esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Partial Correlation between TS and SPEI  
Since the TS\({}_{\text{EVI}}\) and TS\({}_{\text{GPP}}\) were both highly correlated with the SD of the mean GS SPEI, we analyzed the partial correlation between the TS\({}_{\text{EVI}}\) and TS\({}_{\text{GPP}}\) and the SD of the mean GS SPEI with the mean growing season temperature (TMP) and mean growing season shortwave radiation (RAD) controlled. The correlation between the SPEI and TS\({}_{\text{EVI}}\) was similar to that between the SPEI and TS\({}_{\text{GPP}}\) among the DNFs with a negative result. However, the EVI and GPP for DBFs had positive results as well as a weak correlation with the water balance (Figure 5). The \(r\) value from both the EVI and GPP for DBFs was less than 0.1. This could explain the significant difference between TS\({}_{\text{EVI}}\) and TS\({}_{\text{GPP}}\) when the TS values of all the valid pixels were analyzed together. These results suggest that the canopy greenness and photosynthetic rates responded to droughts differently. Furthermore, DNFs were more sensitive to changes in the water balance than DBFs. We also calculated the partial correlation coefficients of TMP and RAD for reference. The complete results are shown in Table 1.  
Figure 4: The temporal stability (TS) of forests versus the corresponding mean and standard deviation in North China, 2001–2018. The subplots are (**a**) TS calculated from growing season (GS) EVI (TS\({}_{\text{EVI}}\)) vs. mean value of GS EVI; (**b**) TS calculated from GS GPP (TS\({}_{\text{GPP}}\)) vs. mean value of GS GPP; (**c**) TS\({}_{\text{EVI}}\) vs. standard deviation (SD) of GS EVI; (**d**) TS\({}_{\text{GPP}}\) vs. mean value of GS GPP, for each valid forest pixels in this study. The blue dots are deciduous broadleaf forest grids. The red dots are deciduous needleleaf forest grids.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 2 Related Work
### Geometric Accuracy Evaluation
[49] proposeda framework for the accuracy evaluation of precast concrete components based on 3D laser scanning and BIM, which first determines the inspection list and quality assessment procedures based on construction specifications, determines the scanner and scanning parameters after obtaining the design BIM, and finally decides whether to rework based on the tolerance of the point cloud data and BIM. Existing evaluation systems focus on the geometric accuracy evaluation of aboveground structures [50], there is less research on geometric accuracy evaluation processes for subway stations. Unlike previous studies, this study proposes a coarse-to-fine registration method to overcome the difficulty of obtaining eponymous features in register point clouds with BIM by aligning two datasets to the same survey coordinate system; a hierarchical evaluation index and a system to meet the geometric accuracy evaluation for subway stations are proposed, and a complete evaluation process is established to ensure the geometric accuracy evaluation of different objects in subway stations.
# Plasmonic and Dielectric Metasurfaces: Design, Fabrication and Applications
## 2 Plasmonic Metasurfaces
The spatially-variant polarization state of the OAM-carrying vector beams is also calculated. These results are shown in Figure 7c. Clearly, the power components on the direction of \(\mathbf{e}_{1i}\) (\(\phi\)) are much larger than those on the direction of \(\mathbf{e}_{2i}\) (\(\phi\)). The phase distributions in Figure 7c indicate that the electric field component \(\mathbf{E}_{1i}\) carries OAM with a charge number of \(m=i\) (\(i=1,2,3\)). The spatial distribution of \(P_{x}\), \(P_{y}\) and the polarization state confirm that the OAM-carrying vector beams have a polarization order of \(l=i\) (\(i=1,2,3\)). Fabrication: The proposed metasurfaces to generate broadband OAM carrying vector beams can be fabricated using the existing fabrication techniques. Figure 8 shows the schematic diagram of the fabrication process of the proposed metasurfaces: (1) using standard electron-beam lithography to generate patterns on the glass substrate; (2) 5 nm Cr and 200 nm Au layers are then respectively deposited on the substrate using electron-beam evaporation; (3) removing the photoresist and undesired metal layers.
## 2 Challenging Open Issues
### Other Open Issues
#### 2.2.7 Data Aggregation  
Because energy consumption for communication is much higher than that for computation [17], data aggregation might be considered as a way to save energy. Data received from different sources is combined and fused before being transmitted to the next hop node. Whether this technique is used or not, energy consumption must be considered, as there is a trade off between energy consumption and network load. When data aggregation is not used, this means that the packet size sent to the next hop node will be larger.
