esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Criteria of Response Function Selection
### Step Two--Accuracy of the Probabilistic Moments
The following expression was employed on the example of the expected value:  
\[\Delta_{\text{rel,m1}}=\left|\frac{E[y_{1}(w)]-E\left[y_{1}(w*)\right]}{E[y_{ 1}(w)]}\right| \tag{25}\]  
The initially obtained results confirmed that for different approximations, the shape of the diagrams of the probabilistic moments' relative error \(\Delta_{\text{rel,m}}\) may also differ (Figure 6). The higher error value was not always obtained for the higher moment. Its monotony was not always only increasing in the considered range. Therefore, it was decided to use the maximum of all the values in the interval (0.0, \(\alpha_{\text{max}}\)*) as a measure of accuracy of the given approximation:  
\[\max\Delta_{\text{rel,m}}=\max_{\text{m}=\text{m1,\ldots,m4}} \Delta_{\text{rel,m}} \tag{26}\] \[\alpha_{\text{i}}\in\{0.0125,0.025\ldots,\alpha_{\text{max}}*\}\]  
The results obtained in the validation process confirmed its compliance (Figure 7). At the same time, they indicated a more general, assumed character: for some approximations, despite the significant relative error of the coefficients, the one concerning probabilistic moments was clearly smaller. One of the greatest advantages of the proposed measure of accuracy max\(\Delta_{\text{rel,m}}\) is the possibility of comparing any approximation to a given reference function despite having different mathematical forms, which was not possible in the case of the previous accuracy criterion \(\Delta_{\text{rel,c}}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **A Results** & **B Results** & **C Results** \\ \hline
**Reference base \(POb\)** & 1 & 2 & 5 \\
**Approximation base \(POa\)** & 1 & 2 & 5 \\
**Reference base \(poly\)** & 1 & 3 & 1 \\
**Data-generating function** & \(y_{7}\) & \(y_{9}\) & \(y_{7}\) \\
**Approximating function** & \(y_{7}\) & \(y_{9}\) & \(y_{7}\) \\
**\(RMSE_{\text{mod}}\)** & 35.83 & 42.70 & 41.45 \\
**max\(\Delta_{\text{rel,m}}\)** & \(\leq\)10\({}^{-4}\) & \(7.404\times 10^{-4}\) & \(\leq\)10\({}^{-4}\) \\
**\(\Delta_{\text{rel,c}}\)** & \(3.210\times 10^{-7}\) & 1.002 & 4.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Exemplary results obtained using the adopted criterion (the most important differences are underlined).
## 3 Results and Discussion
### Shielding Properties of Embedded Cu Meshes
According to the solid layer model, a coating with a sheet resistance of 0.2 \(\Omega\)/sq (such as an e-LCM 180) should have an \(SE\) of 59.5 dB, which we see in practice at 10 MHz. At frequencies of 1 GHz and higher, the discrepancy with the continuous layer model increases, as shown in Figure 12c. In the microwave region, even the most low-resistance coatings with sheet resistance less than 0.1 \(\Omega\)/sq have an \(SE\) value of no more than 40-45 dB [21; 23]. Coatings with a continuous structure are less susceptible to this pattern, which is confirmed in the experiment; however, these coatings rarely have a sheet resistance of less than 5 \(\Omega\)/sq [33]. According to the continuous layer model, the \(SE\) value for such a coating should be 31.75 dB, which is in good agreement with the experimental data on the AZO/Ag/TiO\({}_{2}\) structures [33]. Figure 13 shows a comparison of different types of coatings in terms of transparency and \(SE\) parameters at a frequency of 1 GHz. Thus, the comparative graph shows that the e-LCM and e-SCM coatings proposed in our work correlate or surpass the most promising solutions in this direction. Thus, it can be concluded that, from the point of view of balance, the shielding efficiency-optical transparency of LCM is better than SCM. This thesis is valid only for the low-frequency range up to 1 GHz. At frequencies above 1 GHz, meshes with a large cell size demonstrate a fairly rapid drop in shielding efficiency. This is more pronounced for commercial shielding materials in which the cell size is on the order of 500 \(\upmu\)m. Meshes with a small cell size retain high shielding efficiency over a much wider frequency range. Thus, it can be said that, in order to obtain effective coatings in a wide frequency range, it is necessary to reduce the cell size, as well as keep the crack width less than 5 \(\upmu\)m.
# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 3 Experimental Results and Discussion
### Diagnostic Results of the Proposed Model  
The initial learning rate (LR) is set to 0.001, all iterations are set to 30, and the learning rate is halved every 10 iterations by combining the StepLR learning rate decay mechanism. The batch size is set to 32, and use the Cross-Entropy Loss function, which is defined as in Equation (10):  
\[Loss=\frac{1}{batch\_size}\sum_{j=1}^{batch\_size}\sum_{i=1}^{n}-y_{ji}log\ \dot{y_{ji}}-\left(1-y_{ji}\right)log\ \left(1-\dot{y_{ji}}\right) \tag{10}\]  
Common evaluation metrics used in neural networks are accuracy, precision, recall and \(F1\) score, and they are calculated as shown in Equation (11):  
\[\begin{array}{c}Accuracy=\frac{TP+TN}{TP+FN+TN+FP}\\ precision=\frac{TP}{TP+FP}\\ recall=\frac{TP}{TP+FN}\\ F1\_score=2\times\frac{precision\ast recall}{\left(precision+recall\right)} \end{array} \tag{11}\]  
where \(TP\) is True Positive, \(FP\) is False Positive, \(TN\), and \(FN\) were true negative and false negative, respectively.  
We recorded the accuracy and loss after each iteration in the experiment and plotted the accuracy and loss lines as the experimental results show in Figure 10. It is known that these two evaluation metrics of the model fluctuate dramatically at the beginning of the iteration. After completing the 6 th iteration, the accuracy and loss of the model level led off, indicating the ability of the model to learn fault characteristics quickly and eventually reach a steady state, presenting almost zero loss values, as well as fully correct accuracy. In addition, according to the same division, we do experiments with the 1 HP, 2 HP, and 0-2 HP data, respectively. The accuracy can be seen in Table 5, which confirmed that the model has good diagnostic performance under these three load cases.  
Figure 10: Experimental results: **(a)** Accuracy line; **(b)** Loss line.
# Using Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan
## 3 Materials and Methods
### Decision Tree (ID3)  
Decision tree learning is an ML technique used for building classification models capable of predicting the possible value of a specific attribute (i.e., target attribute) based on the values of other attributes (i.e., input attributes). Such classification models are trained on historical data, in which the value of the target attribute is known. The decision tree is represented using a directed graph where each interior node is an input attribute with several branches equal to the possible values of that attribute. Each leaf node contains the value of the target attribute given the values of the input attributes along the path that led to that leaf node starting from the root node. The input attributes that reside in the higher levels of the tree have a higher impact on deciding the value of the target attribute.  
There are several types of decision tree algorithms that can be used to build classification models. In this work, the Iterative Dichotomiser 3 (ID3) algorithm is used. The ID3 algorithm is an algorithm that performs a top-down, greedy search on a given data set to teach the nodes. The direction at each node is determined using Information Gain (IG). IG represents the reduction of disorder produced by splitting the data at each internal node and thus determines how accurately a given attribute can separate the target classes. The disorder in the target attribute is measured using Entropy. Entropy is computed as follows:  
\[E(S)=-\sum_{i=1}^{C}P_{i}*\ Log_{2}(P_{i}) \tag{1}\]  
where \(S\) is the data set used for building the decision tree, \(c\) is the number of classes in the target attribute, and \(Pi\) is the ratio of instances with class I to the total number of instances in the data set. The \(IG\) for feature \(A\) is calculated as follows:  
\[IG(S,\ A)=E(S)-\sum_{v=1}^{n}\left((\frac{|S_{V}|}{|S|})\ *E(S_{V})\right) \tag{2}\]  
where n is the values feature \(A\) can hold, \(S_{V}\) is the instances where \(A\) has value \(v\), \(\mid S_{V}\mid\) the total number of instances in \(S_{V}\), and \(\mid S\mid\) the total number of instances in \(S\).
