esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
## 2 Numerical Method
### Numerical Domain and Boundary Conditions  
The NACA-8412 airfoil's leading edge (LE) was located at the origin of the coordinate system, and the airfoil was fixed at zero degree angle with respect to the \(x\)-axis. The post-stall angle of attack of \(15^{\circ}\) was introduced by decomposing the freestream velocity in the corresponding \(x\) and \(y\) components. Note that all distances have been expressed as function of the airfoil chord \(C\) (see Figure 1). The distance between the inlet and the LE following the \(x\)-axis was set at \(10C\), while the distance between the trailing edge (TE) and the outlet was set at \(18C\). The height of the domain is \(16C\), symmetrically distributed with respect to the airfoil. An almost identical computational domain was recently employed in [4].  
Figure 1: Sketch of the computational domain. The inlet and outlet boundaries have their limits in the points defined by the letters A and B.  
The inlet was defined by the line connecting points A and B (see Figure 1) in a counter-clockwise direction, while the outlet was defined by the line connecting A and B in clockwise direction. At the inlet, Dirichlet boundary conditions for velocity were set by imposing the decomposition of the freestream velocity as it has been previously mentioned. Regarding pressure, Neumann boundary conditions were used. At the outlet, Neumann boundary conditions for velocity and Dirichlet boundary conditions for pressure were imposed. Over the airfoil's surface Dirichlet boundary conditions for velocity were imposed in order to guarantee the non-slip condition, Neumann boundary conditions for pressure were considered.  
The point \(A\) represented in Figure 1, which corresponds to one of the connecting points between the inlet and the outlet, had to guarantee that the angle formed by its tangent was larger than the angle of attack studied (AoA = 15\({}^{\circ}\)). For the present configuration, the tangent line to the inlet curve at the point \(A\) and the horizontal form an angle of 36.87\({}^{\circ}\), complying with the mentioned restriction. Figure 2 introduces some AFC parameters as the jet position, jet width and jet inclination angle. The International Standard Atmosphere (ISA) model at sea level was used to select the air density, the pulsating flow fluid density, as well as the fluid dynamic viscosity. A relatively low Reynolds number was selected in order to have proportionally low mesh computational requirements and computational time. The values of the physical parameters involved in the present study are summarized in Table 1.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types
### Optical Sensors  
Optical sensors measure crop reflectance at specific wavelengths within a short distance and identify the crops at a large scale. They analyze crop attributes and estimate crop production during the harvest. Optical sensors, such as OptRx [58], ClorofiLOG [59], Dualex [27], Multiplex [60], Isaria, Crop Circle [61], SPAD-502 [10], CropSpec [62], GreenSeeker [63], and ALS-2 N [10], provide crop monitoring under day and night conditions [64], as shown in Table 1. The method of analysis involves collecting a large quantity of recordings per field with many measurements per second [65]. These recordings are analyzed to collect data from different crops, such as _Gossypium arboreum_ (L.) [66], _Triticum aestivum_ (L.) [67], _Triticale rimpaui_ ([PERSON] [68], _Zea mays_ (L.) [69], _Hordeum vulgare_ (L.) [70], _Saccharum officinarum_ (L.) [64], and _Solanum tuberosum_ (L.) [71].  
Optical sensors have been successfully implemented for N prediction in a variety of crops, such as _T. aestivum_[27], _S. tuberosum_[63], _Oryza sativa_ (L.) [72], _Z. mays_[73], _Capsicum annuum_ (L.) [74], and _Vitis vinifera_ (L.) [75].  
Optical sensors are used to estimate leaf chlorophyll [76]. Detailed studies were performed by [PERSON] et al. [77], [PERSON] et al. [78], [PERSON] et al. [69] and [PERSON] et al. [31] on _T. aestivum_, _Z. mays_, _O. sativa_, and _Glycine max_ (L.) Merr. Studies of [PERSON] et al. [76] and [PERSON] et al. [64] for estimating chlorophyll reported similar results to that of [PERSON] et al. [69] and [PERSON] et al. [31]. However, Dualex and Isaria sensors are not able to classify crops due to the lack of classification features [77], soil type, and luminosity effects [67]. However, sensors experience multiple problems, such as data damage and redundancy, inference, and data generation differences [78; 79].
