esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical Methods  
The glucose conversion products in the water phase were analyzed using HPLC (High-Performance Liquid Chromatography). The %mole yield of each conversion product was calculated based on the peak area of the HPLC chromatograms. The calculation methods were adopted from previously published reports based on the number of carbon atoms in the products [16,43].  
Based on the HPLC chromatograms analysis, the concentration of each conversion product was determined and the exact concentration of each product was calculated by multiplying the obtained concentration with the dilution factor. Percentages of conversion products were further calculated based on the peak area of the HPLC chromatograms obtained by entering the actual concentration data to the Equations (1)-(4) given below [16,42]. The calculation was defined based on the number of the C atoms present in the conversion products; for example, HMF has 6 C atoms, LA has 5 C atoms, and FA has one atom C. Therefore, the calculation was formulated as follows:  
\[\text{mole\% yield of product}= \frac{\frac{\text{amount of }C_{atom}}{\text{C}_{PDMT}}}{W_{sample}}\times 100\% \tag{1}\] \[\text{mole\% yield of LA}= \frac{\frac{5}{\text{C}_{LA}}}{W_{sample}}\times 100\% \tag{2}\]\[\text{ mole\% yield of HMF}= \frac{\frac{6}{UMF}}{W_{sample}}\times 100\% \tag{3}\] \[\text{ mole\% yield of FA}= \frac{\frac{1}{U_{FA}}}{W_{sample}}\times 100\% \tag{4}\]  
where,  
\(C_{LA}\) = real concentration of LA produced (mole);  
\(C_{HMF}\) = real concentration of HMF produced (mole);  
\(C_{FA}\) = real concentration of FA produced (mole);  
\(W_{sample}\) = weight of sample (gram).  
The details of this method are given in our recently published work [29]. The HPLC retention time data (in minutes) of the standards used were as follows: glucose: 10.26; formic acid (FA): 15.39; acetic acid: 16.67; levulinic acid (LA): 17.49; and hydroxymethylfurral: 33.38-35.86--as shown in the Supplementary Materials.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods
### Mission Preprocess for All Levels of Granularity
#### 2.1.2 Profit Calculation  
One remote sensing mission can be divided into five stages: discovery, identification, confirmation, tracking, and monitoring. Different initial profits \(p_{j}^{ini}\) are defined as constants for each stage of the mission in this paper, as is shown in Table 1.  
Mission profit \(P_{i,j}\) can be expressed as:  
\[P_{i,j}=A_{t_{i}}\times A_{p_{i}}\times p_{j}^{ini}, \tag{7}\]  
where \(i\) is the index of the target. \(j\) is the index of the stage, and \(j\in N^{+},1\leq j\leq 5\). \(A_{t_{i}}\) is a constant coefficient varying with the type of the target. \(A_{p_{i}}\) is also a constant coefficient varying with the position of the target. In this model, it is easy to find out that the profit is only related to the targets.
# Graphene Oxide Adsorption Enhanced by Attapulgite to Remove Pb (II) from Aqueous Solution
## 4 Conclusions  
The as-prepared GO is an easy-to-produce and low-cost heavy metal adsorbent that can be prepared by a simple hydrothermal intercalation reaction of ATP and GO. Compared to other types of adsorbents and GO-based adsorbents reported in the literature, the prepared GO exhibits excellent adsorption and separation properties. The maximum adsorption capacity of Pb\({}^{2+}\) on as-prepared GO at pH 5 and 25 \({}^{\circ}\)C was 450.9 mg/g. The adsorption-desorption experiments indicate that high concentrations of HCl solution can destroy the structure of the composite adsorption material. Low concentrations of HCl, however, can improve the desorption rates. The composite adsorbent retains a high removal efficiency of lead ions after six adsorption-desorption cycles and can be used in the treatment and purification of heavy metal-containing wastewater.  
