esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Low-Resolution Gamma-ray Spectrometer for Monitoring Radioactive Levels of Wastewater
## 1 Introduction  
Nuclear radiation has a wide range of applications [1; 2]. Because radiation is handled in radiation control zones, people who work and visit these areas are at risk of exposure. Radiation safety management encompasses all activities that limit radiation exposure to within acceptable levels [3; 4; 5].  
Radiation safety management comprises three components: source management, human management, and area management. Source management focuses on maintaining the soundness of radiation sources, such as leak tests and radiation surveys. Human management enforces legal obligations for those entering and exiting radiation-controlled zones; these obligations include awareness, blood tests, and personal dosimetry. Area management includes activities that verify whether the legal standards for radiation dose rates inside and outside the radiation-controlled area are satisfied, whether the area is contaminated, and whether radioactive materials deviate from the radiation-controlled area [5; 6; 7].  
In practice, radiation safety management depends on the type of radiation source. Radiation sources are classified as radiation generators or radioisotopes, and radioisotopes are further classified into sealed and unsealed radioisotopes [5].  
The use of unsealed radioisotopes for experiments or medical treatments produces radioactive wastewater. From the perspective of area management in radiation safetymanagement, radioactive wastewater should be stored separately in a decay tank and drained into the general environment only after it has been ascertained that it poses little risk [4; 5; 6]. Because radioactivity decreases with time, the half-life of the radioisotopes must be considered, and the drainage standards must be satisfied. If the specific activity of the wastewater sample, including that of the stored radioisotopes, does not meet the criteria, the storage, sampling, and measurement processes should be repeated until the standards are satisfied.  
The specific activity of wastewater is primarily measured using gamma spectroscopy systems with a semiconductor detector, which can analyze nuclides and quantities simultaneously. Another method is to use a gamma counter, including a proportional counter, which can only count the amount of radiation emitted by a sample.  
The sample collection of radioactive wastewater from decay tanks to analyze the nuclides and specific activity of radioisotopes is a time-consuming process because decay tanks are typically installed in basements to restrict public access, and the level of specific activity that escapes into the natural environment is similar to the minimum detectable activity (_MDA_), requiring several hours of measurement time per sample.  
To reduce this inconvenience, a regional monitoring system was used in this study to measure count rates in a fixed place continuously, and the applicability of this system was evaluated.
# Real-Time Maintenance Optimization with Industrial Internet of Things
## 3 Models of Conventional and Real-Time Optimization
### Maintenance Strategy Optimization in Conventional Production Systems
\right)^{\vartheta}-c_{SYS}^{MAIN}.\tau^{-\epsilon}\to max., \tag{6}\]  
where \(P\) is the total profit of the production system, depending on the cost of the performed maintenance operations and the income of the production operations, \(p_{max}\) is the total number of maintenance cycles, \(c_{SYS}^{PROD}\) is the initial income generated by the production operations of the production system, \(c_{SYS}^{POSTM}\) is the reliability of the system after the maintenance operations in the maintenance period \(p\), \(\vartheta\) is an influencing factor, which defines the characteristics of the relationship between the reliability and the income, depending on the productivity, \(c_{SYS}^{MAIN}\) is the initial maintenance cost, and \(\epsilon\) is an influencing factor, which defines the characteristics of the relationship between the length of the maintenance period and the specific maintenance costs. The constraint defines that the decision variable of the optimization problem must be selected in such a way that the reliability of each entity falls within a predefined minimum and maximum value:  
\[\forall p,i:r_{i}^{p,min}\leq r_{i}^{pREM}\leq r_{i}^{p,max}, \tag{7}\]  
where \(r_{i}^{p,min}\) is the lower limit of reliability of entity \(i\) in the maintenance period \(p\), \(r_{i}^{p,max}\) is the upper limit of reliability of entity \(i\) in the maintenance period \(p\). For solving this NP-hard problem, metaheuristic approaches offer powerful alternatives. Several software tools and libraries support metaheuristic algorithms. For example, Google OR-Tools v9.12 provides a suite of optimization tools, including support for constraint programming and routing problems. In Python 3.13.1, libraries such as DEAP (for genetic algorithms), PyGAD, and pyswarm (for particle swarm optimization) offer flexible frameworks for implementing custom metaheuristic strategies. In addition to these, spreadsheet-based tools like Excel Solver and OpenSolver are widely used for smaller-scale or educational applications. We have chosen OpenSolver 2.9.3 to solve the above-mentioned optimization problem; the numerical results are discussed in Section 4.
