esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Assessing the Adequacy to the LDH Enzymatic Test  
Observing that the tested colorimetric methods have limitations regarding the release of the dyes, we verified an enzymatic method using the cytosolic enzyme lactate dehydrogenase (LDH) for the determination of viable cells in the spheroids. The results of the optical density of aggregates with 4 days of formation presented adequate O.D. readings between 0.4 and 1.2 and were sensitive to differing cellular densities, with significant differences between 20,000, 30,000, and 40,000 cell spheroids (\(p<0.05\)). The time of formation of the aggregates seems to interfere with the O.D., since the readings obtained with spheroids on the 7 th day are lower than with spheroids on the 4 th day (\(p<0.05\)) (Figure 8).  
To evaluate the behavior of the bone cell aggregates in a cytotoxicity test, we also performed a dose-response assessment by exposing 4-day spheroids to different concentrations of extracts of two commercial polymers, namely latex and PLA. Figure 9A shows that the LDH test employing the 3D model was sensitive to the latex extract, being capable of measuring cell death with a high level of correlation with the concentration of the extracts, in the best linear fit (R\({}^{2}\) = 0.77), allowing the determination of an IC\({}_{50}\) of 175 mg/mL for the latex extract. On the other hand, the biocompatible PLA did not cause significant cytotoxicity, regardless of the concentration employed (Figure 9B).  
Figure 8: LDH test of aggregates with 4 days (**A**) and 7 days (**B**) of formation. All groups were significantly different in the same experimental time. Bars represent the mean \(\pm\) SD (n = 5) reduction in optical density at 340 nm after 25 min reaction. Black bars indicate the results obtained with monolaminar cell culture of MC3T3-E1 cells form the same origin as those used in the aggregates. An asterisk indicates significant differences from other groups in the same experimental time (\(p<0.05\)).  
Figure 9: LDH test of aggregates at 4 days after seeding (30,000 cells), submitted to extracts of different proportions of commercial latex (**A**) or polylactic acid (PLA) (**B**). Results represent mean \(\pm\) SD survival, as a % of the control (unexposed cells) of two independent experiments in quintuplicates.
## 3 Verification of the Composite Main Reflector  
### Verification of the Manufacturing of the Composite Main Reflector  
In this paper, we manufactured a reflector with four process variables and fabricated and verified the performance of a total of twelve reflectors, with three for each case. In this paper, only the representative verification results for each case are described.  
As a first verification, the weights of four types of composite reflectors manufactured according to the process variables defined in Table 3 were measured. The weight of the manufactured main reflector was calculated to be lower than that of the main reflector estimated in the initial design stage. This is because the weight per area of the actual supplied prepreg was lower than that predicted in the initial design stage. The weights of Case 3 and 4, excluding the adhesive film, were lower than Case 1 and 2.  
In the case of Case 1, the reflector surface of the main reflector was first cured at a curing pressure of 40 psi, and it was manufactured without voids or pin-holes on the reflector surface. However, in the case of Case 2, the entire main reflector was manufactured at a curing pressure of 10 psi and through a co-curing process, and pin-holes were inspected on the reflector surface as shown in Figure 10. The pin-holes are very small holes with a diameter of less than 0.01 mm, and they were caused by low molding pressure. Because they are not considered to affect the electrical performance of the reflector antenna, they were not judged to be manufacturing defects.  
Non-destructive testing (NDT) was performed using TTU (Through-Transmission Ultrasonic)-type C-Scan equipment with a frequency of 0.5 to 5 MHz. A reference specimen was produced to establish a standard for non-destructive testing, and the part measured below 12 dB based on the fact that the sound part of the reference specimen is indicated as a sound part in green or blue in the C-Scan results and the part measured above 12 dB is indicated as a defective part in red in the C-Scan results, allowing for the determination of defects.  
Figure 9: Manufacturing of sandwich composite main reflector.  
The results of the non-destructive test are shown in Figure 11. In the case of the main reflectors in Cases 1 and 2 that applied the adhesive film, only a difference in the measurement signal (blue, green zone) according to the lamination pattern was observed, and no separate defects were detected. In the case of the main reflectors in Cases 3 and 4 that did not use the adhesive film, a defect signal (red zone) was detected in some edge areas, but the defect size was very small (less than 1 inch), so it was determined that there was no structural abnormality.
