esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Deblurring Results.
For all of these--\"Img02,\" \"Img07,\" and \"Img21,\"--the PSNR and SSIM were the highest for the proposed method. More comprehensive comparisons were carried out on all 24 tested images in the Kodak Lossless True Color Image Suite, which was introduced at the beginning of Section 3[34]. The statistics related to the PSNR and SSIM, as determined from a comparative analysis of all the simulated motion-blurred images, are shown in Figures 8 and 9, respectively. In the deconvolution procedure, any slight noise or outliers in an estimated blur kernel can cause errors in the image gray values; the shapes and offsets of the blur kernels can also affect the pixel positions in the deblurred results. In addition, the testing ground truth images were wrapped with gray or black boundaries. As the PSNR value is based on the MSE of the images, it mainly depends on image contrast and gray scales. Additionally, the SSIM value uses the image absolute means and the standard deviations. These two metrics have their advantages and make \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Images** & **FRIQA** & \begin{tabular}{c} **Method in** \\ **[**35**]** \\ \end{tabular} & \begin{tabular}{c} **Method in** \\ **[**36**]** \\ \end{tabular} & \begin{tabular}{c} **Method in** \\ **[**37**]** \\ \end{tabular} & \
\begin{tabular}{c} **Method in** \\ **[**38**]** \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline \multirow{2}{*}{“RedDoor.png”} & PSNR & 28.660 & 26.126 & 27.126 & 28.633 & 28.986 \\  & SSIM & 0.9751 & 0.9570 & 0.9660 & 0.9764 & 0.9787 \\ \hline \multirow{2}{*}{“Flowers.png”} & PSNR & 27.411 & 26.082 & 27.429 & 27.826 & 28.240 \\ \cline{2-6}  & SSIM & 0.9049 & 0.9120 & 0.9173 & 0.9122 & 0.9177 \\ \hline \multirow{2}{*}{“Lighthouse.png”} & PSNR & 25.197 & 24.297 & 24.887 & 25.577 & 25.841 \\ \cline{2-6}  & SSIM & 0.8816 & 0.8724 & 0.8651 & 0.8791 & 0.8867 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Full-reference image quality assessment data of the comparison results of Figures 5–7. Figure 7: Comparison results for the “Lighthouse.png” image: (**a**) blurry image; (**b**) method in [35]; (**c**) method in [36]; (**d**) method in [37]; (**e**) method in [38]; (**f**) proposed method. up for each other's shortcomings. In this paper, therefore, we use both metrics to evaluate the deblurring results. As can be seen in Figures 8 and 9, the PSNR values of our results for \"Img03\" and \"Img10\" are lower than others, but their SSIM values are the highest. Similarly, the phenomena of \"Img04\" and \"Img22\", with lower SSIM values, but the highest PSNR values, derive from the same reasons. Once again, it can be seen that the proposed method was superior to other methods in most cases.
###### Abstract  
In this paper, the authors propose a novel partially connected hybrid beamforming (PC-HBF) architecture, which employs variable phase shifters (VPSs) and constant phase shifters (CPSs) for analog beamforming to harness the potential of these two types of phase shifters. In the proposed architecture, the system sum rate optimization to determine the analog precoders can be formulated as a combinatorial problem. However, its exact solution is intractable, and in massive multiple-input multiple-output systems, exhaustive search to solve the corresponding combinatorial problem is practically infeasible. To resolve this problem, we employ a greedy algorithm that provides a near-optimal solution with reduced complexity. The simulation results obtained herein show that by optimally combining VPSs and CPSs, the proposed architecture achieves performance close to that of the VPS-based PC-HBF architecture. Furthermore, its energy efficiency is up to 27.3% higher than that of the CPS-based fully connected HBF scheme.  