[PERSON] designed the research and wrote original draft preparation, [PERSON] and [PERSON] collected samples and conducted experiments, [PERSON] and [PERSON] conducted experiments and analyzed samples, [PERSON] conceptualized and reviewed the paper.  
This research received no external funding.  
This work was supported by the National Natural Science Foundation of China (No. 51668032), the Foundation of a Hundred Youth Talents Training Program of Lanzhou Jiaotong University (No. 152022) and the Natural Science Foundation of Gansu Province of China (No. 145 RJZA209).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 9: (**a**) Effect of HCl concentration on the desorption capacity of Pb\({}^{2+}\); (**b**) adsorption–desorption cycles (\(C_{0}\) = 100 mg/L, m/v = 0.3 g/L, the HCl concentration is 0.5 mol/L, T = 298 K).
# A Review of Degradation and Life Prediction of Polyethylene
## 5 Prediction Techniques for Polyethylene Materials
### Equal Conversion Rate Method
#### 5.3.2 Friedman Method  
The integral and differential approaches make up the equal conversion rate method [120; 129]. In the differential approach, one of the simplest ways to determine the activation energy is by the Friedman method [130] as shown in Equation (20):  
\[\ln\frac{d_{\alpha}}{d_{t}}=\ln A+\ln f(\alpha)-\frac{E_{a}}{RT} \tag{20}\]  
Equation (20) can be further expressed as follows when several runs with various constant heating rates are taken into account and given values for [131]:  
\[\ln\beta\frac{d_{\alpha}}{d_{t}}=\ln A+\ln f(\alpha)-\frac{E_{a}}{RT} \tag{21}\]  
According to the experimental thermal spectrum captured at the heating rate, the graph of \(\ln(\beta d_{\alpha}/d_{t})\) vs. \(1/T\) should be a straight line for the conversion \(\alpha=\) constant [100]. \(\alpha\) is constant when \(\ln A+\ln f(\alpha)\) is constant, despite the fact that the heating rate \(\beta\) is varied [132]. By taking fixed readings of the conversion rate \(\alpha\), temperature \(T\), and reaction rate \(d_{\alpha}/d_{t}\), the activation energy \(E_{a}\) may be estimated from the slope denoted by \(\ln(d_{\alpha}/d_{t})\) for tests carried out at various heating rates [131].  
The Friedman approach is more precise than the integral method while not using mathematical approximations such as other integration methods. However, given the potential variability of the reaction rate, the Friedman approach necessitates a high base of thermal analysis equipment [120; 133]. Any test, including dynamic and isothermal tests, can be subjected to the Friedman model. Due to experimental flaws or the inherent uncertainty of the differential approach, this method's sensitivity to noise makes it potentially less reliable for kinetic data acquired by thermogravimetric analysis (TGA) [131].
## 4 Results and Discussion
### Effect of the Bending Angle of Sealing Sheets  
The sealing sheet usually has a bending angle (Figure 2b) along the rotation direction of the rotor to prevent mechanical wear between the sealing sheets and the sector plate. Figure 9 shows the effects of different bending angle on the air leakage in different pressure difference cases. Obviously, the leakage amount at the negative bending angle is smaller than that at the positive bending angle. When the fluid flows to the second sealing sheet, a recirculation zone was formed between the sealing sheets under the action of the bending angle, where the energy of mainstream was dissipated and the leakage was reduced. For the tri-sectional RAPH, the direct radial leakage takes place at two locations. One is the interface between the primary air and the flue gas section and the other is the interface between the secondary air and flue gas. The leakage at the former location should be handled first, because the higher primary air pressure can cause more serious leakage problem. Therefore, it is recommended for the rotor of tri-sectional air preheater to pass through flue gas section, secondary air section, and primary air section in sequence. In addition, with the pressure difference increasing,  
Figure 8: Sketch of (**a**) the effect of spacing of adjacent sealing sheets on direct leakage and (**b**) fluid streamline.  
the inertial force gradually becomes much larger than the viscous force. As a result, the air leakage gradually increases.  
