esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Fault Diagnosis Methodology
### The Proposed S-ECNN Model
#### 3.3.3 Distance Layer and Output Layer  
Two 1D-CNNs are merged in the distance layer. The distance between two discriminative features is calculated based on the pairwise Euclidean distance. Suppose \((X_{j}^{1},X_{j}^{2},y_{j})\), \((j=1,2,\ldots,M)\) be a pair of inputs that is randomly selected from the collection \(\mathbf{\Lambda}\), where \(y_{j}=0\) if two inputs belong to the same fault category, otherwise, \(y_{j}=1\). \(M\) is the total number of input pairs. The discriminative feature vectors of each input are expressed as \(\mathbf{h}_{j}^{2}\) and \(\mathbf{h}_{j}^{2}\), respectively. The Euclidean distance can be denoted by:  
\[D_{j}=\left|\mathbf{h}_{j}^{1}-\mathbf{h}_{j}^{2}\right|. \tag{6}\]  
After that, a fully connected layer with a sigmoid activation function is followed. The neurons in the fully connected layers are dropped out with a probability of 0.3. This neuron computes the prediction of the input pair as:  
\[p(X_{j}^{1},X_{j}^{2})=\sigma(\sum_{j}\alpha_{j}\left|\mathbf{h}_{j}^{1}-\mathbf{h}_{ j}^{2}\right|), \tag{7}\]  
where \(\sigma(\cdot)\) is the sigmoid non-linearity function, and \(\alpha_{j}\) is a learnable parameter representing the importance of \(D_{j}\). The output \(p(X_{j}^{1},X_{j}^{2})\) is between zero and one, which scores the probability of the label. At the same time, the output \(p(X_{j}^{1},X_{j}^{2})\) is the normalized representation of the difference between inputs. The normalized representation is transformed by the sigmoid non-linearity function mentioned above. The value of the output \(p(X_{j}^{1},X_{j}^{2})\) is between zero and one. The bigger the value of \(p(X_{j}^{1},X_{j}^{2})\) is, the bigger the difference is. Let \(S(X_{j}^{1},X_{j}^{2})\) be the similarity between two inputs. Thus, it is calculated as:  
\[S(X_{j}^{1},X_{j}^{2})=1-p(X_{j}^{1},X_{j}^{2}). \tag{8}\]  
The value of the similarity is also between zero and one. The bigger the value of \(S(X_{j}^{1},X_{j}^{2})\) is, the bigger the similarity is.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers** & **Filters** & **Stride** & **Output Size** & **Layers** \\ \hline Convolutional-ReLU & 128 filters size of \(3\times 100\) & 1 & \(128\times 30\times 1\) & Convolutional-ReLU \\ Max-Pooling & 3 & 3 & \(128\times 10\times 1\) & Max-Pooling \\ Convolutional-ReLU & 32 filters size of 3 & 1 & \(32\times 10\times 1\) & Convolutional-ReLU \\ Max-Pooling & 3 & 3 & \(32\times 4\times 1\) & Max-Pooling \\ Flatten layer & - & - & \(128\times 1\) & Flatten layer \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structure of the 1D-CNN.  
The binary cross-entropy function is used as the loss function. It aims to minimize the distance between samples of the same category while maximizing the distance between samples of different categories. The loss function has the following form:  
\[Loss=\sum_{j=1}^{M}y_{j}\log(p(X_{j}^{1},X_{j}^{2}))+(1-y_{j})\log(1-p(X_{j}^{1},X_{j}^{2})). \tag{9}\]
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 5 Ongoing R&D
### Putting an IoT Gateway in Space
#### 5.4.1 On-Board NOMA Demodulator Implementation
Firstly, the E-SSA software is enriched by self-monitoring functions to detect memory corruptions. Such functions may combine checks-sum verification with a keep-alive mechanism that is triggered periodically by the OS. Secondly, multiple redundant boards, equipped with the same software and fed by the same input, can be installed as the actual on-board payload. The boards will either manage one another to guarantee that at least one is performing correctly, as proposed in [33], or rely on an external controller that is able to reboot a board in some conditions. Since the NVIDIA Jetson is equipped with GPIOs (general purpose inputs/outputs), the controller could be a simple hardware watchdog circuit with relays (clearly, the controller itself must be defined to be fault-tolerant, but this task is largely affordable since its functions are extremely simple). The baseline approach has been that of an on-board processing based on the NVIDIA Jetson TX2 board or the latest NVIDIA Jetson AGX Xavier [33] board (see Figure 21) that has already been studied for CubeSat applications [34]. In particular, the NVIDIA Jetson AGX Xavier is an artificial intelligence (AI) computer for autonomous machines, delivering GPU workstation performance with an unparalleled 32 TeraOPS (TOPS) of peak computing in a compact 100 \(\times\) 87 mm module form factor with user-configurable operating modes at 10 W, 15 W and 30 W (the power consumption can be reduced to 7.5 W with lower performance or reducing functionalities). The main demodulation results based on recent laboratory tests on the Jetson TX2 and Xavier in a non-optimized environment are listed in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Board** & **Modcod** & **Bandwidth** & **Doppler Shift** & **Measured Performance** \\ \hline \multirow{3}{*}{Jetson TX2} & FSIM & \multirow{3}{*}{2.5 MHz} & \multirow{3}{*}{No} & \multirow{3}{*}{55 Kbps, 45 kpkt/s, 1 iSIC} \\  & & SF 128, size 150 bytes & & \\ \hline \multirow{3}{*}{Xavier} & FSIM-like & \multirow{3}{*}{200 KHz} & \multirow{3}{*}{Yes, LEO like} & \multirow{3}{*}{7 kbps, 23 pkt/s, 2 iSICs} \\  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Non-optimized demodulation performance for GPU device usable on small LEO satellite. Figure 21: The Jetson TX2 module (credit card size) (**left**) and Jetson AGX Xavier (**right**).
