esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Experimental Results
This suggests that the vibratory SP method is better than the traditional SP method. \begin{table}
\begin{tabular}{l l l l} \hline
**Material** & **Applied Process** & **Process Parameters** & **Residual Stress** \\  & & & **(MPa)** \\ \hline
8620 & SP & 12–14 & \(-389.3\pm 36.4\) \\  & & 18–20 A & \(-420.1\pm 17.7\) \\  & & 24–26 A & \(-436.5\pm 27.4\) \\ \cline{2-4}  & VSP & 30 s \(-\)2 mm & \(-209.22\pm 12.4\) \\  & & 60 s \(-\)2 mm & \(-212.4\pm 65.1\) \\  & & 30 s \(-\)4 mm & \(-374.28\pm 32.7\) \\  & & 60 s \(-\)4 mm & \(-388.9\pm 33.7\) \\ \hline
1020 & SP & 12–14 A & \(-222.9\pm 12.8\) \\  & & 18–20 A & \(-224.9\pm 15.2\) \\  & & 24–26 A & \(-310.6\pm 21.2\) \\ \cline{2-4}  & VSP & 30 s \(-\)2 mm & \(-310.50\pm 26.4\) \\  & & 60 s \(-\)2 mm & \(-313.4\pm 24.6\) \\  & & 30 s \(-\)4 mm & \(-412.56\pm 52.4\) \\  & & 60 s \(-\)4 mm & \(-418.3\pm 57.6\) \\ \hline
4140 & SP & 12–14 A & \(-400.3\pm 20.0\) \\  & & 18–20 A & \(-402.7\pm 14.5\) \\  & & 24–26 A & \(-499.3\pm 28.6\) \\ \cline{2-4}  & VSP & 30 s \(-\)2 mm & \(-256.24\pm 12.2\) \\  & & 60 s \(-\)2 mm & \(-264.8\pm 29.0\) \\  & & 30 s \(-\)4 mm & \(-395.47\pm 22.4\) \\  & & 60 s \(-\)4 mm & \(-404.2\pm 17.3\) \\ \hline
5140 & SP & 12–14 A & \(-423.0\pm 49.0\) \\  & & 18–20 A & \(-433.0\pm 21.6\) \\  & & 24–26 A & \(-437.5\pm 9.3\) \\ \cline{2-4}  & VSP & 30 s \(-\)2 mm & \(-385.25\pm 56.4\) \\  & & 60 s \(-\)2 mm & \(-409.4\pm 45.4\) \\  & & 30 s \(-\)4 mm & \(-425.25\pm 52.4\) \\  & & 60 s \(-\)4 mm & \(-477.5\pm 15.9\) \\ \hline \end{tabular}
\end{table} 
Table 6: Residual stress values of all materials. In order to determine the effect of SP and VSP processes in more detail, the samples were analyzed using SEM. Based on the findings, Figure 6 depicts the alterations in the grain structure following the SP procedure. Figure 7 displays the macro and SEM results of vibratory shot-peened samples. Upon closer inspection, Figure 7 demonstrates that the vibratory shot-peened samples exhibit greater surface alterations than the shot-peened samples. The vibrating SP process causes more surface roughness and negatively affects the surface compared to the traditional SP approach. Figure 4: Residual stress values of materials at different SP parameters. Figure 5: Optical microscope images of the specimens used in the study: (**a**) SP process for AISI 4140 steel; (**b**) VSP process for AISI 1020 steel.
## 3 Construction Time-Cost Dynamic Optimization
### Construction Time-Cost Optimization Process  
Combined with the project data, the time-cost optimization process based on BIM technology and genetic algorithm is established, as shown in Figure 3. This process mainly includes the BIM module and GA module. The ultimate goal of the BIM module is to obtain project parameter information for feasible solutions. The main function of the GA module is to dynamically optimize the time-cost through MATLAB programming. The specific steps are as follows:  
(1) This study establishes a BIM model and exports engineering quantity files based on BIM calculations. Based on the budget quota, the time of each work is calculated and the network diagram is drawn, as shown in Figure 1.  
