esa-sceva/satcom-chunk-collection huggingface dataset:
# An InSAR Interference Fringe-Matching Algorithm Based on Mountain Branch Points
## 5 Conclusions  
The main objective of this study was to address the primary technical challenge associated with using interference fringe matching for navigation, namely, the poor performance of traditional algorithms in this context. First, the image characteristics of interference fringes were analyzed, and it is concluded that traditional algorithms fail to make use of the rich terrain features of interference fringes, which leads to a poor matching effect. Based on this, the proposed algorithm was first designed to extract the mountain line features and to detect the branch points of the mountain range as the keypoints for feature matching based on the online features; second, the SIFT descriptor was improved by using the terrain features around the branch points. Using real measurement data, we demonstrated that the results are significantly improved compared with those of the traditional algorithm, providing a new idea for terrain data matching. The method proposed in this paper can accurately detect mountain lines using the algorithm. Future research should focus on improving the accuracy of mountain line detection, which will enhance the algorithm's performance. As the algorithm detects the mountain branch point at a non-sub-pixel level, it is also worth exploring methods to locate the mountain branch point at sub-pixel level. Additionally, an interference fringe is an image with topographic features, and the method proposed in this paper is based on this feature. Therefore, the study of any remote sensing image matching problem with terrain features can be inspired by this paper.  
Conceptualization, B.W., M.X., N.L. and G.S.; investigation, G.S. and LL.; software, G.S. and R.S.; writing--original draft preparation, G.S.; writing--review and editing, G.S., L.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 62073306. This work was also supported by the Youth Innovation Promotion Association CAS.  
**Institutional Review Board Statement:** Not applicable.
## 1 Introduction  
The emergence of new telecommunications services, such as: 4 K/8 K ultra-HD video, virtual reality, multimedia, and cloud computing among others, and the constant improvement of existing ones, makes it necessary to implement technologies capable of satisfying the demands of both speed and bandwidth; the means of transmission that offers advantages in terms of data transmission speed is optical fiber (O.F.) which is why these technologies base their architecture on this means of transmission. Passive optical network (PON) has become popular as an O.F. access network solution, achieving in its third generation an architecture capable of offering a total network performance of 40 Gbps, thanks to the next generation passive optical networks standard, next-generation passive optical network 2 (NG-PON2). These 40 Gbps links can be achieved through the combination of heterogeneous systems with low data transmission speeds, 2.5 Gbps and 10 Gbps, which allows for analysis of the behavior of the spectral efficiency in a quasilinear propagation regime in each combination.  
A passive optical network is a network system that sends the signal all, or almost all, of the way to the final user; it is characterized by having a point-to-multipoint distribution model, point-to-multipoint communication (P2 MP) using passive components [1]. The development of PON networks continued with the emergence of gigabit-capable passive optical network (GPON) under the ITU-T-G.984 recommendation, which specifies symmetric and asymmetric transmission speeds of 2.5 Gbps for this technology. Traffic demands of up to 10 Gbps lead to the evolution from GPON to NG-PON, which is divided into two phases. The first phase refers to a PON system with a capacity of 10 Gbps in at least one direction, while, in the second phase, there is great speeds of up to 40 Gbps downstream and 10 Gbps upstream [2].  
This is how fiber optic networks aim to support a wide variety of data traffic generated by the diversification of applications with different speed requirements, needs that current legacy single line speed networks (SLR) with predefined modulation formats cannot supply, because they do not offer enough flexibility to meet the requirements of demands with such a wide range of granularities. Therefore, future optical fiber networks are expected to be heterogeneous. When speaking of heterogeneous optical networks, reference is made to those networks that allow the implementation of different access capacities for different modulation formats [3; 4].
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 2 Model Architecture  
The mixture alignment FastSpeech2 model comprises four main components: Tibetan text preprocessing, the mixture alignment encoder, the decoder, and the HiFi-GAN vocoder [23]. Figure 1 provides an overview of the model structure.
