esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Radioation Patterns  
Figures 3a-h demonstrate the simulated radiation patterns of the antennas at 3.5 GHz, 5.7 GHz, 7 GHz and 10 GHz. The radiation patterns have been plotted as plane-wise, such as elevation and azimuth planes.At 3.5 GHz, the antenna radiates omnidirectional in the XY plane, but in the XZ plane there is a null noticed in the 120 degree angle. At 5.7 GHz in the XY plane, the radiation pattern looks the same as the 3.5 GHz, but less null is noticed at the XZ plane in the 120 degree angle in comparison with 3.5 GHz. At higher frequencies such as 9 GHz, the radiation patterns in both planes look more distorted compared with lower frequency bands.
# Effect of Curing Conditions on Microstructure and Pore-Structure of Brown Coal Fly Ash Geopolymers
## 4 Results
### Microstructure  
Figure 7 shows the microstructure of the 50 mm geopolymer mortar at 8, 10, 12 & 14 h. At 8 h, the geopolymer displays a porous, less compacted structure with a number of unreacted fly ash grains present. The images at 10 and 12 h show a more compact homogeneous structure. Some unreacted fly ash grains were still observed at 10 h, but by 12 and 14 h few remained, indicating that the gelation process reached a conclusion. Micro cracking is also observed at 10, 12 and 14 h, with a significant increase in the number and size of the micro cracks at 14 h. This is attributed to the extended curing at high temperatures which has been observed to increase cracking [42].  
The changing microstructure of the 50 mm geopolymer concrete cube at 8, 10, 12 and 14 h is illustrated in Figure 8. Similar to the mortar specimens, the geopolymer concrete shows a porous less compacted structure with unreacted/partially reacted fly ash spheres at 8 h. However, with the increase in the curing time, less unreacted fly ash and more geopolymeric gel was observed. This resulted in a more compacted homogeneous aluminosilicate gel structure evident at 10, 12 and 14 h. It was also noted that a significant reduction in the number of micro cracks was observed after 14 h of curing compared to that observed after 12 h of curing. This is in contrast to the microstructure of the mortar, which showed an increase in the number of micro-cracks between 12 and 14 h of curing.  
Figure 6: The compressive strength development profile of geopolymer mortar and concrete.  
Figure 7: The microstructural development of geopolymer mortar (50 mm cube) at 8, 10, 12 and 14 h of heat curing.
## 3 Methodology
### Introducing Continuous Wavelet Transform (CWT) with Ricker Wavelet
Its mathematical definition is as follows:  
\[\text{Ricker}(t,\sigma)=(1-2\pi^{2}f^{2}t^{2})e^{-\pi^{2}f^{2}t^{2}}, \tag{1}\]  
where \(t\) is the time variable, \(f\) is the central frequency of the wavelet, and \(\sigma\) is the width parameter that determines the scale of the wavelet. The Ricker wavelet was selected based on its established success in prior works for transient signal detection (e.g., [8; 9]). Its second-derivative Gaussian shape is particularly effective at isolating high-frequency microbursts in network traffic, which align with the rapid packet exchanges characteristic of low-latency applications. The application of a wavelet transform enhances the feature representation by decomposing time-related features into distinct frequency components. This transformation allows the model to identify patterns in different frequency bands, enabling it to capture both short-term and long-term variations in network behavior. This capability is vital for accurate and robust classification of low-latency Internet traffic, which often exhibits complex, multi-scale patterns due to the nature of real-time applications. Furthermore, low-latency traffic such as video conferencing or online gaming generates frequent, small data throughput bursts that may appear as noise in conventional time-domain analysis. However, by applying wavelet transform, we can isolate and emphasize these throughput bursts, which are characteristic of low-latency traffic, thus distinguishing them from other traffic types such as FTP or video streaming, which have different temporal dynamics. For instance, the wavelet transform helps to separate high-frequency components corresponding to the rapid exchanges in low-latency traffic from the slower, more consistent patterns seen in bulk data transfers. Figures 5 and 6 illustrate this transformation for throughput and the ratio of sent to received packets, respectively, highlighting the distinct frequency components that the wavelet transform extracts from the raw time-domain features. Figure 4: Features from time domain to wavelet domain. Using these enhanced feature representations, our ANN model becomes more adept at recognizing the unique patterns associated with low-latency traffic. This addresses the core challenge in traffic classification, where the goal is to accurately identify and prioritize time-sensitive data flows among a diverse mix of network activities.
