esa-sceva/satcom-chunk-collection huggingface dataset:
# The Accuracy Assessment of Lithospheric Density Models
## 2 Numerical Model
### Sub-Lithospheric Mantle Geoid
#### 2.2.4 Lithosphere-Stripped Disturbing Potential  
The lithosphere-stripped disturbing potential \(T^{\rm CL}\) is obtained from the mantle disturbing potential \(T^{\rm M}\) after subtracting the lithospheric-mantle gravitational potential \(V^{LM}\). We then write the following:  
\[T^{\rm CL}=T^{\rm M}-V^{LM}. \tag{36}\]  
For a lateral density distribution function within the lithospheric mantle, the lithospheric-mantle gravitational potential \(V^{LM}\) is defined by the following:  
\[V^{LM}\big{(}r_{g},\Omega\big{)}=3\frac{\rm GM}{\rm R}\,\frac{\rm\pi}{\rm p ^{Earth}}\,\sum_{n=0}^{\eta}\frac{1}{2n+1}\]  
\[\times\sum_{k=0}^{n+2}\binom{n+2}{k}\frac{(-1)^{k}}{\rm R^{k+1}(k+1)}\sum_{m=- n}^{n}\left(\rm L^{(k+1)}_{n,m}-M^{(k+1)}_{n,m}\right)\,Y_{n,m}(\Omega), \tag{37}\]where the Moho coefficients \(\mathrm{M}_{\mathrm{n,m}}^{(k+1)}\) describe the geometry and lateral density (contrast) distribution at the Moho interface, and the LAB coefficients \(\mathrm{L}_{\mathrm{n,m}}^{(k+1)}\) describe the geometry and lateral density (contrast) distribution at the LAB.  
The Moho coefficients \(\mathrm{M}_{\mathrm{n,m}}^{(k+1)}\) in Equation (37) are defined in the following form:  
\[\sum_{\mathrm{m}=-\mathrm{n}}^{\mathrm{n}}\mathrm{M}_{\mathrm{n,m}}^{(k)}\; \mathrm{Y}_{\mathrm{n,m}}(\Omega)=\frac{2n+1}{4\pi}\iint\limits_{\Phi}\delta \rho^{LM}\big{(}\Omega^{\prime}\big{)}\;M^{k}\big{(}\Omega^{\prime}\big{)}\; \mathrm{P}_{\mathrm{n}}(t)\;d\Omega^{\prime}, \tag{38}\]  
where \(M\) denotes the Moho depth (see Equation (34)). The (lateral) lithospheric mantle density contrast \(\delta\rho^{LM}\) in Equation (38) is computed as the difference between the adopted (lithospheric mantle) reference density \(\rho^{LM}\) and the lithospheric mantle density \(\rho^{LM}\); i.e., \(\delta\rho^{LM}(\Omega^{\prime})=\rho^{LM}-\rho^{LM}(\Omega^{\prime})\).  
The LAB coefficients \(\mathrm{L}_{\mathrm{n,m}}^{(k+1)}\) in Equation (37) read as follows:  
\[\sum_{\mathrm{m}=-\mathrm{n}}^{\mathrm{n}}\mathrm{L}_{\mathrm{n,m}}^{(k)}\; \mathrm{Y}_{\mathrm{n,m}}(\Omega)=\frac{2n+1}{4\pi}\iint\limits_{\Phi}\delta \rho^{LM}\big{(}\Omega^{\prime}\big{)}L^{k}\big{(}\Omega^{\prime}\big{)}\; \mathrm{P}_{\mathrm{n}}(t)\;d\Omega^{\prime}, \tag{39}\]  
where \(L\) is the LAB depth. If we consider a current resolution of global lithospheric density models (\(1\times 1\) arc-deg or lower) that corresponds (by means of a half wavelength) to a spectral resolution up to the degree 180 of spherical harmonics, the lithospheric-mantle gravitational potential can sufficiently be computed by using a binomial series in Equation (37) up to only the third-order term.
