esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Estimated Demand for Aviation  
The ICAO Long-Term Aspirational Goal Task Group (LTAG-TG) has been working to revise the long-term goal for sector decarbonisation; the on-going activity [27] clearly shows that sustainable aviation fuels will play a crucial role in sector GHG impact mitigation. At the European level, the ReFuelEU aviation proposal sets a clear target for the sector. The mandatory shares of sustainable aviation fuels have been proposed as progressively increasing from 2025 onwards, including a sub-target for synthetic fuels. According to the proposed definition, synthetic fuels are fuels that are renewable and of non-biological origin (as defined in Directive (EU) 2018/2001: \"liquid or gaseous fuels which are used in the transport sector other than biofuels or biogas, the energy content of which is derived from renewable sources other than biomass\"). A part of the aviation synthetic fuels could also be referred to as eFuels or Power-to-Liquids (see Table 1).  
In order to assess the expected demand for SAFs, the consumption in 2030 was needed. Due to the COVID-19 pandemic, ICAO estimated a demand reduction of 60% in the total world passengers in 2020 [28]. According to the latest data reported from the same source, the recovery rate is still uncertain, for both domestic (EU27) and international flights. The impact assessment accompanying the ReFuelEU proposal quantified the 5% mandate at 2.3 Mtoe, clarifying that the early stage of the SAF ramp-up would be supplied by advanced biofuels (ATJ route) and Part B biofuels (HEFA route). Given the current technological and commercial maturity levels of the two technologies, a 70% (HEFA)-30% (ATJ) ratio was considered.  
A figure of 2.3 Mtoe corresponds to the 5% of the demand volume: 46 Mtoe (lower than the pre-COVID-19 EU figure which was 55 Mt in 2018 [16]). Considering the expected growing trend for the sector, and the still controversial signals about the recovery, we decided to estimate the mandate volume against the more conservative figure of 55 Mt. This resulted in 2,365 Mt (4.3% of SAFs), approximately equal to 2.39 Mtoe.
## 2 Materials and Methods
### Pet/Ct  
All patients fasted for at least 6 h prior to \({}^{18}\)F-FDG PET/CT imaging, and blood glucose levels were \(<\)180 mg/dL at the time of tracer injection. Both \({}^{18}\)F-FDG and \({}^{18}\)F-FCH PET/CT unenhanced scans were acquired using a PET/CT Ingenuity TF (Philips Healthcare, Best, The Netherlands) 60 min after tracer administration (activity range 200-300 MBq, according to body weight) [37; 38]. All scans were performed in three-dimensional mode. An emission scan was performed in the caudocranial direction, from the upper thigh to the base of the skull (4 min for each bed position). Iterative image reconstruction was completed with an ordered subset expectation maximization algorithm (2 iterations, 28 subsets). A T 4-slice multi-detector helical CT scanner was used (detector row configuration, 4 \(\times\) 5 mm; pitch, 1.5; gantry rotation speed, 0.8 s per revolution; table speed, 30 mm per gantry rotation; 140 kV and 80 mA). Attenuation-corrected emission data were obtained using filtered back projection CT reconstructed images (Gaussian filter with 8-mm full width at half maximum) to match the PET resolution. Transaxial, sagittal, and coronal images and co-registered images were examined using Philips IntelliSpace Portal, Image and information management software version 9.0 (Phillips Medical Systems, Veenplus, Best, The Netherlands). Unenhanced co-registered CT images were retrieved and evaluated with a dedicated workstation for post-processing and analysis (Horos Free/Open source DICOM Viewer for Mac OSX, LifeX Free/Open source DICOM Viewer for Microsoft Windows). Experienced nuclear medicine physicians and radiologists in consensus analyzed the CT studies, blinded to the PET results.
# Physicochemical Properties and Sensory Attributes of Yanggaeng Treated with Citrus Peel Powder
## 2 Materials and Methods
### Antioxidant Properties of Yanggaeng  
To evaluate the antioxidative attributes of Yanggaeng, 2.5 g of Yanggaeng was dissolved in 50 mL of 70% ethanol and incubated for 24 h. In succession, Yanggaeng was separated by centrifugation at 3000\(\times\)\(g\) for 10 min. Quantification of total polyphenols, total flavonoids, scavenging activity against DPPH and ABTS radicals, and FRAP were determined following established protocols [17; 26; 27; 28; 29]. To measure the DPPH and ABTS radical scavenging activity, DPPH solution (300 uM, Sigma-Aldrich Co., St. Louis, MO, USA) and ABTS solution (7 mM, Sigma-Aldrich Co.) were separately added to the sample solution.
