esa-sceva/satcom-chunk-collection huggingface dataset:
# Mdpi
## 2 Background
### Webshell Classification  
According to its size and function, each webshell can be classified within three categories: a simple webshell, an upload function webshell, and a multi-functional webshell, as follows.  
1. Simple Webshell  
Figure 1: Webshell attack flowchart.  
A simple webshell refers to a webshell that contains only one line of code. This type of code is used to accept the data submitted by the attacker through the client and perform the corresponding operations. It is the most common form of webshell. A webshell written in PHP is shown in Listing 1.  
```
<php @eval(_POST[\"a\"]);?>
``` In this example, the attacker sends a POST HTTP request, passes the command code through parameter \"a\", and executes the malicious operation using the \"eval\" command. The parameter \"a\" is also called the password of the webshell.
2. Upload Function Webshell The uploading webshell is used as a springboard for multi-function webshell files. Usually, the website will impose certain restrictions on the size or type of files uploaded. The attacker will upload this webshell and then use it to upload a multi-function webshell. The key code for the uploading webshell is in Listing 2.  
```
<php if(isset($_POST[\"upload\"])){  @file_put_contents($_POST[\"path\"],$_POST[\"content\"]); }?>
```  
Listing 2: Upload Function Webshell.  
The webshell can write new files to a specified directory and facilitate latent control.
3. Multi-Functional Webshell The multi-function webshell is usually large and feature-rich, providing functions such as operating files, port scanning, command execution, and database operations. Due to the large size of the file, it is usually uploaded to a victim's server via the upload function webshell.
## Main Results  
### Joint Angle Prediction Experiment Based on LSSVM  
In this study, the elbow joint rehabilitation training was studied, and the positions of the sEMG and the pose sensor in the experiment are shown in Figure 2. In order to prevent the electrode from shifting during the movement, the sensor was fixed with medical tape and a wrist guard.  
First, the range of parameters \(C\) and \(\sigma\) were determined in the three-dimensional space, as shown in Figure 3a. After that, the method of subsection local optimization was adopted. Since the selection of hyperparameters did not have a great impact on the range of \(RMSE\) values, a smaller step size was not necessary, as shown in Figure 3b. According to the grid color, the influence of the two different ranges on \(RMSE\) was determined as shown in the figure, and the hyperparameter of the experimental object was finally determined as (\(C\), \(\sigma\)) = (4.53, 51.56). The time difference compensation processing was performed on the LSSVM, and the result is shown in Figure 4. It was obvious that the error between the desired value and the estimated value was acceptable.  
The backpropagation neural network (BPNN) was chosen to compare with the LSSVM algorithm. The number of nodes in the hidden layer of the BPNN was set to 5, the number of iterations was set to 1000, the network learning rate and the final target error were set to 0.01 and 0.00001, and the interval between the hidden layer and the output layer  
Figure 4: Joint angle prediction error with LSSVM.  
Figure 3: Hyperparameter piece-wise optimization: (**a**) hyperparameter with a small step size and (**b**) hyperparameter with a large step size.  
Figure 2: Joint angle prediction experiment platform.  
was selected. The transfer functions were the sigmoid function and the linear transfer function. In order to visually compare the prediction performance of the two methods, the error is shown in Figure 5. It can be seen intuitively from the figure that the output error of the LSSVM prediction algorithm was smaller compared with the manual adjustment of the optimal parameters; therefore the LSSVM algorithm was better than the BPNN at predicting the joint angle error of patients.
## 4 Early Warning of Rockburst Based on Microseismic Indices
### Early Warning Cases of Multi-Index Machine Learning  
Based on the idea of multi-index comprehensive early warning, taking the Lingnan TBM construction section of the Hanjiang-to-Weihe River Diversion Project as the engineering background, a machine learning model for the early warning of rockburst is established. Taking one day as a unit, the space from 10 m in front to 10 m behind the working face of the day was taken as the early warning area, and 6 input parameters were calculated and counted based on field data and microseismic data (as shown in Figure 5). The variance of the microseismic energy in the past day can be defined as:  
\[D_{E}=\frac{\sum\limits_{i=1}^{n}\left(E_{i}-\overline{E}\right)^{2}}{n} \tag{9}\]  
where \(E_{i}\) is the energy of the \(i\)th microseismic event; \(\overline{E}\) is the mean of \(E_{i}\). The data of \(n\) consecutive days are connected to generate a sequence with day as the stepping unit. Taking the occurrence and level of rockburst on the \(n+1\) day as two output parameters, the LSTM layers are used to establish a deep neural network model. Figure 5 shows the model structure and number of neurons.  
