esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
[83], who showed that several pathways, such as that of tyrosine, glycine, taurine, fatty acids, and glycerophospholipids metabolism, are dysregulated in NTCP knockout mice. Therefore, we investigated the relationship between _SLC10A1_/NTCP expression and endogenous metabolism using an untargeted metabolomics approach. A significant correlation of both mRNA expression and protein levels with certain metabolites like betaine or specific acylcarnitines was observed. The highest correlation, which was significant even after correction for multiple testing, was found for betaine, an osmolyte with an important role as methyl donor in hepatic metabolism [84]. Because betaine is not an NTCP substrate (Supplementary Figure S7), we postulate that the observed correlation is indirect and results from the fact that NTCP expression correlates with that of ALDH7A1, which metabolizes aldehyde substrates, including betaine aldehyde to betaine [85]. The underlying mechanism of the relationship between NTCP and ALDH7A1 needs further investigation. Additionally, we identified a correlation with cAMP levels. cAMP has previously been shown to increase plasma membrane levels of NTCP in rat hepatocytes and in hepatic cells transfected with human NTCP [86; 87]. In conclusion, we could demonstrate that genetic variation of _SLC10A1_ cannot explain the inter-individual variability of NTCP and its function. In contrast, non-genetic (i.e., smoking, alcohol consumption) and epigenetic factors, as well as endogenous metabolites, contribute to the inter-individual variability of NTCP indicating the complexity of expression regulation of human transporter proteins.
###### Abstract  
Vascular calcification is calcium deposition occurring in the wall of blood vessels, leading to mechanical stress and rupture due to a loss of elasticity and the hardening of the vessel wall. The role of the Transient Receptor Channel Vanilloid 4 (TRPV4), a Ca\({}^{2+}\)-permeable cation channel, in the progression of vascular calcification is poorly explored. In this study, we investigated the role of TRPV4 in vascular calcification and the development of abdominal aortic aneurysm (AAA). Experimental mice were randomly divided into four groups: wild-type (WT) sham operated group, WT CaCl\({}_{2}\)-induced aortic injury, TRPV4-KO sham operated group, and TRPV4-KO CaCl\({}_{2}\)-induced aortic injury. The TRPV4-knockout (TRPV4-KO) mice and wild-type (WT) mice were subjected to the CaCl\({}_{2}\)-induced abdominal aortic injury. In histopathological analysis, the aorta of the TRPV4-KO mice showed extensive calcification in the tunica media with a significant increase in the outer diameter (\(p<0.0001\)), luminal area (\(p<0.05\)), and internal circumference (\(p<0.05\)) after CaCl\({}_{2}\) injury when compared to WT mice. Additionally, the tunica media of the TRPV4-KO mice aorta showed extensive damage with apparent elongation and disruption of the elastic lamella. These results indicate a protective function of TRPV4 against vascular calcification and the progression of AAA after CaCl\({}_{2}\) injury.  
a pacs: 11
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 4 Implementation of USDAA Defense Algorithm
### Implementation of Primary User Adversarial Attacks and Defense
#### 4.3.3 Adversarial Attack Defense  
By adversarial pre-training and fine-tuning, the USDAA defense models are obtained to defend two types of white box adversarial attacks: FGSM and PGD. The decision threshold \(\lambda\) is determined by the given false alarm probability. The detection criteria of the defense model against adversarial samples \(D^{adv}\) are shown in Equation (18), where \(prob(D^{adv},H_{1})\) is the output of the defense model \(R_{\theta}\), indicating the probability of \(D^{adv}\) being judged \(H_{1}\); thus, the defense against white box attack is realized.  
\[\begin{array}{l}H_{1}:prob(D^{adv},H_{1})\geq\lambda\\ H_{0}:prob(D^{adv},H_{1})<\lambda\end{array}. \tag{18}\]  
In summary, the USDAA algorithm is shown in Algorithm 1.  
```
0: adversarial pre-training sample set \(D\) and \(D^{adv}\), adversarial sample set \((D^{adv},L)\) for fine-tuning, number of rounds \(k_{0}\) for pre-training and number of rounds \(k_{1}\) for fine-tuning.
