esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Sequential Sampling Method
### Infill Criterion I: Mean Squared Error  
The mean squared error (MSE) criterion is the prediction variance (\(\hat{s}^{2}(\mathbf{x})\)) of the kriging model. The MSE is derived from the curvature of the augmented log-likelihood function as [2; 11; 13; 14]  
\[\hat{s}^{2}(\mathbf{x})=\hat{\sigma}^{2}\Big{(}\mathbf{1}-\ \hat{\mathbf{\psi}}^{T}\mathbf{ \Psi}^{-1}\hat{\mathbf{\psi}}\Big{)}. \tag{11}\]  
As shown in Figure 3, the predictions of the unknown samples can be represented as the Gaussian process with a mean (\(\hat{y}(\mathbf{x}_{new})\)) in Equation (9) and prediction variance (\(\hat{s}^{2}(\mathbf{x})\)) in Equation (11). The MSE can be used as an infill criterion to search the infill sample that has a large prediction uncertainty (larger \(\hat{s}^{2}(\mathbf{x})\)). In Figure 3, there are two unknown samples (\(x=3\) and \(x=4\)) with their prediction distributions (represented by purple lines). Based on Equation (11), \(\hat{s}^{2}(3)\) is much smaller than \(\hat{s}^{2}(4)\). This indicates that the bigger MSE is obtained far apart from the training samples. Stated differently, The MSE can be used as a measure of the sparseness of the input space. The infill sample of the MSE (\(\mathbf{x}_{MSE}^{n+1}\)) is chosen by maximizing the MSE as  
\[\mathbf{x}_{MSE}^{n+1}=\underset{\mathbf{x}}{\mathrm{argmax}}\hat{s}^{2}(\mathbf{x}) \tag{12}\]  
Figure 2: An illustration of the sequential sampling method.
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 5 Conclusions  
The judicious selection of inversion methods and attribute combinations is essential for enhancing the accuracy of coal seam thickness determination systems. While combining log-constrained impedance inversion offers complementary advantages, its effectiveness is influenced by several factors, including, as follows: geological characteristics; the quantity and distribution of drilling wells; seismic data resolution; and the signal-to-noise ratio. In this context, the seismic multi-attribute method shows greater adaptability, particularly when addressing complex geological conditions. The BP neural network, by adjusting parameters through error feedback, demonstrates robust capabilities in non-linear mapping. Constructing BP neural network models to determine coal thickness through optimized combinations of seismic attributes helps to reduce the ambiguity in seismic interpretation, thereby improving the precision of the results. The accuracy of the applied methods is dependent on the quality and resolution of the available seismic and well-logging data, which could limit their effectiveness in areas where data are sparse or of low quality. Continued research in three-dimensional geological statistics and multi-attribute fusion will pave the way for intelligent, automated mining processes and open new avenues for understanding complex geological environments.  
Conceptualization, T.D. and Y.W.; methodology, L.W.; software, L.Z.; validation, T.D., Y.W. and L.W.; formal analysis, T.D.; investigation, T.D. and L.Z.; resources, Y.W. and T.D.; data curation, L.Z.; writing--original draft preparation, T.D.; writing--review and editing, T.D., Y.W. and L.W.; visualization, L.W.; supervision, L.W.; project administration, T.D., Y.W. and L.W.; funding acquisition, Z.N. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully thank the National Key Research and Development Program of China (2022 YFC2904005) and the Innovation Fund Project of Hebei University of Engineering (SJ2401002064) for financial support.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.  
Figure 17: Statics correction distribution map of the entire area.
