esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study highlights the potential of ANNs as a valuable tool for predicting and optimizing the tribological properties of dental glass ceramics, specifically lithium disilicate (Li\({}_{2}\)SiO\({}_{3}\)). By combining experimental data with advanced computational techniques, such as hyperparameter optimization through a GA, this study successfully demonstrated the capacity of ANNs to model the complex, nonlinear relationships between material properties, environmental conditions, and operational parameters. The results confirmed that ANNs can effectively predict wear and friction behaviors, which are essential for understanding the performance and longevity of dental materials under various conditions.  
The findings also underscore the significance of a surface finish on the tribological performance of lithium disilicate, with distinct differences observed between polished, glazed, and ground surfaces. The tribological tests conducted in artificial saliva, simulating realistic oral conditions, revealed that the wear and friction coefficients vary considerably depending on the surface treatment, providing valuable insights into the material's performance in practical applications.  
Moreover, this research contributes to the growing body of literature on the application of ML in material science, particularly in the optimization of dental glass ceramics. It bridges the gap between traditional experimental methods and modern computational tools, offering a more efficient and scalable approach to material development. By reducing the reliance on exhaustive experimental testing, ANN-based models can accelerate the design process and lead to the creation of enhanced materials with superior tribological properties.  
In conclusion, this study demonstrates that integrating ANNs with experimental data presents a powerful approach for the prediction and optimization of tribological properties, offering significant potential for advancing material science and engineering, particularly in the field of dental materials. This research shows the way for future studies that explore the broader applications of ML in material optimization, potentially revolutionizing the development of high-performance materials for various industries.  
Conceptualization, M.P. and A.D.; methodology, M.P.; software, S.P.S.; validation, S.J., Z.S. and M.R.; formal analysis, M.R.; investigation, S.J.; resources, M.P.; data curation, A.D.; writing--original draft preparation, S.P.S.; writing--review and editing, Z.S.; visualization, S.J.; supervision, Z.S.; project administration, M.R.; funding acquisition, M.R, S.J. and Z.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Methods
### Local Defect Resonance Concept  
Recent investigations showed that the use of high power ultrasonic equipment is not necessary, if vibration-based thermography is combined with the concept of local defect resonance (LDR) [16; 17; 18; 19; 20]. The concept of LDR is based on the fact that a structural damage (e.g., the debonded face layer of a sandwich) leads to changed stiffness and damping properties for a certain mass of the material in this region, and consequently has its own resonance frequencies [17]. Is the host structure excited at resonance frequencies of the damage, energy is specifically transferred into the damage, which leads to a local temperature increase due to the before mentioned effects. [PERSON] et al. [16] first introduced an analytical formula to approximate the LDR frequency of a near-surface delamination with respect to a circular shaped damage geometry  
\[f_{0}=\frac{1.6H}{r^{2}}\sqrt{\frac{E}{12\rho(1-\
u)^{2}}}, \tag{1}\]  
where \(H\) is the residual thickness, that is,thickness of the debonded plate, \(r\) is the radius, \(E\) is the Young's modulus, \(\rho\) is the density and \(\
u\) is the Poisson's number. An equivalent solution for the resonance frequencies of square shaped delaminations with a side length \(s\) is introduced by [PERSON] [18], and given by  
\[f_{0}=\frac{4\pi H}{3s^{2}}\sqrt{\frac{E}{6\rho(1-\upsilon)^{2}}}. \tag{2}\]  
The problem in practical use of these analytical equations are the assumed boundary conditions, therefore finite element simulations (FEM) are generally used to visualize the LDR vibration patterns and to evaluate the LDR frequencies [3; 16; 17]. In industrial applications, the damage size and shape are unknown, thus their corresponding resonance frequency is generally unknown. Therefore, broadband excitation is usually employed for applying the concept of LDR without knowing the specific resonance frequency [19; 20]. Recent investigations utilize periodic chirps or other commonly used broadband signals to excite a damage and identify its resonance frequencies by local temperature changes. In the present research a periodic chirp as broadband excitation together with a SLDV are used to identify the resonance frequencies of the considered sandwich face layer debonding.
