esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures
#### 2.3.3 Electrochemical Measurements  
The Cyclic Voltammetry (CV) measurements were carried out by GCE and aGCE and by SPCE and aSPCE to study the DCF electrochemical behavior. With the aGCEs, the following measurement parameters were used: time for equilibration: 15 s; potential step: 5.0 mV; potential range: 0.0-0.9 V; scan rate: 0.02-0.175 V s\({}^{-1}\). The CV signals were recorded on solution 7\(\cdot\)10\({}^{-8}\) mol L\({}^{-1}\) DCF at different pH and medium: pH 2.0, with 1 \(\times\) 10\({}^{-2}\) mol L\({}^{-1}\) hydrochloric acid; pH 2.0, with 2.5 \(\times\) 10\({}^{-2}\) mol L\({}^{-1}\) phosphoric acid; pH 4.7, with 2.5 \(\times\) 10\({}^{-2}\) mol L\({}^{-1}\) acetic buffer; pH 7.0, with 2.5 \(\times\) 10\({}^{-2}\) mol L\({}^{-1}\) phosphate buffer.  
With the aSPCE, the following measurement parameters were used: time for equilibration: 30 s; potential step: 5 mV; potential range: 0.0-0.9 V; scan rate: 0.05 V s\({}^{-1}\). The CV signals were collected on solutions of DCF at different concentrations (in the range 3.3 \(\times\) 10\({}^{-6}\)-82 \(\times\) 10\({}^{-6}\) mol L\({}^{-1}\)) kept at pH 2.0 with 1.0 \(\times\) 10\({}^{-1}\) mol L\({}^{-1}\) phosphate buffer.  
Differential Pulse Voltammetry (DPV) was applied using both aGCE and aSPCE.  
The electrochemical experiments were carried out at room temperature. The potential scan was carried out between 0.0 and 1.0 or 0.1 and 0.6 V with a step height (\(E_{\text{step}}\)) of 5 mVfor aGCE and of 4 mV for aSPCE. The other DPV parameters were optimized by a Design of Experiments (DoE) approach for both aGCE and aSPCEs, as described below.
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
## Materials and Methods
### MODIS Surface Reflectance and Vegetation Index Data  
To examine the effects of the wildfires on different plant characteristics and properties, the 8 day scale, 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data (MOD09A1) were used to produce three vegetation indices, including NDVI, EVI, and LSWI. All available, and reasonable, 16 day MODIS data were used; this equates to 23 images per year. There were a few notable exceptions when snow cover was present. The values for the MODIS data were then interpolated and smoothed using the cubic method in Python to generate the values shown. For each \(3\times 3\) km plot, there were between thirty and thirty-six MODIS pixels. The NDVI, EVI, and LSWI were calculated for this study using MODIS data downloaded from Google Earth Engine. The average of the pixels within individual \(3\times 3\) km plots was computed for each individual image of the specific indices.
## 3 System Model and Problem Formulation
### Transmission Rate  
Signal-to-interference-plus-noise ratio (SINR) directly affects the achievable data rate. According to the Shannon capacity formula, the achievable data rate of \(i_{th}\) CUE, \(J_{th}\) D2D pairs, and \(k_{th}\) SUE link over the sub-carrier,\(f\) can be expressed as follows:  
\[R_{i} =B\text{log}_{2}(1+\gamma_{i}^{M}). \tag{13}\] \[R_{j} =B\text{log}_{2}(1+\gamma_{j}^{D}).\] (14) \[R_{k} =B\text{log}_{2}(1+\gamma_{k}^{S}). \tag{15}\]  
Therefore, the total throughput of the entire network will be as follows:  
\[R_{total}=\sum\limits_{i=1}^{C}R_{i}+\sum\limits_{j=1}^{D}R_{j}+\sum\limits_{k =1}^{S}R_{k}\]
## 3 Results  
In this chapter, the results of the conducted research are presented. The first part discusses the results of the validation of the LUCAS 2018 data based on the current orthophoto  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Formula** \\ \hline ACC (accuracy) & (TP + TN)/(TP + TN + FP + FN) \\ PPV (precision or positive predictive value) & TP/(TP + FP) \\ TPR (sensitivity, recall, hit rate, or true positive rate) & TP/(TP + FN) = 1 \(-\) FNR \\ TNR (specificity, selectivity, or true negative rate) & TN/(TN + FP) = 1 \(-\) FPR \\ NPV (negative predictive value) & TN/(TN + FN) = 1 \(-\) FOR \\ FNR (miss rate or false negative rate) & FN/(FN + TP) = 1 \(-\) TPR \\ FPR (fall-out or false positive rate) & FP/(FP + TN) = 1 \(-\) TNR \\ FDR (false discovery rate) & FP/(FP + TP) = 1 \(-\) PPV \\ FOR (false omission rate) & FN/(FN + TN) = 1 \(-\) NPV \\ S/CSI (Threat score (TS) or critical success index (CSI)) & TP/(TP + FN + FP) \\ MCC (Matthews correlation coefficient) & (TP \(*\) TN \(-\) FP \(*\) FN)/[(TP + FP) \(*\) (TP + FN) \(*\) (TN + FP) \(*\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy metrics used in machine learning.  
