esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation Method
In order to verify the accuracy of the present CFD method used in the dynamic stall characteristic simulations of rotor airfoil, a typical case of dynamic stall about the NACA0012 airfoil is presented under the SFV condition in this section. The freestream Mach number is 0.283, the variation of AoA is \(\alpha=14.91^{\circ}+9.88^{\circ}\sin(\omega t)\), and the reduced frequency (k) is 0.151. As shown in Figure 4, it is indicated that the simulated \(C_{l}\) by the present CFD method is correlated well with the test data of National Aeronautics and Space Administration (NASA) [35]. Meanwhile, the calculated results of the present CFD method are better than that calculated by Fluent software with SST k - \(\omega\) turbulence model. Since we lack appropriate test data of rotor airfoil under dynamic stall conditions coupled with variational velocity, the theory of [PERSON] [36] is employed to verify the accuracy of the present CFD method under OFV condition, as shown in Figure 5. It is illustrated that the numerical results correlate well with the theoretical data at \(\lambda=0.4\), and the numerical results with \(\lambda=0.8\) are basically close to the theoretical value. The deviation of the \(C_{l}\)/\(C_{l0}\) at \(\lambda=0.8\) could be attributed to the fact that the airflow compressibility and viscidity are neglected in the [PERSON] theory, where \(C_{l0}\) represents the lift force at steady freestream velocity. Consequently, it is indicated that the present CFD method is suitable for simulating unsteady characteristics of a rotor airfoil under dynamic stall conditions coupled with oscillating freestream velocity. Figure 4: Comparison of simulated \(C_{l}\) with test data. Figure 3: Comparisons of pressure coefficient (Cp) and velocity profiles of NACA4412 airfoil with test data. (**a**) Cp distribution. (**b**) Velocity profiles.
## 2 Data and Methodology  
[PERSON] et al. [19] investigated the cointegration and price transmission through a bivariate analysis between the ethanol (ETH) and seven important commodities in the Brazilian GDP. Those agricultural commodities consist of the sugar (SUG), cotton (COT), live cattle (LCA), Arabica coffee (ARA), Robusta coffee (ROB), corn (COR), and soybean (SOY). The presence of breakpoints in the ethanol price series was identified using the Bai-Perron algorithm [41], and the period and sub-periods were classified as follows; Full-period (Jan/2011 to Dec/2018), Sub-period 1 (\(P_{1}\): Jan/2011 to May/2012), Sub-period 2 (\(P_{2}\): May/2012 to Nov/2013), Sub-period 3 (\(P_{3}\): Nov/2013 to Sept/2015), Sub-period 4 (\(P_{4}\): Sept/2015 to Oct/2017) and Sub-period 5 (\(P_{5}\): Oct/2017 to Dec/2018). Table 1 summarizes the cointegrations found by [PERSON] et al. [19].  
The data was obtained from the Center for Advanced Studies on Applied Economics/University of Sao Paulo (CEPEA/USP) and the CEPEA methodology for the daily pricing of these products can be found on its website www.cepea.esalq.usp.br.  
We propose the \(EI\), calculated using \(H\) and \(d_{A}\), introduced by [PERSON] and [PERSON] (2018) and described later to assess the market efficiency, for the ethanol and agricultural commodities. The main idea is to observe the distance of the actual market state with respect to an ideal benchmark index for the full-period, and for the sub-periods \(P_{1}\), \(P_{3}\), and \(P_{5}\). Additionally, we employ the \(EI\) in two different perspectives, where we firstly consider \(EI\) as a unique value representing each of the cointegrated TS as described in Section 2.3. Secondly, we explore \(EI\) using a \"Rolling Window Approach\", that allows the visualization of the dynamics of the index along time. Moreover, we investigate in Section 2.4 the predictability of the price TS using the Lyapunov exponent.
## 3 Results  
### Spatial-Temporal Pattern of Land NPP  
The average land NPP in the Han River Basin from 2001 to 2019 showed evident spatial heterogeneity, with high values in the west and low values in the east (Figure 2a). Land with NPP values greater than 700 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) accounted for 9.81% of the basin area, mainly in Qinling Mountains located in the northwest and in Daba Mountains distributed in the southwest. Land with NPP values between 600 and 700 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) accounted for 18.10% of the basin area, primarily in northern Hanzhong and southern Ankang and Shiyan. Land with NPP values between 400 and 600 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) accounted for 66.15% of the basin area and was concentrated in the middle and upper reaches. Land with NPP values less than 400 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) accounted for 5.94% of the basin area, mainly around Nanyang and in the lower reaches of the Han River (Figure 2a).  
