esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning
## 4 Result and Discussion
### Performance of SPP
#### 4.1.1 Single-Frequency
From Table 4, we can conclude that the RMS of the horizontal  
Figure 11: Positioning error scatters of B3I SPP without DCB or TGD correction at BRCH station. Figure 12: Boxplot of the 3D positioning errors for single-frequency SPP distributions in the non-corr (green), dcb-corr (red) and tgd-corr (blue) and schemes for the period of April 2020.  
component can be up to 2-4 m, and the RMS of the vertical component is about 6 m, when the TGD or DCB is uncorrected. In the \"tgd-corr\" and \"dcb-corr\" schemes, a significant improvement can be considered in positioning accuracy, because the code bias is corrected by TGD or DCB parameters. From Table 4 we can derive that the positioning accuracy can reach 2 to 4 m in the vertical component and 1 to 2 m in the horizontal component based on the TGD and DCB parameters correction schemes. However, B3I signal is relatively special, and we have mentioned before that the positioning results were not affected by the code bias because the satellite clock of broadcast ephemeris was referred to the B3I signal. To further verify our conclusions, Figure 13 describes the pseudorange residuals in different schemes at station BRCH; in addition, the different satellites in the figure are represented in different colors. As we can see from the figure, the pseudorange residual of each satellite calculated by the \"non-corr\" scheme has a significant systematic bias, and the offset range related to the satellite is several meters. On the contrary, it is noteworthy that the systematic bias from the results of the \"tgd-corr\" and \"dcb-corr\" schemes are significantly reduced. Furthermore, pseudorange residuals distribution based on the TGD and DCB parameters correction schemes look more reasonable and closer to zero-mean random noise. Therefore, these findings further confirm that the TGD and DCB parameters should be modified when the BDS-3 BII, BIC and B2a signals are used for single-frequency positioning. Furthermore, daily variation in the number of BDS-3 satellites and the value of positioning dilution of precision (PDOP) on 1 April 2020 is also given, as shown in Figure 5:0 We found that the number of BDS-3 satellites has increased significantly worldwide at the present stage; therefore, the correction of TGD or DCB parameters is even more important for stations around the world.
# Can IMU Provide an Accurate Vertical Jump Height Estimate?
## 4 Discussion
A study by [14] has determined the reliability of an IMU placed on the back of the participant. The ICC value of 0.89 and an McV of 7.1% were reported. The presented results further contribute to the previously mentioned findings of [17], who used an IMU placed above the ankle to calculate the flight time of a drop jump (DJ) from vertical acceleration, as well as [26] who used a commercial Polar V800 and a stride sensor to estimate VJ height in SQJ and CMJ tasks using the FT method. Taken as a whole, the findings indicate that VJ height can be effectively calculated from accelerometer data only when the sensor is placed on the foot. The presented approach is an alternative to thefrequently used positioning of the sensor on the back of the participant [12; 14; 21] in terms of simplifying the calculation of the JH as there is no need to calculate the orientation of the sensor unit to extract the vertical acceleration component during the movement of the trunk. In addition, the present study provides the data related to the VJ height estimate in elite athletes using the proposed sensor placement which is lacking in [26]. The main limitations of the present study are related to the small size of the used sample and its high homogeneity in terms of JH. The fact that the sensor is mounted on one foot can present a general limitation in terms of FT measurement. However, this is not supported by the presented results obtained on elite athletes.
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 5 Results  
### Main Effect  
We first evaluated the main effects of predictors and how they influenced the response variable. Figure 4a shows the mean effect at the levels of NI. The mean ISR is approximately 98% when NI is 250, and it decreases to 59% when NI is increased to 500. The mean ISR is 40% and 33% at NI equal to 750 and 1000, respectively. From Figure 4a, NI shows a negative effect on the ISR.  
Figure 4b shows the mean effect at the levels of NN. The mean ISR was approximately 58% when NN was nine, and it decreased to 43% when NN was 25, 41, and 49. In Figure 4b, the NN showed a non-linear effect on the ISR. By grouping NN into two categories NN less than 10 and NN greater than 10, NN showed a negative effect on the ISR.  
