esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 4 Polarization Modulation in Graphene Metasurfaces
### General Polarization Control in Graphene Metasurfaces
The design induces a strong Fano resonance only when the polarization is along the bar direction, i.e., the y direction. The Fano resonance dip in y polarization shifts with the voltage, and the x polarization is unaffected. (**c**) The tilt angle is adjustable with constant ellipticity by changing the biasing voltage as \(-200\) V, 0 V, and 250 V at 7.72 \(\upmu\)m. Reproduced with permission from [100], Copyright The authors, 2017. (**d**) Graphene-loaded slot-shaped metasurface for polarization conversion due to strong resonance along the short edge and weak interaction along the long edge. (**e**) Reflection phase difference between x and y polarization states under different voltages. The phase differences of 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) are obtained at 7.1 \(\upmu\)m with proper voltage applied. (**f**) The 61\({}^{\circ}\) linear polarized light is changed to LCP, cross-LP, and RCP when the voltage is 34 V, 89 V, and 170 V relative to the charge neutral point (CNP) for encoding and multiplexing applications. Reproduced with permission from [101], Copyright WILY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2015. (**g**) Unit cell of a tunable polarization rotator composed of 45\({}^{\circ}\)-rotated bi-layer metallic gratings and orthogonal bi-layer graphene gratings. The polarization rotation is enhanced by the Fabry–Perot interference between the top and bottom gratings and dynamically changed via modification of the graphene conductivity (**h**) The polarization rotation angle varies continuously from 20\({}^{\circ}\) to 45\({}^{\circ}\) and 45\({}^{\circ}\) to 70\({}^{\circ}\) by sequentially tuning the Fermi level of the orthogonal graphene gratings with a high transmission efficiency of above 75% in a wide band between 0.83 and 1.2 THz. Reproduced with permission from [102], Copyright Elsevier Ltd., 2018.  
sequence. In addition, graphene gratings and metallic gratings are stacked with proper orientation in Figure 10g to rotate the polarization direction of LP beam dynamically from 20\({}^{\circ}\) to 70\({}^{\circ}\) with a high transmission efficiency of above 75% in a wide band between 0.83 and 1.2 THz [102], taking advantage of the Fabry-Perot interference and the dynamic graphene conductivity. Such controllable optical activity without chiral material holds good prospects in biology and spectroscopy.
## 3 System Model
### Communication Overhead for Flooding  
In the next step, link failure information must be communicated to other nodes in the network to avoid passing through a failed link. Both the link-state and hop-by-hop routing schemes utilize the flooding technique to broadcast link-state information. The flooding method broadcasts the link-state data to every node in the network. Thus, all available links are utilized to transmit the link-state information, which requires the recalculation of the routing table. In defining the communication overhead, the links used to broadcast link-state information are considered. Subsequently, to minimize the communication overhead during flooding, the solution is obtained by minimizing the objective function, as follows:  
\[\mathrm{Minimize:}\sum_{v\in V}f(v_{(n,m)},v_{(n^{\prime},m^{\prime})})\times e _{(n,m),(n^{\prime},m^{\prime})}, \tag{4}\]  
\[\mathrm{s.t.}\quad f(v_{(n,m)},v_{  
value of one; otherwise, it is zero. According to Equation (4), overhead is minimized when a link failure occurs in LEO satellite networks.
## 3 Analysis of Crack-Characteristic Stress  
### Calculation Method of Crack Strain  
In the process of rock deformation and failure, microcracks in rock undergo the process of closure, initiation, propagation, and coalescence. The evolution of microcracks can be used as the mechanism of rock micro-damage, and the crack strain can be used as an index to describe the evolution of microcracks in rock. Crack strain refers to the axial and lateral deformation caused by crack change under external load; those changes contain the initiation, propagation, and coalescing of primary cracks and the initiation of new cracks under external loads [16]. In the test process, the volumetric strain of rock consists of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample ID** & **Confining Pressure (MPa)** & **Peak Strength (MPa)** \\ \hline X1-1, X1-2, X1-3 & 5 & 96.86 \\ X2-1, X2-2, X2-3 & 10 & 120.02 \\ X3-1, X3-2, X3-3 & 15 & 146.04 \\ X4-1, X4-2, X4-3 & 20 & 161.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test scheme.  
