esa-sceva/satcom-chunk-collection huggingface dataset:
# Antidiabetic Potential of Monoterpenes: A Case of Small Molecules Punching above Their Weight
## 4 Antidiabetic Potential of Monoterpenes
### In Vitro Protective Effects
Hence, such biological effect is not addressed here as the major mechanism of action of these compounds for their antidiabetic effects. It is interesting to note however that when monoterepenes are incorporated into other structural groups such as flavonoids as exemplified by saturein, a far better direct antioxidant and enzyme inhibitory effects were obtained [46]. A number of other in vitro experiments have been carried out to assess the effect of monoterepenes on cultured pancreatic \(\beta\) cells. This has shown remarkable results as concentrations in micromolar range have been demonstrated to potentiate insulin secretion (Table 1). Various experiments on adipocytes, hepatocytes and muscle cells also confirmed that these compounds can effectively increase glucose uptake through upregulation of the glucose transporter (GLUT4) translocation. As discussed in the following sections, some key pharmacological targets at molecular level including the insulin signaling pathways have been implicated. Genipin and geniposide as well as iridoids and their glycosides have also been shown to display potential antidiabetic effects in vitro. In the cultured muscle myotube cells, an increase in the phosphorylation of insulin receptor substrate-1 (IRS-1) and other signal transduction pathways leading to increased intracellular calcium concentrations have been reported (Table 1). The AMPK pathway has also been shown to be involved in the protection of pancreatic \(\beta\) cells by geniposide [32; 33]. Furthermore, activation of the glucagon-like-1 receptor has been implicated in the insulin secretion promotion by geniposide [34]. The role of PPAR\(\gamma\) and phosphatidyl inositol 3-kinase (PI3K) are also shown to be involved in the effect of these compounds in neuronal cells. As discussed in the following texts, the PI3K involvement, which has been confirmed by using specific inhibitor (wortmannin), is a further interesting insight into the possible mechanism of action of monoterepenes [31]. Hence, at concentrations as low as 10 \(\upmu\)M, these compounds can protect pancreatic \(\beta\) cells and other cells such as neurons; promote insulin secretion and facilitate glucose uptake. Moreover, amelioration of the pro-inflammatory cytokines (e.g., TNF) in adipocytes and lipid accumulation in liver cells have been shown to be induced by geniposide. The involvement of the GLP-1R signaling pathway in the antidiabetic effect of geniposide has also been confirmed by specific receptor antagonist, exendin [38; 39]. The production of TNF and free fatty acids (FFAs) in adipocytes and macrophages in vitro could also be ameliorated by monoterepenes such as paeoniflorin [42; 43], swertiamarin [47] and thujone [48].
## 4 Hardware Description
### Circuit Schematic of the System
#### 4.1.2 Node 2 Transmitter Unit  
Figure 5 displays the node 2 transmitter circuit schematic. It comprises a motor, ultrasonic sensor, radio module, Arduino Mega 2560, and LCD relay circuit. Pin 8 of the microcontroller is mounted to the relay circuit. Pin 8 of the microcontroller is connected to the ultrasonic sensor. The module RF is linked to the microcontroller's Rx1 and Tx1. The data pins D4-D7 of the LCD are attached to digital pins 5, 4, 3, and 2 of the microcontrollers. Register Select (RS) and enable (E) pin of LCD is connected to digital 12 and 11 of the microcontrollers respectively. The contrast levels are adjusted using a potentiometer of 10K in which the wiper of the potentiometer is connected to the VEE pin of LCD, one pin is attached to the ground and the other to VCC (+5 V).  
Figure 4: Circuit diagram of transmitter Node 1.