## 4 Simulation and Analysis
### Simulations
#### 4.1.4 Performance Comparison Results of the Proposed Algorithm and Other Algorithms
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline PLL bandwidth, \(B_{w,PLL}\) & \(10\) Hz \\ FLL bandwidth, \(B_{w,FIL}\) & \(20\) Hz \\ _Wn_ of carrier PLL & \(B_{w,PLL}/0.53\) \\ _Wn_ of carrier FLL & \(B_{w,FIL}/0.53\) \\ Phase discrimination equation & \(Q_{p}\cdot sign({I_{p}})/sqrt({I_{p}}^{2}+{Q_{p}}^{2})\) \\ \hline Frequency discrimination equation &
\begin{tabular}{c} \(P_{cross}\cdot sign({I_{dot}})/({I_{p}}^{2}+{Q_{p}}^{2})\) \\ where \(P_{cross}={I_{p-1}}\cdot{I_{p}}+{Q_{p-1}}\cdot{Q_{p}}\), \\ \(P_{dot}={I_{p-1}}\cdot{Q_{p}}-{Q_{p-1}}\cdot{I_{p}}\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The specific parameters and corresponding discriminator algorithms of second-order FLL assisted third-order PLL. Figure 10: Comparison results of frequency estimation errors by three algorithms under different SNR.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 2 Status of Research
### Camera Parameter Constraints
#### 2.1.2 Performance Analysis  
As shown in Table 1, because most poses of the Human3.6 dataset are standing poses and motions, e.g., sit or sit down, contain more ambiguities and occlusions, it provides sufficient proof of the disambiguating effectiveness in [PERSON] et al. [14]. Although it does not use any ground truth information in the estimation process, it still achieves low errors, especially for ambiguous motions such as photo and walk together. In contrast, the disambiguating effects are limited in [PERSON] et al. [12] and [PERSON] et al. [13], which use the weak-perspective model. For a reasonable comparison with other supervised methods, the unsupervised method of [PERSON] et al. [16] is compared with 2D groud-truth annotations and even outperforms some supervised methods.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 1 Introduction
To address the aforementioned challenges, we propose **DK-SLAM**, a novel **de**ep **k**eppoint-based **SLAM** system that incorporates deep keypoint extraction through meta-learning, coarse-to-fine feature tracking, and an efficient online learning-based loop closing. Our DK-SLAM employs an enhanced Model-Agnostic Meta-Learning (MAML) strategy to extract robust deep local features, improving the system's ability to perform feature extraction in diverse and dynamic environments. This meta-learning strategy enables our DK-SLAM system to adapt to diverse conditions without the need for additional training during deployment, thereby enhancing its applicability in real-world scenarios. Additionally, we introduce a novel feature matching strategy tailored for deep learned keypoints. This strategy involves a two-stage process: initial estimation of relative poses using patch photometric loss optimization, followed by refinement through 3D-2D relationships, result ing in improved matching accuracy and robustness. Furthermore, to enable a more effective and efficient loop closing, we propose an online learning-based Bag-of-Words (BoW) model. Our model compresses learned features into binary descriptors and utilizes images from previous timesteps within an online learning framework. It constructs a dynamic tree structure for Bag-of-Words, facilitating loop closure detection in new environments. Experimental evaluation conducted on public car-driving and drone datasets demonstrates the superior performance of our proposed DK-SLAM compared with representative traditional visual SLAM such as ORB-SLAM3 and LDSO, as well as learning-based systems such as LIFT-SLAM. Notably, compared with the representative monocular ORB-SLAM3, DK-SLAM achieves a translation accuracy improvement of approximately 17.7% and a rotation accuracy enhancement of 24.2% on the KITTI dataset [15], and it surpasses ORB-SLAM3 by approximately 34.2% in translation accuracy on the EuRoC dataset [16]. Our contributions can be summarized as follows:  
* We propose DK-SLAM, a novel monocular visual SLAM system with deep keypoint meta-learning. Our deep feature extractor, trained using a MAML strategy, enhances adaptability to diverse scenes. * We develop a coarse-to-fine two-stage keypoint matching strategy that estimates relative poses through patch photometric loss optimization and refines them based on the 3D-2D relationship, resulting in improved accuracy. * We introduce an online learning-based Bag-of-Words (BoW) model that effectively and efficiently utilizes binarized deep keypoints. This model dynamically fine-tunes itself to ensure accurate loop detection in the long-term operation. The reminder of this paper is organized as follows: Section 2 contains a survey of related work; Section 3 introduces our proposed deep keypoint-based monocular SLAM framework; Section 4 evaluates DK-SLAM in both car-driving and drone scenarios, and conducts extensive ablation studies; and Section 6 finally draws conclusions.