(2) This study imports engineering quantity files into BIM pricing software. Combining budget quotas and pricing standards, pricing files are generated. The direct cost of the project is 5.7331 million yuan, and the indirect cost is 770,200 yuan.  
Figure 2: Chromosome composition.  
(3) Based on the BIM model, a 3D site layout is conducted and a 3D site layout model file is generated.  
(4) The network diagram file, 3D site layout model file, and pricing file are imported into the BIM5D construction management platform to complete the association of model, schedule, and cost information. The feasibility of the plan is judged through BIM5D construction simulation. If the plan can be successfully implemented, it indicates from a qualitative perspective that the plan is feasible. For qualitatively feasible solutions, quantitative parameters such as labor, funds, and materials are output through the BIM5D platform. If the output parameters meet the actual needs of the project, it indicates that the scheme is feasible from a quantitative perspective. For schemes that meet the requirements for qualitative and quantitative evaluation, the required project parameter information for optimization is output through the BIM5D platform.  
(5) Through MATLAB programming, the dynamic optimization of time-cost is carried out based on the genetic algorithm. According to the solution steps introduced in Section 3.2 of this article, after iterative convergence, the optimized solution is obtained.  
(6) According to the optimized plan, the pricing file and network diagram are adjusted and step (4) is repeated. If the optimization plan is feasible in both qualitative and directional aspects, the optimal plan is obtained. Otherwise, steps (1)-(6) will be repeated until the solution is feasible.
## 2 Methods
### Data Quality Control, Gap filling, and WUE Computation  
The 30 min mean fluxes of CO\({}_{2}\) and H\({}_{2}\)O were calculated as the covariance of vertical wind speed and the concentration of CO\({}_{2}\) and H\({}_{2}\)O, consistent with previous data processing procedures [42; 46; 48; 49]. Standard quality checks and corrections were applied for spike detection [51], planar fit coordinate rotation of wind vectors [52], correction of the time lags between scalar concentrations and wind speed (covariance maximization), air density fluctuation [53], and high [54] and low pass filtering [55]. Flux gap-filling of missing data and partitioning of NEE30 min into gross primary productivity (GPP) and ecosystem respiration (RE) was conducted following the protocol provided by the Max Planck Institute for Biogeochemistry ([[https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProCWeb](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProCWeb)]([https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProCWeb](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProCWeb)), accessed on 20 February 2019). Data gaps for latent heat (LE) were filled using the relationship between observed ET and grass reference evapotranspiration estimated by the FAO Penman-Monteith method. The total 30 min ET was converted from LE (W m\({}^{-2}\)) using the formula: ET = LE \(\times\) (0.01800/44000) \(\times\) 3600 \(\times\) 0.5 [43].  
Daily GPP (g C m\({}^{-2}\) d\({}^{-1}\)) was computed as the difference between daily RE (g C m\({}^{-2}\) d\({}^{-1}\)) and NEE (g C m\({}^{-2}\) d\({}^{-1}\)). Daily ET (kg H\({}_{2}\)O m\({}^{-2}\) d\({}^{-1}\)) was the sum of 30 min ET over 24 h. These data were extracted from [37; 46] and monthly averages were computed. Ecosystem WUE (g C kg H\({}_{2}\)O\({}^{-1}\)) was calculated as the ratio of GPP to ET according to [56; 57]. No further filtering on WUE was done.
