esa-sceva/satcom-chunk-collection huggingface dataset:
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 7 Application: Nanotechnology and D-Limonene
### Applications
#### 7.2.3 Medicine  
A large number of experimental results have shown that D-limonene has excellent clinical pharmacological activity and antimicrobial activity against many clinically pathogenic microorganisms, but its use and efficacy are hampered by reasons such as its solubility and stability so the use of nanoencapsulation technology to improve its properties for clinical application is the direction of the investigation.  
Doxycyclineine is an important antineoplastic anthracycline chemotherapeutic drug, but it has cumulative cardiotoxicity. In a study, nanodelivery systems (nanoemulsion, niosomes, and polylactic nanoparticles) of doxycycline and D-limonene were prepared with a maximum loading rate of up to 75.8%, which had desirable stability and antioxidant properties, and exhibited enhanced anticancer activity against liver cancer cells as well as lower cytotoxicity against normal liver cells [209].  
In the study, D-limonene was found to have anticancer activity against both melanoma cell line A-375 and breast cancer cell line MDA-MB-468 with IC\({}_{508}\) of 246.05 and 2118.94 \(\mu\)g/mL, respectively, which was significantly improved by chitosan nanoencapsulation, with IC\({}_{508}\) reduced to 30.24 and 650.7 \(\mu\)g/mL, respectively, which may be attributed to the nanoencapsulation improving the hydrophobicity of D-limonene or possibly of the use of its lipophilicity to improve permeability [210; 211].  
Researchers believe that D-limonene can act as a permeation enhancer, fluidizing or disrupting the integrity of the stratum corneum of the skin for the purpose of transdermal drug delivery, decreasing systemic adverse effects or increasing bioavailability [212]. The study showed that benzoaine-loaded poly(D,L-lactide-co-glycolide) nanoparticles with added D-limonene significantly enhanced permeation rate and prolonged anesthetic efficacy and reduced cytotoxicity [211], whereas another study showed that D-limonene-containing nanovesicles showed higher encapsulation efficiency and transdermal delivery of asenapine maleate compared to cineole and hydromiscible cosolvent (transcutol\({}^{\circledapprox}\)). In addition, this was probably due to the effect of structural activity, which allowed better transdermal delivery of lipophilic molecules of hydrocarbon terpen (D-limonene) than ketone terpenes (cineole), and a significant increase in the bioavailability of transdermal administration compared to oral administration of 3% to 54.5% [213].  
Through nanoencapsulation, the physicochemical properties of D-limonene can be effectively improved and the available range can be expanded, but the current research mainly stays focused on in vitro cellular experiments or animal experiments and putting into application in the clinic needs more review studies.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 1 Introduction
### Previous Works on GFDM  
The principle of GFDM in dividing the input bits stream into several subcarriers and several subsymbols, and applying the impulse response of an appropriate pulse shaping filter circularly to each subcarrier, are the main factors in reducing the OOB emissions and PAPR experienced by OFDM [28]. On the other side, the cyclic pulse shaping filters lead to losing the orthogonality between the subcarriers, and thus the adjacent subcarriers may interfere causing inter-carrier interference (ICI). This leads to a degradation in the performance of the GFDM system [14]. As the pulse shaping filter has a clear impact on the performance of GFDM, there are many studies in the literature about this topic [28; 29; 30; 31; 32; 33]. In [28], the widely used filter in GFDM systems is the raised cosine (RC) filter, which is considered a non-casual ideal filter. In practice, this type of filter increases the OOB signal radiations, and thus system delay. For that reason, in [29], different types of improved Nyquist pulse shaping filters are used in combination with the GFDM ZF receiver. The study showed that the proposed filter has lower OOB emissions and a better symbol error rate (SEP) performance compared with root-raised cosine (RRC) in the case of using 16-quadrature amplitude modulation (16-QAM) transmission over an additive white Gaussian noise (AWGN) channel. The OOB emissions and SER of ramp-based pulse shaping filter of GFDM system with RRC and Xia filters for AWGN channel are compared in [30]. This type of filter has better SEP performance and lower OOB emission compared with RRC and Xia pulses, while Xia filter outperforms RRC in terms of OOB radiation. In [31], the authors introduced the results of the proposed new pulse shaping filter from the linear combination of the two pulse shapes. The proposed pulse resulted in better OOB emissions and better resistance to ISI owing to the vertical and horizontal pulse sharpness compared with RRC filter. Another solution was presented in [32], where the quadratic programming (QP) approach is applied as a newly designed pulse shaping filter to reduce the OOB emissions. Moreover, the authors in [33] presented how to modify the GFDM modulation technique to give the same FBMC performance. Therefore, the study introduced the BER performance of GFDM and FBMC schemes under three different channel models: AWGN, time-invariant frequency-selective, and time-variant frequency-selective. This modification led to achieving a linear filtering behavior that can provide an improvement in the OOB emissions.
