esa-sceva/satcom-chunk-collection huggingface dataset:
# Variation in Crop Yield Depending on the Tractor Tire Contact Area and Clay Loam Soil Moisture
## 5 Conclusions  
The results of this specific practice study provide helpful indications and appropriate choices of tractor chassis configurations to meet soil structure requirements on heavy clay loam soils, thereby reducing soil compaction. The research determined that the soft wheeling could keep the soil moist and could improve the spring crop yield in dry weather conditions.  
1. It was settled that by increasing the tire contact area the grip with soil was improved and slippage was reduced. The soil surface displacement also has decreased by increasing tire contact area. The biggest positive effect was achieved by adding double wheels. The scale of the effect was influenced by soil moisture. Increasing tire contact area and reducing slippage enhance the environmental friendliness of tractors.
2. It was determined that on wheeled clay soil the soil aggregates were pulverized and showed a significantly better structure than in the control plot. The germination with a better spout count was bigger after rutting the soil with a double tire contact area; in the control plots, the seeds germinated weakly--there were a smaller number of sprouts. Soil compacting kept the soil moist, but wheeling with the smallest tire contact area created deeper ruts and dried top-soil layers.
3. It was established that the soil plots that were compressed with the lowest tire contact area gave less crop yield; in the plots that were compressed with the greatest tire contact area, the crop yield increased compared to the control plot without any compression of soil.
4. The effect of increasing the tire contact area on the results was less pronounced when working on dry soil. Increasing tire contact area, especially with double wheels, has been proven and recommended for wet soil.  
Conceptualization, V.D. and D.J-R.; methodology, V.D.; software, D.J.-R.; validation, V.D. and D.J.-R.; formal analysis, D.J.-R.; investigation, V.D.; resources, V.D.; data curation, D.J.-R.; writing--original draft preparation, V.D.; writing--review and editing, D.J.-R.; visualization, V.D.; supervision, V.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The paper presents research findings, which have been obtained through long-term research programme \"Productivity and Sustainability of Agricultural and Forest Soils\" implemented by Lithuanian Research Centre for Agriculture and Forestry.  
The authors declare no conflicts of interest.
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 4 Results and Discussion
### Analyzed Rooms
Based on these examples, it is concluded that a clear relationship between individual room characteristics and the insulation power cannot be established. Figure 4b illustrates the equivalent values of the ambient noise level at all measurement locations. The noise measurement results reveal significant variations in ambient conditions from one location to another. The highest values range from 26 dB(A) to 32 dB(A), with most values well below 30 dB(A). Regulations in Serbia limit the permissible ambient noise level to 30 dB(A). Consequently, it can be inferred that the analyzed measurement locations exhibit a low level of ambient noise. This is likely attributed to the facade elements possessing good insulating properties, the devices within the rooms producing low noise, and a lack of substantial human activity in neighboring rooms. Figure 3: Frequency dependence of reverberation time for all measurement locations. Figure 4c depicts the spectra of ambient noise at all measurement locations, expressed in dB. It can be observed that, in most cases, the highest levels of ambient noise occur in frequency ranges below 400 Hz. At several measurement locations, maximum noise levels can be noticed beyond 1000 Hz, which is a result of specific human activities at those locations. The figure also illustrates the averaged spectrum of ambient noise from all locations to determine the shape of the ambient noise spectrum. The average spectrum exhibits a monotonically decreasing trend towards higher frequencies, which is coherent with data from the literature [54]. For the proposed parameter _SPI_, the equivalent noise level expressed in dBA is used, resulting in a weighted spectrum with the A-weighting characteristic. The A-weighting characteristic introduces significant attenuation at low frequencies, resulting in similar noise levels for different frequency ranges. It can be concluded that the frequency content will not affect the calculated parameter value. It should be noted that the measured values of the sound reduction index for the partitions at all measurement locations fall within the range of 20 to 65 dB. The equivalent level of ambient noise in the receiving rooms from all measurement locations is within the range from 22 to 42 dB(A). This means that partitions with substantially different insulating properties and ambient noise conditions were analyzed. Therefore, adequate input data is provided to properly validate the proposed speech privacy evaluation parameter. Figure 4: **(a) Measured sound reduction index for all rooms; (b) equivalent level of ambient noise in receiving rooms; (c) spectrum of ambient noise at all measurement locations (The gray lines represent the results of all measurements. The black line represents the average value. Dots represent values in 1/1 octave bands.). **
