esa-sceva/satcom-chunk-collection huggingface dataset:
# Approaching Nuclear Safety Culture in Fission and Fusion Technology
## 2 Nuclear Safety Culture in Fission  
### Czech Republic Nuclear Power Plants and Research Reactors  
The Czech Republic is characterized by a large density of nuclear facilities located in the whole country, as shown in Figure 2. It hosts six NPP units placed in the Temelin [17] and Dukovany sites. The two in Temelin are VVER 1000/320, while in Dukovany four VVER 440/213 units EDU [18] are placed. In addition, in both the Temelin and Dukovany sites, the future construction of new units is planned based on future tenders. The sites of Temelin and Dukovany can host two new units each, with an installed power of approximately 2400 MWe in each site [19; 20].  
Several research facilities have been built in order to perform research activity and isotope production. In Husinec Rez, the CVR hosts two research reactors, LVR-15 [21] and LR-0 [22]. The first one is one of the main suppliers of isotopes in Europe, also used for several different campaigns for material testing under radiation damage, for Boron Neutron Capture Therapy research and it hosts several different loops and an irradiation channel built into the framework of the SUSEN project, supported by the European Commission [23]. The LR-0 is scaled 1:1 radially  
Figure 2: Czech Republic nuclear facilities map.  
is used for neutronic benchmarking activity, codes validation and neutron flux experiments on activity detection. An additional research reactor, VR-1 [24], is located inside Prague in the Faculty of Nuclear Engineering and Physics (FJFI). This 0-power research reactor is used mainly for experimental activities and student/training programs.  
In the Czech Republic, future new experiments are under preparation. The FJFI is building a new sub-critical experiment called VR-2 [25]. Certain interest is also being given in the Czech Republic to the new fusion research facility, which is going to be designed and constructed in the Institute of Plasma Physics--Czech Republic (IPP-CR) [26] and named COMPASS-U [27].  
Nuclear energy is well accepted by the public in the Czech Republic [28]. In addition to the growing interest in energy self-sufficiency, this phenomenon may also be based on the fact that this is a well-established energy source and a traditional research field in the Czech Republic. Nuclear research in the Czech Republic has been going on since the 1950s (the first research institute in the field of nuclear energy and NS was established in 1955 and the first nuclear research reactor was commissioned in 1957 [29]).  
Additionally, the fact that science is perceived positively in the Czech Republic in the long term may be a contributing factor. On the other hand, the construction of a new NPP is a delicate topic in the Czech Republic, given that it is not only a question of nuclear safety, energy self-sufficiency or strategically selected national energy mix, but also a burning issue in the political scene.
## 2 Theoretical Background
### Artificial Neural Networks  
For automatic pattern recognition, different machine learning models can be used. Among them, artificial neural networks (ANNs) are one of the most popular and simple models [35]. ANNs are inspired by the functioning of the human brain. These networks consist of interconnected neurons organized into layers, namely, the input layer, hidden layers, and the output layer, as shown in Figure 2. Each neuron receives a set of weighted inputs which are multiplied by their respective weights and summed. Subsequently, a nonlinear activation function, such as log-sigmoid, tan-sigmoid, and linear transfer functions, is applied to this weighted sum to determine the neuron's output. Information flows from the input layer through one or more hidden layers before reaching the output layer. Backpropagation is used to train the network, adjusting the weights so that the output approximates the desired output. Here, different training algorithms such as Levenberg-Marquardt, scaled conjugate gradient, and variable learning rate backpropagation, among others, can be used. In each algorithm, different settings such as the number of epochs, minimum error, and type of performance measurement, among others, can be selected. Further details can be reviewed in [35].  
The mathematical formulation for a single neuron is as follows:  
\[y=f\Bigg{(}\sum_{i=1}^{I}\omega_{i}x_{i}+b\Bigg{)} \tag{10}\]  
where \(y\), \(\omega_{i}\), \(x_{i}\), \(b\), \(f(\cdot)\), and \(I\) are the output, weights, inputs, bias, activation function, and total number of inputs, respectively.  
