esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Downscaling Method and Model Construction  
Currently, empirical statistical modeling is widely used in SIF downscaling studies. The basic principle of this approach is to first select driving variables that are highly correlated with the downscaled variables and then aggregate these variables to match the spatial resolution of the target variables to establish spatial correspondence. Based on the theory of invariant spatial-scale relationships, high-resolution driving variables are used to predict new high-resolution data [46; 47; 48; 49; 50; 51]. To improve the accuracy of downscaling products, this study selected six models for downscaling, including:  
1. Convolutional Neural Network (CNN): CNN is a fundamental deep learning algorithm, composed of convolutional, pooling, and fully connected layers. The convolutional layer, as the core component, extracts features from the input data. The pooling layer reduces the spatial dimensions of the feature map, which not only decreases the model parameters but also helps mitigate overfitting. Through the weight connections, CNNs can effectively learn and represent complex data features [52].
2. Random Forest Regression (RF): Proposed by [PERSON], RF is a regression model that integrates multiple decision trees. Each tree is trained on a different subset of samples and features, with predictions made based on the majority vote of the trees. After constructing the model, the out-of-bag (OOB) error is computed. which provides a reliable estimation of the model's future performance [53; 54].
3. Extreme Random Tree Regression (ET): ET is similar to the RF model in that it is also a regression model composed of multiple decision trees. However, ET differs from RF in that it uses the entire training set without random sampling and trains all the trees on this full dataset [55]. After dividing the features, a feature value is randomly selected to divide the decision tree [56].
4. Adaptive Augmented Regression (ADA): The AdaBoost model iteratively trains a series of weak regressors and combines them into a strong regressor, each weak regressor is trained on the weighted training set, with the weights adjusted based on the errors from the previous iteration, allowing the model to focus on the samples that were previously misclassified [57; 58].
5. Gradient boosted tree regression (GBDT): GBDT builds a stronger model by combining multiple decision trees, with each decision tree learning the residuals of the previous one. This iterative process helps to improve the accuracy of the overall model [59; 60] by minimizing the difference between the original data and the predicted values.
6. Stacking integrated learning: Stacking is a powerful machine learning technique that combines multiple base learning models to improve the overall model prediction performance. In this study, we use RF, ET, ADA, and GBDT to construct the Stacking integrated learning model. The accuracy evaluation indices of each model are compared, and the model with better accuracy is selected to construct the SIF downscaling model [61; 62].
## 5 Conclusions  
The spherical tokamak Globus-M2 MHD activity was observed to lead to a deterioration in plasma confinement. This was seen in the form of transitions from high confinement (H-mode) to the L-mode and the discharges would ultimately end in disruptions. These MHD modes were found to be tearing modes (TMs). They are a result of the formation of magnetic islands in the tokamak plasma. The width and velocity of the TM are key parameters that determine the impact of the mode on plasma confinement.  
Different diagnostics were used to investigate the TMs on the Globus-M2. They were observed as fluctuations in the magnetic probe signals. The array of magnetic probes was used to determine the mode structure _m/n_ = 2/1 of the TMs. The Doppler backscattering method was demonstrated as an effective tool to study this MHD activity. Two DBS systems were used to detect TMs on the Globus-M2. The eight available channels with probing frequencies 20, 29, 39, 48, 50, 55, 60, 65 GHz correspond to the area of detection with normalized minor radii \(\rho\) = 0.5\(-\)1.1. The steerable antenna systems probed the plasma with O-mode microwaves in experiments. The TM was found in the DBS phase derivative signals at the same time as the fluctuations in magnetic probe signals.  
Different types of TMs were observed on the Globus-M2. One was a TM with a narrow magnetic island width of less than 3 cm. It developed at the early stage of the discharge during the plasma current growth and did not lead to detrimental consequences to plasma performance. It developed around the magnetic surface \(q\) = 2. The other TMs had wide magnetic islands with a width estimated from 8 to 10 cm. They developed during the plateau stage of the plasma current. Such TMs caused significant degradation of plasma confinement, such as transition from the H-mode to the L-mode, and their development was followed by a disruption of the discharge.  
