esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Article_  
**An Analytical Study on Penetration and Pore Pressure Dissipation of Piezocone Test in Typical Normally and Over-Consolidated Silty Clays**  
**[PERSON] 1, [PERSON] 1,*\({}^{\text{*}}\)**\({}^{\text{(c)}}\), [PERSON] 2, [PERSON] 3,4, [PERSON] 5,*\({}^{\text{(c)}}\) and [PERSON] 2,3**  
1 College of Marine Geosciences, Ocean University of China, Qingdao 266100, China  
2 Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering, Ocean University of China, Qingdao 266100, China  
3 Laboratory for Marine Geology, Pilot National Laboratory for Marine Science and Technology, Qingdao 266061, China  
4 College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China  
5 State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221000, China  
*Correspondence: [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (Z.T.)  
**Abstract** The degree of the over-consolidation ratio (OCR) of silty clay affects the soil's mechanical properties and in situ test results. The present study utilized numerical analysis to investigate the behavior of cone penetration and pore pressure dissipation in typical silty clay soils, while also taking into account the impact of the over-consolidation ratio (OCR). Effective stress finite-element analyses, which accounted for considerable deformation, were carried out at various OCRs. The model assumed the soil as a homogeneous material obeying the modified Cam Clay (MCC) model. The significant advance of this work is the evaluation of the effect of OCR on penetration resistance and pore pressure test data and the calculation formula of OCR and \(c_{\text{h}}\) in typical silty clay. An inversion method based on the results of piezocone penetration tests was proposed in terms of strength and the over-consolidation ratio of the silty clay, which is of great importance for the inversion of soil parameters in the Yellow River Delta region. This paper presents a consolidation coefficient inversion method of typical normally and over-consolidated silty clays and corrected the disadvantage that traditional conversion methods could not take OCR effects into account.  
**Keywords:** silty clay; cone penetration; consolidation coefficient; finite-element modelling; site investigation
## 6 Conclusions  
FHE offers a powerful solution to the challenges of privacy and security in data processing within smart home environments. By enabling computations on encrypted data, FHE ensures that sensitive information remains fully protected while still allowing valuable insights and functionalities to be derived. In this paper, we compared the performance of three machine learning models, XGBoost, Random Forest, and Decision Classifier, on a smart home dataset, with and without FHE. The results revealed that the Decision Classifier maintains high accuracy with minimal degradation when FHE is applied, demonstrating its robustness even with the added encryption overhead. Interestingly, the Decision Classifier with FHE not only performed better than the other models but also surpassed its own accuracy in plaintext, highlighting the potential for encryption to enhance model performance under certain conditions. These findings underscore the efficiency of Random Forest in terms of execution time and low prediction errors when handling FHE, making it a strong candidate for encrypted data processing in smart homes. This study demonstrates that FHE can indeed be transformative in setting high privacy standards in smart homes, paving the way for more secure and privacy-preserving technologies.  
Future research can explore the integration of HE with federated learning, as well as the combination of FHE with emerging technologies like blockchain and edge computing, to further enhance the security and privacy of smart home environments. Continued advances in encryption techniques and machine learning algorithms will be crucial to fully utilizing the potential of privacy-preserving smart home systems.  
[PERSON] led the study design, implemented machine learning models, conducted primary experiments, and performed comparative analysis. [PERSON] assisted with the implementation, literature review, background research, and drafting of the initial sections of the manuscript. [PERSON] provided expert guidance on encryption techniques, supervised the entire project, and reviewed and refined the manuscript before submission. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the research group \"Transformation in Care and Technology TransCareTech\". This project is funded by the Ministry of Culture and Science of the State of North Rhine-Westphalia, and the grant number is PROFILNRW-2020-095.  
Datasets are available online at [[http://casas.wsu.edu](http://casas.wsu.edu)]([http://casas.wsu.edu](http://casas.wsu.edu)) (accessed on 26 August 2024).  
This research is also supported by the research training group \"Dataninja\" (Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis), funded by the German federal state of North Rhine-Westphalia.
## 3 Test Results and Discussion
### Dynamic Stress-Strain Characteristics
#### 3.2.2 Dynamic Compressive Test  
Under the impact load, the dynamic compressive stress-strain curves of the concrete specimens treated at different temperatures are shown in Figure 9. At the initial stage of loading, the stress-strain curve increased linearly, the specimens were in the elastic stage, and quickly reached the compressive strength of the specimens. The elastic modulus of the specimens treated at different temperatures was basically the same. With the increase in the impact load, the stress of the specimens decreased gradually. When the strain was about 0.4%, the stress of the specimen had a minimum value, then a small increase, and finally presented a plastic flow until failure. The dynamic compressive strength change curves of the specimens treated at different temperatures are shown in Figure 10. The dynamic compressive strengths of the specimens at 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, and 400 \({}^{\circ}\)C were 86.04%, 81.74%, 78.69%, and 67.21% of the normal temperature, respectively. With the increase in the temperature, the peak stress decreased significantly. During the heating process, the free water and partially bound water inside the concrete produced water vapor pressure, which caused micro-cracks and the concrete to be strained, resulting in concrete damage and a decline in the axial compression strength and splitting strength. The peak strain of the concrete specimens showed a decreasing trend, and the elastic modulus of the concrete was basically unchanged, indicating that a high temperature had an obvious deterioration effect on the concrete, and the deformation ability and strength were reduced gradually.  