## 3 Functions of Dextran and Its Derivatives
### Dextran Preparations Used as a Blood Plasma Substitute
Dextran molecules have high water-binding capacity; for example, 1.0 g of dextran-40 holds up to 30 mL volume of water, while 1.0 g of dextran-70 is able to take up to 20-25 mL of water. The unique property of dextran is that after dissolving it in normal saline to form a solution of 6%, \(w/v\), it demonstrates colloidal osmotic pressure and viscosity, which are identical to human blood. The isotonic versions of dextran solutions are prepared at 6.0 and 10%. Dextran has been applied as a plasma volume expander. Two products of molecular weights of 40 and 70 kDa, dextran-40 and 70, are used in cases of shock or impending shock after hemorrhage, burns, and trauma [20]. In an investigation, dextran-40 was used to accomplish an in vitro evaluation of haemodilution affecting the coagulation profile. The analytical assays were performed using thrombo-elastometry and multiple electrode aggregometry [21]. Although the application of albumin in patients with septic shock is helpful, the two factors that affect its use are the high cost of clinical-grade albumin and its regulated availability. Therefore, the use of a suitable alternative to albumin was investigated, where dextran-70 was tested during resuscitation for its impact on organ failure or mortality in patients (n = 778) suffering from severe septicmia or septic shock, in addition to the use of albumin and crystalloids. The evidence collected through investigation did not detect any harmful impact of dextran-70 on organ failures or mortality in patients diagnosed with critical sepsis [22]. Alternatives to albumin, used for the expansion of plasma, consist of crystalloids in the form of 0.9% sodium chloride solution as the normal saline, whereas the plasma protein fraction is used as alternate protein colloids. Dextran, a non-protein colloid, worked as an albumin alternative. These alternatives, whether the crystalloids or non-protein colloids have not proved superior to albumin; however, these materials are reasonably cost-effective choices. Dextrans are added in 0.9% sodium chloride isotonic medium to form a component of colloid solutions. A report on albumin and related products has informed that a hypertonic mixture Rescueflow can be used for its prehospital use during bleeding for the management of hypotension [23]. An article on fluid resuscitation and early management reported that colloid replacement was frequently needed in young pediatric burn patients who suffered injuries from major burns. It could be effective because the concentration of serum protein decreases rapidly during burn shock. In such cases, it is reported that dextran colloid is normally applied during resuscitation cases, where children had main injuries from burns [24]. Dextrans are also used in hematology. The following section presents information on applications of dextran products in the pharmaceutical industry, where various dextran derivatives, dextran conjugates, nanoemulsions, and micelles have been studied for their use as nanocarriers for nanomedicine drug delivery.
## 4 Numerical Simulation  
### Numerical Simulation Method and Set-Up  
To further investigate the flow field and the noise reduction mechanism, large eddy simulation (LES) was performed for the baseline cylinder (the #0 cylinder) and the wavy cylinder (the #2 cylinder). The commercial solver Fluent was used. To model the sub-grid-scale stress (SGS), the dynamic Smagorinsky-Lilly model was used in the LES. The bounded central-differencing method was used for the spatial discretization, and the bounded second-order implicit method was used for the transient formulation. Figure 16 shows the computational domain and boundary. The inlet was 20D far from the cylinder, and the outlet was 40D far from the cylinder. The inlet velocity was set to be 60 m/s, and the outlet pressure was set to be 98,000 Pa. The translation periodicity boundary condition was used at the upper and lower boundaries in the spanwise direction. A non-slip wall boundary condition was set for the cylinder surface.  
The O-type topology grid was used around the baseline cylinder and wavy cylinder, as shown in Figure 17. The span of the cylinder was kept as 132 mm, i.e., equal to the wave length of the wavy cylinder. About 450 nodes were distributed around the cylinder, and 100 nodes were used in the spanwise direction. The height of the first layer grid was \(5\times 10^{-6}\) m, leading to y+ \(<1\).  
Figure 16: Computational domain and boundary.  
Figure 15: Sound power of all the cylinders with different aspect ratio (U = 30 m/s).
