esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodologies
### Multicollinearity Analysis  
To construct a susceptibility assessment index system, several factors related to the occurrence and development of geological disasters were selected. However, these factors are not entirely independent and may exhibit some degree of correlation. If not properly addressed, overlapping influence weights among the indicator factors can lead to errors or inaccuracies in assessment results. Therefore, it is crucial to filter the factors during selection to ensure their independence while maintaining the accuracy of the input parameters. To avoid interference and overlap, factors with a minimal influence on landslide susceptibility or overlapping weights should be eliminated. This study utilized the Pearson correlation coefficient method to analyze the correlations between influencing factors.  
The method proposed by the statistician [PERSON] [31] quantifies the correlation between factors using the correlation coefficient \(r\). The primary objective was to describe differences in the target variables. The closer the absolute value of \(r\) is to 1, the greater the correlation between the factors. The Pearson correlation coefficient is a commonly used metric that can be calculated by dividing the covariance of two factors by the product of their standard deviations, as shown in Equation (2):  
\[r_{(X,\ Y)}=\frac{Cov((X,Y))}{\sqrt{Var[X]Var[Y]}}, \tag{2}\]  
where \(Var[X]\) represents the variance of \(X\), \(Var[Y]\) represents the variance of \(Y\), and \(Cov((X,Y))\) is the covariance between \(X\) and \(Y\).  
The influencing factor data were imported into the SPSS statistical analysis software, where the \"Bivariate\" tool was adopted for analysis. The correlation coefficients between the influencing factors are shown in Figure 4. A high correlation coefficient (0.44) was identified between elevation and distance to rivers, leading to the exclusion of the elevation factor from this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Influencing Factors** & **Classification** & **FR** & **Influencing Factors** & **Classification** & **FR** \\ \hline \multirow{8}{*}{NDVI} & \(-\)0.2–0.1 & 0.10 & \multirow{8}{*}{TWI} & \(<\)6 & 0.69 \\  & 0.1–0.2 & 2.59 & & 6–7 & 1.28 \\  & 0.2–0.3 & 6.05 & & 7–9 & 1.69 \\  & 0.3–0.4 & 2.65 & & 9–10 & 1.64 \\  & 0.4–0.6 & 1.58 & & \(>\)10 & 0.89 \\ \hline \multirow{8}{*}{Distance to river (m)} & 0–500 & 1.73 & \multirow{8}{*}{Distance to fault (m)} & 0–1000 & 1.10 \\  & 500–1000 & 1.30 & & 1000–3000 & 0.84 \\  & 1000–2000 & 0.56 & & 3000–4000 & 1.44 \\  & 2000–3500 & 0.17 & & 4000–4500 & 1.02 \\  & \(>\)3500 & 0.03 & & \(>\)4500 & 0.73 \\ \hline \multirow{8}{*}{Slope (\({}^{\circ}\))} & 0–10 & 0.69 & \multirow{8}{*}{Flow path length (m)} & 0–500 & 0.46 \\  & 10–15 & 0.97 & & 500–1000 & 1.32 \\ \cline{1-1}  & 15–35 & 1.14 & & 1000–2000 & 2.60 \\ \cline{1-1}  & 35–40 & 0.92 & & 2000–3000 & 1.89 \\ \cline{1-1}  & \(>\)45 & 0.67 & & \(>\)3000 & 0.00 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 2 Computational Methods  
Calculations of the geometrical structures of the TSs of the conformational interconversions and their vibrational spectra, corresponding to the local minima on the potential (electronic) energy hyper surface, have been performed at the DFT B3 LYP/6-311++G(d,p) level of QM theory [24; 25; 26] by Gaussian'09 program package [27], which was successfully approved in our previous studies for the calculations of the heterocyclic compounds [28; 29]. A scaling factor of 0.9668 has been used to correct the harmonic frequencies for the investigated structures [30]. Intrinsic reaction coordinate (IRC) calculations in the forward and reverse directions from each TS, which have been confirmed by the presence of one and only one imaginary frequency in the vibrational spectra, have been performed using Hessian-based predictor-corrector integration algorithm [31].  
All calculations were performed for the quercetin molecule as their intrinsic property, that is adequate for modeling of the processes occurring in real systems [16; 17; 32].  
Electronic and Gibbs free energies under normal conditions have been calculated by single point calculations at the MP2/6-311++G(2 df,pd) level of theory [33; 34; 35].  
