esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antioxidant Activity  
The biocompatibility of biomaterials is limited by oxidative stress that results in inflammation, chronic diseases and other problems. Hence, there is a need to design a material which possesses antioxidant properties in order to increase biocompatibility. DPPH free radical scavenging assay method was used to determine the antioxidant activity of CuFe\({}_{2}\)O\({}_{4}\) and ZnFe\({}_{2}\)O\({}_{4}\) nanoparticles. It is evident from the results that both CuFe\({}_{2}\)O\({}_{4}\) and ZnFe\({}_{2}\)O\({}_{4}\) nanoparticles exhibited potent antioxidant activity at different concentrations (Figure 5). However, between CuFe\({}_{2}\)O\({}_{4}\) and ZnFe\({}_{2}\)O\({}_{4}\), no significant difference was observed for antioxidant properties tested at all concentrations. However, free radical scavenging activities of both nanoparticles were inferior to the standard (ascorbic acid). The highest scavenging activity was observed in ascorbic acid (58.11% \(\pm\) 1.52%) followed by ZnFe\({}_{2}\)O\({}_{4}\) nanoparticles (30.57%) and CuFe\({}_{2}\)O\({}_{4}\) nanoparticles (28.69% \(\pm\) 1.14%) at 125 ug/mL concentration. With increased  
Figure 4: (**a**) Hysteresis loop of CuFe\({}_{2}\)O\({}_{4}\) nanopowder at room temperature; (**b**) Hysteresis loop of ZnFe\({}_{2}\)O\({}_{4}\) nanopowder at room temperature.  
doses of nanoparticles, the antioxidant activity was also increased correspondingly. This antioxidant activity could be linked to the transfer of free electrons from the oxygen atom of nanoparticles to free radicals present at the nitrogen atom of DPPH molecules. It has been reported that many of the metal nanoparticles can scavene free radicals and act as antioxidants [49]. Moreover, a study by [PERSON] et al. (2013) [36] reported that the antioxidant activity is mainly due to high surface to volume ratio of the nanostructures. Similarly, ZnO nanoparticles have shown to possess 91% scavenging activity at 250 mg/mL concentration [50]. In another study, CuO nanoparticles at 120 mg/mL concentration showed 85% DPPH quenching activity [36]. Also, CoFe\({}_{2}\)O\({}_{4}\), Fe\({}_{2}\)O\({}_{4}\) and NiO nanoparticles were shown to exhibit relatively good antioxidant characteristics compared to their bulk materials [51; 52; 53]. However, systematic studies on the antioxidant properties of ferrite nanoparticles are not well documented. The result of our study is promising and provides a lead in the exploration of CuFe\({}_{2}\)O\({}_{4}\) and ZnFe\({}_{2}\)O\({}_{4}\) nanoparticles as a new source of antioxidants.
## 4 Simulation and Verification
### Duisburg Simulation with Intercity Traffic  
In this section, intercity traffic is added to the traffic demand. As the road network remains the same as in the last section, some trips' departure and arrival roads/edges are out of the network. Under this circumstance, these departure or arrival roads/edges are moved to the closest edge on the network. For example, if a trip starts from Duisburg center to Duiseldorf center, the road network does not contain the city of Dusseldorf, but the trip remains as a traffic demand. Here we change the destination from Dusseldorf center to the road/edge nearest Dusseldorf center in city Duisburg. To achieve this purpose, we redefined the TAZ out of city Duisburg and distributed the outer city TAZ onto different roads/edges of interior city areas.  
The simulation results show that traffic jams are more likely to occur on the streets/edges at the city limits. The reason could be, that the outer city traffic puts extra pressure on the  
Figure 3: Map (Source: HERE map), road network, and traffic demand partition of city Duisburg.  
boundary of the city. Another reason could be that, if a trip has its departure and arrival road/edge both out of the city, originally there is a chance to finish this trip by staying out of the city. However, if we link all the outer city TAZ with its nearest road/edge (Case B), all trips with both departure and arrival road/edge outer the city must come onto the road network. It generates unrealistic traffic jams for the simulation, especially on the boundary of the city, where lots of TAZ are linked. To solve this problem, in Case C, we remove all the trips with both departure and arrival road/edge out of the city. Traffic interior the city, traffic of incoming and outgoing city Duisburg are retained in the simulation. The simulation result this time no longer has the problem of a crowded city boundary for Case C. However, in this case, the trips that would originally pass the city are also ignored in the simulation. This circumstance assumes that all the trips that have both departure and arrival road/edge out of the city do not use the road network interior of the city. Detailed simulation results will be compared with other cases and real traffic data in the following sections.
