esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
Let us consider the dataset \(\mathbf{X}\) consisting of \(N\) images \(\mathbf{X}=\{\mathbf{x}^{(1)},\mathbf{x}^{(2)},...,\mathbf{x}^{(N)}\}\). The assumption of the generative model is that the observed images are generated by some stochastic process, involving an unobserved random variable \(\mathbf{z}\). To be specific, the latent representation \(\mathbf{z}^{(i)}\) is generated from a prior distribution \(p(\mathbf{z})\), and the image \(\mathbf{x}^{(i)}\) is generated from a conditional distribution \(p_{\theta}(\mathbf{x}|\mathbf{z})\) where \(\theta\) is the generative model parameter.  
To efficiently approximate posterior inference of the latent variable \(\mathbf{z}\) given an observed value \(\mathbf{x}\), a recognition model \(q_{\phi}(\mathbf{z}|\mathbf{x})\) is introduced where \(\phi\) is the recognition model parameter. This model is an approximation to the intractable true posterior \(p_{\theta}(\mathbf{x}|\mathbf{z})\), and also referred as a probabilistic _encoder_. Instead of encoding an input image \(\mathbf{x}\) as a single vector, the encoder produces a probabilistic distribution of the compressed feature \(\mathbf{z}\) over the latent space. Similarly, \(p_{\theta}(\mathbf{x}|\mathbf{z})\) is a probabilistic _decoder_ since given a latent feature \(\mathbf{z}\) it produces a probabilistic distribution over the possible corresponding values of \(\mathbf{x}\).  
The VAE is a structure that implements an encoder \(q_{\phi}(\mathbf{z}|\mathbf{x})\) and a decoder \(p_{\theta}(\mathbf{x}|\mathbf{z})\) as a neural network as shown in Figure 2.  
Figure 1: The proposed model of VAE for condition-invariant feature extraction in a changing environment. After training the model, the same place can be recognized by extracting encoded features from images obtained in different environments.  
Then, parameters \(\phi\) and \(\theta\) become the weights of the neural network. The objective is to find the \(\phi\) and \(\theta\) maximizing the variational lower bound \(\mathcal{L}(\theta,\phi;\mathbf{x})\) on the marginal likelihood [18] as the following:  
\[\mathcal{L}(\theta,\phi;\mathbf{x})=\mathbb{E}_{q_{\phi}(\mathbf{z}|\mathbf{x} )}[\log p_{\theta}(\mathbf{x}|\mathbf{z})]-D_{KL}(q_{\phi}(\mathbf{z}| \mathbf{x})||p_{\theta}(\mathbf{z})) \tag{1}\]  
where \(D_{KL}(\cdot)\) stands for the Kullback-Leibler divergence, which measures the difference between two probability distributions. The objective function consists of a reconstruction likelihood and a regularization term. The prior distribution \(p_{\theta}(\mathbf{z})\) is usually set to a Gaussian distribution so that the reparameterization trick can be used to train the network [18].  
After training the VAE, it can compress the input image to the low-dimensional latent vector \(\mathbf{z}\). Since the encoded vector \(\mathbf{z}\) contains the information of the whole input image, it can be used as a global descriptor for comparing similarities between images [19].
# Deep Learning Based Airway Segmentation Using Key Point Prediction
## 3 Results
### Agreement Analysis  
Using agreement analysis, 61 samples were extracted and the manually measured value and deep learning network predicted value were compared for both volumes and coordinates. The total volume was the most correlated intra-class correlation coefficient (ICC) value in the oropharynx (0.986), followed by the hypopharynx (0.964), and the nasopharynx (0.912). The intra-class correlation coefficient (ICC) value for the coordinate CV2(x) was the most correlated (0.963) and the least correlated at CV4(y) (0.868). All ICC values are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average** & **SD** \\ \hline Volume (mm\({}^{3}\)) & & \\ Nasopharynxx & 48.620 & 49.468 \\ Oropharynx & 37.987 & 43.289 \\ Hypopharynx & 50.010 & 57.891 \\ Total volume & 85.256 & 86.504 \\ Distances between M and DL (mm) & & \\ PNS & 2.817 & 1.806 \\ VP & 2.837 & 1.924 \\ CV1 & 2.571 & 2.028 \\ CV2 & 2.896 & 2.205 \\ CV4 & 4.156 & 2.379 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurements of the differences between manual analysis and deep learning analysis (N = 61).  
