esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Microencapsulation of _C. ternatea_'s Phytochemical
### Antioxidant Activity of Microencapsulated C. ternatea Extract  
Oxidative stress contributes to the development of degenerative and chronic diseases such as cardiovascular and neurodegenerative diseases, cancer, and autoimmune disorders. The isolation and application of antioxidants from natural sources have many benefits towards human health [97; 98; 99]. Previous studies have been conducted on the antioxidant activity of _C. ternatea_ flower extract using antioxidant assays such as the 2,2-diphenyl-1-picryhydrazyl radical (DPPH) radical scavenging and Thiobarbituric Acid (TBA) tests.  
[PERSON] et al. [79] spray dried _C. ternatea_ flower water extract with some levels of Arabic gum concentration (0, 2, 4, 6, 8, and 10%) and examined the antioxidant activity with two different methods, namely, DPPH and TBA. The results demonstrated that microcapsules with 8% of Arabic gum indicated the best radical scavenging activity due to the most appropriate condition or non-clotting state of the microcapsules powder, which were capable of performing the antioxidant activity completely. Similarly, the antioxidant activity examined by the TBA test showed that microcapsules with 8% of Arabic gum had the highest activity. Nevertheless, the antioxidant activity detected with the TBA test was lower than the DPPH method. This may be caused by the lipid peroxidation in the secondary stage, which is higher than the initial stage [79]. Moreover, there is a peak concentration of Arabic gum, which is the most effective concentration to maintain the antioxidant activity of microencapsulated extracts. This result is in line with the report by [PERSON] et al. [6], which encapsulated phenolic extract from propolis with different concentrations of Arabic gum and showed a peak of the most effective Arabic gum concentration to preserve the phenolic content in microcapsules.  
One study compared three different drying methods to microencapsulate _C. ternatea_ extract with gelatin as the wall material; the freeze-dried _C. ternatea_ flower extract indicated the strongest inhibition of DPPH radicals, although the total anthocyanin contents were the lowest compared to the spray-dried and oven-dried samples [80]. This phenomenon conforms with [PERSON] [100], who reported that the antioxidant activity of freeze-dried raspberry was higher than the spray-dried sample due to the higher drying temperature, which caused a significant decrease in DPPH free radical scavenging activity. This might occur due to the existence of other phenolic compounds in the freeze-dried microencapsulation system, although the total anthocyanin contents were lower than the ultra-spray dried sample. Moreover, despite the fact that the total anthocyanin content is lower, the efficacy and quality of the bioactive compounds in the freeze-dried sample is better than the spray-driedsample due to its low temperature process, which can preserve the antioxidant capacity of the bioactive compounds during drying.
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## 2 Preliminaries and Related Works
### Convex Optimization  
For the following optimization problem  
\[\min_{\beta}\{f(\beta)+g(\beta)\}, \tag{10}\]  
where \(f(\beta)\) is a convex and differentiable, \(g(\beta)\) is proper closed, convex but non-smooth regularization term. Proximal gradient descent (PGD) algorithm perform well in solving such optimization model [41]. The iterative formula utilized for the PGD is  
\[\beta^{k+1}=\text{prox}_{ag}(\beta^{k}-\alpha\
abla f(\beta^{k})), \tag{11}\]  
where \(\alpha>0\) is the step size of each iteration and \(\
abla f(\beta^{k})\) is the gradient of \(f(\beta^{k})\). \(\text{prox}_{g}(\cdot)\) is the proximal operator of \(g\), which is defined as  
\[\text{prox}_{g}(\beta)=\arg\min_{v}\bigg{\{}\frac{1}{2}\|\beta-v\|_{2}^{2}+g(v )\bigg{\}}. \tag{12}\]  
As a special case, if \(g(\beta)=\lambda\|\beta\|_{1}\), then its proximal operator \(\text{prox}_{\lambda\|\cdot\|_{1}}\) is the element-wise soft thresholding function  
\[\text{prox}_{\lambda\|\cdot\|_{1}}(\beta_{i})=\text{sgn}(\beta_{i})\max\{| \beta_{i}|-\lambda,0\}=\left\{\begin{array}{cc}\beta_{i}-\lambda,&\beta_{i} \geq\lambda,\\ 0,&|\beta_{i}|<\lambda,\\ \beta_{i}+\lambda,&\beta_{i}\leq-\lambda.\end{array}\right. \tag{13}\]  
where \(\beta_{i}\) is the \(i\)-th element of \(\beta\).
