esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Imaging Algorithm Based on Modified Chirp Fourier Transform
### Azimuth Fast Compression Based on MCFT
\begin{array}{cccccccc}W^{0}&W^{0}&W^{0}&\cdots&W^{0}&\cdots&W^{0 }\\ W^{0}&W^{1+\frac{5}{2\Omega}\omega}&W^{2+\frac{5}{2\Omega}\omega+4}&\cdots&W^{( \omega-1)+\frac{5}{2\Omega}(\omega-1)^{2}}&\cdots&W^{-1-1+\frac{5}{2\Omega}( \omega-1)^{2}}\\ W^{0}&W^{2+\frac{5}{2\Omega}\omega}&W^{4+\frac{5}{2\Omega}\omega}\omega&\cdots&W^ {2(\omega-1)+\frac{5}{2\Omega}\omega 2(\omega-1)^{2}}&\cdots&W^{2(\omega-1)+\frac{5}{2 \Omega}\omega 2(\omega-1)^{2}}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ W^{0}&W^{(k-1)+\frac{5}{2\Omega}(\omega-1)}&W^{(k-1)+\frac{5}{2\Omega}(\omega -1)+4}&\cdots&W^{(k-1)(\omega-1)+\frac{5}{2\Omega}(\omega-1)(\omega-1)^{2}}& \cdots&W^{(k-1)(N-1)+\frac{5}{2\Omega}(\omega-1)(N-1)^{2}}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ W^{0}&W^{N-1+\frac{5}{2\Omega}(N-1)}&W^{2(N-1)+\frac{5}{2\Omega}(2N-1)^{2}}& \cdots&W^{(k-1)(\omega-1)+\frac{5}{2\Omega}(\omega-1)(\omega-1)^{2}}&\cdots&W ^{(N-1)(N-1)+\frac{5}{2\Omega}(\omega-1)(N-1)^{2}}\end{array} \right] \tag{22}\]  
For the matrix form shown in Equation (17), if the length of the data and the rotation ratio \(K_{\Omega\omega}\) of the target are known, the phase function \(\mathbf{W}_{FN}\) can be stored in advance, similar to the phase factor function in FFT calculation, so that fast MCFT calculation can be realized. We can further optimize the phase function. In order to avoid the influence of strong and weak scattering points on the sub-signals of the same range cell, the windowing function can be used in MCFT. Without windowing the azimuth raw data, in the reconstructed ISAR image, high sidelobes appear next to the main peaks [3]. Thus, the windowing function should be applied to reduce the sidelobes at the cost of a slightly reduced resolution. Common Hamming windows can be used for this purpose, defined by:  
\[w_{n}=0.54-0.46\cos\left(\frac{2\pi}{N-1}\right)n\qquad(n=0,1,\cdots,N-1) \tag{23}\]  
where \(w_{n}\) is the weighting coefficients of the Hamming window function and \(N\) is the length of the window, that is, the length of discrete azimuth data.
## 3 Mathematical Modelling of Ship Power Positioning Systems
### Mathematical Models of Environmental Disturbances
#### 3.5.1 Wind Disturbance Model  
Let the absolute wind speed be \(V_{T}\), the absolute wind angle be \(\psi_{T}\), the relative wind speed be \(V_{R}\), the relative wind angle be \(\psi_{R}\), \(V\) is the ship's speed, \(u_{R}\) and \(v_{R}\) are the components of the relative wind speed in the kinematic coordinate system, \(\psi\) is the bow angle, and \(\beta\) is the drift angle, and the relationship between the relative wind and the wind pressure and moments can be expressed as [28]  
\[V_{R}=V_{T}-V \tag{16}\]  
\[u_{R}=u+V_{T}\text{cos}(\psi_{T}-\psi) \tag{17}\]  
\[v_{R}=-v+V_{T}\text{sin}(\psi_{T}-\psi) \tag{18}\]  
\[V=\sqrt{u^{2}+v^{2}},\quad V_{R}=\sqrt{{u_{R}}^{2}+{v_{R}}^{2}} \tag{19}\]  
It can also be expressed as  
\[\begin{cases}V_{R}\text{cos}\psi_{R}=V_{T}\text{cos}(\psi_{T}-\psi)-V\text{ cos}\beta\\ V_{R}\text{sin}\psi_{R}=V_{T}\text{sin}(\psi_{T}-\psi)+V\text{sin}\beta\end{cases} \tag{20}\]  
\[\beta=tan^{-1}\Big{(}-\frac{v}{u}\Big{)} \tag{21}\]  
After the calculation, we obtain  
\[V_{R}^{2}=V_{T}^{2}+V^{2}+2V_{T}V\text{cos}(\psi_{T}-\beta) \tag{22}\]  
Wind disturbance dynamics are expressed as \(\omega_{wind}=[X_{wind}Y_{wind}N_{wind}]\); for the sum of  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Numeric Value** \\ \hline Ship length \(L\)/m & 69.213 m \\ Ship width \(B\)/m & 10.90 m \\ Draft \(d_{m}\)/m & 3.5 m \\ Weight load \(m\)/kg & 1500 t \\ Displacement \(\
abla\)/t & 1450 t \\ Design speed \(Vd\)/kn & 18.0 kn \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of intelligent ship.  