## 3 Inherent Optical Properties (IOPs)
### Ocean IOPs--Bio-Optical Models  
In open ocean water, it is customary to assume that the IOPs of particulate matter can be parameterized in terms of the chlorophyll concentration. In coastal water, the IOPs will depend on the presence of \"impurities\" consisting of inorganic (mineral) particles, organic (algae) particles, and Colored Dissolved Organic Matter (CDOM) in addition to pure water. Due to the complexity of coastal water, we introduce three bio-optical models that have been adopted to represent different types of water. The CoastColour Round Robin (CCRR) model [58] is a useful proxy for turbid coastal water frequently observed in estuary areas, where suspended sediment (i.e., mineral) particles have a strong influence on water IOPs. The Santa Barbara Channel (SBC) and Garver-Siegel-Maritorena (GSM) bio-optical models described below provide useful representations of clean to moderately turbid water, where the IOPs are primarily dominated by algae. By varying the slope parameter \(S\) that describe the CDOM spectral absorption (see Equation (109) below), the GSM model may be used to represent CDOM dominated water.  
As mentioned in Section 3.4.3, for pure water we may adopt the real part of the refractive index of pure water from [41], and we use the absorption coefficient \(\alpha_{\mathrm{w}}(\lambda)\) based on published data [42; 43; 44] for wavelengths between 340 and 700 nm, and other data [45] for wavelengths between 720 and 900 nm. Pure water scattering coefficients \(\beta_{\mathrm{w}}(\lambda)\) are based on published data [7], and the Rayleigh scattering phase function is given by Equation (9) with depolarization ratio \(\rho=0.039\), and thus \(f=(1-\rho)/(1+\rho)=0.925\) (see Section 3.1.1).
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 3 Motivation
### Collusion Apps to Evade SVM Detection
#### 3.3.3 Evasion Attack on SVM  
Apart from splitting the app into two or more apps, malware developers also have the incentive to add benign functions to hide its malicious behavior. In addition, malware developers have the incentive to hide their malicious behavior by using some advance technique [26], such as obfuscation, reflection, and class encryption, etc. It is called _Evasion Attack_. Researchers [16; 17; 26] have formulated the optimal evasion problem to different optimization problems. In general, all the proposed attacks attempt to find a sample \(x^{*}\in X\) that evades detection by minimally manipulating the original malware feature \(x\in X\), where the amount of manipulations is characterized by a suitable distance function.  
For example, in [16], the optimal evasion is formulated as  
\[\begin{array}{l}x^{*}=argmin_{x^{\prime}}c(x^{\prime},x),\\ \text{s.t.}f(x^{\prime})<0,\end{array} \tag{6}\]  
where \(c(x^{\prime},x)\), with \(c:X\times X\mapsto\mathbf{R}\), is the distance of the manipulated sample from the original malware, \(c(x^{\prime},x)\) is usually evaluated by the Hamming distance, and \(x^{\prime}\) is classified as legitimate.  
Under our perfect knowledge assumption, the direct gradient-based attack would be the best way to perform an evasion attack [27]. The procedure of Gradient-based evasion attack is as shown in Algorithm 1. The idea is to move a step towards the discriminant boundary in each iteration until  
Figure 2: Best fitness score of individuals.  
evading the detection. The gradient represents the fastest direction to the evasion point, so following the gradient detection to evade can address the worst case. The gradient of SVM differs according to the kernel. For example, the gradient of Linear SVM (Equation (1)) is \(\forall f(x)=w\).