hybrid beamforming; massive MIMO; variable phase shifters; constant phase shifters Article
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 4 Methods
### INS Error Model  
INS error model is described by the system of nine differential equations [5,7,9]. Errors in components of Coriolis acceleration and gravity force vector are not accounted because their contribution is negligible compared to inertial sensors' errors. These equations are given by  
\[\left[\begin{array}{c}\dot{\alpha}\\ \dot{\beta}\\ \dot{\gamma}\end{array}\right]=\widetilde{\boldsymbol{\omega}_{\mathbf{n}}} \left[\begin{array}{c}\alpha\\ \beta\\ \gamma\end{array}\right]+\left[\begin{array}{ccc}f\wp/R_{\lambda}&0&0\\ 0&-1/R_{\wp}&0\\ 1/R_{\lambda}&0&0\end{array}\right]\boldsymbol{\delta\mathbf{v}_{\mathbf{n}}^ {\mathrm{e}}}+\left(\Omega\cos\varphi+\frac{V_{E}}{R_{\lambda}\cos^{2}\varphi} \right)\cdot\left[\begin{array}{c}\delta\varphi\\ 0\\ 0\end{array}\right]-\mathbf{C_{n,s}}\boldsymbol{\delta\omega_{\mathrm{s}}},\] \[\boldsymbol{\delta\dot{\mathbf{v}}_{\mathbf{n}}^{\mathrm{e}}}= \left[\begin{array}{ccc}f_{N}&0&-f_{U}\\ -f_{E}&f_{U}&0\\ 0&-f_{N}&f_{E}\end{array}\right]\left[\begin{array}{c}\alpha\\ \beta\\ \gamma\end{array}\right]+\mathbf{C_{n,s}}\boldsymbol{\delta\mathrm{f}_{s}}, \tag{1}\] \[\boldsymbol{\delta\dot{\mathbf{p}}}=\left[\begin{array}{ccc}0&1/R_{ \wp}&0\\ 1/R_{\lambda}\cos\varphi&0&0\\ 0&0&1\end{array}\right]\boldsymbol{\delta\mathbf{v}_{\mathbf{n}}^{\mathrm{e }}}+\frac{V_{E}\sin\varphi}{R_{\lambda}\cos^{2}\varphi}\cdot\left[\begin{array}[ ]{c}0\\ \delta\varphi\\ 0\end{array}\right],\]  
Figure 4: Devices installed onboard a common foundation in the car body. 1—GNSS compass, 2—IMU, 3—reference inertial navigation systems (INS).  
where \(\beta,\gamma,\alpha\) are pitch, roll, and heading errors; \(\mathbf{p}=\left(\varphi,\lambda,h\right)^{T}\), \(\delta\mathbf{p}=\left(\delta\varphi,\delta\lambda,\delta h\right)^{T}\) is the vector of geographic coordinates including latitude, longitude and altitude, and its error vector; \(\mathbf{V_{n}^{e}}=\left[V_{E},V_{N},V_{U}\right]^{T}\), \(\delta\mathbf{V_{n}^{e}}=\left[\delta V_{E},\delta V_{N},\delta V_{U}\right]^{T}\) is the vector of object linear velocity with respect to e-frame projected on n-axes and its error vector; \(\widetilde{\mathbf{\omega_{n}^{i,n}}}\) is a skew-symmetric matrix containing the components of n-frame angular velocity vector with respect to i-frame projected on its axes \(\mathbf{\omega_{n}^{i,n}}=\left[\omega_{E},\omega_{N},\omega_{U}\right]^{T}\); \(R_{\lambda},R_{\varphi}\) are the curvature radii of the Earth normal sections; \(\Omega\) is the Earth's angular velocity; \(\delta\mathbf{\omega_{s}^{i,s}}=\left[\delta\omega_{X_{s}},\delta\omega_{Y_{s} },\delta\omega_{Z_{s}}\right]^{T}\) are FOG errors in rotation angular velocity with respect to i-frame projected on s-axes; \(\mathbf{\delta f_{s}}=\left[\delta f_{X_{s}},\delta f_{Y_{s}},\delta f_{Z_{s} }\right]^{T}\) are specific force errors projected on s-axes; and \(\mathbf{C_{n,s}}\) is the directional cosine matrix (DCM) characterizing transition from the rotating s-frame to n-frame.
