esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability-Based Optimization of Impeller Structure Using Exponential Function Approximation
## 4 Reliability-Based Optimization of an Impeller Structure
### Sensitivity Analysis of Design Parameters  
The sensitivity coefficients were computed for the key design parameters of the impeller structure. Table 3 presents the sensitivity analysis results, including both absolute sensitivity coefficients and normalized values for comparison.  
The results indicate that the inlet temperature mean has the highest sensitivity coefficient (0.452), followed by the inlet pressure mean (0.318). The standard deviations of both parameters show relatively lower sensitivities, suggesting that the mean values of the operating conditions are more critical for structural reliability than their uncertainties.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Inlet** & \multirow{2}{*}{**Temperature**} & **Inlet** & \multirow{2}{*}{**Objective**} & **Failure** & **Coefficient of** & **Number of** \\  & **(\({}^{\circ}\)C)** & & & & & **Function** \\ \hline Double-loop IS & \(-\)158.68 & 5.6200 & \(-\)2.2291 & \(1.00\times 10^{-4}\) & 0.0292 & \(4.7\times 10^{5}\) \\ Double-loop SS & \(-\)161.16 & 5.4153 & \(-\)2.1991 & \(9.44\times 10^{-5}\) & 0.0965 & \(2.0\times 10^{6}\) \\ First-order & & & & & & \\ exponential & \(-\)153.65 & 5.6200 & \(-\)2.1978 & \(3.20\times 10^{-5}\) & 0.0709 & \(2.4\times 10^{4}\) \\ approximation (IS) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reliability-based optimization results of the proposed decoupling first-order approximation method and double-loop methods.  
Sensitivity analysis provides valuable insights into the actual design and operation of impellers. The high sensitivity to the mean inlet temperature indicates that precise temperature control is essential to maintain structural reliability. Lower inlet temperatures typically result in reduced thermal stresses, which improves reliability. Design engineers should prioritize robust temperature control systems and insulation. Second, the high sensitivity to mean inlet pressure emphasizes the importance of pressure regulation systems. Higher inlet pressures increase the mechanical load on the impeller structure, which can lead to a higher probability of failure. Proper pressure control valves and monitoring systems are critical. Further, the relatively low sensitivity to standard deviation suggests that while controlling average operating conditions is critical, reducing the impact of operating uncertainty on reliability is secondary, but still relevant. This finding supports prioritizing investment in control systems over uncertainty reduction measures.  
In summary, the first-order exponential approximation method not only achieved optimization results comparable to those obtained by traditional methods, but it also offered significant advantages in terms of computational efficiency. Indeed, the application of the proposed iterative decoupling reliability approximation optimization method provided an impeller design capable of exhibiting suitable performance and safety in the presence of various uncertainties.
# Energy Storage Overflow-Aware Data Delivery Scheme for Energy Harvesting Wireless Sensor Networks
## 5 Conclusions  
In EH-WSN, there is the problem of \"energy hungry and surplus coexistence\". That is, some nodes suffer from serious energy shortage while some other nodes waste energy duo to ESO. This problem is alleviated by the proposed EAMP data delivery scheme. The EAMP takes ESO into account and  
Figure 8: Comparison of residual energy and ESO. (**a**) Total residual energy; (**b**) ESO.  
Figure 9: The ESO-aware multiple path (EAMP) scheme with variation in \(\alpha\). (**a**) Impact of \(\alpha\) on residual energy; (**b**) Impact of \(\alpha\) on ESO.  
can alleviate overflow of energy storage in the nodes so that the EH-WSN nodes can consume energy evenly. With the EAMP, a node can optimally split data into multiple pieces and simultaneously deliver these pieces over multiple paths to mitigate ESO. The EAMP is applicable to EH-WSNs and enables the EH-WSN nodes to deliver data for a longer period without suffering from interruption due to energy shortage.  
It should be pointed out that the proposed schemes may not achieve satisfactory performance when they are applied in cases where multiple source nodes simultaneously deliver data to their respective destinations, meanwhile some source-to-destination paths with different sources share some nodes. In the multiple sources case, the proposed schemes can be applied in time division multiple access (TDMA) manner, i.e., the sources deliver data one after the other. We will investigate extending the proposed schemes to the case of simultaneously delivering data over multiple source-destination pairs in the future.  