# Recent Advances in Femtosecond Laser-Induced Surface Structuring for Oil-Water Separation
## 4 Oil-Water Separation Mechanism
In this case, the apparent contact angle, \(\theta_{tr}\), is an average of the flat surface, \(\theta\), and the value for suspending over the flat surface (that is, \(180^{\circ}\)) is given by [53]:  
\[\cos\theta_{tr}=r_{f}f\cos\theta+f-1 \tag{4}\]  
where \(f^{\prime}\) defines the fraction of the projected solid surface that is wetted by the liquid and \(r_{f}\) is the roughness ratio of the wet area. When \(f=1\) and \(r_{f}=r\), Equation (4) turns into the Wenzel equation. In the extreme case of a superhydrophobic-underwater superoleophobic surface (Figure 6f), the droplet volume is adsorbed by the surface structures in air, but when an oil droplet is placed on the surface while it is immersed in water, it exhibits a high contact angle for oil. This is explained by extending the Cassie-Baxter or transition states for the oil droplet. To realize oil-water separation, two different approaches are commonly used, namely superhydrophobic-superoleophilic membranes or superhydrophilic-underwater superoleophobic membranes. In superhydrophobic-superoleophilic membranes, oil is adsorbed while the water is obstructed due to the non-wetting properties. However, a superhydrophobic-underwater superoleophobic membrane repels oil under water. The wetting response of the latter surface depends on the medium; thus, it is superhydrophilic and oleophilic in air, but when it is immersed in water, it becomes superoleophobic. As a consequence of this response, water passes through the membrane, whereas oil is obstructed, resulting in oil-water separation.
## 5 Results and Discussion
### Discussion
#### 5.3.1 NLP-LSTM  
As can be seen in Figure 10, the test accuracy (97%) and training accuracy (97%) obtained with LSTM were acceptable. Again, it can be seen that the validation accuracy line advanced very close to the training accuracy; However, at Epoch 10, there was a drop in accuracy to less than 95%. This drop may be due to an overfitting problem. Among the four DL algorithms, LSTM was the worst performer.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & **K = 1** & **K = 2** & **K = 3** & **K = 4** & **K = 5** & **Mean** \\ \hline LSTM & 94.12 & 95.76 & 96.48 & 98.36 & 98.84 & 96.71 \\ BiLSTM & 94.26 & 95.95 & 97.96 & 98.75 & 99.08 & 97.20 \\ GRU & 95.03 & 95.90 & 98.26 & 98.41 & 99.28 & 97.29 \\ BiGRU & 95.22 & 96.53 & 98.55 & 98.84 & 98.84 & 97.39 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean accuracy in percent with K-fold cross-validation with K = 1 to K = 5.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithm** & **L** & **Micro Avg** & **Weighted Avg** & **F1-Score** \\ \hline LSTM & 1000 & 0.91 & 0.88 & \\ BiLSTM & 1000 & 0.98 & 0.96 & \\ GRU & 1000 & 0.99 & 0.98 & \\ BiGRU & 1000 & 0.99 & 0.98 & \\ \hline LSTM & 500 & 0.94 & 0.92 & \\ BiLSTM & 500 & 0.98 & 0.97 & \\ GRU & 500 & 0.99 & 0.98 & \\ BiGRU & 500 & 0.99 & 0.98 & \\ \hline LSTM & 200 & 0.97 & 0.95 & 0.93 \\ BiLSTM & 200 & 0.98 & 0.96 & 0.74 \\ GRU & 200 & 0.98 & 0.97 & 0.94 \\ BiGRU & 200 & 0.98 & 0.97 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Micro avg, weighted avg, and F1-score.