Figure 4c shows the mean effect at the levels of RB. The mean ISR was approximately 9% when RB was 1 Mbps, and it increased to 38% when NI was 5 Mbps. The mean ISR was 65% and 99% at RB equal to 10 Mbps and 20 Mbps, respectively. In Figure 4c, RB shows a positive effect on the ISR. The ranges of the four boxplots were quite different compared to the other predictors. Although the ISR appeared to increase according to a line in view of their medians, it also seemed to increase according to a log(RB) in view of their ranges. There were some outliers at RB = 20, which is extremely small compared to other observations.  
Figure 4d shows the mean effect at the levels of RD. The mean ISR was approximately 45% at each level of RD, which indicated the absence of a main effect. In Figure 4d, RD shows no effect on the ISR.  
In addition to mean plots of the four main effects, we performed [PERSON]'s HSD test (Table 2). For NI, all pairs 250-500, 250-750, 250-1000, 500-750, 500-1000, and 750-1000 were significantly  
Figure 4: Mean plot for the main effect: (**a**) ISR as a function of the number of interest (NI), (**b**) ISR as a function of number of nodes (NN), (**c**) ISR as a function of router bandwidth (RB), and (**d**) ISR as a function of router delay (RD).  
different from each other. For NN, Group 1, which included nine nodes, was significantly different from the group, which included 25, 41, and 49 nodes. For RB, all pairs 1-5, 1-10, 1-20, 5-10, 5-20, and 10-20 were significantly different. For RD, none of the pairs were significantly different. The estimates in Table 2 show the size of the difference in ISR means for the pairs.
# Implementation of Tools for Lessening the Influence of Artifacts in EEG Signal Analysis
## 4 Implementation
### Calculation of Artifact-Free Unmixing Matrix:  
_fta_artifacts_free_ica_partially_removed_components_method_  
This function calculates an artifact-free version of the ICA unmixing matrix to obtain ICs without artifact influence, making use of an MSF. The correlations between EOG data and independent components are used to remove these components at the samples marked as artifacts by the MSF.  
This is the syntax to use this function:  
_cleaned_data_=_fta_artifacts_free_ica_partially_removed_components_method_(data_eeg, MSF, fig); this function also plots an illustration of the correlation between EEG and EOG signals before and after the process, when _fig_ = 1.  
The steps of this method are as follows:  
1. Obtain VEOG and HEOG channels.  
Obtain the unmixing matrix from ICA of the EEG data free of artifacts.
* Recalculate the ICs of the complete EEG data using the artifact-free unmixing matrix calculated previously.
* Calculate the correlation coefficients between the ICs and EOG signals, as in the previous method.
* Perform the partial elimination of the ICs related to ocular artifacts using the correlation coefficients, as in the previous method.
* This function makes use of the following:
* _ft_selectedata(cfg, data_egg)_ to select EOG data.
* _fta_ica_from_artifact_free_data_ to obtain artifact-free ICs and the unmixing matrix.
* _ft_componentanalysis_ to apply ICA using the artifact-free unmixing matrix.
* _fta_corroeff_ to calculate the correlation coefficients.
* _fta_select_comp_from_coeff_correlation_ to select the ICs with a high correlation with EOG signals.
* _fta_channels_from_ic_partial_reject_components_ to partially reject ICs, where MSF = 1. The inputs to this function are as follows: 1. _data_egg_: EEG data with artifacts.
2. _MSF_: Logical sequence with Fieldtrip structure compatible with _data_egg_, where artifacts are marked as 1 and other samples as 0.
3. _fig_: Logical value that takes the value of 1 to plot the sum of the correlation coefficients, before and after applying the method. The output of this function is as follows: 1. _clean_data_: Fieldtrip-structured data with the influence of ocular artifacts diminished by partially removing artifacts using an artifact-free ICA unmixing matrix and MSF. In the examples section, a small illustrative excerpt of Matlab code will be shown.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 3 SON Applications  
### Basic SON Applications in 2G3G/4G/5G Networks and Operation Rationale  
As far as the key applications for existing mobile networks are concerned, 3 GPP and NGMN overlap in some of them; however, the major ones are the following [21; 22; 23; 27; 40; 41; 57; 58; 59; 60; 61; 62; 63; 64]:  
1. Automatic neighbor relation (ANR).
2. Automatic radio network configuration (initial radio and core parameters setting).
3. Mobility Load Balancing optimization (MLB).
4. Coverage and capacity optimization (CCO).
5. Mobility Robustness Optimization (MRO).
6. Automated configuration of physical cell identity (Cell ID (CI) and PCI optimization).
7. Interference reduction.
8. Minimization of drive testing (MDT).
9. Energy saving (ES).  
Figure 6: Interconnection of SON server cluster with UMTS and LTE OSS (centralized architecture).  
