esa-sceva/satcom-chunk-collection huggingface dataset:
# Bi-Photon Entangled Airy Beams through Unstable Ocean Turbulence
## 2 Methods
\begin{split}&\rho_{\rm{oc}}^{-2}=\frac{1}{3}\pi^{2}k^{2}ze^{-1/3} \chi\tau\beta A^{2}\int_{0}^{\infty}\kappa^{3}\frac{1+C_{1}(\kappa\eta)^{2/3}} {4\pi(\kappa^{2}+\kappa_{0}^{2})^{11/6}}\\ &\times\Bigg{\{}\exp\left[-\frac{(\kappa\eta)^{2}}{R_{T}^{2}} \right]+\frac{1}{\varpi^{2}\theta}\Bigg{[}-\frac{(\kappa\eta)^{2}}{R_{S}^{2} }\Bigg{]}-\frac{1+\theta}{\varpi\theta}\Bigg{[}-\frac{(\kappa\eta)^{2}}{R_{TS }^{2}}\Bigg{]}\Bigg{\}}\mathrm{d}\kappa,\end{split}  \tag{14}\]  
based on the following two integral expressions  
\[ \begin{split}&\int_{0}^{\infty}\frac{\kappa^{3}}{(\kappa^{2}+ \kappa_{0}^{2})^{11/6}}\exp\left[-\frac{(\kappa\eta)^{2}}{R_{j}^{2}}\right] \mathrm{d}\kappa\\ &=\frac{\exp\left(\frac{\eta^{2}\kappa_{0}^{2}}{R_{j}^{2}} \right)\left(5+6\frac{\eta^{2}\kappa_{0}^{2}}{R_{j}^{2}}\right)\Gamma\left( \frac{1}{6},\frac{\eta^{2}\kappa_{0}^{2}}{R_{j}^{2}}\right)-6\left(\frac{\eta ^{2}\kappa_{0}^{2}}{R_{j}^{2}}\right)^{1/6}}{10\left(\frac{\eta^{2}}{R_{j}^{2} }\right)^{1/6}},\end{split}  \tag{15a}\] \[ \begin{split}&\int_{0}^{\infty}\frac{\kappa^{11/3}}{\left( \kappa^{2}+\kappa_{0}^{2}\right)^{11/6}}\exp\left[-\frac{(\kappa\eta)^{2}}{R_ {j}^{2}}\right]\mathrm{d}\kappa\\ &=\frac{\sqrt{\pi}\kappa_{0}}{2}\left[\frac{1\mathrm{F}_{1}\left( \frac{11}{6};\frac{1}{2};\frac{\eta^{2}\kappa_{0}^{2}}{R_{j}^{2}}\right)}{ \sqrt{\frac{\eta^{2}\kappa_{0}^{2}}{R_{j}^{2}}}}-2\frac{\Gamma\left(\frac{7}{ 3}\right)_{1}\mathrm{F}_{1}\left(\frac{7}{3};\frac{3}{2};\frac{\eta^{2}\kappa_ {0}^{2}}{R_{j}^{2}}\right)}{\Gamma\left(\frac{11}{6}\right)}\right],\end{split}  \tag{15b}\]  
where \({}_{1}\mathrm{F}_{1}(\alpha;\gamma;\tau)\) is the confluent hypergeometric function, the final expression of the spatial coherence radius of a spherical wave propagating in unstably stratified oceanic turbulence is derived as  
\[\rho_{\rm{oc}}=\Big{\{}0.262\beta A^{2}k^{2}ze^{-1/3}\frac{\chi\tau}{\varpi^{2 }\theta}\Big{[}\varpi^{2}\theta\Lambda_{T}+\Lambda_{S}-\varpi(1+\theta)\Lambda _{T}\Big{]}\Big{\}}^{-\frac{1}{2}}, \tag{16}\]  
with  
\[ \begin{split}&\Lambda_{j}=\frac{\kappa_{0}^{1/3}\mathrm{e}^{ \vartheta_{j}}\big{(}5+6\theta_{j}\big{)}}{10\vartheta_{j}^{1/6}}\Gamma\left( \frac{1}{6},\vartheta_{j}\right)-\frac{3}{5}\kappa_{0}^{1/3}\\ &+\frac{C_{1}\sqrt{\pi}}{2}\eta^{2/3}\kappa_{0}\left[\frac{{}_{1} \mathrm{F}_{1}\left(\frac{11}{6};\frac{1}{2};\vartheta_{j}\right)}{\sqrt{ \vartheta_{j}}}-2\frac{\Gamma\left(\frac{7}{3}\right)}{\Gamma\left(\frac{11} {6}\right)}{}_{1}\mathrm{F}_{1}\left(\frac{7}{3};\frac{3}{2};\vartheta_{j} \right)\right],\end{split}  \tag{17}\]  
where \(\vartheta_{j}=\big{[}\eta\big{/}\big{(}L_{0}R_{j}\big{)}\big{]}^{2}\) is a turbulence factor determined by the inner- and outer scale of oceanic turbulence. From Equation (16), the effects of oceanic turbulence on the spherical wave can be regarded as the co-effects of temperature fluctuations, salinity fluctuations and coupling fluctuations.
