esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Integration of Multi-Sensory Information for Target Identification  
### Classification Algorithms  
Multiple Signal Classification (MUSIC) algorithm is used for spectral density estimation of all sensory modalities to allow for a unified data processing and data integration.  
MUSIC estimates the eigenvectors of the sample correlation matrix and it uses the pseudo-spectrum estimate of the mean corrected input signal for each sensor at normalized frequencies at which the pseudo spectrum is evaluated [11; 12; 13]. After applying the MUSIC algorithm for signal processing, we classify the estimated power spectra.  
To demonstrate the classification capability of our system, we use Weka [14] as a classification tool on the collected data. We run our data through three classifiers: Support Vector Machines, Multi-Layer Perceptron, and Random Forest:  
* _Support Vector Machines (SVMs)_ are a set of supervised learning methods used for classification and regression analysis.
* _Multi-Layer Perceptrons (MLPs)_ are feed-forward neural networks that are trained with the standard back propagation algorithm. MLPs are widely used for pattern classification.
* _Random forest_ is an ensemble classifier. It consists of many decision trees and outputs the classification that has the most votes over all the trees. It maintains good accuracy when portion of data is missing. However, it is prone to over fitting in some cases.
## 3 Results  
### HIVECs Cultivation on Type I Collagen and Fibronectin  
The primary purpose of this study is to explore the respective effect of soluble and bound VEGF on endothelial cells. Therefore, instead of using the commercial matrix (such as Matrigel(r)), which already contains growth factors, pure collagen type I and fibronectin were used to make the in-house biogel with a definite combination. Type I collagen as the primary substrate provided the endothelial cells with attachment sites when the cells  
Figure 1: Exons of VEGF-A165, in which exon-7 provides the ability to bind fibronectin. A VEGF-blocking peptide (VBP) was made with the amino acid sequence derived from exon-7. Amino acid numbers in the VEGF-A165 sequence are also labeled at two VBP ends.  
moved. Fibronectin attached to the collagen surface provided cell attachment and binding sites for soluble VEGF initially contained in the culture medium to form bound VEGF. The cell suspension was added to a 12-well plate coated with collagen with or without fibronectin. The initial number of endothelial cells was 80,000 cells per well, and the cell distribution images were recorded every 6 h.  
The endothelial cells were cultivated under four conditions, considering whether fibronectin was coated on the collagen and whether VEGF was added to the culture medium. The endothelial cells in the four cases formed the network to different extents. The results are as shown in Figure 2. In the two cases with no VEGF but with or without fibronectin addition (\(-\)F\(-\)V and \(+\)F\(-\)V), HUVECs started the morphological change from round to elongated shape at about 6 h, and the change became evident at 12 h. The cells in these two cases were found to undergo a rapid cell death process that the number of adherent endothelial cells continually decreased. At 24 h of incubation, only a small number of cells remained in the sight field. It is worth mentioning that the cells could express the morphological change even without VEGF added.  
Under the culture condition without fibronectin but with VEGF (\(-\)F\(+\)V), many cells with morphological changes appeared at 6 h. However, the cell network could not ultimately form afterward. In the \(+\)F\(+\)V case, the beginning of morphological change occurred a bit later than in the \(-\)F\(+\)V case. The originally round-shaped cells became elongated, and the dividing boundary for adjacent cells became blurred at 12 h. These characteristics were more evident subsequently, which shows that VEGF stimulated the endothelial cells continuously. The cell-free area in the visual field became apparent as a result of cell aggregation. The cell colony evolved in the direction of forming the capillary-like network.  
Figure 2: HUVECs morphological forms and cellular networks in different cell culture conditions: without fibronectin (\(-\)F), with fibronectin (\(+\)F), without VEGF (\(-\)V), and with VEGF (\(+\)V). Black arrows in the enlarged photos indicate the cells with morphological change into the elongated shape, while white arrows indicate the cells still in the original cobblestone shape.
# Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation Learning
## 6 Conclusions  
This paper presents a novel solution to detect vulnerability in source code by learning vulnerable programming patterns automatically, which aimed to improve the effectiveness of vulnerability detection. The granularity of our proposed method is a minimum intermediate representation that extracts vulnerability relevant information based on dependency analysis. It covers not only intra-procedural but also inter-procedural vulnerabilities, and it alleviates the long-term dependency issue and provides precise vulnerability information. We transfer the common language features in an unlabelled dataset to the task of specific vulnerability detection. Representation learning is leveraged to abstract high-level features. It mitigates the impact of the OoV and the lack of vulnerability dataset issues. We implemented a prototype and performed systematic experiments to validate the effectiveness of our method. The experimental results show that our method has an obvious improvement over the state-of-the-art methods.  
The proposed approach has several limitations which can be further investigated. First, the problem of long-term dependency is merely alleviated by eliminating the irrelevant code in the pre-processing stage. This is an inherent deficiency of the sequential structure. To improve this limitation, the graph embedding method can be applied. By transforming the source code into a graph structure, the element is connected directly to its context. Second, the method in this paper does not clean out the mislabeled samples in the dataset. Mislabeled samples will interfere with the learning of vulnerable programming patterns. Therefore, sample selection could be conducted in further research. Finally, our method falls into the category of static analysis. It means that our method cannot be applied to detect vulnerability in compiled software. This can be solved by transforming the compiled software into a common intermediate representation, such as the intermediate representation of Low Level Virtual Machine (LLVM).  
Conceptualization, X.L.; methodology, X.L.; software, X.L., L.W.; validation, X.L., L.W. and Y.X.; formal analysis, X.L., L.W.; investigation, X.L.; resources, Y.X., L.W. and Y.Y.; data curation, Y.X., Y.Y.; writing--original draft preparation, X.L.; writing--review and editing, X.L.; visualization, X.L., L.W.; supervision, Y.X., Y.Y.; project administration, Y.X.; funding acquisition, Y.X., Y.Y. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by \"National Key R&D Program of China under Grant 2017 YFB0802300\", \"Major Scientific and Technological Special Project of Guizhou Province (20183001)\", \"Foundation of Guizhou Provincial Key Laboratory of Public Big Data (No. 2018 BDKFJ021)\",and, \"Foundation of Guizhou Provincial Key Laboratory of Public Big Data (No. 2017 BDKFJ015).\"  
The authors declare no conflict of interest.
## 3 Radar Micro-Doppler Analysis of Vibrating Structures
### Mechanical Model of a Vehicle  
A basic mechanical model of how a vehicle moves is shown in Figure 1[22; 23]. In this model, the main parts that add weight are the chassis (vehicle frame) and the engine. The suspension system is treated like a combination of springs and shock absorbers. The wheels also add weight, and the tires act like additional springs because of their rubber structure.  
In this setup, the small shifts in Doppler frequency and strength that are measured may come from the suspension and tires softening or slightly reducing the vibration frequencies. The materials of the car frame and other shock-absorbing parts also help reduce these frequencies. It is important to understand that the natural frequency of a system (how it vibrates on its own) is lower when damping (resistance) is involved. This matters especially when the system is hit with quick, sudden forces.  
However, when the engine is running steadily at a certain speed (RPM), its vibrations match that speed rather than the natural vibration frequency of the car's structure. Also, engine vibrations are not just simple, steady tones--they include a range of frequencies. That is why tools like accelerometers or radar sensors often do not pick up the engine's exact RPM. Instead, they detect how the vehicle's parts--like the doors or windows--vibrate when the engine is running.  
The weight of passengers also affects the car's vibration, changing both how strong the vibrations are and how fast they happen. Engine vibrations do not directly shake parts like the windows or body panels--they spread through the entire car in complex ways. Still, machine learning algorithms can learn these patterns without needing to understand all the detailed paths the vibrations travel.  
