esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Switch Regions of CDC42 Are Not the Main Binding Sites for the GRDs
#### 2.2.1 Mutations in CDC42 Switch Regions Only Mildly Affect GRD Binding  
Proposed interacting mode of GRD with the switch regions of CDC42 (RASGAP mode binding) was deduced from the IQGAP2 GRD2 structure in complex CDC42Q61L GTP [12] and two CDC42 mutation variants within the switch I and II regions (2 xSW and 8 xSW) and a 11-residues mutant variant within the GRD of IQGAP2 C795 (11 xGRD) were generated as illustrated in Figure 2A. Identical and highly conserved residues within the interacting interface highlighted in Figure 2B, were all replaced by alanine. All variants were stable in their purified forms and Far-UV CD spectroscopic measurements excluded any improper folding as compared to the wild-type proteins (Supplementary Figure S2).  
Figure 2: Analysis of CDC42 switch region and IQGAP1 GRD mutants. (**A**) The selection of GRD2 and CDC42 mutations is based on the GRD2/CDC42Q61L structure (PDB: 5 CJP). Interacting residues colored on both proteins were selected for mutational analysis. For more details see also Table S1. (**B**) Multiple sequence alignments of switch regions of RHO GTPases and IQGAPs highlight identical or homologous interacting residues that have been replaced in this study by alanine for analyzing their impact on IQGAP binding. Conserved residues are shaded in grey. Mutations in CDC42 switch regions include 2 xSW (bolded residues) and 8 xSW (all eight residues, as indicated), and 11 xGRD in IQGAP2 C795. (**C**) Fluorescence polarization measurements of mGppNHp-bound CDC42 WT, 2 xSW and 8 xSW with IQGAP1 GRD1 or IQGAP2 GRD2. (**D**) The \(\rm K_{d}\) values for the interactions of IQGAP1 C794, IQGAP2 C795 and C795 11 xGRD with the CDC42 variants in mGppNHp- and mGDP-bound form were determined using fluorescence polarization. n.b. stands for no binding observed. C794 and C795 CDC42 WT measurements are included from Figure 1B for simple comparison. (**E**) Observed rate constants (\(\rm\kappa_{obs}\)) for the IQGAPs association with mGppNHp-bound CDC42 WT, 2 xSW and 8 xSW were measured using stopped-flow fluorimetry. (**F**) Pull-down of endogenous IQGAP1 FL from HEK293 lysates with GST-CDC42 in GppNHp-bound or GDP-bound state. Cell lysate was used as an input control. Densiometry evaluation of relative IQGAP1 binding to GST-CDC42 proteins (a. u., arbitrary unit) from triplicate experiments is shown as bar charts.  
We first determined the K\({}_{\text{d}}\) values for the GRD1 and GRD2 interaction with the mGppNHp-bound CDC42 WT, 2 xSW and 8 xSW. Interestingly, we found a two to three-fold reduction in the binding affinity of GRD1 but no notable reduction for GRD2 with the CDC42 variants as compared to CDC42 WT (Figures 1B, 2C and S3). As the effect of 2x and 8x introduced mutations on the proposed crucial interaction sites of CDC42 and GRD2 did not result in a decrease of affinity, our data clearly indicates that the association of CDC42 switch regions with IQGAP must be through other sites rather than the GRD.