Figure 8: Dynamic splitting tensile strength at different temperature.  
Figure 9: Dynamic stress–strain curve of specimens in dynamic compressive test.
# Development of Effective Infrared Reflective Coatings
## 3 Results and Discussions  
### Chromatic Parameters  
The chromatic (L*, a* and b*) and brightness values are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Short Name of Coating** & **Substrate** & \(\Delta\)E* & \(\Delta\)a* & \(\Delta\)b* & \(\Delta\)L* & **Brightness [\%]** \\ \hline Traditional & white black & 0.560 & 0.440 & 0.270 & \(-\)0.230 & 84,990 \\  & black & 0.500 & 0.400 & 0.290 & \(-\)0.080 & 84,750 \\ \hline L95/CR28 & white black & 1.300 & 1.050 & 0.020 & \(-\)0.760 & 84,750 \\  & black & 1.420 & 1.100 & 0.020 & \(-\)0.900 & 84,090 \\ \hline S84/CR28 & white black & 0.750 & \(-\)0.340 & \(-\)0.350 & \(-\)0.570 & 49,810 \\  & black & 0.880 & \(-\)0.410 & \(-\)0.360 & \(-\)0.690 & 67,760 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Colorimetric and brightness values measured on coatings applied on white and black primers.  
Figure 1: Colorimetric coordinates, CIE color system L*, a*, b*. L*: represents the differences between light (100%) and dark (0%). a*: displays the difference between red (+a*) and green (\(-\)a*). b*: corresponds to the difference between yellow (+b*) and blue (\(-\)b*).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measured \(\Delta\)E* & **Visual Observation** \\ \hline \(\Delta\)E* \(<\) 1.5 & not observable \\ \(1.5<\Delta\)E* \(<\) 3 & observable \\ \(3<\Delta\)E* \(<\) 6 & well observable \\ \(6<\Delta\)E* & significant \\ \hline \hline \end{tabular}
\end{table}
Table 3: The color differences and their consequence.  
The chromatic parameters clearly show that there are no significant differences between color and brightness values, whether the coating was applied on white or black primer, i.e., in both cases these coatings could properly cover the substrate; there were no significant differences in color parameters. On the other hand, using black and white backgrounds beneath the coatings, we wanted to see which composition was the best or worst for infrared light reflection. The difference in the brightness parameters was due to the fact that in one case the coatings were applied on white, in the other case on black, primer. It is clear that the brightness depends on the primer. The white one reflects the infrared waves; the black one absorbs them. On the other hand, the difference in the brightness parameters could depend on the composition: the S84/CR28 coating shows less brightness on both primers than the S84/CR28/Lacquer coating, proving the importance of the concentrations of pigments, as they differ only in that. The parameters of \(\Delta\)a* and \(\Delta\)b* developed on black and white primers using the same coatings did not change significantly. In the \(\Delta\)L* values measured on the same upper coat that were developed on very unlike primers, there are no remarkable differences (one exception is the \"traditional\" coating).
## 2 Methods
### Physics-Informed Neural Networks  
PINNs are functionally similar to DNNs, except for how they handle their loss function. In DNNs, the loss is generally composed of only one term but with PINNs there are two distinct terms,  
\[\text{Loss}=a_{1}\cdot MSE_{U}+a_{2}\cdot MSE_{f}, \tag{3}\]  
where constants \(a_{1}\) and \(a_{2}\) are hyperparameters that can be adjusted as needed for optimal training performance. The first term, \(MSE_{U}\), is reserved for specifically satisfying the initial and boundary conditions of the PDE, and is represented by the following equation,  
\[MSE_{U}=\frac{1}{N_{U}}\sum_{n=1}^{N_{U}}|U^{n}-U(x_{U}^{n},t_{U}^{n})|^{2}. \tag{4}\]  
Figure 1: Deep neural network example, with an input layer consisting of 2 nodes, 1 node in the output layer, and 2 hidden layers containing 3 nodes each.  
