esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain for the Healthcare Supply Chain: A Systematic Literature Review
## 4 Discussion  
### Technical Overview  
In our study sample, private (a blockchain in which access is restricted to a specific group of individuals or organizations) and consortium-based (a blockchain that is owned and operated by a group of organizations) blockchains were the most commonly used types, while hybrid (a blockchain that combines features of both public and private blockchains) and public (a blockchain that is open and accessible to anyone) blockchains were less prevalent.  
Non-public blockchains, also known as permissioned blockchains, offer several advantages over public blockchains, including increased control, higher data confidentiality [58], lower energy consumption, and lower costs [59]. These advantages may have led researchers to choose this technology over a fully decentralized solution for efficiency purposes. A possible disadvantage of a public blockchain is the scalability issue, as reported by [PERSON] and colleagues [30], who deemed their public-blockchain-based solution as not fully scalable. Blockchain needs to be scalable if the technology is intended to be used on a large scale [60]. As the data flow provided by users increases, performance must remain viable. For this reason, an energy-consuming solution, such as a public blockchain, may notbe the best choice when dealing with SCHMs. Our analysis shows that the scalability issue is not a prerogative of public blockchain alone, given that even studies investigating private, consortium, or hybrid blockchains did not explore scalability [31; 32; 33; 34; 35; 36; 37; 39; 31; 32; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 52; 54; 55; 56; 57] and, in one case, failed to achieve it [30]. In the article published by [PERSON] et al. [38], scalability issues were solved thanks to the Raft consensus. [PERSON] et al. [53] dealt with scalability issues by proposing a proof of authority (PoA) instead of a proof of work (PoW). The results emerging from this review suggest that further studies are needed to assess the scalability of blockchain technologies before real-world implementation might be commenced.  
Many articles in this review chose ETH as the pre-blockchain platform of choice [30; 37; 39; 41; 53; 54; 56; 57], likely due to its open-source nature and cross-industry focus, which allows for decentralized applications [61].  
Another notable feature of blockchain technology is the use of smart contracts, which offer an automated and transparent way to exchange data and contracts without the need for a third-party intermediary [62]. While the use of smart contracts was widely theorized and simulated in these articles, the technological readiness level of the proposed solutions appears to be too low to determine their effectiveness in real-world settings.
## 3 Data Collection and Processing
### MIC Maximum Information Coefficient Analysis  
MIC maximum information coefficient analysis [25] is one of the methods to indicate the strength of nonlinear relationships between parameters and is built on the machine-learning concept of feature selection. The larger the MIC, the stronger the association between parameters. The collected data set of 25 critical parameters affecting blast furnace production is analyzed for the MIC, and the blast furnace permeability index is analyzed for the MIC with other critical parameters. Twelve critical parameters (\(x_{2},x_{3},x_{4},x_{5},x_{6},x_{7},x_{8},x_{9},x_{14},x_{15},x_{17},x_{21}\)) with MIC coefficients greater than 0.1 were selected, as shown in Figure 6. The specific concept involves selecting a data set consisting of two crucial parameters that are distributed within a two-dimensional space. To analyze this data, an \(m*n\) grid is employed to partition the data space and calculate the mutual information of the random variables x and y. The MIC analysis is calculated as shown in Equation (6):  
\[MIC(x,y)=\underset{m*n<B}{max}\frac{p(x,y)log_{2}\int\frac{p(x,y)}{p(x)p(y)}dxdy}{ log_{2}min(a,b)} \tag{6}\]  
where: \(m\), \(n\) denote the number of grid divisions on \(x\), \(y\); \(p(x,y)\) denotes the frequency of data points falling in the \((x,y)\) grid; \(p(x)\) refers to the frequency of data points falling on the \(x\)th row; \(p(y)\) refers to the frequency of data points falling on the \(y\)th row; and \(B\) is the 0.6 power of the data volume.  
