esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Loss Function  
The loss function used in our network is a composite of reconstruction loss and regularization terms. This combination is designed to ensure that the network not only accurately reconstructs the desired output but also maintains generalization capabilities and prevents overfitting [38]. The reconstruction loss directly measures the difference between the predicted and true values, ensuring precise output generation. Meanwhile, the regularization terms add constraints that promote smoother and more robust model predictions, enhancing the overall performance and stability of the network [39]. The VAE component of the loss  
\[\mathcal{L}_{\text{V}}=\|\hat{D}_{k}^{\text{V}}-\mathcal{D}_{k}\|_{2}^{2}+ \beta\text{KL}(q(z|\mathbf{I})\|p(z)) \tag{2}\]  
consists of a Mean Squared Error (MSE) reconstruction loss (left) and a Kullback-Leibler (KL) divergence for regularization (right). Here, \(\beta\) is a weighting factor, \(q(z|I)\) is the posterior distribution, and \(p(z)\) is the prior distribution.  
For the modified U-Net component,  
\[\mathcal{L}_{\text{U}}=\|\hat{D}_{k}^{\text{U}}-\mathcal{D}_{k}\|_{2}^{2}+ \lambda_{\text{TV}}\sum_{i,j}\sqrt{(\hat{D}_{i+1,j}^{\text{U}}-\hat{D}_{i,j}^{ \text{U}})^{2}+(\hat{D}_{i,j+1}^{\text{U}}-\hat{D}_{i,j}^{\text{U}})^{2}} \tag{3}\]  
there is again an MSE term (left), but additionally a Total Variation (TV) regularization [40] term (right), controlled by weighting factor \(\lambda_{\text{TV}}\) which encourages spatial smoothness in the predicted depth map by penalizing large gradients [41] and reducing noise [42], leading to improved generalization and robustness [43].  
The final output from the MLP ensemble is optimized using a combined loss function from both VAE and U-Net losses:  
\[\mathcal{L}_{\text{final}}=\alpha\mathcal{L}_{\text{V}}+\gamma\mathcal{L}_{\text {U}} \tag{4}\]  
Statistically, the integration of these loss terms ensures that the model benefits from both global structural understanding (via VAE) and local detail refinement (via U-Net). The MLP ensemble leverages this dual optimization, effectively balancing bias and variance trade-offs [44]. This approach mitigates the risk of overfitting through regularization while maintaining high reconstruction accuracy, resulting in a model that performs well across varied and unseen data distributions [45].
## 4 Parallel Performance and Application in Actual Scenarios
### Performance on Tianhe II  
Based on the test results on Era, we decided to conduct tests on a larger scale. The hardware environment was Tianhe II. The processor was an Intel Ivy Bridge-E Xeon E5-2692V2:2.2 GHz. The operating system edition was Red Hat Enterprise Linux Server release 6.5 (Santiago). The compilers were Intel composer_xe_2013_sp1.2.144 and MPICH 3.1.3. In the above hardware environment, we conducted tests on the same three code editions. As shown in Figures 8 and 9, the fully optimized LICOM achieves a good speedup when 9600 and 19,200 processor cores were used. The computing speed reached 12.6 model years per day, which was twice the speed of the original LICOM. However, as shown in Figure 9, a non-intuitive phenomenon occurred. The speedup for the semi-optimized code on 9600 PEs was smaller than that on 4800 PEs. Similarly to the phenomenon on Era, this may have been due to the communication overhead and load imbalance. In contrast, the fully optimized code achieved good speedups on 9600 and 19,200 PEs. This showed that our optimization worked well in improving the scalability of the code.  
Figure 6: Elapsed time and speed of LICOM on Era.  
Figure 8: Elapsed time and speed of LICOM on Tianhe II.  
Figure 7: Speedup of LICOM on Era.