Figure 10 shows the contours of velocity at different bending angles. The bending angle has great effect on the distribution of flow field and the generation of recirculation zone, resulting in the difference of leakage. In the positive bending angle case, the streamlined structure cause less energy loss. By contrast, the sheets with negative bending angel would generate a local recirculation zone in front of the sealing sheet, resulting in greater resistance.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 3 Multi-Attention Video Description Model
### Attention Calculation
#### 3.2.1 Spatial Attention  
From a video, CNN can extract the video features \(V=\{v_{1},\ v_{2},\ldots\ldots,\ v_{n}\}\), where \(v_{i}\) is the feature of each frame with a dimension of \(K*K*C\). We can represent the video feature \(v_{i}\) as \(v_{i}=\{r_{i1},r_{i2},\ldots\ldots,r_{i2}\}\), where \(r_{ij}\) represents the \(j\) region feature of the \(i\) frame with a dimension of \(C\). The spatial attention for the \(ij\) region at time \(t\) can be formulated as follows:  
\[e_{ij}^{(t)}=w_{att-s}^{T}\text{tan}\,\text{h}\Big{(}W_{att-s}h_{t-1}+U_{att-s }r_{ij}+b_{att-s}\Big{)} \tag{9}\]  
\[\alpha_{ij}^{(t)}=\text{exp}\Big{\{}e_{ij}^{(t)}\Big{\}}/\sum_{l=1}^{k^{2}} \text{exp}\Big{\{}e_{il}^{(t)}\Big{\}} \tag{10}\]  
\[v_{i-s}^{(t)}=v_{i}*\alpha_{i}^{t} \tag{11}\]  
\[X_{att-s}^{(t)}=f_{att-s}\big{(}V,\alpha^{(t)}\big{)} \tag{12}\]where \(W\), \(U\), and \(b\) are network weight parameters that can be learned. \(\alpha_{i}^{(t)}=\Big{\{}\alpha_{i1}^{(t)},\ \alpha_{i2}^{(t)},\ldots,\alpha_{i 2}^{(t)}\Big{\}}.f_{att-s}\) means applying a special attention operation on all video frames as in (9)-(11); finally, the video features through spatial attention are obtained: \(X_{att-s}^{t}=\{v_{1-s},\ v_{2-s},\ \ldots,\ v_{n-s}\}\), whose dimension is the same as \(V\).
## 3 Results and Discussion
### Flexural Strength and Young's Modulus  
Figure 14 illustrates the influence of Si\({}_{3}\)N\({}_{4}\) reinforcement on the flexural strength (in MPa) and Young's modulus (in GPa) of the fabricated composites. The graph demonstrates that an increase in the amount of reinforcement leads to higher values of both flexural strength and Young's modulus, reaching peak values of 125 MPa and 98 GPa, respectively. These findings align with the observations made by researchers in [39]. In their study, it was noted that the addition of graphite to the Mg alloy resulted in a decrease in flexural strength.
# Solar Potential in Saudi Arabia for Flat-Plate Surfaces of Varying Tilt Tracking the Sun
## 3 Results
### Annual Energy Sums

Table 2: _Cont._  
Figure 3: Variation of the maximum annual solar energy yield across the 82 selected sites in Saudi Arabia on horizontal surface (green lines), on inclined flat-plate surfaces of mode (i) static (purple lines), of mode (ii) dynamic (red lines), and of mode (iii) dynamic (brown lines) systems. The solid lines represent the variation of the annual yield across all sites, while the dashed ones represent the means of the curves. As a summary, Table 2 reports the total annual solar energy sum per site for all cases of possible installation modes of solar collectors in Saudi Arabia. Contrary to the recent works by [PERSON] et al. [1,2], where three solar energy zones (SEZs) with three different tilt angles for the solar installations were defined and adopted, the present work is independent from these SEZs because the tilt angles of the inclined surfaces are not constant but continuously variable. As far as other similar works to the present one are concerned, the study by [PERSON] et al. [18] is worthy of mention. That study divided the country into five geographical regions (central, eastern, southern, western, and western inland) for the purpose of analysing the solar radiation data from [19]. However, the partitioning by [PERSON] et al. did not follow any solar radiation criteria, and, therefore, it is of limited practical value. Another study by [PERSON] et al. [15] estimated 12 optimal angles \(\beta\) for the 12 months of the year for Tabuk (#8 in Table 1), Al Jawf (#9), Riyadh (#10), Jeddah (#16), and Abha (#24). These angles were derived from modelling and, therefore, have a purely theoretical value, since it is not practical at all to change the tilt angle of the solar panel frame every month. From the international literature, no other work has appeared in relation to modes (ii) or (iii) solar systems for Saudi Arabia.