# Mapping Based Quality Metrics for Mesh Deformation Algorithms Using Radial Basis Functions
## 2 RBFs Method Based on Generalized TPS  
Radial basis functions (RBFs) interpolation can be applied to obtain the displacement of the internal fluid mesh nodes based on the deformation of the structural boundary. The basic form of RBFs interpolation can be expressed as:  
\[s(\mathbf{x})=\sum_{j=1}^{N}c_{j}\varphi\Big{(}\|\mathbf{x}-\mathbf{x}_{\eta_{j}}\|\Big{)} \tag{1}\]  
where \(s\) is the interpolation function, describing the displacement in the whole domain. \(\mathbf{x}_{\eta_{j}}\) are the centers in which the values are known. In this research, the coordinates are of the \(j\)th boundary node, \(N\) is the total quantity of boundary nodes, \(\mathbf{x}\) is the centers in which the values are unknown. In this research, the coordinates are of the random internal fluid mesh node, \(c_{j}\) is the weight coefficient of the \(j\)th RBF, \(\varphi\) is the given RBF, and \(\|\mathbf{x}-\mathbf{x}_{\eta_{j}}\|\) represents the Euclidean distance of vector \(\mathbf{x}\) and \(\mathbf{x}_{\eta_{j}}\). In 3D space, components of \(\mathbf{x}\) and \(\mathbf{x}_{b_{j}}\) are given as \(\mathbf{x}=[x\quad y\quad z]\) and \(\mathbf{x}_{b_{j}}=[x_{b_{j}}\quad y_{b_{j}}\quad z_{b_{j}}]\), and distance \(\|\mathbf{x}-\mathbf{x}_{b_{j}}\|\) has the following formulation:  
\[\|\mathbf{x}-\mathbf{x}_{b_{j}}\|=\sqrt{\left(x-x_{b_{j}}\right)^{2}+\left(y-y_{b_{j} }\right)^{2}+\left(z-z_{b_{j}}\right)^{2}} \tag{2}\]  
The weight coefficients \(c_{j}\) are determined by the interpolation conditions:  
\[s(\mathbf{x}_{b})=\mathbf{d}_{b} \tag{3}\]  
where \(\mathbf{d}_{b}\) are the actual displacements of the boundary nodes. With a \(N\times N\) matrix containing the evaluation of the basis function:  
\[\mathbf{\varphi}_{b,b}(i,j)=\varphi\Big{(}\|\mathbf{x}_{b_{i}}-\mathbf{x}_{b_{j}}\|\Big{)} \tag{4}\]  
The weight coefficients vector \(\mathbf{c}=(c_{1},\cdots,c_{N})^{T}\) can be obtained:  
\[\mathbf{c}=\mathbf{\varphi}_{b,b}{}^{-1}\mathbf{d}_{b} \tag{5}\]  
The values of internal mesh nodes displacement \(\mathbf{d}_{in}\) can be derived by calculating the interpolation Equation (1) after calculating the coefficients vector \(\mathbf{c}\):  
\[\mathbf{d}_{inj}=s(\mathbf{x}_{inj}) \tag{6}\]  
Where \(\mathbf{x}_{inj}\) is the coordinates of the \(j\)th internal mesh node.  
Several RBFs can be selected in literature, which are appropriate for this research. They can be divided in two categories: compact functions and global functions [6; 33]. The compact function is generally scaled with a support radius \(r\) to control the compact support. The mesh nodes inside a circle (2D) or sphere (3D) with radius \(r\) around a boundary node are influenced by the corresponding displacement. The global functions are not equal to zero outside a certain radius \(r\) and operate on the whole interpolation space. Two categories RBFs are shown by [PERSON] (2018), and the frequency used formulations are presented in Tables 1 and 2.  
We chose the thin plate spline (TPS) as the RBFs in this research, which generates the highest mesh quality of adapted mesh ([PERSON], 2018).
## 3 Methodology Steps
### Modeling of Communication and Coordination Between Agents  
A structured communication model is designed, establishing efficient data exchange strategies:  
1. Hierarchical communication with the orchestrating agent (EOO) for global decisions.
2. Direct interaction between specialized agents to minimize latencies.
3. Negotiation based on priorities and availability in the allocation of resources.  
Use of middleware and standard communication protocols (JADE, MQTT, ROS) to ensure interoperability in digital environments.
## 3 Results and Discussion
### Chemical Composition of Substrates
It plays the role of a nutrient, has buffering capacity and exerts inhibitory or toxic action in the solution. Total nitrogen is combined with ionized ammonium nitrogen (NH\({}_{4}\)+) and unionized ammonium nitrogen (free ammonia form, NH\({}_{3}\)), whose form heavily depends on the pH and temperature of the medium. [PERSON] et al. [47] studied the inhibition effect of ammonia on the anaerobic hydrolysis of carbohydrates and lipids, using biomass adapted to 4.9 g NH\({}_{4}\)+-N/L, and concluded that concentrations of total ammonia nitrogen between 2.4 and 7.8 g NH\({}_{4}\)+-N/L (283-957 mg NH\({}_{3}\)-N/L) do not inhibit the hydrolysis process. Unlike the ammonium ion, free ammonia is known as the main inhibitory form of methanogenesis since it can diffuse through the cell membrane and inhibit the cell functioning [48]. The free ammonia form increases from 1 to 10% of the total ammonia by varying the pH from 7 to 8, respectively, while maintaining the temperature at 35 \({}^{\circ}\)C. Looking at the review by [PERSON] et al. [11], the effect of ammonia levels on the anaerobic digestion process is categorized as \"Beneficial\", \"No antagonistic effect\", \"Inhibition (especially at higher pH values)\", and \"Complete inhibition or toxic at any pH\" for the concentrations of 50-200; 200-1000, 1500-3000 and \(>\)3000 mg NH\({}_{4}\)+-N/L, respectively. According to this information, the PE tested, containing 3000 mg of NH\({}_{4}\)+-N/L, is positioned in the upper range of the \"Inhibition\" category, confirming its effective inhibitory capacity and the advantage of digesting it with another effluent that complements and dilute its composition. The advantage of joining substrates was also mentioned by [PERSON] et al. [49], who note that the joint digestion of corn stalk and pig manure provided a greater production of biogas/methane than the individual process because the materials blend makes the process more resistant to harmful inhibitory effects. [PERSON] et al. [50] found that the limited buffering capacity of whey mono-digestion can be resolved by adding cattle manure to stabilize methane production and decrease acid accumulation. The interest in applying the concept of complementary substrates lies not only in avoiding substrate pre-treatments but also in the quality of the digested flow. If piggery effluent is anaerobically digested alone, the digested effluent usually contains large amounts of nutrients, mainly nitrogen, which must be removed by additional processes [51; 52]. Thus, the concept of complementary substrates can also avoid the need to apply a post-treatment to the digestate.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 2 Algorithms of Pedestrian Detection from Images
### YOLO Algorithm  
One of the most popular and influential algorithms in recent years has been YOLO [8] (i.e., \"You Only Look Once\"). The algorithm delivers precisely what the name suggests, i.e., the detection is performed by iterating over every image sector only once. Rather than viewing object detection as a classification problem, YOLO follows the route of regression detectors. The image frame is divided into smaller segments that can vary in size. By performing detection over these segments, the algorithm cannot only determine the class of an object but also its bounding boxes relative to the image segments.  
YOLO achieves much of its efficiency by utilizing the MobileNet [9] neural network. Similar to the YOLO algorithm, MobileNet is highly optimized for speed and resources. In essence, this means that the neural network is primarily designed for low-power computing systems, e.g., unmanned aerial vehicles or mobile devices (see the comparison of the performance of YOLO v3, v4, and v5 by Nepal and Eslamiat [10]). However, given its efficient design, it can be utilized by more computationally capable systems as well.  
Despite its efficiency and popularity, the YOLO algorithm faces several problems, including limitations in how many objects can be detected per image, the efficiency of detection in varying image resolutions, and small object detection. These problems canbe alleviated (as YOLO is highly configurable) but they can also provide challenges in certain implementations.  
The YOLO algorithm was used by [PERSON] et al. [11] for the detection of pedestrians when using UAVs (similar to this research). The authors achieved an AP (average precision) of 93.2% for YOLOv3, while mask R-CNN achieved 95.5%; the method based on YOLOv3, called AIR-YOLOv3, reached 91.5%. Other similar [12] research using the YOLOv3 tiny method reported 99.8% accuracy in testing. [PERSON] et al. [13] used a more novel version of YOLOv4; the authors reported an even smaller AP for the detection of pedestrians--YOLOv3 (59.54%) and YOLOv4 (74.34%).
# Graph Convolutional Networks with POS Gate for Aspect-Based Sentiment Analysis
## 2 Method
### Embeddings and Bi-LSTM  
Given a sentence \(\{w_{1},w_{2},...,w_{n}\}\) where \(w_{i}\) indicates \(i\)-th word, we get a sequence of POS tags \(\{p_{1},p_{2},...,p_{n}\}\) using NLTK [29] POS tagger. Aspect is a sub-sequence of the word sequence, and there may exist one or more aspects in a sentence. The words and POS tags are passed to the word embedding layer and POS embedding layer. The embedding layers embed the words and POS tags in vector spaces with word embedding matrix and POS embedding matrix \(E^{w}\in\mathbb{R}^{d_{e}\times|V|},E^{p}\in\mathbb{R}^{d_{e}\times|P|}\), where \(d_{e}\) is the dimension of embedding, \(|V|\) denotes the vocabulary size, and \(|P|\) indicates the number of unique POS tags. Pre-trained GloVe [22] is used for the word embedding, and the POS embedding is generated using training data with POS tags. The word embeddings and POS embeddings are passed to two different bi-directional LSTM layers (i.e., Word LSTM and POS LSTM), and they yield a sequence of word hidden state vectors \(\{h_{1}^{w},h_{2}^{w},...,h_{n}^{w}\}\) and a sequence of POS hidden state vectors \(\{h_{1}^{p},h_{2}^{p},...,h_{n}^{p}\}\), where \(h_{i}\in\mathbb{R}^{k}\) is a concatenation of hidden states of forward and backward LSTM at \(t\)-th word.  