Establishment of characteristic parameters. Based on the characteristic parameter selection methods of blast furnace permeability and gas flow coupling mechanism analysis, Spearman correlation analysis, and MIC analysis, and summarizing the results of the three analyses and process experience, 14 key parameters were finally selected as the input parameters of the data-driven prediction model. The input parameters are shown in Table 2.  
Figure 6: MIC maximum information coefficient.
## 3 Parameter Selection for the MSFGWO Algorithm  
A simulation experiment was conducted using the Starlink constellation. The data from the Starlink constellation on 19 July 2024 were used, with the ground point set at Zhengzhou (N34.76\({}^{\circ}\), E113.65\({}^{\circ}\)) and an observation elevation angle of 10\({}^{\circ}\). A total of 6623 Starlink satellites were simulated, with satellite data updated every second and a duration of one hour for observation (equivalent to 3600 s). On average, there were 13.68 satellites visible per second in the simulation experiment, with a maximum visibility of up to 20 satellites and a minimum visibility of 7 satellites.
## 6 Discussion
### Challenges  
Application of blockchain technology in intelligent knowledge-based networks also brings in a set of challenges such as difficulty in processing big data, high energy consumption due to blockchain, resource management difficulties, lack of standardization and interoperability, low scalability, increased latency and limited throughput, security vulnerabilities in blockchain, additional resource demand, etc. The next segments examine these negatives.
## 2 Description of the Simulation Models and Experimental Set up
### Calibration of EBT3 Radiochromic Films used for Mapping of the Radiation Field  
The dosimetry assessment during high power laser-thin solid target interaction performed using the EBT3 radiochromic films (RCF) proved to be useful [39] and offers two-dimensional information on the radiation dose. The EBT3 films have a symmetrical layer's structure as the active layer (28 \(\upmu\)m thick) is sandwiched between two 125 \(\upmu\)m matte-polyester substrates (Ashland Advanced Materials). These polyester layers prevent the formation of Newton ring interference patterns when the irradiated EBT3 films are scanned with flatbed scanners [39].  
The EBT3 films were calibrated using both high energy photons generated by a 40 kV X-ray source [40] and electrons with an energy of about 5 MeV using a classical medical accelerator. In the paper of [PERSON] et al. [41], it was shown that EBT3 RCF can be used for dosimetry measurements of photon, electron and proton beams as their calibration curves (in the same dose range), are similar. The uncertainties of the EBT3 calibration curves presented there were within 1.5% for photons and protons and 2% for electrons [41].  
A high precision measurement of the optical density of each EBT3 film is essential for the evaluation of the ionizing particles radiation dose values inside the target chamber.  
The EBT3 films exposed during the high-power laser thin solid target experiments, as well as those used for the determination of the calibration curve (see Figure 3b) were scanned using an EPSON Expression 11000 XL professional scanner with a resolution of 4800 dpi in transmission mode. The scanned area was about 0.5 \(\times\) 0.5 cm\({}^{2}\) for each film.  
The scanned images of the EBT3 films were processed with ImageJ software in order to convert the pixel values into standard optical density (OD) [42]. For this purpose, a Kodak calibrated optical density step tablet [42] has been used and the images were converted into 8 bits grey scale. The pixel values range from 0-255 gray units. A so-called Rodbard calibration function [43,44] was used for pixel values to obtain an optical density transformation: \(y=d+(a-d)/(1+(x/c)^{b})\) where \(a=80.147399\), \(b=0.102551\), \(c=1.109687\times 10^{12}\), \(d=8.304216\). The R\({}^{2}\) was 0.997. This function was identified to be suitable for pixel values to OD calibration in medical applications and for the establishment of dose response curves [43,44]. The graph of the Rodbard function is presented in Figure 3a.  
The calibration graph presented in Figure 3b was obtained for a 40 keV photons beam. The calibration curve obtained for 5 MeV electrons is similar (within \(\pm\)7%) with that one from Figure 3b. Both curves were used for calculation of the radiation dose inside the target chamber as well as for the electron maximum energy (of about 13 MeV) estimation. The black squares represent the measured data values and in red is the curve used for fitting. As fitted function, we used an exponential one. The fitting parameters was: R\({}^{2}\) = 0.997, and standard error was about 0.069.
