esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Chemometric Analyses  
Univariate statistical analyses were implemented via the Student's \(t\)-test to identify VOCs differentially expressed between the sample classes of interest. Two approaches were undertaken to screen for VOCs that were potentially useful for monitoring the efficacy of pitavastatin treatment at high doses. For both approaches, only VOCs that were identified to have a \(p\)-value \(<\) 0.05 for all weeks of cancer and control or between cancer week 3 and control were included for analysis. Approach 1 screened for differences between treatment and cancer in a time-dependent fashion through four comparisons: pitavastatin at high doses (pita high, PH, 8 mg/kg body weight) week 1 vs. cancer week 1, PH week 2 vs. cancer week 2, PH week 3 vs. cancer week 3, and all PH (weeks 1-3) vs. cancer (weeks 1-3). Approach 2 screened for differences relative to cancer week 3 through four comparisons: control vs. cancer week 3, PH week 1 vs. cancer week 3, PH week 2 vs. cancer week 3, and PH week 3 vs. cancer week 3. These comparisons are useful, as tumor-bearing mice at this point in time (cancer week 3) should show the most significant differences relative to healthy controls. For both approaches, VOCs that were significant for multiple comparisons (at least two of those listed above) were identified and further utilized. Pitavastatin at low doses (pita low, PL, 4 mg/kg body weight) was not used to probe VOC differences initially, as this treatment did not show antitumor capabilities [29]. \(P\)-values were adjusted using the Benjamini-Hochberg procedure to account for false discovery rates [36].  
Volcano plots were generated by plotting statistical significance against FC values to visualize the difference between PH vs. cancer and PL vs. cancer. Hierarchical heatmaps were generated for VOCs that were identified through approach 1. Multivariate chemometric analyses were also carried out to observe global data patterns and build classification models. PCA was applied to VOCs with a \(p\)-value \(<\) 0.05 across two or more comparisons for both approach 1 and approach 2 to visualize the highest amount of variation in the sample data with relatively low dimensions. Forward feature selection coupled with LDA was then applied to VOCs highlighted through approach 1 and approach 2 to build classification models and test the pitavastatin-treated samples. For the VOCs identified using approach 1, LDA was initially trained to classify cancer weeks 1-3 from healthy control samples. Samples belonging to PH weeks 1-3 and PL weeks 1-3 were independently tested using this classification model. On the other hand, for VOCs that were significant for approach 2, LDA was trained to classify cancer week 3 from control samples. Then, samples from cancer weeks 1-2, PH weeks 1-3, and PL weeks 1-3 were independently tested using this model of VOCs. Two-tailed Student's _t_-tests were utilized to demonstrate significant differences in the multivariate output (both PCA and LDA) between the sample classes of interest (control, cancer weeks 1-3, PH weeks 1-3 and PL weeks 1-3).
## 2 Materials and Methods
### Animal Models  
Fifty-eight male Sprague-Dawley rats (6 weeks old; 150-220 g), provided by Orientbio Inc. (Gyeonggi-do, Seongnam, Korea), were cared for in a temperature-controlled room (12 h light/dark cycle) and had free access to food and water. After one week of acclimation, eight rats were randomly assigned to the normal diet (N) group and given Teklad Certified Irradiated Global 18% Protein Rodent Diet 2918C (Envigo RMS Inc., Madison, WI, USA). To induce hypertriglyceridemia among the 50 rats that were randomly assigned to the high-fructose diet (HFrD) group, they were given rodent diets (D02022704) with 10 kcal% fat and 60% fructose (Research Diets Inc., New Brunswick, NJ, USA) for two weeks, before undergoing drug treatment. Forty rats that exhibited hypertriglyceridemia were chosen from the 50 rats. They were then randomly divided into five groups with eight rats each: HFrD control, EPK 60, EPK 125, EPK 250, and omega-3. For four weeks, the rats in all groups were continuously fed with HFrD; EPK (60, 125, and 250 mg/kg) and omega-3 (500 mg/kg)were administered by oral gavage once a day to the designated groups (Table 1). Their body weights were determined once a week. In addition, TG, total cholesterol (TC), low-density lipoprotein (LDL), and HDL cholesterol levels in the serum were measured by biochemical analysis once every two weeks.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 2 Materials and Methods
