esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spectrum Management Issues
### Cognitive Radio
#### 3.2.2 Throughput Enhancement  
In [118], the authors have investigated equilibrium strategic behavior for two classes of users, i.e., Primary User (PU) and Secondary User (SU) based on three information cases, namely, no queue length information, partial queue length information, and full queue length information for CR systems. An efficient approach using theoretical and numerical analyses is derived to enhance PU and SU performance. The study has concluded that with minimum sojourn time, improved QoS and throughput can be achieved, but the increase in service rate does not necessarily improve the data rate of the CR network. Therefore, a more robust and precise algorithm is required. In the account of future work, the designed model can be further extended to trilateral work among the PU, SU, and manager of the CR system. A detailed study on the congested routes for PUs in the CR network is performed [119]. The undercover routing protocol technique is introduced with a joint venture of three-layer routings and beamforming methods. The proposed protocol is evaluated via NS2 simulations, and the results show that it enhances the goodput to up to 250% in comparison with other routing protocols with lower overhead. For future recommendations, a technique to improve group construction time remains a challenge.
However, in view of pursuing biohybrid materials for photonics, it would be strongly desirable to achieve a higher purity degree of the metal-doped biosilica, keeping unaltered both the structural properties of the integer silica shells and the photoluminescence of the incorporated organometallic complexes. We aimed to explore the in vivo incorporation in another microalgal species, _Phaocdatylum tricornutum_, a pennate diatom with raphe dimension of \(\sim\)12 \(\upmu\)m. We also extended the investigation to a series of four organometallic complexes, to evaluate the applicability of our protocol for different luminescent transition metal complexes. _Ph. tricornutum_ was selected instead of _Thalassioira weissflogii_ because it is a robust and prolific species providing good amounts of extracted silica per culture batch (more than 100 mg from 5 to 7 mL with a cell density of 10\({}^{8}\) cells/mL). Its genome, biomineralization, and adhesion properties are well established [21]. Moreover, contrary to the planktonic _Th. weissflogii_ diatom, _Ph. tricornutum_ is an adherent species forming living biofilms on various substrates, such as conductive indium tin oxide (ITO) glass. This feature observed our interest because in vivo incorporation of luminescent complexes into _Ph. tricornutum_ not only may represent a protocol to produce luminescent biosilica by isolation of diatom frustules after incorporation, but it is also a suitable method to develop luminescent biosilica-based living biofilms adherent to a variety of substrates, with potential applications in photonics. Besides the orange emitting [Ir(ppy)\({}_{2}\)bpyl]\({}^{+}\)[PF\({}_{6}\)]\({}^{-}\), already investigated for _Thalassioira weissflogii_, the red phosphor [Ru(bpy)\({}_{3}\)]\({}^{2+}\) [PF\({}_{6}\)]\({}^{-}\) was used as a further model complex with similar octahedral structure, to explore possible effects of a different transition metal ion [Ru(III) vs. Ir(III)] on the incorporation into diatoms. We also extended our study to neutral, commercially available AlQ\({}_{3}\) and ZnQ\({}_{2}\) complexes, widely used as both electron transporting and green or yellow light emitting materials for optoelectronics, respectively [22; 23; 24; 25; 26; 27; 28]. Except for ZnQ\({}_{2}\), the complexes were successfully incorporated into frustules, and viability tests on cell cultures revealed a good level of biocompatibility since doped diatoms were still capable of colonizing flat surfaces and forming biofilms. The photoluminescence of incorporated emitters was retained even after harsh acid/oxidative (H\({}_{2}\)SO\({}_{4}\), HCl/H\({}_{2}\)O\({}_{2}\)) purification cycles, leading to highly purified valves with integer nanostructure and preserved metal complex photoluminescence.