When the transmission shifts gears, the engine load changes temporarily. This causes brief changes in engine speed (usually between 500 and 1000 RPM) as the system adjusts for changes in power loss from the drivetrain. Gear shifts can also create sudden jolts as the parts inside the drivetrain connect more tightly. These jolts depend on the specific type of drivetrain but happen in predictable ways within similar vehicles. They show up as broad vibrations that people often feel as the car shifts gears.
## 4 Conclusions  
The grain growth behaviors and magnetic properties of SCLFCO systems with SiO\({}_{2}\) addition were investigated. Typically, when a dopant is added, the grain growth is suppressed by the solute drag and Zener effects. However, abnormal grain growth behavior of SCLFCO was observed by changing the sintering temperature and the amount of SiO\({}_{2}\) added. Thus, the grain growth behavior cannot be explained solely based on the solute drag and Zener effects. The effects of Si addition on the intrinsic magnetic property, \(M_{s}\), were studied by changing the processing procedure. It was shown that Si can diffuse into the unit cell and change the intrinsic properties. At the same sintering temperature, the cell volumes of the pre-Si samples were smaller than those of the post-Si samples, which meant that a larger amount of Si was substituted into the hexaferrite lattice in the pre-Si method. The grain growth behavior of SCLFCO with SiO\({}_{2}\) addition can be explained by a two-dimensional nucleation grain growth mechanism with a non-linear relationship between the grain growth rate and the driving force for grain growth. As the amount of SiO\({}_{2}\) increases, the critical driving force for grain growth increases, which results in abnormal grain growth with a bimodal distribution in the 0.5 wt% SiO\({}_{2}\) samples sintered at 1240 \({}^{\circ}\)C. When more SiO\({}_{2}\) was added, the critical driving force was larger than the driving force for growth of all grains, which resulted in no grain growth in the initial stage of sintering. The growth mechanism of SCLFCO can be explained by the migration of the facet plane with the two-dimensional nucleation grain growth mechanism. The magnetic properties of \(M_{s}\) and \(H_{c}\) can be altered through the control of the Si addition process and the abnormal grain growth in hexaferrites.  
[PERSON]-[PERSON] and [PERSON]-[PERSON] designed the experiments. P.-y.Y. performed the experiments and analyzed the samples. [PERSON]-S.M. analyzed and explained the grain growth behaviors of the samples. All the authors discussed the data and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Research Fund of the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Science, ICT, and Future Planning (Grant No. 2017R1C1B2002394). This study was supported by the materials and parts technology development (Grant No. 20010938), funded by the Ministry of Trade, Industry & Energy (MoTIE, Korea).  
The authors declare no conflict of interest.
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 3 Proposed Algorithm
### Low-Altitude Signal Source Detection  
The similarity of two signals can be evaluated using the cross-correlation [19]. The original signal received at time \(jt_{a}\) is shown in Figure 4a. In order to detect a low-altitude signal source, a matching template is designed to calculate the cross-correlation with the received signal. This template is shown in Figure 4b and is given by \(m=[m_{1},\ldots,m_{L}]\), \(L=10.645\) MHz/\(f_{w}\). The cross-correlation using the proposed template and other templates using wavelet shapes such as the Daubechies, Morlet, and Mexican Hat templates are shown in Figure 4c. This shows that the proposed template provides the best performance. At time \(jt_{a}\), the forward cross-correlation between the sliding template and the received signal can be expressed as  
\[\dot{R}_{jk}=\left\{\begin{array}{ll}\frac{\sum\limits_{i=1}^{L}\left(m_{i}- \overline{m}\right)\left(\dot{y}_{(k+i-1)}-\overline{v}\right)}{\sqrt{\sum \limits_{i=1}^{L}\left(m_{i}-\overline{m}\right)^{2}}\sqrt{\sum\limits_{i=1}^ {L}\left(\dot{y}_{(k+i-1)}-\overline{v}\right)^{2}}},&k=1,2,\ldots,(2.5-2.4) \times 10^{9}/f_{w}-L\\ 0,&k=(2.5-2.4)\times 10^{9}/f_{w}-L+1,\ldots,(2.5-2.4)\times 10^{9}/f_{w} \end{array}\right. \tag{6}\]  
where \(k\) is the \(k\)th sliding position at time \(jt_{a}\); \(k=1\), \(2\), \(\ldots\), \((2.5-2.4)\times 10^{9}/f_{w}-L+1\), \(j=1,\ldots,8/t_{a}\), \(L\) is the length of the template, \(\dot{y}_{(k+i-1)}\) is the power at frequency \((k+i-1)f_{w}+2.4\times 10^{9}\) and time \(jt_{a}\), \(v=\left[\dot{y}_{jk},\ldots,\dot{y}_{(k+L-1)}\right]\), \(\overline{v}\) is the mean of the vector \(v\), \(\overline{m}\) is the mean of the template \(m\), and \(\dot{R}_{jk}\) is the \(k\)th cross-correlation coefficient at time \(jt_{a}\).  
