esa-sceva/satcom-chunk-collection huggingface dataset:
# Accuracy and Wear Evaluation of the Customized Zirconia Guided Sleeves
## 3 Results  
Table 1 lists the deviations of the implant installation with respect to multiple tooth positions and varying drill sleeve distances. The results of 60 analyzed cases revealed horizontal, vertical, and angular deviations. The maximum horizontal deviations did not significantly differ among the three drill sleeve distances (0.01 mm, 0.21 \(\pm\) 0.17 mm; 0.02 mm, 0.21 \(\pm\) 0.18 mm; and 0.03 mm, 0.26 \(\pm\) 0.19 mm; \(p\) = 0.603). The maximum vertical deviations did not significantly differ among the three gap distances (0.01 mm, 0.59 \(\pm\) 0.38 mm; 0.02 mm, 0.72 \(\pm\) 0.36 mm; and 0.03 mm, 0.86 \(\pm\) 0.51 mm; \(p\) = 0.145). The angular deviations did not significantly differ among the three gap distances (0.01 mm, 2.1\({}^{\circ}\)\(\pm\) 2.15\({}^{\circ}\); 0.02 mm, 3.17\({}^{\circ}\)\(\pm\) 2.45\({}^{\circ}\); and 0.03 mm, 3.92\({}^{\circ}\)\(\pm\) 1.87\({}^{\circ}\); \(p\) = 0.046). A wider gap distance was associated with an increased tendency of the occurrence of accuracy errors. The upper-right first molar site exhibited the highest horizontal deviation among the four quarter sites (\(p\) < 0.001). The lower left first molar site exhibited the highest vertical value (\(p\) = 0.001) and angular deviation (\(p\) < 0.001). Table 2 presents the operation time and wear results for multiple drill sleeve distances and positions. Drilling time did not differ significantly among the three drill sleeve distances (\(p\) = 0.970) and anatomical sites (\(p\) = 0.627). Drill wear did not differ significantly among the three drill sleeve distances (\(p\) = 0.677) and anatomical sites (\(p\) = 0.791). However, compared with other positions, the positions of 16 and 46 exhibited higher mean values for the maximum height (8.34 and 8.88 \(\upmu\)m, respectively) related to drill roughness. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Time (s)**} & \multicolumn{2}{c}{**Sz (\(\upmu\)m)**} \\ \hline
**Varies** & **M \(\pm\) SD** & **\(p\)** & **M \(\pm\) SD** & **\(p\)** \\ \hline Position & & 0.627 & & 0.791 \\
16 (\(n\) = 15) & 20.67 \(\pm\) 2.84 & & 8.34 \(\pm\) 1.64 & \\
26 (\(n\) = 15) & 20.86 \(\pm\) 2.13 & & 8.17 \(\pm\) 1.58 & \\
36 (\(n\) = 15) & 20.17 \(\pm\) 3.11 & & 7.81 \(\pm\) 1.40 & \\
46 (\(n\) = 15) & 20.29 \(\pm\) 2.59 & & 8.88 \(\pm\) 2.62 & \\ Distance & & 0.970 & & 0.677 \\
0.01 (\(n\) = 20) & 20.46 \(\pm\) 2.66 & & 8.04 \(\pm\) 1.78 & \\
0.02 (\(n\) = 20) & 20.52 \(\pm\) 2.63 & & 8.17 \(\pm\) 1.46 & \\
0.03 (\(n\) = 20) & 20.50 \(\pm\) 2.75 & & 8.70 \(\pm\) 2.29 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Operation time and implant surface wear for multiple drill sleeve distances and positions.
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 3 Application Cases
### Pak B Low-Pressure Turbine Cascade
Minor differences are noted in the separation region on the SS compared to experimental data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Axial chord length (mm) & 159.5 \\ Blading pitch (mm) & 141.2 \\ Inflow angle (\({}^{\circ}\)) & 55 \\ Outflow angle (\({}^{\circ}\)) & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The main parameters of Pak B.  