## 3 Results and Discussion
### Optical Observations of the Laser-Structured Surfaces
These periodic surface structures diffract incident light in separate directions according to their different wavelengths [20]. This diffraction generates a colourization effect on the laser-structured surfaces. This variation of structural colours on the laser-structured regions indicates that the surface structures present have different types of morphologies with different dimensions depending on the laser processing parameters [27]. It has also been published that the oxide films formed on the surface of the titanium play a role in colourization due to thin oxide film interference [7; 22]. When incident white light is illuminated on the laser-structured surfaces, reflection from the sides of the oxide film will happen and, in case of the interference of the reflected lights, the colourization effect will happen [20]. From the EDX analysis, it was noted that there was a formation of thin oxides on the surface of CP Ti after laser processing. Similarly, [PERSON]'s analysis established that the rutile phase of TiO\({}_{2}\) was present on the surface of the laser-processed CP Ti. The presence of a thin film of oxides on the surfaces of the laser-processed CP Ti implies that it could have contributed to the colourization of the laser-processed CP Ti [46]. The colour seen on the laser-structured surfaces depends on the thickness of the oxide, and the refractive index [20; 22]. [PERSON] et al. [27] also explained that plasmon resonant oscillations of electrons in nanostructures bring about the colourization of Ti surfaces and these colours are commonly referred to as plasmonic colours. In ultrafast laser processing, the laser-induced nanostructures whose dimensions are less than the wavelength of incident light induce localized surface plasmon resonance (LSPR) [22]. This phenomenon happens when incident light causes the electrons at the nanostructure's surface to oscillate collectively, which enhances absorption and scattering at specific wavelengths [59]. These interactions depend on the wavelength of the incident light and the dimensions of the nanostructures [22]. Due to the existence of the LIPSS, the oxide layer, and nanostructures on the laser-structured surfaces of the CP Ti, it is difficult to determine the percentage contribution of the periodic surface micro-/nanostructures, oxide film, or nanostructures quantitatively to the final colour state of the laser-processed surface. Therefore, further research is recommended in this area.
# Reliability Analysis of C\({}^{4}\)ISR Systems Based on Goal-Oriented Methodology
## 1 Introduction
[PERSON] et al. [14] applied the GO method to complete a reliability analysis of a hydraulic transmission oil supply system (HTOSS) under high-speed conditions and the results show that GO method is suitable for reliability analysis of repairable systems with multiple fault modes. [PERSON] et al. [15] implemented the GO methodology to solve low precision and insufficient efficiency problems in reliability assessment of mechanical equipment, the results indicate that the GO method provides useful reference in addressing the complicated, multiple states and time sequence problems in some engineering cases. [PERSON] et al. [16] analyzed the reliability of a braking system by the GO method, concluded that the GO method is applicable for operation and maintenance investigations of mechanical systems like braking systems. [PERSON] [17] established a GO model for an emergency management system, indicates that the GO method is feasible and efficient for conducting the reliability analysis of social systems. [PERSON] et al. [18] combined the GO method with fuzzy theory, accordingly, established a fuzzy GO model that is proved to be more feasible for reliability analysis. Overall, the GO methodology is a success-oriented system reliability analysis method with clear structural representation, that is, system configurations can be directly mapped to the GO model without complicated mapping algorithms [19; 20]. Compared with the commonly used reliability analysis method such as FTA [21; 22], FMEA [23; 24], MC [25], and BN [26; 27], the GO method has obvious advantages [28; 29; 30; 31]: (i) GO models are easy-to-construct, which are able to map from a physical structure of an actual product, by which the functional diagrams are not required; (ii) GO models are easy-to-calculate, quantitative and qualitative analyses of GO models can be obtained through multiple GO operations so that he complicated analysis process (like FTA and BN) can be avoided; (iii) results of GO models are convincing and credible thanks to the specifically designed GO operators. Accordingly, the GO method was selected to represent the failure configuration and structure properties of the C\({}^{4}\)ISR system studied in this paper. The rest of this paper is arranged as follows: Section 2 introduces the modeling and analysis of the GO method. Section 3 presents the case study. Results are demonstrated in Section 4. Discussions are settled at Section 5. The conclusions are listed in Section 6.