## 5 Classification Experiments
### Classification of the Supervised Dataset
#### 5.2.2 Baseline Results for Fine Label Classification
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories** & **ConvBlock (m \(\pm\)**\(\sigma\)**)** & **ResNet (m \(\pm\)**\(\sigma\)**)** & **AST (m \(\pm\)**\(\sigma\)**)** \\ \hline car & 75.1 \(\pm\) 9.1 & 71.0 \(\pm\) 14.9 & 68.2 \(\pm\) 7.0 \\ motorbike & 81.4 \(\pm\) 7.7 & 87.1 \(\pm\) 8.8 & 70.1 \(\pm\) 5.3 \\ cleaning-truck & 66.7 \(\pm\) 0.0 & 86.7 \(\pm\) 16.3 & 60.0\(\pm\) 13 \\ voice & 74.8 \(\pm\) 5.4 & 73.6 \(\pm\) 4.5 & 71.5 \(\pm\) 3.9 \\ music & 67.6 \(\pm\) 10.5 & 61.2 \(\pm\) 6.1 & 58.2 \(\pm\) 9.7 \\ dog & 45.0 \(\pm\) 34.8 & 21.7 \(\pm\) 19.4 & 100.0 \(\pm\) 0.0 \\ storm & 93.3 \(\pm\) 13.3 & 88.3 \(\pm\) 14.5 & 88.3 \(\pm\) 14.5 \\ impact & 51.7 \(\pm\) 17.0 & 20.0 \(\pm\) 24.5 & 21.6 \(\pm\) 19.5 \\ machinery & 33.3 \(\pm\) 16.7 & 20.0 \(\pm\) 18.7 & 21.6 \(\pm\) 11.3 \\ \hline Macro recall & 65.4 \(\pm\) 5.1 & 58.9 \(\pm\) 8.0 & 62.2 \(\pm\) 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recall values (mean and standard deviation) for entire audio clip classification averaged through the cross validation iterations. Data is in %. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories** & **ConvBlock (m \(\pm\)**\(\sigma\)**)** & **ResNet (m \(\pm\)**\(\sigma\)**)** & **AST (m \(\pm\)**\(\sigma\)**)** \\ \hline car & 67.0 \(\pm\) 10.5 & 64.0 \(\pm\) 10.2 & 64.4 \(\pm\) 8.3 \\ motorbike & 75.3 \(\pm\) 6.8 & 79.4 \(\pm\) 4.8 & 76.8 \(\pm\) 9.5 \\ cleaning-truck & 78.5 \(\pm\) 5.2 & 76.7 \(\pm\) 5.6 & 77.1 \(\pm\) 8.7 \\ voice & 87.3 \(\pm\) 1.6 & 89.4 \(\pm\) 3.2 & 87.5 \(\pm\) 4.0 \\ music & 68.4 \(\pm\) 8.1 & 64.8 \(\pm\) 5.1 & 70.1 \(\pm\) 3.8 \\ dog & 37.5 \(\pm\) 13.8 & 40.4 \(\pm\) 15.5 & 48.0 \(\pm\) 9.4 \\ storm & 97.7 \(\pm\) 3.1 & 97.5 \(\pm\) 2.2 & 92.5 \(\pm\) 8.8 \\ impact & 7.6 \(\pm\) 8.6 & 3.6 \(\pm\) 5.1 & 15.8 \(\pm\) 9.5 \\ machinery & 63.8 \(\pm\) 16.4 & 64.4 \(\pm\) 18.5 & 64.2 \(\pm\) 1.8 \\ \hline Macro recall & 64.8 \(\pm\) 4.3 & 64.4 \(\pm\) 5.1 & 66.3 \(\pm\) 3.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recall values (mean and standard deviation) for fragment-level classification averaged through the cross-validation iterations. Data is in %. classified are confused with the other categories of the same group. \"Voice\" and \"Music\" categories are also mixed to some extent. This is probably due to the fact that many of the occurrences of music are sung songs.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### Interference and Channel Model