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 5 Measurement Uncertainty Evaluation  
Acquiring optimal measurement uncertainty is the target of a measurement system. In this paper, the methods to decrease every possible error were the emphasis. Limited by the experiment condition and hardware of the system, the measurement uncertainty was a few tens of nanometers, obtained by estimation. The analysis on measurement uncertainty of the system is given in this section.  
Firstly, the mathematical model is given in Equation (7):  
\[l=\frac{N\lambda_{0}}{2n(p,t_{\mathrm{air}},f)}+\alpha L(20-t_{\mathrm{s}})+ \delta l_{\mathrm{diff}}+\delta l_{Abbe}+\delta h+\delta l_{\mathrm{cos}} \tag{7}\]  
where \(\lambda_{0}\) is vacuum wavelength of laser, \(n(p,t_{\mathrm{air}},f)\) is air refractive index obtained by [PERSON]'s formula (1998 Version), \(p\) is air pressure, \(f\) is air humidity, \(t_{\mathrm{air}}\) is air temperature, \(a\) is the linear thermal expansion coefficient, \(t_{\mathrm{s}}\) is the material temperature, \(\delta l_{\mathrm{diff}}\) is the correction for the laser beam diffraction, \(\delta l_{\mathrm{Abbe}}\) is the correction for the Abbe error, \(\delta h\) is the correction for the flatness derivative of the step, and \(\delta l_{\mathrm{cos}}\) is the correction for the cosine error.  
In Equation (7), \(\delta l_{\mathrm{diff}}\) is expressed as follows:  
\[\delta l_{\mathrm{diff}}=\frac{\lambda^{2}}{4\pi^{2}\omega_{0}^{2}}L \tag{8}\]  
where, \(L\) is the measured length, \(\lambda\) is laser wavelength, and \(\omega_{0}\) is the radius of waist of laser beam.  
Since the laser source used for length measurement was a semiconductor laser and the laser beam directly output without any collimation, the beam waist radius of the laser is estimated to be 0.1 mm, \(\delta l_{\mathrm{diff}}\) 0.1 nm for L 0.1 mm was obtained by Equation (8).  
Figure 17: Bidirectional measurement results and the convergence of average result.  
Table 1 gives the information used for the measurement uncertainty evaluation. As a whole, error sources can be classified into two types: random errors and system errors. For random errors, the Type A evaluation method based on statistics is used. For example, the measurement uncertainty component caused by the repeatability is obtained by Type A method. For system errors, the Type B evaluation method is used [14; 15; 16]. The detailed evaluation information and combined standard uncertainty are also given in Table 1. Since the length value was 0.1 mm, the length-dependent components were so small to be negligible, so the length-independent components were the main contributions to total measurement uncertainty. These components included repeatability, resolution, Abbe error, and the surface flatness of the step measured.
## 8 Practical Considerations for Real-World Applications  
### Real-World Applications and Challenges of Hybrid DRL Methods  
Hybrid DRL approaches that integrate value-based and policy-based reinforcement learning have gained significant attention due to their potential to combine the sample efficiency of value-based methods with the stability of policy-gradient techniques. Recent studies highlight both the advantages and the existing challenges of these methods in mobile robot path planning.  