# Design of Time-Synchronized Switch Based on Zynq
## 2 WR Technology  
WR combines three mature technologies: synchronous Ethernet (SyncE) [2], Precision Time Protocol (PTP), and Digital Dual Mixer Time Difference (DDMTD) [13; 14]. SyncEensures that all clocks within the network operate at a common frequency [15]. PTP is responsible for achieving coarse synchronization of the clock phase information, while DDMTD measures finer clock phase differences to ultimately achieve clock synchronization across all network nodes [16], allowing the system's synchronization accuracy to reach sub-nanosecond levels.
## 3 Current WSNs Designed for Do nana
### ARTICA 1 Network  
ARTICA 1 was our initial prototype. It was designed in 2008, and it was based on a TelosB platform. Figure 3 shows a node of the ARTICA 1 network. These nodes are powered by small solar panels and are designed to obtain the following measurements:  
* Temperature semiconductor sensor in the -40-80 \(\,\)C range, calibrated to obtain an accuracy of 0.1 \(\,\)C in the 0-70 \(\,\)C temperature range.
* Relative humidity, with a semiconductor sensor that works in the 0-100% RH range.
* Solar radiation in visible light (in the [320 nm to 1,100 nm] wavelength range) and NIR (Near-InfraRed in the [320-730 nm] wavelength range) using photodiodes.  
These nodes were designed as a general device system that allows distributed and collaborative data processing over the network, using IEEE 802.15.4 2.4 GHz radio transceivers.  
The initial ARTICA 1 network was composed of four nodes that were spread over an area with a distance of up to 200 m between them. These nodes were designed to read the environmental monitoring information from the sensors every minute, sending the information to them. The maximum distance between nodes is 200 from the central server after its capture. These nodes are being designed to work up to a week in complete darkness. Figure 4 shows a graphical GUI of the environmental information obtained from this network, through the central server.  
To allow for reduced power consumption, it is necessary to use adapted communication protocols. Currently, efficient routing protocols with low power consumption continue being a main issue for WSNs; for example, see the recent review in [7]. Several routing solutions have been proposed in the literature [8], which are mainly focused on energy consumption [9,10], security issues [11,12] or fault-tolerant capacity [13].  
Figure 3: Node of the ARTICA 1 network.  
Currently, the ARTICA 1 network and the subsequent prototypes use a routing algorithm that is based on SOM (Self-Organizing Maps) [14], adding a synchronization mechanism that allows the hibernation of nodes, including the radio transceiver, most or a portion of the time. These networks are working with a sampling time of one measurement from each sensor per minute.
## 5 Conclusions  
The present study investigated the extraction, purification, and antioxidant activity of sulfated polysaccharides from _Sargassum lilifolium_. Fucose, galactose, xylose, and uronic acids were detected in the sulfated polysaccharide monosaccharide composition. The DPPH, ABTS, and reducing activity tests revealed the radical scavenging activity of the sulfated polysaccharide samples, indicating that they might function as potent antioxidants. The molecular docking experiment was carried out for anticancer and antioxidant target proteins. The protein NADPH oxidase showed the highest content among the antioxidant targets with the lowest binding score (\(-\)7.169 Kcal/mol). Similarly, the cellular tumor antigen p53 protein showed the highest content among the anticancer targets with the lowest binding score (\(-\)6.205 Kcal/mol). The highest inhibition of anticancer activity against in vitro HepG2 cells and IC\({}_{50}\) value was 71.33 \(\upmu\)g/mL. These findings report the _S. lilifolium_ biological roles of polysaccharides and develop potential anticancer drugs.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122413010/s1](https://www.mdpi.com/article/10.3390/app122413010/s1)]([https://www.mdpi.com/article/10.3390/app122413010/s1](https://www.mdpi.com/article/10.3390/app122413010/s1)), Table S1: Physico-chemical Property Solubility of fucoidan from _S. lililifolium_ in different solvents. Table S2: Anticancer activity of Fucoidan from _Sargassum lilifolium_. Table S3: The molecular docking experiment was carried out for the compound fucoidan against the target protein of antioxidant and anticancer studies. Their molecular interaction, bond length with glide scores were listed.  
