esa-sceva/satcom-chunk-collection huggingface dataset:
# Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach
## 4 Experiments and Discussions
### Simulated Speckle Image Experiments  
#### 4.2.1 Simulated Speckle Image Generation  
A computerized method for generating speckle images [16; 17] is used to randomly generate the simulated speckle images. The simulated speckle function is defined as follows:  
\[\left\{\begin{array}{l}I_{1}(x,y)=\sum\limits_{k=1}^{n}I_{0}e^{-\dfrac{{{(x-x _{k})}^{2}+{(y-y_{k})}^{2}}}{{R^{2}}}}\\ I_{2}(x,y)=\sum\limits_{k=1}^{n}I_{0}e^{-\dfrac{{{(x-x_{k})}^{2}+{(y-y_{k})}^{2 }}}{{R^{2}}}}\end{array}\right. \tag{6}\]  
where \(I_{1}(x,y)\) and \(I_{2}(x,y)\) represent the gray scale of the speckle images generated before and after the deformation, respectively. \(I_{0}\) is the light intensity at the center of the speckle, \(n\) is the number of speckles, and \(R\) is the radius of the speckle. \((x_{k},y_{k})\) and \((x_{k}{{}^{\prime}},y_{k}{{}^{\prime}})\) are the spot center position of the speckle pattern before and after the deformation, respectively.  
The stretching, compressing, translating, and rotating of a speckle image can be simulated using the following equation:  
\[\left\{\begin{array}{l}{{x_{k}}^{\prime}}=x_{k}+u_{0}+x_{k}u_{x}+y_{k}u_{y} \\ {{y_{k}}^{\prime}}=y_{k}+v_{0}+x_{k}v_{x}+y_{k}v_{y}\end{array}\right. \tag{7}\]  
where \(u_{0}\), \(v_{0}\) is the displacement along the x and y direction; \(u_{x}\), \(u_{y}\) are the displacement gradients in the \(x\) direction, \(v_{x}\), \(v_{y}\) are displacement gradients in the \(y\) direction.  
The simulated speckle images were generated with \(I_{0}\) = 80, \(n\) = 2000, ROI = 512 pixels \(\times\) 512 pixels, \(R\) = 2 pixels, and a sample which is shown in Figure 5.  
The dataset generation methods are as follows:  
1. Translation Dataset Generation Method: First, speckle images are generated using the method described earlier, and one of them is randomly selected as the reference image. Next, the selected speckle image is translated to the right along the horizontal direction \(x\) in steps of 0.01 pixels for a total of 100 steps, generating 100 target images. Finally, using the square dataset expansion method described in Section 4.3.1, the translation dataset is expanded, resulting in a total of 80,200 speckle images.
2. Stretching Dataset Generation Method: First, speckle images are generated using the method described earlier, and 50 of them are randomly selected as reference images. Next, the selected speckle images are stretched in the horizontal direction \(x\) with \(u_{x}=0.001\)  
Figure 5: Simulated speckle image.  
and \(u_{y}=0.01\) using Equation (7), and the stretched speckle images are used as target images. Finally, using the square dataset expansion method described in Section 4.3.1, the stretching dataset is expanded, resulting in a total of 20,100 speckle images.
3. Experimental Dataset Composition: The datasets obtained from the above two steps are combined to form the complete experimental dataset. Of the sample data, 80% are used for training, and the remaining 20% are used for testing.
## 3 Results  
Figure 1 depicts an example of the HR data collected from all three devices in the whole test. There was not one single case where the chest strap underperformed. No visible artifacts nor sudden reading interruptions were identified. Thus, the accuracy of the HR data from the chest strap was assumed. The test for differences between the male and female participants revealed nonsignificant outcomes for both comparisons (for HR\({}_{\text{chest}}\) vs. HR\({}_{\text{temple}}\) t\({}_{\text{3319}}\)) = 1.92, \(p\) = 0.055 and for HR\({}_{\text{chest}}\) vs. HR\({}_{\text{watch}}\) t\({}_{\text{2796}}\)) = 1.25, \(p\) = 0.21).  
