esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### CFD Simulation Method and Parameter Setting
#### 2.3.2 Meshing and Independence Verification  
The basic geometric model of the NSC was meshed. Area II is the most geometrically complex part of the NSC model. When Area II satisfies grid independence, all the NSC models satisfy grid independence. Therefore, the geometric model of Area II was selected for grid independence verification, and the fluid domain of Area II was divided into a refined grid, medium grid, and coarse grid, according to different mesh growth rates of 1.1, 1.15, and 1.2. The specific parameters are shown in Table 1. This study conducted CFD simulation calculations on the fluid domain under coarse grid, medium grid, and refined grid conditions, respectively. To demonstrate the grid independence, 20 sample points along the wind direction were selected from the above grid simulation wind fields with different resolutions to compare the wind speed values, as shown in Figure 4.  
As shown in Figure 4, the difference in CFD-simulated wind speed between the coarse grid, medium grid, and refined grid is very small, with an average error of 0.012%. Therefore, the use of a coarse grid can meet the grid independence requirement. Considering that the number of coarse grids is 1.93 million lower than that of refined grids, this means that coarse grids consume less computing resources, calculate faster, and compute time is shorter. In this study, coarse mesh was used for CFD simulation. The meshing parameters were set according to the size of the geometric model. The most suitable meshing parameters for the above four typical areas are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Grid Type** & **Elements** & **Nodes** & **Growth Rate** \\ \hline Coarse grid & 16,950,209 & 3,123,716 & 1.2 \\ \hline Medium grid & 20,832,355 & 3,775,180 & 1.15 \\ \hline Refined grid & 28,427,397 & 5,049,341 & 1.1 \\ \hline \end{tabular}
\end{table}
Table 1: Grid independence verification experimental parameters.  
Figure 4: Grid independence verification diagram.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 3 Formation Control: Virtual Linkage Approach
### Formation Reconfiguration Using Virtual Linkage  
As described in Section 2.3, virtual structure approaches should be redesigned if different formation patterns are required, as each virtual structure corresponds to a unique rigid body. In detail, the agents have to be recalled back and refresh their relative position in the virtual structure \({}^{R}r_{i}\) which is saved in each local agent. Another way to update the \({}^{R}r_{i}\) for all agents can be implemented by informing all the agents through communication. If a group of agents needs to be reconfigured for every period of time \(T\), and \(3^{*}K\) bits are needed to record the relative position \({}^{R}r_{i}\) in the virtual structure, then the required bandwidth in units of bits per second is  
\[BW_{1}=3\cdot N\cdot K/T \tag{8}\]  
This turns out to be a heavy burden to the communication network and is not suitable for real-time control, especially when the number of agents \(N\) increases.  
Figure 9: Calculating differences between two homogeneous transforms.  
Differently to the virtual structure, each virtual linkage corresponds to a linkage (or manipulator) rather than a unique rigid body. In mechanical engineering, a linkage (or manipulator) is a kinematic configuration which is able to change its state configuration by changing the value of joints. Therefore, a virtual linkage is able to be reconfigured as different formation shapes by changing the virtual joint angle \(\theta_{d}\) without need to recall back and refresh all the agents' relative position in the \(j\)th virtual link \(\
icefrac{{v}}{{r_{i}}}\).  
In detail, by changing the \(\theta_{d}\) in Figure 8, a different formation shape is able to be presented. It is worth mentioning that the \(\theta_{d}\) is only necessary to broadcast to \(k\) locations which handle the individual formation control of the \(k\) virtual links. Supposing \(K\) bits are required to encode each virtual joint angle, then the communication bandwidth reduces to  
\[BW_{2}=K\cdot k/T \tag{9}\]  
Here, the number of virtual links is not bigger than the number of agents \((k\leq N)\) since each virtual link is a collection of agents. Hence, the bandwidth ratio is  
\[\frac{BW_{2}}{BW_{1}}=\frac{k}{3\cdot N}\leq\frac{1}{3} \tag{10}\]  
Figure 10 shows the bandwidth ratio increases linearly with the number of virtual links k when the number of agents is fixed. The maximum value, \(1/3\), is reached when \(k=N\). This indicates that the virtual linkage requires less than 33.3% bandwidth to reconfigure a formation shape as compared to the virtual structure approach.