# Multidisciplinary Reliability Design Optimization Modeling Based on SysML
## 5 Evaluation and Discussion
### MDO Modeling Application for In-Vehicle Environmental Control Integration System
As shown in Figure 15, the system optimization model for the air conditioning subsystem includes the subsystem objective function, the optimization constraints, and the attribute value constraint blocks within the optimization objectives (optimization variables are not included here for clarity of display). The optimization model of the air supply subsystem, as shown in Figure 16, includes the subsystem optimization objectives and two inequality constraints along with their design variables. The attributes contained within the inequality constraints include the constraint expression, the associated discipline, and upper and lower boundaries, among other details. Figure 15: Air conditioning subsystem optimization model information. As mentioned in Section 4.1, each extended optimization constraint block and optimization objective block is associated with a parametric diagram for equation extraction. As illustrated in Figure 17, using the air supply subsystem's optimization objectives as an example, binding connectors can be effectively used to bind the constraint parameters to the value properties of the modules. Once the optimization models for the two subsystems are built, the defined optimization problem diagram can be utilized to capture the internal structure of the entire optimization problem based on the connections between variables. Figure 18 presents the defined optimization problem for the in-vehicle environment control integrated system, detailing the relationships between system-level optimization objectives, subsystem objectives, design variables, optimization constraints, and optimization objectives. For example, the overall optimization goal is composed of two subsystem objectives, with the subsystem objective \(M_{2}\) associated with the variables \(WP\), \(Vicm\), \(d\), and \(Vdc\). Additionally, the compressor displacement constraint is related to the variables \(HE\) and \(WP\). Figure 16: Integrated air supply subsystem optimization model information. Figure 13:0 Integrated air supply subsystem optimization objective constraint block. In order to parse the optimization information model constructed by SysML modeling tools such as MagicDraw, it is necessary to convert the file to an intermediate XML format. The XML file generated from the constructed SysML optimization model is shown in Figure 13:1 This XML file contains the complete optimization information, including information on optimization variables, optimization objectives, and optimization constraints. The optimization information in this XML format can be extracted to an optimization solver such as MATLAB for optimization solving.
## 4 Conclusions  
This study investigated the effects of different extraction solvents (95% methanol, ethanol, and hot water) on the antioxidant, antiaging, and antibacterial activities of GV. The TPC and TTC were measured and ranged from 0.8 to 4.0 mg TA/g and 0.2 to 0.9 mg QC/g, respectively. The highest TPC and TTC were identified in the GVM and GVE (3.74 \(\pm\) 0.14 mg TA/g and 0.86 \(\pm\) 0.06 mg QC/g, respectively). Stronger antioxidant activities (DPPH, ABTS, FRAP, and antioxidant enzyme activity) were observed for GVM and GVE than for GVW. In addition, the antiaging activities (collagenase, elastase, and tyrosinase inhibition activities) were the highest in the GVM. Finally, the highest antibacterial activity against the six foodborne pathogens was observed for GVE. Overall, the GVE and GVM had the highest TPC and TTC, suggesting that the high antioxidant and antibacterial activities of the GVE and GVM were influenced by the TPC and TFC of the GVE and GVM. These findings suggest that the phenolic and flavonoid compositions of GVM and GVE contribute significantly to their bioactivities and have potential applications in the development of functional agents in diverse industries. However, these results may vary under specific test conditions, and the efficacy of these compounds in real-world applications may vary. Therefore, additional research is needed to confirm these results.  
Figure 4: (a) Variable importance in projection (VIP) scores of partial least squares discriminant analysis and (b) correlation matrix between total polyphenols, flavonoid content, and antioxidant activities in _Gracilaria verrucosa_ extracts. The strength of blue and red colors represents the degree of negative and positive correlations, respectively. GVE: _G. verrucosa_ ethanol extract; GVM: _G. verrucosa_ methanol extract; GVW: _G. verrucosa_ water extract; TPC: total polyphenol content; TFC: total flavonoid content.  
**Author Contributions:** Conceptualization, H.-H.L.; methodology, H.-H.L. and J.-S.K.; formal analysis, H.-H.L., C.C. and J.-S.K.; investigation, H.-H.L., C.C. and J.-S.K.; writing--original draft preparation, H.-H.L. and J.-S.K.; writing--review and editing, H.-H.L. and C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Chosun University in 2020.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this article are available in article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Cascade Model with Prior Knowledge for Bone Age Assessment
## 2 Materials and Methods
### Bone Age Assessment
#### 2.3.3 Model Design for Cascade Training  
Cascade operations are used to embed this vector of bone age labels containing semantic information into the BAA model, allowing the network to utilize information from classification, regression, and distribution learning simultaneously.  
First, the hand bone X-ray image is input into the improved convolutional feature extractor to output feature map \(X\). Then, considering the difference in hand bone development speed due to gender [30], we incorporate it into the network as a trainable neuron. Specifically, \(X\) is compressed into a vector by flattening layers and then feature vector \(Z\) is output by concatenating with gender (G).  
\[Z=Concat(flatten(X),G) \tag{6}\]  
Figure 7: Multi-point distribution of bone age labels with semantic information.  
