esa-sceva/satcom-chunk-collection huggingface dataset:
# Motion Generation for Crane Simulators Using Streamlined Motion Blending Technology
## 2 Motion Generation Technology for Crane Simulator
### The Characteristics of Crane Motion
#### 2.1.2 Motion Decomposability  
There are two kinds of motion in the cab: one is the large-scale low frequency motion caused by the acceleration or deceleration of the trolley. The other is the small-scale high frequency motion caused by various vibration sources. The motion of the crane can be decomposed into a number of large-scale motion components and several vibration components. After these components are calculated separately, the overall motion signal is superimposed after coordination and alignment in the time domain.  
Through the decomposability of crane motion, this paper presents a new rule for the process of high-fidelity motion simulation.  
The acceleration signal that moves along the coordinate axis is defined as \((\overset{\cdot}{x},\overset{\cdot}{y},\overset{\cdot}{z})\), and the rotational speed signal that rotates around the coordinate axis is defined as \((\overset{\cdot}{\alpha},\overset{\cdot}{\beta},\overset{\cdot}{\gamma})\). Then, the motion signal of the crane is \(M=(x(t),y(t),z(t),\alpha(t),\beta(t),\gamma(t))\).  
Let the motion \(M\) contain \(n\) motion components, and each motion component \(M_{i}(t)\) can be independently modeled for solution and can be expressed as:  
\[M_{i}=M_{i}(t)=(x_{i}(t),y_{i}(t),z_{i}(t),\alpha_{i}(t),\beta_{i}(t),\gamma_{ i}(t)),i=1,2,\ldots n \tag{1}\]  
where \(x_{i}(t),y_{i}(t),z_{i}(t)\) is the distance of translation around the X, y and Z coordinate axes, and \(\alpha_{i}(t),\beta_{i}(t),\gamma_{i}(t)\) is the distance of rotation around the X, y and Z coordinate axes.  
Each motion component \(M_{i}(t)\) is triggered at \(t=t_{0i}\). The total amount of motion can be expressed as:  
\[M=\sum_{i=1}^{n}M_{i}(t-t_{0i}) \tag{2}\]  
where \(t_{0i}\) is the initial time of each motion component.  
Therefore, as long as the type and quantity of \(M_{i}(t)\), the solution method of \(M_{i}(t)\) and which \(M_{i}(t)\) is triggered at each moment are confirmed, and the motion simulation problem of crane can be close to completely solved.
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 4 Simulation Results
### Impact of the Number of Sinks and Deployment on EEDOR-VA
The energy consumption is similar because the HCREQ and HCREP messages in the hop count discovery phase, which are responsible for the largest amount of the total consumed energy, are broadcasted between the random distributed sensor nodes in the network topology. In other words, the energy consumed in the hop count phase is related to the sensor nodes and has little to do with the sinks. Moreover, the data forwarding process in EEDOR-VA is not affected by the sink deployment method since the relay nodes that are used for forwarding the data packets are defined during the hop count discovery phase. Figure 16: Pre-planned deployment sinks. Figure 15: Impact of number of sinks on the packet delivery ratio.
## 5 Results and Discussion
### Cost Comparison of Different Levels  
According to Figure 4, with the improvement of intelligent driving levels, the TCO of the same technical route is on the rise due to the increase in the number of sensors and the improvement of hardware performance. The only exception is that the TCO of M2-SSM is higher than that of H-SSM. The reason is that M2-SSM is the conditional automatic driving in the complex scenario realized in 2025, which belongs to a relatively advanced solution. The cost of the lidar and computer used by M2-SSM is still high. According to Table 5, from the perspective of cost composition, with the improvement of the intelligent driving level, the power consumption cost increases significantly with the increase in the system utilization rate, the data traffic cost increases significantly with the increase in the demand for networking, and the maintenance cost increases with the increase in system maintenance demand [42], which leads to the continuous decline of the proportion of hardware cost; from about 70% in the low-level system to about 50% in the high-level one. The software cost share is maintained within a stable range, which means that the absolute increase of software cost is positively related to the intelligence level. Therefore, increasing software investment is a necessary condition to promote intelligence improvement.