## 2 Related Work
### Feature Extraction Based on Attention Mechanisms
#### 2.3.2 Spatial Attention-Based Feature Extraction  
The spatial attention mechanism takes into account the different levels of importance of spatial dimensional information and ultimately results in a spatial attention feature matrix with different weights. The structure of spatial attention used for MARN is shown in Figure 4. Firstly, average and maximum pooling are used on the input data in both directions simultaneously, and subsequently, the results are spliced to generate a transition matrix, which is then downscaled using a \(7\times 7\) convolutional layer, and finally weighted to obtain the spatial feature matrices of the different positional information to determine the positions that need to be attended to. The specific process can be expressed as follows:  
\[H_{\text{s}}(M)=\sigma\Big{(}f^{7\times 7}([Avgpool(M),Maxpool(M)])\Big{)}= \sigma\Big{(}f^{7\times 7}(\big{[}M_{Avg},M_{Max}\big{]})\Big{)} \tag{5}\]  
where \(f^{7\times 7}\) represents the convolution algorithm with seven convolutional kernels.
# Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs
## 8 Case Study
### Proposed Solution  
In the considered conditions, the use of only rules for subscriber devices by hours and days cannot provide a reliable forecast, since the movement of user devices and, as a consequence, the possible overload of some routers and the occurrence of emergency situations, must be taken into account. When building a network model, congestion, emergency, and other situations are considered as context events. These events are analyzed at each step of the forecast execution; depending on the events, policies are assigned to predict the state of the object.  
The context events are suggested to solve the problem are shown in Table 1:  
\begin{table}
\begin{tabular}{c c c} \hline
**Context Event** & **Description** & **Assessment Parameters for the Monitoring System Side** \\ \hline \multirow{3}{*}{\(S_{1}\)} & \multirow{3}{*}{Normal operation} & Traffic—green zone \\  & & Number of registered mobile devices—green zone \\  & & Emergency situation—no \\  & & Traffic—yellow zone \\ \multirow{3}{*}{\(S_{2}\)} & \multirow{3}{*}{Increased load} & Number of registered mobile devices—yellow zone \\  & & Emergency situation—no \\  & & Traffic—red zone (below the threshold value) \\ \multirow{3}{*}{\(S_{3}\)} & \multirow{3}{*}{Overload} & Number of registered mobile devices—yellow or red zone \\  & & Emergency situation—no \\ \multirow{3}{*}{\(S_{4}\)} & \multirow{3}{*}{Emergency} & Traffic—red zone (above the threshold value) \\  & & Number of registered mobile devices—any value \\ \cline{1-1}  & & Emergency situation—yes \\ \hline \end{tabular}
\end{table}
Table 1: Context events.  
For every context event, there are policies to predict the internet traffic on the router. The policy descriptions are shown in Table 2.
# Numerical Coupling between a FEM Code and the FVM Code OpenFOAM Using the MED Library
## 2 The Numerical Platform Environment  
A numerical platform was developed to improve the portability and communication of numerical codes. This numerical platform [14] integrates different physical PDE models with FEM (finite element method) and FVM (finite volume method) discretizations for fluids and solids. Other features of the platform are the coupling of algebraic and PDE models implemented in different regions, typical of CFD for multiscale coupling. The platform creates a numerical environment for multiphysics and multiscale simulations through the computational capabilities of the FEMuS software. The FEMuS software is available on github at [[https://github.com/FemusPlatform/femus](https://github.com/FemusPlatform/femus)]([https://github.com/FemusPlatform/femus](https://github.com/FemusPlatform/femus)), accessed on 26 April 2024. and communication with other solvers. This coupling and data exchange exploit the simulation of different physical aspects, avoiding the need to write new physics solvers that may not be supported. In this way, it is possible to use other highly validated software, saving time and resources that are crucial when it comes to highly complex simulations.  
This numerical platform provides several environments for different user levels: one for implementing engineering applications and another suitable for developing and coupling internal FEMuS in-house code. In the former case, this environment provides data entry for input/output using CAD/Mesh generators and visualization/postprocessing with tools typical of the SALOME computing platform [15], i.e., Paraview [16]. In particular, the input/output data can be handled by the MED and HDF5 libraries. Implementations of PDE (partial differential equation) models on the FE and FV discretizations are available from various codes. The OpenFOAM and FEMuS libraries are the codes of interest for the applications described in this paper, where the two codes have additional routines that support the coupling paradigm of the MED libraries.  
In the following, brief introductions to the FEMuS finite element and OpenFOAM finite volume codes, as well as the SALOME platform and MED library functionalities, are presented.