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 3 Results and Analysis
### Analysis and Discussion Based on the RSM Model
#### 3.2.2 Response Surface Analysis of Shrinkage
The moderate AEA (0.100%) helps introduce uniformly distributed microbubbles, relieving internal stress concentration and improving workability, which further reduces shrinkage caused by capillary tension. On the other hand, when the W/C increases to 0.43 and the AEA increases to 0.300%, the shrinkage rate reaches its maximum. The higher W/C results in more free water in the paste, forming  
Figure 10: Shrinkage Contour chart and 3D Surface chart. (**a**) Effect of W/C and Sand ratio on Shrinkage. Contour chart; (**b**) Effect of W/C and Sand ratio on Shrinkage. 3D Surface chart; (**c**) Effect of W/C and Sand ratio on Shrinkage. Contour chart; (**d**) Effect of W/C and Sand ratio on Shrinkage. 3D Surface chart; (**e**) Effect of Sand ratio and AEA on Shrinkage. Contour chart; (**f**) Effect of Sand ratio and AEA on Shrinkage. 3D Surface chart. interconnected capillary pores as the concrete hardens, which increases shrinkage due to moisture evaporation. Additionally, the higher AEA (0.300%) leads to an increased volume of air bubbles that are unevenly distributed, weakening the structural integrity of the cement paste and reducing the bond between the aggregates and the paste. The increased connectivity between the air bubbles also accelerates moisture migration, leading to quicker pore pressure release, and the combined effects of these factors significantly increase shrinkage. Figure 10e,f show the combined effect of the sand ratio and AEA on concrete shrinkage. When the sand ratio is between 36.0% and 37.0% and the AEA is 0.100%, the shrinkage rate is at its lowest. This is due to a higher proportion of coarse aggregates, which act as a rigid framework to limit shrinkage, and the moderate AEA (0.100%) that introduces small, evenly distributed air bubbles. These bubbles help reduce internal stress and decrease the rate of moisture evaporation. On the other hand, when the sand ratio increases to 42.00% and the AEA rises to 0.280-0.300%, the shrinkage rate is at its highest. The higher sand ratio increases the paste content, which leads to more shrinkage, while excessive AEA introduces larger, unevenly distributed air bubbles that weaken the paste-aggregate bond. These bubbles also create pathways for moisture evaporation, increasing shrinkage stress. The combination of high sand ratio and high AEA amplifies the negative effects, resulting in the highest shrinkage rate.
## 3 Modeling of UAHW Target Recognition Algorithm Based on SMOTE-Stacking
### Active Acoustic Signal Detection System of ULAHW
#### 3.1.3 Frequency Correlation  
In order to achieve fast correlation detection, frequency-domain correlation is used for signal detectors. As presented in Figure 9, frequency-domain correlation uses cyclic  
Figure 8: Signal correlation detection flowchart.  
Figure 7: Signal detection process flowchart.  
convolution, fast Fourier transform (\(FFT\)), and inverse fast Fourier transform (\(IFFT\)) for signal detection. The detection algorithm can be defined using the following:  
\[y_{m}(n,v)=abs(IFFT[FFT(x_{m}(n))*(FFT(s_{v}(n)))^{\prime}]) \tag{10}\]  
where \(x_{m}(n)\) and \(s_{v}(n)\) denote the received and reference signals, respectively.
## 2 Material and Methods
### Assessment of Density and Porosity Parameters
#### 2.6.2 Assessment of True Density  
Test Preparation  
Samples, on which the average density was determined, were used for testing. Each marked sample was cleaned to remove dust with a brush, ground to a grain size of 5 mm and then mixed. Then, the sample was reduced by quartering to 150 g. Then, this sample was again ground to a grain size of less than 1.25 mm, mixed and reduced to 30 g. The prepared sample was ground into powder in a porcelain mortar, poured into a weighing glass or into a porcelain cup, dried to constant weight and cooled to room temperature in a desiccator over concentrated sulfuric acid or anhydrous calcium chloride, after which two portions of 10 g each were weighed.  
Testing  
Each sample was poured into a clean dried pyrometer, and distilled water was poured in such an amount that the pyrometer was filled to no more than half its volume. The pycnometer was placed in a slightly inclined position in a sand or water bath and its contents were boiled for 15-20 min to remove air bubbles. After removing air, the pycnometer was wiped off, cooled to room temperature, refilled to the mark with distilled water and weighed. The pycnometer was freed from the contents, washed, filled to the mark with distilled water at room temperature and weighed again.  
