esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Satellite Data  
#### 2.4.1 TROPOMI SIF  
TROPOMI, carried aboard the Sentinel-5 Precursor satellite, commenced its mission on 13 October 2017, with co-funding from the European Space Agency (ESA) and the Netherlands. This advanced instrument provides a wide swath of approximately 2600 km, delivering high-resolution spatial measurements. Prior to 6 August 2019, TROPOMI boosted a spatial resolution of 3.5 km \(\times\) 7 km, which transitioned to 5.6 km \(\times\) 3.5 km after the specified date. TROPOMI enables the retrieval of SIF data within spectral ranges of 735-758 nm and 743-758 nm. Then, all of its products are normalized to 740 nm using a reference fluorescence spectrum [62].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Time Cover** & **Temporal Resolution** & **Spatial Resolution** \\ \hline \multirow{4}{*}{Ground-based data} & Ground-measured & Maize: 2018–2022 & \multirow{4}{*}{0.5 h} & \multirow{4}{*}{-} \\  & SIF & Wheat: 2019, 2021, 2022 & & \\  & (In situ SIF) & & & \\  & Ground-measured & & & \\  & EVI & & 2019–2022 & \\  & (In situ EVI) & & 2019, 2021, 2022 & \\  & TROPOMI SIF & 2018–2022 & 1 day & 0.05\({}^{\circ}\) \\ Satellite data & MODIS EVI & 2018–2022 & 8/16 days & 0.05\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data collection in this study.  
As detailed in Table 2, the ungridded TROPOMI SIF740 product from 2018 to 2022 was sourced from [[ftp://fluo.gps.caltech.edu/data/tropomi/ungrided/](ftp://fluo.gps.caltech.edu/data/tropomi/ungrided/)]([ftp://fluo.gps.caltech.edu/data/tropomi/ungrided/](ftp://fluo.gps.caltech.edu/data/tropomi/ungrided/)) (accessed on 5 September 2023) (for additional details, refer to: [[https://doi.org/10.22002/D1.1347](https://doi.org/10.22002/D1.1347)]([https://doi.org/10.22002/D1.1347](https://doi.org/10.22002/D1.1347)) (accessed on 5 September 2023)) for utilization in this study. To enhance data quality, measurements with viewing zenith angles (VZAs) exceeding 60\({}^{\circ}\) were excluded due to the substantial uncertainties at the swath edges. Additionally, data influenced by dark/bright scenes and cloud cover were systematically removed [28]. This study utilizes daily average SIF data, adjusting the instantaneous SIF records within a 20 km radius from Shangqiu Station using a daily-length correction factor (denoted as 'dcsif' in the product). Furthermore, instantaneous SIF records featuring a cloud fraction (denoted as 'cf' in the product) exceeding 0.2, identified as cloud-contaminated records, were omitted from the dataset.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues
### Multi-Layered Networks  
GEOs, MEOs, and LEOs operate in distinct orbits, with high- and low-altitude aerial platforms, such as UAVs, airships, and balloons, operating at different levels, and a terrestrial network establishing another layer on the ground. Because of the various requirements of different segments in such a complex layered infrastructure, the overall system performance may be impacted. Maintaining necessary QoS standards needs extensive knowledge, which is a major problem in HSTNs.
## 2 Materials and Methods
### Input Data
#### 2.2.2 Weather Data  
Daily precipitation data from 36 weather stations and two stream gauge stations located in the Xijiang River Basin were obtained from the National Meteorological Information Centre (NMIC) of the Chinese Meteorological Administration (CMA).