Figure 2: The framework of the proposed model. Word and POS sequences are firstly embedded by embedding layers, and the embedding vectors are processed by two BiLSTM models. Multi-layered GCN and attention mechanism incorporate syntactic patterns and POS tags to generate final representation.
# Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network
## 3 Performance Evaluation  
In this section, we derive the closed-from expressions for the key performance merits, i.e., outage probability [20] and ergodic capacity [21] of the considered full-duplex satellite relaying systems. Before delving into the detailed analysis, the satellite channel is firstly presented in what follows.
# Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model
## 4 Hybrid Algorithm and Optimization Process  
### Optimization Algorithm Design  
This paper proposes a hybrid algorithm for solving the global optimal solution. The algorithm selects the optimal space filling design (OSF) [26] experimental design method to obtain the global optimal point in the nonlinear optimization space, and adopts MMFD [27] to search and optimize the local in detail based on the Kriging approximate model [28]. Figure 4 is the hybrid algorithm optimization flow chart. The primary flow can be summarized as follows:  
1. In the design space, the space-filling sample points are generated by OSF.
2. According to the sampling point, the CFD software Fluent is used to simulate the model with different structural parameters. Based on numerical simulation, the annular jet pump efficiency is evaluated.
3. The Kriging approximate model is constructed by determining the type and parameters of the regression model. The structural parameters of the annular jet pump obtained at the sampling point in step one are input variables, and the efficiency \(\eta\) of the jet pump obtained in step two is the output variable.
4. The Kriging approximate model constructed in step three is checked by analyzing the errors of the simulated values and the predicted values: if the error meets the requirements, transfer to step five; otherwise, transfer to step three and continue to update the Kriging model.
5. Based on the Kriging approximate model, the MMFD is used to obtain the structural parameters optimal solution in the annular jet pump;
6. According to the design parameters of the optimal solution, a physical prototype is generated. Then the CFD simulation and optimization results of the physical prototype are verified with each other.  
Figure 3: Experiment and simulation data. CFD: computational fluid dynamics.
# Finite Elements for Higher Order Steel-Concrete Composite Beams
## 3 Proposed Higher Order Finite Elements  
As already mentioned, the closed form solution proposed in [PERSON] et al. [30] suffers for numerical instability problems due to the computation of the exponential matrix in the case of long beams; in this case, a discretization of the beam axis is necessary to consider beam sections of reduced lengths. Furthermore, a closed form approach requires the computation of the particular solution, which depends on the specific load distribution. Finally, the beam discretization is required in the case of beams partially loaded along the axis, or in the case of beams with varying cross section properties. Consequently, a finite element approach for the problem solution, through numerical integration of the equilibrium condition in weak form (17), appears convenient considering its versatility to manage the above issues.  
By dividing the beam axis into \(n_{e}\) finite elements, the displacement \(\mathbf{d}(\eta)\) within the generic element may be obtained by  
\[\mathbf{d}(\eta)\cong\mathbf{N}_{e}(\eta)\mathbf{v}_{e} \tag{40}\]  
where \(\eta\) is the local abscissa, \(\mathbf{v}_{e}\) is the vector of the unknown nodal displacements, and \(\mathbf{N}_{e}(\eta)\) is a matrix containing known interpolating functions.  
Taking into account Equation (40), the equilibrium condition (17) leads to the following global equilibrium system obtained by assembling contributions of all elements:  
\[\mathbf{K}\mathbf{v}=\mathbf{f}_{p}+\mathbf{f}_{\overline{n}} \tag{41}\]  
where \(\mathbf{v}\) is the vector of nodal displacements of the whole model, suitably assembled from \(\mathbf{v}_{e}\) of all the elements. In Equation (41),  
\[\mathbf{K}=\overset{n_{e}}{\underset{e=1}{\wedge}}\int_{l_{e}}(\mathcal{D}\mathbf{N} )_{e}^{T}\mathbf{K}\mathcal{D}\mathbf{N}_{e}\ d\eta \tag{42}\]  
is the global stiffness matrix of the composite beam and  
\[\mathbf{f}_{p}=\overset{n_{e}}{\underset{e=1}{\wedge}}\int_{l_{e}}\mathbf{N}_{e}^{T} \mathbf{p}\ d\eta\hskip 28.452756 pt\mathbf{f}_{\overline{n}}=\overset{n_{e}}{ \underset{e=1}{\wedge}}\int_{l_{e}}(\mathcal{D}\mathbf{N})_{e}^{T}\ \overline{n}\ d\eta \tag{43}\]  
are nodal force vectors due to external loads and stress-independent strains, respectively, obtained by assembling the relevant matrices and vectors defined for the single finite element.