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Summarization  
Considering the reviewed configurations, we can conclude that in addition to common fixed-wing and VTOL configurations, many other categories of aircraft are also being studied at UAM. HTOLs, specifically fixed-wing aircraft are so popular and are classic configurations due to their old history and simple structure. They usually have high endurance, high ranges, and high speeds. The main problem on their way is the need for a runway and infrastructures which makes them challenging solutions for in-city mobility. In contrast, they seem to be the best solution for long transportation. Using a morphing-wing can also empower fixed-wing aircraft with higher adaptability levels and efficiency, although it may make them more complex. In the category of HTOLs, Magnus-effect planes are also some considerable concepts, they have small sizes and acceptable efficiency by providing high lifts. In contrast to some aerodynamic issues due to the use of the Magnus effect, they can provide high stability and resistance. Gyroplanes are also popular for their simple structure and low price. In contrast with other HTOLs, they need a shorter runway.  
VTOLs are at the center of attention due to the lack of enough infrastructure for HTOL configurations in cities. Amongst them, helicopters have been used for many years in different sections including survey, search and rescue, agriculture, and transportation of course. Helicopters are able to hover flight in low altitudes which makes them suitable options in urban environments. But they use a complex system and are not too maneuverable. Their sound pollution is also a major concern. In contrast to helicopters, multirotors have a very simple structure, they are easy to build and maintain. By considering their VTOL capabilities they are one of the best solutions in UAM for in-city usage. In spite of all these benefits, they are less efficient and have low endurance and speed which makes them suitable only for short travels.  
Cyclocopters are another group of VTOL aircraft, they use rotors that are capable of producing 360-degree thrust which makes them maneuverable. They also have good range and endurance which makes them suitable for mid-range flights.  
Another popular group of aircraft are hybrids. They benefit from VTOL capabilities along with the high range and endurance of HTOL aircraft. They do not need a runway which makes them suitable for urban environments, but they may have complex structures and control as tilting aircraft are. Among tilting solutions, tilt-rotors have a more simple structure.  
Dual systems are also a group of hybrids that are under the attention of companies and researchers. They do not have the complexity of the tilting hybrids, especially regarding the transition phase from VTOL to HTOL. However, using two separate systems makes them less efficient.
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion
### Pathogenesis of NAFLD and Progression to NASH, Fibrosis, and Primary Liver Cancer
#### 3.1.2 Inflammation (Steatohepatitisitis) and Multiple-Hit Hypotheses  
Previously, the development of NAFLD was described as a 'two hits hypotheses', consisting of hepatic lipid accumulation (steatosis) and consequent inflammation and fibrosis (steatohepatitisitis) [31]. Aberrant fat deposition in the hepatocytes may result from insulin resistance, followed by a sedentary lifestyle, a high-fat diet, and obesity. For the progression of simple steatosis to NASH, further inflammatory cascade and fibrogenesis signals are involved.  
However, recent evidence suggested that the pathogenesis of NAFLD could be more complicated and multifactorial and proposed a'multiple-hit hypotheses' rather than the two hits. As an etiologic mechanism underlying the development of NAFLD and progression to NASH, lipotoxicity has been proposed to be associated with insulin resistance, oxidative stress, and activation of inflammatory cascade. Hepatic lipotoxicity triggers and amplifies the inflammatory pathways by inducing the production of proinflammatory cytokines and activation of inflammosomes, which are multimeric protein complexes of the innate immune system responsible for the activation of inflammatory responses [32]. Inflammatory pathways, including c-Jun N-terminal kinase activator protein-1 (JNK-AP-1) and IxB kinase-nuclear factor kappa-light-chain-enhancer of activated B cells (IKK-NF-kB), are important for the maintenance of chronic inflammation during the development ofNAFLD [33]. The activation of inflammosomes, such as the Nod-like receptor proteins 3 (NLRP3), in response to free fatty acids, oxidative stress, and other proinflammatory metabolites, suppresses peroxisome proliferatorator-activated receptor-\(\alpha\) (PPAR-\(\alpha\)), and promotes tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\))-induced cell death [34]. Inflammosomes can directly promote fibrosis by activating hepatic stellate cells [35].  
