esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Propulsion Systems for HAPSs
### Hybrid Propulsion
Moreover, hybrid propulsion demonstrated longer endurance compared to internal combustion engines alone, especially during summer, and a more practical HAPS size compared to a fully electric aircraft. Another type of hybrid propulsion system, a turbo-electric distributed propulsion system powered by hydrogen combustion, has been investigated in [63] for use on HAPSs carrying payloads with high electric power demands. Specifically, this system utilizes a lightweight gas turbine engine that burns hydrogen to generate electricity, which powers multiple electric motors and the payload. A small amount of thrust is also produced from the gas expelled through the turbo-generator's nozzle. Applied to a suitably designed stratospheric aircraft, this system demonstrated good endurance capabilities (on the order of days), with slight endurance improvements as the number of fans increases, while simultaneously generating sufficient power for high-demand payloads.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 4 Experimental Validation and Results
### Test Apparatus  
A test apparatus was constructed in the lab to replicate a bridge with a metallic surface, as shown in Figure 6. The structure was 6.5 ft tall and 6.5 ft wide (2 m \(\times\) 2 m) with a 2 ft by 4 ft (60 cm \(\times\) 120 cm) steel plate mounted in the center to serve as a docking platform. Figure 7 shows a schematic of this plate. An electromagnetic shaker (Ling Dynamic Systems V408) was permanently mounted on top of the plate and used to excite the structure during our experiments. The test apparatus was placed inside a drone cage where the OptiTrack Motion Capture system was used for rigid body tracking. This system can provide accurate pose data (position and orientation) for our drone flights.  
Figure 6: Overview of the test apparatus with key components annotated.
# Gaussian Process Modeling of Specular Multipath Components
## 2 System and Signal Models
### Signal Model
The DMCFor the diffuse component, we use a complex Gaussian process where we assume uncorrelated scattering [28] and hence describe \(\
u(\tau;\mathbf{p})\) by the auto-correlation function  
\[K_{\
u}(\tau,u;\mathbf{p})=\mathbb{E}\big{[}\
u(\tau;\mathbf{p})\
u^{*}(u;\mathbf{p})\big{]} =S_{\
u}(\tau;\mathbf{p})\delta(\tau-u), \tag{4}\]  
where \(\mathbb{E}\big{[}\cdot\big{]}\) is the expectation operator and \(S_{\
u}(\tau;\mathbf{p})\) is a power delay profile (PDP), which effectively models the diffuse multipath.
# A Fusion Multi-Strategy Marine Predator Algorithm for Mobile Robot Path Planning
## 1 Introduction
combined an improved artificial bee colony algorithm and evolutionary programming optimization algorithm and changed the selection conditions for the best food point to decrease the path length, path planning time, or search cost [15]. Although the improved algorithms have better performance, they also have problems with complex search mechanisms and more experimental parameters. Due to these problems being difficult to solve in the above algorithms, the marine predator algorithm (MPA) has received special attention. The MPA is a meta-heuristic algorithm proposed by professor [PERSON] in 2020, which is inspired by two strategies in foraging: Levy and Brownian motions [16]. Due to its advantages of simplicity, few parameters, and easy application to practical engineering problems, MPA is widely used in various fields, such as image segmentation [17], electricity generation from solar photovoltaic [18], and identifying photovoltaic models parameters [19]. For example, [PERSON] et al. combined the linearly increased worst solutions improvement strategy with MPA to overcome the image segmentation [20]. [PERSON] et al. proposed an improved marine predator optimization algorithm for solving the combined heat and power (CHP) economic dispatch problem [21]. However, few scholars have proposed an improved MPA to solve the mobile robot path planning problem. Therefore, this paper proposes the FMMPA, which has the advantages of high solving accuracy, high robustness and low probability of falling into a local optima, which is suitable for solving the problem in mobile robot path planning and improving the mobile robot computing power in path planning. In summary, the main contributions of this paper are as follows:  
* To solve the problem of the distance (minimum path length) from mobile robot path planning, a fusion multi-strategy marine predator algorithm (FMMPA) was proposed. * An intensified marine predator algorithm called FMMPA, combined with a spiral complex path search strategy based on Archimedes' spiral curve, nonlinear convex decreasing weights and the golden sine algorithm, is developed to enhance the ability of the original MPA to handle complex optimization applications. The performance of the FMMPA is evaluated on 16 benchmark test functions, the Wilcoxon rank sum test and part of the CEC2014 test functions. * The proposed FMMPA is applied to solve the optimal problem of mobile robot path planning in three normal environments of different sizes and two high complexity environments. The results are compared with those of metaheuristic algorithms in the literature. The remainder of the paper is organized as follows: Section 2 reviews the original MPA. Section 3 describes the details of the proposed FMMPA, along with the mathematical model and computational process. Section 4 analyses the experimental results of the proposed FMMPA. Section 5 describes the application of mobile robot path planning based on FMMPA and experimental results compared with other metaheuristic algorithms. The conclusion of this work is provided in Section 6.