# A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals
## 3 Model Formulation
### Terminal Processes Agent

Table 3: Parameters and variables used in the model used by the terminal processes agent. define the actual amount of cargo processed for each period within the subsets outlined in Table 4 based on the mooring and processing times of the vessel. Three cases are thus possible: cargo processing starting partway through an involved period, cargo processed throughout the entirety of a period, and cargo processing concluding before a period ends. The use of arrival rates in Equations (16) and (18) is appropriate for measuring TEUs, as it is assumed that each arriving truck carries a single TEU. Storage capacity at the import and export areas is considered finite for a maximum \(K_{max}\) TEU. \[h^{S}_{v,i} =\frac{\min\bigl{(}W\cdot i-m_{v},m_{v}+p^{S}_{v}-W(i-1),W\bigr{)} \cdot d^{S}_{v}}{p^{S}_{v}}, \forall i\in S^{1}_{v},\forall v\in\mathcal{V} \tag{11}\] \[h^{B}_{v,i} =\frac{\min\bigl{(}W\cdot i-m_{v}+p^{S}_{v},m_{v}+p_{v}-W(i-1),W \bigr{)}\cdot d^{B}_{v}}{p^{B}_{v}}, \forall i\in S^{2}_{v},\forall v\in\mathcal{V}\] (12) \[h^{B}_{v,i} =\frac{\min\bigl{(}W\cdot i-m_{v},m_{v}+p^{B}_{v}-W(i-1),W\bigr{)} \cdot d^{B}_{v}}{p^{B}_{v}}, \forall i\in S^{3}_{v},\forall v\in\mathcal{V}\] (13) \[h^{S}_{v,i} =\frac{\min(W\cdot i-m_{v}+p_{v}B,m_{v}+p_{v}-W(i-1),W)\cdot d^{S }_{v}}{p^{S}_{v}}, \forall i\in S^{4}_{v},\forall v\in\mathcal{V}\] (14) \[z_{0} =0\] (15) \[z_{i} =z_{i-1}-r^{P}_{i}+\sum_{v\in\mathcal{V}\mid i\in S^{1}_{v}}h^{S} _{v,i}+\sum_{v\in\mathcal{V}\mid i\in S^{2}_{v}}h^{B}_{v,i}+\sum_{v\in \mathcal{V}\mid i\in S^{3}_{v}}h^{B}_{v,i}+\sum_{v\in\mathcal{V}\mid i\in S^{4 }_{v}}h^{S}_{v,i}, \forall i\in\mathcal{I}^{*}\] (16) \[a_{0} =0\] (17) \[a_{i} =a_{i-1}+r^{D}_{i}-\sum_{v\in\mathcal{V}\mid i\in S^{1}_{v}}h^{S} _{v,i}-\sum_{v\in\mathcal{V}\mid i\in S^{2}_{v}}h^{B}_{v,i}-\sum_{v\in \mathcal{V}\mid i\in S^{3}_{v}}h^{B}_{v,i}-\sum_{v\in\mathcal{V}\mid i\in S^{ 4}_{v}}h^{S}_{v,i}, \forall i\in\mathcal{I}^{*} \tag{18}\]  
Finally, a maximum number of internal vehicles \(V_{max}\) is considered available. The amount of TEUs a vehicle can process within a single period is defined as \(G^{B}\) for double mode, \(G^{S}_{I}\) for single mode moves towards the import area and \(G^{S}_{E}\) for the export area. Building on that, Equations (19)-(21) define the amount of vehicles needed per processing mode and vessel for the processing times of vessels as defined by the berth allocation agent. These equations establish the amount of vehicles needed to match the productivity of the used mode and cranes. Then, the total number of internal vehicles \(f_{i}\) needed can be computed from Equation (22). \[e^{S}_{v} =\frac{d^{S}_{v}\cdot W}{p^{S}_{v}\cdot G^{S}_{E}}, \forall v\in\mathcal{V}\mid E_{v}>I_{v}\] (19) \[h^{S}_{v,i} =\frac{\min\bigl{(}W\cdot i-m_{v},m_{v}+p^{S}_{v}-W(i-1),W\bigr{)} \cdot d^{S}_{v}}{p^{S}_{v}}, \forall i\in S^{1}_{v},\forall v\in\mathcal{V} \tag{11}\]0 (20) \[e^{B}_{v} =\frac{d
