esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
## 3 European Electronic Authentication Regulation
### eIDAS Regulation Revision  
Since the COVID-19 pandemic has had a significant impact on digitalization speed, both governmental and commercial sector services are becoming increasingly digitized, and citizens and businesses are expecting high security and convenience when doing online transactions (such as paying taxes, asking for a loan or opening a bank account from a distance, starting a business in another MS, buying online). Thus, today, users are becoming more demanding regarding online public and private services. For example, they are expecting seamless online jouneys, mobile applications, and single-sign-on solutions. However, the eIDAS framework was conceived targeting secure cross-border access to public services, which is a limited portion of individuals' and enterprises' electronic identification needs. A recent evaluation of the framework revealed that the existing regulation does not adequately address several issues regarding user expectations and market demands, which results in a limited uptake among European citizens and companies [22]. The report shows that the services addressed primarily concern the 3% of the EU population who live in a MS other than their birthplace. Additionally, the existing eIDAS architecture prevents users from facilitating the GDPR principles of data minimization and privacy by default by deciding what data to share and with whom. According to the report, only 14% of public service providers offer authentication via eIDAS.  
The European Commission identified the lack of awareness and understanding of the regulation as one of the main blocking factors for the uptake of eID from service providers. Simultaneously, the poor user experience is discouraging them from entering the eIDAS Network since it is perceived as a risk of compromising the quality of the services they provide. Moreover, two main roadblocks to the long-term maturation of the legal framework are the absence of a business model for private Identity Providers and the vagueness of the terms and conditions of access to the eIDAS network for private relying parties.  
The report's conclusion is that the current eIDAS Regulation, with its built-in restrictions on the public sector, the difficulty for online private providers to connect to the system, its inadequate availability in all MSs, and its lack of adaptability to support a range of cases, cannot meet these new market demands. To meet new policy objectives, user expectations, and market demand while also considering recent breakthroughs in digitalization, the eIDAS Regulation must be upgraded in terms of effectiveness, efficiency, coherence, and relevance.
## 2 Materials and Methods  
### Materials  
In this study, wheat flour type 750 was employed as the main ingredient and sourced from a retail store in Lublin, Poland. The flour was analyzed and found to have a moisture content of 12.9% \(\pm\) 0.5 and an ash content of 0.74% on a dry basis. The gluten-related parameters included a wet gluten content of 25.0% \(\pm\) 1.1, dry gluten content of 8.9% \(\pm\) 0.3, water absorption capacity of 16.2% \(\pm\) 1.1, and a gluten index of 99.0 \(\pm\) 0.5. The falling number was measured at 290 \(\pm\) 18 s. The flour particle size was uniform, with 99% of particles passing through a 0.2 mm sieve. According to the manufacturer's nutritional label (per 100 g), the flour contained 1.8 g of fat (including 0.4 g of saturated fatty acids), 71 g of carbohydrates (with less than 0.5 g of sugars), 2.9 g of dietary fiber, 12 g of protein, less than 0.01 g of salt, and an energy content of 1427 kJ.  
The plant-based beverages used in this study included oat (BO), millet (BM), and spelt (BS) beverages, all of which were commercially available products (Hafer Natural, Naturali GmbH, Troisdorf, Germany). The oat (BO), spelt (BS), and millet (BM) beverages were obtained from a retail source in May 2024. The products came from the following batches: oat--batch no. 229/M4, expiry date 15 August 2024; spelt--batch no. 258/M4, expiry date 12 September 2024; millet--batch no. L2:231011, expiry date 10 October 2024. All beverages were analyzed within four weeks of purchase, using unopened packages stored at room temperature in accordance with the manufacturer's recommendations.  
The composition of the oat beverage included water, whole grain oats (15%), sunflower oil, and sea salt. The chemical composition of the oat beverage, as declared by the manufacturer per 100 mL, was as follows: fat--1.3 g (including saturated fatty acids--0.2 g, monounsaturated--0.3 g, polyunsaturated--0.8 g), carbohydrates 7.9 g (including sugars--3.9 g), dietary fiber < 0.5 g, protein--0.8 g, salt--0.13 g, and an energy value of 200 kJ.  