Looking at the HR\({}_{\text{chest}}\) vs. HR\({}_{\text{temple}}\) comparison, a strong correlation was denoted (R\({}^{2}\) = 0.85) when analyzing the whole protocol (Table 2). Stepwise, it varied (0.37 < R\({}^{2}\) > 0.93). For the ICC, the first step had a moderate reliability (ICC = 0.71, 95% CI from 0.53 to 0.81), whilst all others showed an excellent reliability (ICC \(\pm\) 0.94, 95% CI from 0.91 to 0.99). The Bland-Altman plots (Figure 2) revealed that more than 80% of the values fell within the 95% CI boundaries for all situations (separate steps and whole test), which is an indicator of a valid agreement.  
Figure 1: Example of an HR–time profile measured by the chest strap (black dots), wristwatch (gray dots), and optical sensor in the temple (white dots) throughout the entire test.  
Figure 2: Bland–Altman plots of HR\({}_{\rm chest}\) and HR\({}_{\rm temple}\) (**A**) and HR\({}_{\rm chest}\) and HR\({}_{\rm watch}\) (**B**) for the first (**1**), second (**2**), and third (**3**) 30 s periods, the recovery period (**4**), and the whole test (**5**). Y axes represent the difference between measurements and X axes the mean values. Limits of agreement were computed as the mean difference \(\pm\) 1.96 SD. ULoA: Upper limit of agreement; LLoA: Lower limit of agreement. The red lines represent the manually set LoA of \(-\)10 and 10 b-min\({}^{-1}\) mean difference.  
Regarding the comparison of the measurement between HR\({}_{\text{chest}}\) and HR\({}_{\text{watch}}\), as described in Table 2, a moderate correlation was found when looking at the whole test (R\({}^{2}\) = 0.23). However, stepwise, all results revealed a weak correlation between the measuring devices (0.01 \(\leq\) R\({}^{2}\)\(\leq\) 0.06). A poor measurement consistency was found in all analyses (0.03 \(\leq\) ICC \(\leq\) 0.42, 95% CI from \(-\)0.04 to 0.68). Again, all Bland-Altman (Figure 2) plots indicated a valid agreement between the methods, as more than 80% of the values fell within the 95% CI boundaries for all analyses.
## 3 Discrete Formulation of Topology Optimization Problem Statements and Sensitivities
### Compliance Functional  
The compliance can be defined as \(C(\rho)=F\ u(\rho)\), and its sensitivity can be determined as follows [97; 98; 99]:  
\[\frac{\partial C}{\partial\rho_{e}}=F\frac{\partial u}{\partial\rho_{e}}; \tag{43}\]  
since \(u(\rho)=K^{-1}(\rho)F\) and \(\frac{\partial u}{\partial\rho_{e}}=-K^{-1}\frac{\partial K}{\partial\rho_{e} }K^{-1}F\), Equation (43) becomes:  
\[\frac{\partial C}{\partial\rho_{e}}=-FK^{-1}\frac{\partial K}{\partial\rho_{e} }K^{-1}F=-u\frac{\partial K}{\partial\rho}u. \tag{44}\]Considering that \(K=A\int_{\Omega_{e}}B_{a}^{T}C\mathbb{B}_{a}\ dx\), with \(A\) being the assembly operator, and that \(\rho\) is constant in \(\Omega_{e}\), \(\frac{\partial K}{\partial\rho}\) can be rewritten as follows:  
\[\frac{\partial K}{\partial\rho}=\int_{\Omega_{e}}B_{a}^{T}C^{\prime}B_{a}\ dx=\int_{ \Omega_{e}}B_{a}^{T}P^{\rho-1}\mathbb{C}_{0}B_{a}\ dx=\int_{\Omega_{e}}\frac{P }{\rho}B_{a}^{T}CB_{a}\ dx=\frac{P}{\rho}K. \tag{45}\]  
Introducing Equation (45) into (44) leads to:  
\[\frac{\partial C}{\partial\rho_{e}}=-\frac{P}{\rho}\rho^{p}u_{e}^{T}K_{0}u_{e}= -P\frac{E_{e}}{\rho} \tag{46}\]  
where \(u_{e}\) and \(K_{0}\) are the elemental displacement vector and stiffness matrix of a fully solid element (i.e., \(\rho=1.0\)). The term \(\rho^{p}u_{e}^{T}K_{0}u_{e}\) is the strain energy (\(E_{e}\)), missing only the \(\frac{1}{2}\) constant. However, because this constant is applied to all elements, it can be neglected, and set the term \(\rho^{p}u_{e}^{T}K_{0}u_{e}\) equal to the strain energy (\(E_{e}\)) automatically calculated in ABAQUS(r).  