### Networking Protocol and Hardware Selection
#### 2.1.4 Inertial Measurement Unit (IMU)  
The three digital inertial measurement units that are considered in this work along with their primary characteristics are listed in Table 4. Each unit contained a three-axis accelerometer and gyroscope. The MPU-9250 also featured a magnetometer making it a nine-axis device. Devices that did not contain a gyroscope were not considered as it was unwieldy to use an accelerometer alone to determine angular rotation unless it was on the axis of rotation, which in this case was not feasible. The two MPU devices featured an internal Digital Motion Processor(tm) (DMP) capable of calculating quaternions which made them significantly more advanced than the ST LSM9 DS1 as quaternions can be used to calculate the Euler angles (pitch, roll, and yaw) that can be used to determine the orientation of an object, in this application, a bell wheel in space. Both MPU devices had identical sensitivity and DMP update rate, and supported the same communication protocols. The nine-axis MPU-9250 was selected over the MPU-6050 because the latter was a six-axis device that was not recommended for new designs as it would soon be discontinued.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multirow{2}{*}{\begin{tabular}{c} **Header Size** \\ **(Byte)** \\ \end{tabular} } & \multirow{2}{*}{**Speed**} & \multirow{2}{*}{\begin{tabular}{c} **Flow** \\ **Control** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Error** \\ **Checking** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Error** \\ **Recovery** \\ \end{tabular} } \\ \hline UDP & 8 & High & No & Yes & No \\ \hline TCP & 20 & Low & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of communication protocols [18]. (Flow control is the ability to delay packet transmission to go back and request that a dropped packet is resent.)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Ease of Manufacture** & **Area (cm2)** & **Documentation Quality \({}^{*}\)** \\ \hline Requirements & Straightforward & \textless{}50 & Good \\ \hline MKR1000 & Straightforward & 16.25 & Good \\ \hline Pi Zero W & Difficult & 19.50 & Poor \\ \hline ESP8266 Thing & Straightforward & 14.35 & Poor \\ \hline \hline \end{tabular} \({}^{*}\) indicates the authors’ personal opinion.  
\end{table}
Table 3: Comparison of Wi-Fi microcontroller boards [19; 20; 21; 22; 23; 24; 25; 26; 27].
# Dynamic Response of Reinforced Recycled Aggregate Concrete Pavement under Impact Loading
## 3 Experimental Results and Discussion
### Damage Characteristics and Crack Patterns
With the same thickness, reinforcement ratio and concrete grade, RAC has little influence on damage characteristics and crack patterns of RCP specimens; however, the crack resistance of 7 MR is slightly lower than that of 7 MN. The number of cracks on the bottom surface decreased with the increase in the thickness of the slab. With the reduction of the thickness from 70 mm to 60 mm, multiple tightly spaced hairline cracks formed on the bottom surface, while as the thickness increased from 70 mm to 80 mm, the development of cracks was strongly limited. The crack width widened as the number of cracks decreased. These results suggest that there is an association between crack resistance and slab thickness. The crack patterns on the top surface varied with the change of the thickness of the slab. The radial cracks extending from the bottom to the top were found in the 6 MR, 7 MR, and 8 MR specimens, and the circumferential cracks around the impact area were detected only in the 8 MR specimen. These circumferential cracks with a hairline width less than 0.06 mm did not close and developed in the range of a half-circle. The circumferential cracks indicated that localized damage in the form of limited concrete penetration on the impact surface had occurred in the 8 MR specimen. The change in crack patterns was due to the stiffness of the specimen increasing as a result of the increase in the slab's thickness, and partial impact energy needing to be dissipated through local damage deformation during the impact [48,50]. Except for the cracks mentioned above, the remaining area on top surface of 8 MR specimen was nearly undamaged. Both circumferential cracks similar to those in 8 MR and radial cracks similar to those in 7 MR were found on the top surface of 7M1R specimen. In comparison with 8 MR, the circumferential cracks of 7M1R had further distributed distance, thinner width, and smaller range. The development of the radial cracks on the top surface of 7M1R was limited when compared to that of 7 MR. Based on the observed damage and crack development in tested specimens, it was found that the crack pattern was more affected by the thickness than by the reinforcement ratio. More steel reinforcement would induce a localized failure of concrete [23].
# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 3 Results
### Multiple Logistic Regression Analysis of Variables Affecting the Occurrence of Metabolic Syndrome  
Table 3 presents the results of the multiple logistic regression analysis examining HOMA-IR levels, gender, age, and DM status as predictors of MetS. Diabetes Mellitus  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **Diabetes Mellitus (\(n=624\))** & **Non-Diabetes Mellitus (\(n=5672\))** & \(p\) \\ \hline Sex & & & \\ Men & 316 (50.6) & 2479 (43.7) & \\ Women & 308 (49.4) & 3193 (56.3) & \(<\)0.001 \\ Age (years) & 65.69 \(\pm\) 10.94 & 50.44 \(\pm\) 16.83 & \(<\)0.001 \\ Waist circumference (cm) & 90.63 \(\pm\) 9.75 & 83.35 \(\pm\) 10.28 & \(<\)0.001 \\ Triglycerides (mg/dL) & 154.48 \(\pm\) 127.39 & 129.28 \(\pm\) 96.87 & \(<\)0.001 \\ HDL-cholesterol (md/dL) & 48.05 \(\pm\) 12.00 & 53.27 \(\pm\) 12.84 & \(<\)0.001 \\ Systolic blood pressure (mmHg) & 127.68 \(\pm\) 17.04 & 119.06 \(\pm\) 16.37 & \(<\)0.001 \\ Diastolic blood pressure (mmHg) & 73.37 \(\pm\) 9.76 & 75.87 \(\pm\) 9.79 & \(<\)0.001 \\ Fasting blood glucose (md/dL) & 137.91 \(\pm\) 41.52 & 97.65 \(\pm\) 16.46 & \(<\)0.001 \\ HOMA-IR (units) & 3.85 \(\pm\) 4.96 & 2.30 \(\pm\) 3.41 & \(<\)0.001 \\ Metabolic syndrome (ATP III) & & & \\ Yes & 339 (57.4) & 1364 (24.8) & \\ No & 252 (42.6) & 4139 (75.2) & \(<\)0.001 \\ \hline \end{tabular} Data are expressed as the mean \(\pm\) standard deviation or n (%); HDL-cholesterol, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; ATP III, Adult Treatment Panel III.  
\end{table}
Table 2: Comparative anthropometric, clinical, and biochemical characteristics of patients with and without Diabetes Mellitus.  
status emerged as the strongest predictor (OR = 1.914), followed by HOMA-IR (OR = 1.566), gender (OR = 1.479), and age (OR = 1.033).  
Clinically, an odds ratio (OR) of 1.566 for HOMA-IR suggests that each 1-unit increase in HOMA-IR elevates the odds of Met5 by approximately 56.6% when controlling for the other variables. Similarly, an OR of 1.033 for age indicates that for every additional year, the odds of Met5 rise by about 3.3%. These findings underscore the substantial impact of insulin resistance and age on Met5 risk.
# Joint Use of Spaceborne Microwave Sensor Data and CYGNSS Data to Observe Tropical Cyclones
These studies have shown that the assimilation of CYGNSS data can improve the prediction of TC position, intensity, and structure. Nevertheless, as shown in the latter sections of this paper, the tracks of the CYGNSS satellite are narrow and sometimes sparse, which makes it difficult to obtain the complete structure of a TC in order to fix its center and to estimate its intensity when only the CYGNSS data are used. In this paper, the SMAP radiometer was taken as an example of spaceborne microwave sensor, and its data and the CYGNSS data were used together to obtain a more complete structure of TCs. The goal of this study was to measure the surface wind speed around the TC inner core at more frequent intervals compared to the case in which the SMAP radiometer data or the CYGNSS data are employed alone to observe TCs. This paper is organized as follows. A brief introduction to the observation data employed to monitor TCs is provided in Section 2. The SMAP radiometer data and the CYGNSS data were combined to estimate the center position and maximum wind speed of TCs, and the steps used to process the observation data are described in Section 3. The results are discussed in Section 4 and finally, the main conclusions are summarized in Section 5.
