esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Generator  
The specific network structure of the generator is shown in Figure 2. The left encoder is constructed based on the dense convolutional network (DCN) [15], using the Dense_Block and the Trans_Block in as the main modules. These modules are utilized to extract important features from the image. The right decoder estimates the scene information based on the features extracted from the encoder and restores the image to its original size. The structure of the decoder is similar to that of the encoder, including the bottleneck layer (Bottleneck Block) [13], the transmission layer (Transition Block), the residual layer (Residual Block), and the refinement layer (Refine Block) [8].  
Figure 1: An overview of the proposed Dense-HR-GAN image dehazing method. It includes a generator and a discriminator. The generator is responsible for generating physical parameters \(\widehat{t}(x)\) and \(\widehat{A}\). Then, the atmospheric scattering model is inverted to output a dehazed image. Finally, the discriminator discriminates between the dehazed image and the haze-free image.  
In the network architecture, the pre-trained parameters from the front part of DenseNet-201 are used from BaseBlock to the third Trans_Block. This is because the structure in the network can achieve feature reuse by concatenating the obtained features in the channel, and using this connection block in dehazing work helps to preserve important features of the hazy image. In this paper, we replace the batch normalization layer with the instance normalization layer. This modification can normalize the training data and increase the stability of the network during training, thereby avoiding gradient explosion. The Transition Block changes the number of channels through a \(1\times 1\) convolution layer, reorders the features, and then upsamples the refined features. Additionally, instance normalization layers are used in this block. The residual network adds an identity mapping layer on top of the shallow network, which enables the network to maintain performance as the depth increases. A residual layer is often added after the transition block to extract more high-frequency information and help recover more details in the image. To alleviate the gradient vanishing during network propagation, we also add an instance normalization layer in the residual block. The refinement layer merges image information from different scales. Refine Block1 in Figure 2 extracts local average information of different spatial sizes using the average pooling layer, and then refines the output through a \(1\times 1\) convolution layer. Subsequently, the sub-pixel convolution is used as the upsampling operation to enlarge the image to the same size. Finally, the locally reassembled images are refined by Refine Block2 to refine image information and remove blocking artifacts.
## 2 Methodology
### BP-ANN Modeling  
#### 2.2.1 BP-ANN Architecture  
A three-layer BP-ANN model, which includes one input, one hidden, and one output layer, is one of the most popular ANN models. The input variables are introduced to the input layer by a vector (\(X\)). A summation for each node in the hidden layer is conducted by multiplying input values with their respective weights (\(W\)) plus a bias (\(b\)) constant. The summation is processed by an activation function and transferred to the hidden layer. The same procedure is carried out between the hidden and output layers. The predicted values at the output layer are compared with the target values and errors are calculated. Weights and biases among layers are updated iteratively until a user-defined performance goal is achieved. The schematic of a three-layer ANN model is demonstrated in Figure 2.  
In the present study, two groups of three-layer BP-ANN models were employed to investigate the optimal range of the node numbers in the hidden layer for individual response and the influence of the ratio of input variables to the ten variables (indicating the variable completeness) on the prediction performance. In the first group, there were ten nodes in the input layer, one node in the output layer, and the node number in the hidden layer varied from 1 to 20. The preferable node number range in the hidden layer for each response was proposed afterward. In the second group, different combinations of significant input variables for the individual response were selected according to the ANOVA results of the previous work [27] and listed in Table 3 with their contribution summations to the response. For each combination, several BP-ANN models were employed with the node number of the hidden layer in the range proposed from the first group tests. For both groups, the activation function between the input and hidden layers was a hyperbolic tangent sigmoid function [28], and a linear function was applied between the hidden and output layers. The training algorithm was the gradient descent with momentum and adaptive learning rate [28]. Training was terminated by reaching any defined criterion, for instance, a maximum number of epochs (10,000), the minimum performance gradient (\(10^{-5}\)), or a minimum target error (\(0\)).  
Before starting the network modeling, input variables were normalized to a scale of 0.1-0.9 to mitigate the influence of magnitudes. The normalization of a variable (\(X_{i}\)) can be carried out according to Equation (7).  
\[X_{i}=\ \frac{0.1x_{max}-0.9x_{min}+0.8x_{i}}{x_{max}-x_{min}} \tag{7}\]  
where \(x_{max}\) and \(x_{min}\) are the maximum and minimum values of the variable \(x\).  
Figure 2: A schematic of a three-layer artificial neural network (ANN) model.