## 3 3-D Deformable Object Models
### Nodule  
A nodule is represented as a sphere model as shown in Figure 2(a). The central position, radius and X-ray attenuation (_i.e._, transparency of X-ray) of the nodule model are denoted by \(\mathbf{x}^{N}=(x^{N},y^{N},z^{N})\), \(r^{N}\) and \(\alpha_{m}^{N}\), respectively. The X-ray attenuation of the area surrounding the nodule model (such an area is often called an _air_ area) is denoted by \(\alpha_{a}^{N}\).  
In medical diagnosis, the size of a nodule is the most important indicator to determine the stage of disease. Therefore, the radius \(r^{N}\) is more essential to describe a nodule than the other parameters. In this paper, the former (_i.e._, \(r^{N}\)) and latter are called _essential_ and _auxiliary_ parameters, respectively. A nodule model is denoted by \(o^{N}(r^{N};\mathbf{x}^{N},\alpha_{m}^{N},\alpha_{a}^{N})\), where the essential parameter is placed on the left-hand side of the semicolon. The probability of appearance of the nodule model is defined by  
\[p\left(o^{N}(r^{N};\mathbf{x}^{N},\alpha_{m}^{N},\alpha_{a}^{N})\right)=P^{N}\cdot g (r^{N};\mu_{r}^{N},\sigma_{r}^{N}) \tag{1}\]  
Figure 2: 3-D deformable object models that represent nodules, curved blood vessels (CBV) and bifurcated blood vessels (BBV), respectively.  
where \(P^{N}\) is the probability of the set of all possible nodules, and \(g(x;\mu_{x},\sigma_{x})\) is a Gaussian distribution with a mean \(\mu_{x}\) and standard deviation \(\sigma_{x}\).
# Load Testing and Analysis of a Large Span Through Simply-Supported Steel Box Arch Bridge
## 1 Introduction
[27] introduced a method that leverages influence line testing and load transition to assess bridge load-bearing capacity with accuracy and efficiency, validation through real-world bridge tests demonstrated that this method yields smaller errors compared to traditional load tests while also significantly reducing labor, materials, and time, this underscores its efficiency and practicality in bridge assessments. [PERSON] et al. [28] examined the evolution of bridge structural health monitoring technologies, with a focus on the application of knowledge-driven and data-driven artificial intelligence methods; they also suggested future trends toward hybrid knowledge-data-driven approaches. [PERSON] et al. [29] conducted field testing on the Ganjiang Bridge and updated its finite element model based on displacement assurance criteria (DAC) and frequency; this comprehensive assessment of the bridge's mechanical performance established an accurate baseline model for future structural health monitoring endeavors. In the future, as artificial intelligence technology continues to advance, bridge load testing will see the integration of more cutting-edge technologies and algorithms. For example, applying domain relationship extraction models and technologies [30] to test data processing can not only more efficiently extract key information but also provide timely warnings for abnormal data, further promoting the intelligent and efficient development of bridge load testing. Consequently, this paper presents an analysis of a load test conducted on a large span through a simply supported steel box tied-arch bridge. This experimental bridge is situated in Jingmen City, Hubei Province, and constitutes the bridge of Jingchu Avenue. The load testing of this bridge is distinguished by its multiple control sections, diverse loading conditions, and an extensive array of measurement points. During the static load test, various mechanical parameters were measured, encompassing strain, stress, hanger cable force, deflection, and horizontal displacement at the arch supports. The dynamic load test, on the other hand, assessed the bridge's natural frequencies and dynamic responses under a range of excitations, including pulse tests, vehicle-induced vibrations, and braking-induced vibrations. The comprehensive load test of this bridge has yielded invaluable data that contributes to the advancement of bridge design theory and the refinement of construction techniques. Furthermore, it serves as a continuous repository of technical data, providing a scientific foundation and offering a valuable reference for similar bridge structures.
