esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current Supervisory or Regulatory Technologies
### Definition of Several Concepts: Fintech, RegTech, SupTech
#### 2.1.1 FinTech  
FinTech was first coined in 1993 [6] and defined by the Financial Stability Board (FSB) as \"technology-enabled innovation in financial services\" [7]. It refers to using technology to deliver innovative financial products, services, and solutions. It encompasses a wide range of applications, including mobile banking, online payment systems, peer-to-peer lending, robot advisors, digital currencies, digital wallets, etc. For example, a pioneer digital wallet provider, Safaricom, which is also a mobile network provider at the same time, provides M-PESA for clients who can manage money and accounts by themselves within the app [8]. Furthermore, multiple Fintech examples are listed by [9] in both the banking process and the insurance process in terms of provider types (bank, non-bank, insurer, and non-insurer) and interaction types such as B2C, C2C, and B2B, etc. For instance, Fintech products in the B2C business mode are identified, such as video conferencing (HVB, GER) and personal finance management (Mint, US) for Advice services; social media payments (Commonwealth Bank of Australia, AUS) and cryptocurrency (Bitcoin) in Payments services; robot-advisory (UBS, CH) and multi-assets trading (360t, GER) in Investment services; online credit application (Targobank, GER) and Corporate credits (Finpoint, GER) in Financing services; online bank account opening (Fidor Bank, GER) and electronic data safe (SecureSafe, CH) in Cross-process service. FinTech aims to enhance and automate various financial processes, increase accessibility, improve efficiency, and provide new business models. Although the development of fintech has become relatively mature, issues and challenges still exist. The research in [10] identified a series of Fintech challenges in framework and model, regulation and policy, financial literacy, supervision, personal data protection, collaboration, data security, etc. For instance:  
* Fintech needs a practical and systematic framework.
* A stable and efficient public infrastructure and trust in the payment system are required for public policies.
* Absolute regulation may not be suitable or effective enough for Fintech.
* Significant data protection issues are caused by the use of big data and new technologies.
* knowledge of the success factors of equity crowdfunding open to non-accredited investors.
* Issues in secure data storage and processing.
# Investigation of Electro-Elastic Properties for LN Single Crystals at Low Temperature
## 1 Introduction  
Lithium niobate crystals (LiNbO\({}_{3}\), LN) are a type of famous multifunctional materials which have excellent piezoelectric, nonlinear, photorefractive, electro-optical, acousto-optic and pyroelectric properties [1, 2, 3, 4, 5, 6, 7, 8]. Based on these properties, LN crystals are widely used in acoustic wave transducers, acoustic filters, acoustic delay lines, electro-optical Q-switches, optical amplitude modulators and so on [9, 10, 11, 12, 13, 14].  
It is reported that LN crystals have large piezoelectric coefficients, measuring 6-70 pC/N [15, 16]. Particular, LN crystals possess high electromechanical coupling factors. The thickness shear electromechanical coupling factor for X-cut crystals reaches 68%, while the electromechanical coupling factor of quartz crystals is just 0.98% with the same condition [17, 18]. LN crystals with outstanding piezoelectric properties are widely used in transducer applications, such as in the nondestructive testing of single crystal transducers. The piezoelectric coefficients, electromechanical coupling factors, mechanical quality factors and the Curie temperature are the main performance parameters needed to measure the performance of the single crystal transducers, which have an effect on sensitivity, efficiency, timing and temperature stability, etc. Therefore, studying the electro-elastic properties of LN crystals systematically is a great supplement for the piezoelectric materials of transducers.  
In previous studies, much research about the electro-elastic properties for LN crystals has been done, including the electromechanical coupling factors with different orientations, and temperature dependence in different temperature ranges, etc., [19, 20] while studies regarding the electro-elastic properties at low temperatures are rare. In 2012, [PERSON] reported the elastic constants and piezoelectric coefficients, using resonant ultrasound spectroscopy from an ambient temperature to 6 K, and discussed the elastic and piezoelectric properties from the viewpoint of group theory and lattice dynamics [21].  
