esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
###### Abstract  
Small space targets are usually present in the form of point sources when observed by space-based sensors. To ease the difficulty of obtaining real observation images and overcome the limitations of the existing Systems Tool Kit/electro-optical and infrared sensors (STK/EOIR) module in supporting the display and output of point target observation results from multiple platforms of the constellation, a method is provided for the fast simulation of point target groups using EOIR combined with external computation. A star lookup table based on the Midcourse Space Experiment (MSX) infrared astrometry catalog is established by dividing the grid to generate the background. A Component Object Model (COM) is used to connect STK to enable the rapid deployment and visualization of complex simulation scenarios. Finally, the automated output of simulated images and infrared information is achieved. Simulation experiments on point targets show that the method can support 20 sensors to image groups of targets at 128 \(\times\) 128 resolution and achieve 32 frames of real-time output at 1 K \(\times\) 1 K resolution, providing an effective approach to spatial situational awareness and the building of target infrared datasets.  
space-based infrared sensor; star background; point target; visual simulation; Systems Tool Kit; electro-optical and infrared sensors +
Footnote †: journal: Applied  
0000-0002-223-3010][PERSON]. [PERSON]  
0000-0002-223-3010][PERSON]. [PERSON]  
0000-0002-223-3010][PERSON]. [PERSON]
## 9 Enabling Technologies for Energy Efficiency in UAV-Based Cellular Networks
### Cooperative Communications  
Cooperative communication helps to lower the energy consumption of UAVs by employing other devices within the network or other UAVs to assist in data transmission, instead of using the UAV to route all network traffic. For instance, UAVs serving as relays (for one another or between an eNodeB and ground-based terminals) can help to improve network coverage [296], reliability [297], or bypass unfavourable channels. In this way way, data are always relayed through favorable channels between communicating pairs [298], leading to both proximity gain (higher throughput) and significant energy savings. Proximity gains result from the fact that UAVs are deployed closer to user terminals than MBSs, especially when they are deployed to extend the coverage of the MBS. The energy savings in this case are due to the proximity/nearness of the UAVs to the terminals, allowing them to transmit with lower powers and reducing re-transmissions (the probability of error is low when the communicating terminals are close), thereby achieving significant energy savings [299]. In addition, cooperative communication leads to spatial diversity gains since the relays employed in such systems receive and process the signal from different locations than the original transmitter. Thus, cooperative communication improves the system reliability by ensuring that the signal is delivered to the destination even if a channel is in deep fade. It has been shown that in some implementations, cooperation achieves full diversity [300], which can significantly suppress the interference arising from multiple terminals [301], leading to fewer re-transmissions which indirectly leads to higher energy savings. An example is shown in [302] where the authors demonstrated that using cluster heads as cooperative relays lowers the energy consumption of a UAV.  
RISs can also be viewed as a cooperative communication mechanism when combined with UAV networks to improve cellular coverage and throughput. They save energy by relaying transmitted data from the source to the destination. RISs not only reduce the transmit powers needed by UAVs to reach ground terminals but also improve coverage and suppress channel fading. This has been shown to improve the system EE [303] in general and to conserve the energy of the UAV in particular.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 2 Mathematical Formulation  
### Notations  
We define \(\{w\}\), \(\{b\}\), \(\{c\}\) as a world coordinate, body coordinate and camera coordinate, respectively. \(\mathbf{R}_{w}^{c}\) and \(\mathbf{p}_{cw}^{c}\) are the rotation and translation from the world coordinate to the camera coordinate. \(\mathbf{R}_{c}^{b}\) and \(\mathbf{p}_{c}^{b}\) represent the extrinsic parameters, which can be calibrated in advance. \(\mathbf{T}=\left[\begin{array}{cc}\mathbf{R}&\mathbf{p}\\ 0&1\end{array}\right]\) is the \(4\times 4\) homogeneous transformation. \(\mathbf{q}\) is the quaternion representation of \(\mathbf{R}\), \(\otimes\) represents the multiplication between two quaternions. \(\lfloor*\rfloor_{\times}\) represents the skew symmetric matrix corresponding to the vector.