One key advantage of hybrid approaches is their improved learning efficiency and generalization in real-world environments. [PERSON] et al. [71] introduced a goal-conditioned hybrid model that integrates model-based and model-free reinforcement learning, demonstrating superior learning efficiency and generalization performance in complex robotic navigation tasks. Similarly, [PERSON] et al. [72] proposed a systematic approach to combining predictive models with experience-based policy learning, leading to improved sample efficiency and motor skill acquisition.  
However, hybrid methods also pose challenges related to computational complexity and stability. [PERSON] et al. [73] examined the integration of Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) in autonomous robot navigation, reporting that while hybrid models offer enhanced adaptability in unknown environments, they require careful tuning of reward functions and policy updates to prevent instability during training. Additionally, [PERSON] [74] highlighted the challenges of applying hybrid DRL techniques to underwater navigation, where continuous control tasks demand high sample efficiency and robust convergence properties.  
Despite these challenges, hybrid methods continue to evolve with innovative frameworks. [PERSON] et al. [75] introduced the HOPE framework, which fuses reinforcement learning with trajectory-based planning, achieving higher success rates in diverse parking scenarios. Meanwhile, [PERSON] et al. [76] developed a hybrid DRL-based navigation strategy for social robot interaction, demonstrating improved compliance with human navigation patterns in real-world environments.  
Overall, while hybrid DRL methods present promising solutions for mobile robot path planning, further research is required to optimize their computational efficiency and stability for deployment in dynamic and unpredictable settings.
# Fixed Wireless Access in Flexible Environment: Problem Definition and Feasibility Check
## 3 Assumptions and Inputs
### Parameters of the Planned Location  
The parameters for the PL fall into three main groups: geo-location information, radio link parameters, and LoS information. Geo-location information includes proper coordinates, exact ground and building heights at potential antenna locations. Radio link parameters encompass proper rainfall and rain intensity statistics for the geo-location of the PL (see e.g., [7; 11; 35; 36; 37]), possible frequencies (including band, channel raster, and bandwidth of the radio channel) for the radio link(s) [6; 17; 19; 38], and available radio equipment options, such as outdoor unit and antenna sizes (considering construction limitations like wind load), along with the parameters of the transceiver units and their antennas (e.g., transmitted power, receiver threshold level, supported modulation modes, antenna gain for the selected antenna size, see e.g., [18; 39; 40; 41]). Finally, LoS information is needed for the potential optical access connection points from the PL [11].
# Evaluation of Urban Transportation Resilience under Extreme Weather Events
## 2 Dataset
### Dataset Description  
According to the statistical information, the total number of buses in Fuzhou by 2018 was 5370. Each bus was equipped with on-board GPS devices, which were interconnected with the data center of the Fuzhou Municipal Transportation Bureau. GPS trajectory information was transmitted to the central server approximately every thirty seconds during the operation. The total amount of data recorded every day was more than 4 million, and the size of the data was more than 1 GB. The Fuzhou bus GPS trajectory data selected in this study detailed every bus trip that occurred in the city between April 2018 and June 2018 (inclusive of the months). This dataset obtained from the Beidou Big Data Center in the Fujian University of Technology contained approximately 400 million bus GPS trajectory data. Of these, about 80 million trajectory data were used in this study. Figure 1 shows an example of the raw bus trajectory data entries for 1 April 2018, where the BUSRDID, ROUTEID, PRODUCTID, TIMESTAMP, LONGITUDE and LATITUDE, and other information not relevant to this study were recorded for each bus trip.
## 3 Results
### Total Energy Dissipation  
Previously changes in geometry were done in order to improve the energy dissipation across the steps. Many researchers, such as [9; 22; 25], altered the steps geometry and improved the energy dissipation to a certain limit. However, as compared to [9; 22; 25], changing the risers of stepped spillway do not increase energy dissipation. Hence, there was no significant increment in the energy dissipation for models with curved risers. A maximum of 3-4% energy dissipation was obtained as shown in Table 6 and Figure 14. Energy dissipation was calculated by percent head loss as compared to head at upstream. It was found that energy dissipation is dependent on the flow rate. For lower discharges, energy dissipation is more as compared to the high discharges. Details are given in the following Table 6. Q is given in m\({}^{3}\)/s.  
Therefore, curved risers in a stepped spillway model do not increase in energy dissipation. In a practical sense, the steps in the stepped spillways, which were damaged due to cavitation, can be made curved in order to with stand the negative pressures. This can be done without any changes in total energy dissipation.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 3 Methodology  
The integrated spatio-temporal graph neural network (ISTGNN) model is designed to address complex traffic forecasting tasks by integrating spatio-temporal dependencies inherent in urban traffic data. The architecture leverages graph-based neural network layers, regression models, and sophisticated training routines to model traffic dynamics.
