esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 4 Real-Time Energy Management Strategy  
### Problem Description  
The energy management of the vehicle electrical system is a type of optimization problem with boundary conditions in a specific driving cycle. For the optimal fuel economy, the energy management strategy of the vehicle electrical system aims to control the output power of the battery and the alternator, and to maintain the battery SoC within the working mode.  
Figure 6: Equivalent circuit of a battery.  
Figure 7: SoC-OCV correlation curve.  
Therefore, the energy management strategy can be simplified as the control of single degree of freedom. The system controlled variable \(u(t)\) is the battery power \(P_{B}(t)\), the system state variable \(x(t)\) is the battery SoC, and the control objective is to minimize fuel consumption of the engine. Accordingly, the objective function of the energy management strategy under a driving cycle in the duration \([t_{0},t]\) is expressed as follows [15]:  
\[J=\min(\int_{t_{0}}^{t_{f}}\dot{m}_{f}\left(x(t),u(t),t\right)dt), \tag{4}\]  
where \(\dot{m}_{f}\) is the fuel consumption rate of the engine, and its value can be obtained from the characteristic curve of engine mapping in Figure 5 based on the rotational speed \(n\) of the engine and torque \(T\).  
To simplify the study, if we neglect the time-variant parameters of the battery, such as ohmic resistance and polarization resistance, the system state equation can be derived according to Equations (2) and (3), as follows:  
\[\dot{x}\left(t\right)=f\left(x\left(t\right),u\left(t\right),t\right)=-\frac{ I_{B}(x(t),u(t))\beta}{3600 Ah_{nom}}, \tag{5}\]  
\[I_{B}(t)=\frac{V_{o}\left(x\right)-\sqrt{V_{o}^{2}\left(x\right)-4R_{i}P_{B}( t)}}{2R_{i}}, \tag{6}\]  
where \(P_{B}(t)\) expresses the charging and discharging power of the battery at \(t\) moment, and \(V_{o}(x)\) is OCV when SoC is \(x\).  
To avoid overcharging and overdischarging of the battery, the final and initial values of SoC are generally required to be equal, and the battery constantly works within a certain range. In addition, the output power should meet certain constrained conditions when controlled by the working capacity of the alternator and battery:  
\[\left\{\begin{array}{l}x\left(t_{0}\right)=x_{0}\\ x\left(t_{f}\right)=x_{f}=x_{0}\\ x\left(t\right)\in\left[x_{min},x_{max}\right]\\ P_{L}\left(t\right)=P_{B}\left(t\right)+P_{G}\left(t\right)\\ P_{G}\left(t\right)\in\left[0,P_{max}\right]\\ P_{B}\in\left[P_{B\_min},P_{B\_max}\right]\end{array}\right., \tag{7}\]  
where \(x_{\min}\) and \(x_{\max}\) are, respectively, the upper and lower limits of the battery SoC. \(P_{L}\) is the power of thre electrical load; \(P_{\max}\) is the maximum output power of the alternator; and \(P_{B\_min}\) and \(P_{B\_max}\) are the maximum charging power and maximum discharging power of the battery, respectively.
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 4 Predicting Signal Strength
### Foliage  
It is probable that the signal radiating from the satellite passes through foliage; hence, they are further attenuated than the unshadowed signals [54; 55; 56]. Specifically in our case, while evaluating the C/N\({}_{0}\) along the trajectory, we noticed that before way-point C (Figure 2), especially in the first six rounds, the solution proceeds to float or code mode. Looking at the visibility status of PRNs 3 and 21, which have medium elevation, we noticed that they were in an LOS situation, meaning that there was no obstruction by any building. Moreover, we can be sure that there was no blocking building from the high-elevated satellites, which also have a reduction in signal strength in this point. Examining the position, we saw there were three consecutive trees whose canopies covered the street, mainly on the southern side of the street (Figure 10). There are other trees affecting the signal reception in all parts of the trajectory except between way-points D to F. These trees are from very different types and with different geometrical shapes.  
