esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Enzyme Reuse  
Since the immobilized enzyme showed similar activities under both conditions, the other relevant parameter to be considered for the final choice between the use of higher temperature with lower amount of biocatalyst or the use of more biocatalyst and lower temperature, is the operational stability of the enzyme. The reuse of the biocatalyst under conditions I and II was tested, and the results of the repeated batches for ultrasound-assisted butyl acetate synthesis are presented in Figure 3. After four reuses under conditions II (lower amount of biocatalyst and higher temperature), the MCI-CALB was fully inactivated, whereas under condition I, the biocatalyst still kept over 70% of its initial activity after seven reuses. As expected, the effect of temperature improved enzyme activity, but simultaneously decreased enzyme stability. Although the reaction rates were similar, because the operational stability of the biocatalyst under condition I was higher, this was selected for the next experiments.  
MCI-CALB showed slightly higher activity and stability under ultrasound stirring when compared to mechanical stirring, [20]. In relation to Novozym 435, one of the advantages of MCI-CALB under standard stirring was the increased operational stability of the enzyme in esterification reactions. However, under ultrasound stirring, the stability of Novozym 435 was strongly increased, whereas for MCI-CALB the improvement was small under condition I [19]. Thus, in the ultrasound-assisted reaction, the operational stability for MCI-CALB in the condition I was around half of the presented by the commercial preparation under its respective optimal conditions. The main advantage of the new preparation was the activity, which is 1.5-fold higher than Novozym 435.  
Figure 2: Time-course of butyl acetate synthesis at 0.3 M acetic acid: (\(\blacksquare\)) MCI-CALB under optimal condition I; (\(\bigcirc\)) MCI-CALB under Condition II; (\(\bigdiamond\)) MCI-CALB under mechanical stirring conditions; (\(\blacktriangle\)) Novozym 435 under ultrasound conditions; (\(\times\)) Novozym 435 mechanical stirring conditions.
# Electrochemical Removal of Chromium (VI) from Wastewater
## 3 Results and Discussions  
### Electro-Reduction of Chromium (VI)  
#### 3.1.1 Reaction Mechanism  
The electro coagulation process of chromium (VI) [11; 26; 27] involved the following stages of reducing agent formation, and subsequently reducing chromium (VI) and chromium (III). The first step was the formation of Fe\({}^{2+}\), by the oxidation of a steel electrode by a DC power supply:  
\[\mathrm{Fe}^{0}+2\mathrm{H}_{2}\mathrm{O}\rightarrow\mathrm{Fe}^{2+}+2 \mathrm{H}_{2}+\mathrm{O}_{2}+2\mathrm{e}^{-}. \tag{4}\]  
The second step was the reduction reaction between the reducing agent and oxidant. In this process, it was the reaction between Fe\({}^{2+}\) and chromium (VI). The reactions are described in Equations (5) and (6), taking the pH of the wastewater into account.  
\(0.5<\mathrm{pH}<6.5\):  
\[6\mathrm{Fe}^{2+}+\mathrm{Cr}_{2}\mathrm{O}_{7}{}^{2-}+14\mathrm{H}^{+} \to 6\mathrm{Fe}^{3+}+2\mathrm{Cr}^{3+}+7\mathrm{H}_{2}\mathrm{O} \tag{5}\]  
\(\mathrm{pH}>6.5\):  
\[3\mathrm{Fe}^{2+}+\mathrm{Cr}\mathrm{O}_{4}{}^{2-}+4\mathrm{H}_{2}\mathrm{O} \to 3\mathrm{Fe}^{3+}+2\mathrm{Cr}^{3+}+8\mathrm{OH}^{-}. \tag{6}\]  
In this work, the reduction process was different from electro coagulation, as the electrode used was stable and not being corroded. The reducing agent in this work was only a free electron (\(\mathrm{e}^{-}\)) supplied by the DC power supply. The reaction that occurred between the \(\mathrm{e}^{-}\) and chromium (VI) is shown in Equations (7) to (10). The Gibbs free energy of the equations at different reaction temperatures was calculated, and the results are shown in Figure 1. The negative of \(\triangle\mathrm{G}\) indicated that the reduction reaction was feasible in thermodynamics [28].  