## 5 Conclusions  
In this study, we propose a GC hybrid dental implant with enhanced antibacterial and cell proliferation properties. Using a spin-coating technique, GC nanocomposites with various concentrations were successfully coated onto the surfaces of a titanium implant, and the surface properties, including wettability and roughness, were changed. The GC hybrid implant suppressed bacterial activity and biofilm formation, and also enhanced the proliferation of osteoblasts. These findings provide a rational design for the development of a hybrid dental implant, which can also be extended to the development of other implants with changed surface properties. In particular, the proposed GC hybrid dental implant can be used as an effective implant scaffold for achieving enhanced osseointegration.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/14/4888/s1](http://www.mdpi.com/2076-3417/10/14/4888/s1)]([http://www.mdpi.com/2076-3417/10/14/4888/s1](http://www.mdpi.com/2076-3417/10/14/4888/s1)): Figure S1: Effects of graphene-chitosan hybrid implants on bacterial activity. (**a**) Confocal microscopy images of _S. aureus_ on pure glass and a 1% graphene-chitosan hybrid glass substrate. (**b**) Confocal microscopy images of _S. mutans_ on pure titanium and a 1% graphene-chitosan hybrid titanium substrate. Figure S2: Effects of graphene-chitosan hybrid implants on the attachment of mammalian cells. Table S1. Composition of artificial saliva  
**Author Contributions:** Conceptualization, H.-P.L. and J.K.; methodology, H.-P.L. and J.K.; software, H.K. and S.P.; validation, H.K., S.P., M.-K.J., H.-P.L., and J.K.; formal analysis, S.P., H.K., K.S.C., S.K., and Y.G.; investigation, H.K. and S.P.; resources, S.P., H.K., K.S.C., C.P., and S.K.; data curation, H.K. and S.P.; writing-original draft preparation, H.K. and S.P.; writing--review and editing, H.K. and S.P.; visualization, H.K. and S.P.; supervision, J.K.; project administration, H.-P.L. and J.K.; funding acquisition, H.-P.L. and J.K.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korea government (2019R11A3A0106345 and NRF-2019M3A9H1103737). This work was also supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Agriculture, Food and Rural Affairs Research Center Support Program, funded by the ministry of Agriculture, Food and Rural Affairs (MAFRA) (Project No. 714002). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 2020R1F1A1076982). This study was supported by a grant (BCRI19043) of Chonnam National University Hospital Biomedical Research Institute.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 4 Results
The analysis confirmed that, although the differences between the scheduling variants were within 1%, the combined effect on the makespan, time buffer, and savings pointed to a clearly optimal solution (Exp. 3). Additionally, the model proved robust to variations in the prioritization weights, with a \(\pm\)20% change causing a production time deviation of no more than 6 min (\(\approx\)1%). These findings demonstrate both the effectiveness and stability of the proposed scheduling approach. The accuracy of the used RTLS sensors was also evaluated in the experiments. The localization accuracy of the monitored objects within the production halls was tested repeatedly, with the average localization accuracy being \(\pm\)15 cm, which is in accordance with the manufacturer's declared accuracy. Such accuracy enables the effective monitoring of production activities in real time and significantly contributes to the reliability of the simulation results. The results presented clearly demonstrate the practical benefit of the proposed approach to production planning. Compared to the original planning method, the production time (makespan) was reduced by an average of 1 h and 42 min, which directly translated into economic savings of approximately EUR 26,533/month based on internal regulations. These savings confirm the significant efficiency of introducing digital twins and simulations into the planning process. This is a new insight compared to existing approaches, which often do not use real data obtained in real time. Moreover, the clear quantification of these savings confirms the significant scientific contribution of this approach.