## 3 Multi-Objective Optimization of the Motor Topology
### Multi-Objective Optimization and Torque Performance Comparison  
Multi-objective optimization of the rotor topology of the proposed model is performed based on the FAST algorithm [29] in modeFRONTIER. Several multi-objective optimization algorithms in modeFRONTIER are compared in reference [29]. The FAST algorithm uses Response Surface Model (RSM) to speed up the optimization process. The optimization process is faster and the optimization search is better. Therefore, in this paper, the FAST algorithm is chosen for multi-objective optimization.  
The FAST algorithm uses a self-initialization setting and limits the maximum number of iterations to 500. The optimized results are shown in Figure 12. In order to prevent the occurrence of excessive low average torque and excessive torque ripple, the frontier result in Figure 12 is selected as the optimized result through comprehensive consideration. The selection result is visible in the partially enlarged view of Figure 12. The dimensional parameters of the optimized model are shown in Table 3. Figure 13 shows the optimized model rotor single-pole topology of the model. The torques of the three models are shown in Figure 14. From Figure 14, it can be seen that the torque of the optimized model is the largest among the three models and has the least fluctuation. From Table 4, it can be seen that the optimized model improves the average torque by 25.32% and reduces the torque ripple by 76.92% compared to the conventional model. After multi-objective optimization, the torque performance of the optimized model is improved.  
Normally, in the design of the motor ontology, there is some error between the results of finite element simulation and the experimental results. References [12; 14; 30; 31] gave finite element simulation results and experimental results for the output torque errors as shown in Table 5. In Table 5, the output torque errors in all references are less than 4.5% and the average error is 2.93%. Therefore, the errors between the finite element simulation results and the experimental results are much lower in the design of the motor ontology. In this paper, the average torque improvement of the optimized model is far higher than  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Unit** & **Conv. Model** & **Prop. Model** & **Opti. Model** \\ \hline Average torque & N-m & 8.61 & 10.53 & 10.79 \\ Torque ripple & \% & 31.50 & 18.19 & 7.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of torque performance of the three models.  
Figure 12: Results from the multi-objective optimization.  
Figure 13: Optimized model rotor with single-pole topology.  
the average error compared to the conventional model. It can be inferred that the errors do not have much effect on the finite element simulation results in this paper, and that the comparison of the finite element simulation results is still reliable.
# Computational Modeling of Ultrasound C-Scan Imaging Using Transmitted Signal Peak Density
## 2 Finite Element Analysis (FEA) Simulation  
### Model Description  
The first objective of this study was to mimic the experimental analysis of [PERSON] et al. [22]. Therefore, the simulation model geometry was created by imitating the experimental sample. A gelatin phantom containing soft pepper flakes was modeled for the simulation. The pepper flake was modeled as a thin cylinder of 3 mm diameter and 50 \(\upmu\)m height. The gelatin phantom was created as a cube of 5 mm in length at each side. The 3D model geometry is shown in Figure 1. The center pepper flake was placed at 0.2 mm along the negative \(Y\)-axis from the phantom center.  
In the study of [PERSON] et al., two single-element transducers were used for the C-scan imaging, where each transducer crystal diameter was 2 mm [22]. A raster scan pattern was used while selecting the step size as half of the crystal diameter, resulting in an image resolution of 1 mm/pixel. Therefore, in this computational model, the top surface of the 3D model was divided into 25 pixels, with each pixel area having a dimension of 1 mm on each side. Thus, the image resolution became 1 mm/pixel. Similar to the experimental study, ultrasound imaging was conducted in a through-transmission or pitch-catch method. The signal was sent from the top surface and received at the bottom surface (back wall). After receiving the signal, the peak density of the frequency spectrum was measured. The peak density value was then used as the pixel value for the C-scan image reconstruction.