\begin{table}
\begin{tabular}{c} \hline \hline
**Metrics** \\ \hline OA (overall accuracy) NOT ACC \\ PA (producer\_accuracy) = sensitivity, recall/true positive rate (TPR) \\ UA (user\_accuracy) = precision/positive predictive value (PPV) \\ OME = FNR (miss rate or false negative rate) \\ CME = FDR (false discovery rate) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Common accuracy metrics used in remote sensing and machine learning.  
map. The second part presents and compares the results of classification using the algorithms RF, SVM, and CART. The final subsection contains an analysis of the relative feature importance of the classified Sentinel-2 stack, which includes three seasonal Sentinel-2 sets (36 features/bands).
## 3 Results  
Three-point flexural tests were conducted on 12 specimens (Figure 1).  
Initial bending was observed at lower applied stresses for perforated samples (503 MPa) compared to non-perforated ones (900 MPa). The ultimate flexural strength was registered at 513 MPa and 1145 MPa for the perforated and non-perforated samples, respectively (Figure 2).  
Figure 1: Four of the specimens used for the investigation. Perforated and non-perforated BTK and BoneEasy. The picture was taken after the three-point flexural tests, and it is evident of the fracture of the specimen.  
Figure 2: Diagram of three-point flexural strength on four different types of specimens.  
Internal friction tests were conducted to determine the Young's modulus, which was reported from 101 to 107 MPa (Figure 3).  
The analysis with an optical microscope at different pH at 7, 14, and 21 days, respectively, did not reveal any macroscopic alteration of the surface (Figure 4).  
Figure 4: A \(200\times\) optical microscopy analysis: (**a,b**) on specimens exposed at pH 7 for 21 days, (**c,d**) on specimens exposed at pH 4 for 21 days. No macroscopic differences can be seen.  
Figure 3: Graphical representation of elastic modulus and flexural strength values on the four types of specimens.  
The further SEM analysis done on all the samples showed several microscopic defects of the surface on all the samples exposed at pH 4. Thus, it can be concluded that those imperfections are attributed to a corrosion effect (Figure 5).  
The roughness parameters Ra, Rq, and Sigma of the samples are reported in Tables 1 and 2, where Ra is the roughness average, Rq the root mean square, and Sigma is the standard deviation.  
The profile analysis of the lower faces of the BE and BTK specimens at pH 7 showed Sigma values (\(\sigma\)) of 1.165 \(\upmu\)m and 21.555 \(\upmu\)m, respectively. On the upper face, the same samples showed Sigma values (\(\sigma\)) of 1.697 \(\upmu\)m for BE and 13.756 \(\upmu\)m for BTK.  
After pH 4 immersion, the lower faces of the BE and BTK specimens showed Sigma values (\(\sigma\)) of 0.883 \(\upmu\)m and 19.685 \(\upmu\)m, respectively. The upper faces of the same samples had Sigma values (\(\sigma\)) of 1.583 \(\upmu\)m for BE and 9.073 \(\upmu\)m for BTK.  
