esa-sceva/satcom-chunk-collection huggingface dataset:
# HyperIO: A Hypervisor-Based Framework for Secure IO
## 5 Discussion
### Performance  
HyperIO uses a thin hypervisor, which may degrade the overall system performance. To assess this performance degradation, we ran a benchmarking tool called PCMark [38] in four configurations:  
* without a hypervisor;
* with HyperIO's hypervisor;
* with HyperIO (which includes additional interceptions, e.g., key presses);
* with Oracle VirtualBox (6.1.14) as an example of a full hypervisor, running the same version of Windows with 4 GB of RAM.  
Table 3 summarizes the exact configuration of our testing environment. Table 4 presents the overhead percentage of each configuration compared to the base configuration without a hypervisor.  
A thin hypervisor and HyperIO incur a minor performance degradation (\(\approx\)1.35% and \(\approx\)2.88% on average, even without considering the video conferencing results). The only difference between the thin hypervisor and HyperIO is the interception of the input and output on port 0x60. Because the operating system accesses this port only upon an interrupt from the keyboard, a relatively rare event, our tests show a minor performance degradation due to this interception. A full hypervisor, represented by Oracle VirtualBox, showed a much higher impact on the overall system performance (\(\approx\)38.6% on average), while there is clear evidence that HyperIO affects the overall system performance, the impact is sufficiently low to allow its installation even in CPU-intensive workstations.
## Nomenclature  
\begin{tabular}{l l} \(A\) & Area, m\({}^{2}\) \\ CR & Crossover rate of differential evolution algorithm \\ \(F\) & Amplification factor of mutation function of the differential evolution algorithm \\ \(G\) & Current generation of the differential evolution algorithm \\ \(H\) & Height of the solid with internal heat generation, m \\ \(H_{0}\) & Height of the cavity stem, m \\ \(H_{1}\) & Thickness of the superior bifurcated branches of the cavity, m \\ \(H_{2}\) & Thickness of the inferior bifurcated branches of the cavity, m \\ \(k\) & Thermal conductivity of the solid with heat generation, W m\({}^{-1}\) K\({}^{-1}\) \\ \(L\) & Length of the solid wall with internal heat generation, m \\ \(L_{0}\) & Thickness of the cavity stem, m \\ \(L_{1}\) & Length of the superior bifurcated branches of the cavity, m \\ \(L_{2}\) & Length of the inferior bifurcated branches of the cavity, m \\ \(NP\) & Total number of solutions (population size) in the differential evolution algorithm \\ \(NG\) & Total number of generations (total iterations of the differential evolution algorithm) \\ \(OF\) & Objective function, solver of the heat diffusion equation \\ \(q\) & Heat transfer rate, W \\ \(T\) & Temperature, K \\ \(u\) & Solution of the next generation of differential evolution algorithm \\ \(W\) & Width, m \\ \(v\) & Trial vector, i.e., candidate solution of differential evolution algorithm \\ \(x,y\) & Cartesian coordinates, m \\
**Greek symbol** \\ \(\Delta E\) & Difference between objective function cost for the new and best solution. \\ \(\theta\) & Dimensionless temperature \\ \(\varphi\) & Fraction area of the cavity \\ \(\alpha\) & Angle of inferior branch of the cavity \\ \(\beta\) & Angle of superior branch of the cavity \\
**Superscripts** \\ (-) & Dimensionless variables. \\
**Subscripts** \\ c & Cavity \\ min & Minimum \\ max & Maximum \\ m & Once minimized \\ \(nm\) & \(n\) times minimized \\ \(no\) & \(n\) times optimized \\ \(r\) & Random index for operations with population solutions in mutation function \\
0, 1, 2 & Variable indexes of height and length for stem, inferior and superior branches \\ \end{tabular}
## 3 Method and Materials
### Reference Signals
#### 3.2.3 Aperiodic  
An aperiodic signal has non self-similar repetition even with infinite precision data, although mathematically, it can be considered as a periodic function with an infinite period. The selected function is the one that generates samples by a linear frequency sweeping, the _chirp_ function: \(f(t)=f_{0}+k\cdot t\), between \(f_{0}=0\) Hz and \(f_{1}=10\) Hz, with \(k=(f_{1}-f_{0})/T\), being \(T\) the sweep time.  
\[y_{\text{aperiodic}}(t)=\sin\left[2\pi\left(f_{0}t+\frac{k}{2}t^{2}\right) \right]. \tag{6}\]
## 5 Materials and Methods
### Performance Comparison Methodology
#### 5.6.1 Algorithm Configurations and Variants  
The experiment involves three configurations:  
* Standard GA (Baseline): A traditional genetic algorithm with fixed mutation and crossover rates.
