esa-sceva/satcom-chunk-collection huggingface dataset:
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
###### Abstract  
In wireless communication systems, channels evolve when user terminals move. To further understand channel variation, and especially the evolution of clusters in mobile channels, a set of experiments was designed. First, we performed pedestrian mobile measurements in an urban macro (UMa) scenario at 3.5 GHz, and the K-power means-Kalman filter (KPMKF) algorithm was used for clustering and tracking. By this process, the trajectory of different clusters could clearly be described during measurement. The birth and death rate of clusters per snapshot show that the change of one or two clusters in each snapshot takes more probabilities. In addition, the differences of the cluster lifetime between the clustering process with and without the Kalman filter (KF) algorithm are given to show the effect from the KF. Second, channel simulations were implemented based on the above observed results. The spatial-consistency feature was introduced to get closer to the measured channels, which is based on the primary module of International Mobile Telecommunications-2020 (IMT-2020) channel model. Comparisons among measurements and simulations with and without this feature show that adding this feature improves simulation accuracy. To explore a novel method to characterize clusters during linear movement, a gradient boosted decision-tree (GBDT) algorithm is introduced. It uses the above characteristics of clusters and channel impulse responses (CIRs) as the training and validating dataset. The root mean square error (RMSE) shows that this is promising.  
channel measurement; IMT-2020 channel model; clustering; Kalman filter; gradient boosted decision tree; massive MIMO +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this paper, a workable and reliable electrical and meteorological DAS of PV-based microgrid has been established and installed as a prototype for rural energy accessibility projects. The designed DAS monitors parameters simultaneously from three PV power plants, thereby helping in limiting resources and technical skills. Besides, the simple design and easy installation make it freely accessible without the knowledge of a unique programming language. We have equally succeeded in combining mechanical, electric and electronic parts into an enclosed DAS box in an easy assembly kit. Calibration of the voltage signal conditioner shows a maximum difference of 1.5% between the actual and DAS,  
Figure 14: Voltage and current distributions of (**a**) off-grid system, (**b**) grid-assisted system and (**c**) hybrid system.  
thereby making the systems meet up with the key necessities in terms of accuracy and dependability. The findings indicate that the PV current in all three systems were found to increase with the increase in solar irradiance, whereas the subjected load influenced the PV currents. Both the off-grid and grid-connected (hybrid and grid-assisted) were monitored by various shunts to adjust the measured data to the required data logger specifications. The designed DAS can be reliably used for PV performance monitoring for all parameters with a high degree of precision and equally capable of resisting high temperatures of about 45 \({}^{\circ}\)C and challenging climatic environments like heavy rain and dust. One important future direction is to design real-time remote monitoring of electrical and meteorological DAS of PV microgrid for rural electrification projects.  
All authors contributed significantly to this study. Conceptualization, E.L.M. and O.O.A.; setup of the electrical data acquisition system, E.L.M. and O.O.A.; setup of the meteorological data acquisition system, E.L.M. and O.K.O.; periodic data collection and management, O.O.A. and O.K.O.; data analysis and graphs production, E.L.M. and O.K.O.; compilation of manuscript, O.O.A. and O.K.O.; technical review and editing of the manuscript, E.L.M. and O.K.O. All authors have read and agreed to the published version of the manuscript.  
This work was based on the research supported in part by the National Research Foundation of South Africa (Grant number 116763). We also acknowledge the Department of Science and Technology and Govan Mbeki Research and Development Centre for supporting this research.  
Special thanks to the members of staff and the students of the Fort Hare Institute of technology for their technical support of this research.  
The authors declare no conflict of interest.