## 2 Materials and Methods
### Colour Measurements and Calculations  
Colour measurements were conducted at the beginning of the experiment (day 0, before packaging of samples) and during storage (on the 2 nd, 5 th, 7 th, 9 th, 12 th, 14 th, and 15 th day). On day 0, the colour of the samples was measured on the cross-sectional area of the pork loin after 20 min of blooming. The colour was measured using a Konica Chromometer CR-400 (Sensing Inc., Osaka, Japan), with a D65 illuminant, 10\({}^{\circ}\) observer, and aperture of 2.54 cm. Before the measurements, the device was calibrated using a white tile. The values of lightness (\(L^{\star}\)), redness (\(a^{\star}\)), and yellowness (\(b^{\star}\)) were measured in three different locations on the surface of each pork slice. Hue (\(H^{\circ}\)) and chroma (\(C^{\star}\)) were calculated using Equations (1) and (2), as follows [33]:  
\[H^{\circ}=\tan^{-1}\frac{b*}{a*} \tag{1}\]  
\[C^{\star}=\sqrt{\left(a*^{2}+b*^{2}\right)} \tag{2}\]  
To analyse differences in colour between LO and HO pork loin samples at each sampling time, the \(\Delta\)E LO/HO coefficient was calculated. Also, the \(\Delta\)E\({}_{0}\) LO and \(\Delta\)E\({}_{0}\) HO coefficients were calculated to show the colour difference between samples before packaging (day 0) and during storage. All the calculations were performed according to the following Equation (3).  
\[\Delta\mathrm{E}=\sqrt{\left(\Delta L*\right)^{2}+\left(\Delta a*\right)^{2}+ \left(\Delta b*\right)^{2}} \tag{3}\]  
where \(\Delta\)E is colour change; \(\Delta L^{\star}\), \(\Delta a^{\star}\), and \(\Delta b^{\star}\) are changes in \(L^{\star}\), \(a^{\star}\), and \(b^{\star}\), respectively [34].  
Colour changes were evaluated based on \(\Delta\)E values and designated as trace if \(\Delta\)E were below 0.5, slight if \(\Delta\)E was in the range 0.5-1.5, noticeable if \(\Delta\)E was in the range 1.5-3.0, appreciable if \(\Delta\)E was in the range 3.0-6.0, much if \(\Delta\)E was above 12.0 [35].  
The redness index (RI) was calculated according to [PERSON] [33], using the following Equation (4):  
\[\mathrm{RI}=\frac{a*}{b*} \tag{4}\]
# Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning
## 3 Methodology  
This research aims to construct an augmented NER model that goes beyond the reference model. The augmented NER model specializes in recognizing keywords in the field of damage information gathering and analysis in the context of disaster management through transfer learning. The model consists of three main modules. The first module, data augmentation, establishes the compact database of named entities and specific patterns regarding disaster management from the official dictionary for formal terms and from existing inspection reviews, representing the primary target of interest in this work. This module can further split into three tasks: NER, pattern specification, and vocabulary update. The second module, the reference model, utilizes Bi-LSTM-CRF as an approach to general Mandarin NER and serves as the third module's foundation. The third module, the augmented model, implements cross-domain transfer techniques with disparate label sets to reinforce NER for damage information gathering and analysis in disaster management [17]. Specifically, it combines data augmentation and transfers learning based on a reference model.  
Figure 2 illustrated the interaction between the three modules. Initially, this research sets up a reference model as the basis of the augmented model. Afterward, the predetermined corpora generated through the data augmentation module added to the augmented model. Subsequently, the augmented model applies transfer learning and integrates the results of the other two modules. After several iterations of training, the augmented model is finally feasible to use in NER for damage information gathering and analysis in disaster management.  
Figure 2: The interaction between 3 modules.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Parameter Sensitivity Analysis  
Table 1 displays the best parameter settings for the competing algorithms, found by experimentation. For the proposed SP-SSA algorithm, the size of the 2-D embedding window was set to \(5\times 5\) pixels for the IP and Salinas dataset; whereas, for the PU and Houston 2018 dataset, the window size was set at \(3\times 3\) pixels. For the IP and SAL datasets, superpixels were set at 100. However, the amount of superpixels in the PU and Houston 2018 datasets were set to 150 and 500, respectively. The effect of window size variation for different number of superpixels on the classification performance for the experimental datasets is provided in Figure 9.  
