esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 5 Results
### Decadal Regional ASL Trend  
The decadal SA-derived ASL trend was examined and compared with TG for two distinct periods--1 January 2000 to 31 December 2009 and 1 January 2010 to 31 December 2019--using the OLS-estimated linear trend (Equations (6) and (7)). The outcomes are presented in Figure 9, employing various DT calculations such as instantaneous SA data (\(DT_{SA\#TG}\)), hourly TG data (\(DT_{TG}\)), and TG data at the time of the SA cycle (\(DT_{TG\#SA\ cycle}\)). While all three methods yielded slightly different results (as detailed in Section 5.3.2), several main characteristics remained consistent across all approaches. These are described below in terms of (i) a comparison with the long-term trends (1995-2022) and (ii) a comparison between the different decades.  
Figure 9: Decadal sea-level trend using hourly TG records (dark blue) and TG data at the time of SA cycles (light blue) and SA data (red) over (**a**) the period of 2000–2019, (**b**) the 2000–2009 decade,  
and (c) the 2010-2019 decade. The RMSE is computed between two corresponding datasets. The deviation between the estimated trend values (especially during 2000-2009) is due to the data gaps. The whiskers represent uncertainties using 95% CI.
## 3 Numerical Simulation  
As the state of the system switches between stick and slip phases, and the motions of the blade-platform structure and the two dampers are coupled, the dynamic behavior of the blade-damper system needs to be obtained by solving four different sets of governing equations of the blade-platform structure together with four governing equations of the two dampers at the same time, which brings about some difficulties in the numerical computations. A numerical integration scheme implementing Runge-Kutta algorithm appropriate for the second-order differential equations coded in MATLAB  
Figure 7: Flowchart for the numerical implementation in Matlab.  
and capable of dealing with nonsmooth friction and contact behavior is developed to solve them in this paper. The states of the blade-platform structure and the two dampers during vibration, including values of the contact normal forces and tangential friction forces, and the forces on the spring and the two dampers, are monitored at each time step. If the results at the end of a time step do not satisfy the conditions for the system to stay in the same motion phase as at the start of the time step, then the bisection method is used to find the critical point where the dynamics switches from one phase to another phase. After getting the critical point, the current set of equations of motion changes to another set.  
In this numerical example, the external excitation frequency is fixed as 200 rad/s and the amplitude is fixed at 20 N. The time step used in the numerical integrations is 0.0001 s. The simulation results have been visualized by means of time histories, frequency spectra and phase-plane plots. Due to the limited space, only some distinct and interesting results are presented. The basic parameter values used are: \(k=1.0\times 10^{4}\) N/m; \(\Omega=50\) rad/s; \(r=0.46\); \(E_{1}=E_{2}=2.06\times 10^{11}\) Pa; \(\mu_{\rm s}=0.35\); \(\mu_{\rm k}=0.3\); \(\rho_{1}=\rho_{2}=7800\) kg/m\({}^{3}\); \(A_{1}=6\times 10^{-5}\) m\({}^{2}\); \(A_{2}=6\times 10^{-5}\) m\({}^{2}\); \(I_{1}=2\times 10^{-11}\) m\({}^{4}\); \(I_{2}=2\ \times 10^{-11}\) m\({}^{4}\); \(L_{1}=0.2\) m; \(L_{2}=0.04\) m; \(L_{1}\) and \(L_{2}\) are the length of the blade and the platform, respectively. The first natural frequency of the blade-platform structure alone is 32 Hz, while the first natural frequency of the blade platform structure and the two dampers with their cantilevers together as one system is 45.7 Hz.
# Experimental Study of Imbibition Characteristics of Silica Sol in Coal-Measure Mudstone Matrix
## 4 Test Analysis of Key Factors Affecting Imbibition Effect  
### Grutability  
Groutability is a prerequisite for the occurrence of imbibition effect, and the specific research purpose of this study is to investigate the ratio between the particle size of grout and the diameter of microporosity in rock sample chosen as grouting target. The pore size distribution test was conducted using an AutoPore IV 9510 mercury porosimeter. As shown in Figure 3, capillary force increases with mercury intrusion and mercury hysteresis. Mercury hysteresis and retention obviously occur, indicating that the pore structure is extremely complicated. Figure 3b shows that proportion of micropores in the sample with diameters smaller than 20 nm exceeds 50%.  