# Analysis of Grating Lobe Effects on GEO DSC Distributed Antennas
## 4 Suppression of Grating Lobes Using Sparse Arrays  
### Layout of a Single Satellite Antenna Array  
For the purpose of achieving a compact and fewer element configuration with the ability to achieve wide-angle scanning for a single DSC satellite antenna in GEO orbit, the layout of the single satellite array adopts an equilateral triangular arrangement as shown in Figure 9. Taking the transmitting antenna as an example, the operating frequency was set to 20 GHz and the triangular side length was set to \(d_{x}=2.4\lambda\). The array pattern of the single satellite, as shown in Figure 10, exhibited no grating lobes within the range of \(\pm 8.7^{\circ}\). The sidelobe level was 15.88 dB, and the main lobe width was \(2.22^{\circ}\), enabling improved directivity and extended transmission distance.
## 4 Results and Analysis
### Spatial Distributions of TWS
In addition, abundant precipitation causes an obvious increase in the GLDAS, GFO, and GNSS-TWS values around the Appalachian Mountains due to the good water and soil conservation effects. While the oscillation of TWS is weak far from the Appalachian Mountains, this is attributed to the precipitation in flat areas flowing into the Atlantic Ocean through rivers.
## 2 Numerical Investigation
### Experimental Results and Comparison with the Numerical Simulation  
To examine the validity of the original model's numerical results, they were compared with the experimental results under the design conditions. The equipment used in the experiment was a laminar flow multi-nozzle chamber that satisfied Figure 15 of ANSI/AMCA standard 210 [18] and ANSI/ASHRAE standard 51 [19]. Figure 4a shows the schematic diagram of the chamber, and Figure 4b shows the plenum fan test scene.  
Figure 1: Geometric shape of the original model (PRL-560L0).  
Figure 3: Mesh system.  
Figure 2: Computational domain.  
Figure 5 shows that the CFD results and the experimental results are in good agreement with the rated airflow rate and the static pressure conditions.
## 5 Critical Aspects of FSS Technology
### Potential Challenges of FSS Technology  
The implementation of FSS technology faces several challenges, both in design and application. One of the main challenges is the sensitivity of FSS structures to fabrication tolerances. Small deviations during the manufacturing process can drastically affect the electromagnetic performance, especially at higher frequencies like millimeter waves and terahertz bands. Another challenge is scalability. While FSSs are effective in applications involving flat surfaces, adapting these structures for curved or complex geometries can be difficult, which limits their deployment in certain architectural or industrial applications. Furthermore, material constraints also pose challenges; the selection of materials that are both cost-effective and capable of withstanding environmental stressors, such as extreme temperatures or corrosion, is still a critical issue for large-scale deployment.
# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
## 4 Implementation of Beamforming in Open RAN Networks
### Beamforming in Open RAN
\begin{tabular}{c} Scheduling Commands and \\ Beamforming Commands \\ \end{tabular}  } & \begin{tabular}{c} Scheduling information, FFT size, CP \\ length, subcarrier spacing, UL PRACH \\ scheduling, DL and UL beamforming \\ commands (e.g., beam index) \\ and scheduling \\ \end{tabular}  & \(\sim\)slot \\ \cline{2-4}  & \multirow{4}{*}{ \begin{tabular}{c} LAA LBT configuration parameters \\ and requests \\ \end{tabular}  } & \begin{tabular}{c} LBT Configuration parameters such as \\ lbtHandle, lbtDeferFactor, \\ lbtBackoffCounter, lbtOffset, MCOT, \\ lbtMode, sfnsf, lbtCWconfig\_H, \\ lbtCW config\_T, lbtTrafficClass \\ \end{tabular}  & per MCOT/DRS \\ \cline{2-4}  & \multirow{4}{*}{ \begin{tabular}{c} LAA LBT status and responses \\ bufferError, lbtCWR\_Result \\ \end{tabular}  } & \ \begin{tabular}{c} LBT DL indication parameters such as \\ lbtHandle, lbtResult, initialPartialSFs, \\ bufferError, lbtCWR\_Result \\ \end{tabular}  &
\begin{tabular}{c} LBT DL indication parameters such as \\ lbtHandle, lbtResult, initialPartialSFs, \\ bufferError, lbtCinformation beamforming method to achieve a lower packet error rate and high latency to support and reduce the complexity of generating the beamforming weight in O-RU, which requires the high performance of CSI data. The outcome results of this concept can confirm that the channel state information beamforming method is acknowledged as a reliable option in high channel traffic to improve the latency and channel capacity by using less recourse blocks and enhancing the signal power compared to the other three beamforming methods mentioned above. Performing the precoding in O-RU will increase the complexity of implementing in case of the high precoding stream number, resulting in inter-symbol interference in O-RU with non-desired users. To overcome these interference challenges, we suggest using zero-forcing beamforming. The next section explains how the zero-forcing method may be implemented.