## 3 Lower-Level Controller
### Driving Controller
Consequently, the input layer contains the current vehicle speed, desired acceleration, and load weight. The output layer is the control command (engine throttle opening). The current speed is obtained by Global Positioning System (GPS). The desired acceleration is calculated from the current and desired speeds obtained from NMPC, as shown in Equation (24).  
\[a=\frac{v_{\text{desired}}-v_{\text{current}}}{T} \tag{24}\]  
The load weight is calculated by the inertial measurement unit (IMU) data mounted on the vehicle's rear body. The hidden layer uses five neurons, according to [28; 29].  
To obtain a sufficient neural network training dataset, the vehicle speed and acceleration are collected under different load cases with fixed and variable throttle input, as shown in Figure 6. The detailed conditions are as follows:  
* Load: no-load (0 t), half-load (3.5 t), full-load (7 t).
* Throttle commands: 1. Fixed: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%; 2. Varying (5 seconds step): 25% \(\rightarrow\) 50% \(\rightarrow\) 75% \(\rightarrow\) 100%, 30% \(\rightarrow\) 60% \(\rightarrow\) 90%.  
Data are collected five times for each condition. Thus, there are 180 sets of data.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 4 Deep Learning-Based Fault Prediction
### Model Selection  
Three deep learning architectures were evaluated to determine the most suitable model for fault prediction in dynamic production environments: 1D-CNN, LSTM [22], and GRU [23]. The evaluation focused on metrics such as the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), training time, and number of epochs.  
* 1D-CNN: Known for identifying local temporal patterns, 1D-CNNs offer faster training times due to parallel processing, making them suitable for edge environments. Additionally, 1D-CNN models require fewer parameters compared to LSTM or GRU, resulting in lower memory usage and computational overhead. Their simpler architecture enhances interpretability, making it easier to analyze how different sensor inputs contribute to fault prediction.
* LSTM: Although they are effective at capturing long-term dependencies, LSTM models are computationally intensive, requiring extended training times. While they often achieve superior accuracy, their high latency makes them less suitable for real-time applications in edge environments.
* GRU: While GRUs combine efficiency with sequential pattern recognition, they may not fully capture temporal complexities compared to LSTMs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Timestamp** & **Tool\_Wear** & **Temperature** & **Vibration** & **Operating\_Hours** & **Fault\_Percentage** \\ \hline count & 1440 & 1440.000000 & 1440.000000 & 1440.000000 & 1440.000000 & 1440.000000 \\ mean & 2024-07-01 11.59:30 & 0.610982 & 28.146094 & 0.025734 & 1.508333 & 72.903272 \\
25\% & 2024-07-01 05:59:45 & 0.538060 & 23.881144 & 0.016644 & 0.762500 & 64.777758 \\ max & 2024-07-01 23:59:00 & 1.000000 & 37.880941 & 0.053650 & 3.000000 & 100.00000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data description for Machine A.  
Figure 3: Plot of generated data of Machine A (temperature, vibration, tool wear over time).  
As shown in Figure 4 and summarized in Table 3, although the 1D-CNN exhibited slightly higher RMSE and MAE results than the LSTM and GRU, it provided the best tradeoff between accuracy and efficiency. The significantly reduced training time and computational overhead make it a viable choice for real-time deployment in edge computing environments where rapid inference and low resource consumption are critical.
## 3 Proposed Real-Time 3D Mapping-Aided (3 DMA) GNSS-Positioning System
### 3 DMA GNSS Positioning Algorithm
#### 3.3.1 Skymask Context-Based Candidates Sampling
A semi-major axis of 50 m can cover the position error of the initial position in most cases. In comparison, separation with 4 m can reduce the number of distributed candidates while maintaining an acceptable accuracy level. The proposed distribution can effectively distribute the hypothesis position candidates based on the surrounding environment. Figure 3 shows two typical cases in urban canyons. In road intersections, two eigenvalues are nearly the same (Figure 3b), such that the candidates' distribution is almost a circle that covers the whole intersection, as shown in Figure 3a. In contrast, when the initial location is in a straight street, the largest eigenvalue is much larger than the other (Figure 3d). The candidates are most likely distributed on the same street but not the next block. The prevention of candidate distribution at the next block can potentially mitigate the local minima issue caused by the high similarity of building geometry, as shown in Figure 4. Local minima are located on the next street (the high score part in red near the upper right corner). After applying the proposed skymask context-based candidates sampling strategy, the local minima issue can be mitigated. To conclude, the skymask context-based candidates sampling can effectively distribute the position candidates. It has two main advantages. The first is to reduce the computational load by reducing the number of distributed candidates based on the surrounding environment. The second advantage is that candidates are most likely distributed on the same street. Therefore, the local minima on the next street can potentially be excluded. Figure 4: Example of skymask context\(-\)based candidates sampling. Figure 3: Typical cases of candidate sampling in urban environments on intersection (**a,b**) and straight street (**c,d**). Note that the eigenvectors (red and blue lines in (**b,d**) are projected back to azimuth and elevation angle (local frame) for illustration here.