## 7 Discussion
### Future Directions and Research Opportunities  
Digital twin (DT) technology holds immense potential for transforming transportation infrastructure throughout its entire lifecycle, from planning and design to construction, operation, maintenance, and decommissioning. However, realizing this potential requires addressing several critical challenges and exploring new research opportunities. These challenges include standardization, advanced analytics, interoperability, policy development, and privacy considerations. This section highlights these future directions and identifies key areas where researchers, policymakers, and industry experts must focus their efforts to advance the field.
## 3 Network Model Design
### Feature Extraction Layer  
ResNet Model Structure  
To effectively extract features of UWA signals for modulation classification, deep neural networks are typically used. However, in deep CNNs, gradients may vanish or explode as the number of network layers increases, leading to convergence failure. ResNet addresses these issues by introducing residual blocks and shortcut connections. Figure 3 illustrates the structure of a residual block in ResNet, where \"3 \(\times\) 3\" denotes the size of the convolution kernel, \"Conv\" represents the convolution operation, \"BatchNorm\" denotes batch normalization, and \"GeLu\" represents the activation function operation. The input information \(x\) has two paths: one directly transmits to the output end, and the other goes through two convolution operations to obtain \(F(x)\). The final output \(H(x)\) can be expressed as:  
\[H(x)=F(x)+x \tag{9}\]  
Figure 3: The structure of the residual block in the ResNet.  
During forward propagation, when the input x meets the task requirements, the output \(F(x)\) of the residual module becomes 0, achieving an identity mapping and solving the network degradation problem. During backpropagation, thanks to the identity mapping, gradients can be transmitted forward through a single GeLu layer, effectively addressing gradient vanishing and explosion issues.  
The ResNet model structure used in this paper is shown in Figure 4. To extract low-level features and accelerate the network's convergence speed, the input signal is first fed into a convolutional layer and a batch normalization layer. The definition of batch normalization is:  
\[\hat{x}_{i}=\frac{x_{i}-\mathrm{E}[x_{i}]}{\sqrt{\mathrm{Var}[x_{i}]}} \tag{10}\]  
where \(x_{i}\) represents the i-th dimension of the feature batch, and \(\mathrm{E}[\cdot]\) and \(\mathrm{Var}[\cdot]\) denote the calculation of the mean and variance of \(x_{i}\). To enable the neuron output to undergo a nonlinear transformation based on the input probability distribution, the batch-normalized data must pass through the GeLu activation function before being input into the residual block. The GeLu activation function is defined as:  
\[\mathrm{GeLu}(x)=0.5x\left[1+\tanh\left(\sqrt{\frac{2}{\pi}}\left(x+0.047715x^ {3}\right)\right)\right] \tag{11}\]  
The resulting signal is then input into the residual blocks to further extract signal features. Block1, Block2, and Block3 are the modules shown in Figure 3, and the numbers below indicate the number of output channels for each module. Finally, to improve the model's generalization ability, an adaptive pooling layer is used to pool the input feature map into an output feature map of size \(6\times 15\), which is then flattened into a feature vector of size 256 for subsequent algorithm use.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 5 Results
### Logistic Regression  
The hyperparameters that best fit the logistic regression model (_one_\(\times\)_one_) were identified, and the classification results are presented in Table 8. Similarly, the results of the logistic regression (_one_\(\times\)_all_) are shown in Table 9.  
While the results obtained with the SVM show that linearly separating the data into classes was not easy, the results achieved with the logistic regression emphasize that using a nonlinear kernel leveraged the model performance. With a few parameters to be optimized, significant results were achieved using the logistic regression classifier.  
The logistic regression (one \(\times\) one) model performed excellently with 99.5% accuracy when no decimation was applied and had a slightly reduced accuracy (approximately 92-93%) with decimation. The performances across the three dataset variations (Lofar, PCA, and NLPCA) were very similar, indicating that the model's behavior was consistent across these variations. The regularization parameter \(C\) was slightly higher for Lofar, but this did not seem to affect the performance noticeably. The model was highly stable and consistent, with very low standard errors for both with and without decimation, particularly when no data reduction was applied.  
