esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation Analysis and Results
### Macroscopic Numerical Analysis  
To examine the influence of penetration rate and distribution degree of CAVs onMTF at a macro level, the traffic environment was set as follows: Car 1 is the leading vehicle with a preset value of acceleration; the next 20 vehicles include RVs and ACC and CACC vehicles that make up an MTF. The numerical simulation in starting and braking scenarios was divided into four groups according to the response characteristics of RV drivers, namely Type I, Type II, Type III, and Type IV drivers. During the numerical simulation, the interval at which to continuously increase the penetration rate of CAVs was set as \(10\%\), so as to analyze the impact of penetration rate on the fleet. At the same penetration rate, the arrangement of CAVs was divided into two cases: decentralized and centralized. Under the distributed arrangement, the CAVs are dispersed into the fleet at the maximum distance; under the centralized arrangement, the CAVs are gathered in the fleet. The effect of the distribution degree of CAV arrangement on the fleet was analyzed. In the specific situation, the CAVs in the fleet were driven in the separate modes of ACC and CACC.  
Figure 6: The variations of vehicle acceleration over time with Car 5 being an RV (with each of four driver types) or an ACC or a CACC in the braking scenario.
## 2 Design Methods
### Modelling and Similitude Law
#### 2.1.1 Geometric Similarity  
The prototype and model must have the same shape, but they can have different sizes. The requirements for geometric similarities are satisfied if the shape of the reducing model (m) of any object corresponds exactly to the prototype (\(p\)). It involves the reduction in  
Figure 1: Summary of the experimental work.  
all dimensions at the same scale. If \(M_{x}=\text{L}_{\text{px}}/\text{L}_{\text{mz}}\), \(M_{y}=\text{L}_{\text{py}}/\text{L}_{\text{my}}\), and \(M_{z}=\text{L}_{\text{pz}}/\text{L}_{\text{mz}}\) are considered as the scaling numbers for this research, then the following equilibrium \(Mx=My=Mz\) should be established in modelling details [40; 41].  
However, to achieve true dynamic similarity, all the dimensionless parameters (\(\pi\) terms) must possess the same value in both the model (m) and the prototype (\(p\)) (Equation (1) [40]).  
\[Fr_{p}=Fr_{m},\ Eu_{p}=Eu_{m},\ Re_{p}=Re_{m},\ We_{p}=We_{m},\ Ma_{p}=Ma_{m} \tag{1}\]  
The mechanical similarity can be established if both conditions for geometrically and dynamic similarities are satisfied for modeling. Hence, the mechanical similarity is assumed for all modeling stages in this research.  
The basic variables that are commonly used in hydraulic research are the following: geometry (length, \(L\)); flow properties (velocity, \(V\) m/s); pressure difference, \(\Delta P\) (Pa); fluid properties (density of water, \(\rho\) (kg/m\({}^{3}\))); the dynamic viscosity of water, \(\mu\) (N s/m\({}^{2}\)); the surface tension of air and water, \(\sigma\) (N/m); the bulk modulus of elasticity of water, \(Eb\) (Pa); the acceleration of gravity, \(g\) (m/s\({}^{2}\)). The following are the three basic dimensions that are commonly used in fluid mechanics: mass (\(M\)), length (\(L\)), and time (\(t\)).  
[PERSON] [42] stated that the pi theorem implies that the quantities can be grouped into five (\(5=8-3\)) independent, dimensionless parameters, expressed in Equations (2a) and (2b) [40].  
\[F_{2}\left(\frac{V}{\sqrt{gL}};\ \frac{\rho V^{2}}{\Delta P};\ \frac{\rho VL}{ \mu};\ \frac{V}{\sqrt{\frac{\rho}{PL}}};\ \frac{V}{\sqrt{\frac{E_{b}}{\rho}}}\right) \tag{2a}\] \[F_{2}\ (F_{r};\ E_{u};\ R_{e};\ W_{e};\ M_{a}) \tag{2b}\]  
The ratios \(F_{r}\), \(E_{u}\), \(R_{e}\), \(W_{e}\), and \(M_{a}\) are for [PERSON] number (\(F_{r}\)), [PERSON] number (\(E_{u}\)), [PERSON] number (\(R_{e}\)), [PERSON] number (\(W_{e}\)), and Sarrau-Mach number (\(M_{a}\)), respectively.
