd was more easily accumulated than transferred in matureleaves. The cumulative effect of tobacco with HA treatment was significantly lower than that in the CK treatment, indicating that HA applied to soil could decrease Pb absorption in tobacco. The reason for the high contents of Zn and Cu in the leaves is that these elements are essential nutrients for tobacco plants. However, the growth and quality are negatively affected when Cd and Pb occur in excess in tobacco leaves. The distribution of Cu and Zn in the different parts of the leaves exclusively followed a certain order, but the differences were not distinct (Figure 3). According to the distribution of metals in the tobacco leaves, Zn absorbed more easily in the middle leaves and could be transferred to the lower and upper leaves [49]. In contrast, Cu uptake in tobacco is more likely to occur in immature leaves. Therefore, as compared with those of Cu and Zn, the toxic effects of Cd and Pb on tobacco deserve more attention. Research showed that China's tobacco plants have a higher concentration of heavy metal [50; 51]. Thus, it is of great significance to decrease the heavy metal uptake of tobacco by application of HA. This study indicates that HA can be applied to soil contaminated by heavy metals, significantly reducing the bioavailability of heavy metals and inhibiting the migration of metals in the soil-plant system. However, Cu and Zn are essential mineral elements for tobacco growth, and application of HA inhibits their uptake by tobacco, as previous studies observed [52]. Due to the limited complexing capacity of HA [26], the accumulation capacity of tobacco toward different metals varies. When soil has a high content of heavy metals (or has a high effective state content), tobacco still has a certain risk of Cd and Pb accumulation.
## 4 Experiment and Result
### Evaluation Baseline
#### 4.4.2 Deep Learning Classifiers
(**a**) Original dataset; (**b**) Augmented dataset. precision = 87.68%, recall = 87.23% and f1-score = 88.35%). The M-BERT obtained an accuracy of 88.69%, precision of 86.86%, recall of 87.33%, and f1-score of 89.55%. These classifiers' performance indicates that the dataset has a positive influence on performance. It is clear that the performance of all classifiers improves significantly after balancing the dataset. Thus, we trained the existing classifiers with the generated embeddings from BERT and performed the classification on both datasets. Furthermore, our proposed AugFake-BERT model outperformed the other state-of-the-art models. Table 13 shows the performance metrics of all used classifiers on both datasets (imbalanced and balanced) for comparison. The table proves that dataset balancing plays a significant role in classification results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Test Performance**} \\ \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline CNN & 83.34 & 80.23 & 84.48 & 83.52 \\ \hline LSTM & 84.69 & 84.86 & 85.72 & 84.15 \\ \hline Bi-LSTM & 85.10 & 86.86 & 85.43 & 86.85 \\ \hline CNN-LSTM & 88.83 & 87.68 & 87.23 & 88.35 \\ \hline M-BERT & 88.69 & 86.86 & 87.33 & 89.55 \\ \hline
**AugFake-BERT** & **92.45** & **92.86** & **91.23** & **91.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Accuracy, precision, recall, and F1-score on the augmented dataset of deep learning classifiers. Figure 11: Comparison of evaluation metrics of CNN, LSTM, Bi-LSTM, CNN-LSTM, M-BERT, and AugFake-BERT models on the original dataset. (**a**) Precision; (**b**) Recall; (**c**) F1-score.
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 4 Simulation and Results  
To validate the algorithm's performance, the proposed CEL-OLSR protocol based on the TOPSIS-MPR algorithm is compared with PM-OLSR [32], ML-OLSR [33], LD-OLSR [18] and the standard OLSR protocol. Through simulation experiments, their differences in network performance metrics are analyzed, including packet delivery rate, average end-to-end delay, network throughput, and routing control overhead.