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 3 Methodology
### Isolation Forest
#### 3.2.4 Anomaly Detection Indicator  
An anomaly indicator is defined to translate the output of the isolation forest into concrete information on the WT status. The discovered anomalies are assigned to the corresponding turbines, to define which turbines have a larger percentage of anomalous points with respect to the rest of the wind park. The indicator is calculated as the ratio between the anomalous points (\(n_{anomalies}\)) and the total amount of records (\(n_{total}\)) of a turbine for a given period of time, see Equation (3). The idea is that if no clear differences between turbines' behaviors are present, the anomaly indicator will be similar across the wind farm. On the other hand, if a turbine is subjected to different operating conditions, its percentage of anomalies over the total number of records will be higher if compared with other turbines.  
\[\text{indicator}_{\text{anomaly}}=\frac{n_{anomalies}}{n_{total}}. \tag{3}\]
## 4 Experimental Setup
### Dataset Preprocessing  
Each BraTS21 dataset sample comprises four NIfTI files with various MRI modalities. These multiple modalities were layered in the initial phase of data pre-processing, giving each instance the form (4, 240, 240, 155) (the input tensor is in the (C, H, W, D) layout, with C-channels, H-height, W-width, and D-depth). Then, on the edges of each volume, redundant background voxels (with voxel value zero) were chopped, as they give no meaningful information and may be discarded by the neural network. Following that, the standard deviation for every channel was computed independently inside the non-zero zone for each case. By subtraction the average and afterwards dividing by the variance, all quantities were normalized. Because the underlying voxels were not adjusted, their quantity was kept at zero. To discriminate among underlying and normalized voxels with numbers near zero, an auxiliary input stream was established and layered with the input data using one-hot encoding for salient voxels.  
After that, we combine these datasets and convert them into two major classes: malignant and benign. Data augmentation is a strategy for preventing overfitting by increasing a dataset unnaturally during the training phase. During the training stage, the following data augmentations were employed to make our technique more robust:  
1. Flips: Volume was flipped across each axis with a chance of 0.5 for each x, y, and z axis separately.
2. Gaussian Blur: The source volume is subjected to Gaussian fading with the variance of the Gaussian Kernel sampled regularly from (0.5, 1.5) with a likelihood of 0.15.
3. Brightness: A random value is picked consistently from (0.7, 1.3) with a frequency of 0.15, and afterward input volume voxels are amplified by it.  
* Zoom: A random value is collected consistently from (1.0, 1.4) with a frequency of 0.15, and the image size is enlarged to its original size times the sampled value using cubic interpolation, whereas the input data is resized using immediate neighbors interpolation.
* Gaussian Noise: Randomized Gaussian noise with average zero and variance chosen consistently from (0, 0.33) is collected for every voxel and applied to the input volume with a likelihood of 0.15.
* Contrast: With a frequency of 0.15, a random value is collected evenly from (0.65, 1.5), and afterward input volume voxels are amplified by it and cropped to its initial range value.
* Biased crop: A patch of dimensions (5, 128, 128, 128) was randomly clipped from the input volume. Furthermore, with a possibility of 0.4, it is assumed that some salient voxels (with true positives in the underlying data) will be present in the cropped portion of the patch picked using a random biassed crop.