The 3D model used for ray tracing does not include any 3D model for trees. Therefore, we added a rough rectangular cuboid to the 3D model (Figure 10). The model for foliage attenuation explained in [57] assumes the tree as a slab in which the leaves are thin disks, and branches are thin cylinders randomly distributed inside the slab. In Figure 4 of this study, the attenuation for different frequencies is plotted. At a 90\({}^{\circ}\) incident angle, the attenuation for 900 MHz is 7.1 dB/m. The corresponding value for 1.5 GHz is 1.15 dB/m. In Figure 5 of this study, the attenuation is plotted against the incident angle of the signal and the slab for the frequency of 900 MHz.  
Figure 9: Investigation of the receiver antenna gain pattern (exemplary for high-elevated GPS PRN 1): (**a**) Real C/N\({}_{0}\) from the Altus receiver, theoretical C/N\({}_{0}\) from simulation using gain pattern of GNSS 800 series antenna, and the gain pattern of the GNSS 800 series antenna. (**b**) Real C/N\({}_{0}\) from the Altus receiver, theoretical C/N\({}_{0}\) from simulation using modified gain pattern and the modified version of the gain pattern of the GNSS 800 series antenna.  
A polynomial can be extracted for attenuation (dB/m) for 1.5 GHz, and the power loss can be calculated by  
\[L_{F}=(0.000136\theta^{2}-0.02468\theta+2.2696)d_{F}, \tag{16}\]  
with \(L_{F}\) attenuation caused by foliage in dB, \(\theta\) being the incident angle in degrees and \(d_{F}\) being the path length through foliage slab in meters.  
The flowchart in Figure 11 summarizes the required inputs and the sequence of calculating needed parameters in order to predict the C/N\({}_{0}\), as it was explained in detail throughout this section.
# Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
## 1 Introduction
To overcome such defects, this paper developed a novel approach called hierarchical Lempel-Ziv complexity (HLZC) to quantify the complexity from the measured time series. Compared with the MLZC method, HLZC utilizes both the low-frequency components generated by averaging the components and the high-frequency components generated by taking the difference of components to produce the sub time series in each layer. The merits of our proposed HLZC method in fault feature extraction are verified using both synthetic signals and experimental signals. Results demonstrated  
Figure 1: (**a**) The waveform of measured rating machinery vibration signals, and (**b**) its corresponding frequency spectrum. that our proposed HLZC method is superior to LZC and MLZC in extracting fault characteristics with high stability. After the HLZC-based feature extraction, we combined the HLZC with support vector machine (SVM) classifier [27] to accomplish the intelligent fault diagnosis of rotating machinery. The remainder of this paper is organized as follows. Section 2 introduces the fundamentals of our proposed HLZC method. Moreover, the superiority of HLZC is validated using simulated impulsive signal through comparing with LZC and MLZC methods. Section 3 describes the framework of HLZC-based intelligent fault diagnosis method. Section 4 provides the experimental variation using two case studies. Finally, Section 5 draws the final conclusion of this paper.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 2 Study Area and Flood Detection Datasets  
### Description of the Study Area  
Erbil, located in northern Iraq, serves as the focus of this study. Its geographic coordinates are 36\({}^{\circ}\)11/28\({}^{\prime\prime}\) N and 44\({}^{\circ}\)0\({}^{\prime}\)33\({}^{\prime\prime}\) E, as illustrated in Figure 1. The region features a broad plain, interspersed with hills to the east that reach elevations up to 426 m above sea level [58]. The landscape is predominantly covered by Quaternary sediments that have accumulated due to the weathering and erosion of the neighboring highlands. Significantly, in the northern and northeastern parts of the study area, the Quaternary sediments lie atop the Bai Hassan formation, composed of molasses-type rock formations. The central section of Erbil is generally flat, whereas the northeastern and eastern areas present a more rugged landscape [59].  
Erbil's climate is classified as semiarid, exhibiting apparent seasonal shifts in humidity. Summers are marked by low humidity, with temperatures often rising above 45\({}^{\circ}\)C, whereas winters experience moderate humidity, with temperatures frequently falling below 0 \({}^{\circ}\)C.  
The area encounters a cool and wet climate, receiving over 400 mm of average annual precipitation [60]. Rainfall usually starts in mid-October and lasts until May.  
