esa-sceva/satcom-chunk-collection huggingface dataset:
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 3 Experiments
### Experimental Setups  
All experiments were conducted on a machine with an NVIDIA GeForce RTX 4090 GPU and 24 GB of memory, utilizing Python 3.9.7. The embedding dimensions were set as \(d_{sp}=24\), \(d_{t}=24\), and \(d_{f}=24\). The depth of the spatial-temporal attention layers, L, was 6. Both the input and output time steps corresponded to 1 h, i.e., \(T=T^{\prime}=12\). The number of heads for both the spatial and temporal self-attention modules was 4. The learning rate was 0.001, and the batch size was 16. [PERSON] was chosen as the optimizer. Based on the performance on the validation set, the optimal model was determined. If the validation loss converged over a span of 20 continuous steps, an early-stop mechanism was implemented.  
We utilized three metrics to evaluate the predictive accuracy of the model during the experiments: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). We also excluded missing values when calculating these metrics.  
MAE is defined as follows:  
\[MAE=\frac{1}{N}{\sum\limits_{i=1}^{N}}{\left|{y_{i}-\hat{y}_{i}}\right|} \tag{21}\]  
RMSE is defined as follows:  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}}{\left({y_{i}-\hat{y}_{i}}\right) ^{2}}} \tag{22}\]  
MAPE is defined as follows:  
\[MAPE=\frac{1}{N}{\sum\limits_{i=1}^{N}}{\left|{\frac{{y_{i}-\hat{y}_{i}}}{{y_{ i}}}}\right|}\times 100\% \tag{23}\]  
where \(y_{i}\) is the true value and \(\hat{y}_{i}\) is the prediction result.
## 3 Methodology
### Optimal Control
#### 3.3.3 Linear Quadratic Gaussian (LQG)  
While the Luenberger Observer works well in instances where all states cannot be measured, those states that are measured must be noiseless if an acceptable response is to be achieved. However, this is not usually the case, as even expensive sensors can inherit some measurement noise, many dynamic systems also suffer from process noise from high frequency actuators (such as motors used to spin RUAV rotors). The Linear Quadratic Gaussian controller provides the same benefits of a Luenberger Observer, but with the added advantage of improved noise attenuation. The controller architecture is shown in Figure 8 below [21].  
The input \(u\) to the system is the same as for Full State Feedback discussed in Section 3.3.1, but the Kalman-estimated \(\hat{x}\) is used instead of all measured states. The input \(u\) and output \(y\) are then fed back into the Kalman filter to reject Gaussian noise.  
The LQG controller is tuned similarly to the LQR with Luenberger Observer, the Feedback gain \(K\) is tuned in the same way. The Kalman filter is tuned slightly differently, instead of a \(Q\) matrix, there is a Vd (process covariance matrix) matrix, the same dimensions of as the \(Q\) matrix. Tuning Vd requires a knowledge of where the noise is in the system, where each diagonal element in the matrix corresponds to the location of the noise in the system (e.g., Vd (1, 1) corresponds to noise in State (1). The Vn (sensor covariance matrix) matrix, similar to the \(R\) matrix in LQR controller, sets the control effort. If the value is set low, the transient response settles very quickly, but with noise in at steady state. The noise disturbance to the system is assumed to be zero-mean Gaussian. Figure 9 shows how  
Figure 8: Linear Quadratic Gaussian.  
Figure 7: Full State Compensator.  
isolating and increasing the values in the Vd matrix the shape of the transient response changes. At low values, the response is very slow but rejects all noise, whereas Vd at higher values produces a much faster response but is noisy. Comparatively, if Vn is set high, the response is slower, but more noise is rejected from the system. Tuning the parameters of Vn and Vd is a trade-off between aggressiveness of signal estimation and noise attenuation, this tuning process can be difficult for multivariable systems with many states.  
The Kalman gain \(K_{f}\), as seen in Figure 8, can be obtained using the same process as used in Sections 3.3.1 and 3.3.2. The \(Q\) and \(R\) matrices are swapped for the Vd and Vn matrices while the A and B matrices are swapped for A transposed and C transposed.