```
1:
2:the malicious sample, \(x\); a small positive constant: \(\xi\); the gradient step size, \(t\); the maximum number of iteration, \(m\);
3:the closest evasion point to \(x\), \(x^{\prime}\);
4:while\(i>m\) or \(c(x^{i},x)-c(c^{i-1},x)<\xi\)do
5:if\(f(x^{i})>0\)then
6:\(x^{i}=x^{i-1}-t\triangledown f(x^{i-1})\)
7:endif
8:if\(x^{i}\) violates constraints then
9: Project \(x^{i}\) to the feasible domain
10:endif
11:\(i=i+1\)
12:endwhile
13:return\(x^{\prime}=x^{i}\);
```  
**Algorithm 1** Gradient-based Evasion Algorithm.  
However, when malware developers adopt both Collusion Attack and Evasion Attack to evade detection, the evasion problem will become more serious. Unfortunately, it is very likely to take place in the wild because the colluded apps are often disguised as normal apps, and will include the functions that make them look legitimate, which means that they tend to add features. Our experiments in Section 5 will detail the evasion problem on SVM. In the next section, we will describe how to make SVM more secure to avoid these attacks.
# Failure Response of Simultaneously Pre-Stressed and Laser Irradiated Aluminum Alloys
## 5 Conclusions  
The present study experimentally examined the failure behavior of Al-6061 and Al-7075 alloys exposed to combined tensile loading and laser irradiations. Tensile loading was provided by a universal tensile testing machine, and a 1080-nm wavelength CW fiber laser was used for laser irradiations. From the experimental results, the specimen's failure time is found to reduce by increasing either the laser power density or preloaded speed. The effects of increased preloaded speeds on the variation of Al-6061 failure time are significant, but random. For Al-7075, under higher preloaded speeds, there is smaller impact of laser power density on the failure time. However, from the fitting results, it may be noticed that the Al-7075 average failure time variation with laser power density tends to follow some relationship. The non-uniformity in the decrease of failure time is due to different thermo-mechanical responses to varying experimental conditions. The non-uniform and inconsistent changes (depending on the temperature rise rate) of the local temperature and local stress during the irradiation process under different test conditions give rise to differences in the variation trend of thermophysical and mechanical properties, like thermal conductivity, the thermal expansion coefficient, yield strength, etc. From the rupture morphology, it is found that, by the increase of laser power density or preloaded speed, the failure mode changed from brittle to ductile. At higher power densities, local damaging and combustion, melting, necking, ablation and cracks may be seen on the irradiated surfaces. Moreover, the increasing preloading speed makes the local effects expand.  
This work is supported by the National Natural Science Foundation of China (6160-5079) and the Fundamental Research Funds for the Central Universities (30916-014-112-020).  
[PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] helped in analyzing the results. [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 8: Rupture morphology of the Al-7075 specimens fractured under the conditions of 2.7 kW/cm\({}^{2}\) laser power density and various preloaded speeds of (**a**) 0.5 mm/min; (**b**) 1 mm/min; (**c**) 2 mm/min; (**d**) 4 mm/min.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 2 Spot Scanning Surface Defect Evaluation System  
### System Layout  
Figure 1 presents the system layout of the SS-SDES. A short-wavelength laser beam passes through a beam controller to obtain a specific polarization state and is subsequently focused onto the optical surface to be inspected. If there is no defect in the illuminated spot, the light will be specularly reflected by the optical surface. Otherwise, the defects will produce scattered light in all directions. An ellipsoidal mirror is used to collect the scattered light beams with a large solid angle and reflects them into a photomultiplier (PMT) for detection. The basic principle is that each beam of light that passes through a focal point of the ellipsoidal mirror will pass through another focal point after being reflected. Thus, the ellipsoidal mirror is properly placed, making its two focal points coincide with the illuminated spot and the PMT, respectively. The reflected light is led through a hole in the mirror to escape so that the scattered light is detected only. Such an optical configuration can greatly enhance the acquired scattering signal of defects while keeping the background signal at a normal level, thus offering the potential to detect smaller-size defects to the system. The SS-SDES incorporates a polarization measurement channel as well. The scattered light in the blind area of the mirror is led into this channel, where a quarter-wave plate and a polarization beam splitter (PBS) are exploited to separate the light into two beams according to its polarization state. Both of them are finally detected by two additional PMTs, respectively, forming a two-dimensional polarization signal for further defect classification [19; 20].  
To realize a scan for the whole surface, the optical sample is placed on a positioning system, which includes a rotation stage and a two-dimensional translation stage. Under the control of a computer, the positioning system rotates and translates the sample, allowing the whole surface to be scanned along a spiral trace. And the intensity signals detected by the PMTs are synchronously acquired and recorded using a data acquisition card with a specific sampling frequency. Based on the scanning trace, image reconstruction is performed on the acquired signal sequence to obtain defect images. With those, a quantitative evaluation for surface defects can be finally achieved after further image processing and analysis.