## 3 The Basic of GNSS
### Smart Train  
The rail industry, which is constantly improving standards for rail safety, operational efficiency, and the quality of passenger services, is expected to be a potential market for the development of GNSSs already in use and, above all, the implementation of new solutions. These concepts are mainly based on the penetration of information and telecommunications technologies into the railway environment. One of these concepts is the construction of signaling and train control systems, preferably without trackside equipment. The goal of this concept is to transfer decision-making from the tracks to train-related systems and create so-called distributed intelligent train control systems. In other words, this means making trains potentially intelligent in some elements of the transportation process in the rail industry. This concept will ultimately reduce investment, operating, and maintenance costs, as the introduction of intelligent yet partially or fully autonomous trains will help optimize and reduce the human factor, which in many cases can contribute to causing overuse of vehicles and railway infrastructure (for example, imprecise braking of the train set). A fully autonomous train eliminates the need for staff to drive the train. The above can favorably affect the efficient use of rail transportation, which can translate into a competitive nature in relation to other modes of transportation [71].  
Table 5 presents the relationships and functionality of the various segments of GNSS systems.  
Figure 9: Simplified diagram of the system architecture. Prepared on the basis of [70].
# Interference-Induced Phenomena in High-Order Harmonic Generation from Bulk Solids
## 1 Introduction
Clearly, unlike more sophisticated models, it is not able to reproduce all quantitative experimental findings; however, as we shall see, it can help us to clarify the role of different initial states during the process of HHG, which is the primary aim of this work. To this end, we work on a single electron picture and determine the Bloch states and the corresponding energies. The resulting band structure has a bandgap (between the valence band (VB) and the first conduction band (CB)) that is equal to that of the ZnO crystal. We solve the corresponding dynamical equations numerically and calculate the HHG signal using the expectation value of the dipole moment operator. Since, in the velocity gauge, the time evolution of different Bloch states is not coupled, this calculation can be carried out for different VB initial states independently. Our aim is to show that despite this independence, the net HHG signal is strongly influenced by the interference of the charge currents related to different initial states. Note that although spatial interference during propagation [45; 46] can also have a significant effect on the HHG spectra, here we focus on the time domain interference that can be detected at a fixed spatial point. Our paper is organized as follows: first we discuss the physical and numerical aspects of the model to be used (Section 2). Then, in Section 3, the main properties of the calculated HHG spectra are analyzed: a general description (Section 3.1) is followed by a discussion on the suppression of the even-order harmonics (Section 3.2) and the position of the cutoff (Section 3.3). Finally, we summarize our results in Section 4.
## 3 Results and Discussion
### Scenario 2 (52)
#### 3.2.2 Cod  
In Table 12, scheme B was the same as in scenario 1. After implementation, COD environmental capacity reduced by 1.48%. There was a large difference between scheme C and scenario 1. After the implementation of scheme C, COD environmental capacity only reduced by 0.15%, indicating that the location of reclamation conditions had a huge impact on the EC. Schemes D, E and F were the same as scenario 1, which had a large impact on the EC, and COD environmental capacity was reduced by more than 16%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rclamation Scheme** & **Evaluation Result** & **Area (km\({}^{2}\))** & **Appropriate Scale of** \\  & & & **Reclamation (km\({}^{2}\))** \\ \hline A & - & 0.00 & \\ B & feasible & 18 & \\ C & infeasible & 40 & \\ D & infeasible & 86 & \\ E & infeasible & 95 & \\ F & infeasible & 114 & \\ \hline \hline \end{tabular}
\end{table}
Table 10: The maximum allowable area for CR in Haizhou Bay (scenario 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rclamation Scheme** & **Working Conditions** & **Reclamation Area/km\({}^{2}\)** & **Calculated Area of EC/km\({}^{2}\)** \\ \hline A & Current situation (no working condition) & 0.00 & 876.39 \\ B & HZ1 & 18 & 858.39 \\ C & HZ1+HZ3 & 64 & 812.39 \\ D & HZ1+HZ2+HZ3 & 86 & 790.39 \\ E & HZ1+HZ2+HZ3+HZ5 & 105 & 771.39 \\ F & HZ1+HZ2+HZ3+HZ4+HZ5 & 114 & 762.39 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Changes in marine area of Haizhou Bay (scenario 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rclamation Scheme** & **R\({}_{1}\)** & **R\({}_{2}\)** & **R\({}_{3}\)** & **R\({}_{4}\)** & **R** & **Evaluation Result** \\ \hline A & - & - & - & - & - & - \\ B & 10 & 8 & 10 & 10 & 9.5 & feasible \\ C & 0 & 0 & 0 & 0 & 0 & infeasible \\ D & 0 & 0 & 0 & 0 & 0 & infeasible \\ E & 0 & 0 & 0 & 0 & 0 & infeasible \\ F & 0 & 0 & 0 & 0 & 0 & infeasible \\ \hline \hline \end{tabular}
\end{table}
Table 9: Feasibility evaluation on EC (scenario 1).  
