esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning
## 3 Methodology
### Data Augmentation  
As particular structures, sentence patterns, and specific terms in disaster management limit the available data for training, this research must perform data augmentation. For texts used in messaging applications, the formations usually differ from those in large scales, such as newspapers and articles. Furthermore, specialized databases are required to recognize specific jargon in particular domains. In other words, the performance of a general NER model may flaw by the unfamiliar vocabulary used in specific domains and, thus, imprecise recognition. Hence, before building the target model, this research constructs a concise database to strengthen the training process concerning damage information gathering and analysis in disaster management. As a result of the advancement in machine learning, establishing a comprehensive database from scratch is no longer necessary. The data augmentation module can decompose into three tasks: NER, pattern specification, and vocabulary update. Along with these three tasks, the module provides the predetermined training data required for later use and enables the augmented model to perform NER tasks in the target domain.
## 3 Results and Discussion
### Ambient Temperature  
As it can be consulted in Figure 8, the ambient temperature does not affect to electrical production of PV cells of the hybrid panels. Nevertheless, thermal efficiency is positively related to the variation of the ambient temperature. An increase in hot water production is observed when an increment in the ambient temperature occurs. The thermal efficiency of conventional PVT panels with the ambient temperature of 0 \({}^{\circ}\)C and 35 \({}^{\circ}\)C is 57.5% and  
Figure 7: Thermal and electrical efficiency of the studied hybrid panels with the inlet temperature variation.  
68.6%, respectively. The difference between thermal production of conventional and PVT + TEG panels is insignificant. The electrical efficiency of TEGs modules increases with the increment in the ambient temperature, reaching a maximum of 0.47% at 35 \({}^{\circ}\)C. This result makes sense because TEG efficiency is related to the gradient's increment between sides of the module. These temperatures are related to thermal efficiency too. In this way, it is possible to conclude that an increase in the thermal efficiency of a hybrid panel contributes to the increment in the electrical efficiency of the TEGs modules integrated into the panel. Additionally, an overall electrical efficiency increment is expected as well.  
In low ambient temperature situations, the global electrical efficiency of both panels is nearly equal. Consequently, global efficiency is also similar. Nevertheless, with the increase in this parameter, the thermal efficiency of the collectors and electrical efficiency of TEG modules grow up. In this scenario, the global efficiency of the PVT + TEG panel is 0.64% higher than that of a conventional photovoltaics-thermal panel.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods  
### Specification and Experimental Data of Worldview-3 Satellite Images  
The Worldview-3 satellite sensor provides a panchromatic image with a 0.3 m spatial resolution, eight bands with a 1.2 m VNIR wavelength range, and eight SWIR bands with a 7.2 m wavelength range. Although Worldview-3 provides the same VNIR and SWIR bands as Sentinel-2A, it provides images with higher spatial resolution, which not only is an advantage for environmental monitoring and change detection but also allows the analysis of small objects in urban areas. Table 1 presents spectral information by the specifications of the Worldview-3 satellite image.  
In this manuscript, the digital number (DN) value of Worldview-3 was used for pansharpening. To apply the pansharpening technique to Worldview-3 satellite images, panchromatic images are converted to 0.3 m spatial resolution, and the VNIR and SWIR bands are geometrically corrected by 1.2 m and 7.2 m, respectively. The images used in the study involved two (2.88 x 2.88 km) regions, each consisting of 9600 x 9600 pixels based on the 0.3 m spatial resolution band. To verify the pansharpening performance with regard to various types of land cover, one image over Dongducheon, Gyeonggi-do, Korea (site 1) was selected to represent urban areas, and another image over Namhae-gun, Gyeongsangnam-do, Korea (site 2) was selected to represent natural features, such as vegetation, agricultural land, and water systems. Figure 1 shows two images of the experimental regions used to sharpen Worldview-3 satellite images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Band**} & **Central Wavelength** & **Bandwidth** & **Spatial Resolution** \\  & **(nm)** & **(nm)** & **(nm)** \\ \hline Panchromatic & 625 & 350 & 0.31 \\ \hline Band 1-Coastal & 425 & 50 & \\ Band 2-Blue & 480 & 60 & \\ Band 3-Green & 545 & 70 & \\ Band 4-Yellow & 605 & 40 & \\ Band 5-Red & 660 & 60 & \\ Band 6-Red Edge & 725 & 40 & \\ Band 7-Near-IR 1 & 833 & 125 & \\ Band 8-Near-IR 2 & 950 & 180 & \\ \hline Band 9-SWIR 1 & 1210 & 30 & \\ Band 10-SWIR 2 & 1570 & 40 & \\ Band 11-SWIR 3 & 1660 & 40 & \\ Band 12-SWIR 4 & 1730 & 40 & \\ Band 13-SWIR 5 & 2165 & 40 & \\ Band 14-SWIR 6 & 2205 & 40 & \\ Band 15-SWIR 7 & 2260 & 50 & \\ Band 16-SWIR 8 & 2330 & 70 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of Worldview-3 satellite imagery.  