Figure 1: TOP INDUSTRIE Rock 600-50 Rock automatic servo rheometer.  
volumetric strain and elastic strain of the crack formed by the closure of the primary crack or the initiation and propagation of the new crack, which can be expressed as follows [17; 18; 19]:  
\[\varepsilon_{v}=\varepsilon_{v}^{e}+\varepsilon_{v}^{c} \tag{1}\]  
where \(\varepsilon_{v}^{e}\) and \(\varepsilon_{v}^{c}\) are the elastic volumetric strain and crack volumetric strain, respectively.  
According to [PERSON]'s law, the elastic volume strain is  
\[\varepsilon_{v}^{e}=\varepsilon_{1}^{e}+\varepsilon_{2}^{e}+\varepsilon_{3}^ {e}=\frac{1-2v}{E}(\sigma_{1}+\sigma_{2}+\sigma_{3}) \tag{2}\]  
where \(\varepsilon_{1}^{e}\), \(\varepsilon_{2}^{e}\), and \(\varepsilon_{3}^{e}\) are the elastic strain energies corresponding to the principal stress, respectively, and \(\sigma_{1}\), \(\sigma_{2}\) and \(\sigma_{3}\) are the maximum principal stress, intermediate principal stress, and minimum principal stress, respectively; \(E\) and \(v\) are the elastic modulus and [PERSON]'s ratio, respectively.  
During the test, the volumetric strain can be obtained by \(\varepsilon_{1}\), \(\varepsilon_{2}\), and \(\varepsilon_{3}\); its expression is as follows [17; 18; 19]:  
\[\varepsilon_{v}=\varepsilon_{1}+\varepsilon_{2}+\varepsilon_{3} \tag{3}\]  
where \(\varepsilon_{1}\), \(\varepsilon_{2}\), and \(\varepsilon_{3}\) are the strains corresponding to the directions of maximum principal stress, intermediate principal stress, and minimum principal stress, respectively.  
According to Equations (1)-(3), the volume strain of the crack is  
\[\varepsilon_{v}^{c}=\varepsilon_{v}-\varepsilon_{v}^{e}=\varepsilon_{1}+ \varepsilon_{2}+\varepsilon_{3}-\frac{1-2v}{E}(\sigma_{1}+\sigma_{2}+\sigma_ {3}) \tag{4}\]  
In the conventional triaxial loading and unloading test, the confining pressure is applied in the circular direction; thus, the above equation can be simplified as  
\[\varepsilon_{v}^{c}=\varepsilon_{1}+2\varepsilon_{3}-\frac{1-2v}{E}(\sigma_{ 1}+2\sigma_{3}) \tag{5}\]
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 3 Related Knowledge  
### ARIMA Model  
The ARIMA model is called the Auto Regressive Integrated Moving Average Model (ARIMA). It was proposed by [PERSON] and [PERSON] in early 1970, to predict a time-series approach. ARIMA \((p,d,q)\) is called a differential autoregressive moving average model, where AR is autoregressive, \(p\) is the autoregressive term, MA is the moving average, \(q\) is the moving average number, and \(d\) is the time series, which becomes a smooth difference number. The so-called ARIMA model refers to a model that converts a non-stationary time series into a stationary time series, and then returns the dependent variable (including the present and late value of the random error). Assuming that the data at time \(t\) is \(y(t)\), it can be composed of weighted combinations of the \(p\) data before \(t\) time and corresponding errors, which is described in the following equation:  
\[y(t)=a_{1}y(t-1)+a_{2}y(t-2)+\cdots+a_{m}y(m-2)+s(t)+b_{1}s(t-1)+\cdots+b_{n}s (n-1), \tag{7}\]  
where \(a_{1}\) to \(a_{m}\) are the AR factor coefficients, and \(b_{1}\) to \(b_{n}\) are the MA factors. Here, the error sequence is assumed to be a white noise Gaussian distribution. Therefore, to establish the ARIMA model, it is necessary to determine the order of the model, evaluate the coefficients of the model, and predict the time series. Generally, the values of \(p\) and \(q\) are determined by correlation analysis, using an autocorrelation function and a partial correlation function. The parameters of the model are generally  
Figure 3: ALIAN variance of the original gyro signal.  
determined by the Box-Jenkins method, or the least-squares method [10; 11; 12]. In the paper, this is done by using an intelligent optimization algorithm.