#### 3.2.1 Free-Space Path-Loss and Excess Path-Loss  
The free-space path-gain \(l\) is expressed as follows,  
\[l(\varphi)=\bigg{(}\frac{c}{4\pi f_{\rm o}d(\varphi)}\bigg{)}^{2}, \tag{6}\]  
where \(c\) is the speed of light, and \(f_{\rm o}\) is the carrier frequency. The satellite antenna gain \(G\) is assumed to be based on an ideal antenna gain and is obtained as follows,  
\[G =\frac{\text{Sphere area}}{\text{Spherical cap area}}\] \[=\frac{4\pi R_{\odot}^{2}}{2\pi R_{\odot}^{2}\Big{(}1-\cos\frac{ \varphi}{2}\Big{)}}=\frac{2}{1-\cos\frac{\varphi}{2}}. \tag{7}\]  
In the emulation, the ideal antenna model is utilized where all transmit power is uniformly confined to the beam's solid angle. However, the emulator can also take different practical antenna patterns to assess the scenario-specific uplink performance. The excess path-gain is assumed to follow a Gaussian mixture model (GMM) as follows [47],  
\[\zeta[\text{dB}]\sim p_{\rm LoS}\mathcal{N}(-\mu_{\rm LoS},\sigma_{\rm LoS}^{2 })+(1-p_{\rm LoS})\mathcal{N}(-\mu_{\rm LoS},\sigma_{\rm LoS}^{2}), \tag{8}\]  
where \(p_{\rm LoS}\) is the probability of line-of-sight (LoS), \(1-p_{\rm LoS}\) is the probability of non-line-of-sight (NLoS) and \(\mathcal{N}(\mu,\sigma)\) represents the normal distribution with mean \(\mu\) and standard deviation \(\sigma\). Note that the Satellite-to-Ground path-loss model is originally developed for urban/suburban regions. However, using the parameter set in the 3 GPP technical report, we can extend this model to include rural cases [9]. The \(p_{\rm LoS}\) is calculated based on the function derived in [47],  
\[p_{\rm LoS}=\exp\biggl{(}-\frac{\beta\sin\varphi}{\cos\varphi-\alpha}\biggr{)}, \tag{9}\]  
where \(\beta\) is a parameter related to the terrestrial clutter density and height.
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 2 Proposed Methods
### Algorithm Procedure  
The procedure of DDCE is shown in Table 1. Given infrared image \(I_{ir}\) and visible image \(I_{vis}\), feature correspondence \(C\) of \(I_{ir}\) and \(I_{vis}\) is output by DDCE. Consistent edge extraction, common feature detection, feature description establishment, and feature matching are described in Section 2.1, Section 2.2, Section 2.3, and Section 2.4, respectively. Note that steps (1), (2), and (3) are identical for infrared image \(I_{ir}\) and visible image \(I_{vis}\).