## Materials and Methods
### Static Headspace-SPME-GC-MS Analysis  
A Perkin Elmer Clarus 500 GC-MS quadruple bench top system (Beaconsfield, UK) was used in combination with a Combi PAL PAL COMBI-xt autosampler (CTC Analytics, Switzerland, [[https://www.palsystem.com/](https://www.palsystem.com/)]([https://www.palsystem.com/](https://www.palsystem.com/))). The GC column used was a Zebron ZB-624 with inner diameter 0.25 mm, length 60 m, and film thickness 1.4 \(\upmu\)m (Phenomenex, Macclesfield, UK). The carrier gas used was helium of 99.996% purity (BOC, Sheffield, UK). A DVB/CAR/PDMS SPME fibre (needle size 23 ga, StableFlex, for use with autosampler) was obtained from Sigma-Aldrich (#57298U) and pre-conditioned before use, as per manufacturer's instructions, 4 \(\times\) 30 min heating at 300 \({}^{\circ}\)C. A blank was always then run to check the fibre was clean before use.  
Urine samples were placed in a Combi PAL PAL COMBI-xt autosampler (CTC Analytics, Switzerland, [[https://www.palsystem.com/](https://www.palsystem.com/)]([https://www.palsystem.com/](https://www.palsystem.com/))) incubation chamber at 60 \({}^{\circ}\)C for 30 min, followed by the extraction of volatiles from the headspace of the vial and adsorption to the SPME fibre for 20 min (static HS-SPME). The fibre was then inserted into the GC component for desorption at 220 \({}^{\circ}\)C for 5 min. The initial temperature of the GC oven was set at 40 \({}^{\circ}\)C, held for 2 min, increased to 220 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and then held for 4 min, with a total run time of 42 min. A solvent delay was set for the first 4 min and the MS was operated in positive electron impact ionization EI+ mode, scanning from ion mass fragments 10-300 \(m\)/z, with an interscan delay of 0.1 s and a resolution of 1000 at FWHM (Full Width at Half Maximum). The helium gas flow rate was set at 1 mL/min. All samples were randomly ordered (using Microsoft Excel's = RAND0) function) and injected, essentially as described previously by [PERSON] et al. [(6)].
## 2 Materials and Methods
### Sampling Site  
Sampling was carried out in the Mexican regions of Tabasco and Tamaulipas. Contaminated soil samples were recovered from the Tres Bocas town (location of 3a.), Huimanguillo Municipality, Tabasco (17\({}^{\circ}\)55\({}^{\prime}\)18.9\({}^{\prime\prime}\) N, 93\({}^{\circ}\)50\({}^{\prime}\)48.6\({}^{\prime\prime}\) W) and were classified as Actisol (AC) and from Cuenca Burgos, Tamaulipas (26\({}^{\circ}\)00\({}^{\prime}\)51\({}^{\prime\prime}\) N, 98\({}^{\circ}\)29\({}^{\prime}\)45\({}^{\prime\prime}\) O) and was classified as Kastanozem based on the World Reference Base (WRB). Each region (Burgos and Tabasco) constitutes a zone. There were two zones selected. Six spots were randomly sampled for each region; Burgos (SB. R1 and SB. R2 replicates) and Tabasco (ST.R1 and ST.R2). Different soil samples were collected in triplicates, according to [PERSON] et al. (2021), and the soil samples were combined to form a composite sample for each region, giving rise to four composite samples, as shown earlier [44]. The texture was determined by using the method of Bouyocos, and the pH was measured in a water solution (1:2) [45] using three replicates for each composite sample (Figure 2). A \(t\)-test was performed to compare the difference between the physiochemical properties of the soil from Burgos and Tabasco at \(p<0.05\). The hydrocarbon content of the soil was not analyzed as the focus of the study is to determine the bacteria diversity in oil-contaminated soil.
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 4 Research Methodology  
The methodology that is followed to define privacy-preserving blockchain solutions in IoV is divided into the following three steps:  
1. Firstly, there is an extensive search in Scopus search engine (www.scopus.com, accessed on 25 May 2021) for papers relevant to blockchain based solutions in IoV. The purpose of this search is to find the most relevant papers to the search question: \"_privacy-preserving blockchain-based solutions that have been proposed in IoV\"_. Therefore, search terms such as \"IoV\", \"VANET\", \"blockchain\" and \"privacy\" are used to search in the titles, abstract and keywords of publications. The searched query is as follows:  
TITLE-ABS-KEY ((VANET OR \"smart vehicle\" OR \"vehicular network\" OR \"internet of vehicle\" OR \"IoV\" OR \"smart transport\" OR \"smart mobility\" OR \"autonomous vehicle\") AND (blockchain OR \"distributed ledger\") AND (privacy OR \"personal data\"))
2. Subsequently, from those returned papers, the most relevant to our research are selected, including only papers which propose blockchain-based solutions aiming to ensure privacy in different aspects of IoV.
