esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Acquisition and Preprocessing  
### Geometric Model and Simulation Environment  
In the liquefied natural gas process, the main focus is on the changes in methane. When the temperature and pressure of methane continuously increase, it reaches a supercritical state once they exceed a certain threshold [17]. This study conducted four sets of three-dimensional Computational Fluid Dynamics (CFD) simulations, considering straight channels with semicircular cross-sections of 1.2 mm and 1.8 mm in diameter, as well as a regular hexagonal channel. The length of the PCHE was 500 mm in all cases.  
A three-dimensional numerical simulation was conducted in a horizontal channel, taking into account the influence of gravity. In the context of the gasification process within an offshore LNG Floating Storage and Regasification Unit (FSRU), the methane pressure ranges from 6 to 9 MPa, with a pseudo-critical temperature varying between 190 and 215 K. The inlet boundary condition is specified as the mass flow rate, while the outlet boundary condition is set to constant pressure. Based on the general requirements for transcritical methane in offshore FSRU PCHEs, the inlet temperature is set to 111.15 K, and the temperature variation during the simulation spans from 100 to 300 K. To simulate the actual heat transfer conditions between LNG and the intermediate medium, the upper and lower walls are assigned constant heat flux boundary conditions, while the remaining walls are defined as adiabatic boundary conditions.  
With reference to the numerical simulation of transcritical fluid flow and heat transfer, it is considered that the SST k-\(\omega\) turbulence model can reflect the heat transfer characteristics of transcritical fluid by using the SIMPLE algorithm for the pressure-velocity coupling solution and the second-order upwind scheme for pressure dispersion. The momentum equation is discretized by the QUICK scheme, and the energy equation, turbulent kinetic energy equation, and turbulent dissipation rate equation are discretized by the second-order upwind scheme. When the residual value of each equation reaches 1.0 \(\times\) 10\({}^{-6}\) and the monitoring parameters remain unchanged, the calculation is considered to be convergent. At the same time, in order to eliminate the influence of the number of grids on the simulation results, the grid independence verification was carried out in this study. As shown in Figure 1, when the number of grids is greater than 2.1 million, the difference between temperature and pressure is small and tends to be stable, so 2.1 million grids was selected to calculate the final grid.  
The geometric model establishment and calculations in this study were performed using Ansys 2022R1. Feature selection, predictive model development, and other machine learning-related tasks were carried out in PyCharm 2024.3.1. The Python version utilized was 3.10, and the deep learning model was constructed based on PyTorch 1.8.0. The computational hardware included an NVIDIA GeForce RTX 4060 GPU and an Intel 14 th Gen Core i7-14650 HX CPU.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### Tele-Operated Driving
#### 3.1.2 Telco Requirements  
The mobile network plays a critical role in the functionality of tele-operated driving technology. In summary, there are three key requirements. In the first place, the mobile must provide enough bandwidth that the required amount of information can be exchanged between the vehicle and the vehicle control center. An objective measure for this is the situational awareness of the tele-operator who must be comfortable controlling the vehicle remotely. Second, the information that gets exchanged must be up-to-date when it is received. Thus, in addition to the aforementioned minimal delays in the vehicle, a minimal network latency forms another demanding requirement. At last, network reliability (Reliability in a broad sense as e.g., described under \"dependability\" [IEC60050] and \"functional safety\" [IEC61508, ISO26262]) plays a great role for functional ToD. To enable full control of the vehicle for the tele-operator, it has to be ensured that loss of critical information, such as key frames in the encoded video feeds, is kept at a minimum. All above key requirements may suffer if the vehicle is crossing a country border. Ideally, the handover from one MNO to another is not or only minimally perceived by the teleoperator. If this requirement cannot be met, the vehicle may need to come to a standstill such that the MNO handover can take place safely.
