esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### VOCs Extraction and Analysis  
The impregnation procedure was performed on broccoli and courgette, which were thereafter subjected to various drying methods. In the source objects--raw broccoli and raw courgetti--by the HS-SPME technique followed by GC-MS analysis, 56 and 52 VOCs were found, respectively. The whole profiles of VOCs in the samples are presented in Tables 2 and 3. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Compound** & **LRI\({}_{\text{exp}}\)1** & **LRI\({}_{\text{lit}}\)2** & \multicolumn{2}{c}{**Spectrum**} & **[\%]** & **SD** \\  & & 862 & 864 & 90 & 11.03 & 0.18 \\
2-(Z)-hexen-1-ol & & 865 & 866 & 91 & 19.47 & 0.60 \\ Hepten-3-ol & & 870 & 877 & 90 & 6.92 & 0.22 \\ allyl-isovalerate & & 924 & 934 & 92 & 1.22 & 0.06 \\ Pentanoic acid, 3-methyl-, ethyl ester & & 956 & 953 & 95 & 4.46 & 0.51 \\
2-(E)-heptenal & & 962 & 956 & 91 & 0.60 & 0.15 \\ cis-2-methyltetrahydroofuran-3-thiol, cis- & & 966 & 956 & 94 & 3.38 & 0.36 \\ Heptanol & & 971 & 970 & 96 & 0.81 & 0.09 \\ Dec-1-ene & & 980 & 987 & 90 & 0.85 & 0.06 \\ Hexanoic acid & & 987 & 990 & 92 & 0.54 & 0.03 \\ Octan-2-one & & 993 & 990 & 90 & 0.37 & 0.00 \\ Decane & & 1001 & 1000 & 93 & 0.43 & 0.01 \\ Octanal & & 1003 & 1006 & 90 & 0.14 & 0.00 \\
2.4-(E,E)-heptadiene-1-ol & & 1012 & 1019 & 94 & 0.34 & 0.02 \\ Formic acid, heptyl ester & & 1015 & 1024 & 91 & 1.75 & 0.09 \\ Heptanoic acid, methyl ester & & 1025 & 1025 & 90 & 0.14 & 0.01 \\ isoamyl-pyruvate & & 1043 & 1051 & 91 & 0.26 & 0.02 \\
2-(E)-ocetan & & 1054 & 1059 & 93 & 0.07 & 0.00 \\ Acetophenone & & 1060 & 1068 & 95 & 3.96 & 0.17 \\
2-(E)-ocetan-1-ol & & 1070 & 1067 & 91 & 0.27 & 0.01 \\ Octanol & & 1074 & 1076 & 90 & 0.27 & 0.01 \\ Hexanoic acid, 2-propenyl ester & 1081 & 1081 & 92 & 1.44 & 0.24 \\ Nonan-3-one & & 1087 & 1088 & 95 & 0.42 & 0.00 \\ Butanoic acid, 2-(Z)-hexen-1-yl ester & & 1089 & 1099 & 90 & 5.30 & 0.21 \\ Unknown & & 1091 & & & 0.63 & 0.02 \\ Undecane & & 1100 & 1100 & 94 & 15.69 & 0.48 \\
3-octen-2-one, 7-methyl- & & 1108 & 1106 & 90 & 0.34 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: VOCs profile of raw broccoli.
# Normalized Weighting Schemes for Image Interpolation Algorithms
## 3 Results and Discussions  
This part presents results from objective and subjective evaluations or assessments of the quality of images interpolated by traditional and non-traditional image interpolation algorithms. The priority was given to the traditional algorithms such as the traditional nearest neighbor image interpolation algorithm (TN), traditional bicubic image interpolation algorithm (TC), and traditional bilinear image interpolation algorithm (TB). Those developed weighting schemes-based image interpolation algorithms (referred to as non-traditional image interpolation algorithms) to evaluate included: The normalized minimum side-based diameter (MD), normalized hypotenuse radius-based (HR), normalized virtual pixel length-based height for the area of the triangle (AT), and normalized virtual pixel length for hypotenuse-based radius for the area of the circle (AC) algorithms. Note that all these image interpolation algorithms were not only evaluated objectively and subjectively but also in terms of computational efficiency (using basic MATLAB functions to record the start and end time and print the time difference in seconds).