## 2 Theoretical Background
### Formulation of Optimal Design Based on Reliability  
The deterministic optimal design problem can be formulated as shown in Equation (5).  
\[\begin{array}{ll}\text{Find}&d\\ \text{To minimize}&f(d,p)\\ \text{subject}&g_{j}(d,p)\leq 0,\ \ j=1,\cdots,m\\ &x_{i}^{L}\leq x_{i}\leq x_{i}^{U},\ i=1,\cdots,n\end{array} \tag{5}\]  
In this equation, \(d\) represents an \(n\)-dimensional design variable vector, \(x^{L}\) denotes the lower limit of the design variable vector, \(x^{U}\) signifies the upper limit of the design variable vector, \(p\) is a fixed system parameter, \(f\) is the objective function, and \(g\) represents a constraint. These elements are used to formulate the design problem in a way that reflects the designer's intentions. In deterministic optimal design, variables such as geometric dimensions, loads, and material properties are assumed to have fixed values without any variability. The design process is then executed, and a suitable safety coefficient is introduced to ensure the feasibility of the design plan. However, in this method, the safety coefficient is based on empirical knowledge, which often results in a conservative design or a suboptimal solution that is not practically applicable.  
In contrast, reliability-based optimal design allows for the direct incorporation of uncertainties into the design process. This approach is represented by Equation (6).  
\[\begin{array}{ll}\text{Find}&d\\ \text{To minimize}&f(d,p)\\ \text{subject}&\text{p}[g_{j}(x,p)>0]<\Phi\big{(}-\beta_{i}^{t}\big{)},\ \ j=1,\cdots,m\\ &x_{i}^{L}\leq x_{i}\leq x_{i}^{U},\ i=1,\cdots,n\end{array} \tag{6}\]  
In this equation, \(x\) represents a vector of design variables that accounts for uncertainty, \(\Phi\) denotes the cumulative distribution function (CDF) of a standard normal distribution, and \(\beta_{i}^{t}\) signifies the target reliability index for the \(i\)-th probability constraint. The differences between the DDO and RBDO approaches are illustrated in Figure 4.  
The reliable design range is defined as the area where both the constraints \(g(x_{1})\) and \(g(x_{2})\) are less than zero, whereas all other ranges are classified as design failures. In the DDO method, the optimal point is computed as the value that maximizes or minimizes the objective function based on the given constraints. As a result, the optimal point lies at  
Figure 4: Comparison of DDO and RBDO.  
the boundary of the constraints, and the design failure region falls within the probability distribution's range, leading to a low level of reliability. In contrast, the RBDO approach ensures a high level of reliability by determining the optimal point within the constraints of the probability distribution range, reducing the likelihood of design failure.
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 2 Materials and Methods
### Roti  
ROTI is one of the two standard parameters that is calculated. The Total Electron Content (TEC) is obtained first, according to Holding [18]:  
\[P_{i}=\rho+\Big{(}\delta_{R}-\delta^{S}\Big{)}+c\Big{(}B_{R}-b^{S}\Big{)}+I_{i }+T+M_{PR_{i}}+\varepsilon \tag{1}\]  
where \(P\) is the geometric distance including errors; \(i\) is 1 or 2, representing two frequencies; \(\rho\) is the true geometric distance between the satellite and receiver; \(c\) is the speed of the light; \(\delta_{R}\) is the receiver clock error; \(\delta^{S}\) is the satellite clock error; \(B_{R}\) is code bias caused by receiver hardware delay; \(b^{S}\) is code biases caused by satellite hardware delay; \(I\) is the ionospheric delay; \(T\) is the tropospheric delay; \(M_{PR}\) is the multipath error (i.e., multipath pseudo range error); and \(\varepsilon\) represents other unconsidered errors. Then raw TEC values can be calculated with Equations (2)-(4):  
\[\frac{I_{1}}{I_{2}}=\frac{f_{2}^{2}}{f_{1}^{2}} \tag{2}\]  
\[P_{1}-P_{2}=I_{1}-\frac{f_{1}^{2}}{f_{2}^{2}}I_{1} \tag{3}\]  
\[STEC=\frac{1}{40.3}\Bigg{(}\frac{f_{1}^{2}f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}} \Bigg{)}(P_{2}-P_{1}) \tag{4}\]  
where \(f\) is the frequency of the GNSS signal and the subscript \(i\) is as mentioned above. STEC is the slant TEC representing the TEC along the satellite-receiver link. STEC can be converted into vertical TEC (VTEC) using the mapping function defined in [18] as:  
\[\text{VTEC}=\text{STEC}\cos(z^{\prime})=\text{STEC}\sqrt{1-\sin^{2}(z^{\prime} )} \tag{5}\]  
\[\sin z^{\prime}=\frac{R}{R+H}\ \sin z \tag{6}\]  
where \(R\) is radius of the Earth and \(H\) is the height of the ionosphere layer. ROTI is defined as the standard deviation of the rate of VTEC (ROT) as in [19]:  
\[\text{ROT}_{t_{n}}=\frac{\text{VTEC}_{t_{n}}-\text{VTEC}_{t_{n-1}}}{t_{n}-t_{ n-1}} \tag{7}\]  
\[\text{ROTI}=\sqrt{(\text{ROT}^{2})-(\text{ROT})^{2}} \tag{8}\]  
In this study, the ROTI estimated over a 5-min interval is used, which was originally defined by [PERSON] et al. [19]. Additionally, the moving average is applied in ROTI calculation so that the time interval is the same as other parameters.
