esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sensitivity Analysis Experiment  
Sensitivity analysis identifies the sensitive parameters for the model by quantifying the magnitude of the influence of the input parameters on the outputs. Commonly used global sensitivity analysis methods include the Sobol method, [PERSON] method, and Fourier amplitude sensitivity test (FAST) method. The eFAST method adopted in this study combines the advantages of the Sobol method and the FAST method and can obtain fully convergent results with less sampling. The eFAST method is a sensitivity analysis method based on variance decomposition, which treats the model as a function in the following form:  
\[\mathrm{y}=\mathrm{f}(x_{1},x_{2},\dots,x_{k}) \tag{1}\]  
The Fourier transform decomposes the total variance \(V\) of the model into the variance \(V_{i}\) contributed by the changes of the individual parameters and the variance \(V_{i,j,\dots,k}\) contributed by the coupling of multiple parameters. Then, the first-order sensitivity index of the parameter \(x_{i}\) can be defined as follows:  
\[S_{i}=\frac{V_{i}}{V} \tag{2}\]  
and the total sensitivity index can be defined as follows:  
\[S_{Ti}=\frac{V-V_{-i}}{V} \tag{3}\]  
where \(V_{-i}\) is the sum of the variances contributed by all parameters unrelated to the parameter \(x_{i}\)[29].  
The model defines 43 ecophysiological parameters, some of which are phenological parameters obtained through field observations, and some of which are weakly related to the carbon outputs and are not involved in the sensitivity analysis. Others have strong dependence on one another, and only representative ones are selected for the analysis. Based on conducting trial tests, only 19 ecophysiological parameters were identified for further sensitivity analysis. The value range of the parameters has a great influence on the sensitivity analysis results, so we selected the maximum range of values suitable for the deciduous broadleaf forests of the study area from the literature in order to fully explore the actual sensitivity of the model parameters (Table 2). To calculate the sensitivity of parameters once using the eFAST method, the model needs to be run \(n\times p\) times, where \(n\) is the number of samples and \(p\) is the number of parameters. The method considers that the result is valid only when the parameter sampling number is greater than 65. To make the result fully convergent, we took the sampling number \(n\) as 150. We implemented the eFAST method of model parameters in a Python environment using the SALib package ([[https://github.com/SALib](https://github.com/SALib)]([https://github.com/SALib](https://github.com/SALib)) (accessed on 24 October 2022)).
# An Overview of Quantum Machine Learning Research in China
## 3 Algorithms and Applications
### Applications  
The application fields of China's QML research are diverse, with the several popular areas including computer vision, cybersecurity, physical science, and natural language processing, as shown in Figure 3.  
In the field of computer vision, the relevant studies mainly cover applications like image processing, object detection, semantic segmentation, etc. For example, [PERSON] and [PERSON] proposed two image superposition methods, i.e., quantum state superposition and angle superposition. The superposition-enhanced quantum neural network (SEQNN) based on angle superposition could outperform two other similar QML models in classification accuracy on the MNIST dataset [42]; [PERSON] et al. proposed a quantum-classical hybrid deep neural network (QHDNN) model for image anomaly detection. They explored multiple quantum layer architectures and designed a VQC-based QHDNN solution, which could surpass the classical counterpart on both the MNIST and Fashion-MNIST datasets [58].  
With regard to cybersecurity, intrusion detection for preventing network attacks has been a key research direction. For example, a protocol for distributed secure QML was designed to detect eavesdroppers attempting to intercept or interfere with the learning process [29]; [PERSON]'s team developed two intrusion detection systems in succession based on QNN [50] and QCNN [45], respectively.  
Some representative QML applications in the physics field include [PERSON] et al.'s work on the ground-state preparation of a Hamiltonian system [92]; [PERSON] et al.'s investigation into the role of entanglement in QML [93]; and [PERSON] et al.'s study on quantum state tomography [94].  
In the area of natural language processing, the relevant studies mainly cover applications like text classification, speech recognition, machine translation, etc. For instance, [PERSON] et al. proposed a novel QSAM based on variational quantum algorithms for binary and multiple classification on natural language processing datasets, and their model could outperform its classical counterpart and was as good as the state-of-art QSAM [38]; [PERSON] et al. developed a novel QCNN model based on multi-scale feature fusion for text classification, and their model was able to surpass a wide range of state-of-art QNN models [53].  