Second, feature vector \(Z\) is passed through the FC layer and SoftMax activation function \(\sigma\) to output the probability that the bone age label belongs to each month (i.e., each bin), and we set the number of neurons in the FC to 216, corresponding to each month of the age range 0-18:  
\[y^{{}^{\prime}}_{n}=\sigma(W_{1}*Z+b) \tag{7}\]  
where \(y^{{}^{\prime}}_{n}\) denotes the output of the FC layer, i.e., the probability that the network makes predictions each month, \(W_{1}\) denotes the weight between \(z\) and FC, and \(b\) is the bias in network learning. The SoftMax activation function is the key to embedding the semantic distribution information labels into the network, which is represented as follows.  
\[\sigma_{i}(y^{{}^{\prime}}_{n})=\frac{e^{y_{i}}}{\sum_{j=1}^{k}e^{y_{j}}} \tag{8}\]  
Finally, the bone age probabilities obtained in the FC layer for each month are inputted into a regression layer with only one neuron to obtain the final bone age assessment results. This is performed as follows: \(y^{{}^{\prime}}_{n}\) is multiplied with the weight \(W_{2}\) from FC to regression layer plus bias to obtain the final bone age prediction output.  
\[y_{n}=W_{2}*y^{{}^{\prime}}_{n}+b \tag{9}\]  
We call the design of the FC layer to the regression layer a cascade module, which allows the model to utilize classification, regression, and distribution learning simultaneously to maximize the use of the bone age label information.
## 3 Examples
### Bernoulli-type and logistic equations  
Consider stochastic Bernoulli equations  
\[dX(t) = rX(t)(K-[X(t)]^{n-1})dt+\sigma X(t)dW(t) \tag{10}\]  
which are interpreted in Ito sense. The most popular representative of these equations is the case with \(n=2\), also called the logistic growth model with growth rate \(r>0\) and environmental capacity \(K>0\). These equations have numerous applications in mathematical biology (e.g., to model the growth of populations). For simplicity, we suppose that \(X(0)>0\) (_i.e._, we seek only for physically meaningful solutions and additionally avoid technical difficulties of mathematical stopping procedures). To carry out the procedure of the algorithm, we begin with step [1] solving  
\[\mathcal{L}^{0}F(x)=\left(rx(K-x^{n-1})\frac{\partial}{\partial x}+\frac{1}{2} \sigma^{2}x^{2}\frac{\partial^{2}}{\partial x^{2}}\right)F(x)=a_{0}+a_{1}F(x)\]  
Suppose that \(n\
eq 1\). By choosing the parameters  
\[a_{0}=-r(1-n),\quad a_{1}=(1-n)(rK-n\frac{\sigma^{2}}{2})\]one recognizes that the (deterministically known) Leibniz transform  
\[F(x)=x^{1-n}\]  
represents a solution of step [1]. Step [2] asks for the calculation of \(\mathcal{L}^{1}F=\sigma x\frac{\partial}{\partial x}F\). The Leibniz transform gives  
\[\mathcal{L}^{1}F(x)=\sigma(1-n)x^{1-n}=\sigma(1-n)F(x)\]  
hence we may choose the constants \(b_{0}^{1}=0\) and \(b_{1}^{1}=\sigma(1-n)\). Clearly, \(F(x)=x^{1-n}\) is a common invertible solution of both step [1] and [2]. Thus, step [3] provides the solution \(F(x)=x^{1-n}\) with its inverse  
\[F^{-1}(x)=\sqrt[1-n]{x}\]  
Step [4] takes into consideration the linear Ito equation  
\[dU(t)=(1-n)[-r+(rK-n\frac{\sigma^{2}}{2})U(t)]dt+(1-n)\sigma U(t)dW(t)\]  
which can be solved by Theorem 2 from the appendix. Its solution is  
\[U(t)=\varphi(t)\left([X(0)]^{1-n}-r(1-n)\int_{0}^{t}\varphi^{-1}(s)ds\right)\]  
where  
\[\varphi(t)=\exp\left((1-n)(rK-\frac{\sigma^{2}}{2})t+\sigma(1-n)W(t)\right)\]  
with \(t\geq 0\). Finally, step [5] leads to the solution of (10)  
\[X(t)=F^{-1}(U(t))=\exp\left((rK-\frac{\sigma^{2}}{2})t+\sigma W(t)\right) \left([X(0)]^{1-n}+r(n-1)\int_{0}^{t}\varphi^{-1}(s)ds\right)^{1/(1-n)}\]
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 4 Radar Representation
### Spinning Radar  
In contrast to automotive radars, spinning radars utilize a single transmitting and receiving antenna. Similar to LiDAR sensors, they are positioned atop a vehicle and rotate around their vertical axis. Like any FMCW radar, these radars leverage the frequency shift between transmitted and received waves to calculate the object's range. The power of the returned signal provides valuable information regarding the object's reflectivity, size, shape, and orientation concerning the receiver. However, the DOA is determined by the current angle of the spinning radar, generating a polar power spectrum [81; 82].  