## 2 Optimization
### Classical Iterative Optimization Algorithms
#### 2.1.3 Nesterov Accelerated Gradient Descent (NAG)  
Momentum's update \(\mathbf{m}_{t}\) can be interpreted as a two-step movement. First, we move according to decayed update history \(\gamma\mathbf{m}_{t-1}\), and then we make a step in the direction of the current gradient calculated using parameters \(\mathbf{\theta}_{t-1}\) from iteration \(t-1\). If we know that we will move in the direction of history \(\gamma\mathbf{m}_{t-1}\), then we can first make the movement and then calculate gradient from the point \(\mathbf{\theta}_{t-1}+\gamma\mathbf{m}_{t-1}\) in which we arrive instead of calculating gradient in the point from the previous iteration \(\mathbf{\theta}_{t-1}\). The formal update rule for Nesterov accelerated gradient [23] (NAG) is given with  
\[\mathbf{m}_{0} =0\quad\text{(initialize momentum)}\] \[\mathbf{m}_{t} =\gamma\mathbf{m}_{t-1}-\eta\
abla J(\mathbf{\theta}_{t-1}+\gamma\mathbf{m}_ {t-1})\quad\text{(compute momentum update)} \tag{7}\] \[\mathbf{\theta}_{t} =\mathbf{\theta}_{t-1}+\mathbf{m}_{t}\quad\text{(apply update)}. \tag{8}\]  
When overshooting local minimum due to accumulated speed happens, _looking ahead_ in (7) helps NAG correct its course more quickly than in the case with regular momentum.
## 1 Introduction  
The cracks that are present in underground concrete structures are continuously subjected to contraction/expansion [1] due to the temperature and humidity changes that occur, the \"micro-behaviors\" [2] caused by differential settlement, the water pressure, the vehicle vibration, and the environmental effects resulting from the water quality (chemical components), flow rate, and constantly wet condition [3].  
The typical micro-behaviors that cracks are subjected to can be divided into the physical behaviors caused by vehicle operation, cargo storage and movement, human traffic, equipment vibration, earthquakes [4] and settlement, and behaviors due to climate impacts, such as wind, humidity, and temperature changes. In particular, underground concrete structures are also influenced by the micro-behaviors caused by the earth pressure, water pressure, and flow rate due to the constant groundwater flow [5; 6; 7; 8]. This behavioral impact has an adverse effect on the performance of the water leakage repair materials, resulting in long-term water tightness performance (durability) degradation.  
As cracks bring about not only structural safety problems but also water leakage problems, the materials and methods used for the repair of such cracks should be able to respond to the cracks' micro-behaviors [9]. Various materials and methods have been used for the repair of structural cracks in many countries [10], but there are no existing quality control standards that reflect the complex environmental conditions of an underground space, or test methods for evaluating such conditions, which leads to the frequent re-leakage of water resulting from crack repair failures [11; 12]. In particular, the repeated repair works due to the re-leakage of water have posed a social problem, causing a decrease in the safety level of structures, an increase in the maintenance costs, and user anxiety and discomfort, among others.  
As such, this study selected the micro-behaviors of cracks as the factors that have the greatest impact on the performance of water leakage repair materials, and proposed a performance evaluation test method for such materials. In addition, this study sought to verify the performance of the repair materials that are currently being used in the field of water leakage repair by applying the proposed test method, and to thus quantify the quality control of water leakage repair.
