esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Synthesis  
All MBG samples, including un-doped, silver-doped, cerium-doped, and silver-cerium co-doped MBG powders on the composition of 80 SiO\({}_{2}\)-16 CaO-4P\({}_{2}\)O\({}_{5}\), were fabricated using SP with horizontal furnace, and a schematic diagram of SP system is shown in Figure 1. Firstly, to prepare an un-doped MBG solution, 4.35 g of calcium nitrate tetrahydrate (Ca(NO\({}_{3}\))\({}_{2}\cdot\)4H\({}_{2}\)O), 98 wt.%, Showa, Tokyo, Japan), 19.23 g of tetraethyl orthosilicate (Si(OC\({}_{2}\)H\({}_{5}\))\({}_{4}\), \(\geq\)99 wt.%, Aldrich, Schnelldorf, Germany), 1.68 g of triethyl phosphate ((C\({}_{2}\)H\({}_{5}\))\({}_{3}\)PO\({}_{4}\), 98 wt.%, Alfa Aesar, Ward Hill, MA, USA), and 21.0 g of Pluronic F-127 EO\({}_{106}\)Po\({}_{70}\)EO\({}_{106}\) were dissolved into 120 mL ethanol (C\({}_{2}\)H\({}_{5}\)OH, 99.5%, Echo, Tainan, Taiwan). Then, 3 mL of 0.5 M hydrochloric acid (HCl, 37 wt.%, Honeywell, Charlotte, NC, USA) was added to the precursor solution and stirred for 12 h at room temperature using a magnetic stirrer to ensure solution homogeneity. The precursor solution was poured gently into 1080 mL of deionized water and stirred again for 1 h at room temperature. To prepare the doped MBG, 0.59 g of silver nitrate (AgNO\({}_{3}\), 99%, Echo, Taiwan) was used for 2.87 mol% Ag-doped MBG and 1.18 g for 5.75 mol% Ag-doped MBG. For cerium nitrate (Ce(NO\({}_{3}\))\({}_{3}\cdot\)6H\({}_{2}\)O, 99.5%, Alfa Aesar, Heysham, UK), 1.51 g was used for 2.87 mol% Ce-doped MBG, and 3.02 g for 5.75 mol% Ce-doped MBG. As for the preparation of 2.87 mol% Ag/2.87 mol% Ce co-doped MBG, 0.59 g of AgNO\({}_{3}\) and 1.51 g of Ce(NO\({}_{3}\))\({}_{3}\cdot\)6H\({}_{2}\)O were mixed into 1080 mL of deionized water and stirred for an hour at room temperature then poured into the precursor solution and stirred for 1 h to synthesize Ag-doped or Ce-doped MBG. Finally, for the preparation of the co-doped MBG, cerium nitrate and silver nitrate were sequentially introduced into 1080 mL of deionized (DI) water with an interval of 1 h between each addition. Subsequently, the co-doped solution was poured into the precursor solution and stirred for an additional hour.  
Next, for the SP process, the prepared MBG solutions were atomized using an ultrasonic nebulizer (King Ultrasonic Co., New Taipei City, Taiwan) with a frequency of 1.65 MHz. Subsequently, the droplets were transported into a quartz tube furnace (D110, Dengyng, New Taipei City, Taiwan) with preheating, calcination, and cooling zones at 400, 700, and 500 \({}^{\circ}\)C, respectively. Finally, the resulting MBG powders were collected from the steel tube [52; 53] and dried at 70 \({}^{\circ}\)C for 24 h in the oven.  
Figure 1: Schematic diagram of spray pyrolysis.
## 4 Deployment and Model Training of the PLA Scheme
### Dataset Preparation and Model Deployment  
The dataset used for this study originates from the LoRaWAN at the Edge Dataset (LoED) [18], which contains 155,422 entries in one of their files. This dataset is part of a larger series of files collected over four months. For the purposes of this study, only this specific file was selected to ensure focused analysis while maintaining relevance to the proposed PLA scheme. While RSSI values were sourced directly from the dataset, BL and altitude were synthesized to reflect realistic sensor behavior. These attributes were preprocessed, standardized, and balanced through random under sampling to address class imbalance, ensuring fair evaluation of the models.  
