esa-sceva/satcom-chunk-collection huggingface dataset:
However, most of the existing research that applies SVM to the transient stability prediction only extracted the trajectory variables after fault as input features, which did not fully consider the dynamic characteristics of the power system at different time stages. However, some ensemble learning models have been established to improve prediction performance, for example, the data segmentation-based ensemble classification (DSEC) method was proposed for transient stability status prediction with imbalanced data [6], the transient stability prediction by a hybrid intelligent system [33], and so on. The prediction accuracies of existing models using those post-fault input features are usually not high enough, which might not be suitable for subsequent online emergency control. In view of the above concerns, the contribution of this paper includes the following four aspects. Firstly, the influence of trajectory variables in multiple time stages on transient stability is analyzed, and the sampled data covering all three-time stages, namely the pre-fault, during-fault and post-fault, are proposed to be used as the SVM input features. Secondly, according to the rotor motion equation (also known as the \"swing equation\"), the new feature of generators' electromagnetic power is introduced into the transient stability prediction. Case analysis shows that generators' electromagnetic power has higher classification accuracy than other feature types. Then, further feature selection is carried out based on min-redundancy and max-relevance (mRMR), and the optimal feature set for predicting system stability is obtained. Finally, the prediction results of SVM classifiers with different feature sets are combined by the winner take all (WTA) method, to establish a high-precision and conservative transient stability prediction model.
# HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing
## 3 Image Analysis Methods and Results  
### Color Images  
A true color composite image is synthesized from red, green, and blue (RGB) band images captured synchronously by the HPT. The fields of view of the three bands deviate spatially from one another because of the optical alignment of their CCD image sensor modules. While the CCD modules are aligned precisely along the optical axis by the focus adjustment, they have an alignment precision of \(\sim\)100 \(\upmu\)m in the direction perpendicular to the optical axis, which corresponds to a deviation of a few tens of pixels. Therefore, band-to-band registration using a feature-based method is implemented in the image processing procedure. The scale-invariant feature transform method [12] is used for feature matching between the blue and green, and between the blue and red band images. From a single set of the two band images for a certain scene, their matched feature points are limited in number, and they are localized within the frame because of the difference in the spectral radiance of the different spectral bands. The matched feature points, which are usually found at the edges of black or white objects and clouds, are accumulated from many sets of different scenes, and a homography matrix is estimated from all of the matched feature points. The homography matrix describes a transformation that maps points in one plane to the corresponding points in another plane, and it is widely used for georeferencing. Based on each estimated homography matrix, the green and red band images are transformed into the coordinate system of the blue band image. Once a sufficiently precise transformation is accomplished, the same homography matrix can be applied to any other RGB band images.  
Figure 5 shows a true color image composed of the RGB bands selected from the \"first light\" scenes, which were acquired about one month after the launch of the RISING-2 microsatellite. For comparison, a true color image composed of Bands 1, 2, and 3 of the Enhanced Thematic Mapper Plus (ETM+) sensor on the Landsat 7 satellite with 30-m spatial resolution is also shown in Figure 5. The area in the scene is famous for its production of rice in Japan, and many rectangular paddy fields roughly 100 m \(\times\) 50 m in size are visible in the image. Agricultural roads that are typically a few meters wide, which separate the paddy fields, are evident in the RISING-2 HPT image, but invisible in the Landsat 7 ETM+ image. This image demonstrates clear evidence of the 5-m resolution targeted by the HPT.  
Figure 5: A true color composite image of Minami-Uonuma City, Niigata Prefecture, Japan, acquired by the RISING-2 HPT on 2 July 2014, overlaid on a true color composite image acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) on 30 May 2014.
## 3 Results and Discussion
### Chemical Properties  
#### 3.2.1 Total Polyphenols  
For the non-stored samples, the content of total polyphenols in control samples (without fruit powders addition) was approximately 7% higher in products gained after usage of microwaves than in fried ones (Table 3). This pointed out an influence of the processing type on the thermolability of native polyphenols present in snacks.  
