esa-sceva/satcom-chunk-collection huggingface dataset:
# Airborne GNSS Reflectometry for Water Body Detection
## 2 Airborne Bi-Static GNSS-R Configuration  
GNSS-R consists of using GNSS signals received on Earth directly from the GNSS satellites as well as after a reflection on the Earth's surface. In our implementation, we use a GNSS-R dual antenna geometry. The direct GNSS signals are received by a Right Hand Circular Polarized (RHCP) antenna, and the reflected GNSS signals are received by a Left Hand Circular Polarized (LHCP) antenna after specular scattering from different landforms along the flight trajectory as shown in Figure 1. Once a signal hits a reflection point on Earth, scattering occurs primarily from the region of the surface surrounding the specular reflection point.  
On flat areas with no topography, the specularly reflected power can be derived solely from the Fresnel reflection coefficients. In this case, the spatial resolution of the GNSS measurements is mostly linked to the size of the first Fresnel zone which in turn determines the surface reflectivity. In this work, we process the amplitudes of the RHCP and LHCP antenna signals in order to measure the reflectivity of the surface. The reflectivity measurements are defined as the ratio of the amplitudes of the reflected LHCP antenna signals \(A^{r}_{LHCP}\) over the amplitudes of the direct RHCP antenna signals \(A^{d}_{RHCP}\) as shown below:  
\[\Gamma(t)=\frac{A^{r}_{LHCP}(t)}{A^{d}_{RHCP}(t)} \tag{1}\]  
We associate these measurements with 20 ms rate specular point localization.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods  
### Experimental Setup  
The experimental host system operates on a 64-bit Windows 10 operating system, featuring hardware consisting of an Intel(R) Core(TM) i5-13600 KF CPU and an NVIDIA GeForce RTX 4070 TI SUPER GPU. The algorithm development environment uses Python 3.10 and the PyTorch 2.1.2 deep learning framework. During the preprocessing phase, training samples were randomly cropped into patches of size \(128\times 128\) to alleviate the computational burden of training, considering the constraints of hardware performance. Through analysis of the training data, the optimal number of training epochs was determined as follows: 100 epochs for the encoder-decoder stage, 200 epochs for both the feature filtering and fusion stages and 500 epochs for the registration stage, with a consistent batch size of 4. The Adam optimizer was employed, with an initial learning rate of \(10^{-4}\), halved every 20 iterations.  
Based on the design hypothesis, the proposed algorithm is suitable for modular deployment. Therefore, the hyperparameters of each module were individually fine-tuned, resulting in the following network hyperparameters. The attention convolutional sparse encoder is trained for 4 iterations with 8 attention heads and 64 feature dimensions for network hyperparameters. The decoder configuration is the same as the encoder. For the loss function parameters, \(\mu_{1}\), \(\mu_{2}\), \(\mu_{3}\) and \(\mu_{4}\) are set to 5, 0.5, 5, and 1, respectively, to maintain the same order of magnitude for each loss term.  
The fusion results are quantitatively measured using eight metrics [26] as follows: entropy (EN), standard deviation (SD), root mean square error (RMSE), mutual information (MI), visual information fidelity (VIF), Sum of Correlation Differences (SCD), Qab/f, and structural similarity index (SSIM). Except for RMSE, higher values of the metrics indicate better image quality. Time refers to the average processing time for the experimental host to process a single image.
# Prediction Method of Blast Wave Impact on Crew Module for Liquid Rocket Explosion on Launch Pad
## 7 Conclusions  
A numerical method for simulating the rocket explosion on the launch pad was developed in order to study the propagation law of blast wave and quantify the impact on the crew modules. After the analysis and discussion above, the following main conclusions were drawn:  
1. The final blast waveform of explosion model with rocket is conical in the upper and lower, and spherical in the middle.  
Figure 8: The pressure enhancement factor curve with scaled distance.  
Figure 9: Fitting curve of the peak overpressures of blast wave versus scaled distances.  