# Soil Depth Prediction Model Using Terrain Attributes in Gangwon-do, South Korea
## 2 Methodology
### Statistics Analysis
#### 2.2.3 Multi-Linear Regression Analysis
However, the coefficient of determination may be increased by some independent variables having high explanatory power, even if some of the independent variables in the model are not very descriptive. There is a correction coefficient as a statistic that complements these shortcomings of the adjusted coefficient of determination (\(R_{adj}^{2}\)) and is defined as follows:  
\[R_{adj}^{2}=1-\Big{[}\Big{(}1-R^{2}\Big{)}(n-1)/(n-k-1)\Big{]} \tag{3}\]  
where \(n\) is the number of points in the data sample, \(k\) is the number of independent variables included in the regression equation and is the number of variables in the model excluding the constant. These \(R_{adj}^{2}\) can be used to compare the suitability of the model [54]. Additionally, it is the most widely used method through RMSE (root mean squared residual) for error analysis of regression models, and the definition is given as Equation (4). \[RMSE=\sqrt{\sum_{i=1}^{n}\big{(}(Y_{m})_{i}-\big{(}Y_{p}\big{)}_{i}\big{)}^{2} /n} \tag{4}\]  
where \(Y_{m}\) is the measurement value, \(Y_{p}\) is the predicted value, \(n\) is the number of datapoints. To determine a suitable model, \(R^{2}\), \(R_{adj}^{2}\), RMSE for each case are calculated as Table 3, and the results are shown in Figure 6  
1. As the number of variables used in the model decreases, \(R_{adj}^{2}\) decreases: Especially, a large decrease in Cases No. 5 and 6. 2. Since Cases No. 1 and 2 have multi-collinearity problems, suitable models are Cases No. 3 and 4. 3. Case No. 4, compared to Case No. 3, has a relatively large error. 4. Applied Model in this study is Case No. 4, since it uses fewer variables than in Case No. 3. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & \multicolumn{4}{c}{**Igneous Rock**} & \multicolumn{4}{c}{**Metamorphic Rock**} & \multicolumn{4}{c}{**Sedimentary Rock**} \\ \cline{2-10}  & \(R^{2}\) & \(R^{2}_{adj}\) & **RMSE** & \(R^{2}\) & \(R^{2}_{adj}\) & **RMSE** & \(R^{2}\) & \(R^{2}_{adj}\) & **RMSE** \\ \hline
1 & 0.707 & 0.688 & 0.863 & 0.697 & 0.677 & 0.773 & 0.856 & 0.846 & 0.672 \\ \hline
2 & 0.704 & 0.689 & 0.866 & 0.691 & 0.674 & 0.781 & 0.698 & 0.681 & 0.973 \\ \hline
3 & 0.704 & 0.691 & 0.868 & 0.690 & 0.678 & 0.781 & 0.695 & 0.682 & 0.977 \\ \hline
4 & 0.702 & 0.693 & 0.870 & 0.686 & 0.676 & 0.876 & 0.693 & 0.683 & 0.981 \\ \hline
5 & 0.665 & 0.658 & 0.922 & 0.666 & 0.659 & 0.864 & 0.621 & 0.613 & 1.09 \\ \hline
6 & 0.607 & 0.603 & 0.999 & 0.609 & 0.605 & 0.878 & 0.604 & 0.600 & 1.114 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{Case 1: SLOPE, TWI, STT’, SPI, CURV, SCA**, Case 2: SLOPE, TWI, STT’, CURV, SCA**, Case 3: SLOPE, TWI, STT’, CURV, Case 4: SLOPE, TWI, STT’, Case 5: SLOPE, TWI, Case 6: SLOPE.} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the multi-collinearity analysis. Figure 6: Comparison results of six regression models for selecting suitable models.