average wind speed and gusts:  
\[\left\{\begin{array}{ll}X_{wind}=\overline{X}_{wind}+\widetilde{X}_{wind}\\ Y_{wind}=\overline{Y}_{wind}+\widetilde{Y}_{wind}\\ N_{wind}=\overline{N}_{wind}+\widetilde{N}_{wind}\end{array}\right., \tag{23}\]  
where the average wind and moment are expressed as  
\[\left\{\begin{array}{ll}\overline{X}_{wind}=\frac{1}{2}C_{X}(\psi_{T})\rho_{ a}V_{R}^{2}A_{T}&(\text{N})\\ \overline{Y}_{wind}=\frac{1}{2}C_{Y}(\psi_{T})\rho_{a}V_{R}^{2}A_{L}&(\text{N} )\\ N_{wind}=\frac{1}{2}C_{N}(\psi_{T})\rho_{a}V_{R}^{2}A_{L}&(\text{N}\text{ m})\end{array}\right. \tag{24}\]  
In the formula, \(C_{X},\ C_{Y},\ C_{N}\) are wind force (moment) coefficients, \(\rho_{a}\) is air density (kg/m\({}^{3}\)), \(A_{T}\) is the side projected area of the ship subjected to the wind (m\({}^{2}\)), \(A_{L}\) is the side projected area of the ship subjected to the wind (m\({}^{2}\)), \(L\) is the total length of the ship (m), and \(V_{R}\) is the relative wind speed (kn).
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 3 Biochemical Effects of EGCG
### Inhibitory Effect on Matrix Metalloproteinases  
Matrix metalloproteinases (MMPs) include the family of zinc-dependent endopeptidase-dases commonly classified on the basis of their substrates and the organization of structural domains, including collagenases (MMP-1, -8, -13, and -18), gelatinases (MMP-2 and -9), stromelysins (MMP-3 and -10), matrilysins, membrane-type (MT) MMPs, and other metalloproteins. This enzyme family has the combined ability to degrade the organic components of the connective tissue matrix [53]. Generally, the structure of these enzymes includes a propeptide sequence, a hinge region or linker peptide, a hemopexin domain, and a catalytic metalloproteinase domain containing a catalytic zinc ion [142].  
Shifts in MMP expression and activity are observed during common biological processes like pregnancy and wound healing. Moreover, such changes have also been implicated in cardiovascular diseases such as atherosclerosis, aneurysms, and varicose veins, musculoskeletal disorders such as osteoarthritis and bone resorption, and various cancers [142]. Therefore, changes in the profiles of MMPs can be used as an important biomarker to track cardiovascular disease, musculoskeletal disorders, and cancer development in the human body.  