This 2D polar spectrum is able to generate a less sparse radar representation compared to the heavily processed point cloud generated by the automotive radar. However, due to this dense representation, the Doppler and RCS component are often not included in spinning radar open datasets. Another issue is that a spinning radar setup is unable to determine any height information due to the lack of antennas. Thus, spinning radars are generally used in 2D scenarios [81; 82].  
It is important to note that radar datasets differ not only in their respective type, i.e. spinning and automotive, but also in their exact sensor configurations. E.g., although two systems use an automotive radar configuration, they can drastically differ in their working range and resolution, generally depending on the price of the sensor. The same goes for spinning radars. Radars with a higher spinning frequency will provide a higher resolution range-angle image, yielding in higher quality data.
## 7 Results of Simulation
### The Impact of Heat Convection  
As already mentioned, convection is the primary heat transfer mechanism between a bearing and its surroundings. Moreover, free convection, mainly on the elements of the bearing seat, significantly depends on the temperature of the elements of the seat and the outer ring. On the other hand, forced convection, occurring mainly on the shaft and the inner ring, is constant for a given rotational speed of the bearing. Through convection, the designer of the bearing seat has the ability to influence the thermal state, as the amount of heat that flows to the environment is proportional to the surface of the elements from which the heat flows. By selecting the size of all surfaces, it can achieve the desired thermal state. All the tests described in items 6 were carried out for a virtual bearing seat in which the ratio of the surface with free convection to the surface with forced convection was 2. This was due to the construction of the body of the bearing seat and the shaft (Figure 2(b)). To assess the effect of convection on temperatures in the bearing, simulation tests were also carried out for the variant of heat sources V1, but for a different surface ratio, equal to 1 (due to structural changes to the seat body). Figure 10 shows the temperature courses on the inner and outer rings for both variants of convection, i.e., for the quotient of 2 and 1.  
Figure 9: Comparison of temperatures on the inner (Tir) and outer (Tor) rings for three different contact areas: (**a**) heat sources V1, (**b**) heat sources V3; BV1, BV2 and BV3—designation of Bonded contact models for contact areas corresponding to variants V1, V2 and V3.  
The conclusion from these studies is quite obvious: due to the fact that the free convection coefficient is lower than the forced convection coefficient, the change in the proportion of heat emitting surfaces to the environment affects the temperature distribution. In the example in Figure 10, the temperature in the steady state changed by approx. 5 \({}^{\circ}\)C.  
Figure 11 presents a summary of temperatures for two values of the area quotients, 2 and 1, for all variants of heat sources V1-V6. For these variants of heat sources there is a similar temperature difference of 4 \({}^{\circ}\)C for the inner ring and 4-6 \({}^{\circ}\)C for the outer ring, except that for the inner ring there is Tir2 > Tir1, and for the outer ring Tor2 < Tor1.
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 2 Functions of Heat Transfer and Mechanical Performances  
### Selected Model  
The schematic of cooling arrangement in a turbine blade is shown in Figure 1. In regions \"1\" and \"2\" are set some traditional internal cooling structures that could improve cooling efficiency and protect the blade from high temperature; however, these structures scarcely concern both heat transfer and mechanical performances. Herein, considering better performances of heat transfer and mechanics, LS was set at thin walls of the blade. Some new internal cooling channels would be established at the thin walls of the blade and LS was placed upon these channels as a component of cooling structure. PLS and XLS used herein are shown in Figure 2. Boundary conditions of LS were given as follows:  
In both PLS and XLS, the height of the cooling channel (\(H\)) was considered as a constant in order to simplify optimization calculation. The left and right surfaces were velocity inlet and pressure outlet, respectively. The bottom wall was applied with a constant  
Figure 1: Schematic of cooling arrangement in a turbine blade.  
heat flux which corresponded to the outer heat source surface of the thin wall of the blade, and was called target surface. Other surfaces of PLS and XLS were adiabatic. Fundamental material of PLS and XLS was nickel-base superalloy IN718 with the density of 8240 kg/m\({}^{3}\), the Young's modulus of 199.9 GPa, and the Poisson's ratio of 0.3 (Table 1).  
The size of the cooling channel was 80 \(\times\) 80 \(\times\) 40 mm\({}^{3}\). Diameter (\(D\)) and inclination angle (\(\omega\)) of optimal LS were geometric variables. According to the study shown in reference [36], when \(\omega\) of PLS was selected as 45\({}^{\circ}\), the heat transfer performance and mechanical properties of cooling structure were relatively optimum. Therefore, parameters of nonoptimal PLS and XLS (initial LS) were selected as \(D\) = 2 mm and \(\omega\) = 45\({}^{\circ}\), as shown in Table 1.