* In different scenarios of rocket explosion on launch pad, the third-stage explosion is the most harmful to the crew module, and the first-stage explosion is the least. In order to prevent crew hazards, necessary protective measures should be taken for the crew modules.
* The rocket model has a marked effect on explosion strength. When the scaled distance is less than 1.0 m/kg\({}^{-1/3}\), the maximum of the pressure enhancement factor is about 17 times, and the minimum is about 4 times. When the scaled distance is greater than 1.0 m/kg\({}^{-1/3}\), the pressure enhancement factor is about 4 times. Therefore, the rocket model cannot be ignored when studying the effect of blast wave on the crew module.
* Prediction formula of peak overpressureacting on the crew module in the explosion mode on the launch pad is established. It can quickly predict the peak overpressure of the blast wave and evaluate the damage to the crew module of the rocket explosion on the launch pad.  
Predicted results are expected to be useful for hazards assessment associated with rocket explosion on the launch pad. Better understanding of explosion overpressure would improve the ability of crew modules protection.  
Methodology, Y.W., H.W.; software, Y.W., B.Z.; formal analysis, C.C.; investigation, Y.W.; resources, Y.W., H.W., C.C.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W.; visualization, Y.W.  
This research received no external funding.  
The authors greatly appreciate the comments from the reviewers, whose comments helped to improve the quality of the paper.  
The authors declare no conflict of interest.
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 4 Proposed Method  
### Method Overview  
The primary objective of this paper is to enhance the generalization performance of the model in multi-source few-shot fault-diagnosis scenarios. To achieve this, a federated multi-source few-shot algorithm called FedCDAE-MN is proposed, which involves two main processes: local training and federated parameter aggregation. During local training, each source domain executes the CDAE-MN algorithm locally to obtain domain-specific parameters. As depicted in Figure 4, CDAE-MN comprises a convolutional denoising auto-encoder (CDAE), a classifier denoted as \(\theta_{\text{C}}\), and a metric module referred to as \(\theta_{M}\). However, since the model solely focuses on similarity information based on metric learning, it disregards the attribute information associated with each specific fault category, resulting in the underutilization of labeled data from the source domain. To address this, supervised learning is performed before metric learning to fully capture fault-specific characteristics. To enhance feature generalization and robustness, CDAE is employed as a feature extractor. Specifically, each source domain utilizes locally labeled fault data to obtain probability distributions of different fault types through CDAE and classifiers. The loss function consists of the reconstruction loss \(L_{R}\) and the classification loss \(L_{\text{C}}\). The encoder, obtained through supervised learning, is then utilized as the feature extractor in subsequent training stages. The second step involves conducting metric learning in the feature space. Initially, few-shot tasks with the same settings as the target domain (\(N\)-way, \(K\)-shot) are constructed using local data. Subsequently, with fixed parameters obtained in the first step, the samples are transformed into the feature space. The metric module is trained in this feature space through episodic training, enabling accurate measurement of similarity between samples. In the following, we elaborate on the details of our proposal.
## 3 Experimental Results and Discussion
### Basic Physical Parameters
#### 3.1.2 Effect of High Temperature on Wave Velocity  
The wave velocity is usually considered as a typical indicator of the integrity of the internal physical and mechanical properties of concrete materials. However, the velocity parameter is directly influenced by the temperature. In this experiment, the ZBL-U510 nonmetallic ultrasonic detector was placed on both sides of the concrete specimen to measure the wave velocity after the heating temperature treatment. The results are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Temperature/\({}^{\circ}\)C** & **Diameter/mm** & **High/mm** & \begin{tabular}{c} **Mass before** \\ **Heat/g** \\ \end{tabular} & \
\begin{tabular}{c} **Mass** \\ **after** \\ **Heat/g** \\ \end{tabular} &
\begin{tabular}{c} **Mass Loss/\%**  
It can be seen from the above experimental results recorded in the Table 5 that the wave velocity of ordinary concrete and basalt fiber-reinforced concrete is descending with the temperature rise. Thus, it can be seen that the high temperature produces a detrimental effect for the mechanical property of concrete.