## 2 Autonomous Orbit Determination Model
### Rotation Correction Model
#### 2.2.3 Algorithm for Global Satellite Constraints  
The estimated value of the inclination angle \(i\) and the right ascension of the ascending point \(\Omega\) in the orbit of the orbit elements obtained in the autonomous orbit determination of Satellite \(m\) is \(\sigma^{\prime}_{m}\big{(}i^{\prime}_{m},\ \Omega^{\prime}_{m}\big{)}\), and the estimated value of the inclination angle \(i\) and the right ascension of the ascending point \(\Omega\) among the real orbital elements of the satellite is \(\sigma_{m}(i_{m},\ \Omega_{m})\). Due to the unobservable satellites of constellation rotation, when an overall rotation of a rotation quantity \(\theta\big{(}\theta_{\alpha},\theta_{\beta},\theta_{\gamma}\big{)}\) occurs, the following relationship will exist between \(\sigma^{\prime}_{m}\) and \(\sigma_{m}\):  
\[\sigma=\widetilde{\sigma}+H\theta \tag{19}\]  
In the above equation, the partial derivative matrix of \(H\) Kepler orbital roots to the constellation rotation error is given without additional derivation:  
\[H=\frac{\partial\sigma}{\partial\theta}=\left(\begin{array}{ccc}\frac{ \partial i}{\partial\theta_{\alpha}}&\frac{\partial i}{\partial\theta_{\beta} }&\frac{\partial i}{\partial\theta_{\gamma}}\\ \frac{\partial}{\partial\theta_{\alpha}}&\frac{\partial}{\partial\theta_{ \beta}}&\frac{\partial}{\partial\theta_{\gamma}}\end{array}\right)=\left( \begin{array}{ccc}\cos\Omega_{m}&\sin\Omega_{m}&0\\ -\sin\Omega_{m}\text{coti}_{m}&\cos\Omega_{m}\text{coti}_{m}&1\end{array}\right) \tag{20}\]  
To estimate the rotation quantity \(\theta\big{(}\theta_{\alpha},\theta_{\beta},\theta_{\gamma}\big{)}\), the errors in the inclination angle \(i\) and in the right ascension of the ascending point \(\Omega\) of at least two satellite orbital elements are needed. In fact, in estimating a more optimal constellation rotation quantity \(\theta\), it is smaller the more satellites there are, and these satellites should be distributed as evenly as possible on each orbital plane. If the errors in the inclination angle \(i\) and in the right ascension of the ascending point \(\Omega\) of the left and right satellites in the constellation are known, and Equation (19)of each satellite is listed, we have  
\[U=X-H\theta \tag{21}\]  
In the equation, \(U=(U_{1},U_{2},\ldots,U_{m},\ldots,U_{n})^{T}\), \(H=(H_{1},H_{2},\ldots,H_{m},\ldots,H_{n})^{T}\), and \(U_{m}=\sigma^{\prime}_{m}-\sigma_{m}-H_{m}\theta\). The optimal estimated value of \(\theta\) can be determined under the least squares criterion.  
\[\hat{\theta}=\Big{(}H^{T}PH\Big{)}^{-1}H^{T}PX \tag{22}\]  
After estimating and obtaining the constellation rotation error of the orbit solved by filtering relative to the reference orbit, the results obtained in autonomous orbit determination can be directly corrected.
# Forward/Backward Decomposition for Dispersive Wave Propagation Measurements
## 4 Experimental Validation
### Experimental Results and Discussion
The average relative error for the high-frequency case is 17.37%, whereas the average relative error for the low-frequency case is 17.71%, indicating that the identified signal components account for more than 80% of the measurement data. The relative error metric is very sensitive to errors in phase, so note that even errors on the order of 20% to 30% appear to give decent reconstructions, as seen for example in Figures 5 and 6. It is also noteworthy that the errors trend upwards towards the boundaries of the scan region; this is potentially due to the necessary truncation of the sampling time of the response, which results in some signals having entered the scan region and appearing in the outermost measurements but not having traversed the entire scan region or reaching the interior scan locations. The average error in the center of the scan region, for example, from points 15 to 30, is lower than average, by about 10%. Figure 8: Decomposed low-frequency (500 Hz) beam responses showing the forward- and backward-propagating wave components for sensor 40. The relative error between the reconstructed signal and the measured signal is 115.41%. In observing the relative errors at the various scan points, we can see that the 40 th scan point for the low-frequency case exhibits a high error compared with most of the other points. In Figure 8, we can see that the measured signal is quite small in amplitude, and similarly the reconstructed signal is small due to apparent destructive interference between the forward- and backward-propagating components. This causes the relative error to be quite high, since the norm in the denominator is relatively small. In Figure 10, we look instead at the 39 th scan point for the low-frequency case, which has a relative reconstruction error of 16.75%. The reconstructed signal in Figure 10 is more similar to the measured signal, as suggested by the error metric.