Here, \(x_{U}^{n}\) and \(t_{U}^{n}\) are the depth and time values used as training data for the initial and boundary conditions, \(N_{U}\) is the number of training points, and \(U^{i}\) denotes the corresponding true values to the training data. The training points \(x_{U}^{n}\) and \(t_{U}^{n}\) strictly consist of boundary and initial condition data obtained from PAC1D, with no collocation points. The second term, \(MSE_{f}\), contains an optimized form of the PDE and is represented by,  
\[MSE_{f}=\frac{1}{N_{f}}\sum_{n=1}^{N_{f}}|f(x_{f}^{n},t_{f}^{n})|^{2}. \tag{5}\]  
Here, \(x_{f}^{n}\) and \(t_{f}^{n}\) denote the depth and time collocation points, respectively. The function \(f(x,t)\) refers to the optimizable form of the PDE, which can be obtained by moving all terms to one side of Equation (1) as follows:  
\[\frac{\partial U}{\partial t}-\alpha\frac{\partial^{2}U}{\partial x^{2}}-Q=0=f (x,t). \tag{6}\]  
Training PINNs uses a variation of the process used to train DNNs. Satisfying the initial and boundary conditions utilizes the same training method for comparing the true and predicted values of the model. The true values are taken from the PAC1D data generation and are used to hone the models' prediction accuracy. The second term of the PINN's loss function requires extra inputs of \(x\) and \(t\), called collocation points.  
Collocation points in the experiments that follow are randomly generated values in the domains of their respective variables. These points can be selected using any sampling method such as Latin hypercube sampling (LHS) [18]. Collocation points feed into the automatic differentiation component of the PINN, as depicted in Figure 2, and the model parameters' derivatives are taken with respect to \(x\) and \(t\) from the collocation points, following the formula in Equation (6). This process returns a value that is not equal to zero at first and the optimizer will simultaneously adjust the model parameters to reduce the overall loss. In doing so, both terms will tend toward zero, satisfying the initial and boundary conditions, and the general form of the PDE, yielding an approximation to a unique solution.
## 3 Results and Discussion
### Characterization of Camelina Cake and Diets
#### 3.1.1 Antioxidants  
The antioxidant activity calculated by DPPH scavenging activity in cake and diets is shown in Tables 1 and 3. The camelina cake had a very high antioxidant activity that is due to the high content of phenolic substances. It is interesting to note that the antioxidant activity of the control diet is relatively low and the addition of camelina in the diets increases the capacity of scavenging (Table 3). This is associated with an increase in phenolic compounds (TPC, flavonoids, and tannins) in diets containing camelina. To this, it must be added the contribution of tocopherols present in the camelina cake oil, which enhance the antioxidant capacity of the diets. In fact, these compounds resulted higher in CAM10 and CAM20 than in the control diet (Table 3).
# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 3 Materials and Methods
### Fine-Tuning Description  
Fine-tuning is a transfer learning technique that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem [42]. Since CNNs are composed of numerous layers and a huge number of parameters, e.g., AlexNet has 650 K neurons and 60 M parameters (as an example, a graphical representation of the AlexNet architecture is shown in Figure 3), the network training phase should benefit from the use of databases rich in examples that avoid the problem of overfitting. Unfortunately, it is not always possible to take advantage of such large databases. Furthermore, training a CNN effectively better on a large database is demanding both in terms of computation time and the computational resources required.  
Fine-tuning arises from the need to make up for these deficiencies. This method consists in the possibility of using a Neural Network, pre-trained on a large database, through a further training phase with another database, even a small one. The output level is replaced with a new softmax output level, adjusting the number of classes to the classification problem being faced. The initial values of the weights used are those of the pre-trained network, except for the connections between the penultimate and last level whose weights are randomly initialized. New training iterations (SGD) are performed to optimize the weights with respect to the peculiarities of the new dataset (it does not need to be large). Fine-tuning can be done in two ways. One way is to freeze the weights of some layers and carry out new training cycles to modify the weights of the remaining layers. The concept of fixing the weights of the layers is defined as freezing of the layers. Generally, they are the first layers to be frozen as the first layers capture low level features. The greater the number of frozen layers, the less the fine-tuning effort in terms of time and resources is required.  
In this case, the weights of the first CNN levels are frozen and the remaining parameters/weights are trained. The other way is to have the architecture re-train entirely on the new database. This method is called training from scratch. It is intuitive that the greater the number of frozen layers the lower the computational cost of training, so training from scratch is the most expensive form of computational training.
## 3 Results and Discussion  
### MTX Loading  
Various ratios of MOFs and MTX were tested to determine the optimal loading conditions for UiO-66 and UiO-66-NH\({}_{2}\) (Table 1).  
Three different concentrations of MTX (3 mg/mL, 1.5 mg/mL, and 0.750 mg/mL) were tested with constant concentration of UiO-66 (3 mg/mL) and UiO-66-NH\({}_{2}\) (3 mg/mL). The results of LC% in Table 1 are presented for three independent loading experiments out of >20 independent repeats.  
