esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Scale Control Using Dynamic GMDH Neural Network Forecasting Based on Real Measurement Data
## 4 Discussion  
The weekly forecasting results for the UTC(PL) time scale show very good convergence with the UTC time scale. During the presented period (the whole year of 2024), the difference between the UTC(PL) and UTC did not exceed \(\pm\)4.4 ns. Moreover, for an interval exceeding two years, the analogous difference is within \(\pm\)5 ns. Additionally, the forecasts for the last day of forecasting (Wednesday) in a given week are very consistent with the forecast results for the first day of the new forecasting week (Wednesday). This proves the high quality of the forecasting and the universality of the procedure developed by the author, using a GMDH-type neural network.  
Currently, the author, together with the research team from the Institute of Metrology, Electronics and Informatics at the University of Zielona Gora, together with the Department of Time and Length in the Central Office of Measures in Warsaw, is conducting research on the application of the hydrogen maser frequency drift to forecast the Polish Time Scale UTC(PL), using a non-linear regression method. The research is at an early stage, but the initial results, presented in [24], are promising. Nevertheless, the author does not intend to stop usingthe dynamic forecasting of the Polish Time Scale UTC(PL), using real measurement data. The introduction of a new forecasting method will allow for even greater possibilities of acquiring knowledge from the obtained forecast results in order to control the UTC(PL) more precisely.
## 2 Materials and Methods
### Laboratory Equipment
#### 2.2.3. Electromyography  
Electromyography (Delsys(r) Inc., Natick, MA, USA) is a wireless system (Delsys Trigno TM Wireless system) was used to calculate the muscular electrical activities using wireless electrodes that were adhered to the skin of the participant's dominant leg with double-sided adhesive tape. The electrodes can collect the muscular electrical activity if they are put on the muscular sensor location following the European Biomedical Health and Research Program instructions, called SENIAM (Surface Electromyography for the Non-Invasive Assessment of Muscles) [13]. The Vicon, AMTI, and Delsys systems collected the data synchronously.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 8 Conclusions  
This paper presented three applications of the parameter optimization algorithm (paraOpt) and showed the superiority of the approach in CPU time and accuracy. In the _fingerprint-based indoor localization system using IEEE802.15.4 devices (FILS15.4)_, the number of fingerprints for each detection point, the fingerprint values, and the detection interval are optimized together by _paraOpt_, which achieves the average detection accuracy with higher than 99%. In the _human face contour approximation model_, the half circle and quadratic curve functions were presented. The center coordinates, radii, and coefficients of simple functions to represent the model are optimized, which can well approximate the face contour of various persons. From the results, the quadratic curve function is more accurate. In the _computational fluid dynamic (CFD) simulation_, the thermal conductivity is optimized, which minimizes the average temperature difference between the estimated and measured ones. From the results with two patterns, _pattern2_ is more approximate to the measurement data.  
In future works, we will improve the parameter optimization algorithm and evaluate it in other applications.  