As each superpixel is reconstructed individually, smaller window sizes are preferred since they lead to better image reconstructions. Using a large window may smooth the results too much and result in mixing errors. A 2D-SSA algorithm was presented in [29] for feature extraction in HSI, where various window sizes, such as \(5\times 5\), \(10\times 10\), \(20\times 20\), \(40\times 40\), and \(60\times 60\), were examined. The IP and SAL datasets produced the best classification accuracy when the window size was set at \(10\times 10\). When analyzing the PU and Houston dataset, the window sizes of \(5\times 5\) showed the best classification results. Since the optimal window size may vary depending on the dataset, ref. [31] adopts a multiscale strategy to improve the generalization ability.  
Figure 9: Effect of window size variation for different number of superpixels on the classification performance for the (**a**) Indian Pines (**b**) Pavia University, and (**c**) Salinas, and (**d**) Houston 2018 datasets.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 6 Results
### Node Active Time and Delivery Time  
Node active time measures the time spent by a node in the VLC network, whilst delivery time indicates the time difference between a correct reception and first try. Node active time is presented normalized in respect to the simulation time. Hence, it corresponds to the network availability from  
Figure 12: Obtained average Queued Packet Delivery Probability (QPDP) versus distance. (**a**) Depicts unsaturated traffic whilst (**b**) corresponds to saturated traffic.  
Figure 13: Obtained average End-to-end Packet Delivery Probability (EPDP) versus distance. (**a**) Depicts unsaturated traffic whilst (**b**) corresponds to saturated traffic.  
the node's viewpoint. It must be taken into account that frame generation does not stop during disconnection periods. Therefore, reduced node active times may also reduce the EPDP due to packet losses. Figures 14 and 15 present the normalized node active time and delivery time respectively.  
Comparing saturated and unsaturated traffic conditions, it is observed that demanding traffic requirements affected node active time, slightly diminishing it. A larger collision probability due to extensive channel occupation led to an increased disconnection probability.  
Regarding delivery time, it can be observed in Figure 15 that Scenario 1 conserves a good performance for all the link ranges, thanks to a proper carrier sensing. However, Scenario 2 loses performance beyond 2.5 m approximately since visibility between nodes is lost. This trend is broken in Scenario 3, which only has a single hidden node. In this case delivery time is conserved regardless of link distance. The minimum theoretical time a packet spends in the queue (considering the 50-slots  
Figure 14: Obtained average normalized node active time versus distance. (**a**) Depicts unsaturated traffic whilst (**b**) corresponds to saturated traffic.  
Figure 15: Obtained average delivery time versus distance. (**a**) Depicts unsaturated traffic whilst (**b**) corresponds to saturated traffic.  
buffer) is approximately 14 ms in this work. Depending on the application, the resulting overall latency (delivery time plus queue time) may be unacceptable, and GTS should be used instead.
# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
###### Abstract  
Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a domain discriminator. The feature extractor and the two label classifiers form the first adversarial mechanism to achieve class-level alignment. Moreover, the discrepancy between the two classifiers is measured by Wasserstein distance. Meanwhile, the feature extractor and the domain discriminator form the second adversarial mechanism to realize domain-level alignment. In addition, maximum mean discrepancy (MMD) is used to reduce the distance between the extracted features of two domains. The DAN-DAM model is verified by multiple transfer experiments on some datasets. According to the transfer experiment results, the DAN-DAM model has a good diagnosis effect for the domain shift samples. Moreover, the diagnostic accuracy is generally higher than other mainstream diagnostic methods.  
fault diagnosis; domain adaptation; variable speed; adversarial mechanism; Wasserstein distance +
Footnote †: journal: Applied Sciences
## 2 Precise Calculation of the Aluminum Extraction Flow Rate
### Unscented Kalman Filter  
The UKF is a filtering method designed to address nonlinear-state estimation problems. Unlike the standard KF, the UKF does not require the system model to be linear. The UKF employs a technique known as \"Unscented Transformation\" to handle the nonlinear characteristics of the system. Unscented Transformation primarily uses a set of deterministic sampling points, called sigma points, to approximate the probability distribution of the state variables. The specific principles are as follows [17; 18; 19].