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 2 Working Mechanism of VVSDSI Bearing
### Theoretical Study of VVSDSI Bearings
#### 2.4.2 Theoretical Hysteresis Model of VVSDSI Bearing  
The VVSDSI isolation system consists of two parts: the upper disc spring set and the lower disc spring set. By combining the hysteresis models of both, the hysteresis model for the VVSDSI disc spring system considering friction effects can be obtained, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Series** & \(f_{M}\) & \(f_{R}\) \\ \hline A & 0.005–0.03 & 0.03–0.05 \\ B & 0.003–0.02 & 0.02–0.04 \\ C & 0.002–0.015 & 0.01–0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Friction coefficients of disc springs.  
Figure 5: Dimensions of disc spring.  
According to the characteristics of the proposed bearing, it is essential to analyze the mechanical properties of the bearing in both scenarios. The working stage of the VVSDSI bearing includes two parts: the seismic isolation stage and the protection stage. During the seismic isolation stage, the upper and lower disc spring assemblies function collaboratively. At this time, the current loading and unloading stiffnesses of the bearing are as follows:  
\[K_{1}=\frac{k_{1}k_{3}}{k_{1}+k_{3}} \tag{5}\]  
\[K_{2}=\frac{k_{2}k_{4}}{k_{2}+k_{4}} \tag{6}\]  
where \(k_{1}\) and \(k_{3}\) are the loading stiffness of the upper disc spring group and the lower disc spring group, and \(k_{2}\) and \(k_{4}\) are their unloading stiffness, which can be calculated by Equations (1)-(4).  
During the protection phase, the upper disc spring group becomes inactive, while only the lower disc spring group continues to function. The current loading and unloading stiffness of the bearing is as follows:  
\[K_{3}=k_{3} \tag{7}\]  
\[K_{4}=k_{4} \tag{8}\]  
furthermore, the VVSDSI bearing's theoretical hysteresis model can be acquired.  
Loading phase:  
\[F=\left\{\begin{array}{cc}K_{1}x&(0\leq x\leq x_{1})\\ K_{1}x_{1}+K_{3}(x-x_{1})&(x_{1}<x<x_{3})\end{array}\right. \tag{9}\]  
Unloading phase:  
\[F=\left\{\begin{array}{cc}K_{2}x&(0\leq x\leq x_{2})\\ K_{2}x_{2}+K_{4}(x-x_{1})&(x_{2}<x<x_{3})\end{array}\right. \tag{10}\]  
where \(x\) denotes the vertical displacement of the bearing: \(x_{1}=(1+k_{1}/k_{3})\)\(x_{0}\) represents the vertical displacement of the bearing when the stiffness changes during loading:  
Figure 6: Hysteretic model of VVSDSI bearing considering the friction effect. (a) Upper disc spring assembly: (b) Lower disc spring assembly: (c) VVSDSI disc spring system.  
\(x_{2}=(1+k_{2}/k_{4})\)\(x_{0}\) is the vertical displacement of the bearing when the stiffness changes during unloading: \(x_{0}\) is the maximum travel displacement of the upper and lower disc spring sets; and \(x_{3}\) is the maximum vertical displacement of the bearing.
## 4 Highly Dynamic Simulations and Results  
In order to prove the correctness and advantages of the proposed method, a full digital simulation platform based on Matlab was constructed. The orbital distribution and operation cycle of GPS (Global Position System) is generated according to actual GPS parameters; the IMUs' information is selected from the data book of Analog Devices Inc. In this part, the norm-based analysis method is verified first, and after combining with three different kinds of MEMS gyros' parameters, the actual result of the parameter self-calibration method is put forward.