## 3 Research Methods
### Correlation Analysis  
#### 3.4.1 Pearson Correlation Analysis  
The Pearson correlation coefficient is defined as the covariance of two variables divided by the product of their standard deviations [19]. The formula is defined as follows:  
\[r=\frac{\sigma_{xy}}{\sqrt{\sigma_{x}^{2}\sigma_{y}^{2}}} \tag{1}\]  
In Equation (1), \(r\) represents the correlation coefficient; \(\sigma_{x}\) represents the standard deviation of variable \(X\); \(\sigma_{y}\) represents the standard deviation of variable \(Y\); and \(\sigma_{xy}\) represents the covariance between variables \(X\) and \(Y\). The value of \(r\) ranges between \(-1\) and \(1\), denoted as \(|r|\leq 1\). A value of \(|r|\) closer to \(1\) indicates a higher degree of correlation between the variables \(X\) and \(Y\). When \(|r|\geq 0.8\), the variables are highly correlated. When \(0.5\leq|r|<0.8\), the variables are moderately correlated. When \(0.3\leq|r|<0.5\), the variables are lowly correlated. When \(|r|<0.3\), the variables have a weak correlation and are essentially unrelated [20].  
Due to the rising and falling of the reservoir water level, the following criteria are established for calculating the correlation between the reservoir water level and the cumulative displacement of the landslide.  
During the rising phase of water levels, a positive correlation exists between monthly increases in water level and differences in monthly cumulative displacement. A higher correlation coefficient (\(r\)) signifies a stronger relationship, indicating that larger monthly increases in water level correspond to greater cumulative displacement of the landslide. Conversely, during the decreasing phase of reservoir water levels, a positive correlation between monthly decreases in water level and differences in monthly cumulative displacement occurs when the correlation coefficient (\(r\)) is negative. In this scenario, a higher absolute value of \(r\) suggests that larger monthly decreases in water level correspond to greater cumulative displacement, while a smaller absolute value indicates the opposite.
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 6 Conclusions  
This paper proposes incremental and decremental TAS selection algorithms with low complexity to efficiently reduce the number of RF chains for the MU-STLC systems. It is analyzed that the proposed algorithms achieve significantly reduced complexity than the existing greedy algorithm for the MU-STLC systems. Thus, they achieve a better tradeoff between BER performance and computational complexity for large antenna dimensions. The proposed MMSE decremental TAS selection algorithm obtains better BER performance than the existing greedy algorithm for the MU-STLC systems with relatively low antenna dimensions. Its BER performance is close to that achieved by exhaustive search-based optimal MMSE-based TAS selection algorithm for the \((8,4,4)\) MU-STLC system. Its complexity is significantly lower than the other algorithms especially for large \(N_{S}\). Moreover, it is analytically shown that decreasing the number of active transmit antennas in the MU-STLC systems damages the detection SINR. Its simulation results are shown to agree with the asymptotic received SINR penalty. As possible future research, it  
Figure 10: BER the proposed TAS selection algorithm for \((8,N_{S},K)\) MU–STLC system with \(N_{S}=K\) under SNR = 16 dB.  
Figure 9: BER the proposed TAS selection algorithm for \((8,N_{S},K)\) MU–STLC system with \(N_{S}=K\) under SNR = 12 dB.  
would be worthwhile to consider the joint transmit/receive antenna subsets selection or joint transmit antenna/user subset selection for the MU-STLC systems.  
This study was supported by research funds from Dong-A University.  
Not applicable.  
This study was supported by research funds from Dong-A University.  
The author declares no conflict of interest.