## 3 Results and Discussion
### Detection of Protein Overexpression in Gels Using the IPBBIS Algorithm
#### 3.3.3 Effectiveness of the IPBBIS Method Using Known Concentrations
A relationship was established between the molecular weights of the ladder or control (provided by the manufacturer, Bio-Rad S.A. Mexico D.F.) and the multiple maxima obtained by the IPBBIS method. Due to the absence of background noise, the positions of the control proteins were automatically detected and marked with blue lines for identification, as shown in the gel image of Figure 11C. At the same time, the data were stored in an array. The stored data represent the positions of the control proteins and the values of their molecular weights. They were processed by applying numerical methods of linear interpolation, nearest interpolation, and cubic interpolation to obtain an equation. This equation was used to know and predict the molecular weight of proteins present in any position of the remaining samples of the gel image to be analyzed (see Figure 12 and Table 3). Figure 12 shows the interpolation methods used to predict the molecular weight of the detected proteins. The X-axis corresponds to the pixel positions of the bands, while the Y-axis represents the molecular weights. Figure 11: (**A**) Molecular weight control was obtained from lane one or the ladder in Figure 10A. (**B**) The IPBBIS plot applied to (**A**). (**C**) Bands automatically detected by IPBBIS marked with blue lines. Figure 12: Results of the interpolation methods for the molecular weight of the detected proteins. The results obtained in Table 3 indicate that the linear interpolation method showed the lowest error, with a value of 3.35%, when correlating the actual weight of the GPN protein (34.56 kilodaltons, kDa) with that predicted by the interpolation method used. Therefore, the formula used to identify the molecular weight of the proteins detected in the rest of the samples corresponds to the linear interpolation method and is defined by Equation (1):  
\[y(x)=y_{i}+\frac{\left(y_{i+1}-y_{i}\right)(x-x_{i})}{\left(x_{i+1}-x_{i} \right)} \tag{1}\]  
where \(0\leq x_{i}\leq 400\) corresponds to the interval containing the number of pixels corresponding to the image height of each sample, and \(0\leq y_{i}\leq 250\) corresponds to the molecular weight of the proteins.
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Tiling
#### 3.2.5 Divide and Conquer  
Research by [PERSON] and [PERSON] [37] proposed a divide and conquer approach to increase the bandwidth efficiency of the 360 VR video streaming system. The hierarchical resolution degrading enables a seamless video quality-switching process hence providing a better user experience. Compared to the other method which uses equirectangular projection [37], implements hexaface sphere projection as illustrated in ([37] Figure 4), and significantly saved 72% bandwidth compared to other tiling approaches without viewport awareness. To improve the performance of this approach, an adaptive rate allocation method for tile streaming based on available bandwidth is needed.
## 5 Conclusions  
The developed system can be applied to assistant robots working in indoor domestic environments. With its help, it is possible, for example, to build a map of the environment, enriched with information about articulated objects. The information stored in the map can be used by the robot to safely interact with articulated objects.  
The result of the system's operation can also serve as information a priori to another module estimating object parameters based on observations, so that such a module will achieve convergence faster. For example, a robot, knowing beforehand that the object it wants to interact with is, with a certain probability, a rotating object, will start making movements to confirm this hypothesis. This will allow it to obtain faster convergence on the model, and thus the task of opening such an object will also be completed faster. The robot will not waste time checking other hypotheses. It also decreases the probability of destroying such objects.  
The developed system has some limitations. It is unable to detect articulated objects that do not have visible handles. This limitation is challenging to overcome because of ambiguity. Parameters such as the axis of rotation of articulated objects are impossible to estimate from a single image, for a human, without characteristic features like handles. The current system also ignores rotating objects that have the axis of rotation placed horizontally, due to the problems that occur when creating a dataset. However, this problem can be eliminated by assigning such objects a certain probability of belonging to one of the two classes when marking the training data. The system also has a problem with estimating the rotation axis when the handle does not lie on the opposite side relative to the location ofthe rotation axis. This limitation could also be addressed by assigning some probability as to the axis of rotation in such cases.  
Future work includes utilizing the neural network to estimate the state of the articulated object during robot-object interaction in a scenario that is not affected by the aforementioned limitations. We also plan to utilize the sequence of images by running a recurrent neural network that only presents the initial guess about the parameters of articulated objects, but later, during the interaction, the parameters are adjusted to include the response of the object.  