# A Co-Rotational Meshfree Method for the Geometrically Nonlinear Analysis of Structures
## 2 Theoretical Formulation
### Governing Equations  
The above equations have been constructed from the arc length of the beam axis, \(s\), at the deformed configuration. For the convenience of later derivation, the equations can be represented with respect to the arc length of the undeformed beam axis, \(S\), by the relationship of the chord length between the undeformed and deformed configuration as follows:  
\[df/ds=(1+F_{z}/EA)^{-1}(df/dS). \tag{29}\]  
Substituting Equation (29) into the kinematics and equilibrium equations leads to the governing equations:  
\[dX/dS=(1+F_{z}/EA)\left[\frac{\varphi\psi}{\theta^{2}}(1-\cos\vartheta)+\frac {\chi}{\vartheta}\sin\vartheta\right] \tag{30}\]  
\[dY/dS=(1+F_{z}/EA)\left[\frac{\psi\chi}{\theta^{2}}(1-\cos\vartheta)-\frac{ \varphi}{\vartheta}\sin\vartheta\right] \tag{31}\]  
\[dZ/dS=(1+F_{z}/EA)\left[1-\left[1-\left(\frac{\psi}{\theta}\right)^{2}\right] (1-\cos\vartheta)\right] \tag{32}\]  
\[d\varphi/dS=\frac{(1+F_{z}/EA)}{2\theta^{2}}\big{[}2\omega_{x}\varphi^{2}+ \theta^{2}(\omega_{z}\chi-\omega_{y}\psi) \tag{33}\]  
\[+2\varphi(\omega_{y}\chi+\omega_{z}\psi)+\vartheta(-\omega_{y}\varphi\chi- \omega_{z}\varphi\psi+\omega_{x}(\chi^{2}+\psi^{2}))\cot\frac{\vartheta}{2}\]  
\[d\chi/dS=\frac{(1+F_{z}/EA)}{2\theta^{2}}\big{[}2\chi(\omega_{x}\varphi+ \omega_{y}\chi)+\omega_{x}(\varphi^{2}+\chi^{2})\psi+\omega_{x}\psi^{3} \tag{34}\]  
\[-\omega_{z}(\varphi^{3}-2\chi\psi+\varphi(\chi^{2}+\psi^{2}))+\vartheta(- \chi(\omega_{x}\varphi+\omega_{z}\psi)+\omega_{y}(\varphi^{2}+\psi^{2})) \cot\frac{\vartheta}{2}\big{]}\]  
\[d\psi/dS=\frac{(1+F_{z}/EA)}{2\theta^{2}}\big{[}(\omega_{y}\varphi-\omega_{x} \chi)(\varphi^{2}+\chi^{2})+2(\omega_{x}\varphi+\omega_{y}\chi)\psi\]  
\[+(2\omega_{z}+\omega_{y}\varphi-\omega_{x}\chi)\psi^{2}+\vartheta(\omega_{z} (\varphi^{2}+\chi^{2})-(\omega_{x}\varphi+\omega_{y}\chi)\psi)\cot\frac{ \vartheta}{2}\big{]}\]  
\[dF_{x}/dS=(1+F_{z}/EA)(\omega_{z}F_{y}-\omega_{y}F_{z}-f_{x}) \tag{36}\]  
\[dF_{y}/dS=(1+F_{z}/EA)(\omega_{x}F_{z}-\omega_{z}F_{x}-f_{y}) \tag{37}\]  
\[dF_{z}/dS=(1+F_{z}/EA)(\omega_{y}F_{x}-\omega_{x}F_{y}-f_{z}) \tag{38}\]  
\[dM_{x}/dS=(1+F_{z}/EA)(\omega_{2}M_{y}-\omega_{y}M_{z}+F_{y}-m_{x}) \tag{39}\]  
\[dM_{y}/dS=(1+F_{z}/EA)(\omega_{x}M_{z}-\omega_{z}M_{x}-F_{x}-m_{y}) \tag{40}\]  
\[dM_{z}/dS=(1+F_{z}/EA)(\omega_{y}M_{x}-\omega_{x}M_{y}-m_{z}) \tag{41}\]  
where  
\[\omega_{x}=M_{x}/EI_{xx}+\omega_{x0}-(1+F_{z}/EA)d(F_{y}/\kappa_{y}GA)/dS \tag{42}\]  
\[\omega_{y}=M_{y}/EI_{yy}+\omega_{y0}+(1+F_{z}/EA)d(F_{x}/\kappa_{x}GA)/dS \tag{43}\]  
\[\omega_{z}=M_{z}/GJ+\omega_{z0} \tag{44}\]  
The 12 nonlinear ordinary differential equations, Equations (30)-(41), are derived to solve the spatial beam with naturally curved and twisted beams undergoing large deformations.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 3 Earth Observation
### Natural Disasters
(2015) proposed a method for monitoring volcanoes using high-resolution EO data from CubeSats, specifically the Doves constellation. The authors studied the Fuego volcano in Guatem, known for its frequent and hazardous activity. Also, they demonstrated the usefulness of these data for mapping various eruptive phenomena and analyzing morphological changes, and using images captured before, during, and after eruptions showed how this technology could track ash clouds, pyroclastic density currents (PDCs), lava flows, and morphological alterations with exceptional detail. This method, which offers high spatial resolution and frequent updates, is essential in overcoming the challenges of Fuego's constant activity and inaccessible terrain. It can aid in volcanic hazard mitigation efforts in Guatem and other active volcanic regions. The authors' comprehensive analysis and mapping efforts, integrated into a GIS platform, offer valuable insights into the dynamics of volcanic processes and highlight the potential of CubeSats based EO for enhancing volcano monitoring and hazard assessment worldwide. However, satellite images are large data sizes due to high spatial resolutions, so their processing time can be lengthy. In