esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of a General Relay-Node Selection on Intersection
### Procedure of the Relay-Node Selection on General Intersection
#### 3.2.1 Intersection Phase  
Before a message reaches the intersection range, it is assumed to be delivered on a straight road, which is one branch of the intersection. When a selected node finds itself in the Intersection Region, it is assigned a function to select a node closest to C. We refer the node first in the Intersection Region as Hunter (H) and the node closest to C as Relay Node in Intersection Phase (Relay\({}_{1}\)). The Intersection Region is defined as a circle region centered at C with radius of \(r\), which is shown as the shadow area in Figure 2.  
Figure 2: General relay-node selection on intersection.  
The criterion for the selection of \(r\) is to cover every possible node which is nearer to C than H. We consider the case that H is located at the border of Intersection Region, i.e., H is \(r\) away from C. In this case the node superior to H is most unlikely out of the Intersection Region. So \(r\) should satisfy the inequity as  
\[\left\{\begin{array}{l}R^{2}=r^{2}+(l_{\rm B}^{n})^{2}-2 rl_{\rm B}^{n}\cos( \theta_{n}-\theta_{m})\\ l_{\rm B}^{n}\geq r\end{array}\right. \tag{2}\]  
where \(n\) is the index of the \(n\)-th branch (\(n\)\(\
eq\)\(m\)), and \(l_{\rm B}^{n}\) is the coverage of H on the \(n\)-th branch. Thus, we can derive  
\[r\leq R/2 \tag{3}\]  
The larger the range in one-hop, the faster the message disseminates. Thus, in our relay-node selection method, \(r=R/2\).
###### Abstract
Adding biochar can regulate the soil temperature and soil water content [26; 29], and many studies have also shown that biochar can stabilize the soil carbon pool, reduce the mineralization and decomposition of soil organic carbon [30; 31; 32], and increase the storage of soil carbon [29; 33; 34; 35]. However, previous studies into carbon sequestration and reducing emissions using biochar were mostly conducted in paddy fields in humid areas or incubation experiments in laboratories [36; 37; 38; 39; 32]. Clearly, the ecological environment, soil quality, and farming measures differ in dry farmland in non-humid areas. However, few studies have investigated the effect of biochar application on soil carbon sequestration in dry farmland and whether the application of biochar can reduce soil carbon emissions under the RFRH planting mode. Wheat is the food crop with the largest planting area and trade volume in the world [40]. Coupled with the fact that it is used for food up to 65% [41], it has an irreplaceable role in meeting global food demand. Wheat has a wide range of adaptability to environmental conditions [41]. It is also an important cereal crop in dryland regions [42]. Carrying out research on wheat planting is important for both agricultural production in arid and semi-arid areas and global food security. Thus, in the present study, different amounts of biochar were applied under the RFRH planting mode using wheat as the test crop in order to: (1) investigate the effects of biochar application on the soil hydrothermal characteristics and gaseous carbon (CO\({}_{2}\) and CH\({}_{4}\)) emissions, as well as the relationships between carbon emissions and soil hydrothermal factors under RFRH planting; (2) determine the effects of biochar application on the crop yield under RFRH planting; and (3) understand the effects of biochar application on the carbon balance under RFRH planting, and the regulatory effects of biochar application and the associated mechanisms on soil carbon sequestration and reductions in emissions under RFRH planting in a dry farmland environment in a non-humid area. We aimed to identify a suitable technical approach to save water, increase yields, reduce emissions, and facilitate carbon sequestration, thereby providing a theoretical basis for high-quality production on farmland in arid and semi-arid areas.
# Two-Way Linear Probing Revisited
## 3 The Proposal
### Two-Way Linear Probing  
To avoid any ambiguity, we consider this definition.  
Definition 1: A two-way linear probing algorithm is an open addressing hashing algorithm that inserts keys into cells using a certain strategy and does the following upon the arrival of each key:  
1. It chooses two initial hashing cells independently and uniformly at random, with replacement.
2. Two terminal (empty) cells are then found by linear probe sequences starting from the initial cells.
3. The key is inserted into one of these terminal cells.  
To be clear, we give two examples of inefficient two-way linear probing algorithms.  