In order to determine whether a UAV is present or not at time \(jt_{a}\), an improved cross-correlation threshold method is used which is given by  
\[\dot{R}_{j}=\max(\dot{R}_{jk}),\;k=1,2,\ldots,(2.5-2.4)\times 10^{9}/f_{w} \tag{7}\]  
The cross-correlation threshold is set to 0.7 so \(\dot{R}_{j}\) exceeding this value indicates that a UAV is present.  
Figure 3: **(a)** The time–frequency matrix in an ideal environment with the unmanned aerial vehicle (UAV) present, (b) the matrix in a real-world environment with the UAV present, (c) the matrix after filtering and normalization in a real-world environment with the UAV present, and (d) the matrix in a real-world environment with no UAV present.
## 4 Millimeter-Wave CP DRA  
The rapid increase in the demand for mobile internet, the internet of things (IoT), and automated vehicles has motivated researchers to focus on highly efficient antennas, which exhibit wide bandwidth, high data rates, and good radiation characteristics to handle information exchange. Thus, millimeter-wave (mm-wave) bands have received significant consideration for their competency in avoiding interference with the overcrowded lower frequency spectrum. A 270 GHz bandwidth ranging from 30 to 300 GHz has been allocated for mm-wave communication systems. Various antennas such as patch, monopole, and dipole antennas have been proposed for mm-wave applications. However, these antennas suffered from a narrow impedance bandwidth and radiation efficiency problems because of lossy silicon substrates. Therefore, the DRA is a good option for mm-wave use owing to its high radiation efficiency, light weight, flexibility in resonator shape, and feeding scheme. At this mm-wave frequency band, DRAs will exhibit a wider impedance bandwidth and higher radiation efficiency, where the conductor losses of metallic patches are significant. Compared to the DRA operating in the microwave band, the sizes of the mm-wave DRAs are extremely small, and therefore, the practical realization issue becomes the main concern. Several studies have been carried out to overcome this drawback. However, most of them focused on LP designs [73; 74; 75] and very little attention has been paid to the mm-wave CP DRA [76; 77; 78; 79; 80]. In [76], four parasitic dielectric resonator elements are sequentially rotated with a 90\({}^{\circ}\) phase angle to achieve circular polarization, as shown in Figure 17. The proposed DRA is mounted on four layers of RT6010 substrates and fed by a printed gap waveguide (PGW). The function of the PGW is to eliminate the losses and dispersion of the substrate. The parasitic elements will cancel the cross-pol E-field component, thus, improving the CP performance. The proposed antenna has no aperture coupling and feed network system. Therefore, it leads to a reduction in the complexity of the feed and ohmic loss in mm-wave
# Enhancing the Content of Hesperidin and Nobiletin in Citrus Wines through Multi-Strain Fermentation
## 2 Materials and Methods
### Fermentation Process  
The inoculation rates and fermentation temperatures of the fermentation process were adapted from a previously described method [17]. Sc and three non-_Saccharomyces_ yeast strains (Ce, Hg, and Ht) were cultured separately in aseptic YPD medium at 28 \({}^{\circ}\)C and 160 rpm (Shanghai Yiheng Scientific Instrument Co., Ltd., THZ-300, Shanghai, China) for 24 h by shock for two consecutive times. The seed culture was centrifuged at 4000 rpm for 5 min to collect the yeast cells, which were then used for sequential and mixed fermentation of Nanfeng tangerine wine. In the context of pure fermentation, a concentration of 10\({}^{7}\) CFU/mL