esa-sceva/satcom-chunk-collection huggingface dataset:
# Decoupling Control of Multi-DOF Supporting System of MLDSB
## 3 Mathematical Model of the Multi-DOF Supporting System
### Coupling Relationship Analysis  
When the rotor is in the rotation center, the initial gap is \(\delta_{0}\). According to the geometric relation, the gap at any time is shown as follows.  
\[\left\{\begin{array}{l}\delta_{x}^{A}=\delta_{0}+(x_{c}-l_{a}\sin\theta_{x}) \\ \delta_{x}^{B}=\delta_{0}+(x_{c}-l_{b}\sin\theta_{x})\\ \delta_{y}^{A}=\delta_{0}+(y_{c}-l_{a}\sin\theta_{y})\\ \delta_{y}^{B}=\delta_{0}+(y_{c}-l_{b}\sin\theta_{y})\\ \end{array}\right. \tag{9}\]  
where, \(\delta_{0}\) is initial gap, the unit is m. \(\delta_{x}^{A}\) is gap of bearing \(A\) in \(x\)-direction, the unit is m. \(\delta_{x}^{B}\) is gap of bearing \(B\) in \(x\)-direction, the unit is m. \(\delta_{y}^{A}\) is gap of bearing \(A\) in \(y\)-direction, the unit is m. \(\delta_{y}^{B}\) is gap of bearing \(B\) in \(y\)-direction, the unit is m.  
So  
\[\left\{\begin{array}{l}x_{A}=\delta_{x}^{A}-\delta_{0}=x_{c}-l_{a}\sin\theta_ {x}\\ y_{A}=\delta_{y}^{A}-\delta_{0}=y_{c}-l_{a}\sin\theta_{y}\\ x_{B}=\delta_{x}^{B}-\delta_{0}=x_{c}+l_{b}\sin\theta_{x}\\ y_{B}=\delta_{y}^{B}-\delta_{0}=y_{c}+l_{b}\sin\theta_{y}\\ \end{array}\right. \tag{10}\]  
According to Equations (7)-(10), there is interference between the \(x\) and \(y\) direction of the left unit and right unit. In other words, there is a coupling effect between the spatial positions and postures which can affect the operation stability of the MLDSB. There is no coupling between the axial direction and radial direction. Additionally, there is a space position coupling between the radial direction and rotation direction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Viscosity** & **Initial Pressure** & **Bias Voltage** & **Bias Current** \\ \(\mu\)**/(Pa-s)** & \(p_{10}\)**/MPa** & \(u_{0}\)**/V** & \(i_{0}\)**/A** \\ \hline
1.3077 \(\times\) 10\({}^{-3}\) & 1 & 5 & 1.2 \\ \hline Air permeability & \(\mathrm{Rotor\ mass}\) & \(\mathrm{Supporting\ area}\) & Stiffness factor \\ \(\mu\)/(\(\mathrm{H/m}\)) & \(\mathrm{m/kg}\) & \(Ae\)/\(\mathrm{mm}^{2}\) & \(k_{1}\)/(\(\mathrm{L/min/V}\)) \\ \hline
4\(\pi\times\) 10\({}^{-7}\) & 10 & 2937 & \(1\times 10^{-4}\) \\ \hline Turns of coil & \(\mathrm{Angle}\) & \(\mathrm{Liquid\ resistance}\) & \(\mathrm{Supporting\ cavity}\) & \(\mathrm{flow}\) \\ \(N\)/Dimensionless & \(\theta\)/\({}^{\circ}\) & \(\mathrm{\dot{R}_{h}}\)/(\(\mathrm{N\cdot s/m}\)) & \(q\)/(\(\mathrm{L/min}\)) \\ \hline
633 & 22.5 & 6.8\(\times\)109 & 5.6471 \\ \hline Flow coefficient & \(\mathrm{Magnetic\ area}\) & \(\mathrm{Liquid\ film}\) & \(\mathrm{Extrusion\ area}\) \\ \(\overline{B}\)/Dimensionless & \(A_{s}\)/\(\mathrm{mm}^{2}\) & \(h_{0}\)/\(\mathrm{\SIUnitSymbolMicro m}\) & \(A_{b}\)/\(\mathrm{mm}^{2}\) \\ \hline
0.71 & 1080 & 30 & 2500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the MLDSB.
