esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method  
In this study, the transfer learning approaches were experimentally evaluated with the following representative applications of classification, object detection, and segmentation, as shown in Table 2: (1) cervical cancer classification, (2) skin lesion classification, (3) pressure ulcer segmentation, and (4) chest X-ray object detection and classification. Because chest X-ray object detection extracts the RoI and transforms it into a classification problem, it can be included in the other three classification and segmentation applications. Consequently, the experiments were performed with three applications. Cervical cancer analysis is an image classification problem, and the presence of cancer can be diagnosed by observing the cervical cancer image captured through colposcopy. The NCI dataset, with 978 labeled images and 44,031 unlabeled images, was used in this study for the classification [86]. These images are different from medical images that show the external surface of the skin, such as skin lesions or pressure ulcers, because elements irrelevant to the classification of cervical cancer, such as medical devices, are captured simultaneously with the cervix when it is enlarged during a colposcopy. For a transfer learning approach suitable for cervical cancer analysis, ref. [7] proposed patch self-supervised learning, which can use unlabeled images and learn the self-information by separating unnecessary parts of the image. Skin lesion classification is performed after extracting the region of the lesion from the skin. This study used the HAM10000 dataset, which consists of 10,015 labeled images with seven types of lesions photographed during dermoscopy [87]. Because the skin and lesion parts are clearly separated in the skin lesion images, a transfer learning approach using only the encoder of the U-Net model is used after training to extract the RoI to improve the classification performance. There has also been a study of multi-task learning with a two-stream structure that performed segmentation and classification simultaneously [88]. Pressure ulcers occur when the skin is under pressure for an extended period. The amount of data available for segmentation is significantly smaller than other datasets because it takes a long time for the disease to appear. Therefore, in this application, the performance can be improved by transfer learning with a wound image similar to the pressure ulcer image. In this study, we propose transfer learning approaches to solve the chronic problems of medical images, as shown in Figure 2. In the given source and target tasks, we train the target task through the transfer learning method of the trained model for the source task. The dataset used in source task is different from the dataset used in the target task. In Equation (1), \(X_{s}\in\mathbb{R}^{h\times w\times c}\) and \(X_{t}\in\mathbb{R}^{h^{\prime}\times w^{\prime}\times c^{\prime}}\), where \(h\), \(w\), and \(c\) are the height, width, and
# The Deformation Law of a Soft-Rock Cavern by Step Excavation in a Pumped Storage Power Station
## 2 Methodology
### Laboratory Rock Mechanics Testing
#### 2.1.3 Triaxial Compression Creep Test  
The FSTX-100 frozen soil dynamic and static triaxial apparatus developed and produced by GCTS Company in the United States were selected for the experiment, as shown in Figure 1a. This testing machine was mainly composed of five parts: a dynamic loading frame, pressure instrument, servo control-mode system, temperature control system, and hydraulic pump station. It can complete static, stress path, and dynamic tests (liquefaction strength, modulus/damping measurement, dynamic strength, and rebound modulus, etc.) for three testing methods (unconsolidated undrained, consolidated undrained, and consolidated drained). Its technical specifications were: static load range \(\pm\) 100 KN, accuracy 0.05%; the maximum axial displacement was 100 mm; the confining pressure range was 0-20 MPa, with an accuracy of 0.25%; and the temperature control range was \(-\)30 \({}^{\circ}\)C to +80 \({}^{\circ}\)C, with a fluctuation of \(\pm\)0.1 \({}^{\circ}\)C. The testing process was completely controlled by a computer and data collection was automatic; the local displacement sensor is shown in Figure 1b. The test steps are shown below.  
Based on the conventional compressive strength test results of the rock samples at the same sampling point, determine the incremental loading under each lateral pressure and use the Chen's compression method to load in five stages;  
Load the axial and lateral pressures at a loading rate of 0.05 MPa/s to the predetermined lateral pressure, and load the axial pressure at a rate of 0.2-0.5 MPa/s to the predetermined axial pressure, while collecting the stress and strain values;  
During the testing process, the automatic collection and storage of the pressure and strain values should be conducted. The data collection interval should not be longer than 5 min in the early stage of the experiment; during the deceleration creep stage, the data collection interval should not be longer than 30 min. The stability standard for each level of loading was a displacement less than 0.001 mm/h or a specified duration.  
Based on the relationship curve between the axial strain, radial strain, and time, fit and determine the triaxial compression creep model and parameters of rocks.
