esa-sceva/satcom-chunk-collection huggingface dataset:
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 5 On the General Structure of Radiation Processes in Linear Quantum Antenna Systems
### Comparative Analysis of the Three Fundamental Types of Antennas  
At this point, it is instructive to give a bird's eye view on the various genera of antenna theories available to us so far. _Classical antennas_ (c-antennas) involve excitation with an external electric field \(\mathbf{E}^{\mathrm{ex}}\), which in turn induces a current on the antenna via the current Green's function [28; 46; 47; 48].22 This is illustrated in Figure 3a. Here, it is essential to note that the \"system input\" is a classical field, while the \"system output\" is _also_ the classical radiated fields \(\mathbf{E}\) and \(\mathbf{H}\). On the other hand, the _quantum-optics_ approach to defining _quantum antennas_, which is the one currently most often mentioned in connection with applications to quantum communications [1; 2; 13], treats the input as a classical source while the output is a quantum state \(|\alpha(t)\rangle\)[10; 13; 62; 83]. This is illustrated in Figure 3b, where the q-antenna current Green's function \(\mathbf{\bar{F}}_{q}\) is _still_ a _classical_ function, just like the induced radiating current \(\mathbf{J}_{q}(\mathbf{x}^{\prime})\).  
Figure 2: The anatomy of the q-antenna’s propagator-based quantum radiation process.  
Consider now the _third_ type of antennas shown in Figure 2(c). The input to the system \(J(x^{\prime})\) (exactly like the previous two antenna types) is still a classical source, but now the radiating source \(J_{q}(D_{\mathrm{s}})\) is an _operator_ that can describe quantum particles emission (quantum radiation) from within the q-antenna's source spacetime region \(D_{\mathrm{s}}\). In fact, we may rewrite (38) in the revealing integral form  
\[J_{q}(D_{\mathrm{s}}):=\int_{x^{\prime}\in\mathbb{M}^{4}}\mathrm{d}^{4}x^{ \prime}F_{q}(D_{\mathrm{s}},x^{\prime})J(x^{\prime}), \tag{48}\]  
where the q-antenna current Green's function in this case is simply given by  
\[F_{q}(D_{\mathrm{s}},x^{\prime}):=\begin{cases}\phi^{\dagger}(x^{\prime}),&x^{ \prime}\in D_{\mathrm{s}},\\ 0,&x^{\prime}\
otin D_{\mathrm{s}}.\end{cases} \tag{49}\]  
Clearly, this is quite different from the two cases depicted in Figure 2(a),b. In the case Figure 2(c), captured by the expression (49), the current Green's function itself is a _quantum field_, i.e., an operator-valued function on spacetime. The source \(J_{q}\) reproduced by the Green's function superposition integral is also an operator, and the ultimate \"output\" coming out from the relativistic QFT-based antenna system is the probability amplitude \(A(q)\) of annihilating a particle at some generic observation point \(x\in D_{\mathrm{r}}\subset\mathbb{M}^{4}\).
###### Abstract  
A packaged resonant tunneling diode (RTD) with rectangular waveguide output for highly integrated wireless communication is proposed. The output signal of the RTD chip is radiated to the rectangular waveguide through an E plane probe transformer. Next, the RTD chip is electrically connected to the transformer by using gold wire bonding. The packaged RTD exhibits an oscillation frequency of 92 GHz, while the maximum radio frequency (RF) output power is \(-7\) dBm. A miniaturized and high data rate wireless communication system with a 7 Gbps on-off keying (OOK) module by using the RTD is demonstrated, and the bit error rate (BER) of the 7 Gbps is below \(3.8\times 10^{-3}\).  