## 3 Materials and Methods
### Proposed Models
#### 3.1.1 Proposed CNN-LSTM Model  
CNNs can effectively learn the internal representations of time-series data [47]. The one-dimensional convolutional layer filters out the noise, extracts spatial features, and reduces the number of parameters. The causal convolution ensures that the output at time \(t\) derives only inputs from time \(t-1\). RNNs are considered the best sequential deep-learning models for forecasting time-series data. To this end, we combine a one-dimensional CNN and an LSTM in a new model: CNN-LSTM. The CNN-LSTM model consists of (1) a one-dimensional convolutional layer, (2) an LSTM layer, (3) a batch-normalization layer, (4) a dropout layer, and (5) a dense layer.  
To determine the best-performing parameters, we examined different variants of the model: the number of hidden layers (1 and 2), the number of neurons (64 and 128), the batch size (32 and 64), and the dropout rate (0.2 and 0.5).  
The best-performing CNN-LSTM model comprised a one-dimensional convolutional layer with 32 filters of size 3 with a stride of 1, causal padding, and the ReLU activation function; an LSTM layer with 128 units and tanh activation function; a batch-normalization layer; a dropout layer with a rate of 0.2; and a dense layer with a prediction window size of units and the ReLU activation function. Figure 1 illustrates the architecture of the proposed CNN-LSTM model, while Table 1 summarizes the configuration.
## 2 System Model
### Discrete Received Signal Pattern  
The passband signal in Equation (7) transferred through the doubly selective UWA channel is sampled into the receiver by an analog to digital converter (ADC). Corresponding to the model in Equation (2), the discrete received signal is given as [39]:  
\[\begin{split}\hat{y}(n)=\mathfrak{N}\Bigg{\{}\sum_{i=1}^{N_{p}}\sum _{n=1}^{N}A_{i}x_{n}\text{exp}\bigg{\{}j2\pi\frac{n}{\kappa_{c}}((1+\alpha) \frac{n}{f_{s}}-n_{\tau_{i}})\bigg{\}}g((1+\alpha)\frac{n}{f_{s}}-n_{\tau_{i} }-n\kappa_{b})\bigg{\}}\\ +v(\frac{n}{f_{s}})\end{split} \tag{8}\]  
where \(n_{\tau_{i}}=\tau_{i}f_{s}\), \(\kappa_{c}=f_{s}/f_{c}\), \(\kappa_{c}=f_{s}/R_{b}\), \(R_{b}\) is the symbol rate, \(f_{s}\) is the sampling frequency of ADC.
### Atomic and Electronic Structure of Composites Based on [M(Salen)] and Carbon Nanomaterials
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Measured**} & \multicolumn{2}{c}{**Photon Source,**} & \multirow{2}{*}{**Spectra Calibration**} & \multicolumn{2}{c}{**Functionalization Method of**} & \multirow{2}{*}{**Ref.**} \\  & **Photoelectron Region** & & **Spectrometer** & & & & **Carbon Surface** & \\ \hline \([\)Mn((OH)-salen)\(]\)/MWCNTs & Survey, O 1s, Mn 2\(p\) & & & & & & [134] \\ \([\)Co(OH)-salen\(]\)/MWCNTs & Survey, O 1s, Co 2\(p\) & & & & & [135] \\ \([\)Ni(OH)-salen\(]\)/MWCNTs & Survey, O 1s, Ni 2\(p\) & & Mg K\(\alpha\) & & & [103] \\ \([\)Cu(OH)-salen\(]\)/MWCNTs & Survey, O 1s, Cu 2\(p\) & & & & [137] \\ \([\)VO(OH)-salen\(]\)/MWCNTs & Survey, O 1s, O 2\(p\) & & & & [138] \\ \hline \([\)Co(Salen)\(]\)/MWCNTs & Survey, N 1s, Co 2\(p\) & & Al K\(\alpha\) & & & \\ \([\)Cu(Salen)\(]\)/MWCNTs & Survey, Cu 2\(p\) & & ESCALAB 250 Xi & C 1s-284.6 & & functionalization with amino (NH\({}_{x}\)-CNT) & \\ \hline poly-[Ni(salphen)]/MWCNTs & Survey, Ni 2\(p\) & not specified & not specified & & homogeneous suspension of & \\  & & & & & & & \\  & & & & & & & \\ \hline poly-[Co-Tos(Salen)]/CNTs & Survey, Co 2\(p\) & not