## 4 Discussion
### Effects of Height Ratio, Mass Ratio, and Inertial Interaction
Consequently, a variation of \(\Delta\)PSD with ratio of Max.RRS\({}_{\mathrm{II}}\) (i.e., ratio of Max.RRS\({}_{\mathrm{II}}\) of adjacent structure to that of reference structure, with Max.RRS\({}_{\mathrm{II}}\) values at L_Input intensity, was taken for each structure to reduce the non-linear behavior of soil) was plotted in Figure 12. There was a reduction in response of structure with higher inertial interaction or greater Max.RRS\({}_{\mathrm{II}}\) value and an increment in response of structure with lower inertial interaction, in agreement with previous observations [16; 17; 18; 30; 31; 32]. In SSSI phenomena between the two structures, the vibration of a larger structure with a higher response or greater Max.RRS\({}_{\mathrm{II}}\) value can transfer and increase the response of smaller structure nearby by rocking interaction stiffness between structures through the soil [16; 47]. The effects of input level on SSSI could be explained. SSSI effects are more significant at low input intensity (i.e., in Figure 12a,b) because the inertial interaction is more remarkable as shown in Figure 4b. When input intensity increases, the inertial interaction of soil-foundation-structure systems will decrease, thus resulting in less variation of \(\Delta\)PSD. Figure 10: Cont. Figure 10: Variation of APSD with height ratio suffered from (**a**) artificial EQ at L_Input; (**b**) Hachinohe EQ at L_Input; (**c**) artificial EQ at H_Input; and (**d**) Hachinohe EQ at H_Input. Figure 11: Cont. Figure 11: Variation of \(\Delta\)PSD with mass ratio suffered from (**a**) L_Input of artificial EQ; (**b**) L_, Input of Hachinohe EQ; (**c**) H_Input of artificial EQ; and (**d**) H_Input of Hachinohe EQ. Figure 12: Variation of \(\Delta\)PSD with ratio of Max.RRS\({}_{\rm II}\) suffered from artificial earthquake at (**a**) L\({}_{\rm Input}\) and (**b**) H\({}_{\rm Input}\).
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 2 The Planar Circular Photodiode Array (PCPA)  
### Background of Operation  
The operation of the proposed method is based on the directional sensitivity of the photodiodes. Figure 1 shows the measured normalized angular response of a photodiode, type SFH205F [14], as a function of angle \(\varphi\) of the incoming light. The left-hand side is shown in a polar system, while the right-hand side in a Cartesian coordinate system. Notice that the maximum sensitivity value was set to unity, hence the name normalized. Apart from the direction, the detected light intensity (i.e., the current flowing through the photodiode) also depends on the emitted light intensity and the distance between the source and the sensor.  
Using multiple photodiodes, the dependence of the distance and the source light intensity can be cancelled, thus angle \(\varphi\) can be measured. Our goal was to create a sensor and a corresponding measurement method which can determine the angle of the incoming light in 2 dimensions. Thus it is supposed that the light source and the sensor are placed (approximately) on the same plane. The proposed measurement setup will be introduced in the next Section.  
Figure 1: The normalized radial sensitivity of a photodiode.
###### Abstract
These changes should lead to an alteration in the cell's functionality, for example, an increase in its rigidity [46; 47]. Deformability is one of the vital features of RBCs. Under circulating conditions, healthy erythrocytes are easily deformed, facilitating their passage through narrower microvessels. RBCs with increased rigidity weaken perfusion and oxygen delivery to tissues [48; 49; 50; 51] and can block microvessels [51; 52; 53]. Relatively rigid (e.g., aged) RBCs prevent the passage of the cells in the spleen vasculature and increase splenic RBC sequestration and destruction [54; 55; 56]. Previously, we demonstrated that the transfusion of stored units with a high fraction of rigid (undeformable) RBCs provoke impaired blood skin perfusion [57]. Therefore, the alteration in cell deformability induced by mechanical stress (for example, during perioperative salvage [18]) is highly significant. In a previous study [58], we examined how cell deformability is altered during the preparation of donated blood for storage as a packed RBC (PRBC) unit. This procedure, which includes centrifugation and filtration, affects cell membrane composition, leading to mixed results [56; 59; 60]. We have previously shown [58] that, depending on the initial level of the donor RBCs' deformability, both a decrease and an increase in the fraction of undeformable cells can occur. The change in the overall deformability depended on the initial level of undeformable cells (%UDFC); it increased when the baseline %UDFC was low and decreased when it was high. Based on our data, we hypothesized that exposing cells to high shear stress during the preparation of a PRBC unit, is likely to have two opposite effects: first, the destruction of undeformable RBCs, thereby reducing their percent in the population; second, mechanical damage to the RBC membrane with a subsequent increase in the cell's rigidity (thereby increasing the %UDFC). Consequently, the final effect of the process of unit preparation is determined primarily by the initial concentration of undeformable cells in the blood collected from the donor. To substantiate this hypothesis, we examined the shear stress effect on RBC deformability using two other methods of shear stress application: (1) in vitro application of mechanical stress to RBCs and (2) blood salvage procedures applied in the operating rooms for auto-transfusion of patients' blood.