Silica sol particle size was tested using a ZS90 nanoparticle size analyzer. Particle sizes of P.O 425R cement and MP364 were tested by a Microtrac S3500 laser granulometer (Microtrac Inc., North Largo, FL, USA). \(N\),\(N\)-dimethylformamide (Shanghai Demand Chemical Co., Ltd, Shanghai, China) was used as a dispersant when testing MP364. The test results are presented in Figure 4.  
Figure 4: Particle size cumulative distribution of different grouts.  
Figure 3: Microporce size analysis. (**a**) Graph of pore volume vs. pressure showing Hg hysteresis and retention; (**b**) pore diameter distribution.  
The fracture width or pore diameter should be greater than triple the particle size. Maximum frequency size of silica sol (\(d_{\text{max}}\)) is 8-12 nm. The diameter of the grout cumulative distribution curve corresponding to 95% (\(d_{95}\)) for silica sol is 62 nm. The proportion of particle size more than triple the \(d_{\text{max}}\) on the cumulative distribution curve of sample (\(R^{3\times d_{\text{max}}}\)) is 49.31%, and the proportion of particle size more than triple the \(d_{95}\) on the cumulative distribution curve of sample (\(R^{3\times d_{95}}\)) is 41.10%. At the same time, this paper takes \(R^{3\times d_{\text{max}}}\) and \(R^{3\times d_{95}}\) as indexes of theoretical groutability. As shown in Table 1, the theoretical groutability ratio of silica sol is optimum compared with MP364 and P.O 425R.
# Image Error Concealment Based on Deep Neural Network
## 4 Experiments
### Objective and Subjective Performance Comparison
In addition, the algorithm that we implemented is not optimized; for example, we used an exhaustive search to collect similar samples. These two reasons made our algorithm time-intensive. More specifically, our algorithm required about half an hour per corrupted image under \(16\times 16\) isolate loss mode and a \(256\times 256\) image size. Therefore, our algorithm is computationally prohibitive for online applications. Although the proposed algorithm requires a longer time than the compared method, the reconstruction quality of our method is better than the others in terms of average PSNR and SSIM, as shown in Tables 2-4. Therefore, our future works will improve our algorithm in two ways. One is to optimize the algorithm and reduce the computational complexity. The other is to use a pre-trained network to avoid training the network for each image.
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 2 Materials and Methods
### Color Measurement, Image Capture and Pairing  
In the color control of textile products, current fabric color measurement methods are classified according to different ways of obtaining chromaticity values using colorimeters: visual comparison, photoelectric integration, spectrophotometry, and digital color measurement. Spectrophotometric colorimetry has gradually replaced the traditional visual comparison method. Although desktop spectrophotometers have extremely high testing accuracy, they have low testing efficiency [15]. Digital color measurement is a color testing technology that has been based in recent years on digital imaging [16]. It can measure color on irregular and rough object surfaces with high-throughput. In recent years, its acceptance has gradually increased, and it has great potential for application.  
Digital color measurement is conducted under a stable standard light source environment, with a high-precision digital camera installed at a fixed position. The camera is white-balanced and color-corrected using a whiteboard and standard color card. With suitable and fixed camera shooting parameters (such as shooting distance, focal length, shooting mode, aperture size, exposure speed, and sensitivity), high-resolution digital photos are taken. Then, the pixel color values of the samples in the specified positions and ranges of the photos are calculated by a program to obtain the comprehensive color values of the samples. This is a non-contact color testing method that preserves the natural state of the sample surface and normal lighting effects, reproducing the true visual color of the sample, making it very suitable for color testing of textured textiles. In 2003, the UK-based VeriVide introduced the DigiEye Digital Imaging System, a typical representative of digital color measurement. Since DigiEye preserves the true surface state of the fabric by simulating a real lighting environment, it is closer to the results of visual observation [17].  
The purpose of this study is to provide color management references for silk printing practitioners, thus requiring images to be taken under standard conditions. In the textile field, the DigiEye system is commonly used for color measurement and evaluation. The DigiEye system used in this paper to capture images of digitally printed silk fabrics is equipped with a Nikon D7000 camera, a lightbox, standard D65 illumination, and a dedicated capture head to maintain standard acquisition conditions.  