# A Simulation of Contact Graph Routing for Mars-Earth Data Communication
###### Abstract  
In this study, we develop a simulation of Contact Graph Routing (CGR) for data communication between Mars, Earth, and relay satellites. Due to the changing of the satellites' distances to Mars and Earth, respectively, there are specific contact windows between NASA's Mars rovers and orbiting relay satellites, and specific contact windows between these relay satellites and NASA's global system of antennas on Earth. The barrier in communication develops delays caused by link propagation, so it needs a Delay Tolerant Network (DTN) for routing networks among the nodes (satellites and antennas), which is the concept of storing and forwarding data whenever the windows are open. We construct an efficient algorithm for CGR, which puts all objects into a general framework of numbered nodes, so that we can easily develop another application of a network with a larger number of nodes. Simulated data are generated randomly to mimic the unpredicted data volumes that are sent from Mars to Earth. We construct some cases involving delivering data for one Martian day, and the simulation performs well in carrying, storing, and forwarding data from Mars to Earth, even though the relay satellites are not able to contact Earth for a period of time.  
contact graph routing; deep space network; optimization; stochastic data generation Article
## 4 Asymptotic Performance Analysis in a Multicast System
Then, similarly to (20) and (21), the theoretical ideal performance, denoted as \(\bar{R}_{k,m}\), and its upper bound, denoted as \(\bar{R}_{k,m}\), can be obtained as follows:\[\bar{R}_{k,m}=\log\biggl{(}1+\rho_{k}\xi_{k}^{2}a_{k}|c_{k}|^{2} \Bigl{(}\sum_{n=1}^{N}\bigl{|}f_{k,n,m}\bigr{|}\Bigr{)}^{2}\biggr{)}\] \[\leq\log\left(1+N^{2}\rho_{k}\xi_{k}^{2}a_{k}|c_{k}|^{2}\left( \bar{\beta}_{k,m}+\bar{\beta}_{k,m}\frac{\sum\limits_{n}\left|\tilde{f}_{k,m,m }\right|}{N}\right)^{2}\right)\] \[\stackrel{{\Delta}}{{=}}\bar{R}_{k,m}, \tag{39}\]  
where \(\bar{\beta}_{k,m}=\sqrt{\xi_{k,m}/\left(\kappa_{k,m}+1\right)}\) and \(\tilde{f}_{k,n,m}\) is the NLoS component of \(f_{k,n,m}\). As \(N\) increases to infinity, we derive the asymptotic upper bound of \(\hat{R}_{k,m}\) following a similar approach as in (22), as follows:  
\[\hat{R}_{k,m} \leq\log\Bigl{(}1+N^{2}\rho_{k}\xi_{k}^{2}a_{k}|c_{k}|^{2}\bigl{(} \bar{\beta}_{k,m}+\bar{\beta}_{k,m}\bigr{)}^{2}\Bigr{)}\] \[R_{k,m}=\log\left(1+\rho_{k}\xi_{k}^{2}a_{k}|c_{k}|^{2}\Big{|}\sum_{n=1}^{N}f_ {k,n,m}^{*}e^{i\theta_{k,n}}\Big{|}^{2}\right). \tag{35}\]0  
From (34) and (40), we can observe that the minimum value of (40) for all \(m\in\mathcal{M}_{k}\) will be:  
\[R_{k,m}=\log\left(1+\rho_{k}\xi_{k}^{2}a_{k}|c_{k}|^{2}\Big{|}\sum_{n=1}^{N}f_ {k,n,m}^{*}e^{i\theta_{k,n}}\Big{|}^{2}\right). \tag{35}\]1  
From (38) and (41), it is evident that the proposed passive beamformer achieves a theoretical ideal performance with an SNR order of \(\mathcal{O}\bigl{(}N^{2}\bigr{)}\), establishing the optimality of the proposed scheme. Note that the proposed passive beamformer in the multicast system can achieve near-ideal performance if the value of \(N\) is sufficient relative to \(M_{k}\). This holds true even when the number of passive elements is not infinite, as demonstrated in the simulation results presented in Section 5. Similarly to (29) and (32), the ergodic achievable rate and its outage probability can be obtained, respectively, as follows:  
\[R_{k,m}=\log\left(1+\rho_{k}\xi_{k}^{2}a_{k}|c_{k}|^{2}\Big{|}\sum_{n=1}^{N}f_ {k,n,m}^{*}e^{i\theta_{k,n}}\Big{|}^{2}\right). \tag{35}\]2 \[R_{k,m}=\log\left(1+\rho_{k}\xi_{k}^{2}a_{k}|c_{k}|^{2}\Big{|}\sum_{n=1}^{N}f_ {k,n,m}^{*}e^{i\theta_{k,n}}\Big{|}^{2}\right). \tag{35}\]3  