Conceptualization, W.L. and Y.-h.Z.; methodology, W.L. and Y.-h.Z.; writing--original draft preparation, W.L.; writing--review and editing, Y.-h.Z. and K.C.; [PERSON] coded the Matlab program for verification; [PERSON] collaborated in the development of the idea.  
This research was funded in part by the Natural Science Foundation of China under grant Nos. 61432015, 61772470, and 61472367.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Phytoliological Compatibility  
To evaluate any possible effect on plant development, several biological parameters linked to basal metabolism were recorded on hazelnut seedlings after being foliar-treated with the same suspension used in the in vivo antibacterial assay. At 1, 7, 14 and 21 dpt, the leaf area, the chlorophyll and flavonol content in leaves, and the nitrogen balance index, calculated from the ratio between these last two values, can be seen in Table 3. From the statistical analysis, the full compatibility of tested CNC and LNP was confirmed. No detrimental effect was noted on the canopy of treated plants that was comparable with the used controls (water and copper oxychloride at field dose). These results suggest that the proposed nanomaterials could be easily used in field alone or as nanocarriers for other active substances, without any harmful effect on the growth of the plants. In this sense, according also with previous results, it is possible to asses a future role for CNC and LNP in a sustainable hazelnut plant-protection strategy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**21 dpi**} \\ \cline{2-3}
**Treatment** & \
\begin{tabular}{c} **Disease Incidence** \\ **(\%) (\(p\) < 0.01)** \\ \end{tabular} &
\begin{tabular}{c} **Disease Severity** \\ **(Necrosis cm\({}^{-2}\)) (\(p\) < 0.05)** \\ \end{tabular} \\ \hline Water & 48.8 \(\pm\) 6.0 a & 1.02 \(\pm\) 0.08 a \\ Copper oxychloride & 24.3 \(\pm\) 4.1 b & 0.67 \(\pm\) 0.06 b \\ HP CNC & 29.9 \(\pm\) 4.2 b & 0.90 \(\pm\) 0.04 a \\ HS CNC & 25.7 \(\pm\) 1.6 b & 0.90 \(\pm\) 0.03 a \\ HP LNP & 30.0 \(\pm\) 2.5 b & 0.92 \(\pm\) 0.10 a \\ HS LNP & 24.8 \(\pm\) 2.1 b & 1.08 \(\pm\) 0.09 a \\ \hline \hline \end{tabular}
\end{table}
Table 2: Disease incidence (DI) was calculated as percentage ratio between symptomatic and total leaves per plant. Disease severity (DS) was calculated as the number of necrosis per cm\({}^{2}\) on symptomatic leaves. Data are represented as the mean and SD, and different letters show significantly different values after one-way ANOVA followed by [PERSON]’s LSD post hoc test were performed.
## 4 The Proposed TransTLA Method
### Transfer Learning Implementation
This approach, as described by Equation (21), gradually decreases the learning rate as the training advances, enabling rapid convergence in the early stages and facilitating precise parameter tuning in the later stages. \[LR_{i}=LR_{0}\cdot(1+\gamma\cdot i)^{-\delta} \tag{21}\]  
where \(LR_{i}\) represents the learning rate at the \(i\)-th iteration, \(LR_{0}\) is the starting learning rate, \(\gamma\) is the coefficient for learning rate growth, and \(\delta\) is the coefficient for learning rate decay. Figure 6: The training process for the TransTLA model. The pseudocode implementation of the entire training and evaluation process of the TransTLA model algorithm is shown in Algorithm 1. ```
0: Time series data of home appliance sales from both source and target domains
0: Predicted sales volume result in the target domain under a given forecast time step
1: Data Preprocessing: Clean, normalize, split the datasets, and process with sliding windows
2: for both source and target domain data. 3: Train the Model with the Training Set:
3: for epoch in range(epochs) do // for each iteration