# Information Propagation and Bionic Evolution Control of the _Sebar_ Model in a Swarm System
## 3 Equilibrium Points and Basic Reproduction Number  
### Equilibrium Points  
Assume that the set of \(N_{s}\) individual types is expressed as \(Net_{state}=\{S,E,B,A,R\}\). Let \(X=(S,E,B,A,R)\). When the proportions in each state no longer change during the  
Figure 1: State transition diagram of the \(SEBAR\) model.  
information propagation, the swarm system is considered to be in a steady state; then there we have the following:  
\[\frac{dS(t)}{dt}=0,\frac{dE(t)}{dt}=0,\frac{dB(t)}{dt}=0,\frac{dA(t)}{dt}=0,\frac {dR(t)}{dt}=0 \tag{2}\]  
From Formulas (1) and (2), the equilibrium points are calculated using the following:  
\[X_{1}=(1,0,0,0,0) \tag{3}\]  
\[X_{2}=\left\{\begin{array}{l}\frac{p_{6}p_{8}(p_{4}+p_{5})}{\langle k\rangle (a_{1}+a_{2}+a_{3})}\\ \frac{a_{9}(\langle k\rangle(a_{1}+a_{2}+a_{3})-a_{5}-a_{6})}{\langle k\rangle (a_{1}+a_{2}+a_{3})(a_{5}+a_{6}+a_{7}+a_{8}+a_{9})}\\ \frac{a_{7}(\langle k\rangle(a_{1}+a_{2}+a_{3})-a_{5}-a_{8})}{\langle k\rangle (a_{1}+a_{2}+a_{3})(a_{5}+a_{6}+a_{7}+a_{8}+a_{9})}\\ \frac{a_{6}(\langle k\rangle(a_{1}+a_{2}+a_{3})-a_{5}-a_{8})}{\langle k\rangle (a_{1}+a_{2}+a_{3})(a_{5}+a_{6}+a_{7}+a_{8}+a_{9})}\\ \frac{p_{6}p_{8}(p_{4}+p_{5})(\langle k\rangle(a_{1}+a_{2}+a_{3})-a_{5}-a_{8} )}{\langle k\rangle(a_{1}+a_{2}+a_{3})(a_{5}+a_{6}+a_{7}+a_{8}+a_{9})}\\ \end{array}\right. \tag{4}\]  
where \(a_{1}=p_{1}p_{4}p_{8}\), \(a_{2}=p_{2}p_{6}p_{8}\), \(a_{3}=p_{3}p_{5}p_{6}\), \(a_{4}=p_{3}p_{5}p_{8}\), \(a_{5}=p_{4}p_{6}p_{8}\), \(a_{6}=p_{4}p_{7}p_{8}\), \(a_{7}=p_{5}p_{6}p_{7}\), \(a_{8}=p_{5}p_{6}p_{8}\), and \(a_{9}=p_{6}p_{7}p_{8}\).  
From Formulas (3) and (4), it can be deduced that when the swarm system is at equilibrium point \(X_{1}\), only individuals in state \(S\) remain, which means that the information does not effectively propagate. When the system is at equilibrium point \(X_{2}\), the information spreads to some extent, and the proportions of each state are in dynamic equilibrium.
## 4 Results
### Time-Domain Analysis  
#### 4.1.1 Time-Domain Analysis for Extracted Signals  
Figure 7(a) shows the original data in channel no. 1, which is the input D matrix (data matrix) of de-noising algorithms. Figure 7(b)-f show the extracted pulse wave signals by five de-noising methods, which are the computed low-rank matrices. Compared with traditional wavelet transform and VMD methods, RPCA based methods, particularly the proposed and PRCA methods, had better performance on the feature extraction of pulse wave signals from multiple cycle data. Furthermore, our algorithm outperformed RPCA in terms of accurate amplitude peaks.  
The de-noising and feature extraction effects shown in Figure 8 were similar to channel no. 1. RPCA-based algorithms could retain more information with significant de-noising performance. It is worth noting that there was a slight jitter of key physiological point P1 in the original signal. All algorithms could successfully remove the jitter and restore the signal's physiological information.  