# Evaluation of the Radiological and Chemical Risk for Public Health from Flour Sample Investigation
###### Abstract  
Flour investigation, in terms of physical and chemical pollutants and mineral content, is of great interest, in view of its high consumption for nutritional purposes. In this study, eleven types of flour (five samples for each one), coming from large retailers and employed by people for different cooking food purposes, were investigated through high-purity germanium (HPGe) gamma spectrometry, in order to estimate natural (\({}^{40}\)K) and anthropogenic (\({}^{137}\)Cs) radioisotope specific activity and thus, to assess the radiological risk due to the flour ingestion. Inductively-coupled plasma mass spectrometry (ICP-MS) and inductively-coupled plasma emission spectroscopy (ICP-OES) were also employed to evaluate any possible heavy metal contamination and the mineral composition, and to perform multivariate statistical analysis to deduce the flour authenticity. The evaluation of dose levels due to flour ingestion was performed, for the age category higher than 17 years, taking into account the average yearly consumption in Italy and assuming this need to be satisfied from a single type of flour as a precaution. All obtained results are under the allowable level set by Italian legislation (1 mSv y\({}^{-1}\)), thus excluding the risk of ionizing radiation effects on humans. As far as heavy metal contamination is concerned, Cd and Pb concentrations turned out to be lower than the threshold values, thus excluding their presence as pollutants. Finally, the multivariate statistical analysis allowed to unambiguously correlate flour samples to their botanical origin, according to their elemental concentrations.  
flour; natural and anthropogenic radioactivity; heavy metals contamination; mineral concentration; high-purity germanium gamma spectrometry; inductively-coupled plasma mass spectrometry; inductively-coupled plasma emission spectroscopy; effective dose; ingestion
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Big Integer-Based Mixing Method
#### 2.2.1 Segmentation and Overflow Buffering  
* Each audio track is segmented based on a specific number of samples, resulting in multiple segments with an equal number of samples.
* At the beginning of each sample within an audio segment, a few bits (determined by floor(\(\log_{2}(k)\)), where \(k\) is the number of mixed tracks) are added as overflow buffer codes. This is to handle potential overflow issues during subsequent mixing.
* Finally, each audio segment is stored as a big integer.
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
###### Abstract  
Traditional hydro-pneumatic spring suspensions are limited to a single vertical degree of freedom, which cannot accommodate the significant technological changes introduced by the new in-wheel motor drive mode. Integrating the motor into the vehicle's hub creates a direct motor drive mode, replacing the traditional engine-transmission-drive shaft configuration. Together with the dual in-wheel motor wheelset structure, this setup can achieve both drive and differential steering functions. In this study, we designed a six-arm suspension wheel-leg device based on hydro-pneumatic springs, and its structural composition and functional characteristics are presented herein. The external single-chamber hydro-pneumatic springs used in the six-arm structure suspension were analyzed and mathematically modeled, and the nonlinear characteristic curves of the springs were derived. To overcome the instability caused by inconsistent extension lengths of the hydro-pneumatic springs during horizontal steering, the spring correction force, horizontal rotational torque, consistency, and stiffness of the six-degree-of-freedom hydro-pneumatic spring wheel-leg device were analyzed. Finally, with the auxiliary action of tension springs, the rotational torque of the hydro-pneumatic springs and the tension resistance torque of the tension spring counterbalanced each other, keeping the resultant torque on the wheelset at approximately 0 N-m. The results suggest that the proposed device has excellent self-stabilizing performance and meets the requirements for straight-line driving and differential steering applications. This device provides a new approach for the drive mode and suspension design of the dual in-wheel motor wheelset.  
in-wheel motor; wheelset structure; multi degree of freedom; hydro-pneumatic spring; suspension device; self-stabilization Article  
Design and Stability Analysis of Six-Degree-of-Freedom  
Hydro-Pneumatic Spring Wheel-Leg.
# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
## 3 Improved Genetic Algorithm Combining the Historical Optimal Population
### The Overall Flow of CHOP-IGA  
In summary, the flow of the improved genetic algorithm combining the historical optimal population is shown in Figure 12. The termination condition of the algorithm is set when the maximum number \(GENERATION\) of iterations is reached or the fitness value is unchanged for successive \(ITERATION\) generations.  
Figure 11: Comparison of iteration curves (Whether or not local optimization operation is used).  
Figure 12: Flowchart of the CHOP-IGA algorithm.
## 3 ANN Method for Maximum Power-Point Tracking (MPPT)  
### Neural Network Construction for MPPT  
ANNs have recently been developed further, not only in theory but also in practice. A common ANN for MPPT [34; 35], showed in Figure 4, has three layers: input, hidden and output layers. In this paper, the ANN input can be the PV-array environmental parameters such as irradiance or temperature and the output is the maximum power. The ANN output is used as the reference power for the DC/DC controller as Formula (4) shows. The input vector \(\mathbf{x}_{i}\) in Figure 4a is equal to \(\mathbf{G}_{i}\), which indicates the PV cells' irradiance for the weights \(\gamma_{j}\) and \(\lambda_{ij}\), while \(b_{j}\) and \(c\) are the biases for each joint from the input layer to the output layer.  
\[\left\{\begin{array}{c}V_{m}=\theta(\sum\limits_{j=1}^{m}\gamma_{j}\phi_{j} (\mathbf{G}))+c\\ \phi_{j}(\mathbf{G})=\rho_{j}(\sum\limits_{i=1}^{n}\left(\lambda_{ij}G_{i}+b_{ j}\right))\end{array}\right. \tag{4}\]  
The training data are determined by using MATLAB/Simulink (R2016b, Mathworks, Natick, MA, USA) to simulate the PV array based on parameters supplied by the manufacturer. For every case,  
Figure 4: Structure of ANN in MPPT: (**a**) Typical ANN structure; (**b**) ANN structure proposed in this paper.  
the specific irradiance and temperature values are recorded as the input data to the ANN. The MPP corresponding to each input combination is also recorded as the output data of the ANN. The network is implemented for determining the maximum power of the PV array, when the PV array is subjected to various environmental parameter conditions. The network is obtained through training (supervised) using a training function--the Levenberg-Marquardt algorithm. However, the parameters of a specific PV array have changed over the years; hence, the data used to train the ANN may not be accurate. Therefore, the output reference power is not the exact optimal power. Nevertheless, this reference operation point is close to the MPP.  
The DC-DC boost-converter proposed in this paper shows clear advantages over conventional methods, especially under the condition of irradiance mutation.
## 2 Results and Discussion
### Effect of HPP and TS Processing on the Quality of Marinated Lotus Root Slices
#### 2.1.2 Color, Texture, and TVC
The \(\Delta\)E value of the samples after TS processing was 36.41, which was significantly higher than that of the HPP samples (\(\Delta\)E = 32.20). This indicates a darker brightness and a redder and more yellowish color of the TS samples compared to the HPP samples. These color changes may be related to cell membrane disruption, thermal denaturation, and the Maillard reaction [34; 35].