## 3 Results
### Analysis of Correlation between H1299 Cells and Nuclei Distributions in Response to MMW Irradiation
\begin{table}
\begin{tabular}{|c c c c|} \hline \hline
**Group** & **Cell Size** & **Nuclear Size** & **Nuclei/Cells (\%)** \\ \hline
**Ra (Day 1, not trans)** & **NC** & **NC** & \(32\pm 5.4\) \\
**Rb (Day 1, trans)** & **NC** & **NC** & \(36\pm 6.3\) \\
**Rc (Day7)** & **NC** & **NC** & \(34\pm 8.1\) \\
**Rd (Day14)** & **NC** & **NC** & \(33\pm 5.7\) \\
**2 Ma (Day1)** & \(\downarrow\) & \(\uparrow\) & \(42\pm 3.9\) \\
**2 Mb (Day7)** & \(\uparrow\) & \(\downarrow\) & \(28\pm 5.0\) \\
**2 Mc (Day14)** & \(\uparrow\) & \(\downarrow\) & \(29\pm 2.6\) \\
**4 Ma (Day1)** & \(\downarrow\) & \(\uparrow\) & \(52\pm 3.3\) \\
**4 Mb (Day7)** & \(\downarrow\) & \(\uparrow\) & \(57\pm 6.5\) \\
**4 Mc (Day14)** & \(\downarrow\) & \(\uparrow\) & \(66\pm 7.6\) \\
**10 Ma (Day 1)** & \(\downarrow\) & \(\uparrow\) & \(78\pm 5.4\) \\ \hline \end{tabular}
\end{table} 
Table 3: Ratio of average areas—Nuclei/Cells. Averages of the cell and nuclei sizes of each subgroup were used for N/C ratio calculation. The distribution of cells and nuclei sizes in the control groups showed a strong positive correlation (c = 0.85-0.91). Cells exposed for 2 min showed a weak positive correlation (c = 0.04-0.35) while the cells exposed for 4 and 10 min showed a negative correlation (c = (-0.11)-(-0.41) and (-0.35)) i.e., an inverse correlation between the distributions. The N/C decorrelation effect as observed for the irradiated cells was apparently caused by MMW irradiation. This experiment suggests that the desired effect of targeted mortality of H1299 cells in response to MMW treatment is directly dependent on the dosage of energy within the applied power density. Specifically, it indicates that the threshold effect is observed when the N/C ratio crosses beyond 70%. The subsequent experiments investigate this surmise using cytological assays to determine the efficacy of the different treatment regimes in terms of exposure durations.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 2 UAV Technology in Wireless Communication
### UAV Communication Scenarios  
From a wireless perspective, a UAV in a 3D environment could potentially act as a mobile BS and mobile EU. Detailed consideration of both of these scenarios is provided below.
# The Synthesis and Biological Evaluation of 2-(1H-Indol-3-yl)quinazolin-4(3H)-One Derivatives
## 3 Materials and Methods
#### Synthesis of 2-(1\(H\)-indol-3-yl)quinazolin-4(3\(h\))-one (**3a**) [80]
\({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) = 11.78 (br.s, 1H), 8.78-8.75 (m, 2H), 8.15-8.13 (m, 1H), 7.82-7.74 (m, 2H), 7.53-7.50 (m, 1H), 7.46-7.43 (m, 1H), 7.25-7.20 (m, 2H), and 5.97 (s, 2H) ppm; \({}^{13}\)C[\({}^{1}\)H] NMR (100 MHz, DMSO-\(d_{6}\)) \(\delta\) = 161.0, 151.0, 147.4, 135.8, 133.8, 133.0, 127.0, 126.7, 125.8, 125.0, 122.8, 122.1, 120.6, 118.4, 111.6, and 107.7 ppm. HRMS (ESI) Calcd for C\({}_{16}\)H\({}_{13}\)N\({}_{4}\)O [M + H]\({}^{+}\) 277.1084; Found 277.1082. 3.3.5. Synthesis of 2-(5-methylfuran-2-yl)-2,3-dihydroquinazolin-4(1H)-one (**6u**) [88]  