## 2 Integrated System Solution  
The system solution for the factor graph integrated navigation method assisted by the CNN-GRU neural network proposed in this paper is illustrated in Figure 1. When GNSS signals are strong, the factor graph is used to fuse GNSS and INS data and output navigation results, while simultaneously training the CNN-GRU prediction model. The training and prediction processes of the CNN-GRU model are detailed later in the text. If a GNSS signal interruption occurs, the CNN-GRU prediction model generates pseudo GNSS position information to supplement the missing information in the integrated navigation system. The basic idea of using the CNN-GRU prediction model to assist the integrated navigation system is to train the model using INS and GNSS measurement information when the GNSS reception signal quality is good, and to predict GNSS position data using the trained model when GNSS signals are unexpectedly interrupted. The INS and GNSS factors are connected via undirected edges to form the factor graph model. Additionally, it is necessary to continuously correct the INS using the integrated navigation information to eliminate the error accumulation in inertial navigation, thereby enhancing the accuracy and robustness of the integrated navigation system.
# Step 12: Compile the grid search model: compile_model(grid_search_model)
## 3 Results and Discussion
### Formation of Distribution Routes  
Three distribution centers (DC) \(i\) will be determined from the 38 demand points, with 21 demand points in the \(DC_{1}\) cluster, 7 demand points in the \(DC_{2}\) cluster, and 10 demand points in the \(DC_{3}\) cluster. These outcomes serve as the input layer \((X_{1},\ldots,X_{n})\) in the construction of transportation routes. Using Equations (5) to (6), the hidden layer uses the distribution of the location of the request point \(\left(L_{11},\ldots,L_{ij}\right)\) to produce an output layer; namely, the route \((Y_{1},\ldots,Y_{n})\). Table 3 shows the results of using the deep learning model approach to form distribution routes.  
For small networks, off-the-shelf software such as CPLEX can solve the problem. The problem cannot be solved as the network size grows, and it is demonstrated that when the number of request points exceeds 11, CPLEX cannot solve the problem on personal computers due to insufficient memory, demonstrating the need for a metaheuristic algorithm. To test the deep route planning (DRP) model's performance in constructing the proposed route, we extracted a small network from the network shown in Figure 7 and ran experiments to compare route formation results using permutations and deep learning models. The network consists of four candidate DCs, 38 demand points (numbered 1  
Figure 8: Predictive model training graph.  
through 38), and ten vehicles. All tests were performed using Python code on a computer with an Intel Core i5-8250U 1.60 GHz CPU and 8 GB RAM. Table 4 summarizes the findings.  
Table 4 shows that DLRP was able to obtain fewer routes than permutation theory for all networks in DC. The results of route formation in the transportation network with 3 DC candidates, 38 demand points, and 10 vehicles assigned in the network, are that the permutation theory produces 5.10909E+19 and the DLRP model produces 387,471,048 combined routes, where route selection will be performed with 363,798 routes. By reducing the number of route formation results, computation time is reduced, allowing the model to be used in real-world situations such as when a disaster occurs, requiring quick decisions and strategies based on large-scale data in humanitarian logistics.
## 3 Methodology
### An Extension to the Proposal: DLT as a Means to Create Trust-Based Environments  
In the scenario we are moving into, we consider that the network nodes are not evenly distributed throughout the study area, but rather are homogeneously distributed in small areas, and these areas are heterogeneously distributed in a geographical area where communication and reliability of the data collected is already a challenge. In this extension, we propose an extension to the proposal we have made which consists of using a lightweight DLT to create clusters in which each node assumes that its group of communicating nodes is trusted. As shown in Figure 4:As can be seen in Figure 4, we create two levels of communication. In the first level (device farm), the nodes use the proposed cryptographic protocol to create a secure group. These nodes communicate with the designated CH (which may vary according to the modification we have proposed to the LEACH protocol). The CH nodes are part of a second level where the trustworthiness is imposed by the DLT itself. Communications between CHs is guided through the DLT update protocols, and the messages exchanged are supported by Message Queueing Telemetry Protocol (MQTT), a machine-to-machine protocol commonly used in the IoT paradigm to message end exchange data between IoT devices [35].  
This second layer, which connects through IPFS to the CH nodes, allows us to make use of the smart contracts stored in the EWS instances that receive the information from all the CHs. A smart contract has been developed to analyze the coherence of the data received by each CH according to the temporal sequence.  