## 4 Conclusions  
The objective of this study was to determine the optimum processing condition for high-protein, gluten-free, and low-calorie laver chip by applying RSM to process the laver chip via air-frying and reaction flavor technologies. The optimum composition ratio of the batter through RSM was 20% (\(w/w\)) laver, 21.3% (\(w/w\)) surimi, and 58.7% (\(w/w\)) rice flour. To achieve this, GDL 3 g, NaHCO\({}_{3}\), 1.5 g, soybean oil 4 g, corn syrup 8 g, salt 1 g, saccharin 0.01 g, 126 mL of distilled water, and 14 mL of RFIS were added and mixed as the minor ingredients to process a batter. The batter was then dried (50 \({}^{\circ}\)C and 1-2 h) to produce pellets. The pellets were then air-fried in an air-fryer (195 \({}^{\circ}\)C and 52.5 s). The measured values of brittleness and puffing ratio of the laver chip were 6.93 \(\pm\) 0.33 N and 116.19 \(\pm\) 0.48%, respectively, which were all within 10% of the predicted values. In addition, the RFIS was produced by adding the precursor materials (methionine 0.54% (\(w/v\)), threonine 3.30% (\(w/v\)), glycine 2.40% (\(w/v\)), glutamic acid 0.90% (\(w/v\)), and glucose 3% (\(w/v\)) to the distilled water (100 mL) and heated (121 \({}^{\circ}\)C and 90 min). The measured QDA (baked-potato and savory odors) values were 6.00 \(\pm\) 0.78 and 4.00 \(\pm\) 0.91, respectively. These were also within 10% of the predicted values. The major flavor components of the laver chip were identified as aldehydes (3-methylhiopropanol, 5-methyl-2-furancarboxaldehyde, decanal, nonanal) and S-containing compounds (dimethyl disulfide, dimethyl trisulfide, methanathiol). The laver chip produced in this study had a high-protein content (24.26 g%) and was low in calories (371.56 kcal). In addition, further research will be needed on the physical properties of air-fried chips according to the heating conditions of air-frying in the processing of air-fried chips.  
G.-T.J.: data curation, writing--original draft, software, validation. C.L.: data curation, methodology, visualization. E.C.: data curation, methodology. S.M.: data curation, methodology. Y.-J.C.: data curation, writing--review and editing, D.Y.: supervision, conceptualization, funding acquisition, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was a part of the project titled 'Development and commercialization of HMR seafood products using new process technology to enhance preference of MZ generation' funded by the Ministry of Oceans and Fisheries, Korea. And this research is funded by the rising professor Financial Program at Changwon National University in 2023.  
**Institutional Review Board Statement:** This study was approved by the Institutional Review Board of Changwon National University (IRB No. 7001066-202211-HR-071).  
**Data Availability Statement:** The data presented in this study are available in article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CPR: & carrier phase recovery \\ \(\Delta\
u T_{s}\): & combined linewidth symbol duration product \\ n-PSK: & \(n\)-level phase shift keying \\ BPS: & blind phase search \\ DSP: & digital signal processing \\ Sq-QAM: & square quadrature amplitude modulation \\ OSNR: & optical signal-to-noise ratio \\ V\&V: & Viterbi and Viterbi \\ AWGN: & additive white Gaussian noise \\ PRBS: & pseudorandom bit sequence \\ BER: & bit error rate \\ BPSSq-mQAM: & blind phase search in square multilevel quadrature amplitude modulations \\ BPSC-mQAM: & blind phase search in circular multilevel quadrature amplitude modulations \\ FEC: & forward error correction \\ PM: & phase modulator \\ EDFA: & erbium doped fiber amplifier \\ OBPF: & optical band pass filter \\ LO: & local oscillator \\ \end{tabular}  
\begin{table}
\begin{tabular}{l c c} \hline _Algorithm_ & _Reduction Factors [DC = 1, M\({}_{i}\) = M\({}_{k}\) = 19] (Multipliers \(\mid\) Address)_ & _Specifications_ \\ \hline _BPS Sq-16 QAM_ & 2.4 \(\mid\) 1.8 & M = 19, \(\delta\) = 32, \(DC\) = 0 \\ _BPS Sq-16 QAM_ & 15.2 \(\mid\) 16.6 & M = 19, \(\delta\) = 32, \(DC\) = 1 \\ _BPS Sq-64 QAM_ & 1.4 \(\mid\) 0.9 & M = 19, \(\delta\) = 64, \(DC\) = 0 \\ _BPS Sq-64 QAM_ & 31.8 \(\mid\) 32.5 & M = 19, \(\delta\) = 64, \(DC\) = 1 \\ _BPS C-16 QAM_ & 15.2 \(\mid\) 16.6 & M = 19, \(\delta\) = 32, \(DC\) = 1 \\ BPS C-64 QAM & 16.1 \(\mid\) 16.5 & M = 19, \(\delta\) = 32, \(DC\) = 1 \\ \hline \end{tabular} Note: \(DC\) = 1 implies hard decision (worst-case complexity). \(DC\) = 0 implies soft decision.  