4: Extract spatial local features through the TCN layer. 5: Extract temporal features through the LSTM layer. 6: Q = Hidden state output from the last time step by LSTM. 7: K = Sequence of hidden states output by LSTM. 8: V = Sequence of hidden states output by LSTM. 9: Optimize features through the attention mechanism layer: Attention(Q, K, V). 10: Calculate domain adaptation loss L_adapt. 11: Get prediction output through fully connected layers. 12: Calculate the regression loss of source domain L_source and target domain L_target. 13: Calculate composite loss L = \(\alpha\times\) L_source + \((1-\alpha)\times\) L_target + \(\beta\times\) L_adapt
14: Backpropagate and optimize model parameters with the Adam optimizer based on the loss function. 15: Perform dynamic adjustment of the learning rate. 16: Evaluate performance with the target domain validation set, update and save the best model. 17: endfor
18: Load the best model. 19: Model Evaluation: Test on the target domain test set and calculate evaluation metrics. 20: Return: The predicted sales results in the target domain. ```  
**Algorithm 1** Process of the TransTLA Model Algorithm
# Research on the Influence Mechanism of Foam Slurry Wall Formation
## 4 Influence of Various Factors on Mud Cake Membrane Formation
### Impact of Foamed Slurry Mix Design on Filter Cake Formation  
The computational results from Schemes 6, 20, and 21 (Table A1) show that appropriately increasing the swelling ratio can accelerate the membrane formation process, as shown in Figure 9a. However, when the swelling ratio is too low (e.g., 2.5%), it becomes difficult to form an effective mud cake. This means that the higher the bentonite particle concentration in the slurry, the more easily aggregates form on the soil surface, thus more effectively clogging the pores.  
Figure 8: Influence of parameters (b) and (m) on membrane formation time. (**a**) Plot of membrane formation duration (T) versus parameter (b). (**b**) Plot of membrane formation duration (T) versus parameter (m).  
Figure 9: Effect of foamed slurry mix ratio on membrane formation outcome. (**a**) Effect of water absorption ratio on membrane formation outcome. (**b**) Effect of foam concentration on membrane formation outcome.  
The computational results from Schemes 6 and 22 to 24 (Table A1) show that a stable mud cake can form at foam concentrations of 10% and 20%. When the foam concentration is 10%, the final membrane formation time increases by about 33% compared to a foam concentration of 20%. However, when the foam concentration increases to 30%, a stable mud cake has still not formed before the calculation ends, as shown in Figure 9b. This suggests that the foam concentration for foam slurry clogging effectiveness has an optimal value, whereby when the foam concentration is below the optimal value, the more foam there is, the better the clogging effect. However, when the foam concentration exceeds this optimal value, the clogging effect decreases. This may be because, at a high foam concentration, the number of foam particles accumulated at the pore channel entrance is relatively large. The shrinkage and deformation of these foam particles under the influence of infiltration pressure become more significant and the flow gradient at the pore channel increases, making it difficult to form stable foam particle agglomerates, thus failing to achieve effective clogging.
## 2 Materials and Methods
### Models' Architecture Setup
#### 2.2.1 ConvLSTM  
A ConvLSTM is essentially an LSTM where the internal gates are modified to operate over two-dimensional data, which allows it to be used for spatial-temporal prediction. This is achieved by using convolution operations in the state-to-state and input-to-state transitions of the traditional LSTM gates [38].  
A multi ConvLSTM layer network was built and tested. The general structure was built by stacking multiple ConvLSTM cells in sequence, to test how the number of hidden dimensions, the kernel size and the number of layers affected performance.
