esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### Fuzzification  
In fuzzification, input variables like signal strength (T), Reliability (R), Battery power (B), Mobile terminal velocity (U), Data Rate (V) are given a degree of suitable fuzzy sets. The burst inputs are a combination of T, R, B, U and V.  
The different possibilities for each attribute are given as below:  
* Signal Strength: (Good, Medium, Bad)
* Reliability: (High, Medium, Low)
* Battery Power: (Good, Medium, Bad)
* Mobile Terminal Velocity: (Good, Medium, Bad)
* Data rate: (Very fast, fast, slow)  
To input and output variables, Figures 4-9 represent member function. For efficient computation, triangulation functions are used to design member function that gives excellent result.  
In Table 4, T, R, B, U and V are taken as input and the output represents best network in the routing layer. The following are the fuzzy sets with combination as depicted in Table 4:  
* Signal strength (T)
* Reliability (R)
* Battery Power (B)
* Mobile terminal velocity (U)
* Data Rate (V)  
Figure 5: Reliability’s Degree Member Function.  
Figure 6: Battery Power’s Degree Member Function.  
If all the conditions satisfy, then it gives the best network.  
If any one condition is low, or medium, then the result gives a fair network.  
If any one condition satisfies, but the remaining is low or medium, then it gives the result as a bad network.  
Figure 8: Data rate’s Degree Member Function.  
Figure 7: Mobile terminal velocity’s Degree Member Function.
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 2 System Model
### Spectrum Sharing and Interference Management  
In this paper, we proposed sharing the whole spectrum of iSMS with in-building small cells of aTMS per building. Within each building, CGC stations serve satellite UEs, whereas SBSs serve small cell UEs. Since both CGC stations and SBSs within a building operate at the same satellite spectrum, co-channel interference can be generated when both CGC stations and SBSs serve their respective UEs at the same time. According to [26], a total of eight co-channel interference links may exist in a hybrid iSMS/aTMS. However, the long-distance between terrestrial components and a satellite station as well as the high external wall penetration loss of a building cause the interference strength between a satellite station and either a CGC station, a SBS, or a small cell UE to be insignificant. Hence, due to this reason, we can consider managing co-channel interference only among terrestrial components.  
Figure 2: Sample space for the deployment of SBSs and CGC stations in an apartment.  
Note that for deployment option 2, due to the absence of a CGC station, no ABS based enhanced intercell interference coordination (elCIC) is needed to be applied. However, for deployment option 4, the ABS based eICIC technique is considered by applying it to avoid co-channel interference between a SBS and a CGC station in an apartment (Figure 3). We assumed similar deployment statistics for all buildings over the coverage of a macrocell.  
For deployment option 4, we considered that the satellite spectrum could be shared by the collocated SBSs with the CGC stations by the following method based on the ABS based eICIC technique: If a SBS is collocated with a CGC station in an apartment in a building, and at least one satellite UE is present in the same apartment so that the satellite UE is within the common coverage of the SBS and the CGC station, a SBS can operate only during non-ABSs, whereas a CGC station can operate only during ABSs. Otherwise, the SBS, even though it is collocated with a CGC station, can operate in all TTIs at the satellite spectrum, as shown in Figure 3.
Remote sensing methods of mapping regional vegetation change and land degradation have been widely used across southern Africa for examining spatial and temporal variations in semi-natural ecosystems that are important for biodiversity and ecosystem services (e.g., [22; 26; 27; 28; 44; 45]). Specifically, NDVI values from time series of remote sensing data can be used to identify which regions are undergoing a decrease in NPP values [46; 47] and are thus experiencing degradation, compared to those regions where NPP values are increasing (e.g., [48]). This in turn has implications for identifying sustainable development strategies for regions already experiencing climate stresses that impact on ecosystem vigor and ecosystem service provision [49; 50; 51; 52]. A previous study on ecosystem service valuation in South Africa [53] used national GDP data in order to calculate the economic values of different ecosystem-dependent sectors, such as agriculture and tourism. This approach addresses only a narrow application of ecosystem services and does not look at the nature of the ecosystems themselves. Figure 1: General classification of ecosystem services (adapted from [11]). This study aimed to map and evaluate national- and regional-scale land-cover change and associated ecosystem services values across South Africa for the period 2001-2019 using MODIS satellite data. Changes in productivity of each land-cover class were used to calculate changes in economic values over time and space. Results of this study provide a first-order analysis of the economic values of ecosystem services in South Africa, which has not been done before at this scale using remote sensing data. This study further highlights the differences in aggregated values between different ecosystems as well as spatial variations at the province scale, which has not been previously done. This analysis provides a baseline for incorporation of ecosystem services as a fully valorized component of relevance to sustainable development strategies.
