esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The traditional use of clayey materials existed in ancient times in Africa (Egypt), Asia (China), America (Mexico), Europe (Roman), etc. [1]. Clay is a material which has a particle size less than 2 \(\upmu\)m and belongs to the family of minerals with similar chemical compositions and common crystal structural characteristics [2]. Raw clay materials are mineral resources with major importance in industries because of their numerous uses [3, 4]. Presently, more than a third of the world's population uses clayey products, due to their quality, weather resistance, plasticity and malleability. The behavior of clay materials is related to their mineralogy and chemical composition, associated with certain geotechnical characteristics (particle size, plasticity, etc.). Their chemical compositions vary depending on both the physical and chemical changes in the environment where clay deposits are found [5]. The use of raw clay materials has grown in several and varied applications(points, plastics, cosmetics, pharmaceuticals and the production of ceramic materials of wide distribution) [6; 7; 8]. Indeed, the fields of application of clay minerals are more and more numerous because they are used in several industrial sectors, the most developed of which are the ceramic industry for the manufacturing of porcelain and earthenware, the agricultural industry to dilute pesticides and the pharmaceutical industry for the manufacturing of medication [6; 7; 8]. Studies have been carried out on the applications of ceramic clays in several areas around the world [9; 10; 11; 12]. However, several researchers have been interested in the ceramic applications of clay materials in Africa [13; 14; 15]. Several works have focused on raw clay materials in Cameroon. Clay materials have been identified in several geological environments in Cameroon [16; 17; 18; 19; 20]. Other works have been carried out on physicochemical and mineralogical characteristics of the catalytic properties and thermal behavior of some clay materials [21; 22]. In Cameroon's coastal basins, studies were carried out for the physicochemical and mineralogical characterization of clay materials in order to understand their origin and paleoenvironment [23; 24] and to determine their properties for various applications [25; 26; 27]. In the present context, the natural, technological and mineralogical properties of the clays in the Douala sub-basin are not very well know and few works have been conducted on the ceramic aspect.  
The aim of this study is to carry out a mineralogical and physicochemical characterization of the clay materials at the eastern part of the Douala sub-basin (Cameroon, Central Africa) in order to determine their nature and identify their technological classification and suitability for various ceramic applications.
# Virtual Signal Processing-Based Integrated Multi-User Detection
## 3 Design of VSP-IMUD  
In this section, we present the design of VSP-IMUD and describe the receiver structure employing VSP-IMUD. By exploiting interactions among wireless signals, the detection of multiple users' signals can be achieved with fewer receiving antennas.
## 2 Key Steps in a FRB Search Pipeline  
The survey technologies employed at present reduce and analyse roughly ten Exabyte of raw data to find a single FRB (e.g., [7]). This analysis can be thought of as a number of distinct functional steps, even if these are sometimes optimised into one another at deployment. This data analysis pipeline has to process large volumes of data, but has strong constraints on latency, too. To fully utilise the scientific potential of FRBs, it is important to localise the bursts by imaging the radio sky at the time of the FRB and sending alerts to other observatories. This can help constrain any multi-wavelength emission that may be concurrent to the event. This is only possible if both the FRB is detected and the corresponding radio data are saved in real-time. This means that FRB detection software must be extremely efficient and exceedingly fast. This has led to vast developments in making every component within the FRB search methodology highly optimised. We illustrate this sequence in Figure 1 and describe its parts in detail below. We provide a (not exhaustive) list of publicly available software that implements all the steps described below in Appendix A.  
Figure 1: The various steps required in a FRB search and their impact on the time-frequency plane. Fiducial rates and parameter values are taken from [7]. Listed at the top for each step are the relevant data and compute and candidate rates. From left to right, data arrive from the telescope. These are cleaned from RFI. Order \(10^{4}\) dispersion trials are next formed and each is searched over \(\sim\)10 matched filters. The large number of candidates are next clustered and graded until \(\sim\)\(10^{3}\) remain, of which one is a new FRB. The FRB in this example was found after 5% data loss to RFI, at a DM of 456 pc cm\({}^{-3}\) and a downsampling factor of 4.  