## Materials and Methods
### Numerical Simulations  
In order to characterize and understand the pulse dynamics inside this cavity and have a prospect of the output performances, we performed numerical simulations using commercial software (Fiberdesk, Ref. [30]) allowing us to resolve the extended nonlinear Schrodinger equation (NLSE):  
\[\frac{\partial A}{\partial z}=g(\mathrm{E})A+\frac{ig}{\Delta\Omega_{g}^{2}} \frac{\partial^{2}A}{\partial t^{2}}+\sum_{m\geq 2}\frac{i^{m+1}}{m!}\beta_{m} \frac{\partial^{m}A}{\partial t^{m}}+i\gamma|A|^{2}A, \tag{1}\]  
where \(A\) is the slowly varying complex pulse envelope, \(\beta_{m}\) is the \(m\)th order dispersion coefficient, the \(ig/\Delta\Omega_{g}^{2}\) term denotes spectral filtering due to the limited gain bandwidth, and \(\gamma\) corresponds to the contribution from Kerr non-linearity through self-phase modulation and is classically defined as:  
\[\gamma=\frac{n_{2}\omega_{0}}{cA_{eff}}, \tag{2}\]  
where \(n_{2}\) is the nonlinear index and \(A_{eff}\) is the effective mode field area. The effect of gain saturation is indicated by \(g\), which stands for the saturated gain that is related to low signal gain \(g_{0}\) by:  
\[g(E)=g_{0}/\left(1+\frac{E}{E_{sat}}\right), \tag{3}\]  
where \(E_{sat}\) is the saturation energy transferable from the pump.  
Equation (1) is solved using the split-step method along the cavity elements, as depicted in Figure 2 and with the parameters listed in Table 1. The effect of the real R-SAM and of the polarization controller are modeled as a single effective SAM with 90% modulation depth, which is a modulation depth that is fairly rational for the nonlinear effect of R-SAM and the NPE combined.
# Formation and Breakup of an Immiscible Compound Jet with Density or Viscosity Stratification
## 4 Results and Discussion
### Baseline Case
The intermediate satellite drops, which have larger volumes compared to the inner satellite drops, after breakup are carried away by the outer fluid motion. Whereas, the inner satellite drop having size less than 2 grid spacing are so small that after the breakup it disappears (Figure 4a,d). So, for such very tiny inner drops, we neglect its formation [13]. Figure 4: Snapshots of phase boundaries of compound liquid jets for \(\rho_{13}=1\), \(\rho_{23}=1\), \(\mu_{13}=1\), \(\mu_{23}=1\), with the rest of the parameters as \(Re=37.5\) and \(We=1.5\), with streamlines superposed in the right halves at non-dimensional times of: (**a**) \(\tau=23.68\); (**b**) \(\tau=24.08\); (**c**) \(\tau=24.56\); (**d**) \(\tau=27.04\) and (**e**) \(\tau=28.08\). Readers can refer to Video “S1:Baseline case” in the Supplementary Materials for better understanding of the breakup process of the baseline case. We define the jet breakup length as the length of the jet measured right after the breakup, as shown in Figure 4b where \(L_{i}\) stands for jet breakup length of the inner jet, and in Figure 4c where \(L_{m}\) denotes breakup length of the intermediate jet. In Figure 4b,c, the jet breakup lengths of both jets lie in the dripping region. The drops formed in this case are single-core. The breakup of a compound jet in the dripping region may also produce multi-core compound drops, i.e., a compound drop enclosing more than one core drop, which can break the periodicity of the drop formation. Such cases are discussed in Section 4.2.
# An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
## 7 Conclusions  
The integration of Machine Learning and Deep Learning methods for cloud masking holds significant promise, offering considerable advantages over traditional approaches such as the thresholding-based method. Ongoing advancements in Deep Learning, including unsupervised and semi-supervised learning techniques, along with the creation of specialized architectures designed for cloud detection tasks, suggest a trajectory toward even more precise and reliable results. The utilization of Deep Learning has already demonstrated substantial enhancements in accuracy, with further refinements expected as novel techniques and architectures emerge. However, a critical consideration lies in the usability, amount, and quality of labelled data. The process of acquiring and labeling substantial datasets can be resource-intensive, potentially limiting the method's applicability. Additionally, the efficacy of the model can be influenced by their quality, as inaccurate or inconsistent labels may introduce noise and bias during training. Consequently, ongoing research is imperative to devise strategies that address challenges related to the data. Investigations should explore the potential of transfer learning techniques, exploiting pre-existing labelled datasets or models. This approach holds promise in diminishing the labelled data requirements for model training while enhancing accuracy and efficiency. Weakly supervised and unsupervised learning is a promising area of research for general image segmentation, which may also have positive implications in the field of remote sensing, overcoming the problematic need for ground truth. Another avenue for research involves the development of ways to make the existing and new models, nonspecific to a given sensor. In summary, as Deep Learning techniques advance and specialized architectures for cloud detection evolve, these methods are expected to become more accurate, efficient, and widely applicable in the future.  