where \(\hat{R}_{\text{MC},k}\) is the asymptotic achievable rate of multicast UE \(k\) and  
\[R_{k,m}=\log\left(1+\rho_{k}\xi_{k}^{2}a_{k}|c_{k}|^{2}\Big{|}\sum_{n=1}^{N}f_ {k,n,m}^{*}e^{i\theta_{k,n}}\Big{|}^{2}\right). \tag{35}\]4  
Similarly to the unicast system, (43) shows that the outage probability in the multicast system decreases as \(N\) increases, verifying that an RIS can offer reliable multicast SatCom for a large \(N\).
# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
## 3 Methods
### Optimization and Evaluation Processes
These areas represent the differences in position over time between the simulated and real data; thus, a smaller area (and therefore a smaller sum of these areas) indicates a more accurate simulation. The GA optimizes the parameters of the simulation to achieve the lowest possible total error, improving the fidelity of the simulation model relative to the real-world conditions. To ensure optimal performance of the GA, a systematic evaluation of its parameters--population size, mutation rate, number of generations, and crossover rate--is conducted, considering the trade-offs between computational efficiency and solution quality. To balance the algorithm's exploration and exploitation capabilities and guarantee convergence to an optimal or nearly optimal solution without incurring undue computing costs, these parameters must be carefully chosen. The population size determines how many potential solutions (sets of calibration parameters) are evaluated in each generation. A larger population provides a diverse set of solutions, enhancing the algorithm's ability to explore the search space but increasing computational load. A population size of 32 is chosen based on a trade-off between computational efficiency and solution quality. This size is sufficient to maintain genetic diversity without overwhelming computational resources [31]. In order to avoid stagnation at local optima, the mutation rate introduces new genetic material into the population by regulating the likelihood of random changes in the parameters of the individuals. A mutation rate of 0.1 is selected to provide a balanced introduction of variability. This rate is commonly used in GA applications and is effective in maintaining diversity within the population [32]. The crossover rate in the implemented GA is set at 100%, meaning crossover is applied in every reproduction step. This decision is based on the need to maximize exploration of the solution space by ensuring a consistent combination of genetic material from parent solutions. While a 100% crossover rate might not suit every problem, it is a common practice in GAs, particularly when paired with an effective mutation operator [32]. The mutation rate, set at 10%, complements the crossover process by maintaining genetic diversity and preventing premature convergence. This combination proves effective, as demonstrated by the algorithm's ability to improve fitness values and reduce calibration errors across generations iteratively. The number of generations defines how many iterations the GA will perform, influencing the depth of the search process. A total of 200 generations is deemed appropriate to allow the algorithm to converge toward an optimal solution while keeping computation time reasonable.
## 4 Methodology
### Experimentation and Test Route  
The experimentation consisted of real driving within the Tecnologico de Monterrey University Campus in the city of Toluca, Mexico. The defined route was selected so that it included various differentiable scenarios. For this series of experiments, the selected route had three different zones that were defined as class elements for the classification task.  
