esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunable Fano Resonances in an Ultra-Small Gap
## 3 Results and Discussion
### Effect of Nanocube-Substrate Spacing on Fano Resonance  
In the experiment, four different Al\({}_{2}\)O\({}_{3}\) film thicknesses were prepared (2 nm, 3 nm, 4 nm, and 6 nm). Figure 3a shows the experimental scattering spectra of NCoM structures with the different gaps. When the gap thickness was 6 nm, the NCoM scattering spectrum had only one dominant peak at 640 nm over the range 500-800 nm. The broadening of the scattering spectra is mainly caused by the loss of the metal itself. An unsmooth metal surface can increase the loss, and lead to spectral broadening.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure** & **Parameters** \\ \hline The face-to-face width of the cube (\(d\)) & 75 nm \\ The rounded corner size (\(r\)) & 10 nm \\ The thickness of the Al\({}_{2}\)O\({}_{3}\) film & 2 nm \\ The thickness of the silver film & 80 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural model parameters of numerical simulation.  
Figure 2: (a) Scattering spectrum of a single silver nanocube on a 4-nm spacer layer. (b,c) The electric field magnitude profiles of peak II and peak I on the x-z plane, respectively. (d,e) The plasmon charge distribution of peak II and peak I on the x-z plane, respectively.  
When the gap was reduced to 4 nm, the scattering spectrum had two resonance peaks at 622 nm and 672 nm, with a dip at 662 nm. When the gap is reduced to 3 nm and 2 nm, the scattering spectra also have two resonance peaks, and the Fano depletion dips and red-shifts to 672 nm and 691 nm, respectively. This red-shift is caused by the damping of plasmon hybrids that is associated with the coupling strength between the NP and the film, which has been well studied [43; 44; 45]. In the NPoM structure, the coupling strength between the NP and the film is enhanced as the gap decreases. When the gap is less than 4 nm, the observed dip spectra indicate that hybridization between different resonance modes mediated by the substrate is a near-field interaction. In Figure 3b, numerical simulations indicated a good agreement with the experimental results in Figure 3a. However, the experimental linewidths are larger than the calculated linewidths, which may be caused by the high loss from unsmooth metal film
## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.1. **Evaluation Metrics for Image Processing:**
NRMSE is computed as the ratio of RMSE to the dispersion of actual data values, which is typically expressed as a percentage. A lower NRMSE indicates that the model's predictions are closer to the actual values. mDCSRN [137] used this metric to evaluate the high-resolution image generated by the model. _Mean Opinion Score (MOS):_ The mean opinion score (MOS) is a subjective metric often used in image processing tasks like deblurring or resolution enhancement. It gauges the quality of an image based on human perception. Scores are assigned by a group of human evaluators on a predefined scale, such as 1 to 5, with 'bad' to 'excellent' quality. SRGAN [90] uses this metric as one of the evaluation metrics to evaluate the generated image HR Image. _Fully Convolutional Network Score (FCN-Score):_ The FCN-Score is an evaluation metric used to assess the quality of samples generated by models such as GANs like CGAN [81]. Leveraging the fully convolutional network (FCN) architecture, it extracts features from real and generated images and measures the differences in their distributions. Lower FCN-Scores suggest that the generated images closely resemble the real ones, signifying a well-performing model, while higher scores indicate a deviation from the real data, pointing to a less effective model. _Realism Score:_ The realism score is a standard for evaluating the quality of images after they have undergone modifications such as inpainting or blending. GP-GAN [134] is one such model that uses this metric to evaluate its output images. This scoring system evaluates how closely these altered images mimic their unmodified, natural counterparts. So, when an image scores highly on the realism score, the modifications have been integrated so seamlessly that it becomes nearly impossible to distinguish the final result from the original image. _Human perceptual metric:_ Human perceptual metrics are key in image processing and computer vision, offering a benchmark that aligns closely with human visual perception. These metrics quantify an image's quality or distinct features or visual result based on the human perceptual understanding of colors, shapes, patterns, and intricate details. Models like GCC-GAN's [64], LDEdit [49], and TAGAN [67] use the human perceptual metric as one of their evaluation metrics.
# Synergetic Synthesis of Nonlinear Laws of Throttle Control of a Pneumatic Drive
## 2 Methods
### Formulation of the Control Problem for EPS and Methods of Its Implementation  
The investigated dynamic system is a nonlinear dissipative system. Namely, in such systems, the properties of self-organization of movement are manifested due to the presence of attractors in their phase space.  