Conceptualization, A.P. ([PERSON]) and A.Y.; methodology, A.P. ([PERSON]) and G.K.; software, A.P. ([PERSON]) and N.Z.; validation, V.M., Y.P. and N.S.; formal analysis, A.Y.; investigation, A.P. ([PERSON]), I.B. and N.Z.; resources, E.K., V.M., Y.P. and N.S.; data curation, A.Y.; writing--original draft preparation, A.Y. and A.P. ([PERSON]); writing--review and editing, A.Y., G.K., Y.P. and N.S.; visualization, A.P. ([PERSON]); supervision, A.Y.; project administration, A.Y.; funding acquisition, A.Y. All authors have read and agreed to the published version of the manuscript.  
The work is supported by the Russian Science Foundation (Project No. 18-72-10028).
## 2 Materials and Methods
### Film Characterization
The dishes were placed in a desiccator with silica gel, and their weights were measured before and after a 24 h period. The film thickness was measured at six points for calculations. The mass loss of the dish equaled the water permeated through the film. The permeability to water vapor was determined by plotting water passing through the film against time and calculating the slope. The value obtained was subsequently divided by the film's area, denoted as A, to calculate the WVTR as per Equation (5). \[WVTR=(\Delta m/\Delta t)/A \tag{5}\]  
To determine water vapor permeability (WVP) in accordance with Equation (6), the thickness of the film (d\({}_{\text{film}}\)) was multiplied by the WVTR value. The resultant product was then divided by the pressure differential in water vapor across the two sides of the film (T = 20 \({}^{\circ}\)C, \(\Delta\)p = 2339 Pa). \[WVP=\frac{WVTR\times d_{film}}{\Delta p}=\frac{[(\Delta m/\Delta t)/A]\times d _{film}}{\Delta p} \tag{6}\]  
**Antioxidant Activity. ** The present study assessed the antioxidant activity of the films using the DPPH (2,2-diphenyl-1-picrylhydrazyl) test. The experimental procedure was based on that by [PERSON] et al. [29], using a Shimadzu Spectrophotometer UV-1800 (Shimadzu, Kyoto, Japan). The percentage of antioxidant activity (AA) of the films was determined by measuring the absorbance (ABS) at 516 nm, with the absorbance of the DPPH solution serving as the control according to the following equation:  
\[AA\ (\%)=\frac{ABS_{control}-ABS_{sample}}{ABS_{control}}\times 100 \tag{7}\]  
**Mechanical Properties. ** Tensile experiments were carried out utilizing an Instron 3344 dynamometer, following the guidelines of ASTM D882 [30] with a crosshead velocity of 10 mm/min. Specimens for the tensile tests were in the shape of a dumbbell (with central dimensions of 5 \(\times\) 0.5 mm thickness and a gauge length of 22 mm), which were prepared using a Wallace cutting press. Each sample underwent a minimum of five trials, and the outcomes were combined to determine the average values for Young's modulus, tensile strength, and elongation at fracture.
## 3 Results and Discussion
### Characterization Studies
#### 3.1.3 FT-IR Spectra  
The FTIR spectra of Cd-I-MA-_co_-AN before and after Cd(II) elution are shown in Figure 3. In the Cd-I-MA-_co_-AN spectrum before elution, bands of around 3400-3700 cm\({}^{-1}\) are attributed to the hydroxyl group in -COOH, the C-H bond of the backbone appears at 2930, 2872 and 1450 cm\({}^{-1}\), respectively. The peak at 2241 cm\({}^{-1}\) can be attributed to the -CN group of AN, the peak at 1708 cm\({}^{-1}\) is ascribed to the C=O bond stretching vibrations of the MA. In the Cd-I-MA  
1450 cm\({}^{-1}\), respectively. The peak at 2280 cm\({}^{-1}\) is ascribed to the -CN bond of AN. The peak at 1738 cm\({}^{-1}\) can be assigned to the C=O stretching vibrations of the MA, showing the formation of Cd-I-MA-_co_-AN [30]. There is a peak at 2241 cm\({}^{-1}\), which shifted to 2280 cm\({}^{-1}\), verifying that the N groups were related to Cd(II) adsorption. The reason for the change in wavenumber is that electron transition occurs when N supplies electrons to the Cd(II) [27]. The peak at 2241 cm\({}^{-1}\) on the Cd-I-MA-_co_-AN shifted to 2280 cm\({}^{-1}\) before elution. This further evidences the occurrence of the imprinting process [31]. These results suggest that Cd-I-MA-_co_-AN contained the functional groups -COOH and -CN from MA and AN, which are successfully immobilized in the microsphere.
# Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm
## 5 Conclusions  
In this paper, we proposed a time-optimal trajectory planning method based on a 3-5-3 piecewise polynomial interpolation combined with GoldS-PSO algorithm. Aiming to optimize the trajectory of woodworking manipulator in joint space and improve processing efficiency. Firstly, a brief introduction was provided to the structure and operating principles of the woodworking manipulator. Then, we used a modified D-H parameter method to establish a kinematic model of the manipulator for forward and inverse kinematic analysis and used a 3-5-3 piecewise polynomial interpolation to fit three segment trajectories between four path points in joint space. By introducing a Circle chaotic map for population initialization, proposing an S-curve type nonlinear decreasing method to update inertia weight, and integrating the Gold-SA for improving the particle position update formula to obtain the GoldS-PSO, we utilized the commonly used benchmark functions to test the performance of GoldS-PSO, and compared it to basic PSO, SPSO, CFPSO and Gold-SA. Afterward, under the limitations of joints' displacement, velocity, and acceleration, the GoldS-PSO was used to optimize the trajectory interpolation time of each joint, with the target of obtaining the minimum total running time. The experimental results demonstrated that GoldS-PSO decreased each segment's maximum time of 5 s by 2.6811 s, 2.046 s, and 2.3953 s, respectively. Therefore, the total running time obtained via GoldS-PSO optimization was 7.8876 s, which had reduced by 47.483%, 15.695%, and 8.461% form 15 s before optimization, the 9.3442 s for PSO optimization, and the 8.6057 s for Gold-SA optimization, respectively. These findings indicate that GoldS-PSO converges faster and more accurately. The optimized trajectory displacement and velocity curves are smooth and stable, and the acceleration curve is continuous without sudden changes. Our results demonstrate the feasibility and superiority of the proposed method by continuously passing through all the given path points in the Cartesian space in optimal time. In future work, the structure of GoldS-PSO needs further adjustment to improve algorithm performance, and we will carry out multi-objective trajectory optimization for the woodworking manipulator in terms of energy consumption, time, and impact. The GoldS-PSO algorithm proposed in this paper can provide guidance for relative agricultural and forestry engineering optimization problems.  
Conceptualization, S.C. and C.Z.; funding acquisition, C.Z.; methodology, S.C.; resources, C.Z.; software, S.C.; supervision, C.Z.; validation, S.C. and J.Y.; visualization, S.C.; writing--original draft, S.C.; writing--review and editing, S.C. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds of CAF, grant number CAFYBB2020 SY043.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.
## 2 Materials and Methods
### NDVI and LST
#### 2.3.1. Radiometric Calibration and Spectral Reflectance  
Radiance determination begins with the generation of Level 1 (L1) products, in which the calculation of pixels with Level 0 (L0) product value, the raw value, has data converted into absolute radiance units using 32 (thirty-two) bit floating points. Subsequently, the absolute radiance values are scaled to 8-bit data and thus become calibrated digital numbers before the output to media distribution [24].  
To calculate the spectral radiance of each band, the digital number (DN) of each pixel in the image is converted into monochromatic spectral radiance. This radiance represents the solar energy reflected by each pixel, per unit area, time, solid angle and wavelength, measured at Landsat satellite level. Landsat-8 satellite images only require the radiometric calibration of band 10 (thermal infrared) using Equation (6) [25].  
\[L_{\lambda}=M_{L}\times ND+A_{L} \tag{6}\]  
where  
\(L_{\lambda}\) = Spectral radiance in (watts/m\({}^{2}\times\) sr \(\times\)um);  
\(M_{L}\) = Band-specific resizing multiplicative factor;  
\(ND\) = Quantized value calibrated by the pixel in DN;  
\(A_{L}\) = Band-specific additive scaling factor.  