## 2 Materials and Methods
### Data Acquisition  
The first step of the proposed camera-aided algorithm is data acquisition. For image acquisition, we have selected a fisheye camera model VIVOTEK FE8181/81V with 180\({}^{\circ}\) panoramic view and 360\({}^{\circ}\) surround view. This choice was made because we need an acquisition device capable of seeing the entire sky-related scene. The GNSS pseudoranges (acquired by any GNSS receiver, such as the one mounted on the vehicle carrying the fisheye camera) and pseudorange rates are computed with the software receiver GEMS [7] using the MC tracking loop with the multipath detection activated. The second step of the camera-aided algorithm supposes the transmission of these measurements into the EKF prediction step, and thus the associated RAIM can exclude faulty satellites. This step ensures the functioning of the proposed system in normal propagation conditions.
# Low-Rank Approximation of Frequency Response Analysis of Perforated Cylinders under Uncertainty
## 3 Free Vibration of Perforated Shells  
Shells of revolution have symmetry-induced clusters of eigenmodes. With the exception of torsion modes, in free vibration, every eigenmode is a member of a cluster of size at least two. This is a simple consequence of the eigenmodes having integer-valued wave numbers in the angular direction. In a non-perforated case, this wave number increases with the rate \(\sim\)\(1/\sqrt[4]{t}\) as \(t\to 0\).  
For regular perforation patterns, the asymptotics for the lowest eigenmode are given in terms of a conjecture.  
**Conjecture 1** (Asymptotics of Perforated Parabolic Shells of Revolution, [7]).: _Given a \(g\times g\) regular perforation pattern, there exists a critical dimensionless thickness \(t_{c}\) at which the angular wave number \(k_{c}\approx g/2\). For thicknesses \(t<t_{c}\), the asymptotics of the lowest eigenmode do not conform to those of the non-perforated case._Geometrically, this means that there exists a thickness at which, within the subspace containing the lowest eigenmode, at least one pair of eigenmodes is in perfect alignment with the perforation grid so that one mode acts (has maximal amplitudes) on the holes and the other in the areas in between.  
In many structures, the perforation patterns may be locally regular--for instance, in shells made out of panels with local perforation patterns. Once the angular symmetry is removed, the eigenmodes are no longer pure, but linear combinations of periodic functions in the angular direction. Removal of axial symmetry affects the local axial amplitudes only. Here, the trommel screens with precisely these properties are considered. In Figure 1, an example of the axial effect (Figure 1a,b) and the saturation with high wave numbers in response (Figure 1d) are illustrated. Currently, even a case of two adjacent but different regular perforation grids is open in the sense that the asymptotics cannot be predicted.
# Korean Passori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding
## 1 Introduction
The model integrates max pooling layers for downsampling and corresponding max unpooling layers for upsampling only in the frequency axis to pool only in the frequency dimension, facilitating an effective feature extraction and reconstruction process. Notably, the model incorporates a self-attention and channel attention mechanism specifically within the bottleneck. The self-attention layer added onto the final feature map of the encoder reduces the computational complexity because the frequency axis is reduced in final feature map of the encoder and the time axis in this stage is the same as the input because of the one-dimensional pooling layer. The model acquired from the source task trained on the vocal melody dataset is an essential component for F0 estimation in Pansori vocal note transcription in the target task. This model provides crucial insights and features for estimating fundamental frequency values (F0) in the target Pansori audio data. To precisely estimate the vocal notes in the [PERSON] performance, the Viterbi decoding technique was employed, a core process within hidden Markov models (HMMs) [17]. Viterbi decoding involves the representation of different states (such as silence, onset, and sustain states) that correspond to various aspects of the vocal notes of Pansori within a defined range. The transition matrix is formulated to model the probabilities associated with transitioning between distinct vocal states within the HMM. This matrix reflects the likelihood of moving from one state (e.g., silence) to another (e.g., onset or sustain) during the progression of Pansori vocal performances. Similarly, the observation matrix is computed to assess the probability of observing specific vocal notes or events within Pansori music. It quantifies the likelihood of various vocal characteristics, such as note transitions, sustained notes, or silence, observed within the performance. By leveraging the Viterbi algorithm in conjunction with the constructed transition and observation matrices, we aim to identify the most probable sequence of hidden states (representative of Passori vocal notes) in the audio recordings. This sequence considers the observed vocal characteristics and the probable transitions between these vocal states. The application of these techniques seeks to offer a systematic and robust methodology for accurately transcribing and analyzing Pansori vocal performances, providing insights into the structured vocal elements and note transitions inherent in this traditional Korean music.