One of the initial stages of the synthesis of the control law (CC) according to the ADAR method is the selection of control objectives invariants that are set based on the technological problem, taking into account the physical essence of the dynamics of the processes occurring in the system under study [40]. Since the considered EPS is described by the equations of the dynamics of the piston motion and the equations of thermodynamic equilibrium, it is necessary to set technological and thermodynamic invariants, which will be the final goal of the state of the system. According to the task of control, pneumatic drives are divided into positional and tracking; thus, the ultimate goal of control technological invariants can be positioning the co-ordinate of the piston 1 to a given position, as well as changing the speed of the piston \(V\) according to a given time law.  
Thermodynamic invariants should reflect the \"internal\" balance of forces of the system, which will ensure the fulfillment of the final technological control task. Similar forces in the system under study are the pressure of compressed air in the filling chamber \(p_{1}\), which is essentially the \"working\" pressure affecting the movement of the piston, as well as the pressure in the exhaust chamber \(p_{2}\), a change that affects the deceleration of the piston and, consequently, its speed movement. Thus, a systemic control model is traced, in which the equilibrium of the system state variables will correspond to certain control objectives.  
According to the ADAR method, the number of control channels must correspond to the number of specified target functions. Control in the EPS is carried out through two channels:  
* Control of the incoming mass air flow, which forms the pressure \(p_{1}\) in the filling chamber, is carried out by changing the cross-sectional area \(f_{1}\) of the pneumatic distributor valve PR1;Control of the mass flow rate of air leaving the exhaust chamber, which is physically reflected in the form of pressure \(p_{2}\), is carried out by changing the cross-sectional area \(f_{2}\) of the PR2 valve.  
In industrial pneumatic automation, two methods of controlling pneumatic drives [40] have been patented, which, in their essence, consist of the method of braking the piston at the end of the working stroke: these are throttle control and backpressure control.
## 2 Numerical Analysis
### Earthquake Excitations  
Three earthquake waves were selected from the strong earthquake database of the Pacific Earthquake Engineering Research (PEER) Center; they are Taft, El Centro, and Northridge earthquake waves. In addition, another artificial earthquake wave (Rgbtongan) that is commonly used for testing buildings in China was selected. The peak accelerations (PGAs) of each seismic wave were adjusted to 0.30 g and 0.40 g to consider two different earthquake levels [32; 33]. The maximum PGA was set as 0.40 g to be a relatively safe value, above which the structure could be damaged. It ensures that all the test scenarios can be carried out with the structures having good mechanical performance. The four earthquakes in the sequence of Taft, El Centro, Northridge, and Rgbtongan were continuously inputted in the X-direction from PGA level of 0.30 g to 0.40 g during the simulation. This arrangement is to minimize the damage accumulation of consecutive earthquakes. Note that the earthquake responses of structures subjected to the Taft, El Centro, and Northridge waves are similar but moderately less than that under the excitation of the Rgbtongan wave.  
Both numerical analysis and the test (Section 3) were then performed using the above seismic excitation data. Figures 6 and 7, respectively, show the time history data and the acceleration response spectra for these four earthquake waves, and Table 5 presents the characteristics of the four seismic waves.  
The model test was conducted first on the story isolation model and then on the non-isolated model. The acceleration response of the mass center of each story and the inter-story displacement between two stories were measured after all the earthquake excitations were input. Note that a total of 16 simulation conditions (four seismic waves \(\times\) two PGAs \(\times\) two models) were carried out in this simulation. Modal analysis was also performed to understand the first natural period of the simulated model. This is to compare the modal analysis result from the simulated model with that from the fabricated model. The comparison result can be found in Section 3.4.
## 5 Simulation Results and Discussion
### Delay Issue  
The total delay required is defined as the total transmission delay. For a fixed transmission bandwidth \(B\), we assume that the symbol period is approximately \(T_{S}\approx 1/B\). The total delays in the case of SISO communication is defined as  
\[T_{SISO}=T_{S}\left(\sum_{i=1}^{N_{t}}\frac{L_{i}}{b_{l_{t}}}+\frac{1}{b_{SISO} }\sum_{i=1}^{N_{t}}\frac{L_{i}}{r}\gamma_{i}\right)+t_{da}, \tag{15}\]  
Where \(b_{l_{t}}\) is the transmission bit size at the transmitter side local communication and \(b_{SISO}\) is the transmission bit size for long haul SISO transmission. \(t_{da}\) is the time taken for data aggregation.  