The Sigma values confirmed that the BTK samples were rougher than the BE ones and also that the BTK ones had two different surfaces with different degrees of roughness (Tables 1 and 2, Figure 6).  
Figure 8: Energy-dispersive X-ray spectroscopy on BE samples (**a**) and BTK samples (**b**). X-axis represents the energy (KeV) and Y-axis represents the number of counts. The highest peak is titanium but an average of 1.25% and 3.65% of carbon can be detected in the BE and BTK samples, respectively.  
Figure 7: SEM internal defects on BTK (**a**) and BE (**b**) samples.
###### Abstract  
Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.  
Keywords:Indoor localization; ultra-wide band (UWB) signals; time of arrival (TOA); deep learning; long short-term memory (LSTM); trilateration algorithm +
Footnote †: journal: Applied Physics Letters
# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 4 Experiments
### Evaluation Metrics  
In order to assess the integrity of the mathematical or machine learning models, evaluation metrics are widely used. In the paper, to test the scratch and fine-tuned pre-trained models, the five most significant evaluation metrics, accuracy, specificity, precision, sensitivity, and f1 score, were used. These metrics are defined as follows:  
\[A_{c}\ =\ \frac{TP\ +TN}{TP+FP+TN+FN} \tag{6}\]  
\[S_{p}=\frac{TN}{TN+FP} \tag{7}\]  
\[P_{r}\ =\frac{TP}{TP+FP} \tag{8}\]  
\[S_{t}\ =\frac{TP}{TP+FN} \tag{9}\]  
\[F1\ Score\ =\ 2\ \times\ \frac{P_{r}\ \times\ S_{t}}{P_{r}\ +\ S_{t}} \tag{10}\]  
In the above equations, \(A_{c}\), \(S_{p}\), \(P_{r}\), and \(S_{t}\) denote accuracy, specificity, precision, and sensitivity. Additionally, \(TP\), \(FP\), \(TN\), and \(FN\) refer to true positive, false positive, true negative, and false negative.
## 6 Conclusions  
Aiming at the problem of identifying complex power quality disturbances, a method for fast and accurate identification of power quality complex disturbance based on DWT and H-ELM is proposed. The simulation results of the example are as follows.  
(1) The feature extraction is performed by DWT, the feature selection is performed by the PSO feature selection algorithm, and the feature combination with the best classification performance is selected. Based on the selected classification features, a network with good generalization performance can be trained.  
(2) The data enhancement method is used to deal with the problems of data imbalance in the power quality disturbance data and the small amount of data, and to enhance the generalization ability of the network. H-ELM is faster than traditional machine learning algorithms, it can extract more important information from features, and its generalization ability is stronger.  
(3) Compared with the deep learning algorithm, the advantage of this method is that it adopts a lightweight structure that combines the ability to process big data and maintain rapidity. Compared with most machine learning methods, the training speed of this method and the ability to process a large data volume is outstanding. It can be used for offline large data volume processing as well as for online analysis.  
In the area of disturbance identification of power quality, how to build the local knowledge map of power systems, analyze the underlying data, and realize streaming online processing is the direction needed to improve the real-time classification of power quality disturbance, which needs further research.  
[PERSON] contributed to establishing the model and writing the paper. Many ideas were suggested by [PERSON] to support the work, and [PERSON] performed the simulations. [PERSON] analyzed the data, reviewed the work and modified the paper. In general, all authors cooperated during all stages of the research.  
\begin{table}
\begin{tabular}{c c c} \hline
**Classifier** & **Classification Accuracy (\%)** & **Algorithm Runtime (s)** \\ \hline PCASVM & 83.94 & 1.28 \\ ELM & 89.73 & 0.34 \\ PNN & 84.93 & 0.98 \\ H-ELM & 92.56 & 0.12 \\ PSO-H-ELM & 93.01 & 0.08 \\ \hline \end{tabular}
\end{table}
Table 8: Comparison of classification accuracy and algorithm running time of five methods.  
\begin{table}
\begin{tabular}{c c} \hline
**Signal Type** & **True Signal Accuracy (\%)** \\ \hline Sag & 91.6 \\ Swell & 93.1 \\ Harmonics & 92.0 \\ Oscillatory transient & 98.4 \\ Waveform distortion & 88.9 \\ Overall accuracy & 93.0 \\ \hline \end{tabular}
\end{table}
Table 9: True signal classification result.  