* GA with Adaptive Mutation (GA-AM): An improved GA where mutation rates dynamically adjust based on population diversity.
* GA with Adaptive Mutation and Tabu Search (GAAM-TS): The most advanced configuration, integrating Tabu Search to refine local solutions and prevent premature convergence.  
Each algorithm variant is configured with the following parameters described in Table 1:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Standard GA** & **GA-AM** & **GAAM-TS** \\ \hline Population Size & 100 & 100 & 100 \\ Generations & 100 & 100 & 100 \\ Crossover Rate & 0.8 & 0.8 & 0.8 \\ Mutation Rate & 0.05 (Fixed) & Adaptive & Adaptive \\ Selection & Tournament & Tournament & Tournament \\ Elitism Ratio & 0.15 & 0.15 & 0.15 \\ Fitness Parameter (\(\gamma\)) & 0.8 & 0.8 & 0.8 \\ Local Search & None & None & Tabu Search \\ Tabu Iterations & N/A & N/A & 10 \\ Tabu List Size & N/A & N/A & 15 \\ Tabu Improvement & N/A & N/A & 0.01 \\ Stopping Criteria & 100 Gen. & 100 Gen. & 100 Gen. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of genetic algorithm variants. GA-AM refers to the genetic algorithm with adaptive mutation, while GAAM-TS includes both adaptive mutation and Tabu Search.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 1 Introduction  
Being followed by [PERSON]'s law, the demands from micro-electronics industry drives more and more powerful transistors with higher integrating density and lower power consumption in integrated circuits (ICs). Because of such a high scaling, local electrical fields increase and many side effects, e.g., short channel effects (SCEs) are created [1]. To minimize SCEs, not only device substrates were changed from bulk silicon to silicon on insulator (SOI) [2], but the device structures transformed from two dimensional (2D) planar ones to three dimensional (3D) devices. Through, this technological evolution transistors e.g., fin field effective transistor (FinFET), gate all around devices and nanowire or nanosheet were manufactured because of their excellent short-channel control [3; 4; 5; 6; 7]. Therefore, unlike 2D devices, to implement such 3D devices needs more efforts on process technologies because not only new structures are introduced, but also new materials are involved. Due to these developments many challenges in processing also emerged when 2D to 3D transition occurred.  
There are many articles dealing with technology of 2D planar transistors as well as 3D ones but so far none has focused on the challenges in processing which appear due to this technology transition. Therefore, this article presents the challenges and difficulties in complementary metal oxide semiconductor (CMOS) process including advanced lithography for patterning nano-scaled transistors, process integration, (wet and dry) etching, stress engineering with an emphasis on SiGe epitaxy for source/drain (S/D), dopant implantation, gate formation including deposition of high-\(k\) material andmetal gate using atomic layer deposition (ALD) technique, and III-V material for high carrier mobility in channel for FinFETs. The discussions have a focus on 2D to 3D transition in FETs.