Figure 4: Boxplots of the differences between manual analysis and deep learning analysis (N = 61). In the boxplots, ‘x’ within the box marks the mean of volume differences.
# In Vitro Genotoxicity Assessment from the _Glycyrrhiza_ New Variety Extract
## Materials and Methods
### In Vitro Chromosome Aberration Test  
A chromosome aberration test evaluates the potential for a substance to induce structural and/or numerical chromosome aberrations in cultured Chinese Hamster Lung (CHL/IU) cells, which were purchased from the American Type Culture Collection (Manassas, VA, USA). An in vitro chromosome aberration test was conducted according to OECD Guideline 473 [26]. Structural abnormalities and polyploidy as chromosome aberrations were examined in cultured CHL cells with or without S9 mix from the rat liver treated with Aroclor 1254. A preliminary range finding test was conducted to establish WG extract's solubility and toxicity in CHL/IU cell growth. Relative increase in cell count (RICC) was calculated by using the following formula:  
\[\text{RICC}\left(\%\right)=\frac{\text{Increase in number of cells in treated culture (final \ - starting)}}{\text{Increase in number of cells in negative control cultures (final \ - starting)}}\times 100\]Maximum concentration was determined based on turbidity, cytotoxicity, pH, and RICC. B[a]P (20 \(\upmu\)g/mL) and 4 NQO (0.4 \(\upmu\)g/mL) were used as positive control with or without S9 mix, respectively. Sterile water was the vehicle and was used for the negative control whether with or without S9 mix. CHL cells were seeded (5 \(\times\) 10\({}^{4}\) cells/T25 flask) and maintained for 3 days before treatment of the reagents. WG extract (350-1400 \(\upmu\)g/mL) or negative/positive controls were treated for 6 h in CHL cells, and consequently 18 h recovery with (6 + 18) or without (6 - 18) S9 mix (final 2%, \(v/v\)). WG extract or negative/positive controls were treated for 24 h, and then 0 h recovery without S9 mix (24 \(-\) 0). After 22 h, colchicine (final 1 \(\upmu\)M) was treated for 2 h. The cells at metaphase were harvested, treated with 75 mM KCl, and finally fixed by methanolacetic acid (3:1, \(v/v\)). Samples were stained with 5% (\(w/v\)) Giemsa solution after being air dried. Two samples per flask were prepared. Chromosome aberrations were estimated in 150 metaphases per sample under the microscope at 1000\(\times\) magnification. Structural aberration of chromosome was categorized into chromosome breaks (csb) or exchanges (cse), chromatid breaks (ctb) or exchanges (cte) with or without gaps. More than 10 aberrations including gaps (multiple aberration) or chromosome fragmentation were calculated by 1 aberration, which is categorized into 'others'. Numerical aberration of chromosome was categorized by the number of centromeres into diploid (23-36 centromere), polypoid (more than 36 centromere), and endoreduplication.
## 4 Discussion  
### Irrigation Uniformity Coefficient of Micro-Spraying System  
Timely and appropriate supplementation of irrigation water is an effective means to achieve coordinated improvement of crop yields and WUE in water-deficient areas [22; 23; 24]. As a low-cost and small-flow irrigation technology in the field, micro-spraying irrigation has great advantages in realizing the high-efficiency and water-saving of pipe irrigation systems. In recent years, the micro-spraying irrigation system has been widely adopted in cereal crops such as wheat and maize in well-irrigated areas of the NCP [25]. With micro-spraying irrigation, SMC in the 0-60 cm soil layers after irrigation decreased with the distance from micro-spray tapes and borders. A similar result was also observed in the literature [26]. In 80-100 cm soil layers, SMC basically kept constant after irrigation events, indicating that the 75 mm irrigation quota did not increase soil percolation in deep layers as expected and was effective in ensuring better irrigation uniformity.  