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
## 3 Data Analysis for Interferometry and Real-Time Tomography
### Tomographic Reconstruction  
The goal of tomographic reconstruction algorithms is to estimate a 3D density distribution based on its measured projections along directions with different angles. If rotational symmetry can be assumed, an Abel inversion provides an analytic method to reconstruct the 3D density distribution from a single projection. Problems where rotational symmetry cannot be assumed demand for another reconstruction method. These kinds of problems arise frequently in medical physics when a density image of tissue is desired, but only projections from X-ray scans or PET (Positron Emission Tomography) data are available. An efficient algorithm is the so-called back projection. As computation power increased drastically, more sophisticated algorithms were developed. One of these is the Maximum Likelihood-Expectation Maximization (ML-EM) algorithm [19], which is an iterative method with good convergence properties compared to the back projection, method but, on the other hand, having a higher computational cost.  
The idea of a simple back projection is to redistribute (back-project) the measured projections homogeneously along the projection lines. This procedure can be implemented efficiently, but it can only provide rough information about the distribution. The reconstructed images become blurred, albeit infinite projections are available [19].
## 2 Numerical Model
### Contact Model  
The contact model allows computing the nonlinear forces \(\mathbf{F_{nl}}=[T_{x},N,T_{y}]\) generated by the relative displacements between strip and blade nodes. A 3D contact model [37] is generally adopted in literature, in which the contact elasticity is approximated with three springs acting between each node respectively of the strip and of the blade as shown in Figure 2a,b.  
The contact stiffness values assigned to each contact node (i.e., \"local\" contact stiffness) \(k_{n}\), \(k_{tx}\) and \(k_{ty}\) depend on the global contact stiffnesses of the strip and on the number of contact nodeschosen to represent the contact. For instance, for \(k_{n}\): \(k_{n}=k_{ng}/N_{C}\), where \(k_{n}\) and \(k_{ng}\) are the local and global contact normal stiffness respectively, and \(N_{C}\) is the total number of the couples of nodes which approximate the contact between strip and blade platforms. The same relationship holds for \(k_{tx}\) and \(k_{ty}\). A uniform distribution of contact parameters among the different contact nodes is typically adopted under the assumption of a uniform contact patch (different assumptions can be considered if experimental evidence, i.e., wear trace, suggests a non-uniform contact [38]). The global contact stiffness values used for this investigation have been taken from experimental results on a rigid damper undergoing contact pressures in the range of those experienced by the strip damper [39], since no direct experimental investigation is available on the blade-strip contact.  
The relation between forces and displacements at the contact depends on the contact state: if the contact is stuck this relation is linear and governed by the contact springs described above. If the contact state is slip, normal and tangential forces are proportional (i.e., Coulomb's law of friction). If the contact state is lift-off contact forces are null as the contact nodes are separated.
## 4 Prediction Results and Analysis
### Evaluation and Comparison of Model Prediction Performance  
In order to assess the prediction efficiency and generalisation ability of different rockburst intelligent prediction models, the study selected the Accuracy, Precision, Recall and F1 Score to evaluate the evaluation performance of each method (26)-(29) [28], and the calculated results are shown in Table 4.  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{26}\]  
\[Precision=\frac{TP}{TP+FP} \tag{27}\]  
\[Recall=\frac{TP}{TP+FN} \tag{28}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{29}\]  
\(TP\) represents true positive, \(TN\) true negative, \(FP\) false positive and \(FN\) false negative.  
Table 4 demonstrates the evaluation results of the different models, and it can be seen that the best prediction performance is the NGO-CNN-BiGRU-Attention model with an accuracy of 98.28%, which has a precision of 97.92%, a recall of 97.73% and an F1 score of 97.82%, which are higher than the other models. The order is CNN-BiGRU-Attention, RF, BiGRU, ELM, CNN, KNN and SVM, with accuracies of 96.55%, 94.83%, 93.10%, 89.66%, 86.21%, 82.76% and 79.31%, respectively. It is not difficult to find that there is a difference in the performance of traditional machine learning methods in the rockburst intensity level prediction, which may be due to a single model with poor robustness. Since this study  
Figure 10: Confusion matrix diagram.  
makes the model better able to capture the complex patterns of the data by combining CNN and BiGRU and introduces the attention mechanism, the model's attention and prediction accuracy can be improved. In addition, the NGO algorithm facilitates the model to better seize the global information. So, although the prediction performance of the CNN-BiGRU-Attention rockburst prediction model shows good performance, with a precision rate of 94.95%, a recall rate of 94.95% and an F1 score of 94.95%, there is still a certain gap compared with the NGO-CNN-BiGRU-Attention model, which also reflects the characteristics of the NGO algorithm's automatic optimisation of the search effect being good and having strong robustness.  