RACH (Random Access Channel) optimization.
* Inter-cell interference coordination (ICIC).  
We believe that the most popular applications are ANR, MLB and CCO; thus, we explain their operation rationale, the factors that led to the need for them, and the issues that they resolve.  
Before the development of SON systems, configuration, optimization, and healing processes were performed manually based on a time-consuming and error-prone lifecycle. As an example, problems such as interference, capacity, and coverage holes mitigation were identified through KPIs monitoring, CM/PM data collected from the OSS, alarms collection, drive test campaigns or even subscriber complaints. The next step was the manual analysis of KPIs and drive test results, and as a last step the network parameters reconfiguration and the monitoring of the new KPIs after the changes were committed to the network. In some cases, drive testing repetition was needed as well for a new evaluation.  
As expected, these procedures frequently led to time-consuming and error-prone actions, especially in the cases of optimization and healing since capacity, coverage, and interference must be faced in real time and with a holistic approach. Moreover, the results after manual network parameter changes cannot be regarded as beneficial in all scenarios since they trigger a chain reaction of additional needed network changes for the neighboring cells, especially in dense areas. As expected, OPEX spending occurs as well.  
As explained earlier, all SON applications are ruled by the Snapshot-Action-Feedback lifecycle. At this point, it should be noted that we can configure and tune the trigger thresholds of the snapshot phase so that the frequency of SON activities in the OSS is increased or decreased. Very frequent activities lead to system and OSS load, while rare activities do not have a fast outcome; thus, trigger balance is a key point.
## 3 Results and Discussion
### Ceramic-Metal Interface Analysis
Nanoparticles, due to their small size and high curvature, are able to enhance the interfacial contact tightness, allowing the weak interaction forces to exhibit locally a bonding strength similar to that of chemical bonds. Based on this principle, [PERSON] in a study conducted at the National Center for Energy Development of Spain, succeeded in the preparation of Al2O3 particles on the surface of a Fe-based heat storage tank by introducing the reinforcing heat transfer agent nanoparticles into the Fe-based vessel surface. Al2O3-Fe composites were prepared, which further validated the mechanism of nanoparticles in metal-ceramic interfacial bonding [66]. In summary, metal-ceramic interfacial bonding mainly relies on mechanical interlocking, physical adsorption, and chemical bonding mechanisms. The optimization of the coating bonding strength can be achieved by adjusting the surface roughness of the substrate, enhancing the chemical adsorption, and introducing nanoparticles to improve the stability and durability of the metal-ceramic interface. Thus, the ideal metal-ceramic interface system should have the following requirements: (i) excellent bonding strength between ceramics and substrate; (ii) small diffusion coefficients between the two phases of the interface; (iii) similar thermomechanical and physical parameters between ceramics and substrate; and (iv) moderate thickness of the interface with good mechanical properties, which can absorb the residual stress and inhibit the growth of cracks.
## 3 Results and Discussion
### Real-Data Verifications  
In order to understand the capability and performance of stability and the charts provided by the couple models, these models were verified by conducting a comparative analysis and justification of the ground information obtained from the extensive Alavian earth-fill dam body and lake to observe its behavior during the construction and service phases. Among these instrumentations, the vibrating-wire piezometers, mechanical piezometers, hydrogeological boreholes, and stand-pipe piezometers are used for measurement of the water level in the dam body and lake. A total water-level drawdown of about 25.45 m in 114 days was measured and applied to the models. This drawdown was  
Figure 7: The trend of safety factors versus drawdown ratio by FEM: (a) R = 0.1 m/day, (b) R = 0.2 m/day, (c) R = 0.4 m/day, (d) R = 1 m/day.  