## 2 Materials and Methods
### Cell Viability Assay  
To investigate cell viability after CDES, a WST-1 assay was used 1 day and 3 days after the treatment. The WST-1 assay is a cell viability assay that uses water soluble tetrazolium salt as an agent, which reacts with mitochondrial dehydrogenase and produces formazan that can be detected quantitatively by spectrometry. For the viability assay, HeLa cells were seeded on 24-well plates at a density of \(5\times 10^{4}\) cells per well. After 1 day and 3 days, CDES was applied to each well. After each period, cell viability was measured using the WST-1 (EZ3000, Daeil Lab Service Co., Seoul, Korea) assay. Each well was washed with PBS, 200 uL of the 1:10 WST-1 agent diluted to the culture media was added and cultured for an hour, and 100 uL of each well was transferred to a 96-well plate to read at a 450 nm wavelength ELISA plate reader (SUNRISE, TECAN, Grodig, Austria).
## 4 Displacement Analysis of Bowden Cable-Fabric Transmission
### The Swing Model of the Lower Leg  
In the flexible exoskeleton, the force is provided by the motor driving the contraction of the Bowden cable to drive the end fabric. One end of the Bowden cable is fixed to the motor, and the other end is fixed to the fabric. In the walking process of the human body, the lower limb will continue to repeat the swing process. When the knee joint flexes, the wire rope is moved by the pull from the lower leg and then increases the length of the transmission mechanism. So, it is necessary to establish the swing model of the lower leg.  
The swing model of the lower leg is mainly used to compensate for the interference caused by lower leg movement when providing assistance during walking. In order to minimize this interference, it is necessary to measure the Bowden cable-fabric length at different leg swing angles to avoid the impact on the lasso-fabric movement during human  
Figure 9: Stiffness model of the Bowden cable—fabric system.  
movement. In order to establish the motion model, the servo motor outputs a constant 20 N preload force, and the DC brushless motor at the knee joint drives the leg lever to swing slowly for 10 cycles. The encoder position values of the two motors were recorded. The fitting results are shown in Figure 10. The dotted line is the raw data from ten sets of experimental tests, and the solid lines are the fitting models with linear functions.  
\[x_{2}=k_{2}\theta_{2}+p_{2} \tag{11}\]  
where \(x_{2}\) is the desired displacement of the wire rope, \(\theta_{2}\) is the angle of the lower leg swing, \(k_{2}\) is the primary term coefficient, and \(p_{2}\) is a constant.  
The 0 degree is in the upright position, the negative angle indicates knee flexion, and the positive angle indicates knee extension. After data fitting, the parameters \(k_{2}\) and \(p_{2}\) are determined to be 0.706 mm/deg and 22.17 mm. Through calculation, the regression decision coefficient \(\mathrm{R}^{2}=0.998\) was obtained, indicating that the regression model has a good fitting effect.
###### Abstract  
In microscopic imaging, the key to obtaining a fully clear image lies in effectively extracting and fusing the sharp regions from different focal planes. However, traditional multi-focus image fusion algorithms have high computational complexity, making it difficult to achieve real-time processing on embedded devices. We propose an efficient high-resolution real-time multi-focus image fusion algorithm based on multi-aggregation. we use a difference of Gaussians image and a Laplacian pyramid for focused region detection. Additionally, the image is down-sampled before the focused region detection, and up-sampling is applied at the output end of the decision map, thereby reducing 75% of the computational data volume. The experimental results show that the proposed algorithm excels in both focused region extraction and computational efficiency evaluation. It achieves comparable image fusion quality to other algorithms while significantly improving processing efficiency. The average time for multi-focus image fusion with a 4K resolution image on embedded devices is 0.586 s. Compared with traditional algorithms, the proposed method achieves a 94.09% efficiency improvement on embedded devices and a 21.17% efficiency gain on desktop computing platforms.  
multi-focus image fusion; Laplacian pyramid; embedded device; down-sampling; real-time processing Article