To a solution of 5-methylfuran-2-carbaldehyde (**1s**) (0.76 mmol, 76 \(\mu\)L) in DMSO (1.0 mL), anthranilamide (**2a**) (0.69 mmol, 94 mg) was added. The reaction mixture was heated at 100 \({}^{\circ}\)C for 3.5 h (TLC control). Then, the reaction mixture was poured into H\({}_{2}\)O (50 mL). The formed precipitate was filtered. The product was purified using column chromatography on silica gel using the mixture of petroleum ether/EtOAc (1:1) as an eluent and recrystallized from ethanol. A yield of 0.098 g (62%) was obtained and it was a pale yellow solid. Mp. = 168-169 \({}^{\circ}\)C (EtOH) (lit. [88] 177-178 \({}^{\circ}\)C). \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) = 8.24 (br.s, 1H), 7.61 (br.d, \(J\) = 7.6 Hz, 1H), 7.25-7.21 (m, 1H), 7.10 (s, 1H), 6.75 (br.d, \(J\) = 8.4 Hz, 1H), 6.69-6.65 (m, 1H), 6.13 (d, \(J\) = 3.2 Hz, 1H), 5.98 (br.s, 1H), 5.68 (br.s, 1H), and 2.22 (s, 3H) ppm; \({}^{13}\)C[\({}^{1}\)H] NMR (101 MHz, DMSO-\(d_{6}\)) \(\delta\) = 163.1, 152.5, 151.3, 147.1, 133.1, 127.1, 117.1, 114.9, 114.4, 108.0, 106.2, 60.3, and 13.2 ppm. 3.3.6. Synthesis of 2-(thiophen-2-yl)-2,3-dihydro-4_H_-benzo[_e_][_1,3]oxazin-4-one (**3 ad**) [89]  
To a solution of thiophene-2-carbaldehydeNa\({}_{2}\)SO\({}_{4}\), and concentrated to dryness. The product was purified using column chromatography on silica gel using the mixture of petroleum ether/EtOAc (5:1) as an eluent. A yield of 0.030 g (52%) was obtained and it was a yellow oil. \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) = 9.14 (br.s, 1H), 7.79 (br.d, \(J\) = 7.6 Hz, 1H), 7.62-7.61 (m, 1H), 7.53-7.49 (m, 1H), 7.28-7.27 (m, 1H), 7.14-7.11 (m, 1H), 7.05-7.03 (m, 2H), and 6.69 (br.s, 1H) ppm; \({}^{13}\)C{\({}^{1}\)H} NMR (101 MHz, DMSO-\(d_{6}\)) \(\delta\) = 162.0, 156.1, 140.7, 134.5, 127.8, 127.7, 127.3, 126.7, 122.4, 118.3, 116.9, and 80.5 ppm.
# A Multilevel Switched Capacitor Inverter with Reduced Components and Self-Balance
## 3 Power Losses Analysis
### Switching Losses  
The switching losses are produced by the overlapping of the voltage and current during the switch on/off process, which includes the switch-on loss (\(P_{\rm sw,on}\)) and switch-off loss (\(P_{\rm sw,off}\)). For simplicity, the switching losses are calculated according to the linear approximation of the voltage and current. According to the calculation method in [16], the \(P_{\rm sw,on}\) and \(P_{\rm sw,off}\) of the \(i\)-th switch are expressed as  
\[\begin{split} P_{\rm sw,on,}i&=f_{\rm sw}\int_{0}^{t _{\rm on}}v_{\rm sw,}(t)i(t)dt\\ &=f_{\rm sw}\int_{0}^{t_{\rm on}}\left(-\frac{V_{\rm sw,i}}{t_{ \rm on}}(t-t_{\rm on})\right)\left(\frac{t_{\rm sw,i}}{t_{\rm on}}t\right)dt, \\ &=\frac{f_{\rm sw}V_{\rm sw,i}t_{\rm on,i}t_{\rm on}}{6}\\ P_{\rm sw,off,}i&=f_{\rm sw}\int_{0}^{t_{\rm off}}t _{\rm sw,i}(t)i(t)dt\\ &=f_{\rm sw}\int_{0}^{t_{\rm off}}\left(\frac{V_{\rm sw,i}}{t_{ \rm off}}t\right)\left(-\frac{t_{\rm off}}{t_{\rm off}}(t-t_{\rm off})\right)dt \\ &=\frac{f_{\rm sw}V_{\rm sw,i}t_{\rm off,i}t_{\rm off}}{6},\end{split} \tag{11}\]  
where \(f_{\rm sw}\) is the switching frequency of the inverter, \(V_{\rm sw,i}\) represents the stress of the \(i\)-th switch, \(t_{\rm on}\) and \(t_{\rm off}\) are the turn-on time and turn-off time of the switch, respectively, and \(I_{\rm on,i}\) and \(I_{\rm off,i}\) represent the current through the \(i\)-th switch after turning on and before shutting off, respectively. Thus, the \(P_{\rm sw}\) of the all switches is expressed as  
\[P_{\rm sw}=\sum_{i=1}^{12}(P_{\rm sw,on,i}+P_{\rm sw,off,i}). \tag{12}\]