## 2 Results and Discussion  
### Volatile Compounds in Prepared Green Teas  
Thirty-eight aroma volatile compounds were tentatively identified from the 24 green tea samples (Table 1). Table 1 also provides information on potential flavors associated with those compounds from prior literature. Because sensory characteristics were not measured in this study, those characteristics merely are examples of potential associations and what might be expected given the chemical composition. Thus, such information must be considered only a tentative association [28].  
\begin{table}
\begin{tabular}{l l l} \hline
**Volatile Compounds** & **Odor characteristics** & **Reference** \\ \hline _Aliphatic Alcohols_ & & \\
1-Penten-3-ol & Butter, mild, green & [29] \\
1-Pentanol & Somewhat sweet, balsamic & [29] \\
2-Penten-1-ol & Jasmine, green, plastic, rubber & [29, 30] \\ (_Z_)-3-Hexen-1-ol & & \\ Linalool & Bergamot oil, French lavender & [29] \\ Geraniol & Pleasant geranium & [29] \\ _Aromatic Alcohols_ & & \\ Benzeneethanol & Floral, rose & [29] \\
1-\(\alpha\)-Terpineol & Lilac & [29] \\
2-Methoxy-4-methylphenol & & \\
2,6-Dimethyl-cyclohexanol & & \\ _Aliphatic Aldehydes_ & & \\
3-Methyl-butanal & Unpleasant & [29] \\ Pentanal & Pungent, almond, malt & [29, 30] \\ Hexanal & Fruity & [29] \\ _cis_-3-Hexenal & herbal apple, green leafy & [29] \\ Nonanal & Fatty, citrus, green & [29, 30] \\
1H-Pyrrole-2-carboxaldehyde & & \\ _Aromatic Aldehydes_ & & \\ Benzaldehyde & Bitter almond & [29] \\ Benzeneacetaldehyde & Hyacinth, Lilac & [29] \\ _Other Aromatic Compounds_ & & \\ Toluene & Paint & [30] \\
1,4-Dimethoxybenzene & Aromatic ether, sweet clover & [29] \\
1,4-Bis(1,1-dimethylethyl)-benzene & & \\ Styrene & Penetrating, balsamic, gasoline & [29, 30] \\
2-Hydroxy methyl ester benzoic acid & & \\ _Ketones_ & & \\
3,5-Octalien-2-one & & \\ Jasmone & Jasmine & [29] \\ \hline \end{tabular}
\end{table}
Table 1: 39 volatile compounds identified in 24 green tea samples and their odor characteristics reported as the literature.  
Of the 39 volatile compounds identified and quantified in this study, 15 volatiles present in more than four samples are shown in Table 2 and the remaining compounds, found in only a few samples, are shown in Table 3. Some of the compounds (e.g., geraniol, linalool, indole, and jasmine) are similar to those found in brewed Japanese green tea samples [18]. Because the focus of the study was to determine the presence of compounds and the distribution of those compounds in green teas grown around the world, the semi-quantification method using an internal standard was employed. Thus, differences in concentrations among tea samples were not determined statistically for this research. Standard deviations were typical for studies of this type, usually between 10 and 50% of the mean value, suggesting low to moderate variation.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 1 Introduction
### Network Covert Channel  
When malicious programs invade air-gapped networks, they often aim to establish communication channels for command control and data transmission. To avoid triggering security device alarms such as firewalls, malicious programs usually utilize covert channels to establish communication. A network covert channel refers to a channel where the communicating parties encode and transmit information by modifying the value, characteristics, or status of shared resources. Over the years, academia has proposed various types of network covert channels, such as hybrid covert channels and behavioral covert channels [3]. These covert channels can typically evade detection by devices like firewalls in order to achieve long-term control over the target. However, traditional covert channels are based on network connections; without a network environment, an air-gap channel cannot be established.