## 3 Results and Discussion
### Electrochemical Performance of Negative Plates
cell (150.3 mA h g\({}^{-1}\)), the initial specific discharge capacity of the cell with HPC additives increased at different levels. The cell doped with LC material shows the largest specific capacity, i.e., 175.1 mA h g\({}^{-1}\) with an increase of 16.5%, which is followed by the cell doped with PLC (169.4 mA h g\({}^{-1}\)), and the cell with HPC (167.6 mA h g\({}^{-1}\)). The addition of HPC improved the initial specific discharge capacity of cells to a certain extent. Among them, LC performed best, which was consistent with the conclusion of CV. Figure 6b illustrates the specific capacity curves of the four cells at different discharge rates. Among them, LC cell enjoys the largest specific capacity under different discharge rates. For example, at 0.1 C, the specific capacity of the LC cell is 175.2 mA h g\({}^{-1}\), which is higher than that of the control cell (150.8 mA h g\({}^{-1}\)) by 16.1%. At 2 C, the specific discharge capacity of the control cell is only 52.8 mA h g\({}^{-1}\). In contrast, the LC cell is as high as 95.1 mA h g\({}^{-1}\), which is higher than that of the control cell by 80.0%. LC cell still has a high capacity under the condition of high current charge and discharge. After 2 C rate, we adjusted all cells' rates back to 0.1 C and found that the specific capacity of LC cell recovered to 166.5 mA h g\({}^{-1}\), about 94.9% of the initial specific discharge capacity. The specific capacity of the control cell is only 132.3 mA h g\({}^{-1}\), which is 87.7% of its initial specific discharge capacity. The addition of LC material is available for a significant improvement of the batteries' stability and high rate discharge performance. The charge and discharge cycle curves of LABs at 1 C are shown in Figure 6c. The cycle performance of all the cells with HPC material is better than that of the control cell at 100% depth of discharge. It shows that the addition of HPC material is beneficial to the negative plates. The specific capacity of LC cell is always stable at about 100 mA h g\({}^{-1}\). PLC and HPC cells can maintain high specific capacity only in the first 200 cycles. The specific capacity of control cell is always low. Figure 6c shows the Coulombic efficiency of the LC cell, which is stable. Combined with the LSV results, the hydrogen evolution reaction may have intensified after about 200 cycles, which caused a rapid drop in the specific capacity of HPC and PLC cells. Even though the HPC material benefits the cycle performance of the cells, the far lower density would cause the undermixing with PbO. The beneficial effect is reduced. The data show that LC material could benefit the cells. LC material is able to eliminate the density difference between lead and carbon. It can disperse HPC uniformly in NAM. The -Pb-COO chemical bond in the LC material can also enhance the stability of the lead-carbon electrode. These characteristics would maximize the benefit of HPC material and improve the cycle performance of cells.
# Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography
## 3 Results  
### Spiral Volumetric Optoacoustic Tomography  
A whole-body (neck to hind paws) SVOT image, reconstructed using the frames selected with the proposed motion rejection approach, is displayed in Figure 2a. In these experiments, an average of 32% of frames were rejected per transducer position. The effectiveness of the motion rejection approach is demonstrated by analyzing three specific regions (dashed squares in Figure 2a). Specifically, we compare the image combining all the frames, with the one obtained by averaging the selected (static) frames as well as the image obtained by averaging the rejected (motion) frames. Note that the red square partially captures the thoracic region. It can be observed that a small vessel, clearly visible in the image rendered from the selected frames (Figure 2b, red arrow), cannot be resolved in the image reconstructed using all the frames. Furthermore, the former image features a clear motion artifact in the form of a 'double vessel' (red arrow), thus concealing the small vessel. The green square captures the region around the liver. A vertical vessel appears regular and complete in the selected-frames image (red arrow), whereas the same vessel appears disrupted in the all-frames image. Also here, the rejected-frames image discloses an artifact responsible for distorting the all-frames image. Finally, the blue square captures part of the abdomen. Clearly, small vessels are better resolved in the selected-frames image (red arrows). Notably, different structures appear to be blurred (yellow arrows) in the rejected-frames images with respect to the selected-frames images. A comparison between amplitude profiles of structures labeled by dashed yellow lines in Figure 2b further emphasizes the effectiveness of the motion rejection algorithm (Figure 2c) with the signal amplitude typically improved by 10% to 30% in the selected-frames images. Likewise, fine details appear more prominent in the selected-frames images, which is evinced by additional, fine peaks in the amplitudes profiles.  