0: the parameters of feature extraction network \(B_{\xi}\) and spectrum sensing classifier \(R_{\theta}\) are optimal;
1:for epoch = 1 to \(k_{0}\)do
2:\(D\) and \(D^{adv}\) are input into two encoders, respectively, intermediate features \(Q_{i}(i=1,2)\) are extracted, and the potential representation of features \(Z_{i}(i=1,2)\) are obtained according to Equation (7);
3: The hidden variable \(Z_{i}(i=1,2)\) are fed to the decoders, which reconstruct \(D\) and \(D^{adv}\) according to potential feature representation \(Z_{i}(i=1,2)\);
4: The contrast loss, KL divergence and reconstruction loss are calculated by using Equations (6), (8) and (9), the total loss is obtained by using Equation (5);
5: The parameters of encoder and decoder are optimized according to Equations (10) and (11) and the parameters \(\xi\) of encoder are saved.
6:endfor
7: The parameters of the encoder \(B_{\xi}\) are loaded, which is obtained by adversarial pre-training as the feature extractor of the defense model;
8: After the feature extractor, a classification layer is added to obtain complete defense model.
9:for epoch = 1 to \(k_{1}\)do
10: The fine-tuning samples \((D^{adv},L)\) are input into the defense model;
11: The parameters \(\theta\) of defense model are fine-tuned by using Equations (12)-(14).
12:endfor
13: Return a trained defense model \(R_{\theta}\).
```  
**Algorithm 1** USDAA Algorithm.
# Bio-Sourced and Biodegradable Membranes
## 3 Biodegradable Antifouling Membrane Properties
### Polylactic Acid (PLA) Membranes  
Recent studies have shown that polylactic acid (PLA) biopolymers can be an excellent alternative to conventional thermoplastic membranes used in separation applications [179; 180; 181; 182]. The polar oxygen linkage in PLA membranes contacts the water molecules over the separation of aqueous solutions and results in a higher permeate flux and decreased foulant attachment [183]. [PERSON] et al. [180] prepared an ultrafiltration PLA membrane through the phase inversion method and evaluated its antifouling property using the BSA solution. After one filtration cycle, the flux recovery ratio was about 57% for membranes containing 15 wt% PLA polymer, and it reached 93% as the PLA concentration increased to 22 wt%. For membranes containing 22 wt% of PLA, the permeate flux recovery ratio after the second cycle was about 85% (decreased only 8%), indicating high fouling resistance of the fabricated membranes. Although PLA membranes demonstrated good antifouling properties, their hydrophobic characteristic and brittleness at room temperature may hinder their use in various applications. Therefore, a number of techniques, including blending, self-assembly, and surface grafting and coating have been used to enhance the hydrophilicity, mechanical stability, and fouling resistance of those membranes [1,184]. In terms of fouling resistance and hydrophilicity, [PERSON] et al. [7] proposed hydroxyapatite (Hap) as a filler and polydopamine (PDA) as the surface coating for PLA membranes. It was reported that the water permeability of PLA membrane improved by 120% when only 2 wt% Hap/1-h PDA was employed in the system. After three complete filtration cycles, the flux recovery ratio (FRR) improved from 20% for pristine PLA membranes to 54% for composite membranes. The irreversible fouling of non-organic material removal was also decreased from 79.7% to 70.7%. In fact, all of those promising findings were the result of improvements in membrane contact angle, porosity, and mean pore size. In another research, the bio-based \(\beta\)-cyclodextrintrim (\(\beta\)-CD) was also employed as the pore foaming to improve water permeability in the PLA membrane due to its hydrophilic exterior [185]. The SEM images of the surface and cross-section of the PLA membrane show that it was composed of a dense skin layer with interconnected internal pores. However, the addition of 5 wt% of \(\beta\)-CD to PLA solution led to the fabrication of membranes with homogenous pores on the surface (mean size of about 1 \(\upmu\)m) and some finger-shaped pores in cross-section. As a result of changes in membrane morphology, the pure water flux and the flux recovery ratio improved by about 230% and 50%, respectively.
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
## 4 Experimental Evaluation
### Efficiency of Synthetic Dataset  
In this subsection, we investigate the efficiency of the synthetic dataset.  
**Effect of grids:** For SYN_A1 dataset, we set facility type \(m=4\). Both of the number of preferable facility types and unpreferable facility types are two. We vary the number of grids \(n^{2}\) with \(32\times 32\), \(64\times 64\), \(128\times 128\), \(512\times 512\) and \(1024\times 1024\) respectively. For dataset SYN_A2, a larger dataset than SYN_A1, we fix the number of facility type \(m=2\), which consists of one preferable facility type and one unpreferable facility type. The number of objects is set to \(obj=2000\). The number of grids are varying as \(100\times 100\), \(500\times 500\) and \(1000\times 1000\).  