# [PERSON] and Mendell-Elston Estimation of the High-Dimensional Multivariate Normal Distribution
## 5 Results
### Mean Execution Time  
Relative mean execution time, \(t_{ME}\) and \(t_{MC}\) for the me and mc algorithms respectively, is summarized in Figure 2 for 100 replications of each algorithm. As absolute execution times for a given application can vary by several orders of magnitude depending on com  
Figure 1: Estimation error in Genz Monte Carlo (mc) and Mendell-Elston (me) approximations. (mc only: single replication; requested accuracy \(\epsilon=0.01\).)  
puting resources, the figure presents the ratio \(t_{\text{ME}}/t_{\text{MC}}\) which was found to be effectively independent of computing platform.  
For estimation of the mvn in moderately few dimensions (\(n\lesssim 30\)) the me approximation is exceptionally fast. The mean execution time of the mc method can be markedly greater--e.g., at \(n\approx 10\) about 10-fold slower for \(\rho=0.1\) and 1000-fold slower for \(\rho=0.9\). For small correlations the execution time of the mc method becomes comparable with that of the me method for \(n\approx 100\). For the largest numbers of dimensions considered, the Monte Carlo method can be substantially faster--nearly 10-fold when \(\rho=0.3\) and nearly 20-fold when \(\rho=0.1\).  
The scale properties of mean execution time for the me and mc algorithms with respect to correlation and number of dimensions may be important considerations for specific applications. The me method exhibits virtually no variation in execution time with the strength of the correlation, which may be an attractive feature in applications for which correlations are highly variable and the dimensionality of the problem does not vary greatly. For the mc method, execution time increases approximately 10-fold as the correlation increases from \(\rho=0.1\) to \(\rho=0.9\), but is approximately constant with respect to the number of dimensions. This behavior would be desirable in applications for which correlations tend to be small but the number of dimensions varies considerably.
In this context, some studies analyze specific mortar production factories, thus considering the real distances between them and their suppliers: a different factory location affects distances as well as transport environmental impacts and overall results [6]. [PERSON] et al. [17] adopted a complex approach, linking LCA with geospatial analysis, to estimate transportation-related impacts. In this way, different mixtures were assessed for the specific factory location, finding that the transportation impact of each mix highly varied depending on the factory location and on the construction site; raw materials suppliers and surrounding street networks were taken into consideration as well [17]. Other research examines different transport scenarios for raw and secondary materials suppliers [31]. For instance, the study by [PERSON] et al. aimed to establish the limit in delivery distance of recycled aggregate, to determine at what point recycled scenarios show no benefit over the conventional ones [30]. In the specific case studied, the sensitivity of transports demonstrated that long delivery distances (i.e., 100 km or more, one-way) would result in outweighing of the environmental benefits [30]. Based on this literature background, the contribution of the present study is twofold. Firstly, it aims to evaluate the environmental performance of mortars that incorporate composite materials from industrial waste as fine aggregates. To the best of authors' knowledge, the investigation of mortars incorporating recycled composite and carbon dust constitutes a novelty in the field of mixtures environmental performance assessment. Secondly, the research investigates the role of transports on the environmental performance of mortars with recycled aggregates, by performing both global sensitivity and scenarios analyses. The paper is organized as follows. Section 2 presents the overall research framework, i.e., the phases of the research in terms of which different evaluation approaches to the impact of materials' transport have been explored. Section 3, \"Materials and Methods\", describes the investigated materials, i.e., the mortars with recycled aggregates and their corresponding reference mixtures, and it considers the methods of the LCA study in relation to the reference international standards. Section 4 reports the results of the study. Discussion of the study and its main conclusions can be found, respectively, in Sections 5 and 6.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 2 Related Works  
Our work intersects three key research areas: collaborative caching systems, privacy protection mechanisms, and MANET-based solutions. Although these areas have been studied separately, our approach uniquely combines elements from each to create a distributed LBS system that preserves privacy. We build on collaborative caching techniques for efficient data distribution, privacy protection mechanisms for user security, and MANET architectures for infrastructure-independent operation. This section analyzes relevant work in each area and explains how our approach advances beyond their limitations.