## 2 FMCW LiDAR
### FMCW LiDAR System Architecture
The selection of these scanning technologies in FMCW LiDAR systems depends on specific application requirements, such as scanning speed, precision, system size, and cost, to achieve efficient and precise target detection and distance measurement [5]. The signal acquisition and processing unit comprises a high-speed acquisition card, a computer, and measurement software. This unit collects the photonic signals after beat frequency via the high-speed acquisition card, where the signals undergo preprocessing such as amplification and filtering to enhance signal quality. Initially, the signals pass through a broadband, low-noise filter, followed by a low-pass filter to reduce noise and interference, thereby improving the signal-to-noise ratio. After filtering, the signals are amplified by a variable gain amplifier (VGA) and undergo analog-to-digital conversion (A/D). The digital signals are further processed in the measurement software, including digital filtering to select the required frequency bands and extract useful spectral components. Subsequently, fast Fourier transform and spectral subdivision are performed to extract signal frequency values, thus estimating target characteristics. Depending on the specific configuration of the system and application requirements, these data can be further processed to determine the spatial location of the target or other relevant characteristics.
## 3 Volatile Organic Compounds Emitted from WBPs  
Wood, WBPs, and their products are present in most indoor environments, representing an important indoor source of VOCs. Several factors can influence the emission of VOCs from these products, such as the nature of the wood material and the degradation of its components, the addition of resins, varnishes, or other additives, and the steps involved during their industrial production. The composition of wood can be divided into the main structural components (cellulose, hemicellulose and lignin) which are common in all types of wood, and the minor low-molecular-weight non-structural components (usually up to 10%) which are characteristic of a wood species in type and quantity [45, 46]. VOCs such as terpenes, terpenoids, alcohols, aldehydes, and ketones constitute an important portion of wood extractives [47, 48].  
The type and amount of VOCs present and emitted from natural wood, and therefore, the wood material of WBPs, are significantly dependent on wood type and species [48], as well as factors such as age [49, 50], weather and environmental conditions [47], time of cutting, storage conditions and drying conditions [50, 51]. Furthermore, although significant differences have been reported in the content of VOCs emitted from the sapwood and heart-wood regions, the overall composition of the emitted compounds is very similar [52, 53].  
Softwood is used to designate wood from conifer trees (e.g., pine, spruce, larch, fir), which are rich in resin acids, terpenes, fatty acids, and flavonoids [54]. Although volatile terpen can make up to 90% of softwood VOC emissions, they also emit low amounts of secondary emissions (aldehydes and acids) [48]. Flowering trees or angiosperms such as birch, cherry, maple, and oak are designated as hardwoods. In contrast to softwoods, hardwoods have significantly lower emissions of terpenes, as volatile terpenes are usually absent; however, these woods emit a variety of secondary emissions such as aldehydes, ketones, carboxylic acids, and alcohols [48].  