## 2 Methods  
### General Kinetic Equation for Dynamic Conditions  
If we introduce Equation (9) into Equation (8), we obtain the equation also found in the literature [27], and earlier in [28]:  
\[\frac{\mathrm{d}\alpha}{\mathrm{d}T}=f(\alpha)\cdot g(\alpha)\cdot\frac{E}{RT^ {2}} \tag{11}\]  
Presenting Equation (11) in form:  
\[\int\frac{\mathrm{d}\alpha}{f(\alpha)\cdot g(\alpha)}=-\frac{E}{RT}+C \tag{12}\]  
the integral on the left-hand side of the equation after substitution: \(\mathrm{d}\alpha=f(\alpha)\cdot\mathrm{d}g(\alpha)\) leads to the form:  
\[\ln[g(\alpha)]=-\frac{E}{RT}+C \tag{13}\]  
Equation (13) satisfies the criteria of the mathematical formalism for different kinetic functions \(g(\alpha)\)-however, deviations from the assumed activation energy can be expected. Table 13 uses the proposition from the book [31] having in mind explicitly given variables and coefficients depending on the definition of the function \(f(\alpha)\). It can also be considered that these are the most common kinetic models. It should be noted that Table 13 (see: Appendix A) does not present all known functions-0 th kinetics (R1) and higher orders, e.g., Johnson-Mehl-Avrami [27], D4 Ginstein-Brouhnstein [32], Dahme-Junker [33], or more complicated ones like Sestak-Berggren (SB) [32; 34], are missing. Thus, the intercept in Equations (12) and (13) can be represented as (\(\ln[g(\alpha)]\) vs. \(1/T\)):  
\[C=\ln[g(\alpha_{0})]+\frac{E}{RT_{0}} \tag{14}\]As in the work [23], the functions \(g(\alpha)\) were replaced with sufficient accuracy by the first term of the expansions into a power series (\(\ln[g(\alpha_{0})]\) for small \(\alpha_{0}\) in Table 11). The constant written in the form of Equation (14) contains the sought after two unknowns as the coordinate \([T_{0};\alpha_{0}]\) at the initial point and requires a special solution, when the kinetic function is a priori unknown. Expressions for the simplified constant \(C\left(C=n\ln\left(\frac{\alpha_{0}}{m}\right)+\frac{E}{RT_{0}},\ n,\ m=\text{ const}\right)\) are included in Table 11.  
For arbitrary data, the combination of Equation (8) to Equation (10) was proposed for the coordinate \([T_{0},\alpha_{0}]\) establishing the relation:  
\[f(\alpha_{0})\cdot g(\alpha_{0})=\frac{RT_{0}}{E} \tag{15}\]  
The integration constant in Equation (14) can be transformed into a complex form with the following components:  
\[C=\ln[g(\alpha_{0})]+\left[f(\alpha_{0})\cdot g(\alpha_{0})\right]^{-1} \tag{16}\]  
The pre-exponential factor is determined from Equation (9) after reference to temperature \(T=T_{0}\). \[\ln A=\ln[g(\alpha_{0})]+\frac{E}{RT_{0}}+\ln\left(\frac{qE}{RT_{0}^{2}}\right) \tag{17}\]  
According to Equation (14) the first two terms to the right of Equation (17) are the integration constant, hence we obtain:  
\[\ln A=C+\ln\left(\frac{qE}{RT_{0}^{2}}\right) \tag{18}\]  
Equation (18) is valid for all kinetic models, including those for which \(f(\alpha_{0})\
eq 1\), as can be shown by inserting the logarithmized Equation (9) into Equation (14) for \(T=T_{0}\).