Fruit powders added to pellets (3%; _w/w_) were obtained from blackcurrant, chokeberry and cranberry spray-dried juice gained with addition of maltodextrin. The dominant groups of polyphenols in blackcurrant powders were: anthocyanins (cyanidin- and delphinidin-3-O-rutinoside being the most abundant), flavan-3-ols, flavonoids and phenolic acids [33], whereas in chokeberry five major groups of polyphenols were identified: phenolic acids, anthocyanins, flavonoids, flavan-3-ols and procyanidins (UPLC analyses; data not shown). On the other hand, in cranberry juice powders the dominant groups were: flavonoids, followed by anthocyanins, phenolic acids and flavan-3-ols (UPLC analyses; data not shown). The addition of fruit juice powders into pellets resulted in an increase in total polyphenols content in following order: chokeberry (approximately increase of 29.8%), cranberry (approximately 26.4%) and blackcurrant (approximately 23%) for products gained after application of microwaves and in the same order but to a slightly lower extent (26.8; 26.7 and 15.9%, respectively) in fried products when compare to control samples (Table 3). This showed an influence of the processing type on the thermolability of polyphenols originated from fruit juice powders in snacks and proved their better retention after application of microwaves used for snacks preparation. It was indicated that the microwave vacuum drying process resulted in better retention of selected polyphenolic compounds during dehydration of plum products [33; 34], thus, this method can be recommended for the preparation of products with improved functional properties. Previously, [PERSON] et al. [35] tested addition of commercial spray-dried powders into extruded corn breakfast cereals and it was observed that usage of selected types of fruit powders improved the total polyphenics contents in products analysed and differed in terms of the chemical composition of fruits. Storage caused a linear decrease in total polyphenols contents that after 5 weeks was 27.5% lower in case of microwaved and 40.4% for fried products when compared to control samples. This indicated that application of microwaves for snacks preparation seems to be more suitable for preservation of bioactive components in such products when compared to these gained after frying.
# Hybrid Active-Passive Reconfigurable Intelligent Surface for Cooperative Transmission Systems
## 3 Performance Analysis
### OP of the Full Active RIS System
\begin{split} F_{\gamma_{a}}(z)&=1-\sum_{k=0}^{m_{S }-1}\sum_{p=0}^{k}\sum_{q=0}^{m_{D}-1}\binom{k}{p}\binom{m_{D}-1}{q}\frac{2 \left(\frac{\mathrm{Y_{S}}\sigma_{R}^{2}}{NP_{S}}\right)^{k}\left(\frac{ \mathrm{Y_{D}}\sigma_{R}^{2}}{NP_{A}}\right)^{m_{D}}}{k!\Gamma(m_{D})}\left( \frac{\mathrm{NY_{S}}P_{A}\sigma_{R}^{2}}{\mathrm{Y_{D}}P_{S}\sigma_{D}^{2}} \right)^{\frac{q-p+1}{2}}\\ &\times\gamma_{th}^{m_{D}+k}e^{-\left(\frac{\mathrm{Y_{S}}\sigma_ {R}^{2}}{NP_{S}}+\frac{\mathrm{Y_{D}}\sigma_{D}^{2}}{NP_{A}}\right)\gamma_{th}} K_{q-p+1}\left(2\sqrt{\frac{\mathrm{Y_{S}}\mathrm{Y_{D}}\sigma_{R}^{2} \sigma_{D}^{2}}{N^{3}P_{S}P_{A}}}\gamma_{th}\right)\end{split}  \tag{25}\]  
where \(\gamma_{th}\) is a given SNR threshold. The OP of the full A-RIS system is the probability that the instantaneous SNR is below \(\gamma_{th}\), which can be written as follows,  
\[P_{outa}=\Pr(\gamma_{a}<\gamma_{th})=F_{\gamma_{a}}(\gamma_{th}) \tag{26}\]
# Manipulator Control of the Robotized TMS System with Incurved TMS Coil Case
## 2 Calibration Between the Robot Manipulator and Neuronavigation System  
To control the robot manipulator using the coordinate data from the navigation system, we first express the poses measured by the camera to the coordinate system of the robot manipulator's base. This section describes the method used to achieve the calibration.  