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
## 3 The Cross-Entropy Algorithm
Let us now formulate the CE algorithm for the multiple change-point problem. Let \(C_{1},C_{2},\ldots,C_{N}\) be a sequence representing the locations of change-points. Since the location space is finite, we can carry out the CE combinatorial optimization algorithm, and generate \(M\) rows of random samples. So here we deal with the objects \(C^{(j)}=(C_{1}^{(j)},C_{2}^{(j)},\ldots,C_{N}^{(j)})\overset{iid}{\sim}f( \cdot,v)\), \(j=1,2,\ldots,M\). The external parameters are \(N_{max}\), \(\beta\), and \(\varepsilon\); we apply the CE.AR algorithm at \(1\leq N\leq N_{max}\). The normal distribution is served as \(f(\cdot,v)\). In practice, the users need to specify the external parameters. The main steps of our proposed CE.AR algorithm are as follows (Algorithm 2). ```
1: Set initial parameters of normal distribution for \(\mu_{(0)}=\left(\mu_{(0),1},\mu_{(0),2},\ldots,\mu_{(0),N}\right)\) and \((\sigma^{2})_{(0)}=\left((\sigma^{2}_{(0),1}),(\sigma^{2}_{(0),2}),\ldots,( \sigma^{2}_{(0),N})\right)\). The length of both vectors is \(N\) corresponding to \(N\) change-points. Set \(k=1\). 2: Generate a sequence of random samples \(C^{(1)},C^{(2)},\ldots,C^{(M)}\) from the normal distributions with parameters \(\left(\mu_{(k-1)},\sigma^{2}\right)_{(k-1)}\), where \(C^{(j)}=\left(C_{1}^{(j)},C_{2}^{(j)},\ldots,C_{N}^{(j)}\right)\), for \(j=1,2,\ldots,M\), where the vector \(\left(C_{1},C_{2},\ldots,C_{N}\right)\) is a vector of simulated change-points. 3: For each \(j=1,2,\ldots,M\), arrange \(C_{1}^{(j)},C_{2}^{(j)},\ldots,C_{N}^{(j)}\) from smallest to largest and store into \(c^{(j)}\). 4: Evaluate the objective function (8) for each \(c^{(1)},c^{(2)},\ldots,c^{(M)}\). Choose the elite samples \(\{c^{(j)},F(c^{(j)})\}\), where \(j=1,2,\ldots,M_{\mathit{ell}}\). Updating level parameter \(\hat{\alpha}_{k}\). 5: For all \(i=1,2,\ldots,N\), estimate the parameters \(\mu_{(k),i}\) and \((\sigma^{2}_{(k),i})\) by solving the equations: \[\widehat{\mu}_{(k),i}=\frac{\sum\limits_{j\in J}c_{i}^{(j)}}{M_{\mathit{ dilute}}},\qquad\left(\widehat{\sigma}^{2}_{(k),i}\right)=\frac{ \sum\limits_{j\in J}\left(c_{i}^{(j)}-\mu_{(k),i}\right)^{2}}{M_{\mathit{ elite}}},\] where \(J\) is the set of indices of the best performing samples. 6: Stopping criterion is \(\max_{i}(\sigma^{2}_{(k),i})<\varepsilon\). 7: If the stopping criterion is met, so stop the process, then identify the combination of the positions of change-points and compare its performance score with the score of \(N=0\) from (9). If \(\mathrm{MDL}_{N\geq 1}(\cdot)>\mathrm{MDL}_{N=0}(\cdot)\), then there are no change-points. Estimate the parameters of the model for all obtained segments. If the stopping criterion is not met, set \(k=k+1\) and iterate from step 2. ```  