## 2 Double-Ridged Waveguide Rotman Lens Design in the K/K\({}_{\text{a}}\)-Band
### Feeding Port Design  
A schematic of the double-ridged waveguide feeding port with associated parameters is provided in Figure 2. A double-ridged waveguide implementation is preferred as it leads to a shorter transition within the PPW for the same cutoff frequency when compared to a single ridge design. These port designs are of interest because they excite a quasi-TEM mode in the PPW section, providing true time delay operation within the lens cavity. They also enable smaller port-to-port spacing when compared to standard H-plane rectangular waveguides [33]. The design of the feeding port is similar to the one used in the Q-band Rotman lens design reported in [38], implementing a straight transition design here as this provided acceptable performance while simplifying the mechanical design. The feeding port has a constant height value, \(b\), which is also the height of the PPW section.  
The main parameter for the sizing of the Rotman lens is the aperture width of the feeding ports, \(d\). Ideally, the value should be as small as possible, while ensuring good matching over the considered operating bandwidth. The starting point for the reference double-ridged waveguide was set to the WRD180 standard, as its nominal fundamental mode bandwidth (18-40 GHz) and cut-off frequency (15.12 GHz) are compatible with the targeted K/K\({}_{\text{a}}\)-band operation. The cross-section was slightly adjusted to improve the response, specifically in the lower part of the frequency range, using a full-wave model implemented with the commercial software Ansys HFSS. The corresponding design parameters are \(a=7.70\,\text{mm}\), \(b=3.40\,\text{mm}\), \(r_{1}=1.92\,\text{mm}\) and \(b_{r}=0.98\,\text{mm}\), where only \(a\) and \(r_{1}\) have been adjusted compared to the WRD180 dimensions.  
Figure 1: Rotman lens design parameters [20; 37].  
Figure 2: Double-ridged waveguide feeding port design parameters: top view (**left**) and isometric view (**right**).  
design parameters are \(d=8.86\,\)mm, \(r_{2}=3.99\,\)mm, \(l_{1}=9.66\,\)mm and \(l_{2}=13.55\,\)mm. The nominal feeding port design was optimized in an embedded configuration, including two adjacent ports on both sides, to reduce both reflections and port-to-port coupling with a goal of \(-20\,\)dB over the operating frequency bandwidth. Section 2.2 details the Rotman lens constructed from this feeding port design.
## 4 Experimentation and Results
### Visualization of Compression Results  
To validate the effectiveness of the hybrid compression scheme, we conducted separate tests using both JPEG and the hybrid compression methods and visualized the reconstruction performance. As shown in Figure 5a-c, we present the original and reconstructed two-dimensional images of three variables: sea surface height, flow velocity u, and flow velocity v. The left images represent the original data, the middle images represent the data after JPEG compression, and the right images represent the reconstructed results after the application of our hybrid compression scheme. From Figure 4, it can be inferred that both JPEG compression and our hybrid compression scheme preserve almost all spatial information with only minimal changes imperceptible to the naked eye. This indicates that the compression ratio is maintained while ensuring high-quality data reconstruction.  
Figure 4: MSE losses under different learning rates.  
Numerically, when using the hybrid compression scheme, the mean absolute error (MAE) of data reconstruction is smaller compared to JPEG compression, with a maximum reduction of 22%. The peak signal-to-noise ratio (PSNR) is also improved compared to single-channel compression, with a maximum increase of 4.6%. This suggests that compared to JPEG compression, the hybrid compression scheme better captures the spatial characteristics between data points. Additionally, Figure 4 shows that the PSNR of each reconstructed image varies from 51.21 to 59.54. This variation is due to the different data distributions of each variable, leading to changes in the quantization of the codebook values and affecting the reconstruction quality. However, previous work has shown that a PSNR in the range of [30; 60] is already considered good enough for various scientific applications,  
Figure 5: Visualization of reconstructed data: (**a**) JPEG compression maze: 0.0029, psnr: 57.56; hybrid compression maze: 0.0024, psnr: 59.54. (**b**) JPEG channel compression maze: 0.0072, psnr: 48.96; hybrid compression maze: 0.0056, psnr: 51.21. (**c**) JPEG compression maze: 0.0073, psnr: 50.77; hybrid compression maze: 0.0058, psnr: 52.67.  
providing high visual quality. Therefore, our model achieves stable performance in data compression and reconstruction.
