esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Lattice Structures in Medical Devices and Applications
### Lattice Structures Using PAEK/PAEK Apatite Composites
Additionally, the customization possibilities afforded by 3D printing allow for implants to be tailored to the specific anatomical requirements of each patient. By creating lattice structures that match the patient's spine, implants are able to provide more effective support for spinal loads, reduce the likelihood of complications, and speed up recovery [149]. Another significant advantage of lattice structures is their ability to reduce the weight of spinal implants, without compromising strength or performance [5, 6]. Spinal implants need to balance strength, flexibility, and minimal added weight [64]. Lattice structures allow for reduced material usage, while still maintaining the necessary structural integrity. This makes the implants lighter and more comfortable for patients, as well as reducing the production costs, particularly when manufactured through 3D printing. By optimizing both material efficiency and the performance, lattice-based PAEK composite implants offer a cost-effective solution that maintains the high standards required for medical devices [138]. PAEK composite lattice structures represent a promising advancement in spinal implant technology, enhancing biocompatibility, the mechanical performance, and customization, while reducing the weight and costs [118]. These innovations are paving the way for next-generation spinal surgeries, improving patient outcomes through more effective, personalized, and sustainable implant solutions.
## 2 Materials and Methods
### Electrochemical Impedance Spectroscopy  
Electrochemical impedance spectroscopy (EIS) is a non-destructive technique used to study the electrical properties of materials in electrochemical systems. It involves applying a small AC voltage to the system, and measuring the resulting current response, over a range of frequencies. By analyzing the impedance data, EIS can provide information on parameters such as the resistance, capacitance, and charge transfer processes in the system, making it a powerful tool for studying corrosion and other electrochemical processes. To perform electrochemical measurements, a classical setup, consisting of a single-compartment glass cell and a three-electrodes system, was used. The system was composed of a reference electrode (saturated calomel (SCE)), a counter electrode made from a platinum grid with a large surface area, and a working electrode made from carbon steel, employing the Corrtest potentiostat/galvanostat model CS350, controlled by CS Studio5 software. The electrochemical impedance spectroscopy (EIS) tests for the carbon steel immersed in 0.1 mol/L HCl, both with and without the formulations, were carried out, applying an alternating current within the frequency range of (0.01-105) Hz, and using an AC signal of 10 mV. Prior to the EIS tests, the open circuit potential (OCP) was monitored, until a stable potential was achieved, ensuring that the system had reached a steady state, and thereby providing a more accurate reflection of the corrosion behavior under equilibrium conditions.
## 4 Results and Discussion
### Feature Selection  
After preprocessing, we used three feature-selection approaches to filter the features further to retain only informative features. The RFE retained the highest number of features in both datasets followed by PCA as shown in Table 2 and Figure 1.  
The graph-based feature-selection approach retained 80 for the SCLC dataset and 134 features for the NSCLC dataset. This is because the graph considers only the connecting features and the filtering step using maximal cliques retaining only the features that had the highest maximal clique score. Figure 2 presents the networks for the two datasets before and after filtering using maximal cliques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Feature Selection Method**} \\ \hline
**Dataset** & **Number of Features** & **Preprocessed** & **Graph** & **PCA** & **RFE** \\ \hline GSE60052 & 28,089 & 3423 (12.2\%) & 80 & 86 & 198 \\ GSE81089 & 28,089 & 12,145 (43.2\%) & 134 & 208 & 270 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Output normalization and feature selection using PCA, RFE and a graph-based approach.  
Figure 1: Features selected by each of the methods from the two datasets.
# From DevOps to MLOps: Overview and Application to Electricity Market Forecasting
## 4 Case Study 1: Forecasting an Hourly Day-Ahead Electricity Market Using an MLOps Pipeline  
The reliable operation of the electric power grid relies on a complex system of markets, which ensures that electric power consumption and generation are matched at every point in time. This matching is crucial for the stability of the grid, since the grid cannot store electric energy. From an MLOps perspective, forecasting the price of any electricity market depends on the market schedule, and the ML experts do not need to understand how the market contributes to stabilizing the grid. The timing characteristics of the market represent important knowledge for the developers of time-series forecasting solutions. In particular, two characteristics are crucial:  
* Does the price change weekly, daily, hourly, or at some other interval?