The regeneration of bones and their repair depend on many regulated mechanisms. MMP regulation plays a key role in this process. MMPs, produced by bone cells in an active state, are essential for chondrocyte formation and development processes and the viability and functionality of osteoclasts, osteoblasts, and osteocytes [143]. Particularly, MMP-9 or gelatinase-B is also a type IV collagenase. It is primarily produced by cell types such as osteoblasts, dendritic cells, fibroblasts, keratinocytes, T-cells, macrophages, and granulocytes [142; 144]. The expression of MMP-9 in osteoclasts is significantly higher than in other cell types [53]. According to [PERSON], MMP-2, MMP-9, and MMP-13 play especially significant roles in bone tissue [143]. MMP-2 appears to alter bone growth by inducing osteoclast and osteoblast activity and differentiation. Animals deficient in MMP-9 exhibited abnormal bone formation mainly during endochondral ossification.  
In an in vitro study, EGCG was shown to inhibit osteoclast formation and reduce MMP-9 expression in osteoblasts, which contributed to the suppression of bone resorption [37]. Additionally, in cultures of rat osteoclast precursor cells and mature osteoclasts, the inhibition of MMP-2 and MMP-9 enzymatic activity was noted, and along with decreased MMP-9 mRNA expression in precursor cells, it significantly impaired both the formation and differentiation of osteoclasts [53].
## 3 System Model Design
### Algorithm Complexity Analysis  
To describe the extensibility and applicability of fuzzy recognition and the decision-making system, we rigorously built the Fuzzy pattern cognition and decision model (shown as Algorithm 1) to show the time complexity of this system. Specifically speaking, in the process of determining the membership degree of each message user, \(n\) attribute features may correspond to three levels of fuzzy subsets (_low_, _medium_ and _high_) that contain \(3n\) membership degrees values, which presents the time complexity of \(O(3n)\). Additionally, the time complexity of the analytic hierarchy process [30] is \(O(n)\), because there exist \(n\) different attribute features which need to be assigned \(n\) different weight values. Ultimately, in the process of fuzzy decision-making for message routing and forwarding, source nodes and message carriers transmit data packages to their future two-hop nodes by gathering and comparing the value of \(TV\), therefore the corresponding time complexity is \(O(log_{2}n)\). Consequently, the total time complexity of fuzzy recognition and decision-making models for opportunistic social networks can be calculated by \(O(3n+n+log_{2}n)=O(n)\), which highlights a relatively low calculation consumption of this model.  
Figure 3: The fuzzy decision-making process for data transmission in opportunistic mobile social networks.  
```
0: social attributes \(X_{1},X_{2}\cdots X_{n}\) of users \(node_{i}\)
0:\(TV_{node_{i}},TV_{node_{2}}\cdots TV_{node_{n}}\)
1:Acquiring data related to attribute features of nodes;
2:Data preprocessing and feature extraction;
3:Quantifying feature information as \(Fl_{node_{x}}(i)\);
4:Defining decision function \(F(D_{node_{x}},B_{node_{x}})\);
5:for(\(i=1;i\leq n;i++\)) do
6:\(Fl_{node_{x}}(i)=|ei(X_{i})|\);
7:\(FS_{node_{x}}(Fl_{node_{x}}(i))\)= \(\{MD_{low}(Fl_{node_{x}}(i))\), \(MD_{medium}(Fl_{node_{x}}(i))\), \(MD_{high}(Fl_{node_{x}}(i))\}\);
8:endfor
9:for(\(i=1;i\leq n;i++\)) do
10: Obtain the eigenvector \(B_{(i)}=\sum_{j=1}^{n}Rb_{(i,j)}\);
11: Computing the weight value of the \(i-th\) fuzzy input \(w_{i}=\frac{B_{(i)}}{\sum_{i=1}^{n}B_{(i)}}\);
12:endfor
13:Computing the weight vector \(W=[w_{1},w_{2}\cdots w_{n}]\);
14:Checking the consistency of the weight assessment matrix \(CM\);
15:Defining the factor set \(U^{\prime}_{node_{x}}\) and the evaluation set \(V_{node_{x}}\);
16:Constructing the fuzzy relationship matrix \(R_{f}\);
17:\(B_{node_{x}}=W_{(}w_{1},w_{2}\cdots w_{n})\circ R_{f}\);
18:Calculating \(B_{node_{x}}\) based on the maximum membership principle;
19:\(TV_{node_{i}}=tanh(D_{node_{x}}+B_{node_{x}})\);