\[\mathrm{Cr}_{2}\mathrm{O}_{7}{}^{2-}+14\mathrm{H}^{+}+6\mathrm{e}^{-} \to 2\mathrm{Cr}\mathrm{O}_{4}{}^{2-}+2\mathrm{H}^{+} \tag{7}\]  
\[\mathrm{H}\mathrm{Cr}\mathrm{O}_{4}{}^{-}+7\mathrm{H}^{+}+3\mathrm{e}^{-} \rightarrow\mathrm{Cr}^{3+}+4\mathrm{H}_{2}\mathrm{O} \tag{8}\]  
\[\mathrm{Cr}\mathrm{O}_{4}{}^{2-}+4\mathrm{H}_{2}\mathrm{O}+3\mathrm{e}^{-} \rightarrow\mathrm{Cr}(\mathrm{OH})_{4}{}^{-}+4\mathrm{OH}^{-}\quad\mathrm{E} ^{0}=-1.30\mathrm{V} \tag{9}\]  
Figure 1: Relationship between \(\Delta\mathrm{G}\) and the temperature of the reduction of chromium (VI).
## 4 Numerical Implementation and Analysis of the Phase-Locked Loop Statistics
It can be seen that, the peak value of \(P(\phi)\) appeared at \(\phi=0\) and was monotonically decreasing, while remaining symmetric with respect to the phase-error axis as \(\alpha\) was increased. For a very high SNR (\(\alpha=1\)),\(P(\phi)\) resembled a unity Gaussian distribution, while for very low SNR (\(\alpha\to 0\)), it became flat and approached a uniform distribution from \(-\pi\) to \(\pi\), with the PDF \(P(\phi)=1/(2\pi\) rad), implying that the phase was completely unknown. The function \(q(\phi,\tau)\) for the primary cycle slip was essentially identical to the \(p(\phi,\tau)\) function until a significant amount of probability mass was lost at the absorbing boundaries at \(\phi=\pm\pi\). The inclusion of absorbing boundaries around some subinterval of \(\phi\), e.g., \([-\pi,\pi]\), enabled one to determine the probability that the phase error had not yet exceeded the limits of that subinterval at any prior time. The end points were held at zero to simulate the effect of the slip boundary. Figure 20 shows the time-evolution of primary cyclic slipping density function \(q(\phi,\tau)\). The curves were plotted for the conditions SNR \(\alpha=1\), where three values of detuning factor, \(\gamma=0\), \(\sin(\pi/4)\) and \(\sin(\pi/2)\), respectively, are depicted. The results provide us a meaningful conceptual link between the stochastic process and information geometry. Through the above examples of physical realizations, the results underscore the natural, intuitive interpretation of information profiles and changes associated with the stochastic process in the phase-locked loop, and can be utilized for optimizing various desired outcomes. Table 1.9 The time-evolution of modulo-\(2\pi\) phase-error: (**a**) PDF, \(P(\phi,\tau)\) and (**b**) cumulative probability distribution function, for SNR \(\alpha=1\) (\(\gamma=0\)). Figure 2:0 Variation of modulo-\(2\pi\) phase-error steady state: (**a**) PDF and (**b**) cumulative probability distribution function at \(\tau=0.625\) for various SNR values (\(\gamma=0\)). Figure 2:1 _Cont._  
Figure 2:2 Variation of modulo-\(2\pi\) phase-error steady state: (**a**) PDF and (**b**) cumulative probability distribution function, for SNR \(\alpha=1\) with various detuning factors \(\gamma\) (\(\tau\geq 5\) or steady-state).
# Linking Off-Road Points to Routing Networks
## 1 Introduction
###### Research Hypothesis.  
Given a (global) routing network that is based on a geometric representation, we want to enhance the network in such a way that queries based on geometric information are answered by matching the geometric position with the network. This should be conducted in such a way that respects the nature of the geometric representation.  
Nowadays, most current spatial systems are based on the World Geodetic System [2]. Its underlying model shapes the Earth as a spheroid. In fact, when neglecting local height differences, e.g., mountains and valleys, it is a good approximation.  