Figure 3: Application fields involved in China’s QML research, such as computer vision [38; 42; 51; 52; 54; 55; 58; 59; 60], cybersecurity [29; 45; 50], physical science [49; 67; 92; 93; 94], and natural language processing [38; 53].
## 3 Coherent \(\pi\)-Electron Angular Momentum and Current of a Molecule Having Two Aromatic Rings
### Two Ring Components of \(\pi\)-Electron Angular Momentum and Current
#### 3.1.1 Decomposition of Angular Momentum and Current into Atomic Orbital Components  
The expectation values of the coherent electronic angular momentum and current operators of an aromatic ring molecule excited by visible or UV light are expressed as [87]  
\[\left\langle\hat{O}(\overset{\rightarrow}{r},t)\right\rangle=n\int d^{3}r_{1} \cdots d^{3}r_{n}\delta\Big{(}\overset{\rightarrow}{r}-\overset{\rightarrow} {r}_{1}\Big{)}\Psi^{*}(t)\hat{O}\Big{(}\overset{\rightarrow}{r}_{1}\Big{)} \Psi(t). \tag{35}\]  
Here, \(\hat{O}\Big{(}\overset{\rightarrow}{r}_{1}\Big{)}\) is a one-electron operator for the angular momentum or current, \(\Psi(t)\) is an electronic wave function at time \(t\), \(n\) is the number of electrons, and \(\overset{\rightarrow}{r}_{i}\) denotes the electron coordinates of the \(i\)-th electron.  
\(\Psi(t)\) is expanded in terms of electronic configurations, the ground electronic \(\Phi_{0}\) and singly excited electronic ones \(\Phi_{\alpha}\), as \(\Psi(t)=C_{0}(t)\Phi_{0}+\sum_{\alpha}C_{\alpha}(t)\Phi_{\alpha}\). Here, \(\Phi_{0}\) is given as \(\Phi_{0}\Big{(}\overset{\rightarrow}{r}_{1},\cdots,\overset{\rightarrow}{r} _{n}\Big{)}=\|\phi_{1}\cdots\phi_{a}\cdots\phi_{b}\cdots\phi_{n}\|\) with \(\phi_{n}\equiv\phi_{n}\Big{(}\overset{\rightarrow}{r}_{n}\Big{)}\). \(\phi_{a}\) and \(\phi_{b}\) are among the \(n\) occupied orbitals, and \(\Phi_{\alpha}\) is given as \(\Phi_{\alpha}\Big{(}\overset{\rightarrow}{r}_{1},\cdots,\overset{ \rightarrow}{r}_{n}\Big{)}=\|\phi_{1}\cdots\phi_{a^{\prime}}\cdots\phi_{n}\|\), which represents a single-electron transition from occupied MO \(a\) to unoccupied MO \(a^{\prime}\). Within a linear combination of atomic orbitals-molecular orbitals(LCAO-MO) approximation, a \(\pi\) orbital \(\phi_{k}\) is expanded as \(\phi_{k}=\sum_{i}c_{ki}\chi_{i}\) (\(k=a,a^{\prime},b,b^{\prime}\)), where \(\chi_{i}\) is \(i\)-th AO and \(c_{ki}\) is the MO coefficient.  
Equation (35) can be expressed within the MO approximations as follows:  
\[\left\langle\hat{O}(\overrightarrow{r},t)\right\rangle=2 in\sum_{\alpha<\beta }\text{Im}\rho_{\beta\alpha}(t)\sum_{ij}\left(\delta_{ab}c_{a^{\prime}i}^{*}c _{b^{\prime}j}+\delta_{a^{\prime}b^{\prime}}c_{ai}^{*}c_{bj}\right)\chi_{i}^{*} \hat{O}(\overrightarrow{r})\chi_{j}, \tag{36}\]  
where the electronic-state density matrix element \(\rho_{\alpha\beta}(t)\) is defined as \(\rho_{\alpha\beta}(t)\equiv C_{\alpha}(t)C_{\beta}^{*}(t)\). Let us now express the expectation value of the coherent angular momentum operator and that of ring current operator of a space-fixed aromatic ring molecule with two aromatic rings connected with a single bond.
## 4 Obstacle Avoidance Planning
### Improved Method  
The artificial potential field method [27; 28] is used for obstacle avoidance planning. It has the advantages of a simple and intuitive mathematical model, low computational complexity, and good real-time performance. However, there may be unreachable target points or local minimum value defects during the operation. These defects make it difficult to complete the obstacle avoidance planning successfully [29]. Now, this work analyzes the two defects of the traditional artificial potential field method and improves them.