## 2 System Model and Problem Formulation
### Problem Formulation
Therefore, it is necessary to develop a more flexible INAC scheme that mitigates the signal processing burden on navigation users while ensuring navigation performance. Furthermore, we hope to minimize modifications to the communication transmitter to make it easier to integrate navigation and satellite communication systems.
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 3 Results  
### Screening of Cyanobacterial Strains for Toxin, Biomass and Polysaccharide Production  
#### 3.1.1 Toxicity Assessment  
None of the tested strains caused high mortality (>50%) of brine shrimp larvae after 24 and 48 h (Table 1). The highest mortality was expressed by a 16-week old culture (_Tolyothrix_ sp. L4) after 48 h of exposure. By causing the death of only 10.2% and 16.1% of larvae in BG11 N+ and BG11 N-, respectively, this strain together with the other strains could be regarded as low-toxic or non-toxic. High standard deviations were observed in some samples (Table 1) but the overall toxicity related to strains L1-L6 was low and the high standard deviations were mainly caused by single outliers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Strain**} & \multirow{2}{*}{**Cyanobacteria**} & \multirow{2}{*}{**Medium**} & \multicolumn{2}{c}{**7-Week-Old Cultures**} & \multicolumn{2}{c}{**16-Week-Old Cultures**} \\ \cline{3-6}  & & & \multicolumn{2}{c}{_Artemia salina_ **Mortality (\%)**} & \multicolumn{2}{c}{_Artemia salina_ **Mortality (\%)**} \\ \cline{3-6}  & & & **24 h** & **48 h** & **24 h** & **48 h** \\ \hline \multirow{2}{*}{L1} & _Chroaccidiopsis_ & N+ & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 6.6 \(\pm\) 0.6 \\  & sp. & N\(-\) & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 6.8 \(\pm\) 1.3 \\ \hline \multirow{2}{*}{L2} & _Nostac_ sp. & N+ & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 4.5 \(\pm\) 3.9 \\  & N\(-\) & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 5.9 \(\pm\) 5.6 \\ \hline \multirow{2}{*}{L4} & _Tolyothrix_ sp. & N+ & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 10.2 \(\pm\) 3.4 \\  & N\(-\) & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 16.1 \(\pm\) 4.7 \\ \hline \multirow{2}{*}{L5} & _Nostac_ sp. & N+ & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 7.2 \(\pm\) 2.6 \\  & N\(-\) & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 8.5 \(\pm\) 3.5 \\ \hline \multirow{2}{*}{L6} & _Nostac_ sp. & N+ & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 2.1 \(\pm\) 3.6 \\  & N\(-\) & 0.0 \(\pm\) 0.0 & 2.4 \(\pm\) 4.1 & 6.4 \(\pm\) 0.2 & 8.5 \(\pm\) 3.5 \\ \hline PCC 7806 & _Microcystis_ sp. & N+/\(-\) & 87.7 \(\pm\) 5.5 & 96.0 \(\pm\) 3.5 & 93.8 \(\pm\) 6.3 & 97.9 \(\pm\) 3.6 \\ \hline \end{tabular}
\end{table}
Table 1: Toxicity of isolated cyanobacterial loess strains. _Artemia salina_ bioassay results on total toxicity of seven and 16 weeks old cultures grown in BG11 N+ and BG11 N\(-\) media. _Microcystis_ PCC7806 from the Pasteur Culture Collection of Cyanobacteria was used as a reference strain for the toxicity assessment as it is a known producer of microccystin. The strain was grown in a modified BG11 N+/\(-\) medium (0.9 g/L NaNO\({}_{3}\)).
# Galile Acid Induces S and G2 Phase Arrest and Apoptosis in Human Ovarian Cancer Cells In Vitro
###### Abstract  
Ocean cancer (OC) is among the top gynecologic cancers in the US with a death tally of 13,940 in the past year alone. Galile acid (GA) is a natural compound with pharmacological benefits. In this research, the role of GA on cell proliferation, cell apoptosis, cell cycle-related protein expression was explored in OC cell lines OVCAR-3 and A2780/CP70. After 24, 48 and 72 h of GA treatment, the IC50 values in OVCAR-3 cells were 22.14 \(\pm\) 0.45, 20.36 \(\pm\) 0.18, 15.13 \(\pm\) 0.53 \(\upmu\)M, respectively and in A2780/CP70 cells IC50 values were 33.53 \(\pm\) 2.64, 27.18 \(\pm\) 0.22, 22.81 \(\pm\) 0.56, respectively. Hoechst 33,342 DNA staining and flow cytometry results showed 20 \(\upmu\)M GA exposure could significantly accelerate apoptosis in both OC cell lines and the total apoptotic rate increased from 5.34%(control) to 21.42% in OVCAR-3 cells and from 8.01%(control) to 17.69% in A2780/CP70 cells. Western blot analysis revealed that GA stimulated programmed OC cell death via a p53-dependent intrinsic signaling. In addition, GA arrested cell cycle at the 5 or G2 phase via p53-p21-Cdc2-cyclin B pathway in the same cells. In conclusion, we provide some evidence of the efficacy of GA in ovarian cancer prevention and therapy.  