This research article was produced as a collaboration between the authors. Conceptualization, A.M., W.-C.L., B.B. and R.B.; Writing original manuscript, A.L. and B.B.; Methodology, data curation, and formal analysis, A.L. and V.M.; Molecular docking, software, and revision, M.A. and R.B.; Review and editing, B.B., W.-C.L. and M.A.; Organizing the working groups, B.B.; Supervision and visualization, M.A.; Interpretation and review/revision, W.-C.L., R.B., B.B. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
All the authors are thankful to their respective Universities and Institutes for their support. The authors thank the International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, for computational studies using Schrodinger software.  
The authors declare no conflict of interest.
# Influence of Soil Particle Size on the Engineering Properties and Microstructure of a Red Clay
## 1 Introduction
Experimental and numerical simulation results manifest how the pore ratio varies nonlinearly when the binary mixture contains other fine particles, and a series of experimental results for the particle size ratio is presented. [PERSON] et al. [15] reported that the angle of internal friction and the maximum horizontal shear stress of uniform sands increase with increasing particle size, and the larger the gradation, the higher the shear strength. Furthermore, a theoretical method correlating particle size to the macroscopic response has also been proposed. [PERSON] et al. [16] studied the effect of particle size on hysteretic sediment transport. By assessing rainfall patterns, flow rates and sand content data were measured for seven particle size classes. It was found that the hysteresis rate varied with the particle size, decreasing when particles were more prominent than 50 \(\upmu\)m. [PERSON] et al. [17] conducted an experimental study on the shear strength characteristics of sandy soils with different particle sizes. They found that sandy soils with larger particle sizes have larger friction angles than smaller ones, determining a direct relationship between soil friction angle and soil particle characteristics. [PERSON] [18], [PERSON] et al. [19], and [PERSON] and [PERSON] [20] also reported that the particle size distribution of granular soils has a significant effect on their mechanical properties. The above research mainly studied the impact of particle size on soil mass, but few were concerned about the impact of red clay with different particle sizes on engineering characteristics. Therefore, this paper aims to determine how the engineering characteristics of red clay will change by varying particle sizes. The red clay in Guilin is chosen as the research material, and the selected particle sizes are 0.25 mm, 0.5 mm, 2 mm, and 5 mm. Multiple tests are conducted on red clay samples with different particles and dry densities, including unconfined compressive strength (UCS) tests, wetting-drying (WD) tests, and low-temperature nitrogen adsorption tests, to explore the impact of particle size on the engineering properties and microstructure of red clay.
# Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews
## 3 Model Architecture
### Gated Fusion Layer  
Although the mutual learning of different reviews at the review level and the word level can provide abundant interactive information to express user preferences and item features, there are also some user (item) reviews with few reviews or no review. Therefore, when review information is sparse, it is insufficient to use review information as a representation of users and items. To solve the above problem, we embed users and items to extract the user ID and item ID and use the ID embedding to enhance the user and item representation.  
Given ID embedding \(V_{u}\) of user \(u\), it is known that \(w_{u}\) comes from the review information of user \(u\); therefore, we integrate \(w_{u}\) and \(V_{u}\) to enhance the comprehensive performance of user \(u\). At present, some existing approaches use simple addition or concatenation operations to fuse nonhomologous features, which may not be able to effectively integrate ID embedding and review features. To extract richer information from ID embeddingand review features, a gated neural fusion layer is designed to integrate \(w_{u}\) and \(V_{u}\). The calculation formula of the gating layer is as follows:  
\[h_{u}=1-\sigma\big{(}\big{(}W_{g}w_{u}+b_{g}\big{)}\odot V_{u}\big{)} \tag{9}\]  
where \(\sigma\) is the sigmoid activation function, \(\odot\) denotes element multiplication, \(W_{g}\) denotes the parameter matrix, and \(b_{g}\) denotes the bias.  