resonant tunneling diode; rectangular waveguide; highly integration; W band; wireless communication +
Footnote †: journal: Electronics
## 3 Results and Discussion
### Characterization of the Zeolite Sediments
Figure 2: SEM analysis of natural and modified zeolite sediments. NZ-M, natural zeolite with microparticle size; NZ-N, natural zeolite with nanoparticle size; LDH-M, LDH-zeolite with microparticle size; LDH-N, LDH-zeolite with nanoparticle size; HCl-M, HCl-zeolite with microparticle size; HCl-N, HCl-zeolite with nanoparticle size; NaOH-M, NaOH-zeolite with microparticle size; NaOH-N, NaOH-zeolite with nanoparticle size; NaCl-M, NaCl-zeolite in the microparticle size; NaCl-N, NaCl-zeolite in the nanoparticle size. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **BET Surface** \\ **Area** \\ \end{tabular} & \
\begin{tabular}{c} **Pore** \\ **Volume** \\ \end{tabular} &
\begin{tabular}{c} **Pore** \\ **Size** \\ \end{tabular} \\ \hline  & **(m\({}^{2}\) g-1)** & **(cm\({}^{3}\) g-1)** & **(A)** \\ \hline LDH-M & \(19.7\pm 0.1\) & 0.051 & 103.852 \\ LDH-N & \(37.2\pm 0.3\) & 0.046 & 51.551 \\ HCl-M & \(14.8\pm 0.1\) & 0.028 & 76.746 \\ HCl-N & \(36.1\pm 1.3\) & 0.115 & 124.087 \\ NaOH-M & \(15.4\pm 0.2\) & 0.067 & 175.916 \\ NaOH-N & \(35.1\pm 0.7\) & 0.112 & 128.437 \\ NaCl-M & \(11.3\pm 0.1\) & 0.027 & 96.939 \\ NaCl-N & \(23.4\pm 0.3\) & 0.025 & 49.919 \\ \hline \hline \end{tabular}  
* NZ-M, natural zeolite with microparticle size; NZ-N, natural zeolite with nanoparticle size; HDI-M, LDI-zeolite with nanoparticle size; HCl-M, HCl-zeolite with microparticle size; HCl-N, HCl-zeolite with nanoparticle size; NaOH-M, NaOH-zeolite with nanoparticle size; NaCl-M, NaCl-zeolite in the microparticle size; NaCl-N, NaCl-zeolite in the nanoparticle size.  
\end{table} 
Table 4: _Cont._  
Figure 3: FTIR spectra of natural and modified zeolite sediments. (**A**) NZ-M, natural zeolite with microparticle size; LDH-M, LDH-zeolite with microparticle size; NaOH-M, NaOH-zeolite with microparticle size; HCl-M, HCl-zeolite with microparticle size; NaCl-M, NaCl-zeolite in the microparticle size. (**B**) NZ-N, natural zeolite with nanoparticle size; LDH-N, LDH-zeolite with nanoparticle size; HCl-N, HCl-zeolite with nanoparticle size; NaCl-N, NaCl-zeolite with nanoparticle size; NaOH-N, NaOH-zeolite with nanoparticle size.
## 3 Solution Method
### Revised Sample Average Approximation
#### 3.1.3 Summary of the SAA Algorithm  
The proposed SAA algorithm is summarized in the following steps.  
1. For \(s=1,2,\cdots S\), repeat steps 1-2. 1 For \(m=1,2,\cdots M\), repeat steps i-ii. i. Generate \(N\) scenarios of the random vector \(\zeta\); ii. Solve the proposed model by MODE to obtain the solution as \(\hat{x}\) and the objective value as \(\hat{v}\). 2 Sort the \(M\) optimal values in an ascending order, and denote the Lth value \(\hat{v}_{L}\) as \(\hat{v}_{Ls}\).
2. The upper bound is defined as \(\hat{v}=\frac{1}{S}\sum\limits_{s=1}^{S}\hat{v}_{Ls}\), and the minimum value from \(\hat{v}_{L1}\) to \(\hat{v}_{Ls}\) is taken as the optimal objective value \(\overline{v}_{o}\). The corresponding optimal solution \(\hat{x}\) can also be obtained.