The irrigation uniformity coefficient is an important indicator to reflect irrigation quality and is also one of the important parameters necessary for planning and designing an irrigation system. It was reported that the hydraulic performance of micro-sprinking hose irrigation system had little impact on irrigation uniformity at different growth stages of wheat [27]. In fact, the effect of plant barriers on irrigation quality during the middle to late growing period was significant, as reflected by the obvious reduction in uniformity coefficients of soil moisture distribution with border widths increasing. Most of the sprayed water was blocked by wheat stems and leaves close to micro-spray tapes, and the amount of water sprayed away from the micro-spray tapes was generally small. A similar result was also observed in the literature [14]. The results of soil moisture distribution also showed that the average proportion of canopy water penetration to total irrigation water was 51.1% (1 April), 85.1% (29 April) and 69.7% (20 May), respectively. Our result indicated that even during the late growing period of wheat, canopy water penetration was still a major way that the irrigation water reached the ground soil surface. Generally, redistribution of SMC after irrigation made soil moisture in wet layers decrease to varying degrees, which helped soil water move from a high water content area to a low area [28].
# Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
## 3 Dataset Description  
The measurement data of 200 peaches constitutes the dataset used in the experiment. The measurements are performed in two consecutive days. On the first day, the first group of 100 peaches is taken out of the refrigerator and their features are measured, while on the second day, the measurements are performed on the second group of the same size as the first one. The peaches used in the experiment were bought on the market and kept in the refrigerator at 4 \({}^{\circ}\)C before being measured.  
Multiple techniques are used for measuring peach features to obtain the dataset on which the experiment has been conducted. Using the alternate voltage of 500 mV amplitude and 10 kHz frequency, the peach impedance is measured. The measured electrical impedance of a peach gives insight into the structural characteristics of its tissue [29]. Changes in fruit ripening reflect changes in the membrane structure, and the latter are related to changes in dielectric properties [30; 31; 32]. The measured electrical impedance that is part of the experiment's dataset is represented using magnitude _Zs_ and phase angle _Angle_ values. By combining juice extracted from a peach and an alkaline solution, the tittatable acidity _TA_ of the peach is obtained. Using the juice extracted from a peach and a refracometer, the soluble solids content _SSC_ is obtained. The peach firmness is measured using a penetrometer. Peach firmness is a good maturity indicator [33] and is therefore used as an outcome feature in this research. The Delta E value represents the peach's color.  
In order to improve the effectiveness of machine learning algorithms [34], the dimension of the initial dataset, which consisted of nine measured features, had to be reduced. The process of dimension reduction is conducted using the expert's domain knowledge, which resulted in the dataset of 200 observations and six features that are used in the experiment. The mass and volume are combined into _density_, while the ratio of SSC and TA is used as a single feature. The categorical feature _appearance evaluation_ is not used in the experiment. The distribution of the measured features that are part of the final dataset is shown in Figure 1. The ratio of soluble solids content and titratable acidity follows a normal distribution skewed to the right. The same is true for the density feature, yet the color feature follows a symmetric normal distribution. The impedance magnitude, impedance phase angle, and peach firmness follow a bimodal distribution. From the firmness distribution, which is skewed towards lower values, it is possible to conclude that most of the peaches that make up the dataset have a lower firmness value and therefore are riper [33].