Figure 1: _Cont_.
###### Abstract  
Low Earth Orbit (LEO) Satellite Internet Network (LEO-SIN) is a promising approach to global Gigabit per second (Gbps) broadband communications in the coming sixth-generation (6G) era. This paper mainly focuses on the innovation of accuracy improvement of simulation modeling of the Doppler Power Spectrum (DPS) of satellite channels in LEO-SIN. Existing DPS modeling methods are based on [PERSON]'s Sum-of-Sinusoids (SOS) which have obvious modeling errors in scenarios with main signal propagation paths, asymmetrical power spectrum, and random multi-path signals with a random Angle of Arrival (AOA) in LEO-SIN. There are few state-of-art researches devoted to higher accuracy of DPS modeling for simulation. Therefore, this paper proposes a novel Random Method of Exact Doppler Spread method Set Partitioning (RMEDS-SP). Distinct from existed researches, we firstly model the DPS of LEO-SIN, which more accurately describes the characteristics of frequency dispersion with the main path and multi-path signals with random AOA. Furthermore, piecewise functions to the Autocorrelation Function (ACF) of RMEDS-SP is first exploited to converge the modeling error supposition with time by periodic changes, which further improve the accuracy of the DPS model. Experimental results show that the accuracy of DPS in our proposed model is improved by 32.19% and 18.52%, respectively when compared with existing models.  
Doppler Power Spectrum; sum-of-sinusoids; simulation method; LEO satellite; random AOA; piecewise convergence +
Footnote †: journal: _electronics_  
Article
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 4 Simulation Results
### Tracking Precision Evaluation  
This subsection evaluates the tracking precision obtained by different methods under different \(C/N_{0}\) conditions.  
Figure 6 shows the code phase and carrier phase tracking precision under different \(C/N_{0}\) conditions. These results are derived from the error statistics obtained at \(C/N_{0}\) in the range 35 to 50 dB-Hz. An analysis of the figure indicates the following. (1) The code phase and carrier phase tracking precision improves as the \(C/N_{0}\) increases, as expected. This indicates that better signal tracking precision can be achieved by increasing the useful signal power. (2) The MVDR-like and MMSE algorithms can only improve the code phase precision compared to the PI algorithm. However, the carrier phase precision of these algorithms deteriorates accordingly whether the signal strength is low or high. (3) The code phase precision of the proposed method is the highest among all methods when the signal strength is weak, and it is comparable to the MVDR-like and MMSE algorithms when the signal is strong. (4) The proposed method can improve the code tracking precision while not affecting the carrier tracking precision, unlike the MVDR-like and MMSE algorithms. Therefore, it has a unique advantage in terms of improving the tracking precision.
# Computational Test for Conditional Independence
## 2 Computational Testing with Categorical Variables
### Establishing a Null Distribution by MCCV  
A fundamental challenge in statistical testing lies in controlling the significance level, defined as the false positive rate or type I error rate. We manage this error rate by estimating an empirical null distribution of metric scores using Monte Carlo Cross-Validation (MCCV) in combination with permutation. Generally, statistical testing based on permutation is a robust alternative when fundamental assumptions of parametric and nonparametric tests are violated [16]. MCCV is a resampling technique typically used to assess ML models' performance by randomly partitioning data into training and testing sets. For each iteration, MCCV randomly selects a subset of data for training, and the remaining observations serve as the test (validation) set.  
The first step in setting up a test is to choose between a classification model for \(Y\) or for \(X\). Tree-based models, which are robust against overfitting when predicting new data, are probably the best choice. However, we will discuss this further in Section 2.3. For now, let us assume that we have a robust classification model for \(Y\) and therefore \(M(P(Y=k\mid X^{*},Z))=M(P(Y=k\mid X,Z))\) is the null hypothesis.  
