esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 5 Error Analysis and Discussion
### Rotating Disk System Error  
The machining accuracy and abnormal installation of the system are also the main reasons for measuring errors. Figure 11 shows a schematic diagram of the non-ideal installation state of the rotating disk and LDV in space.  
Three right-handed Cartesian coordinate systems, O-XYZ, O-X\({}_{1}\)Y\({}_{1}\)Z\({}_{1}\), and O-X\({}_{2}\)Y\({}_{2}\)Z\({}_{2}\), are established at the mass center O of the rotating disk. Define the actual rotation axis of the rotating disk as Z, the normal of the rotating disk plane as Z\({}_{1}\), and the vertical direction of space as Z\({}_{2}\). Due to unsatisfactory processing and installation, Z, Z\({}_{1}\), and Z\({}_{2}\) may not coincide. Theoretically, they are not necessarily in the same plane except for the intersection  
Figure 11: Schematic diagram of the non-ideal installation state of the rotating disk and LDV in space. (**a**) Three coordinate systems of the rotating disk system; (**b**) Schematic diagram of the installation position of the rotating disk and LDV.  
point O. The coordinate system O-X\({}_{2}\)Y\({}_{2}\)Z\({}_{2}\) is an ideal space coordinate system. Theoretically, the laser spot P should be located on the X\({}_{2}\)-axis. However, due to installation errors, the actual measuring point P\({}^{\prime}\) may be found on the coordinate axis X\({}_{1}\) (the thickness of the rotating disk is ignored). Set the X-axis in the OX\({}_{1}\)X\({}_{2}\) plane, and assume that the included angles between Z\({}_{1}\) and Z and Z\({}_{2}\) in space are \(\theta\) and \(\varphi\), respectively. To study the error introduced by the installation accuracy, it is necessary to analyze the combined effect of these two angles.  
Theoretically, angles \(\theta\) and \(\varphi\) can be coupled into an infinite number of situations in space. However, these two angles can always be decomposed into the planes OX\({}_{2}\)Z\({}_{2}\) and OY\({}_{2}\)Z\({}_{2}\) and are denoted as \(\theta_{y}\), \(\varphi_{y}\), and \(\theta_{x}\), \(\varphi_{x}\), respectively. These two sets of angles are superimposed to form the roll angle \(\alpha_{y}\) and the pitch angle \(\alpha_{x}\) of the rotating disk, respectively. However, the two situations affect the LDV values and dynamic measuring results differently and thus need to be analyzed and discussed separately.
## 2 Materials and Methods  
### Carrier Dynamics in Graphene upon Ultrafast Optical Excitation  
We start by discussing the ultrafast dynamics of non-equilibrium electrons in graphene [44], specifically when hot carriers are excited optically with an ultrafast laser pulse, typically in the femtosecond domain. We focus on interband excitation, when the incident pump photon energy \(\hbar\omega_{b}\) is high enough to promote electrons to the conduction band, that is, when \(\hbar\omega_{b}>2E_{F}\), with \(E_{F}\) being the Fermi energy. When such an ultrafast laser pulse impinges on graphene, electrons from the valence band are excited to the conduction band. Therefore, two populations of non-thermalized particles are created: the excited electrons in the conduction band and the corresponding holes in the valence band. These non-thermalized carriers can hinder the transfer of additional electrons to the conduction band, and they evolve over time into separate thermal distributions, each characterized by its own temperature. The true equilibrium of such hot carriers is ultimately attained at a later stage when the excess energy is released to the lattice and the additional carriers created by light recombine.  
The dynamics of these hot carriers in graphene is nowadays understood to occur via several stages and decay channels [44] (see Figure 1). Initially, the promptly excited carriers characterized by a spectrally narrow distribution primarily undergo carrier-carrier scattering on a 10 fs timescale [47], and two populations of hot carriers, one of electrons and another of holes, are formed. These populations can be described as Fermi-like distributions, featuring different chemical potentials while sharing a common temperature, distinct from that of the lattice [37]. Over longer timescales, the relaxation of hot carriers toward their equilibrium state is accomplished via several stages and decay channels, such as optical phonon emission [41], Auger recombination, and hot plasmon emission [45; 46; 47], on a timescale of hundreds of femtoseconds, as well as direct and disorder-assisted acoustic phonon emission on picoseconds scale. Thermalization with the lattice is achieved before the electron populations in both conduction and valence bands reach their equilibrium values (i.e., the electrons' and holes' quasi-Fermi levels become equal (\(F_{e}=F_{h}=E_{F}\))). The recovery of the equilibrium concentrations of electrons and holes is the slowest process in graphene.
