esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 5 Prognostics  
Earlier, we covered the various ways the literature has achieved fault prognosis in satellites, spacecraft, and UAVs. The next step to advancing the health monitoring of the studied agents is to forecast the effect of the diagnosed malfunctions and obtain their RULs, namely, conducting fault prognosis. Performing prognostics for satellite and spacecraft applications is captivating, especially since their launch and operation are costly. Therefore, fault prognosis is a favorable approach to lengthening the lifetime or optimizing the use of satellites by knowing their RUL and time-to-failure. For UAVs, prognostics is not as pressing an issue since their flight time is usually limited and they can undergo maintenance between flights. Nonetheless, predicting their RUL and prolonging it is favorable. In addition, embedded analytical redundancy techniques for performing prognostics could incur high computational burdens, impeding the UAVs' main functionalities. As a result, UAVs have not received extensive attention in the field of prognostics from researchers. This section reviews the work that has currently been conducted on these agents' fault prognoses. Tables 2-4 list the strengths and limitations of each work on fault prognostics of single-agent systems. Table 6 outlines the main features of the fault prognosis works in a manner similar to Table 5. The reviewed literature indicates that, to the best of the authors' knowledge, very few published works exist on the actuator and sensor fault prognosis of a team of satellites or UAVs.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 4 System Model
### Satellite Orbit Model  
Unlike many previous studies in mobile edge computing, a satellite cannot always communicate with a terminal and the cloud computing platform. Therefore, the latency of waiting for a satellite connection needs to be considered during mission execution.  
The space geometry of the link between the satellite and a fixed location on the ground is shown in Figure 5.  
Here, \(\alpha\) is the elevation angle of the mobile device, \(\beta\) is the half-angle of view of the satellite, \(R_{e}\) is the radius of the earth, and \(h\) is the altitude of the satellite. Data transmission is only possible when the satellite is in the window of the mobile device. Regardless of the influence of other factors, when \(\alpha>0\), the satellite is in the communication windows. According to the geometric relationship, the expression of \(\alpha\) can be calculated as follows:  
\[\alpha=\arctan\frac{\cos\Delta\lambda\cos\varphi_{t}\cos\varphi_{s}+\sin\varphi _{t}\sin\varphi_{s}-\frac{R_{e}}{R_{e}+h}}{\sqrt{1-\left(\sin\varphi_{t}\sin \varphi_{s}+\cos\Delta\lambda\cos\varphi_{t}\cos\varphi_{s}\right)^{2}}} \tag{10}\]  
Figure 5: The space geometry of a communication link between a satellite and a terminal.  
Here, \(\Delta\lambda=\lambda_{t}-\lambda_{s}\), \(\lambda_{t}\) and \(\varphi_{t}\) are longitude and latitude of the terrestrial terminal, respectively. \(\lambda_{s}\) and \(\varphi_{s}\) are longitude and latitude of the satellite, respectively.
## 4 Experimentation and Analysis
### Transfer Learning  
To validate the transfer learning capability of the DMR-GCN model, experiments were conducted across multiple datasets. Specifically, the model was trained on the KITTI dataset and then tested on the Apollo, Indian, and a validation set of self-collected datasets. During testing, categories that did not exist in the corresponding dataset were excluded to ensure a fair evaluation. The experimental results are shown in Table 3 and Figure 9c.  
Figure 8: Model validation visualization results on self-selected dataset.  
Figure 7: Model validation visualization results on KITTI dataset.  
The experimental results demonstrated that the transfer learning model exhibits robust performance across all target datasets, underscoring the model's capacity for generalization. The transfer learning model achieved CLAL and CLAR scores of 0.88 and 0.87, LCL and LCR scores of 0.86 and 0.85, and accuracy and Recall rates of 0.94 and 0.92 on the Apollo dataset. These results outperform the model trained and tested solely on the KITTI dataset, demonstrating its resilience and adaptability to diverse scenarios.  