Now we can estimate the empirical null distribution(s). For each MCCV iteration, we first permute \(X\) into \(X^{*}\), then randomly split the data into a training set of size \(pN\) and a testing set of size \((1-p)N\). We then train a classification model for \(Y\) using \(X^{*}\) and the conditioning set \(Z\) with the training set data. We then calculate the classification performance metric using the testing set. After many iterations, this approach theoretically derives the empirical distribution of metric scores under the null being true.
## 2 Materials and Methods
### Surface Analysis  
The surface morphology of the samples post-laser cleaning was analyzed using Field Emission Ring Scanning Electron Microscopy (SEM) (Carl Zeiss, GeminiSEM300, Oberkochen, Germany). For characterizing the chemical composition of the surfaces before and after the cleaning process, Energy-Dispersive X-ray Spectroscopy (EDS) and X-ray Diffraction (XRD) (SmartLab, Tokyo, Japan) were employed. The SEM and surface roughness of each specimen were determined by averaging the measurements of four different surface points. The XRD measurements encompassed a 20 diffraction angle range from 10\({}^{\circ}\) to 80\({}^{\circ}\) with incremental steps of 0.02\({}^{\circ}\). The surface roughness of the cleaned samples was quantified using Laser Scanning Confocal Microscopy (CLSM) (Olympus, OSL5000) with parameters varied to assess their impact. Microhardness evaluations pre- and post-laser cleaning were conducted using a digital micro-Vickers hardness tester, applying a load of 500 g. The microhardness for each specimen was ascertained by averaging measurements from four distinct surface points.
## Materials and Methods  
### Data-Driven Approach  
We used a data-driven approach to develop our global continuous gridded SIF with 0.05\({}^{\circ}\) spatial resolution and an 8-day time step based on discrete OCO-2 SIF soundings along with continuous MODIS data and MERRA-2 meteorological data. This method applied the Cubist regression tree model, which is an extension of [PERSON]'s M5 model tree. Cubist creates rule-based predictive models from training samples, and each rule has an associated multivariate linear regression model. The  
Figure 1: Examples of producing 1\({}^{\circ}\), 8-day solar-induced chlorophyll fluorescence (SIF) maps from Orbiting Carbon Observatory-2 (OCO-2) soundings: (**a**) By Nadir mode in 9–16 January 2015; (**b**) by all modes in 9–16 January 2015; (**c**) by Nadir mode in 9–16 July 2015; and (**d**) by all modes in 9–16 July 2015.  
resulting linear models can be overlapped and are not necessarily mutually exclusive. A prediction is made using the linear regression model at the terminal node of the tree, and the final prediction is the average value from multiple output values. Cubist has shorter run times while producing comparable results with other machine learning approaches [48], and it has been widely used for the estimation of biophysical variables and carbon fluxes, such as leaf area index (LAI) [49], forest biomass [50; 51], net ecosystem exchange (NEE) [52], and GPP [12]. More details on regression tree approaches and Cubist were described in [48; 52]. Cubist uses three statistical measures to evaluate the quality of the constructed predictive models: Mean absolute error (MAE), relative error (RE), and the product-moment correlation coefficient (R). MAE is calculated as follows:  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}| \tag{1}\]  
where N is the number of training samples. \(y_{i}\) and \(\hat{y}_{i}\) are the observed and predicted values of the response variable, respectively. RE can be calculated as:  
\[\text{RE}=\frac{MAE_{T}}{MAE_{\mu}} \tag{2}\]  
where \(\text{MAE}_{T}\) is the MAE of the developed model and \(\text{MAE}_{\mu}\) is the MAE that would result from always predicting the mean value.
# Frequency-Separated Attention Network for Image Super-Resolution
## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.1 Comparison and Visualization of PSNR and SSIM Results  
In this section, we compare our FSANet with state-of-the-art methods, including SRCNN [3], FSRCNN [9], VDSR [6], HDRN [48], CARN [49], MemNet [7], IMDN [50], LAPAR-A [51], SRMD [52], A2F-L [53], and FENet [54].  
Table 4 presents the values of the PSNR and SSIM metrics for different networks at magnification factors of \(\times\)2, \(\times\)3, and \(\times\)4 on five benchmark datasets commonly used in super-resolution. Figure 11 displays a visual quality comparison of image reconstructions at a magnification factor of 4\(\times\). The images reconstructed by FSANet exhibit more vivid details compared to those from other networks, as shown in the figures.  