**Author Contributions:** Conceptualization, Y.H. and N.F.; methodology, Y.H.; software, Y.H. and P.P.; validation, K.H., M.K., Y.Z. and K.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank the reviewers for their thorough reading and helpful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Antibacterial Ceramics for Dental Applications
## 3 Results and Discussion
### Antibacterial Properties of Zinc Oxide
Zinc serves as an important agent for the development of cell structure and functions and acts as a direct or indirect regulator affecting the communication betweenociceptor neurons and the host immune systems, modulating the inflammatory response and host defense against bacterial infections [84]. Information on the antibacterial activity over a longer period of time is missing, and this must be investigated. The development of piezoceramics, known for their superior osteogenic properties with additional antibacterial activity, opens up new possibilities for the regeneration of diseased bones. The development of multifunctional bone tissue engineering ceramics offers promising potential for repairing complex tissue defects. [PERSON] et al. [17] investigated the effect of ZnO-doped dental crown restoration materials on the prevention of secondary dental caries. Amorphous sodium alumino-silicate (NAS) doped with 3 resp. 6 wt% ZnO. _S. mutans_ bacteria was used to investigate the antibacterial activity of NAS/3 Zn and NAS/6 Zn ceramics. As shown in Figure 11, both doped Zn-ceramics possess antibacterial activity, which increases with the ZnO content of the ceramic [17]. The authors also investigated the release of Zn ions into the environment, which was not interrupted during the test period of 0-40 days (Figure 11c). These results are important for the long-term effectiveness of the dental device. According to [PERSON] et al., Zn\({}^{2+}\) ions destroy the cell membrane structure, induce the leakage of bacterial protein, and lead to the death of bacteria. NAS/Zn ceramics have superior biocompatibility and can be used as dental restoration materials with additional antimicrobial activity to prevent secondary caries [17]. The typical problem with doped biocearamics is maintaining long-term antibacterial activity. When the bactericidal ions responsible for the antibacterial effect of the ceramics are completely released from the ceramic, the antibacterial effect is lost. The results of this  
Figure 11: Evaluation of antibacterial effectiveness of NAS/Zn ceramics on _S. mutans_ bacteria: (**a**) bacterial culture plate colony incubated with different samples, (**b**) antibacterial ratio of _S. mutans_ after incubation with different samples, (**c**) cumulative release of Zn ions from NAS/Zn [17]. Reprinted with permission from Elsevier. Figure 10: In vitro antibacterial properties of different groups of piezoelectric ceramics samples. (**A**) The results of plate counting. (**B**) The quantitative results of CFU. (**C**) The live/dead bacterial staining [21]. Reprinted with permission from Elsevier. study are promising for the development of ZnO-functionalized ceramics with long-term antibacterial effects.
## 4 Open Issues
### QoS-Aware Communication Protocols  
In order to efficiently support QoS in WSANs, communication protocols need to be designed with in mind the platform heterogeneity, specifically the heterogeneity between sensors and actuators that are involved in the communication. For this reason, state-of-the-art QoS-aware MAC, routing, and transport protocols devoted to WSNs may not be suitable for WSANs.  
As an essential component of QoS, service differentiation should be supported by communication protocols. As mentioned above, WSANs may be used in cyber-physical systems encompassing diverse applications, which may differ significantly in terms of QoS requirements. Obviously, the best-effort service offered by current wireless networking technologies such as Zigbee and Bluetooth cannot provide different QoS to different applications. Therefore, the communication protocols for WSANs should be designed to perceive the service requirement of each type of traffic so that it can be guaranteed a specific service level. From a practice perspective, the best-effort service is likely to be the standard for the foreseeable future [9, 10]. It is therefore necessary for all new QoS mechanisms to be layered on top of the existing networks.  
Cross-layer design has proved to be effective in optimizing the network performance and hence may be incorporated in the development of QoS-aware communication protocols for WSANs. Much work can be conducted in this line. For example, the prioritization of traffic at lower layers might be associated with the application performance at the application layer.
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
## 3 Overview of Non-Terrestrial Networks
### Channel Models for NR-NTN
The power delay profiles (PDPs) of selected TDL-A and TDL-E are given in [1; 9]. The tap amplitudes follow Rayleigh distributions, and in line-of-sight cases, first tap amplitudes follow Rician distributions; hence, from these PDPs, the one tap of Rayleigh and Rician are used as a single-path channel to analyze link-level performance.