## 3 Results and Discussions
### Experimental Validation
#### 3.4.2 Effective Strain and Stress Estimation  
The ductility of a billet material is essential for the evaluation of material behavior in forming applications because it determines the limits of plastic deformations. In addition, the cold forging backward extrusion process is most suitable option for forming moderately  
Figure 19: Variation between optimal and experimental values of punch force at different reduction ratios.  
Figure 18: Comparison of DE optimal and experimental values for punch force.  
ductile materials. For the estimation of effective stress experimentally, the deformation or the strain was determined during the cold forging process. An analytical model comprising a slab and power analysis was implemented to evaluate the effective strain. The three-zone model is presented in Figure 20a and has been an acceptable choice to describe the forming behavior during cold forging backward extrusion.  
As shown in Figure 20a, plastic deformation is represented by the first and second zones, while, the third zone is the rigid one. These are separated by the surface of discontinuity \(r_{1}\) and \(r_{2}\). The process geometry is described by punch diameter \(\mathcal{O}\)d, die diameter \(\mathcal{O}\)D, current bottom thickness t, and billet thickness T [39].  
[PERSON] [40] proposed effective strain as a measure of deformation for zone I and zone II with Equations (10) and (11), respectively, whereas zone III is free from stresses. The equivalent strain distribution is evaluated as a function of punch position into the billet and found to be large in zone II. [PERSON] [41] compiled excerpts on effective strain distribution in backward extrusion with a set of algorithms. The distribution zones in the backward extruded cup are depicted in Figure 20b.  
\[\epsilon_{eff\_zone\ I}=ln\left(\frac{T}{t}\right) \tag{10}\]  
\[\epsilon_{eff\_zone\ II}=ln\left(\frac{T}{t}\right).\left[1+\frac{d}{4\times (D-d)}\right] \tag{11}\]  
The comparison of the effective strain distribution obtained by experimentation showed good agreement with the values obtained from DE optimization, as evident from the plot in Figure 21.  
The effective stress can be estimated using the following Equation (12) with the strain values obtained from experimentation.  
\[\sigma_{eff\_}=\mathrm{K}(\varepsilon)^{n} \tag{12}\]  
where \(n\)--strain-hardening exponent and K--strength coefficient.  
The values of constants \(\mathrm{K}\) and \(n\) for AISI 1010 material are 715.67 MPa and 0.22, respectively, and a comparison is shown in Figure 22 [28].  
Figure 20: (**a**) Three-zone representation of the cold forging backward extrusion process. (**b**) Strain distribution areas for the three-zone representation.
## 4 Structural Crashworthiness
### Influence of Thin-Walled Inner Tube Thickness  
To study the impact of incorporating a thin-walled inner tube on the overall energy absorption performance of the anti-climb energy-absorbing device, a thin-walled inner tube with varying wall thicknesses \(t_{3}\) of 2, 3, 4, and 5 mm was designed. This thin-walled inner tube is nested on the exterior of the guide beam. The collision force and energy absorption of the anti-climb device were calculated under these different wall thickness conditions. The trends in collision force and energy absorption of the anti-climb energy-absorbing device with varying thin-walled inner tube thicknesses are illustrated in Figure 6.  
From Figure 6a, it can be observed that changes in the thickness of both the thin-walled inner and outer tubes exhibit the same trend in collision force variations for both the initial peak force and the second peak force. This suggests that the square thin-walled inner tubes designed for the anti-climb energy-absorbing device have the same load-bearing effect under the impact of a collision. However, unlike the thin-walled outer tubes, the thin-walled inner tubes maintain consistent displacement at the valley peak force, with no significant displacement difference observed across different parameters. Therefore, adjusting the wall thickness of the thin-walled inner tube does not alter the displacement upon which the valley peak force acts, which is distinctly different from the effect of changing the wall thickness parameter of the thin-walled outer tube.  
From Figure 6b, it can be seen that as the thickness of the thin-walled inner tube increases, so does its energy absorption capacity. At a displacement of 538 mm, the energy absorption of the anti-climb energy-absorbing device with a 2 mm thick inner tube is 238 kJ, and it increases to 297 kJ with a 5 mm thick inner tube, resulting in an increase of 59 kJ and a 24.8% enhancement in energy absorption. This increase is less pronounced compared to the 116% improvement achieved by increasing the wall thickness of the thin-walled tube by 5 mm. Thus, when the energy absorption of the anti-climb energy-absorbing device is on the lower side and there is a need for enhancement, one can consider increasing the wall thickness of the thin-walled square tubes to improve its energy absorption capacity, while also enhancing the vertical and lateral stiffness of the device.