# An Analytical Design of Simplified Decoupling Smith Predictors for Multivariable Processes
## 2 Materials and Methods
### System Performance and Robustness Analysis
#### 2.5.3 Robust Stability Analysis  
The robustness of a control system is one of the most important issues in any controller design. The dynamics of real plants usually have many sources of uncertainty, which cause poor performance or even instability in the control systems. In this paper, the robust stability was studied by considering the multiplicative output uncertainty in each loop of the diagonal element of the decoupled matrix, as shown in Figure 6. It can be expressed as follows:  
\[\mathbf{\Pi}_{o}:\ \mathbf{Q}_{p}(s)=[\mathbf{I}+\mathbf{E}_{o}(s)]\mathbf{Q}(s) \tag{51}\]  
where \(\mathbf{E}_{o}(s)=\mathbf{\Delta}(s)\mathbf{W}_{o}(s)\), and \(\mathbf{\Delta}\) denotes a normalized perturbation with \(\mathrm{H}_{\infty}\) norm less than 1 (\(\overline{\sigma}(\Delta_{i}(j\omega))\leq 1\ \forall\omega,i\)); \(\mathbf{Q}(s)=\mathbf{G}(s)\mathbf{D}(s)\) is the nominal decoupled plant model; \(\mathbf{\Pi}_{o}\) denotes the set of possible perturbed process models; and \(\mathbf{Q}_{p}(s)\) is the diagonal decoupled transfer, which is considered as the function of the perturbation of its nominal model \(\mathbf{Q}(s)\) due to uncertainty in the multiplicative output \(\mathbf{E}_{o}(s)\).  
The \(\mathbf{M}-\mathbf{\Delta}\) structure of the DSP structure is shown in Figure 7 where the transfer function matrix from the outputs to the inputs of \(\mathbf{\Delta}(s)\) can be determined by  
\[\mathbf{M}(s)=-\mathbf{W}_{o}(s)\mathbf{Q}(s)\mathbf{G}_{c}(s)[\mathbf{I}+ \mathbf{Q}_{o}(s)\mathbf{G}_{c}(s)]^{-1}. \tag{52}\]  
According to the \(\mu-\) synthesis, the control system will remain stable under multiplicative output uncertainty if the following constraint inequality is satisfied:  
\[\mu[\mathbf{M}(j\omega)]=\mu\big{\{}\mathbf{W}_{o}j\omega)\mathbf{Q}(j\omega) \mathbf{G}_{c}(j\omega)[\mathbf{I}+\mathbf{Q}_{o}(j\omega)\mathbf{G}_{c}(j \omega)]^{-1}\big{\}}<1,\forall\omega. \tag{53}\]
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis
### Experiments on CWRU Datasets  
#### 4.1.1 Experimental Setup and Datasets  
Our datasets were constructed on CWRU bearing datasets [32] to apply the proposed method. As shown in Figure 2, the experimental test rig included an electric motor (left), a torque transducer/encoder (center), a dynamometer (right), and a control circuitry (not shown). The deep groove ball bearings of the type 6205-2 RS JEM SKF at the drive end (DE) was used for vibration signal collection. The vibration signals were collected by accelerometers at 4 different motor speeds of 1797 rpm, 1772 rpm, 1750 rpm and 1730 rpm, where the sampling frequencies were 12 kHz and 48 kHz. The faults were set by an electric discharge machine with diameters of 0.007 inches, 0.014 inches, 0.021 inches and 0.028 inches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Features in the Time Domain**} & \multicolumn{2}{c}{**Features in the Frequency Domain**} \\ \hline \(f_{0}=\frac{\sum_{i=1}^{N}x(n)}{N}\) & \(f_{6}=\frac{\sum_{i=1}^{N}(x(n)-f_{i})^{4}}{(N-1)f_{i}^{2}}\) & \(f_{12}=\frac{\sum_{i=1}^{N}s(k)}{k  
There were 4 bearing categories, including 3 fault categories and 1 normal category. One hundred fifty samples were acquired in each category and each sample contains 1024 continuous data points. Two same datasets were constructed at the motor speeds of 1750 rpm and 1772 rpm. Table 2 shows the details of the datasets. The training process and the test process were carried out at the same speed. The training set included 420 samples and the testing set included 180 samples.