Figure 2: Motion rejection results for spiral volumetric optoacoustic tomography (SVOT). (**A**) Sagittal maximal intensity projection (MIP) of a volumetric image of the mouse reconstructed with the selected-frames (scale bar—1 cm). (**B**) Zoom-in of three regions marked in red, green, blue, respectively, in (**A**). Each image is reconstructed with (left) all-frames, (center) the selected (static) frames (right) the rejected (motion) frames (scale bar—1 mm). Structural differences are marked (yellow and red arrows). (**C**) Amplitude profiles marked in b (yellow dashed lines) for images reconstructed from all the frames (dashed lines) versus selected frames (solid lines).
## 3 Information Centric Networking (ICN)
### Mobility  
Mobility refers to the changes in speed and direction of nodes over time. Mobility support at the network layer is a strength of ICN that addresses one of the limitations of IP networks. Consumer mobility is naturally supported in ICN architecture, so a change in the physical location of the consumer node does not affect the delivery of the content. However, producer mobility is more difficult to support as the name resolution system or routing tables in named-based routing need to be updated. To support producer mobility in ICN, different techniques are suggested in the literature [53]. [PERSON] [54] presented an adaptive content dissemination solution for VANET networks that includes a hierarchical context-aware content naming scheme that enables safety-critical and non-safety applications. Fog-computing-assisted mobility support with information-centric IoV is addressed in [55].
# Multi-Modal X-ray Imaging and Analysis for Characterization of Urinary Stones
## 2 Materials and Methods
### X-ray Diffraction (XRD)  
The XRD analysis was used here as the reference standard for stone composition analysis. To determine the structure of poly/single crystalline compounds of the three urinary stone samples, a small piece of stone samples was cut and examined using XRD methods. Two-dimensional wide-angle X-ray diffraction (2D-WAXD) was performed on a STOE IPDS-II, using Mo-\(K_{\alpha}\) radiation (0.71073 A) at 40 mA, 50 kVp for 30 min with a beam diameter of 500 \(\upmu\)m in the transmission mode. The sample was placed perpendicular to the beam to allow the X-rays to pass through the sample. The WAXD patterns were recorded on an image-plate detector system with a 340 mm diameter placed at a distance of 200 mm from the sample. The diffraction images were recorded covering a 26 range from 1.3\({}^{\circ}\) to 40\({}^{\circ}\). For the phase analysis, 1D scans were obtained through a 360\({}^{\circ}\) radial integration of the 2D images.  
While the IPDS-II detection system allows for the study of the overall structural state of the samples (crystallinity, ordering), it is limited in resolution. With highly crystalline samples, for a better peak separation and indexing, a PANalytical X'Pert Powder instrument was used in a Bragg-Brentano configuration on powder samples. Data was collected at room temperature using Cu-\(K_{\alpha}\) radiation (\(\lambda\) = 1.5406 A). The phase analysis was carried out using the program Highscore Plus from Malvern Panalytical [41].
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Social RS-Based Proxies  
The social dimension dealing with the status of people in a community is one of the most important components in DRM. Yet, few reliable RS-based proxies exist to address it. This is mostly because the social dimension of disasters embraces an individual status of community members, diversity of livelihoods (economic activities they may engage in), level of inequality, community cohesion, and level of services. Hardly any of these are directly captured through physical infrastructure or other spatial data that can be extracted using RS data. Tables 6 and 7 show existing RS-based proxies in the social environment for urban DRM. To discuss and explain the proxies in detail we separate them into two categories: services and infrastructures, and socio-economic status.
## 5 ICMP-Based Attacker AP Detection
### Attacker AP Detection  
Based on the analysis mentioned above, we know that when an attacker uses a public AP to collect CSI measurements, the number of ICMP ping packets in the network is far greater than normal cases. Thus, whether there exist ICMP ping packets and the number of ICMP ping packets per unit of time can be used to detect whether there exists a target public AP in public places. If a public AP is detected to have plenty of ICMP ping packets during different time periods, then it is very likely caused by an attacker who is pinging the public AP at a high rate, and we think there exists an attack in the public place.  
However, in public places, there will be many public APs, and the attacker may use two or more public APs to improve the success rate of the CSI-based attack. [PERSON] et al. [41] demonstrated that using multiple APs can improve the accuracy of gesture recognition. Therefore, in order to protect the input sensitive information thoroughly, users need to surf all public APs to detect how many public APs the attacker used.  