## 4 Results and Discussion
In Figure 7b, the SNR is fixed at 30 dB, i.e., the SNR value is fixed when the maximum value of mutual information is reached for all modulation formats, which is 3 bits/symb, 2 bits/symb and 1 bit/symb for 8 PSK, QPSK and BPSK, respectively. We observe the sensitivity of this mutual information to the change in the standard deviation of the phase noise. Mutual information for 8 PSK begins to decrease as early as \(\sigma_{\varphi}=5^{\circ}\), while mutual information for QPSK begins to decrease at \(\sigma_{\varphi}=15^{\circ}\), and mutual information for BPSK becomes sensitive to standard deviation of phase noise at \(\sigma_{\varphi}=25^{\circ}\). The higher the order of modulation, the stronger the mutual information sensitive to phase noise. As was mentioned, the mutual information for BPSK, QPSK and 8 PSK when there is no phase noise under given conditions is 1 bit/symb, 2 bits/symb and 3 bit/symb. However, when phase noise appears and if the standard deviation of phase noise is 20\({}^{\circ}\), the mutual information for BPSK, QPSK and 8 PSK is 1 bits/symb, 1.48 bits/symb and 1.86 bits/symb, respectively. Depending on the fading conditions in a certain environment, increasing SNR causes transmitted information to also increase towards its limiting value for the specific modulation format, but at varying rates. Moreover, phase noise causes the limiting values to decrease, which introduces another level of complexity into analysis. To address this, we analyze the power penalty that the system incurs due to multipath fading when phase  
Figure 7: **(a)** Mutual information performance of BPSK and QPSK receiver for different values of phase noise standard deviation. **(b)** Mutual information performance of QPSK and 8 PSK modulation formats in the presence of phase noise. noise is present. Taking the transmitted information limit for a fixed phase noise level as a reference, we calculate the amount of additional power that is required in order to recover certain level of transmitted information in fading conditions. This is shown in Figure 8 for the 8 PSK modulation example. From the figure, we can conclude that unfavorable fading conditions corresponding to the case when specular components are not dominant over diffuse components (\(K=5\,\)dB) impose significant power penalties when a less than 10% decrease in transmitted information is allowed. On the other hand, LOS (\(K=15\,\)dB) causes only negligible penalty that is under 1 dB for the considered conditions.
## 2 Materials and Methods
### Soil Salinity Survey  
Soil properties and salinities were surveyed and characterized within a footprint area of 30 m \(\times\) 30 m around the ET monitoring stations in each site. At each site, soil cores at four distinct depth ranges (0-30, 30-60, 60-90, and 90-120 cm) were taken from five sampling locations. A comprehensive laboratory analysis was conducted on the composite soil samples for each depth range.