A disturbed intestinal microbiome is also implicated in the development and progression of NAFLD via the overproduction--and increased absorption--of fatty acids in the bowel [36; 37]. Excessive free fatty acids activate inflammatory signaling pathways and release of proinflammatory cytokines such as interleukin (IL)-6, IL-1\(\beta\) and TNF-\(\alpha\) to induce chronic hepatic inflammation [38]. Microbial products such as lipopolysaccharideacharide also interact with receptors on inflammatory cells as well as with hepatic stellate cells to promote inflammation and fibrosis [38; 39]. Along with steatosis and lipotoxicity, the regulation of cell death by apoptosis and autophagy is also defective in NAFLD [40; 41; 42].
## 4 Discussion
### Temperature and Amplitude  
Temperature has been shown to exert a major impact on the results of cyclic fatigue testing. The modern endodontic instruments are flexible and ductile due to proprietary heat treatment procedures [1]. During active canal preparation, instruments were used inside the oral cavity and, thus, the surrounding/working temperature changed from room temperature to a temperature close to body temperature. [PERSON] et al. [34] reported that the mean intracanal temperature during root canal preparation is about 35.1 \({}^{\circ}\)C (\(\pm\)1 \({}^{\circ}\)C) [42].  
The crystal lattice of NiTi depends on the A\({}_{\text{f}}\) temperature. Whereas conventional super elastic (SE) NiTi instruments are in the austenitic phase at or below body temperature and exhibit superelastic properties [1], heat-treated alloys such as M-Wire, R-Phase, CM-wire, Gold, and Blue-Wire present higher A\({}_{\text{f}}\) temperatures and are essentially in the martensite/R phase state in the working environment at body temperature. This enhancement offers promising results, concerning the cyclic resistance, but the impact on torsional failure is inconclusive [22]. However, conditions during testing should be as realistic as possible and experiments were, therefore, performed at body temperature.  
Water-based glycerin at 37 \({}^{\circ}\)C--constantly refreshed--served as a lubricant in order to reduce friction of the instruments to the walls of the artificial canals and to avoid additional development of heat. Common alternatives for this purpose represent air cooling or using synthetic oil [22], but a water-based lubricant seemed to be closer to clinical practice and was established in cyclic fatigue tests [24].  
Root canal preparation is always a dynamic process. Manufacturers demand keeping the instruments in motion and not static. Depending on the instrument used and the manufacturer's instructions, the amplitude varies. Picking motions from 1 to 3 mm or even larger strokes from 5 or more mm may be suited for proper mechanical preparation. As a compromise, a 3 mm axial oscillating motion served for the simulation of dynamic preparation in the present study.
# Design of Dual Circularly Polarized Sequentially-Fed Patch Antennas for Satellite Applications
## 3 Dual-CP 2-by-2 Array Antenna for Satcom Applications
As it is apparent, the presence of monopoles results in an increase of antenna gain for large elevation angles (\(\theta>20^{\circ}\)), as well as in an enhancement of the polarization purity, since the cross-polar gain is remarkably reduced of 10 dB with respect to the case without monopoles. The proposed array antenna was designed to be used as primary feeder of reflectors operating in S-band, because of this, the stability of phase center position versus frequency has to be verified in order to ensure efficient reflector illumination. For such kinds of arrays, in which geometric symmetries with respect to \(xz\) and \(yz\) planes, the phase center has to lie on the \(z\)-axis. If no phasing is imposed on the antenna aperture, the phase center is exactly on it, at the central frequency. When a frequency shift occurs, in general a linear phasing is present, hence the phase center moves a little bit on the \(z\)-axis. However, in our case the phase center displacement was almost negligible, since the operating bandwidth was not so large and the antenna size was smaller than the operating wavelength. In [30] further theoretical details can be found. Figure 4: (**a**) Side-view of the proposed 2-by-2 patch array operating in the higher UHF band. (**b**) Reflection coefficient (S\({}_{11}\),S\({}_{22}\)) versus frequency over the operating bandwidth. (**c**) Gain versus frequency over the operating bandwidth. (**d**) Simulated broadside axial ratio (\(\theta=0^{\circ}\)) versus frequency, for different elevation and azimuthal angles. As it is apparent, the monopoles are able to increase significantly antenna polarization purity.