3. Finally, the included papers in our survey are divided into categories, according to the IoV service area that they focus on to provide a privacy-preserving blockchain-based solution. Thus, in Sections 5 and 6, the papers are grouped, analysed and presented using these categories.  
Our scientific interests revol around blockchain and privacy. Blockchain is a well-known technology for the immutability, availability and integrity of the stored data. As a promising technology, it does not have any built-in privacy mechanism. In this paper, we study literature to record what technologies can be combined to expand blockchain and add a privacy aspect. Particularly, the aim of this review paper is to explore existing applications or proposals regarding the use of blockchain technology in the field of IoV that simultaneously try to protect the privacy of involved parties. More specifically, in our analysis of this paper, we aim to answer, in total, the following seven research questions:  
1. Which IoV service areas utilise privacy-preserving blockchain-based solutions?
2. What kind of privacy protection is provided in the existing solutions?
3. What types of blockchain technology are used?
4. Which blockchain frameworks do solutions utilise?
5. What are the underlying privacy mechanisms that support blockchain-enabled IoV solutions?
6. Do the proposed schemes provide a security analysis?
7. What are their implementation maturity levels?
###### Abstract  
A modeling approach combining the lattice Boltzmann (LB) method and the cellular automaton (CA) technique are developed to simulate the faceted front to equiaxed structure transition (FET) of directional solidification of multi-crystalline silicon. The LB method is used for the coupled calculation of velocity, temperature and solute content field, while the CA method is used to compute the nucleation at the silicon-crucible interface and on SiC particles, as well as the mechanism of growth and capturing. For silicon, the interface kinetic coefficient is rather low, which means that the kinetic undercooling can be large. A strong anisotropy in the surface tension and interfacial kinetics are considered in the model. A faceted front in conjunction with a sufficiently high carbon content can lead to equiaxed growth by nucleation on SiC. The temperature gradient in Si melt at the interface is negative, which leads to the occurrence of a faceted interface. The higher the absolute value of thermal gradients, the faster the growth velocity. Due to differences in the degree of undercooling, there will be the unification of facets in front of the solid-liquid interface. Transitions from faceted front to thermal equiaxed dendrites or faceted equiaxed grains are observed with smaller or larger impurity contents, respectively.  
facet; dendrites; cellular automaton; lattice Boltzmann; semiconducting silicon; directional solidification +
Footnote †: journal: Applied Physics Letters B
## 3 Remote Sensing Challenges and Solutions to Increase Agricultural Productivity  
### Data Issues  
Regardless of geographic area, developing high-quality RS approaches to monitor agricultural activities that can produce accurate and robust results may require standardised inputs and, thus, data pre-processing [161]. Any uncertainties within the data inputs and pre-processing steps may result in low predictive accuracy. Here, we outline briefly some of the key challenges for RS-based approaches.
# RIS-Assisted Fixed NOMA: Outage Probability Analysis and Transmit Power Optimization
###### Abstract
RIS can boost cell-edge users' received signal power, while also reducing interference from nearby cells [12]. The power loss due to long haul communication can be overcome by RIS-SWIPT [13]. It helps mobile edge computing-based internet of things (IoT) networks function better in terms of latency [14]. It acts as an EM signal reflection hub, allowing the massive connectivity of device-to-device (D2D) networks [15]. It can improve PLS by intelligently canceling unauthorized transmissions using passive beamforming [16]. It can be effectively used for indoor rate-hungry applications like virtual reality (VR). The RIS-coupled visible light communications (VLC) and wireless-fidelity (Wi-Fi) ensure zero blind spots [17]. It can be utilized in vehicular ad hoc networks (VANET), autonomous vehicular networks, robotics networks [18], UAV networks [19], autonomous underwater vehicular networks, intelligent sensor networks, etc. The following is the order in which the rest of the manuscript is presented: Related works on RIS and NOMA are discussed in Section 2. Analytical expressions for outage probability are developed in Section 3 for RIS as a smart reflector (RIS-SR)-assisted FNOMA system. Most of the traditional works integrating RIS and NOMA assume this RIS-SR-FNOMA configuration, with passive RIS installed far from the BS and UE. Because of propagation losses and shadowing, RIS may receive very weak signals and may not beamform the received signals to the intended users. As a result, RIS as an AP (RIS-AP)-assisted FNOMA configuration is proposed, in which the passive RIS is located near the AP. Analytical expressions for outage probability are developed in Section 4 for RIS-AP-FNOMA. In Section 5, analytical expressions are derived for assigning optimal powers to both the near user (NU) and far user (FU) of the NOMA system. In Section 6, extensive Monte Carlo (MC) simulations are carried through MATLAB R2023a to corroborate the developed analytical expressions. This research aimed to demonstrate that the RIS-APFNOMA configuration outperforms the traditional RIS-SR-FNOMA configuration in terms of outage and sum capacity. The research findings as well as future works are described in the Conclusions.