On 17 December 2021, Erbil and the Kurdistan Region of Iraq faced an unusually severe rainfall following one of the driest years in recent history [61]. Starting at 4 a.m., the rain led to extensive flooding in several districts of Erbil, such as Dara Too, Qush Tappa, Shamamk, Zhyan, Roshinbiri, and Bahrka, especially in the northern and eastern areas of the city. This heavy downpour caused substantial destruction, damaging homes, buildings, and vehicles, and tragically resulting in loss of life [62].
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 4 Solution Approach
### Variable Neighborhood Search for the 2E-EVRP-TW
#### 4.3.1 Standard Shaking Operators  
_Random cyclic exchange:_ This operator was originally introduced by [55]. It transfers a node sequence (consisting of customers and/or charging stations) from one route to another in a cyclic way. This operator is quite advantageous for many sequence-based combinatorial optimization problems as it enables the generation of a large variety of moves with a single operator.  
```
1:input:\(S_{\textit{shake}}\)
2:\(S_{\textit{VND}}:\) Solution obtained after applying a local search operator
3:Set \(h\gets 1\)
4:while\(h\leq h_{\textit{max}}\)do
5: Generate \(S_{\textit{VND}}\) from the \(h\)th local search neighborhood of \(S_{\textit{shake}}\) (\(S_{\textit{VND}}:N_{h}^{local}(S_{\textit{shake}})\))
6:if\(f_{\textit{ext}}(S_{\textit{VND}})\) < \(f_{\textit{ext}}(S_{\textit{shake}})\)then
7:\(S_{\textit{shake}}\gets S_{\textit{VND}}\)
8:\(h\gets 1\)
9:else
10:\(h\gets h+1\)
11:endif
12:endwhile
13:output:\(S_{\textit{shake}}\)
```  
**Algorithm 3** Variable Neighborhood Decent (VND)  
The cyclic exchange operator we have applied takes two parameters as input: (1) the number of routes (\(\zeta\)) to be involved in the cyclic move, and (2) the maximum number of nodes (\(\theta_{max}\)) to be transferred from one route to another. First, the operator randomly selects \(\zeta\) routes. Then, a random integer number \(\theta\) from the interval [1, \(\theta_{max}\)] is independently determined for each route involved in the cyclic move. This value refers to the route-specific length of the node sequence to be transferred. Finally, \(\theta\) consecutive nodes are randomly selected from each route and transferred to the next route in the cyclic move. If \(\zeta\) is greater than the total number of routes existing in a solution, then \(\zeta\) is set to the total number of routes. Similarly, if \(\theta\) is greater than the total number of nodes in a route, then \(\theta\) is readjusted. The optimal values for both parameters are determined by parameter tuning (see Section 5.2). Figure 4 illustrates a cyclic exchange move with three routes.  
_Random sequence relocation:_ This operator selects a node sequence from one route and transfers it to another route. The origin and destination routes, the node sequence to be relocated, and the insertion position in the destination route are determined randomly. Parameter \(max_{n}\) limits the number of nodes to be transferred. The optimal value for \(max_{n}\) is determined by parameter tuning (Section 5.2).