## 2 Theoretical Background and Mathematical Formulation
### Development of Analytical Impact Force Models Using Optimization  
A numerical model for impact force has been developed based on 10 experimental test data results. The optimization technique is selected to seek an optimum analytical impact force model. Typically, an optimization problem with constraints can be expressed as:  
\[\text{Minimize}\,f(x) \tag{2}\]  
\[\text{Subjected to}\,\,\left\{\begin{array}{lcl}g_{i}\left(x\right)\leq 0&:&i=1,2, \ldots.a\\ h_{i}\left(x\right)=0&:&j=q+1,\ q+2,\ \ldots m\end{array}\right. \tag{3}\]  
where \(f(x)\) is an objective function, \(g(x)\) are the inequality constraints, \(q\) is the number of inequality constraints, \(h(x)\) are the equality constraints, and \(m\)-\(q\) provides the number of equality constraints [14].  
During the optimization procedure, a sum of squares of the difference between the experimental test data and the numerical data was taken as the objective function, expressed in Equation (4), in this study. Therefore, the optimum model must minimize the objective function. The Nelder-Mead sequential simplex algorithm, _i.e._, the _fminsearch_ function in MATLAB (R2015b, The MathWorks Inc., Natick, MA, USA, 2015) [22], was applied to solve the problem.  
\[f\left(x\right)=\sqrt{\sum\left(x_{exp}-x_{num}\right)^{2}} \tag{4}\]  
where \(f(x)\) is the objective function, \(x_{exp}\) is an experimental test data, and \(x_{num}\) is generated data using the proposed impact force model.  
With respect to the developed analytical impact force model, the impact exposure level was calculated using Equation (1), and the analytical impact force model was verified through a comparison with an allowable range presented in KS.  
Figure 2: Overall procedure of computing Impact Exposure Level: (**a**) time series data; (**b**) dividing into and converting to \(1/1\) octave band center frequency; and (**c**) computed impact exposure level.
# Neuro-Inspired Computing with Spin-VCSELs
## 2 The Theoretical Model  
### The Spin-VCSEL  
The spin-flip model as described in [26] is used to simulate the spin-VCSEL, as it has been shown to correctly describe the experimentally observed behavior of these lasers. We extend the model to incorporate the optical feedback, which gives us the following rate equations:  
\[\dot{E}^{\pm}=\frac{1}{2\tau_{p}}(1+i\alpha)(\mathcal{N}\pm n-1)E^{\pm}-\big{(} \gamma_{a}+i\gamma_{p}\big{)}E^{\mp} \tag{1}\]  
\[-\big{(}\epsilon_{a}+i\epsilon_{p}\big{)}\big{|}E^{\pm}\big{|}^{2}E^{\pm}+ \eta e^{i\Omega}E^{\pm}(t-\tau_{D})\]  
\[\dot{\mathcal{N}}=\gamma\Big{[}J^{+}(t)+J^{-}(t)-\mathcal{N}-(\mathcal{N}+n) \big{|}E^{+}\big{|}^{2}-(\mathcal{N}-n)\big{|}E^{-}\big{|}^{2}\Big{]} \tag{2}\]  
\[\dot{n}=\gamma\Big{[}J^{+}(t)-J^{-}(t)-(\mathcal{N}+n)\big{|}E^{+}\big{|}^{2}+ (\mathcal{N}-n)\big{|}E^{-}\big{|}^{2}\Big{]}-\gamma_{s}n \tag{3}\]  
Here, \(E^{\pm}\) stands for the right (+) and left (-) circularly polarized components of the slowly varying amplitudes of the electric field, \(\mathcal{N}\) is the total population inversion in the laser with a decay rate \(\gamma\) and \(n\) is the population difference between the spin-up and spin-down electrons with a decay rate \(\gamma_{s}\) due to spin relaxation. The photon lifetime and linewidth enhancement factor are given by, respectively, \(\tau_{p}\) and \(\alpha\). The amplitude and phase anisotropies of the laser cavity are given by \(\gamma_{a}\) and \(\gamma_{p}\). The term \(\big{(}\epsilon_{a}+i\epsilon_{p}\big{)}|E^{\pm}|^{2}E^{\pm}\) takes into account saturation effects associated with the amplitude and phase of the field. In the last term of Equation (1), \(E^{\pm}(t-\tau_{D})\) is the optical feedback after a delay \(\tau_{D}\), \(\Omega\) is the constant feedback phase and \(\eta\) is the feedback rate. \(J^{\pm}\) are the time-dependent pumping rates of spin-up (+) and spin-down (-) electrons. In practice, as mentioned in [26], the spin-VCSEL has an electrical pump \(J_{0}\) and a pulsed optical spin injection. The electrical pump contributes equally to the spin-up and spin-down populations, whereas the individual populations can be pumped separately with the optical spin injection. The pumping mechanism is further explained in Section 2.2, since it is also the mechanism used to inject data.  