3. Calculate the optimality gap by \((\vartheta-\overline{v}_{o})/\overline{v}_{o}\times 100\%\).
## 2 Materials and Methods
### Genomic and Expression Analyses
#### 2.3.3 Polymerase Chain Reaction (PCR)  
PCR amplification was carried out in an iCycler Thermal Cycler 96-well instrument (Bio-Rad, GmbH, Munchen, Germany) using a 25 \(\upmu\)L reaction mixture containing 1 \(\upmu\)L of genomic DNA or cDNA, 1\(\uptimes\) PCR buffer containing 1.5 mM MgCl2, 0.2 \(\upmu\)M of RAPD primer, 100 \(\upmu\)M of each of the four dNTPs (deoxyribonucleotide triphosphate), and 1U of Taq DNA polymerase. The PCR conditions were as follows:initial denaturation at 95 \({}^{\circ}\)C for 7 min, followed by 43 cycles of denaturation at 94 \({}^{\circ}\)C for 1 min, annealing at 37 \({}^{\circ}\)C for primers 10 bp and 48 \({}^{\circ}\)C for primers 15 bp for 1 min and extension at 72 \({}^{\circ}\)C for 2 min with a final extension at 72 \({}^{\circ}\)C for 10 min. The amplified PCR products were resolved by electrophoresis on 1.5\(\times\) agarose gel, in 0.5\(\times\) TBE (Tris-borate EDTA) buffer at 100 V for 3-4 h, and visualized with ethidium bromide staining. Every PCR reaction was repeated twice to check reproducibility of bands and a negative control (no DNA) was used in all reactions to avoid erroneous interpretations.
# A Low-Profile Wideband Monocone Antenna Using Bent Shorting Strips
## 1 Introduction  
Monocone antennas are attractive for industrial, military, and commercial wireless communication systems because they have an omnidirectional radiation pattern with a wide impedance bandwidth. However, these antennas require a large ground plane in wireless transmission applications [1]. To overcome the drawback of a large ground size, methods for miniaturizing monocone antennas have been investigated. However, the reduced ground size causes a reduction of the bandwidth. To increase the bandwidth of monocone antennas, approaches such as resistive loading or the utilization of ferrite materials have been used [2; 3; 4]. Even though resistive loading of the monocone antenna expands the impedance bandwidth in the low-frequency band, this results in a decrease in the antenna efficiency over the entire band. The use of ferrite in the monocone antenna mounting expands the low-frequency band, but its use is costly and limited to the high-frequency band. The impedance bandwidth of monocone antennas has been improved by various methods, such as adding a shorting-pin [5; 6; 7; 8; 9; 10; 11; 12], employing dual-sleeve structures on the ground plane [13; 14; 15], loading a dielectric substrate in the feed region [16], loading high-permittivity materials around the antenna [17], adding a top-cross plate [18], and cutting slots on the top-loaded patch [19]. However, the profiles of these monocone antennas are peaked relatively sharply, resulting in impedance bandwidths that are not sufficiently wide.  
In this paper, a wideband monocone antenna with bent shorting strips, and parasitic and circular sleeves is proposed. The -10 dB reflection coefficient bandwidth ranges from 820 MHz to 5250 MHz. The size reduction of the monocone antenna is achieved by shorting the monocone antenna to the ground plane by four bent shorting strips, with four parasitic and two circular sleeves.
# Parametric Investigation of Die-Sinking EDM of Ti6 Al4V Using the Hybrid Taguchi-Rams-RATMI Method
## 4 Results and Discussions
### Optimization Using the RAMS-RATMI Method  
Since MRR and a depth of cut must be maximized while surface roughness and TWR must be minimized, normalization was achieved using Equation (2) followed by the addition of weights (0.5405 for MRR, 0.1802 for depth of cut, 0.0688 for surface roughness, and 0.2015 for TWR) calculated by using the AHP method. Furthermore, an optimal alternative for all response criteria was calculated followed by the construction of a decomposition matrix (Table 5). This matrix was further used to construct two sets of components and their magnitude. Finally, the components and their magnitude were used to compute the MCRAT score and the RAPS score, which were used to compute the RATMI majority index (Table 6). It was noted that the RATMI majority index (E\({}_{i}\)) was higher in the case of R1 while it was lowest in R12. Hence, it can be concluded that the T\({}_{\text{on}}\) of 500 us, duty cycle of 8%, peak current of 40 Amp, and voltage of 20 V can be considered for optimizing the machining characteristics.