Figure 18: Computational domain and grid distribution of Pak B cascade. Figure 19b further compares the time-averaged spanwise-averaged velocity distributions at various positions on the suction surface (50%, 60%, 70%, 75%, 80%, 85%, and 90% of \(C_{x}\)). Prior to the 60%\(C_{x}\), position, the boundary layer thickness increases slowly due to the accelerating airflow in this region, which exerts significant stretching forces on the fluid, thereby suppressing velocity fluctuations. However, beyond 60%\(C_{x}\), especially past the pressure peak on the SS, the boundary layer begins to experience adverse pressure gradients. At 75%\(C_{x}\), the tangential velocity in the near-wall region is nearly zero, indicating an impending inflection point in the velocity profile and subsequent boundary layer separation. At the 80%\(C_{x}\), the boundary layer thickness increases rapidly after separation. A slight deviation between the numerical simulation results and experimental data is observed at the reattachment point (85%\(C_{x}\)), which is attributed to increased flow field complexity. At 90%\(C_{x}\), the turbulent boundary layer post-reattachment shows a good match with the experimental velocity distribution. Additionally, the time-averaged streamlines and pressure contours in Figure 19c clearly depict the locations of separation bubbles on both the PS and SS. Figure 20 presents instantaneous coherent structures on the Pak B suction surface, identified using the Q-criterion and colored by the Mach number. It is evident that, under adverse pressure gradients, the boundary layer decelerates and separates, forming a full-span Kelvin-Helmholtz (K-H) structure. This structure gradually rolls up during  
Figure 19: Pressure and velocity distribution of Pak B.  
downstream convection, evolving into a typical hairpin vortex structure and eventually developing into a turbulent boundary layer. The turbulent boundary layer sheds at the trailing edge and mixes with the pressure side boundary layer, forming a wake. Overall, the methods used in this study demonstrate high accuracy and resolution in predicting separation and transition induced by separation in low-pressure turbines, which is highly valuable for blade design.
## 3 Results
\begin{table}
\begin{tabular}{l} \hline \hline
**Initialize:** \\ \hline Input: Optimal feature subset, cycle duration (\(d\)), trained DT, trained kitNET, Anomaly threshold (\(Th_{a}\)) classification threshold (\(Th_{c}\)) \\ While \(i<d\) \\ Do nothing \\ \(k=1\) \\ Compute optimal features \\ Apply kitNET and assign the anomaly score \\ If anomaly score \(<(Th_{a})\) \\ Increment \(k\) by 1 \\ End \\ Else \\ Increment \(a\) by 1 \\ End \\ If \(a\geq 2\) \\ Apply DT and compute the CPE for each known class \(c\) \\ If \(\max\limits_{c}\)\(\mathit{CE}\)\(>(Th_{c})\) \\ If \(\max\limits_{c}\)\(\mathit{CE}\) for \(c\) = nominal \\ Increment \(k\) by 1 \\ End \\ Else \\ Fault corresponding to class \(c\) occurred \\ Stop the machinery \\ Break \\ Else \\ A possible novel behavior has been detected \\ Generate an alarm \\ Break \\ End \\ End \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Pseudo-code of the online condition monitoring. three cycles. In this case, it would mean that at least 12.8 products would be discarded for quality issues. Note that the algorithm was not stopped in the online condition monitoring if a known fault was recognized in order to test the methodology. However, in real applications, if a fault is detected, the machinery should be stopped as soon as possible. In the first scenario, the algorithm correctly classified observations belonging to the nominal class, fault 1, and fault 2. On the contrary, the change from the restored condition and fault 3 was not detected. Hence, observations belonging to fault 3 were wrongly considered nominal. Moreover, in eighteen cases, the kitNET detected two anomalies in a row. However, the DT classified them as nominal with a class probability membership value equal to 1. Therefore, no alarm was generated. Note that, as models were trained with four classes in this scenario, the classification threshold was set equal to 0.5. In the second scenario, observations belonging to the nominal condition and fault 1 were correctly classified. When fault 2 occurred, the kitNET detected two anomalies in a row. Hence, the DT was applied, which assigned a class probability value of 0.87 and 0.13 for the fault 1 and nominal class, respectively. As models were trained with only two classes in this scenario, the classification threshold was set equal to 0.9. Hence, when fault 2 occurred, an alarm indicating that a possible novel behavior occurred was generated.