In order to evaluate the performance of the ANNs, various metrics, namely, accuracy, recall, specificity, precision, and F1-score, can be used. Their mathematical formulation for binary classes is as follows:  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{11}\]  
\[recall=\frac{TP}{TP+FN} \tag{12}\]  
\[specificity=\frac{TN}{TN+FP} \tag{13}\]  
\[precision=\frac{TP}{TP+FP} \tag{14}\]  
Figure 2: General architecture for ANNs.  
\[F1\_score=\frac{2 TP}{2 TP+FP+FN} \tag{15}\]  
where _TP_, _TN_, _FP_, and _FN_ represent true positives, true negatives, false positives, and false negatives, respectively. In general, accuracy represents the proportion of overall correct predictions. Recall indicates the proportion of true positive cases correctly identified, while specificity represents the proportion of true negative cases correctly identified. Precision measures the proportion of correct positive predictions, and _F1_score_ is a metric that provides a balance between precision and recall, being useful when there are class imbalances. Each metric addresses a specific aspect of the model's performance, allowing for a more comprehensive and detailed evaluation.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 1 Introduction
On the contrary, sensor-based approaches such as the one described in this work do not require ground intervention. In particular, this approach represents an improvement over traditional EKF-based navigation using magnetometer data. To meet onboard requirements such as limited power consumption and computational effort, the EKF employs an onboard IGRF magnetic field model to derive the state estimates, and it is aided by a Neural Network to overcome the model discrepancies with respect to the CHAOS-7 magnetic field. The overall process represents an accurate and lighter alternative to the direct implementation of the CHAOS-7 model in the EKF and a backup solution for the navigation of small satellites during GPS outages. The algorithm was trained and tested on a simulated Sun-synchronous orbit considering J2, J3, third-body, and atmospheric-drag perturbations. The geomagnetic model used to provide the measurements was the CHAOS-7 magnetic field model [16], while the filter implemented a spherical harmonics expansion using the 13 th-Generation International Geomagnetic Reference Field (IGRF) coefficients up to the 13 th order [17]. This manuscript is organized as follows. Section 2 describes the main frameworks used in this work; Section 3 explains the parameter optimization process; Section 4 describes the dataset generation; and Section 5 presents the obtained results. Conclusions and future works on this topic are presented in Section 7 at the end of the manuscript.
## 3 Derivation of The Method
### Definition of the Piecewise Spline Functions
If \(P_{i-1/2}>0\) and \(P_{i+1/2}<0\), then from (7) we have \[\alpha_{i}^{6}\tilde{y}_{i-1}+\beta_{i}^{6}\tilde{y}_{i}+\gamma_{i}^{6}\tilde{y}_ {i+1}=\delta_{i}^{6},\] (14) where \(\alpha_{i}^{6}=-\lambda_{i-1}A_{i}\beta_{i}^{6}=\lambda_{i-1}A_{i}\cos( \lambda_{i-1}h)+\lambda_{i}\sin(\lambda_{i-1}h)B_{i},\gamma_{i}^{6}=-2\lambda_ {i}\sin(\lambda_{i-1}h),\delta_{i}^{6}=\lambda_{i}\sin(\lambda_{i-1}h)(B_{i}-2 )\frac{Q_{i+1/2}}{P_{i+1/2}}+\lambda_{i-1}A_{i}[\cos(\lambda_{i-1}h)-1]\frac{Q _{i-1/2}}{P_{i-1/2}}.\)