## 3 Application Example
### Axis Trajectory Experimental Data  
The detailed experimental conditions are shown in Figure 9. Vibration displacement signals were collected under nine different experimental conditions on the rotor test bench, as detailed in Table 1. The original axis trajectories synthesized from the unprocessed vibration displacement signals are shown in Figure 10.  
Figure 8: Test bench details. (**a**) Multi-disk rotor test rig; (**b**) arrangement of eddy current displacement sensors.  
Figure 9: Experimental operating condition configuration. (**a**) Adding counterweights; (**b**) configuration of rotor type 1; (**c**) configuration of rotor type 2; (**d**) configuration of rotor type 3.  
In the first rotor arrangement, increasing speed directly alters the shape of the axis trajectory, exacerbating the unbalanced state. Adding weights makes the axis trajectory more convoluted, further complicating the unbalanced condition. In the second rotor arrangement, changes in speed and weight have a relatively minor effect on the axis trajectory shape. In the third rotor arrangement, changes in speed affect the axis trajectory shape similarly to the first type, altering its shape.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} 0 \\ 0 \\ 1 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific experimental condition settings.  
Figure 10: Original axis trajectory. (**a**) Experimental condition 1; (**b**) experimental condition 2; (**c**) experimental condition 3; (**d**) experimental condition 4; (**e**) experimental condition 5; (**f**) experimental condition 6; (**g**) experimental condition 7; (**h**) experimental condition 8; (**i**) experimental condition 9.
# MultiAspect Graphs: Algebraic Representation and Algorithms
## 2 MultiAspect Graph (MAG)
### MAG Adjacency  
Two composite vertices are considered adjacent if they share the same MAG edge, i.e., given two composite vertices \(\mathbf{u},\mathbf{v}\in\mathbb{V}(H)\) are adjacent if and only if there is a MAG edge \(e\in E(H)\) such that \(\mathbf{u},\mathbf{v}\in\{\pi_{o}(e),\pi_{d}(e)\}\). Similarly, two MAG edges are considered adjacent if and only if they share a same composite vertex, i.e., two given edges \(e_{1},e_{2}\in E(H)\) are adjacent if and only if there is a composite vertex \(\mathbf{u}\in\mathbb{V}(H)\) such that \(\mathbf{u}\in\{\pi_{o}(e_{1}),\pi_{d}(e_{1})\}\) and \(\mathbf{u}\in\{\pi_{o}(e_{2}),\pi_{d}(e_{2})\}\).  
Figure 1 shows an illustrative example of three MAG edges. The figure depicts a four aspects MAG, where each set of colored circles represents one aspect, and each edge has two elements of each aspect.  
The isolated edge \((A1,A2,A3,A4,C1,C2,C3,C4)\) on the leftmost side of Figure 1 exemplifies the composite vertex adjacency concept. In this case the composite vertices \((A1,A2,A3,A4)\) and \((C1,C2,C3,C4)\) are adjacent. The two edges \((E1,E2,E3,E4,H1,H2,H3,H4)\) and \((H1,H2,H3,H4,K1,K2,K3,K4)\) exemplify a case of edge adjacency. Since the composite vertex \((H1,H2,H3,H4)\) is shared by both edges, they are adjacent.  
Although the structure of a MAG edge is similar to an even uniform hypergraph edge, the adjacency definition used on MAGs is not the usual one adopted on hypergraphs. The adjacency concept found on a MAG is close to the one associated with traditional directed graphs, where a MAG edge can be seen as a relation between two composite vertices, which are composite objects constructed from aspect elements. Therefore, a MAG edge expresses a relationship between two (composite) objects in the same way as a directed graph edge. This concept leads to the isomorphism between MAGs and directed graphs, as well as the close relation between walks, trails, and paths on MAGs and directed graphs.
# A Self-Calibration Method for Robot End-Effector Using Spherical Constraints
## 4 Simulation Error Calibration Experiment
### Simulation Experiment  
Based on the D-H convention and the geometric parameters of the KUKA KR5 R1400 robot, the D-H parameter table was constructed, as shown in Table 1.  
The kinematic model of the robot was simulated using the Robotics Toolbox in MATLAB. The simulation results, as shown in the Figure 5 below, confirmed the validity of the established robot model.  
According to the simulation experiment procedure, the first step was to preset the robot's geometric error parameters and the end-effector deformation error parameter. These errors were introduced to simulate real-world conditions. The preset geometric error parameters are listed in Table 2, and the deformation error parameter of the robot's end-effector \(c_{t}\) was set to 0.002.  