# Querying and Extracting Timeline Information from Road Traffic Sensor Data
## 3 Timeline Modeling
### Congestion
Because this congestion is not affected by the other congestion, it is independent. The remaining congestion in the path is affected congestion; thus, dependent. **Example 4**. : _Figure 6 illustrates the affectedness of congestion. Six road segments \(L_{1},L_{2},L_{3},L_{4},L_{5}\), and \(L_{6}\) are depicted in an example road network in Figure 5(a). The table in Figure 5(b) contains speed information of road segments from \(L_{1}\) to \(L_{6}\) for every five minutes. The red cell in the table indicates congested road segments. The road networks in the top right show how these road segments are organized. Figure 5(c) explains how to understand the congestion dependency using a graphical view which is drawn by considering only values of red cells in Figure 6b. A gray circle and a black circle denote a START event and a STOP event of a congestion, respectively. Congestions in the path \(L_{1},L_{2},L_{3},L_{4}\) and the path \(L_{5},L_{6}\) are connected sequentially by a dotted line, representing the affectedness of a congestion. This happens because the next congestion occurs when the previous road segment is still congested. For example, a congestion of \(L_{2}\) happens due to the previous congestion of \(L_{1}\), illustrated by the green check mark. However, the congestion in \(L_{4}\) and \(L_{5}\) are not connected, because the START event of congestion \(L_{5}\) occurs when congestion \(L_{4}\) has stopped, illustrated by the red cross mark. The initial congestions in a congestion path can be considered independent congestions (\(L_{1}\) and \(L_{5}\)), whereas the other congestions are considered dependent congestions (\(L_{2},L_{3},L_{4}\), and \(L_{6}\)). As a result, the independent congestions and dependent congestions are depicted as a red solid arrow and a blue solid arrow, respectively, in Figure 6c._
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 4 Experiment and Result Analysis  
In order to verify the effectiveness of the proposed target tracking algorithm, MATLAB R2022a is used for experiment based on KITTI data set [25; 26]. Then, GM-APD LiDAR data are used to verify the algorithm, and center location error (CLE) is used to evaluate the performance of the tracking algorithm. CLE refers to the Euclidean distance between the real target center position and the predicted target center position in the tracking process. The smaller the Euclidean distance, the higher the tracking accuracy. CLE calculation formula is as follows:  
\[CLE=\sqrt{\left(x_{p}-x_{r}\right)^{2}+\left(y_{p}-y_{r}\right)^{2}+\left(z_{ p}-z_{r}\right)^{2}} \tag{38}\]  
where \(p\) is the predicted target center location; \(r\) is the real target center location; the KCF tracking algorithm uses a 4x4-sized grid to extract HOG feature, the histogram direction is 9, the sampling window size is 1.5 times of the initially selected region; the regularization parameter \(\lambda=10^{-4}\), and the Gaussian Kernel bandwidth \(\sigma=0.5\).
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 3 Our Proposed AMC Method
### Training Procedure  
In summary, based on the above modules, the full training procedure for the proposed AMC method is described in Algorithm 1.  
Figure 8: Principle of RandMix.  
```
0:
1:\(D\): Training dataset;
2:\(T\): Number of training iterations;
3:\(B\): Number of batches in a training iteration;
4:\(N\): Length of each datum;
5:\(M\): Number of segments for each datum;
6:\(\theta\): Parameters of the lightweight CVResNet;
7:\(lr\): Learning rate; Data augmentation:
```
1:\(D_{rotate}=\left\{D,D_{\pi/2},D_{pi},D_{3 pi/2}\right\}\gets Rotate\ D\); Training procedure:
2:for\(t=0\) to \(T-1\)do:
3:for\(b=0\) to \(B-1\)do:
4: Sample a batch training dataset \((\mathbf{x},\mathbf{y})\) from \(D_{rotate}\). Forward propagation:  Evenly split \(\mathbf{x}\).
5:\(\mathbf{x}\rightarrow\left\{\mathbf{x}_{0}^{N/M-1},\mathbf{x}_{N/M}^{2N/M-1}, \cdots,\mathbf{x}_{(M-1)N/M}^{N-1}\right\}=\mathcal{X}\); Randomy mix \(\mathcal{X}\).
