esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiments Results and Discussion
### Discussion
#### 6.4.2 Comparisons with State-of-the-Art Speakers Identification System  
This section compares the results obtained in this work with state-of-the-art speaker identification models. The comparison is affected by the dataset used and the model was developed. We compared the proposed novel features and other used features in such systems. Table 11 briefly compares the most related papers that represent speaker identification features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiments** & **Used Features** & **Accuracy (\%)** \\ \hline
**1** & Pitch only (F0, \(\Delta\)F0, and \(\Delta\)\(\Delta\)F0) & 69.75 \\ \hline
**2** & Energy only (E, \(\Delta\)E, and \(\Delta\)\(\Delta\)E) & 64.83 \\ \hline
**3** & MFCCs only (MFCCs, \(\Delta\)MFCCs, and \(\Delta\)\(\Delta\)MFCCs) & 91.20 \\ \hline
**4** & Pitch and Energy (F0, \(\Delta\)F0, \(\Delta\)\(\Delta\)F0—E, \(\Delta\)E, and \(\Delta\)\(\Delta\)E) & 78.40 \\ \hline
**5** & Pitch and MFCCs (F0, \(\Delta\)F0, \(\Delta\)\(\Delta\)F0—MFCCs, \(\Delta\)MFCCs, and \(\Delta\)\(\Delta\)MFCCs) & 91.69 \\ \hline
**6** & Energy and MFCCs (E, \(\Delta\)E, \(\Delta\)\(\Delta\)EE—MFCCs, \(\Delta\)MFCCs, and \(\Delta\)\(\Delta\)\(\Delta\)MFCCs) & **95.52** \\ \hline
**7** & All features (F0, \(\Delta\)F0, \(\Delta\)\(\Delta\)F0—E, \(\Delta\)E, \(\Delta\)\(\Delta\)EE—MFCCs, \(\Delta\)MFCCs, and \(\As we can see from Table 11, our introduced features introduced a new method that uses the first and second derivatives with the selected features. Our proposed features outperform all state-of-the-art results. The developed speaker identification system in [13] uses our selected dataset. The YOHO dataset was recently used on speaker verification systems, which are not in our field. On the other hand, the identification systems that were developed using spectrograms as features showed excellent results.  
Figure 12 presents a comparative evaluation against several state-of-the-art models to evaluate the proposed approaches' performance. The proposed BLSTM achieved a 92.70% competitive with most state-of-the-art methods. However, the modified approaches of DNN, especially CNN, provide higher accuracy than our two models.
###### Abstract  
In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user's signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush-Kuhn-Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.  
deep learning; LSTM; NOMA; optimization; KKT conditions +
Footnote †: journal: 11 May 2022
## 4 A Typical Application: Wuhan Huoshenshan Hospital
### Conceptual Design  
In the conceptual design phase, designers of the Huoshenshan Hospital were to adopt a building configuration similar to that of Beijing Xiaotangshan temporary hospital (located in Changing District, Beijing), which has been proven effective during the SARS outbreak in 2003. The FDS model (see Figure 15) of the conceptual design was rapidly constructed by utilizing the FDSGenerator program.  
Considering that the height of the air outlet may have significant effects on other aspects of the final design scheme, the proposed simulation method was adopted to determine a suitable height range for the air outlet. Figure 15 shows the locations of air outlets and fresh-air intakes in the conceptual design. The height of the fresh-air intake was taken as 2 m, while several air outlet heights ranging from 4 m to 8 m were considered. To reduce analysis time, only cases with wind speeds of 1.9 m/s (i.e., Case-1.9-N, Case-1.9-E, Case-1.9-S, and Case-1.9-W) were modeled and simulated in this design phase. The volume of each air outlet was taken as 0.5 m\({}^{3}\)/s.  