of a singular yeast strain was introduced to Nanfeng tangerine juice. In the case of sequential fermentation, Sc was inoculated into the tangerine juice for 48 h subsequent to the introduction of non-_Saccharomyces_ yeast. At this juncture, the residual sugar content measured 115 g/L. Four different combinations (Ce.Hg)-10-Sc, (Ce.Ht)-10-Sc, (Hg.Ht)-10-Sc, and (Ce.Hg.Ht)-10-Sc were used, with an inoculum ratio of 1:1 and a cell quantity of 10\({}^{7}\) CFU/mL, respectively. In mixed fermentation, Sc was inoculated concurrently with non-_Saccharomyces_ yeast species at the same cell concentration of 10\({}^{7}\) CFU/mL (Sc:(Ce.Hg)-1:1, Sc:(Ce.Ht)-1:1, Sc:(Hg.Ht)-1:1, and Sc:(Ce.Hg.Ht)-1:1). To minimize Sc competition and the effect of high ethanol concentrations, as well as to reduce the workload in comparison with that of sequential fermentation, we performed mixed fermentation with an inoculum ratio of 1:100 10\({}^{5}\) CFU/mL Sc plus 10\({}^{7}\) CFU/mL non-_Saccharomyces_. Four combinations were grouped (Sc:(Ce.Hg)-1:100, Sc:(Ce.Ht)-1:100, Sc:(Hg.Ht)-1:100, and Sc:(Ce.Hg.Ht)-1:100). The experimental groups are listed in Table 1.  
The experiment was conducted with three repetitions, maintaining a fermentation temperature of 28 \({}^{\circ}\)C. The progress of tangerine wine fermentation was monitored by daily measurements of residual sugar concentrations and yeast cell counts. Fermentation completion was determined by observing constant residual sugar concentrations. Following fermentation, both the tangerine juice and wines underwent centrifugation at 4500 rpm for 10 min to remove yeast cells and precipitates. The resulting supernatants were stored at 80 \({}^{\circ}\)C until further analysis.
## 3 Method
### Suggested Method
#### 3.2.3 Predicting pCR using Both MSFI Embedding and Radiomics Features  
For effective pCR prediction, we seek to use diverse characteristics of tumor areas by considering both MSFI embedding and radiomics features. Radiomics features are extracted through mathematical analysis of each MRI sequence and mainly capture shapes, voxel intensity histograms, and the texture of tumor areas. MSFI embedding is generated through deep learning of multi-parametric MRI and consists of novel volumetric features highly related to pCR prediction.  
Figure 5 presents an overview of our pCR prediction method. Given three MRI sequences, {T2, DWI/ADC, and CE}, we extracted 512-dimensional MSFI embedding and 3740 radiomics features, as shown in Figures 2 and 4. Then, we obtained a novel multi-parametric MRI embedding, a compact and effective representation of multi-parametric MRI, by combining MSFI embedding and radiomics features and compressing them into 150 features using kernel principal component analysis (PCA) [42].  
Figure 4: Radiomics feature extraction.  
Kernel PCA is a dimension reduction method that modifies linear PCA [43] by replacing the linear kernel with a Gaussian kernel. Thus, non-linear transformation is performed so that feature vectors can be represented in a linearly separable feature space. We use this multi-parametric MRI embedding as input to a random forest classifier for pCR prediction [44]. We build our pCR classifier based on the random forest model, because random forest classifiers on radiomics features have shown high performance in predicting pCR after nCRT for rectal cancer--sometimes higher than qualitative MRI assessment by radiologists [8; 45].  
Note that a deep neural network classifier is likely to overfit when trained with multi-parametric MRI embeddings, because they are no longer images; thus, data augmentation or transfer learning cannot be applied.