imposed at a different rate (with an average rate of 0.22 m/day) because of the modeling of the existing conditions. Details of changing water levels in the reservoir and the measured pore-water pressures are indicated in the set of figures prepared to analyze the case. Figure 11 provides information about the locations of the instrumentations that were installed to measure the pore-water pressure in the dam and reservoir. To estimate the rapid drawdown conditions, the model was verified by measured data in pure flow and hydrostatic pore-pressure variations in the dam's upstream shell and core. The pure flow defined in the coupled analysis systematically leads to lower water pressure than the uncoupled (pure flow) approximation during the first stages of the dissipation. On the other hand, the classical hypothesis for the drawdown analysis is considered an undrained or pure flow. This is due to the effect of the initial state after drawdown, controlled by the change in stress. Since pressures dissipate faster the stiffer the soil, this situation changes after some time and the water pressure records may cross at a particular time, which depends on the position of the considered point in the slope. Note also that full steady-state conditions were not reached at the end of the simulation period [18]. Figure 12 provides the comparative results of the coupled FEM/LEM modeling with instrumentation results on the shell and core of the studied dam. According to this figure, the applied models provide reliable results to estimate the rapid drawdown simulations and critical state stability analysis.  
Figure 8: The comparative variation of FS based on LEM/FEM models (Note: the results of the FEM model are more conservative than the LEM model).  
Figure 10: _Cont._  
Figure 9: The F.S vs. drawdown ratio in the shallow sliding surface based on LEM/FEM models.  
Figure 11: The location of the instrumentations on the studied dam.  
Figure 10: The F.S vs. drawdown ratio in deep sliding surface based on LEM/FEM models.
# Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoders
## 2 Multi-Size Circular-Shift Network for 5G NR LDPC Decoder
### Multi-Size Circular-Shift Network
In the Rotator-In-Series (RIS) structure, the first barrel rotator, rotates the input data by \(c\). The result obtained from the first barrel rotator is then rotated by the second barrel rotator in the same direction by \(P-Z\)[17]. In addition, the output data are selected from the outputs of the barrel rotators and then combined by MUXs to become the output data. One of the most straightforward ways to employ an MSCS network block would be using a MUX network. An \(P\)-to-1 (\(w\)-bit) MUX can be used to connect \(P\) inputs to an output since the output data can be selected from \(P\) input data. The main drawback of this structure is that many wide-input MUXs  
Figure 1: (**a**) Benes network \(2\times 2\) switch; (**b**) Benes network when \(P=8\); (**c**) Banyan network when \(P=8\). are required. These wide-input MUXs are normally implemented by cascading a lot of 2-to-1 MUXs. To improve the performance of the MUX network structure, [PERSON] [16] introduced a pre-rotator structure exploiting the size-common-divisor property, as shown in Figure 3. The input data are rotated by pre-rotators so that the amount of input data is the greatest common divisor (GCD) of all block sizes before being processed by a MUX network. More precisely, with the maximum block size \(P\), the MSCS network is composed of \(P_{g}\)\(g\)-input pre-rotators and additional stage of \(P\)\(P_{g}\)-to-1 MUXs, also referred to as adaptation network (AN), where \(g\) is the GCD and \(P=g\times P_{g}\). This structure substantially reduces the MUX input width from \(P\) to \(g\). However, the MUX network still occupies a significant area. Specifically, the equivalent number of 2-to-1 MUXs is \(\left[\lceil log_{2}g\rceil\times P+(P_{g}-1)\times P\right]\), where the first term and the second term are for the pre-rotators and the MUX network, respectively. In [19; 24], the MUX network in the AN is substituted with the Benes network and the RIP structure, respectively, to enhance the complexity of the MSCS network block. The previous MSCS network structures are proposed to support multiple submatrix sizes in the QC-LDPC decoders. However, previously proposed structures are not suitable for 5G NR LDPC decoders where the submatrix size varies largely from 2 to 384. The considerable variation in submatrix sizes would result in the high inefficiency in hardware use and the high complexity in the interconnecting network in conventional switching  
Figure 2: (a) RIP structure; (b) RIS structure. network structures. To overcome the limitations of existing MSCS network structures, the design considered in this paper, referred to as low-complexity multi-size circular-shift network for 5G NR LDPC decoders, requires significantly low hardware complexity compared to conventional structures.