# Analysis of the Water Circulation Structure in the Paldang Reservoir, South Korea
## 3 Results
### Analysis of Monitoring Data
#### 3.1.1 Hydraulic Data
At PC1, downstream flow with 86.8% prevailed over upstream flow in the surface layer. Similarly, downstream flow with 81.5%, which was even greater than downstream flow in the surface layer, prevailed over upstream flow in the lower layer at PC1. The average flow velocity was approximately 0.07 m/s near the point 6 m below the water surface, and it decreased closer to the water or bottom surface. In all layers at PC1, the average flow velocity exceeded 0.04 m/s. In the surface layer at PC2, downstream flow with 97.4% prevailed over upstream flow, and in the lower layer at PC2, downstream flow with 85.5% prevailed over upstream flow. The average flow velocity was approximately 0.07 m/s near the point 3 m below the water surface, and it decreased closer to the water or bottom surface. At PC2, the average flow velocity exceeded 0.03 m/s in all layers. At PC3, the highest average flow velocity, approximately 0.09 m/s, was observed near the point 6 m below the water surface, and it decreased closer to the water or bottom surface. At PC1, upstream and downstream flows occurred alternately in the lower layer during the initial stage of observations. This pattern can be attributed to the differences in water temperature, which were observed at the beginning of observations, between the South Han River (lower temperature) and the North Han River (higher temperature). At PC2 and PC3, in particular, downstream flow was observed in all layers. Figure 4: _Cont._  
Figure 4: Velocity vectors for the different depth layers and periods. Figure 5: Profiles of the residual current for each station and period. At each station, distinct vertical distributions of residual current were observed for each layer during the stratification period (Figures 4b and 5b). In the surface layer at PC1, upstream flow with 64.8% prevailed over downstream flow, but different from the pattern observed in the surface layer, downstream flow with 93.2% prevailed over upstream flow in the lower layer. Upstream and downstream flows were observed in the upper and lower areas, respectively, around the point 3 m below the water surface. The average flow velocity was close to 0 at a depth of 3 m below water surface, and it increased closer to the water surface. In contrast, below the 3 m point, the average flow velocity increased until near the point 6 m below water surface, and then it decreased until the bottom surface was reached. At PC2, downstream flow with 89.6% prevailed over upstream flow in the surface layer, whereas upstream flow with 60.2% prevailed over downstream flow in the lower layer. Furthermore, similar to PC1, two-layer flow occurred, but in the reversed direction; upstream flow was observed until near the point 4 m above the bottom surface, and downstream flow was observed above the 4 m point. At PC3, similar to the vertical mixing period, downstream flow was observed in all layers.
## 2 Materials and Methods
### Signal Segmentation
#### 2.6.1 Signal Synchronization  
The synchronization task (i.e., the 10 jumps) was performed at the same phase of the collection session, ensuring the same initial conditions for all participants. The first step was to match the sampling frequencies of the signals from the MuscleBANs with the ones acquired with the Hub by downsampling the first ones to 350 Hz. Following this, the alignment of the signals acquired by the 3 devices (HUB, Trapezius MuscleBAN, and Bicep MuscleBAN) was performed by initially choosing the ACC axis where the jumping motion was most evident and, finally computing the full cross-correlation, thus determining the dephasing between them [41].
## 2 Data and Methods
### Model Descriptions
#### 2.3.3 The TG Model  
The TG model, developed by [PERSON] _et al._[8], estimates vegetation GPP using MODIS, LST, and EVI products. An important merit of this model is the independence of climate variables. GPP is estimated as a product of the scaled EVI and scaled LST:  
\[\mathrm{GPP=m\times EVI_{scaled}\times LST_{scaled}} \tag{8}\]  
here, \(m\) is a scalar with the unit of gC/m\({}^{2}\)/eight-day, and EVI\({}_{scaled}\) has a function similar to that of the AVM, but EVI\({}_{base}\) is set to be 0.1 [8,11].  
The \(\mathrm{LST_{scaled}}\) is based on the determination of optimum temperature for GPP. GPP presents a maximum value for the \(\mathrm{LST_{scaled}}\) of 1.0 when LST is equal to 30 \({}^{\circ}\mathrm{C}\) and a minimum value of 0 when LST declines to 0 \({}^{\circ}\mathrm{C}\) or increases to 50 \({}^{\circ}\mathrm{C}\).  
\[\mathrm{LST_{scaled}}=\min[\left(\frac{\mathrm{LST}}{30}\right);\left(2.5-0.05 \times\mathrm{LST}\right)] \tag{9}\]
# Flame Retardance and Smoke Suppression of CFA/APP/LDHs/EVA Composite
## 3 Results and Discussion
### Thermal Analysis of the Prepared LDHs and Composites.  
Figure 3 illustrates the TGA and DTG curves of three LDHs. The first stage corresponds to the mass losses of physical absorbed water and interlayer water. The mass losses of LDH-Ni, LDH-Co, and LDH-Cu are 14.8%, 15.3% and 15.6% in the TGA curves (30-200 \({}^{\circ}\)C), respectively. The second stage is associated with the dehydroxylation of the metal hydroxide layers, the degradation of carbonates and stearate at a higher temperature interval (200-800 \({}^{\circ}\)C). Furthermore, the maximum mass loss rate temperatures of hydroxyls and carbonates are at 315 and 349 \({}^{\circ}\)C for LDH-Ni and LDH-Co, respectively. In addition, the maximum mass loss rate temperatures of the stearate, residual hydroxyls and carbonates are at 375, 375 and 339 \({}^{\circ}\)C for LDH-Ni, LDH-Co and LDH-Cu in the DTG curves, respectively.  