## 3 Receiver Structure
### Turbo Equalization
#### 3.2.2 Low-Complexity CE-Based MMSE Algorithm
\begin{array}{l}\mathbf{f}^{-m}=K\cdot\mathbf{f}^{m}\\ K=\left(1+\Big{(}1-\overline{v}^{m}\Big{)}(\mathbf{s}^{m})^{\mathrm{H}}\mathbf{f} ^{m}\right)^{-1}\end{array}  \tag{18}\]Based on Equations (16)-(18), the mean and variance of \(\hat{x}_{n}^{m}\) can be computed by  
\[ \begin{array}{ll}\mu_{n,i}^{m}&=K\cdot(\mathbf{f}^{m})^{\mathrm{H}}\Big{(}\mathrm{E} \Big{(}\mathbf{y}_{n}^{m}\Big{|}\mathbf{x}_{n}^{m}=\alpha_{i}\Big{)}-\mathrm{E}(\mathbf{y}_ {n}^{m})+\mathbf{s}^{m}\overline{\mathbf{x}}_{n}^{m}\Big{)}=K\cdot\alpha_{i}(\mathbf{f}^{m })^{\mathrm{H}}\mathbf{s}^{m}\\ \Big{(}\sigma_{n,i}^{m}\Big{)}^{2}&=K^{2}\cdot(\mathbf{f}^{m})^{\mathrm{H}}\mathrm{Cov }\Big{(}\mathbf{y}_{n}^{m},\mathbf{y}_{n}^{m}\Big{|}x_{n}^{m}=\alpha_{i}\Big{)}\mathbf{f}^{ m}\\ &=K^{2}\cdot(\mathbf{f}^{m})^{\mathrm{H}}\Big{(}\delta_{\eta}^{2}\mathrm{I}_{N}+ \mathbf{H}^{m}\mathbf{V}\;\;(\mathbf{H}^{m})^{\mathrm{H}}-\overline{v}^{m}\mathbf{s}^{m}(\mathbf{s }^{m})^{\mathrm{H}}\Big{)}\mathbf{f}^{m}\end{array}  \tag{19}\]  
Combing Equation (19) with Equation (8), the extrinsic information \(L_{ext}^{E}\Big{(}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\
## 2 System Description
### Road Holding and Handling Stability Analysis
Select the appropriate  
From (89), there exist \(\gamma\) and \(\
u\) that satisfy  
\[\gamma\geq\lambda_{\min}\Big{(}P^{\frac{-1}{2}}QP^{\frac{-1}{2}}\Big{)}-\frac{1} {\gamma_{1}}\lambda_{\max}\Big{(}P^{\frac{1}{2}}FF^{T}P^{\frac{1}{2}}\Big{)}- \frac{1}{\gamma_{2}}\lambda_{\max}(P) \tag{90}\]  
\[\
u\geq\gamma_{1}Z_{0}^{T}Z_{0}+\gamma_{2}Z^{T}Z \tag{91}\]  
Then, the inequality (88) can be rewritten by  
\[\dot{V}_{a}\leq-\gamma V_{a}+\
u \tag{92}\]  
We can integrate both sides of (92) to get  
\[V_{a}\leq\bigg{(}V_{a}(0)-\frac{\
u}{\gamma}\bigg{)}e^{-\gamma t}+\frac{\
u}{ \gamma}\leq V_{a}(0)e^{-\gamma t}+\frac{\
u}{\gamma} \tag{93}\]  
Obviously, we can know  
\[|x_{i}(t)|\leq\sqrt{\bigg{(}V_{a}(0)e^{-\gamma t}+\frac{\
u}{\gamma}\bigg{)}/ \lambda_{\min}(P)}\,\ i\ =\ 3,4 \tag{94}\]  
Based on the relative tire force condition in (12), the tire forces \(F_{st}\) and \(F_{dt}\) can be calculated as  
\[F_{st}(z_{u},z_{r},t)\leq k_{t}\sqrt{\big{(}V_{a}(0)e^{-\gamma t}+ \frac{\
u}{\gamma}\big{)}/\lambda_{\min}(P)}+k_{t}\|z_{r}\|_{\infty} \tag{95}\] \[F_{dt}(z_{u},z_{r},t)\leq c_{t}\sqrt{\big{(}V_{a}(0)e^{-\gamma t}+ \frac{\
u}{\gamma}\big{)}/\lambda_{\min}(P)}+c_{t}\|\dot{z}_{r}\|_{\infty}\]  
Substituting (95) into (2), we can obtain  
\[|F_{st}+F_{dt}|\leq(k_{t}+c_{t})\sqrt{\bigg{(}V_{a}(0)e^{-\gamma t}+\frac{\
u }{\gamma}\bigg{)}/\lambda_{\min}(P)}+k_{t}\|z_{r}\|_{\infty}+c_{t}\|\dot{z}_{r }\|_{\infty} \tag{96}\]  
Based on (96) the RTF constraints are guaranteed by selecting the design parameters \(\gamma_{1}\), \(\gamma_{2}\), and matrix \(P\) so that \(|F_{st}+F_{dt}|\leq(m_{s}+m_{u})g\) is ensured. Besides, the suspension deflection condition (13) can be expressed as  
\[|z_{s}-z_{u}|\leq|x_{1}|+|x_{3}|\leq\overline{\kappa}\rho(0)+\sqrt{\bigg{(}V_ {a}(0)e^{-\gamma t}+\frac{\
u}{\gamma}\bigg{)}/\lambda_{\min}(P)} \tag{97}\]  
Then the suspension deflection constraints will be ensured by selecting the PPF coefficients \(\overline{\kappa}\), \(\underline{\kappa}\), \(\rho(0)\) and design parameters \(\gamma_{1}\), \(\gamma_{2}\), \(P\) so that \(|z_{s}-z_{u}|\leq z_{R}\). Based on the above analysis, we can conclude that the three objectives of suspension performance requirements are improved by designing initial conditions and suitable control parameters, i.e., the mechanical structure and safety condition of the pneumatic active suspension are satisfied. **Remark 8**. : _Adaptive backstepping control based on PPF can guarantee not only the steady-state error limitations but also the transient response of chassis displacement. Moreover, by selecting the appropriate PPF and feedback control parameters, the developed method can enhance the suspension requirements._