## 1 Introduction
To keep pace with growth in data traffic demands, future terabit links require higher order modulation (multi-level signals) to increase the spectral efficiency and channel bandwidth compared to on-off keying (OOK) in long-haul transmission links. Thus, it becomes necessary to analyze the digital performance of the modulators, which is estimated by a clearly distinguishable constellation and low bit error rate (BER). Recently, many authors have proposed a variety of (different) designs for high-speed MZI electro-optic modulators (EOM) [12; 18; 19; 20; 21; 22; 23]. However, most of these designs have bandwidths limited to below the sub-THz domain, and do not discuss the effects of device orientation, distortion analysis and digital performance of the device. They also do not provide an option for microvia connections, which become essential for thin electrode designs. In this work, we propose the ultrathin-film electrode design along with the study of the effects of orientation on device performance, SFDR and digital performance calculation for high bandwidth and low \(V_{\pi}L\) EOMs, targeting futuristic applications in aerospace, photonic RADAR, telecommunication and satellite communication industries. This paper is divided into five sections; the effects of various design parameters have been analyzed in detail in the design section, and the study of alteration in material and electro-optic parameters with change in orientation of the device has been described in the effects of orientation section. The theory of intermodulation distortion has been presented in the distortion calculation section. Electro-optic and radio frequency (RF) responses, along with modulation efficiency, \(V_{\pi}L\), SFDR and digital performance of the proposed novel design have been analyzed in the results section. A comparison with recently proposed devices in the literature and a summary are conferred in the discussion.
# ICARUS--Very Low Power Satellite-Based IoT
## 4 The ICARUS Tag  
The most important condition for a viable animal transmitter (tag) is that the species under study must tolerate wearing the tag as a true 'wearable for wildlife'. As recommended by ethics committees, the tag should not exceed 3% of the animal's body weight to avoid influencing the animal's behavior or even endangering it. Since it was also planned to equip small animals with the tags, the upper limits for the size and weight were very difficult to meet. The trackers based on mobile or analog radio, which were conventional at that time, were ruled out for animals under 1 kg, meaning that 75% of all bird and mammal species could not be studied. The blackbird was chosen by the MPIAB as a reference animal due to the long-term focus on this songbird within the observation program. Prototypes of the ICARUS tag were tested, preferably on these seemingly familiar birds whose migration behavior still brings up questions, though that could only be answered by means of continuous monitoring. Weighing in at 4.5 g, the lightest version of the tag is just light enough for the blackbird, assuming its use is limited to adult male specimens (see Figure 17). For  
Figure 16: CCDF of inter-pilot channel power difference.  
all other species to be fitted with a transmitter according to current ICARUS planning, the recommendation is easily fulfilled.  
Along with the radio and location technology, the tags contain multiple sensors as well as enough memory to store the movements and environmental data for a single animal during its entire life. Up to 20 sets of position data are transmitted to the ISS during each overhead pass, which generally occurs daily but can take place every three days at lower, equatorial latitudes. The limited amount of data is due to the brief contact window of only 8 s (of which 3.4 s are used for transmission) and the low bandwidth and high-spreading factor of the UL signal. The fact that a miniature radio with only six milliwatts of transmit power can communicate with a satellite is an important property of this RA-vLPWAN system. The tags use the regularly transmitted ISS ephemeris data along with their own position to calculate the next time of contact. They prepare to receive and transmit during the calculated time window, but mostly remain in stand-by mode to save power. Based on the regularly transmitted tracking data that is compiled in the database at movebank.org [3], researchers have already gained valuable insights (see Section 6). However, another component is needed to access the entire data trove accumulated by the tags, including the environmental data.