Figure 3: TGA-DTG curves of LDHs, (**a**) LDH-Ni; (**b**) LDH-Co; (**c**) LDH-Cu.  
Figure 2: (**A**): XRD pattern; (**B**) IR spectrum of LDHs. (**a**) LDH-Ni; (**b**) LDH-Co; (**c**) LDH-Cu.  
Figure 4a,b illustrate the TGA and DTG curves of EVA and its composites with 20 wt % loading of flame retardants, respectively. The TGA curve of EVA showed two main mass loss steps with two mass loss rate maximums at about 353 \({}^{\circ}\)C and 433 \({}^{\circ}\)C in the DTG. The first step is attributed to the deacetylation of acetate side groups. The second step is due to the chain scission of ethylene leading to full thermal degradation at high temperatures [33; 34]. From the DTG curves, it can be seen that, although the maximum mass loss rate temperature of the acetate side groups for all composites is almost the same as that of the pristine EVA at 353 \({}^{\circ}\)C, their maximum mass loss rate temperature of ethylene-based chains is higher than 433 \({}^{\circ}\)C of the pristine EVA, for ELDH-Ni and ELDH-Co at 473 \({}^{\circ}\)C, ELDH-0 and ELDH-Cu at 470 \({}^{\circ}\)C. The above thermal analysis results indicated that the flame retardants obviously enhanced the thermal degradation temperature of ethylene-based chains of the composites. The reason is that the flame retardants' released H\({}_{2}\)O, CO\({}_{2}\), N\({}_{2}\) and NH\({}_{3}\) effectively delay the scission of ethylene-based chains. Furthermore, it can be seen that the maximal mass loss speed rate of ethylene-based chains for ELDH-Ni and ELDH-Cu is lower than the EVA, ELDH-Co and ELDH-0.
## 1 Introduction  
Preservation of the current standard of living for the human population is not further possible without the development of ecologically sustainable chemical processes and technologies [1; 2]. One of the methods for contributing to responsible handling with raw materials and energy resources is the utilization of recyclable catalysts [1; 2; 3; 4; 5]. The strategy of how to obtain recyclable catalysts belongs the immobilization of known homogeneous catalysts to the solid carriers [3; 4; 5]. Organic polymers, especially commercially available cross-linked pearl-like copolymers of styrene e.g., Merrifield resin(tm)[6] and Jandajel resin(tm)[7] belong among the most relevant carriers. The remarkable advantage of polymer-supported catalysts lies in their simple separation and reusability, their low price, and also in the possibility of their usage in selective continuous-flow procedures [5; 7]. The immobilization of the catalyst can be performed by the reaction of ligands or catalysts with suitably chemically activated polystyrene (post-modification strategy) [7]. Another possibility is the grafting of the ligand or catalyst containing a double bond from the reaction, with monomers generating the scaffold of the polymeric carrier (copolymerization strategy) [7]. The convenience of one or other strategies cannot be generally stated--it depends on many factors for each concrete case. Nowadays, there are many examples in chemical databases of recyclable catalysts based on complex compounds immobilized on different polymers. This review is focused on the area of highly efficient recyclable catalysts based on Cu(I) and Cu(II) complexes immobilized on polystyrene carriers. The aim of this review was to summarize, discuss and evaluate recently published results of catalysts, which were designed particularly for reactions, where new carbon-carbon respective carbon-nitrogen bonds are formed.
