esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Model Effectiveness for Other Types of Battery  
To demonstrate the effectiveness of the model for other types of batteries, a simplified model was used to estimate SoH for two Lithium Cobalt Oxide (LCO) batteries tested for 220 cycles under ambient conditions similar to the experimental set up shown previously for NMC batteries. The LCO sample used in this experiment was a MOLICEL ICR-18650M (MOLICEL(r), Taipei, Taiwan) with a typical capacity of 2.8 Ah [32], and its electrodes were composed of LiCoO\({}_{2}\) and a graphite anode. The charging current set in the experiment was 1 A, and discharge was 1 C-rate (= 2.8 A). Due to the limitation of time and instruments, only two samples were tested as the purpose was simply to demonstrate its effectiveness in principle. The estimation difference is shown in Table 7, and the average differences are 1.77% and 1.03%, respectively. The difference is found to be less than 2 percent. The SoH comparison between the ECBE and semi-empirical model is shown in Figure 9, which includes the discharge curve of the LCO battery as an inset.  
The above fitting results demonstrate that the semi-empirical fading model used in this work can rapidly estimate SoH of batteries accurately. It is also observed that the difference values are always less than 2% whether the SoH estimation is done using its own extracted k values or another cell's k values. Another important observation is that this method provides accurate SoH estimation even when the first 60 cycles are included in SoH estimation, where rapid capacity decrease is observed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cell** & **Averaged Error** \\ \hline A1 & 0.46\% \\ A3 & 0.53\% \\ A5 & 0.81\% \\ A7 & 0.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average error during charge estimation of Li-ion batteries’ charge at different cycles using cycle number higher than 250.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Sample Number** & **Difference from ECBE** \\ \hline A1 & 1.77\% \\ A2 & 1.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The estimation difference of Lithium Cobalt Oxide (LCO) batteries.  
Figure 9: SoH estimation curve for Lithium Cobalt Oxide (LCO) cell to compare the difference between experimental SoH and estimated SoH. Inset graph shows the discharge curve of LCO batteries.  
This rapid capacity decrement increases the estimation difference and can be further reduced if the first 60 cycles are not included in the SoH estimation. However, we have included the first 60 cycles in this work for SoH estimation to show the accuracy of the simplified model in SoH estimation. It will be very interesting to check the credential of this method when batteries degrade to even lower SoH values, and it will be one of our future works. In short, our model provides a path for SoH estimation of several cells in a pack by just the extraction of k values for a single cell in that pack.
## 6 Conclusions  
This study demonstrated through multi-dimensional assessments that the ecological restoration of the Liaohe Estuary Wetland has significantly improved water quality (100% compliance rate for Class III or higher standards), enhanced landscape connectivity (dynamic index of 27.19%), and elevated ecosystem service functions (12.8% increase in carbon sequestration capacity). By implementing the \"returning aquaculture ponds to wetlands\" initiative and tidal channel dredging projects, aquatic ecological corridors connecting native habitats were successfully established, offering a global coastal wetland restoration paradigm characterized by \"anthropogenic disturbance mitigation coupled with natural process synergy\". The application of multi-source data integration methods provided robust technical support for the dynamic monitoring and precise regulation of ecological restoration efforts.  
However, this study has limitations: (1) The evaluation period was relatively short (only one year post-restoration), limiting comprehensive insights into long-term ecosystem stability. (2) The mechanism underlying heterogeneous water quality improvement remains unclear and requires further elucidation through hydrological modeling. (3) The biodiversity assessments relied on macro-level indicators and lacked habitat suitability analyses for keystone species, such as spotted seals.  
Future studies should prioritize prolonging the monitoring period to 5-10 years, incorporating micro-scale indicators (e.g., soil microbial communities and benthic fauna), and developing coupled \"restoration effectiveness-socioeconomic\" models to evaluate the contributions of ecological recovery to regional sustainable development.  