\end{table}
Table 3: Computational complexity reduction factors relative to two-stage n-PSK partitioning algorithm employing optimal decision boundaries in the symbol decision circuit module.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 4 FPGA Implementation  
This section describes in detail how the decoder, whose general architecture is elaborated and characterized in the previous section of the paper, is implemented in the FPGA. The data flow in input consists of 10 M words per second at 12-bit, as sourced by the ADC. The tasks necessary to carry out in real time the calculations on such a data flow are subdivided, as visible in Figure 8, between the NIOS II soft-processor and other blocks that act like efficient mathematical specialized co-processors. This subdivision, quite common in FPGA projects, allows the exploitation of the advantages belonging to both the microcontroller (\(\upmu\)C) and the dedicated logic. The former is programmed in 'C' language, making it quite easy to apply changes, make corrections, investigate new possible approaches; the latter grants the required calculation power to process the data flow that the \(\upmu\)C alone would never grant. Nevertheless, as it would be clear in the following, the \(\upmu\)C must complete its tasks inside timing windows strictly correlated to the bit-length.  
It should be noted that the architecture spans two separated clock domains: data is sourced from the ADC at 10 Msps, synchronized to a 10 MHz clock; while the \(\upmu\)C, the I/Q demodulator and the phase calculation run at the higher 100 MHz clock. This choice leaves more execution power for the \(\upmu\)C and the processing logics. The circular buffer that, as clarified later, is based on dual-port memory, represents the ideal bridge to cross the two clock domains. A description of the functional blocks visible in Figure 8 follows, organized per single block.
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 3 Results
### Influence of Different Climatic Factors  
Figure 8 shows the spatial correlations of WUE with precipitation and temperature. The change of WUE in most areas of the North China Plain has little relationship with temperature (the area with slight correlation is 85.27%), and only a small part of the change of WUE is affected by temperature (14.73%), and scattered in the North China Plain. Among them, the area of WUE that increases with temperature is only 3.42%. The area of WUE that increases with the increase of precipitation accounts for 23.74% of the area of the North China Plain, and is mainly distributed in the QLMDF, CJPEF and HHPMF ecological functional areas. Similar to temperature, the change of WUE in most areas of the North China Plain has little relationship with precipitation (the area with slight correlation is 76.03%).
## 3 Normalization Algorithms
### Pseudo-Invariant Point Feature-Based Normalization
#### 3.2.2 Pseudo\(-\)Invariant Point Features with Multiple Bands (Point\(-\)Multi)  
To account for the effects of adjacent bands, all bands in the reference images were compared using multivariate regression. The equation for invariant features with multiple\(-\)band regression is expressed as:  
\[Y_{i}=\sum_{i=1}^{n}a_{i}X_{i}+b \tag{5}\]  
where \(a_{i}\) is the \(i\)th band normalization coefficient and \(n\) is the band number of the subject image.  
Figure 4: Statistical results by pseudo\(-\)invariant point feature with single\(-\)band data.  
The coefficient and fitting error were estimated from invariant point features using multivariate regression (Table 3). R\({}^{2}\) and RMSE show that the fitting accuracy of the red, green, and blue bands is not the same as for bands 4-6. This appears to be because the first three bands have low reflectance in both Landsat\(-\)8 OLI and Sentinel\(-\)2A images. Fit using multiple bands was consistently more accurate than fit using single bands, but with low fluctuation (Figure 3). Except for band 4, all bands are significantly affected by adjacent bands, some with negative influences.