emergencies, it is crucial to generate quick information, so reducing the computational time required for satellite image processing is essential. [PERSON] and [PERSON] (2016) propose an innovative onboard deep learning-based video scene analysis method for nanosatellites equipped with video payload cameras, mainly focusing on disaster monitoring missions. The authors implement a CNN+LSTM-based model trained on the ground; the proposed approach alleviates the computational burden onboard while ensuring efficient scene analysis. Through extensive experimentation utilizing Nvidia Jetson TX2 as the Onboard Computer (OBC), the method achieved an impressive 89% test accuracy and demonstrated a substantial 30% reduction in downlink costs. Moreover, the proposed approach streamlines ground station operations by selectively transmitting mission-relevant video frames, reducing human resources, enabling nanosatellites to handle HD video payloads, and utilizing low-bandwidth communication channels like S-band. Using CubeSats for natural disaster monitoring and management significantly advances remote sensing. Proposed constellations and innovative technologies promise enhanced data collection frequency and real-time transmission, which are crucial for effective disaster response. The studies highlight the broad applicability of CubeSats in disaster monitoring and management, showcasing their potential to offer high-resolution, timely, and accurate data crucial for mitigating the impacts of natural disasters. Table 4 summarizes the methods used and evaluation metrics for each study regarding natural disasters.
## 4 Experimental Setup
### Ablation Study
The scenarios included flat terrain, mountainous terrain, urban railways, and varying weather conditions such as rainy, snowy, and dry environments. Performance was measured using accuracy, recall, F1-score, and a robustness index, which quantifies performance stability across different conditions relative to the best-performing baseline. The results, summarized in Table 5, demonstrate that the proposed approach maintains consistently high performance across all scenarios. The model achieves an average accuracy of 91.9% with only minor variations across conditions. Notably, performance remains stable in complex terrains, such as mountainous regions (92.4% accuracy) and urban railway environments (91.1% accuracy), indicating the model's ability to generalize to different railway topographies. Similarly, under adverse weather conditions, the model retains competitive performance, with a recall of 88.2% in rainy conditions and 87.6% in snowy conditions. The robustness index remains above 0.95 for all tested scenarios, confirming the model's resilience to environmental variability. These results highlight the capability of the proposed approach to adapt to real-world railway deformation risks regardless of the underlying conditions. The model effectively captures spatiotemporal patterns across different terrains and environmental settings, ensuring reliable risk prediction even in challenging scenarios.
## 2 Mathematical Modelling
### Energy Consumption  
Energy consumption includes the electrical energy for fans, and the thermal energy for sorbent regeneration. The fan power is calculated by Equation (21) using the Ergun equation as in Equation (22) for the evaluation of pressure drop over the bed [3,80,87] (see Equations (21) and (22)):  
\[P_{f}=\frac{2.72\cdot 10^{-5}Q\cdot\Delta P}{\mu_{f}\cdot\mu_{m}} \tag{21}\]  
\[\frac{\Delta P}{L}=\frac{150\cdot\mu\cdot v_{gas}}{d_{p}^{2}}.\frac{(1-\varepsilon )^{2}}{\epsilon^{3}}+1.75.\frac{\rho_{gas}\cdot v_{gas}}{d_{p}}.\frac{(1- \varepsilon)}{\epsilon^{3}} \tag{22}\]  
where \(Q\) is the fan volume (m\({}^{3}\)/h), \(\Delta P\) is the pressure drop (cm of water column in Equation (21) and Pa in Equation (22)), \(P_{f}\) is the fan power (kW), \(L\) is the bed height (m), \(\mu\) is the gas viscosity (Pas), \(v_{gas}\) is the gas velocity (m/s), \(\varepsilon\) is the bed void fraction, \(\rho_{gas}\) is the gas density (kg/m\({}^{3}\)), \(d_{p}\) is the sorbent diameter (m), \(\mu_{f}\) is the fan efficiency, and \(\mu_{m}\) is the motor efficiency.  