The logistic regression model using the one \(\times\) all classification strategy showed an excellent performance, with a 99% accuracy without decimation across all the datasets. With decimation, the accuracy decreased slightly (92% for Lofar and 93% for PCA and NLPCA), indicating that decimation had a small negative impact on the performance. However, the overall performance remained high for all the datasets, with very low standard errors, indicating consistent and stable predictions.
## 4 Result and Analysis
### Characteristic Distribution of Groundwater Storage Capacity
#### 4.2.2 Calculation of Groundwater Recharge  
The groundwater runoff modulus method calculates the groundwater recharge in the basin by converting the average runoff over a specified time interval into the annual groundwater runoff modulus, which is then used to determine the natural recharge for each sub-basin. The exploitability coefficient approach is employed to assess groundwater exploitability in representative years for each sub-watershed.  
To enhance the accuracy of the estimation results, the average mining coefficient (\(\rho\) = 0.45) for several hydrogeological zones in Jiamusi City was utilized for the calculations, and the findings are presented in Tables 6 and 7.  
The years 2013, 2016, and 2018, which exhibited the largest groundwater reserves in the simulation results, were chosen to calculate the groundwater recharge and exploitable groundwater, as presented in Table 7. It is evident that in sub-basins Nos. 6, 10, 18, 27, 28, and 29, the groundwater recharge markedly exceeds that of other sub-basins, particularly those situated in the southwestern mountainous region of the study area. The forested  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Average Runoff** & \multirow{2}{*}{**Year**} & **Average Runoff** & \multirow{2}{*}{**Year**} & **Average Runoff** \\  & **Modulus** & & & **Modulus** & **Modulus** \\  & **(L/s-km\({}^{2}\))** & & & **(L/s-km\({}^{2}\))** & & **(L/s-km\({}^{2}\))** \\ \hline
2010 & 0.22 & 2013 & 0.31 & 2016 & 0.34 \\
2011 & 0.17 & 2014 & 0.27 & 2017 & 0.32 \\
2012 & 0.43 & 2015 & 0.18 & 2018 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Annual runoff modulus in the study area.  
Figure 5: Distribution maps of the groundwater reserves from 2010 to 2018 at a specific scale.  
terrain in this mountainous zone exhibits substantial water conservation and transmission capabilities, resulting in abundant groundwater resources. In 2013, the exploitable volume was remarkable, attaining 1.5 billion m\({}^{3}\).a\({}^{-1}\), and the study area was predominantly in a robust mining condition.
## 3 Multi-Model Modeling of the CO\({}_{2}\) Capture Process
### Derivation of the Multi-Linear Model
\begin{array}{c}x_{j}(k+i)=A_{j}x_{j}(k+i-1)+B_{j}u(k+i-1)\\ =A_{j}\big{[}A_{j}x_{j}(k+i-2)+B_{j}u(k+i-2)\big{]}+B_{j}u(k+i-1)\\ =A_{j}^{2}x_{j}(k+i-2)+A_{j}B_{j}u(k+i-2)+B_{j}u(k+i-1)\\ \hskip 56.905512 pt\ldots\ldots\\ =A_{j}^{i}x_{j}(k)+\sum\limits_{l=0}^{i-1}A_{j}^{l}B_{j}u(k+i-l-1)\\ y_{j}(k+i)=C_{j}x_{j}(k+i)+D_{j}u(k+i)\\ =C_{j}A_{j}^{i}x_{j}(k)+C_{j}\sum\limits_{l=0}^{i-1}A_{j}^{l}B_{j}u(k+i-l-1)+D _{j}u(k+i)\end{array} \right. \tag{15}\]  
By weighting the outputs of the local linear models, the outputs of the multi-linear model can be obtained:  
\[y(k+i)=\sum\limits_{j=1}^{3}\psi_{j}(CR)y_{j}(k+i) \tag{16}\]  
where  
\[\sum\limits_{j=1}^{3}\psi_{j}(CR)=1 \tag{17}\]  
\(\psi_{1}\), \(\psi_{2}\), and \(\psi_{3}\) are the scheduling functions for the local linear models of 50%, 80%, and 90% CO\({}_{2}\) capture rate (CR), respectively. The schematic diagram of the multi-model system is illustrated in Figure 7.  