## 2 Summary of the Paper
### Overlay and Underlay Transmission for Cognitive Radio  
In current cognitive radio, there are two approaches to use the spectrum more efficiently: the overlay cognitive radio transmission and the underlay cognitive radio transmission (UWB). In underlay CR, a very wide bandwidth is occupied by the transmission with a very low power spectrum density. This extremely low spectrum density of underlay CR transmission avoids significant interference to existing primary users operating in the range of the underlay CR transmission. In overlay cognitive radio, frequency agile transmitters discover unused spectrum \"holes\" and transmit over those unused frequency bands. By doing so, interference to existing wireless systems is avoided.  
However, both underlay CR transmission and overlay CR transmission are not without drawbacks. In underlay CR, the transmission power is extremely limited in order to avoid interference to primary users, which significantly decreases the available channel capacity. In overlay CR, only unused frequency  
Figure 5: Illustration of quickest detection.  
bands are exploited to transmit signal and all underused bands are not touched at all, which decreases the available bandwidth and channel capacity. In Section 10., we will address this issue.
# FPGA Implementation of Mutual Authentication Protocol for Medication Security System
## 2 Background Review  
### Medication Errors  
For a healthcare information system, medication management is a complex, multifaceted operation involving multiple people and numerous data transmission steps; medication errors can occur throughout the system network. Medication error is an important cause of patient morbidity and mortality [10]. In efforts to prevent medication errors, the healthcare information system needs to give priority to keeping their networks secure by permitting only authenticated users or messages to gain access to their protected resources.  
Nevertheless, it is challenging to provide consistent solutions to eliminate or minimize recurrent events and work toward improving patient safety [11]. If a proper medication system is proposed considering the circumstances of administrating medication safety, it will be able to help to prevent errors from occurring, whether it be a fault due to personnel or system failure. Due to advances in medical sensors and low-power network systems, the use of the IoT in electronic health (e-health) management systems has emerged in recent years to prevent the growing incidence of medical errors [12].
## 5 Flight Verification and Data Analysis
### Analysis of Heat Transfer Characteristics On-Orbit
#### 5.5.1 Heat Transfer Characteristics of TCB in Module
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Name of the Payload** & **Power \(\mathbf{\varphi_{1}}\) (W)** & **Valve Opening** \\ \hline
1 & Load equipment 1–3 & 268 & \(K_{\text{v}0}=30\%\) \\
2 & Payload rack 1 & 345 & \(K_{\text{v}1,1}=35\%\) \(K_{\text{v}1,2}=30\%\) \(K_{\text{v}1,3}=30\%\) \\
3 & Payload rack 2 & 420 & \(K_{\text{v}2,1}=32\%\) \(K_{\text{v}2,2}=5\%\)\({}^{1}\)\(K_{\text{v}2,3}=47\%\) \\
4 & Payload rack 3 & 95 & \(K_{\text{v}3,1}=24\%\) \(K_{\text{v}3,2}=5\%\)\({}^{1}\)\(K_{\text{v}3,3}=5\%\) \\
5 & Payload rack 4 & 185 & \(K_{\text{v}4,1}=28\%\) \(K_{\text{v}4,2}=5\%\)\({}^{1}\)\(K_{\text{v}4,3}=36\%\) \\
6 & Payload rack 5 & 72–1050 W & \(K_{\text{v}5,1}=38\%\)\(K_{\text{v}5,2}=4\%\)\({}^{1}\)\(K_{\text{v}5,3}=5\%\) \\
7 & Payload rack 6 & 0 & \(K_{\text{v}6,1}=5\%\)\({}^{1}\)\(K_{\text{v}6,2}=5\%\)\({}^{1}\) \\
8 & Payload rack 7 & 360 & \(K_{\text{v}7,1}=22\%\)\(K_{\text{v}7,2}=40\%\) \\
9 & Total of the module & 1745–2723 & / \\ \hline \hline \end{tabular} \({}^{1}\) When the valve opening is 5%, this represents that the load of the branch is not working.  