Thermal energy for regeneration is evaluated according to Equation (23) [18,88,90,91,92,93,94,95,96,97,98]:  
\[Q_{r}=\frac{1}{q_{w}}\cdot c_{p,s}\cdot(T_{de}-T_{ad})+\Delta H_{co_{2}} \tag{23}\]  
where \(Q_{r}\) is the thermal regeneration energy (J/mol), \(q_{w}\) is the working capacity of sorbent, being the difference between the loading at the end of adsorption and desorption stage (mol/kg), \(C_{p,s}\) is the specific heat capacity of sorbent (J/kgK), \(T_{ad}\) and \(T_{de}\) are the adsorption and desorption temperatures, respectively (K), and \(\Delta H_{CO2}\) is the absolute value of heat of adsorption (J/mol). The overall thermal energy considers the sensible heat and adsorption heat. The sensible heat required to elevate the adsorbent to the desorption temperature while the second term represents the latent heat needed to break the bond between the carbon dioxide and the adsorbent for regeneration.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 5 Reward Design
### Alternative Goals  
Sometimes, the objective is not available during the episode, such as when minimizing total tardiness [66; 174] or final makespan [22; 38; 54; 247; 307], forcing the approaches to utilize alternative or estimated metrics for the reward calculation. Ref. [22] demonstrate that, for their formulation, the makespan and the resource utilization are equivalent and use the resource utilization increments as reward. Some estimation examples use tardiness [66] or worst possible task completion [38; 91].  
When the agent's role is not to optimize, but to **assist** other optimization strategies in hybrid approaches, the reward might not be related to the overall objective function. In [252], the agent output updates the optimization parameters for a simulation software. The resulting design-performance value is used to update the critic network using mean squared error. For the constraint programming approach in [254], the agent is penalized at every step, encouraging it to reach feasible solutions faster. In the bee colony optimization of [263], where metaheuristic parameters are given by the agent, the reward depends only on the metaheuristic finding a better solution. Lastly, in [276], no reward is used since the authors use imitation learning.  
Learning through **proxy** metrics can be the only viable choice when the direct objective value is not available. This also enables hybrid approaches to optimize for their particular task and not for the overall problem. However, it may lead to unintended behaviors if the alternative metrics are not fully representative of the true goal.
## 2 The Experiment
### Channel Characterization and Measurements  
Sound speed profile (SSP) measured by the R/V Gunnerus is shown by the left panel of Figure 2. The sound speed profile has a surface channel and a negative gradient down to about 50 m. At deeper depths, the sound speed increases nearly linearly. With the conditions of the SPP and the bathymetry, the PlaneRay ray-tracing program [18] is used to illustrate the acoustic propagation during the trial.  
Figure 1: Block diagrams of the transmitted signals using different shaping pulses shown in the parentheses. \({}^{\mathrm{a}}\) The symbol rate was 1 kilo-symbol/s; \({}^{\mathrm{b}}\) The symbols rate was 2 kilo-symbol/s.  
Figure 2: Measured SSP (the left panel) and the ray traces (the right panel) from a source on the left. The source was at a depth of 20 m.  
The right panel of Figure 2 shows that ray traces and the bathymetry from a source at 20 m depth to the receiving array located at a distance of 7.4 km. The sound propagation dominated by the sound channel at about 25 m and the positive gradient below 50 m. It is shown that there are several almost horizontal paths in the sound channel as well as several deep refracted paths, and all other possible ray paths are blocked by the seamount at 4 km.  
Figure 3 shows the simulated responses to the vertical array with five hydrophones spanning the depth of 0.5 m to 4.5 m. The results are plotted as a function of reduced time, which is the actual travel time with the nominal gross travel time of 4.9333 seconds subtracted. The transmitted pulse used in the simulations was a short transient with 2 ms duration. There is a group of arrivals followed by a second group arriving about 40 ms later. This structure can be understood from the ray tracing with the first group is due to the sound channel paths and the second is the deep refracted paths. Each of the groups has several multipath contributions probably caused by a multitude of surface and bottom reflections occurring in shallow area near the receiving array.  