Conceptualization, Y.H., N.H. and J.Z.; methodology, Y.H.; software, N.H.; validation, Y.H., N.H. and Y.G.; formal analysis, L.Z. and Y.G.; investigation, Y.G.; resources, Y.G.; data curation, Y.H., C.T. and L.Z.; writing-original draft preparation, N.H., C.T. and L.Z.; writing--review and editing, Y.H. and N.H.; visualization, N.H. and J.Z.; supervision, C.T.; project administration, Y.G. and L.Z.; funding acquisition, Y.H. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the opening fund of Key Laboratory of Geohazard Prevention of Hilly Mountains, Ministry of Natural Resources (Grant No. FJKLGH2025K008), and the Liaoning Province Science and Technology Tackling Key Problems Special Fund (Grant No. 2023)H1/10400012).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company Liaoning Nonferrous Geological Exploration and Research Institute Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Techno-Economic Analysis of a Solar Thermal Plant for Large-Scale Water Pasteurization
## 3 Plant Model Design
### Heating Coils  
The thermal energy storage allocates three different heating coils: two of them are connected to the energy sources (i.e., the solar field and the gas burner), while the third, connected to the plate heat exchanger, is dedicated to the pasteurization. In the limit \(C_{r}\simeq 0\), the effectiveness can be obtained as [36]  
\[\varepsilon=1-e^{-\text{NTU}}. \tag{10}\]  
The overall thermal transmittance \(U\) of the heating coils is computed as  
\[U=\left(\frac{1}{\alpha_{i}A_{i}}+\frac{\ln\left(d_{e}/d_{i}\right)}{2\pi \lambda L_{c}}+\frac{1}{\alpha_{e}A_{e}}\right)^{-1}, \tag{11}\]  
being \(\alpha_{i}\) and \(A_{i}\) the internal heat transfer coefficient and surface, \(d_{i}\), \(d_{e}\) and \(L_{c}\) respectively the internal and external diameter and length of the coils, \(\lambda\) the thermal conductivity of the pipe, and \(\alpha_{e}\) and the external heat transfer coefficient and surface. The Nusselt number inside the tubes is in this case computed as [40]  
\[\text{Nu}=0.015\,\text{Re}^{0.83}\,\text{Pr}^{0.42}\left(\frac{\mu}{\mu_{w}} \right)^{0.14}, \tag{12}\]  
while the Nusselt number on the outer part of the tubes [41]  
\[\text{Nu}=\left(0.6+\frac{0.387\,\text{Ra}^{1/6}}{\left(1+\left(0.559/\text{ Pr}\right)^{9/16}\right)^{8/27}}\right)^{2}, \tag{13}\]  
with Rayleigh number given by [41]  
\[\text{Ra}=\frac{\beta\,g\,D_{t}^{3}\left(T_{wall,ext}-T_{\infty}\right)\rho}{ \mu\,\kappa}, \tag{14}\]  
where \(\beta\) is the volume expansion coefficient, \(g\) is the gravitational acceleration, \(D_{t}\) is the characteristic dimension, which in this case is assumed to be the external diameter of the tank, \(T_{\infty}\) is the temperature of the fluid far from the wall (quiescent temperature) and \(\kappa\) the thermal diffusivity of the fluid. The external wall temperature has been computed as  
\[T_{wall,ext}=T_{int}-\left(R_{int}+R_{wall}\right)\Phi, \tag{15}\]  
being \(T_{int}\) the internal temperature of the fluid, \(R_{int}\) the internal convective thermal resistance, \(R_{wall}\) the material thermal resistance and \(\Phi\) the exchanged thermal power. In this case, an iterative procedure on thermal power and external convective heat transfer coefficient is adopted to obtain the wall temperature.
## 2 Methodology
### Numerical Wavefield Modeling Conditions  
The accuracy of wavefield modeling generally needs to meet two key numerical conditions: the Courant-Friedrichs-Lewy (CFL) condition and the least sampling number of grid points for shortest wavelength in wavefield modeling. The CFL condition satisfies the following inequality:  
\[\frac{\Delta t}{\min_{\Omega_{e}}(h_{e}/v)}\leq C \tag{13}\]  
where, \(v\), \(h\) and \(\Delta t\) indicate the velocity model, grid size and time step for numerical modeling, respectively; \(C\) is the Courant number, generally ranging from 0.3 to 0.5.  
[PERSON] and [PERSON] [47] pointed out that for a high accurate modeling by SEM, the number of grid points \(N_{e}\) in the shortest simulated wavelength should meet the following condition:  
\[N_{e}=p_{se}\cdot\min_{\Omega_{e}}\left(\frac{v}{\bar{h}_{e}}\right)\approx N_ {emp} \tag{14}\]  
where, \(p_{se}\) is the shortest period of frequency band based on target modeled wavefield, \(\overline{h}_{e}\) is the average grid size. \(N_{emp}\) represents the number of grid points in a single wavelength that can maintain the numerical simulation accuracy, and it is fixed at 5 in the SPECFEM3D program used for this study. As shown in Equation (13), the smaller the shortest period of modeled wavefield, the smaller the required grid dimension.  
Based on the above conditions, we know that after determining the highest frequency of modeled wavefield, the average grid size can be determined through Equation (14) and time step for modeling is then limited by Equation (13). Equation (14) shows that when the highest frequency of modeled wavefield is doubled, the grid size in each dimension will be reduced to half of the original one, and the number of 3D grid points will become eight times of the original number. Equation (13) indicates that as the grid dimension is reduced by half, the time step for modeling that maintains the stability of the time scheme will also be decreased by half. Therefore, when the highest frequency of modeled wavefield is doubled, the wavefield calculation will be 16 times the original one. This is why we propose the multi-scale strategy (also corresponding to the multi-grid operation depending on wavefield frequency content) to reduce the storage requirements and the computational cost.