## 3 Theoretical Background
### Dielectric Properties in the FIR \(\rightarrow\) NIR Region  
For InP\({}_{1-\mathrm{N}}\)Sb\({}_{\mathrm{x}}\) ternary alloys, the contribution of lattice phonons to the dielectric response function \(\widetilde{\varepsilon}_{\mathrm{L}}(\omega)\) in the FIR \(\rightarrow\) NIR region can be evaluated by exploiting a classical \"Drude-Lorentz\" model [38]:  
\[\widetilde{\varepsilon}_{\mathrm{L}}(\omega)=\varepsilon_{\mathrm{\infty}} \Bigg{[}1-\frac{\omega_{\mathrm{P}}^{2}}{\omega\big{(}\omega+\mathrm{i}\gamma _{\mathrm{P}}\big{)}}\Bigg{]}+\sum_{\mathrm{j}=1,2}\frac{\mathrm{S}_{\mathrm{ jr}}\omega_{\mathrm{TOj}}^{2}}{\omega_{\mathrm{TOjr}}^{2}-\omega^{2}-\mathrm{i} \Gamma_{\mathrm{jx}}\omega}. \tag{6}\]  
In Equation (6), the term \(\varepsilon_{\mathrm{\infty}}\) represents a weighted high-frequency dielectric function; \(\omega_{\mathrm{TOjr}}\) signify the InSb-like and InP-like TO-mode frequencies; \(\mathrm{S}_{\mathrm{jr}}\) represents the oscillator strengths; \(\gamma_{\mathrm{jr}}\) represents the broadening values of TO phonons; \(\omega_{\mathrm{P}}\) implies the plasma frequency and \(\gamma_{\mathrm{P}}\) is its damping constant. The plasma frequency \(\omega_{\mathrm{P}}\bigg{(}\equiv\sqrt{\frac{4\pi\mathrm{m}\varepsilon^{2}}{ \mathrm{m}^{*}\varepsilon_{\mathrm{\infty}}}}\bigg{)}\) and \(\gamma_{\mathrm{P}}\Big{(}\equiv\frac{\mathrm{e}}{\mathrm{m}^{*}\mu}\Big{)}\) of free carriers (electrons or holes) in doped materials can be assessed from the effective mass m\({}^{*}\), the carrier concentration \(\eta\), the magnitude of the electron charge e, and mobility \(\mu\). To simulate n\((\omega),\kappa(\omega),\varepsilon_{1}(\omega),\varepsilon_{2}(\omega), \mathrm{R}(\omega)\), and \(\alpha(\omega)\) for InP\({}_{1-\mathrm{N}}\)Sb\({}_{\mathrm{x}}\)/n-InAs epilayers, the required \(\widetilde{\varepsilon}_{\mathrm{L}}(\omega)\) of the substrate is calculated independently. To attain the best-fit parameter values in Equation (6), we followed an efficient Levenberg-Marquardt [52] algorithm and used the non-linear simulations to minimize the error function \(\Xi\) over \(n\) data points:  
\[\Xi=\frac{1}{n}\sum_{i}^{n}\Big{|}\mathfrak{P}_{i}^{exp}-\mathfrak{P}_{i}^{cal} \Big{|}^{2}, \tag{7}\]  
where \(\mathfrak{P}_{i}^{exp}\) and \(\mathfrak{P}_{i}^{cal}\) are the experimental and calculated values, respectively.
## 2 Design, Modeling, and STOP Simulation of an Optical Laser Head for LAAM
### Structural-Thermal-Optical-Performance (STOP) Modeling
The temperature distribution was computed by finite element method (FEM) based thermal analysis. Combined with the integrative constraints on the optical system design and housing structures, the deformations of the optical components and structural housing were calculated by FEM base structural analysis under the thermal-force coupling conditions (thermal expansion coefficient). Next, based on the temperature-dependent thermo-optic dispersion coefficient and deformed optic lens from structural analysis, the ray-tracing optical simulation model computed the optical performance such as ray paths, spot size, laser beam intensity, and deposited ray power. If the convergence criterion (self-consistent) was unsatisfying, the process with the adjusted deposited ray power went back to the heat sources. Such an iterative process was repeated until it met the convergence criterion.
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
###### Abstract  
With the dynamic development of imaging technologies and increasing demands in various industrial fields, neural networks are playing a crucial role in advanced design, monitoring, and analysis techniques. This review article presents the latest research advancements in neural network-based imaging, thermal, infrared, and X-ray technologies from 2005 to 2024. It focuses on two main research categories: 'Technology' and 'Application'. The 'Technology' category includes neural network-enhanced image sensors, thermal imaging, infrared detectors, and X-ray technologies, while the 'Application' category is divided into image processing, robotics and design, object recognition, medical imaging, and security systems. In image processing, significant progress has been made in classification, segmentation, digital image storage, and information classification using neural networks. Robotics and design have seen advancements in mobile robots, navigation, and machine design through neural network integration. Object recognition technologies include neural network-based object detection, face recognition, and pattern recognition. Medical imaging has benefited from innovations in diagnosis, imaging techniques, and disease detection using neural networks. Security systems have improved in terms of monitoring and efficiency through neural network applications. This review aims to provide a comprehensive understanding of the current state and future directions of neural network-based imaging, thermal, infrared, and X-ray technologies.  
neural network; image sensors; thermal imaging; infrared detectors; X-ray +
Footnote †: journal: applied sciences