The rockburst records, construction records, and microseismic monitoring data from 15 April 2019 to 24 December 2019 were obtained. A total of 252 samples were created taking \(n=\) The previous 80% of the samples were used as the training set, and the last 20% of the samples were used as the verification set to train the model 50 times. Finally, the accuracy of the validation set for predicting the occurrence of rockburst was found to be 92.2%, and the accuracy of the validation set for predicting the level of rockburst was 82.4%.  
Table 2 shows part of the performance of the trained neural network model in the early warning of rockbursts. A comparison between the actual rockburst situations and the prediction results of the model shows that the prediction result of the model is reliable, indicating that the established DNN model can be applied to the early warning of rockbursts.  
Figure 5: Schematic of early warning model for rockbursts based on machine learning.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging
### Measurement Principles
#### 2.1.4 Summary  
The three main measurement principles share the goal of measuring TOF but have very different capabilities and applications (see Table 1). The pulsed approach is based on an incoherent measurement principle, as it is based on the detection of intensity, and can show resolutions at the cm level, being operative under strong solar background with large ambiguity distances. Its main advantage is the simplicity of the setup based on indirect detection of intensity, which is stable, robust and has available general-purpose components. The main disadvantage involves the limit in range due to low SNR at long ranges and the emission limit fixed by eye-safety levels. AMCW methods have been commercialized several years ago and are exceptionally well-developed and efficient in indoor environments. They have stable electronics architectures working in parallel in every pixel and are based on the well-known CMOS technology. They present an equivalent resolution to those of pulsed lidars, and the complexity of determining the reliability of the phase measurements using low SNR signals. Further, this SNR limitation constrains their applications outdoors. Finally, the FMCW approach presents relevant advantages which appear to place them as the future natural choice for autonomous vehicles, as its coherent detection scheme enables improvements in resolution of range measurements between one and two orders of magnitude when compared to the other methods, and the use of FFT signal processing enables to measure speed of the target simultaneously. Despite these advantages, it is a coherent system that needs to be significantly stable in its working conditions to be reliable, and aspects like temperature drift or linearity of electronics become important, which is significant for an application that demands robustness and needs units performing stably for several years.
# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
## 2 Materials and Methods
### Data Source  
In the study conducted by SHE X [33], vegetation indexes of five different seasons were extracted from Landsat 7 and Landsat 8 images, and the results showed that vegetation indexes in summer and autumn had a good correlation with forest biomass. Therefore, September and October, when forest vegetation grows and matures, were selected as the time period of remote sensing images.  
The Landsat image was a Landsat 8 OLI remote sensing image of Liaoning Province in September and October 2019 downloaded from the Geospatial Data Cloud website (www.gscloud.cn, accessed on 1 March 2022), with a spatial resolution of 30 m \(\times\) 30 m.  
Sentinel-1A images were remote sensing images of Liaoning Province downloaded from the EARTHDATA website (www.earthdata.nasa.gov, accessed on 16 March 2022) in September and October 2019, with spatial resolutions of 5 m \(\times\) 20 m and polarization modes of VV and VH.  
The ground-measured data in this paper were the data of 354 fixed plots in the continuous forest resource inventory of Liaoning Province from 2014 to 2019. The data were from the National Ecological Science Service Center (www.nesdc.org.cn, accessed on 16 March 2022) and were divided into vegetation information and topographic information. Vegetation information included plot number, plant name, diameter at breast height, tree height, etc. Other topographic information included plot attribute table, including longitude, latitude, altitude, slope, etc.
