esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed DEIDV-Hop Algorithm
### Process of Calculating the Average Distance per Hop  
From the traditional method on the unbiased estimation criterion to calculate the average distance of per-hop \(HopSize_{i}\), let the value of the Equation (13) be zero.  
\[f_{1}=\frac{1}{m-1}\sum_{i\
eq j}^{m}\left(d_{i,j}-HopSize_{i}\cdot h_{i,j} \right) \tag{13}\]  
The minimum mean square error criterion is used to calculate the average distance per hop, and calculated by minimizing the Equation (14).  
\[f_{2}=\frac{1}{m-1}\sum_{i\
eq j}^{m}\left(d_{i,j}-HopSize_{i}^{\prime}\cdot h _{i,j}\right)^{2} \tag{14}\]  
Let \(\frac{\partial f_{2}}{\partial H\partial S\partial z_{i}^{\prime}}=0\), the estimated average distance per hop based on the minimum mean square error criterion can be obtained as:  
\[HopSize_{i}^{\prime}=\frac{\sum\limits_{i\
eq j}^{m}h_{i,j}d_{i,j}}{\sum \limits_{i\
eq j}^{m}h_{i,j}^{2}} \tag{15}\]  
The average distance per hop is determined through Equation (16):  
\[HopSize^{\prime}=\frac{\sum_{i=1}^{m}HopSize_{i}^{\prime}}{m} \tag{16}\]  
After calculating \(HopSize^{\prime}\), each anchor node broadcasts its average distance per hop \(HopSize^{\prime}\) in the system through controlled flooding. When the unknown node \(u\) retrieves information from the anchor node \(i\), it uses Equation (17) to determine the distance between the unknown node \(u\) and anchor node \(i\).  
\[d_{u,i}^{\prime}=HopSize^{\prime\prime}\times h_{u,i} \tag{17}\]  
Among them, \((x_{u},y_{u})\) is the coordinate of the unknown node, \((x_{i},y_{i})\) is the coordinate of the anchor node, and \(d^{\prime}_{u,i}\) is the estimated distance between the unknown node \(u\) and the anchor node \(i\).  
According to the aim function \(f(x_{u},y_{u})\), the fitness function is expressed as:  
\[\begin{array}{l}\mathit{fitness}(x_{u},y_{u})\\ =\sum\limits_{u=1}^{n}{\left(\frac{1}{h_{u,i}}\right)^{2}}f(x_{u},y_{u})\\ =\sum\limits_{u=1}^{n}{\left(\frac{1}{h_{u,i}}\right)^{2}}\left(\sum\limits_{ i=1}^{m}{\left|\sqrt{\left(x_{u}-x_{i}\right)^{2}+\left(y_{u}-y_{i}\right)^{2}}-d^{ \prime}_{u,i}\right|\right)}\end{array} \tag{21}\]
## 3 Results and Discussion
### Characterization of AuNPs  
#### 3.2.1 Morphological Observation--TEM  
The shape, size, and aggregation of _Z. officiale_-mediated green synthesis of AuNPs was investigated by TEM. The active metabolites from ginger aqueous extract have the potential to form spherical nanoparticles, with well-dispersed nano-sized objects in the range of 5-53 nm (average size: 15.11 \(\pm\) 8.5 nm, Figure 2A,B). This is of the same order of magnitude as the gold nanoparticles synthesized by the mycelium of _Mucor plumbus_ (i.e., 13-25 nm; average size: 17 \(\pm\)4 nm, [54]). Consistent with this study, [PERSON] et al. [55] reported that the size of AuNPs fabricated by leaf aqueous extract of _Dillenia indica_ was between 5 and 50 nm, whereas the average particle sizes of AuNPs synthesized by aqueous extract of _Garcinia mangostana_ peel and _Mentha longifolia_ leaf were 32.9 \(\pm\) 5.3 nm and 36.4 nm, respectively [39; 56]. The variation in average particle sizes could be attributed to metabolites that varied between the plants used as reducing, capping, and stabilizing agents. [PERSON] [57] compiled the characteristics of a series of AuNPs prepared by plant-extract-mediated synthesis. Most of the NPs were spherical, with a large variety in size (from 10 to 120 nm) depending on the plant; some examples of cubic, hexagonal, or triangular structures have also been reported. The biological activities of synthesized nanomaterials depend on various factors, such as size, aggregation, shape, surface area, and surface charge [58]. For instance, glucose oxidation by gold nanoparticles was shape-dependent; cubic and spherical shapes were most active, followed by rods and polyhedrons [59]. The activity of nanomaterials is usually size-dependent; (a) smaller particles are more active than large ones, and (b) a large surface area is more favorable for biological and chemical reactivity. For instance, selenium nanoparticles fabricated by an aqueous extract of garlic displayed higher antimicrobial activity at a size of 21-40 nm than larger sizes (in the range of 41-50 nm) [60]. Therefore, the detection of size, shape, surface charge, and aggregation of synthesized NPs is a crucial step to understand their activity.