The computational domain covered a volume of 300 m \(\times\) 300 m \(\times\) 30 m, which is in agreement with the AIJ [38] best practice guideline. In terms of the grid scheme, the grid size of the domain under the height of 12 m was set to 0.5 m \(\times\) 0.5 m \(\times\) 0.5 m, whereas that of the remaining domain was set to 1.0 m \(\times\) 1.0 m \(\times\) 1.0 m. The total number of cells was \(1.026\times 10^{7}\). According to the FDS user guide [33], the \"PERIODIC\" boundary condition was adopted for the inlet, outlet, and lateral boundaries, while the \"GROUND\" and \"OPEN\" boundary conditions were used for the ground and top boundary, respectively. The total simulation time was set as 400 s, of which the first half was used to achieve a stable flow field while the remaining half was used to record the concentration data of the exhausted harmful air at the monitoring points near fresh-air intakes.  
Given that the knowledge of the transmission mechanism of SARS-CoV-2 was quite limited during the design of the Huoshenshan Hospital, it was challenging for engineers to quantify the effect of filtration devices on pollutant concentration. As a result, the function of filtration devices was ignored in all simulations introduced in Section 4, which would facilitate a safer design of the hospital. Provided that the harmful air concentration at the air outlet was defined as 100%, the relationship between the maximum value of the time-averaged concentrations near all fresh-air intakes and the air outlet height can be illustrated by Figure 16. It can be seen that the maximum harmful air concentration near all the fresh-air intakes decreases with the increase in the height of the air outlet. When the air outlet height reaches 8 m, the maximum harmful air concentration near all intakes can be reduced to approximately 0.1% of that at the air outlets.  
Figure 15: FDS model of the conceptual design (building height is 4 m) of the Huoshenshan Hospital.
## 3 Proposed Design Method for Dual-Mode PUF  
To design a dual-mode SPUF, the transistors in the SRAM cell need to be sized such that the quality expectations can be met for both modes. For memory mode, the memory failure probability should be kept as low as possible, whereas for PUF mode, the randomness, uniqueness and reliability of response bits should be made as high as possible. As randomness and uniqueness can be easily enhanced by fuzzy extractor at a smaller cost, this paper focuses on the reliability, which is more critical and difficult to improve for SPUF. In addition, failure probability and PUF reliability, area, leakage current and dynamic power constraints using a targeted process technology will also be considered.
## 3 Results and Discussion
### Numerical Results of DFT Full Spectrum
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rot. Speed**} & \multicolumn{8}{c}{**Amplitudes (m)**} \\ \cline{2-13}  & \multicolumn{4}{c}{**Linear**} & \multicolumn{4}{c}{**Nonlinear**} \\ \cline{2-13}  & \(-3\times\) & \(-2\times\) & \(-1\times\) & \(1\times\) & \(2\times\) & \(3\times\) & \(-3\times\) & \(-2\times\) & \(-1\times\) & \(1\times\) & \(2\times\) & \(3\times\) \\ \hline
15 Hz & - & - & \(1.10\times 10^{-7}\) & \(1.74\times 10^{-7}\) & - & - & - & - & \(1.42\times 10^{-7}\) & \(2.17\times 10^{-7}\) & - & - \\
30 Hz & - & - & \(1.07\times 10^{-6}\) & \(1.96\times 10^{-6}\) & - & - & - & \(4.12\times 10^{-8}\) & \(1.44\times 10^{-6}\) & \(2.55\times 10^{-6}\) & \(3.97\times 10^{-8}\) & - \\
35 Hz & - & - & \(4.94\times 10^{-6}\) & \(9.31\times 10^{-6}\) & - & - & \(2.43\times 10^{-7}\) & \(8.27\times 10^{-5}\) & \(6.97\times 10^{-4}\) & \(4.32\times 10^{-5}\) & \(5.23\times 10^{-7}\) & \(254\times 10^{-7}\) \\
45 Hz & - & - & \(1.64\times 10^{-6}\) & \(3.36\times 10^{-6}\) & - & - & - & \(8.38\times 10^{-8}\) & \(1.87\times 10^{-6}\) & \(3.80\times 10^{-6}\) & \(7.54\times 10^{-8}\) & - \\
60 Hz & - & - & \(5.83\times 10^{-7}\) & \(1.31\times 10^{-6}\) & - & - & - & - & \(7.84\times 10^{-7}\) & \(1.73\times 10^{-6}\) & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Magnitudes of the most relevant peaks of Figure 11—three-lobed bearing (preload 0.25). Figure 11: DFT full spectrum of simulated results in three-lobed bearing 1 (preload 0.25): (**a**) linear, (**b**) nonlinear. Analyzing the decomposition of the signals by DFTs, it is possible to conclude that, for low preloaded bearings, for both elliptical and three-lobed cases, the nonlinearities are more detectable through the appearance of higher order harmonics, such as \(2\times,3\times\) (forward) and \(-2\times\), \(-3\times\) (backward). On the other hand, for the case of three-lobed bearings with high preload, the nonlinearities are evidenced by the increased growth in the backward component \(-1\times\).