## 2 Experimental Methodology
### Uncertainty Analysis  
In the present experiments, the measurement error of thermocouple and pressure sensor was \(\pm\)50 Pa and \(\pm\)6 K, respectively. The error of pressure sensor could cause an uncertainty of less than 2.5% for the oscillation amplitude under SETAI condition. Moreover, the error of thermocouple could introduce an uncertainty of 1 m/s for the sound speed, and then the resulting error for \(f\) was less than 0.5%. In addition, the measurement error of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(a)**} \\ \hline
**Cases** & \multicolumn{2}{c}{\(L_{\rm P}\)**(m)**} & \(\varphi\) \\ \hline
1–12 & 0.55, 0.65, 0.86, 1.06, 1.25, 1.46, 1.65, 1.85, 2.06, 2.26, 2.66 & 0.9 \\
13–27 & 0.55, 0.65, 0.86, 1.06, 1.25, 1.46, 1.65, 1.85, 2.06, 2.26, 2.46, 2.66, 2.86, 3.06, 3.26 & 0.7 \\ \hline \multicolumn{4}{c}{**(b)**} \\ \hline
**Cases** & \(L_{\rm 1}\)**(m)** & \(L_{\rm 2}\)**(m)** & \(\varphi\) \\ \hline
28–38 & 2.06 & 0.55, 0.65, 0.86, 1.06, 1.25, 1.46, 1.65, 1.85, 2.26, 2.46, 2.66 & 0.9 \\
39–48 & 1.65 & 0.55, 0.65, 0.86, 1.06, 1.25, 1.46, 1.85, 2.26, 2.46, 2.66 & 0.9 \\
49–62 & 2.66 & 0.55, 0.65, 0.86, 1.06, 1.25, 1.46, 1.65, 1.85, 2.06, 2.26, 2.46, 2.86, 3.06, 3.26 & 0.7 \\ \hline \multicolumn{4}{c}{**(c)**} \\ \hline
**Cases** & \(L_{\rm 1}\)**(m)** & \(L_{\rm 2}\)**(m)** & \(\varphi\) \\ \hline
63 & 3.87 & 3.87 & 0.9 \\
64 & 1.65 & 3.87 & 0.9 \\
65 & 2.86 & 0.55 & 0.7 \\ \hline \multicolumn{4}{c}{**(d)**} \\ \hline
**Cases** & \multicolumn{2}{c}{**Premixed chamber 1**} & \multicolumn{2}{c}{**Premixed chamber 2**} \\ \cline{2-5}  & \(L_{\rm 1}\)**(m)** & \(\varphi_{\rm 1}\) & \(L_{\rm 1}\)**(m)** & \(\varphi_{\rm 2}\) \\ \hline
66 & 2.66 & 0.9 & 1.65 & 0.7 \\
67 & 2.66 & 0.7 & 1.65 & 0.9 \\ \hline \multicolumn{4}{c}{**(e)**} \\ \hline
**Cases** & \multicolumn{2}{c}{\(\varphi_{\rm 1}\)} & \multicolumn{2}{c}{\(\varphi_{\rm 2}\)} \\ \hline
68–71 & 0.7 & 0.8, 0.9, 1.1, 1.3 & \\
72–73 & 0.9 & 1.1, 1.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: (a) Cases under symmetric supply structure and combustion condition. (b) Cases under asymmetric air/fuel supply structure. (c) Cases under asymmetric supply system with two premixed chambers differing by a half wavelength. (d) Cases under asymmetric air/fuel supply structure and asymmetric combustion condition. (e) Cases under asymmetric combustion condition (\(L_{1}=L_{2}=1.85\) m).  
flowmeter was \(\pm\)0.0174 L/min in propane flow rate and \(\pm\)0.1006 L/min for air flow rate. These errors could contribute to a maximum error of 2.8% for \(\varphi\).
## 3 Materials and Methods
### Measurement Procedure and Scenarios  
The mobile phone was placed at one position on a car dashboard in a phone holder for testing each scenario. The LSProbe was also placed in one position (in the seat next to the car driver) on a styrofoam block (see Figure 1). This arrangement allowed us to keep the same distance between the mobile phone and the LSProbe for each scenario.  
Figure 1: Overview of the experimental setup: (**a**) block diagram and (**b**) photos.  
Because the availability of RF energy depends on both the surrounding environment (e.g., base station antenna distance, LoS or NLoS, etc.) and cellular network traffic, the measurements were divided into two groups: measurements of EMFs in different wireless communication scenarios and control measurements of EMFs.  
Details of the wireless communication scenarios are summarized in Table 1. The conditions for the control measurements of the EMFs are summarized in Table 2.  
We measured the EMFs in each scenario at two locations: Location 1 and Location 2, described in Section 3.2.  