The spatial distribution of land NPP in sub-basins of the Han River Basin also presented significant regional differences. Specifically, land showing the highest NPP values was in the watershed above Danjiangkou, with an average of 591 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), land exhibiting moderate NPP values was in the mainstream watershed below Danjiangkou, with an average of 482 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), and land displaying the lowest NPP values was in the Tangbai River Watersed, with an average of 449 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\).  
The average annual land NPP in the Han River Basin between 2001 and 2019 ranged from 140 to 1033 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), with an average of 486 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). The highest was in 2015, while the lowest was in 2001. The interannual variation of land NPP exhibited a fluctuating upward trend. The growing linear trend reached the significant level (\(p<0.01\)) with an average change rate of 4.90 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). The increasing trend between 2001 and 2010 was more pronounced (\(p<0.01\)), with an average growth rate of 10.76 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), while the increasing trend between 2011 and 2019 was less pronounced (\(p>0.5\)), with an average growth rate of 3.33 g C\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) (Figure 2b).  
The average growth rates of land NPP in the upper, middle, and lower reaches of the basin were 5.78, 3.88, and 3.29 g C\(\cdot\)m\({}^{-2}\).a\({}^{-1}\) (Figure 2c), respectively. Specifically, the growth rate of land NPP in the watershed above Danjiangkou from 2001 to 2019 was faster than the basin-wide average (4.90 g C\(\cdot\)m\({}^{-2}\).a\({}^{-1}\)), whereas the growth rates of land NPP in the Tangbai River Watershed and the mainstream watershed below Danjiangkou were slower than the basin-wide average.
# Federated Learning for Secure In-Vehicle Communication
## 2 Backgrounds and Preliminaries
### CAN Protocol  
The real-time state of a vehicle is accurately described through CAN messages, which are widely used in the automotive industry due to their low production cost and robustness to electrical noise [12]. The CAN is an ISO-certified international standard for serial communication [13]. The standard frame format in Figure 2 is used to transmit CAN messages. In the standard CAN frame format, a frame is uniquely identified by an 11-bit CAN-ID field (Identifier) [14]. The CAN protocol guarantees reliable performance even under difficult circumstances, is economical, and is resilient in fault handling [15]. It is a prominent standard in automotive and industrial applications due to its robustness, broad acceptance in the semiconductor industry, and adaptability for high-speed, short-message transmission [13]. The frame format in Figure 2 is outlined as follows.  
Frame Start (SOF): use of 1 bit to present a new frame in the CAN bus [16].  
Data Length Code (DLC): refers to the use of four bits to specify the number of data bytes [17].  
Data field: are the data transmitted between 0 and 8 bytes (up to 64 bits) [9].  
Cyclic Redundancy Check (CRC): uses 16 bits for error detection [10].  
Acknowledgments (ACK): take advantage of 2 bits for signaling receipt [18].  
End of Frame (EOF): marks the end of the transmission by setting 7 bits [16].  
This structure ensures efficient and robust data exchange in automotive and other embedded systems.
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 3 Proposed Scheme
### Registration Phase
#### 3.1.2 Cloud Server Registration  
This sub-phase is initialized by each \(S_{j}\) in order to be part of the system. The details of each step are shown in Figure 3.  
Step 1: \(S_{j}\) generates a random nonce \(n_{S}\), obtains the current timestamp value \(T_{S}\) and computes:  
\[PSID_{j}=h(T_{S}\parallel n_{S}) \tag{6}\]  
\[S_{j}\Rightarrow CS:M_{3}=\left\{SID_{j},PSID_{j}\right\}\]  
Equation (6) is used to compute the pseudo-identity of each server.  
Step 2: After receiving the registration request message \(M_{3}\), \(CS\) verifies the validity of \(SID_{j}\). If \(SID_{j}\) is correct, \(CS\) computes:  
\[B_{1}=h\left(SID_{j}\parallel PSID_{j}\right) \tag{7}\]  
\[B_{2}=h\left(PSID_{j}\parallel h\left(ID_{CS}\parallel z\right)\parallel h \left(ID_{CS}\parallel y\right)\right)\oplus h\left(ID_{CS}\parallel z\right) \oplus h\left(ID_{CS}\parallel y\right) \tag{8}\]  
\[B_{3}=h\left(SID_{j}\parallel PSID_{j}\parallel h\left(ID_{CS}\parallel z \right)\parallel h\left(ID_{CS}\parallel y\right)\right)\oplus PSID_{j} \oplus h\left(z\parallel y\right) \tag{9}\]  
\[STORES\ B_{1}\text{ {in a database}}\]  
\[CS\Rightarrow S_{j}:M_{4}=\left\{B_{2},B_{3}\right\}\]  
\(CS\) registers \(S_{j}\) by Equation (7). Equations (8) and (9) are security parameters for future authentication purpose.  