\end{table} 
Table 4: Typical operating conditions and parameters on orbit. Figure 22: Hydraulic and heat transfer characteristics of thermal control system: (**a**) operating parameters of each rack on orbit; (**b**) system temperature and heat transfer characteristic curves.
## 3 Results and Discussion
### Textural Properties of Baked Meat, Hybrid, and Plant Burgers  
An important distinguishing feature of meat products is their texture, which affects their sensory attractiveness. The texture is defined as a combination of the rheological and structural properties of food that result from the mutual arrangement and interaction of food ingredients, as well as the way in which they are perceived by the human senses [78]. Objective assessment of the textural features of food is possible using instrumental methods. Among instrumental techniques for determining the texture (including tenderness and hardness) of meat and meat analogs are mechanical destructive methods such as the Warner-Bradzler shear test and a penetration test [79; 80]. Therefore, in this research, baked burgers were tested using the indicated instrumental methods, and the results are presented in Figure 2a,b.  
Figure 1: Browning Index of the surface of meat, hybrid, and plant burgers. For the denomination of hybrid burger samples, see Table 1. Different lowercase letters above columns for the same recipe variant indicate significant differences (grouping variable: sample type) (\(p\leq 0.05\)). Different uppercase letters above columns for the same sample type indicate significant differences (grouping variable: recipe variant) (\(p\leq 0.05\)).  
It was observed that reducing the meat content in the burgers had a significant impact on decreasing the shear force compared to the sample without plant additives, regardless of the recipe variant (Figure 2a). This meant that the hybrid and plant-based burgers were softer and easier to cut into smaller pieces. The results were consistent with the study of [PERSON] et al. [81], who investigated the effect of partial or total replacement of meat with plant-based proteins on the textural properties of chicken sausages.  
Similarly, the penetration force of burgers decreased with an increase in the content of plant parts in the recipe. The difference was that burgers based on recipe variant II were characterized by significantly (\(p\leq 0.05\)) higher values of penetration force at each level of the proportion of meat to plant parts than burgers obtained from recipe variant I (Figure 2b). It can therefore be concluded that the structure of burgers containing a mixture of plant ingredients was more disintegrated under the influence of a point-applied mechanical force at the same level.  
The greater tenderness (less hardness) of hybrid and plant burgers, compared to the meat sample, could be caused by the probable competition for water between plant and meat ingredients [82] and a reduction in the content of fibrous structures typical of meat. In hybrid burgers, there is a lack of substances that bind plant and meat ingredients into one integral structure. Thus, the observed structural differences may be reduced by using structured plant proteins (to imitate the structure of meat muscles) [79] and/or adding effective binding agents [83].
# The Leaky-Wave Perspective for Array-Fed Fabry-Perot Cavity and Bull's-Eye Antennas
## 2 Fabry-Perot Cavity Antennas  
Fabry-Perot cavity antennas represent a class of resonant radiating systems. The first evidence of such structures was found in the last quarter of the 19 th century, when Fabry-Perot interferometers were developed at the time presenting exceptional, unprecedented spectroscopic resolving powers [20]. The idea behind FPCAs, first proposed in [21], stemmed from the intuition that such a high optical wavelength resolution could be turned into angular resolution and hence high elevation directivity in the microwave range.  
A plane-parallel cavity represents the core of the structure, enclosed by a metal ground plane and a partially reflective sheet (PRS), either uniform (e.g., a high-permittivity super  
Figure 1: Pictorial representation of a Fabry–Perot Cavity antenna (FPCA), with the relevant reference system.  
strate) or quasi-uniform (e.g., a periodic metal screen with spatial periods that are small with respect to the free-space wavelength) [22; 23] (see Figure 1).  
Even if the first attempt to theoretically obtain a collimated beam at broadside was based on geometrical optics [21; 24; 25], the leaky-wave method has proven to be more physically insightful and effective in the design procedure [26; 27]. In this light, antenna feeders have the role of efficiently exciting cylindrical leaky waves; hence, they are typically simple, nondirective radiating elements such as dipoles, slots, or patch antennas. The source choice, however, directly affects the azimuthal dependence, as well as the polarization of the relevant excited field [7].