# Food Processing with UHP Waterjets
## 2 Waterjet Characteristics
### Waterjet Power Density  
The power density of the waterjet is defined as the power per unit area, and it correlates to the cutting capability of the jet. The denser the power, the more effective the jet becomes for cutting, as stated by [PERSON] [3]. Jet spreading, as shown above, will reduce the power density. However, we will develop an expression below for the power density to recognize the parameters that affect it. The waterjet velocity \(V_{w}\) can be calculated from the pressure \(P\) and the water density \(\rho_{w}\).  
\[V_{w}=C\ \sqrt{\frac{2P}{\rho_{w}}} \tag{1}\]  
Figure 1: The 350 MPa waterjet coherency at different upstream conditions.  
In the above equation, \(C\) is a lumped coefficient for the coefficient of velocity and the effect of water compressibility, as stated by [PERSON] [3]. This equation can be used to express the water flow rate \(Q_{w}\) as:  
\[Q_{w}=C_{d}A_{n}\sqrt{\frac{2P}{\rho_{w}}} \tag{2}\]  
In the above equation, \(A_{n}\) is the cross-sectional area of the orifice while \(C_{d}\) is its overall coefficient of discharge. Now, the waterjet power \(E_{w}\) can be determined from the following equation:  
\[E_{w}=\ P\ Q_{w} \tag{3}\]  
Using Equation (2) in Equation (3), the waterjet power can be expressed as:  
\[E_{w}=\ C_{d}\ \ \ \ \sqrt{\frac{2}{\rho_{w}}}\ \ \ \ A_{n}\ P^{1.5} \tag{4}\]  
Defining the jet power density \(E_{wd}\) as the jet power per unit area, we obtain:  
\[E_{wd}=\frac{E_{w}}{A_{n}}=\sqrt{\frac{2}{\rho_{w}}}\ \ \ C_{d}\ \ P^{1.5} \tag{5}\]  
For water, we replace \(\sqrt{2/\rho_{w}}\) with \(K_{w}\) as a water constant, and we obtain:  
\[E_{wd}=K_{w}\ C_{d}\ P^{1.5} \tag{6}\]  
Typical values of the waterjet power density are shown in Figure 2 below for \(C_{d}\) values of 0.7, 0.6, and 0.5, and water density of 1.0 kg/dm\({}^{3}\). Increasing the pump pressure from 400 MPa to 600 MPa, a 50% increase in pressure, will increase the power density by 83%. As can be noticed from the above equation, the power density depends on the pressure for a given liquid and orifice coefficient of discharge. The higher the coefficient of discharge, the higher the power density as more flow rate will flow out of the same orifice at the same pressure. This places important demand on orifice design. For example, an orifice with a \(C_{d}\) of 0.7 has 16% more power and accordingly more power density than an orifice with \(C_{d}\) = 0.6. This is important for food cutting as several orifices are typically used on a single waterjet cutting system using a single pump. Accordingly, power losses will affect the overall efficiency and productivity of the system. A \"Food grade\" orifice is one with a high \(C_{d}\) value, a sharp edge, and is typically made of diamond.  
Observe that the waterjet power density is in the range of \(10^{5}\) W/mm\({}^{2}\) which is about the same or one order of magnitude less than lasers used for cutting. However, some high-power lasers may reach power densities a few orders of magnitude higher, for example, \(\sim\)\(10^{14}\) W/mm\({}^{2}\).