The resizing factor variables to calculate the radiance of Landsat-8 images are available in the image metadata and were applied automatically using.xml files.  
The monochromatic reflectance of each band represents the ratio between the radiation flux, reflected by each band, and the incident radiation flux and is determined by Equation (7) [26].  
\[\rho_{\lambda i}=\frac{M_{\rho}\times ND+A_{L}}{cos(Z)\times\frac{1}{Z^{2}}} \tag{7}\]  
Figure 4: Sample of masks generated to represent synthetic clouds.  
Figure 5: SEBAL model steps used in the image processing.  
where  
\(\rho_{\lambda i}\) = Monochromatic reflectance of each band;  
\(Z\) = Sun elevation angle (degrees);  
\(ND\) = Quantized value calibrated by the pixel in DN;  
\(d\) = Inverse of the square of the relative Earth-Sun distance and the Earth-Sun distance, on a given day of the year (astronomical units);  
\(M_{\rho}\) = Multiplicative reflectance scale factor for each band.  
By applying the reflectance values, it is possible to obtain the \(NDVI\) and the \(LST\).
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### OFDM-OQAM Transceiver
#### 4.1.1.2 Channel Description
For example, if \(\dot{H}[\ddot{k}]\) represents the channel coefficients across the \(\ddot{k}\)th sub-carrier for \(\ddot{k}=0,1,\ldots,M-1\), then \(\dot{H}[0]\) to \(\dot{H}[F_{L}-1]\) are chosen to equal the \(H[0]\) obtained from Equation (12).
# Research on the Influence of Rectifying Orifice Plate on the Airflow Uniformity of Exhaust Hood
## 1 Introduction
In this study, we employed a CFD approach using Fluent to simulate and analyze the effects of the two parameters of the rectifying orifice plate on the airflow uniformity of the exhaust hood. Our goal was to construct a model of an exhaust hood equipped with a double-layer rectifying orifice plate that achieves optimal airflow uniformity with the lowest wind velocity non-uniformity by adjusting the aforementioned parameters. Finally, we conducted experimental measurements to verify the validity of the design and computational method.
## 1 Introduction  
In recent years, the integration density of electric devices has risen rapidly, and device sizes have gradually developed to the nanoscale [1]. As bridges of device integration, nanowires have gained extensive research and development [2]. Nanowires, especially metal oxide nanowires, have been used in a variety of applications due to their excellent electrical, optical, magnetic, and biological properties, such as in micro-nano functional devices [3], sensors [4], and batteries [5]. The effective interconnection of nanowires is inextricably linked to effective device integration. Therefore, achieving the interconnection of nanowires with high quality and low damage has become a vital issue in the manufacturing process of micro-nano devices.  
In order to obtain better nanowires interconnection, scholars have proposed a variety of approaches to obtain nanowire interconnection, such as joule melt connection [6], electron beam/ion beam connection [7; 8], brazing [9], and cold welding [10]. Laser welding nanowires technology has developed rapidly in the past few years [11; 12; 13; 14; 15]. Different from conventional laser (continuous laser, nanosecond laser, etc.)-induced \"thermal processing\" methods, the ultrafast laser, especially femtosecond laser welding, has a very high pulse energy density, allowing nano-welding at a low average laser power. It reduces damage to the nanowires and substrate [16], thus offering great manufacturing advantages in the field of nano-welding [17]. Its main mechanism is to induce plasmon photothermal effects on metal nanomaterials, thereby realizing the effective connection of the metal-metal and metal-metal oxide nanowires. However, laser welding of metal oxide nanowires is limited.  
The absence of a plasmonic excitation photothermal effect and relative high point result in significant obstacles for the interconnection of metal oxide nanowires. To solve this issue, [PERSON] [18] used a high-energy focused laser beam to irradiate the ZnO nanowires, achieving a hot-melt connection. However, the joint melt size was large, and laser irradiation caused obvious excess ablation to the substrate after welding. [PERSON] [19] trapped the electron-hole plasma energy by lattice defects based on the femtosecond laser two-photon absorption to realize the interconnection of the ZnO nanowires. However, this approach is only feasible for wide bandgap nanowires and cannot work well with other metal oxide nanowires.  