# A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
## 3 Result Analysis and Discussion
### The Influence of Poisson's Ratio of Cement Sheath on Casing
#### 3.4.2 The Influence of Poisson's Ratio of Cement Sheath on Casing Eccentricity  
When the Poisson ratio of cement sheath increases from 0.12 to 0.33, the stress reduction under different eccentricity ratios is shown in Table 9. According to the results in Table 9, it can be observed that when the Poisson ratio of the cement sheath is relatively large, the stress value on the outer wall of the casing is lower, which is beneficial for improving the casing's load condition.
# A System for Sustainable Usage of Computing Resources Leveraging Deep Learning Predictions
## 2 Materials and Methods
### Automatic Resource Cluster Scaling  
The practice of automatically scaling clusters up and down based on various metrics is widely discussed in academic literature and actual implementations. Kubernetes is an open-source container management system developed initially as an internal project for Google [8; 9]. It allows scaling services and applications to higher and higher loads, catering both to individual teams using it and for entire organizations that deploy highly-distributed systems.  
Recently, many researchers have focused on the topic of automatic scaling, as it can be attractive to have an elastic system which handles peak traffic gracefully. [PERSON] et al. identify one of the biggest disadvantages of modern Kubernetes auto-scalers, the fact that they are based on static measurements and do not adapt to their current system usage [10]. There is the built-in vertical pod autoscaler, as well as the 3 rd party [11] scaler and the one provided by OpenFaas, which [PERSON] et al. outperform via a simple linear regression algorithm, showing the potential of improving these algorithms.  
[PERSON] et al. designed another system for resource-based automatic scaling of Kubernetes applications coined RUBAS [12]. They report a modest improvement in CPUusage of around 10%. The largest downside of the system described is the lack of a truly dynamic aspect in the prediction process, as scaling decisions are performed every minute and are based on a static formula that takes into account only a limited window from the past.  
[PERSON] et al. make the point that scaling provided in Kubernetes is often reactive rather than proactive [13], highlighting the need for a more dynamic approach to scaling. They propose a solution based on machine learning algorithms, integrating an LSTM solution as well. However, the data are not collected from a real production cluster, but rather through the usage of an experimental setup. Although it seems like a good solution, the data collected appears to be relatively smooth and does not feature the noise one would generally find in real service usage.
## 3 Results
### Positioning Performance of the Single Systems
In general, GPS-only has the best positioning accuracy of all the single-system kinematic PPP solutions. Meanwhile, convergence time is used to assess positioning performance. Figure 5: Distribution of the convergence time and RMS of static PPP solutions in three single systems (G is short for GPS, E is short for Galileo, and C is short for BDS-3) in September 2022. (**a**) Horizontal component of convergence time, (**b**) Horizontal component of RMS, (**c**) Vertical component of convergence time, (**d**) Vertical component of RMS. Overall, the higher convergence performance of GPS-only is particularly apparent in kinematic PPP. Thus, GPS-only PPP solutions have the best positioning performance in both positioning accuracy and convergence time among all the single-system PPP solutions.