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 3 Algorithms  
The overall idea of the algorithm is shown in Figure 3.  
The training process is divided into two stages.  
The first stage trained the autoencoder network in an unsupervised manner as a pre-trained model. Using the autoencoder to extract features suitable for trajectory clustering  
Figure 3: The overall flow of the algorithm.  
ensured that the model could extract the general features of most samples in the target domain and had basic clustering capabilities.  
In the second stage, the trained clustering models were formed into a Siamese neural network and fine-tuned with labeled data. The supervised and unsupervised parts of the network were simultaneously trained through the design of a hybrid loss function at fine-tuning. While strengthening the classification ability of label categories, we tried to ensure the model's ability to extract the overall clustering features.  
Finally, the similarity matrix of the target dataset was obtained and clustered according to the Siamese network model. The following describes the specific algorithm model in stages.
# Variation Trends of Fractal Dimension in Epileptic EEG Signals
## 4 Conclusions  
In this study, two opposite variation trends of fractal dimension in epileptic EEG signals were demonstrated, which could both be consistent with the literature, and the influences of denoising process were analyzed. Higuchi and RSE (RSE-f1, RSE-f1 sc, RSE-f2 and RSE-f2 sc) algorithms were used to calculate the \(D_{\text{c}}\) values of the EEG signals. Based on the CHB-MIT Scalp EEG database, 109 groups of EEG signals which met the 800-s requirement and had the correct epileptic seizures status were employed to carry out the comparative study. The \(D_{\text{c}}\) values of the seizure and normal statuses were calculated by using various algorithms of raw and denoised signals. To further illustrate the influences of denoising, the following ratio and calculated time were also compared. This study would be promising to help to make more understandings of the nonlinear nature and scaling property of EEG signals. The conclusions of this study could be summarized as follows:  
1. \(D_{\text{c}}\) could quantify the complexity of EEG signals, and the noise had a significant influence on \(D_{\text{c}}\) variation trends of EEG signals. After denoised, the following ratio calculated by all algorithms could be increased from 72% to 99%;
2. After using the scaling region interception method, \(D_{\text{c}}\) obtained by RSE-f1 sc and RSE-f2 sc were more favorable to distinguish the seizure status from normal status, because \(D_{\text{c}}\) values in normal status were generally below 1, which indicated the non-fractal nature of EEG signals in such cases. The capability of RSE algorithm to quantify the complexity of non-fractal features could be promising for the analysis on EEG signals. The underlying mechanism of RSE algorithm and the fractal nature of EEG signals in more clinical events would be investigated in our future study.  
Data curation, Z.L., J.L. and Y.X.; Funding acquisition, P.F. and F.F.; Investigation, Z.L., J.L., Y.X. and F.F.; Project administration, P.F. and F.F.; Visualization, Z.L. and F.F.; Writing--original draft, Z.L.; Writing--review & editing, J.L., Y.X., P.F., F.F. All authors have read and agreed to the published version of the manuscript.  
This study was supported by National Natural Science Foundation of China under Grant No. 51875311, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2020A1515011199, and Shenzhen Foundational Research Project under Grant No. WDZC20200817152115001.  
Not applicable.  
The data presented in this study are available on request with reasonable causes from the corresponding author.  
The authors would like to thank [PERSON], [PERSON], [PERSON] and [PERSON] of Tsinghua University for their contributions in the establishment of RSE algorithm.  
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.
## 3 Results and Discussion
### Validation of MW-FLR Method
#### 3.3.3 Robustness and Ruggedness  
The robustness of the method is defined as its ability to maintain its analytical performance upon minor changes in its experimental conditions. The proposed MW-FLR method, involving direct measuring of the native fluorescence of DUV in an aqueous solution without any experimental conditions, is very simple and considered as a robust method.  