## 2 Materials and Methods
### Preprocessing  
The EEG data were filtered using a 256 th-order finite impulse response (FIR) filter in the band below 1 Hz and above 40 Hz to remove records that did not represent brain activity. Then, all unnecessary signal fragments, such as breaks between experimental conditions, were removed from the dataset. Line noise was removed next with CleanLine EEGLab extension [46] using a sliding window which adaptively estimates sine wave amplitude to subtract. Bad channels were also removed by the means of EEGLab (using clean_rawdata EEGLab procedure with default parameters), following the parameters of the automated algorithm. Channels were identified as bad and removed if the signal is flat for more than 5 s. What is more, channels with a large amount of noise were removed based on their standard deviation (maximal acceptable threshold was set on the default value: 4) and when they were poorly correlated with other channels (the rejection threshold for channel correlation was set to default value: 0.8). Removed channels were then interpolated. The next step of the procedure was additional removal of bad data periods based on a set number of channels (default 25%) passing a standard deviation threshold in a time window. The data were subjected to the process of re-referencing the signal (CAR, common average reference) for all channels except those that had been marked as bad.
# An Overview of GEO Satellite Communication Simulation Systems
## 4 Comparative Evaluation of Simulation Tools
### Coverage Analysis  
Coverage analysis is essential for evaluating satellite visibility and service availability. STK stands out with its sophisticated visualization capabilities, real-time satellite visibility simulations, and detailed access time calculations, regularly applied in mission-critical assessments by space agencies and satellite operators. MATLAB and other tools typically rely on externally generated coverage data from STK, providing limited native support for comprehensive coverage computations, serving primarily as analytical extensions rather than standalone solutions.
## Materials and Methods
### Performance Assessment of the Time-Series Smoothing Methods
#### 2.3.3 Elevation vs. Phenology Parameters from Satellite NDVI Data  
Besides evaluating the performance of the smoothing methods for reconstructing time-series, we evaluated their abilities for extracting phenological metrics across a spatial gradient. The assumption of the experiment was that in this area the variation of vegetation phenology, such as start of the season (SOS), end of the season (EOS), length of the season (LOS) and small seasonal integral (SI) along the gradient, are generally related to average air temperature [(23; 51)], and that this is partly controlled by the local elevation. Other factors, such as solar radiation, hydrology, soil type, etc., also affect the relationship [(23)], but we assume that for comparing the different smoothing methods the influence of these is negligible. We extracted SOS, EOS, LOS and SI by TIMESAT based on smoothed MODIS NDVI time-series. To avoid inter-annual variations we averaged the phenology variables over the 10 years between 2001 and 2010, which reduces influences of trends and extreme events, and emphasizes phenological variation along the elevation gradient. To avoid effects of non-linearity we used Spearman's rank correlation coefficient for detecting the relationships. For extracting phenological metrics from time-series, suitable thresholds to define start and end of season are required. Since NDVI has a strong response to snow [(70)] and MOD09Q1 does not have snow flags, using high amplitude thresholds is necessary to avoid capturing snow dynamics rather  
Figure 7: Examples of least-squares fits to asymmetric Gaussian (AG) and DL model functions.  
than dynamics of vegetation. Therefore, the thresholds for start and end of season were set to a high value of 72% of the amplitude, which was based on Fennoscandian studies by [71; 72]. For the same reason we did not use derivatives for determining SOS and EOS, which would have been strongly influenced by snow seasonality during early and late parts of the seasons.
## 1 Introduction  
The diversification of oil supplies and potential price increases in the global market call for a reassessment of enhanced oil recovery (EOR) methods to increase production. As such, there is an urgent need to explore and implement solutions focused on creating new technologies or applying existing ones, deepening our understanding of the underlying phenomena, and generally enhancing knowledge and practices related to field production.  
Scalating global expenses associated with making hydrocarbon deposits accessible are compelling companies to invest in innovative technologies [1]. However, each new method presents unique challenges in terms of design, decision-making, and execution, requiring thorough consideration before application. This involves economic and logistical comparisons with conventional drilling methods [2]. The overarching objective of these endeavors is to decrease the well-drilling costs while maximizing the exploitation of existing deposits. Given the increasing importance of supporting the decision-making process, there is a critical need to minimize the risk associated with investments, especially concerning technologies like directional drilling or hydraulic fracturing. This is crucial to avoiding ill-considered decisions and ensuring a sensible approach to technological progress in the oil industry.  