Calculation of test results:  
The true density \(\rho_{w}\), g\(\cdot\)cm\({}^{-3}\), was calculated by the formula:  
\[\rho=\frac{m\rho_{w}}{m+m_{1}-m_{2}} \tag{7}\]  
where \(m\) is the weight of the sample of powder dried to constant weight, g; \(\rho_{w}\) is the density of water, taken to be equal to 1 g\(\cdot\)cm\({}^{-3}\); \(m_{1}\) is the mass of the pycnometer with distilled water, g; and \(m_{2}\) is the mass of the pycnometer with a sample and distilled water after removing air bubbles, g.  
The result was calculated as the arithmetic mean of two parallel tests.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 4 Methodology
### Sentiment Cue Extraction Model  
Our SS-SCE approach conceptualizes the SCE task as a sequence labeling problem. This requires performing a binary classification for each token \(x_{i}\) within the input \(X\). Consequently, the architecture of the SCE model is highly analogous to that of the sentiment classification model, with a key distinction: while the sentiment classification model focuses on classifying the [CLS] token to infer the overall sentiment of the input, the SCE model extends this classification to all tokens within \(X\). Therefore, the SCE model can be formalized as follows:  
\[f_{\text{sce}}(X)=\text{softmax}(\text{FC}^{768\times 2}(\text{BERT}(X))) \tag{15}\]  
where \(\text{BERT}(X)\) produces a sequence of 768-dimensional vector representations for each token in \(X\). The fully connected layer, denoted as \(\text{FC}^{768\times 2}\), maps each 768-dimensional vector to a 2-dimensional output, corresponding to the binary classification for sentiment cue detection. The softmax function is applied to these 2-dimensional vectors, yielding a probability distribution over two classes (cue vs. non-cue) for each token in \(X\).  
After generating pseudo labels for each instance \(X\) in the train set, these labels will be utilized as the ground truth for training the SCE model, \(f_{\text{sce}}\). It is important to note that each \(X\) is augmented with [CLS] and [SEP] tokens at the beginning and end, respectively. While these tokens are essential for BERT's processing, they should not be overlooked by \(f_{\text{sce}}\). Consequently, their corresponding labels in the pseudo label sequence are fixed to 1. However, we do not consider these specific tokens ([CLS] and [SEP]) as sentiment cues.
## 5 Discussion  
For this effort, three lightweight architectures (MobileNet, ShuffleNet, and SqueezeNet) and five heavyweight architectures (AlexNet, DenseNet, ResNet, ResNeXt, and Wide ResNet) are evaluated.  
From Section 4.2, the free-space data set provides interesting insight in performance differences. Table 5 shows DenseNet performing the best; however, the ResNet family of architectures follow very closely in achieved accuracies.  
The main conclusion from the underwater results in Table 4 is to avoid the SqueezeNet architecture. All other architectures seem to give comparable performance.  
Another observation in comparing Tables 4 and 5 is the difference in accuracies between the two. The underwater data sets have great performance, even with the high signal-to-noise ratio. Why do the CNNs perform so well with the attenuated versus the turbulent images? The answer is likely due to the fact that, the overall shape of the attenuated images remains constant, where turbulence causes displacement of the image patterns. The CNNs have to work harder to learn many potential patterns that belong to a specific OAM set. In addition, the turbulence patterns have twice the number of image patterns to learn.  
Transitioning to out-of-training-set testing provides even further insight into architecture robustness. Tables 7-10 have one consistent top performer, DenseNet. DenseNet took about the same amount of time and number of epochs to train as the ResNet family. It was also smaller than the ResNet family of architectures by at least a factor of 3. Therefore, it was a consistent top performer, while also having significantly fewer parameters than many of the close performing architectures.  