In this particular case, the first three steps of the generic optimization process can be simplified and rewritten as follows:  
* Find \(u\) such that: \(K(\rho)u=F(\rho)\). This procedure can be performed using an ABAQUS(r) FEA;
* Take \(\lambda=-u\), since the problem is self-adjoint;
* Compute: \(\frac{\partial C}{\partial\rho_{e}}=-P\frac{E_{e}}{\rho}\). Note that \(E_{e}\) can be obtained from the ABAQUS(r) FEA executed in the first step.  
This information is included to allow an easier understanding of the code implementation and its correlation with the formal mathematical formulation.
# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 4 Constrained MDP
### Cost and Constraint Functions  
We define here the cost function to minimize OPEX of the service provider. Since \(\rho\) is the unit cost for using one resource unit during the contract time, the total cost for the services can be calculated as \(\rho\sum\limits_{i}A_{i}\), which can be considered as the cost function \(r(S,A)\). That is, \(r(S,A)=\rho\sum\limits_{i}A_{i}\).  
We define the constraint function \(c(S,A)\) to maintain the average response time of the services below their requirements. Basically, the average response time of the service \(i\) is proportional to the queue length of the service \(i\). That is, the queue length of the service \(i\) can represent the response time of the service \(i\). Therefore, the constraint function \(c_{i}(S,A)\) for the service \(i\) can be defined as \(c_{i}(S,A)\)=\(Q_{i}\).
## 6 Evaluation
### Overall Evaluation  
The results described in Sections 6.1 to 6.4 are presented from different perspectives in Figures 13-15. The battery consumption rate of victim terminals and the storage usage of carrier terminals are shown in Figure 13. In the existing methods, even if the batteries of victim terminals are fully charged when a disaster occurred, they run out within one hour, rendering these terminals unusable. Both _Epidemic Routing_ and _Spray and Wait_ take up considerable storage capacities of carrier terminals. The proposed method is free from these problems. Thus, the objective shown in Figure 3 is achieved.  
Figure 14: Delivery rate including bundles kept and the number of bundles generated.  
Figure 12: Average delivery time.  
Figure 13: Battery consumption rate of victim terminals and the storage usage of carrier terminals in each method.  
The delivery rate including bundles kept and the number of bundles generated in the network are shown in Figure 14. The delivery rate including bundles kept of the existing methods is lower than that of the proposed method in spite of the fact that the existing methods generate a larger number of bundles. Thus, the proposed method is superior to the existing methods in terms of the delivery rate including bundles kept and the number of bundles generated. Again, the objective shown in Figure 3 is achieved.  
The battery consumption rate of victim terminals and delivery time are shown in Figure 15. As was mentioned above, the proposed method is superior to _Epidemic Routing_ or _Spray and Wait_ in terms of battery consumption, storage usage, the number of bundles generated, and delivery rate with bundles kept. However, it has a problem with delivery time, which is more varied and is often longer than the other methods. This is an area where the proposed method has room for improvement.