###### Abstract
These plantations provide key industrial raw materials and sequester large amounts of carbon dioxide, playing a vital role in the global carbon cycle. Climate change has intensified droughts in Southeast Asia, negatively affecting rubber plantation growth. Limited in situ observations and short monitoring periods hinder accurate assessment of drought impacts on the gross primary productivity (GPP) of rubber plantations. This study used GPP data from flux observations at four rubber plantation sites in China and Thailand, along with solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photosynthetically active radiation (PAR) indices, to develop a robust GPP estimation model. The model reconstructed eight-day interval GPP data from 2001 to 2020 for the four sites. Finally, the study analyzed the seasonal drought impacts on GPP in these four regions. The results indicate that the GPP prediction model developed using SIF, EVI, NDVI, NIRv, and PAR has high accuracy and robustness. The model's predictions have a relative root mean square error (rRMSE) of 0.22 compared to flux-observed GPP, with smaller errors in annual GPP predictions than the MOD17A3 HGF model, thereby better reflecting the interannual variability in the GPP of rubber plantations. Drought significantly affects rubber plantation GPP, with impacts varying by region and season. In China and northern Thailand (NR site), short-term (3 months) and long-term (12 months) droughts during cool and warm dry seasons cause GPP declines of 4% to 29%. Other influencing factors may alleviate or offset GPP reductions caused by drought. During the rainy season across all four regions and the cool dry season with adequate rainfall in southern Thailand (SR site), mild droughts have negligible effects on GPP and may even slightly increase GPP values due to enhanced PAR. Overall, the study shows that drought significantly impacts rubber the GPP of rubber plantations, with effects varying by region and season. When assessing drought's impact on rubber plantation GPP or carbon sequestration, it is essential to consider differences in drought thresholds within the climatic context.
## 5 Experiments
### Influence of Number of Nodes Having Data to Send Within the Frame on Algorithm Performance  
The number of nodes having data to send in a time frame and their placement in the network topology determines the path length and ability of nodes to be activated concurrently. Therefore, scenarios consisting of less data sending nodes may result in significantly simpler mathematical programming tasks and lower computation time. On the other hand, as all nodes must have the ability to communicate with the base station, activating a too-small number of nodes implies more data collection rounds and slows down the process. To assess the appropriate sender number, a set of experiments was carried out where its number was equal to \(\sqrt{N}\) in every time frame. As all nodes except node 0 (base station) must be served, allocations for \(\sqrt{N}\) of frames were calculated by selecting randomly without returns \(\sqrt{N}\) nodes having data to send. A lacking node was randomly duplicated to provide an equal number of sending nodes in every frame. The frame of eight slots was used in all experiments.  
The data presented in Tables 3 and 4 show results aggregated for \(\sqrt{N}\) calculated frames. For the algorithm using total power consumption as the performance index, the dependence of computation time on sending nodes number is visible even if a problem size (see Table 5) is considered. The resulting problems were so demanding that the calculation of one of the frames exhausted all computer memory and had to be terminated. It must be noted that a problem with more nodes having data to send is harder to solve as not only more paths must be calculated, but what is supposed to be more critical, the paths must be coordinated to prevent interference. The last condition is challenging as all paths must end in a single base node, and the number of slots in the frame is limited. The computation times for the algorithm using the maximal power consumption index are much more favorable. However, still, there is a strong influence, visible especially in maximal computation time values. For a relatively broad number of scenarios, the sending nodes number for further experiments was set to 4 to compare both algorithms.  
Figure 3: Example scenario for the network of 16 nodes and four frames. Nodes having data to send are marked in red, base station is in a larger black one. Note that every node except the base station can send its data in one of the frames.
## 3 Surgical Navigation System
### Registration for Construction of Navigation System  
A registration phase is required to construct a surgical navigation system. Registration implies finding a transformation matrix between two different coordinates: an image coordinate and a patient coordinate. In this study, registration between the image coordinate system and the surgical tool coordinate system was needed for the operation of the proposed navigation system. An optical tracking system (OTS: Northern Digital Incorporated, Waterloo, ON, Canada) was used in the registration process to measure the position and orientation of the surgical tool in relation to the patient.  
Firstly, registration was performed by applying the paired point registration (PPR) method [30] between image coordinates and patient coordinates. As shown in Figure 15, we selected five points in an image beforehand and the OTS was then used to find matching points on the patient, who was replaced by a phantom in this study. The latter points matched the points defined in the image coordinates and if more than three points matched, the relationship between patient coordinates and image coordinates (\(l_{p}^{l}T\)) could be obtained from the PPR.  
We could determine the relationship between the patient coordinate and the surgical tool coordinate (\({}_{T}^{p}T\)) using the OTS output, with these relationships depicted in Figure 16.  
Figure 16: Registration process in the proposed navigation system.  
Figure 15: Points set for image coordinates (Green).  
The process of establishing a surgical navigation system was completed by solving the relationship between the image and tool coordinates given by Equation (6):  
\[\begin{array}{c}\overset{I}{\underset{T}{\phantom{T}}}T=\overset{I}{\underset{P} {\phantom{T}}}T\overset{P}{\underset{T}{\phantom{T}}}T\end{array} \tag{6}\]
## 3 Accelerated Environmental Conditions and Test Methods
### Long-Term Freezing  
A hybrid panel may freeze in winter, making it more susceptible to fracture in response to an applied load. We therefore evaluated the mechanical characteristics of hybrid panels after freezing at \(-5\)\({}^{\circ}\)C for 50 days.