So far, there has been no suitable laser welding method to achieve metal nanowire interconnection. This paper proposes a method to interconnect metal oxide nanowires by melting metal nanoparticles solder using a laser-induced plasmonic excitonic photothermal effect. This paper investigates the effect of femtosecond laser fluence on the melting morphology of Ag solders and the irradiation time on nanowire soldering interconnections. The electrical properties of nanowires are characterized before and after soldering. The photoelectric property of the ZnO-Ag-ZnO interconnect structure under UV light is tested.
## 5 Results of QoE\({}_{i}\) Modeling and Discussion
### Predictive Models of \(QoE_{i}\)
#### 5.5.3 Predictive Models Created by Using an Automatic Modeling Method
Nevertheless, the results show that the relative error achieved by the ensemble prediction is greater than the RE of the existing model and amounts to RE = 0.404. Higher prediction accuracy, i.e., a lower RE value compared to the existing C&R tree model was achieved with a _reference model_. This is the first model in the ensemble of 10 created components shown in Table 6, with a relative error smaller by 0.027 than the previously considered RE = 0.274 amounting to RE = 0.247,  
Figure 10: Structure of the created C&R tree model. which corresponds to the prediction accuracy \(A=69.7\%\). The prediction accuracy A can be represented by the following expression:  
\[A=1-\frac{\sum\limits_{i=1}^{n}\left|QoE_{ip}-QoE_{i}\right|}{QoE_{ip\text{max }}-QoE_{ip\text{min}}} \tag{9}\]  
where \(n\)--the number of input-output vectors from the dataset for testing, \(QoE_{ip\text{max}}\)--the maximum value of the prediction variable \(QoE_{i}\), \(QoE_{ip\text{min}}\)--the minimum value of the prediction variable \(QoE_{i}\). In addition to the value of A, Table 6 shows the number of inputs for each component, and the size of the model expressed by the number of nodes. Apart from the smaller relative error achieved by the reference model, based on Table 6, it is necessary to point out another advantage of this model compared to the others. That advantage is the smaller number of inputs (9 inputs), which favors its simplicity.
# Accelerometer-Based Pavement Classification for Vehicle Dynamics Analysis Using Neural Networks
## 1 Introduction
This article emphasizes real-time categorization via the integration of acoustic analysis and machine learning models, in contrast to previous studies that mostly use mechanical vibration models or geometry approaches for assessing road surfaces. This method is characterized by its practicality; acoustic sensors may be mounted directly within the vehicle, enabling the continuous monitoring of road conditions throughout transit. This technique may be readily modified for commercial trucks, hence diminishing the need for more intricate and costly technology like LiDAR or other optical and laser sensors often used to evaluate road conditions.
# Detecting Underground Geospatial Features for Protection of Rights in 3D Space: Korean Cases
## 3 Results  
In this study, 3D data were acquired from the Gwangbok Underground Shopping Center in Busan Metropolitan City, Korea using a direct survey method. The representative 3D data acquisition methods for underground spaces include using a total station and terrestrial LiDAR equipment. A total station is an optical instrument commonly used in surveying and civil engineering. It is useful for measuring horizontal angles, vertical angles, and distance. In this study, the data were acquired using the terrestrial LiDAR since the method of employing the total station has the disadvantage that it is difficult to capture the detailed shape of the data, although the volume of data acquired is smaller than that acquired using the terrestrial LiDAR equipment.
# Nucleotides as Bacterial Second Messengers
## 2 The c-di-GMP Nucleotide
### Cellulose Biosynthesis  
Cellulose biosynthesis in _G. xylinus_ was the first process discovered in bacteria to be regulated by c-di-GMP. The cellulose synthase enzymes have a PilZ domain at the C-terminal end, suggesting that these enzymes can be allosterically regulated by c-di-GMP. Indeed, it has been observed that the PilZ domain of _G. xylinus_ cellulose synthase can join c-di-GMP, increasing the activity of this enzyme. In vitro, cellulose biosynthesis has been shown to require c-di-GMP, UDP-glucose, and membrane fractions of _G. xylinus_[41].