# Switched Observer Design for a Class of Non-Linear Systems
## 2 System and Observer Description  
Consider the following class of systems with Lipschitz non-linearity.  
\[\begin{split}\dot{x}(t)&=Ax(t)+B_{f}f(x(t))++g(y(t ),u(t))+B_{d}d(t)\\ y(t)&=Cx(t)\end{split} \tag{2}\]  
where \(x(t)\in\mathcal{X}\subset\mathbb{R}^{n_{x}}\), with \(\mathcal{X}\) being a compact domain, is the state vector, \(u(t)\in\mathbb{R}^{n_{u}}\) is the input vector, \(y(t)\in\mathbb{R}^{n_{y}}\) is the output measurement vector, and \(d\in\mathcal{L}^{n_{d}}_{d}\) is an exogenous disturbance vector. The known functions \(f:\mathbb{R}^{n_{x}}\to\mathbb{R}^{n_{f}},g:\mathbb{R}^{n_{y}}\times\mathbb{ R}^{n_{u}}\to\mathbb{R}^{n_{x}}\) are locally Lipschitz on the domain of interest. The matrices \(A\in\mathbb{R}^{n_{x}\times n_{x}}\), \(B_{f}\in\mathbb{R}^{n_{x}\times n_{x}}\), \(B_{d}\in\mathbb{R}^{n_{x}\times n_{d}}\), and \(C\in\mathbb{R}^{n_{y}\times n_{x}}\) are known constant matrices of appropriate dimensions. The initial condition is \(x(0)=x_{0}\in\mathbb{R}^{n_{x}}\).
## 6 Result and Discussion
### Target Localization Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Iteration Number** & **Coordinates/mm** & **Error/mm** & **Iteration Number** & **Coordinates/mm** & **Error/mm** \\ \hline
1 & \((57.14,86.39,41.07)\) & 10.92 & 8 & \((63.67,93.15,44.64)\) & 1.07 \\
2 & \((58.12,89.99,42.38)\) & 7.61 & 9 & \((63.66,93.15,44.55)\) & 1.06 \\
3 & \((61.58,92.07,43.54)\) & 3.40 & 10 & \((63.64,93.14,44.44)\) & 1.05 \\
4 & \((63.17,92.87,44.32)\) & 1.57 & 11 & \((63.62,93.15,44.30)\) & 1.06 \\
5 & \((63.64,93.11,44.57)\) & 1.10 & 12 & \((63.60,93.14,44.11)\) & 1.10 \\
6 & \((63.71,93.15,44.67)\) & 1.05 & 13 & \((63.55,93.13,44.00)\) & 1.16 \\
7 & \((63.69,93.15,44.68)\) & 1.07 & 14 & \((44.00,63.50,93.11)\) & 1.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The errors of localization results during each iteration. Figure 11: The errors of localization results at each iteration. a dispersive estimated target area, as shown in Figure 6. However, the IMWLE method, which was based on the density of pseudo-targets, gradually narrowed down the estimated target area during iteration to increase localization accuracy, as shown in Figure 10. Moreover, IMWLE gave a point estimate of the target, making it possible to evaluate the quality of the localization results, while the probability-based ToF method was not able to provide a point estimate.
## 4 NMPC Controller Design for Aircraft Engines with HGWO
### Prediction Model  
The corresponding output data (low pressure turbine speed) are obtained using Gaussian white noise as input data (fuel quantity). Based on the acquired input and output data, the BP method is used to train the corresponding neural network model off-line, and the recursive method is then used to establish the prediction model.  
Where \(y\) and \(u\) represent \(n_{l}\) and \(w_{f}\), respectively, i.e., low pressure turbine speed and fuel flow, the BP neural network model is  
\[y(k)=f_{nn}[y(k-1),\ldots,y(k-n_{1}),u(k-1),\ldots,u(k-n_{2})]. \tag{21}\]  
In this formula, \(k\) represents the current moment, and \(n_{1}\) and \(n_{2}\) represent the order of output \(y\) and input \(u\), respectively.  