Before explaining the calibration method, the notation of the transformation matrices is described as follows: \({}^{x}T_{y}\) expresses the pose of \(x\) described by the coordinate system of \(y\). More clearly, \({}^{x}T_{y}\) is defined as follows:  
\[{}^{x}T_{y}=\begin{bmatrix}{}^{x}R_{y}&{}^{x}\mathbf{t}_{y}\\ 0&1\end{bmatrix}, \tag{1}\]  
where \({}^{x}R_{y}\) is the rotation matrix that describes the orientation of \(x\) based on the coordinate system of \(y\) and \({}^{x}\mathbf{t}_{y}\) is the position vector of \(x\) based on the coordinate system of \(y\). The letters at the location of \(x\) and \(y\) are defined as follows: \(b\) is the base of the robot manipulator, \(c\) is the camera of the navigation system, and \(t\) is the tool, which is the TMS coil. These expressions are illustrated in Figure 2. Hereafter, the position of the TMS coil means the center of the coil's floor, and the coordinate system of the TMS coil is the coordinate system whose axes are parallel to those of the end-effector of the robot manipulator and whose origin is the location of the TMS coil. Using these expressions, we can describe the relationship between the robot base coordinate and the camera coordinates using the following equation:  
\[{}^{b}T_{c}={}^{b}T_{c}^{e}T_{t}^{i}T_{c}. \tag{2}\]  
Theoretically, if the robot manipulator and camera do not move, \({}^{b}T_{c}\) is constant regardless of the location of the TMS coil. According to (2), \({}^{b}T_{c}\) can be obtained using three values according to (2): the pose of the end-effector of the robot manipulator expressed by the coordinate system of the base of the robot manipulator, the pose of the TMS coil  
Figure 2: Transformation matrices of the coordinate. Each matrix describes the coordinates of the arrival point of the arrow using the coordinate system of the starting point of the arrow.  
expressed by the coordinate system of the navigation camera, and the CAD information of the TMS coil. Meanwhile, (2) can be rewritten as follows:  
\[{}^{b}T_{c}={}^{b}T_{t}{}^{t}T_{c}. \tag{3}\]  
Therefore,  
\[{}^{b}T_{t}={}^{b}T_{c}{}^{c}T_{t}. \tag{4}\]  
Using (4) after fixing \({}^{b}T_{c}\), the coordinates estimated by the camera can be converted to the robot base coordinate system.
## 3 Simulation and Experimental Setups  
### Simulated Tissue-Mimicking Phantom Study  
We first used Field II [46; 47] to generate the simulation data. The simulated phantom consists of a cyst target with a radius of 3.5 mm, and twelve point targets, including two laterally and two axially closely spaced points. The scatters used to simulate the phantom were randomly distributed and the amplitude was random. The density was 20 scatters in a resolution cell. The linear transducer had 128 elements and the pitch was 0.30 mm. The fractional bandwidth of the transducer was 67%, and the transmitted pulse had 2.5 cycles. The center frequency was 5.208 MHz and the sampling frequency was 20.832 MHz. In the simulation, 75 plane-waves were transmitted at angles of \(-16^{\circ}\) to \(16^{\circ}\) with an angle spacing of 0.43\({}^{\circ}\). The parameters were set the same as those used in experiments.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 3 Results and Analysis
### Comparison with Zhong's Method  
In the individual tree segmentation method of [PERSON] et al. [11], the value of recall varies from 80.36% to 89.13%, with a total value of 83.52% (Table 3). The value of precision varies from 88.64% to 95.35%, with a total value of 92.31%. The value of F1-score changes from 0.86 to 0.92, with a total value of 0.88. In this paper, the results of individual tree  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Plot** & **Actual Tree Number** & **TP** & **FP** & **FN** & **Recall (\%)** & **Precision (\%)** & **F1-Score** \\ \hline Plot 1 & 47 & 39 & 4 & 8 & 82.98 & 90.70 & 0.87 \\ Plot 2 & 52 & 42 & 4 & 10 & 80.77 & 91.30 & 0.86 \\ Plot 3 & 46 & 40 & 3 & 6 & 86.96 & 93.02 & 0.90 \\ Plot 4 & 33 & 27 & 2 & 6 & 81.82 & 93.10 & 0.87 \\ Total artificial forest & 178 & 148 & 13 & 30 & 83.15 & 91.93 & 0.87 \\ BR04 & 56 & 44 & 4 & 12 & 78.57 & 91.67 & 0.85 \\ K CA09 & 39 & 30 & 3 & 9 & 76.92 & 90.91 & 0.83 \\ Total mixed forest & 95 & 74 & 7 & 21 & 77.89 & 91.36 & 0.84 \\ Total & 273 & 222 & 20 & 51 & 81.32 & 91.74 & 0.86 \\ \hline \end{tabular}
\end{table}
Table 2: Segmentation results based on the CHM segmentation method.  