## 2 Materials and Methods
### Data Source  
#### 2.2.1 Measured Data Source  
In 2016, the Urumqi Institute of Desert Meteorology, part of the China Meteorological Administration, established the Middle Tien Shan Grassland Land-Air Interaction Observation and Experimental Station in the Ulastai area of Baiyanggou, Urumqi. The station is equipped with an eddy covariance system, a radiation observation system, and a gradient detection system (Figure 3a). The data for this study were mainly obtained from three systems: (I) The eddy covariance system, which consists of a 3D sonic anemometer (CSAT3, Campbell Scientific, Logan, UT, USA) and an open-path infrared gas analyzer (Li7500A, Licor, Lincoln, NE, USA) (Figure 3c); we measure carbon flux using these two instruments. (II) The radiation observation system, which includes a net radiation sensor (CNR01, Kipp&Zonen, Logan, UT, USA, Figure 3b), a soil moisture sensor (CS616, USA), and a heat flow plate (HFP01, The Netherlands), was used to measure long-and short-wave radiation, soil moisture, and soil heat flux at different depths, respectively. (III) The gradient observation system includes a temperature and humidity sensor (HMP45C, Finland) for monitoring meteorological elements, such as soil temperatures at depths of 0 cm, 5 cm, 10 cm, and 20 cm, as well as air temperatures at heights of 2 m and 10 m. Figure 3d displays a 2D anemometer used to measure wind speed and wind direction. It is installed at a height  
The measured data were output through a data collector (CR3000, Logan, UT, USA) at time intervals of 10 s, 1 min, 30 min, and 1 h. The radiation observation system and the gradient observation system were acquired at a frequency of 1 Hz, while the eddy covariance system was acquired at a frequency of 10 Hz. The time scale of this study is from April to September 2018, and all times used are local time.
## 2 Materials and Methods
### Evaluation Metrics  
The evaluation of the two modules is connected because the missing tooth region detection model takes segmented masks from the segmentation model as input. The mean Average Precision (mAP) was used to evaluate both the segmentation and detection models. mAP is the average of each class's AP values, with AP being the value obtained by calculating the area under the Precision-Recall curve. When the Intersection over Union (IoU) between ground truth and prediction is larger than the threshold, we considered it a correct prediction. mAP (0.5) is calculated by predicting the correct answer when IoU is >0.5, and mAP (0.5:0.95) is calculated by averaging the performance of IoUs within 0.5-0.95 in 0.05 steps.