In conclusion, by comparing the predictive capabilities of various models, it is not difficult to find that the NGO-CNN-BiGRU-Attention model, as a superior model, makes the overall model have a stronger characterisation ability by combining a variety of deep learning techniques and is able to forecast the intensity level of rockbursts with a high accuracy rate and provides certain guiding significance for the research of rockburst disaster prevention and control.
# A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment
## 3 The Proposed Framework for Building Dynamic Grid Map
### Dynamic Object Segmentation and State Estimation  
Two-dimensional (2D) Lidar can directly obtain the distance information (ranges) and angle information (thetas) to the object. The range and angle difference of the adjacent measurement laser points on the same object should be within a certain threshold; that is, the same object satisfies \(abs(ranges[i+1]-ranges[i])<\Delta_{range}\) and \(abs(\text{thetas[i+1]}-\text{thetas[i]})<\Delta_{theta}\). The \(\Delta_{range},\Delta_{theta}\) are the designed threshold. When traversing \(ranges\) and \(theta\) to copy it to \(break\_ranges\) and \(break\_tthetas\), inserting \(-1\) if the continuous condition is not satisfied. Then, the measurements can be divided as shown in Figure 5. Figure 5a is the storage structure of the raw measurements, and Figure 5b is the storage structure after inserting \(-1\) between two consecutive measurements that do not meet the continuity condition.  
Then, we calculate the grid coordinates on the dynamic grid map for all the laser points in each part divided by \(-1\), respectively, and get the maximum and minimum grid coordinates. Considering the movement distance of the object in the sampling interval, a rectangle region \(\Omega\) can represent the range of the dynamic object. The rectangle information includes the starting point information \(start\_xy\) and the length information \(length\_x\), \(length\_y\), as shown in Figure 6.  
Then, we only need to perform statistical calculations on the grid cells in the green box, as shown above. The motion information of grid cell \(c\) includes speed and acceleration information. Taking \(\overline{v}^{c}_{x}\) as an example, the calculation formula is:  
\[\overline{v}^{c}_{x}=\frac{1}{\sum\limits_{i=1:N^{c}_{p,k+1}}w^{i,c}_{p,k+1}} \sum_{i=1:N^{c}_{p,k+1}}w^{i,c}_{p,k+1}v^{i,c}_{p,k+1} \tag{37}\]  
where \(v^{i,c}_{p,k+1}\) represents the x-direction velocity of the posterior persistent particle \(x^{i,c}_{p,k+1}\) in the grid cell \(c\), and the other motion information is calculated in the same way.  
Figure 5: (a) The raw measurements stored structure; (b) The measurements stored structure after segmentation.  
After calculating the motion information of each grid cell, due to the small physical size of the dynamic objects in the indoor environment, the average motion velocity of the dynamic objects that can be applied to obstacle avoidance algorithms can be further calculated. For example, the calculation method for the velocity of the dynamic object in the region \(\Omega_{1}\) obtained by the above segmentation method is finding the grid cells \(\Omega_{occ}\) that satisfies \(m_{k+1}^{c}(O_{k+1})>\text{min\_occ}\) in the rectangular region \(\Omega_{1}\), the number of which is \(\Omega_{num}\), and calculating its average velocity as the speed of the dynamic object:  
\[\overline{v}=\frac{1}{\Omega_{num}}\sum_{i\in\Omega_{occ}}\overline{v}^{i}. \tag{38}\]
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
## 5 Verification for Robust Estimation Strategy  
In order to verify the effectiveness of the proposed robust estimation strategy based on the ESMF algorithm, a series of numerical experiments are carried out employing CarSim and Matlab/Simulink.  