Based on Equation (21), the dynamic recursive neural network prediction model is established using the recursive method:  
\[\begin{array}{l}\hat{y}(k+1)=f_{nn}[y(k),\ldots,y(k-n_{1}+1),u(k),\ldots,u(k -n_{2}+1)]\\ \hat{y}(k+2)=f_{nn}[\hat{y}(k+1),\ldots,y(k-n_{1}+2),u(k+1),\ldots,u(k-n_{2}+2) ]\\ \vdots\\ \hat{y}(k+P)=f_{nn}[y(k+P-1),\ldots,y(k-n_{1}+P+1),u(k+P-1),\ldots,u(k-n_{2}+P +1)]\end{array} \tag{22}\]  
where \(P\) is the prediction horizon, and \(\hat{y}\) is the output value of the prediction model.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 6 Challenges
### Connectivity Challenges  
Bandwidth limitations pose a critical challenge to ITS. [PERSON] et al. [108] noted that in ITS scenarios, mobile peers (vehicles) have only a few seconds of connectivity and a limited bandwidth. Reconnecting peers face challenges receiving stored events due to limited bandwidth and a small connectivity window. Therefore, this may create backpressure in the system and pose a risk that essential safety-critical information may not reach the peer in time. This risk increases by delivering irrelevant or incorrect information instead of life-saving information. Moreover, in some ITSs, it is necessary to ensure an exactly-once delivery of subscribed messages, as it is a requirement for some reasons, such as jurisdictional reasons [108]. Durable notification services must store all events a peer has subscribed for while the subscriber is offline. However, meeting this condition may be challenging for publishers with limited resources and lacking broker infrastructures to store the messages.  
[PERSON] et al. [109] mentioned the lack of portability across systems. The current platforms are designed for specific agencies and require the agency's application to access its services. Developing applications that can work across multiple transportation systems is non-trivial. For example, political, legal, and technical challenges impede the development of a single bus application that commuters can use across cities, let alone different countries.  
Efficient data delivery for mobile nodes is also a significant challenge. [PERSON] et al. [110] demonstrated that maintaining a stable and efficient communication path for mobile vehicles connected to a core network through wireless communication technology is challenging. Each network attachment point (e.g., RSU) serves as a node in the network. The vehicle's dynamic movement results in frequent changes in network attachment points, requiring adaptation of the communication path.
## 4 Results
### Prediction with Machine Learning Models  
4.5.1. Model Evaluation  
Hyperparameter optimization was conducted for Random Forest, XGBoost, NGBoost, and SVM models. XGBoost outperformed other models, demonstrating the lowest MAE (0.3522) and MSE (0.3123) and the highest R\({}^{2}\) (0.6762), indicating superior predictive accuracy. SVM performed the worst, with the highest RMSE (1.0055) and a negative R\({}^{2}\) (\(-0.0483\)). The performance values can be found in Table 5.
# Unit Cell Optimization of Groove Gap Waveguide for High Bandwidth Microwave Applications
## 2 Materials and Methods
### Fabrication of MGGW  
To validate the simulation results, the proposed unit-cell-based MGGW structure was fabricated. In the fabrication process for testing and feeding the MGGW, we selected the EIA standard waveguide adapter WR284 physical dimensions as a reference with a recommended frequency range of 2.6\(\sim\)3.95 GHz. The air gap between the top face of the unit cell and the parallel metal plate was taken as 4.036 mm. The Alumina 6061-T6 material was used for fabrication because it has better tensile strength and better corrosion resistance as compared to T4. Additionally, it is low cost as compared to copper or other equivalent metals with similar electrical properties. The fabrication was carried out using computer numerical control (CNC) milling operation with an accuracy of 50 microns (to achieve tolerance, a 1 mm T-slot cutter was used for the fabrication of heads in the unit cell structure). The MGGW was fabricated with four independent components that include a bottom surface with the metallic EBG periodic structure, front and back metal walls, and a top metal cover plate. The waveguide was fabricated with independent components to provide a high level of flexibility for quick fabrication and ease of assembly. To achieve the precise assembly of components and alignment of waveguide adapters for measurement of results, 1 mm deep metal engraving was carried out for all four independent components. The assembly of the components was completed by using metallic screws. Details of the fabricated model are shown in Figure 5.  
The geometric dimensions of the fabricated MGGW are presented in Table 3. The MGGW is designed to cover 1.5\(\uplambda\) (electrical length) of a signal at a center frequency of 3 GHz for measurement and analysis. The electrical length of the fabricated model was kept at 1.5\(\uplambda\) only to minimize fabrication cost. The results of the fabrication design are also compared with simulations for analysis in Section 3.  
Figure 4: (**a**) Design of proposed MGGW in straight structure, well suiting the WR284 standards, utilizing optimized unit cell configuration. Design constructed by employing lossy aluminum, with open boundary conditions (ensuring adequate spacing), and waveguide ports to support excitation. (**b**) MGGW design with single 90\({}^{\circ}\) bend and double 90\({}^{\circ}\) bends.