esa-sceva/satcom-chunk-collection huggingface dataset:
# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 4 Experiments
### Ablation Study  
In our ablation study, we evaluate two core components of TGA-GS: the Thermal Gradient Alignment Loss and the SR module, as demonstrated in Tables 3 and 4 and Figure 7. Tables 3 and 4 reveal that the SR module significantly impacts rendering quality due to its direct influence on the final output. While the Thermal Gradient Alignment Loss, as an intermediate training constraint, also affects rendering results, its performance gaps can be mitigated by the SR module during post-processing. Conversely, Figure 7 illustrates that the SR module has minimal effect on reconstruction quality, whereas the Thermal Gradient Alignment Loss plays a critical role in determining geometric fidelity. This discrepancy  
Industrial Practitioners: The perception ability of autonomous vehicles, unmanned aerial vehicles, etc., in low-light or severe conditions is enhanced. The use of TGA-GS cross-mode alignment can ensure the synchronous perception of thermal barriers (such as pedestrians) and RGB traffic signs. Meanwhile, super-resolution of low-resolution thermal input can detect distant obstacles without affecting latency.  
Industrial and Building Inspectors: The multimodal calibration method proposed in this paper can better align the relevant information between thermal and RGB images and associate thermal anomaly areas (such as thermal leaks) with RGB texture details (such as rust or cracks). Meanwhile, by combining the guidance super-resolution module of TGA-GS, the quality of detection can be further improved.
## 3 Results
### Potassium Adsorption Isotherms
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Treatment** & **Regression Equation** & **R2** & \(\chi^{2}\) \\ \hline \multicolumn{4}{c}{**Langmuir-1**} \\ \hline Soil & y = 0.060 + 0.00006x & \(-\)0.1017 ns & 0.018 \\ CLB & y = 3.884 \(-\) 0.0034x & 0.2331 ns & 1.072 \\ B1 & y = 0.145 \(-\) 0.0002x & 0.0083 ns & 0.049 \\ B2 & y = 0.194 \(-\) 0.0003x & 0.1547 ns & 0.051 \\ B3 & y = 0.345 \(-\) 0.0005x & 0.2021 ns & 0.129 \\ \hline \multicolumn{4}{c}{**Langmuir-2**} \\ \hline Soil & y = \(-\)0.00008 + 0.078x & 0.9072 ** & 2.44 \(\times\) 10\({}^{-4}\) \\ CLB & y = \(-\)0.0036 + 4.163x & 0.3106 ns & 1.87 \(\times\) 10\({}^{-3}\) \\ B1 & y = \(-\)0.0004 + 0.189x & 0.7715 * & 5.68 \(\times\) 10\({}^{-4}\) \\ B2 & y = \(-\)0.0004 + 0.241x & 0.7550 * & 3.62 \(\times\) 10\({}^{-4}\) \\ B3 & y = \(-\)0.0008 + 0.429x & 0.5983 ns & 9.53 \(\times\) 10\({}^{-4}\) \\ \hline \multicolumn{4}{c}{**Langmuir-3**} \\ \hline Soil & y = 2765.355 \(-\) 4.025x & \(-\)0.3332 ns & 5328 \\ CLB & y = \(-\)472.558 + 1172.972x & 0.9817 ** & 119 \\ B1 & y = \(-\)912.392 + 320.233x & 0.2222 ns & 2447 \\ B2 & y = \(-\)1501.352 + 458.057x & 0.5804 ns & 1151 \\ B3 & y = \(-\)1315.974 + 521.343x & 0.7081 * & 928 \\ \hline \hline \end{tabular} Note: ** significant at P = 0.01, * significant at P = 0.05, ns not significant at P = 0.05.  