Step 3: After receiving the registration response message \(M_{4}\), \(S_{j}\) stores \(B_{2}\) and \(B_{3}\).
# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**GPS(L1)-only**} & \multicolumn{2}{c}{**GLONASS-only**} & \multicolumn{2}{c}{**Multi-GNSS**} \\ \cline{2-7}  & **SPP** & **Differential GPS** & **SPP** & **Differential GPS** & **SPP** & **Differential GNSS** \\ \hline RMS (m) & 2.84 & 0.42 & 2.98 & 0.64 & 2.37 & 0.41 \\ Mean (m) & 2.39 & 0.36 & 2.58 & 0.44 & 1.99 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis results of position accuracy for GPS, multi-GNSS, and differential methods. Figure 1: Range-domain DGNSS (**a**) and Position-domain DGNSS (**b**). As described in the next section, there have been many attempts to improve the GNSS performance of smartphones to the level of DGNSS. However, smartphone and GNSS chipset vendors do not allow general users to feed the DGNSS correction to the positioning module or access the raw GNSS pseudorange. Thus, current applications remain purely conceptual approaches. In this paper, we consider the single-point positioning (SPP) algorithm of a GNSS chipset, and suggest a practical solution for improving the position accuracy. The remainder of this paper is structured as follows: Section 2 discusses the motivation behind this work and lists the most important contributions. Section 3 describes the assumptions and verification process of the algorithm for the GNSS chipset considered in this study. We also introduce the proposed solution for implementing DGNSS suitable for the smartphone chipset. The results of static and dynamic tests to verify that our algorithm works well with the smartphone are presented in Section 4. We conclude this paper in Section 5 with a discussion and present some ideas for future work.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 2 UAV Technology in Wireless Communication
### UAVs in 5G Networks  
As commonly known, 5G will transform multiple aspects of society. UAVs will likely be a significant tool used to demonstrate the full potential of 5G technology. UAV connection  
Figure 4: UAVs as base stations in a future ultra–dens heterogenous network.  
Figure 5: UAVs as normal users in a future ultra–dense heterogenous network.  
may even be possible with 4 th generation (4G) LTE, which would be advantageous. UAVs are currently used as flying sensors linked to 4G networks. These sensors can convey data over great distances while remaining securely outside the pilot's line of sight.  
The International Telecommunication Union (ITU) provided an overview of the differences between 4G and 5G networks. This agency developed the capabilities that differentiate broadband cellular network generations. When discussing wireless networks, two definitions must be highlighted: upward and Verizon. In 2009, telecommunication operators deployed 4G and continued its management until 2019. During that time, 4G was widely employed, allowing users to download movies and use the GPS in cars. In 2019, Verizon pioneered 5G, launching a commercial 5G ultra-wideband mobile network in different sections of two cities. 5G enables rapid data transmission speeds due to the massive amounts of data acquired from simulating linked devices. Overall, 5G includes high data rates, low latency, energy, cost efficiency, increased system capacity, and widespread device connectivity.  
The rate at which data is successfully transmitted across a network is referred to as throughput. Peak data rates of up to 10 Gbps can be achievable with 5G. At this level, driverless vehicles, fabrication, and virtual reality (VR) can rapidly advance. This further indicates that UAVs will be capable of transmitting large volumes of data. 5G technology allows devices to communicate at speeds of up to 500 km/h. Commercial UAVs will be able to inspect vast lengths of highways in minutes while maintaining network connection in such a way that data can be promptly transmitted. The 5G network can service millions of devices in a single square kilometer. Numerous organizations can be completely transformed and developed, ranging from home parcel delivery to search and rescue operations. The energy efficiency of 5G ultra-wideband will also be enhanced. Delays will further reduce, signifying the impact of lower latency. It is not uncommon that audio and visual images lag from time to time. 5G data transmission speeds will be at much faster magnitudes than the blink of an eye, with an end-to-end reaction time of roughly 10 milliseconds. This process provides UAVs and sensor operators with a near-real-time experience. With low latency, autonomous UAVs can navigate with tremendous precision due to instant communication.