Figure 9 shows the original and extracted signals from five de-noising algorithms in channel no. 17. Obviously, the signal-to-noise ratio (SNR) of the original signal was low. Wavelet transform and VMD could not eliminate the noise well. On the contrary, RPCA eliminated some informative signals, while WRPCA caused distortion in some cycles (see the red curve flattening in subfigure WRPCA).  
Figure 7: Original and extracted signals from five de-noising algorithms in sorted channel no. 1. (**a**) Original signal; (**b**) wavelet transform; (**c**) variational mode decomposition (VMD); (**d**) robust principal component analysis (RPCA); (**e**) weighted robust principal component analysis (WRPCA); (**f**) cross-channel DWRPCA (proposed method).  
Figure 8: Original and extracted signals from five de-noising algorithms in channel no. 5 (medium energy). (**a**) Original signal; (**b**) wavelet transform; (**c**) VMD; (**d**) RPCA; (**e**) WRPCA; (**f**) cross-channel DWRPCA (proposed method).  
Figure 9: Original and extracted signals from five de-noising algorithms in channel no. 17 (low energy). (**a**) Original signal; (**b**) wavelet transform; (**c**) VMD; (**d**) RPCA; (**e**) WRPCA; (**f**) cross-channel DWRPCA (proposed method).  
Figure 10 shows the results in channel no. 25. Similarly to channel no. 17, RPCA eliminated some informative signals mixed with noises. The de-noising effect of wavelet transform and VMD was not obvious, while RPCA overprocessed the signal. WRPCA distorted the signal slightly and was inferior to the proposed method.
## 4 Conclusions  
In this paper, a non-redundant NF/FF transformation with a hemispherical scan for volumetric AUTs having one or two predominant dimensions as compared to the other one(s) has been developed. It makes use of the image principle to properly take into account the presence of the PEC ground plane, ensuring the repeatability of the measurements when dealing with heavy antennas. Moreover, it adopts a very flexible source model to reduce the volumetric redundancy of the combined AUT (AUT plus its image), which results when tackling the problem in its equivalent formulation, according to the image principle. This modelling is able to fit any kind of volumetric AUT, since a proper choice of its parameter allows one to develop a sampling representation using the minimum number of NF samples. A mirroring rule is used to synthesise the NF data, which would be collected if the scanning were carried out over the whole sphere and not just on the upper hemisphere. Then, the acquired and synthesised NF samples have been effectively interpolated via an efficient OSI algorithm to evaluate the NF value at any point of the classical grid required by [PERSON]'s NF/FF transformation [15]. It is so possible to avoid that a severe truncation error affects the FF reconstruction, as occurs if the NF data required by the NF/FF transformation [15] and falling on the lower hemisphere were not available.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**AUT Case**} & \multirow{2}{*}{**Here Proposed**} & **NF/FF Transformation [15] in Presence of PEC Ground Plane** \\ \hline Case A & 14,521 & 20,736 \\ \hline Case B & 13,297 & 36,864 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of the amount of required NF data.  
Figure 11: Far-field pattern. Blue solid line: exact. Red dots: interpolated from the non-redundant samples: (**a**) \(\theta\)—component on the cut plane at \(\varphi=90^{\circ}\); (**b**) \(\theta\)—component on the cut plane at \(\theta=90^{\circ}\).  
The numerical results have assessed the reliability and precision of the devised sampling strategy and of the corresponding OSI algorithm.  
Conceptualization, F.F., C.G. and R.G.; Methodology, F.F. and R.G.; Software, F.F.; Validation, F.F.; Formal analysis, F.F., R.G. and G.R.; Investigation, F.F.; Writing--original draft, R.G.; Writing--review & editing, C.G., R.G. and G.R.; Visualization, G.R.; Supervision, C.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 4 Engineering Application
### Details of the Dam and Sensors  
The development of the platform needs to be based on practical application requirements. The proposed platform is for a high concrete gravity dam with a foundation elevation of 41 m and a maximum height of 112 m, as illustrated by the physical structure in Figure 6. The dam was constructed in September 2007 and completed in December 2011 for power generation. A total of three foundation reinforcements were subsequently carried out (the last in August 2014). The layout of the sensors is shown in Figure 7.