The composition of the millet beverage included water, millet (16%), sunflower oil, and sea salt. The chemical composition of the millet beverage, as declared by the manufacturer per 100 mL, was as follows: fat--1.1 g (including saturated fatty acids--0.1 g, monounsaturated--0.3 g, polyunsaturated--0.7 g), carbohydrates 11 g (including sugars--5.5 g), dietary fiber < 0.5 g, protein--0.7 g, salt--0.09 g, and an energy value of 240 kJ.  
The composition of the spelt beverage included water, whole grain spelt (17%), sunflower oil, and sea salt. The chemical composition of the spelt beverage, as declared by the manufacturer per 100 mL, was as follows: fat--1.0 g (including saturated fatty acids--0.1 g, monounsaturated--0.3 g, polyunsaturated--0.5 g), carbohydrates 8.7 g (including sugars--6.7 g), dietary fiber < 0.5 g, protein < 0.5 g, salt--0.13 g, and an energy value of 190 kJ.
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 3 Literature Review
The sensor nodes must be attached to the appropriate buoy to ensure constant data delivery [23]. Another critical issue that affects the data gathering and forwarding operations between sensor nodes and buoys is the successful handover in underwater environments. Unlike handovers in ground-based WSNs, the delay time in UWSNs is longer, and it is more difficult to test signal strength. According to recent studies [1; 5; 24], handover in UWSNs solely depends on location-based potentials. The cited works do not provide their datasets nor their source code; thus, it is impossible to reproduce their experiments. The use of any machine learning classifier model (e.g., decision tree) with different parameter values results in different accuracy rates. The authors of [1; 5; 24] did not discuss the parameters of the utilized machine learning models. Moreover, the obtained handover prediction scores are considered very low: less than 56% for the top rank 1 prediction. To the best of our knowledge, handover prediction techniques for UWSNs utilizing marine data have only been proposed in [1; 5; 24]. This lack of previous research and standards leads to an unsystematic system for researchers in the future. This paper is the first attempt to address the solution of the handover problem using ensemble learning for large UWSNs, as shown in Figure 1.
###### Abstract  
The recent development of the smartphone Global Navigation Satellite System (GNSS) chipsets, such as Broadcom BCM47755 and Qualcomm Snapdragon 855 embedded, makes instantaneous and cm level real-time kinematic (RTK) positioning possible with Android-based smartphones. In this contribution we investigate the instantaneous single-baseline RTK performance of Samsung Galaxy S20 and Google Pixel 4 (GP4) smartphones with such chipsets, while making use of dual-frequency L1 + L5 Global Positioning System (GPS), E1 + E5a Galileo, L1 + L5 Quasi-Zenith Satellite System (QZSS) and B1 BeiDou Navigation Satellite System (BDS) code and phase observations in Dunedin, New Zealand. The effects of locating the smartphones in an upright and lying down position were evaluated, and we show that the choice of smartphone configuration can affect the positioning performance even in a zero-baseline setup. In particular, we found non-zero mean and linear trends in the double-differenced carrier-phase residuals for one of the smartphone models when lying down, which become absent when in an upright position. This implies that the two assessed smartphones have different antenna gain pattern and antenna sensitivity to interferences. Finally, we demonstrate, for the first time, a near hundred percent (98.7% to 99.9%) instantaneous RTK integer least-squares success rate for one of the smartphone models and cm level positioning precision while using short-baseline experiments with internal and external antennas, respectively.  