On the Indian dataset, the transfer learning model showed high prediction accuracy in various traffic scenarios, with CLAL and CLAR scores of 0.87 and 0.86, LCL and LCR scores of 0.85 and 0.84, and accuracy and Recall rates of 0.94 and 0.92, respectively.  
For the self-collected dataset, the transfer learning model achieved CLAL and CLAR scores of 0.89 and 0.88, LCL and LCR scores of 0.87 and 0.86, and accuracy and Recall rates of 0.94 and 0.92. These results further validate the model's high fidelity and adaptability across different traffic datasets, demonstrating its broad applicability and reliability in practical applications.  
The results of the ablation experiment, comparison experiment, and transfer learning experiment are shown in Figure 9. It is clear from the figure that the DMR-GCN model outperformed the other models across all metrics. Specifically, the ablation experiments demonstrated the contribution of each component to the overall model performance; the comparison experiments showed that DMR-GCN significantly outperforms the baseline model and other existing methods in terms of accuracy, Recall, and F1 scores; and the transfer learning results validated the ability of DMR-GCN to generalize across different datasets. These findings fully demonstrate the superiority and robustness of the DMR-GCN model in handling complex traffic scenarios.
## 2 Computational Framework
### Present Investigation Case
Figure 3: MAV and propeller geometrical descriptions, (**a**) MAV in top view, (**b**) airfoil profile, (**c**) MAV in 3D view, (**d**) aerodynamic force decomposition, (**e**) propeller azimuth angle, and (**f**) propeller geometrical information(chord and pitch angle distribution). reduced the lift-to-drag ratio dramatically and the drag for all configurations considered was nearly the same. The effects of the fuselage were also investigated in the present study. The slow-flyer propeller was chosen for the current MAV study. The propeller was installed at a distance \(\mathrm{d_{t}}=0.068\) m ahead of the wing planform. It had a diameter of 8 in, the pitch was 4 in, and the hub diameter was 0.014 m. Figure 3e shows the propeller geometry and the blade azimuth angle. The propeller rotated in an anti-clockwise direction viewing from the front, and Figure 3f shows the propeller geometric characteristics. The aerodynamic balance determined a horizontal component X and a vertical component Y of the total force acted on the MAV model (Figure 3d). To obtain the overall lift and drag force on the model, horizontal and vertical components (i.e., X and Y components) were transferred into L and D components (i.e., based on the incoming freestream coordinates), as shown in Equation (7). \[ \begin{array}{c}L=L_{T}+L_{W},\textit{and}\ D=(-T_{T})+D_{W}\\ L_{T}=Y_{T}cos\alpha+X_{T}sin\alpha,\textit{ and}\ L_{WF}=Y_{WF}cos\alpha-X_{WF} sin\alpha\\ T_{T}=-Y_{T}sin\alpha+X_{T}cos\alpha,\textit{ and}\ D_{WF}=Y_{WF}sin\alpha+X_{WF} cos\alpha\end{array}  \tag{7}\]  
A time step of 1.5724 \(\times\) 10\({}^{-5}\) s with 30 sub-iterations was applied for this study (is equivalent to 0.5 degrees per time step). To have reasonable numerical results, the y\({}^{+}\) value of the first grid point in the order of O(1) was required. Figure 4 shows the numerical aerodynamic force coefficients versus the blade azimuth angle. It shows that periodic pulses were produced. This type of signature was found to be relatively independent of the advance ratio and appeared to be mainly associated with the local loading on the propeller itself. A steady state flight condition is defined as one for which all motion variables remain constant with time relative to the body-fixed axis system XYZ. Mathematically speaking, steady state flight conditions are \(\overset{\rightarrow}{V}_{p}=0\), and \(\overset{\rightarrow}{\omega}=0\), and implies that MAV does not have any acceleration in any direction (i.e., \(\frac{du}{dt}=\frac{dv}{dt}=\frac{dw}{dt}=0\)), and that the roll, yaw, and pitch rates are zero. Therefore, a propeller with rotational speed of 555 rad/s satisfied the steady state flight condition, as shown in Figure 5. Namely, the lift equaled to the weight and drag equal  
Figure 4: Propeller time history aerodynamic forces coefficient development, (**a**) drag coefficient, (**b**) lift coefficient, (**c**) side force coefficient, (**d**) rolling moment coefficient, (**e**) yawing moment coefficient, and (**f**) pitching moment coefficient.