When the user detects plenty of abnormal ICMP ping packets in the current network, the system will first detect on which channel the target public network AP is working. Channel detection is easy to implement, and there are many commercial applications that can support channel detection functions, such as WiFi analyzers.
## 5 Future Development
### Integration of L-Band and P-Band  
[PERSON] et al. believed that the further development of integrating the L-band and P-band would make it possible to use TomoSAR technology to more accurately extract forest vertical structures, which would not only solve the problem of forest classification, but also provide strong support for the next generation of Earth Explorer BIOMASS mission [69]. [PERSON], Gabon, [PERSON] et al. compared the CHM and AGB models of a tropical forest obtained by an airborne P-band and L-band synthetic (SAR) Tomosar tomography [70]. In the forests located in Paracou, French Guiana and South America, [PERSON] et al. analyzed the applications of airborne P-band TomoSAR and LiDAR, showing that both could directly lead to high-resolution surface, height, and profile models. The results demonstrated that airborne-based products had higher quality due to stronger penetration. For the forest of an average height of 30 m at Paracou, a RMSE of less than 5 m for tree height estimation was obtained [71]. Moreover, [PERSON] et al. proposed a robust TomoSAR imaging procedure to obtain local high-resolution L-band images of forests for the areas of interest [72].  
The aforementioned studies demonstrate the strong penetration of L-band SAR signal in forests, and reveal the key factors that affect the penetration ability of the L-band SAR signal in forests. However, the studies did not explain the attenuation mechanism of the SAR signal in forest canopies and also did not account for the response process of microwave transmittance and penetration depth. In the estimation of forest height, the improvement in estimation accuracy was mainly achieved by developing algorithms and enhancing computation performance. Obtaining the results, to some extent, was fortuitous.  
In addition, the existing experiments focus mainly on temperate and boreal forests, and rarely take place in subtropical forests. Compared with the temperate and boreal forests, the structures of subtropical forests are more complex. Generally, subtropical forests often consist of three layers including tree, shrub, and grass, and have higher canopy closure and moisture inside the forests, which will lead to stronger impacts on the penetration of signals. Therefore, the results from the temperate and boreal forests will be less effective, and their applicability is limited in subtropical forests.  
L-band SAR data provide the potential for advancing forestry remote sensing technologies by exploring forest vertical structures from the canopy surface to the interior of forests and from 2D- to 3D-model reconstruction 1 [73], and are also critical in realizing the technologies of TomoSAR and penetrating remote sensing in the future [74].
## 5 Case Study Implementation of the Decision-Support Framework
### Calculation of Effect and Life Extension
#### 5.4.3 Quantitative Analysis of Life Cycle Energy Consumption and Environmental Emissions  
(1) Normal operation Energy Consumption and Environmental Emissions  
According to the normal operation fuel consumption of plan 1, Equations (19)-(21) and Table 8 are used to calculate energy consumption and environmental emissions for Plan 1. The other methods are the same, and the normal operation energy consumption and environmental emission costs of plan 1 during the analysis period are shown in Table 22.  
(2) Environmental emissions and energy consumption costs of maintenance activities  
Referring to the \"Highway Engineering Budget Quota\" (JTG/T B06-02-2007) and \"Highway Engineering Machinery Shift Cost Quota\" (JTG/TB06-03-2007), the energy consumption during the production, transportation, and construction stages of slurry seal raw materials, as well as the fuel consumption of various machinery, can be calculated by combining equation (16), the unit price of environmental emissions, and the energy consumption unit price. The calculation results are shown in Table 23.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Year** & **ECC\({}_{\mathbf{CO_{2}}}\)** & **ECC\({}_{\mathbf{HC}}\)** & **ECC\({}_{\mathbf{NOX}}\)** & **ECC\({}_{\mathbf{CO}}\)** & **NC\({}_{\mathbf{oper}}\)** \\ \hline