## 2 Materials and Methods
### Step I: Data Set Preparation for Urban Land Use Suitability Analysis  
When preparing the datasets for urban land use suitability analysis, criteria were selected under the categories of topography, land use, accessibility, soil capability, and geology. Under the topography category, criteria such as slope, elevation, and aspect were used. For accessibility, distance to highways and primary roads, distance to streams, distance to bus stops, distance to ports, distance to the coastline, and distance to commercial and industrial areas were utilized. Additionally, LULC, soil capability, and geology criteria were included in the analysis, each forming separate groups. The selection of these criteria, along with their sub-criteria, was based on the location, scale, and characteristics of the study area, as well as the lessons learned from the literature review and the standards outlined in the Turkish Spatial Planning Regulation. The geographic data layers used for these criteria are presented in Table 1, indicating their sources, resolution, and scale. After determining these factors, the AHP method was employed using the open-source QGIS platform to determine the weights of the criteria. The **elevation** of land plays a significant role in determining urban suitability analysis. Areas with flat and low elevation have historically witnessed more frequent construction activities [14]. The northern part of the Mersin Province comprises the Middle Taurus Mountains. Consequently, the elevation increases as one moves northward from sea level. Existing settlements have generally developed along the east-west axis or on flat terrains [25]. Similarly, **slope**, akin to elevation, is crucial in the evaluation of suitability for settlement. As slope increases, construction costs rise, and soil stability decreases. This situation also increases the risk of erosion and landslides [26]. Mersin Province is located in the Mediterranean region, and is a metropolitan city with a Mediterranean climate. Figure 1: Study area. Therefore, **aspect** plays a significant role in slope assessment. Land facing south and east is considered more suitable for urban development due to its exposure to sunlight and warmth during winter, compared to land facing north and west [14; 26]. For the criteria of elevation, slope, and aspect, publicly available ALOS PALSAR digital elevation model (DEM) data with a spatial resolution of 12.5 m were utilized. Using QGIS modules, maps for slope and aspect criteria were generated from this DEM data, as seen in Figure 2. The elevation and slope data were classified into five classes each (using natural breaks), while aspect data, covering all directions and flat areas, were divided into nine classes and mapped.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 4 Emerging Trends and Future Directions
### Transfer Learning for Limited Data Scenarios  
Transfer learning approaches, where models pre-trained on large datasets are fine-tuned for specific tasks with limited data, are gaining traction in time series analysis. This approach has shown promise in domains where labeled data are poor or expensive to obtain, such as fault detection in industrial equipment or rare disease diagnosis from medical time series.
# Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network
## 3 Performance Analysis
### Throughput Analysis  
In this subsection, we derive and analyze the throughput of the proposed system configuration. Throughput is defined by evaluating the outage probability, \(\mathcal{P}_{m}\), at a fixed-source transmission rate, i.e., R (bits/sec/Hz) in the delay-limited transmission mode. Based on the attainable OP, the system throughput may be represented as follows:  
\[\mathcal{T}_{system}=\sum_{m=1}^{M}R_{m}(1-\mathcal{P}_{m}) \tag{30}\]
## 3 Results
### Changes in the Content of CBDA (CBD) and in Radical Scavenging Activity  
The amount of CBDA and CBD was determined only in the leaves and infloresences of the female plants, since females accumulate significantly greater cannabinoid content than male plants (predominantly in the infloresences) and are therefore commonly used for cannabinoid production [27]. This study was focused on CBDA and CBD, because the detected amounts of THC (tetrahydrocannabinol) and other cannabinoids were much lower. The results of the HPLC analysis of CBDA and CBD content in the leaves and infloresences of female plants are presented in Figure 6a.  
The amount of CBDA in the leaves of the hemp growing from vacuum- and EMF15-treated seeds did not differ from the control, but the CBDA in the leaves of the CP5 group decreased by 41%. No CBD was detected in the leaves. The infloresences of the control plants contained 2.5 times more CBDA compared with the leaves, but the amount of CBD was 10 times lower compared with the amount of CBDA. Seed treatment with a vacuum increased the amount of CBDA by 26% in the infloresences, but it reduced the amount of CBD by 7 times. The average amount of CBD in the infloresences of the EMF15 group was more than two times lower compared with that of the control; however, this difference was not statistically significant. There was no CBD found in the infloresences of the CP5 group. It is assumed that the acidic forms of cannabinoids (e.g., CBDA) are synthesized in hemp tissues, and their neutral forms (such as CBD) originate from the non-enzymatic decarboxylation of corresponding carboxylated forms [18; 19], particularly during the extraction process. Since the CBD detected in the extracts was formed from CBDA, the total amount of CBDA + CDB could be regarded as the important marker of changes in cannabinoid production. The amount of CBDA + CBD was \(0.91\pm 0.02\), \(1.11\pm 0.05\), \(0.51\pm 0.02\), and \(0.83\pm 0.03\) in the extracts of the infloresences of female plants in the control, vacuum, CP5 and EMF15 groups, respectively. Thus, despite the decrease in the CBD amount, the CBDA + CBD content in the extract of the vacuum group was 22% larger compared with the control (\(p<0.05\)). The reason why the extent of CBDA decarboxylation to CBD was decreased in the vacuum group remains to be established.  