Figures 13 and 14 show the results. From the results in Figure 13, we find that the processing time increases with the number of grids increasing. Furthermore, the processing time of GASKY algorithm increases faster than MRGASKY algorithm when \(n^{2}\) is larger than \(256\times 256\). The results in Figure 14 show that processing time of GASKY algorithm increases faster at the beginning while MRGASKY has a relatively steady increase. In other words, GASKY algorithm takes more time in \(min-max\) computation [5] and Voronoi Diagram building. Thus, MRGASKY has better scalability with the increase of the number of grids.  
Figure 13: Processing Time of SYN_A1.  
**Effect of facility types:** In SYN_B, we conduct the effect on the number of facility types. We set the number of objects as \(obj=10,000\) and the number of grids as \(128\times 128\). \(m\) is varied with 2, 4, 6 and 8 respectively. Both the number of preferable facility types and unpreferable facility types are set to be the same values.  
Figure 15 demonstrates the results. We can observe that the average processing time of GASKY algorithm increases linearly when \(m\) changes. Conversely, the average processing time of the MRGASKY algorithm is smaller than GASKY and tends to be stable. The result shows that the proposed algorithm outperforms the GASKY, which justifies that our algorithm has good scalability on the facility types.  
**Effect of objects:** In SYN_C, we set \(m=2\), \(n^{2}=128\times 128\). The number of objects is varied with 4000, 8000, 12,000 and 16,000.  
The result shows in Figure 16. The performance of the MRGASKY algorithm is much better than the GASKY algorithm. Overall, the MRGASKY algorithm maintains sufficient stability to handle \"big data\".  
Figure 14: Processing Time of SYN_A2.  
Figure 15: Processing Time of SYN_B.
## 2 Materials and Methods
### Determination of pH, Titratable Acidity (TA), Total Solids Content (TSC), and Color Measurements  
For pH and TA determination, samples (10 g) were collected and diluted in 90 mL of sterile saline solution (0.9% NaCl) throughout the storage period and measured directly at 25 \({}^{\circ}\)C using a pH meter (CP-411, Elmetron, Zabrze, Poland). TA was determined by mixing 10 mL of the prepared dilutions with 10 mL of distilled water and titrating with 0.01 M NaOH solution, using phenolphthalein (0.1%, \(w/v\) in 95% ethanol) as an indicator [4]. Total solids content (TSC) of the samples was evaluated following the standard method (no. 925.23) of AOAC (Association of Official Agricultural Chemists) [27]. All samples were measured for color during the storage time by a Konica Minolta CR-5 colorimeter (Konica Minolta, Osaka, Japan). The values measured were L * (white 100/black0), a * values (red positive/green negative) and b * values (yellow positive/blue negative). Additionally, \(\Delta\)E (total color difference) compared to non-refmented samples (used as standards), was also calculated as follows  
\[\Delta\mathrm{E}=[(\mathrm{L_{standard}}-\mathrm{L_{sample}})^{2}+(\mathrm{a_{ standard}}-\mathrm{a_{sample}})^{2}+(\mathrm{b_{standard}}-\mathrm{b_{sample}})]^{0.5}\]
## 1 Introduction
Despite the promising performance of DeepLabv3+, it has not been widely exploited for OD segmentation in glaucoma detection. Our previous study in chest X-ray segmentation showed that DeepLabv3+ outperformed other state-of-the-art deep semantic models such as _U_-net, fully connect convolutional (FCN) network and SegNet [26]. Inspired by these results, here we adopted DeepLabv3+ to segment OD. Five different CNNs are substituted as the encoder part and their empirical results are compared. For classification stage, the studies in [6; 20; 21; 22; 23; 24; 25] showed that deep CNNs are commonly applied individually using transfer learning or fine-turning the weights. However, using an individual deep CNNs may produce unsatisfactory results as deep CNN models are rule-based mechanisms to build and predict the hypothesis space. Ensemble of deep CNNs has been shown to be more accurate and effective than those based on an individual CNN [27; 28]. As presented in [3; 5], the ensembles of deep CNNs have been applied to classify glaucoma images and achieved more accurate results. However, these methods obtained better results; the methods adapted on a small number of deep CNN models. Therefore, here, we carried out an experimental comparison among eleven different models of deep CNNs (i.e., (P1) pretrained deep CNNs for transfer learning, (P2) pretrained deep CNNs as the features extractors followed by a SVM classifier and (P3) an ensemble of methods, P1 and P2). The main contributions of this study are summarized as follow:  