The feasibility evaluation results of EC indicated that the evaluation index \(R_{1}\) of schemes B and C was 10, so schemes B and C were feasible (Table 12). The evaluation index \(R_{1}\) of schemes D, E and F was 0, so schemes D, E and F were not feasible, that is, working conditions HZ2, HZ4 and HZ5 were prohibited.
# DC Voltage Induces Quadratic Optical Nonlinearity in Ion-Exchanged Glasses at Room Temperature
###### Abstract  
We demonstrate that applying DC voltage at room temperature to an ion-exchanged glass induces quadratic optical nonlinearity in a subsurface region of the glass. We associate this with the EFISH (Electric-Field-Induced Second Harmonic) effect due to the Maxwell-Wagner charge accumulation in the subsurface region of the glass, in which a conductivity gradient forms as a result of the ion exchange processing. The second harmonic (SH) signal from the soda-lime glass subjected to potassium-for-sodium ion exchange is comparable with one from the same glass after thermal poling. The signal linearly increases with the duration of the ion exchange. The lower mobility of the potassium ions results in a higher SH signal from the potassium-for-sodium exchanged glass than that from the silver-for-sodium ion-exchanged one. This phenomenon is resistant to thermal annealing: only a 500 \({}^{\circ}\)C anneal caused noticeable degradation of the SH signal after \"charging\" the specimen. The phenomenon found is of interest for characterizing graded conductivity regions and providing and controlling second-order optical nonlinearity in transparent isotropic media.  
soda-lime glass; ion exchange; second optical harmonic generation; Maxwell-Wagner charge accepted: 9 March 2024 9 March 202  
nonlinear integrated optics since the technique of waveguide formation using IE is already well established [3].  
We associate the phenomenon discussed in this study with the Maxwell-Wagner effect--the accumulation of an electrical charge at interfaces of materials with different conductivities under the application of DC/AC voltage [19; 20; 21]. This charge forms a strong inner electric field, which, in turn, is responsible for SON--the so-called EFISH (Electric-Field-Induced Second Harmonic) effect. However, in the case of IE glass, the conductivity of the subsurface layer enriched with invasive ions differs from the interior of the glass containing only domestic ions, and instead of a sharp interface, we are dealing with a diffusion region a few microns thick. It should be noted that the SH (second harmonic) generation of laser radiation has recently been demonstrated in amorphous thin films under the influence of an electric field due to the Maxwell-Wagner charge [22]. Also, the Maxwell-Wagner effect is often taken into account in the analysis of frequency-dependent dielectric constants of materials in sandwich and composite structures [23; 24; 25].
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 2 Related Work
### Image Classification Methods  
In recent years, to more effectively carry out image classification, researchers have proposed various technical solutions and strategies. The existing image classification methods can be summarized into the following categories: manual feature-based image classification methods, CNN-based image classification methods, and GP-based image classification methods.
## 10 Emerging Topics and New Trends
### Room Equalization with Moving Microphone  
One of the main issues of multi-point equalization is the measurement of the RIRs, which requires a long time to achieve a certain spatial resolution inside the listening area. A solution to this problem can be found by using time-variant system identification techniques [280; 281]. Here RIRs are measured by applying a dynamic method based on the use of one moving microphone instead of estimating the RIRs independently. This procedure allows to obtain a dense grid of RIRs from one spatially continuous measurement that can be used in multi-point equalization to estimate the prototype function and equalization filters.
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 1 Introduction
This method's novelty lies in its simplicity and low computational demands, making it highly adaptable;
5. Adaptive threshold method: establishing a dynamic approach that varies the number of consecutive frames analyzed, improving reliability for extended driving sessions--an area often neglected by the current literature. This method provides significant enhancement by adapting to different driving conditions;
6. Evaluation: performance analysis using metrics like accuracy, sensitivity, and confusion matrices;
7. Comparison: benchmarking against state-of-the-art techniques, emphasizing the reduced training requirements, cost efficiency, and real-time performance of the proposed algorithms. The purpose of this research is to develop efficient, real-time drowsiness detection algorithms leveraging behavioral parameters. The objectives to accomplish this purpose include the following:  
1. To extract and analyze behavioral indicators such as ECR and MAR for detecting drowsiness;
2. To implement a static threshold method for immediate detection of drowsiness;
3. To design and evaluate an adaptive threshold method for enhanced detection during extended driving sessions;
4. To compare the proposed methods with state-of-the-art systems, highlighting their cost-efficiency and computational simplicity. The use of adaptive frame thresholds sets this research apart from existing studies, which often use fixed thresholds or require intensive training and high computational power. The adaptive threshold approach dynamically adjusts based on the duration of driving, which is essential for accuracy in long drives. The following list highlights improvements over the state-of-the-art (SOTA) methods:  
1. Training independence: unlike machine learning methods, the proposed algorithms do not need extensive training data, making them suitable for deployment in real-time applications;
2. Cost efficiency: the algorithms are designed to work with existing cameras and sensors without the need for specialized, expensive hardware;
3. Enhanced long-duration detection: the adaptive method specifically addresses challenges encountered in long driving sessions, providing a solution that many current systems overlook.