* When does the market participation occur and, thus, how far into the future should forecasts be available?  
There are numerous markets in a single country, and significant differences can exist between different countries. Thus, an ML team working on electricity price forecasting would do well to avoid hardcoding assumptions related to the questions above, and should instead parameterize them. A common market structure in Europe and elsewhere is the hourly day-ahead market [162; 163], which used as a concrete example in this paper. Such markets have the following characteristics:  
* The market interval is one hour; in other words, there is a separate price for each hour.
* The market participants need to place their bids on the previous day; for example, before the market deadline today, separate bids should be placed for each hour of the next day.  
In our case study, we look at a specific hourly day-ahead market--the Finnish Frequency Containment Reserves for Normal Operations (FCR-N) market. The FCR markets compensate participants for maintaining a reserve that can be activated to generate or consume energy in case such an activation is required due to a momentary imbalance in the power grid. An offline neural-network-based forecasting solution for this market is presented in [164].
### Literature Review  
This field of study has been subjected to extensive research in recent years. Various metaheuristic and analytical methods have been employed by researchers to estimate the parameters of the solar cell and module. They are the performance-guided JAYA (PGJAYA) algorithm [6], differential evolution (DE) [10], genetic algorithms (GAs) [11], particle swarm optimization (PSO) [12], the war strategy optimization (WSO) algorithm [13], SEDE [14], an efficient salp swarm-inspired algorithm (SSA) [15], improved JAYA (IJAYA) [16], RAO [17], modified artificial bee colony (MABC) [18], improved moth-flame optimization (IMFO) [19], the shuffled frog leaping algorithm (SFLA) [20], triple-phase teaching-learning-based optimization (TPTLBO) [21], the improved chaotic whale optimization (ICWO) algorithm [22], the sine-cosine algorithm (SCA) [23], a new hybrid algorithm based on the grey wolf optimizer and cuckoo search (GWO-CS) [24], the coyote optimization algorithm (COA) [25], marine predators algorithm (MPA) [26], adaptive genetic algorithm (AGA)-based multi-objective optimization [27], an improved equilibrium optimizer (IEO) [28], the new stochastic slime mold algorithm (SMA) [29], and orthogonally adapted Harris hawks optimization (OAHHO) [30]. An overview of some of these research papers is presented in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **PV Type** & **PV Model** & **Disadvantage** & **Advantage** \\ \hline PGJAYA [6] & RTC France Si cell and PhotoWatt-PWP201 & SDM, DDM, PVMM & Insufficient reliability & Acceptable accuracy \\ \hline DE [10] & SM55 module & SDM & The parameters need to be adjusted and insufficient capability for exploitation & Accurate performance under a variety of operating conditions \\ \hline PSO [12] & Not specified & SDM, DDM & Stuck in local minima and convergence at the beginning & High level of accuracy in the solution \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A review of the solar PV parameter extraction methods.
## 4 Experimental Simulation Analysis
### Effects of Different Chaotic Maps on System Performance  
In order to explore the influences of different chaotic maps on system performance, the control variable method is used to control the initial values of the four chaotic maps to remain equal, all of which are 0.6, the same number of replications \(P\), the same semi-spreading factor, and only the changed type of chaotic map. After several adjustments to the data, this experiment finally selects four representative sets of data for display, and the simulation results are shown in Figure 7.  
Figure 6: Effects of different \(Ps\) on the SR-ODBR-DCSK system error performance. (**a**) \(\beta=32\); (**b**) \(\beta=128\).  
It can be observed from the experimental results in Figure 7 that when the initial value and the number of replications \(P\) are constant, the semi-spreading factor is small. When the signal generated by the chaotic signal generator is mapped by tent, the error performance of the system is worse than that of the signal generated by other mappings. With the increase in the semi-spreading factor, when the signals generated by four typical discrete chaotic maps are used as reference signals, the bit error rate performance is close, and the difference is not obvious. Controlling the initial value and half spread factor unchanged, when \(P\) is small, the system error performance is better; as P becomes larger, the system error performance becomes worse.