Keywords:ovarian cancer; apoptosis; gallic acid; cell cycle arrest +
Footnote †: journal: Applied
## 2 Materials and Methods
### Information Fusion in Chain-Based IOT Platform using Proposed Method
#### 2.8.2 Method of Performance Evaluation
Noise variance is upper bounded to 0.7 and sensor measurements are lower and upper bounded by \(-10\) and \(10\) respectively. As measurements are bounded by \([-10,10]\), we bound the quantized value of a fused output by previous range. Any fused result higher than 10 is set to 10 and a fused result lesser than \(-10\) is set to \(-10\). We assume that the network measures a deterministic parameter \(\theta\), which is equal to 1.8 and bounded by \([-10,10]\) (i.e., \(W^{2}=100\)). This upper and lower bounding is very practical. Consider a sensor network measuring the frequency of a transmission line having an expected value of 50 Hz. Here, the measurements can be lower and upper bounded by 45 Hz and 55 Hz, respectively, similar to what we have done in these experiments which is very practical. For fair comparison of the proposed method with EBDKF in [48] we consider the general multi-dimensional state-space model given in [44; 48] as shown in Equations (31) and (32). \[x_{i}=F_{i-1}x_{i-1}+G_{i-1}w_{i-1} \tag{31}\]  
\[z_{k,i}=H_{k,i}x_{i}+v_{k,i} \tag{32}\]As only one parameter is estimated in our context, the coefficient matrices \(H\), \(G\), and \(F\) in Equations (31) and (32) reduce to one dimension. Even though the process noise (Q) is set to 4 in [44], we select the state noise (\(w_{i-1}\)) as zero which will result zero process noise variance (\(Q_{i}\)). This is because the addition of a process noise even worsens the BER performance of EBDKF for update equations in [48]. In particular, we chose the best-case performance wise scenario for EBDKF to compare with the proposed method which exists when there is no process noise. Then, it will be crystal clear that if a data fusion method outperforms the accuracy of EBDKF under its best performance scenario, then the considered fusion method will outperform EBDKF under any other scenario. Therefore, the value of G is not relevant for zero process noise scenario as it can be observed from Equation (31); but we set it to zero. We set \(H=1\) because observations are not scaled by a factor according to the way that we have modeled sensor observations for proposed method and Average as it was shown in Equation (1). In order to have a fair comparison with AVG and proposed method, the sensor observations of EBDKF are modeled in the same manner. That is the true parameter added with Gaussian noise. Therefore, \(H=1\) for the state-space model. The sensor measurement noise variances (\(R_{k,i}\)) are generated as given in Equation (30). \(\hat{x}_{k,0}^{ind}\)\({}_{-1}\) is modeled as a continuous uniform random variable in range [\(-10\):\(10\)], \(\hat{P}_{k,0}^{ind}\)\({}_{-1}=n_{k}\), where \(n_{k}\) is the degree of the \(k\)th node. We first evaluate the accuracy of estimating a slowly varying(deterministic) parameter for the considered time interval and then later consider the accuracy of a dynamic parameter using the three different fusion algorithms.
## 5 Validation of Proposed Method
### Evaluation Results
#### 5.3.1 Avoidance Duration  
As the pedestrian density increases, the avoidance duration becomes longer to prevent collisions and PS invasions by surrounding pedestrians (Figure 15). Therefore, it can be observed that the scenes where the proposed method should suppress the PMV's avoidance behavior are more likely in higher pedestrian density conditions.  
Figure 14: Relationship between the correlation and the pedestrian position at the prediction timing.  
Figure 15: Comparison of avoidance duration among different pedestrian densities.  
In the condition with 10 pedestrians (pedestrian density 0.04/m\({}^{2}\)) and 20 pedestrians (pedestrian density 0.08/m\({}^{2}\)), the average avoidance duration per simulation trial was significantly longer for the proposed method compared to the existing method (_p_ < 0.05). On the other hand, in higher pedestrian density conditions, the proposed method's avoidance duration is shorter. Specifically, in the condition with 50 pedestrians (pedestrian density 0.21/m\({}^{2}\)), the duration was significantly shorter for the proposed method compared to the existing method (_p_ < 0.05). This result suggests that the proposed method can suppress avoidance, especially in situations where the avoidance duration becomes longer due to high pedestrian density.