A smart contract is triggered by the occurrence of a predefined event, such as a CH recording inconsistent behaviour. The smart contract relies on the ability to store information in the DLT itself, specifically in a storage system called the state-database, which stores what is necessary for the execution of the contract. The result of the execution of a contract modifies the content of the database and is stored in the DLT blockchain. It is most common to find contracts designed to be stored sequentially in Amazon Elastic Cloud 2 (EC2) allowing developers to customize the resources of nodes based on requirements and provide an effective way to monitor the project [36]. Recently, it has become possible to launch more than one thread successfully. In our case, we show how this extension means the ability to reduce the power consumption in the CHs since the sending of information is optimized thanks to IPFS (a file system distributed among all the CHs) and how the existence of a contract that analyses the behavior of the CHs, in addition to gathering all the data sent from each CH to later be inserted into Big Data pipelines, means that the performance is not affected at all. Figure 5 shows how we were able to process up to eight CH concurrently in the EC2 instance.  
Figure 4: DLT and AWS EC2 architectures.
# High Resolution Imaging Using Micro-Objectives Fabricated by 2-Photon-Polymerization
## 1 Introduction
Furthermore, an extension of the Finite Refractive Index Selective Propagation (FRISP) method [19] to simulate the imaging properties of refractive micro structures is presented and the simulation results are compared with experimental results. The use of bi-convex micro-optics offers new degrees of freedom for the geometry of micro structures used in high-resolution near-field microscopy and overcomes the limitations of micro spheres. We demonstrate the fabrication of shape fidelity micro structures with aperture diameters of less than 25 \(\upmu\)m, and curvature radii \(<\)15 \(\upmu\)m to improve imaging quality. In addition to demonstrating the high-resolution imaging properties of bi-convex spherical micro-optics, this study addresses critical aspects of micro structure fabrication, including shape fidelity and geometric accuracy, and highlights the potential of 2 PP for resolution-enhancing applications.
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 2 Related Work
Using the security and threshold properties of the threshold signature, the treatment can be carried out when the threshold number is reached, and medical data can be uploaded to the consortium blockchain. Despite being application-domain-dependent, the security analysis and performance analysis showed that the scheme has advantages in safety and performance and is suitable for the medical environment to a certain extent. Within the medical data domain as well, [PERSON] et al. developed the Pseudonymization and Anonymization with the XACML (PAX) modular system in [35]. The proposed system uses a random pseudonym to separate personal information about patients' data, anonymity to hide subjects' information, and XACML to create distributed access control policies to authorize subjects' requests to objects' records in Electronic Health Records (EHRs). It provides a security and privacy solution to the problem of safe-access decisions for patients' data in the EHR. The results of theoretical and experimental security analysis proved that PAX provides security features in preserving the privacy of healthcare users and is safe against known attacks. However, the validity of the solution has not been demonstrated for other application domains.
## Appendix A Estimating Space-Frequency Bandwidth Utilization  
In order to estimate scene complexity with Equation (16), one needs to give a numerical estimator for \(\text{SPB}_{\text{H}}\). Two approaches come to mind. First, one can follow the visual approach and binarize the phase space signal using a small \(\varepsilon>0\), evaluating area larger than the threshold. This approach is robust in the sense that, the area will vary only by a margin, due to resampling, for operations such as scene space rotations, translations (provided unitary propagations are used), superposition/interference, de-/magnification, changes to the surface roughness, and perspective projection. The downside is, that the approach is sensitive to the dynamic range of \(H\) and hence during comparisons of holograms, their dynamic range needs to be normalized. The method is specific to the chosen parameters of the PSR, as well as the value of \(\varepsilon\). Results are not transferable otherwise.  
A second approach foresees the calculation of the Shannon entropy of a spectrogram or the S-method [63], which remains signal scale invariant as long all PSR parameters are kept fixed. For some non-stationary signals one might consider also the Renyi entropy paired with any PSR from [PERSON]'s class [64]. At least in the case of the WV PSR, the Renyi entropy might not exist for arbitrary DHs and might remain too sensitive to phase differences of neighboring PSF or resampling artifacts.
