esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
[31] reported different parameter values of the stomatal conductance model of _Quercus like_ between marine-influenced sites and continental sites, indicating that the values of the model parameters depend on the environmental growth conditions of the trees. Actually, the optimum air temperature for stomatal opening depends on the growth conditions of _F. crenata_ seedlings in Japan possibly because of an acclimation to the air temperature [32]. Therefore, the application of the stomatal conductance model of [PERSON] et al. [19] to the whole Japan may cause estimation errors of COU. Furthermore, [PERSON] et al. [7] indicated a higher sensitivity in the high-fertil _F. crenata_ seedlings due to the higher COU. In a forest area with a higher soil nutrient status, stomatal conductance may be higher via photosynthetic enhancement and be limited to some extent with shorter leaf longevity [33], indicating nutrient-dependent \(g_{\max}\) and \(f_{\text{phen}}\). In the present study, nutrient-dependent \(f_{\text{phen}}\) may be partly considered because \(f_{\text{phen}}\) was determined from the seasonal changes in EVI provided by satellite data. However, \(g_{\max}\) was regarded as a constant in the present study, which could cause estimation errors of COU. The nutrient-induced modification of \(g_{\max}\) in the calculation of COU is an issue to be considered in the future. On the other hand, the responses of CO\({}_{2}\) uptake to COU can differ depending on the growth conditions because the environmental conditions, such as atmospheric CO\({}_{2}\) concentration and soil drought, affect the leaf detoxification capacity to O\({}_{3}\)[34; 35]. However, information on the effects of tree growth environment on the responses of CO\({}_{2}\) uptake to COU is insufficient to date. In order to perform a more accurate evaluation of the impact of O\({}_{3}\) on the photosynthetic CO\({}_{2}\) uptake by forest tree species, the effects of environmental growth conditions on COU and responses of CO\({}_{2}\) uptake to COU should be considered.
## 2 Materials and Methods
### Methods
#### 2.3.3 One-Dimensional Convolutional Neural Network  
CNNs have been used in several vegetation remote sensing applications, including main crop classification and tree crown delineation [27,50]. CNNs make use of convolutions, which can be seen as sliding a filter over input data to detect local patterns, with the weights of this filter optimized during training to identify discriminative features [51,52]. A key advantage of CNNs is weight sharing across all time stamps, reducing the number of parameters to be tuned [17,51].  
The 1D-CNN was implemented using the PyTorch (v2.0.1) library in Python [53]. The model architecture consists of three sequential convolutional blocks, each consisting of a one-dimensional convolutional layer, a batch normalization, and a ReLU activation function (Figure 5). These three convolutional blocks are followed by two fully connected blocks, each consisting of a linear layer, a batch normalization, and a ReLU activation function (Figure 5). The batch normalizations standardize the inputs of each input batch and thereby help regularize the network and reduce overfitting [17].  
For training the network, we used the minibatch stochastic gradient descent approach and implemented it using the SGD optimizer in Pytorch [54]. For the loss function, we used binary cross-entropy at the first level of the hierarchy and cross-entropy at the second level of the hierarchy because binary and multiclass classifications require different loss functions [54]. Using the Ray Tune library in Python, we optimized the following hyperparameters for the network: window size and stride for the convolutional layers, and batch size, learning rate, number of epochs, and dropout for training the network (Table 2) [55]. For each input data set, the same 50 hyperparameter combinations were tested out, and bad trials were terminated using the median stopping rule. Instead of a 5-fold cross-validation, the training data (75% of the dataset) was split once in an 80-20 split, stratified by the classification group. This means that, in total, 60% of the entire reference dataset was used for training the models on various hyperparameter sets and 15% for evaluating. The remaining 25% was allocated to the validation set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameters** & **Search Space** \\ \hline RF & n\_estimators & 200, 300, 400, 500 \\ \hline TSF & n\_estimators & 200, 300, 400, 500 \\  & min\_interval & 3, 5, 7, 9 \\ \hline CNN & batch\_size & 8, 16, 32, 64 \\  & learning rate & 0.001–0.1 \\  & num\_epochs & 15, 20, 25, 30 \\  & dropout & 0.1, 0.2, 0.3, 0.4 \\  & kernel\_size & 3, 5, 7, 9 \\  & stride & 1, 2, 3, 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter search spaces for each model.
# Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism
## 4 Methodology  
### The HGKT Model  
As illustrated in Figure 2, the proposed model architecture comprises four core components: (1) a hypergraph-enhanced exercise-knowledge concept embedding representation, (2) a learning module (modeling knowledge gain), (3) a forgetting module (modeling knowledge decay), and (4) a prediction module. The hypergraph neural network explicitly constructs heterogeneous correlations between exercises and knowledge concepts through hyperedge connections, employing a dual-layer hypergraph convolution mechanism to capture high-order exercise interaction patterns. Both the learning and forgetting modules incorporate temporal features to dynamically regulate knowledge gain and decay, ensuring adaptive alignment with learners' evolving cognitive states.
# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 2 Methodology
### Feature Fusion Based on Dynamic and Static Features
#### 2.2.1 Feature Fusion Based on Cholesky Variation  
The Cholesky variant is mainly used in matrix factorisation, which decomposes a positive definite Hermitian matrix into the product of a lower triangular matrix and its conjugate transpose. In feature fusion, the Cholesky variant is used to combine static and dynamic features, considering the relationship between the two. Assuming that S represents static feature vectors and M represents dynamic feature vectors, it is first necessary to check whether there exists a matrix that can be represented by S and M as shown in Equation (5).  
\[\begin{bmatrix}A\\ B\end{bmatrix}=\begin{bmatrix}1&0\\ \rho&\sqrt{1-\rho_{1}^{2}}\end{bmatrix}\times\begin{bmatrix}S\\ M\end{bmatrix} \tag{5}\]  
The following inferences can be drawn from the above matrix:  
\[\begin{split} A&=S\\ B&=\rho_{1}S+\sqrt{1-\rho_{1}^{2}}M\end{split} \tag{6}\]  
Meanwhile, exchanging the positions of S and M can obtain Equation (7), similar to Equation (5):  
\[\begin{bmatrix}T\\ D\end{bmatrix}=\begin{bmatrix}1&0\\ \rho_{2}&\sqrt{1-\rho_{2}^{2}}\end{bmatrix}\times\begin{bmatrix}M\\ S\end{bmatrix} \tag{7}\]  
Similarly, the following conclusions can be drawn:  
\[\begin{split} T&=M\\ D&=\rho_{2}M+\sqrt{1-\rho_{2}^{2}}S\end{split} \tag{8}\]  
Equations (6) and (8) lead to the conclusion that B and S are only related to \(\rho_{1}\), and similarly D and M are only related to \(\rho_{2}\). If we find suitable \(\rho_{1}\) and \(\rho_{2}\) based on \(\rho_{2}=\sqrt{1-\rho_{1}^{2}}\), we have B = D for any S and M. Then both B and D can be used as fusion features C, i.e., C = B = D. The next task to be carried out is to experimentally find suitable \(\rho\) 1 and \(\rho\) 2 so that the above equation holds as much as possible. In general, depending on the dataset, \(\rho\)1 and \(\rho\)2 will have different contributions [12].