## 4 Discussion
### Application of Classified Grouting in Engineering  
In many deep coal mines in China, one type of seepage water that is difficult to plug by grouting is the water exuding through the pores of sandstone in deep-mine shafts. At present, there are few experimental and theoretical studies on the pore structure and permeability mechanism of sandstone, which leads to the randomness of grouting parameters and grouting effects and seriously affects the acceptance and long-term stability of the shaft. Based on the study of sandstone pore structure and its influencing factors of permeability, this paper presents a new method for the classification and evaluation of sandstone pore permeability from the perspective of grouting, which is helpful to correctly select grouting methods and grouting parameters and improve the grouting effect.  
For instance, in the Southwest Shandong coalfield, the three most widely distributed layers of sandstone aquifers are the Permian Shanxi Formation sandstone (P\({}_{1}\)s), Permian Shihezi Formation sandstone (P\({}_{1-2}\)sh) and Jurassic sandstone (J\({}_{3}\)k\({}_{1}\)s), which are often seen in the shafts and roadways of deep coal mines and have an important impact on coal mining. Therefore, the classified grouting method was first applied in deep coal mines such as the Tangkou, Longyun, and Anju coal mines in the Southwest Shandong Coalfield. For example, in the process of plugging the pore water seepage of Jurassic sandstone and Shanxi Formation sandstone in the main and air shaft of the Tangkou Coal Mine and the pore seepage of the Shihezi Formation sandstone in the shaft bottom of the Longyun Coal Mine, engineers take samples of sandstone to determine the type of sandstone based on its permeability and plan the appropriate grouting scheme. Suspension slurry grouting is mainly used for the subcategory I\({}_{1}\) sandstone and chemical grouting is used for subcategory I\({}_{2}\) sandstone and subcategory II\({}_{1}\) sandstone. Different types of sandstone are plugged with different types of slurries and grouting parameters, which have received good grouting results: increased groutability, greatly reduced permeability after grouting and good economic benefits.
## 4 New Routing Protocol for MWSNs
### Overview of New Protocol  
Based on our efficient routing strategy, a new centralized discrete PSO routing protocol is presented to solve the problem of routing in MWSNs.  
The new protocol consists of two phases, the various steps are depicted in the flowchart as shown in Figure 3. When a mobile sink moves in the sense area, the routing tree from nearest source nodes towards it will be established in phase 1. When a mobile sink moves away its agent, or its relay node fails, its original routing path is recovered in phase 2. The detailed description can be seen in Section 4.1.  
Figure 3: Flowchart of new routing protocol.  
It can be seen from Figure 3 that the GMDSPO is the core algorithm of our new protocol, which is used to build the optimal tree from the nearest source nodes to the mobile sink in both phase 1 and phase 2.
# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 1 Introduction
The deep learning models within Amazon Rekognition are continuously updated, ensuring that the system benefits from the latest advancements in object detection research. Amazon Rekognition's real-time processing capabilities enable swift analysis of images, facilitating rapid decision-making in surveillance scenarios. The system can promptly identify and report detected objects, aiding in timely response and intervention. To align with the latest developments in image processing for object detection, this research incorporates insights from recent advancements in deep learning models, transfer learning techniques, and optimization strategies. The integration of Amazon Rekognition represents a practical application of cutting-edge technology in the context of aerial surveillance [14]. The significance of this research extends beyond the optimization of aerial surveillance operations. The integration of Delaunay triangulation with multiple-UAV systems provides the potential to enhance object detection accuracy in various domains of interest, including security, emergency response, and environmental monitoring. In practical scenarios, constraints in aerial surveillance operations can manifest in various forms, significantly impacting the effectiveness and scope of surveillance activities. These constraints can include challenges related to limited coverage, especially in vast or intricate terrains, restricting the ability to achieve comprehensive surveillance and detect objects or events. Additionally, constraints tied to the payload capacity of UAVs can impact the deployment of necessary sensors and equipment. Operational limitations such as battery life, range, and adverse weather conditions pose challenges, affecting the duration, distance, and effectiveness of surveillance efforts. Regulatory constraints pertaining to airspace usage, flight altitude, and privacy considerations, as well as the need for efficient communication and coordination among multiple UAVs, further contribute to the complex landscape of constraints encountered in practice. Understanding and addressing these constraints is essential for designing and implementing effective aerial surveillance systems. By providing enhanced surveillance capabilities in unconstrained scenarios, this approach contributes to the development of more efficient and effective surveillance strategies.