Figure 9: Small tree in the red box is not detected based on CHM segmentation method (the distance between the breast height of the two trees in the red box is 1.09 m).  
segmentation based on the improved DBSCAN method are compared with the segmentation method of [PERSON] et al. [11]. As shown in Figure 4, individual tree segmentation of TLS based on the improved DBSCAN method improves the results of recall, precision, and F1-score. Compared with [PERSON]'s method, the total recall, precision, and F1-score of our method are improved by 7.32%, 3.07% and 0.05, respectively. Like the individual tree segmentation method based on CHM, the detection effect of small trees under tall trees is poor. The segmentation errors of small trees account for 77.8% of under segmentation.
## 3 Results and Discussion
### Spectral Profiles and Selection of Optimal Wavelengths
Therefore, in this study, the number of optimal bands considered were two or three wavelengths for further image analysis, as there was no significant difference in classification accuracy when more wavelengths were added. \begin{table}
\begin{tabular}{c c c c c} \hline
**Classifier** & **Pretreatment** & **1 st Band (nm)** & **2 nd Band (nm)** & **3 rd Band (nm)** \\ \hline \multirow{5}{*}{SVM} & Raw & 396 & 554 & 669 \\  & SNV & 458 & 846 & 961 \\  & Max normalization & 607 & 621 & 631 \\  & Mean normalization & 396 & 611 & 870 \\  & Range normalization & 401 & 640 & 669 \\  & Smoothing & 396 & 501 & 664 \\ \hline \multirow{5}{*}{SVM with RBF} & Raw & 401 & 640 & 769 \\  & SNV & 463 & 468 & 635 \\ \cline{1-1}  & Max normalization & 396 & 621 & 674 \\ \cline{1-1}  & Mean normalization & 396 & 401 & 875 \\ \cline{1-1}  & Range normalization & 405 & 420 & 659 \\ \cline{1-1}  & Smoothing & 401 & 635 & 750 \\ \hline \end{tabular}
\end{table} 
Table 2: Wavelengths of the three most important bands determined by the sequential forward selection (SFS) method following various classifier pretreatments. Figure 3: Mean spectra of diseased and sound (non-diseased) rice seeds and standard deviation bars after preprocessing with the standard normal variate (SNV) method.
# A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
## 4 System Measurement
### Frequency Division Duplex Measurement  
For the FDD measurement, the communication distance is set to 0.8 m, as the measured transmitted power drops around 6 dB for the FDD operation mode compared to the HD. The reason for such a power drop is the output power of the LO signal feed to the 12 GHz I/Q mixer and the 24 GHz harmonic up-mixer are dropped when the loading network changes to drive both the transmitter and receiver chains. The performance measurement is performed with four different test conditions: firstly, the signal is transmitted from channel 2 and received at channels 1 and 3, and secondly, data are sent from channels 1 and 3 and received at channel 2. For all the measurements, the received baseband output signal \(T\) is taken by the oscilloscope and then demodulated directly using VSE software. Here, the Rohde and Schwarz RTO 1044 is used as a baseband oscilloscope that samples the received signal at 10 GSym/s. The SI signal here is removed by the bandpass digital filter in VSE software directly. Figure 11 shows the measured constellation diagram with the average EVM results. The performance is better than the HD measurement, and it meets the EVM requirement of MCS12.2.  
Such an EVM improvement occurred due to three reasons. Firstly, the reduced communication distance increases the received signal power by 5.5 dB. In other words, the power drop of the transmitter in the FDD mode is compensated by the communication distance. Secondly, the smaller input signals improve the linearity of the VGA, mixer, and PA. Finally, the high resolution and high sampling rate of the oscilloscope completely removed the SI noise in the digital baseband processor. The measured average EVM across the four test conditions for 500 symbols under 16-QAM are \(-23.2\) dB, \(-23.27\) dB, \(-22.78\) dB, and \(-22.83\) dB, respectively, and \(-21.65\) dB, \(-21.26\) dB, \(-20.97\) dB, and \(-20.59\) dB, respectively, for the QPSK data symbols.  
Figure 10: The measured EVM for (**a**) 64-QAM raw data and (**b**) the MCS12.6 frame.