Conceptualization, D.B. and A.M.; methodology, D.B. and A.M.; software, A.M.; validation, A.M., P.G. and K.M.; formal analysis, A.M. and D.B.; investigation, A.M.; writing--original draft preparation, K.M. and D.B.; writing--review and editing, A.M., P.G., K.M. and D.B.; visualization, A.M. and K.M.; supervision, D.B.; project administration, D.B.; funding acquisition, D.B. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the National Science Centre, Poland, under research project no. UMO-2019/35/D/ST6/03959.  
Not applicable.  
The authors declare no conflict of interest.
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Personalization and Adaptivity (Based on Input Hypothesis Theory)
#### 4.3.1 Dynamic Content Adaptation in VR  
Virtual reality (VR) has emerged as a paradigm-shifting medium for content dissemination, boasting the capability to metamorphose dynamically in alignment with individual learner archetypes. Given the spectrum of learner proficiencies, educational modalities, and inclinations, this malleability becomes profoundly indispensable in linguistic pedagogy. Drawing cues from the input hypothesis theory, it becomes imperative to furnish learners with linguistic stimuli marginally transcending their present proficiency, colloquially delineated as 'i + 1', to ascertain pinnacle educational outcomes.  
This dynamic content transmogrification manifests in multifarious avenues within the VR linguistic and educational sphere. Instantaneous evaluations can discern a learner's linguistic stature, calibrating the intricacy of subsequent linguistic challenges. Neophytesmay grapple with rudimentary lexicon and syntactical frameworks, while their seasoned counterparts delve into labyrinthine grammatical edifices and intricate lexemes.  
VR can architect a lifelike tableau wherein learners liaise with ethereal entities. These virtual denizens can recalibrate their linguistic delivery in tempo, lexicon breadth, and intricacy, ensuring perpetually decipherable communication.  
Feedback apparatus within VR can illuminate discrepancies, furnishing rectifications, thereby enriching the educational odyssey's customization. As an exemplar, should a learner recurrently flounder with verbs of yesteryears, the VR mechanism can proliferate tableau accentuating that specific grammatical facet.  
The dynamism of content transfiguration in VR, anchored in the tenets of the input hypothesis theory, guarantees that learners perpetually find themselves on the precipice of challenge without tumbling into the abyss of bewilderment, laying the foundation for efficacious linguistic mastery.
## 3 Model and Methods
### Model Evaluation Metrics  
The accuracy of the eight-day GPP estimation was assessed against 84 EC flux sites in the OYC, LOS, LSS, LGS, and LFS. The difference in GPP simulation between the TL-CLUE model and the TL-LUE model was explored in terms of spatial heterogeneity and seasonal change. Additionally, the accuracy of GPP estimation in the TL-LUE and TL-CLUE models was evaluated by root mean square error (\(RMSE\)) (Equation (14)), \(Bias\) (Equation (15)), relative to the mean predictive Bias (\(R_{bias}\)) (Equation (16)), and coefficient of determination (\(R^{2}\)) (Equation (17)) as follows:  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}{(v_{oi}-v_{mi})}^{2}}{n}} \tag{14}\]  
\[Bias=\frac{\sum\limits_{i=1}^{n}{v_{mi}-v_{oi}}}{n} \tag{15}\]  
\[R_{bias}=\frac{\overline{v_{m}-\overline{v_{o}}}}{\overline{v_{o}}}\times 100 \% \tag{16}\]  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}{(v_{mi}-\overline{v_{o}})}^{2}}{\sum\limits _{i=1}^{n}{(v_{oi}-\overline{v_{o}})}^{2}} \tag{17}\]  
where \(v_{o}\), \(v_{m}\), \(i\), and \(n\) are the observed value, estimated value, value ordinal number, and sample size, respectively.
## 4 Conclusions  
Although the sizing of defects in IR thermography has been researched for decades, sizing a blurry defect accurately is still a challenge. In this work, the sizing of subsurface defects in square pulse thermography of metallic plates is accomplished by employing the oriented gradient of histograms, which is used in a Pb contour detector found in the natural image processing literature. The oriented gradient of histograms is calculated based on the distribution of the gray level value in a local region rather than the gray level value of a single pixel.  