# A Study of Regular and Irregular Neutrosophic Graphs with Real Life Applications
## 5 Complete Bipartite Neutrosophic Graphs
### Road Transportation Network  
A road transportation network can be modeled by a neutrosophic graph, due to its imprecise dataset, neutrosophic information for vertices as the geometric location, the grade of significance, and so on; in addition to the arcs as the width, length, quality, traffic, and so on. Therefore, we can represent a road transportation network properly by using neutrosophic graphs where the vertices and the edges represent the cities and routes between two cities, respectively. [PERSON]'s algorithm [PERSON] (1984); [PERSON] (1984) is a popular and well-known algorithm to solve the shortest path problem (SPP). It is an algorithmic approach to find the shortest path between two cities within the road transportation network. [PERSON] also contributed much to the efficient route-finding algorithms, hence the famous [PERSON]'s algorithm to find the shortest paths in any network with weights. The SPP is very useful to model the network routing protocol. In any shortest path algorithm, we consider a node as the source node and find all the shortest paths between the source vertex and destination vertex using all other nodes in the graph. Hence, the proposed \(d_{m}\)-degree of the source node will pave a new way to find the shortest path. When its efficiency is proven, it could be considered as the best one in the road transport network.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Integration of \(\Delta SM_{SMAP}\) with Seismic Records  
A more in-depth assessment of the grids with an \(MDI>0\) was conducted to better understand the relationships between \(\Delta SM_{SMAP}\) and the seismic records. In order to qualitatively examine the relationship between \(\Delta SM_{SMAP}\) and the geotechnical aspects of the target earthquake data, \(\Delta SM_{SMAP}\) maps were synthesized with ground failures reportedby reconnaissance reports, DPMs produced by ARIA, and near-real-time liquefaction and landslide hazard maps developed by USGS. In this section, \(\Delta SM_{SMAP}\) data were analyzed using their native spatial resolution to take advantage of the higher spatial resolution of the original SMAP data rather than the coarser resampled resolution. For example, Figure 7a-d show the \(\Delta SM_{SMAP}\) grids for the seismic zone in Petrinja, Croatia at a 9 \(\times\) 9 km resolution located inside the previously specified 25 \(\times\) 25 km isolated grids for which the \(MDI\) was higher than the corresponding MDTs, which were 0.08, 0.04, 0.02, and 0 cm\({}^{3}\)/cm\({}^{3}\), respectively. Event-specific discussions on how soil moisture change maps correlate with seismic response characteristics are presented for seven target earthquakes below. Integrated maps for the New Zealand 2016 and Chile 2015 earthquakes are shown in Figures 14 and 15 of Appendix A, respectively.
## 3 Analysis of BER for SISO System
### Scenario 1
#### 3.1.2 BER during Jamming Idle  
In the absence of jamming, when the system is only subjected to AWGN, we can derive the following expression from (4) and (10)  
\[\begin{split}& f_{\gamma_{2}}(\gamma_{2})=\frac{\alpha_{p}\beta_{ p}\xi_{p}^{2}}{2\sqrt{\tau_{2}\gamma_{D}A_{0p}\Gamma(\alpha_{p})\Gamma(\beta_{p})}} \frac{\alpha_{q}\beta_{q}\xi_{q}^{2}}{A_{0p}\Gamma(\alpha_{q})\Gamma(\beta_{q}) }\\ &\qquad\times\mathcal{G}_{2,6}^{6,0}\left(\begin{array}{c} \alpha_{p}\beta_{p}\alpha_{q}\beta_{q}\\ A_{0p}\alpha_{q}\end{array}\right)\sqrt{\frac{\tau_{2}}{\gamma_{D}}}\left| \begin{array}{c}\xi_{q}^{2},\xi_{p}^{2}\\ \xi_{p}^{2}-1,\alpha_{p}-1,\beta_{p}-1,\xi_{q}^{2}-1,\alpha_{q}-1,\beta_{q}-1 \\ \end{array}\right.\end{split} \tag{22}\]  
According to [31], ABER in a system affected by AWGN is given by  
\[P_{e1i}(\overline{\gamma}_{D})=0.5\int_{0}^{\infty}\textit{erfc}\bigg{(}\frac {\sqrt{\gamma_{2}}}{2\sqrt{2}}\bigg{)}f_{\gamma_{2}}(\gamma_{2})d\gamma_{2} \tag{23}\]  
where _erfc_ can be expressed by Meijer-G function [32].  
