esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 NB-IoT Frame Structure
### NSSS Sequence  
The NSSS is constructed by multi-parameter frequency domain Zadoff-Chu sequences. The parameters contain cell ID, radio frame number and their derived parameters. There are 2016 different NSSS sequences in all, which makes the computation complexity of NB-IoT cell ID identification much higher than LTE. Equation (2) shows the formulation of NSSS,  
\[\begin{split} b(n)&=g_{q}(m)e^{-j2\pi\theta/n}e^{-j \frac{\pi m^{\prime}(n^{\prime}+1)}{131}}\\ n&=0,1,\ldots,131.\end{split} \tag{2}\]  
Table 2 describes the derivation of the parameters in the above equation. \(r_{f}\) represents the frame number. Because NSSS can only be mapped on even frames, the frame number needs to fulfil \(r_{f}\bmod 2=0\). Furthermore, \(N_{ID}^{Ncell}\) denotes the cell ID. As NB-IoT is a multi-cell communication system, the total number of cell IDs can be as high as 504. Moreover, \(g_{q}\) consists of four binary sequences defined in [2].
# OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles
## 4 Algorithms
### A Genetic-Algorithm-Based Path Planning with VRAOA (GA w/VRAOA)
#### 4.2.1 Population Initialization  
A population \(P^{m}\) is the collection of chromosomes of UAV \(m\), where a chromosome represents a possible solution. In the proposed OADC scheme, a chromosome \(C_{I}^{m}\) represents a possible traveling path that starts from the BS for a UAV. Each chromosome is a collection of genes, \(g_{0},...,g_{i},...,g_{Bm}\), where \(i=0,...,B_{m}\). \(B_{m}\) is the number of sensors assigned to UAV \(m\). Each gene, \(g_{i}\), represents an individual sensor, and \(g_{0}\) is the BS. The initial population is generated by changing the positions of the genes randomly. Note that the position of the gene \(g_{0}\) remains fixed in all chromosomes. The initial population \(P^{m}\) can be represented as a \(n_{pop}\times B_{m}\) matrix, where \(n_{pop}\) is the number of the population and each row is an individual chromosome (i.e., row \(l\) corresponds to \(l^{th}\) chromosome \(C_{l}^{m}\) in the \(P^{m}\) of UAV \(m\)). For example,  
\[P^{m}=\begin{bmatrix}C_{1}^{m}\\ \cdot\\ \cdot\\ C_{1}^{m}\\ \cdot\\ \cdot\\ C_{2}^{m}\end{bmatrix}=\begin{bmatrix}g_{0}...g_{i}...g_{B_{m}}\\ \cdot\\ \cdot\\ g_{0}...g_{i}...g_{B_{m}}\\ \cdot\\ \cdot\\ g_{0}...g_{i}...g_{B_{m}}\end{bmatrix} \tag{12}\]
# GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization
## 5 Experiments
### Main Results and Analysis
#### 5.2.2 Node Classification  
Previous works often observe a trade-off between model fairness and accuracy [35; 36]. Our method not only benefits fair graph condensation, but also improves the overall performance in node classification tasks. As Table 2 shows, GCARe achieves a higher accuracy over GCond and DosCond in almost all cases, and it also takes a further step toward recovering the potentials of the original large graphs. In particular, on the Recidivism dataset, GCARe even achieved a higher accuracy than training on the full graph (81.97 vs. 81.01). In addition, the statistical parity regularization approach also surpassed the baselines. These results demonstrate that our method provides better representations for condensed graphs.  