Using the DigiEye image capture system, digital inkjet samples of silk twill and silk crepe satin were collected, generating a total of 5252 sets of digital printing color data and corresponding fabric images. To reduce edge distortion and fit the input size for the Pix2 Pix model, the collected images were uniformly processed to 256 \(\times\) 256 pixels.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## Abbreviations  
\begin{tabular}{l l} ADC & Analog-to-Digital Converter \\ AI & Artificial Intelligence \\ AI/ML & Artificial Intelligence/Machine Learning \\ AO & Adaptive Optics \\ APD & Avalanche Photodiode \\ AWGN & Additive White Gaussian Noise \\ BER & Bit Error Rate \\ CM/CD & Coherent Modulation/Coherent Demodulation \\ CNN & Convolutional Neural Network \\ Comsat & Communications Satellite Corporation \\ COW & Cell on Wings \\ \end{tabular}  
\begin{tabular}{c l}
**DGG** & Double Generalized Gamma \\
**DQL** & Deep Q-Learning \\
**DSP** & Digital Signal Processor \\
**EDFA** & Erbium-Doped Fiber Amplifier \\
**Eutelsat** & European Telecommunications Satellite Organization \\
**FOLODE** & First-Order Linear Ordinary Differential Equation \\
**FoV** & Field-of-View \\
**FSO** & Free-Space Optical \\
**GEO** & Geostationary Orbit \\
**GNSS** & Global Navigation Satellite Systems \\
**GPS** & Global Positioning System \\
**HALE** & High Altitude Long Endurance \\
**HALE-UAS** & High Altitude Long Endurance Unmanned Aerial System \\
**HAPS** & High-Altitude Platform Station \\
**i.i.d** & Independent and Identical Distributed \\
**IM/DD** & Intensity Modulation/Direct Detection \\
**INMARSAT** & International Maritime Satellite Organization \\
**INS** & Inertial Navigation System \\
**Intelsat** & International Telecommunications Satellite Organization \\
**IoT** & Internet of Things \\
**IRS** & Intelligent Reflecting Surface \\
**KARI** & Korea Aerospace Research Institute \\
**LD** & Laser Diode \\
**LDPC** & Low-Density Parity Check \\
**LED** & Light Emitting Diode \\
**LEO** & Low Earth Orbit \\
**LO** & Local Oscillator \\
**LoS** & Line of Sight \\
**LTE** & Long-Term Evolution \\
**MDM** & Mode-Division Multiplexing \\
**MEO** & Medium Earth Orbit \\
**MIMO** & Multiple-Input Multiple-Output \\
**MMW** & Millimeter Wave \\
**MPPT** & Maximum Power Point Tracking \\
**MTD** & Machine-Type Device \\
**NFP** & Networked Flying Platform \\
**NTN** & Non-Terrestrial Network \\
**OAM** & Orbital Angular Momentum \\
**OOK** & On-Off Keying \\
**PAT** & Pointing Acquisition-Tracking \\
**PCS** & Probabilistic Constellation Shaping \\
**PDF** & Probability Density Function \\
**PHASA** & Persistent High Altitude Solar Aircraft \\
**PIN** & Positive-Intrinsic-Negative \\
**PLL** & Phase-Locked Loop \\
**PPM** & Pulse Position Modulation \\
**PSK** & Phase Shift Keying \\
**PV** & Photovoltaic \\
**QAM** & Quadrature Amplitude Modulation \\
**QoS** & Quality of Service \\
**QPSK** & Quadrature-Phase-Shift-Keying \\
**RF** & Radio Frequency \\
**RIS** & Reconfigurable Intelligent Surface \\
**RLN** & Rician-Lognormal \\
**SDM** & Spatial Division Multiplexing \\
**SES** & Societe Europeene des Satellites \\
**SNR** & Signal-to-Noise Ratio \\
**SWIPT** & Simultaneous Wireless Information and Power Transfer \\ \end{tabular}
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 2 Materials and Methods
### Experimental Setup
#### 2.6.1 Transmitter  
The transmitter is composed of two containers: one for the basic solution and another for the acid solution. These containers with the solutions are connected through a silicone tube to peristaltic pumps. Because we have an acidic and a basic solution, we used two peristaltic pumps to transmit the solutions individually and avoid contamination. There is also a third pump that removes the liquid, working at the same speed as the other two pumps mentioned above. These pumps feature a flexible tube, giving an open flow path for the liquids to pass. The pumps we chose were 12 V aquarium pumps working at 5000 RPM. As these pumps work with a high current, a driver is used. We control when they are on or off and how fast they run by using a microcontroller (a simple Arduino UNO microcontroller was adopted) connected to a computer, as in Figure 5. According to the transmitted information, a certain quantity of an acid solution or a basic solution is released to the channel.