The values used for the different parameters are summarized in Table 1. The parameters of the spin-VCSEL were chosen to be the same as in [26], where a bitstream was being modulated at speeds of 240 Gbit/s. The electrical pump and optical pump amplitude were chosen after a few exploratory trials and in Section 3.3 an extensive scan was performed to find the optimized values. The number of nodes was scanned from 5 to 100, as the typical number lies in this range [19; 20; 21]. The node spacing is scanned over a range from 0.5 ps to 10 ps, which is centered around the period of the polarization oscillation \(\frac{\pi}{\gamma_{P}}\), because the node spacing is typically of the same order of magnitude as the fastest time scale present in the system [3; 14].
## 3 Method
### Scene Graph Generation and Graph Embedding Representation  
To analyze economic activities in urban environments, the study first applies semantic segmentation and object detection models to generate scene graphs from satellite or street view images, extracting critical spatial and structural information such as buildings, roads,  
Figure 1: The architecture of Two Stream Network.  
and green spaces. Figure 2 presents an example of an urban area analysis image. Specifically, the Mask R-CNN algorithm is employed to detect and segment relevant elements in the images [11]. The resulting scene graph encodes the structure of economic activities, with nodes representing semantic objects (e.g., buildings, parks) and edges reflecting spatial relationships between these objects. This representation provides a detailed visualization of the spatial layout of various urban zones, such as commercial, residential, or industrial areas, serving as foundational data for further economic activity analysis.  
\[I\in\mathbb{R}^{H\times W\times C} \tag{1}\]  
where \(I\) is the input satellite image or street view image, \(H\) and \(W\) are the height and width of the image, respectively, and \(C\) is the number of color channels.  
\[S=(V,E), \tag{2}\]  
Figure 2: Urban area analysis.  
where \(S\) is the generated scene graph, consisting of the set of nodes \(V\) and the set of edges \(E\).  
\[V=\{v_{1},v_{2},\ldots,v_{n}\}. \tag{3}\]  
Each node \(v_{i}\in V\) represents a semantic object, such as a building, road, or green space.  
\[E=\{e_{ij}:v_{i},v_{j}\in V\}, \tag{4}\]  
where each edge \(e_{ij}\in E\) represents the spatial relationship between nodes \(v_{i}\) and \(v_{j}\).
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 2 Literature Review
The system was assessed using machine learning models and a distributed multi-layer classifier. The number of network nodes did not impact the system's performance because it was set up using Amazon Web Services. To improve content distribution and management, this study suggests a machine learning-based, zonal/context-aware, equipped-content pre-caching technique for VANET. The suggested methodology improves accuracy and cache hit ratio while lowering average delay and network congestion [27; 28]. Another research paper implemented in this context focused on solving issues with overheads, resource utilization, slow startup times, and the expense of running mobile applications. This study suggests a dynamic decision-based task-scheduling technique for microservice-based mobile cloud computing applications (MSCMCC). The suggested approach increases mobile server utilization while successfully lowering the cost of various applications [29]. The presented literature focused on developing different algorithms for routing, secure transactions, and communication between the vehicles and RSU. Although multiple methods and models are presented, secure weather forecasting information through routing techniques has not been presented in the literature. Redundancy, latency, collision, and incorrect message reception towards the destination are all potential effects of a VANET. One of the most crucial types of information for network control is the weather, which offers a better network simulation environment. The two main issues found within the network are packet losses and network traffic delays. Based on the discussion above, in the literature we present novel weather forecasting information through secure routing with efficient transmission in VANETs. In the next section, we present the proposed methodology.