# Numerical Simulation of the Hydrodynamic Performance and Self-Propulsion of a UUV near the Seabed
## 3 Numerical Methods
### Meshing Settings  
Ansys fluent mesh is applied to discrete the computational domain. The stationary domain is a mixed mesh containing polyhedral mesh and hexahedral mesh, and the rotating domain is a polyhedral mesh, as shown in Figure 3. In order to effectively and accurately predict the hydrodynamic properties of the UUV and propeller, two layers of encrypted regions are set up around the UUV and propeller, as shown in Figure 3a. Figure 3b shows the nose grid and Figure 3c shows the stern and rotation domain grid. To capture the shape of the propeller, the grid size of the leading and trailing edges of the propeller is 0.05 mm. Figure 3d shows the grid of the propeller blade surface. Prismatic boundary  
Figure 2: Schematic of flow field.  
layer meshes are set for all the non-slip boundaries in the flow field domain, and the height Y+ of the mesh is controlled to be around 1 to meet the needs of the turbulence model. A total of 15 boundary layers are set up. In order to capture the effects of the seabed on the hydrodynamics of the vehicle, the bottom wall of the stationary domain is arranged with 100 nodes in the length of \(1L_{A}\).  
A grid independent validation is performed in this paper to confirm that the numerical results do not depend on the quantity of grids. Three grid resolutions, 3.2 M (coarse grid), 6 M (medium grid) and 8.4 M (fine grid), are set for the self-propelled UUV case with \(Hd\) of 2 and oncoming velocity of 1 m/s. Table 4 summarizes the numerical results for different grid resolutions. It can be seen that the numerical results of the fine and medium meshes are close to each other, which predicts that further increment of the number of mesh will not significantly affects the numerical results of CFD. Therefore, the medium resolution grid is used to complete the subsequent numerical simulations. In order to eliminate the influence of grid resolution on the numerical results of different simulation cases, the medium grid resolution is used for the bare UUV simulation cases and the self-propelled UUV cases.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 3 Design of Current Controllers  
The simulation study of the current control techniques described in Section 2 was performed in MATLAB/Simulink using the parameters summarized in Table 1. The LC filter was designed for a cut-off frequency of 1 kHz, which is a decade below the switching frequency of 10 kHz to attenuate the high switching frequency noises generated by the inverter. For the PLL, typical parameters available in Simulink were used. The \"Backward-Euler\" technique in MATLAB/Simulink was used to discretize the simulations. The simulations were performed using MATLAB's \"ode23 tb\" variable-step solver.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline DC Voltage (\(V_{\text{DC}}\)) & 400 V \\ Grid Frequency (\(f\)) & 60 Hz \\ Grid Voltage (\(V_{g}\)) & 208 V (L-L) \\ Grid Impedance (\(Z_{grid}\)) & \((0.135+j0.169)\Omega\) \\ Switching Frequency (PI, PR, and ADP) (\(f_{\text{sw}}\)) & 10 kHz \\ Switch Dead-time (\(t_{d}\)) & 2 \(\mu\)s \\ Filter Inductance (\(L\)) & 10 mH \\ Filter Resistance (\(r\)) & 0.1 \(\Omega\) \\ Filter Capacitance (C) & 3.3 \(\mu\)F \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters; PI: proportional-integral; PR: proportional-resonant; ADP: adaptive dynamic programming.  
Figure 6: Supplementary ADP (adaptive dynamic programming) controller. (**a**) Schematic diagram. (**b**) detailed structure showing the action and critic network.
# Influence of Pre-Treatment on the Seasonal Variation of Smoked Baltic Strat Texture and Color
###### Abstract  
The seasonal variation of Baltic sprat chemical composition leads to a change in smoked fish texture and color, which may pose challenges for industrial processing. This research aimed to evaluate the dependence of smoked sprat texture and color on the catching season and pre-treatment applied before the smoking of fish, following one year of frozen storage. Various proportions of sodium chloride (NaCl), calcium chloride (CaCl\({}_{2}\)), and acetic acid were used in the solution for fish pre-treatment. The introduction of salts during the pre-treatment process for late-season fish has been found to enhance the texture of the smoked product. The color components of the lightness (L-) and redness (a*) measured on the smoked fish surface remained consistent throughout the catching season, whereas the yellowness (b*) showed a tendency to decrease towards the end of the season. Moreover, when acetic acid was applied to late-season fish, the yellowness of the smoked sprat surface increased compared to that of the sample without this pre-treatment. These findings suggest that the choice of pre-treatment methods can significantly improve the texture and color attributes of the smoked sprat, which is crucial for maintaining quality standards, especially in the context of industrial processing.  