## 3 Results and Discussion
### MOF Processing
#### 3.3.2 Membranes  
MOFs materials were processed into filters using the PMMA as the support. MOFs in the powder form were suspended in a PMMA solution in dichloromethane and cast into a mold. After solvent evaporation, an MOF@PMMA membrane was obtained (MOFs preserving their structure and crystallinity, Figure S3).  
As observed in Figures 10 and 11, HKUST and Mg-MOF-74 are homogeneously distributed over the entire membrane. Membranes with a higher amount of MOF material can also be obtained, but they are fragile and brittle. A MOF:PMMA mass ratio of 1:3 is the highest possible without compromising the membrane elasticity. Humidity and temperature tests of these membranes were carried out. Due to the insolubility of PMMA in water, membranes show no change at high RH. On the other hand, at temperatures above 140 \({}^{\circ}\)C, membranes become fragile and break.  
Figure 10: MgMOF-74 and HKUST supported in a PMMA membrane.  
Figure 9: Powder X-ray diffraction of HKUST pellets after different cycles of increase–decrease of temperature; (inset) images of the pellets after each cycle of thermal treatment.
# Study of the Effects of Plasma Pretreatment on the Microstructure of Peanuts
## 1 Introduction
[22] found that plasma treatment did not significantly change peanut oil quality, acid value reduction was more conducive for peanut storage, and the molecular structure of peanut oil was no damaged. [PERSON] et al. [23] discovered that peanut protein's water absorption, emulsification properties, and solubility were significantly enhanced. [PERSON] et al. [24] observed that plasma treatment significantly altered the molecular structure and particle size distribution of peanut protein, thereby significantly improving peanut protein functionality. Despite the remarkable achievements of plasma technology in detoxifying peanuts and enhancing their quality, there have been few reports exploring its impact on peanut microstructure. Moreover, plasma ionization is influenced by numerous factors, including power level, relative humidity, gas composition, and treatment duration [25]. Therefore, this study aims to fill this gap by investigating the effects of cold plasma treatment on the microstructure of peanut seed coats and embryo surfaces under a scanning electron microscope (SEM). The findings could provide essential theoretical support and practical guidance for peanut cultivation, efficient oil extraction, and deep processing, contributing to the sustainable development of the peanut industry.
# Optimization of Coverage and Capacity Using Smart Antennae
## 4 Evaluation
### Different Numbers of BSs  
The test area remained at 1000 m by 1000 m, with a fixed number of 1000 user equipment (UE) items. However, the number of base stations (BSs) was varied, including 2, 4, 6, 8, 10, and 12. The experimental parameters were set as shown in Table 5.  
The results of the experiments are presented in Figure 8, which shows that the coverage rate increased as the number of BSs increased. This was because, with a higher number of BS, there were more base stations available to provide service to the UEs. As a result, UE items had a higher chance of being served by at least one of the base stations, leading to an improvement in the overall coverage rate. Taking the number of base stations into consideration, when the base station count increased to 12, utilizing omnidirectional antennas covered only 38.3% of the service area. However, employing smart antennas enhanced the signal coverage rate to 78.1%. This approach effectively adjusted the radiation pattern of the base stations, transmitting signals in the direction of users, thereby substantially improving the signal coverage rate.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 2 Background
### Software-Defined Radios
#### 2.2.3 SoapySDR  
SoapySDR is a general purpose library for interacting with a variety of SDR devices [55]. It enables researchers and developers to build software that operates across many different SDR platforms using the same functional API. SoapySDR is part of the open-source Photoshware SDR ecosystem [56], which provides SDR development tools including a GUI similar to GNURadio Companion.