# A Method to Reduce Eddy Current Loss of Underwater Wireless Power Transmission by Current Control
## Nomenclature  
The following is the nomenclature of the parameters involved in this paper:  
S1-S4 Primary-side MOSFETs D1-D4 Secondary-side rectifier diodes \(L_{p}\) Self-inductance of the primary-side coil in air \(L_{p1}\) Primary-side compensation inductance \(L_{ps}\) Self-inductance of the primary-side coil in seawater \(L_{0}\) Filter inductance \(C_{P2}\) Primary-side parallel compensation capacitor \(C_{P3}\) Primary-side series compensation capacitor \(C_{S2}\) Secondary-side parallel compensation capacitor \(C_{S3}\) Secondary-side series compensation capacitor \(C_{0}\) Filter capacitor \(R_{in}\) Equivalent internal resistance \(R_{P}\) Internal resistances of primary-side coil in air \(R_{Ps}\) Equivalent resistance of the primary-side coil in seawater \(R_{S}\) Internal resistances of secondary-side coil in air \(R_{Ss}\) Equivalent resistance of the secondary-side coil in seawater \(R_{L}\) Load resistance \(R_{ot}\) Equivalent resistance of rectifier circuit and filter circuit \(R_{ecl}\) Eddy current loss resistance \(R_{ref}\) Reflection impedance in real form \(Z_{ref}\) Reflection impedance in plural form \(M\) Mutual inductance of coils \(M_{air}\) Mutual inductance of coils in air \(M_{sea}\) Mutual inductance of coils in seawater \(K_{sa}\) Modulus ratio of mutual inductance coefficient between seawater and air \(\theta_{diff}\) Phase difference of the mutual inductance between seawater and air \(U\) DC power supply \(U_{in}\) Equivalent AC power supply \(I_{in}\) Current on the primary-side resistance \(R_{in}\) \(I_{C_{P2}}\) Current on the primary-side compensation capacitor \(C_{P2}\) \(I_{P}\) Current on the primary-side coil \(I_{S}\) Current on the secondary-side coil \(I_{C_{S2}}\) Current on the secondary-side compensation capacitor \(I_{out}\) Current on the secondary-side resistance \(R_{L}\)
# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 1 Introduction
The main contributions of this paper can be given as follows:  
1. A depth image inpainting algorithm based on NLM is proposed to address the issue of depth image hole repair. The utilization of the NLM algorithm for mitigating noise and speckle artifacts through filtering operations is discussed in [18; 19; 20; 21]. However, the investigations highlighted above primarily concentrate on the realm of filtering procedures and have not encompassed the integration of the NLM algorithm within the context of depth image for hole restoration. 2. By introducing intelligent block factors, we enable the automatic determination of optimal search and repair patch sizes for voids of different dimensions, resulting in a significant reduction of the intricate debugging efforts in engineering applications. The strategy of improving the performance of the algorithm by manipulating the pixel weights is discussed in [5; 14; 15; 16; 17]. Nonetheless, it is worth noting that the acquisition of these weights relies predominantly on manual calibration, a process that demands a substantial investment of time. The remaining structure of this paper unfolds in subsequent sections. The depth image hole repair algorithm is introduced in Section 2. The experimental results are presented along with their corresponding analysis in Section 3. And conclusions are drawn in Section 4.
# Potential of Liquid-Crystal Materials for Millimeter-Wave Application
## 3 Millimeter Wave Fresnel Lens Fabricated Using 3D Printer  
One LC device application style in the MMW region involves scaling up some actual LC devices used in the optics region, such as lenses and prisms; these are classified asquasi-optical devices. Although this device type generally has a simple device structure for attaining a specific functionality, the device size increases according to the wavelength. The Fresnel lens is an important and common component for controlling MMW propagation like an optical beam. If we introduce LC materials to the passive components, variable and/or switchable lenses can be obtained. However, simple scale up of the LC cell structure, which is utilized normally for optical devices, causes fatal degradation of the response speed because the wavelength of MMW is significantly longer than that of the optics region. Thus, we tried introducing a PDLC structure to attain an extremely thick LC layer using porous polymer materials [33; 34; 35]. In this section, we show a fabrication method of a mm-size device structure using a 3D printer, which will be used as a substrate of the MMW LC device combined with porous polymers to introduce the PDLC structure in our next work.