7. If \(P_{i-1/2}>0\) and \(P_{i+1/2}=0\), then from (7) we have \[\alpha_{i}^{7}\tilde{y}_{i-1}+\beta_{i}^{7}\tilde{y}_{i}+\gamma_{i}^{7}\tilde{y }_{i+1}=\delta_{i}^{7},\] (15) where \(\alpha_{i}^{7}=-\lambda_{i-1}h,\beta_{i}^{7}=\lambda_{i-1}\cos(\lambda_{i-1} h)h+\sin(\lambda_{i-1}h),\gamma_{i}^{7}=-\sin(\lambda_{i-1}h),\delta_{i}^{7}=- \frac{1}{2}Q_{i+1/2}\sin(\lambda_{i-1}h)h^{2}+\lambda_{i-1}h[\cos(\lambda_{i- 1}h)-1]\frac{Q_{i-1/2}}{P_{i-1/2}}.\)
8. If \(P_{i-1/2}=0\) and \(P_{i+1/2}<0\), then from (7) we have \[\alpha_{i}^{8}\tilde{y}_{i-1}+\beta_{i}^{8}\tilde{y}_{i}+\gamma_{i}^{8}\tilde{y }_{i+1}=\delta_{i}^{8},\] (16) where \(\alpha_{i}^{8}=-A_{i},\beta_{i}^{8}=A_{i}+\lambda_{i}B_{i}h,\gamma_{i}^{8}=- 2\lambda_{i}h,\delta_{i}^{8}=-\frac{1}{2}A_{i}Q_{i-1/2}h^{2}+\lambda_{i}h\frac {Q_{i+1/2}}{P_{i+1/2}}(B_{i}-2).\)
9. If \(P_{i-1/2}=0\) and \(P_{i+1/2}>0\), then from (7) we have \[\alpha_{i}^{9}\tilde{y}_{i-1}+\beta_{i}^{9}\tilde{y}_{i}+\gamma_{i}^{9}\tilde{y }_{i+1}=\delta_{i}^{9},\] (17) \[\alpha_{i}^{9}=-\sin(\lambda_{i}h),\beta_{i}^{9}=\sin(\lambda_{i}h)+ \lambda_{i}\cos(\lambda_{i}h)h,\gamma_{i}^{9}=-\lambda_{i}h,\delta_{i}^{9}=- \frac{1}{2}Q_{i-1/2}\sin(\lambda_{i}h)h^{2}+\lambda_{i}h[\cos(\lambda_{i}h)-1 ]\frac{Q_{i+1/2}}{P_{i+1/2}}.\]  
We remark that there are \(n-1\) linear equations including \(n+1\) unknowns. In order to solve the system uniquely, we need two additional conditions, which can be obtained from the boundary conditions (2). The continuity conditions (7) combined with the boundary conditions (2) form a system of linear equations with \(n+1\) unknowns, which can be rewritten in the matrix form as  
\[\left( \begin{array}{ccccc}1&0&&&&\\ \alpha_{1}^{j_{1}}&\beta_{1}^{j_{1}}&\gamma_{1}^{j_{1}}&&\\ &\alpha_{2}^{2}&\beta_{2}^{2}&\gamma_{2}^{j_{2}}&&\\ &&\ddots&\ddots&\ddots&\\ &&&\alpha_{n-2}^{j_{n-2}}&\beta_{n-2}^{j_{n-2}}&\gamma_{n-2}^{j_{n-2}}\\ &&&\alpha_{n-1}^{j_{n-1}}&\beta_{n-1}^{j_{n-1}}&\gamma_{n-1}^{j_{n-1}}\\ &&&0&1\end{array} \right)\left( \begin{array}{c}\tilde{y}_{0}\\ \tilde{y}_{1}\\ \tilde{y}_{2}\\ \vdots\\ \tilde{y}_{n-2}\\ \tilde{y}_{n-1}\\ \tilde{y}_{n}\end{array} \right)=\left( \begin{array}{c}A\\ \delta_{1}^{j_{1}}\\ \delta_{2}^{j_{2}}\\ \vdots\\ \delta_{n-2}^{j_{n-2}}\\ \delta_{n-1}^{j_{n-1}}\\ \delta_{n}^{j_{n-1}}\end{array} \right) \tag{18}\]  
Here the value of the superscript \(j_{k}(k=1,2,...,n-1)\) may be one of 1-9, which is determined by \(P_{i-1/2}\) and \(P_{i+1/2}\). The coefficient matrix of the linear system (18) is tridiagonal, which can be solved easily by Thomas algorithm.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Data-Driven Intent Recognition Methods
#### 2.2.2 Deep Learning Approaches  
In recent years, deep learning has made remarkable progress in the field of intention recognition, showing a wide range of cross-domain application potential. From gesture recognition to airborne target intent inference to motion intent analysis, deep learning models offer new solutions for intent recognition with their efficient feature learning capabilities and robustness.  