## 1 Introduction
By improving the temporal resolution of the acquired images, the different phases of the cardiac cycle can be visualized and quantitative structural and functional information analyzed. Different methods based on focused or unfocused transmit beams were developed for ultrafast echocardiography, including plane waves (PW) [20], diverging waves (DW) [21] or MLT [6; 22] approaches. For increased contrast and spatial resolution, the combination of several images is used for these techniques. Therefore, in this case, motion compensation algorithms [23; 24] are required to increase the image quality affected by large tissue displacements. Moreover, complex blood flow quantification [25] or artery elasticity assessment [26] could also be analyzed with ultrafast methods. When using 3-D field of view at a high frame-rate, full quantitative Doppler flow analysis can be performed on a large region of interest, leading to much more information and improved functionality for the clinician [27]. In addition, the accuracy of 3-D power Doppler can also be analyzed. This paper presents a new powerful fully programmable US platform developed with specific performance for implementing 4-D (real-time 3-D) advanced US strategies with custom data processing. It includes 4 US systems with 256 channels each synchronized together and a US probe with a matrix array of 32 \(\times\) 32 elements. The main features of this imaging system include its capability of simultaneous arbitrary waveform generation, full access to the RF data and the possibility of performing 4-D dual-mode US imaging and/or therapy. In addition, the platform is fully modular up to 1024 elements in full or downsampled regular or sparse array, portable and appropriate for diagnostic and/or therapy purposes. The technical implementation and validation of this platform is described here. Moreover, the feasibility of the fast volumetric US B-mode and Power Doppler imaging approach is also described, opening a large field of vascular and cardiac applications.
# Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments
## 3 Methodology
### V-R-MATD3 Strategy
#### 3.2.4 Reward Shaping  
We propose a hybrid reward function, named Prior-Guided Adaptive Hybrid Reward (PGAHR), which consists of two parts: an intrinsic reward and an extrinsic reward. The intrinsic reward is curiosity-driven, including rewards for newly explored map areas, as well as penalties for robots becoming too close to each other and their own historical trajectories. The extrinsic reward includes an ongoing negative reward based on time steps, as well as a final reward upon the completion of the task. This function leverages prior knowledge to optimize overall rewards and improve learning efficiency. Specifically, we simplify the state space by subsampling the LiDAR data and reduce the robot's action space by using a Voronoi-based spatial segmentation method. This approach helps prevent excessive closeness between robots, ultimately accelerating the convergence of rewards.  
The general total reward \(R\) is defined as  
\[R=\sum_{t=1}^{T}R^{t}=\sum_{t=1}^{T}(R^{t}_{m}+R^{t}_{p}+R^{t}_{c}+R^{t}_{ls}), \tag{7}\]  
where \(R^{t}\) represents the reward obtained by the robot system at time step \(t\), which is slightly different from \(r^{j}_{i}\) for the \(j\)-th trajectory as defined in Equation (1). \(R^{t}_{m}\) is the newly explored map reward, \(R^{t}_{p}\) is the proximity penalty, \(R^{t}_{c}\) is the task completion reward, and \(R^{t}_{ls}\) is a time step penalty.  
\(R^{t}_{m}\) is calculated as  
\[R^{t}_{m}=k\cdot(\mathcal{C}(t)-\mathcal{C}(t-1))-C_{0}, \tag{8}\]  
where the weight \(k\) is dynamically adjusted to maintain an effective exploration strategy and avoid local optima. When \(\mathcal{C}(t)<50\%\), \(k=k_{0}-\Delta k_{0}\); when \(\mathcal{C}(t)>50\%\), \(k=k_{0}+\Delta k_{0}\). The hyperparameters \(k_{0}\) and \(\Delta k_{0}\) are adjusted based on map size and task complexity. \(\mathcal{C}(t)\) is the global map exploration rate at time \(t\), and \(C_{0}\) is a bias term. \(R^{t}_{p}\) consists of two parts:  
\[R^{t}_{p}=n_{1}\cdot{R^{t}_{p}}^{\prime}+n_{2}\cdot{R^{t}_{p}}^{\prime\prime}, \tag{9}\]  
where \(n_{1}\) represents the counts of distances between the robot's next target point and historical target points below threshold \(d_{1}\), and \(n_{2}\) represents the number of distances to other robots' next target points below threshold \(d_{2}\). \({R^{t}_{p}}^{\prime}\) and \({R^{t}_{p}}^{\prime\prime}\) are the negative reward values, respectively.  