In this study, to conduct a complete investigation of the electro-elastic properties of LN crystals and be able to extensively use LN crystals in a wider temperature range, the electro-elastic properties, including the dielectric permittivities, elastic compliances, electromechanical coupling factors and piezoelectric coefficients, were measured and determined by using the impedance method from \(-150\)\({}^{\circ}\)C to \(150\)\({}^{\circ}\)C.
## 3 Results and Discussion
### Continuous LBI Measurement
#### 3.2.4 Chemical Properties  
Table 2 presents the results of the comparison between the chemical properties before (Day 0) and after sedimentation (Day 30). Most of the parameters studied showed significant differences before and after sedimentation, with the exception of \(\delta\)-tocopherol and \(\gamma\)-tocopherol. Among all the changes in chemical properties, the ash content was not detectable after sedimentation. The total contamination decreased by 99.9% and the phosphorus content decreased by 99.5%, followed by water content (90%) and carbon residue (87%). The chemical properties that were strongly associated with the oil oxidation process--i.e., oxidation stability, vitamin E, and acid value--changed to a lesser degree. For pressed sesame oil, [PERSON] et al. reported a phosphorus content of 1.14-3.94 mg/ kg [40]; [PERSON] et al. found an oxidation stability of 5.8 h and \(\gamma\)-tocopherol of 456 g/g [41]; [PERSON] et al. recorded an acid value of 3.68 mg/kg [42]. Differences between these values and those in our study may be attributed to variations in sesame seed quality or variety, extraction conditions, or storage methods.  
Figure 7: (a) Regression between interface height and mean grey value intensity I\({}_{\text{mean}}\) of segmented images. (b) Regression when the interface height is less than 100 mm.  
Among all the measured chemical properties, ash content, phosphorus content, total contamination, and carbon residue should be mostly related to the particles present inside the crude oil. The settling of the particles caused more than an 86% drop in these values in the sesame oil. As for the 90% decrease in water content, [PERSON] and [PERSON] related it to high-temperature evaporation [15]. Since the sesame oil was within 21 \(\pm\) 3 \({}^{\circ}\)C, it is more likely that the decrease in water content was also influenced by the consumption of H\({}_{2}\)O in the oil hydrolysis process. The decrease in oxidation stability and the increase in acid value are typical changes that occur during oil oxidation. However, the values of the two types of vitamin E (\(\delta\)-tocopherol, \(\gamma\)-tocopherol) did not change significantly during the oxidation process. This could be because sesame oil contains additional antioxidants such as sesamin, sesamol, and sesamolin, which protected vitamin E from decomposition [6]. After sedimentation, the measured chemical values did not meet the requirements of combustion engines (total contamination upper limitation, 24 mg/kg) [43] but were suitable for food (acid value) [44] and pressure stoves (acid value and total contamination) [14; 45].