## 2 Background
By solving the diffusion approximation for a highly scattering medium (\(\mu_{a}\)\({}^{n}\)\({}_{s}\)), the value of \(\frac{\partial A}{\partial\rho}\) is related to the relative absorption coefficient of the specific wavelength as follows:  
\[K*\mu_{a}(\lambda_{\text{j}})=\frac{1}{3*\left(1-h*\lambda_{\text{j}}\right)}* \left[In*\left(10*\frac{\partial\text{A}(\lambda_{\text{j}})}{\partial\rho} \right)-\frac{2}{\rho}\right]^{2} \tag{3}\]  
Equation (3): Unknown constant for scattering in the near-infrared region (\(K\)), absorption coefficient (\(\mu_{a}\)), specific wavelength (\(\lambda_{\text{j}}\)), normalized slope of \(\mu_{s}\) along \(\lambda\) (h), change of light attenuation along the photodetectors' distance (\(\partial A/\partial\rho\)), and mean emitter-detectors distance (\(\rho\)). Where \(h\) is invariable among tissues and subjects, \(h\) = 6.3 \(\times\) 10\({}^{-4}\) (mm\({}^{-1}\)/nm) is used [20]. Furthermore, it is possible to calculate the relative concentrations of both chromophores by solving the following equations system for at least two wavelengths(770 nm and 880 nm). Where \(\varepsilon_{ij}\) are the extinction coefficients of \(i=[HbO_{2}]\), \([HHb]\) and \(j=\lambda 1\), \(\lambda 2\). \[ \begin{bmatrix}K*HbO_{2}\\ K*HHb\end{bmatrix} = \begin{bmatrix}\varepsilon_{ij}\end{bmatrix} ^{-1}* \begin{bmatrix} K*\mu_{a}(\lambda_{1})\\ K*\mu_{a}(\lambda_{2})\end{bmatrix}  \tag{4}\]  
Equation (4): Unknown constant for scattering in the near-infrared region (\(K\)), oxyhemoglobin concentration ([ \(HbO_{2}\)]), deoxyhemoglobin concentration ([ \(HHb\)]), absorption coefficient (\(\mu_{a}\)), and specific wavelength (\(\lambda\)). These concentrations are used to find the total oxygenation index (\(TOI\)). \[TOI=\frac{K*[HbO_{2}]}{K*[HbO_{2}]+K*[HHb]} \tag{5}\]  
Equation (5): Total oxygenation index (\(TOI\)), unknown constant for scattering in the near-infrared region (\(K\)), oxyhemoglobin concentration ([ \(HbO_{2}\)]), and deoxyhemoglobin concentration ([ \(HHb\)]).
## 4 Experimental Results and Analysis  
### Experimental Preparation and Experimental Steps  
The vehicle detection experiment used the UA-DETRAC dataset [26], which consists of over 140,000 frames with annotations of more than 8000 vehicle objects in Beijing and Tianjin, China. The dataset covers diverse traffic scenarios including highways and road intersections, and vehicle categories are classified into two types: buses and cars. The weather conditions in the dataset include sunny, cloudy, rainy, and night conditions, making it suitable for simulating various traffic scenarios for vehicle detection research.  
A self-built test dataset for vehicle detection was created by collecting video data at the overpass in the east section of Xi'an's Second Ring Road East, which mimics the  
Figure 9: Partial comparison chart of CDnet dataset.  
surveillance video shooting perspective. The dataset comprises of 1200 images and includes sunny, cloudy, and rainy weather conditions. The vehicle categories are classified into cars and buses, and the labeling was performed using Labellmg. XML labeling files were generated to show the category and corresponding positions of the vehicle targets, followed by the normalization of the vehicle coordinates into txt files, with the car and bus categories represented by numbers 0 and 1, respectively.  
Experimental platform: The network model training and model testing were performed using the cloud platform AUTODL, 7-core Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40 GHz CPU, 12 G 3060 RTX GPU, Pytorch1.9.0, Python3.8, CUDA11.1, and the network training parameters were configured as shown in Table 1.  
The experimental steps are as follows:  
1. To improve the analysis of vehicle contours via the network, vehicle feature extraction is conducted on the CDnet dataset using a moving vehicle target extraction method.
2. The annotated images and self-built datasets, which have undergone vehicle profile feature extraction, are labeled with.xml files that are converted into.txt files to augment the UA-DETRAC dataset.
3. A lightweight version of the YOLOv5 model was built and the hyperparameters were tuned for training. The network was trained on the UA-DETRAC and self-built datasets using transfer learning, and the performance was evaluated using mean average precision (mAP) and frame per second (FPS).