\(R^{t}_{c}\) is designed as  
\[R^{t}_{c}=\begin{cases}{R^{t}_{c}}^{\prime}\cdot\frac{\mathcal{C}(t)}{\mathcal{ C}_{\text{target}}}&\text{if }\mathcal{C}(t)\geq C_{\text{target}},\\ 0&\text{otherwise},\end{cases} \tag{10}\]where \(C_{\text{target}}\) is the target coverage rate, and \(R_{c}^{t^{\prime}}\) is the base reward value for reaching the target coverage rate.  
\(R_{ts}^{t}\) is defined as a constant negative value:  
\[R_{ts}^{t}=-R_{0},\quad R_{0}>0. \tag{11}\]
## 5 The Improved BPNN
### The BPNN Description  
The three-layer neural network consisting of one input layer, one hidden layer, and one output layer is adopted in this paper, which is competent for most of the complicated problems. Figure 6 illustrates the structure of the BPNN. We assume that the input signal and the output signal for the structure are \(\mathbf{U}=[u_{1},\,u_{2},\,\ldots,\,u_{l}]\) and \(\mathbf{Y}=[y_{1},\,y_{2},\,\ldots,\,y_{Y}]\), respectively. \(U,\,R,\) and \(Y\) denote the number of neurons of the input layer, hidden layer, and output layer, respectively. Then the outputs of the hidden layer and the output layer can be calculated by:  
\[h_{j}=f_{v}(\sum_{i=1}^{U}w_{ij}u_{i}+b_{j}) \tag{14}\]  
\[y_{k}=f_{v}(\sum_{j=1}^{R}w_{jk}h_{j}+b_{k}) \tag{15}\]  
\[f_{v}(v)=\frac{1}{1+\exp(-v)} \tag{16}\]  
where \(h_{j}\) and \(y_{k}\) represent the outputs of the \(j\)th neuron in the hidden layer and the \(k\)th neuron in the output layer, respectively. \(w_{ij}\) denotes the weight value connecting the \(i\)th neuron in the input layer and the \(j\)th neuron in the hidden layer, and \(w_{jk}\) indicates the weight value connecting the \(j\)th neuron in the hidden layer and \(k\)th neuron in the output layer. \(b_{j}\) and \(b_{k}\) are the biases of the \(j\)th neuron in the hidden layer and the \(k\)th neuron in the output layer, respectively. \(f_{v}(v)\) is the activation function of the hidden layer and the output layer. The overall output is usually different from the expected output and the error function is thus employed, which is to be minimized and is given by:  
\[e_{err}=\frac{1}{2}{\sum_{k=1}^{Y}{e_{k}}^{2}}=\frac{1}{2}{\sum_{k=1}^{Y}{(y_ {k}-y_{k}^{\prime})}^{2}} \tag{17}\]  
where \(y_{k}^{\prime}\) represents the expected output of the \(k\)th neuron in the output layer. By propagating the error backward, the weights and biases can be adjusted based on the gradient descent method. Hence, the error can be reduced gradually. The adjustments for the weights and the biases can be obtained by:  
\[w_{ij}(t+1)=w_{ij}(t)-\eta\frac{\partial e_{err}}{\partial w_{ij}(t)} \tag{18}\]  
\[w_{jk}(t+1)=w_{jk}(t)-\eta\frac{\partial e_{err}}{\partial w_{jk}(t)} \tag{19}\]  
\[b_{j}(t+1)=b_{j}(t)-\eta\frac{\partial e_{err}}{\partial b_{j}(t)} \tag{20}\]  
\[b_{k}(t+1)=b_{k}(t)-\eta\frac{\partial e_{err}}{\partial b_{k}(t)} \tag{21}\]  
where \(\eta\) is the learning rate that should be set appropriately so as to speed up the training process, and \(t\) denotes the number of training times. The training does not cease until the error is decreased to a certain value or the preset number of training times is reached. However, the fixed learning rate sometimes cannot achieve high efficiency during the training. Accordingly, this paper employs an adaptive adjustment method for \(\eta\) as described in next section.