For a resource constrained system that cannot support the size of DenseNet or other heavy-weights, what is the next best performer? The options come down to ShuffleNet, SqueezeNet, and MobileNet. Interestingly, in reviewing tables from Sections 4.2 and 4.3, ShuffleNet consistently performed as well as or better than the other architectures most of the time. Additionally, it is considerably smaller than the other two architectures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **TB5** & **TB10** & **TB15** \\ \hline AlexNet & 97.4 & 82.0 & 59.8 \\ SqueezeNet & 98.6 & 83.2 & 61.1 \\ MobileNet & 97.6 & 91.3 & 74.0 \\ Wide ResNet & 98.6 & 95.1 & 75.2 \\ ShuffleNet & 99.0 & 93.5 & 78.5 \\ ResNeXt & 97.9 & 94.1 & 79.3 \\ DenseNet & 99.0 & 96.7 & 81.8 \\ ResNet & 98.9 & 96.9 & 84.8 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Free-space TB5-10 inter-set test. Architectures trained on TB5-10 and tested against all TB data sets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **TB5** & **TB10** & **TB15** \\ \hline SqueezeNet & 98.6 & 83.9 & 62.4 \\ AlexNet & 97.4 & 86.4 & 58.0 \\ ShuffleNet & 97.6 & 92.2 & 75.9 \\ MobileNet & 98.8 & 93.9 & 77.0 \\ Wide ResNet & 98.6 & 94.5 & 82.2 \\ ResNeXt & 98.9 & 95.4 & 81.4 \\ ResNet & 98.8 & 97.8 & 87.8 \\ DenseNet & 99.6 & 97.9 & 85.5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Free-space TB5 inter-set test. Architectures trained on TB5 and tested against all TB data sets.
## 2 Materials and Methods
### Total Element Content Measurements
#### 2.3.2 Instrumentation  
Ten elements (Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, and Zn) concentration in the samples were measured. Digested samples and water extracts (samples for MARA test) were determined in parallel using inductively coupled plasma high-resolution optical emission spectrometry (ICP-HROES). A PlasmaQuant PQ 9000 Elite (Analytic Jena GmbH, Jena Germany) spectrometer was used. The standard conditions were used: Radio Frequency (RF) power 1.2 kW, nebulizer gas flow 0.5 L min\({}^{-1}\), plasma gas flow 12 L min\({}^{-1}\), auxiliary gas flow 0.5 L min\({}^{-1}\), and axial plasma view. High-resolution optics were an echelle double monochromator and charge-coupled device (HR CCD), which was cooled to \(-10\)\({}^{\circ}\)C using a Peltier system. The following analytical wavelengths (emission lines) were applied: Al 396.152 nm, As 188.979 nm, Cd 214.441 nm, Cr 267.716 nm, Cu 324.754 nm, Fe 261.187 nm, Hg 194.159 nm, Ni 231.604 nm, Pb 220.353 nm, and Zn 206.200 nm. The signal was measured in 5 replicates. The sample was analyzed 3 times (\(n=3\)). Argon was used as an internal standard (Ar 420.068 nm). The limits of detection (LODs, as 3-sigma criteria) were found at the level of 0.01 (mg kg\({}^{-1}\)) of dry weight (\(w/w\)) for all elements presented. The standard addition method was used for traceability measurements. Recoveries were in the acceptable range (80-120%). The uncertainty for the complete analytical process (including sample preparation) was at the level of 20%. In the case of outliers (e.g., surprisingly high element content), the complete analytical process was repeated.
# Optimization of Multimodal Discrete Network Design Problems Based on Super Networks
## 3 Formulating the Multimodal Discrete Network Design Problem
### The Generalized Cost Formulas of Transfer Links  
(1) Travel time  
It has been mentioned above that the transfer modes mainly consist of walking and bike-sharing. Therefore, the travel time of transferring to the transportation mode \(k\) can be formulated as:  
\[T_{ij}^{T,k}=t_{walk,ij}^{T,k}+t_{wait,ij}^{T,k},\;\forall(i,j)\in A^{T,k},k\in K \tag{15}\]  
or:  
\[T_{ij}^{T,k}=t_{cycling,ij}^{T,k}+t_{wait,ij}^{T,k},\;\forall(i,j)\in A^{T,k},k\in K \tag{16}\]  
where \(t_{walk,ij}^{T,k}\): the average walking time for transfer to the transportation mode \(k\), \(t_{cycling,ij}^{T,k}\): the average cycling time for transfer to the transportation mode \(k\), \(t_{wait,ij}^{T,k}\): the waiting time for transfer to the transportation mode \(k\), and we set \(t_{wait,ij}^{T,k}=\frac{1}{2f},\forall(i,j)\in A^{T,k},k\in\{\,p,r\}\), \(f\) is the departure frequency and \(t_{wait,ij}^{T,k}=0,\;\forall(i,j)\in A^{T,k},k=\{\,c\}\).  