Figure 11: A qualitative comparison of classic state-of-the-art SR models for the \(\times\)4 upscaling task. Ours (FSANet) can restore more accurate and sharper details than the other models. We crop the SR image according to the position of the red box to clearly show the details. On the far left is the ground-truth image, while the images labeled “HR” on the right represent cropped portions of the ground-truth image.
## 4 Discussion
### Key Factors Regulating HMGR Expression  
Hormones are important regulators of terpenoid synthesis, functioning by influencing HMGR activity. Several studies have demonstrated that exogenous application of hormones, including ABA and indole-3-acetic acid (IAA), regulates HMGR activity, while CTX counteracts HMGR inhibitors to support cell growth [39; 40; 41; 42]. [PERSON] et al. [26] found that BR treatment in Kyoho grapes inhibited _HMGR_ expression and promoted fruit coloration. Here, our study showed significantly higher BR levels in FaHMGROE strawberries than in FaHMGRR.  
Several key proteins interact with HMGR to regulate its activity, including Protein Phosphatase 2A (PP2A), sucrose nonfermenting-1 (SNF1)-related kinase 1 (SnRK1), HIGH STEROL ESTER 1 (HISE1), Phytoene synthases 1 (PSY1), GATA24, and PHYTOCHROMEINTERACTING FACTOR 3 (PIF3). PP2A, a multi-stage regulator of plant HMGR, negatively impacts HMGR activity through post-translational regulation and is involved in signal transduction for hormones such as ABA, IAA, BRs, and ETH [40]. SnRK1 helps maintain energy homeostasis by post-translationally regulating HMGR and TPS5 [43]. Protein HISE1 participates in the downregulation of HMGR to prevent sterol overproduction in the endoplasmic reticulum, as evidenced by the loss-of-function mutant _hise1_, which displays elevated levels of HMGR and sterol-producing activity [44]. Additionally, tHMG1 complexes with ZmPSY1 and PaCRT1 to significantly boost carotenoid accumulation in rice endosperm by enhancing MVA pathway flux and creating a metabolic sink for carotenoids [45]. In _Vitis vinifera_, twelve amino acid residues of VvGATA24, including Pro218B, directly bind to the GATA-box cis-element of _VvHMGR2_ to mediate wax terpenoid biosynthesis [46].
## 2 Experiments and Results
### Picosecond Amplifier System at 1 kHz
#### 2.1.4 Side-Pumped Amplifier  
Finally, the 34 mJ picosecond pulses from end-pumped amplifiers were injected into the last amplifier, a module consisting of a \(\Phi\)8 \(\times\) 185 mm\({}^{3}\) Nd:YAG side-pumped by CW laser diodes. The maximum allowed pump power of 1000 W could be obtained at the wavelength of 808 nm. The cooling water system maintained at 14 \({}^{\circ}\)C was used for the heat dissipation of the crystal. In this experiment, a pump power of 750 W was used and a 64.8 mJ picosecond laser pulse at 1 kHz was obtained. Figure 9 shows the power curve and beam quality, where a good beam quality of \(M^{2}<2\) is illustrated.
## 2 Prerequisites
### Segmentation and Feature Extraction  
Before extracting features, to filter out the noise, the point cloud is segmented to reduce noise interference. \(\mathbf{D}_{t}=\{\mathbf{d}_{1},\mathbf{d}_{2},\ldots,\mathbf{d}_{n}\}\) is the point cloud information acquired at time \(\mathbf{t}\); \(\mathbf{d}_{\tau}\) is a point in \(\mathbf{D}_{t}\); \(\mathbf{D}_{t}\) is projected onto a range image; the point \(\mathbf{d}_{\tau}\) represents a pixel of the image; and the pixel value \(\mathbf{r}_{\tau}\) represents the Euclidean distance from the point \(\mathbf{d}_{\tau}\) to the lidar. An image-based segmentation method [17] is applied to the range image to group points into many clusters. Points of the same category have the same label (the ground is a particular category). Features are then extracted from ground points and segmented points, with the corresponding edge points and plane points obtained by calculating the curvature \(\mathbf{E}\) of each point. To evenly extract features from all directions, the range image is divided horizontally into several equal sub-images. Then, the points are sorted in each row of the sub-image based on their curvature values, \(\mathbf{E}\). The curvature \(\mathbf{E}\) is computed by Equation (4).  