## 2 Principle
### PS Based on Single-Layer LUT
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Input** & \(E[|X|^{2}]\) & **Output** & \(E[|Y|^{2}]\) \\ \hline
0101 & 2 & 0000 & 18 \\
0111 & 2 & 0010 & 18 \\
1101 & 2 & 1000 & 18 \\
1111 & 2 & 1010 & 18 \\
0001 & 10 & 0001 & 10 \\
0011 & 10 & 0011 & 10 \\
0100 & 10 & 0100 & 10 \\
0110 & 10 & 0110 & 10 \\
1001 & 10 & 1001 & 10 \\
1011 & 10 & 1011 & 10 \\
1100 & 10 & 1100 & 10 \\
1110 & 10 & 1110 & 10 \\
0000 & 18 & 0101 & 2 \\
0010 & 18 & 0111 & 2 \\
1000 & 18 & 1101 & 2 \\
1010 & 18 & 1111 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The contents of the LUT. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \(\mu\) & \(1/11\) & \(1/9\) & \(1/7\) & \(1/5\) & \(1/3\) & \(1\) \\ \hline \multirow{2}{*}{BER} & \(3.3003\) & \(3.3003\) & \(4.1254\) & \(3.3003\) & \(4.4004\) & \(6.6007\) \\  & \(\times\)\(10^{-5}\) & \(\times\)\(10^{-5}\) & \(\times\)\(10^{-5}\) & \(\times\)\(10^{-5}\) & \(\times\)\(10^{-5}\) & \(\times\)\(10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The impact of the parameter \(\mu\) on BER performance. Here, we define a flag signal to indicate the mapping state of each symbol. A flag value of 0 indicates an unshaped symbol, while a value of 1 represents a shaped symbol. This flag signal plays a key role in the probability decoding at the receiver. When the flag is 0, the symbol remains unchanged. When the flag is 1, the input and output energy must be evaluated. Notably, the LUT is used in reverse in this case, meaning that in the invLUT, the 'output' corresponds to the input for the current step, while the 'input' represents the output. When \(E[|Y|^{2}]<E[|X|^{2}]\), the low-energy symbol is converted back into the corresponding high-energy counterpart. Similarly, the LUT-PS method can be extended to higher-order constellations, such as 64 QAM. The main idea is to divide all constellation points into three layers based on their symbol energy levels, then map the outermost layer to the innermost layer while keeping the middle layer unchanged. Figure 2a shows the mapping rules of the second quadrant of the 64 QAM constellation diagram, and the same principle applies to the other three quadrants. It results in the corresponding probabilistic distribution for 64 QAM, as illustrated in Figure 2b.
# Glucose Isomerase: Functions, Structures, and Applications
## 3 Structure
### Xylitol Binding to the Active Site of GI  
Structure-based inhibitor studies of GI are helpful for industrial applications such as HGFS and bioethanol production [37], by identifying the parameters of enzyme engineering to prevent product inhibition. The isomerase activity of GI can be inhibited by some divalent cations such as Ca\({}^{2+}\), Cu\({}^{2+}\), Zn\({}^{2+}\), Ni\({}^{2+}\), Ag\({}^{2+}\), and Hg\({}^{2+}\)[1]. In addition, molecules such as xylitol, arabitol, sorbitol, mannitol, lyxose, and Tris also inhibit GI activity [43]. However, the mechanism of GI inhibition by these compounds remains largely unclear.  
Among these compounds, the inhibition of the activity of GI by xylitol, which is a structural analogue of xylose, has been well studied [44; 45]. To date, six crystal structures of xylitol-bound GI have been deposited in PDB (PDB code 1 XIG, 2 XIS, 3 GNX, 4 DUO, 5Y4), and 7 DFK) [31; 33; 37; 46; 47]. All these structures reveal that the xylitol molecule binds at the M1 site in the active site of GI. Three oxygen (O2, O3, and O4) atoms from xylitol interact with the metal ion at the M1 site, and the metal ion is stabilized by octahedral coordination, by the metal-interacting residues of the enzyme, and the xylitol molecules [31; 33; 37; 46; 47]. Therefore, xylitol binding in the M1 site prevents substrate access to the metal ion. The crystal structure of one-metal-binding mode of GI shows that the metal ion at the M2 site is not essential to facilitate xylitol binding to the M1 site [33]. Furthermore, xylitol binding to the M1 site stabilizes the geometry of the metal-binding sites.  