**Funding:** This research was funded by SGCC program: Research on Extensive Application and Benefit Evaluation of Typical Power Substitution Technology Considering Power Quality Influence, grant number 52182018000H.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Vulnerable Node Identification
In general, the node that is strongly coupled to the other nodes will have a wider and deeper influence on the power system when compared to a node that is weakly coupled to the other nodes. We regard these nodes as the vulnerable nodes. As shown in Figure 1, nodes in a power system are electrically coupled to their neighbor nodes, forming several coupling regions. Due to the dynamic feature of mutual state coupling among regions and the nodes in the regions, the level of electrical coupling coefficient of the node belonging to different regions will change with a change in the electrical coupling coefficient of its neighboring nodes. For example, the RECE value of node \(v_{2}\) is first calculated based on the values of nodes \(v_{1}\) and \(v_{3}\). As their regional electrical coupling state are dynamic correlated to each other, the RECE value of node \(v_{2}\) will change according to its neighboring nodes and finally reach a balanced state. Thus, when we evaluate the RECE values of nodes that reflect the electrical coupling level between nodes and the power system, the dynamic state coupling process should be taken into account necessarily. After further considering the dynamic coupling of nodes on fault propagation route, as well as the power-dependence relationship between neighboring nodes, we adopted a modified PageRank, called the coupling tracking rank algorithm (CT Rank), to conduct the bidirectional propagation of electrical coupling features of neighboring nodes so as to identify the influence of individual node fault upon system:  
\[CT_{i}=C_{i}+\sigma(\frac{w_{ik}}{\sum\limits_{k\in\Omega_{i}^{out}}w_{ik}}CT _{k}+\frac{w_{k^{\prime}i}}{\sum\limits_{k^{\prime}\in\Omega_{i}^{in}}w_{k^{ \prime}i}}CT_{k^{\prime}}) \tag{8}\]  
\[w_{ik}=\sqrt{P_{ik}^{2}+Q_{ik^{\prime}}^{2}},w_{k^{\prime}i}=\sqrt{P_{k^{ \prime}i}^{2}+Q_{ki}^{2}}\]  
where \(\Omega_{i}^{out}\) is the neighboring node set of output current at absorption point, and \(\Omega_{i}^{in}\) is the set of neighboring nodes that make current output to node \(i\). \(w_{ik}\) is the apparent power that node \(i\) supplies to node \(k\), \(k\in\Omega_{i}^{out}\). \(w_{k^{\prime}i}\) is the apparent power that node \(i\) absorbs from node \(k^{\prime}\), \(k^{\prime}\in\Omega_{i}^{in}\). \(P\), \(Q\) are the  
Figure 1: Electrical coupling of multiple regions. active power and reactive power in the power transmission line. \(\sigma\) is the damping coefficient with \(\sigma=0.85\)[16]. The modified PageRank algorithm, with the neighboring electrical coupling coefficient \(C_{i}\) of the node as an initial value and the power supply and demand relationship between nodes as the coupling efficiency, seeks the transient coupling between nodes and the grid through an iterative loop so as to obtain the vulnerability index of node \(CT_{i}\), ultimately identifying the vulnerable nodes that have a large influence upon the operation stability of the distribution system.
# Carbon Mineralization in a Soil Amended with Sewage Sludge-Derived Biochar
## 3 Results and Discussion  
### General Properties of the Soil, Sewage Sludge, and Biochars  
The soil chemical and physical properties are presented in Table 1. In general, the clayey soil (580.8 g clay kg\({}^{-1}\)) showed low nutrient contents (Ca\({}^{+2}\), Mg\({}^{+2}\), P, and K\({}^{+}\)) and low cation exchange capacity (CEC). This is a representative tropical acidic Oxisol (pH value of 5.1) from the Central Region of Brazil.  