The Shorter Probe Sequence: ShortSeq Algorithm  
Our first algorithm places each key into the terminal cell discovered by the shorter probe sequence. More precisely, once the key chooses its initial hashing cells, we start two linear probe sequences. We proceed, sequentially and alternately, one probe from each sequence until we find an empty (terminal) cell where we insert the key. Formally, let \(f\), \(g:\mathcal{U}\to\{0,\ldots,n-1\}\) be independent and truly uniform hash functions. For \(x\in\mathcal{U}\), define the linear sequence \(f_{1}(x)=f(x)\), and \(f_{i+1}(x)=f_{i}(x)+1\mod n\), for \(i\in[n]\); and similarly define the sequence \(g_{i}(x)\). The algorithm, then, inserts each key \(x\) into the first unoccupied cell in the following probe sequence: \(f_{1}(x)\), \(g_{1}(x)\), \(f_{2}(x)\), \(g_{2}(x)\), \(f_{3}(x)\), \(g_{3}(x),\ldots\), as shown in Figure 1. We denote this algorithm that hashes \(m\) keys into \(n\) cells by ShortSeq\((n,m)\), for the shorter sequence.  
The Smaller Cluster: SmallCluster Algorithm  
The second algorithm inserts each key into the empty (terminal) cell that is the right neighbor of the smaller cluster among the two clusters containing the initial hashing cells, breaking ties randomly. If one of the initial cells is empty, then the key is inserted into it, and if both of the initial cells are empty, we break ties evenly. Recall that a cluster is a group of consecutively occupied cells whose left and right neighbors are empty cells. This means that one can compute the size of the cluster that contains an initial hashing cell by running two linear probe sequences in opposite directions starting from the initial cell and going to the empty cells at the boundaries. So practically, the algorithm uses four linear probe sequences. We refer to this algorithm by SmallCluster\((n,m)\) for inserting \(m\) keys into \(n\) cells (Figure 2).  
In Section 4.2, we show that algorithms ShortSeq and SmallCluster have unexpected poor performance. Indeed we prove that such algorithms, which always insert any key upon arrival into the empty cell of its two initial cells whenever one of them is empty, produce a cluster of size \(\Omega(\log n)\), asymptotically almost surely. To overcome the problems of these algorithms, we introduce _blocking_.
## 3 System Model and Problem Formulation
### Channel Model  
We calculate the mean path loss as the propagation loss of the wireless signal according to [19]. Path loss is divided into free space path loss and additional loss [20]. Here, we only consider the LoS situation between the drone and the UE:  
\[PL=20 log(\frac{4\pi f_{c}d}{c})+\eta, \tag{1}\]  
where \(f_{c}\) indicates carrier frequency, \(d\) represents the Euclidean distance between drone and UE, \(c\) is the speed of light, and \(\eta\) denotes the mean additional loss for LoS.  
Then, the received signal power \(P_{r}\) for UE \(n\) from drone \(u\) can be formulated as  
\[P_{nu}^{r}=P_{t}-PL, \tag{2}\]  
where \(P_{t}\) is the total transmit power from drones.  
Thus, the signal to interference and noise ratio, \(SR_{nu}\), for a drone-UE pair can be formulated as  
\[SR_{nu}=\frac{p^{r}}{P_{N}+\sum_{i=1,i\
eq n}^{U}P_{ni}^{r}}, \tag{3}\]  
where \(P_{N}\) represents the additive white Gaussian noise power.  
In practice, if the \(SR_{nu}^{t}\) served by drone \(u\) in time slot \(t\) is greater than the threshold \(SR_{th}\) and the number of UEs served by the drone does not exceed the upper limit, the UE \(n\) is considered to be covered by the drone \(u\) with acceptable QoS. If the number of users served by the drone \(u\) has reached the upper limit or the QoS is lower than the threshold, the next closest drone will be tried for use. If all drones cannot provide services, the UE is considered disconnected in time slot \(t\).