This sequence produces and searches an order of \(10^{12}\) realisations of the data for every FRB that is discovered. It is instructive to compare this stupendous number against the odds of finding the proverbial needle in the haystack. To match the effort required in a FRB search, the grassy pile would need to rival the Great Pyramid of Giza.
## 1 Introduction
### Paper Contributions  
In this paper, we propose a cross-layer framework for the performance analysis of ALOHA-based multi-static backscattering communication systems. The considered architecture consists of a single illuminator (i.e., carrier emitter), which provides powering signals for a set of passive backscattering nodes, and a set of spatially deployed receivers. Given the considered scenario, we propose the following contributions:  
* We present a framework that describes propagation phenomena and their impact on medium access control protocol dynamics. Specifically, the channel abstraction considers log-normal fading statistics and includes cascade communication links that affect both nodes' powering and backscattering signals' detection. To mitigate multiple access interference, we assume that channel contention is driven by a Framed Slotted ALOHA (FSA) [13] protocol and we show how propagation impairments affect detection probability and collision occurrence.
* We characterize the statistical properties of the multiple access interference in terms of Signal-to-Interference Ratio (SIR), which does not admit a closed-form representation in terms of Probability Density Function (PDF) in the proposed settings. To this aim, we approximate the SIR by means of a log-normal random variable for which we determine the unknown parameters via moment matching. The proposed approximation rationale is based on the technique described in [14] and here extended to the peculiar features of the backscattering propagation cascade channel, including nodes' powering.
* By exploiting the proposed approximation, we are able to study the capture effect at a given receiver in terms of capture distribution. Furthermore, when exploiting the spatial diversity offered by multiple detection points we describe the concept of capture diversity that was first introduced in our prior work [11] in the context of RFID systems. In particular, we show how different samples of fading and interference processes available at different receivers can be exploited in multi-static systems to resolve simultaneous nodes' transmissions during collision events.
* Based on the proposed framework, we study the performance of the considered multi-static architecture with focus on different parameters such as fading severity, number of deployed receivers and network topology. Moreover, we discuss the impact of FSA settings on the optimal achievable performance in the presence of capture diversity and propagation channel impairments. Specifically, we show how the capability of multi-static systems to resolve simultaneously transmitted data packets substantially reduces the optimal frame length of the FSA and definitely improves channel access performance.
## 4 Materials and Methods
### Model Specification
#### 4.1.3 Spatial Durbin Model  
To validate H3, this study employs a spatial Durbin model (SDM) to examine the spatial spillover effects of GPP on urban carbon emissions intensity, as specified below:  
\[CEI_{it}=\rho W\ CEI_{it}+\beta_{1}\ GPP_{it}+\beta_{2}W\ GPP_{it}+\beta_{3} \ Control_{it}+\beta_{4}WControl_{it}+\alpha_{i}+V_{t}+\varepsilon_{it} \tag{3}\]  
where \(CEI_{it}\) represents the dependent variable and \(GPP_{it}\) is the core independent variable. \(Control_{it}\) encompasses all control variables, with subscript \(i\) and \(t\) for prefectural city and year, respectively. \(W\) is the spatial weight matrix; \(\rho\) signifies the spatial autoregressive coefficient, which captures the inherent spatial dependence in the sample; \(\beta_{1}-\beta_{4}\) measure the direct and indirect effects of the independent and control variables; \(\alpha_{i}\) accounts for regional fixed effects; \(V_{t}\) captures time fixed effects, and \(\varepsilon_{it}\) is a random error term.