In the field of gesture intention recognition, researchers use LMC to obtain gesture data and propose a variety of deep learning strategies based on the Visual Geometry Group (VGG) convolutional neural network [39]. These strategies include classical VGG-16 networks, VGG-16 combined with dynamic time warping (DTW), and classification methods based on principal component analysis (PCA) data dimensionality reduction. Three deep learning methods based on a fully connected network, CNN, and RNN are proposed to solve the problem of target intent recognition in complex air combat environments [40]. These methods are combined with expert experience to simulate air combat scenarios and extract target attitude information and script labels for model training. Compared with the traditional Dempster-Shafer theory (D-S) inference method, this method shows stronger robustness and universality. In multi-target formation intent recognition, a new information fusion method is proposed by combining deep learning with D-S [41]. This method generates uncertain information through a deep learning network and introduces fuzzy discount weighting rules to improve the reliability of evidence. In the field of rehabilitation and assisted motion exoskeleton, a ResNet based motion intention recognition method is proposed [42]. By introducing a multi-source data fusion module, convolutional blockattention module (CBAM), and adaptive pooling module, the stability and recognition accuracy of the model are improved in the case of insufficient data. Through the analysis of lower limb motion data, the feasibility and potential application value of the algorithm in the field of exoskeleton are verified.  
Deep learning-based intent recognition methods excel in automatically extracting critical features from high-dimensional data, eliminating the need for manual feature design. They demonstrate remarkable cross-domain applicability, from gesture recognition to motion assistance, and offer robust performance in handling uncertainty and complex environmental data, enhancing model generalization. However, these methods require large-scale labeled datasets for training, limiting their application in data-scarce domains. They also demand significant computational resources, which can hinder real-time deployment and scalability. Additionally, the black-box nature of deep learning models results in a lack of interpretability, posing challenges in high-trust applications.
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 4 Methods
### Automatic Tree Detection and Forest Inventory Parameter Determination  
From the leaf-off TLS and MLS data, an automatic tree detection was performed and forest inventory parameters of interest, such as tree position, DBH and tree height were automatically derived using the method presented in [PERSON] et al. (2017) and [PERSON] et al. (2018).  
First, the digital terrain model (DTM) is generated with a grid size of \(s_{DTM}\) = 50 cm. Next, tree detection was performed using three horizontal slices with a height \(h\) of 1.5, 2 and 2.5 m above the ground. Within the section, a cluster search algorithm and point density analysis are used to search for point accumulations ([PERSON], 2017). Point clusters with more than \(n_{min}\) points are stored as potential tree objects. Due to the kinematic recording and the recording direction, the MLS point cloud has a lower point density and the trunks are mapped as circular segments rather than cylinders (see point density in Table 1). To successfully detect point clusters in the less dense MLS point cloud, the minimum number of points (\(n_{min}\)) is increased for the MLS dataset.  
For the classification of the point cluster as stem sections, a circle fitting with all cluster points was performed. As exclusion criteria the standard deviation of unit weight (\(\sigma_{0}\)) and the fitted diameter of the circle fitting are used to classify the tree objects. All detected tree objects are then subjected to a DBH fitting.  
In addition, the azimuth and distance of the DBH from the plot center and the 3D coordinates of the circle fitting are stored as tree positions. The tree height is determined by the difference from the highest and lowest Z coordinate of the segmented tree point cloud. This simple difference height determination is only possible when there are no scattering points above the treetops and below the terrain. Since both scan datasets were recorded by a time-of-flight distance measurement, they are free of scatter points.
## 4 Results and Analysis  
### Performance Investigation of Traditional Models  
#### 4.1.1 Results of Standard Artificial Intelligence Models  
The prediction results obtained by two standard AI models are shown in Figure 6 and Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **NSE** & **MAPE (\%)** & **RMSE (mm)** & **PPTS (10)** \\ \hline BP & 0.49 & 83.35 & 45.46 & 4.69 \\ Elman & 0.43 & 108.36 & 50.00 & 4.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of monthly runoff predictions by two standard AI models.  
Figure 6: Forecasted and observed monthly streamflow during the validation period by the BP and Elman models.  
