esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### Effects of the OIS's Load Ratio on Various Aspects of Performance
#### 4.3.2 Handover Frequency  
Figure 23 shows the performance of the OIS's handover frequency in relation to its load ratio in various channel environments. In nonfading environments, as shown in Figure 23, the handover frequency of the toss-and-catch algorithm is lower than those of the two control groups, as the algorithm is more capable of identifying more suitable SBSs. Control Group 2 has a higher handover frequency as its OIS lacked the score-based selection mechanism.  
According to Figure 23b-d, in all three methods, the handover frequencies declined in response to increasing OIS load ratios in most conditions. This is because under higher OIS load ratios, there are fewer SBS options, and the handover frequencies decreased as a result. When Nakagami-3 fading is employed, the overall effective signal coverage area of the SBSs is slightly smaller due to the slight fading effects. As a result, there are slightly fewer SBS options for this OIS. In this environment, the toss-and-catch algorithm has a slightly higher handover frequency compared to the control groups, as its OIS has more chances to perform handovers with suitable SBSs and prevent disconnections. When Nakagami-1 fading is employed and when the OIS load ratio exceeds 50%, the handover frequencies for all three methods declined, particularly those of Control Groups 1 and 2. This is because the toss-and-catch algorithm had a better ability than Control Groups 1 and 2 in identifying suitable SBSs for communication. For example, at an OIS load ratio of 100% and in comparison with Figure 23b, the handover frequencies of the toss-and-catch algorithm and Control Group 1 had decreased by 0.014 Hz, while those of Control Groups 1 and 2 had decreased by 0.021 Hz and 0.022 Hz, respectively. When Nakagami-1/2 fading is employed, the handover frequencies for all three methods further decreased by around 0.020 Hz (in comparison with Figure 23c), which indicates that there were more disconnections. This is because in such fading environments, the number of SBSs deployed is unable to handle the OIS load. In other words, when the channel environment is harsh to the point that there are no options available, more SBSs should be deployed in order to overcome this problem. At this point, the process is controlled by the number of SBSs deployed.
## 3 Results and Discussion
### The Fluid Dynamics of the Flue Gas
#### 3.1.2 The Pressure Distribution of the Flue Gas  
As analyzed above, the pressure drop in the thermal fluid is one of the most important indexes for matching the driving power of the induced draft fan, while the pressure distribution is the key factor affecting the pressure drop. Accordingly, the pressure distribution of the flue gas with different inlet velocities was investigated, and the results are shown in Figure 5.  
Figure 4: The velocity distributions corresponding to the different inlet flue gas velocities. (**a**) Velocity nephogram for \(v_{fg}=3\) m\(\cdot\)s\({}^{-1}\). (**b**) Velocity nephogram for \(v_{fg}=5\) m\(\cdot\)s\({}^{-1}\). (**c**) Velocity nephogram for \(v_{fg}=7\) m\(\cdot\)s\({}^{-1}\).  
Obviously, it can be concluded from the figures that the pressure at the entrance of the FGD is evenly distributed owing to the fact that the \(v_{fg}\) in this area is stable; however, the pressure decreases rapidly after the back bending, just as in the trend of the \(v_{fg}\) mentioned above, and a similar phenomenon arose in the other three back bendings. As a result, the pressure difference between the inlet and outlet of the HE is huge, and a greater driving force is needed. Similar to the analysis in Section 3.1, the inlet \(v_{fg}\) is proportional to the outlet \(v_{fg}\), and the outlet pressure is also proportional to the outlet \(v_{fg}\), which might owe to the fact that the higher the inlet \(v_{fg}\) is, the higher the inlet pressure will be even if the turbulence degree at the final stage increases with the decrease in \(v_{fg}\). Moreover, the nephograms shown in Figure 5a-c indicate that there are six vortexes for each nephogram and that the low pressure areas are often located at the vortexes, while the pressure gradually decreases from the outside to the inside of the vortexes owing to the fact that the velocity at the center of the vortex is minimum. Moreover, there are two strong turbulence phenomena at the last two stages, which are the main areas hindering the flow of flue gas. From the perspective of the FGD design, the vortexes and the turbulence phenomenon should be avoided for the purpose of optimizing the allocation of the driving force. Accordingly, based on the analysis above, several recommendations can be made for optimizing the FGD, which are listed as follows:  
1. A cambered surface design at the intersection of the vertical wall and transverse wall might be one of the effective methods to decrease the flow resistance of the flue gas.