## 2 Materials and Methods
### Experimental Evaluation
#### 2.2.6 Different Lengths of the Feature Window  
The length of the feature window determines the time interval in which the features, mean and slope, are calculated with MATLAB functions, mean and polyfit (set 'Degree of polynomial fit' to 1, meaning 'linear fitting') respectively. The length of the feature window can affect classification performance. Thus, we investigated how classification results varied depending on the length of the feature window (\(\mathrm{L_{FW}}\), in s). We tested nine different feature window lengths (0.6, 1, 3, 5, 7, 9, 11, 13, and 15 s). The longest length, 15 s, corresponds to the length of a hand-closing period (task period). The shorter lengths, except for 0.6 s, were determined by narrowing down the maximum length with a decrement of 2 s. The shortest one, 0.6 s, matches with five data points. Additionally, delay times in the task-induced hemodynamic response (\(\mathrm{D_{HR}}\)) from 0 to 10 s were introduced, ranging in increments of 1 s. These delays are time gaps between the onset of hand-closing and the visible change in hemoglobin concentrations.
# Evolutionary System Design with Answer Set Programming
## 6 Experiments
### Experimental Evaluation
\begin{table}
\begin{tabular}{l r r r} \hline \hline  & **AVG-HD** & & **MAX-HD** \\
**C** & **AVG-R** & **C** & **AVG-R** \\ \hline ARB-BR-H2-L & 9.1 & BOU-BR-H1-F-8 & 7.9 \\ BOU-BR-H1-F-8 & 10.1 & BOU-BR-H2-L & 8.2 \\ ARB-BR-H1-L & 10.2 & BOU-BR-H2-F-8 & 8.3 \\ BOU-BR-H2-L & 10.3 & BOU-BR-H2-F-4 & 8.8 \\ BOU-BR-H2-F-4 & 10.6 & BOU-BR-H1-L & 8.8 \\ BOU-BR-H2-F-8 & 10.8 & BOU-BR-H1-F-2 & 9.5 \\ BOU-BR-H1-F-2 & 11.0 & BOU-BR-H1-F-4 & 9.9 \\ BOU-BR-H1-L & 11.1 & ARB-BR-H2-L & 10.9 \\ XYZ-B-52 & 11.4 & ARB-BR-H1-L & 12.2 \\ BOU-BR-H1-F-4 & 11.9 & BOU-BR-H2-F-2 & 12.6 \\ BOU-B-S2 & 12.6 & BOU-B-H1-L & 15.4 \\ BOU-BR-H2-F-2 & 13.8 & BOU-B-H2-L & 15.4 \\ BOU-B-H1-L & 18.1 & **XYZ-B-S2** & 16.5 \\ BOU-B-H2-L & 18.1 & **BOU-B-S2** & 17.8 \\ BOU-BR-H1 & 22.8 & BOU-B-H1-F-8 & 18.4 \\ BOU-BR-H2 & 22.9 & BOU-B-H2-F-8 & 18.4 \\ BOU-B-H1-F-8 & 23.1 & BOU-BR-H2 & 18.8 \\ BOU-B-H2-F-8 & 23.1 & BOU-BR-H1 & 19.0 \\ ARB-BR-H1-F-8 & 23.8 & **XYZ-B-H2-L** & 21.0 \\ ARB-BR-H2-F-8 & 23.9 & **XYZ-B-H1-L** & 21.1 \\ BOU-B-H1-F-2 & 27.6 & **BOU-B-H1-F-4** & 22.0 \\ BOU-B-H2-F-2 & 27.6 & **BOU-B-H2-F-4** & 22.0 \\ XYZ-B-H2-L & 28.2 & **XYZ-BR-H1-L** & 22.1 \\ XYZ-B-H1-L & 28.4 & **XYZ-BR-H2-L** & 22.5 \\ ARB-BR-H2-F-4 & 29.3 & **BOU-B-H1-F-2** & 23.4 \\ BOU-B-H2-F-4 & 29.4 & **BOU-B-H2-F-2** & 23.4 \\ BOU-B-H1-F-4 & 29.4 & **ARB-BR-H1-F-8** & 26.4 \\ XYZ-BR-H1-L & 30.4 & **ARB-BR-H2-F-8** & 26.6 \\ XYZ-BR-H2-L & 30.8 & **XYZ-BR-H1-F-8** & 28.3 \\ ARB-BR-H1-F-4 & 32.7 & **BOU-B-H1** & 28.7 \\ BOU-B-H1 & 33.5 & **BOU-B-H2** & 28.7 \\ BOU-B-H2 & 33.5 & **ARB-BR-H2-F-4** & 29.2 \\ ARB-BR-H2-F-2 & 34.2 & **XYZ-BR-H2-F-8** & 30.2 \\ XYZ-BR-H1-F-8 & 36.4 & **XYZ-BR-H1-F-4** & 31.9 \\ ARB-BR-H1-F-2 & 37.1 & **ARB-BR-H1-F-4** & 32.9 \\ XYZ-BR-H2-F-8 & 38.2 & **XYZ-B-H1-F-4** & 32.9 \\ XYZ-BR-H1-F-4 & 39.4 & **XYZ-B-H2-F-4** & 32.9 \\ XYZ-B-H1-F-4 & 40.0 & **XYZ-BR-H2-F-4** & 33.1 \\ XYZ-B-H1-F-8 & 40.0 & **XYZ-B-H1-F-8** & 33.3 \\ XYZ-B-H2-F-4 & 40.1 & **XYZ-B-H2-F-8** & 33.3 \\ XYZ-B-H2-F-8 & 40.1 & **XYZ-BR-H2-F-2** & 33.3 \\ XYZ-B-H2-F-8 & 40.1 & **XYZ-BR-H2-F-2** & 33.3 \\ XYZ-B-H2-F-4 & 40.6 & **ARB-BR-H2-F-2** & 33.9 \\ XYZ-B-S1 & 41.3 & **XYZ-B-H1-F-2** & 34.2 \\ BOU-B-S1 & 41.5 & **XYZ-B-H1-F-2** & 34.8 \\ XYZ-BR-H1-F-2 & 41.8 & **XYZ-B-H2-F-2** & 34.8 \\ XYZ-B-H1-F-2 & 42.1 & **XYZ-BR-H1** & 35.6 \\ XYZ-B-H2-F-2 & 42.1 & **XYZ-BR-H2** & 35.9 \\ XYZ-BR-H2-F-2 & 42.6 & **ARB-BR-H1-F-2** & 37.1 \\ XYZ-BR-H2 & 43.1 & **XYZ-B-H2** & 37.7 \\ XYZ-BR-H1 & 43.3 & **XYZ-B-H1** & 37.9 \\ BOU & 50.6 & **BOU** & 44.5 \\ XYZ & 54.1 & **XYZ** & 45.5 \\ ARB & 60.9 & **ARB** & 60.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average rank regarding average and maximum Hamming distance after cutoff time with instances where strategies were applicable. Successful strategies are highlighted in bold.
## 3 Results and Discussion  
### Steady State Results  
In the measurements made in this work, the solar radiation meter used only measures direct radiation, which means that it provides minimum values when the sun is obstructed by a cloud, for example. This causes the measured radiation values to vary between 100 W/m\({}^{2}\) and 1200 W/m\({}^{2}\) in a few seconds. To solve this problem, between 3 to 5 measurements were made for each established volumetric flow, at the same time of the day and during days in summer and autumn that normally have close radiation values. Once such considerations were made and the measurements were performed, values of each variable were extracted during an interval of approximately 5 min and in which both the radiation and the volumetric flow were kept constant (Figure 12). The temperatures normally increased, however, and for the analysis, an average of each variable was used.  
\begin{table}
\begin{tabular}{c c c} \hline
**Therophysical Properties** & **Range** & **Precision** \\ \hline Flowmeter, \(\mathrm{LT/min}\) & 0.3–6 & \(\pm\)5\% FS \\ Pyranometer, \(\mathrm{W/m^{2}}\) & 1–1999 & \(\pm\)5\% FS \\ Inlet temperature, \(\mathrm{T}_{\mathrm{i}}\), \({}^{\circ}\)C & \(-\)20–100 & 1 \\ Outlet temperature, \(\mathrm{T}_{\mathrm{o}}\), \({}^{\circ}\)C & \(-\)20–150 & 1.5 \\ Environment temperature, \(\mathrm{T}_{\mathrm{a}}\), \({}^{\circ}\)C & \(-\)20–100 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Specifications of measuring instruments.  