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
## 8 Conclusions  
The problems derived from the presence in the aquatic environment of heavy metals, which are not susceptible to degradation, and organic pollutants, usually characterized by their low concentration, request the application of separation technology, such as adsorption. In this review, the recent advances in the efficient production of engineering biochar that increase the application of adsorption process as the primary separation technology are highlighted. The production and modification of biochar produced from biomass wastes open up new paths for the production of engineered adsorbents and demonstrate advantages over commercial activated carbon. The feedstock and biochar manufacturing process (operating conditions, pre or post physical-chemical treatments) have demonstrated to meaningful influence over the removal of the pollutants attaining upright removal efficiencies. The synthesis of the biochar according to the target pollutant is postulated to be a next step in the research. Thus, the introduction of metals, chemical treatment and so forth attained a significant adsorption enhancement in the removal of organic and metallic pollutants caused by the increase in the surface area, pore area and reactivity of the adsorbent. Furthermore, the probed regeneration of these carbonaceous materials increases the usability of these renewable sources for the treatment of huge amounts of wastewater. However, experimental and modelling studies in packed columns are highly recommended for future studies in order to validate the real use and scalability of the synthesized biochars.  
Conceptualization, A.F.-S.; formal analysis, A.F.-S.; data curation, A.F.-S., E.R.; writing--original draft preparation, A.F.-S., E.R.; writing--review and editing, M.A.S.; visualization, M.P.; supervision, M.A.S. and M.P.; project administration, E.R.; funding acquisition, M.A.S. and M.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Spanish Ministry of Science, Innovation and Universities, Xunta de Galicia and ERDF (Grant No. CTM2017-87326-R and ED431C 2017/47).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 1 Introduction
Consequently, it is essential to evaluate the performance of concretes incorporating CS as SCMs and RA as aggregate under ESA; however, to the best of the authors' knowledge, no comprehensive studies have yet examined this combination. While this study provides valuable qualitative and semi-quantitative microstructural insights into degradation mechanisms, it does not attempt to quantitatively model the relationship between chemical transformations (e.g., M-S-H formation) and mechanical performance loss. Accordingly, this study evaluates the performance of concrete mixtures incorporating CS as a SCM (15 vol%) and recycled coarse aggregate (RCA) derived from CDW as a partial or total replacement of natural coarse aggregate at replacement levels of 0%, 20%, 50%, and 100% by volume. The mixtures were subjected to a 5% magnesium sulfate (MgSO\({}_{4}\)) solution and cured in water. To assess the behavior of the mixtures under these conditions, visual inspections, compressive strength tests, and microstructural analyses--including scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric-differential analysis (TGA)--were conducted.
## 3 Results  
Statistical analysis covered individual soil properties as well as comparison between soils. Table 1 shows general soil properties, including nutrients. Table 2 records estrone concentrations and enzymatic activity statistics, which are then visualized in Figure 1--enzymatic activity and Figure 2--estrone in soils.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soils** & **Cambisol** & **Fluwisol** & **Chernozem** \\ \hline Latitude & 49\({}^{\circ}\)33\({}^{\prime}\)15\({}^{\prime\prime}\) N & 50\({}^{\circ}\)09\({}^{\prime}\)50\({}^{\prime\prime}\) N & 50\({}^{\circ}\)07\({}^{\prime}\)40\({}^{\prime\prime}\) N \\ Longitude & 15\({}^{\circ}\)21\({}^{\prime}\)02\({}^{\prime\prime}\) E & 15\({}^{\circ}\)09\({}^{\prime}\)24\({}^{\prime\prime}\) E & 14\({}^{\circ}\)22\({}^{\prime}\)33\({}^{\prime\prime}\) E \\ Site & Humpolec & Patek & Suchdol \\ texture & sandy loam & silty clay & loamy \\ pH & 6.5 & 7.5 & 7.0 \\ WTC 1 & 14.77 & 13.71 & 14.78 \\ C (mg\(\cdot\)kg\({}^{-1}\)) & 20,512 & 76,509 & 28,304 \\ N (mg\(\cdot\)kg\({}^{-1}\)) & 2055 & 2882 & 2333 \\ C/N ratio & 10 & 26 & 12 \\ C/H ratio & 2 & 12 & 4 \\ K (mg\(\cdot\)kg\({}^{-1}\)) 2 & 140 & 390 & 340 \\ P (mg\(\cdot\)kg\({}^{-1}\)) 2 & 60 & 30 & 40 \\ Ca (mg\(\cdot\)kg\({}^{-1}\)) 2 & 1540 & 26,290 & 6480 \\ Mg (mg\(\cdot\)kg\({}^{-1}\)) 2 & 160 & 370 & 220 \\ Mn (mg\(\cdot\)kg\({}^{-1}\)) 2 & 80 & 30 & 160 \\ S (mg\(\cdot\)kg\({}^{-1}\)) 2 & 60 & 150 & 80 \\ Zn (mg\(\cdot\)kg\({}^{-1}\)) 2 & 2.88 & 4.02 & 5.66 \\ Fe (mg\(\cdot\)kg\({}^{-1}\)) 2 & 26 & 28 & 6 \\ Cu (mg\(\cdot\)kg\({}^{-1}\)) 2 & 2.68 & 0.91 & 3.81 \\ Cr (mg\(\cdot\)kg\({}^{-1}\)) 2 & 0.24 & 0.09 & 0.09 \\ \hline \hline \end{tabular}  
* soil water holding capacity calculated for 60% in mL/100 g dw soil; 2 available element contents released by Mehlich III extraction procedure [30].  