## 2 Materials and Methods
### InfoVAE Architecture  
Various architectures of the variational autoencoder (VAE) [18] family were tested as alternatives to models of the generic adversarial network family. The best results were obtained for the model of the Information Maximizing Variational Autoencoders (InfoVAE) [19] family, which uses the Maximum Mean Discrepancy (MMD) function [20]. A basic autoencoder architecture comprising two symmetrical neural networks, and encoder and decoder, connected with a latent layer, was proposed as a generalization of the PCA technique for dimensionality reduction problems. Such architectures lacked, however, content creation capabilities because of irregularities in the latent space. To handle this deficiency, significant modification was proposed, which led to defining the variational autoencoder (VAE). Any VAE architecture is non-symmetric by definition, where the encoder maps a data instance to a smooth distribution over the latent space, rather than to a single point. This, in turn, helps to reproduce the complicated patterns present in a given data set, also enabling new data generation. The InfoVAE consists of two neural networks, an encoder and a decoder, where the second network acts as a generator. The encoder network consists of a common part and two analogous paths responsible for the vector of expected value and vector of variance. The common part consists of 6 layers. The first layer takes 6 phase space parameters as input and converts them into 200 parameters (6, 200). The second layer expands the 200 parameters to 400 (200, 400). This is followed by 4 layers of 400 neurons each. Then, a layer of (400, 400), (400, 200), and (200, 4) follows for each of the two paths mentioned. Thus, the chosen architecture features a 4D latent space. After each of these layers, except for the last one, a batch normalization layer and leakyReLU activation functions are added. The decoder consists of 9 layers. The first layer takes 4 parameters as input and extends them to 200 (4, 200), and the second one extends the received 200 to 400 (200, 400). The next 5 layers have 400 neurons, followed by a layer that reduces the number of parameters from 400 to 200 (400, 200), and, finally, a layer that reduces this from 200 to 6 parameters that represent parameters of the generated photon (200, 6). After each of the layers in the decoder, except for the last one, the ReLU activation function is used. In the case of the InfoVAE model, parameter truncation is not used. The decoder network is used to generate new photons by supplying it with a 4D Gaussian noise vector at the input.
## 2 Results and Discussion
### Physicochemical Parameters in Juice
\begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **STW (g)** & **SL (cm)** & **SD (cm)** & **SW (g)** & **SSI** \\ \hline LI1 & 77.57 \(\pm\) 11 \({}^{\mathrm{\,e}}\) & 0.90 \(\pm\) 0.17 \({}^{\mathrm{\,b}}\) & 0.71 \(\pm\) 0.03 \({}^{\mathrm{\,b}}\) & 0.22 \(\pm\) 0.01 \({}^{\mathrm{\,c}}\) & 1.27 \(\pm\) 0.06 \({}^{\mathrm{\,a}}\) \\ TU1 & 162.52 \(\pm\) 16.04 \({}^{\mathrm{\,b}}\) & 0.99 \(\pm\) 0.03 \({}^{\mathrm{\,b}}\) & 0.79 \(\pm\) 0.03 \({}^{\mathrm{\,b}}\) & 0.37 \(\pm\) 0.01 \({}^{\mathrm{\,b}}\) & 1.25\(\pm\) 0.12 \({}^{\mathrm{\,a}}\) \\ TU2 & 110.31 \(\pm\) 24.01 \({}^{\mathrm{\,a}}\) & 0.81 \(\pm\) 0.03 \({}^{\mathrm{\,a}}\) & 0.63 \(\pm\) 0.02 \({}^{\mathrm{\,a}}\) & 0.25 \(\pm\) 0.01 \({}^{\mathrm{\,c}}\) & 1.29 \(\pm\) 0.05 \({}^{\mathrm{\,a}}\) \\ TU3 & 66.90 \(\pm\)9.38 \({}^{\mathrm{\,a}}\) & 0.72 \(\pm\) 0.03 \({}^{\mathrm{\,a}}\) & 0.53 \(\pm\) 0.03 \({}^{\mathrm{\,a}}\) & 0.24\(\pm\) 0.01 \({}^{\mathrm{\,c}}\) & 1.34 \(\pm\) 0.05 \({}^{\mathrm{\,a}}\) \\ AP2 & 210.24 \(\pm\) 15.23 \({}^{\mathrm{\,ab}}\) & 1.18 \(\pm\) 0.03 \({}^{\mathrm{\,a}}\) & 1.05 \(\pm\) 0.03 \({}^{\mathrm{\,a}}\) & 0.51 \(\pm\) 0.02 \({}^{\mathrm{\,a}}\) & 1.12 \(\pm\) 0.05 \({}^{\mathrm{\,a}}\) \\ BA1 & 140.77 \(\pm\) 50.22 \({}^{\mathrm{\,d}}\) & 0.90 \(\pm\) 0.04 \({}^{\mathrm{\,b}}\) & 0.69 \(\pm\) 0.06 \({}^{\mathrm{\,b}}\) & 0.37 \(\pm\) 0.03 \({}^{\mathrm{\,b}}\) & 1.31 \(\pm\) 0.05 \({}^{\mathrm{\,a}}\) \\ DC & 240.78 \(\pm\) 16.52 \({}^{\mathrm{\,a}}\) & 1.30 \(\pm\) 0.05 \({}^{\mathrm{\,a}}\) & 0.97 \(\pm\) 0.04 \({}^{\mathrm{\,a}}\) & 0.50 \(\pm\) 0.01 \({}^{\mathrm{\,a}}\) & 1.16 \(\pm\) 0.09 \({}^{\mathrm{\,a}}\) \\ \hline \end{tabular} Different letters on the numbers in the same column indicate significant differences (\(p\leq 0.05\)) between the seeds of accessions.  