1. A comparative study of OD segmentation using five different deep CNNs as the encoder in DeepLabv3+ architecture;
2. Comparison of eleven pretrained CNNs as the glaucoma classifier using transfer learning techniques;
3. Comparison of eleven pretrained CNNs as the feature extractors using SVM classifier
4. Ensemble of the diverse CNN based learners from P1 and P2 using probability averaging strategy.
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
## 4 Optimization Problem
### Angles, Angular Velocities, and Angular Accelerations  
In Section 3, trajectories of hip position, torso angle, swing ankle position have been defined. Then knee angles can be calculated using inverse kinematics when the related ankle position and hip position are known. In this paper, we do not use a numericalsolution method but an analytical solution method to calculate joint angles. According to the geometric relationship, one can obtain the joint angles as follows:  
\[\left\{\begin{array}{l}q_{1}(t)=a\cos\frac{x_{1}}{k_{1}}-a\cos\frac{k_{1}^{2}+ \hat{x}_{1}^{2}-\hat{x}_{2}^{2}}{2k_{1}l_{1}}-\frac{\pi}{2}\\ q_{2}(t)=\pi-a\cos\frac{\hat{x}_{1}^{2}+\hat{x}_{1}^{2}-\hat{x}_{1}^{2}}{2k_{1 }l_{2}}\\ q_{3}(t)=\theta(t)-q_{1}(t)-q_{2}(t)\\ q_{4}(t)=\frac{\pi}{2}-a\cos\frac{x_{2}^{2}}{2k_{2}}+a\cos\frac{k_{2}^{2}+\hat {x}_{2}^{2}-\hat{x}_{2}^{2}}{2k_{2}l_{4}}-q_{1}(t)-q_{2}(t)-q_{3}(t)\\ q_{5}(t)=a\cos\frac{\hat{x}_{1}^{2}+\hat{x}_{2}^{2}-\hat{x}_{2}^{2}}{2k_{4}l_{ 5}}-\pi\end{array}\right., \tag{26}\]  
where \(x_{1}=x_{h}(t)\), \(x_{1}=x_{h}(t)\) and \(k_{1}=\sqrt{x_{1}^{2}+x_{1}^{2}}\) is the virtual length of stance leg; \(x_{2}=x_{a}(t)-x_{h}(t)\), \(x_{2}=x_{a}(t)-x_{h}(t)\) and \(k_{2}=\sqrt{x_{2}^{2}+x_{2}^{2}}\) is the virtual length of the swing leg.  
Next, we introduce how to calculate angular velocity and angular acceleration. Define a vector \(r(t)=[x_{h}(t);x_{h}(t);\theta(t);x_{a}(t);x_{a}(t)]\) whose components were defined in Section 3. According to the kinematic relationship, \(r(t)\) can be expressed in terms of \(q=[q_{1};q_{2};q_{3};q_{4};q_{5}]\). Its derivative with respect to time is  
\[\dot{r}=J_{r}\dot{q}, \tag{27}\]  
where \(J_{r}\) is the Jacobian matrix and it is invertible. Therefore, angular velocity can be calculated as follows:  
\[\dot{q}=J_{r}^{-1}\dot{r}. \tag{28}\]  
Differentiating Equation (27) once more yields  
\[\ddot{r}=J_{r}\ddot{q}+\dot{J}_{r}\dot{q}. \tag{29}\]  
Then, one can obtain angular accelerations as follows:  
\[\ddot{q}=J_{r}^{-1}\Big{(}\ddot{r}-\dot{J}_{r}\dot{q}\Big{)}. \tag{30}\]
## 2 Materials and Methods
### ANFIS Model  
The adaptive neuro-fuzzy inference system utilizes a neural network to generate and optimize decision rules based on Takagi-Sugeno fuzzy inference system. Among the five layers of ANFIS architecture, layer 1 is the fuzzification layer. Nodes in this layer are adaptive nodes with a membership function. There are eight types of MFs that can be used for fuzzification of input data. The parameters of MFs in layer 1 are referred to as premise parameters. Layers 2 and 3 are the rule and normalization layers, respectively, with fixed nodes. Layer 4 is the defuzzification layer. Nodes in this layer are adaptive nodes with a MF. Parameters of nodes in this layer are referred to as consequent parameters. Layer 5 is the output layer with a single fixed node. A hybrid training method, which combines the least-squares and back-propagation algorithms, was employed to fine-tune the premise and consequent parameters of the ANFIS model in the present study.  