## 7 Discussion
### Trust Enhancement  
This study shows that various factors, including security, privacy, regulation, and transparency, greatly influence the adoption of blockchain technology in digital payment systems. These factors are crucial in bolstering user trust. It identifies security and privacy challenges in interoperable blockchains, including hash time lock contracts, private key attacks, and network analysis [31]. Security and privacy measures, such as the implementation of consensus algorithms, mixing, anonymous signatures, encryption, secure multiparty computation, non-interactive zero-knowledge proof, and secure verification of smart contracts [38], can be employed to offer users the confidence that their transactions are impervious to unauthorised modifications and that records remain unchangeable. This measure successfully reduces the likelihood of fraudulent acts and promotes an atmosphere characterised by heightened trust. The implementation of robust privacy protocols with adherence to data protection regulations like the General Data Protection Regulation (GDPR) of the European Union [37; 39] engenders a sense of confidence among consumers that their personal information and financial transactions are shielded from unauthorised intrusion.  
Furthermore, well-defined regulatory frameworks provide clients with a sense of legal certainty, incorporating measures to protect consumers and address the issue of money laundering [55]. The inherent transparency of blockchain technology is derived from its publicly available ledger and decentralised system [29; 40]. This feature enables users to independently verify transactions and uphold the system's integrity. As a result, this fosters an elevated level of assurance in the digital payment system.  
**Proposition 1**.: _Trust in blockchain technology for digital payments relies on security, privacy, regulation, and transparency. When used responsibly, blockchain can instil trust in digital payment systems. However, stakeholders must address these critical aspects to ensure widespread implementation and reception of blockchain technology._
###### Abstract  
The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO\({}_{2}\) from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO\({}_{2}\), and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO\({}_{2}\) emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO\({}_{2}\)Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis.  
Atmospheric CO\({}_{2}\) concentration was estimated from the GOSAT satellite. The variables GPP and CO\({}_{2}\)Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km\({}^{2}\) of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km\({}^{2}\) and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO\({}_{2}\) concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO\({}_{2}\) capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO\({}_{2}\)Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region.  
carbox, 2023, 15, 2780. [[https://doi.org/10.3390/rs15112780](https://doi.org/10.3390/rs15112780)]([https://doi.org/10.3390/rs15112780](https://doi.org/10.3390/rs15112780))
## 2 Experimental Procedures
### Analysis of Nanoindentation Quantities  
Figure 3, adapted from [PERSON] et al. [27], illustrates the schematic representation of a complete evolution of typical loading-unloading cycle. This indentation cycle depicts the maximum test load or force (\(P_{max}\)), maximum depth (\(h_{max}\)), final depth after loading (\(h_{f}\)) and the slope (\(S\)) of the unloading curve (Equations (1) and (2)). This slope is referred to as the elastic contact or unloading stiffness of the sample. Loading-unloading curves were analysed using the Oliver-Phar method [38] with the software provided by Micromaterials. The hardness, \(H\) and the reduced elastic modulus, \(E_{r}\) are defined by Equations (1) and (2), respectively:  
\[H=\frac{P_{max}(h_{max})}{A(h_{c})} \tag{1}\]  
where \(A\) and \(h_{c}\) are the contact area (between the indenter and the sample) at maximum load and contact depth, respectively;  
\[E_{r}=\frac{S}{2\beta}\sqrt{\frac{\pi}{A(h_{c})}} \tag{2}\]  
where \(\beta\) is a correction factor which varies slightly for various indenter geometry (1.034 for a Berkovich indenter) and \(S=\frac{dP}{dh}\). The reduced elastic modulus accounts for the elastic displacement of both indenter and sample. The elastic modulus of the sample, \(E_{s}\) can be derived from the reduced elastic modulus, \(E_{r}\) (from the tests) and the indenter elastic modulus, \(E_{i}\) (usually 1141 GPa for the diamond) by Equation (3):  
\[\frac{1}{E_{r}}=\frac{\left(1-{v_{s}}^{2}\right)}{E_{s}}+\frac{\left(1-{v_{i} }^{2}\right)}{E_{i}} \tag{3}\]  
where \(v_{s}\) and \(v_{i}\) are [PERSON]'s ratios of the polymer sample and diamond indenter tip respectively, approximately 0.2 for polymer and 0.07 for diamond. The diamond indenter tip is much stiffer than  
Figure 2: (**a**) Diagrammatic illustration of the nanoindentation test system, (**b**) a typical Berkovich indenter tip for nanoscale measurements and (**c**) symmetrical indentations at 30 μm apart (not to scale) on the HF/PCL biocomposite sample.  