The total delays in the case of cooperative MIMO communication is defined as  
\begin{table}
\begin{tabular}{l l} \hline \(f_{c}\) = 2.5 GHz & \(\eta\) = 0.35 \\ \(G_{t}G_{r}\) = 5 dBi & \(N_{0}\) = –171 dBm/Hz \\ \(B\) = 10 KHz & k = 2 for local com \\ \(P_{b}=10^{-3}\) & k = 3 for long haul com \\ \(N_{f}\) = 10 dB & \(M_{l}\) = 40 dB \\ \(P_{syn}\) = 50.0 mW & \(P_{mix}\) = 30.3 mW \\ \(E_{da}\) = 5 nJ/bit/signals & \(P_{LNA}\) = 20 mW \\ \(P_{fil_{r}}\) = 2.5 mW & \(P_{fil_{r}}\) = 2.5 mW \\ \hline \end{tabular}
\end{table}
Table 1: System Parameters.  
Figure 6: Total energy consumption over distance.  
\[T_{CO} = T_{S}\left(\sum_{i=1}^{N_{t}}\frac{L_{i}}{b_{l_{t}}}+\sum_{i=1}^{N_{t }}\frac{\frac{L_{i}}{r}\gamma_{i}}{b_{l_{t}}}+\frac{1}{b_{MIMO}}\sum_{i=1}^{N_{t }}\frac{L_{i}}{r}\gamma_{i}\right) \tag{16}\] \[+T_{S}\frac{1}{b_{l_{r}}}\left\{\sum_{i=1}^{N_{t}}\frac{L_{i}}{r} \gamma_{i}\right\}+t_{da}+t_{ch},\]  
Where \(t_{ch}\) and \(t_{da}\) are the channel estimation and data aggregation delays respectively. The term \(T_{S}\sum_{i=1}^{N_{t}}\frac{L_{i}}{b_{l_{t}}}\) is for the delay due to the local transmission from sensors to the cluster head. The next term is due to the local transmission from cluster head to the sensors. \(T_{S}\frac{1}{b_{MIMO}}\sum_{i=1}^{N_{t}}\frac{L_{i}}{r}\) term is caused by the long haul MIMO transmission. The next term is due to the local transmission at the receiver side. The assisting nodes first quantize each symbol they receive into \(n_{r}\) bits, then transform all the bits into symbols using \(b_{l_{r}}\) and transmit to the cluster head to do the joint detection.  
The delay difference is calculated using the following equation. We assume the value of \(t_{ch}\approx 0\).  
\[DD = T_{SISO}-T_{CO} \tag{17}\] \[= T_{S}\left(\frac{1}{b_{SISO}}\sum_{i=1}^{N_{t}}\frac{L_{i}}{r} \gamma_{i}-\sum_{i=1}^{N_{t}}\frac{\frac{L_{i}}{r}\gamma_{i}}{b_{l_{t}}}\right)\] \[-T_{S}\frac{1}{b_{MIMO}}\sum_{i=1}^{N_{t}}\frac{L_{i}}{r}\gamma_{i }-T_{S}\frac{1}{b_{l_{r}}}\sum_{i=1}^{N_{t}}\frac{L_{i}}{r}\gamma_{i},\]  
The value of \(n_{r}\) is chosen at the receiver based on the optimized transmitted constellation size. The delay difference is a measure of delay performance by which the cooperative MIMO can be compared with SISO. Positive delay difference indicates the SISO is facing larger delay compared to C-MIMO. In Figure 7, delay difference is compared where proposed C-MIMO outperforms SISO after 60 meters.