The verification structure for the proposed robust estimation strategy is shown in Figure 8. The input of the ESMF-based vehicle state estimator is composed of the basic signals output by CarSim and the result obtained from the calculation of tire forces. The algorithm steps for vehicle state estimator are also integrated via the \"S-Function\" module. Moreover, during the whole simulation, the noises with the bounds of \([-0.003,0.003]\), \([-0.003,0.003]\) and \([-0.0001,0.0001]\) are added on the signals including the longitudinal acceleration, lateral acceleration and yaw rate to simulate the practical measurement signals, respectively. The sampling time is set as 0.01 s.  
Vehicle parameters are set as: \(m=1416\) kg, \(I_{z}=1523\) kg \(\cdot\) m\({}^{2}\), \(a=1.106\) m, \(b=1.562\) m, \(t_{f}=t_{r}=1.539\) m, \(R=0.31\) m. The double lane change (DLC) maneuver and slalom maneuver are respectively set in CarSim for verification. Furthermore, considering that the UKF is most widely used in the traditional estimation strategies, we present the estimation results using the UKF-based strategy and the reference values from CarSim for making comparison.
# Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
## 4 Experiments
### Part Segmentation on the ShapeNet Dataset  
Compared with classification, part segmentation is a much more challenging task. The proposed method was evaluated on the ShapeNet dataset [22]. ShapeNet dataset is composed of 16,881 shapes with 16 categories, which are labeled with 50 parts in total. Following the data splitting operation in [6], 2048 points were randomly picked as the input of the proposed network. Furthermore, the part segmentation performances were compared using the average category mIoU and the average instance mIoU.  
For target recognition, the set of points with real true labels was assumed to be \(A\), and the set of points marked by the algorithm as the \(A\) type was \(B\). Then, IoU can be calculated as follows:  
\[IoU=\frac{[A\cap B]}{[A\cup B]} \tag{6}\]  
where \([A]\) denotes the number of points in \(A\). The average category mIoU describes the mean \(IoU\) of the overall classes, while the average instance mIoU describes the mean \(IoU\) of the overall instances.  
The results of shape part segmentation on rotated shapes are shown in Table 2. AP-GCN achieves a class mIoU of 83.6% and an instance mIoU of 85.4%, which are both optimal results. The class mIoU increases by 1.3% compared with DGCNN, which obtained the second best result. Compared with the best additional-input method, AP-GCN is obviously better than PiontNet++ [7] and SO-Net [11], while AP-GCN only takes an xyz coordinate as input. These results showed that AP-GCN can capture more accurate structural information because of the addition of both vertices and edges. Figure 3 shows some examples of part segmentation. It can be seen that AP-GCN can segment parts correctly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Input** & **Points** & **OA (\%)** & **Time Consumption (ms)** \\ \hline Pointwise-CNN [34] & xyz & 1k & 86.1 & 6985 \\ PointNet [6] & xyz & 1k & 89.2 & 189 \\ PointNet++ [7] & xyz & 1k & 90.7 & 8761 \\ PointCNN [10] & xyz & 1k & 91.7 & 5136 \\ Ours & xyz & 1k & 92.1 & 4218 \\ DGCNN [19] & xyz & 1k & 92.2 & 5562 \\ SO-Net [11] & xyz & 2k & 90.9 & - \\ Kd-Net (depth = 15) [33] & xyz & 32k & 91.8 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shape classification results (%) based on the ModelNet40 benchmark.  
Figure 3: Several part segmentation results obtained by AP-GCN.  
Additionally, it should be noted that AP-GCN lag behind other methods in the aero, guitar, laptop, and mug classes. Compared with other classes, these four classes suffer much more small structural parts. Due to this reason, AP-GCN may miss some local features. In view of this, we need to add more local information extraction structures to AP-GCN.