20:for(\(i=1;i\leq n;i++\)) do
21: Output \(TV_{node_{i}}\);
22:endfor
23:for(\(i=1;i\leq n;i++\)) do
24:if(\(node_{i}\_hasMessage()\)) then
25:if(\((TV_{i}>TV_{m})\bigwedge(TV_{m}>TV_{n})\)) then
26:\(node_{i}\) sends data to \(node_{m}\) and \(node_{n}\);
27:endif
28:endif
29:endfor
```  
**Algorithm 1** Fuzzy pattern cognition and decision model
# Quick Overview of Face Swap Deep Fakes
## 2 Face Swapping Process
### Evaluation Methods  
There are two evaluation methods commonly used in face swapping: objective and subjective. Objective evaluation involves quantifying the difference or correlation between the output and the target using some form of metric. The metrics used in these works are typically based on loss functions such as SSIM (structural similarity) [3], Euclidean distance for shape, pose, and expression [3; 6; 7; 8; 9; 10], or ID retrieval [6; 7; 8; 9; 10]. As a subjective evaluation, the developers do not implement additional tests. However, as a review, samples of the performance of the described many-to-many algorithms are shown in Figure 2, based on the comparison provided by the DiffFace authors [10] and our experiments.  
Based on the subjective evaluation of the results, it can be concluded that DiffFace achieved the best outcomes regarding the quality of the generated images--this is a feature of diffusion models. However, it should be noted that, currently, these are significantly slower than other models.  
When comparing the rest of the models, which are trained based on GANs, it can be observed that SimSwap provided the best results in preserving attributes. Unfortunately, this comes at the expense of accurately representing identity.  
Ghost-A achieved high-quality results due to the application of additional postprocessing actions. In terms of transferring identity, HiFiFace yielded the best results.  
In conclusion, each model has its strengths and weaknesses. DiffFace offers superior image quality but at the cost of speed. SimSwap excellently retains attributes but struggles with identity preservation. Ghost-A shows high-quality results with additional postprocessing, and HiFiFace stands out for its ability to transfer identity accurately. Future developments in face swap models could aim to combine these strengths to improve overall performance.
## 2 Materials and Methods  
### Porcine and Ovine Pericardium for Decellularization  
The porcine pericardium was obtained from a local slaughterhouse (Steinhauser s.r.o, Skalice nad Svitavou, Czech Republic). The Large White breed of pigs with a weight of approximately 100 kg and age 5-6 months were used. The fresh tissues were extracted during the slaughter process. The tissue was then transported to our laboratory in phosphate-buffered saline (PBS) with 100 mg/mL Gentamicin and 10 mg/mL Fluconazole (both LEK PHARMACEUTICAL D.D., Ljubljana, Slovenia).  
In the laboratory, the pericardial tissue was mechanically cleaned from fat and other residues. Then the tissue was cut into smaller pieces (approx. 6 \(\times\) 10 cm) to fit into the decellularization chamber described below, and frozen in a DMEM (Dulbecco's Modified Eagle Medium) medium with 10% FBS (Fetal Bovine Serum), 1% ABAM (Antibiotic Antimycotic Solution, contains 100 units penicillin, 0.1 mg streptomycin, and 0.25 \(\upmu\)g amphotericin B per ml of culture media) and 10% DMSO (Dimethyl sulfoxide). The freezing gradient was set to 1 \({}^{\circ}\)C/min up to \(-\)80 \({}^{\circ}\)C. All chemicals unless otherwise specified were from Sigma-Aldrich (Sigma-Aldrich, St. Louis, MO, USA).  
The ovine pericardium was obtained from experimental sheep (Suffolk breed, animals were used for another experimental work; these tissues were harvested fulfilling 3R). After animals were euthanized, a median sternotomy was performed and was followed by thoracotomy. The pericardium was extracted and mechanically cleaned from surrounding tissue, mainly fat. The same protocol for handling and cryopreservation, as described above, was used. Due to a limited number of experimental sheep, optimization procedures were mainly done on the porcine pericardium.