## 3 Laboratory, Field, and Design Strengths  
As the field mixing and curing conditions are quite different from the laboratory mix test, the field-stabilized soil strength is usually different from the laboratory-stabilized soil strength. The field-stabilized soil has a relatively large strength variability. The average strength, \(\overline{q_{ul}}\), and deviation of the laboratory stabilized soil and the average strength, \(\overline{q_{uf}}\), and deviation of field stabilized soil are schematically shown in Figure 3. The design standard strength, \(q_{uck}\) is derived from \(\overline{q_{uf}}\) by incorporating the strength deviation as Equations (1) and (2). The magnitude of the parameter \(K\) can be determined by the probability, which is defined as the frequency of the strength lower than the \(q_{uck}\), as a portion drawn with a hatch in Figure 3.  
\[q_{uck}\leq\overline{q_{uf}}-K\cdot\sigma \tag{1}\]  
\[\overline{q_{uf}}=\lambda\cdot\overline{q_{ul}} \tag{2}\]  
where \(K\) = coefficient, \(q_{uck}\) = design standard strength (kPa), \(\overline{q_{uf}}\) = average unconfined compressive strength of field stabilized soil (kPa), \(\overline{q_{ul}}\) = average unconfined compressive strength of laboratory stabilized soil (kPa), \(\sigma\) = standard deviation of field strength (kPa), and \(\lambda\) = ratio of \(\overline{q_{uf}}/\overline{q_{ul}}\).
## 4 Field Engineering Application  
To monitor the coal pillar and the surrounding rock state of the roadway after roof cutting and pressure relief, two measuring stations are set in the roof cutting and pressure relief section. The first measuring station is located in the haulageway 100 m away from the open-off cut of the 1203 working face, and the second measuring station is 50 m away from the first measuring station.  
In the field engineering application, to prevent air leakage from the coal pillar to the adjacent goat, the coal pillar is sprayed with a thickness of 200 mm. According to the statistics of roadway deformation of the 1203 haulageway during tunneling, displacement monitoring data are obtained, as shown in Figure 13. With the continuous excavation of the gob-side entry, the deformation trend of the surrounding rock increases towards stability. The surrounding rock deformation of the roadway is mainly caused by right wall deformation and roof subsidence. The deformation of the right wall accounts for 60% of the deformation of the two sides and the roof deformation accounts for 87% of the displacement of the roof and floor. The deformation speed of the surrounding rock is fast, within 25 days of roadway formation. With time, the deformation speed of the surrounding rock slows down and the roadway enters a stable state. Currently, the maximum displacements of the roadway roof, floor, and left and right sides are 109.4 mm, 16 mm, 179 mm, and 122.8 mm, respectively. The above situation indicates that the roadway is in good use.  
Figure 12: Deformation curve of the roadway surrounding rock with coal pillars of different sizes: (**a**) displacement of two sides of the roadway; (**b**) displacement of the roof and floor of the roadway.  
The vertical stress inside the 5 m small coal pillar is monitored on site and the overall stress distribution is symmetrical. The maximum stress in the center of the coal pillar is 4.42 MPa and the stress on both sides gradually decreases, as shown in Figure 14. It can be seen that the internal stress distribution of coal pillar after roof cutting and pressure relief is in good agreement with the results of the numerical simulation (see Figure 10b).