The user terminal was configured to operate in different modes (2G only, 3G only, etc.) to perform the different sub-scenarios.  
The duration of each voice call, video call and data transmission in each sub-scenario was 6 min. More than 1600 measurements of the E-fields for each sub-scenario were performed.  
Measurements in each sub-scenario were performed on both weekdays and weekends.  
To assess the background EMFs, we measured the EMFs at the beginning and the end of each measuring campaign. Control measurements were performed outside and inside the car. During these control measurements, the user terminal was switched off.
# _Forentation_
## 3 Results
### Antagonist Activity  
In order to rule out the potential antagonistic activity (including killer activity) between the two evaluated yeast strains, in vitro antagonistic analyses were carried out. No antagonistic effect was observed in assays using either _P. kudriavzevii_ as a lawn and streaked with _S. cerevisiae_ or _S. cerevisiae_ as a lawn and streaked with _P. kudriavzevii_. On the other hand, _S. cerevisiae_ was shown to exhibit an antagonistic effect on _Candidida glabrata_ sensitive strain NPCC106 and a weak sensitivity to the reference _S. cerevisiae_ K2 killer strain. _P. kudriavzevii_ was neither able to show an antagonistic effect against the _C. glabrata_ sensitive strain nor sensitivity to the reference _S. cerevisiae_ K2 killer strain.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Pure Culture Inoculations** & \multicolumn{2}{c}{**Simultaneous Inoculations**} & \multicolumn{2}{c}{**Sequential Inoculations**} \\ \cline{3-7}  & **P** & **S** & **Sim 1:1** & **Sim 1:10** & **Seq P-S** & **Seq S-P** \\ \hline \multirow{3}{*}{Glucose} & t10 & 53.8 \(\pm\) 0.7 a & 12.3 \(\pm\) 0.1 d & 12.7 \(\pm\) 1.2 d & 16.95 \(\pm\) 1.3 c & 19.7 \(\pm\) 0.9 b & 12.5 \(\pm\) 1.0 d \\  & t50 & 206.6 \(\pm\) 8.5 a & 65.8 \(\pm\) 0.1 d & 77.7 \(\pm\) 3.4 bc & 121.1 \(\pm\) 3.0 cd & 84.1 \(\pm\) 1.4 b & 67.3 \(\pm\) 3.5 cd \\  & t95 & 380.4 \(\pm\) 14.6 a & 275.6 \(\pm\) 0.5 c & 279.6 \(\pm\) 11.7 c & 318.0 \(\pm\) 4.1 b & 178.0 \(\pm\) 1.8 d & 281.9 \(\pm\) 13.5 c \\ \hline \multirow{3}{*}{Fructose} & t10 & 76.3 \(\pm\) 13.1 a & 39.1 \(\pm\) 1.8 b & 38.3 \(\pm\) 1.5 b & 41.6 \(\pm\) 2.9 b & 51.4 \(\pm\) 3.3 b & 35.6 \(\pm\) 2.8 b \\  & t50 & 258.3 \(\pm\) 6.4 a & 129.4 \(\pm\) 1.4 c & 128.7 \(\pm\) 0.7 c & 127.0 \(\pm\) 2.0 c & 163.2 \(\pm\) 4.5 b & 127.7 \(\pm\) 2.2 c \\  & t95 & 436.9 \(\pm\) 3.4 a & 235.9 \(\pm\) 2.5 d & 237.6 \(\pm\) 3.7 cd & 222.0 \(\pm\) 3.2 e & 291.2 \(\pm\) 1.5 b & 246.7 \(\pm\) 6.2 c \\ \hline \multirow{7}{*}{Fructose} & \multicolumn{4}{c}{Different letters in the same row indicate significant differences by ANOVA (Tukey’s HSD, \(p\) < 0.05).} \\  & **P**: _P. kudriavzevii_ NPCC1651; **S**: _S. cerevisiae_ NIF8; **Sim 1:1**: Simultaneous fermentation by S:P ratio 1:10; **Sim 1:10**: Simultaneous fermentation by S:P ratio 1:10; **S**:**: Sequential fermentation by _S. cerevisiae_ NIF8 48 h after _P. kudriavzevii_ NPCC1651; **Seq S-P**: Sequential fermentation by _P. kudriavzevii_ NPCC1651 48 h after _S. cerevisiae_ NIF8. 10\%, 150\%, and 190\% represent the time required to decrease 10%, 50%, and 90% of the corresponding sugar in the must. Time (t) is given in hours. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time required for the consumption of 10%, 50%, and 95% of glucose and fructose in fermentations conducted with pure and mixed cultures of _P. kudriavzevii_ NPCC1651 and _S. cerevisiae_ NIF8.