## 3 RLBM Parallel Strategy
### Data Exchange  
In order to ensure that the calculation results of each sub-domain are accurate, a data exchange with adjacent MPI processes is required. When data are exchanged, it is not necessary to exchange possessive data \(f_{i}^{+}\) in all 18 directions; it is only needed to exchange the \(f_{i}^{+}\)  
In 1D domain decomposition along the \(x\) direction, communication data count is  
\[4\ surfaces=4\times 5\times nLatY\times nLatZ=20\times nLatY\times nLatZ, \tag{23}\]  
where \(nLatY\) and \(nLatZ\) are the number of lattice nodes along the \(y\) and \(z\) direction, respectively.  
In 2D domain decomposition along the \(x\) and \(y\) direction, the communication data count is  
\[\begin{split} 8\ surfaces+8\ edges&=4\times 5\times nLatX \times nLatZ+4\times 5\times\frac{nLatY}{nDDY}\times nLatZ\\ &+8\times nLatZ\\ &=20\times\frac{nLatX}{nDDX}\times nLatZ+20\times\frac{nLatY}{nDDY }\times nLatZ\\ &+8\times nLatZ,\end{split} \tag{24}\]  
In 3D domain decomposition along the \(x\), \(y\) and \(z\) direction, the communication data count is  
\[\begin{split} 12 surfaces+24 edges&=4\times 5\times\frac{nLatX }{nDDX}\times\frac{nLatY}{nDDY}+4\times 5\times\frac{nLatY}{nDDY}\times\frac{nLatZ}{nDDZ} \\ &+4\times 5\times\frac{nLatX}{nDDX}\times\frac{nLatZ}{nDDZ}+8\times \frac{nLatX}{nDDX}\\ &+8\times\frac{nLatY}{nDDY}+8\times\frac{nLatZ}{nDDZ}\\ &=20\times\frac{nLatX}{nDDX}\times\frac{nLatY}{nDDY}+20\times\frac {nLatY}{nDDY}\times\frac{nLatZ}{nDDZ}\\ &+20\times\frac{nLatX}{nDDX}\times\frac{nLatZ}{nDDZ}+8\times\frac {nLatX}{nDDX}+8\times\frac{nLatY}{nDDY}\\ &+8\times\frac{nLatZ}{nDDZ}.\end{split} \tag{25}\]  
As is observed from Table 1, various DDMs are compared according to the lattice scale \(512\times 512\times 512\).  
Figure 8: Three-dimensional edges of data exchange.  
From Table 1, it can be noted that the 1D domain decomposition does not change the communication data count. When \(\frac{nLatX}{nDDX}\), \(\frac{nLatY}{nDDY}\), \(\frac{nLatZ}{nDDZ}\) gradually becomes equal, the 3D DDM has less communication data count than the 2D one, and the 2D DDM has less communication data count than the 1D one. Thus, the 3D DDM should have better scalability than the 2D one, and the 2D DDM has better scalability than the 1D one.
# Numerical Study of an Ultra-Broadband All-Silicon Terahertz Absorber
###### Abstract  
In this article we present and numerically investigate a broadband all-silicon terahertz (THz) absorber which consists of a single-layer periodic array of a diamond metamaterial layer placed on a silicon substrate. We simulated the absorption spectra of the absorber under different structural parameters using the commercial software Lumerical FDTD solutions, and analyzed the absorption mechanism from the distribution of the electromagnetic fields. Finally, the absorption for both transverse electric (TE) and transverse magnetic (TM) polarizations under different incident angles from 0 to 70\({}^{\circ}\) were investigated. Herein, electric and magnetic resonances are proposed that result in perfect broadband absorption. When the absorber meets the impedance matching principle in accordance with the loss mechanism, it can achieve a nearly perfect absorption response. The diamond absorber exhibits an absorption of ~100% at 1 THz and achieves an absorption efficiency >90% within a bandwidth of 1.3 THz. In addition, owing to the highly structural symmetry, the absorber has a polarization-independent characteristic. Compared with previous metal-dielectric-metal sandwiched absorbers, the all-silicon metamaterial absorbers can avoid the disadvantages of high ohmic losses, low melting points, and high thermal conductivity of the metal, which ensure a promising future for optical applications, including sensors, modulators, and photoelectric detection devices.  
broadband absorption; all-silicon terahertz absorber; electric and magnetic resonances +
Footnote †: journal: Applied Physics Letters