## 5 DFIG Connected at Electrical Grid
### B2B Power Converter Analysis  
The B2B power converter topology used in this analysis comprises a rectifier and an inverter connected in series to a DC bus in parallel with a capacitor. This converter type is bidirectional,allowing operation in the four quadrants of the XY plane, and, therefore, both converters operate as rectifiers or inverters. The difference between the rectifier and the inverter is the definition of the power sign. The active and reactive powers through the rotor and the stator are controlled by adjusting the amplitude, phase and frequency of the voltage introduced to the rotor. The rotor-side converter provides a three-phase voltage at a variable amplitude and frequency, controlling the generator torque and the reactive power exchange between the stator and the electrical grid. The grid-side converter exchanges the active power with the electrical grid obtained or injected by the rotor-side converter from the rotor. The output frequency of the grid-side converter is constant, while the output voltage varies depending on the exchange of the active and reactive powers with the electrical grid. Because of the greater flow of power injected by the stator generator, the B2B power converter is designed to operate between 25% and 30% of the nominal power of the generator. This reduces the design cost of the power converter and the losses of the generation scheme. Thus, the wind turbine's generated power is distributed between the stator and the supply through the rotor. The three-phase B2B power converter is shown in Figure 6.  
The converter is modeled with bidirectional switches, where the ideal switch is created by a semiconductor with an anti-parallel diode allowing current to flow in both directions. The semiconductor used is an isolated gate bipolar transistor (IGBT) with ideal operation. For the synchronization and power injection of the B2B power converter toward the electrical grid, it is necessary to know the phase's sequence and to synchronize one phase of the grid by the phase-locked loop (PLL) method. This method requires estimating the grid angle to accomplish the necessary transformations using the d-q reference frame of the grid voltage.
## 5 Conclusions  
Within the limitations of the study, we can arrive at the conclusion that the increased number of MDIs reduces the amount of microstrains around implants and in the posterioredentous area. The highest microstrains were achieved under 150 N forces and unilateral loading in the two-MDI model. In the one-MDI model, anterior loading with both 50 and 100 N elicited higher peri-implant microstrains than posterior loads. Posterior loads elicited higher peri-implant microstrains around the posterior than anterior MDIs. Unilateral loadings elicited higher strains than bilateral loads, especially in implants on the loaded side. In the posterioredentous areas, the highest microstrains were recorded in the one- and two-MDI models. Although none of the recorded strains interfered with bone reparatory mechanisms, precautions should be taken, and additional investigations should be conducted for the one- and two-MDI models.  
**Author Contributions:** Conceptualization, N.P. and A.C.; methodology, I.K., D.P., A.C. and N.P.; writing--original draft preparation, D.P. and I.K.; writing--reviewing and editing, N.P. and A.C.; Data analysis: A.C.; Data Interpretation: A.C., N.P., D.P. and I.K.; funding acquisition: N.P. and A.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The raw data supporting the conclusions in this article are available from the authors on request.  
**Acknowledgments:** The authors thank the Institut Straumann AG for the donation of the Ti-Zr (Roxoldid(r)) alloy mini-implants used in this study.  
**Conflicts of Interest:** The authors declare that this study received funding from Institut Straumann AG. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
# Parameterized Algorithms in Bioinformatics: An Overview
## 2 Genome Comparison and Completion
### Genomic Distances
#### 2.1.2 Reversal Distance  
**Problem Description.** A _reversal_[15] is a rearrangement reversing the order of the characters in any substring of the genome. When considering signed genomes, a reversal additionally switches all the signs in the reversed substring (see Figure 2).  
**Results.** Sorting by Reversals is polynomial-time solvable in the signed-permutations model [16]. However, it is NP-hard for unsigned permutations [17] and for strings, even when \(\mathtt{occ}=2\)[18] and for binary signed strings [19]. For unsigned permutations, Sorting by Reversals is easily FPT for \(k\), using the fact that reversals never need to cut at positions that are not breakpoints [20]. For  
Figure 1: Two DCJ operations turn abcdefghxyz into abcxyzghdef (breakpoints are gray arcs): 1. split cd and hx and join cx and dh, and 2. split fg and join zg.  
Figure 2: A signed genome +a-b+c+d-e+f turned into +a+e+c+d+b+f by a signed reversals on +c+d followed by one on -b-d-c-e.  
balanced strings, a possible parameter, \(b_{\max}\), is the number of blocks of the input strings, where a _block_ is a maximal factor of the form \(\mathsf{a}^{n}\) for some character \(\mathsf{a}\). Sorting by Reversals can be solved in \(O^{*}\left(b_{\max}^{O(b_{\max})}\right)\) time in both the signed and unsigned variants. [21]. As for DCJ, the variant restricting scenarios to reversals preserving common intervals is also NP-hard, and FPT for a parameter given by the common interval structure [22].  