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 5 Practical Test on USV  
### USV and Components  
We apply the improved D* Lite to a real USV to verify the correctness and efficiency of the algorithm. The USV is a triple-hulled vessel with a size of 160 cm \(\times\) 85 cm \(\times\) 45 cm, which has been developed for water quality monitoring. It is powered by two propellers installed at the end of two floating vessels. During the navigation, the USV can obtain accurate GPS positions by the Real-Time Kinematic (RTK) GPS receiver. An electronic compass can detect the heading of the USV. A Lidar is deployed in the front of the USV, which can detect obstacles around the USV in real time within 20 m and return cloud points to identify the distance and the angle between the Lidar and the obstacles. An EXO sensor is installed at the end of the middle vessel to collect water quality parameters during the navigation. A camera on the top of the USV can transfer living video to users through 4G networks. There are two 12 V batteries in the floating vessels to provide power for the two propellers and all the electronic devices in the USV. We deploy the improved D* Lite  
Figure 19: Full path planed in unknown environment.  
Figure 18: Local path planned in local environment.  
algorithm in a Raspberry PI to control the autonomous navigation of the USV. The USV can also be remotely controlled by an Android-based app, which can be operated by the users. Figure 20 shows the USV and its components.
## 3 Research Methods and Data
### Spatial Autocorrelation Analysis Based on Moran'I Index  
A global spatial autocorrelation analysis measures the overall distribution of water conservation in Yunnan Province by calculating indicators to analyze the overall spatial correlation and spatial differences, and to evaluate whether it has spatial agglomeration [32]. The formula is as follows:  
\[K=\frac{\sum_{i=1}^{n}\sum_{j\
eq 1}^{n}W_{ij}(X_{i}-\overline{X})\left(X_{j}- \overline{X}\right)}{s^{2}\sum_{i=1}^{n}\sum_{j=1}^{n}W_{ij}} \tag{8}\]  
In Equation (8), \(W_{ij}\) is the spatial weight matrix (1 for spatial adjacency and 0 for non-adjacency). \(n\) is the number of space units. \(X_{i}\) and \(X_{j}\) are the attribute value of the spatial unit \(i\) and \(j\), respectively. \(s^{2}\) is the variance of attribute value. \(\overline{X}\) is the average of the observed attribute values of each unit.  
The local spatial autocorrelation analysis is further used to reveal the heterogeneity characteristics of spatial differences in order to comprehensively reflect the change trend of the spatial differences in regional water conservation. The formula is as follows:  
\[K_{i}=Z_{i}\sum_{i}W_{ij}z_{j} \tag{9}\]  
In Equation (9), \(Z_{i}\) and \(z_{j}\) are the standardized values of regional observations, and \(K_{i}\) is the local spatial autocorrelation index, according to which the research units can be divided into four spatial correlation patterns: \"high-high\" value area, \"low-low\" value area, \"high-low\" value area and \"low-high\" value area.
## 3 Basic Theory
### Time Domain Analysis
#### 3.2.2 Root Mean Square  
The _RMS_ of a signal is defined as the signal's normalized second statistical moment [18]. It is typically used to describe the steady-state or continuous magnitude of a time-varying signal. This study uses the _RMS_ as the first of three \"health indicators\" (_HI_)  
Figure 4: Flowchart of the data analysis procedure.  
to monitor the lubrication condition of the toggle clamping system. The \(RMS\) of a time domain signal is calculated as shown in Equation (1):  
\[HI_{RMS}=x_{RMS}=\sqrt{\frac{1}{N}\sum\limits_{i}^{N}(x_{i}^{2})} \tag{1}\]  
where,  
\begin{tabular}{c c} \(HI_{RMS}\) & Health index derived using \(RMS\) algorithm \\ \(x_{RMS}\) & the \(RMS\) value of dataset \(x\) \\ \(x_{i}\) & the \(i\)th member of dataset \(x\) \\ \(N\) & the number of points in dataset \(x\) \\ \end{tabular}
## 5 Conclusions  
The aim of this research was to formulate functional protein aggregates based on dairy proteins and tart cherry juice. In most studies, additional adsorption of flavor compounds next to polyphenols from fruit sources is neglected; thus the investigation of both types of compounds on formulated aggregates was investigated. Our results showed that, next to polyphenols from tart cherry juice, flavor compounds were also adsorbed on the protein matrix, altering the protein matrix flavor profile, not only the polyphenol profile and color. Formulated dairy-protein-based aggregates can be applied for the enrichment of different products (fruit, dairy, and bakery products) with proteins, as well as polyphenols and flavor compounds. This type of additive could be beneficial in the formulation of novel innovative food products and the improvement of existing ones.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12112104/s1](https://www.mdpi.com/article/10.3390/foods12112104/s1)]([https://www.mdpi.com/article/10.3390/foods12112104/s1](https://www.mdpi.com/article/10.3390/foods12112104/s1)), Figure S1: HPLC chromatogram at 520 nm of sample Cas2%_TC (1--cyanidin-3-glucosyl-rutinoside; 2--cyanidin-3-rutinoside); Figure S2 HPLC chromatogram at 360 nm of sample Cas2%_TC (3--rutin; 4--quercetin); Figure S3 HPLC chromatogram at 320 nm of sample Cas2%_TC (5--chlorogenic acid; 6--p-coumaric acid derivate; 7--neochlorogenic acid; 8--p-coumaric acid); Figure S4 HPLC chromatogram at 210 nm of sample Cas2%_TC (9--(--)-epictechin).  