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 5 Experiments
### Metrics  
To prove the effectiveness of our model, we make sure that all the calculations in the paper are based on geographical distance. For any two pairs of latitude and longitude \((x_{2},y_{2})\) and \((x_{1},y_{1})\), where \(x_{1}\) and \(x_{2}\) are latitude, \(y_{1}\) and \(y_{2}\) are longitude; the geographic distance is calculated using the following formula.  
\[S=2\cdot\arcsin\left(\sqrt{\sin^{2}\left(\frac{x_{2}-x_{1}}{2}\right)}+\cos(x _{2})\cdot\cos(x_{1})\cdot\sin^{2}\left(\frac{y_{2}-y_{1}}{2}\right)\right) \times 6,371,393. \tag{26}\]  
The unit of the calculation here is meters. The geographic distance calculated using the above formula is represented as \"geodesic\". For each sample, the reconstructed location is \((\hat{x}_{i},\hat{y}_{i})\), while the real location is \((x_{i},y_{i})\). Our model is evaluated using the following indicators:  
* RMSE: The equation of this part is \[\text{RMSE}=\frac{1}{m}\cdot\sqrt{\sum_{i=1}^{m}(\text{geodesic}((x_{i},y_{i} ),(\hat{x}_{i},\hat{y}_{i})))^{2}}.\] (27)
* MAE: The equation of this part is \[\text{MAE}=\frac{1}{m}\cdot\sum_{i=1}^{m}\text{geodesic}((x_{i},y_{i}),(\hat{ x}_{i},\hat{y}_{i})).\] (28)
* Rate150: The proportion of reconstructed positions where the error in geographic distance from the actual position is within 150 m. We reckon that if the distance between the reconstructed position and the real position is less than 150 m, this sort of reconstructed position can be regarded as a precise position. The equation for Rate150 is \[\text{Rate150}=N(geodesic((x_{i},y_{i}),(\hat{x}_{i},\hat{y}_{i}))<150\text{ meters})/\text{m}. \tag{29}\]
# S-Band Doppler Wave Radar System
## 4 Results Comparison and Analysis
### Non-Directional Wave Height Spectrum Comparison  
Figure 10 below presents the non-directional wave height spectra measured by both the MORSE system and the buoy. Three-point smoothing was applied on these wave height spectra. The significant wave height measured by the buoy is also illustrated below. Because of a power failure, the data for 04:00 March 24 were missing, and they were replaced by the data of 09:00 March 24. The wind velocity is also plotted in Figure 11.  
Figure 10: _Cont._  
Figure 9: Normalized echo energy density of one antenna and directional wave height spectra. The directional wave height spectra is obtained by using six antennas’ data. (**a**) Normalized echo energy density acquired with No.5 antenna at 16:53 March 23. (**b**) Directional wave height spectrum at 16:47 to 17:05 March 23. (**c**) Normalized echo energy density acquired with No.5 antenna at 04:59 March 25. (**d**) Directional wave height spectrum at 04:53 to 05:11 March 25.  
Figure 10: _Cont._  
The spectra indicate that the targeted ocean area was obviously influenced by the cold front. The cold front reached the experimental site on March 22. The wind velocity reached its maximum on March 25 and, then gradually declined. The non-directional wave height spectra illustrate that the wave height started to increase on March 23 and reached its peak on March 25. On March 27, the wave height returned to a lower level. The data derived from the spectra are consistent with the weather conditions.  
For comparison, the spectra measured by both instruments are more consistent when the sea state is relatively high. However, when the sea state is low, the sea surface is calm and smooth, which leads to a low SNR of echoes. Hence, the spectra measured by MORSE deviate from those measured by the buoy.  
Figure 10: Non-directional wave height spectra comparison.