This article analyzes the structure of high-frequency noise in the time series of the network of GPS stations in Japan (about 1200 points) over 13 years of observations, 2009-2013, with a time step of 5 min. Four characteristics of tremors are considered: the entropy of the distribution of squared orthogonal wavelet coefficients, the entropy of the distribution of power spectrum values, and two estimates of the spectral index, which is understood as the regression coefficient between the logarithm of the power spectrum and the logarithm of the period. The first estimate of the spectral index is traditional, by calculating the power spectrum. Another estimate of the slope is based on orthogonal wavelet decompositions, where the averages of the squares of the wavelet coefficients are considered as the contribution of the frequency band of the detail level of the decomposition to the total signal energy. For successive time fragments 1 day long (288 readings with a time step of 5 min), for measurements at each station, the values of the four statistics described above are calculated after detrending by a 4 th order polynomial. The removal of the trend is aimed at getting rid of the influence of external deterministic factors, such as tidal and thermal deformations, and switching to the analysis of the random high-frequency component of the Earth's surface displacements, which we will call tremors. The anomalous regions of maxima of probability densities of the distribution of extreme values of the studied tremor statistics are determined: entropy minima and spectral index maxima. This definition of tremor anomalous behavior is based on the fact that white noise is characterized by maximum entropy values and a zero spectral index. Consequently, the maximum deviation from uninformative \"white-noise\" behavior should be characterized by entropy minima and spectral slope maxima.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 6 Application to CFD Simulation
### Score Function  
The boundary conditions of the walls have a large influence on the temperature changes in the room. To accurately predict the temperature changes, the values of the boundary condition parameters in _OpenFOAM_ should be optimized. The score function \(S(P)\) is calculated from the given simulation heat flux values \(P\) and the measured temperatures by the following procedure:  
1. Record the simulation temperature every five seconds for one hour.
2. Calculate the absolute value of difference simulation temperature between measurement actual temperature.
3. Calculate \(S(P)\) by: \[S(P)=\sum_{i=0}^{N}\lvert T_{s}^{i}-T_{m}^{i}\rvert\] (6) where \(T_{s}^{i}\) does the \(i\)-th simulated temperature at every five seconds, \(T_{m}^{i}\) does the \(i\)-th measured temperature saved at every five seconds, and \(N\) does the total number of temperatures. In the parameter optimization algorithm, tabu \(t_{i}\) = 10 and \(\Delta p_{i}\) = 10 are used.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment
### Fault Feature Extraction
#### 3.3.2 Entropy  
It can represent the information amount of the image and the non-uniformity of the image texture. If the image is full of texture, the entropy value can reach the top. If there is no texture, the entropy value is equal to 0. The equation is  
\[ent=-\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{N-1}g(i,j)\log(g(i,j)) \tag{13}\]
## 2 Materials and Methods
### Transfer Learning  
The amount of labelled data is scarce, which poses significant difficulties in training a large neural network (130 M parameters). One of the most common approaches to cope with small sample size problems is the use of transfer learning [19]. The CNN is first trained on a large data set of images (ImageNet [14] in our particular study), and then the model parameters are fine-tuned according to the retina image training set. We removed the last fully-connected layer from VGG16 (FC-1000, adjusted to the ImageNet dataset), adding a two neuron layer (FC-2) to distinguish only two classes: patients labelled CAC \(>400\) and CAC \(<400\). These layer parameters where randomly initialized and fined tuned inside the learning process. The model was trained for 60 epochs, batch size of 8, using learning rate 0.0001 and weight decay \(4\times 10^{-2}\). Cross-entropy loss and stochastic gradient descent were used for loss function and optimizer respectively. The use of pretrained filters significantly improves the accuracies, being the first CNN layers local filters for detecting specific image compounds, and the top (classification layers) of the network high level representations of the decision boundary. All the experiments were performed using PyTorch [20] Python library.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN Architecture** & **VGG16** & **VGG19** & **ResNet** \\ \hline Mean & 0.68 & 0.67 & 0.64 \\ \hline StdDev & 0.045 & 0.088 & 0.12 \\ \hline CI (95\%) & (0.64, 0.72) & (0.59, 0.75) & (0.53, 0.75) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of prediction accuracy between DL architectures.