## 4 Results
### Simulation Validation  
#### 4.2.1 Simulation Platform and Scene  
The proposed strategy was tested using a platform that combines PYTHON and SUMO, running on a computer equipped with a 4.9-GHz Core i7-12700F RTX3070 8 GB LHR and 16 GB DDR5. SUMO version is 1.16.0 and Python version is 3.8 is an open-source micro road traffic simulation software that displays network and routing information through a visual interface. The control logic is implemented in PYTHON, which interfaces with SUMO in real-time through the Traci library. The road network of the scene is introduced through the Open Street Map (OSM), which can be used in SUMO, following the proportion of the actual road as much as possible. The Hong Kong-Zhuhai-Macao Bridge, the world's most up-to-date cross-sea bridge spanning the Lingdingyang Sea, was selected to evaluate the proposed strategy. Traffic on the bridge is particularly susceptible to stochastic bottlenecks caused by vehicle interactions, which can lead to a decrease in road capacity. The bridge features a three-lane freeway with a static speed limit of 100 km/h.
###### Abstract  
Impervious surface areas (ISA) are heavily influenced by urban structure and related structural features. We examined the effects of object-based impervious surface spatial pattern analysis on land surface temperature and population density in Guangzhou, China, in comparison to classic per-pixel analyses. An object-based support vector machine (SVM) and a linear spectral mixture analysis (LSMA) were integrated to estimate ISA fraction using images from the Chinese HJ-1B satellite for 2009 to 2011. The results revealed that the integrated object-based SVM-LSMA algorithm outperformed the traditional pixel-wise LSMA algorithm in classifying ISA fraction. More specifically, the object-based ISA spatial patterns extracted were more suitable than pixel-wise patterns for urban heat island (UHI) studies, in which the UHI areas (landscape surface temperature >37 \({}^{\circ}\)C) generally feature high ISA fraction values (ISA fraction >50%). In addition, the object-based spatial patterns enable us to quantify the relationship of ISA with population density (correlation coefficient >0.2 in general), with global human settlement density (correlation coefficient >0.2), and with night-time light map (correlation coefficient >0.4), and, whereas pixel-wise ISA did not yield significant correlations. These results indicate that object-based spatial patterns have a high potential for UHI detection and urbanization monitoring. Planning measures that aim to reduce the urbanization impacts and UHI intensities can be better supported.  
Keywords:impervious surface areas; object-based image analysis; land surface temperature; population density +
Footnote †: journal: Urban Sci. 2018, 2, 2; doi:10.3390/urbansci2010002
## 3 Experimental Results
### Metallic Pipe Penetrations  
Figure 6 presents the fire-resistance test results for firestop systems applied to metallic pipes. Specimens were grouped into four opening-penetrant diameter conditions; in the final (125-150 mm) condition, only the pipe diameter increased while the opening remained fixed.  
Overall, the lower thermocouples recorded the highest temperature rise, attributed to their proximity to the slab, where radiant heat arrives first. As measurements moved to the middle and upper layers, heat transmission was delayed, and the layered resistance of the  
Figure 4: Peak temperature as a function of time for nonmetallic pipe penetrations: (**a**) slab; (**b**) mounting frame; and (**c**) pipe.  
composite insulation mitigated temperature increases. The insulated metallic-pipe surface remained below 100 \({}^{\circ}\)C throughout the test.  
In the 125-150 mm condition, the increased pipe diameter reduced the available space for firestop and insulation materials, decreasing coverage of the thermal barrier. The larger cross-sectional area of the pipe also increased heat conduction, concentrating heat at the lower insulation interface. Consequently, the lower thermocouple recorded the greatest temperature rise, suggesting that, in metallic configurations, the penetrant thermal conductivity and cross-sectional area have more impact on fire-resistance performance than the opening size. This result indicates that in metallic-pipe configurations, the pipe's size combined with the insulation material are major factors impacting heat transfer. These results align with the guidelines suggested by [PERSON] et al. [6]. Furthermore, the insulated  
Figure 5: Post-test condition of nonmetallic-pipe specimens.  