## 2 Related Work
### Multi-Source Data Fusion Methods  
In recent years, the demand for multi-source information fusion [29; 30; 31] in various applications has increased, and data fusion technology has been widely researched. The goal of data fusion is to alleviate the problem of weak recognition of single sensors and to obtain more accurate detection results. At the same time, deep learning has become a very attractive data processing method that can find higher-order abstract features that are difficult to be found by traditional feature extraction methods. Hence, it is widely used in massive multi-source data processing. [PERSON] [32] et al. used extended Kalman filter (EKF) to fuse the camera information and IMU information. Pre-integration and visual information were used for state prediction and update. [PERSON] [33] et al. proposed a new integrated method based on convolutional neural network (CNN), a transfer learning method and support vector machine (SVM) to automatically identify flotation conditions. Compared with the existing recognition methods, the CNN-SVM model could automatically retrieve features from the original images and perform high-precision fault detection. [PERSON] [34] et al. proposed a trend prediction method for large axial flow fans based on vibration signal-power information fusion to solve the problem of the insensitivity of measured signals to internal faults, poor timeliness and low sensitivity of online intelligent fault diagnosis. Extracted feature vectorswere used as the input of particle swarm optimization and cross-validation optimized support vector machine to predict the operation trend of axial flow fan. Experimental results showed that the performance of this method was better than that of the benchmark method. [PERSON] [35] et al. proposed a reliability prediction method for satellite lithium battery. Bayesian model integrating binary performance degradation data and life data were used to predict the remaining life of satellite lithium battery. The results showed that the prediction accuracy of remaining life could be effectively improved by integrating binary performance degradation data and life data. [PERSON] et al. [36] proposed an aeroengine fault diagnosis fusion model based on deep learning that achieved high fault diagnosis accuracy under the influence of measurement error interference. [PERSON] et al. [37] used a three-dimensional laser radar to obtain point cloud data of corn plants and the image data collected by the camera, automatically obtained a green feature binary image by using a super greening algorithm and the maximum interclass variance method, and then projected the point cloud data after clustering analysis onto the target edge frame of the image to build a multi-sensor data fusion support model for feature recognition. [PERSON] et al. [38] proposed a mobile node location method of an underwater sensor array network based on multi-information fusion. The simulation results showed that the location performance of the fused location method was significantly improved.
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 6 Positioning Experiments  
To evaluate the hybrid positioning method using IMES and PDR information, two positioning experiments, robot localization and pedestrian localization, were conducted. In the following experiments, we used the IMES radio propagation model described in Section 3. Figure 7 shows the appearance of the experimental field. The experiments were performed in the corridor of a university building. Figure 8 shows a schematic of the test environment; it was the same as that in the pre-experiment for investigating the trilateration positioning method described in Section 4. A robot or a human subject followed a figure-eight path indicated by blue lines. As the red arrows and the corresponding numbers represent, it starts moving from the bottom-left point and returns to it. The locations of the transmitters are indicated by orange circles. The numbers in the orange circles indicate the corresponding id numbers of each transmitter. These transmitters were attached on the ceiling as the height of the transmitters was set to 2196 mm.  
Figure 7: Appearance of the experimental field.  
The accurate instantaneous location of a robot or a human subject was obtained through a measurement using a laser range finder, and it was considered to measure the actual path. (Note that, in the case of positioning for a human, the path of the receiver antenna was regarded as the human subject's actual path. To calculate it, the human subject wore a pipe with a diameter of 50 cm, and the center of the pipe was obtained by a positioning algorithm using the laser range finder. Then, in consideration of the offset between the center of the pipe and the receiver antenna on the subject's hand, the actual path of the receiver antenna was calculated.) The location was estimated via post-processing using three different algorithms: trilateration, dead reckoning and Kalman filtering. The IMES radio signal was received every 100 ms (10 Hz), and the estimation algorithms were calculated in each time interval. The trilateration method used only IMES radio information, and the Kalman filtering method used both IMES radio information and PDR information. In the cases of dead reckoning and Kalman filtering, we provided both correct and incorrect initial position information to the algorithm to evaluate the error correction performance of Kalman filtering.