6:\(Randmix\ \mathcal{X}\rightarrow\mathbf{x}^{*}\); Get the output of lightweight CVResNet.
7:\(\hat{\mathbf{y}}=f(\theta^{t,b};\mathbf{x}^{*})\); Calculate the loss.
8:\(\mathcal{L}=-\mathbb{E}(\mathbf{y}\log\hat{\mathbf{y}})\) Backward propagation:
9:\(\theta\gets Adam(\
abla_{\theta},\mathcal{L},lr)\)
10:endfor
11:endfor
12:endfor ```  
**Algorithm 1** Training procedure of the proposed AMC method.
## Abbreviations  
FFT-BPM: FFT-based Beam Propagation Method; TE: Transverse Electric; TM: Transverse Magnetic; MIM: metal insulator metal; FDFD: finite difference frequency domain; TM0: transverse magnetics fundamental mode; PWE: plane wave expansion, TMM: Transfer Matrix Method.
## 2 Study Area and Data Preprocessing
### Data Sources  
#### 2.2.1 MODIS Remote-Sensing Data  
MODIS, a medium-resolution imaging spectrometer mounted on the Terra and Aqua satellites, can scan 2330 km (wide) and obtain global observation data every 1-2 days. Due to its advantages of a wide spectral range, simple data reception, and high update frequency, it is widely used in large-scale ecological research. All indicator data are derived from MODIS remote-sensing products ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)), accessed on 7 December 2022). Specific data are shown in Table 1.  
The above MODIS data products, obtained in 2010, 2015, and 2020, are of a high grade and have been corrected for by radiation and atmosphere.  
Table 2 shows the number of pixels, observations, mean, standard deviations, minimums, and maximums of GPP, LAI, LST, NDVI, and WET. The number of pixels is the total number of pixels in 16 county areas for each indicator; NDVI is observed once every 16 days, and GPP, LAI, LST, and WET are observed once every 8 days, so the number of observations in a year is 23 or 46, and the product of the total number of observed counties was the total number of observations, i.e., 46 \(\times\) 16 = 736, and 23 \(\times\) 16 = 368; the mean, standard deviation, maximum, and minimum are the total values of each indicator in 16 counties per year. (Note: GPP, LAI, LST, and NDVI are abbreviations for gross primary production, leaf area index, land surface temperature, and normalized difference vegetation index, respectively).
# Study on Mechanism of Series-Flow of Pollutants between Consecutive Tunnels by Numerical Simulation
## 2 Mathematical Model
### Computational Geometry, Domain, and Grid  
Two-lane and three-lane tunnels with a horseshoe-shaped cross-section were considered and their corresponding hydraulic diameters are, respectively, 6.1 and 8.6 m. In reference to a real tunnel, the tunnel portal's front slope gradient is \(90^{\circ}\). The \(x\)-axis is the center line on the tunnel road along the direction from the upstream to downstream. Distance \(\Delta L\) between upstream and downstream tunnels was set to increase from 20 to 240 m with a step of 20 m. The airflow within a volume of \(\Delta L\) long, 200 m wide, and 50 m high between the upstream and downstream tunnel portals was simulated. The whole domain was meshed by non-uniform hexahedral grids using ICEM-CFD 17.2 (ICEM-CFD 17.2, ANSYS Inc. Pittsburgh, PA, USA, 2016), and in order to ensure the accuracy and reliability of the calculation, the areas between the upstream outlet and downstream inlet were locally refined.  
Figure 1 shows simulation domain and mesh for a three-lane tunnel with \(\Delta L\) = 60 m, and \(y\) coordinate is vertical direction. The number of grids is \(2.84\times 10^{6}\). We conducted numerical simulations with different grid orders of \(0.09\times 10^{6}\), \(0.52\times 10^{6}\), \(2.84\times 10^{6}\), and \(6.31\times 10^{6}\). The result shows that, when the number of grids reaches \(2.84\times 10^{6}\), the simulation results hardly change with the increase of grid number, which means grid independence.  
Figure 1: Simulation domain and mesh for consecutive tunnels.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 2 Background
### Corrosion Detection Process
#### 2.3.5 Image Classification  
The final process is image classification, wherein the groups of pixels are labelled and categorized in accordance with specific rules. This process will label an image based on its visual content. The groups of pixels can be categorized with textural or spectral characteristics. The three types of image classification used were supervised, unsupervised, and object-based analysis.