## 2 Formulation and Theoretical Derivation of the Elastoplastic Phase Field Fracture Model
### Elastic Strain Energy
To characterize rock plastic strain evolution, a non-associated flow rule is adopted [32]. Based on the Drucker-Prager criterion, the yield function is defined as  
\[f(\sigma,\varepsilon^{p})=\sqrt{J_{2}^{\prime}}+\alpha\text{tr}(\sigma)-k \tag{20}\]  
In this formula, \(J_{2}^{\prime}\) is the second deviatoric stress invariant; \(\alpha\) relates to the internal friction angle; \(\text{tr}(\sigma)\) is the volumetric stress; and \(k\) is the hardening parameter. Integrating yielding conditions, the plastic damage evolution equation is revised as  
\[\frac{\mathrm{d}D_{p}}{\mathrm{d}t}=\left\{ \begin{matrix}\frac{1}{\mathrm{c}_{pD}} \Big{\{}K_{p}\mathrm{tr}(\varepsilon^{p})\mathrm{tr}\Big{(}\frac{\mathrm{d} \varepsilon^{p}}{\mathrm{d}t}\Big{)}+2\mu_{p}\mathrm{tr}\Big{(}\varepsilon^{p} \frac{\mathrm{d}\varepsilon^{p}}{\mathrm{d}t}\Big{)}\Big{\}}\,f(\sigma, \varepsilon^{p})>0\\ 0\qquad\qquad\qquad f(\sigma,\varepsilon^{p})\leq 0\end{matrix} \right. \tag{21}\]  
Through the comprehensive analysis of plastic damage, the plastic damage evolution equation is derived, providing a theoretical foundation for optimizing the elastoplastic phase field coupling framework. Furthermore, the enhanced model captures the mechanical behavior and failure mechanisms of rock materials under compressive-shear loading, enabling the precise simulation of crack propagation and failure processes under multiaxial stress conditions.
## 4 Experimental Results
### Comparison with Classical SR Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Params**} & \multicolumn{2}{c}{**MRI13**} & **ADNI100** & **OASIS100** \\ \cline{3-5}  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Bicubic & - & 32.70/0.9126 & 27.95/0.8063 & 28.97/0.8919 \\ SRCNN [2] & 8 K & 35.03/0.9393 & 29.29/0.8527 & 31.27/0.9225 \\ FSRCNN [5] & 13 K & 35.35/0.9406 & 29.48/0.8562 & 30.91/0.7568 \\ VDSR [8] & 666 K & 35.43/0.9449 & 29.83/0.8661 & 31.60/0.9308 \\ DRCN [9] & 1774 K & 35.83/0.9449 & 29.92/0.8672 & 32.18/0.9371 \\ SRDenseNet [13] & 7160 K & 36.25/0.9469 & 30.06/0.8704 & 32.64/0.9477 \\ DDSR [12] & 202 OX & 35.49/0.9433 & 29.68/0.8638 & 31.72/0.9330 \\ EDSR [14] & \(\times 3\) & 1555 K & 36.71/0.9484 & 30.27/0.8735 & 33.26/0.9444 \\ SRMD [41] & 1528 K & 35.91/0.9456 & 29.91/0.8676 & 32.29/0.9385 \\ SRMDNF [41] & 1528 K & 36.09/0.9465 & 30.02/0.8698 & 32.52/0.9405 \\ SRFBN-S [21] & 375 K & 36.09/0.9459 & 29.99/0.8691 & 33.79/0.9453 \\ FAWDN [22] & 7170 K & 36.60/0.9481 & 30.16/0.8719 & 33.00/0.9429 \\ FAWDN+ [22] & 7170 K & 36.73/0.9479 & 30.75/0.8839 & 33.19/0.9450 \\ DMFN(ours) & 583 K & 36.72/0.9485 & 30.17/0.8719 & 34.35/0.9478 \\ DMFN+(ours) & 583 K & 36.75/0.9482 & 30.76/0.8843 & 34.41/0.9493 \\ \hline Bicubic & - & 29.90/0.8591 & 26.37/0.7298 & 26.37/0.7298 \\ SRCNN [2] & 8 K & 31.75/0.8914 & 27.49/0.7388 & 28.47/0.8621 \\ FSRCNN [5] & 13 K & 32.17/0.8934 & 27.66/0.7876 & 28.49/0.6900 \\ VDSR [8] & 666 K & 32.44/0.9007 & 28.00/0.8028 & 28.96/0.8748 \\ DRCN [9] & 1774 K & 32.64/0.9034 & 28.04/0.8029 & 29.29/0.8830 \\ LapSRN [7] & 502 K & 32.68/0.9072 & 27.99/0.8027 & 29.26/0.8837 \\ SRDenseNet [13] & 7160 K & 32.97/0.9075 & 28.18/0.8079 & 29.65/0.8900 \\ DDSR [12] & 2020 K & 32.45/0.9029 & 27.91/0.8011 & 29.15/0.8798 \\ EDSR [14] & 1518 K & 33.31/0.9107 & 28.42/0.8136 & 30.48/0.9003 \\ SRMD [41] & 1552 K & 32.83/0.9060 & 28.05/0.8044 & 29.66/0.8884 \\ SRMDNF [41] & 1552 K & 32.69/0.9062 & 28.13/0.8080 & 28.13/0.8080 \\ SRFBN-S [21] & 483 K & 32.85/0.9069 & 28.20/0.8086 & 31.01/0.9006 \\ FAWDN [22] & 7170 K & 33.22/0.9098 & 28.30/0.8117 & 30.05/0.8957 \\ FAWDN+ [22] & 7170 K & 33.21/0.9086 & 28.81/0.8259 & 30.38/0.8895 \\ DMFN(ours) & 707 K & 33.37/0.9104 & 28.30/0.8117 & 31.47/0.9051 \\ DMFN+(ours) & 707 K & 33.33/0.9094 & 28.94/0.8310 & 31.54/0.9079 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 10: _Cont._
## 3 Analysis of Results
Thus, it can be concluded that the GaAs substrate-backed solar absorber is performing well in the infrared region, which is visible in this electric field response. The highest E-field of 2.5 \(\times\) 10\({}^{6}\) is visible in the response presented in Figure 3. The E-field response presented in the figure shows good field distribution in the metamaterial resonator and substrate. The substrate is also visible in the figure, with a high-field red color response. The result also verified the responses presented in Figure 2 for different wavelengths. The different colors and related fields are also presented in the figure, with the field response inset presented on the right side of the figure. Figure 3: E-field results for the I-shaped metamaterial design backed by gallium arsenide at (**a**) 500 nm, (**b**) 1000 nm, (**c**) 1500 nm, (**d**) 2000 nm, (**e**) 2500 nm, and (**f**) 3000 nm. The color bar shows the electric field values, which are at their highest value of 2.5 \(\times\) 10\({}^{6}\) V/m. The E-field response at 500 nm shown in Figure 3a demonstrates that the electric field is visible at the substrate's surface and in the metasurface. The level of the E-field is low. This electric field response matches the absorption response available at 500 nm, which is around 60%. The E-field response shown in Figure 3b for 1000 nm shows that the electric field is visible not only at the surface like the previous response, but it is also visible going into the substrate a little bit. The E-field response at 1500 nm shown in Figure 3c indicates that the electric field is visible throughout the substrate, with higher energy on the upper part of the substrate and lower energy on the lower part of the substrate. The E-field response at 2000 nm shown in Figure 3d indicates that the electric field is visible throughout the substrate, with higher energy on the upper and middle surfaces and lower energy on the bottom surface. The E-field response shown in Figure 3e for 2500 nm shows that the electric field is visible in the whole substrate, with higher energy on the upper, middle, and lower parts of the substrate, with energy also visible in the second substrate below the SiO\({}_{2}\) substrate. The E-field response shown in Figure 3f for 3000 nm shows that the electric field is visible in the whole SiO\({}_{2}\) substrate, and some part of the energy is also visible in the second substrate of GaAs. This electric field response matches well with the absorption response available at 3000 nm shown in Figure 1, which is more than 96%.