(2) Monetary cost  
Since there is no monetary cost for walking, and only the monetary cost of bike-sharing exists, the monetary cost of transferring to the mode \(k\) can be represented as:  
\[P_{ij}^{T,k}=p_{walk,ij}^{T,k}=0,\forall(i,j)\in A^{T,k},k\in K \tag{17}\]  
or:  
\[P_{ij}^{T,k}=p_{cycling,ij}^{T,k}=\left\{\begin{array}{cc}p_{ij}^{T,k0}&L_{ ij}^{T,k}\leq L^{T,k0}\\ p_{ij}^{T,k0}+\left[L_{ij}^{T,k}-L^{T,k0}\right]*p_{ij}^{T,k}&L_{ij}^{T,k}>L^{T, k0}\end{array},\;\forall(i,j)\in A^{T,k},\;k\in K\right. \tag{18}\]  
where \(p_{ij}^{T,k0}\): the starting fare for cycling, \(L^{T,k0}\): the number of miles corresponding to the starting fare for cycling, \(L_{ij}^{T,k}\): the length of the link \((i,j)\) and \(p_{ij}^{T,k}\): the increasing fare per unit mileage.  
(3) Comfort loss Since the transfer trip is very short and the comfort loss can be ignored, we set the comfort loss of transferring to the transportation mode \(k\) as:  
\[S_{ij}^{T,k}=0,\forall(i,j)\in A^{T,k},k\in K \tag{19}\]  
(4) Risk reserve time  
Similarly, the risk reservation time of transferring to the transportation mode \(k\) is expressed as:  
\[R_{ij}^{T,k}=\left(\rho_{ij}^{T,k}-1\right)T_{ij}^{T,k},\;\forall(i,j)\in A^{T,k},k\in K \tag{20}\]  
where \(\rho_{ij}^{T,k}\): the delay parameter of transferring to the transportation mode \(k\) and \(\rho_{ij}^{T,k}\;>\;1\), \(\forall(i,j)\in A^{T},k\in K\).
## 4 Theoretical Estimations
### Experimental Data for Parameter Estimation  
The parameters of the mathematical expression were estimated using the experimental data of different mixtures of biodiesel (vegetable or animal fat) and diesel at varying temperatures. It should be noted that for simplicity, from now on the biodiesel volume percentage will be referred as BD, while the diesel volume percentage will be referred as D. The theoretical calculations of Equations (3)-(5), (9), and (11) as well as the values of their parameters were determined by fitting through parameter estimation the experimental data of density, temperature, API, and Sg (Tables S1-S4).  
In order to reduce the number of parameters to be fitted, the parameters b\({}_{0}\) (MPa), b\({}_{1}\) (MPa\(\cdot\)K\({}^{-1}\)), b\({}_{2}\) (MPa\(\cdot\)K\({}^{-2}\)), and C in Equation (4) were adopted from the literature [24]. The parameters that were optimized in Equation (5) were a\({}_{0}\) (gr\(\cdot\)cm\({}^{-3}\)), a\({}_{1}\) (gr\(\cdot\)cm\({}^{-3}\cdot\)K\({}^{-1}\)), and a\({}_{2}\) (gr\(\cdot\)cm\({}^{-3}\cdot\)K\({}^{-2}\)). Note that Equations (4) and (5) are solved together with Equation (3). The parameter estimation was performed using data in six groups for vegetable biodiesel and six groups for animal fat biodiesel, while temperature was varied from 288 to 298 K:  
Group 1: D100%-BD0%, D95%-BD5%, D90%-BD10%  
Group 2: D85%-BD15%, D80%-BD20%, D75%-BD25%  
Group 3: D70%-30%, D65%-BD35%, D60%-BD40%  
Group 4: D55%-BD45%, D50%-BD50%, D45%-BD55%, D40%-BD60%  
Group 5: D35%-BD65%, D30%-BD70%, D25%- BD75%, D20%-BD80%  
Group 6: D15%-BD85%, D10%-BD90%, D5%-BD95%, D0%-BD100%  
The parameters that were optimized in Equation (9) were a and b. The parameter estimation was performed using all the data at once for both vegetable and animal fat biodiesel. The parameters that were optimized in Equation (11) were c and d. The parameter estimation was performed using data for both vegetable and animal fat biodiesel separately.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 5 Experiment Results  
To give a more concrete example, we tested the proposed DRL algorithm on the real experimental results which was done in the anechoic tank laboratory as shown in Figures 13 and 14.  