## 5 Conclusions  
A compact 4-DOF LD-based system was designed for measuring the straightness errors and the angular errors of machine tools. In order to reduce the size of the measurement system and realize the on-line measurement, a laser diode (LD) was adopted as the laser source of the measurement system. An active method for shaping the laser spot of the LD without adding additional optical elements is proposed to reduce the measurement error caused by the elliptical laser spot of the LD. In addition, the measurement errors induced by the linear drift and the angular drift of the LD were actively compensated by an improved active error compensator. The stabilities of the horizontal and vertical straightness signals were, respectively, improved from 2.91 \(\upmu\)m and 4.67 \(\upmu\)m to 0.88 \(\upmu\)m and 1.02 \(\upmu\)m, and the stabilities of the pitch and yaw signals were, respectively, improved from 1.35 arcsec and 1.92 arcsec to 0.11 arcsec and 0.17 arcsec when the distance between the sensor head and the detector was set to be 1500 mm. The measurement accuracy of the straightness errors and the angular errors were evaluated to be within \(\pm\)1.1 \(\upmu\)m and \(\pm\)2.0 arcsec, respectively, in the measurement distance up to 1500 mm. Therefore, the effectiveness of the proposed methods and the capability of the constructed LD-based measurement system were verified. The analyses of the uncertainty and systematic error will be carried out in the future work.  
Conceptualization, Y.C. and K.F.; methodology, Y.C. and K.F.; software, K.Y. and Q.F.; validation, Y.C., Y.G. and K.Y.; formal analysis, Y.C., Y.G. and K.Y.; investigation, Y.G. and K.Y.; resources, Y.G., Y.C. and K.F.; data curation, Q.F. and K.Y.; writing--original draft preparation, Y.C.; writing--review and editing, K.F.; visualization, Y.C. and K.F.; supervision, Y.C. and K.F.; project administration, Y.C. and K.F.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (51905078), the National Key Research and Development Program of China (2018 YFB2001400) and Fundamental Research Funds for the Central Universities (DUT19 RC(4)007).  
Not applicable.  
The data presented in this research are available on request from the corresponding author.
## 5 Wing Component Structure Design Optimization Process
### Wing Skin Thickness Size Optimization  
A baseline wing for both segments had a skin thickness of 2 mm for its optimization process. Size optimization (SO) was conducted in a 2D environment, and then the results were extended to a 3D environment, and they were further chosen to design the best possible wing skin [44]. In fact, it is known that SO is an iterative process. Wing skin optimization was performed using the aerodynamic loads calculated with the CFD solver and the XFLR5 code at the maximum speed and full wingspan extension. A 20 mm tetra quadrilateral element size meshing was applied on both segments, as shown in Figure 5.  
The aerodynamic loads were applied, and the design variables were considered as scalar parameters. These parameters were defined for the optimization problem in this investigation because of the fact that the wing skin thickness influences the system responses. The optimization responses were defined for two variables, which were the design volume and the selected stress. The objective function concerned the minimization of the structural mass of the wing skin. The design constraints were determined in this investigation as the lower and upper bounds of the maximum stress values, which was less than 200 MPa due to the stress response based on the design variable. The design variable was defined with an initial value of 2 mm, with a lower bound of 0.5 mm and an upper bound of 2 mm [13; 45]. The optimization problem was formulated as follows:  
\[\begin{array}{l}\min M\\ \sigma_{max}=200\text{ MPa}\\ 0.5\leq T\leq 2\end{array} \tag{14}\]
# Congestion Evaluation of Pedestrians in Metro Stations Based on Normal-Cloud Theory
## 4 Methodology
### Crowd Management Measures  
According to previous literature review [38; 39], four basic strategies for crowd management exist: increasing throughput, preventing blockades, distributing traffic, and limiting inflow. Additionally, this basic strategy framework is widely used in the categorization of crowd management measures. Hence, the crowd management measures in this study were proposed following the four basic strategies proposed by [PERSON] and [PERSON] [38; 39] and NC model results.  