## 1 Introduction
The scintillator detector waveform analysis method identifies neutron and gamma rays through the luminescence waveforms generated by neutron and gamma rays in the scintillator, utilizing the PSD analysis method. Although this method attempts to use waveform information, it only uses a small portion of the data and does not utilize all the information from the waveform. This may affect the results due to the rise and fall of particle deposition or interference from noise. In addition, all four methods require the downlinking of in-orbit scientific data to the ground for particle identification analysis, which is not suitable for resource-constrained satellites, especially in the field of deep space exploration. In this paper, we propose using the powerful classification ability of convolutional neural networks (CNNs) in machine learning to establish a particle identification platform. This platform includes a particle waveform data software preprocessing module, a CNN software (1.0.0) training module, a particle waveform data FPGA forward inference module, and a CNN training set extension module. The main advantages of this CNN-based particle identification platform compared to traditional methods are as follows:  
1. Convolutional neural networks are good at separating different features in the data and finally achieving classification;
2. Convolutional neural networks are analyzed using the data of the waveform as a whole, which are richer in data information, and the data are expected to improve accuracies [8];
3. Convolutional neural networks have good scalability. FPGA offers high parallelism, low power consumption, and reconfigurability [9]; thus, it can realize real-time in-orbit processing and model updating, reduce the use of in-orbit resources, and improve the accuracy of detection. By establishing a particle identification platform based on convolutional neural networks (CNNs), we can further expand traditional particle identification technology, realizing the technical route of \"CNN + traditional particle identification technology\". This approach also improves the physical methods of traditional particle identification and interference rejection, enhancing the accuracy of particle identification and the effectiveness of interference rejection. Additionally, by building a generalized CNN onboard particle waveform signal identification FPGA platform, the in-orbit data rate can be greatly reduced, and both reconfigurable and scalable characteristics can be achieved. Therefore, the establishment of a CNN-based particle identification platform is of great significance for future space science exploration and engineering practice.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 3 Proposed Scheme  
This section describes the proposed scheme from data transmission to signal detection. Using the proposed system, more data symbols can be transmitted and more users can be served simultaneously. First, the transmission model explains how two additional domains can be used to improve spectral efficiency. Second, the signal detection model explains how each signal is detected and reliability can be satisfied. Additionally, in the received SINR, some considerations for the proposed scheme are presented. Finally, the sum throughput between the proposed scheme and conventional schemes is compared. As a result, the throughput performance of the proposed scheme is superior to that of the conventional scheme.
# Mechanical Design and Gait Optimization of Hydraulic Hexapod Robot Based on Energy Conservation
## 3 Robot Modeling  
### Geometric Modeling  
Reasonable leg structure design is vital to the mobility of a legged robot. As shown in Figure 3, a novel leg structure is adopted in the HHR. The hip and knee cylinders are fixed on the thigh, and the installation points of the cylinder are on both sides of the joint connection line. In the structure, the space for installation is reduced and the arm of force for cylinders can be increased. \(L_{1}\), \(L_{2}\) are the length of the thigh and shank. \(\theta_{0}\), \(\theta_{1}\), \(\theta_{2}\) are the angles of the root, hip, and root joint. \(a_{0}\), \(a_{1}\), \(a_{2}\), \(b_{0}\), \(b_{1}\), \(b_{2}\), \(\epsilon_{01}\), \(\epsilon_{02}\), \(\epsilon_{11}\), \(\epsilon_{12}\), \(\epsilon_{21}\), \(\epsilon_{22}\) are variables for the cylinders' installation. \(c_{0}\), \(c_{1}\), \(c_{2}\) and \(l_{0}\), \(l_{1}\), \(l_{2}\) are the cylinders' length and arm of force for root, hip, and knee joint.  