Since review features contain rich semantic information, ID embedding contains the intrinsic features of users, and \(h_{u}\) contains the interaction information between the two, we perform a concatenation operation on \(w_{u}\), \(V_{u}\), and \(h_{u}\) to obtain the final representation of user \(u\). The description is as follows:  
\[o_{u}=h_{u}\oplus w_{u}\oplus V_{u} \tag{10}\]  
where \(\oplus\) is the join operator. For item \(v\), we can use the same method to obtain the final representation \(o_{v}\) of item \(v\).
## 3 Localised Information Retrieval with Query Resolution
### Query Resolution Mechanism
A sink proxy that will be used for localised query broadcasts of queries and local data collections is then selected according to the query resolution result. As show in Figure 2, there are two types of sink proxy selection schemes. One is fixed selection scheme, in which an identical sink proxy (such as node 9 in the Figure 2**A**) will be selected at different times for the queries (such as Q1, Q2, Q3 in Figure 2**A**) that have the same query resolution result; the other is dynamic selection scheme, in which various sink proxies will be selected at different times for the queries that have the same query resolution result, as shown in Figure 2 (B). As for the fixed selection scheme, a simple way adopted in this paper is to select a sink proxy that is the \"centre node\" of the query area. The centre node is defined as the node that is nearest the centre location of the query area. If the closest nodes are equidistant from the centre of the query area, the node with the smallest ID is chosen as the centre node. In Section 3.4 we discuss the dynamic sink proxy selection process in detail. Figure 2: Two Types of Sink Proxy Selections. Consequently, the sink proxy provides the node information at which the query message starts to be broadcasted and the query area gives a region in which a query message can be disseminated. According to the calculated query area and sink proxy, the distribution of a query message is performed by two steps: unicast distribution and geocast distribution. These are introduced in the following subsection.
## 6 Performance and Comparative Analysis
### Transmission Costs  
The communication cost refers to the cumulative computation of various transmissions that have traversed the communication channels during the phases of key agreement and authentication, which is typically quantified in bits. For ease of communication evaluation, we defined the size of different variables such as the timestamp as 32 bits, the random number as 128 bits, the identifier as 128 bits, and the hash value as 256 bits, respectively. Our proposed approach incurs a communication cost of \((128+256+128+128+256+256=1152)\) bits, spread across six messages. As illustrated in Figure 5, the comparative analysis of communication expenses is presented, contrasting our proposed method with alternative related strategies. The comparative assessment reveals that our suggested approach, which integrates the key exchange technique, experiences notably lower estimated communication overhead in comparison to various preexisting methodologies. Specifically, our proposed method achieves a reduction in communication costs of up to 32.55% when compared to the approach discussed in reference [42].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Schemes** & **Total Computation Cost** & **Estimated Time (ms)** \\ \hline
[38] & \(44T_{H}+15T_{\oplus}+127T_{\parallel}+4T_{ran}\) & 0.1236 \\
[39] & \(9T_{H}+27T_{\oplus}+15T_{\parallel}+4T_{ran}\) & 0.0853 \\
[40] & \(25T_{H}+38T_{\oplus}+28T_{\parallel}+2T_{ran}\) & 0.1101 \\
[42] & \(28T_{H}+13T_{\oplus}+38T_{\parallel}+2T_{ran}\) & 0.0656 \\
[44] & \(9T_{H}+17T_{\oplus}+26T_{\parallel}+5T_{ran}\) & 0.0749 \\
[45] & \(10T_{H}+27T_{\oplus}+17T_{\parallel}+2T_{ran}\) & 0.0762 \\ Proposed & \(4T_{H}+6T_{\oplus}+3T_{\parallel}+4T_{ran}+22T_{A}\) & 0.0448 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computation cost comparison.