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 2 Materials and Methods
### Input Data Sources
#### 2.3.2 Meteorological Data Source  
The meteorological data used in this study consists of air temperature at 2 m, dew point temperature at 2 m, wind speed at 100 m, surface pressure, total column water vapour (TCWV), aerosol optical thickness (AOT) at 550 nm and surface geopotential. Those inputs are obtained from the ECMWF ERA5 reanalysis ensemble means dataset [81]. The only exception was AOT which come from the Copernicus Atmosphere Monitoring Service (CAMS) forecast dataset [82], since it is not included in ERA5. Inputs at the time of the satellite overpass are computed by linear interpolation between the previous and posterior reanalysis timestep. Due to the low spatial resolution of the air temperature and wind speed fields (tens of kilometers) they are assumed to represent the surface conditions derived from conditions above the blending height (100 m above the surface) rather then  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CCI-LC** & \(\mathbf{h_{C,max}}\)  
the actual surface conditions. Therefore, air temperature at 100 m is calculated using the 2 m estimate, ECMWF surface geopotential, SRTM DEM and lapse rate for moist air. Those 100 m estimates are then used as inputs into the land surface flux models. AOT together with TCWV, surface pressure, SRTM DEM elevation and solar zenith angle at the time of Sentinel-3 satellite overpass were used to estimate the instantaneous shortwave irradiance on a horizontal surface at the satellite overpass [83; 84].
###### Abstract  
Due to safety concerns of integrating small unmanned aircraft systems (UAS) into non-segregated airspace, aviation authorities have required a set of detect and avoid (DAA) systems to be equipped on small UAS for beyond-visual-line-of-sight (BVLOS) flight operations in civil airspace. However, the development of small UAS DAA systems also requires BVLOS flights for testing and validation. To mitigate operational risks for small UAS BVLOS flight operations, this paper proposes to initially test small UAS DAA systems in BVLOS flights in a restricted airspace with additional safety features. Later, this paper further discusses the operating procedures and emergency action plans for small UAS BVLOS flight operations. The testing results show that these safety systems developed can help improve operational safety for small UAS BVLOS flight operations.  
unmanned aircraft; detect and avoid; unmanned aircraft systems; command and control link; first person view; flight termination system; UA; DAA; UAS; C2; FPV; FTS; VLOS; BVLOS
## 3 Results
### Modelling and Simulation during LEP Event
On the NAA signal trace section, where ionospheric irregularity occurs, the calculated maximum value of electron density is 3.65 \(\times\) 10\({}^{7}\) m\({}^{-3}\), while in unperturbed ionospheric conditions, on the same NAA signal path section, it is 3.39 \(\times\) 10\({}^{7}\) m\({}^{-3}\). On the GQD signal trace section, where ionospheric irregularity occurs, the calculated maximum value of electron density is 3.76 \(\times\) 10\({}^{7}\) m\({}^{-3}\), while in unperturbed ionospheric conditions, on same GQD signal path section, it is 3.39 \(\times\) 10\({}^{7}\) m\({}^{-3}\). In Figure 9, the electron density height profiles in the altitude range 60-90 km, for unperturbed and perturbed conditions due to the analysed LEP event were calculated at the reflection heights in the case of both monitored VLF signals (altitude 86 km for NAA and 85.8 km for GQD in Figure 8b), in the area of the most prominent electron density change along GCPs (e.g., at about 5300 km in the case of NAA and 1100 km in the case of the GQD signal looking from the transmitter towards the Belgrade receiver, i.e., at the approximate location of about 1100 km looking from Belgrade towards both transmitters) are given. Electron density \(N_{e}(z)\) height profiles for GQD (at reflection height 85.8 km) and NAA (at reflection height 86 km) signals, calculated for the altitude range 60-90 km, for 16 November 2004, are given in Figure 10.  
In order to create an easier and more convenient use of results presented in the analysis conducted in this paper, an expression for electron density \(N\!e(z)\) at reflection height is introduced in the form of a polynomial function as given in the equation below:  
\[\log N\!e(z,H^{\prime},\ \beta)=\sum_{i=0}^{1}a_{i}\cdot z^{i}, \tag{3}\]  
where \(z\) is height in km, and dimensionless quantities take values of \(a_{0}=-5.88339\) and \(a_{1}=0.15635\). Figure 9: Electron density changing along GCP path towards the Belgrade receiver, for considered VLF signal waveguides (NAA/24.0 kHz in dashed line and GQD/22.1 kHz in dotted line), for unperturbed (red) and perturbed (blue) ionospheric states, on 16 November 2004 (R is for receiver, T is for transmitter, black arrows on x axis point the direction of view).