**Remark 1** ([6]).: _The representation of the Earth as a spheroid can be formalized by the local parametrizations_  
\[\phi_{j}:[0,\pi]\times I_{j}\to\mathbb{R}^{3},\ (\theta,\psi)\mapsto \begin{pmatrix}a&\sin\theta&\cos\psi\\ b&\sin\theta&\sin\psi\\ c&\cos\theta\end{pmatrix},\]  
_with \(I_{1}=(0,2\pi)\) and \(I_{2}=(-\pi,\pi),j=1,2\), where the variables \(a,b,c\in(0,\infty)\) have to be chosen such that the overall error is minimized [7]. Here, \(\theta\) denotes the inclination (i.e., latitude) and \(\psi\) the azimuth (i.e., longitude). The intervals \(I_{j}\) are chosen in such a way that there are no degenerated points, i.e., each inverse function and its derivate are continuously defined._  
In this article, we show an approach combining a routing network with a geometrical representation that respects distances globally. In simple terms, we augment a given routing network's graph with new vertices in such a way that the new graph can give us routing information from and to the newly created vertices that reference geometrical points that were not yet represented by the routing network. To keep it short, we use the term _linkage_ for such a procedure.
# DEM Simulation of the Load Transfer Mechanism of a GRPS Embankment with a Fixed Geogrid Technique
## 3 Results and Discussions
### Geogrid Tensions and Displacements  
Figure 14 shows the tensile and vertical displacement of the geogrid in the fixed and unfixed geogrid cases. As expected, the geogrids sustained the catenary vertical deformation. The vertical deformation of the geogrids at the center of the subsoil in the fixed geogrid case was similar to that of the unfixed geogrid case, while the deformation of the geogrid near the pile was smaller than that of the unfixed geogrid case. Furthermore, the axial force of the reinforcement could be obtained by monitoring the normal force of the parallel bonds in the models. It can be seen that the tension in the reinforcement for the fixed geogrid case was more evenly distributed between the piles, and the axial force of the reinforcement was generally greater than that of the unfixed geogrid case (Figure 14b). With the increase in the surcharge load, the axial force of the reinforcement in the model of the fixed geogrid increased more obviously. Based on the previous analysis, the fixed geogrid allowed the reinforcement to directly transfer more of the upper load to the pile  
Figure 13: Load-sharing ratio of the subsoil, soil arching effect, and tensile membrane effect in the fixed geogrid case.  
top, while the unfixed geogrid transmitted the load to the pile by interacting with the embankment filler (or sand cushion). Under the surcharge load of 500 kPa, the axial force of the fixed geogrid was about 10 kN larger than the axial force of the unfixed geogrid. This was also the main reason for the increase in efficacy. This indicated that the load transfer mechanism of the fixed geogrid case was different from that of the unfixed geogrid case.
# Nemesis: Neural Mean Teacher Learning-Based Emotion-Centric Speaker
## 3 Results
### Ablation Study
#### 3.2.3 Image-to-Emotion Classifier  
A comparison of the model's performance according to the utilization of different image-to-emotion classifiers can be found in Table 5. These classifiers are: (1) the ResNet-32 classifier pre-trained on the ImageNet dataset (IN), which gave the best performance in the previous work by [PERSON] et al. (2019). (2) The ResNet-50 classifier pre-trained on the Stylized-ImageNet dataset (SIN), which is our proposed classifier.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric** & _Nemesis_ & _Nemesis_\({}_{\text{SCST}}\) & _EGNemesis_ & _EGNemesis_\({}_{\text{SCST}}\) \\ \hline BLEU-1 & 0.539 & 0.711 & 0.479 & 0.700 \\ BLEU-2 & 0.311 & 0.406 & 0.260 & 0.403 \\ BLEU-3 & 0.178 & 0.211 & 0.141 & 0.214 \\ BLEU-4 & 0.106 & 0.113 & 0.080 & 0.115 \\ METEOR & 0.141 & 0.166 & 0.129 & 0.165 \\ ROUGE-L & 0.294 & 0.341 & 0.262 & 0.336 \\ CIDEr & 0.130 & 0.219 & 0.099 & 0.224 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of the results before and after applying the SCST fine-tuning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Phase** & **Visual Encoder** & **Encoding Mode** & **Data Parallelism** & **GPU Type** & **Time Per Epoch** \\ \hline \multirow{3}{*}{XE} & Faster R-CNN & Offline & - & NVIDIA P100 & 1 h \\ \cline{2-5}  & CLIP-RN50 \(\times\) 16 & Online & - & NVIDIA V100 & 4 h \\ \cline{2-5}  & \(BLIP_{ViT-L/16}\) & Online & ✓ & NVIDIA V100 & 1 h \\ \hline \multirow{3}{*}{SCST} & Faster R-CNN & - & - & - \\ \cline{2-5}  & CLIP-RN50 \(\times\) 16 & Online & - & NVIDIA V100 & 7 h \\ \cline{1-1} \cline{2-5}  & \(BLIP_{ViT-L/16}\) & Online & - & NVIDIA V100 & 7 h \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training time based on utilizing different visual encoding modes.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & _EGNemesis_\({}_{\text{IN}}\) & _EGNemesis_\({}_{\text{SN}}\) \\ \hline BLEU-1 & 0.466 & 0.479 \\ BLEU-2 & 0.251 & 0.260 \\ BLEU-3 & 0.137 & 0.141 \\ BLEU-4 & 0.077 & 0.080 \\ METEOR & 0.128 & 0.129 \\ ROUGE-L & 0.253 & 0.262 \\ CIDEr & 0.093 & 0.099 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results with respect to the utilized image-to-emotion classifiers.  