Considering the robot's workspace, the center of the calibration sphere was positioned at \(x=1.34\) m, \(y=-0.24\) m, \(z=0.35\) m in the base coordinate frame, with the calibration sphere having a diameter of 0.06 m. The proposed self-calibration method involves a total  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Link** & \(\theta\) (\({}^{\circ}\)) & \(d\) **(mm)** & \(a\) **(mm)** & \(\alpha\) (\({}^{\circ}\)) & \(\beta\) (\({}^{\circ}\)) \\ \hline
1 & \(-\theta_{1}\) & 400 & 180 & \(-90\) & - \\
2 & \(\theta_{2}\) & - & 600 & 0 & 0 \\
3 & \(\theta_{3}-90\) & 0 & 40 & \(-90\) & - \\
4 & \(-\theta_{4}\) & 620 & 0 & 90 & - \\
5 & \(\theta_{5}+180\) & 0 & 0 & 90 & - \\
6 & \(-\theta_{6}\) & 80 & 0 & 0 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: KUKA KR5 R1400 robot D–H parameters.  
Figure 5: KUKA KR5 R1400 robot simulation model.  
of 28 error parameters, necessitating at least 28 points to identify these errors. In this study, 50 points were randomly generated on the calibration surface, and the inverse kinematics of the robot was used to compute the joint angles with errors, simulating the scenario where the robot's end-effector touches the calibration surface. Additionally, care was taken to ensure no interference occurred between the robot body and the calibration sphere, closely replicating real-world conditions.  
After setting the initial values, a MATLAB(R2021a) program was developed to perform the iterative calculations. The iterative process was depicted in Figure 6. After 50 iterations, the data converged, and the iteration process was successfully completed.
# Examining the Optimization of Spray Cleaning Performance for LiDAR Sensor
## 3 Results and Discussion
### Optimized Cleaning Model for the Entire Target Area
#### 3.1.1 Results of the First Spray  
To calculate the cleaning rate of the first spray, the quadratic model was most suitable as it showed high explanatory power with an R-squared value of 0.8666 and an adj. R-squared value of 0.8158. The adequate (adeq.) precision of the first spray model was 13.9004, exceeding the desired value of 4 and indicating its utility for navigating the design space. Variance analysis results of the first spray model are presented in Table 3.  
In the first spray model, the primary terms of the spray time and washer pressure were significant at a significance level of 5%, and the quadratic term of the spray angle was significant at a significance level of 10%. When the regression coefficients of the factors were estimated based on the method of least squares, the quadratic equation was expressed as  
\[\begin{split}\text{\emph{Cleaning Rate (First spray)}}=-88.6166+10.0455\ \times\text{\emph{ spray time}}+3.7811\ \times\text{\emph{washer}}\\ \text{\emph{pressure}}+5.9701\ \times\text{\emph{washer angle}}-0.1003\ \times\text{\emph{ spray angle}}^{2}\end{split} \tag{1}\]  
The 3D surface and 2D contour graphs drawn based on the quadratic equation of the first spray model are shown in Figure 9. The cleaning rate significantly increased with the washer pressure, and it showed a tendency to increase with the spray time. Initially, the cleaning rate increased with the spray angle, but it started to decrease as the angle continuously increased. Conversely, the cleaning rate barely changed or slightly decreased based on the target point.  
As observed from the ANOVA results, the spray time and washer pressure had the largest impact on the cleaning rate, and the spray angle quadratically affected the cleaning rate.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 4 Experimental Results and Analysis
### Datasets  
The proposed age estimation framework involves five datasets: IMDB-WIKI [32], Morph II [33], MegaAge-Asian [34], CACD [35] and ImageNet [36].  
IMDB-WIKI is currently the largest publicly available age estimation benchmark. It has 523,051 facial images in total, with 460,723 images from IMDB and the remaining images from Wikipedia. The age in IMDB-WIKI ranges from 0 to 100. Since the images from IMDB-WIKI contain too much noise, it is not suitable for evaluating the performance of the age estimation study. IMDB-WIKI is used for pre-training in most previous human age estimation works, e.g., SSR [15] and DEX [32].  