Figure 2: Design methodology of the EDS.  
The acquisition of data was performed by using a Dewesoft's SIRIUS data acquisition system, which included the DS-IMU/GYRO modules and sensors. The test circuit was divided in three zones: \"Transit\", \"Flatland\", and \"Cobblestone\". These zones are shown in Figure 4 and are described as follows:  
Transit: This is the default classification of the parts of the route that do not belong to the other two classes. This zone is characterized for having a top speed restriction of 10 km for pedestrian safety; also, there are several crossings and intersections, and it is expected that pedestrians or other vehicles may roam, so the driver should always be careful and especially attentive.  
Flatland: This zone is a 150 m by 50 m open-space parking lot that was totally empty while the testing was being conducted. It is considered flat because there is no significative difference in the height of the area within the Flatland zone and there are also no obstacles.  
Figure 4: Test route with defined zones.  
Figure 3: Experimentation and algorithm design process (provisional).  
After measuring the height on several points inside the area, the maximum slope registered was 0.007. The route within this zone includes a series of close radius and successive turning maneuvers.  
Cobblestone: This is a small zone of about 70 m long, located inside the \"Transit\" zone; this means that both zones share some characteristics. The difference is the terrain roughness of this area due to it not being asphalted but rather covered with cobblestone. Figure 5 presents a photography of this zone.  
In the transit and cobblestone zones, it is recommended that the vehicle speed is moderated, but it is possible to observe a range of speeds in the gathered data. In the Cobblestone zone, it is expected that the IMU sensors register high values of vibration in all axes, but mostly on the \(z\) axis, and a possible decrease in longitudinal velocity as an effort of the driver to reduce the effects of the road roughness on the passenger comfort.  
In the Flatland zone, the closed turning maneuvers will induce lateral acceleration and yaw-rates that should be characteristic of the zone, both in magnitude and frequency. In addition, the roll angle (rotation around the vehicle's \(x\) axis) is expected to have a distinctive behavior.
## 3 Background Theory
### Advanced Encryption Standard (AES)  
AES is a standard algorithm issued by NIST as a replacement for the Data Encryption Standard (DES). AES is designed as a symmetric key algorithm that manages messages in the form of blocks, often called block ciphers. NIST began announcing AES as a new standard starting from 26 November 2001 [33]. The algorithm chosen for AES is Rijndael, which was designed by [PERSON] and [PERSON] [34]. Rijndael was chosen as the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Research** & **Author** & **Algorithm** \\ \hline \multirow{2}{*}{1} & Extended Model of Secure Communication for Embedded & [PERSON] et al. & LBLOK, AES-128, PRESENT, Trivium \\  & Systems with IoT and MQTT & & [PERSON] \\ \hline
2 & Lightweight Security Scheme for MQTT/MQTT-SN Protocol & [PERSON] et al. & ChaCha20-Poly1305 \\ \hline \multirow{2}{*}{3} & JEDI: Many-to-Many End-to-End Encryption and Key & & \\  & Delegation for IoT & [PERSON] et al. & WKD-IBE \\ \hline \multirow{2}{*}{4} & Secure End-to-End Encryption on the MQTT protocol with & & \\  & Block Cipher-based Lightweight Cryptography & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with Previous Researchs.  
winner of a contest for the encryption standard algorithm held by NIST for 5 years. AES is designed to process data in blocks of 128 bits. AES has three key variations that can be used, namely AES-128, AES-192, and AES-256 [35].  
Structurally, AES has a simple structure so that the encryption and decryption process can use the same structure. The AES process can be divided into three phases, namely the initial round, the main round, and the final round. In the initial round phase, the algorithm processes messages with the add round key function, namely the process of XORing messages with keys. In the next phase, AES process messages with the functions of sub bytes, shift rows, mix columns, and add round key. Meanwhile, the final round performs the same function as the main round but eliminates the mix column function [36]. The AES algorithm above has four main components, namely sub bytes, shift rows, mix columns, and add round key [37].