\end{table} 
Table 3: Seed characteristics of pomegranate genotypes. STW: total seed weight; SL: seed length; SD: seed diameter; SW: seed weight; SSI: seed shape index. The TSS parameter, associated with acidity, was used by [PERSON] et al. (2016) to classify pomergnate cultivars in relation to sweetness. The TSS and its TA ratio (TSS/TA) are the main indices for determining fruit quality. In particular, the TSS/TA ratio is an indicator of flavor, quality, and maturity (S  
Finally, the glucose/fructose ratio ranged from 0.63 to 0.97. These results are in accordance with the glucose-to-fructose ratio reported in previous studies, i.e., [PERSON] et al. (2014) reported that in pomegranate cultivars grown in Spain, the glucose/fructose ratio ranged from 0.88 to 0.96. Considering the different sweetness power of fructose and glucose, their ratio may affect the overall juice taste and therefore the fruit's sensorial quality.
# Measuring Identification and Quantification Errors in Spectral CT Material Decomposition
## Materials and Methods  
### MARS Spectral Scanner  
MARS spectral CT uses the Medipix3 RX photon-counting X-ray detector technology [14; 38; 39; 40]. The scanner is suitable for preclinical spectral imaging of small animal models and human samples of diseased tissue. It consists of a rotating gantry, detector, X-ray source and sample holder shaft, computer hardware and software as shown in the scanner schematic in Figure 1a. It is the photon processing X-ray detector, Medipix3 RX [41], that allows for spectral computed tomography imaging by measuring the number and energy of each X-ray photon, to provide specific identification and quantification (g/cm\({}^{3}\)) of the component material. Medipix3 RX allows simultaneous acquisition of up to eight energy bins at a single X-ray exposure. Energy ranges are user-defined, therefore offering flexibility and customization for a wide range of medical and industrial applications. To reduce the effect of charge sharing [42], the charge summing feature is included in the Medipix3 RX ASIC which allows the total charge for a single interaction to be summed and allocated to the closest pixel [43]. To do this, each pixel communicates with its four neighbours to locate the pixel with the highest charge for a co-incident event, the total charge is then allocated to this pixel [42].  
The threshold settings for the selection of energy bins have a major impact on spectral image quality in terms of image contrast and noise level. The wider the energy bin width is, the lower the noise level is, but the poorer the reconstructed spectral image contrast is. This is mainly because Medipix3 RX detector works as a single photon counting detector using user-specified energy thresholds to gather data for a certain energy range. Figure 1b shows the mass attenuation coefficients of calcium, gadolinium (K-edge energy = 50.2 keV), tissue and adipose and simulated X-ray spectrum with an external 0.375 mm brass filter.  
The MARS scanner incorporated a microfocus poly-energetic X-ray source and a Medipix3 RX detector within a continuous rotating gantry. We used a 2 mm thick CdTe sensor (14 \(\times\) 14 mm\({}^{2}\)), bump-bonded at 110 \(\upmu\)m to a Medipix3 RX readout chip.  
Figure 1: (**a**) Schematic of the MARS small animal spectral scanner; (**b**) the mass attenuation coefficients for calcium, tissue, adipose and gadolinium are shown (left axis) and simulated X-ray spectrum at 120 kVp with 0.375 mm external brass filter together with a 1.8-mm-Al (equivalent) intrinsic filtration (right axis). Vertical dotted lines show user specific energy thresholds.