# Towards Automatic License Plate Detection
## 4 Results and Discussion
### Image Variations Analysis  
To validate the robustness of our developed LPD algorithm, in Figure 5a we show the license plate detection accuracy for five important variations, which are **(i)** image resolution, **(ii)** illumination variation, **(iii)** vehicle shapes, (**iv**) license plate angle, and (**v**) license plate color. For each of the variations, as shown in Figure 5a, there are two towers. The first tower depicts the standard conditions as provided by the PKU dataset, while the second tower shows the performance of the proposed algorithm when the relevant parameter is varied. From Figure 5a, a few important observations are in order:  
* We examine the five different image resolutions, which are 1600 \(\times\) 1200, 1082 \(\times\) 728, 800 \(\times\) 600, 400 \(\times\) 300, and 320 \(\times\) 240 pixels image resolutions. As shown in first pair of towers in Figure 5a, the detection accuracy of the proposed algorithm has barely any effect on performance with the variation in image resolution. In our simulations, we observe that as the image resolution is decreased from 300 \(\times\) 240 pixels, the detection performance of the proposed algorithm slightly starts to degrade.
* As shown in the second pair of towers in Figure 5a, the detection accuracy of the proposed algorithm has a minor effect with the variations in illumination conditions. In the PKU dataset's illumination variations, our proposed algorithm yields 97.30% detection accuracy. However, when we manually varied the contrast of the input images, the accuracy of the proposed algorithm dropped down to 82.78%.
* Our detailed analysis for different shapes of the vehicles outside the PKU dataset reveals that the vehicle detection module is also robust and virtually detects every available vehicle shape.
* In the PKU dataset, the provided license plates have yellow, blue, and white colors. We also tested the performance of our developed algorithm for green and light/dark blue colors. As shown in the fifth pair in Figure 5a, license plate colors variation have the least effect on the detection accuracy of the proposed algorithm. Similarly, the license plate rotation or angle variation has a minor effect on the performance of proposed algorithm. In all of the previously described cases, the license plate detection accuracy is over 95% for the parameters outside the PKU dataset.
## 3 Results and Discussion
### tRNA Aminoacylation for Protein Translation  
Regarding tRNA aminoacylation for protein translation, more specific proteins were identified in the P29 strain than in the G1 strain: 16 proteins _versus_ 3 proteins, respectively (Table 1, Figure 1). The formation of aminoacyl-tRNA (aa-tRNA) is an essential process in protein biosynthesis. Proteins of the aminoacyl-tRNA synthetase family can be classified into two groups, depending on the specificity of amino acids: (i) class I (specific for glutamine, glutamate, arginine, cysteine, methionine, valine, isooleucine, leucine, tyrosine, and tryptophan); (ii) class II (specific for glycine, alanine, proline, serine, threonine, histidine, aspartate, asparagine, lysine, and phenylalanine) [28; 29; 30; 31]. At the middle of the second fermentation in the P29 strain, 9 proteins were identified (Aim10p, Cdc60p, Ded81p, Dia4p, Dps1p, Msk1p, Slm5p, Ths1p, and Wrs1p), and at the end of the second fermentation, 7 proteins (Arc1p, Grs1p, Gus1p, Ism1p, Msk1p, Ses1p, and Vas1p) were identified. These proteins are responsible for binding amino acids such as tryptophan (Wrs1p), threonine (Ths1p), asparagine (Ded81p, Slm5p), lysine (Msk1p), aspartate (Dps1p), serine (Dia4p, Ses1p), leucine (Cdc60p), methionine (Arc1p), glutamate (Arc1p, Gus1p), glycine (Grs1p), isoleucine (Ism1p), and valine (Vas1p). These results suggest that yeast may be activating its transcription and translation machinery to try to cope with cell death and autolysis, and they corroborate those previously obtained by our research group [30].  
On the other hand, the specific proteins identified in the G1 strain were Gln4p and Nam2p, which catalyze the binding of glutamine and leucine, respectively, the nuclear protein Pet112p, required to maintain rho+ mitochondrial DNA [31], and Msm1p; this protein is a mitochondrial methionyl-tRNA synthetase. These results seem to indicate that the protein synthesis machinery in flor yeast (strain G1) is not working, though yeast autolysis may be occurring, which would help the survival of the rest of the population in this harsh condition [32].  