Figure 14: The experiment images.  
Figure 12: The comparison of communication cost.  
Figure 13: The anechoic tank laboratory.  
The experiment is limited by the scale of the laboratory. The designed UAN localization system contains five nodes and one of them is the anchor. Fix the anchor be (0,0) and change other nodes' positions to get three different topologies. We test the localization performance on each topology. TDMA protocol and TOA ranging method are adopted in the designed network system.  
Figure 15 shows the structure of designed UAN system and the topology changes. In this experiment, the network was operated based on nodes' IDs. The anchor is labeled as node 0, and so on. Five tests were done in each network topology.
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 3 Experiments
### Results
#### 3.4.2 Performance of AGLNM on the South China Sea Data Set  
In order to verify the robustness of the AGLNM, this paper compared the above six models on the SEA surface temperature daily data set (4018 days) in the South China Sea (2307 points), and the experimental results are shown in Table 3. First of all, we can see that the performance of the AGLNM in this paper still maintains a great advantage compared with the other methods when the prediction scale is 1 and 3 days. When the prediction scale was 7 days, the AGLNM was slightly behind the CGMP model with a gap of less than 0.01, but it was still slightly ahead of the FC-LSTM and still ahead of the CFCC-LSTM, GED, and SVR models by a large margin. Therefore, the AGLNM still maintained the best overall performance, followed by the CGMP. Secondly, the mean absolute error of temperature prediction was in the range of 0.2, which is the international leading level.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**Prediction Length (Day(s))**} \\ \cline{3-5}  & & **1** & **3** & **7** \\ \hline \multirow{2}{*}{CGMP} & _MSE_ & 0.1714 & 0.4173 & 0.7449 \\  & _MAE_ & 0.3014 & 0.4685 & 0.6301 \\ \hline \multirow{2}{*}{FC-LSTM} & _MSE_ & 0.187 & 0.4708 & 0.7985 \\  & _MAE_ & 0.3091 & 0.4937 & 0.6574 \\ \hline \multirow{2}{*}{CFCC-LSTM} & _MSE_ & 0.1715 & 0.4219 & 0.8233 \\  & _MAE_ & 0.3015 & 0.4794 & 0.6771 \\ \hline \multirow{2}{*}{GED} & _MSE_ & 0.1723 & 0.4933 & 0.8502 \\  & _MAE_ & 0.2956 & 0.5034 & 0.6810 \\ \hline \multirow{2}{*}{SVR} & _MSE_ & 0.4716 & 0.6919 & 1.0047 \\  & _MAE_ & 0.5058 & 0.6140 & 0.7416 \\ \hline \multirow{2}{*}{AGLNM} & _MSE_ & **0.1378** & **0.3057** & **0.5318** \\  & _MAE_ & **0.2629** & **0.4031** & **0.5398** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction results of the Bohai Sea data set (135 points).  
However, the MAE can still outperform the CGMP model ranked second by 0.026 (14.6% difference) at the prediction scale of 1 day. The occurrence of the fault phenomenon reflects that the performance of the AGLNM designed in this paper is significantly superior to other models in nature.