## 2 Material and Methods
### Cloud-Topped Water Vapor  
To approximate the vertical column of water vapor located above marine low-level clouds, we used the following variables: precipitable water \(PW\), surface skin temperature \(T_{S}\) and surface pressure \(P_{S}\)--all provided by the Goddard Earth Observing System (GEOS) [26] version 5.4.1 and collocated to CERES footprints--as well as MODIS mean cloud top and cloud base pressure (\(P_{CT}\) and \(P_{CB}\), respectively). All fields were included in CERES Edition 4 SSF (introduced in Section 2.1).  
First, we approximated the amount of water in the cloud-topped boundary layer. Taking the \(T_{S}\) and the dry-adiabatic lapse rate, we derived the temperature at cloud base level \(T_{CB}\). Applying the relation by [PERSON] [27] (shown in Equation (1)), we determined the saturation vapor pressure \(e_{s}\) at cloud base temperature (here, \(T_{CB}\) in \({}^{\circ}\)C) and then (in Equation (2)) the mixing ratio \(r\). \(W_{w}\) and \(W_{air}\) are molecular weights of water and dry air, respectively.  
\[e_{s}=6.112\exp\left(\frac{17.67\ T_{CB}}{T_{CB}+243.5}\right) \tag{1}\]  
\[r=\frac{e_{s}}{P_{CB}}\frac{W_{w}}{W_{air}}10^{-3} \tag{2}\]  
Figure 3: The setup of thirteen adiabatic cloud experiments. Each experiment arrived at a cloud optical depth of 10 and a cloud top pressure of 875 hPa. Across experiments, both liquid water path (LWP; \(x\)-axis) and cloud-droplet number concentration (CDNC; \(y\)-axis) varied. Accordingly, geometric extent (shown in color) and profiles of cloud droplet effective radii (represented by cloud-top effective radius shown in dot size) changed.  
Assuming well-mixed conditions and thus a constant \(r\) within the cloud-topped boundary layer, we integrated the vertical column of water vapor between surface and cloud-top. Finally, we subtracted the boundary layer integral from \(PW\) to gain the vertical column of cloud-topped water vapor \(CTWV\) (as shown in Equation (3) where \(g\) is the gravitational constant and \(\rho_{w}\) is the water density).  
\[CTWV=PW-\frac{1}{g\rho_{w}}\int_{P_{S}}^{P_{CT}}r\;dp \tag{3}\]  
With regard to Figure 2, boundary layers rarely had a vertically constant mixing ratio and therefore violated the assumption of being well-mixed. By using the ratio at cloud base, we potentially compensated an underestimation in the cloud-free boundary layer with an overestimation between cloud base and cloud top. Our methodology, additionally, depended on a correct vertical position of the cloud. In the case of under- or overestimation of cloud base and top, the effect on errors in cloud-topped water vapor should, however, be regularizing: for example, an overestimation in cloud base and top height would result in lower boundary layer mixing ratios (as the cloud base temperature was erroneously lower) which would be integrated over a higher boundary layer. We mimicked a cloud base and top underestimation of 50 hPa and found a median offset of \(-0.5\) kg m\({}^{-2}\) (\(-7.6\%\)) in cloud-topped moisture. Negative values in cloud-topped water vapor (found in 13% of all footprints) were set to zero.