catching season; frozen storage; work of shear; pre-treatment Article
# Experimental Study of a Novel Prototype of Dual-Cycle Two-Phase Cooling System for a Data Cabinet
## 3 Results and Discussion
### The Effects of the Flow Rate of Each Loop on the Cooling System  
#### 3.2.1 The Effects of PCTCS Internal Loop Flow Rate  
Maintain the temperature of the reservoir at 40 \({}^{\circ}\)C and gradually augment the flow rate within the internal loop for testing purposes. Figure 6 delineates the influence of the internal loop flow rate on system temperature and pressure drop. Throughout the experiment, the flow rate within the internal loop was incrementally raised from 1.3 L/min to 3.0 L/min. When the total flow rate reaches 1.3 L/min, providing an average of 1 g/s of working fluid to each layer of a server is sufficient to meet the normal operating requirements of its two internal CPU chips, demonstrating excellent heat dissipation and energy efficiency performance. With the augmentation in flow rate, T\({}_{105}\), the temperature of the reservoir within the internal loop, exhibited a slight increase, whereas T\({}_{102}\), the evaporation temperature, demonstrated a notable rise. The heat transfer temperature difference of the refrigerant within the internal loop also escalated. The elevation in flow rate augmented the pressure differentials, \(\Delta\)P\({}_{102-103}\) and \(\Delta\)P\({}_{103-105}\), spanning from the evaporator to the reservoir. The augmented resistance within the two-phase region was the principal factor contributing to the elevated evaporation temperature. Concurrently, T\({}_{\text{CPU}}\), the core temperature of the chip, gradually ascended; however, the heat transfer temperature differential between T\({}_{\text{CPU}}\) and T\({}_{102}\) remained relatively constant. This signifies that although increasing the flow rate does not markedly enhance heat exchange within the evaporator, it does amplify the system's heat transfer temperature differential. Additionally, the augmentation in the internal loop flow rate substantially increased the pressure differential, \(\Delta\)P\({}_{101-102}\), from the liquid-phase manifold to the evaporator. Nonetheless, for the external loop, the pressure differentials across various segments remained virtually unchanged.  
Figure 6: Effects of internal loop flow rate on temperature and pressure difference: (**a**) temperature of main measuring point; (**b**) internal and external loop pressure difference.
## 3 The Monte Scarpino Landfill and the SRS Test-Bed  
The case of the Monte Scarpino landfill in Genoa is of particular importance in relation to the monitoring of areas subject to environmental risk protection. The solid urban waste disposal plant covers an area of about half a million square meters (Figure 3a). Established in 1968, the landfill is located on the heights of Sestri Ponente, in the Metropolitan City of Genoa. The waste disposal plant is about 600 m above sea level and is composed of two modules that today are no longer in operation as the maximum allowed volume for filling has been reached. The over 10 million cubic meters of waste disposed of in the landfill continue to feed a biogas transformation system for the production of electricity (on average 60 million kWh per year entered into the national grid). The Liguria Region and the Metropolitan City of Genoa required AMIU (Azienda Multiservizi e d'Igene Urbana),  
Figure 2: Schematic diagram of the RF front-end of a Smart Rainfall System (SRS) sensor.  
the company that operates the landfill, to increase and improve its monitoring through different environmental matrices that influence incoming and outgoing flows of matter and energy.  
In this context, particular attention has been paid to the problem of water pollution [29; 30]. In the event of sudden, intense and highly localized precipitation phenomena, it is necessary to measure rainfall in real time, identifying the \"hot spots\" in which the events occur. There is a distance of 1.3 km and an elevation of about 300 m between the entrance to the landfill, to the north, and the borders to the south of it, where the containment tanks of the percolate are located [29]. The levels in the tank and the inflows must be constantly monitored during the rains. A system that acquires data in real time can also ensure greater safety for operators as well as it can generally allow for the activation of emergency procedures based on nowcasting [11; 12].