# A Review of Internet of Things Technologies for Ambient Assisted Living Environments
## 4 Acceptance of New Gerontechnologies for Assisted Living
Various facilitating conditions, tied with ease of experimentation and acceptable result means that ecosystems based on this principle can also work towards providing new knowledge for building new IoT based assistive services or technically also extending the already supported services, leading to the immediate recognition of added value of the IoT based assistive technologies. In Table 1, we provide several examples of successful or failed to adopt IoT devices, which were aimed at improving the living conditions of ageing people. Our proposed model is in agreement with similar models and frameworks of adopting technology for healthcare domain such as Anchored, Realistic, Co-Creative, Human, Integrated, Evaluated (ARCHIE) model [92] (which emphasizes alignment of technology with social support and understanding of user requirements) and Nonadoption, Abandon \begin{table}
\begin{tabular}{c c} \hline \hline
**Product/Service** & **Reason of Success/Failure** \\ \hline \multicolumn{3}{c}{Examples of Success} \\ \hline Nest Learning Thermostat (3 rd generation) [79] & Simplicity of use, no need of programming, self-learning, adaptability, context-awareness \\ Flic wireless smart button with TAGhelper [80] & Simplicity, intuitiveness, low learning curve \\ IFTTT applet for remote home lighting control [81] & Ease of use, low learning curve \\ SUUNTO smart chestbelt heart rate sensor [82] & Ease of use, usefulness, high reliability \\ Philips HUE Light Bulbs [83] & Ease of use, usefulness, low learning curve \\ \hline \multicolumn{3}{c}{Examples of Failure} \\ \hline Banknote identifier [84] & Difficult to use, complex user input \\ Proshetic Damping Control [85] & Manual control required, low comfortability, high \\ Toto Intelligence Toilet II [86] & Over-complexity, difficult to use \\ Head driven mouse for smartwatch [87] & Low practicality, high learning curve \\ Smart armchair [88] & Low usefulness, complex to adjust and control \\ Home Automation Kit for smart home control [89] & High complexity, difficult to use \\ Smart insoles [90] & Low reliability \\ Garmin Vivofit activity tracker [91] & Low usability, high learning curve \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of successful/failed products (services) employing IoT technologies of AAL. Sustainability (NASSS) framework [93] (which claims that technology adoption requires cognitive participation of its users). The latter aspect has been often missed by some of older technology acceptance models, which have diminished the role of social constructs to a subjective perception [94]. Among the factors noted as a reason for technology non-adoption by older and/or ill people are the lack of social support (e.g., wearable activity trackers [95]), lack of usefulness (wearables and tablets [96]), or low practicality (e.g., mobile health apps [97]). On the other hand, the usefulness of equipment [98] have been explicitly outlined as one of the reasons for technology acceptance in the AAL domain.
## 4 Conclusions  
Wheel-rail interaction studies can obtain significant results for railways and improve their competitiveness. Bogie maintenance and retrofit analysis can contribute to this improvement.  
1. The linear programming model shows that fixing bogies with a high wear rate (e.g, as a result of warp conditions) allows for the wheel to obtain a useful life of nine years (0.81 MM km) and avoids the premature scrapping of the wheel in less than five years (0.45 MM km). Therefore, the concept of maintenance optimization can have a positive impact on reducing wheel costs.
2. Bogies with a high wear rate were identified as having an unevenness greater than 16 mm, as this condition leads the bogie to warp. Therefore, establishing new maintenance parameters is necessary to achieve high performance. The standard criterion  
Figure 11: Wear rate comparison with and without retrofit.  
Figure 12: Comparison of angle of attack with and without retrofit—maximum of \(-3\) mrad with retrofit and \(-5.3\) mrad without retrofit.  
to evaluate friction wedge was considered for operational safety. However, it is not sufficient to evaluate the bogie's performance from the perspective of reducing wheel maintenance costs. Thus, the results suggest the adoption of a friction wedge height difference criterion.
3. The methodology that was developed to analyze the bogie retrofit showed significant reductions in wheel wear of 40%. This percentage gain can increase the wheel life to almost 13 years (1.12 MM km), which is more appropriate for a modern railway. Moreover, the retrofit bogies use less force on the rail, which also reduces friction and can have a positive impact on energy efficiency and rail maintenance. The wear reduction is similar to modern bogie behavior references, which achieved a percentage of 43%. despite the presence of some different bogie conditions.  
In future work, we aim to analyze the behavior of new bogies that have been tested in VLI, which is the motion control. This bogie has a variable damping and already uses polymers as pads and center bowl liners, as in this original design.  