## 3 MIMO Antenna Configuration Analysis
### Comparison among Various MIMO Configurations  
The comparison between four designs differing on the bases of placement of the second antenna element of two-port MIMO antenna is given in Table 1. The results of two-port antennas differentiate on the bases of operational bandwidth, transmission co-efficient, ARBW, peak gain, and radiation bandwidth.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 6 Conclusions  
The HY-2A CAPF is a significant contribution to expanding the usage of HY-2A satellite data. It focuses on upgrading the existing ground application system by addressing users' on-demand processing issues. Based on the review of users' requirements, the framework provides innovative ways to access and process the large ocean remote sensing data. For the staff of SOA, the framework offers pre-defined algorithms and processing workflows to automatically deal with daily work. For researchers, the framework offers a platform to easily translate their research into an operational processing workflow that can be used by other users. In the meantime, once a large number of use cases have been accumulated, more research and development activities are required to meet users' needs in order to enhance the framework usage.  
The development of the HY-2A CAPF project was supported by the NSOAS and SOA. Additional funding was provided by RADI under the [PERSON]'s project (Y6S)2700 CX) for the survey of remote sensing data services.  
The project was supervised by [PERSON] and [PERSON]. [PERSON], [PERSON] and [PERSON] designed and developed the framework, and [PERSON] wrote the manuscript. [PERSON] and [PERSON] provided comments and revised the manuscript.  
The authors declare no conflict of interest.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 5 Results and Discussion
### Test Results of 190 BX-121-RVE Reducers
Taking prototype 3 as an example, its response amplitude at the characteristic frequency of 0.1612 Hz is 1.8268\({}^{\prime\prime}\), which is \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Transmission Error Amplitude (\({}^{\prime\prime}\))**} \\ \cline{3-6}
**Component** & **Frequency (Hz)** & **Prototype 1** & **Prototype 2** & **Prototype 3** & **Prototype 4** \\ \hline Gear shaft & 0.5 & 0.1984 & 1.1922 & 0.7281 & 0.4037 \\ Gear shaft & 0.9917 & 0.2201 & 0.0320 & 0.4676 & 0.0585 \\ Planet gear, Crankshaft, Flange & 0.1612 & 1.6684 & 5.1097 & 1.8268 & 3.4640 \\ Crankshaft, Flange, Pin gear & 0.3223 & 0.7801 & 5.9741 & 4.0161 & 4.0220 \\ Flange, Cycloidal gear & 0.004132 & 3.8579 & 4.0522 & 6.0800 & 8.9639 \\ Cycloidal gear & 0.1653 & 6.4356 & 4.9307 & 14.1126 & 15.7449 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Characteristic frequency response of transmission error for 190 BX-121-RVE reducers. Figure 9: Characteristic frequency response of transmission error for 190 BX-121-RVE reducers. relatively small, suggesting minimal error and impact from the planetary gear, crankshaft, and flange. In this case, the response amplitude at the characteristic frequency of 0.004132 Hz likely results primarily from a significant geometric eccentricity error of the cycloidal gear, while the response at 0.3223 Hz is likely due to the tangential composite error of the pin gear. Therefore, for prototype 3, the main contributors to error are the cycloidal gear and pin gear, and thus, these components should be prioritized for inspection to improve transmission accuracy. For prototype 2, the error amplitudes at these three characteristic frequencies are relatively close, and all three frequencies have a flange component in their source, so the flange should be prioritized for quality inspection to enhance transmission accuracy. For frequencies with multiple sources and less distinct comparisons, further research is needed to accurately pinpoint the specific origin of each characteristic frequency.
## 5 Results
### Moderator Analysis
#### 5.3.2 Meta-Regressions  
Based on the subgroup analysis, the effect size for the levels of each moderator was found to be significant. However, the difference in the effect size between the levels of each moderator was not explored. Accordingly, this paper investigated the moderating effects via meta-regressions. Table 7 shows the results of continuous variables for meta-regression with the method of moments for all covariates employing the random effects models.  