## 2 Materials and Methods
### Point Cloud Data Collection and Generation  
#### 2.2.1 TLS-Based Point Cloud  
The application of terrestrial laser scanning (TLS) technology in the agricultural field has a robust history with the capability to capture accurate three-dimensional models of plants [73; 74]. Increasingly, it has been used for measuring plant canopy structure [75],plant height [76], biomass [77], leaf area index [78], and vertical leaf area profile [79]. The present study implemented a terrestrial laser scanner, FARO FocusS70 (Figure 1a,b). The FARO FocusS70 is a short-range instrument that emits laser pulses at a wavelength of 1550 nm within a maximum field of view of 360\({}^{\circ}\)\(\times\) 300\({}^{\circ}\). Its maximum measurement range is 70 m with a ranging error of 1 mm within the 10-25 m range. Scanning was carried out during weather conditions with wind speeds below 5 m/s. The point clouds for three plant species, rapeseed and maize at two growth points, and cotton at a mature stage, were collected using TLS technology, as depicted in Figure 1.  
The data collection scenario was as follows: The scanning device was mounted on a tripod approximately 1.8 m above ground level and one scan was carried out every 8 m, about 1.5 m away from the nearest plant, resulting in 15 TLS scans in total. Under potted conditions, five scans were conducted at matching height and distance for rapeseed (from the four corners and the center of the planting area, Figure 1c). During the scan, the ranges of vertical and horizontal scanning angles of the scanner were \(-\)60\({}^{\circ}\)-90\({}^{\circ}\) and 0\({}^{\circ}\)-360\({}^{\circ}\), respectively. Owing to minor wind influence during our measurements, some plant replicas exhibited subtle shaking and shadowing. However, the overall plant morphology was not compromised. Each scanning required approximately 11 min to complete.  
Owing to differing coordinate systems of the return TLS data obtained from the four positions, six target spheres were positioned at fixed locations. The FARO SCENE (version 2022.2.0.10355) software was used to collect and preprocess the TLS data, including denoising and point cloud registration. Subsequent steps included further partitioning, individual plant segmentation, and semantic annotation of the point cloud data, as exhibited in Figure 1d.
## 5 Experimental Setup and Performance Metrics  
ASC-RL was implemented in a particular setting using the PPO algorithm [48] and various metric values. The system was established with the right operating methods, principles, and procedures. In Table 3, different parameters, network topology, sensor count, and data generation processes are listed.  
Python was used in the development of ASC-RL, using the basic concept of the PPO algorithm for reinforcement learning. It is useful because it supports the adaptive scheduling solution for IoT cognitive sensors. The \(Gym\) library was used to model the IoT scheduling environment, and \(PyTorch\) and \(stable-baselines3\) were used for different reinforcement learning modules. The \(NumPy\) and \(pandas\) libraries have support for numerical calculations, and we used them for sensor data handling, and \(matplotlib\) was used to visualize performance.  
\begin{table}
\begin{tabular}{l c l} \hline \hline
**Parameter** & **Symbol** & **Metric Value** \\ \hline RL-Algorithm & \(AIRL\) & PPO “(Proximal Policy Optimization)” \\ Learning Rate & \(\tau\) & 0.0003/0.0004 \\ Discount Factor & \(\lambda\) & 0.998/0.989 \\ Clip Range & \(\delta\) & 0.2/0.3 \\ Epochs & \(E_{phacs}\) & 10 \(E_{phacs}\) \\ No. of Sensors & \(N_{stn}\) & 4–6, 10–16 \\ Network & \(NT\) & IoT \\ Dimension & \(|S|\) & 64–128 \\ Data Generation Pattern & \(D_{PD}\) & Poisson distribution (\(\lambda\) = 2–5 packets/s) \\ Sensor Dynamics & – & static \\ Communication Topology & \(N_{T}\) & clustered \\ Transmission Range & \(R\) & 100–250 m \\ Simulation Episodes & \(E_{s/e}\) & 5000–10,000 \\ Framework & \(FW\) & OpenAI Gym + PyTorch \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters, symbols, and metric values.