\[\mathbf{E}=\frac{1}{|\mathbf{S}|\cdot\|\mathbf{r}_{\tau}\|}\,\|\sum\limits_{\lambda\in \mathbf{S}\lambda\
eq\tau}(\mathbf{r}_{\lambda}-\mathbf{r}_{\tau})\| \tag{4}\]  
where \(\mathbf{S}\) is the set of continuous points \(\mathbf{d}_{\tau}\) from the same row of the range image; \(\lambda\) is a point in the set \(\mathbf{S}\); \(\mathbf{E}_{\mathbf{th}}\) is a threshold which is set to distinguish different types of features; the points are called with \(\mathbf{E}>\mathbf{E}_{\mathbf{th}}\) edge features; and the points are called with \(\mathbf{E}<\mathbf{E}_{\mathbf{th}}\) planar features.
## 4 Case Study of Data Collaboration
### Visual Traffic Flow Detection Based on DFA
#### 4.3.5 Target Detection Effect Comparison  
Due to the small number of pixels occupied by vehicles in the high-altitude video captured by UAV, the effect of using yolov2 for object detection is not good. We used the number of real vehicles manually counted frame-by-frame as a baseline to calculate the accuracy. The yolov2 algorithm was selected to compare with the differential fusion algorithm designed in this paper for small target identification, and the accuracy of this paper was higher than that of yolov2. Without loss of generality, we randomly selected two frames for comparison. In Figure 11, the blue is the target object recognized by yolov2, and the green is the target object recognized by the fusion algorithm designed in this paper. Since the target object is a moving object, the proposed algorithm is obviously better than yolov2 in the small target environment as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **Time** \\ \hline DFA & 95.26\% & 0.55 s \\ yolov2 & 78.43\% & 0.41 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy and time consumption comparison.  
Figure 10: Traffic flow at seven real intersections. (**a**) Morning rush hour: 8:00 to 9:00; (**b**) Evening rush hour: 17:30-18:30.  
Figure 9: Box figure of car flow. (**a**) Box figure of car flow in morning rush hour. (**b**) Box figure of car flow in evening rush hour.
## 2 Material and Methods
### Eddy Covariance Tower Establishment and Other Accessory Instruments  
The instrument tower consisted of a complete EC system and other accessory meteorological sensors, including a Gill-WM WindMaster 3-D Sonic Anemometer (Gill Instruments Ltd., Lymington, Hampshire, UK, at 3 m height) and an LI-7500 RS open-path infrared gas analyzer (IRGA, Li-Cor Inc., Lincoln, NE, USA at a 3 m height) integrated with the SMARTFlux System. The software estimates quality of each flux data point using a flux quality flag obtained from the integral results of the steady-state and the development turbulence tests. The flagging policy is in compliance with the documented standards specified by the ICOS (Integrated Carbon Observation System), AmeriFlux, and FLUXNET; which uses numerical values '0', '1', and '2' to indicate high-quality, suitable for budget analysis, and poor-quality data; respectively.  
Supplementary instruments were included in the LI-7900-102 Biomet System Package (Li-Cor Inc., Lincoln, NE, USA). The whole package included an NR Lite2 net Radiometer (Kipp and Zonen Corp., Delft, The Netherlands at 2.5 m height), a Vaisala HMP155 Humidity and Temperature sensor (Vaisala Corp., Helsinki, Finland at 2.8 m height) housed in an RM Young 41005-5 Radiation Shield (R.M. Young Company, Traverse City, MI, USA), three Hulseflux HFP01 SC Self-Calibrating Soil Heat Flux sensors (Hulseflux Thermal Sensors, Delft, The Netherlands at 8 cm depth), three Stevens Hydra Probe II (Stevens Water Monitoring Systems Inc., Portland, OR, USA at 6 cm depth), an LI-200R Pyranometer (Li-Cor Inc., NE, USA at 2.5 m height), an LI-190R Quantum Sensor (Li-Cor Inc., NE, USA at 2.5 m height), a TR-525 USW Rain/Precipitation gauge (Texas Instruments Inc., Dallas, TX, USA at 1.5 m height), and a Sutton Xlite 9210 Data logger (Sutron Corp., Sterling, VA, USA).