polymer sample (\(E_{s}\ll E_{i}\)). Therefore, Equation (3) can be reduced to \(\frac{1}{E_{r}}\approx\frac{\left(1-{v_{s}}^{2}\right)}{E_{s}}\) and \(h_{c}=h_{max}-\varepsilon\). \(\frac{P_{max}}{3}\), where \(\varepsilon\) depends on the shape of the indenter tip (usually 0.75 is recommended for a Berkovich indenter) and \(h_{max}\leq 200\) nm for nano range [39, 40]. However, from Equation (3), elastic modulus of the sample, \(E_{s}\) can be expressed as:  
\[E_{s}=\frac{1-{v_{s}}^{2}}{\frac{1}{E_{r}}-\frac{1-{v_{s}}^{2}}{E_{i}}} \tag{4}\]  
The mean values of the concerned experimental nanoindentation data obtained from the loading-unloading curves of the tests performed at a peak indentation load of 15 mN are later presented and discussed extensively.
# Modeling Open Channel Flows of a Viscous Fluid: Critical Transition and Apparent Bottom
## 5 Enhanced Parametrization for the SW Model
### Apparent Bottom for the SW Model  
Since there does not seem to be a simple way to parameterize \(\beta\) and \(\lambda\) to enhance the SW model in the present configuration, we choose \(\beta=1\) and \(\lambda=1\), which is the common assumption in the SW model, and seek to adjust an \"apparent bottom\" denoted by \(Z_{a}(x)\). We thus consider the SW system  
\[\left\{\begin{array}{l}q=U_{a}\,h_{a}\\ U_{a}\frac{dU_{a}}{dx}+g\cos\theta\frac{dh_{a}}{dx}=-g\cos\theta\frac{dZ_{a}}{ dx}+g\sin\theta-3\
u\frac{U_{a}}{h_{a}^{2}}.\end{array}\right. \tag{27}\]  
where \(h_{a}(x)\) and \(U_{a}(x)\) are the new variables in response to the apparent bottom \(Z_{a}\). Knowing the steady surface height \(Z_{s}(x)\) computed from the NS model, we want to adjust \(Z_{a}(x)\) in order to satisfy the constraint \(Z_{s}(x)=Z_{a}(x)+h_{a}(x)\). The apparent bottom is thus the solution of the equations  
\[\left\{\begin{array}{l}Z_{a}=Z_{s}-h_{a}\\ \frac{dh_{a}}{dx}=\frac{g}{q^{2}}\left(\cos\theta\,\frac{dZ_{s}}{dx}-\sin \theta\right)h_{a}^{3}+\frac{3\,\
u}{q}.\end{array}\right. \tag{28}\]  
The shape of the apparent bottoms \(Z_{a}(x)\) are displayed in Figure 9 for the 16 considered configurations. All these shapes are smoother than the actual semi-circular profile and exhibit a maximum located downstream the actual one. The apparent bottom solution clearly captures the main features of the free-surface, derived from the NS model, which are disregarded in the SW model close to the obstacle. In particular, the obtained apparent bottom roughly incorporates both the actual obstacle and the complex flow cells induced by its presence. FromEquation (28), we can see that the slope of the apparent bottom is proportional to the slope of the free-surface. Since for this combination of Froude numbers and blocking factors the free surface profile is wavy, in agreement with [53; 54; 55; 56], this explains the apparent bottom pattern in Figure 8a-c. The concept of apparent bottom seems, however, more intuitive for the case \(F_{n}=1.75\) since regular bumpy shapes are observed in Figure 8d.