The time \(\tau_{99.9\%}\) necessary to reach 99.9% of the equilibrium concentration of the reactant and product in the system of the reversible first-order forward (\(k_{f}\)) and reverse (\(k_{r}\)) reactions can be estimated by the formula [36]  
\[\tau_{99.9\%}=\frac{ln10^{3}}{k_{f}+k_{r}} \tag{1}\]  
The lifetime, \(\tau\), of the conformers has been calculated using the formula 1/\(k_{r}\), where the values of the forward \(k_{f}\) and reverse \(k_{r}\) rate constants for the tautomerization reactions were obtained as [36]  
\[k_{f,r}=\Gamma\cdot\frac{k_{B}T}{h}e^{-\frac{\Delta\Delta G_{rf}}{RT}} \tag{2}\]  
where the quantum tunneling effect has been accounted by Wigner's tunneling correction [37], successfully used for the double proton reactions in DNA base pairs [28]:  
\[\Gamma=1+\frac{1}{24}\bigg{(}\frac{hv_{i}}{k_{B}T}\bigg{)}^{2} \tag{3}\]  
where \(k_{B}\)--[PERSON]'s constant, \(h\)--[PERSON]'s constant, \(\Delta\Delta G_{rf}\)--Gibbs free energy of activation for the conformational transition in the forward (\(f\)) and reverse (\(r\)) directions, and \(v_{i}\)--magnitude of the imaginary frequency associated with the vibrational mode at the TS.  
The topology of the electron density was analyzed using the program package AIM'2000 [38] with all default options and wave functions obtained at the level of theory used for geometry optimization. The presence of the (3,-1) bond critical point (BCP), bond path between hydrogen donor and acceptor,and positive value of the Laplacian at this BCP (\(\Delta\rho>0\)) were considered altogether as criteria for the formation of the H-bond and attractive van der Waals contact [39].  
In this work, standard numeration of atoms has been used [16; 17]. Numeration of the conformers, which are highlighted in bold in the text, have been used as in the work [16].
## 2 Materials and Methods
### Software Implementation of R2R Communication System  
Client-server-based software was developed using C#.NET programming language in Microsoft Visual Studio 2015 platform to create the WiFi communication between the mobile robot server and the mobile robot clients. C# Multithreaded Socket Programming technique was used to communicate with more than one mobile robot client at the same time in the same network in developed software. The Microsoft.NET framework provides two namespaces: System.Net for providing the TCP/IP protocol and System.Net.Sockets for providing the socket class to send and receive data over the network. In multithreaded socket programming, the mobile robot server receives a connection request from the mobile robot client side; the mobile robot server creates a separate mobile robot client thread on the mobile robot server side to communicate with that particular mobile robot client socket. That means the mobile robot client can communicate independently with their mobile robot client thread on the mobile robot server side.  
The steps involved in establishing a multithreaded TCP/IP socket on the mobile robot server side are as follows:  
* Create a mobile robot server socket with the private static readonly Socket serverSocket = new Socket (AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp) method.  
Figure 5: Data transmission from the robot client to the robot server via WiFi.  
* Bind the socket to an address using the serverSocket.Bind (new IPEndPoint (IPAddress.Any, PORT)) method.
* Listen for connections with the serverSocket.Listen(0) method.
* Accept a connection with the serverSocket.BeginAccept (AcceptCallback, null) method.
* Send and receive data with the Socket current = (Socket)AR.AsyncState object.
* Close all connections with the serverSocket.Close() method.  
The steps for establishing a multithreaded TCP/IP socket on the mobile robot client side are the following:  
* Create a mobile robot client socket using the private static readonly Socket ClientSocket = new Socket (AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp) method.
* Connect the socket to the address of the mobile robot server using the ClientSocket.Connect (IPAddress.Loopback, PORT) method.
* Send data using the ClientSocket.Send (buffer, 0, buffer.Length, SocketFlags.None) method.
* Receive data using the ClientSocket.Receive (buffer, SocketFlags.None) method.
* Close the connection using the ClientSocket.Close() method.  
Figure 6 shows the screenshot of the graphical user interface of the R2R wireless communication software. There are two radio buttons in the software window named \"Server\" and \"Client\" If \"Server\" is selected, the mobile robot works as a mobile robot server, or \"Client\" is selected, and the mobile robot works as a mobile robot client.  
Figure 6: Graphic user interfaces of the R2R wireless communication software: (**a**) mobile robot server side and; (**b**) mobile robot client side.