\end{table} 
Table 17: Regression equations, Coefficient of determination (R2), and Chi-square analysis (\(\chi^{2}\)) for the fit of the Langmuir type 1, 2, and 3 isotherms to the potassium adsorption data of the treatments. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Treatment** & **Regression Equation** & **R2** & \(\chi^{2}\) \\ \hline \multicolumn{4}{c}{**Langmuir-4**} \\ \hline Soil & y = 16.050 \(-\) 0.00002x & \(-\)0.3332 ns & 3.687 \\ CLB & y = 0.441 + 0.001x & 0.9817 ** & 0.079 \\ B1 & y = 7.890 + 0.001x & 0.2222 ns & 2.836 \\ B2 & y = 5.211 + 0.002x & 0.5804 ns & 1.695 \\ B3 & y = 3.732 + 0.002x & 0.7081 * & 1.257 \\ \hline \multicolumn{4}{c}{**Freundlich**} \\ \hline Soil & y = 1.362 \(+\) 0.911x & 0.7972 * & 0.025 \\ CLB & y = \(-\)16.374 + 6.596x & 0.5384 ns & 0.068 \\ B1 & y = 0.163 + 1.367x & 0.7426 * & 0.033 \\ B2 & y = \(-\)0.382 + 1.550x & 0.7530 * & 0.027 \\ B3 & y = \(-\)1.646 + 1.993x & 0.6925 ns & 0.039 \\ \hline \multicolumn{4}{c}{**Temkin**} \\ \hline Soil & y = \(-\)6074.48 + 1752.61x & 0.8527 * & 513.40 \\ CLB & y = \(-\)100384 + 14997x & 0.7650 * & 1368.23 \\ B1 & y = \(-\)12348 + 2819.12x & 0.8369 * & 678.28 \\ B2 & y = \(-\)16236 + 3422.73x & 0.8439 * & 613.18 \\ B3 & y = \(-\)21619 + 4226.44x & 0.8393 * & 719.51 \\ \hline \hline \end{tabular} Note: ** significant at P = 0.01, * significant at P = 0.05, ns not significant at P = 0.05.  
\end{table} 
Table 18: Regression equations, Coefficient of determination (R2), and Chi-square analysis (\(\chi^{2}\)) for the fit of the Langmuir type 4, Freundlich, and Temkin isotherms to the potassium adsorption data of the treatments.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 2 Materials and Methods
### Data and Experiment Details  
#### 2.5.1 HRS Image Data  
In order to effectively extract UGS, this experiment obtained high-resolution remote sensing images (GF-2, 21 July 2022) from the China Resources Satellite Application Center as the data source. The GF-2 PMS (panchromatic multispectral sensor) has a panchromatic band with a spatial resolution of 1 m and four multispectral bands with a spatial resolution of 4 m. The multispectral includes blue, green, red, and near-infrared bands. After ortho-correction, the panchromatic and multispectral images were fused to obtain the GF-2 image with a spatial resolution of 0.8 m. The image shearing operation was performed to obtain the GF-2 image. Finally, the 0.8 m GF-2 image was subjected to image cropping operation to obtain the object image of the study area.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 1 Introduction
Although AC filters are usually configured in converter stations, a great quantity reactive power is absorbed by LCC-HVDC during commutation failure fault. These further deteriorate voltage stability of the AC grid. To overcome the above challenges, this paper presents a UC model for the receiving-end in the AC/DC hybrid grid. The proposed model contains minimum frequency constraints for ensuring sufficient capacity on primary frequency regulation after the HVDC blocked. Moreover, transmission line power limits after completing secondary frequency regulation are also included because of large power translation caused by the same fault. Besides that, voltage stability constraints are added to guarantee the voltage supporting ability in the grid near the HVDC. It is worth mentioning that HVDC power is variable, participating in optimal dispatching of the power grid. Based on characteristics of the model, Benders decomposition and the mixed integer programming algorithm are used to get the optimal transmission power of the HVDC and planning of thermal generators. The rest of the paper is structured as follows. Sections 2 and 3 model the SCUC formulation with AC/DC transmission constraints and Benders decomposition-based solution presented in Section 4. The proposed algorithm is tested with the IEEE 39-bus and Jiangsu power grid in eastern China in Section 5. We summarize the conclusion in Section 6.