Experimental testing was implemented on a corroded steel plate on which nine flat-bottom blind holes with a diameter of 15 mm were machined. Attributed to the non-uniform heating and variation of defect depths, defects presented different observable levels in raw thermograms. The experimental result shows that the proposed approach succeeds in not only sizing distinct defects but also sizing blurry ones with high accuracies. The sizing result of the proposed approach is compared to that of the Canny operator and FWHM. The comparison shows that the proposed approach is superior to the Canny operator in sizing not only distinct defects but also blurry ones. When sizing distinct defects, the proposed approach has an accuracy similar to FWHM. However, it outperforms FWHM when sizing blurry defects. We implemented experiments repeatedly by rotating the tested specimen 90\({}^{\circ}\) each time. The sizing results of the proposed approach are generally stable although serious non-uniform heating was stimulated in the experiments. Experimental results verified that the proposed approach can size both distinct defects and blurry ones with a high and stable accuracy in square pulse thermography of metallic plates. Based on the accurate size information provided by the proposed approach, a more reasonable decision can be made in the maintenance management of metallic components. In the future, we will test the performance of the proposed approach on IR thermography with other excitation means.  
This work was financially supported by the National Key Research and Development Program (Nos. 2016 YFC0303803), the Fundamental Research Funds for the Central Universities of China (Nos. 14 CX02077A) and the Shandong Provincial Natural Science Foundation of China (Nos. ZR2013 EEQ028).  
[PERSON] conducted experimental data analysis and wrote the paper. [PERSON] organized and revised the paper. [PERSON] performed the experiments and drew all figures. [PERSON] provided guidance for methods of data analysis. [PERSON] provided advices for results discussion.  
The authors declare no conflict of interest.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 3 Position Solution and Measurement Exclusion
### Location Solution Based on Angle of Arrival  
For the case of AoA, the GN solution at the \(l\)-th iteration is defined as:  
\[\hat{\mathbf{x}}_{l}=\hat{\mathbf{x}}_{l-1}+(\mathbf{G}_{\text{AoA},(l-1)}^{T} \mathbf{W}_{\text{AoA}}^{-1}\mathbf{G}_{\text{AoA},(l-1)})^{-1}\mathbf{G}_{ \text{AoA},(l-1)}^{T}\mathbf{W}_{\text{AoA}}^{-1}\mathbf{b}, \tag{24}\]where \(\mathbf{G}_{\text{AoA}}\) is the geometry matrix defined as:  
\[\mathbf{G}_{\text{AoA},(2n,1:3)}=\begin{bmatrix}\frac{\partial\phi_{n}}{ \partial x}&\frac{\partial\phi_{n}}{\partial y}&\frac{\partial\phi_{n}}{ \partial z}\\ \frac{\partial\phi_{n}}{\partial x}&\frac{\partial\phi_{n}}{\partial y}&\frac{ \partial\phi_{n}}{\partial z}\end{bmatrix}, \tag{25}\]  
composed by the partial derivatives of the azimuth and elevation angles as a function of UE-BS vector's position coordinates \(\mathbf{P}_{n}^{\prime}\) calculated in (7). The partial derivatives of both azimuth and elevation angles are derived from the expressions in (9) and (10), respectively. Moreover, \(\mathbf{W}_{\text{AoA}}\) is the weighting matrix defined as:  
\[\mathbf{W}_{\text{AoA}}=diag(\sigma_{\phi_{n},CRLB},\sigma_{\theta_{n},CRLB}), \tag{26}\]  
and \(\mathbf{b}\) is the vector of residuals  
\[\mathbf{b}=\begin{bmatrix}\mathbf{b}_{\phi_{n}};&\mathbf{b}_{\theta_{n}} \end{bmatrix}, \tag{27}\]  
being the difference between angle estimation and AoA observables computed in Section 2.2.2. The residuals are wrapped in radians to \(\begin{bmatrix}-\pi&\pi\end{bmatrix}\).  