The ML and DL models were trained on the preprocessed dataset to classify sensors effectively. During deployment, the models continuously process incoming sensor data, analyzing any deviations in RSSI, BL, and altitude. Non-legitimate sensors are flagged as spoofed, providing real-time protection against unauthorized access.  
Figure 4: Phase 2, Continuous authentication and spoofing detection and model training.  
The hybrid approach of combining lightweight ML models for resource-constrained environments with advanced DL models for high-accuracy scenarios ensures a balance between computational efficiency and detection performance. This adaptability makes the proposed scheme suitable for diverse deployment scenarios.
# Statistical Characterization of the Magnetic Field in Space during Magnetic Storms
###### Abstract  
Magnetic storms are an important type of space weather and are usually caused by large streams of charged elementary particles (ions, for example) generated during solar wind production. The occurrence of magnetic storms can pose a threat to the internal electronics of satellites, communication, navigation, remote sensing, etc. Additionally, ground-based electrical facilities may be impacted. In this paper, we focus on the statistical characteristics of the space channel during the occurrence of magnetic storms. By analyzing the observed data for each component of the magnetic field during a magnetic storm and applying the relevant cognitive radio theory, we obtain the probability density function, autocorrelation function, and power spectrum of the magnitude of each component of the magnetic field. The results show that the probability density of the magnitude of each component of the magnetic field gradually deviates from the Gaussian distribution as the Magnetic storm ring current index (Dst index) increases during a magnetic storm, and the autocorrelation function exhibits nonstationary characteristics, which further leads to the time-varying characteristics of the power spectrum.  
magnetic storms; probability density estimation; autocorrelation function estimation; power spectrum estimation
## 1 Introduction  
The development of nutritional science has resulted in the search for food that, apart from its nutritional value, will also have health-promoting properties. Studies of the relationship between diet and human health have shown that, apart from valuable nutrients, food, especially of plant origin, also contains many valuable non-nutritive substances [1].  
_Stevia rehaudiana_ has gained great importance among nutritionists and consumers thanks to two compounds: stevioside and rebaudioside A. These glycosides have strong sweetening properties many times stronger than sucrose. Currently, nine sweet steviol glycosides listed below have been identified, which account for 4-20% of the weight of dried leaves, depending on the growing and cultivation conditions [2; 3]. In the total mass of steviol glycosides extracted from _Stevia rehaudiana_ leaves, about 65% is stevioside, 25% rebaudioside A, and the rest are rebaudioside B, C, D, E, F, duloside A and steviolbioside [2; 4].  
Stevioside is about 300 times sweeter than sucrose. It occurs in the form of a white, crystalline and odorless powder, which is extracted from the leaves of _Stevia rehaudiana_, in an amount of 4-13% [5; 6; 7; 8]. Stevioside consists of three glucose molecules and steviol [9]. Itis responsible for the bitter aftertaste of _Stevia rebaudiana_ [PERSON], remains stable over a wide temperature and pH range, and is non-fermentable. It is characterized by aroma-enhancing properties.  
Rebaudioside A is the most stable of the aforementioned glycosides; its content in fresh leaves oscillates at the level of 2-4% [5]. This compound has a better flavor profile than stevioside and is sweeter [10]. It is characterized by high solubility in water and a longer shelf life [11]. Studies conducted over the last years indicate that steviol glycosides derived from _Stevia rebaudiana_ Bertoni leaves have hypoglycemic, hypotensive, anti-inflammatory, antiviral, anti-caries and immune-modulating properties [12; 13; 14; 15]. Among others, Stevia is the carrier of many antioxidant substances, e.g., the above-mentioned steviol glycosides, which can regulate pro-/antioxidant properties of the body [16; 17; 18].  
It must be emphasized that most of the available studies related to steviol glycosides and Stevia leaves were carried out using diabetic rats, while there is lack of research on healthy rats with induced aberrations, mainly oxidative stress and changes in lipid profile [19; 20; 21].  
The aim of this study was to compare the effect of adding pure steviol glycosides or Stevia leaves to the hypercholesterolemia diet (to cause the above-mentioned changes in the body) on selected growth and biochemical parameters of experimental (non-diabetes) rats.