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 2 Numerical Model  
### Flow Model  
The flow model is the backbone of the sediment-transport model. The proposed sediment-transport model is based on a 2D depth-integrated hydrodynamic-flow model developed by [PERSON] et al. [36] where the continuity equation and the momentum equations for unsteady turbulent flow are:  
\[\frac{\partial h}{\partial t}+\frac{\partial(hu)}{\partial x}+\frac{\partial( hv)}{\partial y}=0 \tag{1}\]  
\[\frac{\partial(hu)}{\partial t}+\frac{\partial(hu^{2})}{\partial x}+\frac{ \partial(huv)}{\partial y}=-gh\frac{\partial\eta}{\partial x}+\frac{1}{\rho}[ \frac{\partial(h\tau_{xx})}{\partial x}+\frac{\partial(h\tau_{xy})}{\partial y }]+\frac{(\tau_{ux}-\tau_{bx})}{\rho}+f_{cor}hv \tag{2}\]  
\[\frac{\partial(hv)}{\partial t}+\frac{\partial(huv)}{\partial x}+\frac{ \partial(hv^{2})}{\partial y}=-gh\frac{\partial\eta}{\partial y}+\frac{1}{ \rho}[\frac{\partial(h\tau_{yx})}{\partial x}+\frac{\partial(h\tau_{yy})}{ \partial y}]+\frac{(\tau_{yy}-\tau_{yy})}{\rho}-f_{cor}hu \tag{3}\]  
where \(t\) represents time (s); \(u\) and \(v\) are depth-integrated velocity (m/s) components in the \(x\) and \(y\) directions, respectively; \(\eta\) is the water surface elevation (m); \(\rho\) is the water density (kg/m\({}^{3}\)); \(h=\eta-z_{b}\) is the local water depth (m) and \(z_{b}\) is the bed elevation (m); \(g\) is the gravitational acceleration (m/s\({}^{2}\)); \(f_{cor}\) is the Coriolis parameter; \(\tau_{yx}\) and \(\tau_{by}\) are shear stresses (Pa) on the bed surface and were calculated as follows:  
\[\frac{\tau_{b(x,y)}}{\rho}=\frac{gn^{2}}{h^{1/3}}\sqrt{u^{2}+v^{2}}(u,v) \tag{4}\]  
\(n\) is [PERSON]'s roughness (m\({}^{-1/3}\)s); \(\tau_{ux}\), \(\tau_{uy}\) are surface wind shear stresses (Pa):  
\[(\tau_{ux},\tau_{uy})=\rho_{air}c_{fa}\sqrt{U_{w}^{2}+V_{w}^{2}}(U_{w},V_{w}) \tag{5}\]  
where \(c_{fa}\) is the friction coefficient at the water surface and \(U_{w}\) and \(V_{w}\) are wind velocity (m/s); and, \(\tau_{xx}\), \(\tau_{xy}\), \(\tau_{yx}\), and \(\tau_{yy}\) are the depth-integrated Reynolds stresses (Pa) including both viscous and turbulent effects and approximated based on the Boussinesq assumption:  
\[\tau_{xx}=2\rho(\
u+\
u_{t})\frac{\partial u}{\partial x} \tag{6a}\] \[\tau_{xy}=\tau_{yx}=\rho(\
u+\
u_{t})(\frac{\partial u}{\partial y}+\frac{ \partial v}{\partial x})\] (6b) \[\tau_{yy}=2\rho(\
u_{t}+\
u)\frac{\partial v}{\partial y} \tag{6c}\]  
where a mixing-length model [21] was adopted to calculate the eddy viscosity \(\
u_{t}\) (m\({}^{2}\)/s).
## 1 Introduction
It aims to define the communication architecture of IoT, analyze the optimal satellite constellation, develop a model of interplanetary communications between Mars and Earth, evaluate Long Range (LoRa) communications on Mars, and identify potential IoT applications on the red planet. To achieve this, various technical and analytical activities will be carried out. This will include the design of communications infrastructure, encompassing IoT nodes, satellites, and interplanetary communication systems. The spatial layout and number of satellites necessary to guarantee adequate coverage on the Martian surface will also be determined. Data transmission systems will be analyzed to overcome the distances and unique conditions of the interplanetary environment for communications between Mars and Earth. Likewise, the feasibility and effectiveness of LoRa communications on Mars will be evaluated, considering factors such as transmission distance and message processing capacity. Specific scenarios, such as atmospheric, geological, and seismic monitoring, will be explored to maximize IoT's potential in exploring the red planet. These activities are essential for developing a suitable communications infrastructure and successfully implementing the IoT in the Martian context. This manuscript starts with Section 2, which is dedicated to examining the architecture suggested for its implementation in detail. Subsequently, Section 3 addresses the study of communication between IoT nodes and satellites in Martian orbit, considering aspects such as transmission distance, message capacity, the design of the satellite constellation, and the frequency of communications through LoRa and interplanetary data communications. This section lays the foundation for Section 4, which presents the results obtained from the simulations. Section 5 details the potential applications of IoT in this context, while Section 6 explains the open research challenges and future directions. The document concludes with the presentation of the conclusions.