An example of these tests is shown in Figure 13. The complete group of measurements, made for all flows and volume fractions analyzed, is shown in [56]. From the figure, it can be seen that the values of each variable can be considered stable, because the maximum deviations were 37 W/m\({}^{2}\), 0.012 LT/s and 0.48 \({}^{\circ}\)C for radiation, flow rate and inlet temperature.  
Figure 12: Steady state of (**a**) solar radiation, (**b**) flow rate, (**c**) temperatures.  
Figure 13: Steady state performance of the radiation, flow rate and inlet temperature.
## 2 Methods
### Data Acquisition
Gas species, liquid saturation, membrane water content and temperature are calculated:  
\[\frac{\delta_{p}e_{p}(1-s)\Big{(}c_{i,p}^{k}-c_{i,p}^{k-1}\Big{)}}{\text{d}t}= \phi_{i,p}+S_{i,p} \tag{4}\]  
\[\frac{\rho_{\text{lq}}}{M_{\text{w}}}\frac{\delta_{p}e_{p}\Big{(}s_{p}^{k}-s_{ p}^{k-1}\Big{)}}{\text{d}t}=\phi_{\text{lq},p}+S_{\text{lq},p} \tag{5}\]  
\[\frac{\rho_{\text{m}}}{EW}\frac{\delta_{p}e\Big{(}\lambda_{p}^{k}-\lambda_{p} ^{k-1}\Big{)}}{\text{d}t}=\phi_{\text{mw},p}+S_{\text{mw},p} \tag{6}\]  
\[\frac{\big{(}\rho_{\text{C}}\big{)}_{p}^{\text{eff}}\delta_{p}\Big{(}T_{p}^{k }-T_{p}^{k-1}\Big{)}}{\text{d}t}=\phi_{\text{T},p}+S_{\text{T},p} \tag{7}\]  
where \(\delta\) (m) is the thickness; \(\varepsilon\) is the porosity; \(EW\) (kg\(\cdot\)mol\({}^{-1}\)) is the equivalent weight of the membrane; \(\omega\) is the ionomer volume fraction of the catalyst layer (CL); \(\lambda\) is the membrane water content; \(C_{\text{p}}\) is the specific heat capacity (J\(\cdot\)mol\({}^{-1}\cdot\)K\({}^{-1}\)); T (K) is the temperature. The superscripts and subscript: \(p\) represents the components of the electrode, including GDLs, micro-porous layers (MPLs) and CLs of the anode and cathode; m represents the membrane; mw represents the membrane water; T represents energy; eff represents the effective coefficient. Nitrogen crossover from the anode to the cathode is calculated:  
\[J_{\text{N}_{2},\text{cro}}=K_{\text{N}_{2}}\frac{p_{\text{N}_{2},\text{c}}- p_{\text{N}_{2},\text{a}}}{\delta_{\text{m}}} \tag{8}\]  
where \(K\) (mol\(\cdot\)m\({}^{-1}\cdot\)s\({}^{-1}\cdot\)Pa\({}^{-1}\)) is the permeability; \(p\) (Pa) is the pressure. The superscripts and subscript: cro represents crossover; a and c represent the anode and cathode, respectively. The electro-chemical model represents the relationship between the current density and output voltage:  
\[V=E_{\text{rev}}-RI-\frac{RT}{n\pi F}\ln\left(\frac{I}{I_{0}}\right) \tag{9}\]  
where \(V\) (V) is the output voltage; \(E\) (V) is the potential; \(R\) (\(\Omega\cdot\) m\({}^{2}\), 8.314 J\(\cdot\)mol\({}^{-1}\cdot\)K\({}^{-1}\)) is the area resistance, and universal gas constant; \(I\) (A\(\cdot\)m\({}^{-2}\)) is the current density; \(n\) is the number of electron transfer; \(\alpha\) is the transfer coefficient; \(F\) (95,486 C\(\cdot\)mol\({}^{-1}\)) is Faraday constant. The superscripts and subscript: rev represents reversible; 0 represents the reference value. The numerical process is done by the in-house C++ code. The simulation results of performance transients in the DEA and anode recirculation modes were compared with experimental results, and a good agreement was achieved that could be found in our previous work [14; 21]. The employed model could well reflect the degradation process of the PEM fuel cell in the DEA and anode recirculation modes, and thus the simulated data can be used as the substitute for experimental data to develop the data-driven model.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 4 Problem Statement
### Multi-Copy Problem  
In receiver-based protocols, the routing process will produce multiple copies of a data packet from source nodes to the sink without control packets. This is called the multi-copy problem. Sender-based protocols avoid the multi-copy problem by selecting several specific receivers. There are two major reasons for redundant packets. One is that a node may send the same packet many times. The other is that multiple paths are naturally produced to forward packets. To solve the first problem, the packet history buffer is used to ensure that a node forwards the same packet only once in a certain time interval.  