## 3 Physical Layer Improvement Proposals for PRIME and G3-PLC  
Based on the future evolutions concluded in [10], three improvement proposals are selected for the physical layer of PRIME 1.3.6 [11], PRIME 1.4 [12], and G3-PLC [13,14] technologies, trying to improve their response against the disturbances present in LV grids. Two types of improvement are studied: implementation changes within the scope of the standard, and proposals for the modification of PLC specifications.  
1. PRIME implementation option: Improvement of the equalizer curve fitting.
2. PRIME modification to the standard: Reed-Solomon outer encoder as an addition to the convolutional encoder.
3. G3-PLC implementation option and modification to the standard: Implementation of tone mapping as defined in the standard, and proposal of technical improvements beyond the standard Tone Mapping.
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 5 Experimental Results
### Spur Suppression Test  
Spurs are unwanted periodic components of the spectrum. Spurs can reduce the signal-to-noise ratio of a wireless communication system and increase signal jitter [28]. For both the passive fourth-order RC and RLC filters, the spur suppression of a signal is evaluated with an output frequency of 1426 MHz. The experimental spur suppression results are shown in Figure 11.  
Since the output frequency is not an integer multiple of the reference frequency, the fractional-N frequency synthesizer necessarily outputs spurious signals [28]. The division ratio modulated by the frequency divider causes the quantization noise. The spectrum of its quantization error causes noise and spuriousness in the corresponding position of its spectrum. In addition to the above spuriousness, reference frequencies appear as spurious signals in the spectrum. As shown in Figure 11, the spurious signals are mainly at 50 MHz, 100 MHz, and 150 MHz. From the experimental data, the passive fourth-order RLC filter is superior to the passive fourth-order RC filter in spur suppression. The spurious signals are all frequency interferences from the reference source. Spur suppression is nearly 70 dB and 90 dB for the proximal and distal phase noise, respectively.  
The spur suppression of the designed frequency synthesizer is more significant than 47.04 dBc/Hz in [25], 58 dBc/Hz in [11], and 45 dBc/Hz in [26]. As shown in Table 8, the spur suppression of the frequency synthesizer in this design is superior to the mentioned references. Therefore, this frequency synthesizer provides good suppression of spurious signals.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Loop Circuit Type** & **Loop Capture Time** \\ \hline passive fourth-order RC filter & 93.69 us \\ passive fourth-order RLC filter & 94.02 us \\ active second-order filter [24] & 2.5 ms \\ active second-order filter [27] & 14.4 ms \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of loop capture times of different filters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Spur Suppression (dBc/Hz)**} \\ \cline{2-4}
**Loop Circuit Type** & **50 MHz** & **100 MHz** & **150 MHz** \\ \hline passive fourth-order RC filter & \(-\)69.48 & \(-\)73.35 & \(-\)85.92 \\ passive fourth-order RLC & \(-\)69.80 & \(-\)73.89 & \(-\)87.12 \\ active second-order filter [25] & & \(<\)\(-\)47.04 & \\ passive third-order filter [11] & & \(<\)\(-\)58 & \\ third-order microstrip filter [26] & & \(<\)\(-\)45 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of spur suppressions of different filters.
# Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
## 6 Experimentation and Discussion
### Liver Volume Assessment  
In this section, we performed liver volume estimation using an SRG algorithm on the 3D binary masks. The algorithm operates within the cropped region and computes volume as the total number of labeled voxels multiplied by voxel size. Table 3 gives the analysis of semiautomatic 3D cropping showing the estimated liver volumes compared to ground truth. Results show strong agreement (mean absolute error below 2.5%) across both Chan-Vese and U-Net pipelines. The exact cropped size (ECS) is obtained by counting the number of voxels inside the cuboid, which is defined automatically from ground truths validated by clinicians. The average time to crop the liver slice volume is 8.841 \(\pm\) 1.505 s. The average semiautomatic cropping time per slice is 11.603 \(\pm\) 1.913 ms. The average percentage of \begin{table}
\begin{tabular}{c c c c c c} \hline
**Chan-Vese** & \multicolumn{2}{c}{**Dice Score**} & \multicolumn{2}{c}{**Time in sec per Slice**} & \multirow{2}{*}{**Speedup w.r.t. Original**} \\
**Volume \#** & & & & & \\
**(512 \(\times\) 512 \(\times\) n)** & & & & & \\ \hline
1 (n = 128) & 0.945 & 0.961 & 0.81 & 0.57 & 1.421 \\ \hline
2 (n = 171) & 0.938 & 0.967 & 0.88 & 0.56 & 1.571 \\ \hline
3 (n = 199) & 0.932 & 0.958 & 0.82 & 0.59 & 1.39 \\ \hline
4 (n = 89) & 0.928 & 0.951 & 0.73 & 0.51 & 1.431 \\ \hline
5 (n = 138) & 0.939 & 0.962 & 0.71 & 0.55 & 1.291 \\ \hline
6 (n = 134) & 0.942 & 0.958 & 0.81 & 0.63 & 1.286 \\ \hline
7 (n = 150) & 0.941 & 0.961 & 0.78 & 0.59 & 1.322 \\ \hline Average & 0.938 & 0.96 & 0.791 & 0.571 & 1.387 \\ \hline Std. Dev. & 0.006 & 0.005 & 0.058 & 0.038 & 0.1 \\ \hline \end{tabular}
\end{table} 
Table 1: Liver segmentation using Chan-Vese. \begin{table}
\begin{tabular}{c c c c c} \hline
**U-Net** & \multicolumn{2}{c}{**Dice Score**} & \multicolumn{2}{c}{**Dice After Post-Processing**} \\ \hline
**Volume \#** & & & & \\
**(512 \(\times\) 512 \(\times\) n)** & & & & \\ \hline
1 (n = 128) & 0.561 & 0.489 & 0.932 & 0.951 \\ \hline
2 (n = 171) & 0.512 & 0.451 & 0.936 & 0.953 \\ \hline
3 (n = 199) & 0.521 & 0.442 & 0.934 & 0.956 \\ \hline
4 (n = 89) & 0.531 & 0.451 & 0.941 & 0.957 \\ \hline
5 (n = 138) & 0.537 & 0.423 & 0.951 & 0.967 \\ \hline
6 (n = 134) & 0.492 & 0.371 & 0.941 & 0.954 \\ \hline
7 (n = 150) & 0.494 & 0.389 & 0.943 & 0.951 \\ \hline Average & 0.521 & 0.431 & 0.94 & 0.956 \\ \hline Std. Dev. & 0.025 & 0.04 & 0.006 & 0.006 \\ \hline \end{tabular}
\end{table} 
Table 2: Liver segmentation using U-Net. maximum unnecessary data to be reduced is 92.031 \(\pm\) 2.731% and semiautomatic cropping helps reduce 90.007 \(\pm\) 3.406% (average) from the volume of liver slices. We compare the performance on LiTS liver data [20] testing on an NVIDIA GPU GeForce GTX 1050. The voxel-based parallel approach improves cropping by 2.289 times compared to slice-based cropping proposed by [PERSON] et al. [30].