\[\textit{erfc}(\sqrt{y})=\frac{1}{\sqrt{\pi}}G_{1,2}^{2,0}\bigg{[}y\bigg{|} \begin{array}{c}1\\ 0,1/2\end{array}\bigg{]} \tag{24}\]  
By substituting (22) and (24) into (23) and integrating using 07.34.21.0013.01, the ABER under jamming idle can be obtained as follows  
\[\begin{split} P_{e1i}(\overline{\gamma}_{D})&=\frac{1}{\sqrt{ \pi}}\frac{2^{(\alpha_{p}+\beta_{p}+\alpha_{q}+\beta_{q}-\gamma)}}{(\pi)^{2}} \frac{\xi_{p}^{2}}{\Gamma(x_{p})\Gamma(\beta_{p})}\frac{\xi_{q}^{2}}{\Gamma(x_ {q})\Gamma(\beta_{q})}\\ &\times\mathcal{G}_{13,6}^{2,12}\left(32\overline{\gamma}_{D}\bigg{(} \frac{A_{0p}A_{0q}}{\pi_{p}\beta_{q}\beta_{q}}\bigg{)}^{2}\bigg{|}\begin{array} []{c}\[PERSON]_{1},\[PERSON]_{2},\[PERSON]_{3},\Gamma\\ 0,\frac{1}{2},\frac{1}{2},-\frac{\xi_{p}^{2}}{2},-\frac{\xi_{p}^{2}}{2},- \frac{1}{2}\xi_{q}^{2},\frac{2}{2}-\xi_{q}^{2}\\ \end{array}\right)\end{split} \tag{25}\]  
where \(\[PERSON]_{1}=\frac{1-\xi_{p}^{2}}{2},\frac{2-\xi_{p}^{2}}{2},\frac{1-\alpha_{p}}{2 },\frac{2-\alpha_{p}}{2}\), \(\Xi_{2}=\frac{1-\beta_{p}}{2},\frac{2-\beta_{p}}{2},\frac{1-\xi_{q}^{2}}{2}, \frac{2-\xi_{q}^{2}}{2}\), and \(\Xi_{3}=\frac{1-\alpha_{q}}{2},\frac{2-\alpha_{q}}{2}\), \(\frac{1-\beta_{q}}{2},\frac{2-\beta_{q}}{2}\).  
The overall ABER of the proposed system for scenario 1 can be expressed as follows  
\[\overline{P}_{e1a}=\rho P_{e1a}\Big{(}\overline{\gamma}_{JD}\Big{)}+(1-\rho)P_ {e1i}(\overline{\gamma}_{D}) \tag{26}\]
# Edge-Event-Triggered Synchronization for Multi-Agent Systems with Nonlinear Controller Outputs
## 3 Edge-Event-Triggered Policy
Let \(\mathcal{U}_{k}^{p}=\max_{t\in[t_{k}^{i},t_{k+1}^{i}]}\|\mathcal{U}_{p}(t)\|\) and \(\Pi_{k}^{p}=\max_{t\in[t_{k}^{i},t_{k+1}^{i}]}\|\Pi_{p}(t)\|\). The measurement error vector \(e_{i}(t)\) satisfies  
\[ \begin{split}\|\dot{e}_{i}(t)\|=&\|\dot{r}_{i}(t)\|\\ \leq&\|\sum_{p\in\mathcal{E}_{i}}h_{ip}w_{p}[f(x_{i}) -f(x_{j})]\|+\mathcal{U}_{k}^{p}\\ \leq&\mathcal{L}\|\sum_{p\in\mathcal{E}_{i}}h_{ip}w_{ p}y_{p}(t)\|+\mathcal{U}_{k}^{p}\\ \leq&\mathcal{L}\|e_{i}(t)\|+\Pi_{k}^{p}.\end{split}  \tag{18}\]  
As \(\|e_{i}(t_{k}^{i})\|=0\) holds, \(\frac{d}{dt}\|e_{i}(t)\|^{2}\) satisfies  
\[ \begin{split}\frac{d}{dt}\|e_{i}(t)\|^{2}\leq& 2\|\dot{e}_{i}(t)\|\|e_{i}(t)\|\\ \leq&(2\mathcal{L}+\beta)\|e_{i}(t)\|^{2}+\frac{( \Pi_{k}^{p})^{2}}{\beta},\end{split}  \tag{19}\]  
with the scalar \(\beta>0\), which implies that  
\[ \begin{split}\|e_{i}(t)\|^{2}\leq&\int_{t_{k}^{i}}^{ t}\frac{\Pi_{k}^{p}e^{(2\mathcal{L}+\beta)(t-s)}}{\beta}\,ds\\ =&\frac{\Pi_{k}^{p}\left(e^{(2\mathcal{L}+\beta)(t- t_{k}^{i})}-1\right)}{\beta(2\mathcal{L}+\beta)}.\end{split}  \tag{20}\]  
Applying the condition (6), the next event does not occur until \(\|e(t)\|^{2}=\sigma_{i}\|g(c\widehat{r_{i}}(t_{k}^{i}))\|^{2}+\frac{\beta_{1}} {\beta_{2}+\beta_{3}t^{2}}\), which yields that the inter-event time interval is lower bounded by \(\tau_{k}^{i}=t-t_{k}^{i}\) with  
\[\frac{\Pi_{k}^{p}\left(e^{(2\mathcal{L}+\beta)t_{k}^{i}}-1\right)}{\beta(2 \mathcal{L}+\beta)}=\sigma_{i}\|g(c\widehat{r_{i}})\|^{2}+\frac{\beta_{1}}{ \beta_{2}+\beta_{3}t^{2}}. \tag{21}\]  
Therefore, it follows that  
\[\tau_{k}^{i}>\frac{1}{2\mathcal{L}+\beta}\ln\left(1+\frac{\sigma_{i}\beta(2 \mathcal{L}+\beta)\|g(c\widehat{r_{i}})\|^{2}}{\Pi_{k}^{p}}\right). \tag{22}\]Equation (22) holds because the function \(\frac{\beta_{1}}{\beta_{2}+\beta_{3}t^{2}}>0\) until \(t\rightarrow\infty\). Therefore, no Zeno behavior occurs. The proof is completed.