2. A smooth and adiabatic wall material design may be one of effective methods to reduce flow resistance.
3. The drained cells on the wall in the last two chambers can be designed to help to avoid the vortex phenomenon and reduce flow resistance.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Risk-Based Access Control Implementation
#### 4.3.3 User History Risk  
In this scenario, all actors including doctors, nurses, social workers, and law enforcement personnel are formally employed by the hospital and are therefore considered trusted users. As a result, their historical risk profiles are assumed to be low based on institutional trust. Accordingly, the initial risk history value for these users is set to 0.2, reflecting a baseline level of trust derived from their organizational affiliation and absence of prior access anomalies.
# Junction Selection Based on Optimal-Weighted Multiple Attributes for VANETs
###### Abstract  
Vehicle ad hoc networks (VANETs) is an emerging ad hoc network technology with a promising future but significant limitations, particularly in communication networks. An effective routing system can significantly enhance the performance of VANETs. However, creating an effective routing protocol in an urban context to transport the data packet to the destination is still challenging due to complex road conditions, sporadic connectivity among vehicles, frequent disconnections, and quick changes in network topology. To enhance the accuracy of routing decisions in an urban setting, we propose a novel \"multiple attributes decision-making junction selection routing (MADMJSR)\" routing protocol based on vehicle position for VANETs. Formerly, most of the research was focused on static weight-based attribute selection for communication rather than optimal values, which led to a drop in junction decision-making accuracy. Our proposed scheme considers the multiple attribute selection and the optimal weight value assigned to each attribute, which significantly improves the routing decision accuracy. The results show a high packet delivery ratio (PDR) gain within minimum latency and less network load. Furthermore, a thorough discussion of the functioning of our proposed protocol is presented along with a detailed performance evaluation in comparison to other protocols.  
Vehicular ad hoc networks (VANETs); junction selection; vehicle to infrastructure (V2I); distance; direction +
Footnote †: journal: Electronics Letters
# Parallel Extreme Learning Machines Based on Frequency Multiplexing
## 2 Experimental System
### Electric Field Description
PM\({}_{2}\) applies a second phase modulation, whose effect is to mix the comb line amplitudes in each comb, generating the hidden layer of the network below:  
\[E^{\text{hidden}}(t)=\sum_{j=1}^{N_{S}}\sum_{k}E^{\text{hidden}}_{j,\,k}e^{-i( \omega_{j}+k\Omega)t}\,\quad E^{\text{hidden}}_{j,\,k}=\sum_{p}E^{\text{in}}_{j,\,p}e^{i\phi^{( 3)}_{j,\,p}}i^{k-p}J_{k-p}(m_{2})\, \tag{5}\]  
where \(\phi^{(3)}_{j,\,p}\) is the phase accumulated by the \(p\)-th line of the \(j\)-th comb while travelling from SF\({}_{1}\) to PM\({}_{2}\). SF\({}_{2}\) allows for two different filter shapes to be set, redirecting the two outputs towards photodiodes PD\({}_{1}\) and PD\({}_{2}\). We define \(F^{\text{out, }i}_{j,\,k}\) as the attenuation that the spectral filter applies to the \(k\)-th line of the \(j\)-th comb before reaching PD\({}_{i}\), \(i\in\{1,\,2\}\). Hence, the electric fields on the photodiodes are as follows:  
\[E^{\text{out, }1}(t)=\sum_{j=1}^{N_{S}}\sum_{k}E^{\text{out, }1}_{j,\,k}e^{-i(\omega_{j}+k\Omega)t}\,\quad E^{\text{out, }1}_{j,\,k}=E^{\text{hidden}}_{j,\,k}e^{i\phi^{(4)}_{j,\,k}}\sqrt{ F^{\text{out, }1}_{j,\,k}}\, \tag{6}\] \[E^{\text{out, }2}(t)=\sum_{j=1}^{N_{S}}\sum_{k}E^{\text{out, }2}_{j,\,k}e^{-i(\omega_{j}+k\Omega)t}\,\quad E^{\text{out, }2}_{j,\,k}=E^{\text{hidden}}_{j,\,k}e^{i\phi^{(4)}_{j,\,k}}\sqrt{ F^{\text{out, }2}_{j,\,k}}\, \tag{7}\]  
where \(\phi^{(4)}_{j,\,k}\) is the phase accumulated by the \(k\)-th line of the \(j\)-th comb while travelling from PM\({}_{2}\) to the photodiodes, assuming that paths towards the two photodiodes have the same optical length. The photodiodes PD\({}_{1}\) and PD\({}_{2}\) provide the following intensity measurements:  
\[I^{\text{out, }1}=<|E^{\text{out, }1}(t)|^{2}>\, \tag{8}\] \[I^{\text{out, }2}=<|E^{\text{out, }2}(t)|^{2}>\, \tag{9}\]  
where \(<\cdot>\) represents a time average.