## 4 Test Results and Discussion  
### Test Results  
Figure 3 shows the pullout test results of both types of reinforcement. The pullout force of case SN showed a marked peak value regardless of normal stress conditions when the front displacement was within 1.2 mm; pullout force then gradually decreased (Figure 3a). The pullout forces of PSI-1, PS2-1, and PS3 rapidly increased when front displacement was within 4 mm, and they continuously increased according to the increment of the front displacement. A number of transverse members increased, and the increment ratio of pullout force increasingly grew (Figure 3b-d). However, there were no effects with stress less than 100 kPa when transverse members were more than 2. This means that pullout resistance initially (within 4 mm) appeared only as friction resistance due to longitudinal members. Therefore, bearing resistance due to transverse members has a significant effect when front displacement and normal stress are more than 4 mm and 100 kPa, respectively.  
Figure 4 shows the test results for Type B in which a location change in transverse members was installed in the same condition of normal stress (100 kPa). PSI-2 and PS1-3, in which one transverse member was installed, had a similar trend in the relationship between pullout force and pullout displacement, as shown in Figure 4a. However, the transferred bearing resistance due to pullout force on PSI-1 was less than that of other cases because the transverse member was installed at a long distance from the front wall, which is affected by wall friction. In the case of two transverse members, PS2-2, where transverse members were installed in the first and third locations, there was a large pullout force compared with that in other cases (PS2-1 and PS2-3). This means that interference was affected by the distance between transverse members when a number of transverse members were installed over two locations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reinforcement Type**} & \multirow{2}{*}{\
\begin{tabular}{c} **Normal Stress** \\ **(\(\sigma_{v}\), kPa)** \\ \end{tabular} } & \multicolumn{3}{c}{**Transverse-Member Type**} & \\ \cline{3-5}  & & **Number of Transverse** & **Location of Transverse** & **Test Classification** \\ \cline{3-5}  & & **Members** & **Member** & \\ \hline Smooth steel strip & 25, 50, 100, 150, 200 & None & - & SN \\ \hline \multirow{5}{*}{
\begin{tabular}{c} Pinning jointed smooth steel strip \\ \end{tabular} } & 25, 50, 100, 150, 200 & & 1 st & PS1-1 \\  & 100 & 1 & 2 nd & PS1-2 \\  & 100 & & 3 rd & PS1-3 \\ \cline{2-5}  & 25, 50, 100, 150, 200 & & 1 st, 2 nd & PS2-1 \\  & 100 & 2 & 1 st, 3 rd & PS2-2 \\  & 2 nd, 3 rd & PS2-3 \\ \cline{2-5}  & 25, 50, 100, 150, 200 & 3 & 1 st, 2 nd, 3 rd & PS3 \\ \hline \hline \end{tabular} Note: test classification = longitudinal member type, a number of transverse member-location of transverse member (e.g., PS 2-1 = pinning jointed smooth steel strip, 2 (number of transverse member)-1 (1 st and 2 nd location of transverse member)).  
\end{table}
Table 2: Pullout testing program.
## 3 Results and Discussion  
### Load-Settlement Curves  
Figure 6 shows the load-displacement curves of the HSCM-1-1 under uplift loading for different \(C_{ref}/s_{u}\) conditions. When \(C_{ref}/s_{u}\) = 20, 40, 60, 80, the curve inflection point was evident, and it was appropriate to use the inflection point method to determine the ultimate bearing capacity, which was 386.5 kN, 386.4 kN, 387.7 kN, and 388.7 kN with displacements of 17.86 mm, 14.34 mm, 13.50 mm, 12.84 mm, respectively. When \(C_{ref}/s_{u}\) = 5, 10, the curve had no obvious inflection point, which was determined by the double tangent method [23]; the uplift ultimate bearing capacity was 179 N and 260 N; the displacement was 8.67 mm and 11.07 mm, respectively. The above results showed that the soil-cement strength could affect the uplift ultimate bearing capacity of the HSCM pile. The uplift ultimate bearing capacity \(P_{1-1}\) of HSCM-1-1 increased with the soil-cement strength and remained constant after reaching a certain peak.  