## Results and Discussion
### Formation of the BCGCuO Electrolyte Films on the LNFO Substrates with the Deposited LBNOnc Sublayer
The thickness of the electrolyte film in the BCGCuO/LBNO\({}_{\text{nc}}\)/LNFO\({}_{\text{P}}\) half-cell was further increased by an additional three cycles of deposition/sintering (intermediate sintering between cycles was carried out at temperatures of 1200 \({}^{\circ}\)C, 2 h; 1350 \({}^{\circ}\)C, 2 h; and final sintering was performed at 1450 \({}^{\circ}\)C, 2 h). Additionally, to enhance the effect of barium preservation, sintering at all the stages, including intermediate ones, was carried out in a protective barium carbonate filler. The total thickness of the BCGCuO film was 21 \(\upmu\)m. According to the EDX analysis data, the barium content in the film was close to the nominal level (-17.1 at.%). Figure 5: SEM images of back sides of the porous electrode substrates after the BCGCuO electrolyte sintering at 1450 \({}^{\circ}\)C, 2 h: (**a**) \(\text{LNFO}_{\text{P}}\) in BCGCuO/LBNO\({}_{\text{nc}}\)/LNFO\({}_{\text{P}}\) and (**b**) \(\text{LNFO}_{\text{ss}}\) in BCGCuO/LBNO\({}_{\text{nc}}\)/LNFO\({}_{\text{ss}}\). Thus, in the case of the BCGCuO/LBNO\({}_{\text{nc}}\)/LNFO\({}_{\text{P}}\) half-cell, the electrolyte with a dense grain structure was formed with a small number of closed pores on the surface (Figure 7a), in contrast to the Ba-deficient film (Figure 6a), which was characterized by a loose structure with a large number of pores. As a result of increasing the electrolyte thickness to 21 \(\upmu\)m, it was possible to retain barium in the BCGCuO electrolyte layer, which is confirmed by the spatial distribution of elements in the coating (Figure 7b,c). According to the EDX data, La diffusion into the electrolyte layer (\(\sim\)1.2 at.%) occurred but its distribution in the electrolyte layer was quite uniform (Figure 7c) compared to the results of deposition of a BCGCuO film on a dense LBNO\({}_{\text{ss}}\) substrate [16], when in the electrolyte film, the sections of its heterogeneous accumulation were visible. The Gd content in the film was slightly reduced (\(\sim\)0.7 at.%) in both films--obtained in this study and in [16]--which may be associated with its diffusion into the substrate. No diffusion of Ni into the film was detected. Figure 7d shows a SEM image of the BCGCuO/LBNO\({}_{\text{nc}}\)/LNFO\({}_{\text{P}}\) half-cell cross section, taken after the electrochemical measurements, according to which a dense layer of sintered barium-containing electrolyte with a thickness of about 20 \(\upmu\)m was formed. Figure 6: SEM images of the BCGCuO/LBNO\({}_{\text{nc}}\)/LNFO\({}_{\text{ss}}\) half-cell after cycling EPD (3 cycles): the BCGCuO electrolyte surface (**a**); EDX mapping images (cross-sectional)—integrated (**b**) and for the individual elements (**c**).
# Model Test on the Behaviors of Deep Excavation with Lateral Confined Water
## 3 Results
### Pore Water Pressure Analysis  
The variations in pore water pressure outside and at the bottom of the excavation are shown in Figure 10, which exhibit convex and linear distributions with depth, respectively. The pore water pressures outside the excavation range from 0.9 to 8.0 kPa, while those at the bottom range from 1.7 to 3.6 kPa. After the soil was filled (initial state), the pore water pressures at all depths were below 4.0 kPa. When the lateral confined water level was raised to 396 mm (O), the pore water pressure outside the excavation significantly increased, while the pressure at the bottom increased only slightly. The average increase rates of the pore water pressure outside the excavation at depths of 20 mm, 260 mm, and 500 mm were 56.9%, 117.9%, and 46.8%, respectively. This indicates that the increase in pore water pressure was most pronounced within the depth range of the lateral confined aquifer. As dewatering and soil excavation progressed (K1-K4), the pore water pressure outside the excavation decreased due to the inward deflection of the diaphragm wall (Figure 12), while the pressure at the bottom of the excavation gradually decreased due to the reduction in upper load. After the excavation was completed, and the lateral confined water level was raised to 432 mm (F), the pore water pressure outside the excavation increased again, with the maximum pressure observed within the depth range of the lateral confined aquifer (8.0 kPa), surpassing the pressure recorded when the lateral confined water level was at 396 mm (7.4 kPa). In contrast, the pore water pressure at the bottom of the excavation did not exceed the pressure observed at 396 mm.  
Figure 9: Bending moment variation on diaphragm wall.  
By analyzing the evolution of pore water pressure, it can be inferred that the changes in pore water pressure at the bottom of the excavation were primarily caused by the dewatering and excavation of the soil layers, with limited influence from the lateral confined aquifer pressure. Based on the anti-inrushing stability check of the bottom [16], the safety factor remained above 1.05 during all phases of the test, indicating that the pit bottom is safe.