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 3 MSE Growth of GaN Thin Films Using Liquid Ga Target
### Effect of Growth Parameters on GaN Thin Films
The improvement in surface morphology is caused by the formation of Ga ad-layer which increase the Ga surface mobility and promote layer growth mode [46]. Another consideration in determining the mixture of the process gas is to control the deposition rate and target poisoning. Target poisoning can occur on the Ga target during reactive sputtering due to the formation of GaN compound on the target. This results in a reduction in sputtering deposition rate over time as the target shifts to poisoned mode. [PERSON] et al. [47] has simulated the process of Ga target poisoning under a purely nitrogen gas ambient and confirms that under low nitrogen ion energy, a smooth GaN surface forms at the surface of the target. Introducing Ar gas into the mixture can assist in preventing target poisoning. The deposition rate of Ga is shown to increase proportional to the Ar partial pressure due to the partial removal of the GaN surface through Ar\({}^{+}\) ion bombardment on the target surface [39]. Increasing the power applied to the target (cathode) is the main way to increase the deposition rate in sputter deposition processes. However, this is also associated with an increased potential drop between the plasma and the cathode which leads to higher kinetic energy of the sputtering gas ions. Upon impact, the ions transfer a majority of their momentum to the atoms in the target, causing, e.g., sputtering and subsurface collision cascades, but a fraction are kinetically reflected as neutrals, back into the chamber. Back reflected neutrals, which may retain a part of their initial kinetic energy, may result in the formation of crystal defects, gas implantation, re-sputtering, etc., if allowed to reach the growing film. For example, [PERSON] et al. [39] discovered an increase in residual compressive stress in nanocrystalline GaN films with increasing negative target voltage. The total chamber pressure affects the concentration, mean free path, and energy of the incoming sputtered atoms as well as the back-reflected neutrals. [PERSON] et al. [38] has demonstrated that the crystallinity of the GaN layer can be improved by decreasing the total pressure of the chamber. At a lower chamber pressure, the number of activated nitrogen radicals is increased, and more Ga atoms with higher kinetic energy arrive on the substrate due to the increased mean free path length; thus, implying a delicate balance in process parameters to achieve a high-quality GaN thin film.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
###### Abstract  
Visual positioning is the task of finding the location of a given image and is necessary for augmented reality applications. Traditional algorithms solve this problem by matching against premade 3D point clouds or panoramic images. Recently, more attention has been given to models that match the ground-level image with overhead imagery. In this paper, we introduce AlignNet, which builds upon previous work to bridge the gap between ground-level and top-level images. By making multiple key insights, we push the model results to achieve up to 4 times higher recall rates on a visual position dataset. We use a fusion of both satellite and map data from OpenStreetMap for this matching by extending the previously available satellite database with corresponding map data. The model pushes the input images through a two-branch U-Net and is able to make matches using a geometric projection module to map the top-level image to the ground-level domain at a given position. By calculating the difference between the projection and ground-level image in a differentiable fashion, we can use a Levenberg-Marquardt (LM) module to iteratively align the estimated position towards the ground-truth position. This sample-wise optimization strategy allows the model to align the position better than if the model has to obtain the location in a single step. We provide key insights into the model's behavior, which allows us to increase the model's ability to obtain competitive results on the KITTI cross-view dataset. We compare our obtained results with the state of the art and obtain new best results on 3 of the 9 categories we look at, which include a 57% likelihood of lateral localization within 1 m in a 40 m \(\times\) 40 m area and a 93% azimuth localization within 3\({}^{\circ}\) when using a 20\({}^{\circ}\) rotation noise prior.  