## 1 Introduction
The use of LIBS to investigate minerals containing lighter elements (H, Li, N, and C) can be particularly useful for studying gemstones. LIBS, coupled with multivariate data analysis, is frequently used to classify different samples with elusive changes in their relative elemental concentrations or other variables [19; 20; 21; 22]. In numerous studies, LIBS enabled the differentiation of many geological materials, even if they were chemically similar and optically indistinguishable [19; 20]. In work by [PERSON] et al. [21], 569 rough sapphire and ruby specimens from 21 localities in 11 countries were analyzed through LIBS and multivariate data analysis. They demonstrated that the quantitative measure is related to the deposit of origin. The potential of LIBS was also used to recognize beryl (Be\({}_{3}\)Al\({}_{2}\)Si\({}_{6}\)O\({}_{18}\)) from the three beryl-bearing zones in the Palermo#1 pegmatite (New Hampshire). Principal component analysis (PCA) has been applied to the broadband LIB spectra of 39 beryl specimens from 11 pegmatite mines in New Hampshire, Connecticut, and Maine (USA) to successfully determine sample provenance [22]. The present work aims at advancing the analytical capabilities of LIBS in the field of gem mineral characterization. The elemental composition and the surface hardness are key parameters in this context. LIB spectra of six different gemstone samples have been analyzed using univariate and multivariate techniques. Trace elements responsible for the coloration of the gemstones were examined. Moreover, the potential of determining the surface hardness of the samples using LIB spectral data was tested based on a comparison with the relative hardness values using Mohs's scale.
## 3 Experimental Results and Analysis
### Materials  
In the first experiment, the evaluation indices are calculated based on the proposed theoretical model and watermarking algorithms respectively. The experimental data contains 20 remote sensing images of Vancouver and 20 remote sensing images from Landsat7 with sensors of Enhanced Thematic Mapper Plus (ETM+) in ScanLinesCorrector (SLC)-off mode. The images of Vancouver are the open datasets downloaded from the City of Vancouver website (available at [[http://data.vancouver.ca/datacatalogue/2011](http://data.vancouver.ca/datacatalogue/2011) facetsGridECW.htm]([http://data.vancouver.ca/datacatalogue/2011](http://data.vancouver.ca/datacatalogue/2011) facetsGridECW.htm)). Their resolution is 16,000 \(\times\) 10,000 with three bands and the size is 457 MB in.tiff format. The images of Landsat7 are provided by Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn))). Their resolution is 4096 \(\times\) 4096 with nine bands and the size is 144 MB in.tiff format. Parts of the experimental data are shown in Figures 9 and 10 with the overview map acquired from World Topographic Map of ArcGIS Online data.  
Figure 9: Parts of experimental data: (**a–d**) are remote sensing images of Vancouver.  
Figure 10: Parts of experimental data: (**a**–**d**) are remote sensing images of Landsat7.
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 2 Literature Review
### Cybersecurity in IoT Architecture
#### 2.1.5 Cybersecurity at the Service Management Layer  
Unlike the technological risks of the other layers, cybersecurity at the service management layer focuses on human and organizational aspects of cybersecurity. Trust and privacy issues are relevant to IoT service management, since these issues affect the usage of the IoT services and applications. A study on farmers' perceptions of agricultural technology shows that trust affects perceived value and perceived risk, and in turn, those affect IoT adoption [44]. Security and privacy threats become prevalent in the use of cloud services [45].  
It is imperative to protect individuals' privacy impacted by personally identifiable information (PII) processing through the protection of device and data [23]. Incorporating privacy protection measures in the early stage of IoT development is critical for building trust and promoting the adoption of IoT systems [46]. However, since most IoT devices are low energy and lightweight, the task of protecting security and privacy is quite challenging [47]. To protect the privacy of patients in IoT-based healthcare systems, [PERSON] et al. [48] propose a framework called PrivacyProtector with the aim of defending against sophisticated cyberattacks such as collusion attacks and data leakage.
# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 4 Performance Evaluation
### Performance Metrics  
The following performance evaluation metrics were adopted to evaluate the proposed algorithm:  
* Perceptual evaluation of the signal quality (\(PESQ\)) measure, defined in ITU-T P862.2 [54].