## 3 Results and Discussion  
### Analysis of Phase One  
Figure 6 depicts how light shelf height affects the daylighting performance of light shelves regarding the uniformity and distribution of WPI levels at distances of 1 m and 7 m from the window of the office where testing was performed. Remarkably, WPI levels in front of the window exceed 750 lux for all height levels used in this study and all design days. Additionally, the WPI levels of LS2H2 and LS3H3 are less compared to LS1H1, especially at midday. The illuminance level at the back daylight area was less than half of the illuminance level of the front area. However, the WPI levels were almost within the recommended range. Consequently, the uniformity index of LS1H1 is less than 0.5 at the winter solstice and during the mid-season due to the low angle of incidence of the Sun's path, as depicted in Figure 2. This leads to a higher contrast between daylight distribution near the window and the back surface. The LS2H2 and LS3H3 configurations achieved the uniformity index value at all times except during the winter solstice. It is worth mentioning that the width of the external light shelves influenced the integrated solar panel in terms of power generation during the last phase. However, its drawback is that there may be infringement concerns regarding the prospect right and damage due to wind pressure [6]. Therefore, the LS2H2 configuration was chosen for the width of the light shelf and 2/3 rd of the window height, which is in line with previous studies [31].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Criteria** & **Performance Indicator of Delighting Quantity and Quality** \\ \hline
**WPI** & WPI recommended 300–750 lux \\ \hline
**UI** & Uniformity index must be greater than 0.5 \\ \hline
**UDI** & 100 lux \textless{} dark area (need artificial light) \\  & 100 lux–2000 lux (comfortable), at least 50\% of the time \\  & \textgreater{}2000 lux too bright with thermal discomfort \\ \hline
**DA** & Set up 300 lx \\ \hline  & 0.35 \textless{} imperceptible glare \\  & 0.35–0.40 perceptible glare \\  & 0.4–0.45 disturbing glare \\  & \textgreater{}0.45 intolerable glare \\ \hline
**Net energy** & Energy production of solar panels—energy consumption of artificial lighting \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance indicators of visual comfort used in this study.
# Safety of Nuclear Power Plants with Respect to the Fault Displacement Hazard
## 5 Sensitivity/Vulnerability of the Fundamental Safety Functions to the PGD  
The basic hypothesis in this paper is that if the limited internal deformations and tilt due to PGD will not threaten the integrity and function of the structures and will not hinder the safety functions of systems and components.  
For the identification of potential safety issues due to PGD, a simple approach is applied in this paper that is based on the judgement on the fundamental safety functions and integrity of physical barriers:  
* For the integrity of the fuel matrix and the cladding control of reactivity, heat removal should be ensured.
* The latter means that the integrity of the boundary of the primary system, emergency core cooling system and the systems transferring the heat to the ultimate heat sink should be ensured for the mechanical loads transferred to the piping and components from the supporting structures. Moreover, the emergency energy supply systems including cables should be available.
* The containment function should be ensured. Such a function is challenged by ground shaking, ground displacement, and, in the case of a loss-of-coolant event, by internal pressure and heat.  
It should be noted that the possible damages and failure modes due to PGD depend on the particular design features of the nuclear power plant and on the peculiarities of PGD phenomenon and PGD foundation-structure interaction. The latter depends on the magnitude of earthquake, depth, style, sense of faulting, the thickness and parameters of the overlying the fault soil layers, the foundation type and rigidity, the load due to structure and foundation, the stiffness of the structure, the position of the structure relative to the fault trace are decisive [42]. The consideration of all these aspects was beyond the scope of a recent paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **IO** & **LS** & **Source** \\ \hline Overall tilt & 1/400 & & EC2, EC3 \\ Reinforced concrete column, wall eccentricity & 0.06 h \({}^{*}\) & & EC2 \\ Reinforced concrete beam & 0.0015–0.010 rad & 0.002–0.020 rad & (FEMA-356) \\ Reinforced concrete column & 0.001–0.005 rad & 0.005–0.015 rad & (FEMA-356) \\ Steel beam or pole end (cross-section class 1) & 1x0y \({}^{*}\) & 6x0y & (FEMA-356) \\ Steel beam or pole end (cross-section class 2) & 0.25x0y & 2x0y & (FEMA-356) \\ Foundation system & 1/4000–1/2000 & 1/1000–1/500 & (EC7) \\ Non-ductile reinforced concrete frames, ISD *** & 0.35\% & 1.36\% & [32] \\ \hline \hline \end{tabular}  
* h wall thickness, ** \({}^{\#}\) by yield rotation, ISD *** inter-storey drift ratio.  