smartphone positioning; real-time kinematic (RTK); multi-GNSS; dual frequency +
Footnote †: journal: Physics Letters B  
1
Footnote 1: email: {[[https://doi.org/10.3390/s21248318](https://doi.org/10.3390/s21248318)]([https://doi.org/10.3390/s21248318](https://doi.org/10.3390/s21248318))}  
Footnote †: journal: Physics Letters B
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Research Objectives  
Before going into detail, a significant item was shared among all the approaches used for this analysis. Based on the results of the general variability, the NS gradient (latitude) was the main driver for VIs and VIs CV (Figure 8). The latitude affected all indices retrieved by RS monitoring, the general variability, VIs development and the semivariogram descriptors (Nugget and CI). Many concurrent causes could explain this figure [43]. First, the absolute VIs values of southern vineyards were lower than the ones in the northern region. Hence, a negative correlation between absolute value and VIs variability could be stated. Second, farm practices could change across latitudes. In northern Italy, where VIs could be more stable in vineyards and showed higher value, rainfall availability, irrigation, fertilisation, and mechanisation may flatten vineyard variability. Finally, soil management or grass cover could enhance vineyard variability by monitoring it via satellite. Usually, northern vineyards are covered by permanent or seeded grass, while in southern Italy, sequential tiling is widespread during summer.
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 3 Results and Discussion
### Testing the Biochar Efficiency as Tartrazine Adsorbent from Synthetic Aqueous Solutions  
In the adsorption process, the effects of some factors such as solution pH, temperature, adsorbent dose, etc., must be taken into account. Thus, the optimal retention conditions for the tartrazine removal from aqueous solutions, using biochar as adsorbent, were determined following the influence of different parameters. For this reason, the contact time between the biochar adsorbent and the synthetic solution, the initial pH of the aqueous solution, the temperature of the solution during the adsorption process, the adsorbent dose during the adsorption, and the tartrazine initial concentration were varied, and the results are presented in the following sections.  
Figure 3: FTIR spectra of initial biochar, tartrazine, and biochar with the adsorbed dye.
## 3 Experimental Results  
### Experimental Setups  
Three different scenarios were designed for the experimental studies. This allows for the evaluation of the generalizability and performance of the developed DRL algorithm in various complex environments. In Scenario 1, a route with a total length of 6600 m was used. A simulation was prepared for this route, which included four traffic lights. For Scenario 2, a more complex route of 8700 m was determined, and the number of traffic lights on this route increased. Finally, Scenario 3 was determined to have the longest distance (15,000 m) and to have a more complex structure as a route. In this study, three different scenarios were specifically designed to represent various aspects of urban traffic management. The first scenario simulates a low-density traffic environment, while the other two scenarios model different real-world conditions with increasing traffic density and more complex road structures. The main reason for this selection is to evaluate the performance of the model in different traffic situations and to create a general infrastructure to provide a wide range of applications. The generalizability of the selected scenarios depends on the compatibility of the simulation parameters used with real city data. For example, variables such as traffic light phase durations, vehicle densities and road structures were determined to mimic real traffic situations. These parameters show the applicability of our method to different scenarios in other cities or traffic systems. In order to ensure the generalizability of the results, the scenarios were expanded to include different traffic densities and vehicle types, and the model was trained to provide optimum speed control in various situations. Thus, the proposed method offers a solution that is applicable not only in a specific scenario but also in more complex and dense traffic environments. In future studies, the generalizability of this model can be further expanded with real-time traffic data and data obtained from different geographical regions. Detailed parameter information for these scenarios is provided in Table 3.  
The vehicle percentages indicated in Table 3 were selected as car- and bus-intensive because the road used was in urban traffic. Although these roads are closed to heavy vehicles, the truck rate was kept low because they are available in small quantities.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 2 Preliminaries and Related Work  
LT codes are the first practical realization of fountain codes invented by [PERSON] [8; 9]. Asymptotically, they are optimal in every erasure channel (e.g., BEC) in terms of capacity achievement. Note that the BEC channel is a simple communication channel, where the receiver receives the transmitted bit/symbol correctly or does not receive it (i.e., erased) with a probability \(\varepsilon\). This probability is referred to as erasure probability.