###### Abstract
NaDES are deep eutectic mixtures comprising two or more natural compounds common in living cells and acting as either hydrogen bond donors (HBD) or hydrogen bond acceptors (HBA) such as choline chloride, amino acids, sugars, and organic acids, among others [19]. These solvents have shown great potential for extraction processes because of the strong hydrogen bonds that take place between the solvents' components and the extracted compounds. These interactions enable obtaining high extraction yields and preserve the extract, protecting the bioactive compounds from degradation [19; 20]. That is why NaDES have been previously employed for the extraction of phenolic compounds from natural sources [17; 19; 21]. In addition, these solvents have enabled the extraction of phenolic compounds in combination with advanced extraction techniques such as ultrasound-assisted extraction (UAE), homogenate (HAE), high hydrostatic pressure (HHPAE), pressurized liquid extraction (PLE), and microwave-assisted extraction (MAE) [22; 23; 24; 25]. The main advantages that these extraction techniques present over conventional ones are the reduction of operation time and adverse ecological effects, the decrease in the amount of solvent employed, economizing the operation cost, and maintaining the quality of the compounds of interest. Therefore, they are considered green extraction technologies [17]. For instance, UAE has been widely used for the extraction of phytochemicals as well as for the treatment of biomass [26]. Furthermore, this technique has been applied, along with NaDES, to extract phenolic compounds [27]. Nevertheless, the combination of UAE with NaDES has not been yet studied for extracting NEPs. This work proposes, for the first time, the development of a sustainable, analytical methodology for the recovery of antioxidant NEPs from _Garcinia mangostana_ L. peels based on the combination of NaDES with UAE technology. Figure 1 shows the procedure carried out in this work for NEP extraction. Different NaDES derived from choline chloride were studied to select the most suitable solvent to extract NEPs from this matrix as well as the best molar ratio between NaDES components. A Box-Behnken design was used to establish the optimal extraction conditions (percentage of water, ultrasound amplitude, and extraction time) to attain extracts with high antioxidant capacity and high proanthocyanidin content. Furthermore, the presence of extractable polyphenols (EPPs) recovered through the conventional extraction method from mangosteen peel was compared with the extraction of NEPs performed by UAE with NaDES, using the optimal conditions. The phenolic profile and the molecular weight of the phenolic compounds extracted were determined by reverse-phase HPLC-DAD and size exclusion (SEC)-HPLC, respectively. Finally, the cytotoxicity of the NEP extracts obtained under optimal extraction conditions was studied.
## 6 Conclusions
\begin{tabular}{c l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(\mathcal{F}\) & the set of followers \\ \(\mathcal{W}\) & the set of leaders \\ \(\mathcal{G}\) & the communication topology graph \\ \(\mathcal{A}\)\(\mathcal{A}\)\((k)\) & the adjacency matrix with fixed \textbackslash{}switching topologies \\ \(\mathcal{G}\)\((k)\) & the in-degree matrix with fixed \textbackslash{}switching topologies \\ \(\mathcal{L}\)\((k)\) & the Laplacian matrix with fixed \textbackslash{}switching topologies \\ \(\mathcal{C}_{\mathcal{O}}(\mathcal{W})\) & the convex hull of the set \(\mathcal{W}\) \\ \(y_{i}(k)\)\(y_{i}(k)\) & the output without with disturbances of the \(i\)th follower agent \\ \(u_{i}(k)\) & the input of the \(i\)th follower agent \\ \(f_{i}(\cdot)\) & the unknown follower dynamics \\ \(\widehat{w}_{i}\) & the convergence value for the \(i\)th follower agent \\ \(w_{l}(k)\backslash\)\(\widehat{w}_{l}(k)\) & the output without \textbackslash{}with disturbances of the \(l\)th leader agent \\ \(\phi_{l}(k)\) & the pseudo partial derivative \\ \(\widehat{\phi}_{l}(k)\) & the estimated pseudo partial derivative \\ \(\xi_{i}(k)\backslash\)\(\xi_{i}(k)\) & the pseudo partial derivative estimation error \\ \(\xi_{i}(k)\backslash\)\(\xi_{i}(k)\) & the local containment error with fixed \textbackslash{}switching topologies \\ \(\rho_{i}\), \(\eta\), \(\lambda\), \(\mu\) & adjustable parameters of controllers \\ \(\mathbf{y}(k)\) & the vector collecting all follower agents' outputs \\ \(\mathbf{u}(k)\) & the vector collecting all follower agents' inputs \\ \(\mathbf{z}(k)\) & the vector collecting all local containment errors \\ \(\mathbf{w}_{s}\backslash\mathbf{w}(k)\) & the vector collecting all stationary \textbackslash{}dynamic leaders' outputs \\ \(w^{+}\backslash w^{-}\) & the upper \textbackslash{}lower bound of stationary leaders' outputs \\ \(\widehat{w}^{+}\backslash\widehat{w}^{-}\) & the upper \textbackslash{}lower bound of dynamic leaders' outputs \\ \(y^{+}(k)\backslash y^{-}(k)\) & the upper \textbackslash{}lower bound of followers' outputs \\ \(\Omega^{+}(k)\backslash\Omega^{-}(k)\) & the upper \textbackslash{}lower bound of the MASs with stationary leaders \\ \(e^{+}(k)\backslash\widehat{e}^{+}(k)\) & the upper global containment error with stationary \textbackslash{}dynamic leaders \\ \hline \hline \end{tabular}
## 6 Results and Discussion
### Advantages and Disadvantages of Particular Artifact-Removal Methods  
Each artifact removal method has its own advantages and disadvantages. Linear regression is notable for its simplicity of implementation, low computational requirements, and very high processing speed, making it ideal for real-time applications. However, its effectiveness is limited to simple, linear interferences and requires reference signals, which restricts its versatility. ICA decomposes the EEG signal into independent components, effectively separating artifacts without the need for artifact indicator signals. Nevertheless, this method is time-consuming and requires expertise in identifying artifact components, which limits its automation. CNN-LSTM networks represent one of the most advanced approaches, offering high effectiveness in removing complex and nonlinear artifacts, as well as full automation once the model is trained. However, such hybrid networks require large training datasets, substantial computational resources, and time-consuming training processes. Once trained, they operate very quickly, making them well-suited for processing large datasets. In summary, linear regression is the most efficient in terms of speed and simplicity; ICA provides a balance between adaptability and automation; and CNN-LSTM architectures offer superior performance and full automation, albeit at the cost of increased computational demands and data requirements. The choice of method depends on the problem's specifics, available resources, and requirements for speed and automation.  