###### Abstract  
_Thymelaea microphylIa_ Coss. et Dur (_T. microphylIa_), a medicinal herb that grows in arid and desert pastures, has been traditionally utilized by Moroccans to treat many diseases, including kidney failure. This study aims to evaluate the nephprotective effect against gentamicin-induced nephrotoxicity (GM), and thus the antioxidant activity of an aqueous extract rich in flavonoids from the aerial part of _T. microphylIa_ (APTM). The antioxidant activity of APTM was assessed using the 2-2-diphenyl-1-picrylhydraz (DPPFH) scavenging test and the \(\beta\)-carotene bleaching assay. The nephprotective effect of this extract was evaluated in two doses of 200 and 400 mg/kg in GM-exposed male rats. Acute toxicity of the APTM was tested out on _Swiss albino_ mice using 2000 mg/kg as the dose limit. The findings showed that the aqueous extract of APTM is abundant in total polyphenols and flavonoids and has significant antioxidant properties against DPPH radicals and \(\beta\)-carotene oxidation. According to the acute toxicity research findings, the administered doses of the APTM extract do not cause toxicity and death. A significant increase in the serum concentrations of creatinine, urea, uric acid, sodium, chloride, calcium, gamma-glutamyl transpeptidase (gamma-GT), and alkaline phosphatase, as well as an increase in urinary volume, water consumption, and relative kidney weight, were all caused by the administration of GM to rats. In addition, a significant reduction in urinary concentrations of creatinine, uric acid, urea, and albumin, and thus the clearance of creatinine and weight gain were observed in rats injected with GM. Also, the administration of GM dramatically raised the malondialdehydedehyde level in the kidneys. Likewise, rats that had been poisoned with GM had histological kidney abnormalities. However, the daily treatment of APTM aqueous extract to rats given GM injections dramatically improved the biochemical and histological parameters affected by GM administration in rats. Finally, APTM extract enhanced GM's biochemical and histological indicators of nephrotoxicity, supporting its use as an ethnomedical.  
Keywords:polyphenols; flavonoids; antioxidant; gentamicin; nephrotoxicity; protective effects; medicinal plants +
Footnote †: journal: Applied sciences
## 6 Conclusions and Future Work  
In this paper, an ABM for connectivity robustness was introduced, integrating component failure with the malware propagation model. ABM uses MCC as a measure of connectivity robustness. Furthermore, the ABM models cascading failures between malware propagation and components, crossing the information domain and the physical domain. Finally, we used NetLogo to evaluate the connectivity robustness of different scenarios and network topologies, and the experimental results were discussed.  
Below are the main conclusions drawn from the results: (1) The presented model can be used to provide evaluation results of the connectivity robustness of WSNs. (2) The network with a higher average degree has better connectivity robustness with only the component failure considered. However, considering the influence of malware propagation, high connectivity WSNs may not be robust. The trade-offs of high connectivity for rerouting  
Figure 8: Simulation results of (**a**) scale-free network and (**b**) small-world network; malware propagation was not considered.  
and network topology for malware propagation resistance need further consideration. (3) The interactions between the physical domain and information domain have apparent impacts on the robustness performance of WSNs, which should be appropriately taken into account in the planning and designing of WSNs. (4) The visualization of the results is easy to understand, and the MCC can be analyzed in each step of time \(t\).  
In future work, the coupling relationship between the physical domain and the information domain should not be limited to one-to-one. The one-to-many coupling in the actual situation should also be taken into account. The new coupling relationship will also affect the modeling of cascading failure. Finally, a new optimization method to obtain the optimal network topology in different scenarios is needed.  
Conceptualization, B.X. and M.L.; methodology, B.X.; programing, B.X.; validation, B.X.; formal analysis, H.Z.; writing--original draft preparation, B.X.; writing--review and editing, M.L., H.Z., and C.P.; visualization, B.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The simulation files can be found at [[https://github.com/pauxavi/cyber-physical-social-system-sims](https://github.com/pauxavi/cyber-physical-social-system-sims)]([https://github.com/pauxavi/cyber-physical-social-system-sims](https://github.com/pauxavi/cyber-physical-social-system-sims)) (accessed on 7 July 2021).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Linearization of JWL's Third Term and Least Square Fitting  
The first stage is addressed by linearizing the JWL expression (28), considering only the third term (Poisson adiabatic) for reduced volume data taken after the threshold, set to \(\
u=15\). Taking the natural logarithm (denoted by \(\ln\)) of the Equation (28) neglecting exponential terms, it is  
\[\ln p_{\rm is}=\ln(C)-(1+\omega)\ln(\
u). \tag{33}\]  
By introducing the notation \(P_{\rm is}=\ln(p_{\rm is})\) and \(V=\ln(\
u)\), the above equation transforms into a linear form:  
\[P_{\rm is}=C_{1}+C_{2}V, \tag{34}\]  
where \(C_{1}=\ln C\) and \(C_{2}=-(1+\omega)\).  