Figure 5: Comparison of field test and simulation results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Pile Type** & **ID** & \(C_{ref}/s_{u}\) & _Dsc (m)_ \\ \hline HSCM & HSCM-1-1 & 5, 10, 20, 40, 60, 80 & 0.32 \\ SDCM & SDCM-1-2 & 5, 10, 20, 40, 60, 80 & 0.32 \\ \hline \end{tabular}
\end{table}
Table 3: Numerical simulation schemes.  
The load-displacement curve of SDCM-1-2 is shown in Figure 7. When \(C_{ref}/s_{u}\) = 5, 10, 20, 40, 60, and 80, the curve inflection point was relatively obvious, and the inflection point method was used to determine the ultimate load-bearing capacity, which was 72 kN, 138.6 kN, 273 kN, 382.8 kN, 386 kN, and 385 kN, respectively, and the displacement was 12.3 mm, 14.4 mm, 18.57 mm, 18.77 mm 16.78 mm, and 15.10 mm. The above results showed that the strength of the soil-cement affects the uplift ultimate bearing capacity of SDCM piles. The uplift ultimate bearing capacity \(P_{1-2}\) of SDCM-1-2 increased with the soil-cement strength and remained constant after reaching a certain peak, similar to the variation law of the uplift ultimate bearing capacity of HSCM-1-1.  
When \(C_{ref}/s_{u}\) = 80, \(P_{1-1}\) and \(P_{1-2}\) were very close to each other with 386 N and 385 N, respectively, but the initial stiffness of SDCM-1-2 was less than HSCM-1-1. As \(C_{ref}/s_{u}\) reduced, the uplift ultimate bearing capacity \(P_{ult}\) of the pile started to decrease, and \(P_{1-2}\) decreased to a greater extent than \(P_{1-1}\), indicating that the failure mode of HSCM-1-1 and SDCM-1-2 had changed, thus causing the difference in the uplift ultimate bearing capacity.
## 4 Numerical Simulation
### Constitutive Model and Calculation Parameters  
Due to the heterogeneity and anisotropy of rock and soil media, the test results of soil are unstable and discrete. In order to determine the calculation parameters, the physical property parameters and compression coefficient in this engineering geological survey report are used and the average value is taken.  
The modified Mohr-Coulomb constitutive model is used for soil material and grouting layer, and the linear elastic material model is used for the tunnel structure. The parameters required for model establishment are determined according to the geological survey report of the monitoring area and the relevant geotechnical experiment, and the specific values are shown in Table 1. Where \(E\) (MPa) is the elastic modulus; \(\mu\) is [PERSON]'s ratio; \(\gamma\) is bulk density; \(c\) is cohesion; \(\varphi\) is the internal friction angle; and \(E_{0}\) is the deformation modulus. As the modified Mohr-Coulomb model needs to be supplemented with other parameters, Midas GTS NX 2021 does not give the value rules for the Jinan area. Since the project site is mainly clayey soil, which is similar to the soil in Beijing, other parameters are set according to the value rules of Beijing in Midas GTS NX help manual, and the specific rules are shown in Table 2. Where \(E_{0}\) is the deformation modulus; \(E_{80}^{ref}\) for the standard drainage triaxial experiment of secant stiffness; \(E_{oed}^{ref}\) primarily oedometer records of tangent stiffness; \(E_{u}\) is the unloading reloading stiffness; and m is the power function of stress correlation, 0.5 for soft soil and 1 for hard soil.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Soil Layer Classification** & **\(E\)/MPa** & \(\mu\) & \(\gamma\) **(kN/m\({}^{3}\))** & \(\varepsilon\)/kPa** & \(\varphi^{o}\) & \(E_{0}\) \\ \hline Plain fill soil & 13.5 & 0.35 & 18 & 8 & 10 & 5.81 \\ Loess-like silty clay & 18 & 0.35 & 25 & 15 & 18 & 6.39 \\ Silty clay & 18.9 & 0.33 & 19.6 & 30 & 16 & 7 \\ Stroke limestone & 500 & 0.27 & 23 & 55 & 32 & 1170 \\ Grouting layer & 10 & 0.38 & 15 & 5 & 5 & 2.29 \\ Tube segment & 3000 & 0.17 & 24 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil layer materials and grouting layer parameters.  