## 1 Introduction  
The oxygen equipment of an aircraft is the first line of defense against the potentially lethal effects of hypoxia and carbon monoxide poisoning. It is the pilot's responsibility that all aboard the aircraft--crewmembers and passengers--know how to use this life-saving equipment safely and efficiently [1].  
Most oxygen systems are composed of a storage system (containers), delivery system and mask or nasal cannula. The oxygen delivery system, also known as an oxygen regulator can be one of three types: continuous flow--delivering continuous flow of oxygen from the storage to the mask (28,000 feet and lower); diluter demand: giving the user oxygen on-demand (during inhalation) stopping the flow when the demand ceases (during exhalation) to conserve oxygen (altitudes up to 40,000) and pressure demand--providing oxygen under positive pressure to over-inflate the lungs (altitudes above 40,000 feet) [2,3].  
Aircraft components require periodical maintenance, repair and overhaul (MRO) activity. For oxygen regulators MRO is normally used a specialized test bench designed to perform tests to the regulators during its work conditions. The test bench is composed of a barometric chamber, a vacuum pump, an altimeter and several flowmeters [4]. The tests are performed placing the regulator in the barometric chamber, where low pressure conditions are forced to simulate altitude conditions and then a flow is forced on the output of the regulator to simulate the inhalation of the user [5]. The relevant test flows are measured by laminar flowmeters [6].  
In this work a way was studied to automate laminar flowmeters used on oxygen regulator test benches. The flowmeters studied have particular requirements of accuracy established by the technical documentation delivered by the test bench manufacturer (1% full scale--fs). This flowmeter is meant to measure flowrate at pressures correspondent to altitudes between sea level altitude and fifty thousand feet. So, in order to achieve precise measurements, they should be corrected considering the pressure and temperature changes. At the core of this work, the correspondence between altitude and pressure was made in accordance to the U.S. Standard Atmosphere [3]. To estimate the error of the flow rate measured by this solution, an uncertainties simulation model and several calibrations were performed. The simulation model was based on the Monte Carlo method. These calibrations were made in accordance with the Guide to the Expression of Uncertainty in Measurement--GUM [7]. In this work, was also developed a data acquisition system (DAS) using a microcontroller board and two microelectromechanical systems--MEMSs. Since these MEMS did not have factory calibration, the uncertainty measurement study was performed. A methodology was developed to test the accuracy of the automated laminar flowmeter.
# Investigation of Segmented Halbach Array Design for Passive Magnetorheological Brake System
## 1 Introduction
Numerical approaches such as finite element analysis (FEA) and boundary element methods (BEMs) enabled the detailed modeling of intricate geometries. [PERSON] et al. analyzed a double cup-shaped gap magnetorheological (MR) clutch excited by Halbach arrays, optimizing its structure through an analytical model considering iron nonlinearity and saturation effects, improving accuracy in predicting electromagnetic performance [14]. [PERSON] et al. proposed a method for determining segment widths in unequal segmented Halbach arrays, enhancing air gap flux density. This demonstrates advancements in analytical modeling techniques in predicting and optimizing the performance of Halbach array permanent-magnet machines compared to conventional approaches [15]. [PERSON] et al. designed and tested a large-scale, lightweight linear Halbach array without a steel backplate, showing strong agreement between the numerical and experimental results [16]. [PERSON] et al. proposed dual-layer segmented Halbach arrays for permanent-magnet machines, analytically determining optimal configurations and demonstrating improved electromagnetic performance over single-layer designs [17]. [PERSON] et al. developed an analytical model for permanent-magnet machines using segmented Halbach arrays, enabling precise predictions of magnetic field distribution and electromagnetic performance [18]. Their findings emphasized that adjusting the design parameters, such as segments per pole and air-gap length, can increase the electromagnetic torque while reducing torque ripple, highlighting the potential for optimizing Halbach array configurations in various applications. The focus of this research is to consider the knowledge gap in the technical literature regarding factors that affect the performance of the permanent-magnet Halbach structure. This research contributed to a deeper understanding of how the dimensions of permanent magnets and the segmented Halbach structure in a cylindrical configuration influence the strength of the magnetic field, and it compared these findings with those of a conventional cylindrical magnet array. The optimized design of a permanent-magnet Halbach structure was implemented in a magnetorheological brake (MRB) to verify the correctness of these simulations.