## 4 Methodology and Experiment Design
### Measurement Table Design  
The measurement table used in this experiment was designed based on existing research. Detailed criteria are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **Code** & **Measures** & **References** \\ \hline \multirow{4}{*}{**Perceived information**} & PIV1 & Detailed product Description & \\ \cline{2-4}  & PIV2 & Get lots of product information & \\ \cline{2-4}  & PIV3 & Be able to fully understand product information & [PERSON] et al., 2010 [31; 32; 33] \\ \cline{2-4}  & PIV4 & Obtained information help to make purchasing decisions & \\ \hline \multirow{4}{*}{**Perceived risks (PR)**} & PR1 & Concerning that the actual product does not match the displayed features & \\ \cline{2-4}  & PR2 & Concerning that actual product is not satisfactory & \\ \cline{2-4}  & PR3 & Concerning that some of the functions of the actual products cannot achieve the advertised effect & [PERSON] et al., 2010 [31; 32] \\ \cline{2-4}  & PR4 & Concerning that using the product will affect how others perceive you & \\ \cline{2-4}  & PR5 & Concerning about using the product and your friends will think you’re funny & \\ \cline{2-4}  & PR6 & Concerning product is inappropriate for using in public & \\ \hline \multirow{4}{*}{**Perceived value (PV)**} & PV1 & make a good impression on others & \\ \cline{2-4}  & PV2 & recognized by other people & \\ \cline{2-4}  & PV3 & Create a positive social image & \\ \cline{2-4}  & PV4 & Like product & \\ \cline{2-4}  & PV5 & Feel happy when using this product & \\ \cline{2-4}  & PV6 & Enjoy using this product & \\ \cline{2-4}  & PV7 & Want to own this product & \\ \cline{2-4}  & PV8 & The actual price of this product is X, which is high cost performance & \\ \cline{2-4}  & PV9 & Price is acceptable & \\ \cline{2-4}  & PV10 & It’s a good buy & \\ \hline \multirow{4}{*}{**Purchase Intention (PI)**} & PI1 & Will choose to by this product & \\ \cline{2-4}  & PI2 & glade recommend this product to people & [PERSON] et al., 2010 [33], \\ \cline{2-4}  & PI3 & If this product is needed, happy to buy it & \\ \cline{2-4}  & PI4 & Choose the same when next purchase & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurements source.  
A questionnaire was designed based on the above measurement table and modified after the pre-experiment. A seven-level Likert scale was used in this experiment.
## 4 Conclusions  
In this work, the possibility of increasing heat transfer in periodic Kelvin cell-based aluminum foams by considering fiber inclination effect was investigated. Four angles of attack (fibers' orientation with respect to main air flow) were analyzed: 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\) for four elliptical struts' (E1-1, E2-1, E1-2, E2-2) configurations. In particular, by means of steady state numerical simulations in Ansys Fluent environment, the thermal-hydraulic behavior was studied and deeply discussed by varying the air inlet velocities (0.5, 1, 2, 3, 4 m s\({}^{-1}\)). Besides, the results were compared in terms of enhanced performances with respect to the circular struts' foams considered as reference case.  
A PEC was used to directly compare the foam configurations. Results revealed that, as the angle of attack increased, a general reduction of the overall performance enhancement was obtained due to a remarkable flow disturbance if compared to no inclination cases. It can be concluded that almost all elliptical struts investigated performed better than circular ones (except E2-2 30\({}^{\circ}\) and 45\({}^{\circ}\)). More specifically, the best result was obtained for E1-2 with 0\({}^{\circ}\) angle of attack, which reached a PEC value which is 22% higher than that for reference case, at Re = 200.  
M.C.: Conceptualization, methodology, investigation, software, writing--original draft preparation. E.D.M.: Investigation, software, writing--review and editing. S.M.: Conceptualization, supervision, methodology, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors wish to acknowledge the support and the computational resources made available by the High Performance Computing Lab at the Department of Management and Engineering (DTG), co-funded by the University of Padova in the framework of the program \"Scientific Research Instrumentation 2015\".  
The authors declare no conflict of interest.  
Figure 15: PEC as function of Re for all the investigated KFMs’ configurations.