## 1 Introduction
The main contributions and innovations of the article are as follows:  
1. The system uses the image-processing algorithm, with higher measurement accuracy and trigger, to measure the characteristic parameters of the 3D model of parts and reconstructs the virtual prototype with the simulation appearance, collision, and interference detection function and simulation of the physical characteristics of the 3D model according to the parametric design process of mechanical parts. 2. The system constructs a knowledge base that can be updated dynamically, infers and updates the data of assembly sequence, assembly benchmark, fit tolerance zone and product interchangeability of the virtual prototype in real-time, and carries out production tests and analyses of the test data. At the same time, the system can freely match the knowledge reasoning program of product tests according to the design requirements, and carry out data analysis flexibly, coherently, and automatically. 3. According to the assembly logic of key parts of agricultural machinery, the system simplifies the assembly behaviour, uses VR equipment to complete the virtual assembly with stronger interactions and higher immersion, and uses the knowledge base to feed back the analysis results of assembly interference data and user assembly operation specifications in real-time. This helps to improve the efficiency of assembly training of small and medium-sized enterprises. 4. Based on the virtual reality engine, this paper constructs an intelligent design system for key parts of agricultural machinery with more powerful real-time and calculated functions. Finally, the third-party software evaluation and practical production application test results show that the system is more in line with the actual production situation of small and multi-batch, and changeable production requirements of small and medium-sized enterprises. It can effectively improve the design and production efficiency and reduce the production cost. The structure of the article is as follows: the second chapter is the process of virtual prototype construction and accuracy verification; the third chapter is the construction of a knowledge base and the verification process of data reliability; the fourth chapter is the overall application process of the system and the analysis of a third-party software test and evaluation data; and the last chapter summarizes the advantages and disadvantages of the system.
## 4 Numerical Results and Analysis  
### GSM Beam Scintillation Index in Space-to-Ground Link  
Table 1 presents the simulation parameters of GSM-beam propagation through a downlink channel. Here, \(F_{0}=\pi{W_{0}}^{2}/(\lambda L)\) is the Fresnel number of the link, and \(\zeta_{s}\) is the source's normalized spatial coherence width. The Fresnel number \(F_{0}\) and coherence parameter \(\zeta_{s}\) (as defined above) are used to compute the turbulence-induced phase variance in the simulations.  
Figure 3 illustrates the variation in the scintillation index for a GSM beam in downlink as a function of transmission distance under different \(\zeta_{s}\) and \(\theta\) values. The simulation parameters are listed in Table 1. The solid red line corresponds to fully coherent light with a source coherence parameter of \(\zeta_{s}=1\). By contrast, the blue and orange dashed lines indicate partially coherent light with \(\zeta_{s}=2\) and \(\zeta_{s}=5\), respectively, and the dashed green line represents fully incoherent light with an infinite source coherence parameter.  
As evident from Figure 3A-E, at a zenith angle of \(\theta=0^{\circ}\), the scintillation index for all four beams increases with transmission distance and decreases as the source coherence parameter increases.  
The scintillation index does not peak until a transmission distances of 10 km. In addition, partially coherent GSM beams exhibit lower scintillation indices than fully coherent beams. Both partially coherent and incoherent GSM beams exhibit lower scintillation indices than fully coherent beams. However, although incoherent beams yield the lowest scintillation index, their received optical intensity is extremely low, resulting in poor system performance. Therefore, incoherent cases are not emphasized in our main analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title 1** & **Title 2** & **Title 3** \\ \hline radius of curvature & \(F_{0}\) & \(\infty\) \\ source coherence & \(\zeta_{s}\) & \(1,2,5,\infty\) \\ parameter & \(\lambda\) & \(1550\) nm \\ laser wavelength & \(D\) & \(1\)–\(10\) cm \\ receiving aperture & \(z\) & \(10\) km \\ beam waist width & \(W_{0}\) & \(5\) cm \\ zenith angle & \(\theta\) & \(0\)–\(88^{\circ}\) \\ wind velocity & \(v\) & \(21\) m/s \\ refractive index parameter & \(C_{0}\) & \(1.7\times 10^{-14}\) m\({}^{-2/3}\) \\ transmission altitude & \(H_{0}\) & \(20\) km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.