The location solution derived in (15) can also be used for the hybrid approach, where the total number of BSs used for the hybrid positioning is the sum of the number of the strongest BSs of the TDoA position based-method and those in LoS condition for the angle position based method, i.e., \(M=M_{\text{BS}_{\text{TDoA}}}+M_{\text{BS}_{\text{AoA}}}\). The WLS classical solution of this trilateration problem is similar to the one in (15) and (24), where the geometry matrix \(\mathbf{G}_{\text{Hyb}}\) is a \(M\times 3\) matrix defined for \(1\leq m\leq M_{BS_{TDoA}}\) as:  
\[\mathbf{G}_{\text{Hyb},(m,1:3)}=\begin{bmatrix}\frac{\mathbf{w}_{1:1}-\mathbf{ k}}{|\mathbf{w}_{1:1}-\mathbf{k}|}-\frac{\mathbf{w}_{1:(m+1)}-\mathbf{k}}{ \|\mathbf{w}_{1:(m+1)}-\mathbf{k}\|}\end{bmatrix}. \tag{28}\]  
and defined for \(M_{\text{BS}_{TDoA}}+1\leq m\leq M\) as:  
\[\mathbf{G}_{\text{Hyb},(m,1:3)}=\begin{bmatrix}\frac{\partial\phi_{n}}{ \partial x}&\frac{\partial\phi_{n}}{\partial y}&\frac{\partial\phi_{n}}{ \partial z}\\ \frac{\partial\phi_{n}}{\partial x}&\frac{\partial\phi_{n}}{\partial y}&\frac{ \partial\phi_{n}}{\partial z}\end{bmatrix}, \tag{29}\]  
and \(\mathbf{W}_{\text{Hyb}}\) is the weighting matrix defined as:  
\[\mathbf{W}_{\text{Hyb}}=blkdiag(\mathbf{W}_{TDoA},\mathbf{W}_{AoA}), \tag{30}\]  
\(\mathbf{W}_{TDoA}\), \(\mathbf{W}_{AoA}\) being the matrix of UTDoA and AoA weighting coefficients, respectively, computed in (18) and (26).
## 5 Conclusions and Future Work  
This paper presents the machine learning algorithm for reducing the BER in UWA OFDM communication systems with a reduction in PAPR. An OFDM system is considered by adding clippers in a communication system, which reduces the PAPR but creates nonlinearity distortion caused by the PA. The proposed FFB method estimates the PA nonlinearity at the receiver with the help of a modern machine learning algorithm. The deep learning method gives us a feasible estimation of the data. The DPD technique is very difficult to implement because most of the underwater acoustic transceivers consume very high energy. In such conditions, one can use this algorithm to reduce the PAPR. Reliable communication can be achieved if the FFB algorithm is performed at both sides of the base station and UWA OFDM modems. The results prove that modern machine learning algorithms can be used in signal processing and communication engineering for reducing distortion, which is caused by high PAPRs in OFDM systems. In future work, the number of neurons can be reduced with more hidden layers and less input-output neurons, and the time frame can be estimated in order to maintain lower BERs.  
Every author in this paper performed a significant role in completing this project and worked on the manuscript. [PERSON] discussed the main idea and proposed this paper. [PERSON] implemented the whole idea and prepared the manuscript. [PERSON] worked on preparing a machine learning algorithm. [PERSON] and [PERSON] performed the simulation in MATLAB using a bellhop channel and made some corrections to the manuscript. Finally, [PERSON] contributed to the manuscript preparation and did some numerical analyses. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the following projects: the Equipment Prophetic Field Fund (No. 61404150301), the Heilongjiang Natural Science Foundation Joint Guidance Project (No. LH2019A006), the Equipment Prestably Ship Heavy Industry Joint Fund (No. 6141B042865), the Underwater Information and Control Key Laboratory Open Fund (No. 6142218061812), and the Xiamen University Education Fund (No. UAC201804). Key research & Development and Transformation Plan of Science and Technology for Tibet Autonomous Region (No. XZ201901-GB-16). Special Fund for Local Universities Supported by the Central Finance of Tibet University 2018.  
There is no conflict of interest regarding the publication of this article.