* Segmental signal-to-noise ratio (\(SegSNR\)) in dB (Equation (5)).
* Bit error rate (\(BER\)): calculated as the percentage number of retrieved binary bits after demodulating the stego channel and have been altered due to noise, divided by the total number of bits in the transmitted payload.
* Average embedding ratio (\(AER\)): AER is defined as: \[Embedding\ rate\ (\%)\ =\ \frac{total\ embedded\ bits}{total\ bits\ of\ the\ signal}\ \cdot 100\] (1) \(AER\) is used to calculate the ratio of the embedded data to the cover signal size.  
F-measure and the area under the ROC curve are two of the most popular computational methods to find a balance between false positives and false negatives. The F-measure is calculated using precision (PP) and recall (R) such as:  
\[F-measure=2\cdot\frac{PP\cdot R}{(PP+R)} \tag{2}\]  
\[PP=\frac{TP}{(TP+FP)} \tag{3}\]  
\[R=\frac{TP}{(TP+FN)} \tag{4}\]  
The ROC is the fraction of true positives (TPR = true positive rate) versus the fraction of false positives (FPR = false positive rate). In this experiment, TP, FP, and FN are defined as follows:  
* True positive (TP): stego-audio signal classified as stego-audio signal;
* True negatives (TN): cover-audio signal classified as cover-audio signal;
* False negatives (FN): stego-audio signal classified as cover-audio signal;
* False positives (FP): cover-audio signal classified as stego-audio signal.
## 3 Circuit Design
### Broadband Design Consideration  
The design of a high-efficiency power amplifier requires obtaining the optimal impedance region within the target frequency band. In this paper, we adopt the method of a load-pull simulation to obtain the optimal impedance. Table 2 gives the load-pull simulation result of the two parallel HEMTs with stability circuits for the power stage of the developed MMIC PA. These optimum impedance points are selected based on tradeoffs among the PAE, \(P_{out}\), and bandwidth. It is also observed that the optimal load impedance of the device is not constant for the whole frequency. Within the frequency range of 7-13 GHz, the \(P_{out}\) and PAE are better than 37 dBm and 54%, respectively. Figure 5 gives the load-pull contours of the \(P_{out}\), PAE, and ideal load impedance at 7 and 13 GHz.  
Figure 4: Simulated results of stability circuits: (**a**) maximum gain; (**b**) stability factor.  
Figure 3: Block diagram of the stability circuits.  
Figure 5: Load–pull contours for \(P_{out}\) and PAE from 7 to 13 GHz and ideal load impedance.  
\begin{tabular}{c c c c} \hline \hline
**Frequency (GHz)** & \(\mathbf{Z_{opt}}\) & \(\mathbf{P_{out}}\) **(dBm)** & **PAE (\%)** \\ \hline
7 & 8.25 + j18.69 & 39.8 & 59.63 \\
8 & 17.59 + j31.16 & 37.52 & 57.51 \\
9 & 6.03 + j16.04 & 38.65 & 57.24 \\
10 & 11.51 + j22.2 & 37.37 & 57.68 \\
11 & 3.95 + j13.23 & 38.37 & 56.59 \\
12 & 4.26 + j11.85 & 37.57 & 55.42 \\
13 & 9.29 + j20.16 & 37.51 & 54.68 \\ \hline \hline \end{tabular}  
To ensure a high-frequency performance of this PA, thereby improving the gain flatness, the optimal load \(Z_{opt}\) is chosen to be 12 GHz. Figure 6 gives the simulation result of the \(P_{out}\) and PAE versus the input power (\(P_{in}\)) at 12 GHz for a 2 \(\times\) 4 \(\times\) 125 \(\upmu\)m GaN HEMT with an optimal load impedance under CW conditions. It can be seen that as the \(P_{in}\) increases, the \(P_{out}\) gradually increases. The peak value of the PAE appears around \(P_{in}\) = 27 dBm, at the same time, the \(P_{out}\) also reaches a relatively high power level.  