\begin{table}
\begin{tabular}{|c c|c c c|c c c|c|} \hline
**Dataset** & **Metric** & **GCond** & **+SP** & **+Ours** & **DosCond** & **+SP** & **+Ours** & **Whole Dataset** \\ \hline \multirow{3}{*}{Cora} & \(acc\uparrow\) & 80.98 & 80.80 & **81.03** & 80.06 & 80.25 & **80.32** & 81.92 \\  & \(\Delta_{acc}\downarrow\) & 9.80 & 9.83 & **9.56** & 9.43 & 9.31 & **9.07** & 8.95 \\  & \(\sigma_{acc}\downarrow\) & 4.50 & 4.41 & **4.28** & 4.18 & **4.14** & 4.15 & 3.96 \\ \hline \multirow{3}{*}{Ogbn-arxiv} & \(acc\uparrow\) & 59.23 & 58.89 & **59.37** & **58.61** & 58.08 & 57.81 & 71.21 \\  & \(\Delta_{acc}\downarrow\) & 31.36 & 30.75 & **30.22** & 31.79 & 30.53 & **29.85** & 22.04 \\  & \(\sigma_{acc}\downarrow\) & 10.98 & 10.75 & **10.71** & 11.15 & 10.63 & **10.35** & 7.57 \\ \hline \multirow{3}{*}{Credit-defaulter} & \(acc\uparrow\) & 74.53 & **74.60** & 74.37 & 74.30 & 74.66 & **74.96** & 75.22 \\  & \(\Delta_{SP}\downarrow\) & 11.21 & 11.20 & **10.98** & 13.99 & **10.10** & 10.90 & 10.23 \\  & \(\Delta_{EO}\downarrow\) & 8.87 & 8.91 & **8.74** & 11.39 & **8.15** & 8.82 & 8.21 \\ \hline \multirow{3}{*}{Recidivism} & \(acc\uparrow\) & 80.04 & **82.43** & 81.97 & 80.08 & 80.88 & **81.16** & 81.01 \\  & \(\Delta_{SP}\downarrow\) & 4.47 & 4.46 & **3.98** & 4.76 & 4.90 & **4.68** & 3.59 \\ \cline{1-1}  & \(\Delta_{EO}\downarrow\) & 3.40 & 2.76 & **2.52** & 3.45 & 3.61 & **3.43** & 1.21 \\ \hline \end{tabular}
\end{table}
Table 2: Results from the Cora, Ogbn-arxiv, and Credit-defaulter datasets. \(acc\) stands for overall accuracy; \(\Delta_{acc}\) for the accuracy gap between the highest and lowest subgroups; \(\sigma_{acc}\) for the std. dev. of the accuracies among all subgroups; \(\Delta_{SP}\) for statistical parity; and \(\Delta_{EO}\) for equal opportunity. GCARe achieved superior performances regarding both accuracy and fairness metrics. All the numbers were averaged over five random seeds. The best results are marked in bold.
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 5 Discussion
### Advantages and Disadvantages of the Exhibition Environment Status Detection Device and System  
#### 5.2.1 Advantages  
* Enhancing Monitoring Precision: The system can monitor environmental parameters such as temperature, humidity, and light intensity in real-time, ensuring that exhibits are maintained in optimal preservation conditions. Utilizing high-precision sensors and advanced data analysis techniques, the system can quickly detect and respond to environmental changes, preventing damage to exhibits caused by environmental factors. Additionally, the system provides long-term data recording and analysis, helping managers understand environmental trends and develop more effective management strategies. These features not only enhance the precision of exhibition environment monitoring but also improve the safety and reliability of exhibit preservation.  
Figure 6: Structural block diagram of the best implementation of the electronic device.  
* Reducing Labor Costs: Traditional exhibition environment monitoring relies on manual inspections, which are labor-intensive and prone to blind spots and oversights. With the introduction of an automated environmental status detection system, managers can remotely monitor the exhibition environment in real time, reducing dependence on manual inspections. The system's automatic alarm function can promptly notify managers of abnormal environmental parameters, preventing losses due to untimely manual monitoring. This not only improves monitoring efficiency but also significantly reduces labor costs and enhances overall exhibition management effectiveness.