It should be noted that all methods considered by us use data from additional EMG channels; therefore, they cannot be used with standard EEG hardware nor directly applied to existing EEG data.
# Spectrum Slicing for Multiple Access Channels with Heterogeneous Services
## 3 System Model
### Key Performance Indicators  
The Key Performance Indicators (KPIs) of interest are described in the following.  
We first define the AoI \(\xi\), which in our case is the number of slots that have passed since the generation of the last correctly received packet. If packet \(i\) is generated in slot and decoded by the receiver in slot \(d_{i}\), while packet \(i+1\) is generated in slot \(g_{i+1}>g_{i}\) and decoded by the receiver in slot \(d_{i+1}>d_{i}\), we have:  
\[\xi(n)=n-g_{i},\ \forall n\in\{d_{i},\ldots,d_{i+1}-1\}. \tag{9}\]  
The PAoI \(\Delta\) is then simply defined as the AoI, measured at the instant of arrival of a new packet:  
\[\Delta_{i}=\xi(d_{i}). \tag{10}\]  
The PAoI is the maximum value of the AoI across a cycle, as depicted in Figure 3.  
The relevant KPI for PAoI-oriented systems is the 90 th percentile of the PAoI, denoted by \(\Delta_{90}\).  
\[\Delta_{90}=\min_{n\in\mathbb{Z}^{+}}\{n:\Pr[\Delta\leq n]\geq 0.9\}, \tag{11}\]  
Latency and age are expressed in slots. \(\Delta_{90}\) allows us to assess the tail distribution of the PAoI in a general scenario, and can be used to compare performance with different values of the slot arrival rate \(\alpha\). In contrast, a widely employed metric called _PAoI violation probability_[3] requires the definition of a specific threshold, either expressed as an absolute time or as a maximum number of slots. Furthermore, it cannot be used to compare the performance under different arrival rates \(\alpha\) since the AoI is greatly determined by the latter.  
For latency-oriented systems, we introduce a similar KPI, which is the 90 th percentile of the latency-reliability for intermittent users. The distribution of latency-reliability is computed by multiplying the distribution of the latency of successfully received packets by their success probability \(p_{s,I}\):  
\[T_{90}=\min_{n\in\mathbb{Z}^{+}}\{n:\Pr[T\leq n]p_{s,I}\geq 0.9\}, \tag{12}\]  
on all packets, not just the successfully delivered ones,  
We can now define the Pareto frontier, which is commonly used in multi-objective optimization:  
**Definition 1**.: _Let \(f:(\mathbb{Z}^{+})^{2}\rightarrow\mathbb{R}\times\mathbb{Z}^{+}\) and \(\mathcal{C}\) be the set of feasible configurations. Next, let_  
\[Y=\{(S_{B},\tau):(S_{B},\tau)=f(c\in\mathcal{C}),\},\]  
_where \(S_{B}\) is the throughput of the broadband user and \(\tau\) is the timeliness of the intermittent user, i.e., \(\Delta_{90}\) or \(T_{90}\). The Pareto frontier is the set_  
\[\mathcal{P}(Y)=\{(S_{B},\tau)\in Y:\{(S_{B}^{\prime},\tau^{\prime})\in Y:S_{B} >S_{B}^{\prime},\tau<\tau^{\prime}\}=\emptyset\}. \tag{13}\]