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Run** & **A (Pulse on** & **B (Duty** & **C (Peak** & **D (Voltage,** & **MRR** & **Depth of** & **Surface** & **TWR** \\
**No.** & **Time (Ton), us)** & **Cycle, \%)** & **Current, Amp)** & **V)** & **mm3/ min** & **Cut, mm** & **Roughness, \(\upmu\)** & **mm3/ min** \\ \hline R1 & 500 & 8 & 40 & 20 & 5.48 & 0.087 & 9.07 & 0.00000223 \\ R2 & 500 & 8 & 40 & 25 & 5.30 & 0.084 & 8.97 & 0.00000111 \\ R3 & 500 &  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Run No.**} & \multirow{2}{*}{**U\({}_{\text{IK}}\)**} & \multirow{2}{*}{**U\({}_{\text{in}}\)**} & \multicolumn{4}{c}{**MCRAT**} & \multicolumn{4}{c}{**RAMS**} & \multicolumn{4}{c}{**RATMI**} \\
## 3 Solution to the Problem
### Effect of Coating Thickness on Image Force  
Figures 6 and 7 shows the variation law of image force with angle under different coating thickness, and the other parameters are: \(h_{2}/h_{1}=100\), \(c_{44}^{(1)}/c_{44}^{(2)}=1.5\), \(h_{0}/k_{y}^{(2)}=1\), \(a=1\) mm, \(F_{0}=c_{44}^{(2)}b_{z}^{2}/(h_{1}+h_{2})\). It can be seen from the figures that image force \(F_{x}\) increases with the increase of \(\theta\), and image force \(F_{y}\) first increases and then decreases with the increase of \(\theta\), but for \(h_{1}=2\) mm, image force \(F_{y}\) decreases with the increase of \(\theta\). For the coating thickness \(h_{1}\), when different values are taken, the image forces \(F_{x}\) and \(F_{y}\) show great changes, indicating that the coating thickness also has a great influence on the dislocation. It is also found that the peak value of image force decreases when the coating thickness is large.  
Figure 5: Variation of \(F_{y}/F_{0}\) with \(\theta\) under different elastic modulus ratios.  
Figure 6: Variation of \(F_{x}/F_{0}\) with \(\theta\) under different coating thickness.
## 2 Method
### Fixed Point Stability Analysis
#### 2.2.1 Fixed Point Stability in the Case of Linear Link Cost Functions  
Let the travel cost functions of the two routes have the following forms:  
\[K_{1}(F)=K^{0}+\gamma F\qquad\quad(\gamma>0)\quad\quad 0\leq F\leq 1 \tag{48}\]  
\[K_{2}(F)=K^{0}+\gamma(1-F)\quad(\gamma>0)\quad\quad 0\leq F\leq 1 \tag{49}\]  
where \(K^{0}\) is a positive constant representing travel cost under free-flow conditions, and \(\gamma\) is a positive parameter representing the sensitivity of travel cost to flow. In spite of their simplified representation of traffic congestion, linear cost functions retain the essential property of being monotonically increasing with flow, and have been adopted in previous experimental studies on repeated route choices [9; 14; 15; 35; 41].  
From (48) and (49) it follows that \(V(F)=\gamma(2F-1)\), so that (19) and (20) become:  
\[Z^{t}=\beta\gamma\Big{(}2F^{t-1}-1\Big{)}+(1-\beta)Z^{t-1} \tag{50}\]  
\[F^{t}=\alpha\left[\frac{1+\varphi\big{\{}\exp\big{(}\mu\big{[}\beta\gamma \big{(}2F^{t-1}-1\big{)}+(1-\beta)Z^{t-1}\big{]}\big{)}-1\big{\}}}{1+\exp(\mu [\beta\gamma(2F^{t-1}-1)+(1-\beta)Z^{t-1}])}\right]+(1-\alpha)F^{t-1} \tag{51}\]  
and the fixed point conditions (21) and (22) can be rewritten as:  
\[\widetilde{Z}=\gamma\Big{(}2\widetilde{F}-1\Big{)} \tag{52}\]  
\[\widetilde{F}=\frac{1+\varphi\Big{[}\exp\bigg{(}\mu\widetilde{Z}\Big{)}-1 \Big{]}}{1+\exp\bigg{(}\mu\widetilde{Z}\bigg{)}} \tag{53}\]  
We can immediately see that \(\bigg{(}\widetilde{Z}=0,\widetilde{F}=0.5\bigg{)}\) is a solution to (52) and (53).  