Regarding the categorical variables, their coefficient represents the marginal effect of the focused category in comparison with the reference category [152]. No significant moderating effects of the three possible moderators were found on the effect size. For example, the moderating effects of the geographical region (\(Q=4.24\), \(df=7\), \(p=0.7514\)), organization size (\(Q=1.06\), \(df=2\), \(p=0.5875\)), and organization sector (\(Q=1.04\), \(df=4\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Relationship** & **Covariate** & **Coefficient** & **Z-Value** &
\begin{tabular}{c} **2-Sided** \\ _p-Value_ \\ \end{tabular} \\ \hline \multirow{6}{*}{PU–BI} & Gender & & & \\  & Intercept & \(1.008\) & \(3.40\) & \(0.0007\) \\  & gender & \(-0.721\) & \(-1.57\) & \(0.1175\) \\  & Age & & & \\  & Intercept & \(1.171\) & \(3.49\) & \(0.0005\) \\  & Age & \(-0.0167\) & \(-1.69\) & \(0.0907\) \\ \hline \multirow{6}{*}{PEOU–BI} & Gender & & & \\  & Intercept & \(0.554\) & \(2.04\) & \(0.0412\) \\  & gender & \(-0.094\) & \(-0.22\) & \(0.8262\) \\  & Age & & & \\  & Intercept & \(0.526\) & \(1.73\) & \(0.0828\) \\  & Age & \(0.001\) & \(0.12\) & \(0.9017\) \\ \hline \multirow{6}{*}{SI–BI} & Gender & & & \\  & Intercept & \(1.344\) & \(2.54\) & \(0.0112\) \\ \cline{1-1}  & gender & \(-1.356\) & \(-1.79\) & \(0.0738\) \\ \cline{1-1}  & Age & & & \\ \cline{1-1}  & Intercept & \(0.3189\) & \(0.59\) & \(0.5521\) \\ \cline{1-1}  & Age & \(0.0071\) & \(0.49\) & \(0.6261\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Moderating effects of these relationships by random effects models (continuous variables).  
\(p=0.9040\)) in the link PU-BI were non-significant. Regarding the continuous variables, the moderating effects of gender in the link between SI and BI were significant at the 10% level (\(Z=-1.79\), \(p=0.0738\)), implying that the effects of SI on the adoption of Industry 4.0 technologies varied significantly in different genders. However, the moderating effects of gender and age were non-significant in these pairwise relationships.
# Study on Shock Initiation Randomness of Energetic Materials on a Macroscopic Scale
###### Abstract  
The shock initiation randomness problem of energetic materials (SIREM) is an important problem in the research field of energetic material safety. With the purposes of solving SIREM on a macroscopic scale and obtaining the statistics, such as the initiation probabilities of energetic materials and the statistical characteristics of the detonation pressure, this paper considers the effect of the randomness of the parameters of the Lee-Tarver equation of reaction rate and the JWL equation of state of energetic materials and the randomness of load intensity parameters--such as fragment shock velocity--on the randomness of the shock initiations of energetic materials. It then decomposes SIREM into an initiation probability problem (IP) and a detonation pressure randomness problem (DPR). Further, with the Back Propagation Neural Networks optimized by the Genetic Algorithm (GABPNN) as the surrogate models of the numerical models of two-phase reactive flow, this paper proposes the approach of solving IP and DPR in turn, adopting Monte Carlo Simulations, which use the calculations of GABPNNs as repeated sampling tests (GABP-MCSs). Finally, by taking the shock initiation randomness problem of Composition B as an applied example, this paper adopts GABP-MCS under the randomness conditions that the means of fragment shock velocities are 1050 m/s and 1000 m/s and that the coefficients of variation (CVs) of BRVs are 0.005, 0.01, 0.015, and 0.02 in order to obtain the initiation probabilities of Composition B and the statistical characteristics, such as the means and CVs of the detonation pressure. It further observes the variation tendencies that these statistics show under various randomness conditions, so as to prove the effectiveness of GABP-MCS in solving SIREM. Therefore, this paper investigates SIREM on a macroscopic scale and proposes a universal technique for solving SIREM by GABP-MCS, in the hope of shedding some light on the SIREM study.  