Conceptualization, L.V. and L.L.; methodology, L.V. and L.L.; formal analysis, L.V. and L.S.R.; supervision, L.L.; revision, L.V. and L.S.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study does not require ethical approval.  
Not applicable.  
The authors declare no conflict of interest.  
Samples of the compounds are available from the authors.
## 3 Results and Discussion
### Cell Culture
#### 3.2.4 Cell Function  
The clinical significance of the liver primarily lies in its two vital functions: synthesis and detoxification via biochemical transformations. In cases of liver failure, patients often encounter various complications stemming from an inability to synthesize plasma proteins and detoxify harmful substances. Among these complications, ammonia accumulation-induced hepatic encephalopathy is the most prevalent. HepaRG is classified as a type of human liver progenitor cell. Because of its high expression of liver functions, including drug metabolism-related enzymes, urea synthesis, and transporter proteins, as well as its similarity to primary human hepatocytes, it is currently a widely recognized cell model [36; 37]. Additionally, HepaRG cells exhibit pipotential differentiation capacity and can differentiate into mature hepatocytes or cholangiocarcinoma under specific culture conditions. Research has revealed that extracellular matrix components can regulate the differentiation of HepaRG cells [38; 39; 40]. To further assess the functional performance of HepaRG cells cultured on PET and PET-COL scaffolds, we analyzed the levels of albumin and urea present in the cell culture supernatant. Our findings revealed that as the duration of cell culture progressed, both the albumin secretion (Figure 10A) and urea synthesis (Figure 10B) abilities of HepaRG cells cultivated on PET-COL scaffolds exhibited a gradual upward trajectory, as did CYP3A4 (Figure 10C) and CPS1(Figure 10D) expression. Furthermore, on culture days 3, 5, and 8, the levels of albumin secretion, urea synthesis, CYP3A4, and CPS1 expression were significantly elevated in PET-COL-cultured cells compared to those grown on PET scaffolds (\(p<0.001\)). These results suggest that the collagen coating has the potential to modulate cellular functions, facilitating the differentiation of human hepatic progenitor HepaRG cells to acquire more mature hepatocyte-like functionalities, ultimately enhancing their albumin secretion, urea synthesis, and expression capacities of CYP3A4 and CPS1.  
Figure 9: Proliferation of HepaRG cells growing on PET and PET-COL fibers on day 1, day 3, day 5, and day 8. (**A**) Cell counting results; (**B**) CCK-8 results. Data are mean \(\pm\) SD, n = 3; *: \(p<0.05\), **: \(p<0.01\), ***: \(p<0.001\).
# Soil Moisture Influence on the FTIR Spectrum of Salt-Affected Soils
###### Abstract  
Soil salinity has a major impact on agricultural production. In a changing climate with rising sea-levels, low-lying coastal areas are increasingly inundated whereby saltwater gradually contaminates the soil. Drought prone areas may suffer from salinity due to high evapotranspiration rates in combination with the use of saline irrigation water. Salinity is difficult to monitor because soil moisture affects the soil's spectral signature. We conducted Fourier-transform infrared spectroscopy on alluvial and sandy soil samples in the coastal estuary of the Red River Delta. The soils are contaminated with NaCl, Na\({}_{2}\)CO\({}_{3}\) and Na\({}_{2}\)SO\({}_{4}\) salts. In an experiment of salt contamination, we established that three ranges of the spectrum were strongly influenced by both salt and moisture content in the soil, at wavenumbers 3200-3400 cm\({}^{-1}\) (2.9-3.1 \(\mu\)m); 1600-1700 cm\({}^{-1}\) (5.9-6.3 \(\mu\)m); 900-1100 cm\({}^{-1}\) (9.1-11.1 \(\mu\)m). The Na\({}_{2}\)CO\({}_{3}\) contaminated soil and the spectral value had a linear relationship between wavelengths 6.9 and 7.4 \(\mu\)m. At wavelength 6.99 \(\mu\)m, there was no relationship between absorbance and soil moisture, but the absorbance was proportional to the salt content (R\({}^{2}\) = 0.85; RMSE = 0.68 g) and electrical conductivity (R\({}^{2}\) = 0.50; RMSE = 3.8 dS/m). The relationship between soil moisture and spectral absorbance value was high at wavelengths below 6.7 \(\mu\)m, resulting in a quadratic relation between soil moisture and absorbance at wavelength 6.13 \(\mu\)m (R\({}^{2}\) = 0.80; RMSE = 5.2%). The spectral signatures and equations might be useful for mapping salt-affected soils, particularly in difficult to access locations. Technological advances in thermal satellite sensors may offer possibilities for monitoring soil salinity.  