* Multiple Patent Certifications: This technology has received several awards, including a gold medal at the 14 th International Invention Exhibition in Poland (2020), a silver medal at the Seoul International Invention Fair in Korea (2021), and a bronze medal at the Paris International Invention Fair (2022). It has also obtained new patents in the Republic of China (M602651), Japan (No. 3230284), Mainland China (No. 15876689), and France. The technology's multiple international patent certifications attest to its high technical content and reliability.
* Industry Application: This technology has been implemented in the collection management bank of the Conservation & Research Center at Cheng Shiu University, the open restoration space at Weiwei Arts Center, and the exhibition areas of the JP Art Center, where it is used for environmental monitoring of art exhibition and storage spaces.
## 2 Theory and Modeling
### Polarization and Depolarization of Reflection and Transmission  
We used degree of total polarization (DoP) to quantify the polarization properties of reflection and transmission. DoP is defined as follows:  
\[DoP=\ \frac{\sqrt{\text{Q}^{2}+\text{U}^{2}+\text{V}^{2}}}{\text{I}}. \tag{32}\]In order to quantify the depolarization effects caused by the sea surface, the depolarization ratio (\(\delta\)) was calculated as follows:  
\[\delta=\frac{I_{r}}{I_{l}}=\frac{\mathrm{I-Q}}{\mathrm{I+Q}}. \tag{33}\]  
For the polarized lidar incident light, \(\delta\) is zero, which means that the vertical component is negligible. After the light propagates through the sea surface, the vertical component arises due to scattering. In general, \(\delta\) is less than 1. A value of \(\delta\) equal to 1 indicates that the polarized light was totally unpolarized. However, \(\delta\) is greater than 1 in some cases where parallel-polarized light becomes partially vertically polarized.
## 2 System Model and Channel Model  
In this section, we introduce the IRS-assisted FL system model, its CSI error model and outage probability as follows.
## 5 Synthetic Data Generation for Network-Based Analyses  
Synthetic data generation is essential for developing and testing network algorithms under controlled conditions. Traditional random graph models have long generated synthetic networks with controlled properties. The Erdos-Renyi model produces random graphs by independently connecting nodes with a fixed probability, yielding networks that serve as null models for graph algorithms [47]. [PERSON] and [PERSON]'s small-world networks interpolate between regular lattices and random graphs, capturing the high clustering and short path lengths already observed in real-life examples [15]. [PERSON] and [PERSON]'s scale-free model introduced preferential attachment, producing graphs with a power-law degree distribution capturing nature's essence in forming networks [16]. These foundational models allow researchers to synthetically create social or transportation network topologies with desired global properties (randomness, small-world characteristics, heavy-tailed connectivity), which are helpful in testing network behavior theories and algorithms' robustness (e.g., community detection or consensus). It is worth mentioning that other techniques, such as Stochastic Block Models (SBMs) [48] and the Lancichinetti-Fortunato-Radicchi (LFR) [49], can capture community structure and realistic degree heterogeneity. In addition, deep generative models enable us to improve synthetic graph generation by incorporating structural patterns from a set of training graphs (e.g., real social networks or road networks) as GraphRNN [50], NetGAN [51], or GraphVAE [52] do. However, we will restrict ourselves to the aforementioned three seminal network models in this work.  
Below, we present two distinct procedures for synthetic data generation based on the consensus dynamics outlined previously. The first scenario considers a _local (one-hop)_ interaction model, while the second scenario extends interactions through an _exponential (multi-hop)_ model. Both approaches begin to generate a network and are subsequently converted into directed networks by assigning orientations to edges, allowing bidirectional connections with a given probability.