Figure 6: Peak temperature as a function of time for metallic pipe penetrations: (**a**) bottom of insulation; (**b**) middle of insulation; (**c**) top of insulation; and (**d**) pipe.  
pipe wall's low temperature is attributed to both the insulation's protective effect and heat dispersion over the pipe's surface. Despite high-temperature exposure, the metallic pipe retained structural integrity without deformation, and the insulation effectively blocked radiant heat. This demonstrates that metallic pipe systems offer higher structural stability than nonmetallic ones and, when paired with an optimized insulation layer, achieve superior thermal-barrier performance. Figure 7 shows the post-test condition of the metallic-pipe specimens. The insulation remained in place without visible failure, although slight surface charring was observed in some areas near the pipe-insulation interface.
# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
## 4 RF-Based Target Localisation
### Design Concept and Methodology
Our method has the additional benefit of improving resilience to noise and sudden changes in the environment that can cause erroneous readings to occur when there are time offsets between readings. Using paired antennae in this way avoids the need to determine the radiation pattern of the antenna, as we can use the instantaneous values for mapping the RSS into a probabilistic direction. To provide sufficient resolution and directionality in a two-dimensional planar coordinate space, a minimum of two pairs should be used at a \(90^{\circ}\) offset between each pair, meaning that there should be a total of four antennae that are perpendicular to each other. Based on this design concept, the proposed angle-based differential RSS localisation approach is introduced here. Figure 3a illustrates how the two pairs of antennae with their reflectors are arranged in the \(x\) and \(y\) axes for two dimensional localisation. Referring to Figure 3a, \(y_{1}\), \(y_{2}\), \(x_{1}\) and \(x_{2}\) represent the individual RSS values received from the emitter, and the black arrow indicates the current heading direction of the robot. After calculating the differential RSS value for each axis (i.e., \(x\) and \(y\) axis), we follow the traditional approach of mapping RSS values to distance. Parameters A1 and A2 are introduced after calibrating the differential RSS with distance mapping. The relative angle, \(\theta\), and distance, \(r\), between the emitter and the robot are calculated using Equations (3) and (4), and illustrated in Figure 3b. Referring to Figure 4, the angle-based differential RSS localisation is designed to provide reasonably accurate direction and distance estimates. The estimates are further enhanced using a particle filter in order to reduce the inherent fluctuations of RSS-based estimation. The differential localisation method is validated in Section 4.3, and the estimation enhancement is explained in Section 4.4. \[\theta=arctan\bigg{(}\frac{(x_{1}-x_{2})}{(y_{1}-y_{2})}\bigg{)} \tag{3}\]  
\[r=\ \sqrt{\big{(}(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}\big{)}} \tag{4}\]  
In our current prototype, we used off-the-shelf components to create a simple, low-cost directional antenna using a 915 MHz omnidirectional antenna and a custom-made cylindrical parabolic reflector dish. Using an emitter at a fixed location, we can verify the directionality of the antenna by running an experiment that measures the RSS and compares it to known direction values. We used an STM32 ultra-low-power ARM-based processor with a low-power Atmel AT86 RF212B transceiver to process the signals from the antenna and provide the necessary RSS values [34]. Figure 3: **(a)** Top view illustration of the physical arrangement of two pairs of antennae with their reflectors aligned along \(x\) and \(y\) axes, and (**b**) calculation of the estimated relative direction \(\theta\) and distance \(r\) based on differential RSS.
## 3 Analysis Method of End Face Stress for Cycloid Gear  
Various methodologies are employed for the computation of stress on gear contact surfaces, including Mechanical Analysis, FEA, Empirical Formulation, the equivalent stress method, and Statistical Analysis. The applicability of these approaches hinges on the specific scenarios they address. While Mechanical Analysis and FEA offer nuanced insights into the stress distribution and magnitude on the contact surface, they mandate a comprehensive understanding of the gear's structure and material properties, accompanied by intricate computational processes. The Empirical Formulation, albeit restricted in its applicability, proffers the advantages of simplicity and expeditious calculations. The equivalent stress method, despite its rapid computational attributes, has a circumscribed domain of applicability, potentially leading to discrepancies in its outputs. The Statistical Analysis approach is adept at mirroring the gear's lifecycle characteristics, but it is inherently sensitive to the volume and distribution of data samples.