## 5 Ficin Use as an Exfoliant Agent in Cosmetic Applications
### Chemical Properties and Structure of Ficin  
#### 5.3.1 Molecular Structure  
Ficin is a cysteine protease, also known as a sulfhydryl enzyme, which is characterized by its essential catalytic cysteine residue within its active site [25; 68]. This protease consists of a single polypeptide chain, and has a molecular mass of approximately 23-27 kDa, although this can slightly vary based on the source and determination method used [68; 89; 90]. Ficin is stabilized by three disulfide bonds which contribute to its overall structural stability [25; 90]. The N-terminal sequences of different ficin isoforms show high identity, with a core sequence that is homologous to other plant cysteine proteases [90]. Ficin's active site is characterized by the presence of a catalytically active cysteine residue and a histidine residue, which are both essential for its proteolytic function [25; 90]. The secondary structure of ficin consists of approximately 22% \(\alpha\)-helices and 26% \(\delta\)-sheets [89]. While ficin can interact with a variety of substrates, it demonstrates a preference for more basic amino acids at the P1 position, such as arginine or lysine, and aromatic residues at the P2 position [91]. Ficin's structural and functional properties align with other cysteine proteases that belong in the C1 papain family, and this enzyme shares many similarities with papain, mainly considering their substrate specificity and catalytic mechanism [89; 90].
## 3 Methods
### Feature Extraction
#### 3.2.3 Discrete Wavelet Transform (DWT)  
For a thorough and precise analysis of ECG signals, our study employed the discrete wavelet transform (DWT), a mathematical tool designed for multi-level signal analysis. This technique allows for the simultaneous investigation of a signal's behavior in both time and frequency domains. Through the DWT, the ECG signal undergoes a detailed decomposition, resulting in a range of wavelet coefficients that represent different frequency bands over specific time periods.  
The application of the DWT meticulously segments the ECG signal into a collection of wavelet coefficients, each of which captures unique frequency details at corresponding time intervals. These coefficients give us a deeper understanding of the time-frequency dynamics of the ECG signal, enabling the detection of specific patterns and variations indicative of various physiological events. Each set of coefficients is linked to a discrete wavelet scale and a particular time frame, offering invaluable insights into the oscillatory components of the ECG during that interval.  
The DWT operates by breaking down the signal into multiple frequency bands through wavelet filters, isolating lower frequency components that provide an approximation of the original signal, and higher frequency components that detail finer aspects. The process is inherently dyadic, meaning that with each successive decomposition level, the signal's frequency range is halved, providing a hierarchical analysis. In our research, we conducted a 12-level DWT in the ECG sub-band with an initial sampling rate of 2000 Hz using the _Daubechies 4_ wavelet. The computation of DWT and its inverse for a signal \(x(t)\) is defined by the subsequent equations.  
\[\text{DWT}x(a,b)=\frac{1}{\sqrt{a}}\int x(t)\psi\bigg{(}\frac{t-b}{a}\bigg{)}dt \tag{2}\]  
where \(\psi(t)\) is the wavelet function, \(a\) denotes the scaling factor, and \(b\) represents the translation factor [40].  
Figure 3: Sample HRV series.
## Appendix A Parsing and Resolving Hard Limits and Linked Equations
### Computing Linked Parameters  
Linked parameters are not randomly generated; rather, they are computed based on other patient variables via user-defined equations. This ensures logical consistency, deterministic relationships, and avoids redundant or inconsistent data. Characteristics of linked parameters are as follows:  
* Logical Consistency: Parameters such as BMI categories derived from continuous BMI values.
* Deterministic Relationships: Variables computed from known relationships (e.g., Heart Rate Variability dependent on Age and Physical Activity).  
* Avoidance of Redundancy: Derived risk scores dependent on multiple patient variables. Example Scenario BMI Category: if (BMI < 18.5, \"Underweight\", if (BMI < 25, \"Normal\", if (BMI < 30, \"Overweight\", \"Obese\"))) Hypertension Status: if (SBP > 140,1,0) Cardiovascular Risk Score: (Age
* 0.1) + (SBP
* 0.2) + (Diabetes
* 1.5) Steps to Resolve Linked Parameters for each linked parameter and each patient are as follows: 1. Substitution: Replace parameter names in the linking equation with actual patient-specific values. Example: Patient 1: Age = 60, SBP = 150, Diabetes = 1 Formula: (Age
* 0.1) + (SBP
* 0.2) + (Diabetes
* 1.5) Substituted: (60
* 0.1) + (150
* 0.2) + (1
2. Evaluation: Compute mathematical or logical operation. Result: 6 + 30 + 1.5 \(\rightarrow\) 37.5
3. Assignment: Assign the computed value to the linked parameter. This ensures that linked parameters are accurately derived, maintaining internal consistency and clinical plausibility within the synthetic patient cohort.