4. Visualization experiments were conducted on two datasets: the public UA-DETRAC dataset and a self-built dataset.
[PERSON]  
4 Department of Cancer Preventive Material Development, College of Korean Medicine, Graduate School, Kyung Hee University, 26, Kyungheedae-ro, Dongdamun-gu, Seoul 02447, Korea; [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (J-H.L.L. NAFLD reflects various liver diseases, ranging from steatosis to non-alcoholic steatohepatitis, which may advance to fibrosis, cirrhosis, and liver cancer. Fructose intake contributes to the synthesis of fats by activating lipogenesis and promoting de novo lipogenesis (DNL), leading to hepatic lipid accumulation [5; 6; 7]. Moreover, it causes hypertriglyceridemia, a condition in which TG levels in the blood are elevated (>200 mg/dL), by directly stimulating hepatic very-low-density lipoprotein (VLDL)-TG secretion [8]. Hypertriglyceridemia is a common clinical condition: it affects 15-20% of the adult population. Previous studies have reported that elevated fasting and non-fasting hypertriglyceridemia or VLDL cholesterol levels are related to a high risk of coronary heart disease, even after adjustment for high-density lipoprotein (HDL) cholesterol concentrations [9; 10]. High fructose intake promotes DNL by driving key transcription factors, such as carbohydrate response element-binding protein (ChREBP) and sterol regulatory element-binding protein 1 ( SREBP-1), which regulate their key target enzymes for lipid synthesis--acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS) [11; 12]. SREBPs have three isoforms (SREBP-1a, SREBP-1c, and SREBP-2) that regulate lipid homeostasis in hepatocytes. SREBP-2 mainly activates genes associated with cholesterol biosynthesis, while SREBP-1 triggers genes involved in fatty acid and TG synthesis preferentially. Hepatic and plasma TG accumulation is fully involved in transcriptional activity and SREBP-1c levels [13]. ChREBP (or MLXIPL) is an adept transcriptional regulator of glycolytic and lipogenic genes; carbohydrate metabolites activate it in key metabolic tissues, including the liver [14]. ChREBP plays important roles in regulating DNL, lipid composition, and plasma TG levels [15]. _Pinus koniaensis_, commonly known as Korean nut pine, grows in the wild in China, Japan, Korea, and eastern Russia. _Pinus koniaensis_ leaf extract has been reported to contain essential oil. _Pinus koniaensis_ leaves have been reported to possess anti-cancer [16], anti-hyperlipidemic, anti-obesity [17], and anti-diabetic properties [18]. In this study, we purposed to inspect the effect of the ethanol extract of _P. koniaensis_ needles (EPK) on mitigating hypertriglyceridemia and NAFLD in fructose (Fr)-induced cells and rat models.
# Decentralized Consensus Protocols on \(SO(4)^{N}\) and \(Tso(4)^{N}\) with Reshaping
## 2 Background
### Properties of \(So(4)\)
\begin{bmatrix}J_{2}&0_{2}\\ 0_{2}&0_{2}\end{bmatrix} +\theta_{2} \begin{bmatrix}0_{2}&0_{2}\\ 0_{2}&J_{2}\end{bmatrix} ,\quad J_{2}= \begin{bmatrix}0&-1\\ 1&0\end{bmatrix} . \tag{15}\]  
The characteristic equation for matrix \(S\) in Equation (15) is \(\text{det}(S-\lambda I_{4})=\lambda^{4}+(\theta_{1}^{2}+\theta_{2}^{2}) \lambda^{2}+\theta_{1}^{2}\theta_{2}^{2}=0\). Comparing with Equation (14), the two PRAs are solved for as  
\[\theta_{1,2}=\sqrt{\frac{\text{tr}_{2}(S)\pm\sqrt{\text{tr}_{2}^{2}(S)-4\det( S)}}{2}}=\sqrt{\frac{a^{T}a+b^{T}b\pm\sqrt{(a^{T}a+b^{T}b)^{2}-4(a^{T}b)^{2}}}{2}} \tag{16}\]  
In the case of a simple rotation where \(\theta=\theta_{1}\), \(\theta_{2}=0\), we have \(\theta=\sqrt{\text{tr}_{2}(S)}=\sqrt{a^{T}a+b^{T}b}\). For an isoclinic rotation where \(\theta_{1}=\theta_{2}=\theta\