# Developing a Decision-Making Framework to Improve Healthcare Service Quality during a Pandemic
## 1 Introduction
Stakeholders normally favor solutions that will improve their department, even if these solutions do not always advance overall system improvement. Similarly, decisions related to improving ICU service require unanimous support among stakeholders, which entails either full agreement or some level of compromise. This proposed framework integrates the best-worst method (BWM) and Multi-Actor Multi-Criteria Analysis (MAMCA) with a Multi-Objective Linear Programming (MOLP) optimization model. Specifically, the BWM first assigns weights to all the criteria based on stakeholder input, and MAMCA is then used to identify the best alternative for each stakeholder group. Since the integrated BWM-MAMCA results only show the best-ranked alternative for each stakeholder group, but not the best common solution among all groups, an MOLP model is utilized to obtain a consensus solution. This research makes the following key contributions:  
1. It identifies a comprehensive list of factors for improving service quality in ICUs based on a detailed review of the relevant COVID-19-related literature. 2. It presents the development of a systematic decision-making framework that integrates the BWM, MAMCA, and MOLP methods. 3. The proposed framework can guide decision makers in situations where there are competing demands and enable them to select the best option for improving the quality of patient service within the context of COVID-19. This paper is structured as follows: Section 2 provides a brief explanation about the criteria, alternatives, stakeholders, and method along with the rationale behind their selection. Section 3 details the development of the proposed integrated framework (BWM-MAMCA-MOLP) for finding consensus solutions in group decision-making scenarios. Section 4 documents the implementation of the proposed framework in two different scenarios, with the results being discussed in Section 5. Finally, the concluding section summarizes the overall framework, discusses its limitations, and outlines some future research directions.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 4 Results
### Nominal Scenario Performance
This high responsiveness of the controller impacts largely the propellant mass expenditure, where it is possible to see that the difference in propellant mass between the two cases is reached in the first minute of controlled action. The navigation error during the thrusted phase is characterised by a Root Mean Squared Error (RMSE) of around 2.5 m in the vertical component, 9.7 m and 2.7 m in the along-track and cross-track components respectively. The associated control errors show 122.6 m, 1273.5 m, and 259.5 m for case A and 172.1 m, 3049.9 m, and 37.6 m for case B, in the vertical, along-, and cross-track components, respectively. It is possible to highlight that, in both cases, most of the error is present in the along-track component, due to the larger velocities involved by the orbital motion in this direction. Overall, we may say that the GNC performance is satisfactory for the nominal scenario under study, giving with both tuning cases the possibility to follow the prescribed guidance, with errors below 500 m, a threshold considered reasonable for the end of the main braking phase, before the terminal descent.
# Study on the \"Two-Zone\" Heights in Lower Slice Mining Under Thick Al Alluvium and Thin Bedrock
## 6 Discussion  
Through numerical simulation, theoretical calculation and field measurement, the development heights of the caving zone and water flowing fractured zone in the overlying strata of the working face after lower slice mining are determined. The results are illustrated in Figure 17. The height of the caving zone, as determined by field measurements, is 15.26 m, while the development height of the water-conducting fracture zone is 55.59 m. The height of the caving zone calculated by the empirical formula ranges from 17.24 m to 27.72 m, while the height of the water-conducting fracture is 41.58 m-71.73 m. The height of the water flowing fractured zone obtained by the BP neural network is 56.83 m, the height of the caving zone obtained by numerical simulation is 16.78 m, and the height of the water flowing fractured zone is 53.45 m.  
By comparing the three prediction methods with the actual measurement results obtained in the field, it was found that the results of the water-conducting fracture zone predicted by the BP neural network and numerical simulation are closer to the actual values. The height of the caving zone and the water-conducting fracture zone calculated by the empirical formula in the'mining specification' is a range value. Compared to other prediction methods, accurate prediction values cannot be achieved; therefore, this formula is utilized solely as a means of verification in this paper. In order to accurately calculate the development height of the water flowing fractured zone, it is essential to comprehensively consider factors such as overburden lithology, coal seam dip angle, burial depth, mining height, dip angle, and the length of the working face inclination [29]. It is important to note that these calculation methods often require extensive analysis of field-measured data in order to derive empirical formulas for the corresponding geological conditions of a water flowing fractured zone.
###### Abstract  
Deep learning (DL) has revolutionized image classification, yet deploying convolutional neural networks (CNNs) on edge devices for real-time applications remains a significant challenge due to constraints in computation, memory, and power efficiency. This work presents an optimized implementation of VGG16 and VGG19, two widely used CNN architectures, for classifying the CIFAR-10 dataset using transfer learning on field-programmable gate arrays (FPGAs). Utilizing the Xilinx Vitis-AI and TensorFlow2 frameworks, we adapt VGG16 and VGG19 for FPGA deployment through quantization, compression, and hardware-specific optimizations. Our implementation achieves high classification accuracy, with Top-1 accuracy of 89.54% and 87.47% for VGG16 and VGG19, respectively, while delivering significant reductions in inference latency (7.29\(\times\) and 6.6\(\times\) compared to CPU-based alternatives). These results highlight the suitability of our approach for resource-efficient, real-time edge applications. Key contributions include a detailed methodology for combining transfer learning with FPGA acceleration, an analysis of hardware resource utilization, and performance benchmarks. This work underscores the potential of FPGA-based solutions to enable scalable, low-latency DL deployments in domains such as autonomous systems, IoT, and mobile devices.  