Figure 4: Channel response at different depths. (**a**) 1 m; (**b**) 3.5 m; (**c**) 4.5 m.  
Figure 3: Modeled channel impulse response calculated by the PlaneRay program to the vertical array with five hydrophones spanning the depth of 0.5 m to 4.5 m.  
Figure 4 shows examples of channel response estimated using the LS method. Within the observations of 15 s, the responses varied with time. In each receiving channel, there are two groups of concentrated arrivals with a time span of 35 ms, and they correlate with the simulated results in Figure 3. It is evident that the channel is sparse. It is apparent that they are similar over the three water depths. In particular, the signals received in Ch #2 and Ch #4 are highly correlated, the correlation coefficient between these two channels is calculated to be 0.68.
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 3 Methodology  
This section provides a detailed description of a deep learning-based pseudolite positioning model: Enconv1d. During the offline phase, the model utilizes pseudolite positioning signals collected within the tunnel as input to optimize its parameters. The trained model parameters are then deployed to the terminal, where they are used to obtain real-time positioning results in the current phase.  
The signals used in this study were collected from a synchronized pseudolite system called GH-LPS [14] (Figure 1), which primarily consists of base stations (BSs) and user receivers. The precise locations of the base stations were determined using a laser total station. Each base station broadcasts navigation signals containing positioning information, which the user terminal receives. The user terminal then decodes the positioning information from the pseudolite distance observation signals. The pseudolite distance observation signals undergo the following preprocessing steps to enable the model to more effectively capture the spatio-temporal features of the positioning signals and enhance its ability to understand the data.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## Appendix A  
```
While(1) { Recv ACK //Compute window's smoothing rate \(\text{B}_{\text{C}}=\text{Fs / (T}-\text{Ts})\); \(\text{B}=\text{(1}-\alpha)*\text{B'}+\alpha*\text{B}_{\text{C}}\) If(B > B') //window's smoothing rate increases { If(B - B') > \(\gamma\)} \(\text{CWND}=\text{CWND'}+\epsilon^{j}\) Else \(\text{CWND}=\text{CWND'}+\zeta\) j++ } Else //window's smoothing rate decreases { i++ if(\(\{\geq 3\}\)) \(\text{If}((\{\sum_{k=1}^{3}(\text{B'}-\text{ B}))<\delta\})\) { If(\(\text{SRTT}\leq\eta*\text{RTTMIN}\)) \(\text{CWND}=\text{CWND'}+\zeta\)  Else \(\text{CWND}=\text{CWND'}\) \(\text{Else}\) \(\text{CWND}=\text{CWND'}\) Else \(\text{CWND}=\beta*\text{CWND'}\) else \(\text{CWND}=\text{CWND'}+\zeta\) }
```  
**Algorithm 1**Pseudo-Code of Congestion Control Algorithm.  
The meaning of the parameters in the preceding pseudo-code are as follows:  
\(\alpha\): The value range is from zero to one, where the value of one refers to the computed throughput of the standard TCP; the closer the value is to zero, the longer the history tracking will be, which is actually a fixed constant that has been tuned and optimized;  
\(\beta\): The value range is from 0.5 to one, where the value of 0.5 indicates standard TCP congestion flow control. The closer the value is to one, the more aggressive it is. Three critical values have been tuned for the acceleration engine: maximum mode (\(\beta=0.9\)), normal mode (\(\beta=0.8\)), and conservative mode (\(\beta=0.6\)). The conservative mode is nearly identical to the congestion control provided by standard TCP;  
\(\gamma\): A constant calculated based on the bandwidth of the acceleration engine, and it has basically the same meaning as the threshold parameter in the standard TCP. This threshold prevents congestion. If the increase in throughput exceeds the threshold value, the throughput increases rapidly. If the throughput continues to increase but remains below the threshold, the bandwidth threshold is reached and the throughput continues to increase linearly;  
\(\delta\): A dynamically computed value based on the bandwidth setting in the acceleration engine. This value is used to judge whether the congestion is avoided or the bandwidth threshold is exceeded;  
\(\varepsilon\): A constant larger than two. The value for the standard TCP is two, and a larger value indicates stronger aggressiveness. The \(\varepsilon\) value of the current BoostTCP has a value of three.  
\(\zeta\): An integer with a value range greater than one; one represents the constant value after tuning and fixing in the linear increase of standard TCP.  
\(\eta\): A constant that indicates the network's jitter tolerance and has a value range from one to two. The larger the constant value is, the less perceptible the response to the network jitter will be, which means that the occurrence of jitter cannot be easily detected. If \(\eta\) equals one, the constant has been tuned out and fixed when the network jitter is judged to be congested.