Figure 1: Total number of specific proteins related to amino acid metabolism in _S. cerevisiae_ P29 (blue colour) and G1 (orange colour) during two sampling times: at the middle of the second fermentation, MF (**A**), and at the end of the second fermentation, EF (**B**).
## 4 Results and Discussion
### Blocking Voltage Test  
The relationships between the blocking voltages across the switches and the selected magnitudes of the DC sources were initially studied from simulation analysis. To verify the blocking voltage relationships in Section 2, the voltage across each switch was measured experimentally. With the VDC value of 40.66 V, as given in Table 6, the measured voltages across all switches are shown in Figure 16, where it can be concluded that all the measured maximum blocking voltages agree with the theoretical relationships given in Equations (1)-(5). The highest measured voltages were found across switches S\({}_{5}\) and S\({}_{6}\) with a value of \(\sim\)325.28 V, which is equivalent to 8 VDC. On the other hand, the blocking voltages across switches S\({}_{7}\) and S\({}_{8}\) were the lowest. They shared the same value of \(\sim\)40.66 V, which was equivalent to VDC. By summing up the blocking voltages, a net value of 1626.4 V was achieved, corresponding to \(\sim\)40 VDC, representing the TSV of the inverter.
# Design and Implementation of Model Predictive Control for a Gyroscopic Inverted Pendulum
## 2 Dynamic System Modeling
### Model Verification
Since both flywheels have the same parameters, the two PID controllers of the inner loop have the same parameters, which are \(K_{P2,3}=0.07\), \(K_{I2,3}=0\), and \(K_{D2,3}=0.001\). Figure 5a compares the roll angle \(\theta\) in 3 s, with the blue solid line indicating the response of the mathematical model and the red dashed line indicating the response of the ADAMS model. The maximum difference between these two models is \(0.002^{\circ}\). In Figure 5b,c the same notations are used to describe the comparison of gimbal angles. In this figure, both models exhibit the same responses for gimbal angles \(\alpha_{1}\) and \(\alpha_{2}\). The difference in the roll angle \(\theta\) is caused by numerical tolerances of different solvers in Matlab and ADAMS. When the inverted pendulum oscillates, the numerical errors are accumulated after each integration and the maximum value after 3 s of simulation is \(0.002^{\circ}\). A comparison of the results shows that the proposed mathematical model (17) models the system exactly and can be used with an embedded hardware to simulate the real-time dynamic behaviours of the system. Figure 4: PID control scheme. PID: proportional-integral-derivative. Figure 5: Comparison of th mathematical model and the ADAMS model: (**a**) roll angle; (**b**) gimbal angle 1; (**c**) gimbal angle 2.
## 2 Materials and Methods
### Numerical Setup  
#### 2.4.1 Fluid Model  
Since there was a wide variety of geometric sizes, we used one with a total volume that was similar to the average volume. RG-2 was selected for the mesh tests for both domains. The fluid mesh test was based on the mesh density, the ratio between the total number of elements, and the geometry's total volume. It was performed using computational fluid dynamics simulations in Ansys Fluent to solve the equations associated with the fluid, using the finite volume method (FVM). Densities from 250 elements/mm\({}^{3}\) up to 3000 elements/mm\({}^{3}\) were included, using both linear and quadratic tetrahedral elements. The chosen scheme was PISO, and spatial discretization was performed using the least-squares cell based on the gradient and the second order for pressure and momentum. The transient formulation was first-order implicit with a time step of 0.0005 [s], 3600 time steps, and 200 maximum iterations per time step.  
The blood model used was employed in previous work that used geometries from the same database [31]. Blood was modeled as a laminar and incompressible Casson fluid with a density of 1065 kg/m\({}^{3}\). The boundary conditions for entry were the Womersley velocity profile, whereas for the exit, a three-element Windkessel model with pressures ranging between 80 and 120 mmHg was chosen. The total simulated time was 1.8 s, slightly more than two complete cardiac cycles, and the considered results were the area-averaged WSS at the diastole (DWSS) and systole (SWSS) from the second cardiac cycle. Simulations were performed using double precision in a machine with an AMD Ryzen 5 5600 G processor and 16 GB of RAM.  