Meanwhile, the xylitol-bound GI structure shows a high B-factor value of the metal ion at the M2 site, although the reason for this observation remains unknown. A recently obtained room-temperature structure of xylitol-bound GI via serial crystallography revealed a correlation between xylitol binding and low metal occupancy at the M2 site [37]. In native GI, the M1 site shows a distorted octahedral coordination, but it is geometrically stabilized upon xylitol binding to the metal at the M1 site [37] (Figure 6A). This causes a rearrangement of the conformation of metal-binding residues in the M2 site (Figure 6C). This induces the distortion of metal coordination at the M2 site, leading to the release of the unstable coordinated metal ion from the M2 site [37]. This metal release induces further conformational changes in the neighboring residues (Figure 6C). Since the metal ion at the M2 site involved in isomerization activity is released as a result of xylitol binding, an additional catalytic metal ion has to be added for an isomerization reaction in industrial applications after the release of xylitol.  
Figure 6: Proposed mechanism of xylitol-induced release of metal at the M2 site of GI. (**a**) Xylitol-bound state of GI. (**b**) Rearrangement of xylitol-binding residues. (**c**) Release of metal ion from M2 site of GI.
_Algorithms_**2012**, \(5\), 529-544; doi:10.3390/a5040529  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
Finite Element Quadrature of Regularized Discontinuous and Singular Level Set Functions in 3D Problems  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Engineering Department, University of Ferrara, via Saragat 1, 44122 Ferrara, Italy;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Structural and Geotechnical Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-0532-974935; Fax: +39-0532-974870.  
_Received: 10 September 2012; in revised form: 22 October 2012 / Accepted: 29 October 2012 / Published: 7 November 2012_  
**Abstract:** Regularized Heaviside and Dirac delta function are used in several fields of computational physics and mechanics. Hence the issue of the quadrature of integrals of discontinuous and singular functions arises. In order to avoid ad-hoc quadrature procedures, regularization of the discontinuous and the singular fields is often carried out. In particular, weight functions of the signed distance with respect to the discontinuity interface are exploited. [PERSON] and [PERSON] (_Journal of Scientific Computing_, 2003, 19: 527-552) proved that the use of compact support weight function is not suitable because it leads to errors that do not vanish for decreasing mesh size. They proposed the adoption of non-compact support weight functions. In the present contribution, the relationship between the Fourier transform of the weight functions and the accuracy of the regularization procedure is exploited. The proposed regularized approach was implemented in the eXtended Finite Element Method. As a three-dimensional example, we study a slender solid characterized by an inclined interface across which the displacement is discontinuous. The accuracy is evaluated for varying position of the discontinuity interfaces with respect to the underlying mesh. A procedure for the choice of the regularization parameters is proposed.  
**Keywords:** 3D-quadrature; regularization; XFEM; interface
## 4 Results  
### Burn-Type Differentiation  
#### 4.1.1 Agricultural Burning Identification  
We used Florida's burn type data to evaluate the performance of the agricultural burning identification algorithm. There were 95,364 records in Florida permits labeled as agricultural burns, 65.35% of which were also identified as agricultural burns by the algorithm. On the other hand, 70,157 permit records were labeled as agricultural burns by the algorithm, of which 88.83% were validated by the burn type in permit records. The results showed that there was good agreement between information obtained from permits and the detection offered by the algorithm. Meanwhile, the algorithm underestimated the number of agricultural burns in Florida.
## 6 Experiment Evaluation
### Division Experiment  
In the process of using FALCON, it was found that the original division operation has a significant limitation for the denominator, which is mainly related to the design principle and algorithm \(\Pi_{\text{Pow}}(P_{1},P_{2},P_{3})\). In addition, mistakes always occur with vectorization processing with inputs: It is found that the division method in FALCON did not handle the results correctly when different values were taken in the vector. Thus, the work performed division experiments with Avgpool on the same denominator to ensure the origin division can normally work.  