**Remark 1. **_Note that the multi-linear model combines three local linear models to predict the nonlinear dynamics of the MEA-based post-combustion CO\({}_{2}\) capture process. Therefore, it has improved accuracy compared to the single linear model. Moreover, the proposed model has a state-space type of a local model expression, thus it can be conveniently used for many practical advanced control system designs._
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 2 Methodology
### Artificial Neural Networks (ANN)  
The ANN is inspired by the biological (brain) neuron system that is well-suited to the modelling of non-linear and complicated tasks such as the estimation and forecasting of rainfall, runoff and river sediment. Generally, the feed-forward multilayer perceptron neural network (MLPNN) is chosen from the various ANN structures, and extensively applied for the classification and regression analysis of nonlinear datasets [55].  
In the neural network, each neuron (node) has input and output variables. The output variables' value is determined by neurons using the net and activation (transfer) function on input variables. Generally, the net function is determined by the linear form, given as  
\[u=\sum_{i=1}^{n}x_{i}W_{i}+b \tag{7}\]  
where \(x_{i}\) is an input variable, \(W_{i}\) is the connection weights from the \(i\)th neuron in the input layer, and \(b\) is the bias or threshold value of the neuron. The net function (\(u\)) at a hidden node is transformed into output (\(y\)) using a non-linear activation function.  
The performance of the neurons can be improved by changing the transfer functions and varying parameters: for instance, the size of the hidden layer, size of the neurons in a hidden layer, gains and thresholds. The neural network is trained by employing an appropriate learning algorithm that altered the connection weights among the neurons of the training dataset. The weights are fixed after the successful completion of network learning. These interconnection weights are fine-tuned by an error convergence technique until the simulated output best matches the targeted output.
## 2 Materials and Methods
### Extract Evaluation and Analysis
#### 2.4.4 Antioxidant Capacity (AC)  
The antioxidant capacity was determined using the DPPH\(\bullet\) scavenging method, as described by [PERSON] and [PERSON] [38]. To prepare the standard curve, 2 mg of Trolox was dissolved in 50 mL of methanol and refrigerated for one hour at 8 \(\pm\) 1 \({}^{\circ}\)C. Additionally, 12 mg of DPPH\(\bullet\) was dissolved in 100 mL of methanol. Trolox solutions of 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 mL were sequentially transferred into test tubes and diluted to 1 mL with distilled water. Next, 3 mL of methanol and 1 mL of DPPH\(\bullet\) solution were added to each tube, stirred, and kept in the dark. After exactly 30 min, the absorbance was measured at 517 nm against methanol. A standard curve for Trolox was plotted based on the absorbance measurement results, taking into account the dilutions and converting the mass of Trolox (250.259 g/mol) into \(\mu\)moles. The equation obtained from the calibration curve of Trolox was y = \(-\)0.042x + 0.6239 (r = 0.9982).  
Similarly, absorbance was measured for a blank by mixing 1 mL of distilled water, 3 mL of methanol, and 1 mL of DPPH\(\bullet\) solution. An amount of 0.1 mL of the tested extract was transferred into the test tubes and made up to 1 mL with distilled water, then 3 mL of methanol and 1 mL of the DPPH\(\bullet\) solution were added, stirred, and set aside in the dark. After exactly 10 min, the absorbance was measured at 517 nm against methanol. The Trolox concentration was determined by reading the calibration curve. Taking dilutions into account, the final result was expressed in \(\mu\)mol Trolox per 1 g of dry weight (DW).