## 3 Results
### Performance Summary  
The performance of the two categories of features extraction and mapping suites (ultra-resolution and very high-resolution suites) is summarized and shown in Figure 15. It is based on the 10-fold cross-validation accuracy (F1-score) using the SoftVoting classifier. For the case of single-date autumn images, the ultra-resolution suite provided 6.3% more accuracy (F1-score) than the very high-resolution suite in the Hakkoda site with 16 land cover and vegetation classes. In the Zao site with 23 classes, the ultra-resolution suite provided 6% more accuracy (F1-score) than the very high-resolution suite. For the classification of 12 classes in the Shiranuka site, the ultra-resolution suite provided 3.6% more accuracy (F1-score) than the very high-resolution suite. On average, the ultra-resolution suite provided 5.3% more accuracy (F1-score) than the very high-resolution suite across the three sites. A similar trend was obtained by using bi-seasonal images (summer and autumn). In the case of bi-seasonal images, the ultra-resolution suite provided 4.9% more accuracy in the Hakkoda site, 6.8% more accuracy in the Zao site, and 3% more accuracy in the Shiranuka site over the very high-resolution suite. On average, the ultra-resolution suite showed 4.9% higher performance than the very high-resolution suite using bi-seasonal images across three sites.  
The addition of summer images with the previous autumn season images increased the classification accuracy substantially for both the ultra-resolution and very high-resolution suites in all sites. In the Hakkoda site, the gain by increasing the temporal images was 10.9% for the ultra-resolution suite and 12.3% for the very high-resolution suite. The Zao site gained 13.2% accuracy in the case of the ultra-resolution suite and 12.4% in the case of the very high-resolution suite. The gain by increasing the temporal images was 14.3% for the ultra-resolution suite and 14.9% for the very high-resolution suite in the Shiranuka site. On average, the ultra-resolution suite gained 12.8% accuracy; whereas 13.2% gain was achieved by the very high-resolution suite across the three sites. The very high-resolution suite gained slightly more accuracy than the ultra-resolution suite while increasing the temporal images.  
Figure 14: Class-wise changes in accuracy (F1-score) by addition of summer images in the Shiranuka site.  
However, it should be noted that the accuracy obtained from the ultra-resolution suite using single-date autumn images was not higher than the accuracy obtained from the very high-resolution suite using bi-seasonal images. Therefore, increasing the temporal images was found to be more crucial than the usage of ultra-resolution suites. Nevertheless, merely increasing the bi-temporal images was not found to be sufficient unless the ultra-resolution suite was applied.
###### Abstract  
The legacy Global Navigation Satellite System (GNSS) satellite clock offsets obtained by the dual-frequency undiferenced (UD) ionospheric-free (IF) model absorb the code and phase time-variant hardware delays, which leads to the inconsistency of the precise satellite clock estimated by different frequencies. The dissimilarity of the satellite clock offsets generated by different frequencies is called the inter-frequency clock bias (IFCB). Estimates of the IFCB typically employ epoch-differenced (ED) geometry-free ionosphere-free (GFIF) observations from global networks. However, this method has certain theoretical flaws by ignoring the receiver time-variant biases. We proposed a new undiferenced model coupled with satellite clock offsets, and further converted the IFCB into the code and phase time-variant mixed observable-specific signal bias (OSB) to overcome the defects of the traditional model and simplify the bias correction process of multi-frequency precise point positioning (PPP). The new model not only improves the mixed OSB performance, but also avoids the negative impact of the receiver time-variant biases on the satellite mixed OSB estimation. The STD and RMS of the original OSB can be improved by 7.5-60.9% and 9.4-66.1%, and that of ED OSB (it can reflect noise levels) can be improved by 50.0-87.5% and 60.0-88.9%, respectively. Similarly, the corresponding PPP performance for using new mixed OSB is better than that of using the traditional IFCB products. Thus, the proposed pseudorange and phase time-variant mixed OSB concept and the new undiferenced model coupled with satellite clock offsets are reliable, applicable, and effective in multi-frequency PPP.  