As is observable, the performance has improved by using our proposed classifier module in all the utilized metrics. For instance, the BLEU-1 score has improved from 0.466 to 0.479, and the CIDEr metric has improved from 0.093 to 0.099. This shows that the decrease in texture bias while increasing shape bias achieved a better performance in our auxiliary image-to-emotion classification task.
## 2 Passband and Equivalent Baseband Transfer Functions
Let us assume that \(H_{i}(\psi=f_{c})=0\); if \(H_{i}(f_{c})\
eq 0\), there would be a constant phase that would not affect the general analysis. Therefore, the modulated signal at the output of this transfer function is that which is drawn in Figure 2, in which \(H_{r}(\psi)\) acts on the two side-bands in phase (real axis), while \(H_{i}(\psi)\) acts on them by producing orthogonal side-bands (imaginary axis). By defining the equivalent baseband transfer function of the direct channel as:  
\[H_{ed}(f)=H_{edr}(f)+jH_{edi}(f) \tag{3}\]  
and the equivalent baseband transfer function that couples it to the orthogonal channel:  
\[H_{eq}(f)=H_{eqr}(f)+jH_{eqi}(f) \tag{4}\]  
in which \(f\) is the baseband frequency, from Figure 2 we calculate the output of the two coherent demodulators by projecting the two side-bands on the cosine axis (direct channel) and on the sine axis (interference). In other words, \(H_{eq}(f)\) is the baseband transfer function that couples the cosine channel to the sine channel, therefore producing an output signal in the sine channel due to modulating signal in the cosine channel. The projections on the cosine axis give [19]:  
\[H_{edr}(f)=\frac{1}{2}[H_{r}(f_{c}+f)+H_{r}(f_{c}-f)] \tag{5}\]  
\[H_{edi}(f)=\frac{1}{2}[H_{i}(f_{c}+f)-H_{i}(f_{c}-f)]\]  
The projections on the sine axis give:  
\[H_{eqr}(f)=-\frac{1}{2}[H_{i}(f_{c}+f)+H_{i}(f_{c}-f)] \tag{6}\]  
\[H_{eqi}(f)=\frac{1}{2}[H_{r}(f_{c}+f)-H_{r}(f_{c}-f)]\]  
The equivalent baseband transfer function that affects the cosine channel because of a modulating signal in the sine channel is given by:  
\[m(t)=V\cos(2\pi ft)\times\cos(2\pi f_{c}t)=\frac{V}{2}\cos(\omega_{c}-\omega)t+ \frac{V}{2}\cos(\omega_{c}+\omega)t \tag{1}\]0  
\[m(t)=V\cos(2\pi ft)\times\cos(2\pi f_{c}t)=\frac{V}{2}\cos(\omega_{c}-\omega)t+ \frac{V}{2}\cos(\omega_{c}+\omega)t \tag{1}\]1 Cartesian plane in which the phasors (vectors) at angular speed \(\omega_{c}\) are fixed (non-rotating). The two side-bands of Equation (1) are shown at the output of the medium transfer function. \(H_{r}(\psi)\) acts on the two side-bands in phase (real axis), while \(H_{i}(\psi)\) acts on them by producing orthogonal side-bands (imaginary axis). \[m(t)=V\cos(2\pi ft)\times\cos(2\pi f_{c}t)=\frac{V}{2}\cos(\omega_{c}-\omega)t+ \frac{V}{2}\cos(\omega_{c}+\omega)t \tag{1}\]2  
Notice a change of sign in Equation (7) compared to Equation (6). Now, from Equations (6) and (7), we notice that \(H_{epi}(f)=0\) only if \(H_{i}(\psi)\) is an odd function of frequency about \(f_{c}\) so that \(H_{epi}(f)=0\), and only if \(H_{r}(\psi)\) is an even function of frequency about \(f_{c}\) so that \(H_{epi}(f)=0\). In the next section, we calculate these transfer functions for channels faded by rain.