## 3 Results  
### Visual Evaluation  
We conducted a visual evaluation to compare the image quality of the brain region in images reconstructed using various filters and projection angles. Figure 3 shows the results of the FDK reconstruction algorithm using various filters and projection angles with added Gaussian noise with a standard deviation of 0.5. We observed that as the projection angle increased, streak artifacts were enhanced in the brain region. Moreover, we found that a projection angle of 1.2\({}^{\circ}\) was the most effective for noise reduction without generating streak artifacts. Of the five filters used in the FDK reconstruction, the Ram-Lak filter exhibited the highest noise level, while the Hann filter was the most effective for noise reduction.
# Applications of Graphene-Based Nanomaterials in Environmental Analysis
## 2 Graphene-Based Nanomaterials and Nanocomposites
### Synthesis of Graphene Aerogels  
[PERSON] et al. [55] showcased in a review seven different ways of preparing graphene aerogels, either with no templates or with a template. The methods that require no template are the hydrothermal method, where a GO colloidal dispersion is prepared in ethanol or water, heated at a certain temperature and freeze-dried, dried in atmospheric and in supercritical conditions, the chemical reduction method, by using a reducing agent, the chemical cross-linking method, to enhance the interaction between the graphene oxide sheets with the usage of glutaraldehyde or poly(vinyl alcohol), and a 3D printing method, with the preparation of a high viscosity ink of SiO\({}_{2}\) and GO suspension. The latter methods that require a template are the chemical vapor deposition method, where 3D graphene is prepared by the chemical vapor deposition of carbon onto a metal foam, like copper or nickel, the ice template method, by freezing a GO suspension and its gradual melting to form a porous aerogel with GO, and the bubble template method, with the utilization of a surfactant under vigorous stirring so that the GO is trapped in the bubbles and begins to form.
###### Abstract  
The compression index (C\({}_{\text{c}}\)) is a critical soil parameter that is used to estimate the consolidation settlement of ground. In this study, the compression index, typically obtained through consolidation tests, was predicted using machine learning techniques after preprocessing data that considered the geotechnical and hydrogeological characteristics of the study area. This approach enabled an analysis of how geotechnical and hydrogeological characteristics affect the performance of machine learning models. Data obtained from geotechnical investigations were used to train models for each classified zone. Suitable models were then selected to predict the compression index, and their performance was evaluated. Predictions that considered the geotechnical and hydrogeological characteristics showed improved accuracy in zones influenced by a single water system or zones near the coast. However, in offshore areas with complex water systems, using the entire dataset proved to be more effective. Differences in the clay mineral of the soil also affected the prediction accuracy, indicating a correlation between clay mineral properties and model performance. These findings suggest that classifying data based on geotechnical and hydrogeological characteristics is necessary when developing compression index prediction models to achieve relatively stable results.  
machine learning; soft ground; compression index; Saemangeum reclaimed tidal land; bedrock Article
A further contribution of this research is that instead of selecting ground motions from places other than the bridge's site and modifying them considerably, strong motion records that match the site conditions are carefully picked out and ten synthetic ground acceleration time histories are generated to be used in the nonlinear response history analysis.
# Characterization of Monovacancy Defects in Vanadium Diselenide Monolayer: A DFT Study
## 3 Results
Importantly, modern X-ray photoelectron and ultraviolet photoemission spectroscopy methods allow us to measure the density of states of the valence bands of nanostructures, which can be derived from the measured spectrum of the binding energy of a matter [97]. There are also experimental studies utilizing DFT-based simulations to detect structural defects in van der Waals crystals observed in STM images and ultraviolet photoemission spectra. Specifically, a redshift of the valence band maximum, because of the presence of a single tin defect observed in the ultraviolet photoemission spectra, has been mapped to an increased electronic state localization related to the defect states deep in the gap, as has been found in DFT calculated LDOS plots [98]. Very recently, a deep-level transient spectroscopy technique has been demonstrated for the non-destructive probe of point defects in layered TMDs. By using experimental STEM imaging and DFT calculations, it has been shown that it is possible to quantify the energy states of vacancy defects in a single-layer MoS\({}_{2}\)[99]. Therefore, this study not only provides an atomic-scale characterization of point defects in terms of their effect on the electronic properties of the VSe\({}_{2}\) monolayer but also facilitates their experimental identification.