\begin{tabular}{c c c} \hline \hline
**Frequency (GHz)** & \(\mathbf{Z_{opt}}\) & \(\mathbf{P_{out}}\) **(dBm)** & **PAE (\%)** \\ \hline
7 & 8.25 + j18.69 & 39.8 & 59.63 \\
8 & 17
# Numerical and Experimental Research on the Laser-Water Jet Scribing of Silicon
## 2 Numerical Simulation and Experimental Setup
### Numerical Setup  
Before setting up the numerical model, the following assumptions were made to reasonably simplify the simulation process:  
1. The density of the substrate remained constant, neglecting the absorption of laser energy. The surrounding temperature was 300 K throughout the whole calculation process.
2. When the temperature of one element reached the melting point, it was assumed to be ablated from the substrate.  
The fluid dynamics of the melting material was neglected considering the scouring effect of the waterjet.
* The influence of the assisted gas atmosphere on the substrate ablation was neglected. The numerical simulation was carried out under the circumstance of COMSOL Multiphysics. The CFD (computational fluid dynamics) module and heat transfer module were used for model setup. A two-dimension symmetrical calculation domain and the corresponding mesh generation is illustrated in Figure 3. The size of the calculation domain was 50 \(\upmu\)m \(\times\) 5 \(\upmu\)m. It should be noted that the LWJ impinged on the upper surface of the domain and the radius of LWJ was 25 \(\upmu\)m. In order to improve the computational accuracy, the upper boundary of the domain was refined. The minimum mesh size was 100 nm (length) \(\times\) 50 nm (width).  
The LWJ machining parameters included but were not limited to laser pulse duration, waterjet pressure, and diameter, in accordance with the experimental setup in Section 3.
# A Comprehensive Methodology for Investment Project Assessment Based on Monte Carlo Simulation
## 3 Application of the Methodology on a Virtual Investment Project
### Creating a Financial Model and Calculating NPV Deterministically  
The economic efficiency of the investment project is assessed using the NPV financial criterion, the value of which is calculated according to the relationship (1). The prediction of annual CF from an operational activity is determined according to relationship (2):  
\[NPV=\sum_{n=1}^{N}C{F_{n}}.\frac{1}{{(1+d_{r})}^{n}}-IC, \tag{1}\]  
\[C{F_{n}}=\left(\sum_{j=1}^{3}S_{jn}-\sum_{j=1}^{3}C_{jn}\right).(1-T_{n})+(D_{n} \cdot T_{n})-\Delta NCWC_{n}, \tag{2}\]  
where \(CFn\) is cash flow in year \(n\), \(n\) number of years of life of the investment, \(N\) life of the investment, \(d_{r}\) discount rate, \(IC\) investment costs, \(S\) sales, \(p\) price, \(C\) costs, \(j\) product range (A, B, C), \(D\) depreciation, \(T\) income tax rate, and \(NCWC\) non-cash working capital.  
Table 4 presents the input variables necessary to calculate the financial criterion NPV deterministically and its calculated value of EUR 3,778,179.
## 1 Introduction
A significant reduction in computational complexity was obtained when evaluating system performance using our derived expressions. The organization of the paper is as follows. Related work is explained in Section 2. Section 3 defines the ASTBC in which signal transmission using two transceivers is described. Section 4 describes the system model in which the pathloss of our model is computed. The closed-form approximations of BER for BCH in Rayleigh fading and BCH-concatenated ASTBC coding are derived. Section 5 contains results and discussion. Last comes the conclusion in Section 6.