Figure 6 shows the evolution of both parameters with respect to the mesh. We chose to use a mesh density of about 700 elements/mm\({}^{3}\) with size refinements at the aneurysm dome and at the zone in contact with the artery wall because at sizes between 500 and 1000 elements/mm\({}^{3}\), the difference was less than 5% and because two-way FSI simulations require considerable computational resources. Additionally, the differences between quadratic and linear elements are negligible; therefore, the former was chosen.
# Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph
## 3 Research Method
### Deep Neural Network Model for Registration of Temporal Subtraction Technique: ResidualVoxelMorph  
In this section, we propose ResidualVoxelMorph as a deep neural network for image registration using a temporal subtraction technique. Figure 4 shows the network model of ResidualVoxelMorph. The parametrization of registration field generation is based on a neural network architecture that introduces residual blocks in a network architecture such as U-net [26], which consists of encoder and decoder sections with skip connections. The residual blocks in both encoder and decoder stages consist of a 3D convolutional layer with a kernel size of three and stride of two, as shown in Figure 5, with a leaky ReLU layer with parameter 0.2 and the addition of skip connections [7]. In the encoder, the residual-block layer is used to halve the spatial dimensions of each layer. Therefore, the encoder's successive layers work with a coarser representation of the input, such as the image pyramid used in traditional image registration. The decoding stage alternates between up-sampling, the residual-block layer, and concatenating skip connections to propagate the features learned in the encoding stage directly to the layer that generates registration. Registration fields are generated by inputting a current and previous CT image patch into the residual U-net. The registration field and previous CT image patch were then input into the spatial transformer network [17] to generate the deformed image patch. The  
Figure 3: Schematic of deep neural network registration for each patch.  
Figure 2: Overall scheme for extraction method of lung field region.  
spatial transformer network used the registration field \(\varphi\) to calculate the (sub-pixel) voxel position \(p^{\prime}=p+u(p)\) in the previous CT image patch \(m\) for each voxel \(p\). Because the image values can be defined only in integer positions, the values of the eight adjacent voxels are linearly interpolated:  
\[m\varphi(p)=\sum_{q\in Z(p^{\prime})}m(q)\ \prod_{d\in\{x,y,z\}}\big{(}1-\big{|}p^{ \prime}_{d}-q_{d}\big{|}\big{)} \tag{1}\]  
where \(Z(p^{\prime})\) is the voxel neighborhood of \(p^{\prime}\), and \(d\) iterates over the dimensions of \(\Omega\). The gradients and sub-gradients can be computed such that errors can be backpropagated during optimization. This model replaces U-net, which is the registration field generation layer of the VoxelMorph [6] proposed by [PERSON], with a residual U-net. In addition, because the residual blocks can improve feature utilization, it is expected to be highly efficient even with a limited training set.
## 3 Materials and Methods
### Extracting Features from a Voice Image
#### 3.2.2 CNN Architecture for Voice Phrase Feature Extraction  
This paper proposes to combine multilayer CNNs and neural fuzzy extractors using the stacking principle. CNNs are used to generate features, which are then fed to the input of the neural fuzzy extractor. This approach improves the quality of classification by combining the strengths of both architectures: deep neural networks for image analysis and feature extraction, and neural fuzzy extractors for rapid additional training of the system on new user data and image verification while ensuring the confidentiality of biometric templates.  
To extract features, we used the autoencoder architecture, which is a deep neural network that allows for extracting informative features. It includes an encoder that compresses input data into a more compact form, which allows us to reduce the dimensionality of the feature space, and a decoder that is trained to restore them. During training, the average spectrum of the voice password is fed to the input and output of the autoencoder, which allows the network to learn to highlight key features of the voice data.  
The autoencoder is based on the VGG19 architecture [29], which was modified to handle averaged spectra. It includes one-dimensional convolutions, layers with batch normalization, and a fully connected layer with a linear activation function (Figures 2 and 3).  
Figure 1: Construction of the averaged spectrum.  
Figure 2: Encoder architecture for feature extraction.  
The training sample was the VoxCeleb2 voice dataset (200,000 short utterances that have a similar duration to voice passwords--from 1.5 to 5 s), the optimization algorithm was Adam, and the error function was binary cross-entropy.