## 5 Methodology
### Fuzzy Logic Based Pricing Strategy
#### 5.1.1 Multiple Metrics  
The proposed pricing model takes five instant factors as the inputs of the fuzzy information system. For any vehicle \(sv_{j}\), the price it can charge \(u_{0}\) is determined by the following parameters: the financial condition of \(sv_{j}\), the distance and relative speed between itself and \(u_{0}\), the computing resources it has and the link reliability of the communication channel between itself and \(u_{0}\). The above-mentioned parameters are denoted as \(w_{j}(t_{0})\), \(d_{uj}(t_{0})\), \(v_{uj}(t_{0})\), \(f_{j}(t_{0})\) and \(r_{uj}(t_{0})\), respectively, where \(t_{0}\) represents the moment when \(sv_{j}\) receives the offloading request from \(u_{0}\). The distance between \(sv_{j}\) and \(u_{0}\) at time \(t_{0}\) can be calculated as (The distance is calculated on the 2D coordinate system and the result is applicable for the 3D coordinate system):  
\[d_{uj}(t_{0})=\sqrt{\big{[}x_{j}(t_{0})-x_{u}(t_{0})\big{]}^{2}+\big{[}y_{j}(t_ {0})-y_{u}(t_{0})\big{]}^{2}}, \tag{12}\]  
where \((x_{j}(t_{0}),y_{j}(t_{0}))\) and \((x_{u}(t_{0}),y_{u}(t_{0}))\) are the coordinates of vehicle \(sv_{j}\) and \(u_{0}\) at time \(t_{0}\). Similarly, the relative speed between \(sv_{j}\) and \(u_{0}\) at time \(t_{0}\) can be calculated as:  
\[v_{uj}(t_{0})=\big{|}v_{u}(t_{0})-v_{j}(t_{0})\big{|}, \tag{13}\]  
where \(v_{j}(t_{0})\) and \(v_{u}(t_{0})\) are the speeds of vehicle \(sv_{j}\) and \(u_{0}\) at time \(t_{0}\). Considering the existence of Line of Sight (LOS) in the VEC networks, the Rician fading channel model is adopted in this paper. The Rician K-factor directly reflects the quality of the channel, and it is related to the distance between the transmitting node and the receiving node. To better describe the vehicle channel condition, we use the Rician K-factor module in [30], and there is:  
\[K_{uj}(t_{0})=39.832-0.288d_{uj}(t_{0}), \tag{14}\]  
where \(K_{uj}(t_{0})\) is the value of Rician K-factor between \(sv_{j}\) and \(u_{0}\) at time \(t_{0}\). A large \(K_{uj}\) means weak channel fading and good channel quality between \(u_{0}\) and \(sv_{j}\), which makes the transmission more reliable. Thus, the link reliability of \(u_{0}\) and \(sv_{j}\) at time \(t_{0}\) is represented by \(K_{uj}(t_{0})\).
# Integrating BIM and GIS for an Existing Infrastructure
## 3 Interoperability Issues
### Georeferencing  
All information in the AECO sector has coordinates defined by CRS. The most popular geographic CRS is the WGS 84, used in GIS tools and by the Global Positioning System (GPS). However, in the AECO sector, relative Cartesian coordinates are used, defined by a XY plane, adding the Z coordinate for three-dimensional cases. The 1989 European Terrestrial Reference System (ETRS89) was established as an official system in Europe. There is a good correlation between the two CRSs, with ETRS89 being more precise. Although the main BIM and GIS tools operate with these CRSs, it is essential to know at all points in the process which CRS is being used and to work with correctly located BIM models, that is, the point of origin (0, 0, 0) must be georeferenced to be able to carry out a good BIM-GIS integration.
## 3 Results and Discussion
### Analysis of Factors Affecting the Spread of HBNG Leakage under Normal Ventilation Conditions
#### 3.2.2 Different Leakage Hole Sizes  
Comparative analyses were carried out for cases 4, 6, and 7. The research produced graphs showing how the gas concentration changed when HBNG leaked through different leakage holes while the ventilation mode was normal (see Figure 9). As the size of the hole rises, the total momentum of the gas leakage jet containing hydrogen increases, resulting in an improved spread of the HBNG. Over the same amount of time, there were mostly no changes in the longitudinal distribution pattern of HBNG on the monitoring line when the leakage aperture conditions were different. The HBNG concentration on the monitoring line at t = 150 s exhibited a progressive and smooth distribution. The mass fractions for the leakage aperture diameters of 4 mm, 5 mm, and 6 mm were 0.00672, 0.01047, and 0.0150, respectively.  
Figure 8: Distribution of leakage flow lines in the pipeline corridor under normal ventilation conditions for HBNG (HBR = 15%), (**a**) t = 10 s, (**b**) t = 50 s, (**c**) t = 150 s, (**d**) t = 380 s.  
Figure 9: Variation of gas concentration in HBNG (HBR = 15%) leakage with different leakage apertures, (**a**) t = 50 s, (**b**) t = 100 s, (**c**) t = 150 s, (**d**) t = 200 s.