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)
### KDN vs. SDN vs. Conventional Network
#### 2.3.6 Application Plane  
In conventional networking, services are provided depending on the capabilities of underlying devices, and the application plane is closely tied to the network infrastructure. This can limit the flexibility of the network and make it challenging to adapt to shifting requirements. Instead of relying on the network infrastructure's capabilities, SDN offers more flexibility and enables services to be supplied in accordance with application needs. In SDN, the application plane is decoupled from the underlying physical network and is dynamically defined and reconfigured according to the requirements of the service. KDN goes a step farther in terms of the application plane's versatility. In KDN, the application is continually optimized depending on the service's requirements and network performance, in addition to being developed dynamically. This strategy enables the more effective use of network resources and may result in better service delivery.
## Appendix A Study of a Preliminary Model  
In this section, we analyze a preliminary model encapsulating basic interaction dynamics:  
\[\frac{dS}{dt} =b(t)S-\beta(t)SW, \tag{11a}\] \[\frac{dI}{dt} =+\beta(t)SW-\alpha(t)I,\] (11b) \[\frac{dU}{dt} =a(t)U-\gamma(t)IU,\] (11c) \[\frac{dW}{dt} =\gamma(t)IU-\mu(t)W, \tag{11d}\]  
which is a simplification of the main model (13)-(17). This system has a trivial critical point where all variables are zero:  
\[(S,I,U,W)=(0,0,0,0)\]  
Also, there is a non-trivial critical point that depends on the coefficients:  
\[(S,I,U,W)=\left(\frac{a\alpha}{b\gamma},\frac{a}{\gamma},\frac{b\mu}{a\beta}, \frac{b}{\beta}\right)\]  
The general Jacobian matrix is calculated as  
\[\begin{bmatrix}-W\beta+b&0&0&-S\beta\\ W\beta&-\alpha&0&S\beta\\ 0&-U\gamma&-I\gamma+a&0\\ 0&U\gamma&I\gamma&-\mu\end{bmatrix},\]  
The Jacobian matrix at the trivial critical point is given by  
\[\begin{bmatrix}b&0&0&0\\ 0&-\alpha&0&0\\ 0&0&a&0\\ 0&0&0&-\mu\end{bmatrix}\]  
with eigenvalues \(\{b,-\alpha,a,-\mu\}\), indicating an (unstable) saddle point. For the non-trivial critical point, the Jacobian matrix is  
\[\begin{bmatrix}0&0&0&-\frac{a\alpha\beta}{b\gamma}\\ b&-\alpha&0&\frac{a\alpha\beta}{b\gamma}\\ 0&-\frac{b\gamma\mu}{a\beta}&0&0\\ 0&\frac{b\gamma\mu}{a\beta}&a&-\mu,\end{bmatrix}\]  
resulting in the characteristic polynomial  
\[-a\alpha b\mu+a\alpha\lambda\mu+\alpha b\lambda\mu+\alpha\lambda^{3}+\lambda^{ 4}+\lambda^{3}\mu 00.\]  
Setting all parameters to \(1\), except \(\beta\) and \(\gamma\), yields the eigenvalues  
\[\{-i,i,\sqrt{2}-1,-1-\sqrt{2}\}, \tag{12}\]  
which remarkably do not depend on \(\beta\) and \(\gamma\). This critical point is an unstable center, marked by a complex eigenvalue pair and a positive eigenvalue \(\sqrt{2}-1\). Other parameter choices do not yield compact forms, at least within the scope of computational algebra. Numerical calculations of eigenvalues with random parameters consistently reveal a positive term, indicating instability in any scenario. This aligns with the theoretical finding,particularly those in (A2), which suggest an oscillatory, non-converging nature. For the actual system (13)-(17), identifying critical points using computational algebra proves to be infeasible.