ImageNet is a large-scale image classification dataset that contains 1.2 million training images and 50,000 validation images of 1000 classes. It is widely used to pre-train deep convolutional neural networks. Although ImageNet does not contain age-specific labels, pre-training on this dataset helps the model to learn general visual features. Therefore, it was employed to fine-tuned our basic Swin Transformer for age estimation task.  
Morph II is the most widely used human age estimation dataset; it includes 55,134 facial images from 13,617 subjects. The ages range from 16 to 77. Popular experimental protocol used for Morph is 80% for training and 20% for testing. Since Morph IIis large enough to carry out real age estimation, we employ it to evaluate the effectiveness of our model and compare with the state-of-the-arts.  
MegaAge-Asian is another large-scale age estimation benchmark, which has 40,000 facial images with multiple image variations involving illumination, pose and expression. Different from Morph II, MegaAge-Asian is collected from Asians. The age ranges from 0 to 70 and we employ 3945 images for testing and the remaining ones for the training.  
CACD is a publicly available cross-age face dataset containing \(166\),\(A17\) images collected from 2000 celebrities, with ages ranging from 14 to 62 years. The dataset is divided into three subsets: the training set consists of 1800 celebrities, the testing set includes 120 celebrities, and the validation set contains 80 celebrities. Given that the dataset includes a variety of facial expressions, illumination, and pose variations, it provides a rich and diverse source of data for training age estimation models. Following previous works, we train our model on the training subset and report its performance on the test subset.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 3 Model and Method
### Fitting Correction Methods
#### 3.3.2 Nonlinear Fitting Correction  
The nonlinear correction method can modify both the mean value and the variation coefficient. The nonlinearly corrected precipitation \(\mathrm{P}_{\mathrm{nonlinear}}\) is calculated as  
\[\mathrm{P}_{\mathrm{nonlinear}}(\mathrm{i},\mathrm{t})=\mathrm{b}_{\mathrm{i}} \times\mathrm{P}_{\mathrm{O}}(\mathrm{i},\mathrm{t})^{\mathrm{c}_{\mathrm{i}}}, \tag{8}\]  
where \(\mathrm{b}\) and \(\mathrm{c}\) are the two parameters determined by the least square fit between precipitation from CMPA and GPM (or SMP) in the calibration period, and the fitting process is conducted on each grid cell.
## 4 Results and Discussion
### Four-Sensor Configurations
#### 4.4.4 Confusion Matrix Analysis for Four-Sensor 50%-Overlapping Configuration
Overall, TCN effectively handles long-term dependencies, underscoring its strength in processing sequential data [28]. Transformer: The Transformer confusion matrix in Figure 13i showcases a strong ability to classify activities, achieving 927 correct classifications for the _stm_wos_ class. Despite some misclassifications, its self-attention mechanism effectively captures complex relationships over longer sequences, demonstrating its applicability for activity recognition. In summary, the 50% overlapping configuration enhances the performance of all models. The TCN stands out as the best performer, achieving the highest accuracy and lowest misclassifications, while the GRU and XGBoost also demonstrate commendable results. These findings underscore the importance of selecting appropriate models based on the specific requirements of human activity recognition tasks, particularly in leveraging overlapping data to enhance classification accuracy.
## 3 Hybrid Neural Network Based on Multiple Time Scale Feature Learning
### Hybrid Neural Network Based on Multiple Time Scale Feature Learning
#### 3.2.3 Learning the Dependencies in Multiple Time Scale Features  
We use three LSTMs to learn time dependencies in features of different time scales. We need to convert feature maps extracted by the CNN into feature sequences suitable for LSTMs by map to sequence layer. As shown in Figure 4, feature maps represent features learned by the CNN. Different colors indicate that these feature maps are obtained by different convolution kernels. The points in the feature map are arranged chronologically from left to right. The feature sequence represents the input of LSTM. The feature vector in the feature sequence is represented by \(fv_{t}\), and the subscript \(t\) corresponds to its order in the series. Each feature vector is generated from left to right on feature maps by column. This means the i-th feature vector is the concatenation of the i-th columns of all the maps.  
Figure 3: The models based on single time scale feature learning. (**a**) The structure of Model based on \(F_{1}\); (**b**) The structure of Model based on \(F_{2}\); (**c**) The structure of Model based on \(F_{3}\).  