###### Abstract  
Realizing thin-film acoustic wave resonators presents many design and fabrication challenges. Actual material specifications always differ from nominal material properties employed in simulations, as they depend on the deposition technique and parameters used and on equipment type and status. Moreover, each deposition process introduces a degree of uncertainty regarding the thicknesses of the layers. All these factors have a substantial impact on the resonance frequency, which often differs from the designed value. This work details the design and fabrication of an aluminum nitride (AlN)-based thin-Film Bulk Acoustic wave Resonator (FBAR) showing one of the highest products of Q-factor and electromechanical coupling of 6895. The design process is based on an innovative, fast, and scalable design and fabrication approach that considers fabrication tolerances. The algorithm returns very fast results on the order of seconds, and successfully estimates the resonance of a designed stack at 2.55 GHz with a very low error of 0.005 GHz (about 0.2%). The FBAR layer stack is suspended on a polymeric membrane and an innovative rapid dissolving sacrificial layer made of Lift-Off Resist (LOR). This new fabrication protocol obtains resonators with an electromechanical coupling factor of 4.7% and a maximum quality factor of 1467, respectively.  
thin-film acoustic wave resonators; flexible resonators; Monte Carlo simulations; rapid prototyping +
Footnote †: journal: Applied Sciences
## 3 Validation Method for the Controller
### Parameterized Trajectory of the UUIV  
A spiral trajectory was selected to be able to control all DOFs of the BlueROV2 (\(x,y,z,\varphi,\psi\)) while moving simultaneously at surge, sway, heave, and yaw. The desired positions \(\eta_{d}\) and velocities \(\dot{\eta}_{\rm d}\) are given by the following equations  
\[\eta_{d}=(x_{d},y_{d},z_{d},\varphi_{d},\dot{\psi}_{d}), \tag{26}\]  
where  
\[x_{d}=x_{i}+Rcos(\omega t),\]  
\[y_{d}=y_{i}+R\ sin(\omega t),\]  
\[z_{d}=z_{i}+(z_{f}-z_{i})\ \frac{t}{t_{s}},\]  
\[\varphi_{d}=0,\ \text{and}\]  
\[\dot{\psi}_{d}=\ \dot{\psi}_{i}+\omega t,\]  
\[\dot{\eta}_{d}=\Big{(}\dot{x}_{d},\dot{y}_{d},\dot{z}_{d},\dot{\varphi}_{d}, \dot{\psi}_{d}\Big{)}, \tag{27}\]  
where  
\[\dot{x}_{d}=-\ \omega\ Rsin(\omega t),\]  
\[\dot{y}_{d}=\ \omega\ R\ cos(\omega t),\]  
\[\dot{z}_{d}=\ \frac{(z_{f}-z_{i})}{t_{s}},\]  
\[\dot{\varphi}_{d}=0,\ \text{and}\]  
Figure 3: Model-free high-order Sliding Mode Control (SMC) with Time Base Generator (TBG) scheme.  
where \(R\) is the radius for the spiral trajectory, \(\omega\) is a constant, \((x_{i},y_{i})\) is the center of the circumference of the spiral, \(z_{i}\) is the initial depth for the trajectory, \(z_{f}\) is the final depth of the trajectory, \(\psi_{i}\) is the initial yaw, and \(t_{s}\) is the simulation time.  
The desired spiral trajectory with \(R=1\) meter, \(\omega=\frac{\pi}{6}\frac{\pi}{8}\), \((x_{i},y_{i})=(0,0)\), \(z_{i}=0\), \(z_{f}=10\) meters, and a simulation time \(t_{s}=30\) s is plotted in Figure 4.
## 3 Methodology
### Feature Extraction and Selection
#### 3.2.9 Waveform Length Feature  
The Waveform Length (WL) features are mainly used in ECG signals to measure the complexity of a signal. These are used to detect human physical activities using wearable inertial sensors. Additionally, they efficiently estimate the complexity of each activity signal by taking the negative of the current sample from the last sample and summing them. These features give the amplitude, frequency, and time information. Its formula is:  
\[Wave_{(form)}(t)=\sum_{n=0}^{N}\left|f_{i}^{t}-f_{i-1}^{t}\right|, \tag{7}\]  
where \(f_{i}\) is the feature vector of the current sample, \(f_{i-1}\) is the past sample, and \(N\) is the total number of samples in the current frame.