The search for innovative solutions to enhance extraction has given rise to the development of radial borehole drilling, executed from a base hole using the high-pressure jet energy of hydraulic radial drilling nozzles. The unique characteristics of this technology, specifically, the capability to achieve an exceptionally short radius of curvature, of approximately 30 cm, and the remarkable increase in the angle of curvature of the hole (100-250\({}^{\circ}\)/30 m), have led to the classification term URRS (Ultrashort-Radius Radial System) [3; 4; 5]. Radial drilling finds application in both existing and new production as well as exploration wells [1; 6; 7]. In this context, \"radial drilling\" does not solely refer to a small radius of curvature but emphasizes the ability to drill in various directions at the same level. Typically, two to four small-diameter boreholes are drilled, depending on the deposit structure, layer characteristics, and production requirements. The exceptionally small radius of curvature and the correspondingly compact dimensions of the drilling set necessitate the application of unconventional drilling techniques. Various methods were considered, but hydraulic radial drilling, utilizing a high-pressure liquid jet expelled from the nozzles of the rotary head, emerged as the most effective. This approach enables a remarkably high concentration of fluid energy per unit area of rock, thereby facilitating hydraulic radial drilling.
# A Maximum Power Point Tracking Control Method Based on Rotor Speed PDF Shape for Wind Turbines
## 5 Conclusions  
The conventional MPPT method mainly focuses on the steady-state performance and ignores the dynamic characteristics of the wind turbine. At present, the proposed MPPT control method aims to accurately track the change in wind speed without considering the influence of wind speed distribution characteristics. The uncertainty of wind speed, the large inertia of the wind turbine, and the slow dynamics caused by the wind turbine system nonlinearity make it difficult for the wind turbine to respond to the high turbulence in stochastic wind timely and accurately.  
To solve the problem caused by blind tracking, a novel MPPT control method is very necessary. Considering the influence of the stochastic distribution of wind speed on the system control performance, the PDF control was introduced, and a novel MPPT control design-based rotor speed PDF shape was proposed. The PDF controller is designed by solving the FPK equation with the LLS method so that the PDF shape of the system state variable can better approach the PDF shape of the goal. Combining this PDF control with the conventional OT control, the novel MPPT control for wind turbines is proposed, which can accurately track the desired PDF and improve the wind energy capture efficiency.  
Simulation results of a 1.5 MW PMSG wind turbine system show that the novel MPPT control can indeed increase the power coefficient of the system under high wind speed, make the system respond to the random change of wind speed more quickly, and effectively improve the overall power generation in a period. In addition, this control method can reduce the fluctuation of rotor speed and torque as well as reduce the mechanical damage.  
The novel MPPT method improves the overall power generation to a certain extent and possesses a huge reference value for the further study of the control problem of stochastic wind power generation system. Future works may include the application of B-spline modeling to design the low-order controller and the novel MPPT method in real variable-speed wind turbines.  
Methodology, X.Z. and L.Z.; project administration, L.Z.; software, X.Z. and Z.Z.; writing--review & editing, X.Z. and J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51976113.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion: Statistical Approach
### Multiple-Regression Analysis  
Bearing in mind the aspects discussed in the previous sections, the procedure for obtaining an expression that simultaneously links the UPV readings with all the mechanical properties is addressed in this section. First, the standardization of the variables is presented, and then the multiple-regression model is approached.
## 4 Discussion  
### Effect of Sensor Mismatch on Bias and Variance of Time Period Estimation  
One of the primary factors affecting time period estimation (TPE) accuracy is the mismatch in the sensor characteristics, particularly differences in thermal and electrical time constants between the two PIR sensors. This mismatch introduces a phase shift \(\Theta_{0}\) between the sensor responses, leading to systematic errors in the TPE.  
From Figure 4a, it is observed that the phase mismatch \(\Theta_{0}\) increases as the thermal time constant \(\tau_{th2}\) derives from \(\tau_{th1}\). This leads to an increasing bias in TDE, as shown in Figure 4b, confirming that larger sensor response mismatches lead to greater bias in the estimated time period.  