# 3D Positioning of Drones through Images
## 3 Methods
### Feature Extraction Network  
In this section, we propose a backbone network MH-PCPVT that simultaneously extracts input image features. The baseline of the model is twin-PCPVT-S, and we have made changes on this basis, mainly in the input part and the attention structure part. Our backbone structure provides a channel for information exchange between the satellite image and the two drone images at the same time as feature extraction, thus improving the efficiency of feature extraction.  
As shown in Figure 6, the input of MH-FPI is two drone images with a size of \(96\times 96\times 3\) and a satellite image with a size of \(384\times 384\times 3\). We divided the drone image into 24 patches and the satellite image into 96 patches, each with a size of \(4\times 4\times 3\) (transformer stage in Figure 7). We then fed them into a linear projection, reshaping them  
Figure 5: The backbone is MH-PCPVT, the neck is FPN, and the detection head restores the feature map to the size of the satellite image through linear interpolation.The blue part is the feature map produced by the large drone map. The red part is the feature map produced by the satellite image. The green part is the feature map produced by the cropped small drone map.  
into two-dimensional patches with sizes \(24^{2}\times\) C1 and \(96^{2}\times\) C1 (C1 is the number of channels in the first stage). Finally, we combined them to create an embedded patch with a size of \((24^{2}+24^{2}+96^{2})\times\) C1.  
Next, we input the merged patch into the Transformer block for feature extraction. After each Transformer block, the absolute position coding is replaced by the position-coding generator (PEG) module [22]. Conditional position coding (CPE) [22] is implemented by the position-coding generator PEG, which is more flexible for input sequences and also preserves translation invariance. This feature has a positive effect on training.  
After each stage of the MH-PCPVT network, the output is reshaped into a feature map. In the first stage, the size of the drone feature map is \(24\times 24\times\) C1, while the size of the satellite feature map is \(96\times 96\times\) C1. The current output feature map is then used as input for subsequent stages and the process is repeated.  
After three stages, each stage generates one feature map of the satellite image and two feature maps of the drone image, which are compressed 4, 8 and 16 times compared to the original image, respectively. Originally, the last layer of the network would continue to compress the feature map once, which is 32 times more compressed than the original map, which is not adopted by us. The reason is that too small a feature map is not conducive to subsequent restoration and is not conducive to the model learning the correlation between drone and satellite images. These compressed feature maps are then fused to produce a final prediction.
## 4 Numerical Simulation and Discussion
### Numerical Results
#### 4.3.2 Thermal and Seepage Variation after Impoundment  
The reservoir began the initial impoundment at the end of 2012, and the reservoir level rises from 370 m to 600 m. Because of the impoundment, the hydraulic head in the rock mass near the valley increases drastically to match the reservoir level. As shown in Figure 16, the previous seepage direction at the bottom of the valley was reversed. Though the waterproof curtain can reduce the horizontal seepage flow. It is not able to prevent infiltration along the vertical direction. Thus, the water in the reservoir will infiltrate downward to the limestone and then bypass the waterproof curtain. The infiltration of cold water will form a cooling zone underneath the river bed which leads to the valley shrinkage.  
Figure 16: _Cont._  
Figure 15: Comparisons between simulated temperature distribution extracted from Line 1 in Figure 14 and the measured values near the dam site shown in Section 2, Figure 3.  
Comparisons between simulated and measured data are as follow. The simulation result of thermal variation extracted from point 1 is consistent with the thermal field measurement represented in Figure 17. The numerical result shows that the temperature will drop by 1 \({}^{\circ}\)C in the next two years before it reaches an equilibrium state.
## 5 Conclusions  
In this paper, a new valve strategy with EMIV is proposed to realize VCD operation without any modification to the exhaust valves. The VCD model, which is established in GT-Power and tuned using the 3D code, is used to analyze the effects of the valve strategy on energy losses, in-cylinder pressure, and the mass fraction of oxygen in exhaust gas. The main conclusions are summarized below.  
1. When the exhaust gas is pressed into the intake manifold, there is a significant boundary layer between the exhaust gas and the fresh air. Through a reasonable control of the IVO and IVC, most of the gas sucked into the inactive cycle is the exhaust gas, which avoids not only high exhaust oxygen but also oil suction.
2. Advancing the IVO and delaying the IVC helps to reduce the total energy losses and increase the minimum in-cylinder pressure of the inactive cycle, so as to avoid the risk of oil suction.