specified & not specified & ethylene diamine treatment & [105] \\ \hline \([\)Co(Salen)\(]\)/SWNHs & Survey, N 1s, Co 2\(p\) & & Al K\(\alpha\) & & & \\  & & & ESCALAB MKII & not specified & sonicating and stirring a toluene & & \\  & & & & & & solution of Co-Salen and SWNHs & [101] \\ \hline \([\)Co(Salen)\(]\)/GO & Survey & Al K\(\alpha\) & & & & & [110] \\ \([\)Co(Salen)\(]\)/GO & Survey & ESCALAB 250 & & & & & [110] \\ \([\)VO(Salen)\(]\)/GO & C 1s, V 2\(p\) & & Al K\(\alpha\) & & & (NH\({}_{x}\)-GO) & [108] \\ \([\)Fe(Salen)\(]\)/GO & C 1s, Fe 2\(p\) & & Kratos AXIS Ultra HSA & & & & [109] \\ \([\)Ni(Salen)\(]\)/GO & C 1s, N 1s & & & & & functionalization with & \\  & & & & & & & 3-Aminopropyl) & \\ \([\)Cu(Salen)\(]\)/GO & Survey, N 1s, Cu 2\(p\) & & & & & trimethoxysilane & \\  & & & & & & (APTMS) & \\ \hline \multirow{2}{*}{poly-[Ni(3-CH3-Salen)]/N-FLG} & C 1s, O 1s, N 1s, Ni 2\(p\), & & Al K\(\alpha\) & & & & nitrogen doping using CVD & \\  & & & Cl 2\(p\) & & & & method with additional & \\  & & & & & & & ammonia precursor & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: XPS data overview of composites based on [M(Salen)] complex with different carbon nanostructures.
# The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA
## 3 Related Works
In [25], the authors considered a mutual information (MI) approach between the output probabilities and validation labels to monitor the best epoch during training. The work of [26] monitors the epoch when the training achieves the minimal difference between the number of profiling and validation traces required to achieve a 90% success rate. The authors proposed a routine to abort training if this difference increases after reaching its minimum value. In our work, we also consider the mutual information metric for comparison. The method proposed in [26] is not considered in our comparative analysis as it is directly adapted to datasets with fixed keys in the profiling set, which is not the case of ASCADr. The method requires estimating the number of traces to reach a success rate of 90%, which implies obtaining the evolution of the success rate concerning the number of validation traces. This means that the success rate is computed \(Q\) times for each epoch, adding a significant time overhead to the process. Finally, [PERSON] et al. suggested an optimized early stopping algorithm that efficiently integrates the GE metric into the training phase [27]. As we can see, none of the mentioned approaches compute GE directly from the validation traces at the end of each training epoch. GEEA was proposed as a fast and more stable GE estimation, but it is not suggested to be used during network training. On the other hand, although GE can be a potential metric candidate, its computation could be very slow if more validation traces are considered (which is required for GE stability), finally providing significant overheads to the training process. Therefore, the SCA community did not consider directly applying GE (including GEEA) as the early stopping metric, especially in the hyperparameter search processes. This work provides a novel evaluation metric called fast guessing entropy and shows that significantly reducing the number of validation traces for GE estimation during training is reliable and efficient for early stopping, benefiting hyperparameter tuning optimization. Further information about deep learning-based side-channel analysis and challenges can be found in [28].