## 5 Numerical Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Numerical Value** \\ \hline \(p_{on}\) & Probability that the PU is ON & 0.3 \\ \hline \(I_{max}\) & Maximum Interference & 0.18 \\ \hline \(BER_{max}\) & Maximum BER & 0.01 \\ \hline \(l\) & Number of channels & 3 \\ \hline \(K\) & Number of CRSN Nodes & 10 \\ \hline \(p_{f}\) & Probability of false Alarm & 0.1 \\ \hline \(f\) & Frequency of transmission & 1 GHz \\ \hline \(N_{0}\) & Noise spectral density & \(1\times 10^{-10}\) \\ \hline \(W\) & Band Width & 1 MHz \\ \hline \(P\) & Maximum Power on each Channel & 100 mW \\ \hline \(\hat{\tau}\) & Maximum Delay for each user & 160 ms \\ \hline \(\mu_{on}\) & Average times of PUs being ON channel & [0.60,0.64,0.86]/s \\ \hline \(\sigma_{s}\) & Standard deviation due to shadowing & 6 dB \\ \hline \(P_{u}\) & PU received power & 10 dB \\ \hline \(G_{t}\) & Transmit Antenna Gain & 2 dBi \\ \hline \(G_{r}\) & Receive Antenna Gain & 0 dBi \\ \hline \(d_{s}\) & Distance between two SUs & 9 m \\ \hline \(d_{p}\) & Distance between CSN node and PU & 80 m \\ \hline \(l\) & Path-loss Exponent & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter values used in the simulations. functions of (17), which we call case 1. The second, third and forth cases are when more weight is put on transmission rate, transmission power and transmission delay, respectively. These cases are summarized in Table 2.
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 4 Results and Discussion
### Comparing the Interference Suppression Capabilities of Different Methods
#### 4.2.3 Suppression of 20 MHz Crosstalk  
In the third set of comparisons, the bandwidth of the interfering signal becomes 20 MHz, as shown in Figure 8a, and the rest of the conditions are exactly the same as those of the second set. The equalization suppression method not only fails to achieve an obvious  
Figure 6: Comparing the interference suppression capability of different methods to in-band spurs by output spectra: (**a**) Original signal without any suppression, (**b**) Signal after equalization suppression, (**c**) Signal after fine notch filtering, and (**d**) Signal after the fusion anti-interference scheme.  
suppression effect, but it also affects the spectrum of the signal to a certain extent, as shown in Figure 8b. Both blind source separation and the fusion scheme achieved a crosstalk suppression capability of 5 dB without impacting signal bandwidth, as shown in Figure 8c.d.  
To sum up, the fusion anti-interference scheme proposed here has better interference suppression capability. At the same time, it neither requires complex controls, like notch filtering, nor does it affect the signal bandwidth, like equalization suppression. Compared with the conventional blind source separation, it has obvious advantages in terms of its signal processing speed.
## 4 Case Study: Economic Analysis 600 TEU Fuel Transition from MFO to LNG  
Based on an economic perspective, this study analyzed the existing 600 TEU container vessel transition from MFO to LNG to determine the life cycle cost (LCC) of a 10-year investment retrofit scheme. The container vessel flies from Tanjung Priok, Indonesia, to Singapore, at a distance of 591 nautical miles. Therefore, the existing vessel will have to be retrofitted to the LNG system, and the major information extracted from the technical analysis of the owners' plans to change fuel, specifically the data concerning the Specific Fuel Oil Consumption from both main and auxiliary engines. The type of fuel used is MFO and Table 1 shows the estimated price of the new build 600 TEU container vessel in the market. PT Samudera Indonesia purchased this vessel for 8.5 million USD in 2018 from Jingjiang Nanyang Shipbuilding China [43]. This container vessel was an MFO-fueled vessel. Table 2 shows 2% inflation per year for new build vessels manufactured with FGSS installed on board culminating in two million USD. The estimated cost is based on 600 TEU newly build MFO fuel container vessel prices from 2018 from PT Samudera Indonesia and calculates 2% inflation each year.  
Furthermore, Figure 9 adapted from the Moore Index 2021 illustrates a container vessel OPEX with various sizes. There are no data for those below 1000 TEU as per the subject size vessel used in this study. For the new build, the analysis was carried out using vessel between 1000 TEU to 1999 TEU. The Moore Index was used to determine each sub-category independently. This study calculated the total OPEX expenditure, which includes maintenance and crew costs using the Moore Index as a reference.
# Role of Blockchain Technology in Combating COVID-19 Crisis
## 7 Conclusions and Future Directions
### Future Directions
#### 7.1.3 Blending Blockchain with Additional Technologies  
To obtain adequate performance in resolving issues that are associated with the COVID-19 pandemic, blockchain can be combined with other available technologies to establish a robust healthcare architecture.  