An in-depth overview of VOCs found in wood and WBPs, together with their wood source, the analytical method used for their determination and the content found is available in the Supporting Information.
# Universal Local Attractors on Graphs
## 2 Background  
### Graph Filters  
Graph signal processing [5] extends traditional signal processing to graph-structured data by defining graph signals \(h\in\mathbb{R}^{|\mathcal{V}|}\) that assign real values \(h[v]\) to nodes \(h\in\mathcal{V}\) (nodes and their integer identifiers are used interchangeably). It also considers node adjacency matrices \(M\) whose elements \(M[u][v]\) hold either edge weights (1 for unweighted graphs) or zeros if the corresponding edges \(u-v\) do not exist. Adjacency matrices are normalized as  
\[\hat{M}=MD^{-1}\text{ or }\hat{M}=D^{-1/2}MD^{-1/2}\]  
where \(D\) is the diagonal matrix of node degrees. Iterative matrix-vector multiplications \(\hat{M}^{n}h\) express the propagation \(n=0,1,2,\dots\) hops away from \(h\). Throughout this work we adopt symmetrically normalized adjacency matrices of the second kind, as they are also employed by graph convolutional networks (GCNs; see Section 2.2). A weighted aggregation of hops defines graph filters \(F(\hat{M})\) and their posteriors \(r\) as  
\[r=F(\hat{M})h\quad F(\hat{M})=\sum_{n=0}^{\infty}f_{n}\hat{M}^{n}\]  
for weights \(\{f_{n}\in\mathbb{R}\,|\,n=0,1,2,\dots\}\), indicating the importance placed on propagating graph signals \(n\) hops away. In this work, we focus on graph filters that are positive definite matrices, i.e., whose eigenvalues are all positive. We also assume a normalization \(\sum_{n=0}^{\infty}f_{n}\leq 1\) for all filters, whose maximum eigenvalue then becomes  
\[\lambda_{\text{max}}=\max_{\lambda\in[-1,1]}F(\lambda)=\max_{\lambda\in[-1,1] }\sum_{n=0}^{\infty}f_{n}\lambda^{n}\leq\sum_{n=0}^{\infty}f_{n}\leq 1\]  
where \(\lambda\in[-1,1]\) is the range of eigenvalues for symmetrically normalized adjacency matrices \(\hat{M}\). Two well-known filters of this kind are personalized PageRank [6] and Heat Kernels [7]. These, respectively, arise from power degradation of hop weights \(f_{n}=(1-a)a^{n}\) and the exponential kernel \(f_{n}=e^{-t^{n}/n!}\) for parameters \(a\in[0,1]\) and \(t\in\{1,2,3,\dots\}\).
## 4 Conclusions
### Implications for Similar Projects  
Geological Condition Correlation: In the Qinghai-Tibet Plateau or similar high-temperature regions, tunnel route selection should prioritize avoiding areas where deep major faults intersect and areas with granite high radioactive heat generation zones to minimize the heat damage risk.  
Graded Control Strategy: It is recommended to design construction plans based on heat damage levels:  
Level I (T \(\leq\) 28 \({}^{\circ}\)C): Standard ventilation is sufficient. Level II-III (28 \({}^{\circ}\)C \(<\) T \(\leq\) 50 \({}^{\circ}\)C): Heat-insulating lining and localized cooling systems are required. Level IV (T \(>\) 50 \({}^{\circ}\)C): It is suggested to optimize the burial depth or bypass high-temperature sections. Data-Driven Decision Making: The \"well logging temperature fitting + numerical simulation\" method proposed in this study can be applied to other geothermal anomaly regions, providing a standardized process for heat damage assessment during the early exploration phase.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mileage/km** & **Thermal Class** & **Percentage/\%** \\ \hline
0–9.4, 31.3–43.2, 49.5–74 & I & 62\% \\
9.4–13.2, 14.8–20.4, 28.6–31.3, 3.2–49.5 & II & 24.9\% \\
20.4–22.4, 2.6–28.6 & III & 10.8\% \\
22.4 to 22.6 & IV & 0.24\% \\ \hline \end{tabular}
\end{table}
Table 5: Statistics of temperature segment prediction along the numerical simulation tunnel.