## 5 Discussion  
There are several parameters such as porosity, mineralogical composition, anisotropy, and granulometry that influence the thermal sensitivity of the rocks [59]. In addition, other properties related to the porous network, such as pore volume, pore access radii, tortuosity or connectivity, condition the water flow. During heating, the creation of microcracks and the thermal expansion of minerals are two contradictory phenomena in the evolution of the microstructure, and the variation of all the above-mentioned parameters leads to differences in their thermal and hydric properties. The degree of cracking depends mainly on temperature and mechanical strains, mineralogical composition, and particle size distribution [60]. In addition, it is possible that microcracks appear during the first cycles, even with a low heating rate (1 to 2 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\)) [61].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & **TT** & & & **TF** & \\ \hline  & **25 \({}^{\circ}\)C** & **90 \({}^{\circ}\)C** & **100 \({}^{\circ}\)C** & **110 \({}^{\circ}\)C** & **120 \({}^{\circ}\)C** & **130 \({}^{\circ}\)C** & **25 \({}^{\circ}\)C** & **200(1) \({}^{\circ}\)C** & **200(5) \({}^{\circ}\)C** \\ \hline CC (CRI5) & 0.26 & 0.26 & 0.42 & 0.29 & 0.53 & 0.51 & - & 0.11 & 0.72 \\ FS (CRI10) & 0.43 & 0.62 & 0.57 & 0.59 & 0.75 & 0.86 & - & 0.48 & 0.71 \\ GA (CRI10) & 0.31 & 0.05 & 0.49 & 0.63 & 0.71 & 0.6 & 0.67 & 0.78 & 0.89 \\ GS (CRI10) & 0.90 & 0.56 & 0.71 & 0.7 & 0.71 & 0.64 & 0.77 & 0.83 & 0.68 \\ \hline \hline \end{tabular}  
* CC: Chalons chalk; FS: Fontainebleau sandstone; GA: Gris Alba granite; FS: Golden Ski granite; TT: Thermal Threshold test; TF: Thermal Fatigue test.  
\end{table}
Table 4: Cooling rate indexes (\({}^{\circ}\)C\(\cdot\)min\({}^{-1}\)) of the 4 rocks before and after treatment at different temperatures (in blue, values lower than the initial values, in red, values higher than the initial values).
# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 5 Discussion
### Addressing Research Questions
RQ3: To what extent do existing federated machine learning (FML) models in cybersecurity address the critical need for explainability, and what are the prevalent methods and their limitations in enhancing model interpretability for high-stakes cybersecurity decision-making? Explainability in FML models for cybersecurity remains an area requiring substantial development. While interpretability tools such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are employed to enhance transparency in both FL and hybrid models, their integration into complex FML architectures remains limited [66; 72; 80; 85; 99; 100]. This limitation impacts transparency and usability, particularly in high-stakes decision-making contexts such as intrusion prevention and fraud detection, where understanding the rationale behind system decisions is essential. Advancing explainability mechanisms, including developing more seamless integration methods and enhancing model interpretability without compromising performance, will be crucial for building user trust and facilitating broader adoption of FML models in cybersecurity applications [101; 102; 103; 104; 105]. RQ4: What are the primary research gaps and emerging trends in federated machine learning (FML) for cybersecurity, and what novel solutions, such as alternative privacy-enhancing technologies beyond blockchain (e.g., multi-party computation), are critical to overcome existing limitations and enhance real-world applicability? Our review identifies several primary research gaps and compelling emerging trends in FML for cybersecurity. Key gaps include the need for more extensive testing on adversarial examples and zero-day attacks, particularly for models involving residual networks [97]. Limitations persist in fully integrating FML with real-time cyber operations, and challenges in coordinating diverse agent types in agentic hybrid AI [63; 82]. Model drift and the lack of standard benchmarks for continual learning in dynamic cyber settings also represent significant hurdles [78, 114, 115]. However, emerging trends offer promising solutions. Quantum-enhanced FL is exploring novel ways to boost accuracy, privacy, and robustness using quantum-inspired optimization and quantum cryptographic primitives [93, 94, 95]. Neuro-symbolic AI is gaining traction for its potential to provide interpretable and adaptive solutions by combining deep learning with symbolic knowledge [110, 111, 112]. Furthermore, agenti hybrid AI is developing autonomous defense agents using reinforcement learning and LLMs for proactive network defense. Beyond blockchain, integrating multi-party computation (MPC) or advanced homomorphic encryption more deeply with FML is critical for achieving even stronger privacy guarantees and enhancing real-world applicability without compromising performance. These novel solutions are essential for overcoming existing limitations and advancing the state of FML in cybersecurity.