###### Abstract  
Mobile Augmented Reality (MAR) is designed to keep pace with high-end mobile computing and their powerful sensors. This evolution excludes users with low-end devices and network constraints. This article presents ModAR, a hybrid Android prototype that expands the MAR experience to the aforementioned target group. It combines feature-based image matching and pose estimation with fast rendering of 3D textured models. Planar objects of the real environment are used as pattern images for overlaying users' meshes or the app's default ones. Since ModAR is based on the OpenCV C++ library at Android NDK and OpenGL ES 2.0 graphics API, there are no dependencies on additional software, operating system version or model-specific hardware. The developed 3D graphics engine implements optimized vertex-data rendering with a combination of data grouping, synchronization, sub-texture compression and instancing for limited CPU/GPU resources and a single-threaded approach. It achieves up to 3\(\times\) speed-up compared to standard index rendering, and AR overlay of a 50 K vertices 3D model in less than 30 s. Several deployment scenarios on pose estimation demonstrate that the oriented FAST detector with an upper threshold of features per frame combined with the ORB descriptor yield best results in terms of robustness and efficiency, achieving a 90% reduction of image matching time compared to the time required by the AGAST detector and the BRISK descriptor, corresponding to pattern recognition accuracy of above 90% for a wide range of scale changes, regardless of any in-plane rotations and partial occlusions of the pattern.  
mobile augmented reality; pattern recognition; vertex-based rendering; geometric instance; camera pose estimation; 3D rendering; ORB; BRISK; OpenCV; OpenGL ES Article
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
###### Abstract  
Confidentiality and efficient bandwidth utilization require a combination of compression and encryption of digital images. In this paper, a new method for joint image compression and encryption based on set partitioning in hierarchical trees (SPIHT) with optimized Kd-tree and multiple chaotic maps was proposed. First, the lossless compression and encryption of the original images were performed based on integer wavelet transform (IWT) with SPIHT. Wavelet coefficients undergo diffusions and permutations before encoded through SPIHT. Second, maximum confusion, diffusion and compression of the SPIHT output were performed via the modified Kd-tree, wavelet tree and Huffman coding. Finally, the compressed output was further encrypted with varying parameter logistic maps and modified quadratic chaotic maps. The performance of the proposed technique was evaluated through compression ratio (CR) and peak-signal-to-noise ratio (PSNR), key space and histogram analyses. Moreover, this scheme passes several security tests, such as sensitivity, entropy and differential analysis tests. According to the theoretical analysis and experimental results, the proposed method is more secure and decreases the redundant information of the image more than the existing techniques for hybrid compression and encryption.  
chaotic maps; encryption; image compression; integer wavelet transform; k-dimensional tree; set partition in hierarchical trees +
Footnote †: journal: Applied Sciences
###### Abstract  
With the goal of automatic sea ice mapping during the summer sea ice melt cycle, this study involved designing a fully automatic sea ice segmentation method based on a deep learning semantic segmentation network applicable to summer SAR images, which achieved high accuracy and the fully automatic extraction of sea ice segmentation during the summer ice melt cycle by optimizing the process, improving the pixel-level semantic segmentation network, and introducing high-resolution sea ice concentration features. Firstly, a convolution-based, high-resolution sea ice concentration calculation method is proposed and was applied to the deep learning task. Secondly, the proposed DF-UHRNet network was improved upon by designing high- and low-level fusion modules, introducing an attention mechanism, and reducing the number of convolution layers and other operations, and it can effectively fuse high- and low-scale semantic features and global contextual information based on reducing the overall number of network parameters, enabling it to achieve pixel-level classification. The results show that this method meets the needs associated with the automatic mapping and high-precision classification of thin ice, one-year ice, open water, and multi-year ice and effectively reduces the model size.  
deep learning; sea ice segmentation; sea ice concentration; Sentinel-1A/B SAR Article  
17 March 2023  
3 May 2023  
Accepted: 4 May 2023  
Published: 6 May 2023