## 3 Results and Discussion
### Frontier Molecular Orbitals and Electrostatic Potential Surface (EPS)  
Molecular orbital analysis of HOMO and LUMO of the ACA, MAY, and LUT were carried out to identify regions of high electron density. These regions are known as pharmacophores, which interact with the active site in a macromolecule. Figure 2 shows the HOMO and LUMO orbitals for the ACA, MAY, and LUT. In the ACA molecule, it was observed that the electronic density of both HOMO and LUMO orbitals were located in the same functional groups (cyclohexane and amino), defining this zone as the pharmacophore of the ACA ligand, which could interact in the active site of the NtMGAM. In the MAY ligand (Figure 2B), the most electron-saturated zone was the HOMO orbital which is located in the ketofucosyl moiety, while the LUMO orbital was located in both groups, the ketofucosyl and throughout the flavonoid luteolin. For this reason, the ketofucosyl moiety was considered as the pharmacophore of the MAY. In addition, in the LUT ligand, the HOMO and LUMO orbitals are distributed throughout all the structures due to the aromatic rings of the molecule, the latter indicates that the whole structure of LUT could interact with the active site of the NtMGAM. This study was also used to explain which zone of the ligands has the recognition ability in the NtMGAM.  
In addition, a critical parameter for drug design is the electrostatic potential surface (EPS) [63]. EPS maps can be employed in the binding sites analysis, focused on the mechanism of recognition of one molecule by another. EPS maps in Figure 3 show the distribution of the electron density in the ligands, which allows the analysis of molecular interactions between ligands and the amino acids present in the active site of the NtMGAM. The electron density surface is colored according to the values of the electrostatic potential in the ligands. Therefore, areas where the molecules can act as an acceptor or donor of hydrogen are plotted in red and blue, respectively.  
Figure 2: Frontier Molecular Orbitals for (**A**) acarbose (ACA), (**B**) maysin (MAY), and (**C**) luteolin (LUT) calculated with the M06/6-31G (d) level of theory.  
As can be observed, the regions with hydrogen donor behavior (blue) are the oligosaccharides-charide and the cyclohexane units of the ACA, as well as the hydroxyl groups. Similarly, in the MAY molecule, the hydroxyl groups are the hydrogen donor zones (blue), while the hydrogen acceptor zone (red) is mainly located at the oxo group of the luteolin ring. The hydroxyl groups along the structure of the LUT presented a hydrogen donor behavior; whereas the oxo group of the C ring can be identified as a hydrogen acceptor.
## 2 Network Model
We adopt the index \(l\in\mathbf{L}=\{m_{u},p_{c},p_{e}\}\) to denote the indication of the above three types of UEs, respectively, where \(m_{u}\) represents MBS UEs, \(p_{c}\) denotes the PBS center UEs, and \(p_{e}\) signifies PBS CRE UEs. The received signal power of a typical UE \(l\) from a BS of the \(k\)th tier at a distance of \(r_{l}\) can be represented as \(P_{k}hr_{l}^{-\alpha}\), where \(P_{k}\) is the transmission power of BS in the \(k\)th tier, the variable \(h\) denotes the channel fast fading gain, which is modeled as Rayleigh distributed with average unit power, i.e., \(h\sim\exp\left(1\right)\), the term \(\alpha\) is the large scale path loss exponent, which is assumed to be the same in both of the two tiers for analysis simplicity. Thus, the SINR of a typical UE \(l\) according to its user type can be expressed as:  
\[\gamma_{l}=\left\{ \begin{array}{ll}\frac{P_{k}hr_{l}^{-\alpha}}{\sum_{l=1}^ {l}I_{k,l}+\sigma^{2}}&\text{if }l=m_{u},\\ \frac{P_{k}hr_{l}^{-\alpha}}{\sum_{l=1}^{l}I_{k,l}+\sigma^{2}}&\text{if }l=p_{c},\\ \frac{P_{k}hr_{l}^{-\alpha}}{I_{2,l}+\sigma^{2}}&\text{if }l=p_{e},\\ \end{array} \right. \tag{1}\]  
where \(I_{k,l}\) denotes the interference from the \(k\)th tier to UE \(l\). We restrict that the PBS CRE UEs can only be scheduled by PBSs in the subframes that correspond to the MBS ABS subframes. Therefore, when \(l=p_{e}\), the interference from MBSs, i.e., tier 1, can be omitted when the CRS interference cancellation is utilized in pico. This is why we just consider \(I_{2,l}\), i.e., the intral-tier interference from PBS tier, in the denominator of the SINR expression when \(l=p_{e}\). Figure 1: The network scenario.