\end{table}
Table 1: Damage limits examples [40].  
Qualitative judgement of the PGD effects on fundamental safety functions is presented below. Although the results of the studies [22; 23; 24], [29; 41] are explicitly not used, these results support the considerations below.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 4 Electronics Design
### Electronic Speed Controller (ESC) [Flame 80A 12S V2.0]  
Most drones utilizing PX4 open-source flight control software employ brushless motors controlled via the flight controller through electronic speed controllers (ESCs). An ESC is a module that converts a signal from the flight controller into the appropriate power level for the motor, allowing for modulation of its angular velocity. PX4 supports various types of ESCs, including PWM-based ESCs as well as those utilizing the OneShot or the DShot protocols.  
In this project, we have chosen the T-MOTOR FLAME 80A 12S to serve as the interface between the motors and the flight controller. When selecting ESCs for this application, their compatibility with the chosen motors was taken into account, as well as their maximum current output to ensure it met the motor's requirements.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 4 Results  
Once the algorithm is implemented, it is possible to calculate the satellite attitude for each measurement acquisition. The objective of calculating this attitude is required to compute the incident flux for each face of the CubeSat, as presented in Section 1. This means that the measured fluxes are corrected from their angle to the observed source. Therefore, the flux corrections are directly related to the Sun LOS and nadir vectors in the spacecraft body frame.  
This section presents the two unit vectors in the satellite's body reference frame for both TRIAD and MEKF methods. The vectors are represented as a function of time according to each of their components in the satellite reference frame.  
For example, if the X component of the Sun LOS is equal to 1, the satellite's +X face is facing the Sun, the normal to the face coincides with the Sun LOS vector, and this would be the case for the \(-\)X face if the Sun LOS X component was \(-1\). In the following studies, only data from the UVSQ-SAT satellite in orbit are used to test the TRIAD and MEKF methods.
# Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner
###### Abstract  
Road vehicle lateral positioning is a key aspect of many assistance applications and autonomous driving. However, conventional GNSS-based positioning systems and fusion with inertial systems are not able to achieve these levels of accuracy under real traffic conditions. Onboard perception systems provide knowledge of the surroundings of the vehicle, and some algorithms have been proposed to detect road boundaries and lane lines. This information could be used to locate the vehicle in the lane. However, most proposed algorithms are quite partial and do not take advantage of a complete knowledge of the road section. This paper proposes an integrated approach to the two tasks that provides a higher level of robustness of results: road boundaries detection and lane lines detection. Furthermore, the algorithm is not restricted to certain scenarios such as the detection of curbs; it could be also used in off-road tracks. The functions have been tested in real environments and their capabilities for autonomous driving have been verified. The algorithm is ready to be merged with digital map information; this development would improve results accuracy.  
positioning; road boundary; curb; lane line; laser scanner; algorithm; accuracy Article
## 2 Materials and Methods
### Comparison from Previous Studies  
Previous studies on drowsiness detection covered a variety of methodologies, with notable attempts to achieve high accuracy using advanced techniques. For instance, Refs. [2; 20] leveraged deep learning and visual characteristic analysis, respectively, to achieve high accuracy and precision. Although effective, these methods often rely on complex processing or large datasets, potentially limiting their applicability in real-time or resource-constrained settings. Similarly, approaches by Refs. [7; 16] demonstrated significant success in facial feature detection and fatigue indicator analysis but may encounter difficulties with extreme head poses or require substantial computational resources owing to their model sizes.  