## 1 Introduction  
Forward stratigraphic modelling (FSM) is a powerful tool to predict reservoir potential in sedimentary basins, including continental, marine, siliciclastic, and carbonate systems. Its applications have been reported to be critical for petroleum exploration, e.g., [1; 2; 3]. FSM is also an important approach to reveal uncertainties in depositional settings, such as sedimentation rate, water discharge, and sea level fluctuations, which are difficult to determine with conventional research techniques [4; 5]. Consequently, FSM is becoming a regular multi-purpose approach to modeling petroleum reservoirs and has been applied commonly in ancient sedimentary basins, particularly in shallow water systems [4; 5; 6; 7; 8]. However, it has rarely been applied in modern siliciclastic settings (see exceptions: [9]). Given that this numerical modelling technique has a forward application in stratigraphic architecture, in this study its application is examined in a well-studied modern deltaic system (i.e., the Fraser River Delta, Canada), and its potential applications to predict future stratigraphic architecture (e.g., coastline morphological changes) are tested. These potential applications may help to prevent and better manage natural geo-hazards, such as slope failures, erosion, and subsidence. FSM has the potential to support multiple forms of multidisciplinary research.  
The Fraser Delta provides an opportunity to test applications of FSM due to its limited anthropogenic modifications (limited to river dykes in the last several decades) and well-studied nature. This study tests (1) whether FSM can be applicable to modern studies, (2) what parameters are the main controlling factors (e.g., water discharge, grain size, etc.)in reconstructing modern environments, and (3) whether FSM can be utilized to predict future changes in modern deltaic environments.
## 4 Experiment and Analysis
### Experimental Results and Analysis
#### 4.2.2 Analysis of Changes in Sugarcane Leaf Bending Angle  
The data on the change in the bending angle of the sugarcane leaf were extracted using a computer, and the bending angle was calculated using the same formula as in the simulation analysis. The experimental data of quick camera were compared with the data from the simulation. The comparison between the experimental value and simulation value of the sugarcane leaf bending angle is shown in Figure 17.  
Due to the characteristics of the leaf and the way it was stored, the initial bending angle was approximately 10\({}^{\circ}\) when the leaf first came into contact with the elastic teeth in the experiment. This was a bit different from the ideal arrangement of the cane leaves in the simulation, which appeared to be straight and flat. Firstly, the bending angle of the sugarcane leaf increased rapidly before 0.14 s in simulation. Secondly, the sugarcane leaf folding angle gradually decreased overall during the period of 0.14-0.58 s. Thirdly, in the period of 0.58-1.34 s, the folding angle generally increased in a fluctuating way, reaching a maximum value at 1.34 s. Finally, the folding angle of the sugarcane generally fluctuated in the range of 12\({}^{\circ}\) to 27\({}^{\circ}\).  
Initially, the bending angle of sugarcane leaf increased rapidly before 0.09 s in the experiment. After that, in the period of 0.09-0.70 s, the bending angle in the experiment generally decreased slowly. Then, in the period of 0.70-1.12 s, the bending angle in the experiment generally increased in a fluctuating way. Lastly, the bending angle generally fluctuated in the range of 25\({}^{\circ}\) to 40\({}^{\circ}\). The overall trend in the bending angle of the sugarcane leaf was the same in the simulation and the experiment.  
Both are made up of four stages, in which the bending angle firstly increased rapidly (stage 1), secondly decreased gradually (stage 2), thirdly increased in fluctuation (stage 3), and finally fluctuated within the range of 15 degrees' difference (stage 4). The correlation coefficient between the experimental and simulated bending angle change curves is 0.557.  
Figure 17: Comparison of experimental and simulated values of bending angle change.  
The experimental value and simulation values of the average value of sugarcane leaf bending angle were 27 \({}^{\circ}\)and 19\({}^{\circ}\), respectively, with a relative error of 29.6%. The moisture content of the sugarcane leaf will affect the posture of the sugarcane leaf. The reason for the large relative error is that the moisture content of the sugarcane leaf in the simulation and the moisture content of the actual sugarcane leaf cannot be guaranteed to be identical.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 3 Mechanical Design Structure
### Configuration of Thrusters  
We calculated the maximum force to move our vehicle up and down to choose the suitable type of thruster and determine whether the thrusters could dive. We used six Blue-robotics T100 thrusters. Compared to bilge pumps, they have a greater thrust-to-physical size ratio [6]. Accordingly, they can provide more free space to install the mechanisms required for the required tasks. However, the T100 thrusters have a high-power consumption at high-speed operations. Equation (14) expresses the computation of the drag force \(D\):  
\[D=0.5A\rho V^{2}C_{d} \tag{14}\]  
where \(D\) is the drag force, \(A\) is the ROV's cross-sectional area, \(\rho\) is the fluid's density, \(V\) is the object's speed relative to the fluid, and \(C_{d}\) is the drag coefficient.  