1 & 3.131607 & 0.154784 & 6.368233 & 0.011757 & 427.644 \\
2 & 3.459422 & 0.171009 & 7.035792 & 0.012979 & 472.410 \\
3 & 3.82261 & 0.188989 & 7.775523 & 0.014332 & 522.008 \\
4 & 4.225284 & 0.208928 & 8.595856 & 0.01583 & 576.998 \\
5 & 4.582375 & 0.226486 & 9.318247 & 0.017205 & 625.756 \\
6 & 5.063108 & 0.250281 & 10.29725 & 0.018997 & 691.406 \\
7 & 5.595587 & 0.276643 & 11.38185 & 0.02098 & 764.122 \\
8 & 6.186111 & 0.305886 & 12.58495 & 0.023176 & 844.765 \\
9 & 6.841251 & 0.338336 & 13.92001 & 0.025611 & 934.232 \\
10 & 7.567882 & 0.374336 & 15.40114 & 0.028307 & 1033.461 \\ \hline \end{tabular}
\end{table}
Table 22: Calculation of the normal operation Energy Consumption and Environmental Emission Costs of plan 1 (10,000 yuan/km).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Year** & **FCC** & **WC** & **RC** & **TTC** & **AC** \\ \hline
1 & 294.1211 & 17.4257 & 0.00154 & 33.3285 & 6.1727 \\
2 & 324.8908 & 19.2620 & 0.00154 & 38.2515 & 6.6986 \\
3 & 358.9778 & 21.2982 & 0.00155 & 43.8971 & 7.2670 \\
4 & 396.7672 & 23.5581 & 0.00156 & 50.36925 & 7.8796 \\
5 & 430.3811 & 25.4968 & 0.00153 & 49.28365 & 9.0551 \\
6 & 475.5033 & 28.1896 & 0.00154 & 57.12204 & 9.8205 \\
7 & 525.4778 & 31.1757 & 0.00155 & 66.18907 & 10.645 \\
8 & 580.8947 & 34.4909 & 0.00156 & 76.67867 & 11.534 \\
9 & 642.369 & 38.1729 & 0.00156 & 88.80832 & 12.491 \\
10 & 710.544 & 42.2615 & 0.00157 & 102.8211 & 13.518 \\ \hline \end{tabular}
\end{table}
Table 21: Calculation table of normal operation user cost of plan 1 (10,000 yuan/km).
## 5 Conclusions  
In this study, two circular-shaped ice-velocity anomalies were observed in Campbell Glacier, East Antarctica by using 13 DInSAR images generated from COSMO-SkyMED one-day tandem pairs obtained in 2010-2011. Firstly, DInSAR was used to define the valley of the glacier with local maximum velocity across the ice flow. Ice bed topography was also obtained from the helicopter-borne IPR surveys in 2016-2017 operated by the Korean Polar Research Institute. The year-long amplitude of velocity anomalies were ~1 cm/day for anomaly A, located 14.7 km from the grounding line of the glacier, and -5 cm/day for anomaly B, located 11.3 km from the grounding line. Both anomalies are located on a bed hill along the glacial valley where stick-slip and pressure melting can occur resulting in temporal variation of ice velocity. Bright radar reflection and the flat hydraulic head at the ice bed of anomaly A observed from the IPR-derived radargram strongly suggest the existence of basal water in the form of a reservoir or film. Smaller friction and a reduced variation of stick-slip motion would have occurred in A than in B. Crevasses begin to appear at B due to tensile stress at the top of the hill and the fast-flowing downhill. Changes of locations in the anomalies suggest complex pressure melting and transportation of the basal water over the bed hill. The location of the grounding line that had remained ambiguously in the DDInSAR image was also confirmed by the bright radar return from the basal water. Long-term observation of SAR is necessary to understand the seasonal variability of velocity anomalies and its relationship with basal water and bed topography.  
Conceptualization, H.L. and H.H.; methodology, H.L. and H.H.; software, H.L. and H.J.; validation, H.L., H.S. and H.J.; formal analysis, H.L. and H.J.; investigation, H.S. and H.J.; resources, H.H. and J.L.; data curation, H.L.; writing--original draft preparation, H.L. and H.S.; writing--review and editing, H.H. and H.J.; visualization, H.L., H.J. and H.S.; supervision, H.L. and J.L.; project administration, H.L., H.H. and J.L.; funding acquisition, H.L., H.H. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF-2019R1F1A10-41389, NRF-2021R1C1C1009621 and No. 2019R1A6A1A03033167) and also by Korea Polar Research Institute (No. PE21080 and PE21070).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. TanDEM-X DEM data were provided by the German Aerospace Center (DLR) under the project DEM_GLAC1183.  
The authors declare no conflict of interest.