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 2 Thermal Conductivity of NSN Nanofluids
### Numerical Predictions and Mechanism Discussion
#### 2.2.4 Rotational Diffusion and Micro Convection  
[PERSON] [73] presented the equivalent field factor for CNTs distributed randomly in the base fluid and constructed a model for CNT nanofluids based on the Maxwell theory. They took the CNT as a rotational elliptical particle with a large aspect ratio. [PERSON] et al. [74; 75] built a model by neglecting the micro convection at the surface of cylindrical particles because the Brownian motion is slow. In addition, the collision model for non-spherical particles is difficult to construct. Models with the Brownian effect are based on spherical particles [76; 77]. [PERSON] et al. [78] found that cylindrical nanoparticles have a larger surface area and more energetic atoms than spherical nanoparticles, resulting in a larger TCE. Moreover, the high-speed rotation of cylindrical nanoparticles accelerates micro convection in the base fluid due to the stirring effect.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Authors** & **Particle Shape** & **Models (\(k_{\textit{off}}/k_{\textit{f}}\) as Default)** & **Remarks** \\ \hline [PERSON] and & & \(\frac{k_{\textit{off}}+(n-1)k_{\textit{f}}-(n-1)(k_{\textit{f}}-k_{\textit{f}}) \phi}{k_{\textit{f}}+(n-1)k_{\textit{f}}+(k_{\textit{f}}-k_{\textit{f}}) \phi}\) & Reduce to Maxwell model when \(n=3\) \\  & & & Unit-cell model \\ [PERSON] and [PERSON] [79] & Cylinder & \(\frac{k_{\textit{f}}/k_{\textit{f}}+K-k_{\textit{f}}(1-k_{\textit{f}}/k_{ \textit{f}})}{k_{\textit{f}}/k_{\textit{f}}+K+(1-k_{\textit{f}}/k_{\textit{f}})}\) & Consider the shape factor \\  & & \(K=2\phi^{h_{2}}\ (21/d)\) for cylindrical particles \\ [PERSON] et al. [54] & Aggregates & \(k_{\textit{off}}=(1-\phi)+3\Phi\int_{0}^{\infty}\frac{\frac{k_{\textit{off}}( 2\phi^{h_{2}})}{k_{\textit{f}}(1-\phi)+3\Phi}\int_{0}^{\infty}\frac{k_{\textit{ f}}(2\phi^{h_{2}})}{k_{\textit{f}}(1+\phi)+3\phi}}{\int_{0}^{\infty}\frac{k_{\textit{f}}(2 \phi^{h_{2}})}{k_{\textit{f}}(1+\phi)+3\phi}}\) & Combine the effective medium \\  & & & approximation and the fractal theory \\ \hline \hline \end{tabular}
\end{table}
Table 2: Typical models for TCE of NSN nanofluids.
# Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin
###### Abstract  
Aiming at the problems of the traditional planetary gear fault diagnosis method of wind turbines, such as the poor timeliness of data transmission, weak visualization effect of state monitoring, and untimely feedback of fault information, this paper proposes a planetary gear fault diagnosis method for wind turbines based on a digital twin. The method was used to build the digital twin model of wind turbines and analyze the wind turbines' operating state utilizing virtual and real data. Empirical mode decomposition (EMD) was used, and an atom search optimization-support vector machine (ASO-SVM) model was established for planetary gear fault diagnosis. The digital twin model diagnoses faults and constantly revises the model based on the diagnostic results. The digital twin fault diagnosis system was implemented in the Unity3D platform. The experimental results demonstrate the feasibility of the proposed early-warning system for the real-time diagnosis of planetary gear faults in wind turbines.  
wind turbine; digital twin; fault diagnosis; real-time perception; Unity3D
# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 4 Results
### Performance Metrics upon Individual Faults
#### 4.2.1. HPU 2 Pump Active  
As seen in Table 6, it is most appealing that the maximum MIP under SVM has reached full score, whilst its minimum MIR is the poorest by approaching zero. Thus, SVM is inapplicable for fault diagnosis on HPU 2 pump active. Regarding predictions on fault-free cases, both LSTM models have inferior MIP ranges compared to RF. However, T2V-LSTM is superior to all other classifiers in its best MIR range (0.7657, 0.9189), which consequently leads to its best fault prediction with the fewest FNs and the optimum range of F-score under T2V-LSTM (0.7555, 0.9026).  