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 2 Materials and Methods
### Regeneration of MIEX(r) GOLD Resin  
Regeneration of PFAS-adsorbed MIEX(r) GOLD resin was initially studied for PFBA and PFOA-loaded resin to identify the most effective regenerant solution. Here, PFBA and PFOA-loaded resins were prepared following the protocol mentioned in Section 2.3 but the initial concentration of PFAS solution was 100 ppm. Then, the PFAS-adsorbed resin was collected carefully and used for the regeneration testing. One complete regeneration study takes a total of 4 h and ends up with BV 10. Four different regenerants were used, namely: (i) 60% MeOH with saturated NaCl, (ii) 60% MeOH with saturated NH\({}_{4}\)Cl, (iii) 60% EtOH with saturated NaCl, and (iv) 60% MeOH with 0.0012% NaOH (pH 9.8). Briefly, 0.5 mL PFAS-adsorbed resin was added into 1.5 mL (3 BV) regenerant and mixed for 1 h at 180 rpm. Then, the resin was separated carefully, and this step was repeated two more times with fresh regenerant. Finally, 1 BV regenerant (0.5 mL) was added to the resin and mixed for 1 h. All regenerants were collected at the end of each cycle and combined (i.e., total 10 BV). The PFAS in the combined solution was measured. The regeneration efficiency of resin was calculated by quantifying the percentage of desorbed PFAS compound from resin with the following equation.  
\[\text{Regeneration efficiency}(\%)=100\times\left(\frac{\text{Total PFAS adsorbed in resin }-\text{Total PFAS desorbed in regenerant}}{\text{Total PFAS adsorbed in resin}}\right) \tag{2}\]  
Further regeneration studies were performed for three different PFAS-adsorbed resins, i.e., PFOA, PFOS, and PFHxS, using 70% MeOH with saturated NaCl, following exactly the same protocol as mentioned above, and LC-MS was used for quantitative analysis. Here, PFOA, PFOS, and PFHxS were loaded in resin following the same protocol mentioned in Section 2.3 (i.e., initial concentration of each PFAS was 1 ppm, and resin amount added was 0.5 mL). Regeneration of PFHxS was also conducted from supersaturated resin following the same regeneration protocol.
## 3 Construction of Carbon Steel Corrosion Prediction Model
### Verification of Prediction Model
#### 3.2.2 Random Forest Regression Prediction Model  
The key parameters affecting the accuracy of the random forest regression prediction model are the leaf size and the number of decision trees. In this paper, the leaf size was set to 5, 10, 20, 50, and 100, and the number of decision trees was set to 200. Figure 11 shows the mean square error of carbon steel corrosion prediction under varying numbers of decision trees and leaf sizes.  
Figure 11 shows that errors in prediction models trained using corrosion data are minimized when the leaf size is set to five. According to the fundamental principle of the random forest regression algorithm, the prediction output of the model represents the average predictions from all decision trees. As the number of decision trees increases, the predicted values tend to stabilize. If the decision trees are sequentially built after stability, the computation time will only increase and even lead to \"overfitting\". As Figure 11 illustrates, the number of decision trees stabilizes at around 60 for carbon steel corrosion  
Figure 11: Parameter selection of random forest regression.  
Figure 10: BP neural network model corrosion prediction.  
prediction. Based on the analysis, the leaf size was set to five, and the number of decision trees to 60, when the random forest regression corrosion prediction model was established for this paper. If the number of samples is large enough while developing the random forest regression prediction model, the \"overfitting\" phenomenon will not occur, because the algorithm adopts a back-sampling cross-experiment for verification. If less data are available, dividing the data into training and test sets is necessary. Therefore, 80% of the total experimental data was selected as the training set, while the remaining 20% served as the test set for building the random forest regression corrosion prediction model.  
Similar to the BP neural network prediction model, the model underwent 30 training iterations, and the top 5 results were selected for plotting the predicted corrosion depth of carbon steel and the error curve, as shown in Figure 12.
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 2 Individual Methods
### Data Collection  
Factors influencing the remaining strength of the pipeline include the outer diameter of the pipeline (\(D\)), the wall thickness of the pipeline (\(t\)), defect length (\(L\)), defect depth (\(d\)), defect width (\(w\)), yield strength of the pipeline(\(\sigma_{y}\)), and ultimate tensile strength (\(\sigma_{u}\)). The remaining strength is characterized by the failure pressure (\(p_{f}\)). The datasets of these influencing factors and the failure pressure are sourced from the published literature [39]. In total, 60 sets of burst test data from defective pipelines were collected. The seven influencing factors are taken as the input variables of the prediction model. The output variable is the failure pressure of the defective pipeline. In Figure 8, histograms are plotted to reflect the distribution of the input variable datasets. The height of each bar represents the relative frequency or probability density of each value, and the total area of the histogram is equal to 1. It is found that the features contained in the dataset have a wide range, the dataset is well-balanced, and most of the input variables follow a normal distribution. This meets the data requirements of machine learning and ensures the effectiveness of the machine learning algorithm constructed based on these data.