# Experimental and Numerical Analyses of Timber-Steel Footbridges
## 4 Laboratory Testing of the Structural Detail of Interest
### The Course of the Experiment  
The stress-strain response of the steel cross-girder to the load caused by transverse acting discrete forces was observed in laboratory tests. Based on the preliminary analyses, the specimens were loaded with two forces situated symmetrically at a distance of 150 mm from the midpoint. The test arrangement of series \"C\" specimens subjected to the four-point bending test as well as the locations of strain gauges and displacement sensors are shown in Figure 9. The following stresses and deformations were registered during the tests:  
* Stresses in the mid-span of the cross-girder (gauges T0, T1, and T2);
* Stresses at the cross-girder-to-timber-block connections (gauges T3, T4, T5, and T6);
* Stresses in the bolts (gauges T7, T8, T9, and T10);
* Vertical deformation in the middle of the cross-girder (sensors S6 and S10);
* Vertical deformations of the overhanging ends of the steel hydraulic press's distribution beam (sensors S3 an S4);
* Bolts pushing into the wooden blocks (sensors S1 and S2);
* Absolute rotations of the wooden blocks (based on the horizontal deformations of the top and bottom edges of the wooden blocks measured by sensors S5 and S7);
* Relative rotations of the end plate against the wooden blocks (sensors S8, S9, and S11).  
Figure 8: Geometrical parameters of laboratory specimens (dimensions are given in mm).  
A hydraulic press was used to produce the pressure needed for specimen testing. At the beginning, every sample was loaded with a force of 5.0 kN divided into two half forces. This force introduced the basic load level of all tested specimens. Consequently, the samples were tested by gradually increasing the force by a step of 5.0 kN in the case of specimens in series \"A\" and 10.0 kN in the case of specimens in series \"B\" and \"C\". After each loading step, the specimens were unloaded to the basic loading level. The test continued until the samples collapsed, which was due to either a bearing failure of the wood or the wood splitting in the direction perpendicular to the grain because of transverse bending of the timber blocks (Figure 10).
# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
## 2 Previous Work and Contributions  
### Previous Work  
The 95 th percentile of horizontal/vertical accuracy in the GPS standard position service (SPS) is 3.4/4.7 m [9]. Similarly, the accuracy of the GPS modules embedded in smartphones is typically 3-5 m under good multi-path conditions; otherwise, it can be above 10 m. There have been several studies on improving the 5-10 m accuracy to the performance of DGNSS or RTK (Real Time Kinematics). These studies can be categorized into two groups: hardware add-on or modification methods and user-developed software.  
A GPSWorld magazine article published in 2015 presented the results of an experiment in which a signal received through an antenna embedded in a smartphone was input to an external software-developed receiver. This report indicated that it is technically possible to achieve accuracy at the level of 10 cm [10]. To this end, however, it is impossible to implement or apply methods that allow smartphone manufacturers or users to improve the GPS accuracy of their devices directly without hardware modification. Moreover, the GPS modules in smartphones provide neither raw measurements (e.g., pseudorange measurements) nor an open port capable of receiving DGPS corrections. As current smartphones simply embed the GPS modules provided by the chip vendor, the necessary functionalities are unlikely to be included until the vendors predict that a new market can be opened up by adding the function or the related module. In this respect, the approach of adding an independent DGNSS/RTK-enabled receiver seems to be more practical. Recent smartphones support the installation of user-developed applications, high-speed processing, and wireless communication to provide a new environment for the Network Transport of RTCM via Internet Protocol (NTRIP). Thus, a smartphone application that feeds DGNSS or even RTK corrections to a GNSS receiver connected to the smartphone via Bluetooth could be developed [11].  