The first category of strategies is to increase the throughput by identifying and possibly adapting the key bottlenecks in the infrastructure of metro station. The available measures are as follows: (a) separating bidirectional flows on path (or using safety barriers, isolation barriers to separate bidirectional flows); (b) creating funnel-shaped corridors; (c) creating two doors; (d) placing the object in front of bottleneck; (e) marking on the floor; (f) using handrail on stairs; (g) having fast and slow walking lanes; (h) having escalator directions; (i) changing the boarding and alighting procedures, and so on.  
The second category of strategies is to prevent blockades, such as (a) using direction gates on platforms, (b) creating waiting locations (or buffer area at the bottleneck), and (c) relocating gates, kiosks, and machines. Measure (a) can be applied to force people to use a particular exit. Measure (b) can be employed by dedicating some waiting location in which interference between flows do not exist, to some extent, it will reduce walking space. Since it will involve the issue of interests, measure (c) may be complicated because the arrival of metro is crucial for using the revenues of the gates, kiosks, and machines.  
The obtainable infrastructure should be fully and efficiently utilized. The third category of strategies is to guide the equilibrium distribution of traffic flow in facilities, such as (a) providing information about the stop position of trains; (b) increased attractiveness of route; (c) clear indication of less-used exits; (d) train stopping position adjustment, (e) walking time information provision, (f) manual intervention, and (g) separation of arrival tracks.  
The fourth category of strategy is to limit the inflow so as to ensure that the number of pedestrians (passenger flow) remains below the critical density in the metro station. It can be done by deployingone-way brakes, temporally closing stations, or changing the schedule of metros. The gating control method can be employed temporarily and in urgent events with rush-hour demands. An example of the application of gating control approach to limit the inflow is modelled for a case in Beijing metro station, China [31]. However, the gating control measure will have some negative effects, such as reducing passenger comfort and increasing their travel time.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 5 Experimental Results  
In this section, we conduct experiments on practical semantic sources and demonstrate the simulation results for our semantic communication system for generation and classification tasks.
## 3 ESCB Routing Scheme
The boundary conditions of optimization are described as:  
\[Min[BW_{link(i)}]\geq BW_{restriction} \tag{17}\]  
\[Max[Delay_{link(i)}]\leq Delay_{restriction} \tag{18}\]  
where \(BW_{restriction}\) and \(Delay_{restriction}\) are restrictions for a specific service. In this algorithm, congestion state can be calculated by Equations (6)-(9); energy state can be calculated by Equations (10) and (11); latency state can be calculated by Equations (12)-(14). A taboo list is made by Equations (6), (7), (17) and (18). Defining Equation (16) as the optimization goal, NTSN can be traversed to find the optimal routing path. During actual operation, all node parameters are propagated through SIP signaling It broadcasts the status of the local node to neighbors at a fixed period. In addition, each node calculates the validity period of the neighbor information to perceive the status of the other node. The algorithm needs to traverse all the node information in the initialization phase. Once the status table is generated, the neighbor node status can be updated through the SIP signaling. Distributed routing computing only needs to be performed on the nodes whose status is updated, and do not need to traverse all the nodes every time. In order to reduce the computing burden of the nodes, the initial computing can be conducted by the ground station. Subsequent information updates can be conducted by each node individually. The main algorithm runs as follows in Figure 8.  
Figure 8: The pseudo code of path finding algorithm. Through the above scheme, the optimal solution and the sub-optimal solution can be found simultaneously as multiple paths. The specific algorithm takes into account both the congestion and energy balance together. The network performance can be greatly improved by it.
# An Overview of Nanofiltration and Nanoadsorption Technologies to Emerging Pollutants Treatment
## 3 Nanotechnological Approaches to the Treatment of Emerging Pollutants
### Nanoadsorbents  
Adsorption is one of the most suitable techniques for removing pollutants from water [54,55]. The interest in this method is due to the relative simplicity of operation, mild conditions for operation, such as room temperature, capacity to remove pollutants in low concentrations (micrograms), cost-energy efficiency, and low impact on the environment, in addition to the possibility to reuse and for the regeneration of the adsorbent [56,57]. Adsorption is a mass transfer unit operation in which a substance is transferred to the surface of a solid through physical or chemical interactions. The surface on which accumulation occurs is called an adsorbent. Adsorbate is a substance that adheres to the surface [54].  