The ANFIS toolbox of Matlab 2016a was utilized to build ANFIS models for predicting the _Salmonella_ population. Each input/output pair contained two inputs (storage temperature and storage days) and one output (_Salmonella_ population). In total, 102 experimental data were randomly separated and used for training (60%), checking (18%), and testing (22%) the model, respectively. The prediction performance of the ANFIS model was estimated using the coefficient of determination (R\({}^{2}\)), root mean squared error (RMSE), and residual predictive deviation (RPD) of the linear regression line between the predicted and experimental values at the training and prediction phases.
###### Abstract  
This article describes the automated computed tomography (CT) image processing technique supporting kidney detection. The main goal of the study is a fully automatic generation of a kidney boundary for each slice in the set of slices obtained in the computed tomography examination. This work describes three main tasks in the process of automatic kidney identification: the initial location of the kidneys using the U-Net convolutional neural network, the generation of an accurate kidney boundary using extended maxima transformation, and the application of the slice scanning algorithm supporting the process of generating the result for the next slice, using the result of the previous one. To assess the quality of the proposed technique of medical image analysis, automatic numerical tests were performed. In the test section, we presented numerical results, calculating the F1-score of kidney boundary detection by an automatic system, compared to the kidneys boundaries manually generated by a human expert from a medical center. The influence of the use of U-Net support in the initial detection of the kidney on the final F1-score of generating the kidney outline was also evaluated. The F1-score achieved by the automated system is 84% \(\pm\) 10% for the system without U-Net support and 89% \(\pm\) 9% for the system with U-Net support. Performance tests show that the presented technique can generate the kidney boundary up to 3 times faster than raw U-Net-based network. The proposed kidney recognition system can be successfully used in systems that require a very fast image processing time. The measurable effect of the developed techniques is a practical help for doctors, specialists from medical centers dealing with the analysis and description of medical image data.  
computer-aided diagnosis; image segmentation; artificial intelligence; kidney disease diagnosis +
Footnote †: journal: Medical Image Analysis  
1 Faculty of Electrical Engineering, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland; [EMAIL_ADDRESS]  
22 October 2  
components that determine the possibility of visualizing abnormalities in the structure of the kidney. Focal changes in the kidneys may be a simple cyst or even an obvious cancerous tumor. The changes have a whole range of different morphological variants and knowing them is very useful in the final diagnosis of a specific disease. The importance of tomography as a helpful tool in the detection of invisible and non-palpable tumors is growing especially in increasingly used renal-sparing treatment. This article focuses on the presentation of the computed-aided method of automatic kidney boundary detection in CT images. This is a very important task from the differentiation of the kidney shape.
## Nomenclature  
\begin{tabular}{l l} \(C_{is}\) & Isentropic flow velocity \\ \(d_{2}\) & Rotor mean inlet diameter \\ \(\dot{m}\) & Mass flow \\ \(N\) & Speed of rotation \\ \(P\) & Pressure \\ \(PR\) & Pressure ratio \\ \(MP\) & Mass parameter \\ \(T\) & temperature \\ \(\eta\) & Efficiency \\ S.s & Suction side \\ P.s & Pressure side \\ \(C_{p}\) & Pressure loss coefficient \\ \(A_{is}\) & Isentropic flow area \\ Tr. vortex & Trailing-edge vortex \\ LR vortex & Leading-edge Root Vortex \\ \(SPR\) & Surface pressure ratio \\ \(U\) & Blade speed \\ \((I\)-\(MFR)\) & Degree of inequality (Equation (5)) \\ \(CFX\) & Ansys code \\ Subscripts \\
0 & Stagnation conditions, Volute inlet station \\
1 & Nozzle inlet station \\
2 & Rotor inlet \\
3 & Rotor exit \\ \(eff\) & Effective \\ \(Inner\) & Inner limb entry \\ \(Is\) & Isentropic process \\ \(O\) & Reference value \\ \(Outer\) & Outer limb entry \\ \(s\) & Static condition \\ \(T\) & Total condition \\ \(t\)-\(s\) & Total to static \\ LP & Low pressure \\ HP & High pressure \\ DCL & Degree in Circumferential location \\ \(\sim\) & Approximate component \\ \end{tabular}