## 3 Results and Discussion
### Elemental Analysis  
The elemental compositions of the PLA-based nanofibrous textiles after the incorporation of AgNPs were analyzed using EDX spectrum curves. Figure 4 depicts the EDX elemental spectra and elemental contents of fabric samples M1-M4. The EDX spectra and elemental analysis in Figure 4 confirm the presence of colloidal silver in all samples. When AgNPs were integrated into M1 in the pre-electrospinning stage, the elemental contents were 54.41% carbon (C), 45.74% oxygen (O), and 1.85% Ag. With the integration of the Ag hydrophilic membrane via pre-electrospinning (M2), the Ag content increased, whereas the carbon content decreased. In contrast, in samples M3 and M4, the presence of Ag increased with the incorporation of Ag after electrospinning via solution casting. The highest Ag content and spectrum peak were observed for sample M4, with a slight decrease in the carbon content. This indicates a good interaction between the Ag and the membrane surface. The peak of the platinum coating was not counted and removed, and atomic elemental analysis was performed. Silver colloids were successfully integrated into the membranes; however, the incorporation of AgNPs through solution casting resulted in a higher quantity of Ag compared to the pre-electrospinning stage. The higher Ag content observed in samples M3 and M4 can be attributed to the presence of PEG-PPG-PEG, which enhanced the retention and absorption of the Ag solution within the nanofibrous structure. PEG-PPG-PEG functions by reducing the attractive forces among NPs, increasing the steric distance between them and improving the hydrophilicity. Consequently, this led to an improved dispersion of the AgNPs and a higher concentration of Ag detected in the elemental analysis [32].  
Figure 4: EDX results of the Ag spectrum of the prepared nanofibrous electrospun cloth as evidence of the presence of Ag in the samples.
# 6G Technology for Indoor Localization by Deep Learning with Attention Mechanism
## 5 Conclusions  
This paper investigates how advancements in 6G technology can propel the future development of indoor positioning systems. Our findings reveal that 6G technology, especially its sophisticated SA and CA mechanisms, can substantially improve the precision and efficiency of indoor localization. These technological enhancements are crucial for a wide variety of applications in smart environments, as the SA and CA mechanisms in 6G demonstrate remarkable capabilities in managing complex and evolving indoor scenarios.  
Moreover, the integration of CNN with SA and CA not only enhances positioning accuracy but also facilitates real-time location awareness. This capability is particularly significant for applications involving vulnerable populations, such as the elderly, enabling caregivers to quickly identify and respond to emergencies or health concerns. Our numer  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ScenariosAlgorithm** & **CNN** & **CNN with SA** & **CNN with CA** \\ \hline
3 Txs & 0.0418 & 0.0235 & 0.0384 \\ Block Tx1 & 0.0593 & 0.0251 & 0.0416 \\ Block Tx2 & 0.0590 & 0.0264 & 0.0446 \\ Block Tx3 & 0.0682 & 0.0253 & 0.0422 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE (m) for four scenarios with 20% noise.  
ical results provide valuable insights into effectively utilizing 6G technology for indoor positioning, highlighting its potential to improve safety measures, mobility, and independence for individuals. This work outlines important directions for future innovations in 6G applications, emphasizing the practical benefits of advanced positioning systems in enhancing quality of life. In future work, we plan to develop more different combined CSI fingerprints, such as frequency channel response and time channel with time delay as an input database for neural networks. In addition, we also plan to apply advanced AI technology, such as generative adversarial networks, to more complex indoor environments.  
Conceptualization, C.-C.C.; Methodology, H.-Y.W.; Software, H.-Y.W. and P.-H.C.; Validation, P.-H.C.; Formal analysis, C.-E.C. and E.H.L.; Investigation, C.-E.C.; Resources, E.H.L.; Data curation, P.-H.C.; Writing--original draft, H.-Y.W.; Writing--review & editing, C.-C.C.; Visualization, C.-E.C.; Supervision, E.H.L.; Project administration, C.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Council Taiwan, grant number NSTC 112-2221-E-032-014-MY2.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.