energetic material; shock initiation randomness; initiation probability; detonation pressure randomness; Back Propagation Neural Network (BPNN); Genetic Algorithm (GA); surrogate model; Monte Carlo Simulation (MCS) +
Footnote †: journal: Applied Sciences
## 4 Advantages and Limitations of MSLP  
### Comparing MSLP and MS  
[PERSON] [28; 29] has shown that there are minor differences between MS and MSLP solutions for normally excited damped systems with cubic nonlinearities. In this specific example with difference coordinates and an excitation that is dependent on the frequency, the MSLP method is more precise. However, not only the method can affect the accuracy of the solutions, the exponent of the small parameter \(\varepsilon\) also affects the feasibility of the analysis and, thus, the accuracy. Comparing the results obtained by MS using \(\varepsilon\) in the first power for the damping and excitation term, in the second power and MSLP with \(\varepsilon\) in the second power leads in the typical Multiple Scales analysis to  
\[\Omega=\omega_{0}+\frac{3\alpha\hat{u}^{2}}{8\omega_{0}\hat{J}}-\frac{15 \alpha^{2}}{256\omega_{0}^{3}\hat{J}^{2}}\hat{u}^{4}\pm\frac{\omega_{0}\varphi _{ij}q}{2\hat{u}}\sqrt{1-\left(\frac{d\hat{u}}{\omega_{0}\varphi_{ij}q\hat{J} }\right)^{2}}. \tag{57}\]  
Using  
\[\ddot{u}+\varepsilon d^{\sim}\dot{u}+\omega_{0}^{2}u+\varepsilon\alpha^{\sim }u^{3}=\varepsilon\omega_{0}^{2}\varphi_{ij}q^{\sim}cos(\Omega t) \tag{58}\]  
and now analyzing only up to \(O(\varepsilon)\) due to the very complex expressions for \(O\big{(}\varepsilon^{2}\big{)}\); the function for the frequency-response curve is  
\[\Omega=\omega_{0}+\frac{3\alpha\hat{u}^{2}}{8\omega_{0}\hat{J}}\pm\frac{ \omega_{0}\varphi_{ij}q}{2\hat{u}}\sqrt{1-\left(\frac{d\hat{u}}{\omega_{0} \varphi_{ij}q\hat{J}}\right)^{2}}. \tag{59}\]  
It is important to recognize that, according to the power of \(\varepsilon\) in the equation describing the system, the expression for calculating the excitation frequency out of the detuningparameter and the angular natural frequency also changes according to \(\Omega=\omega_{0}+\varepsilon^{n}\sigma\) for MS methods or \(\Omega=\omega(1+\varepsilon^{n}\sigma)\) for MSLP.  
The differences in the obtained solutions are shown in Figure 3a,b in comparison to the results simulated in the time domain. MSLP results are accurate even for such large nonlinearities and excitations that subharmonic resonance occurs, MS solutions meanwhile show spurious peaks that are bend to much or in the wrong direction.  
In Figure 3a, the solutions of MS do not cover the whole excitation branch in contrast to the MSLP solution, which matches the point representing the numerically obtained solution very accurate. For the strong excitation in Figure 3b, MS solutions even show very differently bended curves than the numerical solution or MSLP. Furthermore, the MS solution for \(O\big{(}\varepsilon^{2}\big{)}\) is bent backwards and, therefore, not just quantitatively but, also, qualitatively inaccurate. Due to the dependency of the excitation force on the excitation frequency in this case, the MSLP method is more suitable to approximate the equation of motion.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 2 Materials and Methods  
### The Ray Launching Technique  
As stated before, an in-house 3D-RL algorithm has been used for the characterization of radio wave propagation in the considered urban scenario. The detailed operating mode of the algorithm has been previously published in [24]. It has also been validated in large indoor environments [25], intra-vehicle [26] and transportation systems [19; 27; 28]. The principle of the algorithm is that the complete 3D scenario is divided into a fixed number of different cuboids sizes. The position of the transmitter antenna is an input parameter of the algorithm; thus, rays are launched from the transmitter position with a fixed angular and spatial resolution, and propagation parameters are calculated for each cuboid along the ray path until the ray reaches the maximum number of reflections considered. The advantage of deterministic approaches is that it permits the consideration of all the obstacles within the environment, taking into account all the material properties, considering the conductivity and dielectric constant at the frequency range of operation of the system under analysis. Electromagnetic phenomena such as reflection, refraction and diffraction have been considered in the algorithm, leading to an efficient and robust technique already validated for the urban environment [27] and vehicular communications [19].  
Different radiation patterns for the transceivers can be considered, as well as antenna parameters. The frequency of operation, number of multipath reflections, angular and spatial resolution, and cuboid dimensions are introduced. In addition, the frequency dispersive effects due to the movement of the vehicles can be analyzed by means of the Doppler shift and Doppler spread parameters, for different velocities of the vehicles and for the frequency under analysis.