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
## 3 AFC Implementation
### AFC Velocity Input  
Synthetic jets, also called ZNMF actuators, are characterized by transporting the same quantity of fluid in the blowing and suction phases. For this reason, a sinusoidal velocity input as in Equation (23) must be designed with the objective of extracting and injecting moment from the boundary layer. An initial approach must be carried out to estimate a reference value for the velocity input frequency \(f\) and amplitude \(A\).  
\[U_{jet}(t)=A\sin(2\pi ft) \tag{23}\]  
The introduction of periodic forcing is aimed to modify the airfoil vortex shedding frequency and amplitude. The vortex shedding natural frequency for the baseline case must be assessed by analyzing its frequency spectrum. Following the methodology presented in [37], a Fast Fourier Transform for the lift coefficient of the baseline case was performed. The resulting vortex shedding frequency was \(f_{0}\) = 0.6965 Hz. The amplitude \(A\) of the velocity signal corresponds to the maximum velocity of the jet \(U_{max}\) when sucked/injected from/to the boundary layer. The parametric study presented in this paper is designed as follows: the groove position and width were kept constant, and the natural vortex shedding frequency was selected as input frequency. Then, for a given jet inclination angle, CFD simulations were performed for a set of momentum coefficients \(C_{\mu}\) ranging between \(0.0001\leq C_{\mu}\leq 0.1\). The process was performed for three different jet inclination angles of \(\theta\) = 20\({}^{\circ}\), 30\({}^{\circ}\) and 40\({}^{\circ}\). Via following this procedure the optimum momentum coefficient and jet injection angle (from the range chosen) were obtained. As a final step, a set of jet pulsating frequencies ranging between \(0.5\leq F_{\pi}^{+}\leq 7\) were evaluated for the optimum \(C_{\mu}\) and \(\theta\) configuration previously obtained.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 4 Experimental Evaluation
### Datasets  
_MSVD_ is composed of 1970 YouTube open domain videos that are popular for video captioning. Each clip in this dataset shows one single activity. Generally, it spans over 10 to 25 s. The ground-truth captions are annotated by multiple humans. On average, each clip has approximately 40 ground-truth captions in English. For benchmarking, the training, validation, and testing splits are set to 1200, 100, 670 respectively in our experiments referred to [31].  
_MSR-VTT_ is a large-scale benchmark dataset which consists of 10,000 clips that are transformed from 7180 videos. The clips were divided into 20 different categories. In addition, each video clip is annotated by Amazon Mechanical Turk (AMT) workers using 20 single sentences. Following the official evaluation protocol provided by [13], the training, validation, and testing splits include 6513, 497, and 2990 clips, respectively.