## 3 Coverage Methods Utilizing Multiple Heterogeneous Robots
### Handling Broken Robot or Network Delay  
After the network is completely built, a robot may be broken. In this case, sensing holes may appear due to the broken robot. In this case, a frontierPoint of a robot, say \(a_{n}\), can be detected using its local sensor. Suppose that a new robot, say \(n_{N}\), is deployed from a ship to replace the broken robot. Based on Assumption (A3), each frontierPoint of \(a_{n}\) can be located using the local sensor of \(a_{n}\). One frontierPoint on \(a_{n}\) is then chosen as a target position, say \(f_{n_{N}}\). Based on \(I^{s}\), \(n_{N}\) travels along the directed path to reach the target position. In this way, \(n_{N}\) replaces the broken robot.  
Algorithm 1 does not require synchronization among the robots. Suppose that a robot, say \(n_{j}^{s}\), reached its target position. It then transmits a signal to the ship via multi-hop communication using USBL, so that the ship can deploy the next robot, say \(n_{i}^{s}\). It is inevitable that network delay occurs in multi-hop communications.  
This article assumes that a robot can communicate with other robots via multi-hop communication using USBL. However, using multi-hop communication, time delay may occur during data transfer.  
Algorithm 1 is robust to network delay due to data transfer. Suppose that it takes \(\delta\) seconds until \(n_{i}^{s}\) finds the directed path to the nearest frontierPoint based on \(I^{s}\). The robot \(n_{i}^{s}\) begins moving only after it finds a directed path to the found frontierPoint. In addition,no robot moves while \(n_{i}^{s}\) moves. Hence, Algorithm 1 works regardless of how long the delay \(\delta\) is.  
Note that while a robot \(n_{i}^{s}\) travels along the path to the nearest frontierPoint, other robots stand still. Hence, the speed of a moving robot doesn't make effects on Algorithm 1. In the case where a robot moves slower than other robots, it may take longer to make the robot reach its associated frontierPoint. Since only a single robot moves at a time, a robot's speed doesn't disturb the process of Algorithm 1.  
It can be argued that the proposed coverage methods run slow, since we make a single robot move at a time. However, we can speed up the coverage process by increasing the speed of each robot. This speed up is possible, since the speed of a moving robot doesn't make effects on Algorithm 1. Moreover, we can speed up the network building process by letting multiple surface ships drop multiple robots simultaneously.
# Message Action Adapter Framework in Multi-Agent Reinforcement Learning
## 7 Results
### Optimization Strategy  
MAAF has two separate steps in training: base policy training and message adaptation training. Note that this separation can encourage pre-trained RL agents to adapt to communication. When base policy parameters are frozen (stop-gradient and no parameters update), the original action distribution is preserved, and residuals are trained. This will result in a little discrepancy in actions between pre- and post-adaptation. Figure 6 shows the adaptation results for both cases with MAAF-RNN, including a scratch case where both base and adapted policies are trained from the beginning. We observe that the proposed method works to reach the performance of the scratch case when the parameters of base policies are updated. Note that freezing the base policy results in a little discrepancy between the performance at 1M and 2M.  
As mentioned, MARL has more instability than a single RL agent. We observe more failure cases in training when freezing parameters of a base policy. Figure 7 shows the adaptation performance for seven message types after 1M time steps. When the parameters of base policies are not updated, the performance in step 2 is even less than the performance at step 1M (the last checkpoint of the base policy), indicating that the parameters of adaptation policies may have failed at optimizing given the base policy. Therefore, the best optimization setting for performance is training both networks, base policy, and adaptation policy.  
Figure 5: Rendering of three environments. In the spread environment, three agents (purple circles) must spread as far as possible while staying near target locations (small black circles). In the tag environments, three predators (red circles) chase prey (a green circle) in an environment with obstacles. In SMAC environments, two groups of agents fight each other.  
Figure 6: Episode returns for three optimizations (Y-axis) for 2M time steps (X-axis). Adaptations are started at the 1M time step. The results indicate that updating both policies (red) is better than freezing blue). The shaded regions are confidence intervals obtained by three random seeds.  
When the base policy is updated, the communication implicitly contributes to the base policy. However, the two action branches still infer actions conditionally from observation and messages, ensuring separate inferences. In addition, this separation can reduce the training time when multiple MARL adoptions from a single RL are required. For example, when we want to train MARL agents in 10 communication settings, having a base policy will reduce the overall training time as MAAF requires only adaptation training.