Antioxidant activity of the female hemp leaf extracts was determined using a DPPH radical scavenging assay, and the results (Figure 6b) showed that it was significantly lower in the leaves of  
Figure 6: (**a**) The content of cannabidiolic acid (CBDA) and cannabidiol (CBD) in leaves and infloresences of female plants, and (**b**) the radical scavenging activity of female leaves four months after sowing in the field. The means \(\pm\) standard errors are presented (n = 10–12). * Significantly different from the control group (\(p\leq 0.05\)).  
plants growing from treated seeds compared with the control. DPPH radical binding was reduced by 31%, 43%, and 27% in the vacuum, CP5, and EMF15 groups, respectively.
# Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity
## 2 Methodology
### Category Classification of Scientific Articles
#### 2.5.2 Machine Learning Models  
Having readied the training dataset, the four designated machine learning models underwent training using the prepared data.  
* Multinomial Naive Bayes Classification (MNBC) is a prevalent technique for text classification, establishing a classifier from a network of trained data. According to [11], MNBC utilizes the Frequency Estimate parameter to accentuate data frequencies. This feature contributes to the reliability of results and renders MNBC an effective tool for data classification.
* Stochastic Gradient Descent (SGD) is a classifier capable of comprehending increasingly intricate functions and generalizing over parameterized data, as indicated by [12]. It is noteworthy that Dynamic Multiclass Strategy (DMS) frequently initiates learning with straightforward classifiers, deferring the use of complex ones. The rationale lies in preserving the information gleaned from the initial learning phase via the simpler classifiers. Consequently, the DMS initiates learning in a predominantly linear manner.
* Support Vector Machines (SVMs) operate based on the minimum structural risk principle found within the statistical theory [13]. Consequently, SVM plays a pivotal role in data classification by employing linear regression to generate outcomes. SVM transforms a nonlinear input dataset into a linear form by applying a kernel function [14].
* The Decision Tree Classifier (DTC) serves to simplify intricate issues, making them more comprehensible and aiding in decision-making [15]. As described by [16], DTC is characterized as a hierarchical classifier, furnishing multi-level classification and divulging the specific pattern to which each datum belongs. Moreover, it offers adaptability in handling both binary and multi-class classifications.  
The hyperparameters in each model were optimized using GridSearchCV ([PERSON] et al., 2022) [17]. As can be seen in Figure 5, this approach enables a grid of possible values to be created for the hyperparameters and then involves examining all the available combinations so that the best configuration can be determined. The GridSearchCV implementation uses cross-validation to analyze the performance of each set of hyperparameters. By employing this method, we were able to discover the best combination of hyperparameters for each model and thus optimize its effectiveness. The metric chosen for the best  
Figure 4: Distribution by category without duplicates.  
sets of hyperparameters was the average of the cross-validation results. By adopting this methodology, we could pinpoint each model's optimal blend of hyperparameters, elevating its overall performance to its pinnacle.
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## 3 Results
### Comparison between Single and Double Precision for Single-Layer AD Model Simulations  
We conducted a comparative analysis of reflectance and transmittance calculations using both single and double precision. The testing involved all possible combinations of absorption coefficients (\(\mu_{a}=0.1\), \(0.5\), \(1.0\) cm\({}^{-1}\)) and scattering coefficients (\(\mu_{s}=25\), \(50\), \(100\), \(150\) cm\({}^{-1}\)) at \(m=20\). The relative difference between the single and double precision results for reflectance ranged from \(2\times 10^{-7}\) to \(6\times 10^{-5}\); the highest discrepancy occurred with the combination of the highest scattering coefficient and the smallest absorption coefficient. For transmittance, the differences between the single and double precision results were slightly higher, between \(2\times 10^{-6}\) and \(5\times 10^{-4}\), the worst agreement corresponding again to the highest scattering and lowest absorption coefficients. However, these discrepancies are considerably smaller than the typical noise amplitude encountered in experimental spectra, which usually falls within the range of 1%.  