# A New Under-Sampling Method to Face Class Overlap and Imbalance
## 4 Experimental Set-Up  
Extensive experiments were conducted to evaluate the usefulness of the DBMIST-US method when compared to a pool of state-of-the-art under-sampling techniques. In summary, the research questions to investigate in this experimental study were:  
1. What is the classification performance of DBMIST-US in comparison to several state-of-the-art under-sampling algorithms?
2. How robust is DBMIST-US across the use of different classification models?
3. What is the impact of each under-sampling algorithm on the imbalance ratio?
## 2 System Model
### Data Frame of the Proposed System  
A data frame is used for storing the data tables of our proposed system. It is a list of vectors of equal length. For our proposed system, the data from each battery is collected into respective data frames. The main objective in using the data frame is to ensure that only a receiver with a known data frame can decode the data from the battery. In beginning of the frame, the ID of the lead-acid battery is uniquely defined to differentiate the data from multiple batteries, as our proposed system can support multiple n-number of batteries connected to the network. The data frame for monitoring our proposed system is shown in Figure 6 and the details of the data frame functions and units are summarized in Table 1.  
Figure 5: Schematic diagram of the ATmega128a microcontroller for the proposed method.  
Figure 6: Data frame of the proposed system.
## 2 Method Description
### Modal Approximation Model
\begin{split}\mathbf{M}^{p}(\mathbf{q},\mathbf{y})& =\sum_{|\alpha|+|\beta|=0}^{p}\frac{\partial_{\mathbf{q}}^{\alpha }\partial_{\mathbf{y}}^{\beta}\mathbf{M}(\mathbf{q}_{0},\mathbf{y}_{0})}{ \alpha!\beta!}\delta\mathbf{q}^{\alpha}\delta\mathbf{y}^{\beta},\\ \mathbf{K}^{p}(\mathbf{q},\mathbf{y})&=\sum_{|\alpha| +|\beta|=0}^{p}\frac{\partial_{\mathbf{q}}^{\alpha}\partial_{\mathbf{y}}^{ \beta}\mathbf{K}(\mathbf{q}_{0},\mathbf{y}_{0})}{\alpha!\beta!}\delta\mathbf{q }^{\alpha}\delta\mathbf{y}^{\beta},\end{split} \] (3) \[ \begin{split}\lambda_{i}^{p}(\mathbf{q},\mathbf{y})& =\sum_{|\alpha|+|\beta|=0}^{p}\frac{\partial_{\mathbf{q}}^{\alpha }\partial_{\mathbf{y}}^{\beta}\lambda_{i}(\mathbf{q}_{0},\mathbf{y}_{0})}{ \alpha!\beta!}\delta\mathbf{q}^{\alpha}\delta\mathbf{y}^{\beta},\\ \boldsymbol{\phi}_{i}^{p}(\mathbf{q},\mathbf{y})&= \sum_{|\alpha|+|\beta|=0}^{p}\frac{\partial_{\mathbf{q}}^{\alpha}\partial_{ \mathbf{y}}^{\beta}\boldsymbol{\phi}_{i}(\mathbf{q}_{0},\mathbf{y}_{0})}{ \alpha!\beta!}\delta\mathbf{q}^{\alpha}\delta\mathbf{y}^{\beta}.\end{split} \] (4)  