augmented reality; visual positioning; outdoor localization; maps; satellite data 2023
## 4 Results
### \(l_{c}\) Sensitivity Analysis
#### 4.1.1 Standard Phase-Field Fracture  
In this analysis, a sensitivity study of the length scale parameter \(l_{c}\) is performed using a 2D benchmark model within the AT2 standard phase-field fracture framework. All parameters are held constant while \(l_{c}\) is systematically varied between 0.1 mm and 0.2 mm. Figure 5 shows the simulated fracture paths for different \(l_{c}\) values, with a mesh size of 0.05 mm used in the finite element analysis. The reaction force-displacement curves, as shown in Figure 6, reveal two key observations consistent with [PERSON] et al. [61]. Firstly, an increase in \(l_{c}\) leads to a reduction in the peak reaction force. Secondly, across different \(l_{c}\) values, a sharp decline in reaction force is observed after the peak, with only a small increase in displacement.  
It is important to note that \(l_{c}\) cannot only be considered as a numerical parameter within the SPFM approach. The relationship between \(l_{c}\) and material strength has been clarified by simplified analytical solutions [62; 63; 64], while experimental studies have de  
Figure 5: Fracture paths in the 2D single inclusion model have been simulated under tensile loading conditions. Here, \(\phi\) denotes the phase-field fracture parameter, with \(\phi=0\) indicating no damage, and \(\phi=1\) illustrating a complete fracture.  
termined \(l_{c}\) by measuring the critical stress in uniformly stretched specimens [61]. Using [PERSON]'s equation, \(l_{c}\) can be calculated from material properties such as Young's modulus \(E\), fracture energy \(G_{c}\), and tensile strength \(\sigma\) for the SPFM simulations:  
\[l_{c}=\frac{2\mathcal{Z}EG_{c}}{8\sigma^{2}} \tag{20}\]  
Considering Table 3, the calculated length scale parameter for the inclusion phase is \(l_{c}=0.243\,\mathrm{mm}\). Similarly, for the matrix phase, the length scale parameter is \(l_{c}=0.142\,\mathrm{mm}\), and for the interfacial phase, it is \(l_{c}=0.128\,\mathrm{mm}\). As these results suggest, the \(l_{c}\) values are not appropriate for small-scale simulations due to the size of the specimen. Using a larger \(l_{c}\) in small-scale simulations can introduce boundary effects and inaccuracies, particularly in the matrix and interfacial phases where the calculated \(l_{c}\) values are relatively small. This highlights the limitations of the SPFM approach for small-scale simulations, especially for heterogeneous multi-phase microstructures.