# Adaptive Neural-Network-Based Lossless Image Coder with Preprocessed Input Data
## 7 Conclusions  
In this paper, an AdNN-based lossless image coding technique that is less computationally complex and more efficient than other ANN-based methods is presented. Additionally, it does not require initial learning on exemplary images, which is necessary for some newer approaches. The improvement is obtained by introducing preprocessing stage preceding AdNN input. Two concepts of initial processing are shown: in one, pixels are replaced mainly by pixel differences, which results in the shortening of the adaptation process; in the other, the ANN is preceded by four \"classic\" predictors, which means that there are only five ANN input nodes. The results are verified for a set of widely used benchmark images and, as can be seen, it outperforms the JPEG-LS codec by approximately 10%. The coding time for JPEG-LS is three orders of magnitude shorter than for the methods described in the paper. The four \"classic\" predictors in Concept 2 have relatively low computational complexity, but they are not the most efficient ones. The more advanced ones, such as EM-WLS [12], are significantly more complex, but their use in the preprocessing stage may result in much better AdNN performance. Another interesting question is if the preprocessing of data can improve the properties of deep-learning algorithms. These topics need further study. Conceptualization, G.U. ; methodology, G.U. ; software, G.U. ; validation, G.U. and R.S. ; formal analysis, G.U. and R.S. ; investigation, G.U. and R.S. ; resources, G.U. ; data curation, G.U. ; writing--original draft preparation, G.U. and R.S. ; writing--review and editing, G.U. and R.S. ; \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Images** & **JPEG-LS [1]** & **CALIC [4]** & **TS-FNN [37]** & **AdNN [14]** & **AdNN\({}_{+}\)[16]** & **C1** & **C2** \\ \hline Camera & 4.31 & 4.190 & 4.39 & 4.13 & 4.120 & 4.007 & 3.999 \\ Couple256 & 3.70 & 3.609 & 3.73 & 3.60 & 3.561 & 3.435 & 3.441 \\ Noisesquare & 5.68 & 5.443 & 5.44 & 5.18 & 5.201 & 5.166 & 5.135 \\ Airplane & 3.82 & 3.743 & 3.68 & 4.06 & 3.598 & 3.557 & 3.558 \\ Baboon & 6.04 & 5.875 & 5.97 & 5.77 & 5.706 & 5.662 & 5.641 \\ Lennagrey & 4.24 & 4.102 & 4.08 & 3.97 & 3.925 & 3.899 & 3.890 \\ Peppers & 4.51 & 4.421 & 4.33 & 4.27 & 4.201 & 4.153 & 4.146 \\ Shapes & 1.21 & 1.139 & 1.79 & 1.44 & 1.708 & 1.111 & 1.124 \\ Balloon & 2.90 & 2.825 & 2.54 & 2.80 & 2.647 & 2.602 & 2.577 \\ Barb & 4.69 & 4.413 & 4.49 & 4.16 & 3.868 & 3.843 & 3.821 \\ Gold Hill & 4.48 & 4.394 & 4.36 & 4.33 & 4.238 & 4.196 & 4.192 \\ \hline
**Mean** & 4.144 & 4.014 & 4.073 & 3.974 & 3.888 & 3.785 & 3.775 \\ \hline \end{tabular}
\end{table} 
Table 2: Average bit per pixel rates for the first group of standard test images; C1, C2: Concept 1 and Concept 2.
## 2 Materials and Methods
### Methodology
#### 2.2.6 Selection of VI  
Some popular VIs selected in this research are presented in Table 3. Four bands were selected for image fusion to check the quality of image fusion, including Blue (459-479 nm), Green (530-590 nm), Red (640-670 nm) and Near Infrared (850-880 nm), for all MS satellite images. These spectral bands were used in this study as their combinations are good for calculating different VIs. These bands are also commonly used to calculate VIs such as ARVI, Enhanced Vegetation Index (EVI), GNDVI, NDVI, Optimized Soil Adjusted Vegetation Index (OSAVI ) and Soil Adjusted Vegetation Index (SAVI). They are chosen in such a way that they will contain different bands in their VI equation (Table 3). For example, some vegetation indices like NDVI, OSAVI and SAVI have mainly R and NIR bands in their equations. However, EVI and ARVI have NIR, R and B bands in VI equations, and GNDVI has NIR and G bands in VI equation. In our strategy, RVI and FVI are calculated through band math operation from reference MS and fused MS images using the following VI equations (Table 3).