\end{table}
Table 1: Soil properties.  
Figure 1: \(Cont\).  
Figure 1: Estrone concentrations.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Enzymatic Activity**} \\ \hline soils comparison & \(p\)-values \\ equality of variance & Brown–Forsythe test \\  & 0.00466543 \\ ANOVA & Kruskal–Wallis \\  & 0.01272 \\ Post hoc pairwise comparison & Wilcoxon test with Bonferroni corrections \\ Fluvisol-Cambisol & 0.045 \\ Fluvisol-Chernozem & 0.023 \\ Cambisol-Chernozem & 1.000 \\ \hline \multicolumn{2}{c}{Estrone} \\ soils comparison & \(p\)-values \\ soils, individually—0 to 28 days & Friedman test \\ Fluvisol & 0.0262 \\ Cambisol & 0.0142 \\ Chernozem & 0.0121 \\ soils, together—equality of variance & Brown–Forsythe test \\
0 and 28 days & 0.9438856 \\
0 days & 0.1035599 \\
28 days & 0.001856509 \\ ANOVA & One-way ANOVA \\
0 days & 0.0914 \\
28 days & \(1.2\times 10^{-5}\) \\ Post hoc pairwise comparison & Tukey HSD \\
0 days & Fluvisol-Cambisol \\ Fluvisol-Chernozem & 0.3101227 \\ Fluvisol-Chernozem & 0.0804346 \\ Cambisol-Chernozem & 0.5635242 \\
28 days & \\ Fluvisol-Cambisol & 0.0000253 \\ Fluvisol-Chernozem & 0.0000186 \\ Cambisol-Chernozem & 0.7627496 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Soils statistical analysis for changes in enzymatic activity and estrone concentrations.
## 2 Background and Related Work
### Security
In addition, an attack with new hash power implies that an attacker has opted to obtain a more powerful hash power than that of the public chain, having the advantage of not knowing the start of the attack because no hashing power will leave the live network. Apart from the fact that this is a stealth attack, it does not force the difficulty to adjust to the live network as a result of a drop in its hash rate, thereby preventing new miners from joining the live network. In this case, the only factor that affects the live chain's hash rate and complexity is the Bitcoin price itself. By contrast, this attack is twice as costly to execute as the current hash power attack. The execution of a 51% attack with new hash power is shown in Figure 3.  
In sum, according to the literature, current systems have different aspects that make blockchains non-scalable [45]. Three critical aspects stand out in particular:  
* New transactions are sent in broadcast to all the nodes of the network. * All nodes receive each new block. * A set of nodes is responsible for processing all the new transactions involved in the next block. Given these aspects, the computational capacity and bandwidth of the individual nodes must be proportionally increased (vertical scalability). Horizontal scalability, in which additional nodes are added to the network in response to the increased volume of transactions, is preferable.
## 3 Grid-Connected PV/BESS System Description
### Power Conversion System (PCS)  
A power conversion system is a two-way converter (DC to AC and AC to DC). According to the requirements of charging/discharging control, PCS can convert AC power to DC power, and then store it in an energy storage battery or store energy. In addition, the DC power of the battery also can be converted into AC power to be used by the AC load. In this simulation analysis, the conversion efficiency is set to 97.3%, and the working life is set to 10 years.