According to Figure 3a, the length of the hydraulic cylinder and the arm of the force can be obtained according to the geometrical relationship, where  
\[\begin{array}{l}c_{0}=\sqrt{{a_{0}}^{2}+{b_{0}}^{2}+2a_{0}{b_{0}}\cos({ \theta_{0}}-{e_{01}}-{e_{02}})}\\ c_{1}=\sqrt{{a_{1}}^{2}+{b_{1}}^{2}+2a_{1}{b_{1}}\cos({\theta_{
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 4 Sensors
### Satellite and RPAs: A Complementing System  
The use of RPAs versus satellites is often debated in the context of their agricultural applications. Nevertheless, the choice of any of these RS platforms is highly subjective to the needs of farmer and the crop to be studied. However, a minor comparison of these is presented considering their usefulness in RS.  
Limited battery life and flight time hinder extensive spatial coverage using RPAs, despite being regarded as optimal for providing robust, reliable and efficient crop phenotyping [144]. RPAs offer lower operational costs; however, for large amounts of data (to cover larger areas), data processing costs increase exponentially. Similarly, flexibility with flying times and the significant resolution of RPA imagery are desirable characteristics over satellite imagery [7].  
On the other hand, freely accessible satellite data are gaining popularity. Even so, these freely available data come with coarser spatial and temporal resolutions (with the exception of a few commercial satellites), and is being used to generate VIs. In this regard, AI offers leverage to fine-tune coarser satellite data based on high-resolution drone data [5]. Moreover, a large dataset for training AI models, despite poor quality or noisiness, generates substantial results compared to smaller datasets, indicating the importance of huge datasets obtained through satellite imagery [5]. This is why big data holds a remarkable position in this regard. For example, a previously reported study demonstrated  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr.** & **Category** & **Description** \\ \hline
1 & Open & Not requiring any prior operational declaration or authorization. \\
2 & Specified & Requiring an authorization by the competent authority except for standard scenarios. \\
3 & Certified & Requiring certification by the competent authority. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three categories established by European Union Aviation Safety Agency (EASA) for operating RPAs.  
the effectiveness of deep learning associated with the availability of large and high-quality training samples [145]. Furthermore, crop simulation models can be generated beforehand from these data for respective agronomic measures and crop yield estimations. Therefore, a complementing system based on these two RS platforms can supplement the efficiency of RS products.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs
### The Restoration Scheme  
Although the restoration scheme tries to establish a new light path for the interrupted service employing the remaining available resources after failures, it cannot guarantee 100% recovery. Compared with the protection scheme, the restoration scheme does not need to reserve spectrum resources in advance so it usually requires more recovery time. For natural disasters, it is not a disaster-resilient survivability technology, especially when a large number of services need to be recovered after natural disasters. The inadequate available resources may lead to network paralysis. In [121], the authors proposed a fuzzy fault set (FFS)-based multi-link faults restoration algorithm. Experimental results showed that it had better performance compared with the extended limited perimeter vector matching protocol and could improve the restoration success rate under multi-link faults. In [122], the authors proposed a dynamic restoration scheme for EONs based on the SDN framework. The proposed scheme contemporarily exploits centralized path computation and node configuration to avoid contentions during the recovery procedure with the final aim of minimizing the recovery time. In [123], three network operator policies were proposed to yield different restoration mechanisms according to different inter-domain failure exchanges. In [124], the authors proposed a restoration-based survivability strategy, which combines the benefits of both cloud service relocation and service differentiation concepts. In [125], the authors exploited wavelength converters in GMPLS-based wavelength switched optical networks to reduce resource contentions during light-path restoration. In [126], the authors examined dynamic optical service restoration on a large-scale ROADM network. They showed that with dynamic restoration, full service restorability can remain achievable even in the face of four or more concurrent fiber cuts.