UV-vis spectrometry was used to confirm the loading of MTX by measuring the absorbance of the supernatant collected after loading UiO-66 and UiO-66-NH\({}_{2}\)[48,50]. A visual change in coloration of the MOFs after MTX incorporation, changing from white to blue for complexed UiO-66-MTX and UiO-66-NH\({}_{2}\)-MTX, was observed. The maximum LC (-40%) (Table 1) was achieved with a weight ratio of UiO-66:MTX of 1:1, corresponding to the adsorption of 0.371 mg of MTX onto 1 mg of UiO-66. The LC% decreased to ~30% with a 2:1 weight ratio and ~15% with a 4:1 weight ratio. In contrast, the UiO-66-NH\({}_{2}\) results demonstrated a reverse trend as compared to UiO-66 in terms of loading MTX. UiO-66-NH\({}_{2}\) showed a maximum loading capacity of ~21% with a 4:1 UiO-66-NH\({}_{2}\):MTX weight ratio, corresponding to the adsorption of 0.208 mg of MTX loaded per mg of UiO-66-NH\({}_{2}\). Increasing the weight ratio to 2:1 and 1:1 resulted in a decreased LC of ~8% under the same loading conditions. The greater loading capacity of UiO-66 at higher MTX weight ratios can be attributed to its larger surface area and pore size, whereas the surface area and pore size of UiO-66-NH\({}_{2}\) decreased due to the presence of functionalised groups. The surface area and pore volume of UiO-66 have been reported to be (\(\mathrm{{}_{SBET}}\)) 838 m\({}^{2}\)/g and 0.245 cm\({}^{3}\)/g, respectively, compared to UiO-66-NH\({}_{2}\), which has a surface area (\(\mathrm{{}_{BET}}\)) and pore volume of 822 m\({}^{2}\)/g and 0.236 cm\({}^{3}\)/g, respectively, as reported by [PERSON] et al. [46]. Another report of UiO-66 and UiO-66-NH\({}_{2}\) showed a surface area (\(\mathrm{{}_{SBET}}\)) of 857 m\({}^{2}\)/g and 826 m\({}^{2}\)/g, respectively [51]. The observed decrease in loading capacity of UiO-66-NH\({}_{2}\) with an increasing MTX weight ratio is likely due to the presence of the -NH\({}_{2}\) functional group, which results in a partial blockage of the accessible pore volume for MTX molecules, while MTX attachment to the pore surface may also reduce pore size.  
The amine functionalisation in UiO-66-NH\({}_{2}\) alters the surface characteristics, potentially affecting the interactions between the MOFs and MTX. This modification can lead to changes in surface charge, hydrophobicity, or specific binding sites, influencing the adsorption capacity of the drug. Additionally, the amine groups in UiO-66-NH\({}_{2}\) could participate in different types of interactions with MTX compared to UiO-66, affecting the loading behaviour.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 5 Conclusions  
In this article, we have identified different categories of knowledge representation and reasoning formalisms that can be used in telecommunication systems. We further defined a methodology to evaluate the identified categories based on a set of qualitative criteria. The criteria have been defined and the conformance of the different approaches have been discussed. From the evaluation, we could conclude that different use cases are better served by different knowledge-based approaches. Further, we have also identified the application domains and discussed the knowledge-driven evolution of telecommunication concerning use cases targeting network operation, automation, and optimization objectives. Overall, knowledge-driven applications empower wireless networks to become more adaptive, intelligent, and responsive to dynamic conditions, leading to enhanced efficiency, improved services, and better overall performance.  
Generally, current knowledge representation and reasoning applications in wireless communications struggle with dynamic environmental adaptation, real-time decision-making, and efficient resource management, while also facing challenges in ensuring security and user privacy. Future research directions should focus on developing sophisticated, context-aware models for dynamic networks, enhancing self-optimization, improving security protocols, and integrating with emerging technologies like 5G/6G and IoT, all while maintaining energy efficiency and global interoperability. Within the key AI technologies, future studies should focus on advances with LLMs and generative approaches, extensions of knowledge graphs, and enhancements of deep AI technologies for distributed intelligence. To this end, future standards and applications strengthen the importance of knowledge in AI, and its deployment in telecommunication businesses and services is expected to only amplify.  
Conceptualization, G.P.K.; methodology, G.P.K.; validation, G.P.K., and S.S.; formal analysis, G.P.K.; investigation, G.P.K., S.S. and R.M.; resources, G.P.K., S.S. and R.M.; data curation, G.P.K., S.S. and R.M.; writing--original draft preparation, G.P.K.; writing--review and editing, G.P.K., S.S. and R.M.; visualization, G.P.K., and S.S.; supervision, R.M.; project administration, R.M.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
**Acknowledgments:** The authors like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], all from Global AI Accelerator, Ericsson, for fruitful discussions.  
**Conflicts of Interest:** The authors of this research study were employees of Ericsson.The 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.