**Notes.** Other rearrangements can be considered, such as _transpositions_ (two consecutive factors are swapped), and the _prefix_ and/or _suffix_ variants of reversals and transpositions (that is, reversals and transpositions affecting the first or last character of the sequence, respectively) (see, e.g., [23]).  
No polynomial-time algorithm is known for computing these rearrangement distances, although NP-completeness is still open, notably on permutations for signed prefix reversals and prefix transpositions. Fixed-parameter tractability results may be achieved in the permutation model using the relationship between these rearrangements and breakpoints [24], and in the string model using the number of blocks \(b_{\max}\) as a parameter [21; 25].
# Influence of Fiber Mixing Process on the Cracking Resistance of Cold Recycled Asphal Mixture
## 4 Results and Discussions  
### Resistance to Cracking  
#### 4.1.1 The Fracture of Indirect Tensile Tests  
Based on the results of the IDT test, the load-displacement curve and the relationship between splitting strength, fracture work, and fiber mixing processes are shown in Figure 9. It was found that the fiber mixing process has significant effects on the fracture cures, splitting strength, and fracture work of IDT specimens. As presented in Figure 9a, the rise rate of the loading curves of the specimens prepared by process B, process C and process D were smaller than that of the specimen prepared by process A. In addition, the displacement value corresponding to peak load of the specimens prepared by process B, process C and process D were obviously larger than that of the specimens prepared by process A. This indicates that the fracture toughness of fiber CRAM is better than that of CRAM with no fiber. This phenomenon could be attributed to the fact that the fiber plays a role of reinforcement and bridge in the mixture. In addition, the modification effect of fiber on the CRAM is also strongly affected by the fiber mixing process. As illustrated in Figure 9b, for both the splitting strength index and the fracture work, the order of IDT specimens prepared by different processes is: process C > process B > process A > process D. The specimens made by process C have the largest splitting strength and fracture work, while the specimens made by process D have the smallest splitting strength and fracture work. As reported in the literature [18], the larger the splitting strength or fracture work, the better the resistance to cracking of the mixture. Furthermore, it is worth noting that the splitting strength of specimens made by process D is 13.72% smaller than that of specimens made by process A, and the fracture work is 13.80% smaller than that of specimens made by process A. The reason for this phenomenon is that the fiber is well dispersed in the specimens prepared by process C, which contributes to obtaining a better crack resistance. Thus, for the fiber CRAM with improper fiber mixing processes, the addition of fiber will not enhance the crack resistance, but will reduce the crack resistance of the mixture. The fiber mixing process selection is very important in engineering practice.
## 4 Results
### Performance Comparison Based on Energy Concentration  
#### 4.1.1 Measured Signals  
The measured HR signals are down-sampled to 1.33 Hz and analysed using quadratic TFDs including the described methods, ADTFD, AOKTFD, MBD, S-method and the spectrogram where \(N=424\) and \(M=512\). Figure 2 illustrates that the ADTFD results in high energy concentration of both weak and strong signal components while no other TF method was able to achieve high energy concentration for the weak component. The aforementioned experiment is then repeated on 35 such HRV signals, and, for each TFD, the energy concentration measure in Equation (25) is computed. The mean and standard deviation of the energy concentration measure for all the TFDs are shown in Table 1. The ADTFD achieves the minimum value and then also the highest energy concentration. In order to examine if the results are statistically significant between the ADTFD and the other TFDs described in Section 3, we perform a student's \(t\)-test. In all cases, the \(p\)-values are less than 0.0001.  
Figure 2: Time-frequency (TF) analysis of measured heart rate signals; (**a**) adaptive directional time-frequency distribution (ADTFD) (\(a=3\), \(b=8\)); (**b**) Adaptive Optimal Kernel Time-frequency Distribution (AOKTFD); (**c**) modified B distribution (MBD) (\(\beta=0.05\), lag window = 64); (**d**) S-method (Short time Fourier transform (STFT) window length = 91, frequency axis convolution window length = 21); (**e**) Spectrogram (window length = 91).