AI hardware acceleration; convolutional neural network (CNN); deep learning; field-programmable gate array (FPGA); transfer learning Article
## 3 Case Study: Analysis of Urban Pipeline Sensor Placement Optimization
### Urban Pipeline Extraction  
The method presented in this paper successfully extracted pipeline information from multiple sets of urban pipeline images and effectively removed noise from the images. As shown in Figure 9, in the experiments, morphological opening operations helped eliminate  
Figure 8: Dendrogram using average linkage (between groups).  
small black noise spots while preserving the continuity and integrity of the pipelines. By redrawing the pipelines in black, the image clarity is significantly improved.
## 6 Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Rhyodacite** & \begin{tabular}{c} **Basaltic** \\ **Andesite** \\ \end{tabular} & \
\begin{tabular}{c} **Alluvial** \\ **Deposits** \\ \end{tabular} &
\begin{tabular}{c} **Hydroxyl-Bearing** \\ **Aleration** \\ \end{tabular} & **Sulfur** \\ \hline  & \% & \% & \% & \% & \% \\ \hline ASTER & & & & & \\ \hline Debris flow/lacustrine deposits & 32.3 & 16.7 & 30.3 & 54.5 & 51.4 \\ \hline Acidic to intermediary lava flows & 11 & 16.1 & 9.6 & 10.3 & 17.2 \\ \hline Basic to intermediary lava flows & 3.3 & 5.6 & 0.5 & 1 & 0 \\ \hline Alluvial/beach deposits & 12.5 & 23 & 21.4 & 7 & 6.9 \\ \hline Scree deposits & 40.9 & 38.6 & 38.1 & 27.2 & 24.6 \\ \hline Total & 100 & 100 & 100 & 100 & 100 \\ \hline Landsat-8/OLI & & & & & \\ \hline Debris flow/lacustrine deposits & 17.9 & 3.8 & 39.4 & 49.9 & 56.7 \\ \hline Acidic to intermediary lava flows & 8.2 & 19.1 & 13.3 & 12.2 & 4.6 \\ \hline Basic to intermediary lava flows & 4.8 & 5.4 & 0.9 & 0.4 & 1.5 \\ \hline Alluvial/beach deposits & 20.1 & 24.6 & 11.3 & 8.4 & 13.7 \\ \hline Scree deposits & 49 & 47.1 & 35.1 & 29.2 & 23.5 \\ \hline Total & 100 & 100 & 100 & 100 & 100 \\ \hline Sentinel-2 MSI & & & & & \\ \hline Debris flow/lacustrine deposits & 11.7 & 11.3 & 36.7 & 53.5 & 58.1 \\ \hline Acidic to intermediary lava flows & 11.2 & 16.5 & 10.4 & 12.5 & 11.1 \\ \hline Basic to intermediary lava flows & 4.3 & 5 & 1.2 & 0.2 & 0.4 \\ \hline Alluvial/beach deposits & 25.1 & 19.5 & 13.8 & 7.1 & 7 \\ \hline Scree deposits & 47.7 & 47.7 & 38.1 & 26.6 & 23.4 \\ \hline Total & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage of endmember within each general lithologic type present in the study area. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ASTER**} & \multicolumn{2}{c}{**Landsat-8/OLI**} & \multicolumn{2}{c}{**Sentinel-2 MSI**} \\ \hline \multirow{2}{*}{**LSU**} & \multicolumn{2}{c}{**Hydroxyl-Bearing**} & \multirow{2}{*}{**Sulfur**} & \multicolumn{2}{c}{**Hydroxyl-Bearing**} & \multirow{2}{*}{**Sulfur**} & \multicolumn{2}{c}{**Hydroxyl-Bearing**} \\  & \multicolumn{2}{c}{**Alteration**} & & & & \\ \hline  & \% & \% & \% & \% & \% & \% \\ \hline
1 & 27.0 & 1.7 & 21.4 & 5.9 & 20.6 & 7.5 \\ \hline
2 & 23.4 & 4.7 & 21.7 & 0.4 & 27.2 & 3.0 \\ \hline
3 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
4 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
5 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
6 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
7 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
8 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
9 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
10 & 7.0 & 0.2 & 4.7 & 0.5 & 7.5 & 2.6 \\ \hline
11 & 8.5 & 1.3 & 7.4 & 0.1 & 6.7 & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of surface affected by high hydroxyl-bearing alteration within each LSU (described in Section 2).