According to Equation (14), to achieve suitable maneuverability and to introduce lateral translation, four equally spaced thrusters were located on a prevalent plane and were vectored at 45\({}^{\circ}\) angles in the corners as shown in Figure 4a. The two motors were in front of ROV (a forward position), and the two thrusters were in the back of ROV (a backward position). Two vertical thrusters were installed on the vehicle's right and left sides. As a result, the vehicle could achieve six-DOF (namely, Surge, Sway, Heave, Yaw, Roll, Pitch). The 6-DOF of the ROV is illustrated in Figure 4b.  
This configuration allows the thrusters to contribute to the power efficiently towards better propulsion. It also reduces flow interference, considering the overcoming drag that might interrupt the vehicle and the drag of the tether. Each blue-robotics T100 thruster has a maximum forward thruster = 23.15 N and a maximum backward thruster = 17.85 N [24; 25].
## 5 Gut Microbiota-Targeted Interventions in the Prevention and Progression of Atherosclerosis
### Faecal Microbiota Transplantation  
Although it is a concept that dates back to the late 1960s, FMT is currently an attractive method that is increasingly being used to alter the composition and restore the functions of the gut microbiota in pathological states associated with dysbiosis [244]. This procedure involves the collection of filtered faecal samples from carefully selected healthy donors,or from the recipient before the onset of the pathology, in a process known as autologous FMT, followed by their transplantation into the recipient's GIT [57]. FMT has proven to be a highly effective method in treating _C. difficile_ infection [245]. Furthermore, FMT is considered an emerging therapy in cases of extra-intestinal diseases, including CVDs, where a shift in the recipient's bile acid composition has been observed, bringing it closer to that of the donor [246,247]. Additionally, FMT performed from lean individuals to obese recipients has shown improvement in the sensitivity of recipients to triglycerides, resulting in a decrease in their plasma concentration [248]. These observations suggest an anti-atherosclerotic effect, considering the previously mentioned benefits of bile acids, as well as a reduction in dysplasia, one of the main risk factors for the disease. However, FMT remains a limited therapeutic approach due to various associated risks, including the transfer of endotoxins and infectious agents, and its long-term effects are still not fully understood [9,217].  
Current evidence for FMT in atherosclerosis is limited, but evolving. Preclinical models consistently show that FMT from healthy donors can reduce plaque development, modulate systemic inflammation, and lower pro-atherogenic metabolites such as TMAO [249,250]. In humans, only one ongoing clinical trial (NCT04410003) is directly investigating FMT in severe unexplained atherosclerosis [251]. Other small-scale studies on metabolic syndrome, including one involving 32 women [252] and another with 20 men receiving FMT from vegan donors, have demonstrated shifts in microbiota composition, but no significant clinical improvement in TMAO levels, lipid profiles, or endothelial function [253]. Larger trials targeting atherosclerotic outcomes are needed to clarify FMT's therapeutic potential.
# FDBP-InSAR: An Efficient Algorithm for InSAR Imaging via Frequency Domain Back Projection
## 3 InSAR Imaging via FDBPA
### Theory
The detailed description of this conclusion has been proved in References [27; 36]. \(\mathbf{f}(x,y)\) contains the amplitude and phase information of the imaging scene and can be described as follows:  
\[\mathbf{f}(x,y)=\mathrm{Asinc}(ax)\sin c(by)\exp(-j4\pi l/\lambda) \tag{10}\]  
where \(a\) and \(b\) represent for the constant-coefficient relating to InSAR system parameters. Then the interference phase of FDBPA can be obtained by the following formula:  
\[\varphi=\sphericalangle\exp(j4\pi\Delta l/\lambda), \tag{11}\]  
where \(\Delta l=l_{s}-l_{m}\), and \(l_{m}\) and \(l_{s}\) are the distance from the master and slave antenna to the imaging scene. According to the imaging geometric relationship \({l_{s}}^{2}={l_{m}}^{2}+B^{2}-2 Bl_{m}\cos(\alpha+\pi/2-\theta)\), in which \(B\) is the length of baseline, \(\theta\) is the antenna incident angle, \(\alpha\) is the baseline angle of inclination. The height of the target points can be calculated by the following formula:  
\[h=\frac{\lambda R_{1}\sin(\theta)}{2\pi B\cos(\theta-\alpha)}\varphi_{h} \tag{12}\]  
where \(R_{1}\) is the center distance, and \(\varphi_{h}\) is the phase of height obtained by \(\varphi\) after phase filtering, removing of flat earth effect, and phase unwrapping. The basic processing chain of InSAR and detailed information of phase unwrapping are introduced in References [43; 44; 45].