# An Approach for Anomaly Detection in Network Communications Using _k-Path_ Analysis
## 5 Results
### Determination of the \(p\)-Value Threshold (\(r_{0}\))  
To establish the threshold (\(r_{0}\)) for each model, we implemented a process to generate a scenario reflecting normal behavior. For the period from February 1 to February 28, we generated data packets for three edges with specific means and standard deviations: 200 (standard deviation of 45) for the edge (\(A\), \(B\)), 205 (standard deviation of 48) for the edge (\(B\), \(C\)), and 151 (standard deviation of 41) for the edge (\(C\), \(D\)). Peaks were introduced randomly within intervals according to a specific distribution. Each peak was assigned based on predefined values for each edge, using a set of defined weights. For example, the number of peaks was selected from the values 0 and 1 with respective weights of 0.9 and 0.1, thus favoring the value 0 more frequently. This process aims to simulate extreme variations that can occur under real network conditions. Additionally, we ensured that no edge could be involved in more than 14 different exchanges within 30 min. Timestamps were randomly assigned to the generated data packets for each edge. To achieve a false alarm rate of one alarm per day, we selected the tenth smallest \(p\)-value from the resulting list of \(p\)-values, as chosen in the two-state scan path model [4]. The Table 3 illustrates the \(p\)-value thresholds for each model.
# Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
## 4 Experiments
### Ablation Experiment and Discussion
#### 4.4.2 Impact of Head Number  
Table 4 shows the influence of the number of multihead attentions in the Transformer structure on the performance of the model, and the models were all tested on Manga109. In order to more intuitively compare the impact of different numbers of attention heads on RDST. We also drew a scatter line chart for the PSNR indicators of the three zoom scales within and outside the distribution, as shown in Figure 3. For the convenience of presentation, we denote the models as RDST1, RDST2, RDST4, and RDST8.  
Combining the data in Table 4 with the broken line in Figure 6, we can clearly see that for the over-score effect within the distribution, RDST8 obtains the best PSNR value; for the over-score effect outside the distribution, RDST2 obtains the best PSNR value. Through a large number of previous studies, it is known that different numbers of attention heads in the same layer of Transformer can learn information in different subspaces, but the attention patterns of most heads are the same. Therefore, we guess that for the super-resolution tasks in the subdivisions, the scaling factor is small, different feature information can be obtained from different heads, and feature information can be supplemented from heads with similar attention patterns, thereby improving the performance of the model. When the model performs an out-of-distribution super-resolution task, the scaling factor is large, and the heads with the same attention mode cannot achieve good information complementation. On the contrary, unnecessary information similar to noise is introduced, resulting in a large number of heads, causing the model's performance to decline.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**LFF** & **GFF** & **Metric** & \(\mathbf{\times 2}\) & \(\mathbf{\times 3}\) & \(\mathbf{\times 4}\) & \(\mathbf{\times 6}\) & \(\mathbf{\times 8}\) & \(\mathbf{\times 12}\) & \(\mathbf{\times 18}\) & \(\mathbf{\times 24}\) & \(\mathbf{\times 30}\) \\ \hline \hline \multirow{3}{*}{\(\times\)} & \multirow{3}{*}{\(\times\)} & PSNR & 39.32 & 34.26 & 31.24 & 27.36 & 25.09 & 22.51 & 20.58 & 19.47 & 18.51 \\  & & SSIM & 0.9785 & 0.9491 & 0.9179 & 0.8515 & 0.7957 & 0.7141 & 0.6453 & 0.6088 & 0.5827 \\  & & PSNR & 39.38 & 34.36 & 31.35 & 27.44 & 25.14 & 22.54 & 20.59 & 19.46 & 18.5 \\  & & SSIM & 0.9785 & 0.9497 & 0.919 & 0.8535 & 0.7982 & 0.7166 & 0.6471 & 0.6094 & 0.5826 \\  & & PSNR & 39.26 & 34.22 & 31.18 & 27.34 & 25.05 & 22.48 & 20.55 & 19.45 & 18.49 \\  & & SSIM & 0.9783 & 0.9488 & 0.9171 & 0.8507 & 0.7949 & 0.7138 & 0.6452 & 0.6087 & 0.5827 \\  & & PSNR & 39.33 & 34.32 & 31.33 & 27.44 & 25.16 & 22.57 & 20.61 & 19.49 & 18.53 \\  & & SSIM & 0.9784 & 0.9496 & 0.9189 & 0.8532 & 0.7980 & 0.7165 & 0.6470 & 0.6100 & 0.5837 \\ \hline \end{tabular}
\end{table}
Table 3: Add Ablation study of LFF and GFF. Best performance are in red color, respectively.