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
## 4 Conclusions  
We can conclude that the use of nanoparticles for the immobilization of the laccase enzyme through amino functionalization enhanced its catalytic activity for the successful biodegradation of DR23 dye. The CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase showed higher storage stability for 20 days at 4.0 \({}^{\circ}\)C than the free laccase and Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase. Also, the CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase showed higher activity in the pH range of 3.0 to 9.0 and temperature range of 30.0 to 70.0 \({}^{\circ}\)C. This higher laccase activity was attributed to the presence of copper ions that stimulated the laccase activity. Additionally, the CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase showed higher biodegradation ability towards DR23 dye than the free laccase and Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase. The biodegradation of DR23 dye was confirmed by the HPLC results. Also, the immobilized laccase was investigated for the biodegradation of DR23 dye for up to six successive cycles, with good reusability results. Finally, the use of nanoparticles as support for the laccase enzyme can enhance their catalytic activity and enables their applicability in industrial applications.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11178216/s1](https://www.mdpi.com/article/10.3390/app11178216/s1)]([https://www.mdpi.com/article/10.3390/app11178216/s1](https://www.mdpi.com/article/10.3390/app11178216/s1)), Scheme S1: The mechanism of phenolic azo dye biodegradation using laccase enzyme.  
Conceptualization, F.B.R. and A.A.; methodology, N.S.A.; validation, F.B.R., F.M.A. and N.S.A.; formal analysis, K.M.K.; investigation, K.M.K.; resources, H.N.H.; data curation, H.N.H.; writing--original draft preparation, A.A.; writing--review and editing, N.S.A.; visualization, M.A.T.; supervision, M.A.T.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deanship of Scientific Research at King Khalid University. Also, this research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.  
Not applicable.  
Figure 6: The reusability of CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase for DR23 biodegradation up to six successive cycles in optimum conditions.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 3 Proposed Method
### Two-Path Spatial-Temporal Feature Fusion Module  
The two-path spatial-temporal feature fusion module is composed of three different components, namely multi-scale feature extraction (MSFE), frame-level spatial feature extraction (FLSFE) and multi-scale temporal feature extraction (MSTFE). In the following sections, we will furnish a comprehensive description of the structure of each component individually.
## 3 Roasting Process
### Radio Frequency Roasting  
Radio frequency (RF) heating utilizes electromagnetic fields, with frequencies ranging from 13 to 40 MHz to volumetrically heat food products. This heating process leverages the dielectric and thermal properties of food materials. When exposed to an alternating electric field, the molecules within the food undergo thermal and electrical reactions, generating heat. Specifically, ions migrate towards opposite poles (protons to the anode, electrons to the cathode), and dipole molecules, such as water, rotate rapidly in response to the oscillating magnetic field. The friction generated by these molecular motions results in heat generation throughout the food material [42].  
RF heating penetrates deeper into food products compared to microwave heating, due to its longer wavelength. This enables more uniform heat distribution and the processing of larger food items. Moreover, the direct interaction between electromagnetic energy and food can significantly reduce processing time, eliminate harmful bacteria, and enhance sensory and nutritional qualities. For these reasons, RF heating is employed in various food processing applications, including meat cooking, bread baking, and vegetable dehydration. Additionally, RF technology can be used to inactivate microorganisms and enzymes, extending the shelf life of food products [43].  
In contrast to microwave ovens, RF ovens utilize longer wavelengths. RF technology can be integrated with conventional ovens to create hybrid systems, such as hot air-assisted RF ovens. In this study, a 12 kW, 24 MHz RF oven was combined with a hot air system to roast cashew nuts at 120 \({}^{\circ}\)C and 130 \({}^{\circ}\)C for 30 min, with an electrode gap of 8 cm. For comparison, conventional hot air roasting was performed at 140 \({}^{\circ}\)C for 33 min. The resulting products were evaluated for flavor, taste, and composition. GC-MS analysis of the aroma and flavor profiles revealed no significant differences between the two roasting methods. Both techniques produced similar levels of pyrazines and aldehydes. However, RF roasting resulted in a lower furan content and higher ketone and terpene concentrations compared to conventional hot air roasting [34].  
Another application of radio frequency roasting that has been investigated is coffee beans. A 27 MHz, 6 kW radio frequency system was employed in conjunction with a 48 \({}^{\circ}\)C hot air system and continuous agitation to ensure homogenie heating. This method was applied to disinfect coffee beans post harvest, without compromising product quality [44].