## 3 Results and Discussion
### SEM Analysis  
SEM was used to study the surface morphology and fundamental physical properties of the synthesized adsorbents, including the tea-waste-based biochar after thermal treatment. As shown in Figure 4, post-thermal treatment of the biochar showed rough and irregular surface layers similar to a rod with many visible fractures and possible open stomata. The modification of biochar with the rGO composite formed a mesoporous structured composite. The composite showed a more defined surface area than that of biochar alone. The composite also looked like thick fractured sheets with irregular pores that were unevenly spread on the material's surface.  
The composite biochar/rGO showed more developed and well-defined porosity and unique surface characteristics than the starting biochar material. The porous surface area of an adsorbent is an essential indication of its capacity to absorb. The size of the molecule compared to the pore volume impacts the adsorption process's efficiency.  
Moreover, molecules with large particle sizes may not fit into small pores, affecting the adsorption capability [29]. In this case, the composite adsorbent (biochar/rGO) had smaller pores and larger pore volumes, making the material adopt greater characteristics and creating a range of active adsorption sites. Further modification of biochar/rGO with DES-CTAB and DES-glycerol resulted in a completely different structural morphology. The biochar/rGO/DES-CTAB resulted in irregular open cavities and pores spread out unevenly on the surface of the adsorbent. The biochar/rGO/DES-glycerol resulted in sheets of different shapes and sizes with prominent black holes and cavities, suggesting that contaminants can be trapped and absorbed into the surface of the adsorbent.
# Coexistence in Wireless Networks: Challenges and Opportunities
## 2 Related Survey
Concerns about interference between terrestrial networks and satellite services in shared frequency bands have been extensively analyzed. Studies on co-channel interference and out-of-band emissions provide critical guidelines for protecting satellite services while optimizing terrestrial network deployment [18]. Furthermore, intelligent resource allocation strategies have been explored to manage interference in hybrid satellite-terrestrial networks, ensuring efficient coexistence in congested environments [19]. These studies highlight the importance of adaptive interference management, which is a central theme in our research. With the growing interest in expanding spectrum access, particularly in the 12 GHz band, recent research emphasizes the need for adaptive spectrum sharing policies to enable the integration of 5G terrestrial services while mitigating interference with incumbent systems [20]. This focus on dynamic spectrum access aligns with our investigation into flexible resource allocation strategies. Furthermore, advancements in reconfigurable hardware, such as the reconfigurable substrate integrated waveguide (SIW) filtenna operating in the 5G mm Wave band [21], offer promising solutions for reducing adjacent channel interference and enabling passive spectrum coexistence. Similarly, joint multi-dimensional resource allocation algorithms based on block coordinate descent have been proposed to optimize uplink throughput in 5G/Wi-Fi coexisting mm Wave networks [22], addressing the growing demand for high throughput in hotspots. These recent developments illustrate the importance of adaptive techniques in managing interference and optimizing resource allocation, which are the core elements of our research. By incorporating these findings, this review extends prior research and addresses the evolving landscape of coexistence challenges in wireless networks, highlighting the need for dynamic and intelligent resource management strategies to ensure efficient and harmonious spectrum utilization.
## 2 Methods
### CFD Method  
#### 2.1.1 CFD Tools  
The commercial software package NUMCA 14.1 was selected for CFD analysis, in which AutoGrid generated an O-H structured grid from the rotor geometry and Fine Turbo solved the 3D RANS equations. After the calculation was finished, NUMECA CFView completed the postprocessing. All tools were automatically driven by the scripts.  