###### Abstract
Therefore, the real-time estimation of \(R_{0}\) to update \(R_{0}\) is bound to effectively reduce the voltage error and improve SOC estimation accuracy under the premise of offline parameter identification. In this study, based on the FOM of a lithium battery, the adaptive genetic algorithm (AGA) [23; 24] and Dynamic Stress Test (DST) were applied to carry out the offline parameter identification of a lithium battery model. By referring to the method of predicting the square root of covariance used by fractional order square root unscented Kalman filter (FSRUKF) in Ref. [25], a novel FSRCKF was obtained by combining SRCKF and FCKF, and then combined with multi-innovation unscented Kalman filter (MIUKF) [26] to estimate \(R_{0}\) and constantly update \(R_{0}\), so as to derive the MIUKF-FSRCKF algorithm. This algorithm has the advantages of high accuracy and practicability of SOC estimation. The main contributions of this work include the following. (1) We refer to the method of predicting the square root of covariance used by FSRUKF in the literature [25] and propose a new FSRCKF by combining FCKF and SRCKF. The accuracy of this algorithm in estimating SOC is higher than that of FCKF and SRCKF. (2) On the basis of the proposed FSRCKF, MIUKF is used to estimate \(R_{0}\) in the FOM of a lithium battery in real time and constantly update \(R_{0}\), so as to obtain MIUKF-FSRCKF. This algorithm is not only highly accurate but also highly practical. (3) The SOC estimation accuracy of SRCKF, FCKF, FSRCKF and MIUKF-FSRCKF is verified under different operating conditions, and the SOC estimation accuracy of MIUKF-FSRCKF is further verified under different initial SOC values.
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 2 Materials and Methods
### Method
#### 2.3.6 Model Performance Evaluation  
The coefficient of determination (R\({}^{2}\)) and the root-mean-square error (RMSE) were selected as the evaluation indicators of the modeling accuracy and verification accuracy. The closer the R\({}^{2}\) value to 1 and the lower the RMSE value, the better the model fitting effect. The calculation method is as follows:  
\[\text{R}^{2}=\frac{(\sum_{i=1}^{n}y_{i}-\overline{y})^{2}}{(\sum_{i=1}^{n}x_{i }-\overline{y})^{2}}, \tag{3}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1,j=1}^{n}\left(x_{i}-y_{i}\right)^{2}}{n}}, \tag{4}\]  
where \(x_{i}\) is the measured value of the yield; \(y_{i}\) is the predicted value of yield; \(\overline{y}\) is the average yield; \(n\) is the number of samples.
# Bilateral Deficits during Maximal Grip Force Production in Late Postmenopausal Women
## 1 Introduction  
Menopause typically occurs in women's 40s [1], and one third of women's lifespan is spent post-menopause [2]. Progressive reduction of estrogen in postmenopausal women may facilitate more age-related deficits in the central and peripheral nervous system [3, 4, 5, 6]. For example, muscle weakness normally appears in elderly people because of age-induced neurophysiological alterations [7, 8, 9]. Furthermore, asymmetrical interlimb muscle strength interferes with executing bilateral movements that account for 54% of daily activities in the aging population [10, 11]. Importantly, postmenopausal women reveal more significant reduction of muscle strength than premenopausal women and age-matched males [12, 13, 14, 15, 16].  
Bilateral deficit is a phenomenon when individuals reveal lower force outputs produced simultaneously by both limbs than the sum of unilateral forces generated by each limb. Previous studies indicate that bilateral deficit may appear in either upper or lower extremities during various motor tasks such as maximal voluntary contraction (MVC), reaction time [17, 18, 19], and different contraction types (e.g., isometric and dynamic contraction) [20]. Moreover, greater levels of bilateral deficit are associated with increased impairment in bilateral performances (e.g., ballistic push-off and vertical squat jumping) [21, 22, 23], and several aging studies report bilateral deficit patterns in elderly people interfering with various functional movements (e.g., rising from a chair) [24, 25].  