## 3 Results
### Toscanos CGTOS and Velez Valley CGVEL-MAG, with Groups CG13 and CG14
Previous PE studies of Phoenician ceramics found at Ceuta [58], on the African side of the Strait of Gibraltar, suggested a distinction between these two areas, contrasting the presence of ultrabasic rocks, a possible indicator of a Morocco provenance, with a low grade of metamorphics, linked to an Andalusian origin. In our case, it is not possible to suggest or reject a Moroccan provenance for groups CG13 or CG14 until we have further data from known production centres. All these groups exhibit some similarities with groups FG 4-5 [54], and, remarkably, group CG14 exhibits high Al\({}_{2}\)O\({}_{3}\) and Zn concentrations. PXRD analysis enables the identification of different fabrics (Table 4). In the case of CGTOS, the first, CGTOS-I (MOS062 and 064), has a low EFT (800-850 \({}^{\circ}\)C) (Figure 9, bottom left). In the case of CGTOS-II (MOS006, 007, 009, 014, and 080), the crystallisation of pyroxene and gehlenite as firing phases indicates an EFT of (900-950) \({}^{\circ}\)C. Finally, CGTOS-III (MOS030) has an EFT of ca. 1050 \({}^{\circ}\)C due to the decomposition of illite-muscovite, and gehlenite. The presence of calcite is most likely of secondary origin. From all the individuals in CGTOS, SEM-EDX analysis was performed on MOS062 (CGTOS-I), displaying less developed continuous vitrification (Vc\(-\)) with an EFT around (800-850) \({}^{\circ}\)C (Figure 8, MOS062), and MOS007 (CGTOS-II), exhibiting continuous vitrification, possibly advanced (Vc/Vc+), with an EFT estimated between (850/1050-1080) \({}^{\circ}\)C (Figure 8, MOS007). PXRD of VEL-MAG reveals three different fabrics (Table 4). CGVEL-MAG-I (MOS076 and 077) has an EFT of 800-850 \({}^{\circ}\)C if hematite is considered a firing phase, but no clear firing phase can be observed. Andalusite, quartz, plagioclase, and illite-muscovite are also present. Fabric CGVEL-MAG-II (MOS074) is similar but exhibits spessarting, and its EFT should also be in the range of 800-850 \({}^{\circ}\)C. Finally, fabric CGVEL-MAG-III (MOS067) displays the absence of illite-muscovite and calcite and the presence of quartz, hematite, pyroxene, and plagioclase (Figure 9, top left). This suggests that the EFT was over 950-1000 \({}^{\circ}\)C. In the case of CGVEL-MAG, no SEM-EDX analysis was performed. Group CG13 exhibits only one fabric (Table 4), CG13-I (MOS020 and 023), that, after the PXRD and SEM-EDX pieces of evidence (Figure 8, MOS023), suggests an EFT of (850/900-950/1000) \({}^{\circ}\)C. Finally, group CG14 has two fabrics, CG14-I (MOS013, 021, and 065) and CG14-II (MOS061 and 073), whose main difference is the presence or absence of illite-muscovite, providing two different ranges of EFT (850/900-1000) \({}^{\circ}\)C and over 1000 \({}^{\circ}\)C, respectively. The EFT estimated for CG14-II is sharpened with the information inferred from SEM-EDX (Figure 8, MOS061), which shows a Vc+ state corresponding to the range (1050-1080) \({}^{\circ}\)C. It is important to point out the presence of analusite and garnets in these groups.