The constants \(C_{1}\) and \(C_{2}\) are determined using the linear least squares approximation method. First, to estimate the parameters \(C\) and \(\omega\) of the JWL equation, the tail region of the dataset (\(\
u>15\)) is linearized, exploiting the logarithmic form of the JWL pressure Equation (33) for volumes significantly larger than the Chapman-Jouguet point, and fitted using the QR decomposition method.  
The coefficient matrix \(M\) is created, consisting of a column of ones (intercept term) and \((V_{j})\) as the second column:  
\[M=\begin{bmatrix}1&V_{1}\\ 1&V_{2}\\ \vdots&\vdots\\ 1&V_{m}\end{bmatrix}. \tag{35}\]  
Using the QR decomposition [17], the matrix \(M\) is factorized into an orthogonal matrix \(Q\) and a non-singular, upper triangular matrix \(R\):  
\[M=QR. \tag{36}\]  
The coefficients of the linearized equation are computed by solving the triangular system:  
\[R\cdot[C_{1},C_{2}]^{\top}=Q^{\top}\cdot P_{\rm is}. \tag{37}\]  
The coefficients \(C_{1},C_{2}\) are then determined by taking the exponential of the intercept term  
\[C=e^{C_{1}}, \tag{38}\]  
and inverting the formula for the slope  
\[\omega=1-C_{2}. \tag{39}\]  
This approach efficiently isolates \(C\) and \(\omega\) while minimizing numerical instability through the use of QR decomposition, which is particularly advantageous for overdetermined systems.  
Afterwards, setting \(l=[0,0]^{T}\), \(u=[10,5]^{T}\) and starting from \(R_{\rm ini}=[5,1]^{T}\), the non-linear least square algorithm is applied to (2). The constants \(C\) and \(\omega\) obtained by this procedure are then used as constraints in the second stage of the non-linear optimization aimed at determining the JWL isentrope coefficients.
## 2 Methodology
### Mesh Generation  
In the numerical simulation, two trains passing each other and the relative motion between the train and other railway infrastructures (e.g., tunnels, wind barriers, and bridges) can be analysed using the dynamic mesh method [33; 34], sliding mesh method [1; 35; 36; 37], and overset mesh method [12; 21; 38; 39]. Nevertheless, the dynamic mesh method needs to regenerate the mesh at each time step, which may increase the calculation time. To capture small-scale flow structures, we often need to conduct local refinement around a train, particularly in the near wake. However, the number of grids required for the sliding mesh method is greater than that required for the overset mesh method with the same grid refinement method. Furthermore, the overset mesh method is suitable for complex and irregular moving models owing to its decomposition and overlapping techniques [39]. Consequently, the overset mesh method was applied in this study.  
In the present research, the computational fluid dynamics commercial software STAR CCM+ was used to generate the mesh and for the calculations and post-processing. The computational domain contained two types of mesh: the near-wall of the train surface and rails were discretised with prism layers; the remaining computational domain utilised hexahedral grids. Three sets of grids, namely coarse, medium, and fine grids, were generated to verify the grid sensitivity, as presented in Figure 5a, and their detailed information is presented in Table 1. Here, the growth rate was 1.15 for the three meshes. As shown later in the paper, the medium grid was found to be sufficient for this study. Figure 5b shows a schematic of the medium mesh. To ensure a high resolution around the train and reduce the number of computational nodes, four types of mesh regions were constructed in the computation domain, namely wake refinement, fine, medium, and coarse areas. A similar refined method was also utilised in coarse and fine grids. The two MTs always met in the fine region. The local refinement region in the train wake of length and width of 9.11 H and 1.43 H, respectively, was used to capture small-scale vortices. Figure 6 shows the wall \(y\)+ of the train surface of the medium mesh. It can be observed that the \(y\)+ value of most regions on the train surface is less than 2 except for some transition regions, where it is still less than 5. Thus, it meets the requirements of the SST k-\(\omega\) turbulence model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grid** & **Coarse** & **Medium** & **Fine** \\ \hline Train surface & 0.016 H & 0.013 H & 0.01 H \\ Wake refinement & 0.016 H & 0.013 H & 0.01 H \\ Number of prism layers & 16 & 20 & 20 \\ Number of cells (\(\times\)10\({}^{6}\)) & 27.17 & 45.75 & 68.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detail of grids with different density.  
Figure 5: Mesh configuration: (**a**) coarse, medium, and fine mesh; (**b**) medium mesh around the train.  