## 2 The Governing Equation and General Solution
### Governing Equations
\begin{array}{l}\frac{E_{0}h^{3}}{12(1-v^{2})}\big{(}\frac{r}{R}\big{)}^{n} \Big{(}\
abla^{4}w+\frac{2n}{r}\frac{\partial^{3}w}{\partial r^{3}}+\frac{n(n +1+v)}{r^{2}}\frac{\partial^{2}w}{\partial r^{2}}+\frac{n(nv-v-1)}{r^{3}} \frac{\partial w}{\partial r}\\ \qquad\qquad+\frac{n(nv-v-3)}{r^{4}}\frac{\partial^{2}w}{\partial\theta^{2}}+ \frac{2n}{r^{3}}\frac{\partial^{3}w}{\partial\theta^{2}}\Big{\}}=-\
abla^{2} M^{\Delta T},\end{array}  \tag{8}\]  
where \(\
abla^{2}=\frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r}\frac{\partial}{dr} +\frac{1}{r^{2}}\frac{\partial^{2}}{\partial\theta^{2}}\). Equation (8) reveals that the equilibrium equation for the radially FGM circular plate is more complex compared to that of the FGM circular plate with varying material properties in the thickness direction. In the case of axisymmetric bending, the deflections of the radially FGM circular plate remain independent of the angle \(\theta\), i.e., \(w=w(r)\) and \(\partial(\ )/\partial\theta=0\). The bending equilibrium equation, Equation (8), can be simplified as:  
\[\frac{h^{3}E_{0}}{12(1-v^{2})}\Big{(}\frac{r}{R}\Big{)}^{n}\bigg{\{}\
abla^{4} w+\frac{2n}{r}\frac{d^{3}w}{dr^{3}}+\frac{n(n+1+v)}{r^{2}}\frac{d^{2}w}{dr^{2}}+ \frac{n(nv-v-1)}{r^{3}}\frac{dw}{dr}\bigg{\}}=-\frac{1}{r}\frac{dM^{\Delta T}} {dr}-\frac{d^{2}M^{  
a constant [PERSON]'s ratio \(\upsilon\) and [PERSON]'s modulus that varies radially based on a power function, i.e.,  
\[E(r)= \begin{cases}E_{0}^{*}&0\leq r\leq R_{1}\\ E_{0}(r/R)^{n}&R_{1}\leq r\leq R\end{cases} . \tag{16}\]  
The general solution for each section of the FGM-coated circular plate can be obtained separately by directly applying the results from Section 2. It is important to highlight that while the [PERSON]'s modulus \(E_{0}^{*}=E_{0}\overline{R}_{1}^{n}\) within the range \(0\leq r<R_{1}\) varies based on the material index \(n\) of the outer FGM-annular section, the inner portion of the FGM-coated circular plate represents a homogeneous circular plate with a uniform Young's modulus \(E_{0}^{*}\) for a specific value of \(n\). Hence, Equation (13) can serve as the homogeneous solution for the inner portion of the FGM-coated circular plate.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 2 Experimental Setup and Procedures
### Rock Fragment Analysis
#### 2.2.2 Coarseness Index  
The \(CI\) is a nondimensional value that expresses the particle size distribution of rock fragments generated during cutting. \(CI\) has an important relationship with \(SE\). Regardless of the cutting tool type, \(SE\) decreases as \(CI\) increases and is minimized with the maximum value of \(CI\)[14]. Therefore, it can be used as an indicator of the cutting efficiency of mechanical cutting operations [16].  
The \(CI\) is defined as the sum of the cumulative weight percentages of the remaining particles in each sieve used. Therefore, this value depends on the opening size and the number of sieves used [17,18]. For this reason, to use \(CI\) as a rock particle characteristic, values from the same set of sieves should be used. \(CI\) was calculated using Equation (2):  
\[CI=\sum_{i=1}^{n}\frac{W_{i}}{W_{t}} \tag{2}\]  
where \(CI\) denotes the coarseness index, \(n\) denotes the number of sieves used, \(W_{i}\) denotes the weight of the rock chip in the \(i\)th largest sieve, and \(W_{t}\) denotes the total weight of the rock chips used for sieve analysis.