soil; salinity; Fourier-transform infrared spectroscopy; FTIR; Vietnam 1
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 2 Materials and Methods
### GNSS-LiDAR Fusion Positioning Principle
#### 2.2.3 GNSS-LiDAR Orchard Positioning Scheme Based on Dynamic Switching  
To meet the navigation needs of the orchard environment, we utilize the ROS system to subscribe to the GNSS STATUS topic. Based on the assessment of GNSS signal status, we propose a method for dynamically switching between GNSS-LiDAR positioning modes. Figure 8 is a schematic diagram of the work performed by the orchard robotic vehicle. In this diagram, the green areas represent the end of the field, where there is no overhead cover, and the satellite differential signals are good. The yellow areas represent the working areas between the rows of trees, and the black areas are where satellite signals are obstructed. During operation, the orchard robotic vehicle dynamically switches between odometer positioning information and RTK-GNSS positioning information, based on the GNSS signal status. In the green areas, the orchard robotic vehicle navigates using RTK-GNSS positioning information. In the black areas, it determines the switch to laser odometer positioning mode by assessing GNSS signal status. Upon exiting such areas, it switches back to RTK-GNSS positioning mode.
# Long-Term Thermo-Hydraulic Numerical Assessment of Thermo-Active Piles--A Case of Tropical Soils
## 3 Results  
### Geometry and Boundary Conditions  
The finite difference numerical analysis is conducted using the software FLAC\({}^{3\text{D}}\) version 6.0. Unless otherwise stated, groundwater is considered to be static, and natural convection was not considered in this study. The top of the pile is suitably extended by 0.5 m to ensure that the temperature in pile head zones varies with the atmospheric temperature.  
A thermo-active pile with a diameter of 1 m and a length of 9.5 m is set up in the soil mass. The soil properties are adopted from the TRT site. A monolayer is considered in order to reduce the calculation complexity, and its properties are assumed to be homogeneous as summarized in Table 3.  
The finite line heat sources are applied at 10 cm from the pile-soil interface (see Figure 7), representing the real positions of the heat exchanger tubes. They are extruded along the full length of the considered piles, i.e., 9.5 m in this study. The heat power at each gridpoint is equal to \(P(t)/2N\), \(P(t)\) is the total thermal power of pile (W) and is given in Figure 4. Because only half of a pile is built, the simulated thermal power is also half of the total power and \(N\) is the number of all gridpoints that constitute the finite line source.  
\[T(t)=\overline{T}_{year}+A_{T}(\sin(\omega t)+\varphi)\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Properties** & **Units** & **Soil** & **Pile** \\ \hline Density, \(\rho\) & [kg/m\({}^{3}\)] & 2000 & 2500 \\ Thermal conductivity, \(\lambda_{off}\) & [W/m/K] & 2.82 & 1.8 \\ Specific heat capacity, \(\zeta_{p}\) & [J/kg/K] & 2200 & 1000 \\ Hydraulic conductivity, \(k\) & [m/s] & \(1\times 10^{-6}\) & - \\ Saturation, \(S\) & - & 1 & - \\ Porosity, \(n\) & - & 0.5 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The properties of the soil and the pile.  
Figure 7: Geometry and boundary conditions of the 3D thermo-active pile numerical model.  
A mesh refinement in the numerical analysis is considered at the pile proximity. A parametric analysis was conducted to determine the numerical model dimensions to avoid the boundary effects and to define adequate mesh refinement. The considered mesh (composed of 103,704 nodes) for the 3D model for the following analyses is shown in Figure 7.