The studies utilizing the NTHU-DDD dataset highlighted the effectiveness of various machine learning techniques in drowsiness detection. Notable approaches include the use of CNN [15], HOG descriptors [24], SVM [23], and ensemble deep learning architectures with InceptionV3 modules [25]. These methods achieved high accuracy rates, such as 90% with HOG descriptors and SVM, and up to 99.65% train accuracy with an ensemble deep learning approach. The findings emphasize the potential of combining advanced feature extraction and machine learning models for efficient drowsiness detection on low-resource devices like the Jetson Nano.  
In contrast, the proposed method combines facial feature detection using Dlib, head pose estimation with HOPEnet, and traditional machine-learning models to efficiently process detailed information from facial landmarks, head pose, PERCLOS, and POM. This approach not only maintains high accuracy but also ensures a relatively small model size of 5.05 MB, making it exceptionally suitable for real-time applications on low-computing devices, such as the Jetson Nano. By addressing both accuracy and computational efficiency, the proposed method presents a balanced solution for practical drowsiness detection in automotive safety systems, distinguishing it from previous attempts that may not fully cater to the demands of real-world scenarios owing to their complexity or resource requirements.
## 3 Results and Discussion
### Oxide Cross-Section Evaluation
#### 3.2.2 Cross-Sectional Oxide Composition Analyses
Two possible explanations for the drop in Ca/P ratios shown in the outermost oxide layers include differences in magnification used for the EDS collection and differences in EDS below-surface interaction volumes between the surface and cross-sectional oxide chemistry assessments. The relevant EDS sampling areas for each analysis were different, and thus, different values were obtained. Nonetheless, the Ca/P ratios shown in both cases are well within those commonly observed for clinically relevant calcium oxide compounds. Another interesting observation is the addition of substantial Mg uptake in the NOJ MP oxide. Overall, the dopant uptake of Ca, P, and Mg bone minerals in these oxides shows much promise in improving the clinical outcomes of future titanium implants [2]. Figure 7: Representative EDS line scans across the oxide group cross-sections. The elemental distributions across the cross-sectional thickness are divided into two groups. Titanium (Ti) and oxygen (O) distributions are shown in the left column, and calcium (Ca), phosphorus (P), and magnesium (Mg) dopant uptake distributions are shown in the right column. Each scan begins inside the titanium substrate on the left side and progresses through the substrate–oxide interface, the inner oxide, and the outer oxide. The oxide cross-sectional profiles confirm and further explain the bi-layered structure in both oxides with an inner titanium dioxide-rich inner layer and an outer layer consisting of significant Ca, P, and Mg dopant uptake.
# Improved CycleGAN for Mixed Noise Removal in Infrared Images
## 4 Conclusions  
In this paper, we presented a mixed noise denoising algorithm for infrared images based on CycleGAN. To fully learn the feature information of noisy images, we proposed a ResNet-E Block with an EMA attention mechanism, and based on this, we designed the generator by means of skip-connection structure to realize the full fusion of higher-order feature information and lower-order feature information in the process of denoising, which effectively improves the denoising effect of the network. In addition, we added the PSNR loss calculation term in the original cycle consistency loss, which was used to ensure the non-noise feature in the image can be effectively retained. We experimented with the improved network on synthetic noisy data and real noise data, and the results show that our algorithm can effectively remove the mixed noise while retaining the detailed information of the images, and the denoising effect is better than other similar methods.  
Within infrared image denoising, as a research study with a long history, it has become increasingly difficult to improve the evaluation index of the denoised results with the development of denoising methods from traditional to deep-learning-based. We believe that this paper can provide a new way of approaching the denoising task for infrared images and is applicable to denoising tasks containing higher noise and denoising tasks targeting images rich in texture information. In future work, we plan to incorporate more advanced and lightweight feature extraction modules into the network to reduce the number of parameters and increase the training speed.  
Conceptualization, H.W.; methodology, H.W.; software, H.W.; validation, H.Y., J.W. and Z.W.; resources, H.Y.; data curation, H.W.; writing--original draft, H.W. and X.Y.; writing--review and editing, H.Y. and X.Y.; visualization, H.W. and J.W.; supervision, H.Y., X.Z. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant 62005320).  
Figure 9: Contrast of denoising effects of different methods on indoor scene of real noise image.