Analyst-to-analyst and inter-day repeatability of the results were used to assess how robust the procedure was. The suggested MW-FLR approach is robust and reproducible, as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**DUV/ Conc. (ng/mL)**} & \multicolumn{2}{c}{**Relative Standard Deviation (RSD, \%)**} & \multicolumn{2}{c}{**Recovery (\% \(\pm\) SD)**} & \\ \cline{2-5}  & **Intra-Assay a** & **Inter-Assay b** & **Intra-Day a** & **Inter-Day b** \\ \hline Copiktra capsules & & & & \\
100 & 2.41 & 2.82 & 101.2 \(\pm\) 1.1 & 99.2 \(\pm\) 1.6 \\
200 & 2.26 & 1.53 & 98.5 \(\pm\) 1.6 & 100.2 \(\pm\) 1.4 \\
400 & 1.85 & 1.92 & 99.4 \(\pm\) 1.8 & 101.7 \(\pm\) 1.5 \\
500 & 1.62 & 1.84 & 103.2 \(\pm\) 2.2 & 98.8 \(\pm\) 2.5 \\
600 & 1.54 & 2.42 & 102.6 \(\pm\) 2.4 & 101.2 \(\pm\) 2.8 \\ Plasma samples & & & & \\
100 & 3.54 & 4.56 & 97.5 \(\pm\) 5.4 & 104.1 \(\pm\) 4.2 \\
200 & 4.26 & 4.25 & 102.5 \(\pm\) 3.5 & 100.4 \(\pm\) 3.1 \\
400 & 2.89 & 2.45 & 104.2 \(\pm\) 3.8 & 99.8 \(\pm\) 5.6 \\
500 & 2.52 & 5.18 & 101.4 \(\pm\) 4.9 & 102.1 \(\pm\) 3.9 \\
600 & 3.44 & 4.68 & 99.6 \(\pm\) 4.2 & 98.4 \(\pm\) 5.5 \\ \hline \hline \end{tabular}  
* Values are mean of 6 replicates in one run. b Values are mean of 9 replicates: 3 replicates on 3 consecutive days.  
\end{table}
Table 3: Precision and accuracy of the proposed MW-FLR method for determination of DUV in copiktra capsules and plasma samples.  
evidenced by the RSD values for the analysis of capsules and plasma samples not exceeding 2.8 and 5.6%, respectively.
## 2 Methodology  
### Data Selection  
Over the Donghai Bridge we have collected a set of data acquired by a Phased Array type L-band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite (ALOS) under fine beam double (FBD) polarization. Compared to the C-band data, e.g., the Environmental Satellite/Advanced Synthetic Aperture Radar (ENVISAT/ASAR) data and RADARSAT-1/2 data, the L-band PALSAR data can better resist the decorrelation (Figure 2). When we look into the PALSAR data we further find that the Horizontal Vertical (HV)-polarized data (Figure 2b) has better performance on minimizing the amplitude ambiguity in the sea area and suppressing the side lobe effects on the island than the Horizontal Horizontal (HH)-polarized data (Figure 2a). Hence, we finally chose four HV PALSAR datasets as observations to determine the deformation rate of the Donghai Bridge. From the six interferograms generated by the four images we selected three single-master pairs with relatively short perpendicular baselines (see Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Master** & **Slave** & **B\_Perp. (m)** & **B\_Temp. (day)** & **Height Ambiguity (m)** \\ \hline
12 January 2009 & 27 February 2009 & 115 & 46 & 560 \\
12 January 2009 & 14 April 2009 & 822 & 92 & 78 \\
12 January 2009 & 15 July 2009 & 959 & 184 & 67 \\ \hline \end{tabular}
\end{table}
Table 1: Image pairs used for monitoring of Donghai Bridge. B\_perp and B\_temp represent the perpendicular baseline and time interval between the two acquisitions, respectively.  
Figure 1: The location of the Donghai Bridge where a Phased Array type L-band Synthetic Aperture Radar (PALSAR) amplitude image was overlapped and the Google Maps image was taken as the background. The figure is plotted in the World Geodetic System (WGS) 84 coordinate system.
## 3 Results and Discussion
### Ablation Experiment Results and Discussion
#### 3.1.1 Tokenized KAN Block Ablation Experiment
By analyzing the ablation experiment results in Table 6 and Figure 7, integrating the Tok-KAN into U-Net++ not only reduced the parameters and accelerated training but also significantly improved the segmentation accuracy. The Tok-KAN excels in modeling intricate nonlinear relationships and adapting to various scenarios, demonstrating strong potential for remote sensing image segmentation tasks. Its advantages are evident in both multiclass datasets with rich features and single-class datasets with complex details.