The paper compares the division in three different intervals of orders of magnitude (Range-I, Range-II, Range-III). They are \((0,512),(0,1024)\) and \((1024,2^{f})\) respectively, asTable 1. For each interval set of experiments, the algorithm took random values as input, set the divisor to be a positive floating point number to ensure that the original division method in FALCON can work, set the experimental data type length to unsigned int64, and the experiment recorded the computation time (every 20 times), the communication cost, the precision of the division, and the average relative error between the ciphertext and the plaintext division calculation. The experimental results outperform the original work in terms of efficiency and communication in the lower data range, with a speedup of nearly 2\(\times\). In addition, the algorithm improves the division operations being able to calculate in a larger range than the original division method--about a 4-order of magnitude increase. Then, the experiment compares the division respectively based on [PERSON]'s method, the repeated subtraction method (SecureNN), and the Newton-Raphson iteration method (CRYPTEN) in ROSETTA, as Table 2. The experiment results show that Goldschmidt-based implementation of the division achieves a runtime improvement of nearly 2\(\times\) compared to the Newton-Raphson iteration method, and about 5\(\times\) compared to the repeated subtraction method.
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art
### [PERSON] et al.'s GOMEA-Based Proposal  
In [63], the authors addressed the DIR problem by means of the proposal of a real-coded version of the recently contributed Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). Their GOMEA variant tackled the IR problem from a multi-objective view-point. As in their previous contributions ([52; 56]), each IR solution in the Pareto set corresponds to a different trade-off between the objectives of interest. Specifically, the solution that is estimated the most suitable can manually be selected a-posteriori. The main difference between their new GOMEA-based IR method and the ones in the SoTA [56] was that the former has a much higher selection pressure as a result of increasingly enhancing sections of existing Parent solutions. In this new version, the authors separately proposed the estimation of such distribution for each subset of variables in the dependency model. Thus, the solutions are then independently improved by means of sampling new values for these subsets of variables in the linkage model. Then, only those sampled values that lead to an improved solution are accepted, otherwise, the main solution returns to its previous state. Because of the solutions are partially disturbed, it is not necessary to completely evaluate them to assess the contribution of a partial alteration. This strategy allowed their proposal to enhance its efficiency. The authors performed several experiments on three different IR scenarios: (i) An artificial problem with disappearing structures; (ii) a pair of pre- and post-operative breast CT scans; and (iii) a pair of breast MRI scans that were acquired in prone and supine positions. The reported results showed a speed-up of up to a factor of \(\sim\)1600 compared to the previously used algorithms.
ranging from 1-5 mM. ATP serves not only as a building block for RNA but also as a phosphate and energy donor in numerous enzymatic reactions, and acts as a key player in both intra- and extracellular signaling [4; 5]. Guanosine-5\({}^{\prime}\)-triphosphate (GTP) is critical for cellular signal transduction, particularly in relation to GTP-binding proteins (G-proteins) and G-protein coupled receptors [6]. Additionally, in bacteria, GTP and guanosine-5\({}^{\prime}\)-diphosphate (GDP) are involved in the stress adaptation process (stringent response) by serving as precursors for the synthesis of alarmones such as (p)ppGpp [7]. Cytidine-5\({}^{\prime}\)-triphosphate (CTP) is essential for lipid synthesis, while a number of 5\({}^{\prime}\)-NTPs can combine with sugars to form sugarnucleotides [8]. Sugar nucleotides play a crucial role as intermediates in carbohydrate metabolism and the synthesis of glycoconjugates. These molecules consist of a sugar or sugar derivative linked to a 5\({}^{\prime}\)-NDP or 5\({}^{\prime}\)-NMP. Examples include UDP-\(\alpha\)-D-galactose, GDP-\(\alpha\)-D-mannose, or CMP-sialic acid [9].  