## 3 Methodology
### TRFM Trajectory Prediction Model
#### 3.3.1 TRFM Unit  
The core of the Self-Attention mechanism involves calculating the weighted relationships between the current sequence point and all other sequence points using three vectors: \(Q\) (query), \(K\) (key), and \(V\) (value). The Self-Attention calculation structure is shown in Figure 4. The dot product of Q and K vectors represents the degree of correlation between features of two temporal points. The resulting attention weights, obtained through a \(softmax\) layer, are used to weight and sum the features of the current temporal point with those of other temporal points. In each TRFM unit, the \(Q\), \(K\), and \(V\) vectors of each temporal feature point can be combined into their respective \(Q\), \(K\), and \(V\) matrices. This allows for parallel computation when calculating attention weights, thereby improving model efficiency. The attention calculation formula is shown in Equation (23).  
\[Self-Attention(Q,K,V)=Softmax\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V \tag{23}\]  
Additionally, this TRFM model employs multi-head attention, which involves splitting the original temporal point features into multiple heads. Each head corresponds to a specific  
Figure 4: Removal of anomalies and redundant points.  
feature portion and undergoes a Self-Attention mechanism, shown in Equation (24). This calculates the attention weights for each head, which are then used to fuse the features of the current temporal point with those of other temporal points corresponding to that head. The multiple heads are then merged, as shown in Equation (25), and fed into the Feed Forward layer; the structure of the multi-head attention layer is shown in Figure 5. The Feed Forward layer essentially functions as a fully connected layer, further enhancing non-linearity and high-dimensional feature representation.  
\[head_{i}=Self-Attention(Q_{i},K_{i},V_{i}),i=1,2,3\dots\text{n} \tag{24}\]  
\[MultHead(Q,K,V)=Concat(head_{1},\dots,head_{n}) \tag{25}\]  
Residual networks are introduced outside the multi-head attention layer and Feed Forward layer to prevent the occurrence of gradient explosions when stacking deep networks. The outputs of both the multi-head attention and Feed Forward layers are subject to layer normalization, ensuring that the learned feature values are distributed within a certain range.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 6 Conclusions  
This study demonstrated the effect of whitening the features of 1D-CNN-based fault diagnosis models. The features of three commonly known and highly accurate fault diagnosis models were whitened with network deconvolution. Network deconvolution is a recently proposed, implicit and approximative whitening technique. The models modified with network deconvolution were compared to the originally proposed models relying on batch normalisation under two validation case studies. In the first case study, the models were evaluated on a well established bearing fault dataset under varied load domains. The second case study evaluated the model performances in more challenging real thruster fault detection tasks. The first case study showed that whitened features increased the 1D-CNN model performances on average. Furthermore, the same case study showed that the models with whitened features were significantly more accurate at diagnosing the bearing health state from the vibration data acquired with two sensors. However, the second case study showed that the models with whitened features achieved similar fault detection accuracies to the corresponding baseline models. Overall, this study shows that whitening 1D-CNN-based fault diagnosis model features may improve the diagnosis performance. These results are significant for CNN-based fault diagnosis algorithms since the whitening technique employed in this study can replace all batch normalisation layers in any CNN-based fault diagnosis model.  
Conceptualization, J.M., R.-P.N. and J.K.-R.; methodology, J.M.; software, J.M.; validation, J.M., R.-P.N., J.K.-R., F.F. and T.T.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON], R.-P.N. and J.K.-R.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], R.-P.N., J.K.-R., F.F., T.T., S.S. and R.V.; visualization, [PERSON]; supervision, J.K.-R., F.F., T.T., S.S. and R.V; project administration, J.K.-R. and R.V.; funding acquisition, J.K.-R. and R.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Business Finland as part of the Reboot IoT project (grant number 4356/31/2019) and by the Academy of Finland as part of the AI-ROT research project (grant number 335717).  
Not applicable.  
Data Availability Statement: Data sharing concerning the thruster dataset is not applicable to this article due to legal issues. Publicly available datasets were analyzed in this study. This data, concerning the bearing fault dataset, can be found here: [[https://engineering.case.edu/bearingdatacenter](https://engineering.case.edu/bearingdatacenter)]([https://engineering.case.edu/bearingdatacenter](https://engineering.case.edu/bearingdatacenter)).  
**Conflicts of Interest:** 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.