Although connecting an independent receiver to the smartphone would guarantee the positional accuracy, many studies have focused on developing suitable applications that use position information from the GPS modules of smartphones, which will improve portability and reduce costs. To determine the current location with similar precision to a more expensive DGPS solution, a correction is computed by subtracting the difference between the true and observed values at the known point. This is then applied to the coordinates of the features that are surveyed in that session [4; 12].
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 2 Related Work
### Summary of Related Work  
Our study is closely related to [16], wherein we have used properties of edge detection operation as MR, ref. [18] where structural testing is used to check test case adequacy and [4] where [PERSON]'s improved edge detection algorithm is used as the SUT. Based on the critical analysis of the literature review, the research gaps are given below:  
* In the literature survey, we have included the papers of edge detection algorithms that are used to identify edges/tumors in MRI brain images. These algorithms are enhancements of traditional edge detection algorithms such as Sobel, Canny, Prewitt, Roberts, etc. The traditional edge detection algorithms are already tested; therefore, testing of these enhanced algorithms is important. We have selected [PERSON]'s improved edge detection algorithm because amongst all the articles, this is the latest research article to detect brain tumor in MRI images.
* In the existing techniques, random testing is considered unbiased for the generation of test cases, but random testing leads to unfair distribution of parametric values. Therefore, we have proposed a criterion where test cases are generated through black-box testing and white-box testing techniques. In the proposed framework, source test cases are selected randomly through the strong equivalence class testing technique, and later, the adequacy of selected test cases is checked through structural testing.
* In the case of image processing operations, sometimes a test oracle cannot be clearly defined, e.g., comparing two images pixel by pixel may show little difference, but visually they are similar. We have used SSIM for the comparison of two images and then calculated the FDR accordingly.
# Control for Ship Course-Keeping Using Optimized Support Vector Machines
## 2 Problem Formulation
### Particle Swarm Optimization  
Besides SVM, another AI technique, particle swarm optimization (PSO) is also applied in this paper. As a popular parameter optimization tool, PSO is introduced to obtain the optimal parameters in SVM, which exert a vital influence on the performance of SVM. Model selection is an important issue in machine learning, no matter for classifiers or regressors. For SVM, the main concern in model selection is the selection of SVM parameters. Those parameters include insensitivity factor, regularization factor, and kernel parameters. However, how to calculate their optimal values theoretically still remains unsolved. One reason is that it is difficult to determine the VC dimension [32]. A simple and commonly used way to obtain these parameters is the trial-and-error method despite some proposals, for instance analytic parameter selection directly from the training data [33], in-sample and out-of-sample method [34], dynamic particle filter [35], etc. To improve the performance of SVM, this paper makes use of PSO to determine SVM parameters.  
The main idea of PSO is to optimize a problem from a population of candidate solutions (so-called particles). These particles are moved towards the optimal position (best solution) through iteration of velocity and position [36]. This optimization method is easy to use since few or no assumptions are required; for example the optimization problem is not required to be differentiable. Moreover, a globally optimal solution can be guaranteed even if the capacity of candidate solutions is very large. In practice, PSO has been proven an effective universal parameter optimizer. There have been some PSO applications to the parameter optimization of SVM, e.g., [37; 38; 39].  
A standard PSO algorithm adopts the following iteration formulae about the particle's velocity and position  
\[V_{id}(k+1)=W\cdot V_{id}(k)+c_{1}\kappa(p_{id}(k)-X_{id}(k))+c_{2}\eta(p_{gd }(k)-X_{id}(k)) \tag{9}\]  
\[X_{id}(k+1)=X_{id}(k)+V_{id}(k+1),\ i=1,2,\ldots n;\ d=1,2,\ldots q \tag{10}\]where \(V_{id}(k)\) is the velocity of a particle at the \(k\)-th step; \(n\) is the number of particles; \(q\) is the dimension of particle; \(X_{id}\) is the position of a particle; \(W\) is the inertia weight; \(c_{1}\) and \(c_{2}\) are acceleration coefficients; \(p_{id}\) is the best position of a individual particle while \(p_{gd}\) is the swarm or social best position; \(\kappa,\eta\) are both random.