## 2 Higher-Order-Mode Triple Band Circularly Polarized Rectangular Dielectric Resonator Antenna  
[PERSON] et al. [1] proposed a triband circular polarized rectangular dielectric resonator antenna. The DRA is fed by a single coaxial cable, and the wide bandwidth of the proposed DRA is achieved by the coupling feed. The antenna can have the higher-order mode with a low-cost simple excitation mechanism. The antenna exhibits a bandwidth of 48% with a gain of ~6-9 dBic with a broad AR bandwidth. A wide beamwidth of 112\({}^{\circ}\), which is good for better signal reception is also obtained. The authors compared the measured results with the simulated results, and two results are in good agreement.
## 4 Materials and Methods
### SPME-GC-MS Experimental Conditions
#### 4.4.2 Indole and Biogenic Amines (BAs) Analyses  
Sponge samples (1 g) were weighed directly into 4.0 mLamber vials (Sigma Aldrich), the vial filled with a 15% NaCl sterile solution in ultra-pure water (3.0 mL), sealed with drillable caps and put in a thermostatic bath at 37 \({}^{\circ}\)C for 4 h. Then, 0.5 mL were taken by piercing the silicone septum with the needle of a Hamilton Microliter syringe (Supelco) and transferred into a sealed 1.7 mL clear glass vial, containing 1.0 mL of a sterile 15% NaClsolution and a PTFE Teflon coated magnetic stir bar (4 \(\times\) 10 mm) (Sigma-Aldrich). The pH of the resulting solution, in accordance with [36], was adjusted to 12 with 4 N NaOH (3.8 \(\mu\)L), added to the sealed vials with a syringe, and subjected to DI-SPME (polyacrylamide coating). Eventually, fibers were carefully removed by the vials and inserted directly into the GC injector for 2 min for the GC-MS analysis (TIC acquisition mode for indole, SIM for BAs).  
For BAs extraction, the derivatizing agent IBCF (7.5 \(\mu\)L) was added immediately after NaOH addition [36]. The vial was immediately shaken manually for 2 min at room temperature and a polyacrilate fiber was exposed to the solution for 40 min.  
Calibration curve for indole was prepared adding blank samples (without sponge fragments, only 15% NaCl sterile solution) with 1 mL of working solutions in the concentration range 0.012-12 \(\mu\)g/mL, corresponding to 0.003-3 \(\mu\)g/mL in the test tube. The within-day (\(n\) = 3) and between-days (\(n\) = 3 over 5 days) percentage relative standard deviations (RSD %) were calculated at 0.01, 0.05, and 1 \(\mu\)g/mL. Limits of detection (LOD) and quantitation (LOQ) were calculated at a signal-to-noise (S/N) ratio of 3 and 10, respectively. To estimate recovery, indole was added to each specimen in order to have twice the estimated concentration, or the LOD level, and then the ratio between the peak area in the spiked sample and a standard solution at the same concentration was calculated.  
Calibration curves for BAs were prepared adding six blank samples with variable amounts of working solutions in the concentration range 0.04-4 \(\mu\)g/mL, corresponding to 0.01-1 \(\mu\)g/mL in the test tube.  
The within-day (\(n\) = 3) and between-days (\(n\) = 3 over 5 days) percentage relative standard deviations (RSD %) were calculated at 0.05, 0.25, and 1 \(\mu\)g/mL. LODs and LOQs were calculated as described above. To estimate recovery, mix standard solutions containing the analytes at twice the estimated concentration, or the LOD level, were added to each sample, and then the ratio between the areas of the peaks was calculated as described above.  
To eliminate carry over, fibers were always subjected to a second thermal desorption after the chromatographic runs. All experiments were performed in triplicate and blank samples (vials without sponge fragments) were randomly acquired between different samples to exclude fiber signals.
## 4 Results
### RQ6: Issues in WSN and IoT Frameworks
#### 4.6.4 Resource Limitations  
If necessary resources in WSN and IoT are abandoned or not handled efficiently, it may affect the performance of the network. The network consists of many nodes and sensors that require energy to operate well [86]. Various MAC layer protocols have been developed to reduce the energy consumption of sensors or nodes. These energy-efficient algorithms work primarily by regulating the synchronization of network traffic over time and the time period during which a node becomes active in a network [87; 88]. In contrast, the communication medium is another basic requirement, since nodes rely on the Internet for data transmission. There is a constant need for energy, otherwise the network will fail. The nodes have limited resources because battery capacity, correspondence capacity, and computing power are low. Again, security is the main problem, because the security measurement expenses require more resources to maintain the speed of the network, which is not affordable. As a result of low regulated security, attacks can subvert software execution and protocols used in the network [89; 90].