###### Abstract  
This paper proposes a probabilistic method to obtain optimized parameter values for different power-system controllers, such as power-system stabilizers (PSSs) and battery energy-storage systems (BESSs) to improve probabilistic small-signal stability (PSSS) considering stochastic output power due to wind- and solar-power integration. The proposed tuning method is based on a combination of an analytical method that assesses the small-signal-stability margin, and an optimization technique that utilizes this statistical information to optimally tune power-system controllers. The optimization problem is solved using a metaheuristic technique known as the firefly algorithm. Power-system stabilizers, as well as sodium-sulfur (NaS)-based BESS controllers with power-oscillation dampers (termed as BESS controllers) are modeled in detail for this purpose in DIGSILENT. The results show that the sole use of PSSs and BESS controllers is insufficient to improve dynamic stability under fluctuating input power due to the integration of renewable-energy resources. However, the proposed strategy of using BESS and PSS controllers in a coordinated manner is highly successful in enhancing PSSS under renewable-energy-resource integration and under different critical conditions.  
cumulant method; NaS; firefly algorithm; stochastic; probability density function; Gram-Charlier expansion; power-system stability +
Footnote †: journal: Applied
# Aluminum-Based Plasmonic Photodetector for Sensing Applications
## 3 Simulation of the Proposed Structure and Parameter Optimization  
### Description of the Geometry of the Structure  
In order to conduct rigorous electrodynamic simulations of the PPPD structure, we had to choose an idealized geometric profile for the grating that did not significantly deviate from the real structure profile, which could be successfully simulated by available numerical methods. A rectangular profile is the most efficient for numerical simulation, but the results of the calculations for such a profile were poorly correlated with the experimental spectra, presumably because of their deviation from the actual profile. Conversely, different smoothed profiles are far better for describing the actual profile but can undermine the  
Figure 3: Spectral dependences of (**a**) refractive index \(n\) and (**b**) extinction coefficient \(k\) for 15 nm (Curve 1) and 50 nm (Curve 2) thick Al films. Curve 3—literature data [30] for bulk material.  
calculation stability and require more computation with lower accuracy. A trapezoidal profile was the best option for our goals: it allowed for relatively quick exploration of the parameter space while providing sufficient calculation accuracy. Moreover, the groove profiles of aluminum gratings obtained experimentally using interference lithography (Figure 2) were close to trapezoidal. Figure 4 provides a schematic sketch of the simulated PPPD structure, highlighting the metallic (Al) grating and the silicon substrate. The grating parameters were chosen to satisfy the phase-matching conditions that allow for the excitation of SPPs. These resonant modes enhance light absorption within the depletion region of the p-n junction (not shown here), leading to increased generation of electron-hole pairs and resonant photocurrent enhancement.
## 4 Discussion  
The main objective of the C-ITS evaluation is to assess whether notifying the driver of the condition of an incident on the road in front of the driver will improve traffic safety, improve driving comfort, and readiness for the approaching event. A questionnaire survey was conducted to gain insight into the driver's views on C-ITS, the HMI, and the overall warning system.  
This can be seen in the example of the PTVC use-case and its usefulness, and implementation together. Before the ride, the drivers were slightly inclined to believe that they would be distracted by the warning, which was favored by a few more drivers even after the ride. As the end user of C-ITS is a car driver, this should be seen as a good opportunity to obtain data and feedback from surveys on possible improvements and shortcomings of C-ITS and the impact on drivers.  
The main result of the Impact Assessment analysis was a slight increase in caution when driving with C-ITS with an average reduction in driver speed, together with greater uniformity of measured speed and acceleration. It also turned out that the drivers braked less but also accelerated less using the C-ITS equipment. A big challenge is the effort to increase the accuracy of the measured data by filtering out external influences affecting the driver during the evaluation. As it is not possible to create the exact same driving conditions for all drivers, such as the impact of other traffic on drivers or the weather, it is necessary to take such factors into account in the analysis and reduce their effect on the  
Figure 10: HMI of Public transport vehicle crossing use-case.  
overall conclusion by a larger number of evaluated passes. If an effort is made to balance the conditions for all evaluated drivers as much as possible, for example, by road closure or closed-circuit testing, there is a possibility that we expose drivers to an inauthentic environment in which they will not behave as they would in a real event.  
Functional evaluation tells us important findings obtained during the evaluation, which significantly correlated with the opinion of drivers on the evaluated system. In this way, the late incoming messages and the insufficiently large pictograms depicting warnings were detected. One of the important outputs is the following implementation of important findings in C-ITS and its HMI from both functional evaluation and user acceptance. For the subsequent expansion and improvement of the methodology, the possibility of using more vehicles and thus multiple numbers of tested drivers for the same use-case in the same place and time are required. In this way, there is a better opportunity to capture a true view of the C-ITS system for wide publicity, which allows for better subsequent implementation of corrective measures in the HMI and the entire C-ITS system.