To solve the second problem, the holding time mechanism is widely used in receiver-based protocols, such as DBR [7], DVOR [14]. It is very likely that the multiple neighboring nodes of a node are qualified candidates to forward a packet as the next-hop. If all these qualified nodes have the authority to broadcast the packet, high collision and high energy consumption will occur. Therefore, in order to reduce collisions as well as energy consumption, the number of forwarding nodes needs to be controlled. This prompts a node to queue its receivers by holding the packet for a while to schedule the packet forwarding process. During the holding time, if a qualified node detects the same packet from another qualified node, it will drop the packet to prevent duplicate forwarding. Holding time in DBR and some other depth-based methods is estimated mainly by calculating the value of depth difference between the sender and the receiver. Holding time in DVOR calculates based on the number of hops that the receiver is away from the aggregation node.  
Different kinds of holding time mechanism in receiver-based protocols have reduced multi-copy problems effectively. However, the holding time mechanism may be invalid in some cases. For example in Figure 3, there exist nodes \(B\) and node \(C\) in the transmission range of node \(A\). Obviously, node \(B\) and node \(C\) are not within the transmission range of each other. When node \(B\) and node \(C\) receive the same packet from node \(A\), they will not detect this packet from each other during their own holding time. Node \(B\) and node \(C\) will forward the same packet to their neighbors. If this happens once per hop during the transmission to the sink, the final number of received packets will be countless. Eventually, the sink will receive multiple identical packets from multiple paths.  
Generally, it is easy to find pairs of nodes like node \(B\) and node \(C\) in the transmission range of a node in a dense underwater network. The holding time mechanism may be rendered invalid in such cases. Based on the analysis above, it is necessary to guarantee that qualified nodes will receive packets from each other. In view of this situation, CDTP proposes a different holding time mechanism in the design of the protocol.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Perceptron Based Branch Predictor  
The existing 2-bit branch prediction [19] stores two-level scheme branch history information for branch prediction. In order to predict, a pattern history table (PHT) is used. To improve branch accuracy, a larger table is needed. In order to reduce the space overhead, it shares counters, but duplicates may occur. Because of these disadvantages, history length is limited, and the predictor is difficult to learn a long history. Perceptron is a simple neural network, but it has the advantage of learning a long history. This paper proposed a two-level scheme using perceptron instead of a 2-bit counter [9]. A detailed description of this structure is shown in Figure 6. If a branch address is given, the address is hashed to obtain an index. The index obtained by hashing the branch address indexes the entry of the Perceptron Vector Table. The entry has a weight and a bias indicating the correlation between branch addresses. The extracted weights and biases are dot products with the Global Branch History that stores the past branch address to calculate the output value y. If y is negative, the branch is non-taken, otherwise it is taken. Finally, when the actual branch result appears, the weight is updated by comparing it with the predicted branch result. When comparing the prediction rate according to the hardware budget, learned branch predictor improved 14.7% over gshare [20] and 10% over bi-mode [21].  
Figure 5: Learned Inverted Index [8]: The RNN model received the preprocessed data string as an input and predicts the position of the data in the leaf RNN.  
Figure 6: Perceptron Predictor Block Diagram [9]: After selecting the parameter corresponding to the branch address, compute dot product the parameter with the previous branch history, and the predictor decides if the branch is taken or not based on the result.
## 2 Materials and Methods
### Methods  
To assess the spatiotemporal dynamic changes in forest NPP, the method we used included four steps: first, the MOD_17 model was optimized using CMFD data, GLASS LAI/_f_PAR products, and the value of \(\varepsilon_{max}\) for the main forest types in the study area; second, Biome-BGCMuSo was subjected to sensitivity analysis using the EFAST method, and the most sensitive parameters were retained for the next calibration step; third, sample plots representing different forest types were selected in the region, and the process model's sensitive parameters for each forest type were calibrated based on the model-model coupling method; and fourth, the simulated forest NPP was validated using dendrochronological measurements, together with the spatiotemporal dynamic changes in forest NPP and its relationship with climatic factors were analyzed. Figure 2 shows the overall flowchart of the approach used in this study. Details of each step are given in subsequent sections.