Equations (40) and (53) infer that bias decreases at higher signal frequencies, meaning that, at higher vehicle speeds, the impact of sensor mismatch on bias is reduced. This trend is supported by Figure 4c, where speed estimation bias decreases as vehicle speed increases, further confirming the inverse relationship between bias and signal frequency.  
Variance analysis reveals key differences between CCF and CCFT in handling sensor mismatches. In addition, from Figure 5, it is observed that CCF variance increases rapidly with sensor mismatch, making it highly unstable, while CCFHT variance remains relative lower, confirming its robustness against sensor mismatches.
# Mycotoxin Level in Winter Wheat Grain as Impacted by Nitrogen and Manganese Fertilisation
## 4 Discussion
### Fusarium spp. Infestation and Mycotoxin Level
[PERSON] and [PERSON] [58] analysed the intensity of _Fusarium_ head blight and DON level in spelt grain and found no correlation between the symptom intensity and the toxin level. Nitrogen application at 150 kg ha\({}^{-1}\) (without Mn) in the current study caused higher grain contamination with toxins than at 200 kg ha\({}^{-1}\) N. The results of an experiment conducted by [PERSON] et al. [23], [PERSON] et al. [16] and [PERSON] et al. [4] did not show a significant impact of nitrogen fertilisation on infestation by _Fusarium_ spp. or mycotoxin production in wheat grains. As mentioned above, [PERSON] et al. [22] linked mycotoxin occurrence with increased nitrogen fertiliser rates. Supplementation of nitrogen fertilisation with manganese in this study reduced the number of mycotoxins in wheat grain. Micronutrients contribute to the development of plant resistance to pathogens [59]. Manganese is an important micronutrient for plant growth and development [60; 61]. It plays some metabolic roles in various processes of the plant life cycle, such as photosynthesis, respiration and the uptake of reactive oxygen species [62; 63]. The presence of Mn is important for building structures of plant tissues, which is a physical barrier for pathogens [30; 31].
## 7 Case Study: Position Control of a Rotor by PII\({}^{1/2}\)DD\({}^{1/2}\) Control
### Comparison of PID and PII\({}^{1/2}\)DD\({}^{1/2}\) Controls in Discrete-Time Simulation  
Let us consider the same closed-loop system discussed in Section 7.1, with the same values of \(J\) and \(B\), and the same control gains for the three controllers (PID, PII\({}^{1/2}\)DD\({}^{1/2}_{H}\) and PII\({}^{1/2}\)DD\({}^{1/2}_{L}\)). For the discrete-time implementation of the controllers, the half-derivatives  
Figure 9: Bode plots of: a half-zero term with corner frequency of 100 rad/s (blue); two half-zero terms with corner frequency of 100 rad/s (red); a zero term with corner frequency of 100 rad/s (yellow).  
and the half-integrals are calculated by means of sixth order digital filters, according to Equation (6), adopting a sampling time \(T_{s}\) = 0.006 s. These values of filter order and sampling time are compatible with the computational capability of the digital controller used for the experimental validation (Section 8.3).  
The considered trapezoidal position reference is characterized by: (i) a first phase with acceleration of 500 rad/s\({}^{2}\) and duration of 0.2 s, (ii) a second phase with a constant speed of 100 rad/s and duration of 0.6 s, and (iii) a third phase with deceleration of \(-\)500 rad/s\({}^{2}\) and duration of 0.2 s; consequently, the setpoint varies from 0 to 80 rad in 1 s.  
The discrete-time simulations, performed by Simulink, confirm that the PII\({}^{1/2}\)DD\({}^{1/2}\) controllers exhibit a better readiness than the PID control, as already shown by the continuous-time step response (Figure 7). Figure 10 represents the time histories of the angle \(\theta\), of the error \(e_{\theta}\) and of the motor torque \(M\). These results will be validated by experimental tests (Section 8.3) and then discussed (Section 8.4).