## 4 Magma Source Modeling
The magma source related to the recent magma intrusion is most likely coincides with the deep source located near the southwest rift zone, based on the deformation patterns and distribution. Figure 9 shows the refined magma source locations estimated from our 3D-based modeling and previously defined source locations from an earlier study [20]. The areas with colors represent the locations of sources below Kflavaea's summit. The extent of the color boundaries correspond to the uncertainty associated with each source location. In other words, a large area of color indicates a relatively uncertain location of the magma source. In Figure 9, the source very close to the Halema'uma'u crater is the shallow magma reservoir, which lies at a depth of approximately 1.5-2.0 km below the surface [14, 16, 22]. Only this shallow reservoir is presumed to be a point source with a spherical or spheroid shape; the others located at depths of 2.5-4.0 km are presumed to be penny-shaped or rectangular sources. The magma source represented by the large light-red ellipsoid is the deepest source beneath Kflavaea's summit and is related to the magma source involved in the recent volcanic event. The two small areas that are very close to the light-red ellipsoid are the magma source locations from our modeled results. These source locations do not differ significantly from the previous location, but much better precision can be obtained using 3D-based modeling. In particular, the 3D-based model based on a spheroid source provided almost identical source positions in ten thousand Monte-Carlo simulations. The ellipsoid with the dashed outline represents the modeled magma source with the best-fit model parameters based on a spheroid source. The best-fit spheroid model involves a magma body at approximately 2.8 km depth, which is \(>\)1 km shallower than the previous estimate. Figure 9: Refined magma source locations estimated from the 3D displacement maps using rectangular dislocation (Okada) model and prolate spheroid (Yang) model. Areas with colors indicate the source locations below Kflavaea’s summit, as defined by a previous study [20] and our results. The dashed outline represents the magma source boundary of the spheroid model.
## 2 Materials and Methods
### Data Processing
#### 2.1.3 Precipitation Data  
To analyze the relationship between precipitation and crustal deformation observation data, we obtained daily mean surface precipitation for county-level administrative areas in Sichuan and Yunnan provinces for 2010-2021 from the China Meteorological Science Data Center. The selected 24 GNSS stations were located in these administrative areas.
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
## 4 Discussion
### Simulations  
As the absorption coefficient mainly affects the temperature increase on the surface of the skin during irradiation, we decided to assess the absorption coefficient of skin and hair in such a way that the experimental measurements and simulated results were matched at the moment of maximum temperature increase. Figure A3 shows the measured and simulated time course of the temperature change in both subjects following irradiation of hair and skin; we exhibit a fluence of 40 J/cm\({}^{2}\) and a duration of 30 ms, while a fluence of 20 J/cm\({}^{2}\) and a duration of 30 ms gave similar results. As we can see, the measured and simulated results for the epidermis matched very well, and the absorption coefficient of the epidermis \(\upmu\) was 0.069 mm\({}^{-1}\). For hair, the correspondence between the measured and simulated time course of the temperature change was worse; we attribute this to the fact that the absorption coefficient regulates only the temperature increase but has no effect on relaxation, which may very much depend on the thickness of the hair and the proximity of adjacent hairs. The absorption coefficient of the hair \(\upmu\) was estimated at 0.56 mm\({}^{-1}\).  
Figure A2: Time course of the temperature change following irradiation of (i) hair and (ii) skin at two different locations distant from visible hair in a human subject. (**A**) Fluence of 20 J/cm\({}^{2}\) and duration of 30 ms; (**B**) fluence of 40 J/cm\({}^{2}\) and duration of 30 ms.
## 2 Problem Formulation
It is to be noted that we begin with the complex baseband signal sampled at the specific Nyquist rate. Hence, the hypotheses become:  
\[ \begin{split}\mathcal{H}_{0}:&\mathbf{x}(n)\sim \mathcal{CN}\left(\mathbf{0},\mathbf{\Sigma}_{0}\right)\\ \mathcal{H}_{1}:&\mathbf{x}(n)\sim\mathcal{CN}\left( \mathbf{0},\mathbf{\Sigma}_{1}\right)\end{split}  \tag{6}\]  
where \(\mathcal{CN}\left(\mathbf{0},\mathbf{\Sigma}_{h}\right)\)\(h=\{0,1\}\), denotes the complex Gaussian distribution with zero mean and covariance \(\mathbf{\Sigma}_{h}\). Under \(\mathcal{H}_{0}\), \(\mathbf{\Sigma}_{0}\) is an unknown diagonal matrix, since in the absence of the primary signal the observations are assumed to be white. Therefore, the detection problem (6) distinguishes a perfectly white noise from spatially correlated (_i.e._, inter-receiver and inter-antenna correlation) signal. For this problem, we will next present the traditional detector, and then derive the proposed improved detector by introducing the pair Kronecker product.