## 6 Results
### Wake-Up Range  
Using identical sending and receiving boards, we conducted experiments on the wake-up range. The best results were obtained at a distance of 15 cm, with an optimal orientation of the antennas. This distance is ten times shorter than the theoretical maximum distance of 1.5 m obtained in Section 6.1. We deduced that our assumption regarding the shared front-end between BLE and WuR resulting in similar performance was incorrect. The manufacturer suggested that the addition of a low-noise amplifier (LNA) to the reception circuit can lead to enhanced performance, albeit with a rise in energy consumption. As a result, the board can be used exclusively for development purposes or for wake ups within a limited range.
## 2 Antenna Arrays
### Linearly Arranged Concentric Circular Antenna Array (LCCAA)  
Although the centered element raises only one element, it has a significant impact on the beam width and sidelobe level reduction. In Figure 1d, the LCCAA beamformer's system model is explained briefly. Three concentric circular antenna arrays with a central element are positioned linearly in this beamformer.  
As a result, the beamformer is named a linearly arranged concentric circular antenna array (LCCAA). There are 36 elements in each LCCAA beamformer with the central element. Each LCCAA is made up of three rings, with the inner ring containing six elements, the middle ring containing twelve, and the outermost ring containing seventeen, and all three rings sharing one central element. In this proposed beamformer, the total element is 108. The distance between two adjacent items is \(0.5\beta\). The central element of each ring in this suggested beamformer generates a linear structure with \(0.5\beta\) spacing between any two neighboring elements.
## 1 Introduction
They found that using a high laser power while maintaining a low laser energy density could eliminate defects such as pores or warping and significantly improve the forming quality [22]. [PERSON] and [PERSON] used the residual stress coupling model and found that the residual stress of the samples in the scanning direction is higher than that in the vertical direction [23]. Through numerical simulation and microstructure observation, [PERSON] found that an insufficient or too high energy density would lead to spherification or stomata formation during the manufacture of AlSi10 Mg overhang structures [24]. A low energy density leads to incomplete powder melting, while a high energy density leads to droplet splashing. At the same time, the high cooling rate of the LPBF process can cause the stress to exceed the material strength, which can lead to cracking. According to the literature, warping and deformation are the main defects in the manufacture of overhang structures by LPBF. Therefore, it is necessary to understand the processes of warping and dross formation to solve the forming difficulties caused by the overhang structure. In this study, a three-dimensional FE model was established by combining a Gaussian moving heat source, the difference of the thermal physical properties between the AlSi10 Mg powder and the solid, and a layer addition feature. This model was used to obtain the temperature field and stress field. In combination with the experimental result, the process of warping and dross formation was studied. This provided theoretical support for the subsequent optimization of the process of forming an overhang structure and helped to propose a more reasonable optimization method for improving the quality of the parts.
## 4 Conclusions  
In this work, a class of PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs has been facilely fabricated via solvothermal approach for effective separation of Pb(II) from water phase. PEI functionalization of MNPs was vital for Pb(II) capture, and its sorption mechanism was metal-amine complexation. The Pb(II) adsorption process could be well depicted with the pseudo second-order kinetic and Langmuir isotherm model. The maximum adsorbing capacity at 25 \({}^{\circ}\)C and pH 5.0 reached 60.98 mg/g. Moreover, the adsorbing capacity increased with pH or temperature rising, but decreased with addition of electrolyte due to competitive sorption of cations. Besides, the PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs could still maintained high adsorption capacity after recycling five times, indicating the superior reusability. In summary, the PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs can be readily synthesized, and they served as a simple but effective tool for Pb(II) removal from the aqueous environment.  
Conceptualization, Y.T. and T.L.; methodology, T.L.; formal analysis, Y.T.; investigation, C.Z. and Y.T.; writing--original draft preparation, Y.T. and T.L.; writing--review and editing, T.L. and H.Z.; supervision, T.L. and H.Z.; funding acquisition, T.L. and H.Z. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Natural Science Foundation of China (#21878064 and #41977017). Anonymous reviewer's constructive comments were greatly appreciated. The authors declare no conflict of interest.