# Application of Lignin for Slope Bioengineering: Effect on Soil Improvement and Plant Growth
## 4 Results and Discussion
### Compressive and Shear Strength Characteristics
The obtained data suggest that treatment of all three soils with the lignin solution increased the vane shear strength of the soil. Also, similar to the pocket penetrometer test data, Method 1 produced the soil specimens with greater vane shear strength compared to Method 2. Figure 3: Results of vane shear tests conducted at the end of the experiment: (**a**) Soil 1; (**b**) Soil 2; (**c**) Soil 3. The vane shear test is typically conducted on soft, cohesive soils like Soils 1 and 2. However, it was also possible to obtain a vane shear strength value for Soil 3 because, at the end of the long-term experiment, the sand developed apparent cohesion due to soil suction [59]. In addition, the treatment of the sand with lignin solution significantly increased its vane shear strength (more than six times the untreated soil strength, Figure 3c), suggesting that lignin helped to develop adhesive bonding between the sand grains, making it stronger. These results appeared promising for the possible bioengineering treatment of slopes consisting of sand. Effect of lignin on soil strength. The experimental data indicated that adding lignin to each tested soil generally led to an increase in soil strength. The obtained results are replotted in Figure 4 as the percentage of soil strength changes over time in comparison to the strength of the untreated soil specimens. For the lignin-treated Soil 1, the maximum increase in strength occurred after the first ten days, whereas Method 1 was associated with the greatest (more than 100%) increase in strength. Interestingly, the percentage of strength increase gradually diminished over time but remained positive (about 40%) after 40 days. However, for the soil specimens prepared by Method 2 (spray), the change in strength, compared to the untreated soil, became negligible after 30 days. Unlike Soil 1, Soil 2 treated with lignin showed no increase in strength after 10 days, compared to the untreated specimens. However, the strength gradually increased over time, especially for Method 1, when the strength increased by 60% and 120% for the specimens mixed with 1% and 3% lignin, respectively. For the specimens sprayed with 1% lignin, no significant change in the strength was observed after 40 days. The results from the vane shear tests (Figure 5) indicated that for each soil and lignin concentration, the vane shear strength of the treated soil exceeded the strength of the untreated soil. Method 1 significantly improved the soil strength with at least a 100% increase for each soil type compared to the untreated soil. Method 2 also produced soil samples with a greater strength than the untreated soil, but the increase was smaller, generally in the range of 50-100%.
# Efficient Approximation for Restricted Biclique Cover Problems
## Overview of New Results
### Model I: A Node belongs to a Small Number of Bicliques  
In this model, we consider bipartite graphs with minimum biclique covers in which each node participates in only a small number of different bicliques. More formally, we introduce the \((f_{L},f_{R},k)\)-biclique cover decision problem for bipartite graphs:  
**Problem 1** (\((f_{L},f_{R},k)\)-biclique cover problem).: _Given a bipartite graph \(G=(L,R,E)\), and parameters \(f_{L},f_{R}\) and \(k\), distinguish between the following two cases: Yes-instance: There is a biclique cover of \(G\) with \(\leq k\) bicliques, such that \(\forall u\in L\) (resp. \(v\in R\)), \(u\) belongs to at most \(f_{L}\) distinct bicliques in the cover (resp. \(v\) belongs to at most \(f_{R}\) distinct bicliques in the cover). No-instance: No such cover exists._  
When \(f_{L}=f_{R}=f\), we write \((f,k)\)-biclique cover instead of \((f,f,k)\)-biclique cover.  
Notice that this problem is related to the local biclique cover problem where one is only interested in minimizing the number of distinct bicliques covering each node without minimizing the total number of bicliques [16]. Clearly, if a graph satisfies the \((f_{L},f_{R},k)\)-biclique cover condition it also satisfies the \((\max(f_{L},f_{R}))\)-local biclique cover condition, but the converse is not always true.  
**Results 1**.: _Our first result (Section 3.3) shows that the \((f,k)\)-biclique problem remains computationally hard even for small values of \(f\). Specifically, we prove that the \((f,k)\)-biclique problem is NP-Complete for any \(f\geq 5\)._  
On the positive side, we first note that the \((1,k)\)-biclique case is trivial since the graph must consist of disjoint bicliques. We present two results for the non-trivial \((2,k)\)-biclique case. (1) For any graph with a \((2,k)\)-biclique cover, we present a polynomial time algorithm that finds a biclique cover with at most \(\approx 2.83k\) bicliques. (2) For any graph with a \((2,k)\)-biclique cover, we present a polynomial time algorithm that covers at least a \(1-\frac{1}{e}\) fraction of the edges of the graph with \(k\) bicliques. These algorithms do not guarantee that each node is covered by at most 2 bicliques.