Conceptualization, A.A. and A.P.; methodology, A.A. and A.P.; validation, A.A., A.P. and A.T.; investigation, A.A., A.P. and A.T.; resources, A.A., A.P. and A.T.; writing--original draft preparation, A.A., A.P. and A.T.; writing--review and editing, A.A., A.P. and A.T.; visualization, A.A., A.P. and A.T.; supervision, A.A., A.P. and A.T.; project administration, A.A., A.P. and A.T.; funding acquisition, A.A. and A.P. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge supercomputing resources and support from ICSC--Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing--and hosting entity, funded by European Union--NextGenerationEU.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Design of the Learning System and Content Structure
### Section Introduction
#### 3.4.1 Section 1: Verilog Introduction  
This section establishes the foundational knowledge required for digital-circuit design using _Verilog_. The specific topics addressed in this section's exercises are listed in Table 1. It begins with an explanation of the _Verilog_ module construct, detailing the syntax and declaration of input and output ports to familiarize students with the basic building blocks of any design. The section then examines the four essential logic gates--NOT, AND, OR, and XOR--clarifying their roles and behaviors within combinational circuits. As shown in Figure 4, through practical circuit diagrams and guided questions, students can clearly understand the functions of logic gates and their corresponding operators.  
Next, it systematically introduces the reg and wire data types, explaining their respective functions in signal storage versus transmissions and the conditions under which each should be used. This enables students to accurately distinguish between the two core data types. The section then extends to the always block, offering an in-depth comparison of combinational and sequential logic, so learners can obtain a preliminary understanding of how each is described at the _RTL_.  
Finally, the section presents the three principal design abstraction levels--gate level, dataflow level, and behavioral level--contrasting their characteristics and typical application scenarios. By the end of this section, students will have a coherent overview of _Verilog_'s syntax and abstraction hierarchy, laying the groundwork for deeper exploration in subsequent sections and ensuring they possess the basic skills needed to proceed.
## 5 Case Study  
### Space Manipulator Assembly  
The space manipulator is fixed on the spacecraft, which needs a high assembly accuracy to guarantee the stability when the spacecraft is flying. The assembly is executed by shaft and hole connectors, which are shown in Figure 9a. The connector is shown in Figure 9b. The manipulator is the align part and the spacecraft is the reference part.  
Figure 8: Random sampling by Monte Carlo method.  
The upper connector is fixed on the manipulator, and the bottom connector is fixed on the spacecraft. The KACs are the assembly of the connectors. Due to the slight deformation of the spacecraft and the installation error of the bottom connectors, it is difficult for the connectors to accurately assemble at one time during the assembly of the spacecraft and the manipulator. In the original assembly process, it is necessary to try the assembly first, measure the assembly deviation of the clearance and coaxiality of each pair of connectors, make the accuracy compensation, and retry the assembly to ensure the assembly quality. The assembly takes a long time and the connectors are not convenient to be operated on the spacecraft. Therefore, the laser tracker is used to measure the connectors between the spacecraft and the manipulator. The methods in Sections 2 and 3 are taken to evaluate the assemblability based on the measurement data. The method in Section 4 is used to find the key connectors to make the accuracy compensation. The assembly is carried out after the accuracy compensation. In this way, the assembly quality is better guaranteed and the assembly efficiency is improved. The flow of the proposed method and the comparison with the original method are shown in Figure 10.  
As shown in Figure 10, the assembly process is developed toward digital measurement and analysis. The results obtained in the actual assembly and inspection are replaced by the analysis of the  
Figure 10: The comparison of the original and proposed assembly processes.  
Figure 9: Space manipulator assembly.  
measurement data. Therefore, some unnecessary assembly processes are eliminated and the possibility of repeated trial assembly is greatly reduced.  
The assembly accuracy requirements of the connector are coaxiality \(dr\) and clearance \(dc\) on the matching surface, as shown in Figure 9b. The coaxiality requirement is 0.2 mm, and the clearance requirement is 0.1 mm. Assembly accuracy is compensated by gasket compensation, finishing, or position movement according to the deviation.