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 6 Comparison of the Used Commercial Software  
The three chosen software platforms, SolidWorks, ANSYS Mechanical, and ABAQUS, were compared for their optimization capabilities and limitations during the TO workflow. In addition, three case studies, a bell crank lever, a pillow bracket, and a small bridge, were optimized using the above software and their results were compared with respect to the number of used design cycles, the optimization time, their weight after TO, and 3D preparation, as well as their maximum Von Mises stresses and FOS against yield.
## 2 Performance Limitations of LS-MZS
### Insertion Loss  
Insertion loss is a major challenge that limits the scaling up of the switch fabric. Losses can reach up to 2 dB per MZI and 0.2 dB per waveguide crossing. Unfortunately, these two components are needed in high numbers to design any switch matrix. Therefore, the architecture chosen must limit the number of cascaded MZIs and waveguide crossings. The power attenuation can be modeled by a loss factor \(l_{MZI}\) and \(l_{X}\) in the case of an MZI and a waveguide crossing, respectively.  
\[P_{out_{MZI}}=P_{in}l_{MZI} \tag{1}\]  
\[P_{out_{X}}=P_{in}l_{X} \tag{2}\]  
where \(P_{in}\) is the optical input power, \(P_{out_{MZI}}\) and \(P_{out_{X}}\) are the output powers of each component, respectively (Figure 1). Insertion loss is then defined as the loss factor in dB, noted \(Loss_{MZI}\) and \(Loss_{X}\) in the following.
# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
## 5 Artificial Intelligence Applied to Building Height Estimation
### Extracting Spatial Information  
This model provides 2D relative spatial information from the image. However, we would like to obtain 3D real-world spatial information, as we could then measure building height directly. This requires a point cloud to be generated from the input image. There are machine learning models that provide pixelwise depth estimation from single-view images. With the depthmap of the image and camera data such as focal length, we would be able to assign a distance and direction to every pixel of the image and thus create the point cloud. Unfortunately, the precision of the single-view depth estimation models [44] developed to date is insufficient for this purpose. In the KITTI benchmark, the best models present an error of around 8%. This means an error of 8 m in the depth estimation of a building at a distance of 100 m from the camera. We carried out tests with depth models trained on KITTI, but the point cloud we obtained was not accurate. As it was not possible to generate a point cloud from single view-images, we follow two alternative approaches. Firstly, a point cloud can be generated through photogrammetry from multiple images or a video [45]. This implies a high computational cost and takes away versatility from our method, as it is much more difficult to obtain multiple images with the necessary characteristics from a video. The second option is to develop a method that does not require a point cloud, which is the approach we have found to be the most feasible.  
Figure 8: Model decoder. \(\times\) denotes array dimension.  
Figure 7: Atrous spatial pyramid layer.
###### Abstract  
A new coupling method, immersed moving boundary-discrete element method-lattice Boltzmann method (IMB-DEM-LBM), is proposed to simulate particle flow for application in soil mechanics or coastal engineering. In this study, LBM fluid is simulated on the regular Eulerian grid and Lagrangian particle motion is governed by DEM while IMB couples the two algorithms. The new method is promising and robust as it resolves numerical instability near the particle boundary caused by mesh distortion in the conventional grid method. In IMB, the interface lattice solid fraction determines the distribution function ratio of non-equilibrium bounce back and Bhatnagar-Gross-Krook (BGK) collision. The non-equilibrium bounce back at moving boundary results in the fluid momentum change and contributes to the hydrodynamic force on particle. For numerical stability, this paper introduces the hydrodynamic force calculation concept from IB (immersed boundary method) to IMB, and at the same time, proposes a new solid fraction calculation method for sphere that divides the intersection into simple sector and triangle, as well as calculates the intersection area by vector. With this method, approximate inaccuracy is overcome while complicated integration is avoided.  
lattice Boltzmann method; discrete element method; immersed boundary method; particle flow; solid fraction calculation