###### Abstract  
Magnetite (Fe\({}_{3}\)O\({}_{4}\)) have been thoroughly investigated as microwave absorbing material due to its excellent electromagnetic properties (permittivity and permeability) and favorable saturation magnetization. However, large density and impedance mismatch are some of the limiting factors that hinder its microwave absorption performance (MAP). Herein, Fe\({}_{3}\)O\({}_{4}\) nanoparticles prepared by facile co-precipitation method have been coated with citric acid and embedded in a polyvinylidene fluoride (PVDF) matrix. The coated Fe\({}_{3}\)O\({}_{4}\) nanoparticles were characterized by X-ray diffraction spectrometer (XRD), field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR), and vibrating sample magnetometer (VSM). COMSOL Multiphysics based on the finite element method was used to simulate the rectangular waveguide at X-band and Ku-band frequency range in three-dimensional geometry. The citric acid coated Fe\({}_{3}\)O\({}_{4}\)/PVDF composite with 40 wt.% filler loading displayed good microwave absorption ability over the studied frequency range (8.2-18 GHz). A minimum reflection loss of \(-47.3\) dB occurs at 17.9 GHz with 2.5 mm absorber thickness. The composite of citric acid coated Fe\({}_{3}\)O\({}_{4}\) and PVDF was thus verified as a potential absorptive material with improved MAP. These enhanced absorption coefficients can be ascribed to favorable impedance match and moderate attenuation.  
electromagnetic wave; microwave absorbing materials; magnetite; reflection loss; absorption bandwidth +
Footnote †: journal: Applied Science
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
## 4 A Joint Denoising Learning Model for Weight Update STD Method
### The UWA Signal Analysis and Pre-Processing
After processing by the complex processing module, the features with real and imaginary parts separated are obtained, and this separation can destroy the orthogonality of the signal. To minimize this damage, we need to perform orthogonality compression to ensure the uniformity of feature scales. The hyperbolic tangent function is used to compress the real and imaginary components, respectively, and the corresponding Tanh-compressed real component (TR) and Tanh-compressed imaginary component (TI) are obtained as the normalized input features:  
\[\mathbf{TR}(t,f)=\frac{\beta\big{(}1-\mathrm{e}^{-\alpha Z_{\mathrm{R}}}\big{)} }{1+\mathrm{e}^{-\alpha Z_{\mathrm{R}}}}, \tag{7}\]  
\[\mathbf{TI}(t,f)=\frac{\beta\big{(}1-\mathrm{e}^{-\alpha Z_{\mathrm{I}}}\big{)} }{1+\mathrm{e}^{-\alpha Z_{\mathrm{I}}}}, \tag{8}\]  
where \(\alpha\) and \(\beta\) are the restriction factors, taking values of 0.5 and 10, respectively; \(Z\) denotes the objects to be compressed, i.e., \(\big{(}\mathbf{R}_{y},\mathbf{I}_{y}\big{)}\) and \(\big{(}\mathbf{R}_{h},\mathbf{I}_{h}\big{)}\). After compression and normalization, the features change to \(\big{(}\mathbf{TR}_{y},\mathbf{II}_{y}\big{)}\) and \(\big{(}\mathbf{TR}_{h},\mathbf{TI}_{h}\big{)}\).