## 4 Cellular Component of the CRC microenvironment
### Tumor-Associated Macrophages (TAMs)
#### 4.1.1 Metabolic Profile of TAMs  
The functions of TAMs are closely associated with the type of metabolism they engage in. Various stimuli from the TME lead to their differentiation into various subtypes, with functions shaped by specific metabolic profiles. Several factors contribute to the polarization of macrophages, inducing changes in their metabolism. These include pro-inflammatory inducers such as lipopolysaccharidesaccharides (LPS) and IFN-\(\gamma\) associated with classically activated M1 macrophages and IL-4 and IL-10 linked to the anti-inflammatory subtype M2 [120; 121]. Classically, activated M1 macrophages are characterized by intense glycolysis, where glucose is converted to lactic acid and the pentose phosphate pathway (PPP), which is a source of NADPH. The result of this metabolic adaptation is the production of inflammatory inducers and reactive oxygen species. M1 also differs from M2 in arginine metabolism. Due to the presence of nitric oxide synthase 2 (NOS2, iNOS), they produce NO, serving as a weapon in the fight against cancer cells [96; 120]. However, long-term exposure to the products of this type of metabolism in the TME may also have the opposite effect, increasing oncogenic mutations.  
In contrast to M1, M2 macrophages primarily derive energy from oxidative phosphorylation, the tricarboxylic acid (TCA) cycle, and \(\beta\)-oxidation, resulting in the promotion of tumor invasion [122]. Studies confirm that alterations in the metabolism of TAMs regulate their functions and, consequently, the course of the disease [123]. The high metabolic flexibility of TAMs can also be explained by the fact that they must compete for nutrients with transformed TME cells [121].  
Recently, the following eight enzymes: acyl-CoA dehydrogenase medium chain (ACADM), acyl-CoA dehydrogenase short chain (ACADS), glutathione peroxidase 4 (GPX4), glutathione-disulfide reductase (GSR), hydroxyacyl-CoA dehydrogenase (HADH), 3-hydroxy-3-methylglutaryl-CoA lyase (HMGCL), 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1), and isocitrate dehydrogenase (NADP(+)) 1 (IDH1) associated with the characteristic amino acid metabolism of colorectal TAMs have been identified. The loss of one of them--ACADS--influenced the polarization of TAMs to M2, indicating the role of this protein in modifying the CRC microenvironment [124].
In 2020, the total catch of the blackfin flounder was 1492 metric tons in Korea, of which approximately 49% (734 metric tons) was caught by coastal gillnet fisheries [11].  
The blackfin flounder is sometimes caught by being held to the gillnet. However, because of its rounded and flat body shape, it is often caught by pocketing. Therefore, trammel nets are frequently used to increase the catching efficiency for the blackfin flounder. However, in Korea, trammel nets have a high bycatch ratio with high proportions of immature fish and pose the risk of overfishing. Thus, their use is strictly regulated by related law. Recently, tie-down gillnets have been used, in which the lines are tied vertically to a single gillnet at regular intervals so that the net extends to a lesser degree than its original height. The gillnet netting forms a large pocket in the water similar to that formed by a trammel net, and compared with a single gillnet, gillnet netting exhibits a higher catching efficiency for flounders [12; 13; 14; 15].  
Although there have been reported concerns regarding overfishing associated with the use of tie-down gillnets (as is the case for trammel nets), the risk has not yet been studied. There are few studies on tie-down gillnets comparing mesh selectivity, bycatch ratios, and catching efficiency, although some studies have investigated the effects of tie-down gillnets on reducing mortality [14; 15; 16; 17]. The catching efficiency of gillnets has been reported to vary with the target fish species and the type of fishing gear (such as trammel nets and a single gillnet) [1; 18]. Studies have also investigated the catch composition or catching selectivity of a single gillnet or trammel net [1; 4; 19; 20]. Some non-Korean studies have investigated a method for adjusting the headline height using support lines to reduce the bycatch of marine animals (such as sea turtles) when fishing using gillnets [16; 21; 22]. However, studies conducting sea trials using tie-down gillnets are limited.  
This study aimed to determine the catching efficiency of tie-down gillnets. To do this, we conducted sea trials using four types of tie-down gillnets with different mesh sizes for the blackfin flounders found along the east coast of Korea and comparatively analyzed the catch characteristics and selectivity for each mesh size. Our results can be used as a basic reference for establishing plans for the efficient use and management of blackfin flounder stocks.