## 1 Introduction
### State-of-the-Art
Recently, in [36], a two-phase communication system was proposed and analyzed. The system model in [36] is similar to that in [15], so that in the first phase, a group of clustered devices transmit their packets to multiple relays or multiple satellites using a simple slotted Aloha protocol. Then, in the second phase the relays forward the decoded information to a common sink. The channel is modeled considering on-off fading, while expressions are provided for calculating the first phase throughput, in addition to the packet loss rate for a number of relays, considering equal erasure probabilities at all relays. However, in the case of a LEO satellite constellation, it is very likely that the erasure probabilities are not all the same, since some of the satellites may be at different elevation angles from the point of view of the devices. The same is true even for other alternative constellation designs, as the GeoSurf constellation [37], since satellites entering or leaving the service are subject to an additional loss compared to the satellite currently servicing the devices.
## 3 Calculation Method for the Optimized Capacity of the PV and ESSs with 90% Energy Self-Sufficiency  
### Calculation Method for Optimized PVs Capacity  
\[PV_{o}=\frac{Y_{AWh}}{\text{Month}\times H_{AS}\times I_{L}\times c_{L}}\ [ kW] \tag{1}\]  
\(\bullet\)\(PV_{0}\) = Optimized capacity of PVs  
\(\bullet\)\(Y_{AWh}\) = Average energy usage per month in last year (kWh)  
\(\bullet\)\(Month=1\) month (365/12; day)  
\(\bullet\)\(H_{AS}\) = Daily insolation hour of the area in the last year (h)  
\(\bullet\)\(I_{L}\) = Power loss in Inverter (0.965)  
\(\bullet\)\(C_{L}\) = Power loss in Cable (0.9926)  
Figure 4: Flow diagram for finding the optimum capacity of PV and energy storage systems (ESSs).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Average Power** & **Average Number** & **Average of** & **Generation** \\  & **Generated** & **of Generating** & **Generation** & **Efficiency Limited** \\  & **(kWh/day)** & **Hour (h/day)** & **Efficiency** & **to 100\%** \\ \hline January & 8.73 & 3.15 & 88.61 & 88.61 \\ February & 9.61 & 3.47 & 97.57 & 97.57 \\ March & 11.70 & 4.23 & 118.75 & 100.00 \\ April & 11.72 & 4.23 & 118.94 & 100.00 \\ May & 12.52 & 4.52 & 127.09 & 100.00 \\ June & 11.38 & 4.11 & 115.48 & 100.00 \\ July & 9.04 & 3.27 & 91.74 & 91.74 \\ August & 9.52 & 3.44 & 96.63 & 96.63 \\ September & 10.30 & 3.72 & 104.51 & 100.00 \\ October & 9.75 & 3.52 & 98.92 & 98.92 \\ November & 7.06 & 2.55 & 71.62 & 71.62 \\ December & 7.01 & 2.53 & 71.10 & 71.10 \\ Average & 9.86 & 3.56 & 100.08 & 93.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Monthly energy generation of the 905 households in 2017 (unit: kWh).  
\[PV_{Eo}=\frac{PV_{0}}{E_{CL}\times E_{dL}}\ (kWh) \tag{2}\]  
\(\bullet\)\(PV_{Eo}\) = Optimized capacity of PVs considering the charge/discharge losses  
\(\bullet\)\(E_{CL}\) = Energy loss in charging ESS  
\(\bullet\)\(E_{DL}\) = Energy loss in discharging ESS