inter-frequency clock bias (IFCB); observable-specific signal bias (OSB); epoch-differenced (ED); undiferenced; multi-frequency precise point positioning 2024  
6  
H. BDS-3/GNSS Undiferenced Pseudorange and Phase Time-Variant Mixed OSB  
2024  
16 October 2024  
Revised: 24 November 2024  
Accepted: 25 November 2024  
Published: 27 November 2024
## 3 Methodology
### Image Pre-Processing  
Based on local weather data, HJ-1B data, which were under clear-sky conditions, were acquired between July to September from 2009 to 2011. Four CCD bands of the HJ-1B satellite were used to estimate the ISA. The thermal channel (IRS4) in the infrared scanner (IRS) of the HJ-1B satellite was used for the LST retrieval. Radiometric calibration and atmospheric correction were carried out based on the HJ-1A/1B tools V3.0 and FLAASH Atmospheric Correction Model. Subsequently, the brightness temperature of each pixel was calculated from the Plank radiation function using the wavelength of IRS4. In addition, water was masked out in all images to improve the ISA classification.
# Evaluation and Current State of Primary and Secondary Zinc Production--A Review
## 4 Zinc Production from Secondary Resources
One of the most commonly used processes for recycling today is the Waelz process. The recovery of zinc from this process is 91 to 93% [78], and the remaining 7-9% remains in the slag. This slag is directed to landfills. The product obtained in the rolling furnace is zinc oxide, which is the raw material for the process of obtaining metallic zinc in the ISP and solvent extraction-electrowinning processes, while up to 20% of the charge in the roasting-leaching-electrowinning (RLE) process. The use of zinc oxide as a feedstock in zinc smelters has been increasing in recent years; in 2016, it amounted to 878 thousand tons, while in 2019 already 1448 thousand tons [78]. This indicates an average annual increase in the use of ZnO at the level of 20% [36; 38]. All galvanized scrap, regardless of grade, is now recycled in the electric arc furnace. Currently, many methods are also used to recover zinc from the waste generated in the hot dip galvanizing process. An economic method of removing the zinc coating before re-melting the galvanized scrap is also being sought. This is not a new concept. Tin-plated steels are regularly subjected to a metal removal process prior to the recycling of tin-plated products. In fact, some original dezincification solutions were based on industrial processes for removing tin coatings [83]. Tin-coated steel is usually recycled in an alkaline detinning process, leaving the steel substrate to be re-melted. The conducted studies on the recycling of galvanized steel scrap mainly concern alkaline dezincification, i.e., zinc dissolution by hot caustic soda (NaOH) to the form of sodium zincates (Na\({}_{2}\)ZnO\({}_{2}\) and NaHZnO\({}_{2}\))--Figure 8a. Zinc is recovered electrolytically in the form of powder, while uncoated steel is readily used for re-melting [84]. Spontaneous alkaline dissolution of zinc (without heating or using an external electric current source) is thermodynamically possible but kinetically limited [85; 86]. Dissolution will only occur when the zinc is in electrical contact with a metal with a lower hydrogen overpotential, such as platinum, iron, or nickel, or if a strong oxidizing agent is added. Recently, acid dezincification has been quite popular--Figure 8b. Typically very low concentrations of acid make the process safe, and the dissolution reaction occurs spontaneously, without the need for heating or the use of an external source of electricity. The advantage of using dilute acids is the complete dissolution of the zinc coating with little or no attack on the steel substrate, which is eventually recycled while the zinc is recovered from the solution either in metallic form or as a metal salt depending on the type of acid used.
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 2 Calculation Theory for Fracture Diffusion of Bingham Fluid  
The mechanism of grouting diffusion in single materials such as gravel and clay has been studied by predecessors. In gravel, the slurry is often diffused in the form of permeation, while in clay, the slurry is often diffused in the form of fracture. Unlike these single media, the mechanical properties and permeability of the soil-rock mixtures are affected by factors such as stone content, and the physical and mechanical properties of the soil-rock mixtures are between those of soil and stone. Therefore, the slurry diffusion form may be changed, affecting the diffusion radius, an essential parameter in grouting engineering. In this section, the fracture diffusion formula of a Bingham fluid is derived theoretically.