**Algorithm 2** The CE algorithm.
###### Abstract
[PERSON] [8] used the equivalence theory to equate the helical loop with a magnetic loop and applied the finite element method to study the attenuation characteristics of the helical loop antenna for near-bit short-range wireless transmission technology, providing a solid theoretical foundation for near-bit measurements. [PERSON] and [PERSON] [9] proposed a method of adding a thin metal layer outside the helical coil, effectively solving the problem of damage to helical loop antennas during downhole operation. They also used extremely low-frequency electromagnetic waves to excite TEM waves, achieving longer-distance transmission under the conditions of multiple relays. [PERSON] [10] optimized the parameters of downhole transmitting antennas using magnetic loop excitation technology, initially proposing a solution to the issue of electromagnetic wave signal attenuation caused by metal casings. Research on electromagnetic wave wireless transmission often focuses on the wireless transmission technology itself, such as Electromagnetic Measurement-While-Drilling technology. However, for downhole wireless control systems, the transmitting antennas are typically placed in environments surrounded by metal casings. Due to the unique  
Figure 1: Electromagnetic wave wireless transmission model and three types of antennas. properties of these casings, they greatly influence downhole measurements and transmissions. Current research on the effects of metal casings on EM wave signal transmission is relatively scarce, and the underlying mechanisms of how casings affect EM waves are unclear. While the acquisition of real-time downhole dynamic data can significantly aid technical personnel in taking timely measures, especially in guiding development and production, field tests have shown that EM wave transmission distances in cased wells are usually shorter than in open-hole wells. At the same depth, the intensity of EM signals received at the surface is often weaker in cased wells. Moreover, the complexity of the downhole environment renders the signal attenuation patterns of helical loop antennas unclear, posing challenges to the design and deployment of downhole wireless control systems. This paper focuses on exploring the optimization of a third type of helical loop antenna for EM wave transmission. Employing the principle of equivalence, we treat the helical loop transmitting antenna as a point magnetic dipole source, replacing the physical helical loop antenna with an imposed magnetic dipole current. On this basis, a two-dimensional axisymmetric model has been established to clearly define the key mechanisms by which metal casings affect EM wave transmission and propose corresponding solutions to improve the efficiency of EM wave wireless communication transmission. Further, this study delves into the signal transmission characteristics of EM waves in cased wells. By applying the theory of equivalence, we have not only improved the efficiency of model establishment and solution but also provided a new perspective for understanding and resolving practical EM wave transmission issues in operational settings.
## 2 Antenna Design and Analysis of the Proposed Radiator  
The proposed MIMO radiator's feeding mechanism and 3D perspective are depicted in Figure 1a,b. A pair of rectangular DRAs, which were of Eccostock HIK with \(\epsilon\)r = 9.8 and tan\(\delta\) = 0.002, were fitted over a metallic ground plane. A novel-shaped, conformal metal strip was installed on the DRA face by the support of adhesive (Quick Fix). At the same time, the radiators were excited through the singly fed mechanism. At ports 1 and 2, both RDRAs, which were mirror images of each other, were utilized. To improve isolation, an S-shaped DGPS slit was etched into the upper half of the base plane among the two radiators. The desired MIMO antenna was supplied by a 50 \(\Omega\) metalic probes both ports 1 and 2. The optimized parameter for the proposed MIMO RDRA was attained based on rigorous parametric analysis using a finite integration technique (FIT)-based simulation tool, i.e., CST MWS [12], and the values are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Element** & **Parameter** & **Dimension (mm)** \\ \hline PEC Ground Plane & L\({}_{1}\), W\({}_{1}\) & 50, 50 \\ \hline RDRA & H, W, D & 26, 25, 20 \\ \hline DGPS, Arc & h\({}_{5}\), h\({}_{6}\), \(\theta\) & 2.5, 30, 80\({}^{\circ}\) \\ \hline Conformal Metal Strip & h\({}_{2}\), h\({}_{3}\), h\({}_{4}\), w\({}_{2}\) & 12, 2, 10.5, 10 \\ \hline \end{tabular}
\end{table}
Table 1: Optimized values of the proposed MIMO antenna’s parameters.  
Figure 2 illustrates the evolution and building of the intended RDRA antenna. Figure 2a presents the structure that was made up of two rectangular DRs, each of which was fed via a 50 \(\Omega\) coaxial probe. However, a novel conformal metal strip was used in Step 2, which comprises five individual cut strips. As depicted in Figure 2b, a DGPS was introduced at an optimized location to obtain the desired results.  
Figure 1: Graphic diagram of the desired MIMO antenna: (**a**) 3D view point; (**b**) feed and DGPS structure.