The combined analysis shows that neither the BP nor the Elman model reached a prediction accuracy of Grade C or higher. In terms of the NSE evaluation index, the NSE value of the BP model was 0.49, while that of the Elman was 0.43. In terms of the MAPE evaluation index, the MAPE value of the BP model was 83.35%, while that of Elman model was 108.36%. In terms of the RMSE evaluation index, the RMSE value of the BP model was 45.46 mm, while that of the Elman model was 50.00 mm. In terms of the PPTS evaluation index, the PPTS value of the BP model was 4.69, while that of Elman model was 4.37. Taken together, the results indicate that the predictions of the BP model were better than those of the Elman model, but that overall neither model was satisfactory.
## 2 Material and Methods
### The Proposed Hybrid EDBO-BPNN  
The initial parameters of the BPNN are randomly assigned, so the numerous parameters lead to unstable model calculation. Optimizing the BPNN using EDBO can improve the prediction performance of the model. The main idea of an EDBO-BPNN is to update the BPNN's weights and thresholds by constantly updating the location of the dung beetle population until a global minimum is found that represents the optimal solution to the initial weights and thresholds. The flow of an EDBO-BPNN is shown in Figure 3. Firstly, the parameters of the BPNN are determined, including the hidden layer, number of hidden nodes, training cycle, model learning rate, and activation function. Secondly, the optimized BPNN prediction model is trained using the training set. The optimization methods used are EDBO-BPNN, BDB-BPNN, JAYA-BPNN, PSO-BPNN, GWO-BPNN, and WOA-BPNN. Finally, we studied the training model using a test set and evaluated the predictive power of the four models based on statistical parameters such as coefficient of determination (R\({}^{2}\)), RMSE, mean absolute error (MAE), and mean absolute percentage error (MAPE). The predicted values of six kinds of neural network models are compared with experimental values and the predictive ability of the models is analyzed.  
In this paper, six optimization algorithms are used to compare the optimization of the BPNN model, which are EDBO, DBO, JAYA [39], Particle Swarm Optimization (PSO) [40], Gray Wolf Optimization (GWO) [41], and the Whale Optimization Algorithm (WOA) [42].  
Figure 3: Hybridization of EDBO and BPNN.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 4 Proposed Method  
In this section, a snapshot of the instantaneous user distribution is assumed. First, we propose a placement method for UAVs after the user distribution is obtained, then we propose a frequency division method after the placement decision is made. When  
Figure 2: Parameters related to positioning.  
considering this problem, the data rate can be improved by increasing the number of UAVs. However, this has drawbacks in terms of the overall deployment cost and power consumption of the system. For this reason, in this paper, we only consider a scenario with a limited number of UAVs, ranging from 3 to 10. A maximum number of 10 was selected so that UAVs are separated by certain distances to prevent them from colliding with each other in our evaluation area of interest. We would like to investigate the optimal number of UAVs under interference constraints in our future work.  
In this paper, we use the method described in [38] to determine the altitude of the UAV. This method determines the altitude of each UAV coverage circle in such a way that the propagation loss of the user at the edge of the circle is minimized. Therefore, we now describe the proposed method for the horizontal placement of UAVs.
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 2 Related Work
The scheme improves the effectiveness and safety of nodes' storage by making use of the resource advantages of high-end nodes in heterogeneous sensor networks and adopting the method of asymmetric key pre-distribution. [PERSON] _et al._[14] presented a polynomial key pre-distribution scheme. The key pairs can be set up by \(t\)-degree binary symmetric polynomials generated by the server. The network will not disclose any information when the number of captured nodes is less than \(t\). [PERSON] _et al._[15] proposed a key pre-distribution scheme based on a polynomial pool on the basis of combining the concepts of key pool and a polynomial pre-distribution scheme. The scheme increases the network security, but the requirements of storage space and computing ability are higher when the degree of the polynomial \(t\) becomes larger. The key management scheme based on elliptic curves is a typical asymmetric key system method. [PERSON] _et al._[16] and [PERSON] _et al._[17] introduced the elliptic curve concept into public key systems, and the security could be translated into the problem of a discrete logarithm based on elliptic curves. It has been proved that Elliptic Curve Cryptography (ECC) can be applied in key management of WSNs effectively [18]. Its key length is shorter than that of other public key systems while achieving the same safety indexes, and its amount of calculation and the required storage space are all smaller.