Nanomaterials have characteristics of interest for application as adsorbents in the remediation of water contaminated with emerging contaminants. Nanoparticles require lower contact time, faster removal, and lower dosages [54]. Besides their small size, these materials have a high surface area, high reactivity, catalytic potential, and a large number of active sites, enabling the adsorption of several substances. Furthermore, functional groups incorporation improves the adsorption capacity of nanomaterials. There is an increase in the surface area, reactivity, affinity, and selectivity toward the contaminant of interest [54,58,59]. Adsorption capacity is also related to factors such as the nature and size of the contaminant; pH, temperature, and ionic strength of the solution; solid-liquid balance; contact time; and mass transfer rate [54]. The most widely studied nanomaterials as adsorbents are carbon-based nanoadsorbents and metal-based nanoadsorbents.  
Technological applications involving nanoadsorbents have been developed and patented. Patent US10143972B2 [60] describes the development of an ultrafiltration membrane comprising a sulfone polymer membrane and an organic polymer sealing layer, in which the pores are filled with nanoadsorbents. Nanoadsorbents are hollow porous nanoparticles of hydrated zirconium oxide. Patent US10245576B1 [61] describes a method for developing a magnetic polymeric nanocomposite aimed at removing bivalent heavy metals such as Pb (II) and Cd (II). Patent CN114471445A [62] describes a process for producing WO3@NiCoP nanoadsorbent for specific removal of tetracycline from water.
## 2 Materials and Methods
### Chlorination Reaction
#### 2.2.2 Chlorination Experiments  
The conditions used in a typical WWTP were simulated by treating a 10\({}^{-5}\) M OLM solution with 10% hypochlorite (1:1 molar ratio of OLM/HClO by concentration, spectroscopically determined at \(\uplambda_{\rm max}\) 292 nm, \(\varepsilon\) 350 dm\({}^{3}\)/mol cm) and 10\({}^{-3}\) M OLM solutions with 10% hypochlorite (1:5 and 1:6 molar ratio of OLM/HClO by concentration, spectroscopically determined at \(\uplambda_{\rm max}\) 292 nm, \(\varepsilon\) 350 dm\({}^{3}\)/mol cm) for 1 h at room temperature [25]. The presence of OLM was quantified using the UV-Vis spectrophotometer. The absorbance peaks were evaluated at 230 nm. Using standard solutions with known OLM concentrations it is possible to construct a calibration curve; then OLM concentrations were calculated from absorbance peaks by interpolating the points on the corresponding calibration curve. The pH of the solution is measured with the pH-meter; it passes from the initial value of 8.0 to the final value of 10.5. A small amount of the chlorinated solution was quenched by an excess of sodium thiosulphate, filtered, dried by lyophilization; then the residue was solubilized in a saturated sodium bicarbonate solution and extracted with ethyl acetate (EA). HPLC was used to monitor the course of the reaction. The main degradation byproducts (DP10-DP17 for the EA fraction and DP18-DP22 for the aqueous fraction (W); Scheme 1 and Figure 2) were identified by comparing them with commercial reference compounds, or isolated through preparative experiments with an OLM solution at concentration above 10\({}^{-3}\) M, using 6% hypochlorite at room temperature for 2 h. The proposed mechanism of their formation and all other DPs previously isolated from the OLM is shown in Figure 3.  
Figure 3: The plausible mechanism for the formation of DP1–DP17.  
Figure 2: Chemical structures of olmesartan acid and its degradation byproducts DP10–DP22.
# 3D Skeletal Joints-Based Hand Gesture Spotting and Classification
###### Abstract  
This paper presents a novel approach to continuous dynamic hand gesture recognition. Our approach contains two main modules: gesture spotting and gesture classification. Firstly, the gesture spotting module pre-segments the video sequence with continuous gestures into isolated gestures. Secondly, the gesture classification module identifies the segmented gestures. In the gesture spotting module, the motion of the hand palm and fingers are fed into the Bidirectional Long Short-Term Memory (Bi-LSTM) network for gesture spotting. In the gesture classification module, three residual 3D Convolution Neural Networks based on ResNet architectures (3D_ResNet) and one Long Short-Term Memory (LSTM) network are combined to efficiently utilize the multiple data channels such as RGB, Optical Flow, Depth, and 3D positions of key joints. The promising performance of our approach is obtained through experiments conducted on three public datasets--Chalearn LAP ConGD dataset, 20 BN-Jester, and NVIDIA Dynamic Hand gesture Dataset. Our approach outperforms the state-of-the-art methods on the Chalearm LAP ConGD dataset.