The production of nucleotides and their analogs has been a major focus of research for decades due to their wide range of applications in pharmaceuticals, molecular biology, and structural chemistry [2; 10; 11; 12; 13]. 5\({}^{\prime}\)-NTPs are mainly the active forms of most antiviral and anticancer nucleoside drugs. Consequently, nucleotide analogs, which include modifications to the sugar, base, or phosphate components, are used to explore and manipulate biological systems during drug development [14; 15]. In addition to nucleoside analogs, protected 5\({}^{\prime}\)-NDP-/5\({}^{\prime}\)-NTP- derivatives are being studied as potential activated drugs for direct application [16; 17]. In molecular biology, 5\({}^{\prime}\)-dNTPs and 5\({}^{\prime}\)-NTPs are employed as building blocks for DNA and RNA, respectively, in various applications such as PCR, mutagenesis experiments, diagnostic probes, and the study of macromolecular structures and their interactions [12]. Naturally occurring 5\({}^{\prime}\)-NTPs like ATP are also used as cofactors in numerous in vitro assays, including phosphorylations, ligations, and cell-free protein expression [5]. ATP is also one of the most important phosphate donors in enzymatic cascade reactions [18]. Moreover, 5\({}^{\prime}\)-NTPs are key components for the synthesis of mRNA therapeutics by in vitro transcription, which show great promise for treating a wide range of diseases and serving as vaccines [19]. The development of vaccines for COVID-19 represents one of the most efficient vaccine campaigns in history, with modified 5\({}^{\prime}\)-NTPs playing a crucial role by reducing potential toxicity and immunogenicity. Notably, the N1-methylpseudouridine modification significantly improved the efficacy of mRNA vaccines [13]. This makes 5\({}^{\prime}\)-triphosphorylated N1-methylpseudouridine and pseudouridine promising nucleotide analogs for future RNA-based therapies and vaccine developments.
# Low-Photon Counts Coherent Modulation Imaging via Generalized Alternating Projection Algorithm
## 3 Numerical Simulations
### Near-Field Simulation  
The convergency requirement of the CMI method determined other distance parameters; a large sample (pinhole) to modulator distance increases the effectiveness of the support constraint but at the cost of degraded image resolution in the reconstruction. A value between 20 and 40 mm was found to give the best balance. Therefore, the separations between the sample, modulator, and detector were set to 22.1 mm and 21.5 mm according to our experimental settings. Wave propagation between the three planes was calculated with the angular spectrum algorithm. The sampling intervals were 5.04 mm at all planes.  
The performance of CMI-GAP was evaluated with the illumination photon number of \(N\) being \(10^{8},\ 10^{7},\ 10^{6},\ 10^{5}\). Reconstructed object amplitudes and phases, as well as the PSNR and RMS, are shown in Figures 2 and 3, respectively. The iteration number of the traditional algorithm is about 500 and 50 with CMI-GAP. Overall, one can see the CMI-GAP algorithm shows better performance than CMI. When \(N\) is above \(10^{7}\), their reconstruction quality is comparable; as \(N\) decreases, the RMS curve of CMI increases rapidly. When \(N\) drops below \(10^{6}\), the CMI-GAP algorithm can still retrieve details in most of the regions.  
Figure 2: Reconstruction of the sample given near-field CMI simulated data at varying photon flux. In the lower right corner of each image is a larger version of the middle region. As \(N\) decreases, the superiority of CMI-GAP is demonstrated clearly.  
The convergence rate dependence of the CMI-GAP algorithm on the illumination photons was then studied. Figure 4 shows the comparison of the convergence rate for the photon counts of \(10^{6}\), \(10^{7}\), and \(10^{8}\). Notably, the proposed algorithm converges rapidly in the first ten iterations when the number of photons is \(10^{7}\). The RMS value reaches 0.08 before it stagnates, while the RMS using the CMI algorithm converges relatively slowly and reaches an RMS of 0.15 after 30 iterations. As \(N\) decreases further, the performance difference between the two algorithms becomes more apparent.
## 3 Results  
### Signal Acquisition and Median Filtering  
In this paper, the MCU controller collects the dynamic weighing and static weighing signals after filtering by the circuit at a frequency of 5 Hz to analyze the difference between the two weighing methods, as shown in Figure 7. The results show that dynamic weighing and static weighing are significantly different, and there can be a large error when directly reading the weight of a living body by static weighing.  
The interference caused by the activities of poultry on the scale are close to the frequency of the body weight signal, and the analog circuit is difficult to filter out. Therefore, the median mean filter is used to eliminate the interference, as shown in Figure 8. The experimental results show that, after sorting the weight sequence from small to large, the rearranged sequence will always have an interval that overlaps with the actual weight, and the mean interval \((p,q)\) has different values, which will affect the final calculated weight.  
Figure 6: Experiment with chicken feeding ad libitum: (**a**) layout of chicken house; (**b**) live trial.  
Figure 7: Original load cell signal of weight scale.