## 2 Materials and Methods
### Spxy Algorithm  
The Spxy algorithm, developed from the Kennard-Stone (KS) algorithm, is a statistically-based sample partitioning method widely used in spectral data partitioning [41; 42]. Compared to the KS algorithm, it can simultaneously consider two variables--in this case, spectral reflectance and SOM content. By using these two variables to calculate the Euclidean distance, the Spxy algorithm ensures the maximum retention of sample distribution, effectively covering the multidimensional vector space to enhance model stability. The distance calculation formula is given by Equations (3)-(5).  
\[d_{x}(p,q)=\sqrt{\sum_{j=1}^{N}\left[x_{p}(j)-x_{q}(j)\right]^{2}},(p,q\in[1,N]) \tag{3}\]  
\[d_{y}(p,q)=\left|y_{p}-y_{q}\right|,(p,q\in[1,N]) \tag{4}\]  
\[d_{xy}(p,q)=\frac{d_{x}(p,q)}{maxd_{x}(p,q)}-\frac{d_{y}(p,q)}{maxd_{y}(p,q)},(p,q\in[1,N]) \tag{5}\]  
where \(j\) represents the bands in the spectrum, and \(x_{p}(j)\) and \(x_{q}(j)\) denote the spectral reflectance of samples p and q at band \(j\), respectively. \(N\) is the total number of samples. \(d_{x}(p,q)\) represents the Euclidean distance between two samples in the spectral feature space. \(d_{xy}(p,q)\) is an upgraded version of \(d_{x}(p,q)\) in the KS algorithm, ensuring that the sample data have equal weights in both the \(x\) and \(y\) spaces.
# Iterative Learning Control for Vibration Suppression of a Robotic Arm
## 1 Introduction
ILC can achieve good compensation for repetitive error [24; 25], while the non-repetitive part limits the achievable performance of ILC. Motivated by the need to control and suppress the vibration for a robotic arm, this paper proposes a VMD-HHT iterative learning control for the elastic joint (VH-ILC-EJ) method. This method utilizes ILC and considers the elastic-joint dynamics of a robotic arm with the inputs of the feature extraction of a vibration signal analysis by integrating VMD and the HHT. The remainder of the paper offers the following:  
* A forward elastic-joint dynamics model is formulated, and the recursive Newton-Euler method is utilized to solve the inverse model. The controller design involves elastic-torque feedback and input-based iterative learning control. Integrating the HHT and VMD is used to extract the features of the vibration signals, and the features are fed into the controller for vibration suppression. * The VH-ILC-EJ method is numerically validated by a simulation of a laboratory-developed six-degree-of-freedom (6-DOF) robotic arm, and the VH-ILC-EJ method is experimentally evaluated via its implementation in the laboratory-developed 6-DOF robotic arm.
# Measuring the Centrality of DNS Infrastructure in the Wild
###### Abstract  
The centralization of the global DNS ecosystem may accelerate the creation of an oligopoly market, thereby, increasing the risk of a single point of failure and network traffic manipulation. Earlier studies have revealed the level of centralization in terms of the market share of public DNS services and DNS traffic seen by major CDN providers. However, the level of centralization in the infrastructure of the DNS Ecosystem is not well understood. In this paper, we present a novel and lightweight measurement approach that effectively discovers resolver pools from a single probing point. We conduct an Internet-wide active measurement on the client-side as well as the server-side DNS infrastructure to assess the level of DNS centralization in terms of the supporting infrastructure. Our measurement results show that the DNS infrastructure is much more centralized than previously believed. Over 90% of forwarding resolvers are backed by less than 5% (4071) of indirect resolvers. Merely 0.45% (12,679) of all name servers across 1138 gTLDs, operated by just 10 DNS providers, provide authoritative domain resolution service for 48.5% (more than 100 million) of domain names. We also investigated several leading DNS providers in IP infrastructure, load distribution, and service geo-distribution. The findings of our measurements provide novel insights into the centrality of the DNS infrastructure, which will help the Internet community promote the understanding of the DNS ecosystem.  
Keywords:internet centrality; DNS infrastructure; active measurement; infrastructure structure +
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