## 2 Fundamentals Revisit
### Deep Canonical Correlation Analysis  
DCCA is an advanced method that extends Canonical Correlation Analysis (CCA) by incorporating deep neural networks to learn nonlinear transformations of two data views, maximizing their correlation, as illustrated in Figure 2. Traditional CCA identifies linear projections that maximize the correlation between two random vectors. DCCA addresses these limitations by learning flexible nonlinear mappings through deep networks. It jointly trains two separate deep networks, one for each data view, to maximize the correlation of their output representations.  
The objective of DCCA is to maximize the total correlation between the transformed data views. Given two views, X and Y, their respective representations \(f(x)\) and \(g(y)\) are obtained through deep networks. The correlation objective and its associated constraints have been formally defined in Equations (1) and (2).  
The parameters involved in Equations (1) and (2) are defined as follows:  
\[\sum_{xx}=\frac{1}{N}f(x)f(x)^{T}+rI \tag{7}\]  
\[\sum_{yy}=\frac{1}{N}g(y)g(y)^{T}+sI \tag{8}\]  
\[\sum_{xy}=\frac{1}{N}f(x)g(y)^{T} \tag{9}\]  
Here, \(\sum_{xx}\) and \(\sum_{yy}\) are the regularized covariance matrices of the transformed data \(f(x)\) and \(g(y)\), respectively, with \(r\) and \(s\) being regularization parameters to ensure numerical stability. \(\sum_{xy}\) represents the cross-covariance matrix between the two views.  
By incorporating regularized covariance and cross-covariance matrices, these equations ensure that Deep CCA can stably and efficiently maximize the correlation between the learned representations \(f(x)\) and \(g(y)\).
## 5 Experimental Results
### Seismic Effectiveness
However, considering the acceleration peaks, \(J_{1}\), \(J_{2}\), and \(J_{3}\), the reductions observed are generally lower, with percentages of reductions in the range of 25-75%. Instead, the displacement and the base shear and base moment show more reductions compared to accelerations that decrease with the PGA. The same responses evaluated in terms of RMS (Figure 15d) show a percentage of reduction that is almost constant with the PGA, especially for displacement, base shear, and base moment, which have reductions around 80%. Instead, accelerations are mostly reduced at higher floors, with reductions that decrease with the PGA. Figure 15: _Cont._  
Considering the far-field earthquakes, in Figure 16a,b the response indices in terms of peak and RMS values are depicted for El Centro. The acceleration peak values, \(J_{1}\), \(J_{2}\), and \(J_{3}\), do not have the same trend with the PGA varying the floor: the reductions oscillate from a minimum of 50% to a maximum of almost 75%. Instead, displacement, base shear, and base moment have modest variations with the PGA, oscillating with reductions around the 70-75%. By looking at indices in terms of RMS (Figure 16b), it can be seen that \(I_{1}\), \(I_{4}\), \(I_{5}\), and \(I_{6}\) are almost constant with the PGA, with reductions of around 60% for the first floor acceleration and around 80% for the displacement, base shear, and base moment. Instead, the second and third floor acceleration, \(I_{2}\), and \(I_{3}\), have almost the same reductions, decreasing with the PGA from almost 85% to 65%. Figure 16c,d depict the response indices in terms of pack and RMS values for the Hachinohe earthquake. Generally, considering the peak indices (Figure 16c), as observed for El Centro, for the accelerations \(J_{1}\), \(J_{2}\), and \(J_{3}\), the trend of reduction is not regular with the PGA and the floor considered, with reductions from 40-55%. Considering the displacement, \(J_{4}\) modestly varies with the PGA, with reduction from 55% to almost 70%. The base shear, \(J_{5}\), is constant, with a reduction of almost 70%, whereas the base moment, \(J_{6}\), slowly decreases with the PGA, with reductions around 70-75%. The same indices considering the RMS values (Figure 16d) instead have an almost constant trend with the PGA, with reductions of 55-70% for floor accelerations, \(I_{1}\), \(I_{2}\), and \(I_{3}\), and around 80% for displacement, base shear, and base moment (\(I_{4}\), \(I_{5}\), and \(I_{6}\)).