## 3 System & Network Traffic Monitoring  
Communications are critical for search and rescue operations and consequently, the operational status of the equipment and the network needs to be monitored. The system features monitoring and alerting services to inform its operator about the current status of the equipment and produce Common Alerting Protocol (CAP v1.2)-compliant messages when a device is connected/disconnected from the network or has low signal strength. The latter aids in integrating the system with other tools, such as command and control software, commonly used to manage search and rescue operations. The aforementioned services are executed locally, in the central communication node, and the relevant user interfaces are web based, meaning that they can be accessed without any prior need for software installation or configuration.  
The monitoring of the equipment is based on the Nagios Core [28], an open source monitoring and alerting engine, and the Simple Network Management Protocol (SNMP). Through SNMP, Nagios Core is able to retrieve information from the components comprising the system with respect to their status and component specific parameters. Owing to the use of SNMP, which enjoys widespread support, this scheme is both highly modular and widely applicable. Any device hosting an SNMP agent can be integrated with the rest of the system.  
Figure 4a presents the User Interface (UI) of the monitoring service. The connectivity of every component is regularly checked and reported (PING field) and various information specific to each component, such asthe number of wifi clients or 4G connection status, are reported. In part (b) of Figure 4, a graph of the network traffic through selected interfaces can be seen. The full green area represents the download traffic of the interface while the blue line represents the upload. Through it, the operator of the system can receive information about the usage of various resources such as the 4G connection, or the Point to Point link and identify bottlenecks or abnormal behavior.  
Figure 4: _Cont._
## 3 System Design
### Dynamic Core Management and Selection  
#### 3.2.1 Candidate Core Management  
In our multicast approach, each router in the network could undertake core functions. We managed candidate cores by using NRS to maintain the mapping between a fixed MSEID (FMSEID) and the candidate cores. A router could become a candidate core by notifying the NRS to insert its information into the mapping of FMSEID. Each candidate core was set to a working state, which could be Enable, Ready, Busy, or Disable. Only one candidate core was in the \"Enable\" state at the same time.  
\[CD_{i}\ =\ (\sum_{j=1}^{i}d_{ij}+\sum_{j=i+1}^{n}d_{ij})/(n-1) \tag{1}\]  
Figure 1: The architecture model of the ILDM.  
When a router starts, it calculates its center distance (\(CD\)), which refers to the average distance from the router to all other routers in the network. In the network with n network nodes, for node \(i\) (\(0\leq i\leq n-1\)), the distance from \(i\) to other nodes \(j\) (\(0\leq j\leq n-1\) && \(j\
eq i\)) is \(d_{ij}\). The center distance \(CD_{i}\) of node \(i\) can be expressed as Equation (1). Then, the router sends its information including NA, center distance, and degree to the NRS. The NRS inserts the router into the mapping corresponding to the FMSEID.  
NRS arranges all candidate cores in ascending order of center distance, arranges the cores with the same center distance in descending order of degree, and gets the sorted candidate core sequence. Then, NRS uses the index of each candidate core in the sorted sequence as the serial number (SN) of the candidate core. The smaller the SN of the candidate core, the higher the priority to be used. Finally, the working state of the candidate core with SN 0 is set to \"Enable\", and the working state of other candidate cores is set to \"Ready\".  
Due to the need to converge multicast flows, decapsulate packets, and respond to the join and leave requests of receivers, the core faces great load pressure. The load can be the router's CPU load, memory load, port bandwidth load, etc. We set a load threshold for each candidate core. When its load was less than the load threshold, the core could work normally without affecting other services and traffic. When a core's load reaches the threshold, it can notify NRS to update its working state to \"Busy\", and then NRS sets the state of the candidate core which has the smallest SN among all candidate cores in the \"Ready\" state to \"Enable\". When failures or performance bottlenecks occur, the core can notify the NRS to update its working state to \"Disable\" so that it is no need to serve new multicast services.