Each LSTM network learns the time dependencies in its corresponding feature sequence and the process is described as follows. In the LSTM, each cell has three main gates: the input gate, the forget gate and the output gate. Suppose that the input feature vector at the time \(t\) is \(fv_{t}\) and the hidden state at the previous time step is \(h_{t-1}\).  
The input gate \(i_{t}\) is calculated by:  
\[i_{t}=\mathit{sigmoid}(W_{ifo}fv_{t}+W_{ih}h_{t-1}+b_{i}) \tag{2}\]  
The forget gate \(f_{t}\) is calculated by:  
\[f_{t}=\mathit{sigmoid}(W_{ffo}fv_{t}+W_{fh}h_{t-1}+b_{f}) \tag{3}\]  
The output gate \(o_{t}\) is calculated by:  
\[o_{t}=\mathit{sigmoid}(W_{ofv}fv_{t}+W_{oh}h_{t-1}+b_{o}) \tag{4}\]  
The principle of the memory mechanism is to control the addition of new information through the input gate, and to forget of the former information through the forget gate. The old information is represented by \(c_{t-1}\), and the latest information is calculated as follows:  
\[\overline{c}_{t}=\tanh(W_{cfo}fv_{t}+W_{ch}h_{t-1}+b_{c}) \tag{5}\]  
The information stored in the memory unit is updated as follows:  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\overline{c}_{t} \tag{6}\]  
Then the output of the LSTM cell is expressed as:  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{7}\]  
where \(\bigcirc\) means element-wise product.  
After all the feature vectors complete the above process, we will use the final output \(h_{t}\) as the time dependencies learned by the LSTM cell. The time dependences learned by the three LSTMs are denoted by \(D_{1}\), \(D_{2}\) and \(D_{3}\), which are all one-dimensional vectors.  
Figure 4: The explanation of map to sequence layer.
# Glass Ceramic with Improved Structure and Properties Made with Wastes from FeNi Production
## 4 Results and Discussion
### Characteristics of the Final Glass-Ceramic  
The main characteristics of the new material were evaluated by different techniques [21]. Here these results are summarized in Table 5, together with the corresponding data for Slagsitalls [24; 25], which up to now have been the most-produced glass-ceramics in the world. Slagsitalls were developed in the former Soviet Union by Prof. [PERSON] only few years after the discovery of glass-ceramics by Prof. [PERSON] in \"Corning\", New York, in the USA. Their production was mainly based on the use of blast furnace slag from the region of Donbas, Ukraine. In 1966 in the plants of Avtosteklo (Konstantinovka), a manufacturing line with daily capacity of 3000 m\({}^{2}\) was started, and by 1980 the entire volume of produced Slagsitall tiles surpassed 15,000,000 m\({}^{2}\).  
The melting temperatures of the Slagsitalls batches, as well as those of various similar glass-ceramics, were at about 1500-1550 \({}^{\circ}\)C, while the nucleation and crystallization temperatures were at about 600-650 and 900-950 \({}^{\circ}\)C, respectively; the duration of the crystallization cycle was at about 3-4 h.  
Notwithstanding the significantly lower melting and crystallization temperatures, as well as the shorter holding times for melting, nucleation and crystallization, the newly obtained iron-oxide-rich glass-ceramic is characterized by enhanced hardness and bending strength, both of which can be explained by the nano-structure of the material. At the same time, the values for compressive strength and Young's modulus, which are high enough for a modern building material, are lower than the ones reported for the Slagsitalls. This is a consequence of the non-homogeneous structure of new iron-rich materials due to the precipitation of \"preliminary\" spinel. Finally, due to the formation of crystal phases as iron rich pyroxenes and spinel, the density of our glass-ceramic is slightly higher, while the coefficient of thermal expansion is moderate, even a little lower than the one reported for the Slagsitalls.  
Finally, the TCLP tests of the parent glass and the final glass-ceramics, reported in Table 2, demonstrate that the new material is completely inert. In fact, the measured concentrations of heavy metals are two to four orders of magnitude lower than those of the corresponding values for the initial residues.  
Figure 10: BF TEM of single P2 crystal (**a**) and HRTEM from the area in the circle with the corresponding SAED as inset (**b**).