3. On the premise of avoiding high exhaust oxygen and oil suction, the optimal areas for IVO and IVC can be determined by the variation features of energy losses during the transition process. The energy losses in the optimal area decrease with the increase in firing density.
4. Under low load conditions, the fuel economy achieved by VCD is better than that of the EIVC strategy. At 1200 and 1600 rpm, fuel economy is improved 12.5-16.6% and 9.7-14.6%, respectively, compared to the prototype engine when the BMEP ranges from 0.3 MPa to 0.2 MPa.  
The author [PERSON] carried out the main research tasks and wrote the original draft. [PERSON] analyzed the results and the whole manuscript. [PERSON] made suggestions for the manuscript corrections and improvements. [PERSON] contributed to the validation of the study and the data processing.  
This research was funded by the National Natural Science Foundation of China, under grant number 51306090; and Natural Science Foundation of Jiangsu Province, under grant number BK20130762.  
The authors would like to thank [PERSON] of Jiangsu University of Technology for his support and assistance with the 3D simulations.  
The authors declare no conflict of interest.  
Figure 22: Benefits to fuel economy achieved by VCD operations.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 2 Fault Types
### Magnetic Faults  
The demagnetization fault is a unique fault of PMSM. Permanent magnets in the PMSM can be demagnetized by damage to the magnet, a high temperature, large stator currents, large short-circuit currents produced by inverter or stator faults, and the aging of the magnet itself [24]. The main source of this fault is armature reactions. During the normal operation of a PMSM, the stator current produces a reverse magnetic field that constantly resists the magnetic field of the permanent magnet [25].  
During the course of long-term operation, this resistance will gradually cause demagnetization of the permanent magnet. When this demagnetization is on the entire magnetic pole, it is completely demagnetized. And partial demagnetization corresponds to the partial magnetic pole.  
According to some studies [26; 27], when the PMSM-specific demagnetization fault occurs, because of flux linkage ripples, the torque will be insufficient, causing the current increase to provide the required torque, which will raise the temperature and exacerbate the demagnetization in turn. At the same time, the fluctuation of the torque will also produce abnormal vibration and acoustic noise. They can reduce motor performance and efficiency [28], and have a negative effect on the normal operation of the PMSM.
# Deep Learning of Diffuse Optical Tomography Based on Time-Domain Radiative Transfer Equation
## 4 Applications to Multiple Absorbers
### Two Absorbers  
First, we obtain temporal profiles of \(A(t)\) for absorbers located at two different domains. We then apply our method to the data. Once a position of an absorber is predicted, we subtract the profile data of the absorber from the original data. Then, we again apply our detection method to the remaining data after subtraction. Figure 7 shows the results for two absorbers, where orange and green circles are absorbers and predicted positions are indicated by blue squares. These results show that the subtraction method works successfully to predict positions of two absorbers. In most cases, correct positions or close positions are predicted, while in the case shown in the lower right panel, prediction is not so accurate. This can be improved by multi-beam injection as shown below. After we tested 60 cases for two absorbers, we found that the data subtraction method is effective for detecting multiple absorbers at a high accuracy rate of 88%.  
Figure 6: Results of position prediction for absorbers with offsets. Red circles are absorbers, and blue squares are predicted positions.  
These results imply that a linear combination of each absorption profile is a good approximation for most cases of two absorbers. When the distance between two absorbers is large, the linearity is expected to hold good. However, the upper middle panel in Figure 6 shows that linearity is a good approximation even for adjacent absorbers, if they are located parallel to the incident beam direction. On the other hand, as seen in the lower right panel, the adjacent absorbers are located perpendicular to the incident beam direction. In this case, the nonlinear effects seem to be significant.  
As shown here, the data subtraction method is capable of accurately predicting the correct position or nearest positions. In order to evaluate the probability of a correct detection, it is useful to introduce a scoring scheme. As shown in the left panel of Figure 8, the score for a correct position is 2, while the score for correctly predicting adjacent positions is 1. The right panel of Figure 8 shows an example of a total score of 3. In this case, the detection probability of the two absorbers is \(\frac{3}{4}=75\%\) because the score of correct prediction for both absorbers is 4. Based on such scoring rule, we tested 60 examples for two absorbers and found the average of the detection probability to be 72%.