## 2 Materials and Methods
### Data and Methods
#### 2.2.2 Verification Data  
The validation data of this study are mainly multi-sensor remote sensing image data and field photos. Because the study area is large, in order to obtain a reasonable coverage of the validation samples, we collect the reference data from Landsat time series, Gaofen-1 (GF-1) satellite, Mapping Satellite-1, ZY-3, ZY-1 02C, and historical WorldView and Quickbird very high resolution (VHR) imagery from Google Earth during the study period, together with some field photos.  
When selecting the sample data, the ground area of the reference point should be greater than a 30 m, which is the spatial resolution of the Landsat image. The multi-phase image is first registered with the same projected coordinate system as the Landsat imagery. It is then superimposed to facilitate the experimental verification. All together, we collected 54 sample areas including 1131 pixels.
## 4 The Prediction Model When Signal Approaches the Ground Station (Section B)
### Aligning the Satellite Signal Strength  
The predicted signal-to-noise ratio (SNR) was sent back from the ground station to the satellite. The aim was to maintain a constant level of satellite link budget, which is conveyed by the desired signal-to-noise ratio level and may be different from the predicted signal-to-noise ratio level. We used the following model:  
\[SNR_{predicted}\ -\ SNR_{Desired}\ =0\  
The LSTM mechanism comprises several epochs, starting with the entire input sample, reducing to a smaller number, and reducing to one instance at the final stage. We developed a hyper-parameters function and, with several empirical experiments, we identified the optimal parameters converged after four epochs. The parameters are: (a) Loss function: mean absolute error, (b) Optimizer: \"Adam\", (c) Activation function: \"tanh\", (d) Number of epochs to saturation: 4, 3 layers: 2 LSTM, 1 dense--First layer: 98 neurons, Second layer: 20 neurons, Last layer: 1 neuron.  
We used the LSTM process and compared the predicted building attenuation levels to the actual attenuation results executed over a wide variety of frequencies, from 2.4 GHz up to 72 GHz. The experiment started with training the LSTM system using 913 sample records from the STK satellite simulation connected to its corresponding earth station. Figure 9 depicts the results of the experiments performed for each of the five frequencies. The average difference between the predicted and actual results was below 2%. It can be seen, visually, from the presented figures. The \(y\)-axis represents the signal loss through the building in dB. The \(x\)-axis represents times (from the moment the ground station discovers the satellite) in seconds or the satellite's angle (above the ground) in degrees. The blue and orange lines in the top frame represent the actual and predicted attenuation levels, respectively. We can see how close the two charts are and realize that all other figures demonstrate similar charts. We checked the training results, and they resemble the accurate results to a convincing extent. Figure 9a-e depict the comparison of our experiment's predicted/true signal strength for the frequency range of 2.4-72 GHz.
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 3 Our Method
### Network Architecture
#### 3.2.2 Temporal Feature Fusion Module  
The spatiotemporal fusion module is modified from 3D ResNets [42]. Its structure is shown in Figure 5. It consists of three large residual units connected in series. The first residual unit has a step size of 1 and does not downsample the feature map in the time channel, while the last two residual units have a stride of 2, and the feature map is downsampled four times in the time channel in total. Each residual unit comprises four basic blocks connected in series. The first basic block has a different step size, and the remaining basic blocks have a step size of 1. Each basic block is composed of a 3D convolution structure superimposed with a time-adaptive module (TAM) [43]. In the basic block, conv3d, k = 3, s = S represents a 3D convolution with a kernel size of 3 and a stride of S. A standard 3D convolutional combination of batch normalization layers in series with ReLU activation functions is used. The TAM module enhances the network's ability to capture important temporal information by learning adaptive kernels.