Figure 8: The simulation of construction process. (**a**) Lower pipe jacking; (**b**) upper pipe jacking.
# Dynamic Characteristic Analysis of Permanent Magnet Brushless DC Motor System with Rolling Rotor
## 2 Dynamic Model of the Rotor-Bearing-Stator System for the BLDCM  
### Model of the BLDCM  
A BLDCM mainly consists of the rotor, stator, rolling rotor, shaft, sliding bearing and shell, as shown in Figure 1a. To research the dynamic characteristics of the BLDCM, a simplified lumped parameter model is built, as shown in Figure 1b, where the uncertain factors [27] are neglected.  
In Figure 1b, the bearings and supports are described as springs and damping. The symbols \(k_{1}\) and \(c_{1}\) are the lateral stiffness and damping of the rotor, respectively; \(k_{2}\) and \(c_{2}\) are the lateral stiffness and damping of the rolling rotor, respectively; \(k_{i}\) and \(c_{1I}\) (\(i=4,5\)) represent the lateral stiffness and damping of the left sliding bearing and its support, respectively. Similarly, the right bearing and support have the relative parameters, including \(k_{ir}\) and \(c_{ir}\) (\(i=4,5\)); \(k_{6}\) and \(c_{6}\) are the support stiffness and damping of the shell; \(m_{1}\), \(m_{2}\) and \(m_{6}\) are masses of the rotor, rolling rotor and shell, respectively; \(m_{3I}\), \(m_{4I}\) and \(m_{5I}\) are the masses of the left shaft neck, the sliding bearing and the support, respectively; \(F_{ex}\) and \(F_{xy}\) are the electromagnetic force and torque acting on the rotor along the \(x\) and \(y\) directions,respectively; \(F_{bx}\) and \(F_{by}\) are the oil-film forces along \(x\) and \(y\) directions, respectively. In addition, \(\rho\) is the eccentric distance of the rolling rotor.
## 5 Example Analysis
### Analysis of Icing Prediction Method Result
#### 5.3.3 Example 3: Simulation Analysis of Online Icing Prediction of Transmission Lines  
Model testing and evaluation were carried out, and the _Robust STL-BTSF_ and IBWO-LSSVR model was used for icing prediction and model evaluation. The comparison of evaluation index results is shown in Table 7. The residual plot is shown in Figure 12, and the comparison of prediction results is shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Preprocessing Methods** & **Running Time/s** & _MSE_ \\ \hline \(BTSF\) & 13.9989 & 1.5836 \\ _Robust STL-BTSF_ & 11.1227 & 1.4206 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of data preprocessing methods.  
Figure 11: Fitness values.  
Analyzing Table 7, _Robust STL-BTSF_ and IBWO-LSSVR have the lowest error values of the four evaluation indexes, which validates the prediction accuracy of the model. Furthermore, through Figures 12 and 13, compared with other methods, the residual values of the proposed are more evenly distributed on both sides of the central axis, which proves that its prediction effect is better than that of other methods. In addition, the model proposed in this paper has the highest accuracy on the prediction curves with the real ice cover thickness, which further verifies the superiority of the method proposed in this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Evaluation Indicators**} \\ \cline{2-5}  & **MSE/mm** & **RMSE/mm** & **MAE/mm** & **MAPE/mm** \\ \hline _Robust STL\(-\)BTSF_ & 8.0351 & 2.8346 & 2.3285 & 0.2223\% \\ and PSO-LSSVR & 6.6646 & 2.5816 & 2.1156 & 0.1816\% \\ _Robust STL\(-\)BTSF_ & 6.0588 & 2.4615 & 1.9509 & 0.1681\% \\ and IGWO-LSSVR & 6.1377 & 2.4774 & 1.9793 & 0.1707\% \\ and BWO-LSSVR & 5.4632 & 2.3373 & 1.7807 & 0.1475\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison table of evaluation indicators.  
Figure 12: Residual map of test results.  
Figure 13: Comparison of test results.