## 3 Methodology
### Fractional Vegetation Cover Estimation  
The dimidiate pixel model is a vegetation estimation model, which assumes that the real surface of one pixel consists of vegetation-covered and non-vegetation-covered [30]. The spectral information observed by the sensor is linearly weighted by these two-component factors, and the weight of each factor is the ratio of its area in the pixel. The principle model principle is as follows:  
\[P=P_{v}+P_{s} \tag{2}\]  
where \(P\) is the information observed by remote sensing sensors, \(P_{v}\) is green vegetation information, and \(P_{s}\) is the information of non-vegetation, which represents soil area.  
FVC is the ratio of the vertical projection area of the vegetation canopy to the total soil area. Assuming that \(P_{vcg}\) is the pixel information of FVC in remote sensing image, and the pixel information of soil without vegetation is \(P_{soil}\), the contribution of vegetation to the pixel can be expressed as:  
\[P_{v}=P_{vcg}\times f_{vc} \tag{3}\]  
The contribution of soil to the pixel is expressed as Equation (4):  
\[P_{s}=(1-f_{vc})\times P_{soil} \tag{4}\]  
Therefore, the information observed based on remote sensing images is calculated as:  
\[P=P_{vcg}\times f_{vc}+(1-f_{vc})\times P_{soil} \tag{5}\]  
In summary, the fractional vegetation cover is expressed as:  
\[f_{vc}=(P-P_{soil})/\left(P_{vcg}-P_{soil}\right) \tag{6}\]  
The model reflects the relationship between remote sensing information and FVC, and reduces the impact of atmosphere and soil as much as possible to retain only FVC information. Meanwhile, the Normalized Difference Vegetation Index (NDVI) is an important parameter that reflects crop growth and vegetation coverage information and is often used to estimate fractional vegetation cover in the dimidiate pixel model. The value of NDVI is substituted into Formula (6):\[f_{\text{cc}}=\left(NDVI-NDVI_{soil}\right)/\left(NDVI_{vgg}-NDVI_{soil}\right) \tag{7}\]  
Therefore, according to Equation (7), the NDVI value of a pixel (\(NDVI\)) can be expressed as a combination of the information contributed by the vegetation (\(NDVI_{vgg}\)) and soil (\(NDVI_{soil}\)) areas.
# Progressive Poling of Large Area, High r\({}_{33}\) Electro-Optic Polymer SEO100c
## 6 Results
### Poling Electric Field Strength Results  
Data was collected at four frequencies, 40 Hz, 100 Hz, 500 Hz, and 1k Hz. The presented r\({}_{33}\) for each poling voltage is the average r\({}_{33}\) of these four data points. The error bars were determined by calculating the standard deviation of the data set at each poling voltage. The resulting data show two distinct regions of r\({}_{33}\) as a function of poling field: a non-linear region at low poling voltages (<25 V/\(\upmu\)m across the EO core) and a linear region for higher poling voltages as shown in Figure 6. The r\({}_{33}\) values obtained at the lower poling voltage resulted in a non-linear region. This effect has been attributed to there being insufficient voltage to overcome the resistance to chromophore movement in the poled device. This resistances of chromophore movement is caused by the chromophore being tangled within the host polymer [17; 18]. The linear region shows an increase of 0.37 pm/V for every 1 V/\(\upmu\)m of additional poling field strength. This trend can be extrapolated to find an r\({}_{33}\) of 93 pm/V at 100 V/\(\upmu\)m poling, which is the poling field specified by the data sheet to achieve the maximum r\({}_{33}\) value. Higher voltages were avoided as they would increase the risk of device damage, and fields in excess of 77 V/\(\upmu\)m would cause sparking damaging the top electrode.  
The value of 93 pm/V after being poled at 100 V/\(\upmu\)m across the core, though high, is less than the value specified by the data sheet. The data sheet specifies an r\({}_{33}\) of 140 pm/V for a wavelength of 1300 nm. Causes for this were investigated and it was found that though the measured air temperature was reaching 135 \({}^{\circ}\)C the device was only reaching 125 \({}^{\circ}\)C. This led to a slight alteration of poling process to study the correlation between poling temperature and r\({}_{33}\).