Since \(\frac{dV}{dF}=2\gamma\), the local stability condition (44) becomes:  
\[\frac{1}{2}+\frac{2(\alpha+\beta)-\alpha\beta-4}{\alpha\beta\gamma\mu}<\varphi <\frac{1}{2}+\frac{1}{\gamma\mu} \tag{54}\]  
with the lower and upper limits being effectively binding for:  
\[\frac{2(\alpha+\beta)-\alpha\beta-4}{\alpha\beta\gamma\mu}\geq-\frac{1}{2} \tag{55}\]  
\[\frac{1}{\gamma\mu}\leq\frac{1}{2} \tag{56}\]  
Finally, in the particular case of a population of memoryless travelers (\(\beta=1\)) completely free of inertia (\(\alpha=1\)), the range of \(\varphi\) values ensuring fixed point stability reduces to the following interval, which is symmetrical around \(\varphi=0.5\):  
\[\frac{1}{2}-\frac{1}{\gamma\mu}<\varphi<\frac{1}{2}+\frac{1}{\gamma\mu} \tag{57}\]  
subject to (56).
## 2 Methods
### Principles Involved in the Method
#### 2.2.1 Phase Gradient Stacking and Fusion
The final Stacking method is shown in Equation (3):  
\[\text{G}_{\text{k}}=\sum_{\text{n}=1}^{\text{M}}\text{T}_{\text{n}}\Delta \varphi_{0}^{\text{n}}/\sum_{\text{n}=1}^{\text{M}}\text{T}_{\text{n}}^{2} \tag{3}\]  
where \(\text{G}_{\text{k}}\) represents the phase gradient results stacked in each direction, k equals 0, 45, 90, 135, representing the phase gradient stacking results in four directions, M represents thenumber of differential interferograms we used for phase gradient stacking, and T represents the temporal baseline of the two-scene complex image of the differential interferogram. Although some of the noise and the atmosphere are removed by masking the low coherence region and stacked summation, there are still some phase gradient anomaly results caused by random noise. Therefore, in order to better identify the region of interest, we use spatial filtering to remove these noises. Of course, due to the side-view imaging of SAR and the relief of the terrain, there will inevitably be some shadows and overlying areas. DEM and SAR imaging parameters are used to calculate areas of geometric distortion to help identify landslides and reduce landslide detection errors. By stacking the phase gradients in the four directions, the phase gradient stacking results in the four directions can be obtained respectively. Due to the relationship between the morphology of landslide and satellite imaging, the landslide signals in the four results show different forms, and it may be impossible to distinguish the landslide signals from other signals in one result. Thus, the results of the four directions are fused through Equation (4):  
\[\text{G}=(|\text{G}_{0}|+|\text{G}_{45}|+|\text{G}_{90}|+|\text{G}_{135}|)/4 \tag{4}\]  
where G is the combination of the phase gradient stacking results in four directions, and the phase gradient stacking results were averaged in four directions to obtain a more complete landslide boundary signal, which is an improvement over the results obtained in a single direction.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 4 Thermal H\({}_{2}\) Generation from Hydrocarbons: Theoretical Basis
### Partial Oxidation  
Partial oxidation is mostly claimed to be the most appropriate technology for obtaining H\({}_{2}\) from heavy fuel oil and coal. Partial oxidation is an exothermic process used in converting hydrocarbon fuels into a mixture of H\({}_{2}\), CO, and other partially oxidized substances. One of the advantages of this process is that reactions with O\({}_{2}\) are highly exothermic, without the necessity of any external energy source. The product distribution of partial oxidation reactions depends upon the carbon/oxygen (C/O) ratio and is constrained by high reaction temperatures (>1000 \({}^{\circ}\)C). Depending on the sulfur content in the feedstock, the partial oxidation process is either operated without catalyst as a pure thermal partial oxidation process (TPOX) at a temperature >1200 \({}^{\circ}\)C if the sulfur content is high or at a lower temperature, between 800 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, using a solid catalyst if the sulfur content is considerably low.  