**Author Contributions:** Conceptualization, M.K. and J.S.; methodology, A.P., M.K. and J.S.; formal analysis, I.B., I.I., V.K. and L.C.; investigation, I.B., I.C., I.I. and V.K.; data curation, I.B., I.C. and I.I.; writing--original draft preparation, M.K. and I.C.; writing--review and editing, A.P. and J.S.; supervision, M.K. and A.P.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was part of the project PZS-2019-02-1595, which has been fully supported by the \"Research Cooperability\" Program of the Croatian Science Foundation, funded by the European Union from the European Social Fund under the Operational Program for Efficient Human Resources 2014-2020. I.C. acknowledges support from the Croatian Science Foundation program for Training New Doctoral Students (DOK-2020-01-4205).  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Parallel Load-Bearing and Damping System Design and Test for Satellite Vibration Suppression
###### Abstract  
The traditional series-type satellite vibration suppression scheme significantly decreases satellite frequency, which leads to difficulty in controlling the amplitude. In the present work, a new parallel viscous damping scheme is adopted on the Payload Adaptor Fitting (PAF), which aims to integrate a load-bearing design and vibration reduction. The vibration amplitude and weight are the most important design requirements of the damping system. The Finite Element (FE) model of PAF was established. Through a series of analyses, the appropriate number and coefficient of dampers were determined. The damping force was calculated according to the damping coefficient and the relative velocity between the two ends of the damper. Based on the damping force and the installation dimensions, the damping rod was designed. The force-velocity test was carried out on the damping rod prototype, which showed its performance satisfies the requirements. With the topology optimization and sizing optimization technology, the light-weight supports were designed and manufactured. One damping rod and two supports were assembled as one set of dampers. Eight sets of dampers were installed on the PAF. Vibration tests were conducted on the damping state PAF. The results showed that the proposed system is effective at suppressing vibration and maintaining stiffness simultaneously.  
parallel vibration reduction; viscous damper; damping system design +
Footnote †: journal: Applied Sciences
## 3 Quantitative Analysis of Support Effect of the Tunnel Face
### Strengthening Effect of Bolting Support  
Fiberglass anchor bolts are mainly composed of glass fiber polymers and widely used in tunnel face support because of their characteristics of a high tensile strength, low shear strength and easy excavation. To analyze the strengthening effect of bolting support, the bearing capacity of a single bolt in five failure modes was calculated. Referring to the existing study [26; 27], the bolt parameters are \(m=100\), \(I_{\rm{II}}=3\) m, \(I_{\rm{II}}=1\) m, \(d_{\rm{b}}=0.025\) m, \(d_{\rm{h}}=0.042\) m, \(f_{\rm{b}}=556\) MPa, and \(f_{\rm{f}}=1.20\) MPa. The anchoring stresses of the anchor bolt are \(P_{1i}=272.80\) kN, \(P_{2i}=592.80\) kN, \(P_{3i}=17531\) kN, \(P_{4i}=216\) kN, and \(P_{Si}=5844\) kN, and its total supporting stress is \(P_{min}=216\) kN. Therefore, the strengthening effect of bolting support can be studied by increasing the anchor bolt density. The variation trend in the stability coefficient (\(K\)) with increasing anchor bolt density is shown in Figure 11.  