## 3 Results and Discussion
### Effect Concentration Ratios and Evaluation Methods on Analytical Performance
The potential was scanned from 0.0 to 1.2 V.  
method for simultaneous determination of tocophereols in margarines [12], edible vegetable oils [17], nuts, and seeds [30]. From the analytical point of view, determination of precision and accuracy is necessary to obtain relevant results. The precision, defined as the result of variability found with repeated measurements, was calculated for tocophereols mixture (50 umol L\({}^{-1}\)) of each form) measured ten times and presented as relative standard deviation (RSD). Values of 5.48%, 4.88%, and 7.87% RSD for polynomial baseline; 4.78%, 0.11%, and 0.20% RSD linear baseline; 2.01%, 0.15%, and 0.05% RSD for zero base; and 1.66%, 2.75%, and 1.94% RSD for deconvolution were calculated. For a significance level of 5%, it is evident that only the polynomial evaluation method is not satisfactory for analytical purposes. Table 3 shows the comparison of evaluation methods used in determination of accuracy for different model samples having the randomly chosen contents of investigated tocophereols. In this case, the accuracy is presented as recovery values (%). Acceptable accuracy values ranging from 72% to 119% can be accepted only for the deconvolution method, which is comparable with recovery ranges from 71% to 116% and from 77% to 85% recommended for tocophereols determination in nuts and virgin olive oils using QuEChERS-liquid chromatography and reversed-phase HPLC with spectrophotometric detection, respectively [30]. Obtained results showed that it is not yet possible to perfectly split the anodic peaks of \(\alpha\)-TOH, \(\gamma\)-TOH, and \(\delta\)-TOH differing only in the number of methyl groups in chromanol ring. Nevertheless, it was found that the type of working electrode material, composition of detection medium, set parameters of electrochemical technique, and selection of evaluation method have a significant effect in their sufficient resolution so that the individual signals can be quantified. The distance around 100 mV of individual oxidation peaks (0.645 V for \(\alpha\)-TOH, 0.753 V for \(\gamma\)-TOH and 0.857 for \(\delta\)-TOH) has been achieved at optimum working conditions.
## 3 Materials and Methods
### HS-SPME GC-MS Measurements  
Aliquots of 8 mL of sample were placed in a 20 mL vials that were immediately sealed with a silicone rubber Teflon cap and crimped with aluminium seal. Then samples were heated at 60 \({}^{\circ}\)C and kept at the same temperature for 30 min while a polydimethylsiloxane/divinylbenzene SPME fibre (Supelco, Bellefonte, PA, USA) was exposed to the headspace over the surface of each sample in order to collect the compounds in the vapour phase. The exposure time was optimized in preliminary experimental trials. The SPME coating containing the headspace volatile compounds was inserted into the GC injection port and then thermally desorbed at 250 \({}^{\circ}\)C for 10 min in a 6890 GC (Agilent Technologies, Santa Clara, CA, United States). Compounds were eluted by a He gas flow of 1,4 mL/min in split mode (split 1:4) and separated using a 60 m Varian FactorFour WAXms capillary column (film thickness 0.25 mm, 0.25 mm internal diameter) (Varian, Middelburg, The Netherlands). The oven  
Figure 4: Analysis of mascarpone VOCs profile assessed by PTR-ToF-MS for the Manufacturers C (**a**) and M (**b**) plotted by the first and the third principal components. The labels and the selected areas indicate the separation between samples with or without lactose.  
temperature, initially set to 35 \({}^{\circ}\)C, was increased to 210 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, then to 240 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min, and then this final temperature was held for 5 min. The mass spectrometer was set to electron ionization mode (MS-EI) generated at 70 eV, and mass spectra were collected in full scan mode, collecting ions from 39 to 250 \(n\)/z. The volatile compounds studied were identified by comparing their mass spectra and their retention times to those of reference standards analyzed at the same conditions and by comparison with spectra recorded in the Wiley 6 N mass spectral library (Wiley, Hoboken, NJ, USA) and, when needed, to literature references. Due to the lengthy HS-SPME GC/MS analysis, only four samples have been analysed by this method. For each sample, four replicates were analyzed.