The catalytic partial oxidation at lower temperature is more energy-efficient and globally described by the following reaction:  
\[\mathrm{C_{n}H_{m}+\lambda\mathrm{n}\ O_{2}\to nCO+\lambda\mathrm{m}\ H_{2}\ \Delta H<0} \tag{4}\]  
Thermodynamically, in this process, H\({}_{2}\) and CO are the most abundant products above 550 \({}^{\circ}\)C, with CO being a coke precursor that can be removed by its oxidation toward CO\({}_{2}\) or by the WGS reaction (Equation (3)) increasing H\({}_{2}\) production.
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 2 Methods
### Data Preprocessing
#### 2.4.2 Operating Conditions  
Previous research [23; 24; 37] on the C-MAPSS dataset showed that the operating setting in this dataset can be clustered into six distinct groups, each representing a distinct operating condition. Here we used the K-means algorithm to cluster operating setting values in all datasets into 6 clusters as shown in Figure 4. Based on clustering, an operating setting label can be represented as a 6-dimension vector using one-hot encoding.
## 2 Materials and Methods
### Color Measurement  
A Minolta CR-400 (Konica Minolta, INC., Osaka, Japan) chroma meter was used for the reflection color measurement. The instrument measures the CIE Lab color characteristics. The ratio of three different wavelength lights was plotted in a coordinate system called CIE Lab color space. The color results were quantified by the device. The instrument was calibrated with a standard white etalon. Each sample (N = 6) was measured three times on a white plate with the same illumination. The measured attributes were as follows: redness/greenness (a*), yellowness/blueness (b*) and lightness (L*).  
Hue angle (h\({}_{\text{ab}}\)) was also an important color attribute, which was calculated according to Equation (1) [34]:  
\[\text{h}_{\text{ab}}=\arctan\frac{\text{b*}}{\text{a*}} \tag{1}\]
## 3 Effects of TBR on Thermal Management in GaN-on-SiC and GaN-on-Diamond Devices
### Effects of TBR on Thermal Management in GaN-on-Diamond Devices
The results demonstrate that using an interlayer decreases the TBR and that SiN as an interlayer is superior to AlN for decreasing TBR. This finding is ascribed to SiN being more difficult to etch than AlN in a harsh diamond growth environment, resulting in a smoother interface and a lower phonon scattering rate at the interface. In addition, the stronger Si-C bonds formed between the diamond and SiN facilitate phonon transport across the interface, resulting in a lower TBR. In addition to the experimental results, the ab initio calculation results showed a similar trend in the TBR difference between different interlayers. Furthermore, the effective TBRs increased along with the increasing thickness of the SiN interlayer, as shown in Figure 9b. This finding differs from that for the AlN interlayer in GaN-on-SiC devices, where no thickness dependence could be observed. This could be attributed to the thermal conductivity of the crystalline AlN interlayer, which is one order of magnitude higher than that of the amorphous SiN interlayer. Compared with the effects of defects and disorder near the interface, the intrinsic thermal resistance of the AlN interlayer plays a less important role in the total thermal resistance of the GaN/AlN/SiC stacks. Therefore, optimally reducing the thickness of the SiN interlayer is an effective method to reduce the TBR of GaN/SiN/diamond stacks. Although an extremely low TBR of \(\sim\)3.1 m\({}^{2}\) K/GW has been reported, it remains a challenge to reduce the thickness of a SiN interlayer to \(\sim\)1 nm to reduce the TBR without affecting the electrical performance of GaN active layers. In addition to decreasing the SiN interlayer thickness, introducing nanostructured interfaces, such as periodically patterned SiN interlayers, could increase the effective contact area and improve the interface bonding strength and seeding density, resulting in a low TBR. The calculation results of the MD simulations also showed similar effects of reducing the TBR using nanostructured interfaces. Furthermore, the room-temperature SAB method is promising for decreasing the TBR. Figure 9: (a) Effects of interlayer type on the TBR of GaN/diamond interfaces [99; 103; 104; 105; 106]. Using an interlayer decreases the TBR, with SiN being superior to AlN as interlayer in decreasing TBR. (b) TBR of GaN/diamond interface as a function of SiN interlayer thickness [99; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113]. TBR predicted by the DMM is shown for comparison [103]. Only the data with clear interlayer information are included. The effective TBR increases with the increasing thickness of the SiN interlayer, which differs from the case of an AlN interlayer in GaN-on-SiC devices, where no thickness dependence could be observed.