For postmenopausal and elderly women, previous studies report bilateral deficit patterns in lower limb movements such as leg extension and leg press [24, 25, 26, 27, 28]. Specifically, greater bilateral deficit in producing explosive forces increases the time of sit-to-stand performance [25]. In postmenopausal women, increased interhemispheric inhibition and reduced muscle strength potentially induced by deficiency of estrogen and/or progesterone may facilitate bilateral deficit patterns [20, 28, 29]. However, these previous findings are mostly limited to lower limb movements. Given that successful bilateral upper limb movements are additional critical motor functions for older adults [11], determining whether bilateral deficit patterns in upper limb movements appear in late postmenopausal women is necessary. Thus, the purpose of this study was to examine bilateral deficit patterns in late postmenopausal women using voluntary maximal handgrip force tasks.
## 2 Background Study
### Rainfall Rate Conversion
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref.** & **Mechanism** & **Symbol Meanings** \\ \hline
[35] & \(\left\{\begin{array}{ll}H_{0}\\ H_{0}+\log(R/10)\end{array}\right.\) (km) & \(R\leq 10\) mm/h & \(H_{0}\) is the 0\({}^{\circ}\) isotherm height, and \(R\) is the rain rate. \\ \hline
[36] & \(10\log\left(\frac{T_{m}-T_{a}^{\prime}}{1n-T_{a}^{\prime}}\right)\cdot\gamma^ {-1}\) & \(T_{m}\) is the medium temperature, \(T_{a}^{\prime}\), under clear sky, and \(T_{a}^{\prime\prime}\) is under rainy condition temperatures, and \(\gamma\) is the specific attenuation coefficient. \\ \hline
[37] & \(4.5+0.0005R^{1.65}\) (km) & \(R\) is the rain rate in the range 30–70 mm/h. \\ \hline
[38] & \(H_{0}+0.36\) (km), also using rain height map & \(H_{0}\) is the 0\({}^{\circ}\) isotherm height. \\ \hline
[32] & \(\left\{\begin{array}{ll}4.8,&|\varphi|\leq 30^{\circ}\text{ and }R\leq 10\text{ mm/h}\\ 7.8-0.1|\varphi|,&|\varphi|\geq 30^{\circ}\text{ and }R\geq 10\text{ mm/h}\end{array}\right.\) & \(\varphi\) is the latitude in degrees. \\ \hline
[33] & \(\left\{\begin{array}{ll}3+0.028\varphi&0\leq\varphi<36^{\circ}\\ 4-0.075(\varphi-36^{\circ})&\varphi\geq 36^{\circ}\end{array}\right.\) & \(\varphi\) is the latitude in degrees. \\ \hline
[34] & \(\left\{\begin{array}{ll}4&0<|\varphi|<36^{\circ}\\ 4-0.075(|\varphi|-36^{\circ})&|\varphi|\geq 36^{\circ}\end{array}\right.\) & \(\varphi\) is the latitude in degrees. \\ \hline
[39] & \(14.114\cdot R^{-0.6402}\) & \(R\) is the rain rate. \\ \hline
[40] & \(3.6693\cdot R^{0.097}\) & \(R\) is the rain rate. \\ \hline
[41] & \(\left\{\begin{array}{ll}5-0.075(\varphi-23^{\circ})&\text{for}\quad\varphi>23 ^{\circ}\\ 5&\text{for}\leq\varphi\leq 23^{\circ}\\ 5&\text{for}\geq\varphi\geq-21^{\circ}\\ 5+0.1(\varphi+21^{\circ})&\text{for}-71^{\circ}\leq\varphi<-21^{\circ}\\ 0&\text{for}\varphi<-71^{\circ}\end{array}\right.\) & \(\varphi\) is the latitude in degrees. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different techniques to estimate rain height. in other integration times, the rainfall rate needs to be converted. Table 5 lists most of the rainfall rate conversion techniques, as per the literature. Instead of a mathematical conversion procedure, the rainfall rate \"contour map\" can be used to obtain the rainfall rate [40; 46; 47; 48]. Even if the rainfall rate is not readily available, or there are missing data in the collected records, there also exist synthetic techniques to generate rain attenuation time series. Mathematically, Equation (10) defines the rainfall rate in a typical rainfall rate acquisition system:  
\[R_{p}=\frac{R_{tip}\times N}{T} \tag{10}\]  
where \(R_{p}\) is rainfall rate, \(R_{tip}\) is rainfall per tip (mm), \(N\) is number of tips, and \(T\) is the time gap between consecutive tips.