For example, ingenious storage and great computation potential are the fundamental peculiarities that could be benefited from the cloud. Such features could be employed besides the blockchain to deliver a more efficient performance. Additionally, drones support augmenting the contactless observation of the epidemic. Furthermore, using drones is one of the efficient ways to offer requisite conveniences to the people under quarantine where all means of transportation are suspended.
## 2 Characteristic Signal Selection and Wavelet Denoising Processing
### Wavelet Denoising
#### 2.2.1 Basic Principle  
Its principle is to extend into a function space by stretching and shifting the generating function, that is, the wavelet function cluster, to form the wavelet basis.  
\[\psi_{a,b(i)}=\frac{1}{\sqrt{a}}\psi\left(\frac{t-b}{a}\right) \tag{1}\]  
In Formula (1), \(a\) is a scale factor for the extension and shortening of windows, \(b\) is a translation factor for the changing of windows position. By the selection of the generating function, wavelet basis function, and threshold value, the original function is approximated layer-by-layer to find the best mapping of the original function and extract the original signal in complex conditions. Assuming that the energy of the function is finite, then the function is \(x(t)\in L_{2}(R)\) and the wavelet transform is shown in Formula (2).  
\[W_{\varphi}x(b,a)=<x(t),\psi_{b,a}(t)>=\int_{-\infty}^{+\infty}x(t)\psi_{b,a}^{ \ast}(t)dt \tag{2}\]
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 2 Theoretical Background
### Finite State Machine  
The FSM represents a model of a sequential digital circuit in which the operation (its output vector) depends on both the values at the inputs of the circuit and the internal state of the circuit. Naturally, the internal state must be stored in some block of memory. In addition, the FSM contains a combinatorial part, which makes these circuits more complex than simple combinatorial circuits.  
A finite state machine is a mathematical model of computation. The FSM can be described using five elements: \(\{X,Y,S,\delta,\lambda\}\), where \(X\) is the set of input elements, \(Y\) is the set of output elements, and \(S\) is the set of the internal states. The last two elements describe the relationship between the other elements, where \(\delta\) is the transition function and \(\lambda\) is the output function. The general structure of the FSM is shown in Figure 1.  
The key element of the structure in Figure 1 is the memory block labeled DFF, and is most often implemented in the circuit as a parallel register (called state register). It stores a \(K\)-bit current state vector \(S\), where \(K\geq\lceil lg_{2}(card(S))\rceil\). The value of the next state \(S+\) is determined by the transition function \(\delta\): \(X\times S\to S+\). The arguments of this function are the current state vector \(S\) and the \(N\)-bit input vector \(X\), where \(N\geq\lceil lg_{2}(card(X))\rceil\). Naturally, the transition function is realized by a combinatorial block \(\delta\): \(B^{N+K}\to B^{K}\), where \(B=\{0,1\}\). The output value \(Y\), which is an \(M\)-bit vector, where \(M\geq\lceil lg_{2}(card(Y))\rceil\) is determined by the output function \(\lambda\). The output function can take two forms:  
* \(\lambda\): \(S\to Y\) for [PERSON]'s FSM;
* \(\lambda\): \(X\times S\to Y\) for Me  
Figure 1: The block scheme of FSM.  
In the case of [PERSON]'s FSM, the function of the outputs depends only on the current state of \(S\). In the case of [PERSON]'s FSM, the output function depends additionally on the state of the \(X\) inputs. The output function is a combinatorial block which can be represented as:  
* \(\lambda\): \(B^{K}\to B^{M}\) for [PERSON]'s FSM;
* \(\lambda\): \(B^{N+K}\to B^{M}\) for [PERSON]'s FSM.  
Both the transition function \(\delta\) and the output function \(\lambda\) are implemented in blocks marked with the same symbols (hereafter, transition block and output block).  
Naturally, from the point of view of mitigating radiation effects in FSM circuits, the introduction of TMR is crucial.