Figure 9 exhibits the time-domain prediction results in advance of HPU 2 pump active faults. As seen in Figure 9b,c, SVM has the steepest downtrend in its MIR and F-score, while T2V-LSTM goes beyond other classifiers in most test cases. Although T2V-LSTM is  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Scores**} & \multicolumn{3}{c}{**LSTM**} & \multicolumn{3}{c}{**T2V-LSTM**} & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**KNN**} & \multicolumn{3}{c}{**ALL**} \\ \hline \multirow{2}{*}{MIP} & MIN & 0.71277 & 0.744 & **0.7561** & 0.56667 & 0.59375 & 0.54839 & 0.54839 \\  & MAX & 0.90385 & 0.9065421 & 0.91837 & 0.93902 & **1** & 0.78641 & 1 \\ \hline \multirow{2}{*}{MIR} & MIN & 0.6036 & **0.7657658** & 0.35135 & 0.20721 & 0 & 0.27928 & 0 \\  & MAX & **0.91892** & **0.9189189** & 0.82883 & 0.82883 & 0.90991 & 0.72973 & 0.91892 \\ \hline \multirow{2}{*}{F-score} & MIN & 0.65366 & **0.7555556** & 0.49682 & 0.33577 & 0.01786 & 0.38554 & 0.01786 \\  & MAX & 0.88312 & **0.9026549** & 0.85581 & 0.81778 & 0.84519 & 0.75701 & 0.90265 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation scores for HPU 2 pump active.  
Figure 12: (a) MIP, (b) MIR, and (c) F-score for predictions on pitch fatal faults from \(t-1\) to \(t-21\).  
the best predictor for HPU2 pump active faults by correctly predicting 76.57-91.89% of faults, HPU 2 pump active beholds smaller MIR ranges, compared to the predictive results on the four specific faults in Table 11.
## 4 Results and Discussion
### Dynamic Mechanical Tests
It can be seen that there is no obvious change before and after the dynamic Hopkinson compression bar experiment. There is no obvious crack in the internal ceramics, and it is more closely combined with the external metal. Due to the impact force and negative Poisson's ratio effect of the re-entrant hex structure, the internal ceramics are wrapped more tightly, and the ceramics are well protected from fragmentation. Metal-ceramic composite structures can generate the full advantages of high strength and compression resistance of ceramics when enduring high-speed impact loading. At the same time, structural advantages of external metals can protect ceramics from crushing failure. In order to focus on the protective effect of different metal frames on the built-in ceramics, in the subsequent periodic lattice Hopkinson experiment, the mass loss of the built-in ceramics is employed for evaluating the protecting performances. In order to better understand the protection effects of metal frames on built-in ceramics, the method of statistical ceramic volume loss after impact is employed for evaluation. Figure 6f-i illustrates the experimental results comparisons between the traditional conventional hex lattice and the re-entrant hex auxetic lattice. Figure 4f,g demonstrate the typical results of two different lattices after impact (the strain rate is 8.8 \(\times\) 10\({}^{2}\)/s). Figure 6h,i show the corresponding scanning 3D imaging results, and the 3D scanner used in this study is Zeiss Atos Q with a 170 mm focal length lens and 8M pixels. The internal ceramic materials of the two hybrid lattices are damaged into cracks after high-speed impact. Under the strain rate of 8.8 \(\times\) 10\({}^{2}\)/s, the average volume of the ceramic loss of the traditional honeycomb lattice is 65 mm\({}^{3}\). Under the same train rate, the volume loss of the re-entrant hex auxetic lattice is 17.8 mm\({}^{3}\) (Figure 6j). This experimental result is similar to previous theoretical analysis and experimental results of the single unit cell sample, which further shows the mechanical benefits of strength and toughness.