The chemical, physical, and microbiological characteristics of the sewage sludge (SS) and the biochars are shown in Table 2. The biochar produced at pyrolysis temperature of 300 \({}^{\circ}\)C (BC300) showed higher C (total and organic) and N contents compared to biochar produced at pyrolysis temperature of 500 \({}^{\circ}\)C (BC500) and SS. A decrease in the C and N contents was observed from BC 300 to BC 500. Increasing the pyrolysis temperature gradually reduced the percentage of H and the H/C ratio, and increased the C/N ratio. The pyrolysis of SS increased pH values, shifting from an acidic condition (pH = 4.8 in SS) to near neutrality (pH = 6.5 in BC500). Higher pH values in BC500 may be related to increased concentrations of alkali elements, such as Ca and Mg oxides, and their alkaline reactions.  
In general, except for the nitrate and ammonium concentrations, the pyrolysis of sewage sludge enriched the macro- and micronutrients in the biochars. Phosphorus and potassium were higher in the biochars compared to SS, and this enrichment increased with pyrolysis temperature. In addition, the surface area and the pore volume increased with increasing pyrolysis temperature. Increasing the pyrolysis temperature gradually increased the total heavy metal contents. Nevertheless, recently, [PERSON] et al. [3] concluded that heavy metals availability decreases with increasing pyrolysis temperature.
## 3 Results
### Diagnostic Performance of the Elastography  
The predictive values of the elastography were 75% of sensitivity, 98.36% of specificity with a correct diagnosis in 96.92% of the cases (n = 63); the PPV was 75%, and the NPV was 98.36%. No significant differences were found between the BI-RADS classification and the elastography patterns (\(p\) = 0.85). The malignant lesions were associated with stiff elastography patterns (pattern 4), with a \(p\) = 0.04, whereas the benign lesions were associated with soft elastography patterns (patterns 1, 2, and 3, and cyst artifact), with \(p\) = 0.03, 0.02, 0.04, and 0.02, respectively. In the diagnosis of the cystic lesions, a correlation was observed between the elastography diagnosis and the histopathological diagnosis, with statistical significance (\(p\) < 0.001). The sensitivity was 66.7% and the specificity was 94%. The PPV was 76.9% and the NPV was 90.4%.  
As for the improvement in the diagnostic precision--which implies adding elastography to the B-mode ultrasound--in three out of four cases categorized as BI-RADS 4a in B-mode, and which showed stiff elastogram results (pattern 4), the result was malignant in the pathological anatomy, whereas for the rest of the softer patterns (1, 2, 3, and cyst artifact), they all showed a benign histopathological result.
# Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
## 2 RBF-BP Combined Neural Network Model
### BP Neural Network  
In both theory and practice, the BP algorithm has reached a more general applicability in some nonlinear predictions [18]. The learning process consists of two processes: forward propagation of the signal, and backward propagation of the error. Forward propagation is the consequence of feeding training data to the input layer, which is computed layer by layer by the neural network. Backpropagation is accomplished by backpropagating the error to all the units in each layer using the error values to update the iterative solution weight and bias term values [19]. However, this technique has certain unresolved flaws, and its algorithm uses gradient descent as a training method, resulting in a propensity for local optima [20]. Figure 2 depicts the optimum neural network diagram for BP. The BP algorithm is composed of input, hidden, and output layers which belong to the multi-level feedforward neural network. The main feature of the network is that it needs to go through multiple forward propagation networks, and the error is propagated back. The idea is to input a set of samples to achieve the output by calculation, and then to get the difference between the actual output and the expected value to continuously adjust the weight and threshold to minimize the difference. The process is repeated until the difference is less than a predetermined value. In the forward pass, the input layer passes through multiple hidden layers to the output layer. If the output layer cannot get the expected output, it will turn to backpropagation and adjust the network weight and threshold according to the prediction error, so that the predicted output of the BP neural network is close to the expected output.