Figure 6: \(y+\) wall of the medium mesh: (**a**) head car and (**b**) tail car.
# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 3 System Description  
### Power Monitoring System with Wireless Sensor Networks  
Recent advances in embedded systems and WSNs have made it possible to establish dependable and cost-effective power grid systems that can monitor and control the real-time performance of the grid. The cooperative and low-cost nature of WSNs provides technical advantages over typical (wired) electric monitoring systems, including flexible reconfiguration of networking and a reduced installation cost. The WSNs are becoming increasingly popular in IoT, and are mostly utilized in far-off areas to collect data from the surrounding environment and transfer the data to a sink node or base station on regular basis. In monitoring and data collecting systems for PV and wind applications, the IoT and WSN can play a key role. In this framework, the WSNs are utilized to convey critical information from the PV/wind power station to the monitoring center, and cloud data centers are used to store this vital information, as shown in Figure 1. At each PV module and wind turbine, the WSNs are used to collect the sensor data. The sensor nodes, in particular, gather the information and optionally preprocess the data. The sink node integrates the incoming data and delivers it to the power monitor center via internet networks. The monitoring center is a vital part of this framework because it accepts combined information from the sink nodes in the power plant and stores it in separate data units in the private cloud. Different sorts of utilizations for clients are accessible in the created framework. The information transmission channel utilizes the internet, which might include security information for the created framework. This plan is to cater to the various prerequisites of various clients. The cloud data center is liable for validation and information encryption [59]. The system administrator has the highest level of authority, with the ability to monitor the complete PV power plant and gain access to all statistical data and the forecasted power data for various locations.  
Each sink node receives sets of information, similar to a hub, and merges the information of different data. In the power monitoring center, an access interface is built to receive and evaluate data, as well as process the information stored in the cloud as shown in Figure 1. After receiving the data, the cloud data center runs the KRLS forecasting model to forecast PV and wind power production.
# Interval Analysis of Vibro-Acoustic Systems by the Enclosing Interval Finite-Element Method
###### Abstract
In order to retain higher-order terms in the Neumann series expansion, [PERSON] [21; 22] and [PERSON] [23], respectively, presented the Taylor-Neumann-series-based modified interval perturbation finite-element method (TN-MIPFEM). Recently, [PERSON] [24] extended the perturbation theory to analyze the vibro-acoustic system with FE-BEM coupling; moreover, this hybrid numerical method for vibro-acoustic problem can achieve higher precision by introducing the smooth finite-element analysis [25]. With other global optimization means, [PERSON] presented a dimension-wise technique for IPFEM to reduce the effects of the non-conservativeness [26; 27; 28]. By integrating the homogenization-based finite-element method and IPFEM, [PERSON] proposed a homogenization-based interval analysis method to analyze the vibro-acoustic problem involving periodical composites and multi-scale uncertain parameters, which expand the application of IPFEM [29]. In addition, to avoid part of the truncation error from Taylor series expansion, another type of IPFEM, called the Sherman-Morrison-Woodbury-series-based IPFEM (SMW-IPFEM) [30], adopted the dyadic product forms to factorize the uncertainty. As the dyadic product formula is hard to derive in dynamic analysis, the direct application of the SMW-IPFEM has not been extended to vibro-acoustic problem in previous research [22]. Ultimately, in order to carry out the conservative prediction for the uncertain vibro-acoustic problem, the modified interval arithmetic strategy turns into the best choice. This paper is organized as follows: in Section 2, an iterative interval analysis approach named enclosing-IFEM is proposed for solving the uncertain vibro-acoustic coupling problem, inspired by both the EBE-IFEM and mixed-nodal-element assemblage of unimodal components' decomposition. In this method, the vibro-acoustic coupling problem can be transformed into the iterative enclosure formula just as the EBE-IFEM, excluding the interference from Lagrange multiplier. In Section 3, two different iterative methods, called Rump's inclusion method and Neumaier-Pownuk method, for the iterative enclosure formula are introduced, as the purpose to search the best optimal bounds of solution sets by comparison. In Section 4, to demonstrate the performance of Monte Carlo simulation, TN-MIPFEM, SMW-IPFEM, and enclosing-IFEM, all of these approaches are implementedfor two examples, and then the convergence of iteration and relative errors of different IFEMs are analyzed, respectively. At last, conclusion is drawn in Section 5.