As shown in Figure 11, the stability coefficient linearly increases with the density of anchor bolt. The stability of the tunnel face cannot meet the safety construction requirements when the anchor bolt density is less than 0.52 roots/m\({}^{2}\). The stability coefficient is slightly greater than the critical stability coefficient (1.20) when the anchor bolt density is greater than 0.52 roots/m\({}^{2}\), indicating that the stability of the tunnel face is improved, and the strengthening effect is slightly prominent. The anchor bolt density in actual engineering is generally 0.50-2 roots/m\({}^{2}\)[26], as shown in the shaded part in Figure 11, and the stability coefficient calculated from the data in this paper is 1.17-2.09. It can be seen that the improvement effect of tunnel stability depends on the anchor bolt density when the anchoring force is determined; however, it is not possible to blindly increase the anchor bolt density and ignore the cost of engineering, when considering the actual working conditions.  
Figure 10: Relationship between stability coefficient (\(K\)), bottom width (\(b_{1}\)), and top height (\(h\)).  
Based on the above analysis, it can be concluded that the grouting support has the most significant effect on improving tunnel face stability, followed by the reinforcement effect of the anchor bolt and reserved core soil; the reinforcement effect of pipe roofing support has the worst effect. Therefore, the reserved core soil is preferred for providing support if the initial stability coefficient of tunnel face cannot meet the stability requirement, and then the reinforcement through the tunnel face grouting is more effective than other measures when the stability requirements are still not met.
## 4 Results and Discussion
### Validation of Vibration Compensation Capabilities of the MA  
In the following, a measurement of a sine standard with \(100\,\mathrm{\SIUnitSymbolMicro m}\) period length and \(1\,\mathrm{\SIUnitSymbolMicro m}\) peak-to-valley amplitude is performed to illustrate the vibration compensation capabilities of the Mirau setup. For this purpose, two measurements are sequentially executed. First, the sinusoidal surface topography is measured in a disturbance-free environment. The result is depicted in Figure 9a.  
Then, a well-defined vibration is applied. For this purpose, the object to be measured is sinusoidally deflected by a piezo-driven stage with an amplitude of \(200\,\mathrm{nm}\) and an oscillation frequency of \(8\,\mathrm{Hz}\). Figure 9b shows the impact of the disturbance on the measured surface topography. There is a significant difference to the disturbance-free surface topography, and the sinusoidal structure can only be suspected. Besides, the CSI signal the laser interferometric signal of the IDS is detected during the depth scanning process, leading to the course of height values depicted in Figure 10. Due to the acquisition rate of 1000 height values per second, the changing height caused by the depth scan (linear part of the course) and the superimposed oscillation of the vibration could be detected by the IDS. Consequently, the frames captured by the CCD camera can be resorted according to the distance values measured by the IDS. The evaluation of the sorted and trigonometrically interpolated CSI signals using a phase analyzing algorithm leads to the surface topography depicted in Figure 9c. A comparison with the unaffected surface topography (see Figure 9a) shows hardly any deviation. The determined peak-to-valley amplitude of the surface  
Figure 8: Profiles measured on the superfine roughness standard KNT4070/03. The blue profile is obtained by the tactile stylus instrument GD26 and the red one below by the custom-built 20x Mirau CSI.  
obtained from the corrected data corresponds to \(0.998\,\mathrm{\SIUnitSymbolMicro m}\), while the measured amplitude of the surface topography measured without disturbance is \(0.992\,\mathrm{\SIUnitSymbolMicro m}\). In both measurements the period length results in \(100\,\mathrm{\SIUnitSymbolMicro m}\). Therefore, the influences of the disturbance on the measured surface is successfully corrected.
## 6 Future Research Directions
### Hardware Development  
In order to operate in the millimeter, wave and THz bands, hardware components must use a significant amount of energy, and are costly. Some gadgets and terminals only have a finite amount of processing and storage capacity. The learning and recognizing capabilities of AI learning algorithms have improved, but they typically require high levels of computational complexity, power consumption, and enough computer resources. In order to improve the collaboration between hardware components and AI learning algorithms, a lot of study is required. Being able to manage matrix calculations and transfer learning-enabled intelligent communications that can adjust to various limitations in the hardware by transferring one learning framework to other hardware components makes graphics processing units (GPUs) suitable for learning computation [35].