where \(\delta\mathbf{q}=\mathbf{q}-\mathbf{q}_{0}\) and \(\delta\mathbf{y}=\mathbf{y}-\mathbf{y}_{0}\). In the previous equations, the symbols \(\alpha\) and \(\beta\) are multi-indices. Such a notation has been used to cast [PERSON]'s formula in a compact manner. The operators that are used in Equations (3) and (4) for multi-indices are hereafter defined. Given a multi-index, i.e., a \(n\)-tuple of non-negative integers \(\alpha=(\alpha_{1},\alpha_{2},\dots,\alpha_{n})\), its absolute value is \(|\alpha|=\alpha_{1}+\alpha_{2}+\dots\alpha_{n}\), and its factorial is \(\alpha!=\alpha_{1}\alpha_{2}!\dots\alpha_{n}!\). Moreover, given a \(n\)-tuple of unknowns \(\mathbf{x}=(x_{1},x_{2},\dots x_{n})\), the \(\alpha\)-th power of \(\mathbf{x}\) is \(\mathbf{x}^{\alpha}=x_{1}^{\alpha_{1}}x_{2}^{\alpha_{2}}\dots x_{n}^{\alpha_{n}}\), while the \(\alpha\)-th order partial derivative operator with respect to \(\mathbf{x}\) is \(\partial_{\mathbf{x}}^{\alpha}=\frac{\partial^{|\alpha|}}{\partial^{|\alpha|} \alpha_{1}x_{1}\partial^{\alpha_{2}}x_{2}\dots\partial^{\alpha_{n}}x_{n}}\). It is important to note that the coefficients of the Taylor polynomials in (3) can be computed analytically, thanks to the parametric representation of matrices \(\mathbf{M}\) and \(\mathbf{K}\) given by the ERLS theory. In contrast, the coefficients in (4) are yet to be determined, but they can be inferred iteratively from (2), as described in [8].
## Materials and Methods  
InGaN-based blue LEDs were realized by the metal-organic chemical vapor deposition (MOCVD) growth and chip process with a mesa structure. Two-inch c-plane (0001) patterned sapphire substrates (PSS) were used for the LEDs epitaxial growth. Trimethylgallium (TMGa), triethylgallium (TEGa), trimethylindium (TMIn), Trimethylaluminum (TMAI), ammonia (NH\({}_{3}\)), silane (SiH\({}_{4}\)), and magnesium (Cp\({}_{2}\)Mg) were used as precursors and dopants. The LEDs schematic structure is shown in Figure 1a, which is composed of a 30 nm low temperature GaN nucleation layer, a 4.0-\(\upmu\)m mid_GaN, a 3.0-\(\upmu\)m n-GaN, 3 pairs of 2 nm/80 nm In\({}_{0.04}\)Ga\({}_{0.96}\)N/GaN strain released layers, 9 pairs of InGaN/GaN (3 nm/8.5 nm) multiple quantum wells (MQWs), a 20 nm p-Al\({}_{0.15}\)Ga\({}_{0.85}\)N electron blocking layer (EBL), a 100 nm p-GaN main layer and a 15 nm p+GaN contact layer. The thickness of the LQW is designed to be 3.0 nm, 4.7 nm and 5.2 nm, corresponding to an InGaN LQW growth time of 90 s, 135 s and 156 s, respectively. The TEGa and TMIn flow rate for the InGaN QW is set to be \(3.6\times 10^{-4}\) mol/min and \(1.3\times 10^{-4}\) mol/min, respectively. Standard LEDs chips with a size of \(1\times 1\) mm\({}^{2}\) were fabricated. Firstly, Indium Tin Oxide (ITO) was deposited on top of the p-type GaN. The n-GaN layer was exposed by inductively coupled plasma etching with a 1.3 \(\upmu\)m depth and Ti/Al/Ni/Au metals were deposited as the n-contact layer. Finally, the Cr/Pt/Au metals were deposited as the p/n contact pads. A patterned design of mental electrodes with multiple-branches for both the p/n contact pads was used to reduce the current crowding effect [22]. The devices were diced, packaged with silica gel and measured in an industrial calibrated integrating sphere. Figure 1b demonstrates the electroluminescent (EL) intensity mapping of the InGaN high power LED chip under a driven current of 50 mA. A uniform current spreading can be seen, which is helpful to rule out the effect of current crowding on the efficiency droop.