The viscous and inviscid fluxes were determined using second-order Jameson-type dissipation, and an explicit Runge-Kutta scheme was applied for time discretization. The Spalart-Allmara (S-A) model was selected to close the RANS equation. As one of the most successful one-equation turbulence models, the S-A model performs well in the boundary layer and pressure gradient area, so it is widely used to predict complex flows with separation, showing attractiveness in turbomachinery analysis. Moreover, the S-A model in NUMECA has been widely validated and applied [30; 31; 32], and it consumes less additional CPU and memory than the k-\(\varepsilon\) model.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 2 Experiment Program
### Method  
#### 2.2.1 Molding Specimens and Curing Procedure  
To conduct UCS testing, precise cylindrical specimens were carefully crafted to possess a diameter of 50 mm and a height of 100 mm, adhering to the guidelines outlined in (ASTM C39/C39M-20) [33]. Initially, specific dry densities were targeted for the specimens. In this study, two target densities were selected, namely 1500 and 1700 kg/m\({}^{3}\), aligning with the maximum dry density of the clay at its optimal moisture content (17%). To accomplish this, the soil samples were first dehydrated in an oven at a temperature of 105 degrees Celsius for 24 h. It was followed by the process of pulverization using the Los Angeles apparatus. Subsequently, the clay material was passed through sieve number 18 (with an opening of 1 mm).  
Adhering to the predetermined density requirements, the proportions of clay, glass powder, and sintered gypsum were then accurately considered. The specified quantities of dry materials were blended in a moisture-free environment until a consistent dispersion was attained, which usually required at least 5 min. After this, a predetermined quantity of water was incrementally added and mixed with the binder materials until a uniform blend was achieved. The amount of water used was based on the clay's compression curve, which showed two different densities: the highest density (1700 kg/m\({}^{3}\)) and a lower density (1500 kg/m\({}^{3}\)). Three samples were prepared for each mixture. Two of them were  
Figure 1: Glass powder, sintered gypsum, and clay soil particle size distribution.  
subjected to the UCS test, while one was tested for the determination of the initial shear modulus (\(G_{0}\)).  
The blend was later divided into three equal layers and compacted using a split mold to reach the pre-determined density, following the compaction procedure proposed by [PERSON] and [PERSON] [34]. After molding, the sample was delicately removed from the mold, and its dimensions were gauged. Following this, the specimens underwent a curing process by (ASTM C0511-19) [35] for different durations of 7, 28, and 56 days.  
After the curing process, each sample was submerged in water for 24 h, ensuring the highest degree of saturation and assessing its capacity to withstand this long immersion without failure. The table expressed detailed information regarding specimen preparation, the additive contents, and the tests conducted.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 3 Characterization of Solar Irradiance Time Series
### Static Parameters  
A characterization of the data includes position, dispersion, symmetry, and feature of time-series distributions [50]. As performed in Reference [51], the mean is used to compare the different measures. It is the choice of simplicity and has thus left aside the other measures of central tendency (median and mode) and non-central tendency (quartiles, deciles, and percentiles). As it is important to consider the dispersion of the series, the standard deviation is used. The two other parameters are well-known in statistics:  
* Skewness describes how much the statistical data distribution is asymmetrical from the normal distribution, where distribution is equally divided on each side [52; 53; 54; 55]; it is defined by the following: \[Skewness=\frac{\mu_{3}}{\sigma^{3}}\] (1) where \(\mu_{3}\) is the third central moment and \(\sigma\) is the the standard deviation.
* Kurtosis measures the \"tailedness\" of the probability distribution of a real-valued random variable [54; 55; 56; 57] and is defined by the following equation: \[Tailedness=\frac{\mu_{4}}{\sigma^{4}}\] (2) where \(\mu_{4}\) is the fourth central moment.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **2020** & **2019** & **2018** & **2017** \\ \hline Total SIAR station & & 542 & & \\ Active SIAR Stations & & 448 & & \\ Selected * SIAR Stations & 94 & 84 & 82 & 78 \\ Discarded ** SIAR stations & 10 & 2 & 4 & 2 \\ Subtotal & 84 & 82 & 78 & 76 \\ \hline \hline \end{tabular} \({}^{\star}\)Solar data available and ** test failure (Section 2.3).  
\end{table}
Table 1: SIAR stations and quality control.