In terms of computation speed, the single precision computations were, on average, 4.3 times faster compared to double precision when considering the above set of optical parameters. In addition to the faster computation, the advantage of using single precision is that it also has two times lower memory requirements, facilitating the upload of larger datasets to the GPU memory.
## 2 Materials and Methods
### Data Preprocessing  
In preparing the data for ML analysis, particular attention was paid to the selection and treatment of input modalities. In this study, some modalities were dropped to reduce the number of inputs to the models, aiming to reduce redundancy and improve model performance. The preprocessing steps included normalization, feature extraction, and the handling of missing data, which were crucial for preparing the dataset for effective machine learning analysis.  
The preprocessing stage was crucial in preparing the raw sensor data for analysis and modeling. The first step involved defining the sampling rates for various sensors, such as the ACC, BVP, and EDA. This ensured that the data from different sensors were properly aligned and synchronized. One step that differed in our preprocessing implementation, compared to [PERSON] et al., was that the raw signals were preprocessed without converting raw signals from raw values into their respective units. For example, EDA from the RespiBAN was provided in voltage and conversion was required to obtain siemens (S) units.  
For feature-based models, EDA and ACC signals were filtered using a lowpass Butterworth filter with cut-off frequencies of 5 Hz and 13 Hz, respectively. For ECG peak detection, the Automatic Multiscale-based Peak Detection (AMPD) algorithm was based on [48].  
The preprocessing steps for E2E models differed from those for FB models. The steps applied to the signals for E2E models were winsorization, filtering, downsampling, and min-max normalization. For the winsorization, 3-97% was used to remove extreme values. A Butterworth low-pass filter with a 10 Hz cutoff was applied. Signals were then downsampled to reduce dimensionality and decrease the number of learning parameters, which helped reduce computational resource consumption. Finally, signals were normalized using a min-max normalization function. To prepare the data for temporal modeling, 60 s sliding windows were created for each signal with a 30 s stride length, based on initial testing.
## 3 Experiments
### Pump-Probe Nonlinear Magneto-Optical Spectroscopy
#### 3.2.1 Magnetization-Induced Second Harmonic Generation
Although SHG in the electric-dipole approximation is forbidden in EuO due to the centrosymmetry as mentioned above, pronounced MSHG signals are observed (Figure 4b). The obtained MSHG signal well reflects the magnetic properties of the samples and disappears exactly at \(T_{\mathrm{C}}\), which confirms its purely magnetic origin. Neither crystallographic SHG from the Eu\({}_{1-x}\)Gd\({}_{x}\)O film nor SHG from the a-Si capping layer and the YAlO\({}_{3}\) substrate contribute to the net signal, which reveals SHG to be a background-free probe of the magnetic state of the Eu\({}_{1-x}\)Gd\({}_{x}\)O in the a-Si/Eu\({}_{1-x}\)Gd\({}_{x}\)O/YAlO\({}_{3}\) heterostructure. This contrasts with the results on EuSe and EuTe films grown on BaF\({}_{2}\) substrates where SHG signals not coupling to the magnetic order were present, presumably because of the (111) orientation of the corresponding films [48; 49]. The MSHG signal coupling linearly to the magnetization can be also seen in the magnetic field measurements. The upper panel of Figure 4c shows the magnetic field dependence of the MSHG intensity at 10 K in undoped EuO. The MSHG intensity varies by one order of magnitude with the change of magnetic field. Different minima in field increasing and decreasing runs lead to a butterfly shape. Considering that the MSHG intensity is proportional to the square of the magnetization, the dependence of the magnetization on the applied magnetic field is extracted and shown in the lower panel of Figure 4c. The result reveals a pronounced hysteresis and reproduces direct magnetization measurements.