eq 0\), we have \(\theta=\sqrt{\text{tr}_{2}(S)/2}=\sqrt{a^{T}b}\). The challenge of representing the angular rate in four dimensions is discussed in [27]. The ability to represent the angular rate as a vector is unique to three dimensions. For four dimensions and above, the angular rate must be represented by a dyadic or tensor of the second rank. This becomes apparent when defining the four-dimension rotation matrix kinematics. The kinematic differential equation for \(R\in SO(4)\) is [14]  
\[\dot{R}=R\Omega \tag{17}\]  
where  
\[\Omega= \begin{bmatrix}\omega_{1}\\ \omega_{2}\end{bmatrix} ^{\wedge}= \begin{bmatrix}\omega_{1}^{\times}&\omega_{2} \\ -\omega_{2}^{T}&0\end{bmatrix} \in\mathfrak{so}(4) \tag{18}\]and \(\omega_{1},\omega_{2}\in\mathbb{R}^{3}\). Note that, in ref. [14], the kinematics are presented as right-invariant, or \(\dot{R}=\Omega R\), whereas this paper will maintain left-invariant kinematics. The dyadic \(\Omega\) is a representation of the angular rate in four dimensions. Thus, Equation (17) corresponds to the kinematics on \(SO(3)\), namely \(\dot{R}=R\omega^{\times}\), although \((\omega_{1}^{T},\omega_{2}^{T})^{T}\) is not a physical angular velocity vector since the vector representation of the angular rate is unique to three dimensions, as emphasized in [27].
## 2 Results  
### O. vulgaris terpenoids are Modulated by S. littoralis Herbivory  
The volatile profile of _O. vulgare_ undamaged leaves is characterized by the presence of several monoterepenes and sesquiterpenes (Table 1). In general, herbivory increased the total terpene content of _O. vulgare_ leaves; however, the qualitative composition of terpenoids was not changed (Table 1). In particular, herbivore wounded (HW) _O. vulgare_ plants showed a significant increase of two green leaf volatiles, (_E_)-2-hexenal and (_Z_)-3-hexenal, with respect to control undamaged plants. With regards to monoterepenes, HW significantly increased the content of \(\alpha\)-terpinene, limonene, \(\beta\)-phellandrene, _trans_-sabinene hydrate, _cis_-sabinene hydrate, linalool, linalool oxide, \(\alpha\)-terpineol, _trans_-dihydrocrvone, _cis_-dihydrocarvone, carvacrol methyl ether, linalyl acetate and thymol, with respect to controls. No significant difference was found for the remaining monoterepenes, including the major compound carvacrol (Table 1). Considering the sesquiterpenes, herbivory significantly increased the content of almost all identified compounds, with the exception of \(\alpha\)-copaene, \(\alpha\)-humulene and _trans_-farnesol (Table 1).  
We then assessed the expression of some genes involved in _O. vulgare_ terpenoid biosynthesis. Upon herbivory, a strong upregulation was found for _O. vulgare CYP1D180_, which was followed by a 4-fold upregulation of _GPPS_ and an approximate 2-fold upregulation of _DXS. TPS2_ showed minor upregulation after HW. _CYP71D178_, _CYP71D179_ and _CYP1D181_ were slightly downregulated by _S. littoralis_ feeding activity (Figure 1).  
Figure 1: Levels of gene expression in _Origanum vulgare_ leaves upon _Spodoptera littoralis_ herbivory. Data are expressed as fold change (herbivory vs. control). _CYP71D180_ = one of the 4 cytochrome P450 isoforms possibly involved in carvacrol biosynthesis, isolated from _O. vulgare_; _TPS2_: \(\gamma\)-terpinene synthase, isolated from _O. vulgare_ and involved in the production of several monoterepenes, including the potential precursors of carvacrol; _CYP71D178_ and _CYP71D179_ = cytochrome P450 isoforms possibly involved in carvacrol biosynthesis, isolated from _O. vulgare_; _DXS_: Deoxyxylulose phosphate synthase; _GPPS_: Geranyl diphosphate synthase. Three technical replicates were run for each biological replicate. Metric bars indicate standard deviation.